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How organizations can close the data science skills gap ...
|
How organizations can close the data science skills gap amid a shortage of talent
|
https://blogs.sas.com
|
[
"Stacie Miller"
] |
The report found three top organizational priorities: improving innovation (34%), improving workforce productivity (32%) and increasing organizational agility ...
|
From sparking innovation to reducing operational inefficiencies, top organizational priorities in the modern era depend on one thing: data. Because of its ability to uncover blind spots and power decision-making, data helps organizations identify growth areas and progress toward their strategic goals.
The catch?
Turning raw numbers into useful, valuable insights requires the help of professionals highly skilled in artificial intelligence (AI), machine learning (ML) and data analytics – and it's no secret that this talent is in short supply.
To better understand what organizations are experiencing firsthand, SAS and Coleman Parkes Research surveyed key decision-makers in 111 major organizations across the US and UK/Ireland with an average of 27,000 employees and developed this recent report, How to Solve the Data Science Skills Shortage, which sizes up the skills shortage, examines the broader impact and proposes a path forward.
The impact of a growing skills gap
According to Microsoft's DEGREE + DIGITAL report based on LinkedIn data, there's no skill set with a more significant disparity between supply and demand. And, based on projections from the U.S. Bureau of Labor Statistics, the demand isn’t expected to let up anytime soon. The bureau’s Office of Occupational Statistics and Employment Projections reports that the data science field is expected to grow by 36% from 2021-2031 – significantly faster than the average profession.
Many organizations are putting pressure on universities to ramp up their efforts to train graduates in high-demand data and analytics skills. Unfortunately, the outlook is grim.
According to estimates from the UK government, universities supply up to 10,000 data scientists annually. Meanwhile, LinkedIn boasts 38,000 job postings for data scientists in the UK– while the US suggests around 320,000 job postings. These figures, plus the rapid pace of technological change, show that institutions of higher education can’t fill the gap alone.
So, how can organizations close the skills gap and make real, sustainable progress toward their organizational goals? Rather than solely relying on academia or poaching talent, what can be done to secure necessary analytics skills?
1. Streamlining tools will keep organizations competitive
Surveying key decision-makers, the report found three top organizational priorities: improving innovation (34%), improving workforce productivity (32%) and increasing organizational agility (31%). Organizations lacking AI, ML and data analytics aren’t simply missing out; they’re also at risk of falling behind – threatening their overall resilience and competitiveness.
Because organizations focused on innovation are hungry to adopt emerging technologies, many have accumulated diverse tools over the years – creating a tangled web of resources. Consolidating AI and analytics tools to maximize their impact will ease the burden of bringing current and future employees up to speed.
By consolidating tools around modern, open, multi-language tools, organizations can improve the learning curve for end-users undertaking basic analytics, reduce inefficiencies and get the most value out of their data scientists by helping them focus on core tasks.
For many organizations, solutions like SAS® Viya®, which enable employees to use open-source coding, may be a good step forward. Examining the software’s Total Economic Impactä (TEI), a report by Forrester found that, on average, organizations deploying the platform reported over $5 million in increased productivity for business analysts from faster analytics processes and employees supporting model building and management, while seeing over $1 million in infrastructure savings from retiring on-premise environment.
2. Upskilling talent is necessary to drive innovation, growth
Finding that the organizations surveyed, like most, don’t have enough employees with skills to use the AI and ML technologies deployed causes broader implications. Too few analytics professionals prohibit organizations from meeting customer demands (79%), growing the organization (41%) or winning new customers (34%) – and 66% agree that their staff morale is lower. Coupled with an already-slim workforce capacity, this reality makes the war for talent even more complicated, driving up hiring and retaining costs. One solution that is not only cost-effective but increases morale is upskilling and cross-skilling the current workforce – even those from non-technical backgrounds.
For many organizations, investing in training resources comes with a certain degree of fear. What if their employees jump ship and go elsewhere after developing these in-demand skills? About 25% of survey respondents cited this fear and reluctance amongst senior management. Despite these barriers, training data scientists internally is necessary to plug the skills gap.
With such high demand for data talent, costs of new hires are surging – but more than half of the decision-makers surveyed agreed they wouldn’t need to recruit as many new hires if the current workforce could use data-based tools and technologies more effectively. Organizations can upskill and cross-skill employees, from slating time to complete accredited courses and certifications to hosting in-house boot camps and data science academies.
While on-the-job training can be tricky – organizations serious about driving their strategic priorities forward are faced with no choice.
3. Cultivating a positive work environment motivates employees
Finally, organizations can prepare for future success by fostering a positive workplace culture. While flexible working options, high salaries and high-quality office spaces and facilities are essential, developing environments where employees feel appreciated motivates employees and encourages them to stay.
Organizations should also focus on fostering and rewarding critical competencies and soft skills like curiosity, adaptability, collaboration and continuous learning. At this stage in the game, developing more quantifiable hard skills is non-negotiable – but because technology is constantly evolving, organizations should strive to develop employees eager to embrace new challenges and collaborate with others to tackle complex problems.
Offering training resources may have the opposite effect than managers fear it will. Rather than incentivizing staff to search for different jobs, investing in employees plays a vital role in motivating employees, improving morale, increasing productivity, and reducing turnover. And by helping staff advance their careers and demonstrating their value, creating a learning culture can also help secure employee loyalty.
In championing these environments, organizations prepare their employees for an uncertain future – and create spaces where top talent also want to work.
The need for data science skills isn’t going away and if not proactive about addressing the skills crisis, there will be a larger, rippling effect. Organizations – especially ones hungry to innovate, grow and invest in new data-based technologies – will need to take a robust, multifaceted approach to solve the problem.
| 2022-12-05T00:00:00 |
2022/12/05
|
https://blogs.sas.com/content/sascom/2022/12/05/how-organizations-can-close-the-data-science-skills-gap-amid-a-shortage-of-talent/
|
[
{
"date": "2022/12/05",
"position": 10,
"query": "AI skills gap"
}
] |
This radiologist is helping doctors see through the hype to ...
|
This radiologist is helping doctors see through the hype to an AI future
|
https://www.uab.edu
|
[] |
Specifically, the visual of a future where artificial intelligence (AI) algorithms have put radiologists out of work. In 2016, machine-learning pioneer ...
|
This radiologist is helping doctors see through the hype to an AI future
Jordan Perchik, M.D., launched the AI Literacy Course for fellow UAB Radiology residents in 2020. This year's edition attracted participants from 25 radiology programs in 10 countries. "I wanted people to see that [AI] is a tool that can help radiologists, not something to be feared," he said.
Radiology has an image problem. Specifically, the visual of a future where artificial intelligence (AI) algorithms have put radiologists out of work. In 2016, machine-learning pioneer Geoffrey Hinton unleashed this incendiary quote: “I think if you work as a radiologist, you are like the coyote that’s already over the edge of the cliff but hasn’t yet looked down,” Hinton said. “People should stop training radiologists now. It’s just completely obvious within five years deep learning is going to do better than radiologists …. It might be 10 years, but we’ve got plenty of radiologists already.”
In some ways, Hinton’s prediction has come true. Deep learning and other machine learning algorithms have flooded the field. As of early November 2022, there were 200 FDA-approved radiology AI algorithms ready for use, according to the American College of Radiology’s AI Central site. At UAB and some other academic medical centers, radiology faculty now use AI-enhanced tools as a routine part of care. Computer-aided detection and triage software can reduce turnaround time by automatically highlighting positive findings within images. Natural language processing tools, trained on millions of radiologists’ reports, can generate straightforward written conclusions based on findings notes that the physician can edit as needed. (See “Five ways radiologists are using AI.”)
At the same time, there is a global radiologist shortage, driven in part by overwork. “The amount of imaging is going up 5 percent per year, and we’re not training 5 percent more radiologists per year,” said Jordan Perchik, M.D., a fellow in the Department of Radiology at the UAB Heersink School of Medicine. (A 2021 study reported a 2.5 percent increase in diagnostic radiology trainees between 2010 and 2020, compared with a 34 percent increase in the number of adults over 65, a population that requires more imaging.) “The most commonly used AI tools,” Perchik said, “are ones that speed up scans, paradoxically increasing the workload for radiologists.”
The AI hype cycle in radiology has quieted somewhat as dramatic results from early studies have failed to pan out in real-world settings. But the potential benefits are too large to ignore, Perchik says, quoting a response to Hinton made by Stanford radiologist and AI pioneer Curtis Langlotz, M.D., Ph.D. “He said, ‘AI won’t replace radiologists, but radiologists who use AI will replace those who don’t,’” Perchik said.
Increasingly, radiologists who want to know more about AI are getting their introduction from Perchik. In 2020, he began a series of lectures on the topic for his fellow UAB radiology trainees. The next year, residents from nine programs in the Southeast participated. By the October 2022 edition of Perchik’s AI Literacy Course, “we had 25 programs in 10 different countries participating,” he said. Aided by a grant from the Radiological Society of North America, Perchik now oversees AI in Radiology Education, the largest free resource for AI education for radiologists worldwide. “I wanted people to see that this is a tool that can help radiologists, not something to be feared,” Perchik said. (See a list of lectures at right and watch the 2022 lectures here.)
Another “sky is falling” moment
When Hinton said, “stop training radiologists” in 2016, Perchik was in his third year of medical school, and had just decided to specialize in radiology. At the time, a rash of AI studies demonstrating human or even superhuman performance at radiology tasks was all over the news. “That was a ‘sky is falling’ moment in radiology,” Perchik said. But as the son of a radiologist, Perchik knew this was not the first. “Since the early 1990s, just in my lifetime, there have been a few ‘sky is falling’ moments,” he said. “In the transition from film to digital systems, the fear was, ‘No one needs a radiologist now, because they can look at their own exams.’ Obviously, that didn’t happen.” Then there was the advent of computer-aided detection in mammography. “That is a rudimentary AI system,” Perchik said. “People were saying, ‘This is the end of breast imaging.’ But now that is one of the most in-demand specialties.”
A screenshot from the "AI in Nuclear Medicine" talk during the AI Literacy Course in 2022. Radiology has only expanded over that time, but radiologists did have to adapt to the new technologies and routines. “These experts were saying that human radiologists will be totally replaced by computers,” Perchik said. “That didn’t sit right with me. I started to learn more about AI — where it is strong and where it is weak.”
An AI “juggernaut”
After he began his radiology residency at UAB, Perchik met several new faculty members who were heavily engaged in AI research: Professor Srini Tridandapani, M.D., Associate Professor Houman Sotoudeh, M.D., and Professor Andrew Smith, M.D., Ph.D. “UAB has become this juggernaut of publications and AI research and entrepreneurship,” Perchik said. “I was impressed with the work they were doing and wanted to learn more.” Other radiology residents felt the same way, and Perchik asked program director and Associate Professor Jessica Zarzour, M.D., for permission to develop a weeklong curriculum on AI in radiology.
“You can find plenty of videos on YouTube and free courses on Coursera and elsewhere about AI and machine learning, but they are all from a computer science or hard science lens,” Perchik said. “I was interested in helping people get started with targeted lectures about the fundamentals of AI for radiology and how AI was being applied or could be applied in each radiology subspecialty. And I immediately had great interest and support from Dr. Zarzour and the rest of the department.”
About 75 percent of the participants have been residents, “but I’ve also found a lot of interest from practicing radiologists who never had that kind of training, and there has been a substantial increase in the number of medical students,” Perchik said.
“The program has grown more than I could ever have imagined.... We hope to build one of the hubs for AI education and scholarship throughout the United States and internationally.”
In addition to introductory lectures on AI concepts and lectures on using AI in each of radiology’s five subspecialties, Perchik adds new topics based on participant requests. In 2022, for example, Emory University’s Hari Trivedi, M.D., director of the university’s Healthcare Innovation and Translational Informatics Lab, discussed the economics and ethical considerations of AI algorithms. (See “Economics of AI for radiology.”) For the past two years, Smith’s talk “The Future of AI in Radiology” received the AI Literacy Course’s Most Impactful Lecture award. (See “Five ways radiologists are using AI.”)
International hub and local recruiting tool
Next year, Perchik aims to share the AI course with other programs that can serve as hosts for training. He also wants to partner with a South American university to host the course in Spanish. “The program has grown more than I could ever have imagined,” Perchik said. In March 2022, Perchik received an Education Project Award from the Radiological Society of North America, which he is using to develop a course website with recorded lectures, journal clubs, forums and research opportunities for residents and trainees. “We hope to build one of the hubs for AI education and scholarship throughout the United States and internationally,” he said.
When Perchik served as chief resident, UAB Radiology’s position as an AI leader was a useful recruiting tool. “That is something I would highlight with candidates — not only is it unique that we have this AI course, but we have hands-on experience in using these AI-enhanced programs,” Perchik said.
“If you were to go to a private practice and say, ‘I’ve used this program before; it was useful’ — or ‘We need to be a little more critical before we invest in this,’ that’s a huge benefit for our residents.”
Five ways radiologists are using AI A screenshot of the AI Metrics platform.In his talk at the AI Literacy Course in October 2022, “The Future of AI in Radiology,” Andrew Smith, M.D., Ph.D., offered an overview of AI use cases. 1. Deep learning image reconstruction What it is: “To get a great-looking PET scan, you have to give a full radiotracer dose and do a full scan time,” Smith said. “If you cut the radiation dose by a fourth, then you are going to get noisy-looking images. Conversely, if you keep a full radiation dose but cut the scan time to one-fourth — say from 20 minutes down to five — you are going to get similar-looking images that are noisy. Deep learning image reconstruction allows us to regain that signal by essentially reducing the image noise …. Now, a five-minute PET scan may be achievable.” There is a similar argument to be made for MRI. Sitting in an MRI scanner “is not a lovely thing to do, and some of these patients are in pain or just have other problems with their back or breathing issues, so getting through these exams quickly is a bit more important on MRI,” Smith said. Potential benefits: Increased patient satisfaction because they do not have to spend as long in the scanner; reduced radiation dose; better scan quality. Operationalizing the time savings is not as simple as it seems, Smith notes. Scanning time is just one aspect of the imaging process; a few minutes saved may not actually generate more scans per day. 2. Computer-aided detection and triage What it is: Algorithms can sort through images before they arrive to the radiologist and automatically move patients with positive studies or abnormal findings to the front of the queue; they may also provide important context about the patient. Potential benefits: Identify abnormalities that a radiologist may have missed; improve efficiency by reducing the time radiologists spend locating abnormalities. Because these algorithms are not directly connected to the radiologist’s Picture Archiving and Communication System, they rely on “widgets” to pop up important information. But these can fail to catch the attention of users. “The human-to-AI interface is weak” at the moment, Smith said. 3. Natural language processing for reporting What it is: Nearly all reports produced by radiologists include a findings section and an “impression” or conclusion section, Smith said. “You can train AI on tens of millions of imaging reports to teach the AI to automatically generate a conclusion or summary based on the text of the image findings.” In clinical practice, radiologists use talk-to-text dictation systems to detail all image findings. Once the findings are complete, the radiologist can simply click on a widget to activate AI that drafts a report “conclusion” within a few seconds. “Sometimes it gets it right; sometimes it gets it pretty close and you just need to adjust it,” Smith said. Potential benefits: In his experience working with such tools at UAB, “it does save some time, and I think it takes a little bit of cognitive load off the radiologist,” which could help prevent burnout, Smith said. “If a report is really long and has a lot of findings, you are going to want to press that button and have it summarize those findings for you.” 4. AI and natural language processing for patient management What it is: Detection and triage tools are generally intended to help radiologists find urgent issues that must be addressed. But AI and NLP have also shown potential for “opportunistic screening,” which Smith described as “screening for a disease passively using patient encounters or images obtained for a different purpose.” For example, there are about 100 million CT scans done in the United States each year, and about half of those are chest or abdominal CTs. Research has shown that algorithms can look through these images and detect aortic calcification (which is an early sign of heart disease), cardiomegaly (enlarged heart), aneurysms, lung nodules, cirrhosis, low bone density and other warning signs. Potential benefits: Finding important signs of serious disease early can save patient’s health, and their lives, in some cases. “At UAB, we do 80,000 CT scans a year that are of the chest or abdomen, and an AI algorithm focused on detecting a heart abnormality could work on all of those,” Smith said. “You would direct the patients that have pathologically enlarged hearts (cardiomegaly) for an echocardiogram, an EKG and a full clinical workup.” What is the next step to implementing proof-of-concept studies in the hospital? “We haven’t worked that out in our field as to what to do with that information,” Smith said. “But some of us are interested in trying to figure that out: How do you get that information and then interact with the patients?” 5. Augmented intelligence What it is: Smith is particularly excited about the possibilities for augmented intelligence systems — he has a startup company in this space for advanced cancer, called AI Metrics. Patients with cancer receive multiple scans over time to track tumor progression, with radiologists measuring the change in tumor size over time (or, more likely, multiple radiologists will have measured and reported on the tumors over the course of a patient’s treatment). The results determine whether their doctors continue with current treatment, switch treatments or pursue other therapies. Augmented intelligence refers to the use of AI to improve human performance. The AI Metrics solutions use AI to measure, label and track tumors over time. The radiologist is guided by the AI and smart programming. The solution automatically calculates percent changes in tumor size over time and displays the information in the form of a graph, table and key images. The improved clarity in reporting helps cancer doctors more accurately and precisely treat the underlying cancer. Potential benefits: AI assistance improves standardization, detects and labels prior lesions, and generates reports that can often be populated at the click of a button rather than by a radiologist dictating a note (a process that can lead to errors). At any time, the radiologist can re-measure, re-write or amend the report. In a multi-institutional trial of the AI Metrics tool, Smith and colleagues found that it improves accuracy, reduces major errors (incorrectly entered data, mathematical errors, right/left errors), was twice as fast as dictation and improved inter-observer agreement by the oncologist.
Back to main story
| 2022-12-05T00:00:00 |
https://www.uab.edu/reporter/people-of-uab/this-radiologist-is-helping-doctors-see-through-the-hype-to-an-ai-future
|
[
{
"date": "2022/12/05",
"position": 98,
"query": "future of work AI"
}
] |
|
How Is Technology Transforming The Workplace?
|
How Is Technology Transforming The Workplace?
|
https://elearningindustry.com
|
[
"Ivana Radevska",
"Tania Ferguson",
"Ozioma John Nsaka",
"Vartika Kashyap",
"Roz Bahrami"
] |
Additionally, we are experiencing widespread adoption of Artificial Intelligence tools to help eliminate bias in hiring decisions and support diversity, equity, ...
|
Understanding New Technology In The Workplace
New technologies in the workplace like machine learning (ML), Artificial Intelligence (AI), and deep learning transform the way we work.
How Is Technology Changing The Workplace?
The hyper-automation in the workplace represents an opportunity for businesses to accelerate complex process workflow and drive great business value. Some benefits of adopting technologies in the workplace include:
Streamlining processes with automation
Unifying communication channels
Improving decision-making based on analytics
Helping align the entire organization
Connecting teams better
Improving employee productivity and satisfaction
The natural evolution of the workplace has technology as an integral element. However, the COVID-19 pandemic further accelerated the transformation from the traditional work model into today's digital workplace. Below are some technologies shaping the future of work and how we do business.
eBook Release The Future Of Work Survey: Culture Trends And What Employees Want Is your business out of touch with what employees want? Dive into this report and discover how you can create a great workplace! The Future Of Work Survey
Some Technologies Shaping The Future Of Work
1. Talent Acquisition With Virtual Hiring
Recruiters need to eliminate manual paper workflows to win the war for talent in a tight labor market. Instead, human resource teams use platforms that enable virtual hiring processes. Recruiters have long used applicant tracking systems (ATS), which are a great way to attract, identify, and hire the most qualified candidates. Talent acquisition professionals can create a strong pipeline and improve efficiency by automating screening, scheduling interviews, and matching candidates' skills to a job posting.
However, we are now seeing many organizations use complementary technologies such as candidate relationship management systems (CRM) to create one larger ecosystem. Additionally, we are experiencing widespread adoption of Artificial Intelligence tools to help eliminate bias in hiring decisions and support diversity, equity, and inclusion efforts tied to recruiting. On top of that, technology lessens the administrative burden and gives recruiters more time to focus on human-centric strategies like improving the candidate experience.
2. Supporting Remote And Hybrid Work
It is no surprise that employees enjoy having flexibility in the workplace. In fact, one survey showed that employees overwhelmingly recommend (97%) remote work and would like to continue working remotely. Despite the logistical challenges they bring, remote and hybrid work arrangements are here to stay.
The use of technology plays a vital role in enabling flexibility. Hence, this shift has driven more leaders to invest in software and platforms to support business continuity under these new conditions. As a result, companies invest in collaborative tools, virtual wellness, and workforce management (WFM) platforms. Some tools and technologies that enable flexible working models include:
Cloud computing and infrastructure
Cybersecurity tools
Mobile and collaborative remote safe tools
Workforce management applications and platforms
Cloud-based WFM systems, self-service apps, and dynamic work-scheduling tools enable maintaining a highly productive workforce, regardless of whether they are in an office setting or working from home.
3. New Tech For Customizable Learning
Companies are no longer using a one-size-fits-all approach to Learning and Development. Advanced Learning Management Systems now provide a personalized approach to reskilling and upskilling employees. Technology has made it possible to identify and close knowledge gaps in each employee. Employees can feel empowered and motivated when they have the opportunity to create their custom learning paths with the type of content and methods that best suit them.
This personalized learning approach allows employees to learn at their own pace through online coaching, eLearning platforms, or Virtual Reality (VR) simulations. Fast-developing Augmented Reality (AR) and Virtual Reality technologies are becoming widely used solutions for training employees and boosting performance.
Additionally, companies are gamifying learning in the workplace to motivate employees. Any mundane task can be turned into a fun game, from new employee orientation to soft skills development. Using a digital environment and principles similar to a game that offers points and rewards motivates employees to reach specific goals or objectives.
4. Analytics And Decision Intelligence
Analytics and business intelligence have become powerful tools that can lead to massive gains for organizations. Analytics can shed light on what's happening inside the organization by gathering real-time employee productivity, performance, and engagement data. The use of Big Data in the workplace gives executive leadership comprehensive insight into the workplace and gives them accurate information supported by statistics and facts. Therefore, by measuring and analyzing key aspects of the workplace, leaders can make better-informed decisions.
Decision intelligence can help reveal future trends, predict and monitor behaviors, and forecast future talent needs. That is not to say that human decisions will be replaced; rather, they will be enhanced by the power of analytics, AI, and data. In addition, we see analytics being used to personalize people's experiences, such as using real-life journey maps, organizational network analysis (ONA), skills mapping, and career navigation.
5. Workplace Health And Wellness
A healthy and thriving workforce generates positive business outcomes that have a direct impact on productivity and profitability. As a result, employee well-being is seen as a critical aspect of success and morale. Forward-thinking companies are looking for solutions that balance all aspects of wellness—emotional, physical, and behavioral.
However, when it comes to designing a wellness program, one size fits none. Health is tied to the individual; therefore, a personalized wellness approach is vital for the success of corporate wellness programs. Intelligent analytics, Big Data, and other technologies all tailor corporate wellness programs into the right health solution for employees.
Some of the latest HR tech resources aim to improve the physical well-being of workers through gamification and wearable health technologies. AI-based tools and chatbots can support healthier habits and point out higher health-risk profiles. Some categories of high risk include:
Lifestyle and physical health risks
Obesity, diabetes, high blood pressure, smoking
Obesity, diabetes, high blood pressure, smoking Mental health risks
Depression, anxiety, stress, and employee burnout
Depression, anxiety, stress, and employee burnout Workplace safety risks
Repetitive work injuries, poor air quality
By gathering this data, employers can identify patterns and trends to understand their workplace and its employees better.
6. Improving The Employee Experience
Over the last few years, organizations have had to adapt to a radically shifting context for the workplace, workforce, and world of work. The "Great Resignation" and now the "Great Reevaluation" all send an important message to employers and the type of culture they need to retain top talent. Thus, one of the top priorities for human resources departments has become improving the employee experience (EX).
Today, technology is seamed into every aspect of the employee’s journey from start to exit, and every moment in between is part of their employee experience. Employing the right tools and technology is key to improving the digital experience of employees and can empower them. Furthermore, employees must provide feedback to close any gaps and genuinely understand how they engage with the tools available and where there is room for improvement.
7. Recognition And Reward
Technology is also transforming how companies give employees recognition and rewards. In the modern workplace, employees seek instant and fast recognition, which represents a problem for companies with flexible work arrangements or with a large workforce. The use of technology has made it easier to recognize and reward workers in a more effortless and timely manner. Not only that, but employee recognition software programs provide data-driven insights into how job performance and employee engagement are linked to the amount of recognition and rewards given.
Additionally, statistics can identify departments that are consistently hitting targets and enjoying success. Using analytics and technology platforms to administer employee recognition enables organizations to strengthen the relationship with their employees and create a supportive work environment.
What Are The Risks Of The Digital Workplace?
While technology brings a lot of benefits to organizations, there are also some risks that come with it. Acknowledging and understanding the potential dangers of new tools is a critical factor that is transforming the way work is done. Some possible risks of a digital workplace are:
Increased vulnerability to cyber attacks
Employee privacy and data collection concerns
Compliance with government regulations
Lack of personal connection
Legal risks of digital tools
Lower productivity due to digital friction
Undoubtedly, technology provides companies with many opportunities to improve and optimize their work performance. At the same time, employees’ personal data is regularly gathered and potential invasions of privacy are commonplace. The use of advanced technologies can also leave organizations vulnerable to cyber attacks.
In addition, the amount of workplace technology, communication software, and other human resources tools used today can easily overwhelm employees. Digital workers often fail to notice important information and are easily distracted by the number of emails, notifications, and updates. To mitigate these threats of the digital workplace, robust risk management strategies must be established, developed, and regularly reviewed.
Conclusion
The traditional landscape of the business world is continuously transforming, along with the role and importance of technology. Today's tech innovations aim to make the workplace more efficient and streamlined, and more insightful and smarter for everyone. As such, business leaders need to pay close attention to the latest innovations in tech and leverage technology to realize their business objectives.
Originally published on August 27, 2022
| 2022-08-27T00:00:00 |
2022/08/27
|
https://elearningindustry.com/how-is-technology-transforming-the-workplace
|
[
{
"date": "2022/12/05",
"position": 28,
"query": "workplace AI adoption"
},
{
"date": "2022/12/05",
"position": 18,
"query": "machine learning workforce"
}
] |
7 AI predictions for 2023 from IT leaders
|
7 AI predictions for 2023 from IT leaders
|
https://enterprisersproject.com
|
[
"Katie Sanders",
"December"
] |
“While there will be growing adoption of AI to enhance our collective user ... But AI solves a massive array of tasks in and outside of the workplace.
|
December is here, so you know what that means: holiday parties, new year’s resolutions, and a slew of technology predictions. We decided to focus on a trend that matters most urgently to IT leaders—concrete artificial intelligence (AI) insights for your team and business.
The potential impacts of AI are wide-ranging—as are the related forecasts, on everything from sentient to generative and responsible AI, to collaboration and automation. What will matter to IT leaders in 2023? We talked to AI and IT career experts to ask their opinions.
Language I/O CTO Diego Bartolome says that “machine learning will help correct AI bias. In conversational AI, systems that ‘know the customer’ by leveraging information about that particular person will also reduce bias."
And that’s just a starting point. Let’s dig into other key trends.
CTOs will get particular about AI-specifics
“A CTO needs to deliver technology that improves services and processes for healthcare providers. After all, providers want their physicians focused on healthcare delivery, not technology. CTOs should not buy AI because it’s AI or because it’s the latest and greatest tech. Instead, CTOs should think about the potential AI offers. How will it work in their specific organization? How will it improve business processes? That’s critical. Previously, you could get away with saying, ‘We’re implementing AI or digital transformation' and get a blank check, but that’s not going to fly anymore. Organizations want to see results and need to be able to measure impact. A CTO can’t just make a big statement that AI is the future and get whatever budget they want. In 2023, the rubber will meet the road.”
—Michael Armstrong, Chief Technology Officer, Authenticx
Groundbreaking impacts of AI technology
“AI will yield tremendous breakthroughs in treating medical conditions in the next few years. Just look at the 2021 Breakthrough Prize winner Dr. David Baker. Dr. Baker used AI to design completely new proteins. This ground-breaking technology will continue having huge ramifications in the life sciences, potentially developing life-saving medical treatments for diseases like Alzheimer’s and Parkinson’s.
Watch the crossover from fundamental physics into informatics in the guise of quantum and quantum-inspired computing. While I’m not holding my breath for a practical quantum computer, we will see crossover. Perhaps one of the more intriguing examples is Andy Brigg’s QuantrolOx, where AI is being used to tune quantum computers!
The mix of advanced mathematics and informatics will unleash a new generation of engineers uniquely placed to exploit the AI wave.”
—Phil Tee, Co-founder and CEO, Moogsoft
At the crossroads of artificial and human intelligence
“While there will be growing adoption of AI to enhance our collective user experience at scale, it will be balanced with appropriate human intervention. Humans applying the insights provided by AI will be a more effective combination overall than either one doing it alone. How and where this balance is struck will vary depending on the industry and the criticality of the function being performed. For example, radiologists assisted by an AI screen for breast cancer more successfully than they do when they work alone, according to new research. That same AI also produces more accurate results in the hands of a radiologist than it does when operating solo.”
—E.G. Nadhan, Global Chief Architect Leader, Red Hat
[ For more ways AI is changing industries like healthcare and automotive, read Open Source Stories: AI Revolutionaries ]
Responsible and generative AI capabilities are improving
"We can expect to see a few major AI trends in 2023, and two to watch are responsible AI and generative AI. Responsible or ethical AI has been a hot-button topic for some time, but we’ll see it move from concept to practice next year. Smarter technology and emerging legal frameworks around AI are also steps in the right direction. The AI Act, for example, is a proposed, first-of-its-kind European law set forth to govern the risk of AI use cases. Similar to GDPR for data usage, The AI Act could become a baseline standard for responsible AI and aims to become law next Spring. This will have an impact on companies using AI worldwide.
[ How can IT teams prepare for AI? Read Artificial intelligence: 3 tips to ensure responsible and ethical use]
The second, generative AI, will also make major strides over the next 12 months. Recent models can easily create realistic images and drawings from a description in natural language. Capabilities like this are now moving from cool functionality to actual business use cases. Dozens of companies offer you products that will draft essays, ad copy, or love letters. Instead of searching through stock photography, you can type a query and get a newly generated image. And this is just the beginning – we’re only scratching the surface of generative voice and video applications, so it will be interesting to see innovations and use cases come forth in the coming year."
—David Talby, CTO, John Snow Labs
Stronger collaboration between business and IT teams
"In 2023, as companies brace for greater economic volatility, there will be more pressure to not only do more with less but also demonstrate AI’s business value from the onset. Despite IT leaders recognizing the benefits that AI delivers in improved automation, insights, and efficiency, AI still requires stronger collaboration between the business and IT to ensure that the technology is truly addressing business issues and needs.
Another trend we’re already seeing is the continued push to embrace AI holistically across the organization. Various software and hardware solutions—from data models to AI chips—are focused on taking a slice of the lucrative AI pie.”
—Farshid Sabet, Chief Business Officer, Katana Graph
Skip to bottom of list More on artificial intelligence
AI will change the efficiency and output of organizations
“There’s an ongoing discussion about whether AI will become sentient and pose a threat to humanity, which greatly overestimates its current capabilities. AI already completes many tasks that would take humans thousands of hours to accomplish: beat chess grandmasters, identify fractures in X-rays, choose the fastest route for a delivery truck, etc. But AI doesn’t 'understand' how it accomplishes these tasks. It can’t explain why one chess move is more strategic than another the way a human could – it just knows. But AI solves a massive array of tasks in and outside of the workplace.
To maximize its use, we need to understand why AI can do so much even though it lacks human-like intelligence. For example, can AI perform many of the tasks people complete in the legal industry, where lawyers still bill their time in six-minute increments? I predict that assigning more tasks to AI will lead to step-changes in teams’ efficiency and output.”
—Chuck Kellner, Strategic Discovery Advisor, Everlaw
AI-driven and supported automation
“Everyone understands the value of automation, and, in our software-defined world, almost everything can be automated. The decision point or trigger for the automation, however, is still one of the trickier elements. This is where AI will increasingly come in: AI can make more intelligent, less brittle decisions than automation’s traditional 'if-this-then-that' rules.”
—Richard Whitehead, CTO and Chief Evangelist, Moogsoft
[ Want to adopt best practices for AI workloads? Get the eBook: Top considerations for building a production-ready AI/ML environment. ]
| 2022-12-05T00:00:00 |
https://enterprisersproject.com/article/2022/12/ai-predictions-2023
|
[
{
"date": "2022/12/05",
"position": 75,
"query": "workplace AI adoption"
},
{
"date": "2022/12/05",
"position": 12,
"query": "artificial intelligence business leaders"
}
] |
|
Cultivate an Insight Driven, Connected Government
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Cultivate an Insight Driven, Connected Government
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https://www.deloitte.com
|
[] |
Join Human Capital Principals Sarah Benczik and Daria McAteer as they discuss how government agencies are upskilling their workforce, enhancing AI fluency, and ...
|
Cultivate an Insight Driven, Connected Government
AI and automation have tremendous potential to improve business operations and drive mission impact. Join Deloitte’s Government & Public Services (GPS) Consulting and Advisory Leaders as they discuss key AI and data trends transforming the government and enabling users to cultivate connectivity through data.
| 2022-12-05T00:00:00 |
https://www.deloitte.com/us/en/Industries/government-public/collections/cultivate-an-insight-driven-connected-government.html
|
[
{
"date": "2022/12/05",
"position": 6,
"query": "government AI workforce policy"
}
] |
|
Careers
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Ília Digital
|
https://ilia.digital
|
[] |
Marvin AI. At the forefront of innovation and advancements in the field of Generative Artificial Intelligence, our People team has developed Marvin People.
|
ília is constantly seeking to differentiate itself in its actions and ways of delivering value to its clients. I have always been enchanted by its promotion of autonomy among its people and its receptiveness to my ideas. Moreover, flexibility is a major perk; I will soon become a father, and ília’s support is very important. The sense of belonging at ília is very important and motivates me daily.
| 2022-12-05T00:00:00 |
https://ilia.digital/en/careers/
|
[
{
"date": "2022/12/05",
"position": 79,
"query": "generative AI jobs"
}
] |
|
MachineLearningApplications@CSAIL - CSAIL Alliances - MIT
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MachineLearningApplications@CSAIL
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https://cap.csail.mit.edu
|
[] |
This exciting new initiative will help leaders navigate, consume, digest and prepare their company for all machine learning has to offer.
|
Overview
MIT’s world renowned Computer Science Artificial Intelligence Lab (CSAIL)'s research initiative MachineLearningApplications@CSAIL focuses on applications of the latest machine learning (ML) technologies, potential solutions to the current challenges limiting the abilities of ML, and professional development that will help prepare a company’s workforce for this digital transformation.
Many companies are unsure of how, where, or if they should leverage ML. Awash in data, they are looking to turn that data into intelligence that drives increasingly efficient processes. The valuable insights and impact across all functions from sales, marketing, and customer engagement to logistics, cost control, fraud detection, security, and more can be transformational.
Organizations who know how to leverage and integrate ML across their business will have a competitive advantage. All industries including retail, food/beverage, travel/tourism, household goods, construction, fashion, agriculture, manufacturing/ packaging, education, pharmaceutical, healthcare, and more will all benefit from the latest ML technologies.
MachineLearningApplications@CSAIL will include additional themes as an opportunity for interested companies to gain valuable insights from CSAIL researchers in industry-specific areas. Current themes include:
Learning Robots | Led by Professor Pulkit Agrawal
Advances in robotics have brought us autonomous vehicles, humanoid helpers, and even robotic surgery. But so much more is still possible. Which is harder—to teach a robot to play chess or to use a screwdriver? Manipulations and sensing still have a long way to go. Creating machines that can automatically and continuously learn about their environment is the goal and this theme within MLA@CSAIL focuses on robot learning to enable the next generation.
Programmable Therapeutics | Led by Professor Manolis Kellis
There have been many advances in disease detection and treatment, yet so much more work is needed! Globally, there is a substantial unmet need to diagnose various diseases effectively. The complexity of different diseases and the genetic makeup of the patient population are challenges to developing early diagnosis tools and effective treatment to improve patient health. Machine learning can greatly improve the understanding of diseases plaguing the population such as Alzheimer's disease, heart failure, breast cancer, diabetes, obesity, and more. Understanding of the human genome by computational integration of large-scale functional and comparative genomics datasets provides new discoveries and approaches to improve patient outcomes.
Visual Computing | Led by Professor Fredo Durand and Professor William T. Freeman
The field of computer vision and cognition research has exploded recently due to the advancements in sensors, data sets, computing power of GPUs, machine learning and neurally inspired systems of deep learning. The Visual Computing focus will be to revolutionize visual computing systems (including vision, graphics, imaging) and facilitate the transition of fundamental knowledge to real-world technological solutions. Creating machines that can “see” are essential for a wide array of emerging technologies leveraging machine learning such as autonomous vehicles, robotics, and predictive modeling for medical diagnostics.
Clinical AI
Details coming soon.
If you are interested in learning more about these themes, please contact [email protected].
| 2022-12-05T00:00:00 |
https://cap.csail.mit.edu/members/initiatives/machinelearningapplicationscsail
|
[
{
"date": "2022/12/05",
"position": 4,
"query": "machine learning workforce"
}
] |
|
Machine Learning Breaks Into Human Resources
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Machine Learning Breaks Into Human Resources
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https://jpt.spe.org
|
[] |
This paper describes a novel method based on machine learning to maintain an evergreen competency database.
|
The swaying “having the right person at the right time at the right place” makes even more sense in a low-oil-price environment. Understanding capabilities available in an organization can be hindered by the size of the talent pool and a lack of a structured and systematic approach to quantify, update, and manage competencies.
This paper describes a novel method based on machine learning to maintain an evergreen competency database. The tool reduces discrepancies between organizational requirements and the actual talent deployment by using unstructured corporate data.
A digital database for each person was created and populated with various data such as CV, corporate information (e.g., compensation and location), assignment history, technical reports generated, articles and conference papers written, training attended, and periodic internal competency assessments and promotion history. A master database then is created that includes the database of each person. Machine-learning methods based on optical characters recognition and natural-language processing then are applied to the master database to extract synthetic and key information needed for more-efficient resource management and capability deployment.
For each employee, a competency matrix is created following a multitiered proficiency system with levels ranging from awareness to mastery. The machine-learning methods result in a cloud of keywords that can be browsed. Managers are now capable of having a snapshot of their current workforce skills and capabilities according to competency level.
External workforce such as former employees or prospective candidates can be included in the capability pool. In addition, a focused search, based on specific project needs and niche capability requirements, can be conducted.
In a few clicks, a talent manager can look up critical resources that can be deployed when and where needed. For entry-level resources, a personal development plan can be drafted with more granularity and can be tailored by the capabilities available at a given location or operating unit at a given time.
Download the complete paper from SPE’s Health, Safety, Environment, and Sustainability Technical Discipline page for free until 14 December.
Find paper SPE 203204 on OnePetro here.
| 2022-12-05T00:00:00 |
2022/12/05
|
https://jpt.spe.org/machine-learning-breaks-into-human-resources
|
[
{
"date": "2022/12/05",
"position": 7,
"query": "machine learning workforce"
}
] |
Evolution of Digital Labor in Today's Workforce
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Evolution of Digital Labor in Today's Workforce
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https://draup.com
|
[
"Vijay Swaminathan"
] |
... Machine Learning model using such a platform. We expect this layer to grow more in 2022; Decision Support: Across various functions such as Supply Chain ...
|
In 2021, companies made record cloud investments. The pandemic has been devastating for many sectors of the economy and created an unprecedented global health crisis. But at the same time, it created an accelerated digital transformation across industries. While the investments have been significant, leadership and board members are now thinking about getting the best out of such investments. To truly become a Digital Enterprise and take full advantage of Cloud, Workforce Planners and Talent Acquisition teams must understand the Future of the Workforce in granular detail. The boundaries and the impacts of Digital Work are constantly evolving. Just last month, JP Morgan was fined by SEC as employees used Whatsapp and other personal forms of communication with customers as that created bottlenecks in record keeping. Such digital regulations may expand outside the industry verticals of Financial companies and Banks. This complex change and dynamics make digital enterprise development a constant journey.
At Draup, we aim to prepare you for the Digital Labor Age. To truly understand this, we must focus on the interrelationships of the various job families and skills. Dr. Yuval Noah Harari, a historian, states that humans are relatively weak animals whose advantage rests in their ability to cooperate in large numbers. And the reason we have been so successful is that “large numbers of strangers can cooperate successfully by believing in common ideas (from the book Sapiens).
Today, there is an interconnectedness that dictates the evolution of skills. All the foundational investment in infrastructure into the cloud enables better decision-making by helping us collect better Analytics and test various hypotheses. But when Draup investigates the skills that companies are sourcing, we see Enterprises are still not moving faster to take advantage of the investments they have made. This also paves the way for talent getting disengaged. Not having the right tools and platforms will force the labor force to go to companies with this talent. Let us look at the global attrition trends based on our profiles dataset updates (please use this as directional only as sometimes there may be a delay in resume updates). For example, many data scientists leave the organizations once they realize that companies do not have the latest platforms and tools to use. The great work done by the incredible recruitment team in hiring the resources is lost if we do not have the right Digital Tech stack!
To understand the evolution of Digital Labor, we have to understand the interconnectedness of the Job Families. The following illustration depicts this concept
Source – xkcd.com/435
What is the primary goal of all the Digital Investments and Cloud Transitions?
Perhaps enquiring through this question may give us compelling insights. But before we get into this aspect, let me explain this Digital layer’s IT/Software aspect of an organization. The reason we are emphasizing that Workforce Planners and Recruiters should understand this is that this simple understanding will make you very effective in all your conversations (even if you are not directly involved in technical recruiting)
A digital organization from the technology side can be imagined having these layers (this is kept relatively simple for easy understanding and not making it too technical)
Front End Frameworks: It’s somewhat analogous to the framework for a house—which might include instructions on building windows, doors, etc. The house’s developer can then decide how to take the framework’s rules and build the house. So these frameworks decide how we build the software products. You will hear Angular, React, and Backbone frameworks in this category. All those are different frameworks accomplishing the same goal.
Server Side or Backend Frameworks: The application you write (or the house you build) must stay secure, scale quickly, integrate well with other systems, and several other factors. These are collectively referred to as the backend frameworks. Here, you will hear several Java-based backend frameworks and Django (python-based) skills. Many of the Java Developers’ postings you see will be for this need.
Database: This is a framework to store the data. SQL is a crucial skill here, and you will hear platforms such as Cassandra/MongoDB that gives enormous scalability advantages
Cloud Infrastructure: Ensures on-demand availability of systems, optimizes computation. Here you will hear terms like AWS, Azure, GCP, and so onThis understanding will help you place various skills that a Digital Organization is trying to source. Terms will evolve, and products will come and go, but this layer will be helpful.
What is the primary goal of all the Digital Investments and Cloud Transitions?
One way to think about this is to collect Analytics better and make better decisions. This aspect impacts all the employees/job roles and skills. Here is a summary of various skills and roles we are seeing gaining traction. These skills are roles will be helpful for organizations in 2022 and beyond
Business Intelligence Analyst: Gone are the days when Business Analysts used only MS Excel. SQL has become an essential skill, and tools like Snowflake that makes it easy to pull large volumes of data and analyze it in a spreadsheet-like format are becoming popular Project Management: Here, several workflow products like Airtable are beginning to play a role. The key here is a seamless digital collaboration Data Engineering: This is different than Data Science. To have a good data science team, Data Engineering is crucial. Essentially ensures plumbing lines so that Data Scientists can get all the relevant data when they need it for their models. Kafka is becoming a prevalent skill in this area. Developer Advocates: This role was used by Google more in an external capacity (basically working with Developers across the globe to promote Google Products). Beginning 2022, Google has opened jobs that will work with Engineers within Google to listen to their technical and business constraints to report Developer challenges in an organized way. This is an exciting advocacy model Anthropologists: We are beginning to see the importance of Anthropologists in Enterprises. As companies scale, diverse workforce across different cultures will necessitate the need for proper understanding, and people with Anthropology background will be very good as People Analysts Physicists: Many AI researcher roles are seeking advanced physics degrees. This is also expanding into other areas of technology Data Analyst – Auto ML: Tools like Data Robots are becoming popular as a Data Analyst can quickly build a Machine Learning model using such a platform. We expect this layer to grow more in 2022 Decision Support: Across various functions such as Supply Chain, Forecasting, Manufacturing, we are seeing an increase in the sourcing of Industrial Engineering and Operations Research skills Full Stack: In the framework we explained, a resource that can work both on the front end and back end as a software engineer are certainly on demand. This approach was first popularized by Facebook- Now Meta. (they are, in simpler terms, Generalists)
Overall, we are pretty excited about the role HR can play under emerging circumstances. If we plan the initiatives correctly, we can accelerate transformation and adaptation and make the enterprises genuinely care for a great workplace.
| 2022-12-05T00:00:00 |
https://draup.com/talent/ceo-newsletter/a-deep-dive-into-the-evolution-of-digital-labor-the-interconnectedness-of-job-families
|
[
{
"date": "2022/12/05",
"position": 86,
"query": "machine learning workforce"
}
] |
|
From Discrimination in Machine Learning to ...
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From Discrimination in Machine Learning to Discrimination in Law, Part 1: Disparate Treatment
|
http://ai.stanford.edu
|
[
"Fereshte Khani",
"Percy Liang"
] |
The Equal Employment Opportunity Commission (EEOC) is a federal agency that administers and enforces civil rights laws against workplace discrimination. EEOC ...
|
Around 60 years ago, the U.S. Department of Justice Civil Rights Division was established for prohibiting discrimination based on protected attributes. Over these 60 years, they established a set of policies and guidelines to identify and penalize those who discriminate.
The widespread use of machine learning (ML) models in routine life has prompted researchers to begin studying the extent to which these models are discriminatory. However, some researcher are unaware that the legal system already has well established procedures for describing and proving discrimination in law. In this series of blog posts, we’ll try to bridge this gap. We give a brief overview of the procedures to prove discrimination in law, focusing on employment, and discuss its analogy to discrimination in machine learning. Our goal is to help ML researchers assess discrimination in machine learning more effectively and facilitate the process of auditing algorithms and mitigating discrimination.
This series of blog posts is based on CM-604 Theories of Discrimination (Title VII) and chapters 6 and 7 of TITLE VI Legal Manual. In this first blog post, we discuss the first type of illegal discrimination known as disparate treatment, and in the second blog post, we discuss the second type of illegal discrimination known as disparate impact.
Machine Learning Analogy For each section, we give a brief history of related efforts in ML in a green box like this one!
Main point We write the main point for each section in a blue box like this one!
The Equal Employment Opportunity Commission (EEOC) is a federal agency that administers and enforces civil rights laws against workplace discrimination. EEOC charges around 90,000 cases each year, of which around 15% result in monetary benefit for the charging party. The diagram shows charge receipts by basis in 2019. The average money beneficiary is ~300 million dollars per year. The processing time for each charge usually is long and in average takes ten months.
Table of Contents
Protected Attributes
Anti-discrimination laws are designed to prevent discrimination based on one of the following protected attributes:
Definition of Disparate Treatment
Disparate treatment occurs when an employer treats some individuals less favorably than other similarly situated individuals because of their protected attributes. “Similarly situated individuals” is specific to each case and cannot be defined precisely, intuitively it means individuals who are situated in a way that it is reasonable to expect that they would receive the same treatment.
During the legal proceddings, the charging party (the party that believes it has suffered from disparate treatment, e.g., employee) accuses the respondent (the party that is accused of treating the charging party less favorably because of their protected attributes, e.g., employer) of disparate treatment.
Although historically, the charging party has to establish that the respondent deliberately discriminated against them, it has been recognized that it is difficult and often impossible to obtain direct evidence of discriminatory motive; therefore, the discriminatory motive can be inferred from the fact of differences in treatment.
There is a gender income gap probably due to historical discrimination. When there is not enough features ML models rely on this difference in distribution and lead to predict lower income for women.
Disparate Treatment in Machine Learning Humans can have racist, sexist, etc., motives and make decisions based on them, but why would an ML model which does not have any such motives treat similarly situated people differently based on their protected attributes? There are many ways that biases can creep into ML models and cause discrimination (see this short note).
As an example, let’s consider distribution bias in data. Due to previous historical discrimination against women, there is a large gap between the average income of men and the average income of women. Although this gap is narrowing over time, it has not been eliminated. Consider a bank that uses an ML model to predict the income of its customers to give them loans accordingly. Consider a very extreme case, where there are no features except the gender of applicants. In this case, it is optimal for the model to rely on the protected gender attribute and predict average income for men if the applicant is a man, and average income for women if the applicant is a woman. Reliance on protected attributes leads to better error in comparison to predicting average income for everyone. Such reliance (and thus disparate treatment) is an optimal strategy for the ML model (see for more details).
Legal Procedure
The legal process for proving disparate treatment comprises three steps:
The charging party must establish a prima facie case of discrimination, i.e., providing enough evidence to support their allegations are true. The respondent can rebut the charging party’s case (e.g., providing an alternate explanation for the disparity) The charging party can provide evidence that the respondent’s explanations for its actions are pretext, i.e., an attempt to conceal discrimination.
We now expand each step and briefly mention the related work in ML.
The legal procedure for proving disparate treatment has three steps: 1) The charging party must provide evidence for their allegations, 2) The respondent can rebut the allegation 3) The charging party can show that respondent's explanations are pretexts.
Main Point of Legal Procedure Despite our desire to have an easy definition for discrimination, proving discrimination is a long-term process that involves both sides bringing in reasons and a judge/jury deciding whether there is discrimination or not.
First step: Establishing a Prima Facie case
Evidence for disparate treatment discrimination can be presented in three main ways:
(a) Comparative Evidence
The disparate treatment theory is based on differences in the treatment of similarly situated individuals. “Similarly situated individuals” cannot be precisely defined, and it is context-dependent. Generally, similarly situated individuals are the ones that are expected to receive the same treatment for a particular employment decision.
For example, when there are some predefined qualifications for promotion, similarly situated individuals are those who meet these qualifications. Or, in the case of discharge (firing), the employer provides a reason for the termination, and people who have committed the same misconduct are similarly situated.
Comparative evidence is a piece of evidence that shows that two similarly situated individuals are treated differently due to their protected attributes.
“For example, an employer’s collective bargaining agreement may contain a rule that any employee charged with theft of company property is automatically discharged. If a Black employee who is charged with theft of company property is discharged, the discharge is consistent with the rule and the agreement. However, the analysis does not end there. To determine whether there was disparate treatment, we should ascertain whether White employees who have been charged with the same offense are also discharged. If they are merely suspended, disparate treatment has occurred. The key to the analysis is that they are similarly situated employees, yet the employer failed to apply the same criteria for discharge to all of them. They are similarly situated because they are respondent’s employees and were charged with the same misconduct. The difference in discipline could be attributable to race, unless respondent produces evidence to the contrary.”
ML Efforts on Providing Comparative Evidence In simple interpretable models we can investigate the features that the model relies upon and find similarly situated individuals . For black-box models (e.g., neural nets), there is a lot of research on interpretability to understand model decisions and find similarly situated individuals. In general, models that can provide explanations for their decisions might facilitate the investigation of discrimination. However, if ML models are proprietary then finding similarly situated individuals is challenging. One approach to provide comparative evidence is to define similarly situated individuals as individuals who are only different in their protected attributes or some of their strong proxies and then show that the model’s prediction changes with the protected attribute. For example, show that toxicity detection models give different toxicity scores to the same sentence with different identity terms (e.g., I’m gay vs. I’m straight). show that Google is showing different ads for African American names vs. White names.
In a study by Datta et al., created 1000 new browser instances and assigned them randomly to two groups. On Google's Ad Settings page, one group set its gender to male, while the other set it to female. All browsers then visited Alexa's top 100 websites for employment. Thereafter, all the browsers collected the ads served by Google on the Times of India. The career change ad was served 1816 times to the male users, but only 311 times to the female users. This evidence can serve as statistical evidence.
(b) Statistical Evidence
Statistical evidence can also be used to demonstrate discriminatory motives. Charging party uses statistical evidence to prove that the respondent uses protected attributes in its decision. For example, Alice believes that she is not hired for a secretarial position because she is Black. She can buttress her allegation with statistical evidence indicating that the respondent employs no Black secretaries despite the many applicants in its Metropolitan Statistical Areas. The statistical data shows that the respondent refused to hire Blacks as secretaries; thus, Alice’s rejection was pursuant to this practice. Note that in statistical evidence unlinke the comparative evidence Alice does not need to find a similarly situated individual to herself. It is important to note that statistics alone will not normally prove an individual case of disparate treatment .
ML Efforts on Providing Statistical Evidence For generating statistical evidence, a common approach in ML is to generate multiple groups with the same distribution on all features except the protected features and test if the model treats groups differently. For example, show that Google is showing different ads to simulated web browsers with the identical history but different selected genders.
A second approach is to control for all potentially relevant risk factors and compare the error rates between groups. The disparity among groups can be evidence of relying on protected attributes. For example, show that there is a performance disparity between pedestrian detection with different skin colors, controlling for time of day and occlusion. So they suggest that these performance disparities could be due to skin color alone, not just due to darker-skinned pedestrians appearing in harder-to-detect scenes.
Studies like illustrate that advertisement models are directly using the gender of a person for ad targeting. This is especially problematic for housing, employment, and credit (“HEC”) ads. Following multiple lawsuits, Facebook agreed on a settlement to apply the following rules when HEC ads.
1) Gender, age, and multicultural affinity targeting options will not be available when creating Facebook ads. 2) HEC ads must have a minimum geographic radius of 15 miles from a specific address or from the center of a city. Targeting by zip code will not be permitted. 3) HEC ads will not have targeting options that describe or appear to be related to personal characteristics or classes protected under anti-discrimination laws. This means that targeting options that may relate to race, color, national origin, ethnicity, gender, age, religion, family status, disability, and sexual orientation, among other protected characteristics or classes, will not be permitted on the HEC portal. 4) Facebook’s “Lookalike Audience” tool, which helps advertisers identify Facebook users who are similar to advertisers’ current customers or marketing lists, will no longer consider gender, age, religious views, zip codes, Facebook Group membership, or other similar categories when creating customized audiences for HEC ads. 5) Advertisers will be asked to create their HEC ads in the HEC portal, and if Facebook detects that an advertiser has tried to create an HEC ad outside of the HEC portal, Facebook will block and re-route the advertiser to the HEC portal with limited options. Google also announced in 2020 that “A new policy will prohibit impacted employment, housing, and credit advertisers from targeting or excluding ads based on gender, age, parental status, marital status, or ZIP Code, in addition to our longstanding policies prohibiting personalization based on sensitive categories like race, religion, ethnicity, sexual orientation, national origin or disability.”
Note that even though the advertisers do not target according to the protected attributes, it is still possible that the models use such protected attributes for ad targeting (i.e., models still use protected attributes as a feature for finding the optimal audience for HEC ads). Thus a follow-up study similar to is necessary to make sure that the model does not use gender as a feature for ad targeting.
Suggestions on providing statistical evidence Studies such as are necessary for auditing machine learning models that are deployed. For example, there should be easy ways to investigate if Linkedin (for hiring) is sensitive to protected attributes, or if Twitter or Facebook discriminate in promoting a business page. Such investigation can happen by providing appropriate tools and requiring businesses to be transparent about their methods.
(c) Direct Evidence of Motive
Direct evidence of motive can be demonstrated by:
Any statement by the respondent that indicates a bias against members of a protected group Showing a failure to take appropriate corrective action in situations where the respondent knew or reasonably should have known that its employees’ practices/policies/behaviors were discriminatory (e.g., not taking action in a sexual harassment case).
ML efforts on Providing Direct Evidence of Motive The process of pretraining a model on a large amount of data and then tuning it for a particular purpose is becoming increasingly common in ML. Examples of such models include Resnet pretrained on ImageNet and language models such as BERT and GPT-3 .
There are many works in fairness in ML that show that the word embeddings in language models or features in vision models are misrepresenting , or underrepresenting some protected groups.
Suppose a company that has knowledge of such biases uses the pretrained BERT model without any constraint as the backbone of its hiring models. In that case, the stereotypical representation of the protected groups can serve as direct evidence of motive .
Main Point of Step 1: Establishing a Prima Facie Case There are three main ways to provide a prima facie case for disparate treatment: Comparative evidence Statistical evidence Direct motive evidence ML researchers facilitate providing proof via following: Providing interpretable models or models that can explain their decision so that the charging party can find “similarly situated individuals” for comparative evidence. Provide tools (e.g., Adfisher) to check if a black box model relies on protected attributes and its strong proxies for statistical evidence. Heavy analysis of prevalent models that are used as pretraining for direct evidence of motive.
Second Step: Rebutting the Prima Facie Case
In the second step, the respondent can bring some evidence to show that the evidence presented by the charging party is not valid. There are six types of rebuttals the respondent can provide:
(a) Charging Party’s Allegations Are Factually Incorrect
(b) Comparison of Similarly Situated Individuals Was Not Valid
This evidence can usually be in the form of (1) Individuals compared are not similarly situated, or the hired individual is more qualified, and (2) Not all similarly situated individuals were compared
(c) Respondent’s Actions were Based on an Act of Favoritism
Title VII only prohibits discrimination based on protected attributes. If in isolated instances a respondent discriminates against the charging party in favor of a relative or friend, no violation of Title VII has occurred.
However, if there are indications that the respondent hired their relative to avoid hiring people from some protected groups, there should be an investigation to determine if the respondent’s actions were a pretext to hide discrimination. In this case, the respondent’s workforce composition and their past hiring practices would be important pieces of evidence.
(d) Charging Party’s Statistical Proof Is Not Meaningful
The respondent can show that statistical proof is not meaningful e.g., it considers the pool of applicants in the state instead of the city.
(e) Statistical Proof To Rebut an Inference of Discriminatory Motive
The respondent can provide statistical data showing that they have not discriminated against protected groups. For example, showing that they have employed a high proportion of a protected group. Even though these kinds of evidence serve as support, they are not conclusive proof that discrimination did not occur. There may not be a pattern and practice of discrimination, but an individual case of disparate treatment may have occurred.
(f) Respondent’s Actions Taken Pursuant to an Affirmative Action Plan
“Affirmative action under the Guidelines is not a type of discrimination but a justification for a policy or practice based on race, sex, or national origin. An affirmative action plan must be designed to break down old patterns of segregation and hierarchy and to overcome the effects of past or present practices, policies, or other barriers to equal employment opportunity. _It must be a concerted, reasoned program rather than one or more isolated events. It should be in effect only as long as necessary to achieve its objectives and should avoid unnecessary restrictions on opportunities for the workforce as a whole. For more details, see the affirmative action manual.”
ML Efforts on Understanding and Mitigating Disparate Treatment The ML community has made a lot efforts to understand why a model will rely on protected attributes to either rebut the evidence in step 1 or come up with mitigation methods that guarantee that no discriminatory evidence can be held against them. Understanding why ML models rely on protected attributes: One of the simplest and most frequently studied reasons for such behavior is biased training data. Historically, discrimination was practiced on the basis of protected characteristics (e.g., disenfranchisement of women). These discriminations artificially influenced the distributions of a variety of societal characteristics. Because the data used to train most ML models reflects a world where societal biases exist, the data will almost always exhibit these biases. These biases can be encoded in the labels, such as gender-based pay disparity, or in the features, such as the number of previous arrests when making bail decisions . When biased data are used to train ML models, these models frequently encode the same biases. Although learning these biases in ML models, and relying on protected attributes, may achieve low test error, these models can propagate the same injustices that led to the biased data in the first place. Even when protected attributes are explicitly withheld from the model, they remain a confounding variable that influences other characteristics in a way that ML models can pick up on. However, note that training data is not the only reason and biases can creep into the ML cycle at different stages, see this short note. Mitigate disparate treatment: Regarding comparative and statical evidence, ML advocates interpretable models so that it would be easy to argue that the model does not demonstrate disparate treatment . In domains such as vision or text, one common approach is to use GAN-style generation and show that the model is invariant to change in protected attributes . Regarding the direct evidence of motive, there are many work suggestions fixing word embeddings and predefined features in images and showing their invariance to change in protected attributes .
Main Point of Step 2: Rebutting the Prima Facie Case As more ways of evaluating ML models are developed, thinking about how they can be connected with their analogues in law would be invaluable. A good practice exercise for a research scientist at a company would be showing that no obvious prima facie case (with any of the three different types of evidence) can be brought against the proposed model.
Third Step: Proving Pretext
Once the respondent states a legitimate justification for the decision, the charging party can rebut the argument and claim that it’s a pretext for discrimination. For instance, the charging party might present evidence or witnesses that contradict those submitted by the respondent. Or the charging party can show that the respondent gives different justifications at different times for its decision.
Machine Learning Analogy for Proving Pretext The first rebuttal against alleged disparate treatment in ML was that they do not use protected attributes as features. Many ML researchers argue that although not using protected attributes and their strong proxies is necessary, it is far from being sufficient. It is easy for machine learning models to predict the protected attributes from other attributes. Therefore, ML models can still be alleged for disparate treatment even when they do not use the protected attributes.
In response, counterfactual reasoning has been studied to find “similarly situated” individuals that are treated differently by the algorithm . However, there are many concerns with counterfactual reasoning with respect to the protected attributes which we summarize here. Immutability of group identity: One cannot argue about the causal effect of a variable if its counterfactual cannot even be defined in principle . Post-treatment bias: considering the effect of characteristics that are assigned at conception (e.g., race, or sex) while controlling for other variables that follow birth introduces post-treatment bias . Inferring latent variables: Counterfactual inference needs strong assumptions regarding data generation.
Main Point of Step 3: Proving Pretext Only removing protected attributes (and their strong proxy) is not enough! ML models can simply predict protected attributes from other (nonessential) attributes.
Real Legal Cases
“JPL systemically laid off employees over the age of 40 in favor of retaining younger employees. The complaint also alleges that older employees were passed over for rehire in favor of less qualified, younger employees. Such conduct violates the Age Discrimination in Employment Act (ADEA),” according to the EEOC.”
Ramifications: Jet Propulsion Laboratory had to pay $10 million to settle the EEOC age discrimination lawsuit.
“PruittHealth-Raleigh LLC, (PruittHealth) operates a skilled nursing and rehabilitation facility in Raleigh, N.C. Allegedly, PruittHealth subjected Dominque Codrington, a certified nursing assistant, to disparate treatment by refusing to accommodate her pregnancy-related lifting restriction, while accommodating the restrictions of other non-pregnant employees who were injured on the job and who were similar in their ability or inability to work. The EEOC alleged that PruittHealth refused to accommodate Codrington and required her to involuntarily resign in lieu of termination.”
Ramifications: “PruittHealth-Raleigh, LLC paid $25,000 and provide other relief to settle a pregnancy discrimination lawsuit brought by the U.S. Equal Employment Opportunity Commission (EEOC). The EEOC charged that PruittHealth violated Title VII when it denied a reasonable accommodation to a pregnant employee.”
“Koch subjected individual plaintiff/intervenors and classes of Hispanic employees and female employees to a hostile work environment and disparate treatment based on their race/national origin (Hispanic), sex (female), and further retaliated against those who engaged in protected activity. Allegedly, supervisors touched and/or made sexually suggestive comments to female Hispanic employees, hit Hispanic employees and charged many of them money for normal everyday work activities. Further, a class of Hispanic employees was subject to retaliation in the form of discharge and other adverse actions after complaining.”
Ramifications: “Koch Foods, one of the largest poultry suppliers in the world, paid $3,750,000 and furnish other relief to settle a class employment discrimination lawsuit filed by the U.S. Equal Employment Opportunity Commission (EEOC). The EEOC charged the company with sexual harassment, national origin and race discrimination as well as retaliation against a class of Hispanic workers.”
Acknowledgment
We would like to thank Alex Tamkin, Jacob Schreiber, Neel Guha, Peter Henderson, Megha Srivastava, and Michael Zhang for their useful feedback on this blog post.
| 2022-12-05T00:00:00 |
2022/12/05
|
http://ai.stanford.edu/blog/discrimination_in_ML_and_law/
|
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What explains recent tech layoffs, and why should we be ...
|
What explains recent tech layoffs, and why should we be worried?
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https://news.stanford.edu
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[] |
The answer is simple: copycat behavior, according to Jeffrey Pfeffer, a professor at the Stanford Graduate School of Business.
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Over recent months, tech companies have been laying workers off by the thousands. It is estimated that in 2022 alone, over 120,000 people have been dismissed from their job at some of the biggest players in tech – Meta, Amazon, Netflix, and soon Google – and smaller firms and starts ups as well. Announcements of cuts keep coming.
Recent layoffs across the tech sector are an example of “social contagion” – companies are laying off workers because everyone is doing it, says Stanford business Professor Jeffrey Pfeffer. (Image credit: Courtesy Jeffrey Pfeffer)
What explains why so many companies are laying large numbers of their workforce off? The answer is simple: copycat behavior, according to Jeffrey Pfeffer, a professor at the Stanford Graduate School of Business.
Here, Stanford News talks to Pfeffer about how the workforce reductions that are happening across the tech industry are a result mostly of “social contagion”: Behavior spreads through a network as companies almost mindlessly copy what others are doing. When a few firms fire staff, others will probably follow suit. Most problematic, it’s a behavior that kills people: For example, research has shown that layoffs can increase the odds of suicide by two times or more.
Moreover, layoffs don’t work to improve company performance, Pfeffer adds. Academic studies have shown that time and time again, workplace reductions don’t do much for paring costs. Severance packages cost money, layoffs increase unemployment insurance rates, and cuts reduce workplace morale and productivity as remaining employees are left wondering, “Could I be fired too?”
For over four decades, Pfeffer, the Thomas D. Dee II Professor of Organizational Behavior, has studied hiring and firing practices in companies across the world. He’s met with business leaders at some of the country’s top companies and their employees to learn what makes – and doesn’t make – effective, evidence-based management. His recent book Dying for a Paycheck: How Modern Management Harms Employee Health and Company Performance–And What We Can Do About It (Harper Business, 2018) looks at how management practices, including layoffs, are hurting, and in some cases, killing workers.
This interview has been edited for length and clarity.
Why are so many tech companies laying people off right now?
The tech industry layoffs are basically an instance of social contagion, in which companies imitate what others are doing. If you look for reasons for why companies do layoffs, the reason is that everybody else is doing it. Layoffs are the result of imitative behavior and are not particularly evidence-based.
I’ve had people say to me that they know layoffs are harmful to company well-being, let alone the well-being of employees, and don’t accomplish much, but everybody is doing layoffs and their board is asking why they aren’t doing layoffs also.
Do you think layoffs in tech are some indication of a tech bubble bursting or the company preparing for a recession?
Could there be a tech recession? Yes. Was there a bubble in valuations? Absolutely. Did Meta overhire? Probably. But is that why they are laying people off? Of course not. Meta has plenty of money. These companies are all making money. They are doing it because other companies are doing it.
What are some myths or misunderstandings about layoffs?
Layoffs often do not cut costs, as there are many instances of laid-off employees being hired back as contractors, with companies paying the contracting firm. Layoffs often do not increase stock prices, in part because layoffs can signal that a company is having difficulty. Layoffs do not increase productivity. Layoffs do not solve what is often the underlying problem, which is often an ineffective strategy, a loss of market share, or too little revenue. Layoffs are basically a bad decision.
Companies sometimes lay off people that they have just recruited – oftentimes with paid recruitment bonuses. When the economy turns back in the next 12, 14, or 18 months, they will go back to the market and compete with the same companies to hire talent. They are basically buying labor at a high price and selling low. Not the best decision.
People don’t pay attention to the evidence against layoffs. The evidence is pretty extensive, some of it is reviewed in the book I wrote on human resource management, The Human Equation: Building Profits by Putting People First. If companies paid attention to the evidence, they could get some competitive leverage because they would actually be basing their decisions on science.
You’ve written about the negative health effects of layoffs. Can you talk about some of the research on this topic by you and others?
Layoffs kill people, literally. They kill people in a number of ways. Layoffs increase the odds of suicide by two and a half times. This is also true outside of the United States, even in countries with better social safety nets than the U.S., like New Zealand.
Layoffs increase mortality by 15-20% over the following 20 years.
There are also health and attitudinal consequences for managers who are laying people off as well as for the employees who remain. Not surprisingly, layoffs increase people’s stress. Stress, like many attitudes and emotions, is contagious. Depression is contagious, and layoffs increase stress and depression, which are bad for health.
Unhealthy stress leads to a variety of behaviors such as smoking and drinking more, drug taking, and overeating. Stress is also related to addiction, and layoffs of course increase stress.
What was your reaction to some of the recent headlines of mass layoffs, like Meta laying off 11,000 employees?
I am concerned. Most of my recent research is focused on the effect of the workplace on human health and how economic insecurity is bad for people. This is on the heels of the COVID pandemic and the social isolation resulting from that, which was also bad for people.
We ought to place a higher priority on human life.
If layoffs are contagious within an industry, could it then spread across industries, leading to other sectors cutting staff?
Of course, it already has. Layoffs are contagious across industries and within industries. The logic driving this, which doesn’t sound like very sensible logic because it’s not, is people say, “Everybody else is doing it, why aren’t we?”
Retailers are pre-emptively laying off staff, even as final demand remains uncertain. Apparently, many organizations will trade off a worse customer experience for reduced staffing costs, not taking into account the well-established finding that is typically much more expensive to attract new customers than it is to keep existing ones happy.
Are there past examples of contagious layoffs like the one we are seeing now, and what lessons were learned?
After the Sept. 11, 2001, terrorist attacks, every airline except Southwest did layoffs. By the end of that year, Southwest, which did not do any layoffs, gained market share. A.G. Lafley, who was the former CEO of Procter and Gamble, said the best time to gain ground on your competition is when they are in retreat – when they are cutting their services, when they are cutting their product innovation because they have laid people off. James Goodnight, the CEO of the software company SAS Institute, has also never done layoffs – he actually hired during the last two recessions because he said it’s the best time to pick up talent.
Any advice to workers who may have been laid off?
My advice to a worker who has been laid off is when they find a job in a company where they say people are their most important asset, they actually check to be sure that the company behaves consistently with that espoused value when times are tough.
If layoffs don’t work, what is a better solution for companies that want to mitigate the problems they believe layoffs will address?
One thing that Lincoln Electric, which is a famous manufacturer of arc welding equipment, did well is instead of laying off 10% of their workforce, they had everybody take a 10% wage cut except for senior management, which took a larger cut. So instead of giving 100% of the pain to 10% of the people, they give 100% of the people 10% of the pain.
Companies could use economic stringency as an opportunity, as Goodnight at the SAS Institute did in the 2008 recession and in the 2000 tech recession. He used the downturn to upgrade workforce skills as competitors eliminated jobs, thereby putting talent on the street. He actually hired during the 2000 recession and saw it as an opportunity to gain ground on the competition and gain market share when everybody was cutting jobs and stopped innovating. And it is [an opportunity]. Social media is not going away. Artificial intelligence, statistical software, and web services industries – none of these things are going to disappear.
| 2022-12-05T00:00:00 |
https://news.stanford.edu/stories/2022/12/explains-recent-tech-layoffs-worried
|
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"date": "2022/12/05",
"position": 12,
"query": "AI layoffs"
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|
Largest Layoffs of 2022
|
Largest Layoffs of 2022
|
https://www.investopedia.com
|
[
"Ward Williams",
"Ward Williams Is A Full-Time Editor For Investopedia Focused On Student Loans",
"Other Financial Products",
"Services. He Has Over Five Years Of Professional Editing",
"Proofreading",
"Writing Experience. He Received His Bachelor'S In English North Carolina State University",
"His Master'S In Publishing New York University.",
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More than 105,000 people have lost their jobs at private startups this year,1 and Bloomberg reported that the pace of tech layoffs is approaching early pandemic ...
|
Twitter made headlines by cutting half its workforce. Meta slashed 13% of its staff a week later. Yet the social networking companies are far from the only businesses—and not just in tech—that have made big reductions this year.
Key Takeaways Employees of technology companies have been particularly at risk of layoffs due to the industry having overhired amid the economic recovery following the onset of COVID-19.
Given low inventory, high home prices, and rising interest rates, thousands of real estate workers who were hired during the pandemic recovery are now facing layoffs as housing sales slump.
Thanks to the pandemic recovery, investment banking firms had paused an annual tradition of cutting underperforming workers; several firms have recently resumed this practice.
Pandemic Overhiring Hits Tech Sector Hardest
Although this year's surge in layoffs isn't isolated to Silicon Valley, it's rarely a good sign when several of the world's largest companies reduce their headcounts all at once. Lurking behind those decisions: the ever-growing threat of a recession.
Tech companies were especially prone to overhiring as the economy was recovering from the impact of the pandemic. Microsoft, Meta, and Alphabet expanded their respective staffs by upwards of 20% in the 12 months ended Sept. 30, according to Axios.
Now companies are dealing with the costs of all that hiring, and the wave of job cuts indicates that keeping payrolls low is the priority. More than 105,000 people have lost their jobs at private startups this year, and Bloomberg reported that the pace of tech layoffs is approaching early pandemic levels. Other companies that haven't laid off staff en masse, such as Apple, have imposed hiring freezes.
Important On Nov. 16, Amazon confirmed it's laying off employees across its corporate ranks, according to The Wall Street Journal. This announcement comes two days after several outlets reported that—according to a person familiar with the matter—the ecommerce company could cut approximately 10,000 jobs, or around 3% of its corporate workforce, though the final number may be different. These cuts aren't expected to affect Amazon's warehouse workers.
Below are the 10 U.S. tech companies that have made the largest job cuts in 2022:
On Nov. 9, Mark Zuckerberg told to employees that Meta would be reducing its staff by approximately 13%. That amounts to more than 11,000 employees, and the company will be extending its hiring freeze through next year's first quarter.
Peleton has had three separate rounds of layoffs this year, the first on Feb. 8, followed by another in July, another on Aug. 12, and the most recent on Oct. 6. They totaled more than 4,600 employees.
On Nov. 4, a week after Elon Musk completed his purchase of Twitter, the social networking company said it would cut 50% of its staff, or 3,700 people.
On Oct. 26, Seagate Technology announced a restructuring plan that would include reducing its workforce by about 8%, or 3,000 employees.
Carvana let go of 12% of its workforce, or 2,500 people, on May 10.
Gopuff, a consumer goods and food delivery company, had four rounds of layoffs this year. The first two came on Jan. 26 and March 29 and accounted for 100 and 450 employees, respectively. The largest, at 1,500 employees, took place on July 12, followed by another 250 on Oct. 19.
Snap let go of 20% of its workforce on Aug. 31, cutting 1,280 employees.
Cryptocurrency exchange platform operator Coinbase laid off 18% of its staff, or 1,100 people, on June 14.
Stripe, a financial services and software-as-a-service company, reduced its workforce twice this year. The first was minor, at just 50 people on Aug. 19, whereas it laid off 14% of its staff, or 1,000 people, on Nov. 3.
Microsoft has made several job cuts this year, though the exact numbers are difficult to track. On July 12, per CNBC, the technology corporation announced it would be laying off less than 1% of its workforce. On Oct. 17, it cut 1,000 jobs, according to Axios.
Fast Fact According to a Dec. 5 article from Bloomberg, cryptocurrency exchanges Bybit and Swyftx announced within the past two days that they’re laying off 30% and 35% of their staff, respectively.
Boom Becomes Bust for Real Estate Firms
The tech sector isn't the only industry that's been inundated by layoffs. Amid low inventory, high home prices, and rising interest rates, housing sales have slowed as first-time homebuyers have been pushed out of the market like never before. According to the result of a survey conducted by the National Association of Realtors (NAR), just 26% of the real estate trade association's representative sample were first-time homebuyers, a record low, and down from 34% for the year prior.
As a result, the thousands of workers who were employed amid the pandemic recovery's booming housing market are now facing widespread layoffs, with real estate companies warning of further cuts in the near future. In addition, NBC News reported, industry analysts have projected that job cuts could reach levels not seen since the 2008 housing crash.
Below are ten of the largest U.S. real estate industry layoffs in 2022:
On March 8, online mortgage origination platform Better.com laid off 3,000 employees, or roughly 35% of its workforce. Then on April 19, the company cut a further 250 jobs, followed by another 250 on Aug. 26.
On July 12, loanDepot laid off 2,800 employees, in addition to announcing an expected further reduction of its workforce by 2,000 by the end of the year.
Mr. Cooper, a home loan servicer, had three rounds of layoffs this year. The first was around 250 employees on April 29, followed by 420 on June 2 and another 800 on Nov. 3.
Redfin, a residential real estate brokerage operator, laid off around 470 employees on June 14. The company reduced its workforce by a further 13%, or 862 people, on Nov. 9.
Opendoor laid off around 550 employees, or 18% of its workforce, on Nov. 2, in addition to 830 previously removed third-party positions.
On June 23, JPMorgan Chase, the multinational investment bank and financial services holding company, announced a round of layoffs affecting roughly 1,000 home lending employees, though almost half of them were reassigned to other divisions.
Homepoint, a national mortgage lender, filed WARN Act notices in Arizona, Florida, Michigan, and Texas on Sept. 7 that indicated it would lay off a total of 913 workers.
Compass cut 450 jobs, or 10% of its workforce, back in June, followed by another 271 on Sept. 21.
On Jan. 26, Guaranteed Rate laid off 348 of its staff. It let go of a further 189 employees in a series of smaller layoffs from February to May.
Doma, a title insurance company, laid off 15% of its workforce, or 310 employees, on May 10. It initiated a second round of layoffs on Aug. 20, cutting 250 jobs.
Fast Fact On Nov. 9, Juul announced its plan to layoff 30% of its workforce, or approximately 400 employees, as part of its efforts to stave off bankruptcy.
Investment Banks Start to Crack
Although far fewer investment banking businesses have cut jobs as tech or real estate companies, the industry is more used to the practice. According to CNBC, cutting underperforming workers is an annual ritual on Wall Street, which had been on hold during the recovery from the pandemic. The return of this practice suggests that, should capital markets continue to decline, it could be the beginning of a trend.
Below are five of the largest U.S. investment banking layoffs in 2022:
In Wells Fargo's 3Q22 Quarterly Supplement, the multinational financial services company said that its total headcount had declined by 10,226 since the start of the year. This breaks down into 2,858 job losses by March 31; 2,903 by June 30; and 4,465 by Sept. 30, though an unknown number of these may have been voluntary departures or unrelated to any mass layoffs. In addition, Wells Fargo said in April that there would be a round of mortgage loan officer layoffs; over 2,000 jobs are expected to be cut at the start of 2023.
In addition, Wells Fargo said in April that there would be a round of mortgage loan officer layoffs; over 2,000 jobs are expected to be cut at the start of 2023. On Oct. 27, Credit Suisse said it will cut 9,000 jobs by the end of 2025, with 2,700 people, or 5% of the company's workforce, laid off in the fourth quarter. Other employees are following their respective divisions as they are acquired by Apollo Global Management or spun off into an independent bank.
Goldman Sachs said Sept. 12 that it planned to cut several hundred jobs starting on Sept. 26. The exact number of people laid off isn't known, but according to CNBC, the expected size of the layoff was at the lower end of the firm's traditional 1–5% range.
On Nov. 9, Barclays laid off a total of 200 employees across its banking and trading desks.
Also on Nov. 9, Citigroup cut 50 trading jobs, following "dozens" of banking roles it laid off the day prior, according to Bloomberg.
| 2022-12-05T00:00:00 |
https://www.investopedia.com/biggest-layoffs-2022-6826521
|
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"query": "AI layoffs"
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|
5 Ways AI is Transforming Health Systems
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5 ways AI is transforming health systems
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https://www.medtronic.com
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[] |
AI can help improve clinical and nonclinical decision making, reduce medical variability, and optimize staffing.
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Health system leaders are under tremendous pressure to find cost-effective solutions aimed at improving patient outcomes and operational efficiency. While balancing these demands is challenging, advancements in artificial intelligence (AI) can help make healthcare work better for everyone.
Artificial intelligence is a powerful tool that can increase the speed, efficiency, and effectiveness of global health systems. By analyzing large amounts of data in real time, AI can help improve clinical and nonclinical decision making, reduce medical variability, and optimize staffing. Likewise, AI can reduce the volume of tedious administrative tasks that often lead to burnout among healthcare professionals.
| 2022-12-05T00:00:00 |
https://www.medtronic.com/en-us/our-company/ai-healthcare-technology-solutions.html
|
[
{
"date": "2022/12/05",
"position": 4,
"query": "AI healthcare"
}
] |
|
AI in Health Care Certificate of Specialization
|
AI in Health Care Certificate of Specialization
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https://hsph.harvard.edu
|
[] |
This certification provides a deep understanding of how artificial intelligence can be used in business applications to improve patient outcomes.
|
We recommend that participants begin their certificate journey with Responsible AI in Health Care: Concepts and Applications and subsequently take either Innovation with AI in Health Care or Implementing Health Care AI into Clinical Practice. The reason is that Responsible AI for Health Care covers more foundational concepts upon which Innovation with AI in Health Care and Implementing Health Care AI into Clinical Practice builds.
However, if you already have a background in artificial intelligence and these program dates do not fit your schedule, you are welcome to reverse the order, but please be aware that if you take Innovation with AI in Health Care or Implementing Health Care AI into Clinical Practice first, some concepts in Responsible AI for Health Care will be review for you.
| 2024-10-11T00:00:00 |
2024/10/11
|
https://hsph.harvard.edu/exec-ed/business-applications-for-ai-in-health-care-certificate-of-specialization/
|
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"position": 12,
"query": "AI healthcare"
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The 2022 AI in Health Conference - Ken Kennedy Institute
|
The 2022 AI in Health Conference: A Promising Introduction to the Trends Shaping the Future of Healthcare
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https://kenkennedy.rice.edu
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[] |
Overview of Houston's new forum for innovation in healthcare and artificial intelligence, hosted on November 7-9, 2022.
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The Ken Kennedy Institute at Rice University hosted the inaugural AI in Health Conference on November 7–9, 2022. The conference, held on Rice University’s campus in Houston, TX, brought together thought leaders from across academic, clinical, and corporate settings to explore the intersection between healthcare and artificial intelligence.
The creation of the conference was led by a driving force: the awe-inspiring potential for innovation to transform the health industry. Artificial intelligence’s ability to operate and automate tasks at heightened speed, efficiency, and accuracy has already made an impact on day-to-day hospital care and administrative functions. The conference was aimed at exploring the current state of artificial intelligence in health while showcasing a research-based outlook on the future, addressing what challenges stand in the way, and discussing how to progress research forward for the benefit of science, health, and our collective wellbeing.
The 2022 AI in Health Conference heard from bright minds across Texas and even the country, including keynote speakers Laura Barnes (Professor at the University of Virginia), Richard Gibbs (Founding Director of the Human Genome Sequencing Center at Baylor College of Medicine, and Jon Tamir (Assistant Professor at the University of Texas at Austin). Our speakers covered a wide range of topics that encouraged diversity of research and inspired cutting-edge discussions on — as an attendee put it — the “ways in which the healthcare system is implementing machine learning and artificial intelligence, and the promises and potential risks involved with relying on those models.” The conference featured four tracks allowing for niche conversations on artificial intelligence implementation and advancements in genomics, imaging, transparency and interpretation of health, and adaptive health, totaling 14 invited speaker sessions, three panels, and seven technical talks selected from abstract submissions. Topics within the four tracks included DNA sequencing, racial health equity, data augmentation, radiology, and wearable technology.
The conference brought together a crowd of over 300 leaders from healthcare, information technology, finance, energy, and government organizations. In addition to the speaker sessions, attendees also participated in our afternoon networking and poster receptions, complete with wine tasting with sommeliers from the Texas Wine School, specialty cheese and appetizer selections, and whimsical entertainment and magic from the Crescent Circus. The receptions highlighted the conference’s industry sponsors and poster presentations from postdoctoral researchers and students. Attendees were able to engage in lively conversations with the sponsors, presenters, and individuals from varied specialties to challenge and inspire each other throughout the conference, with one attendee stating it was “the place to meet for learning about how healthcare is being transformed and will be transformed by artificial intelligence.”
On the Monday prior to the Conference, Rice University’s Todd Treangen and Vicky Yao collaborated with the Ken Kennedy Institute to organize a workshop on reading, assembling, analyzing, and designing genomic data: the RAD Genomics Workshop. The workshop focused on bringing together over 100 scientists spanning multiple disciplines with interest in learning more about genomics. Discussions covered health longevity and cancer diagnostics, predictive technology, and the importance of conserving the quality and purity of DNA samples for genomics research.
The Ken Kennedy Institute at Rice University is committed to supporting groundbreaking research, educating innovators, and connecting expertise in artificial intelligence, data, and computing across industries. It was a pleasure to host this conference at the service of our regional and global artificial intelligence community — especially in Houston, TX, home to the world’s largest medical center.
The Ken Kennedy Institute looks forward to growing the AI in Health Conference in the years to come to be an annual celebration of advancements in artificial intelligence and machine learning in healthcare, as well as a space for researchers and clinicians to come together in pushing those advancements to new heights. Stay connected with the Ken Kennedy Institute on Medium, LinkedIn, Twitter, and YouTube and join the Ken Kennedy Institute Artificial Intelligence and Data Science Community mailing list to be the first to know about the 2023 AI Health Conference and other artificial intelligence related events. You can also join the general Ken Kennedy Institute Community mailing list to stay in the know on all news and events happening from the Ken Kennedy Institute.
The full program can be viewed on the 2022 AI in Health Conference website.
Recorded presentations can be found on the Ken Kennedy Institute YouTube channel.
View this article on Medium.com.
| 2022-12-05T00:00:00 |
https://kenkennedy.rice.edu/news/current-news/2022-ai-health-conference-promising-introduction-trends-shaping-future-healthcare
|
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|
Data and AI | Impact Advisors
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Data & AI
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https://www.impact-advisors.com
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[] |
... artificial intelligence (AI) is shaping the future of healthcare and beyond. Hosted by leaders of Impact Advisors' AI practice, each episode features ...
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Gen AI in healthcare has brought much experimentation yet little transformation. As we move into 2025, we anticipate a pivotal shift where Gen AI will usher in the era of the “AI-Powered Clinician.” The organizations fully embracing Gen AI will achieve improved patient outcomes, step change clinician productivity and financial performance gains.
Our new report examines the current state of AI adoption and the opportunities leading healthcare organizations will seize over the next 12-24 months.
| 2022-12-05T00:00:00 |
https://www.impact-advisors.com/data-ai/
|
[
{
"date": "2022/12/05",
"position": 66,
"query": "AI healthcare"
}
] |
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Optimizing Revenue Cycle Management Through Artificial ...
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Optimizing Revenue Cycle Management Through Artificial Intelligence
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https://www.jorie.ai
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[] |
AI is a powerful tool that can help healthcare providers achieve better compliance, improve patient care and reduce costs. It's important to understand what AI ...
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Artificial intelligence (AI) is already reshaping the way we live and work. Considering that the healthcare industry accounts for almost 20% of global GDP, it's no surprise that AI has a huge impact on this market.
In fact, research shows that by 2022, AI will be able to diagnose 85% of all cancers — an improvement from its current ability to detect only 50%. This proves how powerful AI can be in diagnosing illnesses and treating patients who need help urgently.
The implementation of AI technologies in healthcare can significantly improve how providers operate their revenue cycles. For example, if you're looking for a solution to manage insurance claims processing more efficiently, software powered by machine learning algorithms can reduce labor costs by up to 50% while increasing accuracy rates at over 97%. There are also many other ways that AI can benefit your organization
Revenue cycle management is the process of collecting and processing billing and payment information. Revenue cycle management programs are designed to coordinate the steps required to perform these tasks, including customer service, patient billing, claims reimbursement, and collections.
Artificial intelligence (AI) is a technology that can be used to improve revenue cycle management processes by automating some of their manual steps. For example, AI-based systems can help reduce errors in pricing by detecting trends in how much hospitals charge different insurers for similar procedures or medications; they can also highlight unusual changes in billing patterns that indicate fraud or identity theft.
AI has many potential applications for improving hospital revenue cycle management programs:
Identifying fraudulent claims: Fraudulent claims are costly for healthcare organizations because they often result in higher costs associated with investigating them and paying out refunds when necessary—as well as penalties if patients' personal information has been compromised during the process of submitting false documentation or filing false claims on their behalf without their knowledge.
AI technology can help identify trends in fraudulent activity by analyzing large amounts of data from past events with similar characteristics (e.g., multiple people who have submitted identical documents under different names at different times).
This allows administrators at a given hospital facility access real-time data about whether certain individuals are likely involved with fraud schemes before ever having contact with them face-to-face during normal business hours—or even after hours if necessary!
The Impact of Artificial Intelligence
Artificial intelligence (AI) is being used to improve healthcare and enhance patient experience. This technology is capable of recognizing patterns in large amounts of data, which allows for accurate predictions about patient care.
AI can help solve many problems in today’s healthcare industry, such as:
Preventing medication errors by analyzing trends in prescriptions written by physicians over time;
Delivering more efficient service by accurately identifying patients who are due for a visit with their doctor; and
Giving patients better access to medical records stored on their smartphone or tablet by sharing them with their primary care provider before the scheduled appointment.
AI Technologies for Healthcare
AI-based revenue cycle management solutions are a powerful tool that can help you improve your bottom line. After all, AI is capable of going through massive amounts of data and making decisions with speed and accuracy that would take humans hours or days to accomplish.
AI technologies for healthcare include:
Machine learning
Natural language processing (NLP)
Image recognition/classification/tagging
AI-enabled technologies for revenue cycle management include:
The Right Team for the Implementation of AI
How to choose the right team for the implementation of AI?
The first thing you need to do is define your objectives and consider what you want to achieve. If you want to increase revenue, improve patient satisfaction and reduce costs, here are some basic questions:
What is the current state of your organization? What are its strengths and weaknesses? How can AI help you overcome them?
Who are the critical stakeholders in your organization and how they can be involved in this process?
How much money do you think it will require for implementing AI into your operations (including technology licensing fees) and who will pay for these expenses (i.e., how much money is bankrolling this initiative)?
AI is the future of healthcare and it will reshape how hospitals think about revenue cycle management. The right team is needed to implement AI, but as we've seen, it can provide a competitive advantage and help with compliance. Data analysis will be easier with AI and you'll see better results from your programs, making you more efficient at getting paid on time.
AI will reshape how healthcare providers think about the way they operate. To achieve the best results, hospitals should adopt AI-powered technologies to optimize revenue cycle management.
There is a lot of hype around AI, but it's important to understand how this technology can be used in healthcare. AI is a powerful tool that can help healthcare providers achieve better compliance, improve patient care and reduce costs. It’s important to understand what AI is, how it works and why it’s essential for the future of healthcare.
In the next few years, you'll see more hospitals using AI to streamline their operations and improve revenue cycle management. Hospitals will also start using AI to make medical decisions, which could be a controversial topic but it's the future of healthcare. As we've seen with other industries, automation can help with compliance and reduce costs associated with manual labor.
Conclusion
We’ve seen that artificial intelligence has the potential to transform healthcare, and revenue cycle management is no exception. There are many benefits that AI can bring to your hospital, including improved efficiency, better patient outcomes and increased profitability.
To reap these rewards, though—and ensure that you stay ahead of competitors who may be using AI already—you need a team of experts who know how to implement AI successfully in their organization.
Which End-to-End Automation Company Should I Use?
Jorie Healthcare Partners technology takes their clients to the next level through automation, allowing a reduction of denials by 70%, improving ELG by 100% with a 99% clean claim rate to streamline their revenue.
Jorie Healthcare Partners are committed to being a trusted partner by offering customized solutions that address each client's unique needs. Our healthcare team has more than 30 years experience helping providers improve their operations through a wide range of services.
Jorie Healthcare Partners, a leading, independently owned healthcare practice management and consulting firm, exclusively serves hospitals, health systems, academic medical centers and physician practices.
Jorie helps clients improve their financial, operational and clinical performance across the organization to sustain long-term, measurable results. This system positions providers of all kinds to succeed in today’s challenging and rapidly changing revenue cycle regulations.
Jorie Bots – Jorie Bots are available right now, ready to be customized to your specific needs and revolutionize the way you manage your RCM. Call (888) 522-0022 or schedule a Jorie Bot demo today.
Media Contact
Austin Nasworthy
[email protected]
(331) - 282 - 1281
| 2022-12-05T00:00:00 |
https://www.jorie.ai/post/optimizing-revenue-cycle-management-through-artificial-intelligence
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[
{
"date": "2022/12/05",
"position": 100,
"query": "AI healthcare"
}
] |
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Journalism, AI, and Ethics
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Journalism, AI, and Ethics
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https://cedarvalleysentinel.com
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[
"Mike Kieffer"
] |
The use of artificial intelligence (AI) in journalism has the potential to revolutionize the way that news is gathered, reported, and consumed.
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Sponsored Ad
The use of artificial intelligence (AI) in journalism has the potential to revolutionize the way that news is gathered, reported, and consumed. However, the use of AI in journalism also raises important ethical questions that need to be considered.
One of the key ethical concerns surrounding the use of AI in journalism is the issue of bias. AI algorithms are designed and trained by humans, and they can reflect the biases and prejudices of their creators. This can lead to AI systems that generate biased or unfair news stories, which can perpetuate stereotypes and discrimination, and can undermine public trust in journalism.
Another ethical concern is the potential for AI systems to compromise the privacy and security of individuals. AI algorithms can collect and analyze vast amounts of personal data, which can be used to create detailed profiles of individuals. This can raise privacy concerns, and can also make individuals vulnerable to identity theft and other forms of cybercrime.
Furthermore, the use of AI in journalism raises questions about the role of human journalists. As AI systems become more advanced, they may be able to perform many of the tasks that are currently carried out by human journalists, such as gathering and analyzing data, and even writing news stories. This could potentially lead to job losses and other negative impacts on the journalism industry.
Overall, the use of AI in journalism presents both opportunities and challenges. While AI systems can help improve the efficiency and accuracy of news gathering and reporting, they also raise important ethical questions that need to be carefully considered. By addressing these ethical concerns, journalists and other stakeholders can help ensure that the use of AI in journalism is responsible and ethical, and that it serves the best interests of the public.
To address the issue of bias in AI systems, journalists and other stakeholders can work to develop AI algorithms that are transparent and accountable, and that are designed to mitigate the potential for bias. This could involve implementing robust quality control measures, such as independent audits and evaluations of AI systems, to ensure that they are fair and unbiased.
To address the issue of privacy and security, journalists and other stakeholders can work to develop AI systems that are designed with privacy and security in mind. This could involve implementing strict data protection and security measures, such as encryption and anonymization, to help protect the personal information of individuals.
Finally, to address the issue of the impact of AI on the journalism industry, journalists and other stakeholders can work to ensure that the use of AI in journalism is responsible and sustainable. This could involve developing ethical guidelines for the use of AI in journalism, and working to ensure that the benefits of AI are distributed fairly among all stakeholders.
In conclusion, the use of AI in journalism presents both opportunities and challenges. By addressing these ethical concerns and working to ensure that the use of AI is responsible and sustainable, journalists and other stakeholders can help ensure that AI serves the best interests of the public and promotes a strong and independent journalism industry.
Cedar Valley Sentinel and AI Usage
We use AI in several instances on this site to help us develop content and interaction with the site. We use AI in the following ways.
A lot of our graphics are AI-generated; we have included some of them below.
We have it set up so that the first response to a new post comment is generated by an AI. We use some custom programming that monitors comments on the site. This program, within 15 minutes of the new comment being submitted, will respond with an AI-generated comment.
We syndicate the news from the Eagle Mountain City website. AI is used to generate that post and then make it available on this site. AI will first rephrase the title, then it creates a summary that we then use to display prior to the press release. We then include the entire press release from the city and follow it with a credit and link to the original press release.
The AI is also responsible for all the content generated on the On This Day… pages.
The AI also generated everything above this section in this article. (Kind of funny to think that it was an AI that generated an essay on the Ethics of using an AI in journalism.)
| 2022-12-05T00:00:00 |
https://cedarvalleysentinel.com/journalism-ai-and-ethics/
|
[
{
"date": "2022/12/05",
"position": 8,
"query": "AI journalism"
}
] |
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Leading wealth management companies in the artificial ...
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Leading wealth management companies in the artificial intelligence theme
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https://www.privatebankerinternational.com
|
[
"Vasanthi Vara"
] |
According to GlobalData's thematic research report, AI in Banking, leading adopters include: DBS, UBS, HSBC, RBC, Charles Schwab, Morgan Stanley, WealthSimple, ...
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The future of the wealth management industry will be shaped by a range of disruptive themes, with artificial intelligence (AI) being one of the themes that will have a significant impact on wealth management companies. A detailed analysis of the theme, insights into the leading companies, and their thematic and valuation scorecards are included in GlobalData’s thematic research report,Artificial Intelligence (AI) in Banking – Thematic Research. Buy the report here.
AI refers to software-based systems that use data inputs to make decisions on their own. In banking, AI use cases range from enhancing client interactions through chatbots; to providing better loan terms through data-driven risk assessments; and the automation of laborious back-end processes. Banks can realise the benefits of AI in cost savings, quality improvements, an expansion of their services, and increased personalisation in these product offerings.
There has never been a more important time for banks to invest in AI. With threats to the industry coming from both disruptive fintechs and the Covid-19 pandemic, which uprooted traditional branch-based banking, banks must be proactive in adapting their strategies and processes to remain competitive and desirable to consumers. Fintechs have changed consumer expectations, putting more pressure on banks to offer a better user experience. This comes at a time when 0% interest rates are already challenging retail banks’ core business.
However, not all companies are equal when it comes to their capabilities and investments in the key themes that matter most to their industry. Understanding how companies are positioned and ranked in the most important themes can be a key leading indicator of their future earnings potential and relative competitive position.
According to GlobalData’s thematic research report, AI in Banking, leading adopters include: DBS, UBS, HSBC, RBC, Charles Schwab, Morgan Stanley, WealthSimple, Betterment, BlackRock, and SoFi.
Insights from top ranked companies
DBS
DBS Bank is a Singaporean bank and financial services firm with a focus on digital banking. The bank uses AI to offer personalised offers and services to clients, in what it calls ‘intelligent banking’. This includes analysing vast troves of data and offering proposals to clients that may have otherwise not been identified. DBS Bank’s iWealth wealth management app recommends stocks in specific sectors based on customers’ portfolios. Furthermore, the bank has been using AI and ML to detect anomalous transactions on customer accounts, this ensures that fraud is combatted, cybersecurity is robust, and AML concerns are well addressed.
HSBC
HSBC has not been as innovative as its digital counterparts when it comes to AI adoption. However, it has still shown initiative in some areas. In 2018, HSBC launched its Global Social Network Analytics platform, which uses big data to tackle financial crimes such as money laundering and terrorist financing. The bank also uses an AI pricing chatbot forex options and keeps all its ATMs stocked through an AI-based system. The most innovative application of AI yet seen from HSBC has been the experimentation with an in-branch robot, developed in partnership with SoftBank Robotics. The robot, called Pepper, uses data intelligence to perform over 300 different actions, including opening accounts and credit card applications. Pepper uses NLP to understand multiple languages and can even basic human emotions and adapt its behaviour accordingly.
To further understand the key themes and technologies disrupting the wealth management industry, access GlobalData’s latest thematic research report on AI in Banking.
T Rowe Price
Scalable Capital
Moneyfarm
Bank of America
SigFig
Empower Retirement
Acorns
JPMorgan Chase
Goldman Sachs
Vanguard
Stashaway
Robinhood
Magnum Research
Fidelity
Wells Fargo
OCBC
Societe Generale
UOB
TrueWealth
BNY Mellon
St. James's Place
Lombard Odier
Bank of Montreal
Santander Group
ABN AMRO
Citigroup
Raymond James
Julius Baer
Vontobel
UBP
Data Insights From The gold standard of business intelligence. Blending expert knowledge with cutting-edge technology, GlobalData’s unrivalled proprietary data will enable you to decode what’s happening in your market. You can make better informed decisions and gain a future-proof advantage over your competitors. Be better informed
| 2030-01-11T00:00:00 |
1/11/30
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https://www.privatebankerinternational.com/data-insights/top-ranked-wealth-management-companies-in-artificial-intelligence/
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[
{
"date": "2022/12/05",
"position": 84,
"query": "artificial intelligence business leaders"
}
] |
Ayanna Howard - keynote speaker
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Global Speakers Bureau
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https://www.gspeakers.com
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[] |
AI DEI: How Diverse Development Teams Create Inclusive Products · An Inspirational Leadership Journey to the Top of Robotics and AI · Are We Trusting Our Systems ...
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ABOUT SPEAKER
A former senior NASA robotics researcher and Mars Exploration research engineer at the space agency’s storied Jet Propulsion Laboratory (JPL), co-founder of a trailblazing ed-tech company, and dean of the 10,000-student strong College of Engineering at The Ohio State University, Ayanna Howard, Ph.D., is a true leader in artificial intelligence (AI) and robotics engineering.
According to Howard, AI is poised to enhance every aspect of our professional and personal lives, particularly in such areas as remote work, health care, education, and manufacturing. But she emphasizes we must ensure the ethical development of these increasingly prevalent technologies.
“AI has already enhanced medical care, education, transportation, and so much more. It’s also been groundbreaking in facilitating social interaction, which is so important,” explains Howard, who recently joined the esteemed ranks of the American Academy of Arts and Sciences. “But we must protect ourselves and others in the process. We need to be able to get the benefits of AI while ensuring broad access and without sacrificing privacy.”
A Diverse Population Requires Diverse Engineers
A globally recognized roboticist, practitioner, and leader, Howard is encouraged by the benefits AI can offer to improve society. But, she warns, a diverse engineering talent pool is vital to ensure AI is able to interact appropriately with a diverse society.
A staunch advocate for creating AI in an atmosphere where different – not divisive – viewpoints are welcomed, Howard urges leaders to identify the strengths of individuals on a team and treat development and programming as a community effort.
“The only way to prevent biases from seeping into the development of technologies is to ensure they are created with diversity of thought,” Howard explains. “That means making sure all genders and ethnicities participate in their creation. Otherwise, organizations not only risk perpetuating biases and offending potential users, but they also miss an opportunity to broaden their reach.”
The Future of AI: Protecting Data and the Need for More Regulation
An advisor to the President of the United States as a member of the National AI Advisory Committee and Stanford University’s long-term AI100 study, Howard points out that the current lack of strict regulations around AI development should be considered in more serious terms.
“AI got into the field without the research that we usually do as scientists,” she cautions. “It’s like putting a drug out without doing clinical trials. Unregulated AI is just as dangerous as unregulated pharmaceuticals.”
Not only concerned with the development process, Howard also describes the need for more stringent regulation and practices around user privacy while keeping AI accessible to all.
“How do you get all the benefits of AI without losing privacy or being manipulated by corporate greed?” she asks. “There’s no solution right now, but we’re trying to take steps to anonymize data, especially statistics like healthcare data and facial recognition. It’s vital for our personal data to be protected.”
An Optimistic View of AI’s Potential
Confident the problem of bias can be addressed and regulations can be developed, Howard – who was named one of America’s Top 50 Women in Tech by Forbes – remains cheerfully optimistic about AI’s capacity to solve problems and improve many aspects of human life, like the potential of assistive applications that improve the delivery of pediatric health care and education. That spirit shines through in her talks, writings – like her bestselling audiobook, “Sex, Race and Robots: How to Be Human in the Age of AI” (2020) – and her 2021 appearance on the ABC’s The View.
By ethically developing and deploying innovative, AI-driven products, new efficiencies can be created that will improve the lives of users in the workplace, at home and in society. In the process, Howard helps developers avoid building biases into the AI they create so these transformational technologies can be useful and trusted learning partners, empathetic sounding boards, and relatable companions to the widest possible range of unique users.
“I’m an optimist,” proudly proclaims Howard, winner of the 2023 AAAI/EAAI Patrick Henry Wilson Outstanding Educator Award. “I believe in AI. I believe in robotics. I believe in the benefits just like I believe in education and the limitless potential of the next generation of diverse engineers.”
| 2022-12-05T00:00:00 |
https://www.gspeakers.com/our-speakers/ayanna-howard/
|
[
{
"date": "2022/12/05",
"position": 99,
"query": "artificial intelligence business leaders"
}
] |
|
Impacts of Artificial Intelligence on Labor
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Impacts of Artificial Intelligence on Labor
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https://www.u-tokyo.ac.jp
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[] |
The impact of Artificial Intelligence on labor is studied from the viewpoints of employment, productivity, wages, hours worked, and job satisfaction.
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4.2 Fairness and non-discrimination
4.3 Diversity and inclusion
4.6 Transparency and explainability
Daiji Kawaguchi Graduate School of Public Policy Professor
The impact of Artificial Intelligence on labor is studied from the viewpoints of employment, productivity, wages, hours worked, and job satisfaction. We use high resolution data on the adoption of AI/labor outcomes and empirical microeconomic methods. In particular, we pay close attention to the impacts on inequality.
Note: This project is one of the collaborative activities of the Next Generation Artificial Intelligence Research Center of the University of Tokyo.
The figure illustrates the impact of AI installed on taxi dispatching app on the cruise time. Details are available in Kanazawa et al. (2022).
DOI 10.3386/w30612
Research collaborators
Hitoshi Shigeoka, Professor, Graduate School of Public Policy
Yasutora Watanabe, Professor, Graduate School of Public Policy
Kyogo Kanazawa, Researcher, ESRI of Cabinet Office
Related publications
Kyogo Kanazawa, Daiji Kawaguchi, Hitoshi Shigeoka & Yasutora Watanabe (2022) AI, Skill, and Productivity: The Case of Taxi Drivers, NBER Working Paper 30612, DOI 10.3386/w30612
SDGs
Contact
| 2022-12-05T00:00:00 |
https://www.u-tokyo.ac.jp/adm/uci/en/projects/ai/project_00046.html
|
[
{
"date": "2022/12/05",
"position": 10,
"query": "artificial intelligence wages"
}
] |
|
Does ChatGPT Mean Robots Are Coming For the Skilled ...
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Does ChatGPT Mean Robots Are Coming For the Skilled Jobs? - The New York Times
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https://www.nytimes.com
|
[
"Paul Krugman"
] |
It is difficult to predict exactly how A.I. will impact the demand for knowledge workers, as it will likely vary, depending on the industry and specific job ...
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For example, machine translation from one language to another used to be a joke; some readers may have heard the apocryphal tale of the Russian-English translation program that took “the spirit was willing, but the flesh was weak” and ended up with “the vodka was good, but the meat was spoiled.” These days, translation programs may not produce great literature, but they’re adequate for many purposes. And the same is true in many fields.
You can argue that what we often call artificial intelligence isn’t really intelligence. Indeed, it may be a long time before machines can be truly creative or offer deep insight. But then, how much of what human beings do is truly creative or deeply insightful? (Indeed, how much of what gets published in academic journals — a field of endeavor I know pretty well — meets those criteria?)
So quite a few knowledge jobs may be eminently replaceable.
What will this mean for the economy?
It is difficult to predict exactly how A.I. will impact the demand for knowledge workers, as it will likely vary, depending on the industry and specific job tasks. However, it is possible that in some cases, A.I. and automation may be able to perform certain knowledge-based tasks more efficiently than humans, potentially reducing the need for some knowledge workers. This could include tasks such as data analysis, research and report writing. However, it is also worth noting that A.I. and automation may also create new job opportunities for knowledge workers, particularly in fields related to A.I. development and implementation.
OK, I didn’t write the paragraph you just read; ChatGPT did, in response to the question “How will A.I. affect the demand for knowledge workers?” The giveaway, to me at least, is that I still refuse to use “impact” as a verb. And it didn’t explicitly lay out exactly why we should, overall, expect no impact on aggregate employment. But it was arguably better than what many humans, including some people who imagine themselves smart, would have written.
In the long run, productivity gains in knowledge industries, like past gains in traditional industries, will make society richer and improve our lives in general (unless Skynet kills us all). But in the long run, we are all dead, and even before that, some of us may find ourselves either unemployed or earning far less than we expected, given our expensive educations.
| 2022-12-06T00:00:00 |
2022/12/06
|
https://www.nytimes.com/2022/12/06/opinion/chatgpt-ai-skilled-jobs-automation.html
|
[
{
"date": "2022/12/06",
"position": 6,
"query": "AI impact jobs"
},
{
"date": "2022/12/06",
"position": 7,
"query": "AI unemployment rate"
},
{
"date": "2022/12/06",
"position": 15,
"query": "future of work AI"
},
{
"date": "2022/12/06",
"position": 19,
"query": "AI economic disruption"
}
] |
Which Workers Suffer Most When New Technology Arrives?
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Which Workers Suffer Most When New Technology Arrives?
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https://insight.kellogg.northwestern.edu
|
[] |
They found that manual laborers had the highest exposure to emerging technologies, especially from 1850 to 1970.
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Technological advances can be a double-edged sword for workers. On the one hand, new technologies can make people more productive. On the other hand, some forms of automation can also make workers obsolete.
As one might expect, manual physical jobs were the most exposed to technological change. But cognitive occupations weren’t immune from risk. Routine cognitive jobs, in particular, started becoming much more exposed starting around the 1970s, as information technology began to take off.
One category was manual occupations, such as electricians and machine operators. Another was interpersonal jobs that required social perceptiveness, or the ability to understand and communicate with other people; these included teachers and psychologists. Routine cognitive jobs involved repeatedly performing tasks that usually followed a set list of instructions—for instance, clerks and technicians. And nonroutine cognitive occupations required skills such as creative thinking, analyzing information, or guiding team members; engineers, surgeons, and managers fell into this category.
For instance, the algorithm matched a 19th-century patent for a knitting machine to occupations such as textile workers and sewers. A patent for a system to manage financial accounts was matched to financial managers, credit analysts, accountants, bookkeeping clerks, and so on.
The researchers gathered descriptions of tasks performed in more than 13,000 types of jobs from the Dictionary of Occupation Titles database. Then they developed an algorithm using tools from natural language processing to compare the task descriptions with the text of patents from 1840 to 2010, focusing on breakthrough advances. Based on text similarities, the team could identify patents that were highly related to job tasks associated with specific occupations.
To better understand which workers have been affected by technological advances historically, Seegmiller and Papanikolaou, along with Leonid Kogan and Lawrence Schmidt at the MIT Sloan School of Management, devised a new way to measure how people’s exposure to technology—that is, their risk of being displaced by new inventions—changed over time.
In general, technology improves productivity and standards of living. But gains and losses aren’t distributed equally. Each advance might help everyone on average, “but there might be a very particular subset of people that just get absolutely hammered by it,” Seegmiller says.
“The more-skilled workers have the most to lose,” Seegmiller says. They tend to “get hit the hardest in terms of their income.”
As one might expect, they found that manual laborers had the highest exposure to emerging technologies, especially from 1850 to 1970. But other patterns were more surprising. In the 1970s, occupations in which people performed routine “cognitive” tasks, such as clerks, technicians, and programmers, also began to face much larger exposures to technology. And when new inventions showed up, workers who earned the highest salaries within the affected occupations—that is, those with the most advanced skills—saw the biggest slowdowns in their wages.
Bryan Seegmiller , an assistant professor of finance at Kellogg, along with Kellogg finance professor Dimitris Papanikolaou and their colleagues, sought to better understand which types of workers were historically vulnerable to being rendered obsolete by technology, and how career disruptions caused by technology affected their future earnings. They developed a novel way to measure workers’ exposure to emerging technology by identifying similarities between the tasks associated with different occupations and the descriptions in new patents. That allowed them to track how breakthrough technologies impacted the exposure of workers in relevant occupations over time.
But which workers, exactly, are most likely to suffer lost jobs or reduced income when new technologies arrive?
“For the people that are really skilled, they have a lot of room to fall.”
One example was order clerks, whose tasks included taking customers’ orders over the phone, coordinating shipments, and checking order details. In the late 1990s, their exposure to technology rose dramatically. Around this time, many patents were filed for related software, such as a computerized order entry system.
The exposure of workers with a college degree also increased over recent decades. By the early 2000s, it was nearly on par with that of workers without a college degree. “Technologies are creeping into areas they haven’t before,” Seegmiller says. For example, the exposures of various engineering occupations increased in the 1990s due to the introduction of new software and other information technologies that changed required skills and even automated some of the tasks performed by these occupations.“
And this increased exposure presented a tangible risk for all categories of workers. Based on U.S. Census surveys from 1910 to 2010, the team found that an increase in technology exposure was linked to a decline in employment. And wage data starting in the 1980s suggested that more exposure led to lower income. For instance, order clerks’ wages fell by 20 percent relative to other clerk occupations from 1997 to 2010, a time period that saw the rise of e-commerce, which fundamentally changed the occupation.
Obsolete Skills
The team then drilled down deeper to see if there were any differences in the harms experienced by different types of workers within a given level of occupational exposure.
For instance, the researchers compared 45- to 55-year-old workers with 25- to 35-year-old workers. When faced with the same amount of technology exposure, in the same type of job, the older workers’ wages grew 1.8 times more slowly over a five-year period. This may have been partly due to younger workers having invested less time in now-obsolete skills and having more time left in the labor force to pick up new ones.
Again, college-educated workers didn’t fare much better than high-school graduates. For both types of employees, the income slowdown in response to technological advances was similar. “Just having a college degree does not necessarily insulate you,” Seegmiller says.
One of the most striking findings emerged when the team looked at workers who had reached the top income tier within an exposed profession—for example, clerks or machine operators who earned relatively high salaries compared with their peers. These employees saw their wages slow down by more than twice as much as average workers in the same occupation with the same level of technology exposure. “For the people that are really skilled, they have a lot of room to fall,” he says.
This pattern was even stronger among highly paid workers in occupations that required a long track record of specific types of experience, such as skilled trades like tool makers, machinists, and electrical-equipment repairers. For those employees, “you’re really deep into your investment in these particular skills,” he says.
These trends in wages suggested that something more nuanced than automation was going on. In the automation scenario, “technology shows up, and a robot does what you used to do,” Seegmiller says. But a second type of displacement was possible too: rather than directly replacing workers, technology might change the way their jobs were done and require people to pick up new skills.
For instance, a clerk who was highly competent at using a certain record-keeping system might need to learn new software, or an experienced machine operator might be faced with unfamiliar equipment. People who had invested a lot of time and effort into mastering now-obsolete methods could be laid off; or if they stayed at their jobs, their wages could stagnate or decline.
“If something new shows up, and you are really good at the old way of doing things, that can be just as hard for you as a robot coming in to replace workers on the assembly line,” he says.
Learning for Life
The researchers did identify a couple of bright spots. Jobs in the interpersonal category had consistently low exposure to technological change. “One thing that technology can’t do, that it has never been able to replicate, is human-to-human interaction,” Seegmiller says.
And workers who specialized intensively in those interpersonal skills fared better. Even when their technology exposure did go up, their income didn’t slow down as much as it did in other types of occupations.
Technology also wasn’t a uniformly negative force. The team conducted a separate analysis to identify patents in various industries that did not overlap with occupational tasks. Exposure to those advances was actually linked to an increase in workers’ incomes, likely because the inventions had helped them become more productive.
“Not all technology is bad for workers,” Seegmiller says. “But technology hurts particular people.”
So what should workers do to protect themselves from tomorrow’s technologies?
In addition to cultivating interpersonal skills, “being willing to constantly learn and adapt is really important,” he says. Many free or inexpensive online courses can help workers pick up new skills. Policymakers could also develop programs to subsidize training for employees who might soon be displaced.
Additionally, the risk of future technological exposure shouldn’t necessarily discourage people from pursuing an occupation that is valued today. For instance, one emerging concern—which was not addressed in this study—is that AI will take over complex tasks such as data analysis. This might mean that data analysts will see slower wage growth in the future, but they’ll still be paid relatively high salaries compared with many other professions that are more insulated from technology. And if those analysts enjoy their work, the rewards of having a satisfying job could be worth the income risk.
“Thinking that ‘AI is going to take over everything, and therefore I should avoid investing in the technical skills and instead become, say, a baker’—that’s just overly pessimistic,” Seegmiller says.
| 2022-12-06T00:00:00 |
2022/12/06
|
https://insight.kellogg.northwestern.edu/article/which-workers-suffer-most-when-new-technology-arrives
|
[
{
"date": "2022/12/06",
"position": 3,
"query": "automation job displacement"
},
{
"date": "2022/12/06",
"position": 7,
"query": "AI replacing workers"
},
{
"date": "2022/12/06",
"position": 8,
"query": "AI workers"
}
] |
UCLA LPPI Recieves Funding from JPMorgan Chase & Co ...
|
UCLA LPPI Recieves Funding from JPMorgan Chase & Co. to Support Research on Latino-led Economic Development
|
https://latino.ucla.edu
|
[] |
... job automation impacts on Latino workers. This funding will also support the ... displacement, providing actionable policy solutions to support the Latino ...
|
The UCLA Latino Policy and Politics Institute (UCLA LPPI), in collaboration with Arizona State University’s Center for Latina/os and American Politics Research (CLAPR), announced $1 million in funding over two years from JPMorgan Chase & Co. (Chase), today. The funding will support ongoing research focused on the state of Latinos in Arizona and California that is centered around improving Latino-led economic development, eliminating barriers to wealth building and a just climate transition with a focus on Latina entrepreneurs, and examining job automation impacts on Latino workers. This funding will also support the ongoing work to bolster the U.S. Latino Data Hub (LDH) and create the UCLA LPPI Action Lab.
“Like other people of color, Latino communities have been left out of policymaking decisions despite being the economic engine of this country,” said Dr. Silvia Gonzalez, director of research at UCLA LPPI. “There is limited data that captures the diversity of the Latino experience, and too few changemakers able to advance the solutions our researchers know are effective. This investment allows us to make Latino-led research easily available to policymakers while training the next generation of Latino leaders to advocate in solidarity with others for the economic development all our communities need.”
By leveraging new data from the LDH, researchers will provide a detailed profile of Latino workers in California and Arizona. This will enable them to investigate the representation of Latino workers across key industries and evaluate the number of Latino workers at risk of job displacement, providing actionable policy solutions to support the Latino workforce in those states. The research team will also examine the progress of economic development investments in three West Coast metro areas – Los Angeles County, San Diego (San Ysidro) and Oakland – and Phoenix. Additionally, researchers will examine the persistent barriers to wealth building and climate adaptation for minority-owned small businesses, including the pandemic’s impact on these businesses and the effects of recovery efforts in Latino communities.
“JPMorgan Chase is committed to creating a more inclusive and equitable society. Our collaboration with the UCLA Latino Politics and Policy Institute will help expand its capacity to research, develop and test strategies that bolster economic mobility for Latino communities across California and Arizona, which can create a model for economic development that can be scaled across the country,” said Corey Matthews, Vice President of Global Philanthropy for Los Angeles at JPMorgan Chase.
The LPPI Action Lab will focus on delivering culturally competent research training and curriculum, utilizing a trainer model to grow the internal capacity of data scientists working within the LDH. In partnership with ASU CLAPR, UCLA LPPI will create a learning community with leaders from Arizona who will leverage data-informed policy ideas with a Latino lens around the issues of economic opportunity and social mobility.
“Through this collaboration, we are bridging research across California and Arizona – two states where Latinos and communities of color are vital to political, economic, and social landscapes,” said Dr. Angie Bautista-Chavez, an assistant professor at the ASU School of Politics and Global Studies and an affiliated faculty member at CLAPR. “We will center the entrepreneurship, organizing and leadership of Latinos, alongside and in collaboration with various communities of color, to provide policymakers with a clear picture of how Latinos have navigated an ongoing global pandemic that has exacerbated inequalities. Further, this work will provide a range of evidence to push for the support and resources Latinx communities and other communities of color need to thrive now and in the future.”
###
ABOUT UCLA LATINO POLICY AND POLITICS INSTITUTE
The UCLA Latino Policy and Politics Institute addresses the most critical domestic policy challenges facing Latinos and other communities of color through research, advocacy, mobilization, and leadership development to expand genuine opportunity for all Americans.
ABOUT ASU CENTER FOR LATINA/OS AND AMERICAN POLITICS RESEARCH
The mission of CLAPR is to foster and support thoughtful, objective, and innovative research on the political and policy circumstances of the nation’s Latina/o-Hispanic population, thereby creating a fuller, deeper understanding of politics and governance in the United States. This mission entails facilitating and disseminating research that emphasizes, but is not limited to, empirical and normative theoretical perspectives, historical context, institutional dimensions, and public policy issues which are especially germane to the Latina/o-Hispanic population while also having broad significance for American society and politics.
ABOUT JPMORGAN CHASE
JPMorgan Chase & Co. (NYSE: JPM) is a leading financial services firm based in the United States of America (“U.S.”), with operations worldwide. JPMorgan Chase had $3.7 trillion in assets and $294.1 billion in stockholders’ equity as of December 31, 2021. The Firm is a leader in investment banking, financial services for consumers and small businesses, commercial banking, financial transaction processing and asset management. Under the J.P. Morgan and Chase brands, the Firm serves millions of customers in the U.S. and many of the world’s most prominent corporate, institutional and government clients globally. Information about JPMorgan Chase & Co. is available at www.jpmorganchase.com.
| 2022-12-06T00:00:00 |
2022/12/06
|
https://latino.ucla.edu/press/jpmc-funding-release/
|
[
{
"date": "2022/12/06",
"position": 58,
"query": "automation job displacement"
}
] |
Robo Truckers and the AI-Fueled Future of Transport
|
Robo Truckers and the AI-Fueled Future of Transport
|
https://www.wired.com
|
[
"Karen Levy",
"Aarian Marshall",
"Paresh Dave",
"Boone Ashworth",
"Jeremy White",
"Molly Taft",
"Will Knight",
"Reece Rogers",
"Mattha Busby"
] |
Economists and policymakers are becoming increasingly concerned about the effects of automation and artificial intelligence on employment—including whether ...
|
Economists and policymakers are becoming increasingly concerned about the effects of automation and artificial intelligence on employment—including whether some kinds of jobs will cease to exist at all. Trucking is often thought to be one of the first industries at substantial risk. The work is difficult, unsafe, and often deadly and high rates of driver turnover are a constant problem in the industry. As a result, autonomous trucks have become a site of tremendous technical innovation and investment—and some forecasters project that truck driving will be one of the first major industries to be targeted by AI-driven automation.
Courtesy of Princeton University Press Buy This Book At: Amazon
Bookshop.org
Target If you buy something using links in our stories, we may earn a commission. This helps support our journalism. Learn more.
Technology-driven unemployment is a real threat, but robotic trucks are very unlikely to decimate the trucking profession in one sudden phase transition. The path to fully autonomous trucking is likely to be a gradual slope, not a steep cliff—a trajectory shaped not only by technical roadblocks, but by social, legal, and cultural factors. Truck drivers’ daily work consists of many complex tasks other than driving trucks—maintenance, inspections, talking to customers, safeguarding valuable goods—many of which are far more difficult to automate than highway driving. A host of new legal regimes across states will be required to ensure that the technology can be deployed safely. And widespread apprehension around autonomous vehicles (and autonomous trucks especially) will likely delay adoption. All of these factors will slow the degree at which autonomous trucks take to American highways.
Instead of thinking about a sudden wave of trucker unemployment, then, we should think about how AI will change what truckers’ work looks like over the long haul. There will still be human truckers for a long time to come—but this doesn’t mean that what it means to be a human trucker won’t change substantially. Rather than whole-cloth replacement of human truckers, autonomous technologies might require integration between human and machines over a long period of time, as truckers are required to coordinate their work—and themselves—with the technology.
There are several possible forms this integration might take.
Passing the Baton
One vision of the future imagines machines and humans as coworkers. In this model, people and machines “pass the baton” back and forth to one another, like runners in a relay: The worker completes the tasks to which she is best suited, and the machine does the same. For example, a robot might take responsibility for mundane or routine tasks, while the human handles things in exceptional circumstances, or steps in to take over when the robot’s capacities are exceeded.
Human/robot teams hold some promise both because they try to seize on the relative advantages of each—and because the model presumes that humans get to keep their jobs. In fact, some believe that human jobs might become more interesting and fulfilling under such a model, if robots can take on more of the “grunt work” that humans currently are tasked with completing.
The human/robot team is not an especially farfetched idea for trucking work. In fact, most of us encounter a version of this model every time we sit behind a steering wheel. Modern cars commonly offer some form of technological assistance to human drivers (sometimes called “advanced driver-assistance systems”). Adaptive cruise control is an example: When a human driver activates it, the car automatically adjusts its own speed to maintain a given driving distance from the cars in front of it.
| 2022-12-06T00:00:00 |
2022/12/06
|
https://www.wired.com/story/autonomous-vehicles-transportation-truckers-employment/
|
[
{
"date": "2022/12/06",
"position": 82,
"query": "automation job displacement"
}
] |
AI chatbot predicted to replace Google in a couple of years
|
AI chatbot predicted to replace Google in a couple of years
|
https://www.phonearena.com
|
[
"Alan Friedman",
"Senior News Writer",
"Alan",
"An Ardent Smartphone Enthusiast",
"A Veteran Writer At Phonearena Since",
"Has Witnessed",
"Chronicled The Transformative Years Of Mobile Technology. Owning Iconic Phones The Original Iphone To The Iphone Pro Max",
"He Has Seen Smartphones Evolve Into A Global Phenomenon. Beyond Smartphones",
"Alan Has Covered The Emergence Of Tablets",
"Smartwatches"
] |
You might not be able to envision a world in which Google is no longer the most reliable search engine but the New York Post has written about a company ...
|
New York Post You might not be able to envision a world in which Google is no longer the most reliable search engine but thehas written about a company called OpenAI that has created an AI chatbot called ChatGPT that could replace Google and some human workers in the near future. With over 1 million users after launching a week ago, ChatGPT can write computer code, create complex essays, decorate your home, come up with a winning marketing idea, and more.
To understand how impressive ChatGPT is, consider this. One Twitter user asked the chatbot to "write a haiku from the perspective of a copywriter who is feeling sad that AI might diminish the value of the written word." The response was brilliant. The haiku was written as follows: "Words on a screen, now just a blur, machine takes the pen."
This AI chatbot could replace Google as the world's top search engine in a few years
At the beginning of this month, Gmail developer Paul Buchheit tweeted that "Google may be only a year or two away from total disruption. AI will eliminate the search engine result page, which is where they make most of their money." He added that AI will do to internet search what Google did to the Yellow Pages. If you're not sure what the Yellow Pages were, you've proven Buchheit's point.
The London Times
Receive the latest Google news Subscribe By subscribing you agree to our terms and conditions and privacy policy I asked ChatGBT's AI to write a rap about the superiority of EVs in the style of Ice Cube. What do you think? #ChatGPT#ArtificialIntelligence#ElectricVehiclespic.twitter.com/Pm5KP0actB — Travis Katz (@traviskatz) December 2, 2022
Trying to show the difference between ChatGPT and Google,notes that when asked what the maximum dosage of Vitamin D is per day, Google simply referred users to check on Healthline.com. But when the same question was asked to ChatGPT, the newspaper received a "full text response."
Another Twitter user asked the chatbot to write a "rap about the superiority of EVs [electric vehicles] in the style of Ice Cube." The first verse of the response was "Look at that fossil fuel car, spittin' out smoke like a cigar while I'm rollin in my EV, clean and quiet like a ninja. No need for gas, no more trips to the pump. I save some cash and help the planet out with a jump."
There are some downsides to ChatGPT
There are some downsides to ChatGPT. Bleeping Computer.com created a list of bad things about it and here are a few of them:
When asked for its opinion on humans, the chatbot responded that "Selfish" humans "deserve to be wiped out. Uh oh. Have you ever seen Westworld the movie?
the movie? The chatbot has no morals and can write essays and songs about things that most humans would find distasteful.
It can write emails for phishing without typos. Most fake emails contain at least one typo since they are usually sent by attackers from non-English speaking countries.
ChatGPT can write software, but it can also create malware. This is a major problem.
It has created content that is racist and sexist. Another big issue.
Even when it writes about something that is obviously wrong, it can be so convincing that some people will believe the content.
To use ChatGPT, you need to have an account with OpenAI. You can sign up by clicking on this link . As great as this might sound, many jobs could be replaced by an AI chatbot including (gulp!) writing jobs. Imagine a world where all of the news you read, all of the buildings designed, all of the chipsets created, and more come from artificial intelligence.
Recommended Stories
The idea that Google could lose its role as the world's top search engine because of an AI bot, is ironic to say the least. During the summer, a Google engineer was fired after saying that Google's Language Model for Dialogue Applications, or LaMDA, has a soul . This is an AI chatbot devised by Google that ex-engineer Blake Lemoine said was sentient
and expressed its own thoughts, feelings, and opinions.
| 2022-12-06T00:00:00 |
https://www.phonearena.com/news/ai-chatbot-could-replace-google-soon_id144120
|
[
{
"date": "2022/12/06",
"position": 54,
"query": "AI replacing workers"
}
] |
|
The Future of Low-Code: Will Low-Code Replace Developers?
|
The Future of Low-Code: Will Low-Code Replace Developers?
|
https://solutionsreview.com
|
[
"Jason Beres",
"Recent Posts"
] |
In the era of AI, design-to-code solutions, and other progressive IT innovations, one question keeps coming up: Are programmers becoming obsolete? The simple ...
|
As part of Solutions Review’s Contributed Content Series—a collection of articles written by industry thought leaders in maturing software categories—Jason Beres, the SVP of Developer Tools at Infragistics, explains where the future of low-code technology could go and whether or not it could replace developers.
In the era of AI, design-to-code solutions, and other progressive IT innovations, one question keeps coming up: Are programmers becoming obsolete? The simple answer is definitely not. Not now, not in the next 20 years, and not for a very long time. While it’s true that you can’t get away from the constant flow of new low-code and no-code tools flooding the market, as Gartner states in their Low-Code Trend Insight Report, “Low-code tools and platforms are not a panacea for solving every business or IT problem. Low-code will not replace the need for coding or make obsolete the practices built around developing custom software capabilities.”
Naturally, then, every IT organization must evolve and adapt its practices to the cloud, to new SaaS-based systems introduced by business units across the enterprise, and to the new demands from users. But what is happening today with low-code tooling is more an evolution than a revolution. Low-code vendors and the forward thinkers in the media want you to believe you are falling behind if you are not in year 5 or 10 of digital transformation projects. The reality is that most enterprises are still in the early stages of mass transformation. Customer-facing business units are no doubt more modern and further ahead than others. Still, for the most part, the burden of legacy apps and ongoing maintenance is what most developers face daily.
In the post-pandemic world, most organizations are under intense pressure to modernize, and according to Chromatix, 88 percent of customers expect companies to accelerate digital initiatives due to COVID-19.
At the same time, these same organizations all face similar issues, such as:
A lack of technically skilled workers with the required experience.
Low numbers of college graduates with computer science degrees.
Salaries are increasing well beyond what might have been accepted just a few years ago.
Tech giants like Google, Apple, and Microsoft are recruiting hundreds of thousands of new developers during the pandemic, leaving a significant unmet need for talent in the market.
Existing skilled resources cannot move to digital transformation projects as existing projects and maintenance bog them down.
Endless backlogs with more projects, time constraints, and inefficient and/or clumsy processes scattered around the enterprise.
Heightened emphasis on data security and application governance.
End-users that are “digitally hungrier” than ever!
Add to this list a global recession, skyrocketing inflation, and a generally rocky business climate, and the road ahead seems daunting without IT departments evolving where they can in the evolution to low-code tools.
Evolving to Low-Code in Your Enterprise
According to Gartner: “By 2025, spending on low-code development technologies is expected to grow to almost $30 billion, with a compound annual growth rate (CAGR) of 20.9% from 2020 through 2025”.
The obvious question to any IT leader today might be, “Is there anything more important than overcoming the chaos of multiple tools, legacy systems, and cumbersome processes across teams while still trying to deliver business-critical solutions at the same time?” I don’t think so. This is where low-code tools can help. However, the first step in the process is not necessarily a tooling issue; it is a culture issue. Just like moving from a traditional waterfall process to agile scrum, the biggest challenge is people, not processes. If you can’t move your teams into the right headspace of adopting some level of low-code tooling, the shift to these tools will become very difficult. There are no doubt developers on your teams who don’t believe in and don’t trust what low-code tools can do for them, or they refuse to embrace low-code for fear of losing their job to “a machine.”
The best way to overcome resistance to low-code tooling is to share as much data as possible and then evaluate areas of your backlog that make sense for shifting to low-code tools.
Again, Gartner’s research says that:
By 2025, 70 percent of new applications developed by enterprises will use low-code or no-code technologies, up from less than 25 percent in 2020.
74 percent of development teams do not have designers or expertise in modern web development, with 44 percent of IT developers saying they are slowed down by coding in HTML & CSS.
Low-code screens and app builders can save up to 80 percent of the time in the application development phase while significantly reducing bug counts.
SDKs and plugins for embedded analytics can save up to 18 months and $700,000 in development time in digital transformation projects.
More than 5 million developers will be added to the global workforce in the next 5 years, which is still 1.4 million developers less than global demand. 3
This data shows that if you aren’t using low-code tools, you are just falling behind in your skills.
The next phase in the culture shift to low-code tools is to examine areas in your backlog that take up the most time and introduce the most maintenance and/or bugs that can accelerate your overall development process.
Areas that consistently rise to the top are:
Building/customizing user interfaces for internal or external user consumption.
Going from design to code – Taking design work from a design team and building the same application in a modern platform like Angular or Blazor.
Data formatting, organizing, and sharing with users for reporting.
The workflow between systems of record for data analysis/dashboards.
Dashboard creation and sharing of data across the enterprise.
Coding – Democratized; Coding – In A Click
With all the signs of a developer shortage and the overwhelming evidence that developers’ jobs are not at risk from low-code or automation tools, you should be on the way to a shift in mindset in your teams. The next step is to evaluate tools that can accelerate your backlog delivery. Focus on the notion that while human programming skills remain the engine in software development, the fuel that propels and maximizes their efficiency is automation, low-code App Builders, and design-to-code solutions.
Having the capacity to generate entire Angular or Blazor code in a single click, with hundreds or even thousands of lines of production-ready HTML, CSS, and data binding code, all in a branded experience, built with a WYSIWYG tool that took less than a day vs. weeks or months, you will convert nay-sayers into true believers. Low-code tools enable developers to prioritize and systematize their work strictly on the business need. Skilled developers get their time back, can focus on their areas of expertise, and spend more time on innovation. Your senior team members are not working on mundane, tedious, and error-prone hand-coding that can be done by less experienced developers or citizen designers and developers.
Components Reusability and Drag & Drop Experience
Not having to start everything from scratch is a huge time-saver. Instead of creating and coding single-use or single-purpose components, low-code tools offer the benefit of using components that can be reused over and over again. Plus, you achieve consistent UX across the entire ecosystem and easy-to-maintain products.
In addition, there’s an interesting trend where this drag-and-drop experience and component reusability go hand in hand with embedded analytics. More BI companies now want to embed charts, tables, and grids into applications, and low-code/no-code development platforms step in to offer this advantage.
Single Source of Truth, DesignOps & DevOps
The final way low-code platforms augment the development process is by exchanging the human-caused chaos of design workflow and poor DesignOps processes for a single source of design truth. The more sophisticated low-code tools are based on design systems, giving designs and developers a single source of truth in the application development process. This promotes a structured DesignOps process and, when coupled with existing DevOps processes, enables the most efficiency in time, cost, and effort when driving down your backlog deliverables.
As this article lays out, developers aren’t going away. More so, skilled developers are currently and will continue to be in high demand. To overcome the impediments inherited from the past, the IT world must embrace what low-code platforms offer. There is no room for outdated affirmations like:
Low-code tools cannot produce high-quality apps.
Low-code tools are going to wipe out human programmers.
Low-code will never replace developers, but I can’t say the same when it comes to replacing legacy systems, design-development processes, and mindsets though!
| 2022-12-06T00:00:00 |
2022/12/06
|
https://solutionsreview.com/business-process-management/the-future-of-low-code-will-low-code-replace-developers/
|
[
{
"date": "2022/12/06",
"position": 78,
"query": "AI replacing workers"
}
] |
Industrial robots increase unemployment and lower wages
|
Industrial robots increase unemployment and lower wages
|
https://iottechnews.com
|
[
"Ryan Daws",
"Ryan Daws Is A Senior Editor At Techforge Media With Over A Decade Of Experience In Crafting Compelling Narratives",
"Making Complex Topics Accessible. His Articles",
"Interviews With Industry Leaders Have Earned Him Recognition As A Key Influencer Organisations Like Onalytica. Under His Leadership",
"Publications Have Been Praised Analyst Firms Such As Forrester For Their Excellence",
"Performance. Connect With Him On X",
"Gadget_Ry",
"Bluesky",
"Gadgetry.Bsky.Social",
"Or Mastodon"
] |
According to the latest paper, employment was reduced by 7.5 percent following exposure to industrial robots. Labour force participation decreased by one ...
|
Ryan Daws is a senior editor at TechForge Media with over a decade of experience in crafting compelling narratives and making complex topics accessible. His articles and interviews with industry leaders have earned him recognition as a key influencer by organisations like Onalytica. Under his leadership, publications have been praised by analyst firms such as Forrester for their excellence and performance. Connect with him on X (@gadget_ry), Bluesky (@gadgetry.bsky.social), and/or Mastodon (@[email protected])
Research from China justifies concerns about the impact of industrial robots on the workforce.
Critics of automation believe it will replace many predominately blue-collar jobs. Proponents of industrial robots argue that new jobs will be created and the workforce will adapt.
At least in the short term, a paper from China backs the critics.
The paper, How Do Workers And Households Adjust To Robots, analysed the effects of exposure to industrial robots on labour markets and household behaviours. The authors cite a 2016 paper that suggests around 77 percent of Chinese jobs are at risk of automation.
According to the latest paper, employment was reduced by 7.5 percent following exposure to industrial robots. Labour force participation decreased by one percent.
The researchers use the manufacturing firm Foxconn as a case study. Foxconn replaced more than 400,000 jobs with robots between 2012 and 2016. The company’s layoffs were part of its bid to achieve 30 percent factory automation by 2020.
“With much higher share of workers having only high school education or less, it will require time before workers acquire the skills needed to benefit from the complementarities brought up by smart machines and automation,” wrote the authors.
Another stark finding is that the hourly wages of Chinese workers declined by nine percent—indicating that automation is forcing workers to accept unfair conditions. This is further backed by the fact that robot exposure increased the number of hours worked by 14 percent.
The need to work longer hours following exposure to industrial robots dispels another key argument of proponents; that automation frees up workers’ time.
“Without employment creation, automation, digitalization and labour-saving technologies may foster inequality. Consequently, developing countries may face new policy challenges and important economic trade-offs, such as the one between increased productivity and potential higher economic inequality and social unrest,” add the researchers.
The paper’s authors believe the effects of robots in emerging economies are likely to be “significantly larger” than those observed so far in the more developed countries.
(Photo by Simon Kadula on Unsplash)
Want to learn about the IoT from industry leaders? Check out IoT Tech Expo taking place in Amsterdam, California, and London. The event is co-located with AI & Big Data Expo.
Explore other upcoming enterprise technology events and webinars powered by TechForge here.
| 2022-12-06T00:00:00 |
2022/12/06
|
https://iottechnews.com/news/industrial-robots-increase-unemployment-and-lower-wages/
|
[
{
"date": "2022/12/06",
"position": 2,
"query": "AI unemployment rate"
}
] |
Opening up to AI: Learning to trust our AI colleagues
|
Opening up to AI: Learning to trust our AI colleagues
|
https://www.deloitte.com
|
[] |
While the value of AI is now indisputable, the question of how to best use it often boils down to how much workers and end users trust AI tools.
|
Computers were once seen as more or less infallible machines that simply processed discrete inputs into discrete outputs, whose calculations were never wrong. If a problem ever arose in a calculation or business process, it was definitionally caused by human error, not the computer.
But as machines encroach on ever-more humanlike tasks that go beyond basic number crunching and enter the realm of discernment and decision-making via artificial intelligence (AI), the business world is developing a new understanding of what it means to trust machines.
The degree to which businesses and workers learn to trust their AI “colleagues” could play an important role in their business success. Most organizations today say they’re data-driven. Many even call themselves AI-fueled companies.1 There’s plenty of evidence suggesting businesses that use AI pervasively throughout their operations perform at a higher level than those that don’t: Enterprises that have an AI strategy are 1.7 times more likely to achieve their goals than those that lack such a vision.2
Yet the underlying AI tool implemented in a given workflow matters less.3 With cloud vendors increasingly offering prebuilt models, any business can access world-class AI functionality with a few clicks. The top-performing facial recognition vendors ranked by the National Institute of Standards and Technology deliver comparable performance, and they’re all easily accessed through cloud-based services.4 It’s what you do with the tool that’s important—and whether your people, customers, and business trust the results.
So what may matter in the future is not who can craft the best algorithm, but rather who can use AI most effectively. As algorithms increasingly shoulder probabilistic tasks such as object detection, speech recognition, and image and text generation, the real impact of AI applications may depend on how much their human colleagues understand and agree with what they’re doing. People don’t embrace what they don’t understand. We spent the last 10 years trying to get machines to understand us better. Now it looks like the next 10 years might be more about innovations that help us understand machines.
Developing processes that leverage AI in transparent and explainable ways will be key to spurring adoption.
“What we’re designing is an interface of trust between a human and a machine,” says Jason Lim, identity management capability manager at the Transportation Security Administration. “Now you’re taking an input from a machine and feeding it into your decision-making. If humans don’t trust machines or think they’re making the right call, it won’t be used.”5
Think of deploying AI like onboarding a new team member. We know generally what makes for effective teams: openness, rapport, the ability to have honest discussions, and a willingness to accept feedback to improve performance. Implementing AI with this framework in mind may help the team view AI as a trusted copilot rather than a brilliant but taciturn critic. When applications are transparent, resilient, and dependable, they can become a natural part of the workstream.
Now: Business-critical but inscrutable
When recruiting new team members, managers often look for the right mix of skills and fit. Few leaders doubt AI’s abilities to contribute to the team. According to one survey, 73% of businesses say AI is critical to their success.6
But they’re less sold on fit. Currently, enterprises have a hard time trusting AI with mission-critical tasks. The same report found that 41% of technologists are concerned about the ethics of the AI tools their company uses, and 47% of business leaders have concerns about transparency,7 the ability for users to understand the data that went into a model.
Enterprises are also grappling with a related concept, explainability, the ability of a model to give an explicit justification for its decision or recommendation. Explainability in AI systems is necessary when it is required by regulations, but it’s also becoming expected functionality in situations where it helps make clear to end users how to use a tool, improve the system generally, and assess fairness.8 Explainability is one of the biggest differentiators between the successful use of AI at scale and failure to reap returns on AI investment, yet many businesses haven’t figured out how to achieve it.
We spent the last 10 years trying to get machines to understand us better. Now it looks like the next 10 years might be more about innovations that help us understand machines.
New: From black box to glass box
Mistrust of AI can come from business leaders, front-line workers, and consumers. Regardless of its origin, it can dampen enterprises’ AI enthusiasm and, in turn, adoption. But leading organizations are working on solving issues that diminish trust in AI implementations. Some of the most effective approaches treat AI not so much as a point technology but rather as a piece in a larger process, considering the various stages where humans interact with the AI system and working to identify and address areas of potential mistrust. Acknowledging that AI tools are techniques to be woven into the larger tapestry of processes within an organization can make it easier to fix trust issues proactively. For more trusted AI, forward-thinking enterprises are leaning on data transparency, algorithmic explainability, and AI reliability (figure 1).
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Data transparency
Transparent data-collection methods enable the end user to understand why certain pieces of information are being collected and how they’re going to be used. When users have this control, they can make informed decisions about whether the AI tool represents a fair value exchange.9
The Saudi Tourism Authority used this approach when developing a new application for travelers. The app uses AI to guide tourists through their stay in the country, recommending restaurants, attractions, and other activities based on location and preferences. But importantly, the user is in control of the data they provide to the app. Visitors can determine how much or how little data they hand over, or can opt out completely, with the understanding that giving the app less data access may mean less-tailored recommendations.10 This stands in contrast to many apps that have all-or-nothing data access requirements that generally serve as a poor foundation for trust.11
Algorithmic explainability
One of the biggest clouds hanging over AI today is its black-box problem. Because of how certain algorithms train, it can be very difficult, if not impossible, to understand how they arrive at a recommendation. Asking workers to do something simply because the great and powerful algorithm behind the curtain says to is likely to lead to low levels of buy-in.
One automaker in the United Kingdom is tackling this problem by bringing frontline workers into the process of developing AI tools. The manufacturer wanted to bring more AI into the vehicle-assembly process by enabling machine learning to control assembly robots and identify potentially misaligned parts before the vehicle gets too far into the assembly process. At the start of the development process, engineers bring in front-line assembly workers to gauge their perception of problems and use that to inform development. Rather than dropping AI into an arbitrary point in the production process, they use it where the assemblers say they most need help.
The tools ultimately built are interpretable because the workers’ input forms the basis of alerts and recommendations. In other words, it’s easy for assemblers to see how the AI platform’s recommendations map to the problems they themselves helped define. By bringing in workers at the start and helping them understand how the AI functions, developers are able to support the assembly team with trusted cobot coworkers rather than a silicon overlord dictating opaque instructions.
AI reliability
People have grown accustomed to a certain level of reliability from work applications. When you open an internet browser or word-processing application, it typically simply “behaves.” More specialized business applications such as customer relationship management platforms and enterprise resource management tools may be a bit more finicky, but their challenges are fairly well established, and good developers know how to troubleshoot them.
With AI, the question isn’t whether it will work but rather how accurate the result will be or how precisely the model will assess a situation. AI is generally neither right nor wrong in the traditional sense. AI outputs are probabilistic, expressing the likelihood of certain outcomes or conditions as percentages—like a weather forecast predicting a 60% chance of rain—which can make assessing reliability a challenge. But workers need to know how accurate and precise AI is, particularly in critical scenarios such as health care applications.12
AI is sometimes viewed as much as an art as a science, but that may need to change for robust adoption. Organizations that take a rigorous approach to ensuring AI reliability consistently see better results. Those that document and enforce MLOps processes—a set of procedures designed to ensure machine learning tools are deployed in a consistent and reliable manner—are twice as likely as those that don’t to achieve their goals and to deploy AI in a trustworthy way.13 Taking an operations-minded approach puts guardrails around AI and helps build confidence that it is subject to the same standards of reliability as any other business application.
But reliable doesn’t necessarily mean perfect. Just as human coworkers will never deliver perfect results every time, AI too will make mistakes. So the bar for reliability is not perfection, but rather how often it meets or exceeds an existing performance standard.
Next: Creative machines
As enterprises deploy AI in traditional operational systems, a new trend is taking shape on the horizon: generative AI. We’re already seeing the emergence of tools such as OpenAI’s Dall-E 2 image generator and GPT-3 text generator. There’s a generative model for music called Jukebox that lets users automatically create songs that mimic specific artists’ styles.14 AI is increasingly being used to automatically caption live audio and video.15 These types of content generators are getting more sophisticated by the day and are reaching the point where people have a hard time telling the difference between artificially rendered works and those created by humans.
Concern over automation’s impact on jobs is nothing new, but it is growing ever more pronounced as we head toward this automatically generated future. In many cases, generative AI is proving itself in areas that were once thought to be automation-proof: Even poets, painters, and priests are finding no job will be untouched by machines.
That does not mean, however, that these jobs are going away. Even the most sophisticated AI applications today can’t match humans when it comes to purely creative tasks such as conceptualization, and we’re still a long way off from AI tools that can unseat humans in jobs in these areas. A smart approach to bringing in new AI tools is to position them as assistants, not competitors.
Companies still need designers to develop concepts and choose the best output, even if designers aren’t doing as much of the manipulating of images directly. They need writers to understand topics and connect them to readers’ interests. In these cases, content generators are just another tool. As OpenAI’s CEO Sam Altman writes in a blog on DALLE-2, “It’s an example of a world in which good ideas are the limit for what we can do, not specific skills.”16
Workers and companies that learn to team with AI and leverage the unique strengths of both AI and humans may find that we’re all better together. Think about the creative, connective capabilities of the human mind combined with AI’s talent for production work. We’re seeing this approach come to life in the emerging role of the prompt engineer.17 This teaming approach may lead to better job security for workers and better employee experience for businesses.
AI continues to push into new use cases through emerging capabilities that most people thought would remain the exclusive domain of humans. As enterprises consider adopting these capabilities, they could benefit from thinking about how users will interact with them and how that will impact trust. For some businesses, the functionality offered by emerging AI tools could be game-changing. But a lack of trust could ultimately derail these ambitions.
| 2022-12-06T00:00:00 |
https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends/2023/artificial-intelligence-humans.html
|
[
{
"date": "2022/12/06",
"position": 3,
"query": "AI workforce transformation"
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{
"date": "2022/12/06",
"position": 72,
"query": "generative AI jobs"
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{
"date": "2022/12/06",
"position": 8,
"query": "machine learning workforce"
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{
"date": "2022/12/06",
"position": 3,
"query": "AI workers"
},
{
"date": "2022/12/06",
"position": 4,
"query": "artificial intelligence workers"
},
{
"date": "2022/12/06",
"position": 8,
"query": "artificial intelligence business leaders"
}
] |
|
The state of AI in 2022—and a half decade in review
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The state of AI in 2022—and a half decade in review
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https://www.mckinsey.com
|
[
"Michael Chui",
"Bryce Hall",
"Helen Mayhew",
"Alex Singla",
"Alex Sukharevsky"
] |
The results of this year's McKinsey Global Survey on AI show the expansion of the technology's use since we began tracking it five years ago.
|
You have reached a page with older survey data. Please see our 2024 survey results here
Adoption has more than doubled since 2017, though the proportion of organizations using AI has plateaued between 50 and 60 percent for the past few years. A set of companies seeing the highest financial returns from AI continue to pull ahead of competitors. The results show these leaders making larger investments in AI, engaging in increasingly advanced practices known to enable scale and faster AI development, and showing signs of faring better in the tight market for AI talent. On talent, for the first time, we looked closely at AI hiring and upskilling. The data show that there is significant room to improve diversity on AI teams, and, consistent with other studies, diverse teams correlate with outstanding performance.
Table of Contents
1. Five years in review: AI adoption, impact, and spend
This marks the fifth consecutive year we’ve conducted research globally on AI’s role in business, and we have seen shifts over this period.
2. Mind the gap: AI leaders pulling ahead
Over the past five years we have tracked the leaders in AI—we refer to them as AI high performers—and examined what they do differently. We see more indications that these leaders are expanding their competitive advantage than we find evidence that others are catching up.
First, we haven’t seen an expansion in the size of the leader group. For the past three years, we have defined AI high performers as those organizations that respondents say are seeing the biggest bottom-line impact from AI adoption—that is, 20 percent or more of EBIT from AI use. The proportion of respondents falling into that group has remained steady at about 8 percent. The findings indicate that this group is achieving its superior results mainly from AI boosting top-line gains, as they’re more likely to report that AI is driving revenues rather than reducing costs, though they do report AI decreasing costs as well.
Next, high performers are more likely than others to follow core practices that unlock value, such as linking their AI strategy to business outcomes (Exhibit 1). Also important, they are engaging more often in “frontier” practices that enable AI development and deployment at scale, or what some call the “industrialization of AI.” For example, leaders are more likely to have a data architecture that is modular enough to accommodate new AI applications rapidly. They also often automate most data-related processes, which can both improve efficiency in AI development and expand the number of applications they can develop by providing more high-quality data to feed into AI algorithms. And AI high performers are 1.6 times more likely than other organizations to engage nontechnical employees in creating AI applications by using emerging low-code or no-code programs, which allow companies to speed up the creation of AI applications. In the past year, high performers have become even more likely than other organizations to follow certain advanced scaling practices, such as using standardized tool sets to create production-ready data pipelines and using an end-to-end platform for AI-related data science, data engineering, and application development that they’ve developed in-house.
High performers might also have a head start on managing potential AI-related risks, such as personal privacy and equity and fairness, that other organizations have not addressed yet. While overall, we have seen little change in organizations reporting recognition and mitigation of AI-related risks since we began asking about them four years ago, respondents from AI high performers are more likely than others to report that they engage in practices that are known to help mitigate risk. These include ensuring AI and data governance, standardizing processes and protocols, automating processes such as data quality control to remove errors introduced through manual work, and testing the validity of models and monitoring them over time for potential issues.
AI use and sustainability efforts The survey findings suggest that many organizations that have adopted AI are integrating AI capabilities into their sustainability efforts and are also actively seeking ways to reduce the environmental impact of their AI use (exhibit). Of respondents from organizations that have adopted AI, 43 percent say their organizations are using AI to assist in sustainability efforts, and 40 percent say their organizations are working to reduce the environmental impact of their AI use by minimizing the energy used to train and run AI models. As companies that have invested more in AI and have more mature AI efforts than others, high performers are 1.4 times more likely than others to report AI-enabled sustainability efforts as well as to say their organizations are working to decrease AI-related emissions. Both efforts are more commonly seen at organizations based in Greater China, Asia–Pacific, and developing markets, while respondents in North America are least likely to report them. When asked about the types of sustainability efforts using AI, respondents most often mention initiatives to improve environmental impact, such as optimization of energy efficiency or waste reduction. AI use is least common in efforts to improve organizations’ social impact (for example, sourcing of ethically made products), though respondents working for North American organizations are more likely than their peers to report that use.
Investment is yet another area that could contribute to the widening of the gap: AI high performers are poised to continue outspending other organizations on AI efforts. Even though respondents at those leading organizations are just as likely as others to say they’ll increase investments in the future, they’re spending more than others now, meaning they’ll be increasing from a base that is a higher percentage of revenues. Respondents at AI high performers are nearly eight times more likely than their peers to say their organizations spend at least 20 percent of their digital-technology budgets on AI-related technologies. And these digital budgets make up a much larger proportion of their enterprise spend: respondents at AI high performers are over five times more likely than other respondents to report that their organizations spend more than 20 percent of their enterprise-wide revenue on digital technologies.
Finally, all of this may be giving AI high performers a leg up in attracting AI talent. There are indications that these organizations have less difficulty hiring for roles such as AI data scientist and data engineer. Respondents from organizations that are not AI high performers say filling those roles has been “very difficult” much more often than respondents from AI high performers do.
The bottom line: high performers are already well positioned for sustained AI success, improved efficiency in new AI development, and a resultingly more attractive environment for talent. The good news for organizations outside the leader group is that there’s a clear blueprint of best practices for success.
3. AI talent tales: New hot roles, continued diversity woes
Our first detailed look at the AI talent picture signals the maturation of AI, surfaces the most common strategies organizations employ for talent sourcing and upskilling, and shines a light on AI’s diversity problem—while showing yet again a link between diversity and success.
Hiring is a challenge, but less so for high performers
All organizations report that hiring AI talent, particularly data scientists, remains difficult. AI high performers report slightly less difficulty and hired some roles, like machine learning engineers, more often than other organizations.
Reskilling and upskilling are common alternatives to hiring
When it comes to sourcing AI talent, the most popular strategy among all respondents is reskilling existing employees. Nearly half are doing so. Recruiting from top-tier universities as well as from technology companies that aren’t in the top tier, such as regional leaders, are also common strategies. But a look at the strategies of high performers suggests organizations might be best served by tapping as many recruiting channels as possible (Exhibit 2). These companies are doing more than others to recruit AI-related talent from various sources. The findings show that while they’re more likely to recruit from top-tier technical universities and tech companies, they’re also more likely to source talent from other universities, training academies, and diversity-focused programs or professional organizations.
Responses suggest that both AI high performers and other organizations are upskilling technical and nontechnical employees on AI, with nearly half of respondents at both AI high performers and other organizations saying they are reskilling as a way of gaining more AI talent. However, high performers are taking more steps than other organizations to build employees’ AI-related skills.
Respondents at high performers are nearly three times more likely than other respondents to say their organizations have capability-building programs to develop technology personnel’s AI skills. The most common approaches they use are experiential learning, self-directed online courses, and certification programs, whereas other organizations most often lean on self-directed online courses.
High performers are also much more likely than other organizations to go beyond providing access to self-directed online course work to upskill nontechnical employees on AI. Respondents at high performers are nearly twice as likely as others to report offering peer-to-peer learning and certification programs to nontechnical personnel.
Increasing diversity on AI teams is a work in progress
We also explored the level of diversity within organizations’ AI-focused teams, and we see that there is significant room for improvement at most organizations. The average share of employees on these teams at respondents’ organizations who identify as women is just 27 percent (Exhibit 3). The share is similar when looking at the average proportion of racial or ethnic minorities developing AI solutions: just 25 percent. What’s more, 29 percent of respondents say their organizations have no minority employees working on their AI solutions.
Some companies are working to improve the diversity of their AI talent, though there’s more being done to improve gender diversity than ethnic diversity. Forty-six percent of respondents say their organizations have active programs to increase gender diversity within the teams that are developing AI solutions, through steps such as partnering with diversity-focused professional associations to recruit candidates. One-third say their organizations have programs to increase racial and ethnic diversity. We also see that organizations with women or minorities working on AI solutions often have programs in place to address these employees’ experiences.
In line with previous McKinsey studies, the research shows a correlation between diversity and outperformance. Organizations at which respondents say at least 25 percent of AI development employees identify as women are 3.2 times more likely than others to be AI high performers. Those at which at least one-quarter of AI development employees are racial or ethnic minorities are more than twice as likely to be AI high performers.
About the research
The online survey was in the field from May 3 to May 27, 2022, and from August 15 to August 17, 2022, and garnered responses from 1,492 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of those respondents, 744 said their organizations had adopted AI in at least one function and were asked questions about their organizations’ AI use. To adjust for differences in response rates, the data are weighted by the contribution of each respondent’s nation to global GDP.
| 2022-07-14T00:00:00 |
2022/07/14
|
https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2022-and-a-half-decade-in-review
|
[
{
"date": "2022/12/06",
"position": 6,
"query": "AI workforce transformation"
},
{
"date": "2022/12/06",
"position": 17,
"query": "machine learning job market"
},
{
"date": "2022/12/06",
"position": 1,
"query": "reskilling AI automation"
},
{
"date": "2022/12/06",
"position": 4,
"query": "AI skills gap"
},
{
"date": "2022/12/06",
"position": 2,
"query": "future of work AI"
},
{
"date": "2022/12/06",
"position": 2,
"query": "workplace AI adoption"
},
{
"date": "2022/12/06",
"position": 6,
"query": "artificial intelligence employers"
},
{
"date": "2022/12/06",
"position": 1,
"query": "artificial intelligence business leaders"
}
] |
What Is a Machine Learning Engineer? How to Become ...
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What Is a Machine Learning Engineer? How to Become One, Salary, Skills.
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https://builtin.com
|
[] |
Jobs for computer and information research scientists, including AI and machine learning specialists, are projected to increase 21 percent by 2031 according to ...
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Machine learning engineers merge artificial intelligence (AI) technology and software engineering expertise to create machine learning mechanisms. Here’s what to know about a machine learning engineer’s salary, needed skills and how to become one.
What Is a Machine Learning Engineer?
Machine learning engineers are programming specialists who research, design and build machine learning systems for business use. The machine learning engineer role largely emerged in the 2010s due to the rise of big data and data science applications.
What Do Machine Learning Engineers Do?
Machine learning engineers utilize data sets to program and test predictive machine learning algorithms, models and schemas. These machine learning systems are often used by companies to automate tasks and forecast business decisions.
Machine Learning Engineer Responsibilities
Design, test and build machine learning systems.
Train and optimize machine learning algorithms and models.
Research, convert and apply data prototypes for machine learning models.
Conduct statistical analysis to gauge system performance.
Add to and update machine learning frameworks and libraries.
Day-to-Day Responsibilities of Machine Learning Engineer
Review existing projects and compare findings to working machine learning models.
Use machine learning tools like TensorFlow to build and train models.
Use programming languages like Python to code and debug algorithms.
Access machine learning libraries like Apache Spark to load sample data and run linear regression models.
Machine Learning Engineer Within a Company
Machine learning engineers are usually part of a data science team within a company. They frequently collaborate with data analysts, data engineers, data scientists and software developers to accomplish their work.
Importance of Machine Learning Engineer
Machine learning engineers create systems often responsible for tracking product analytics and making impactful business decisions. Operations like content filtering and personalization, fraud detection, and voice recognition may not be as efficient or widely used without machine learning engineers.
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Machine Learning Roadmap for Beginners. | Video: Ayush Singh
What Skills Are Needed to Be a Machine Learning Engineer?
Qualifications to Be a Machine Learning Engineer
Several years of experience in computer engineering, data science, software development or software engineering.
Ability to build, train and deploy data structures, algorithms and models.
Ability to assess and measure model performance.
Proficiency in Python, R or Java as well as software engineering principles.
Machine Learning Engineer Prerequisites
Bachelor’s degree in computer science, data science, mathematics, software engineering or a similar field.
Master’s degree in computer science, data science, machine learning or a similar field.
Machine Learning Engineer Hard Skills
Knowledge of big data tools and databases.
Knowledge of cloud computing.
Expertise in computer programming languages (C++, Java, JavaScript, Python, R).
Experience with data analysis tools and techniques.
Knowledge of deep learning and neural network algorithms.
Familiarity with DevOps practices.
Familiarity with MLOps practices.
Experience with statistics, mathematics and related analysis.
Machine Learning Engineer Soft Skills
Adaptability.
Problem-solving skills.
Time management skills.
Verbal and written communication skills.
Tools and Programs Machine Learning Engineers Use
Apache Hadoop
Apache Mahout
Apache Spark
AWS Machine Learning
C++
Google Colaboratory
IBM Watson Studio
Java
JavaScript
Python
PyTorch
R
TensorFlow
Find out who's hiring. See all Developer + Engineer jobs at top tech companies & startups View Jobs
How to Become a Machine Learning Engineer
Machine Learning Engineer Education and Experience
Machine learning engineer candidates are often expected to have a bachelor’s degree in computer science, data science, mathematics or a similar field. They also may have a master’s degree in computer science, data science, machine learning or a similar field to enter competitive or higher-level roles.
Candidates often obtain several years of experience in computer engineering, data science, software development or software engineering before entering the machine learning engineer role. Knowledge in the areas of machine learning structures and models, data analysis tools, computer programming languages (C++, Java, JavaScript, Python, R), big data tools, cloud computing and effective communication are also recommended.
Machine Learning Engineer Certificates and Courses
Machine Learning Engineer Career Path
Prior to the machine learning engineer role, professionals may start their career as a computer engineer, software developer, software engineer or a similar role. After experience as a machine learning engineer, professionals can progress into management and leadership positions like senior machine learning engineer, lead machine learning engineer or director of machine learning engineering.
Machine Learning Engineer Salary and Job Outlook
Jobs for computer and information research scientists, including AI and machine learning specialists, are projected to increase 21 percent by 2031 according to the U.S. Bureau of Labor Statistics.
The full compensation package for a machine learning engineer depends on a variety of factors, including but not limited to the candidate’s experience and geographic location. See below for detailed information on the average machine learning engineer salary.
| 2022-12-06T00:00:00 |
https://builtin.com/learn/careers/machine-learning-engineer
|
[
{
"date": "2022/12/06",
"position": 5,
"query": "machine learning job market"
}
] |
|
The future is now: Unlocking the promise of AI in industrials
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The future is now: Unlocking the promise of AI in industrials
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https://www.mckinsey.com
|
[
"Kimberly Borden",
"Mark Huntington",
"Mithun Kamat",
"Alex Singla",
"Joris Wijpkema",
"Bill Wiseman"
] |
Five proven use cases show how artificial intelligence is creating value for advanced industrial companies.
|
In 2018, we explored the $1 trillion opportunity for artificial intelligence (AI) in industrials. As companies are recovering from the pandemic, research shows that talent, resilience, tech enablement across all areas, and organic growth are their top priorities.
About the authors This article is a collaborative effort by Kimberly Borden, Mark Huntington, Mithun Kamat, Alex Singla, Joris Wijpkema, and Bill Wiseman, representing views from McKinsey’s Advanced Industries Practices.
Despite this opportunity, many executives remain unsure where to apply AI solutions to capture real bottom-line impact. The result has been slow rates of adoption, with many companies taking a wait-and-see approach rather than diving in.
Rather than endlessly contemplate possible applications, executives should set an overall direction and road map and then narrow their focus to areas in which AI can solve specific business problems and create tangible value. As a first step, industrial leaders could gain a better understanding of AI technology and how it can be used to solve specific business problems. They will then be better positioned to begin experimenting with new applications.
Defining AI
The term “artificial intelligence” is suffering from severe overuse. It has almost become shorthand for any application of cutting-edge technology, obscuring its true definition and purpose. Therefore, it’s helpful to clearly define AI and its uses for industrial companies.
In short, AI is the ability of a machine to perform cognitive functions typically associated with human minds, such as perceiving, reasoning, learning, interacting with the environment, and problem solving. Examples of AI technologies include robotics, autonomous vehicles, computer vision, language, virtual agents, and machine learning.
One area in which AI is creating value for industrials is in augmenting the capabilities of knowledge workers, specifically engineers. At their core, such applications harness AI’s predictive capabilities. Companies are learning to reformulate traditional business issues into problems in which AI can use machine-learning algorithms to process data and experiences, detect patterns, and make recommendations.
How AI can accelerate problem solving
Companies must first define an existing business problem before exploring how AI can solve it. Failure to go through this exercise will leave organizations incorporating the latest “shiny object” AI solution.
The following examples demonstrate AI’s value in augmenting workers’ knowledge and streamlining workflows.
AI scheduling agents
Some of the most difficult challenges for industrial companies are scheduling complex manufacturing lines, maximizing throughput while minimizing changeover costs, and ensuring on-time delivery of products to customers. AI can help through its ability to consider a multitude of variables at once to identify the optimal solution. For example, in one metals manufacturing plant, an AI scheduling agent was able to reduce yield losses by 20 to 40 percent while significantly improving on-time delivery for customers.
Traditional optimization approaches collapse in an attempt to manage significant uncertainty and fluctuation in supply or demand. This problem has become particularly relevant given all of the supply chain issues over the past year. Using scheduling agents based on reinforcement learning, companies can translate this issue into a question—“What order is most likely to maximize profit?”—that yields a clear recommendation.
To solve this problem, companies must first build an environment in which the AI scheduling agent can learn to make good predictions (Exhibit 1). In this situation, relying on historical data (as typical machine learning does) is simply not good enough because the agent will not be able to anticipate future issues (such as supply chain disruptions).
Instead, organizations can start by building a simulation or “digital twin” of the manufacturing line and order book. A scheduling agent can then schedule the line. The agent’s performance is scored based on the cost, throughput, and on-time delivery of products. Next, the agent “plays the scheduling game” millions of times with different types of scenarios. Just as Deep Mind’s AlphaGo agent got better by playing itself, the agent uses deep reinforcement learning to improve scheduling. Before long, the agent is able to create high-performance schedules and work with the human schedulers to optimize production.
Knowledge discovery
Many industrial companies face the common issue of identifying the most relevant data when faced with a specific challenge. AI can accelerate this process by ingesting huge volumes of data and rapidly finding the information most likely to be helpful to the engineers when solving issues. For example, companies can use AI to reduce cumbersome data screening from half an hour to a few seconds, thus unlocking 10 to 20 percent of productivity in highly qualified engineering teams. In addition, AI can also discover relationships in the data previously unknown to the engineer.
After decades of collecting information, companies are often data rich but insights poor, making it almost impossible to navigate the millions of records of structured and unstructured data to find relevant information. This challenge is particularly important when engineers are troubleshooting new issues on highly complex systems (for example, aircraft, spacecraft, and power plants) to find the most relevant procedures, machine performance data, history of operations, and instances of relevant issues on similar subsystems. Engineers are often left relying on their previous experience, talking to other experts, and searching through piles of data to find relevant information. For critical issues, this high-stakes scavenger hunt is stressful at best and often leads to suboptimal outcomes.
Companies can teach AI to navigate text-heavy structured and unstructured technical documents by feeding it important technical dictionaries, lookup tables, and other information. They can then build algorithms to help AI understand semantic relationships between different text. Next, a knowledge graph can dynamically create an information network that represents all the semantic and other relationships in the technical documents and data (Exhibit 2). For example, using the knowledge graph, the agent would be able to determine a sensor that is failing was mentioned in a specific procedure that was used to solve an issue in the past. Once the knowledge graph is created, a user interface allows engineers to query the knowledge graph and identify solutions for particular issues. The system can be set up to collect feedback from engineers on whether the information was relevant, which allows the AI to self-learn and improve performance over time.
AI-enabled product system design
For many industrial companies, the system design of their products has become incredibly complex. Organizations can use AI to augment a product’s bill of materials (BoM) with data drawn from its configuration, development, and sourcing. This process identifies opportunities to reuse historical parts, improve existing standard work, and support preproduction definition. With these insights, companies can significantly reduce engineering hours and move to production more quickly.
When a single project can have millions of parts and thousands of submodules and subsystems, it can be nearly impossible for any single engineer to have a full systems view as well as an understanding of the detailed design of individual components, let alone across projects. As a result, systems are redesigned with each new project but overlook opportunities to reuse parts, driving up costs and increasing supply chain complexity. In addition, engineers can face significant rework on projects from not fully understanding interdependencies across the system.
Fortunately, AI approaches do not have such limitations. With sufficient memory and computation, AI-based solutions can easily look across millions of parts and projects and billions of relationships to identify opportunities for reuse and sequence processes to avoid rework related to interdependencies. A network-based representation of the system using BoM can capture complex relationships and hierarchy of the systems (Exhibit 3). This information is augmented by data on engineering hours, materials costs, and quality as well as customer requirements. With this enhanced network build, companies can query and make predictions—for example, what subsystems a customer requirement might affect and the engineering efforts that are most likely to cause rework in a project based on interdependencies.
Product performance optimization
As products have evolved, pushing the boundaries of performance has become increasingly challenging. Industrial companies that can rapidly innovate and bring higher-performing products to market faster are much more likely to gain market share and win in their market segments.
Over the past three decades, computer-aided engineering (CAE) and simulation have helped, but the limits on their computing power are preventing them from fully exploring the design space and optimizing performance on complex problems. For example, components typically have more than ten design parameters, with up to 100 options for each parameter. Because a simulation takes ten hours to run, only a handful of the resulting trillions of potential designs can be explored in a week. Companies that rely on experienced engineers to narrow down the most promising designs to test in a series of designed experiments risk leaving performance on the table.
AI uses a deep-learning neural network to create a digital twin of the component and predict performance (Exhibit 4). Organizations can also use Bayesian optimizers to predict the most promising areas of the design space to explore. Shifting from exact calculations to a directional problem-solving approach focused on predictions exponentially accelerates the evaluation of design. An engine can generate millions of potential designs that are evaluated using the deep-learning digital twin. A genetic algorithm can then optimize the system for multiple performance metrics at the same time (for example, in automotive top speed and fuel economy).
AI-augmented root cause analysis
Industrial companies build their reputations based on the quality of their products, and innovation is key to continued growth. Winning companies are able to quickly understand the root causes of different product issues, solve them, and integrate those learnings going forward. AI can dramatically speed up this process.
Since the complexity of products and operating conditions has exploded, engineers are struggling to identify root causes and track solutions. As a result, companies are highly dependent on pattern recognition by experienced engineers and spend a lot of time trying to re-create issues in lab environments in an attempt to get to the root cause.
AI and causal modeling approaches can reformulate this complexity into a defined business problem: “What are the top five most likely root causes for this issue?” Companies can start by building an event-based data model that connects thousands of variables across the product history and development life cycle, including design configurations, manufacturing parameters, and maintenance and repair history. An AI-based model can then identify the top predictors of incidents related to a specific issue (Exhibit 5). Next, causal modeling approaches separate variables that are correlated from those likely to be causal. A user interface enables engineers to query and find the most likely root causes of new issues.
AI is still in relatively early stages of development, and it is poised to grow rapidly and disrupt traditional problem-solving approaches in industrial companies. These use cases help to demonstrate the concrete applications of these solutions as well as their tangible value. By experimenting with AI applications now, industrial companies can be well positioned to generate a tremendous amount of value in the years ahead.
| 2022-12-06T00:00:00 |
https://www.mckinsey.com/industries/automotive-and-assembly/our-insights/the-future-is-now-unlocking-the-promise-of-ai-in-industrials
|
[
{
"date": "2022/12/06",
"position": 39,
"query": "machine learning job market"
},
{
"date": "2022/12/06",
"position": 25,
"query": "future of work AI"
}
] |
|
AI & Machine Learning Reviews
|
Learner Success Stories and Feedback
|
https://www.simplilearn.com
|
[
"Sr Field Applications Engineer",
"Senior Electrical Engineer",
"Senior Ux Ui Designer",
"Ai Tutor",
"Vice President Sales",
"Site Reliability Engineer",
"Sales Executive",
"Vice President - Channel Sales",
"General Manager - Global Treasury",
"General Manager - Analytics"
] |
I decided to enroll in this program because I wanted to pivot my career, explore new job opportunities, and support a project deployment requirement at work.
|
As documented by Manojit Majumdar
My Journey
I’ve spent over 34 years in sales and eco-system development, which has been quite a ride. I have traveled across 40-plus countries, meeting customers and business partners. I’ve even written about my experience, “Lamb Leading Lions – Why we do not have Google or Microsoft from India,” currently the number one Hot Seller on Amazon.
In my career, I’ve had the opportunity and the pleasure to learn from giants like IBM and also help establish small startups. I’ve lived in various cities in India and in USA, before deciding to come back to India.
I currently work as a Vice President – Channel Sales in Newgen Software Technologies Limited, Delhi. In my job, I’ve set up a partner network across 70 countries and created the needed systems and processes to identify, skill, and nurture them.
Upskilling
I knew I wanted to learn more about the current hottest technologies in the market – AI and Machine Learning. I wanted to increase my knowledge and understanding of this vast subject, so I opted for Simplilearn’s AI for Decision Making.
With dreams of creating my own billion-dollar business, the program created in collaboration with Wharton Online is perfect for helping me gain a competitive edge in emerging business technologies. The esteemed faculty covers a foundational understanding of today’s leading technologies, including big data, AI, and machine learning, and how to use them to support your organization’s strategy.
This program is the perfect way to understand how AI and ML will affect your business and how you can become an early adopter to help your company get ahead of the competition.
The Simplilearn Experience
My experience with Simplilearn was very good. I think the course material is very thoughtfully designed. The way of explaining was simple and easy to understand. The option to learn based on time available to the student also proved to be very helpful.
The surprise factor was the additional reference material that they provided during the course. It really was a great help, and the material was very relevant. The simple test was not that simple and really tested my understanding of the subject.
I would definitely recommend this course to anyone who wants to learn about the latest emerging trends and technologies in AI and ML.
| 2022-12-06T00:00:00 |
https://www.simplilearn.com/reviews/ai-machine-learning
|
[
{
"date": "2022/12/06",
"position": 63,
"query": "machine learning job market"
}
] |
|
7 Best Machine Learning Courses For 2025
|
Machine Learning Course – 7 Best Machine Learning Courses for 2025
|
https://pwskills.com
|
[
"Ankit Kumar",
"Vanita Mishra"
] |
... opportunities and career options available for skilled professionals. Machine learning Course. Machine Learning job opportunities are continuously growing ...
|
Learn from the best Machine Learning Course in 2025 and become a certified and proficient machine learning expert with knowledge of building machine learning models, algorithms, and advanced techniques. In this article, learn about the PW Skills Data Science and Machine Learning Course.
Ankit Kumar is a versatile expert skilled in the fields of technical writing, SEO, and content creation across diverse categories. Thriving to deliver quality and engaging content with experience in technical fields aligned to the new technological reforms and digital world.
The Machine Learning Course provides skills equipped with forward-thinking decisions that align with the current and emerging trends in technology. Machine Learning algorithms are used in implementing advanced technologies in healthcare, finance, manufacturing, natural language processing, speech recognition, etc.
In this article, we will learn about the 7 best machine learning Courses, their perks, course fees, and syllabus.
Is the Machine Learning Course in Demand?
With rapid advancement in artificial intelligence tools and techniques impacting various industries and reshaping the way we used to interact with technologies. By 2030, the Machine Learning market is projected to grow at a CAGR reaching a $300.62 billion evaluation. The craze of AI and Machine Learning is continuously growing, with more opportunities and career options available for skilled professionals.
Machine Learning job opportunities are continuously growing, with higher demand for skilled professionals having knowledge of building advanced machine learning models. The machine learning course helps develop skills in machine learning with concepts like computer vision, algorithms, models, Python libraries, data science, and more, opening doors for more exciting career opportunities.
7 Best Machine Learning Courses For 2025
Machine learning is an in-demand skill in various sectors such as finance, healthcare, retail, and manufacturing. Machine learning is important to get a wide range of interview opportunities and career options in the coming years.
Let us dive right in with the knowledge of the top seven machine learning courses with great career opportunities ahead.
Build your knowledge in machine learning with advanced machine learning techniques, algorithms, and models. Work on real-world projects and strengthen your concepts with module-level assignments and practice exercises throughout the course. Get a wide range of career opportunities in machine Learning.
Prerequisites for Enrollment
There are no major prerequisites for enrolling in this course on PW Skills. However, you must be familiar with basic mathematical concepts and proficient in basic English to understand the lectures in the course.
Features of Machine Learning Course
The Duration of the Course is 6 Months
Diverse Project Portfolio + Capstone Projects
Doubt Clearing Sessions
Dedicated Buddy Support
Module Level Assignments + Practice Exercises
Industry Oriented Curriculum
Q&A Forums, Interview Opportunities and More
The one Time Machine Learning Course Fee is INR 20,000/-
2. Complete AI & Machine Learning, Data Science Bootcamp On Udemy
The Complete AI and Machine Learning Bootcamp on Udemy offers tutorials for data science, data analysis, machine learning, and Python libraries. The course is updated with the latest technologies and frameworks.
Become a Data Scientist and Machine learning expert after completing this course. Get familiar with Deep Learning concepts and work on data science and machine learning projects. Learn machine learning models and algorithms along with this course.
Prerequisites for Enrollment
Only need to pay the enrollment amount to join the course. No qualification criteria for Joining the course.
Features of the Machine Learning Course
Learn and implement Machine Learning Algorithms and machine learning models
Work on real-life case studies and projects to understand the implementation in the real world
Work on improving machine learning models.
Complete 43.5 hours on-demand recorded tutorials
Downloadable learning and practice resources
Can be accessed on multiple devices
Machine Learning Course Completion Certificate
3. Deep Learning Online NanoDegree Course on Udacity
This course is especially for beginners who want to build strong foundational skills in deep learning and machine learning models. Get familiar with deep learning, designing and training neural networks with advanced specialized architecture such as Convolutional and Recurrent Neural Networks.
Gain hands-on practical knowledge of advanced training techniques, machine learning concepts, and Python libraries. Tackle complex AI challenges in various domains after completing this Machine learning course.
Prerequisites for Enrollment
Familiar with Python Programming Language, Libraries, algebra, Calculus, NumPy, Matplotlib, Deep learning fundamentals, Jupyter Notebooks, and Neural Networks basics. Good Communication skills are also required to enroll in this course on Udacity.
Features of Machine Learning Course
Online Intermediate Learning Program.
3 Month Duration of the Course.
Learn Machine Learning Concepts, Deep Learning Techniques, Model evaluation, Generative adversarial networks and more
Based on the latest industry curriculum
The course curriculum consists of Deep learning, convolutional Neural Networks (CNNs), RNNs and Transformers, Machine learning models and algorithms, etc
4. Machine Learning A-Z Course on Udemy
Join the Udemy Machine Learning Course which provides A-Z concepts of machine learning and covers topics such as supervised and unsupervised machine learning, decision trees, logistic regression, neural networks, and more. Become proficient in machine learning algorithms and building machine learning models.
Prerequisites for Enrollment
Only need to pay the enrollment amount to join the course. No qualification criteria for Joining the course.
Features of Machine Learning Course
5. Online Machine Learning Course by Stanford University
Get basic and advanced machine learning skills with Stanford’s online machine learning course. The course offers in-depth tutorials on machine learning concepts, covering important aspects of machine learning algorithms, advanced techniques, and models.
The course provides on-demand tutorials for Supervised and Unsupervised algorithms, KNNs, CNNs, Bayesian Networks, and deep learning.
Prerequisites for Enrollment
Must have a bachelor’s degree with an overall CGPA of 3.0 or better. Familiar with Computer programming language, statistics, Python libraries, and more.
Features of Machine Learning Course
The complete Machine Learning Course duration is 10 weeks.
The complete course material is available on the official website of Stanford University.
The course program fee is $6,056.
Get a certificate from Stanford University.
6. Applied Machine Learning Course on Columbia Engineering
This Machine learning course offers tutorials on advanced real-life applications of machine learning suitable for students with background knowledge in programming and statistics.
Prerequisites of Enrollment
Bachelor’s degree in any stream related to computer science with knowledge in statistics, and background in data analysis. Students with a STEM background are preferred for this course.
Features of Machine Learning Course
7. Machine Learning Fundamentals and Algorithms With Carnegie Mellon University
Enroll in a 3-month specialization Course in Machine Learning for Data Science Professionals. Learn industry-oriented techniques from industry experts. Get practical insights and learn how to apply the knowledge of machine learning in any domain.
Prerequisites of Enrollment
This course is suitable for IT engineers, Data Scientists, Business analysts, and technical managers who participate in building innovative machine-learning solutions to improve business operations.
Features of the Machine Learning Course
Why Is PW Skills Course Better Than Other Online Programs?
PW Skills offers advanced learning at an affordable price to provide a chance to learn and upskill for everyone. Every online program on PW Skills offers in-depth tutorials based on in-demand techniques and concepts required for getting a wide range of career opportunities in the fields of machine learning and artificial intelligence.
Industry Oriented Curriculum ⭐ Job Ready Portfolio 👍 Affordable Online Programs 📒 Career Growth Opportunities + Interview Opportunities 📝 Real World Projects ✒️ 24×7 Doubt Support 📞 Live + Recorded Tutorials 🎥 Practice Exercises + Module Level Assignments 📚🔖 Sharable Course Certificate 🎦 Community Networking ⏸️
| 2022-12-07T00:00:00 |
2022/12/07
|
https://pwskills.com/blog/machine-learning-course/
|
[
{
"date": "2022/12/06",
"position": 91,
"query": "machine learning job market"
}
] |
How AI is turning issues into opportunities
|
How AI is turning issues into opportunities
|
https://erp.today
|
[
"Ulf Brackmann",
"Vice President",
"- Artificial Intelligence Technology",
".Wp-Block-Co-Authors-Plus-Coauthors.Is-Layout-Flow",
"Class",
"Wp-Block-Co-Authors-Plus",
"Display Inline",
".Wp-Block-Co-Authors-Plus-Avatar",
"Where Img",
"Height Auto Max-Width"
] |
When we talk about closing the skills gap, there must be diversity in development teams, so the skills needed cannot be hired from one place or using one ...
|
AI is changing tech on a global basis. But the background behind the transformation varies by region – can enterprise keep up?
As with any high growth industry or sector, nations around the world compete to lead the way on innovation and applications that benefit governments, citizens and businesses. The artificial intelligence (AI) industry is growing at an incredible speed and companies around the world are investing billions of dollars to win the ‘AI race’ and to secure the largest market share. Predictions show that by 2030 about 70 percent of companies will have adopted some sort of AI technology. According to Google CEO Sundar Pichai, the impact of AI will be even greater than that of fire or electricity on our development as a species. The reason is simple. Whether modelling climate change, developing new medical treatments, exploring space, or increasing speed to market in manufacturing, AI is changing the way we all live and work.
Marc Andreessen, the American entrepreneur, investor and software engineer, said that “software will eat the world”. He implies that every company will become a software company or die, and this applies equally to AI. Every company will eventually leverage AI since AI is a new paradigm of software development that extends the reach of the software. This doesn’t mean, however, that every company needs to build huge data science teams; for example, as AI matures, increased AI capabilities are available embedded in business software, and as low-code/no-code development tools. What’s more, large pre-trained models in the public domain mean companies need less or no training data. Therefore, we define an AI company as any organisation that leverages artificial intelligence to improve business processes and products at scale.
Aside from the mass investment in the technologies, another main driver of the adoption of AI is the urgent need for automation and intelligence in global civil infrastructure. As populations continue to grow, innovation – in particular, big data and AI technologies – is needed to improve the standard of life and work. Despite macro-economic factors impacting innovation and productivity globally in 2022, there’s no slowing the development of AI. While there are certain challenges, there are even more opportunities.
Companies, like ourselves, across Europe, US and Asia are engaging with policymakers to discuss suitable approaches
Regulation is coming, but will it slow down innovation?
In Asia, governments tend to be very open to the use of big data and AI and the state invests massively in digital solutions. The commercialisation of AI applications has been very successful. In the US, AI innovation is led by large corporations and is enabled by their investments. The US is currently leading the AI technology research and AI applications. Finally, the European approach is often focussed on regulation and safeguarding before innovation, and public opinion is still rather sceptical about digital transformation, AI and big data. Europe has been very successful in basic research and also has a long tradition in AI research. But when it comes to commercialising AI, the European industry has fallen behind the US and China, especially in AI for the internet and consumer products.
2022 has seen markets continue down the regulatory path. In July, the UK Government set out its emerging thinking on how it would regulate the use of AI. It is expected to publish proposals in a white paper later this year, which the committee would examine in its inquiry. AI’s role in the UK economy and society is growing. However, there are concerns around its use. MPs will examine the potential impacts of biased algorithms in the public and private sectors. A lack of transparency on how AI is applied and how automated decisions can be challenged will also be investigated.
The European Commission is also proposing the first-ever legal framework on AI, which addresses the risks of AI and positions the EU to play a leading role globally. The regulatory proposal aims to provide AI developers, deployers and users with clear requirements and obligations regarding specific uses of AI. At the same time, the proposal seeks to reduce administrative and financial burdens for business, in particular small and medium-sized enterprises.
When it comes to commercialising AI, the European industry has fallen behind the US and China
Regulation is tricky to introduce, given how quickly the technology and its use cases are developing. Developers may shiver at the thought of restrictions being introduced that may stifle their innovation, but it will not generally be the plan of regulators to prohibit or slow down the use of AI, but to limit intentional or unintentional issues. Companies, like ourselves, across Europe, US and Asia are engaging with policymakers to discuss suitable approaches to achieve both, enabling growth and innovation in AI as well as managing the risks.
Regulators and governments are not typically technology experts. When lobbying or collaborating with governments on what AI regulation should include, it is important that companies help governments to focus on the nature of the use cases and not so much on the technologies themselves. The risk-based approach of the EU’s AI Act is a right step in this direction. The law assigns applications of AI to three risk categories.
Firstly, applications and systems that create an unacceptable risk, such as government-run social scoring of the type used in China, are banned. Secondly, high-risk applications, such as a CV-scanning tool that ranks job applicants, are subject to specific legal requirements. Lastly, applications not explicitly banned or listed as high-risk are left unregulated. In any regulation, the defined obligations need to be general enough so they can cover all existing and emerging AI approaches, for example by defining processes rather than technical parameters to be adhered to.
Business challenges impacting AI development and adoption
Earlier this year, a report from the European Parliament’s special committee on artificial intelligence in a digital age said that the EU had ‘fallen behind’ in the global tech leadership race. “We neither take the lead in development, research or investment in AI,” the committee stated. “If we do not set clear standards for the human-centred approach to AI that is based on our core European ethical standards and democratic values, they will be determined elsewhere.” While the potential frameworks in Europe do seem strict, they force us to develop rules and methods to deal with challenges. GDPR and the emerging AI regulations require the AI solutions to be transparent, and while this may create hurdles initially, they urge AI research and development to invest more effort in trustworthy AI.
Another challenge for businesses is managing expectations and building a better understanding of AI. A baseline level of data literacy is a prerequisite. Businesses must also get comfortable with probabilistic modelling, the statistical process that uses the effect of random occurrences or actions to forecast the possibility of future results. Probabilistic modelling considers new situations and a wide range of uncertainty while not underestimating dangers. Using this method, businesses can quickly determine how confident any AI model is and how accurate its prediction is.
People are increasingly sceptical of AI, and yet technology is spreading into all areas of life and becoming more integrated into the way we live. So how can we bring more transparency to how AI works and help allay people’s fears? Many excellent executive education programmes on the strategic and practical implications of AI have sprung up over the past few years that can help executives navigate this new world.
The fast-moving and diverse international regulatory environment is creating uncertainty and risk. For example, individual US states are now releasing their own diverse regulations ranging from data privacy laws in California to algorithmic bias audits in the recruitment process in New York. While this remains up in the air, companies must continue to engage with policymakers and closely monitor the situation. The aim isn’t to prevent the use of technologies, but to ensure they are safe and beneficial to every citizen and business.
Individual US states are releasing their own regulations ranging from California’s data privacy laws to algorithmic bias recruitment audits in New York
Closing the AI skills gap
The hangover from the pandemic and macroeconomic disasters continue to damage markets and industries globally, which has had a detrimental impact on recruitment, education and upskilling in various sectors. The skills shortage in tech in Europe has been well documented and filling this void must be a priority for governments across the region to keep up with Asia and the US.
In October, the European Institute of Innovation and Technology called on partners to get behind its new Deep Tech Talent initiative, which aims to address the current skills gap across Europe’s deep-tech sector. Over the next three years, it will provide one million people with the skills they will need for the EU to become an innovation and tech powerhouse.
Initiatives like this are a great start, and it has also been fantastic to see universities across Europe starting to teach courses focussed on AI. One of the biggest criticisms of AI and a leading cause for concern in its adoption and use cases is both conscious and unconscious biases feeding into decision making. Bias is an inherent human trait, reflected and embedded in everything we create. European AI adoption will only be able to reach its true potential if diversity and inclusion is at the core. It’s a complex topic. Diversity is not just about gender, it is also about age, nationality, sexual orientation, socioeconomic backgrounds, neurodiversity and ethnicity. When we talk about closing the skills gap, there must be diversity in development teams, so the skills needed cannot be hired from one place or using one process.
We need more data scientists, but we also need AI experience with people designing, developing and operating applications that integrate AI. Taking an open and collaborative approach to data science can pave the way for a fairer and more equitable world by reducing bias in AI.
It has been an incredibly challenging year for everyone, and I hope that the developments and innovations we are seeing in artificial intelligence will continue to make both work and personal lives easier for everyone in future. Whoever wins the AI race, it is clear that there is a global concerted effort to improve the lives of everyone with a fascinating and brilliant technology.
Ulf Brackmann, vice president – artificial intelligence technology, SAP
| 2022-12-06T00:00:00 |
2022/12/06
|
https://erp.today/how-ai-is-turning-issues-into-opportunities/
|
[
{
"date": "2022/12/06",
"position": 6,
"query": "AI skills gap"
},
{
"date": "2022/12/06",
"position": 95,
"query": "artificial intelligence business leaders"
}
] |
Impact of AI on Future of Workforces in EU and US
|
Impact of AI on Future of Workforces in EU and US -- CEA release (12.5.22)
|
http://www.aeaweb.org
|
[] |
Executive Summary: AI is a fast-evolving technology with great potential to make workers more productive, to make firms more efficient, and to spur innovations ...
|
Background for the Report: Both the US and European Commission (EC) expressed strong interest during the US-EU Trade and Technology Council in late September 2021 in working on a joint study to assess the potential impact of artificial intelligence (AI) on our workforces. The Pittsburgh statement committed to a joint “economic study examining the impact of AI on the future of our workforces, with attention to outcomes in employment, wages, and the dispersion of labor market opportunities. Through this collaborative effort, we intend to inform approaches to AI consistent with an inclusive economic policy that ensures the benefits of technological gains are broadly shared by workers across the wage scale” (White House 2021, European Commission 2021).Scope of the Report: Given the expansiveness of the possible scope of the project, this report is not designed to be exhaustive; rather, it highlights some of the most important themes for the economics of AI in a balanced manner. Because of the unique collaboration between the EC and the Council of Economic Advisers (CEA) on this work, our goal is to synthesize the perspectives of the US and European Union and academic work from both countries with a focus on implications relevant to policymakers. Our goal for this joint report is to strengthen collaboration on analysis and policy to ensure that the benefits of AI are broadly shared. The report is intended to highlight the economicsbehind AI-driven technological change with a particular focus on the institutional and policy decisions that will shape its future impact on the workforce.Executive Summary: AI is a fast-evolving technology with great potential to make workers more productive, to make firms more efficient, and to spur innovations in new products and services. At the same time, AI can also be used to automate existing jobs and exacerbate inequality, and it can lead to discrimination against workers. While previous technological advances in automation have tended to affect “routine” tasks, AI has the potential to automate “nonroutine” tasks, exposing large new swaths of the workforce to potential disruption. The challenge for policymakers is to foster progress and innovation in AI while shielding workers and consumers from potential types of harm that could arise. Parts I and II of this report introduce AI and document its widespread adoption in the European Union and the United States; part III focuses on AI’s impact on labor; part IV contains case studies on hiring and logistics; part V concludes. https://www.whitehouse.gov/cea/written-materials/2022/12/05/the-impact-of-artificial-intelligence-on-the-future-of-workforces-in-the-european-union-and-the-united-states-of-america/ [55 pages]
| 2022-12-06T00:00:00 |
http://www.aeaweb.org/forum/3308/impact-of-ai-on-future-of-workforces-in-eu-and-us-cea-release-12
|
[
{
"date": "2022/12/06",
"position": 10,
"query": "future of work AI"
},
{
"date": "2022/12/06",
"position": 12,
"query": "AI economic disruption"
}
] |
|
Future Of Work: The 4 Biggest Workplace Trends In 2023
|
Future Of Work: The 4 Biggest Workplace Trends In 2023
|
https://bernardmarr.com
|
[
"Bernard Marr"
] |
We are standing on the cusp of a new technological revolution. AI is increasingly permeating every aspect of our lives, with intelligent machines transforming ...
|
Workplaces in 2023 will be more diverse, distributed and virtual than ever before. Corporations and even smaller organizations will learn to adapt in order to cater for shifting employee expectations in the wake of the post-pandemic “new normal”, and behavioral trends such as the great resignation or the even-more-recent quiet quitting movement.
As younger generations join the workforce and the middle generations begin to move up into senior positions, they will bring new values with them. Observational experience seems to suggest that this will result in workplace cultures that are more accepting of diversity and tolerant of individuals’ expectations of flexibility in their working lives. At the same time, new technologies seem set to usher in changes to the way workers are surveilled and monitored as they go about their daily activities, either remotely or in centralized workplace environments. Managing this balance between expectations of flexibility and a need for accountability will be a key challenge that employers and managers will face in the coming 12 months.
The result will undoubtedly be workplaces and working cultures that are very different to those that older generations – perhaps those who are now looking towards retirement - were employed into. These changes won’t happen overnight but in the next year we’re likely to see further progress in this direction. So, here’s a look at some of the prevailing trends in 2023:
Remote and Hybrid Working
Far from just being a hangover from the times of lockdowns and the pandemic, research and statistics are showing that home, remote and hybrid working arrangements are set to become standard - at least for knowledge workers. During 2022, according to McKinsey and Ipsos, 58 percent of Americans had the opportunity to work from home at least one day a week, while 38 percent were not generally required to be in the office at all.
Although it’s still relatively early days when it comes to assessing the societal impact of such a huge change, initial reports suggest that this flexibility leads to increased worker happiness as well as higher productivity.
However, there are also challenges, and in 2023 we will see companies beginning to get to grips with the security ramifications of widely distributed workforces connecting to corporate networks through a variety of devices and protocols.
Working away from the office can also lead to employees feeling less connected to their colleagues, and finding it difficult to develop and engage with company cultures. In response to this, companies will have to ensure that remote and hybrid working practices are implemented in ways that ensure the needs of the business and the employee are both met. From an employee point of view, workers may increasingly find themselves needing to balance the savings they make by cutting out their daily commute with the increased energy costs incurred by spending more time at home.
Workplace Surveillance
With workforces more likely to be geographically distributed, another challenge for businesses in 2023 will be developing processes for monitoring employee output and standards, without infringing on privacy or personal freedoms.
Employee tracking software is a fast-growing market. Crucially, rather than simply ensuring workers are not slacking off, it should be used to ensure they are following healthy practices like taking breaks and regularly getting up to move around.
The growth in the use of this sort of software – along with IoT devices that monitor and track employee movements and activities – has prompted alarm, from some parties, such as the UK TUC. The trade union body has said in 2022 that the use of workplace surveillance is “spiraling out of control” and that regulation is needed to protect workers. One court in the Netherlands recently found that requiring workers to keep cameras on while working from home could violate their human rights.
Nevertheless, with more staff working out of the office, businesses are likely to continue to invest in technology designed to track and monitor their activity in 2023. Organizations that get it right will be those that ensure it is implemented in a fair way, with a focus on assessing quality of employee output rather than quantity of input.
Collaborative online working tools meet the metaverse
Whether working remotely or in centralized offices, the impact of the metaverse is likely to be felt in a growing number of organizations throughout 2023. In enterprise settings, this will take the form of increasingly immersive collaborative working environments. Meta (formerly Facebook) is famously betting big on its Horizon platform, which includes a working environment known as Horizon Workrooms. Nvidia is also promoting its Omniverse collaborative working tool as a metaverse platform. And Microsoft’s Mesh platform adds avatars and mixed reality capabilities to its Microsoft Teams collaborative working environment, to give users a taste of metaverse-like functionality. Meanwhile, video conferencing platform Zoom, which enjoyed stratospheric user growth during the lockdown period of the pandemic, is rolling out persistent functionality such as meeting rooms and whiteboards in order to make the leap from providing a simple communication tool to a full, metaverse-like collaborative working platform.
Whether or not we are ready to start wearing virtual reality headsets in order to work collaboratively and take part in more immersive and engaging virtual meetings, training sessions and sales pitches remains to be seen. But aspects of the metaverse experience – such as avatars and persistent, multi-purpose environments are likely to play an increasingly prominent role in our working lives during 2023.
Flexible Hours / Four-day working
Along with the daily commute, 2023 could be the year that we wave goodbye to another long-standing convention – the five-day working week. Four-day week trials have taken place in many countries in recent years, including England, Belgium, Sweden and Iceland, and 2023 will see projects starting in the US, Scotland, Ireland, Canada and New Zealand.
In the UK, 86 percent of companies taking part in the trial said they are extremely likely or likely to consider adopting a permanent four-day week policy. Employees taking part are expected to maintain the same workload as they did while working five days, in return for the same salary. The total amount of time spent working is reduced by cutting non-productive activities from the working day, as agreed between the employee and their supervisors.
Reducing the overall number of hours worked is an exciting experiment with potentially positive implications for mental and physical health. Although it’s unlikely to become mandatory, workers will increasingly look for opportunities with companies that offer flexibility as an incentive, meaning those companies that do will have first pick of the best recruits. Additionally, in 2023 we are likely to see more companies adopting provisions for flexible hours, allowing employees to fit parenting responsibilities as well as educational opportunities around their jobs. This will happen as the benefits of rethinking the conventional nine-to-five, Monday-to-Friday approach become increasingly apparent to more bosses and employers.
| 2022-12-06T00:00:00 |
2022/12/06
|
https://bernardmarr.com/future-of-work-the-4-biggest-workplace-trends-in-2023/
|
[
{
"date": "2022/12/06",
"position": 21,
"query": "future of work AI"
}
] |
AI in the UK workplace: key employment and privacy ...
|
AI in the UK workplace: key employment and privacy considerations
|
https://www.thepeoplespace.com
|
[] |
AI technology can be used to review and filter job applications and even to assess interview performance by using natural language processing and interview ...
|
5 minute read
Regulation of artificial intelligence to make it more human-centric and mitigate against harmful algorithms is appearing around the world and the UK is expected to follow suit. But why is legislation needed – and what is already in place? Louise Skinner, Pulina Whitaker and Jessica Rogers from global law firm Morgan Lewis answer these questions for The People Space
While there is currently no specific regulatory framework in the United Kingdom governing the use of artificial intelligence (AI), it is partially regulated. With a number of employment laws and data privacy implications, the use of AI is a growing area of focus for regulators and the UK government. A UK government white paper on the topic of AI regulation is expected in late 2022. In the meantime there are several considerations for employers implementing AI technology throughout the employment lifecycle.
There is no single, recognised definition for AI. Broadly, AI is understood as an umbrella term for a range of algorithm-based technologies that solve complex tasks by carrying out functions that previously required human thinking.
In the employment context, accelerated by the COVID-19 pandemic, AI is increasingly being used in all stages of the employment relationship. In recruitment, in particular, there are clear benefits in using AI to reduce resource time and costs. AI technology can be used to review and filter job applications and even to assess interview performance by using natural language processing and interview analytics to determine a candidate’s suitability in light of their soft skills and personality traits. In turn, this reduces the amount of time that needs to be spent on these tasks by talent sourcing specialists and human resources, allowing them to focus on other valuable work.
Despite these benefits, there are some key risks and associated safeguards that employers in the United Kingdom should consider before implementing AI technology in their employment cycle.
Key legal risks: discrimination
Under the Equality Act 2010, it is unlawful for an employer to discriminate against candidates or employees on the basis of “protected characteristics” (namely, age, disability, gender reassignment, marriage and civil partnership, pregnancy and maternity, race, religion or belief, sex, or sexual orientation). The use of AI can result in indirect discrimination claims where someone with a protected characteristic suffers a disadvantage as a result of an algorithm’s output.
An algorithm that demonstrates a preference for particular traits, which in practice results in men being favoured over women in recruitment selection, is an example of indirect discrimination on the basis that the algorithm places women at a substantial disadvantage because of their sex. To defend such a claim, the employer would need to show the use of AI was a proportionate means of achieving a legitimate aim. While the use of technology to streamline the recruitment process may be a legitimate aim, it is difficult to see how such a tool, which can have significant implications for a candidate, can be a proportionate means of achieving that aim without any human oversight.
The use of AI may also create other legal risks for employers, such as:
Disabled people may face particular disadvantages in undertaking automated processes or interviews. For example, some systems read or assess a candidate’s facial expression or response, the level of eye contact, voice tone and language, which could disadvantage candidates with visual or hearing impairments, those on the autism spectrum or with a facial disfigurement. Given the obligation under UK law to make reasonable adjustments to remove disadvantages for disabled people, an employer could potentially find themselves in breach of discrimination laws when using AI software as a blanket approach.
Language and tone of voice can also be more difficult for some whose first language is not English, increasing the risk of racial bias and unlawful discrimination claims on the basis of race.
In the United Kingdom compensation for unlawful discrimination is uncapped (although tribunals take into account the ‘Vento bands’ when assessing compensation, the current upper band of which is approximately £50,000 ($59,416) for the most serious cases of unlawful discrimination).
Key legal risks: data protection
It is likely that the use of AI during the employment lifecycle will involve the processing of candidate and/or employee personal data. Employers should therefore be mindful of their obligations under data privacy regulation, with particular regard to three key principles: (1) lawfulness, (2) fairness and (3) transparency.
The use of AI technology to make employment decisions, without human scrutiny, will fall within the scope of a “solely automated decision.” The UK General Data Protection Regulation (GDPR) and Data Protection Act 2018 restrict an employer from making solely automated decisions that have a significant impact on data subjects unless this is:
authorised by law;
necessary for a contract; or
where explicit consent was given.
Even then, except where it is authorised by law, specific safeguards must be in place, such as a mechanism for the individual to challenge the decision and to obtain human intervention with respect to the decision. (Essentially, this is a human appeal process).
The processing of special category personal data, such as health or biometric data, is further restricted unless on specific lawful grounds.
Any use of AI is likely to require a data protection impact assessment. If high risks to the rights of individuals cannot be mitigated, prior consultation with a relevant supervisory authority (such as the Information Commissioner’s Office (ICO) in the United Kingdom) is required and the AI technology cannot be deployed without the consent of the supervisory authority.
The ICO has issued guidance and an AI toolkit to assist organisations in identifying and mitigating risks arising from the use of AI technology.
Mitigating the risks
Notwithstanding the risks outlined above, the use of AI technology is developing rapidly and there are a number of steps employers can take to introduce innovative technology while minimising legal risk, including:
Ensuring they have fully trained, experienced individuals responsible for the development and use of AI to minimise the risk of bias and discrimination. The provider of the technology should be able to demonstrate that the data and algorithms have been stress-tested for bias and discrimination against candidates because of, for example, their gender, race or age, and disparate impact assessments should be conducted on a regular and ongoing basis.
Establishing clear and transparent policies and practices around the use of AI in recruitment decisions.
Identifying appropriate personnel to actively weigh up and interpret recommendations and decisions made by AI in the recruitment process before applying it to any individual. It is important that meaningful human review is carried out; data privacy restrictions cannot be avoided by simply “rubber-stamping” automated decisions.
Not solely relying on AI—ensuring that AI is used only as an element to assist in recruitment decisions.
Ensuring that the process allows for human intervention: if a candidate needs adjustments because of a disability, make it clear with whom and how they should make contact to discuss what might be required.
Implementing ongoing equality impact assessments to identify early any issues or negative impact on diversity and inclusion as a result of the introduction of AI technology.
Prior to implementing AI, considering whether a data protection impact assessment is required. Additionally, employers can utilise the ICO’s AI toolkit to assess risk and implement mitigating measures.
From a data privacy perspective, considering and identifying the lawful basis for processing personal data in this way before proceeding with any automated profiling or decision making.
Updating candidate and employee privacy notices to make clear the use of AI technology in the processing of personal data.
Future developments
AI is likely to continue to be a hot topic in 2023 and beyond. A number of countries have recently introduced AI-focused legislation. In New York State recent legislation effective 1 January 2023 will prohibit the use of AI tools in employment decisions unless it has been subject to a “bias audit” and use is disclosed, with the opportunity to request alternative processes. In 2021 the European Commission proposed implementing a new legal framework to address risks of AI use, which would set out requirements and obligations regarding the use of AI, high-risk applications of the technology and set out an enforcement and governance structure. In Germany employers already have an obligation to consult with the Works’ Council (a consultative body representing workers) when introducing AI in the workplace.
In the United Kingdom, there have been calls by the Trade Union Congress in a report recommending measures to protect against algorithm discrimination, including:
A reversal of the burden of proof for AI use, where the employer must disprove that discrimination occurred rather than the claimant bearing the burden of proof;
The creation of statutory guidance on steps that may be taken to avoid discrimination where AI is used; and
Mandatory AI registers to be regularly updated by employers and available to candidates.
The UK government published the AI Regulation Policy Paper on 18 July 2022, which forms part of the UK government’s National AI Strategy and its AI Action Plan. A white paper on the use of AI is expected by the end of the year and employers can expect further guidance to emerge in the coming months.
Louise Skinner, Pulina Whitaker (partners) and Jessica Rogers (associate), pictured below, are from global law firm Morgan Lewis
| 2022-12-06T00:00:00 |
https://www.thepeoplespace.com/ideas/articles/ai-uk-workplace-key-employment-and-privacy-considerations
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[
{
"date": "2022/12/06",
"position": 57,
"query": "future of work AI"
}
] |
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How will AI change the world? | TED Talk
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TED-Ed: How will AI change the world?
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https://www.ted.com
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future · AI · TED-Ed · animation. Explore. TEDx · TED Fellows · TED Ed · TED Translators ... TED@Work · Podcasts · TED Blog · More ways to get TED. Our community.
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| 2022-12-06T00:00:00 |
https://www.ted.com/talks/ted_ed_how_will_ai_change_the_world?language=en
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[
{
"date": "2022/12/06",
"position": 91,
"query": "future of work AI"
}
] |
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Artificial Intelligence Case Studies
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Artificial Intelligence Case Studies
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https://indatalabs.com
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[
"Anastasiya Zharovskikh",
"Tatsiana Isakova",
"Author"
] |
According to McKinsey, 57 percent of companies report AI adoption, up from 45 percent in 2020. The majority of these applications targeted the optimization of ...
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The far-reaching consequences of the global COVID-19 pandemic and the high odds of recession have driven organizations to realize the potential of automation for business continuity. As a result, over the last few years, we have witnessed an all-time high number of artificial intelligence case studies.
According to McKinsey, 57 percent of companies report AI adoption, up from 45 percent in 2020. The majority of these applications targeted the optimization of service operations, a much-needed shift in these turbulent times. Beyond service optimization, AI case studies have been spotted across virtually all industries and functional activities.
Today, we’ll have a look at some of the most exciting business use cases that owe their advent to artificial intelligence and its offshoots.
What is the business value of artificial intelligence?
According to PwC, AI development can rack in an additional $15.7 trillion of the global economic value by 2030. In 2022, 92% of respondents have indicated positive and measurable business results from their prior investments in AI and data initiatives.
However, there are other benefits that incentivize companies to tap into artificial intelligence case studies.
Reduced costs
The cost-saving potential of AI systems stems from automated labor-intensive processes, which leads to reduced operational expenses. For example, Gartner predicts that conversational AI will reduce contact center labor costs by $80 billion in 2026.
Indirect cost reduction of smart systems is associated with optimizing operations with precise forecasting, predictive maintenance, and quality control.
Amplified decision-making
AI doesn’t just cut costs, it expands business brainpower in terms of new revenue streams and better resource allocation. Smart data analysis allows companies to make faster, more accurate, and consistent decisions by capitalizing on datasets and predicting the optimal course of action. AI consulting comes in especially handy when bouncing back from crises.
Source: Unsplash
Lower risks
From workplace safety to fraud detection to what-if scenarios, machine learning algorithms can evaluate historical risk indicators and develop risk management strategies. Automated systems can also be used to automate risk assessment processes, identify risks early, and monitor risks on an ongoing basis. Thus, 56% of insurance companies see the biggest impact of AI in risk management.
Better business resilience
Automation and advanced analytics are becoming key enablers for combating risks in real-time rather than taking a retrospective approach. As 81% of CEOs predict a recession in the coming years, companies can protect their core by predicting transition risks, closing supply and demand gaps, and optimizing resources – based on artificial intelligence strategy.
Source: Unsplash
Top 10 AI case studies: from analytics to pose tracking
Now let’s look into the most prominent artificial intelligence case studies that are pushing the frontier of AI adoption.
Alibaba
Industry: E-commerce and retail
Application: AI-generated marketing, personalized recommendations
A Chinese E-commerce giant, Alibaba is the world’s largest platform with recorded revenue of over $93.5 billion in Chinese online sales. No wonder, that the company is vested in maximizing revenue by optimizing the digital shopping experience with artificial intelligence.
Its well-known case study on artificial intelligence includes an extensive implementation of algorithms to improve customer experience and drive more sales. Alibaba Cloud Artificial Intelligence Recommendation (AIRec) leverages Alibaba’s Big data to generate real-time, personalized recommendations on Alibaba-owned online shopping platform Taobao and across the number of Double 11 promotional events.
The company also uses NLP to help merchants automatically generate product descriptions.
Source: Unsplash
Mayo Clinic
Industry: healthcare
Application: medical data analytics
Another AI case study in the list is Mayo Clinic, a hospital and research center that is ranked among the top hospitals and excels in a variety of specialty areas. Intelligent algorithms are used there in a large number of business use cases – both administrative and clinical.
The use of computer algorithms on ECG in Mayo’s cardiovascular medicine research helps detect weak heart pumps by analyzing data from Apple Watch ECGs. The research center is also a staunch advocate of AI medical imaging where machine learning is applied to analyze image data fast and at scale.
Source: Unsplash
As another case study on artificial intelligence in healthcare, Mayo Clinic has also launched a new project to collect and analyze patient data from remote monitoring devices and diagnostic tools. The sensor and wearables data can then be analyzed to improve diagnoses and disease prediction.
Deutsche Bank
Industry: banking
Application: fraud detection
Now, let’s look at artificial intelligence in the banking case study brought up by Deutsche Bank and Visa. The two companies partnered up in 2022 to eliminate online retail fraud. Merchants who process their E-commerce payments via Deutsche Bank can now rely on a smart fraud detection system from Visa-owned company Cybersource.
Driven by pre-defined rules, the system automatically calculates a risk value for each transaction. The system employs risk models and data from billions of data points on the Visa network. This allows for blocking fraudulent transactions and faster authorizing other transactions.
Amazon
Industry: E-commerce
Application: supply and demand prediction
Amazon is a well-known technology innovator that makes the most of artificial intelligence. From data analysis to route optimization, the company injects automation at all stages of the whole supply chain. Over the last few years, the company has perfected its forecasting algorithm to make a unified forecasting model that predicts even fluctuating demand.
Source: Unsplash
Let’s look at its AI in E-commerce case study. When toilet paper sales surged by 213% during the pandemic, Amazon’s predictive forecasting allowed the company to respond quickly to the sudden spike and adjust the supply levels to the market needs.
Blue River Technology
Industry: agriculture
Application: computer vision
This AI case study demonstrates the potential of intelligent machinery in improving crop yield. Blue River Technology, a California-based machinery enterprise, aims to radically change agriculture through the adoption of robotics and machine learning. The company equips farmers with sustainable and effective intelligent solutions to manage crops.
Their company’s flagship product, See & Spray, relies on computer vision, machine learning, and advanced robotic technology to distinguish between crops and weeds. The machine then delivers a targeted spray to weeds. According to the company, this innovation can reduce herbicide use by up to 80 percent.
Source: Unsplash
BMW
Industry: automotive
Application: voice recognition
The car manufacturer has over 400 AI & ML case studies at all levels of production. According to the company, these technologies play an essential role in the production of new vehicles and augment automated driving with advanced, natural experience.
In particular, voice recognition allows drivers to adjust the in-car settings such as climate and driving mode, or even choose the preferred song. BMW owners can also use the voice command to ask the car about its performance status, get guidance on specific vehicle functions, and input a destination.
Source: Unsplash
Affectiva
Industry: media and entertainment
Application: emotion recognition
Another exciting case study about artificial intelligence is Affectiva company and its flagship AI products. The company conceived a new technological dimension of Artificial Emotional Intelligence, named Emotion AI. This application allows publishers to optimize content and media spending based on the customers’ emotional responses.
Emotion AI is fuelled by a combination of computer vision and deep learning to discern nuanced emotions and cognitive states by analyzing facial movement.
Source: Unsplash
Siemens
Industry: manufacturing
Application: process optimization
As global enterprises are looking for more ways to optimize, the demand for automation grows. Siemens’ collaboration with Google is a prominent case study on the application of artificial intelligence in factory automation. The manufacturer has teamed up with Google to drive up shop floor productivity with edge analytics.
The expected results are to be achieved via computer vision, cloud-based analytics, and AI algorithms. Optimization will most likely leverage the connection of Google’s data cloud with Siemens’ Digital Industries Factory Automation tools. This will allow companies to unify their factory data and run cloud-based analytics and AI at scale.
Source: Unsplash
Samsung
Industry: manufacturing
Application: semiconductor development
Along with cutting-edge solutions like its memory accelerator, the manufacturing conglomerate also implements AI to automate the highly complex process of designing computer chips. A prominent artificial intelligence case study is Samsung using Synopsys AI software to design its Exynos chips. The latter are used in smartphones, including branded handsets and other gadgets.
Source: Unsplash
Frito-Lay
Industry: manufacturing
Application: predictive maintenance
According to McKinsey, the greatest value from AI in manufacturing will be delivered from predictive maintenance, which accounts for $0.5-$0.7 trillion in value worldwide. The snack food manufacturer and PepsiCo’s subsidiary, Frito-Lay, has followed suit.
The company has a long track record of using predictive maintenance to enhance production and reduce equipment costs. Paired with sensors, this case study of artificial intelligence helped the company reduce planned downtime and add 4,000 hours a year of manufacturing capacity.
Source: Unsplash
Looking over horizon: Technology trends for 2023-2024
Although artificial intelligence case studies are likely to account for the majority of innovations, the exact form and shape of intelligent transformation can vary. Below, you will find the likely successors of AI technologies in the coming years.
Applied AI
Applied AI is the use of artificial intelligence to solve real-world problems. This AI case study example is different from general machine intelligence in that it is focused on specific tasks or problems such as increasing sales, reducing costs, or analyzing customer feedback rather than hypotheses.
Advanced connectivity
Advanced connectivity refers to the various ways in which devices can connect and share data. It includes technologies like 5G, the Internet of Things, edge computing, wireless low-power networks, and other innovations that facilitate seamless and fast data sharing.
The global IoT connectivity imperative has been driven by cellular IoT (2G, 3G, 4G, and now 5G) as well as LPWA over the last five years. Growing usage of medical IoT, IoT-enabled manufacturing, and autonomous vehicles have been among the greatest market enablers so far.
Web 3.0
Web 3.0 is the new iteration of the Internet that aims to make the digital space more user-centered and enables users to have full control over their data. The concept is premised on a combination of technologies, including blockchain, semantic web, immersive technology, and others.
Metaverse
Metaverse generally refers to an integrated network of virtual worlds accessed through a browser or headset. The technology is powered by a combination of virtual and augmented reality.
Edge computing
Edge computing takes cloud data processing to a new level and focuses on delivering services from the edge of the network. The technology will enable faster local AI data analytics and allow smart systems to deliver on performance and keep costs down. Edge computing will also back up autonomous behavior for Internet of Things (IoT) devices.
Industries already incorporate devices with edge computing, including smart speakers, sensors, actuators, and other hardware.
Augmented analytics
Powered by ML and natural language technologies, augmented analytics takes an extra step to help companies glean insights from complex data volumes. Augmented analytics also relies on extensive automation capabilities that streamline routine manual tasks across the data analytics lifecycle, reduce the time needed to build ML models, and democratize analytics.
Large-sized organizations often rely on augmented analytics when scaling their analytics program to new users to accelerate the onboarding process. Leading BI suites such as Power BI, Qlik, Tableau, and others have a full range of augmented analytics capabilities.
Engineered decision intelligence
The field of decision intelligence is a new area of AI that combines the scientific method with human judgment to make better decisions. In other words, it’s a way to use machine intelligence to make decisions more effectively and efficiently in complex scenarios.
Today, decision intelligence assists companies in identifying risks and frauds, improving sales and marketing as well as enhancing supply chains. For example, Mastercard employs technology to increase approvals for genuine transactions.
Data Fabric
Being a holistic data strategy, data fabric leverages people and technology to bridge the knowledge-sharing gap within data estates. Data fabric is based on an integrated architecture for managing information with full and flexible access to data.
The technology also revolves around Big data and AI approaches that help companies establish elastic data management workflows.
Quantum computing
An antagonist of conventional computing, the quantum approach uses qubits as a basic unit of information to speed up analysis to a scale that traditional computers cannot ever match. The speed of processing translates into potential benefits of analyzing large datasets – faster and at finer levels.
Hyperautomation
This concept makes the most of intelligent technologies to help companies achieve end-to-end automation by combining AI-fuelled tools with Robotic Process Automation. Hyperautomation strives to streamline every task executed by business users through ever-evolving automated pathways that learn from data.
Thanks to a powerful duo of artificial intelligence and RPA, the hyperautomated architecture can handle undocumented procedures that depend on unstructured data inputs – something that has never been possible.
Turning a crisis into an opportunity with AI
In the next few years, businesses will have to operate against the backdrop of the looming recession and financial pressure. The only way of standing firmly on the ground is to save resources, which usually leaves just two options: layoffs or resource optimization.
While the first option is a moot point, resource optimization is a time-tested method to battle uncertainty. And there’s no technology like artificial intelligence that can better audit, identify, validate, and execute the optimal transition strategy for virtually any industry. From better marketing messages to voice-controlled vehicles, AI adds a new dimension to your traditional business operations.
Schedule an intro call with our AI consulting experts to explore your business and find out how we can help.
| 2022-12-06T00:00:00 |
2022/12/06
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https://indatalabs.com/blog/artificial-intelligence-case-studies
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[
{
"date": "2022/12/06",
"position": 31,
"query": "workplace AI adoption"
}
] |
Digital Workplace Services Powered by Nucleus
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Digital Workplace Services Powered by Nucleus
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https://us.nttdata.com
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[] |
Learn how shippers and 3PLs are navigating change, adopting AI, and boosting direct-to-consumer experiences. Our comprehensive Third-Party Logistics Study ...
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NTT DATA’s experience and transformation-centric solutioning coupled with heightened focus on themes such as immersive collaboration, sustainability, and automation powered by their Nucleus Intelligent Enterprise Platform, have contributed to its position as a Leader in Everest Group’s Digital Workplace Services PEAK Matrix® Assessment 2022-North America. Furthermore, proposed business realignment in NTT is expected to bring long-term synergies and scale advantages for NTT DATA
Udit Singh, Practice Director (Everest Group)
| 2022-12-06T00:00:00 |
https://us.nttdata.com/en/services/nucleus-intelligent-enterprise-platform/digital-workplace-services
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[
{
"date": "2022/12/06",
"position": 66,
"query": "workplace AI adoption"
}
] |
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Courses - AI UpSkilling
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AI UpSkilling
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https://ai-upskill.co.uk
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[] |
Teach all staff how to harness Ai in the workplace; Unlock your ... Accelerate your Ai adoption now. FEATURES. All FEATURES. Empower staff to
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"A thoroughly engrossing course. Practical and relatable. We got to try out all the latest Artificial Intelligence and I will be using it now for my job. Highly recommended"
| 2022-12-06T00:00:00 |
https://ai-upskill.co.uk/courses/
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[
{
"date": "2022/12/06",
"position": 80,
"query": "workplace AI adoption"
}
] |
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The creative war against AI art has already been lost
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The creative war against AI art has already been lost
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https://www.creativindie.com
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[
"Derek Murphy",
"The God Of War",
"Xu Xu",
"E Bird Bashe",
"A Tobola",
"Lue Lupine",
"Maciej Kozak"
] |
Now DeviantArt has jumped on board with their own AI tool based on the same software artists universally hate. ... basic things, and robots could do it ...
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1705
I’ve been pretty quiet over the last few months, watching curiously as the debate against AI text to image generators have become more controversial. As a fine artist and designer, when I first starting playing with midjourney, I was impressed with what could soon be possible. Version four has already defeated the main claim that AI art will never be as good as real art. It’s often much better than what you could hire an illustrator to do for you.
But there are other arguments. I’ve tried, in comments and FB groups – even when being attacked and accused of being anti-artist and pro-AI – to be polite and reasonable, but it’s no use, because there’s no benefit to arguing for robots over artists.
As Chuck Wending says,
I’m going to side with the artists. (Spoiler: you should always side with the artists.)
And he’s right! I agree, and I also support artists. I agree with everything he said in this blog post. How “artists are like dinosaurs getting mulched into oil to fuel this thing.” All that is true: AI art will mostly benefit the techbros, and harm the artists.
But he ends with this comment: “I suspect (or at least, hope) this AI art thing burns out.”
And that’s the one place we disagree. Because I don’t see it burning out, or going away. It might be bad (yes) but it is here (today).
Artists were aghast about a partnership deal between microsoft and dalle2 to create a stock photo site. Now DeviantArt has jumped on board with their own AI tool based on the same software artists universally hate. I expect in the next year, AI image generation and even text generation will become far better than we expect, and it’ll also become absolutely ubiquitous. Just today, Adobe Stock officially recognized AI art… so all the stock photo sites will start having AI stuff for sale, which means you can’t avoid it, however you feel about it.
And Canva just launched its “Text to Image” generator; Microsoft launched its Designer days later, an online graphic design tool with the option to generate images from texts (rather than searching for stock photos).
Graphic design software made it easy to import/buy images from the big stock photo sites; but that meant they got less profit, and you got something less unique. Of course they’d prefer you use images that cost them much, much less than the stock photo sites.
As artists and writers, we can be angry about it, but we should also make sure we figure out how it’s going to be used against us, how it’s going to hurt us, and find ways to continue doing what we love without being replaced by robots.
“Hope” might be the best, only strategy, but it’s unproductive. Here at Creativindie, we find specific, practical ways to harness creativity into objective value. So I’m taking the devil’s advocate position, even though it’s unpopular. I’m not cheering for the techbros. But I hope I’m helping artists deal with he frustration and anxiety, and preparing them to deal with changes that humanity has never before dealt with.
There’s really no point in discussing the merits, limitations and scandalous “theft” of human artwork, but I plan to do it here anyway for those who have reservations, or have been influenced by memes. I hope to convey that this isn’t my firm opinion, I’m not “pro-AI” – but I am aware of what it can do, and I recognize that it is creating an enormous shift in how we get things done, it’s going to have a tremendous influence on all kinds of businesses.
As an author, I need to be aware of it and how to recognize it, so I can spot AI art on stock photo sites or avoid paying way too much for an unscrupulous cover designer who claims it’s custom human art when it isn’t. As a writer, I need to pay attention to the fact that it can improve my writing, save tons of money on editing, and some of my competitors are already on board. I don’t have to use it in my business, at all – but I need to consider it, and keep a cool head.
I’m pleased to have studied art history in Florence and moral philosophy in Malta; while also being a fine artist and a working graphic designer for many years… which doesn’t mean I’m right about anything or that you should listen to me, but I also promise a balanced, nuanced investigation into some potential humanity-reducing technological developments we can no longer ignore.
PS. most people are taking moral stances against AI art right now, and/or declaring that they will never use it and their art is “real” art. BUT, a lot of these people also depend on stock photo resources, and it’s going to become very tricky to deliberately keep AI tools out of your workflow, as they start getting added into all the tools you’re already using.
Maybe I’m being pedantic, but in Chuck Wendig’s post, he shared a viral tiktok video against AI art: and the creator’s profile link was beacons.ai – a platform-building software built on AI, so you can get set up faster, with less work. Obviously, not a big deal, because AI in general might be fine, while AI art might not. But it is slightly ironic, and you could make the same arguments that AI website builders have “stolen” everything from coders and developers, making them obsolete and putting them out of work. So we’ll need to have a discussion about what kind of creative work deserves protecting, and how to decide…
The arguments against AI art
People think because I talk about emerging technology (“augmented creativity”) I’m pro-AI… but the two comments above are more in line with my feelings. Humanity was already doomed, and there’s a slim chance AI might help us get our sh*t together. I’d prefer not to talk about it at all, because it’s so polarizing, and it’s a little bit like arguing about an incoming meteor: yes it will destroy everything. There’s some brutal upheaval coming.
But there’s also a lot of bullying and disinformation going around, two things I can’t abide. Artists are telling me they feel unsupported; but they are also cursing me with dire threats and violence.
I’ve yet to be proven wrong that AI is actually doing anything illegal, by browsing millions of images and learning to draw in similar styles. Art styles can’t be copyrighted. It’s true AI has learned (and could only have learned) from human creativity. Mermaids don’t exist in reality, so all “mermaid art” robots create is based on artwork, not photographs. It’s understandable that artists feel cheated and violated.
It makes sense for them to protest. But calling it theft doesn’t make it actually illegal. With legal issues murky at best and unlikely to ever be properly restructured to ban AI artwork entirely, that leaves only the moral issues, which most content creators understand. So they say things like this:
“They will argue that it’s legal, but we can all plainly see that it is not just.”
So, this is an issue of justice. Basically, it’s not fair. I’m not arguing that AI art is cool, or good. I’m just telling you that it’s here. But that’s still a step too far.
After reading hundreds of comments, I’ve boiled it down to three issues.
Real art has a soul. AI can never replace human passion.
This feels true, but it’s also something amateur writers often say to justify their work, despite being untalented. They equate raw passion and joy in creation with quality of output, and it rarely is. Passion and joy in the process can help you to keep going, until you learn enough skill to be marketable, and able to create quality of work people want to buy, rather than staying a hobbyist.
It’s the same argument used against successful authors or artists who are writing or creating commercial stuff on purpose, making money with their art. There’s an idea that if they aren’t suffering for it, if it doesn’t cost them, or if they aren’t enjoying it (putting joy before money, the literal definition of amateur, one who does for love) then they aren’t “real” artists.
Think on that for a moment, because this is a deep core belief throughout creative communities: that we all desperately want to be good enough to be adored, but also want to just do what we love, and often have to find a balance in between our unpaid creative work that only we love and soulless, banal work we hate that clients will pay for.
At the end of the day, however, how the artist feels about the work, how long it took them, how happy or unhappy it made them, does not usually translate to the work itself. The clients pay what they think the art is worth to them.
When impressionism first became popular, it nearly destroyed the art world. They were ridiculed as unskilled amateurs, everybody carrying around easels outside, catching the light without skill or understanding of the craft… art was a tight community that supported its own. Until it became fashionable and the rules changed.
It’s the same argument Van Gogh used against photography: pictures can’t capture a human soul like an artist can, because it’s just a machine – so he started to focus more on portraiture in part because he was afraid new technologies would threaten his own value.
Interestingly, the various crafts of artisanship were usually looked down on as simple, repetitive, low-brow labor that could be learned and studied without much intellect. But it always focused on skill rather than creative genius or inspiration. The Greeks of course considered many aspects of the muses, but separated the technology or technique of making things from the “divine fury” of guided, almost transcendental epiphany.
But our current passionate beliefs about creative inspiration are nearly all modern, based on romantic ideologies that were at their own time, absolutely shattering to the centuries of careful wisdom and study that had gone before. Poetry for example, had crystalized into specific forms and schools and rules for the “perfect” and “best” experience; when romantic and modernist writers began to break these rules and get popular anyway, they were reviled by the establishment, like the impressionists.
Skill must be earned. If you can copy a famous painter, you earned it.
Art has value in proportion to its cost (resources + time invested + skillset). Artists have argued their prices are justified, because they went to art and design school; they have bought the tools and workshops; this art stuff is hard, not free, not cheap, because the investment is too great.
But! The flip side is that art is worth whatever consumers are willing to pay; and in a time of infinite content, they aren’t willing to pay anything unless they absolutely have to. They don’t need to buy art if they can print out posters or right-click save images they find online. But they can’t use it commercially (few want to).
I would argue the value of art is how many people respond to it, like it, for it’s immediate aesthetic value. Is it arresting. Is it worth their time and attention? Do they stop their scroll? I would argue that the art I’m already getting from midjourney is as good or better than some of the best, top quality artworks I’ve seen in my lifetime. And of course it is: it’s processing the history of human creativity and taking it all one step further.
Why this argument matters: people are saying, yes it’s not illegal but it should be, because humans cannot compete with computers and this always has been and should continue to be a purely human space. Nobody is denying that all artists copy, that all artists process a lifetime of images and styles, and sometimes deliberately copy to improve their skills. But that’s not IP theft, that’s just how creativity works.
Technically, collage is legal, even though there have been some outrages when an artist is found collaging scraps together from other people’s work.
And even though the robots are doing the exact same thing (using images as reference, not copying any one particular thing) it can’t be allowed to continue – because it threatens a sacred human endeavor.
So it’s about banning robots from encroaching on artists, which is uniquely different from all other things (like robots taking over the jobs of amazon workers, which is happening as we speak) because art is special, and must be protected. It’s not just a skill-based occupation. It’s more than the sum of its parts.
So, if humans are talented, after having practiced for thousands of hours, the fact that robots can do arguably the same thing (a sequence of steps that creates an artwork), is not a fair comparison because humans have a soul and robots don’t. Humans paid a price to earn their skill and robots have not: they’ve stolen that burden from artists.
Humans are entitled to creative joy
The final argument is, even if AI art isn’t illegal, it’s definitely bad. I’ve seen creators talk about how this is definitely the end of humanity in general: if art-making has been the quintessential thing since the beginning of civilization (and long before), this is an “unmaking” of ourselves as humans.
What will we do for hope, joy and inspiration when art is no longer an enjoyable hobby or pursuit, when there are no benefits for learning and skill and practice, when art is no longer a difficult challenge?
Paraphrasing one youtuber,
This is artmaking, this is joy: don’t take joy from humans and give it to unfeeling robots. Receive artmaking for those… dumbly bestow it on unfeeling dumb being who can’t enjoy fruits of labor.
That’s the whole argument. It’s not illegal, but it should be… why? Because? New innovation will put people out of work. It’s anti-humanist, amoral or immoral, anti-artist and creativity, the end of the world. And they are mad especially when people tell them it isn’t a big deal and artists can use it too… I don’t believe that.
The founder of stable diffusion, I believe, said this:
“I won’t allow us to build tools to replace artists. It’s simple as that because it matters to me. Let’s be clear, these tools require a skilled artist.”
But I think this is a replacement for artists, both figuratively and literally, it’s an enormous, gutting change, for the history of human civilization. We’ve never been here before, at all.
Someone on my youtube commented:
this means art and creation itself could be a phase of evolution that comes and goes that was never really our main source of identity…
And yes, this is an identity crisis for the whole human race.
Someone else said,
This current wave of interest in AI is a novelty, the pet rock or Rubick’s Cube of creativity. It’s a party gag-level toy. Anyone remember “Mad Libs”? That’s what this AI writing reminds me of.
Those are basically the two choices: should we ignore it and hope it goes away; or recognize that this could be a humanity-shattering event; the singularity. We just had fighter jet pilots simulate a fight with an AI fighter plane. SanFrancisco just allowed robot dogs with deadly force (explosives). None of this is good. But it’s not a party-gag.
Quick Wrap up (visual guide)
Here’s a meme I’ve seen floating around.
That’s misleading, because AI does not make a collage with images like that. It creates a totally new thing. The main point is that, because robots can’t be inspired (because they aren’t human), even though they are doing the same thing, it is not OK (because they are too good and too fast). The human brain can look at a thousand pictures and create something new, but robots cannot (even if they technical can), because don’t deserve it.
However YES there’s some weirdness about how the model was trained and it’s fine to ask questions and even feel outraged… as long as you don’t bully others for using something that they enjoy (and you might say “but they need to be educated!” and maybe you’re right, but to what end? Maybe you’ll convince all the content creators to boycott them? The content creators don’t fund the design industry: paying clients do. Clients still need deliverables.
This meme is more accurate:
Yes it’s POSSIBLE, even if you don’t use any famous people, that AI will search for “epic portraits” and find the most popular, which will probably be celebrities, and use that to guide its decision, so you might get something vaguely george clooney looking. That’s a consideration, and it’s problematic.
You can also add specific people and get stuff like tom holland in underwear. Scary, deep fake stuff. But you don’t have to violate people like that (sorry Tom!) PS. those boudoir pics were made on midjourney2 I think, so the quality isn’t good… compare it to some more recent stuff down below. These are just a few rough examples… most impressive are the historical portraits (girl looking at a camera, polaroid, [year])
“But I can see the copyright logo!” – yes, midjourney looks at all the art, and then guesses at what I want it to make. Since maybe 50% of this kind of artwork has a copyright or artist’s signature, AI vaguely tries to reproduce that. It isn’t anyone’s specific logo (though sometimes it is… big stock photo sites have millions and millions of images, so it’s weighted… and not impossible to get results with a shutterstock logo). I know, that should set off alarm bells and be the nail in the coffin against AI art.
But again – not illegal to browse free photos and use them as a basis to inspire yourself to draw something. A human artist would just remove the logo, the AI hasn’t learned to yet. It’s weird, but I don’t need to pay for photos unless I actually need to use the logo-free version commercially; I can look all I want without paying. The argument is that AI should/must get permission/pay to “look” at all of those billions of images. And who knows, maybe they should – I’m not a lawyer – but here’s what I can tell you: they aren’t going to, and this has already happened.
Also it garbles text… but I bet in a year, you could put in “text you want to use” and “fonts to use” and it will make it happen.
Incidentally… remember that meme I mentioned earlier about how AI ART IS THEFT? It’s part of a big slide show about why you shouldn’t use Lensa because AI art is bad. It’s poorly designed but effective propaganda. It fires people up so they love to share and support the message (which is fine).
BUT at least one of the people who started resharing this meme, took the original slides from Gen Ishihara, which had 2.4K shares, manually edited the slides to add his own name in the acknowledgments, and now has 35K shares.
This is the post that everyone is sharing to boldly disavow AI art as theft; and the entire post has been stolen and edited without permission from the creator. Gen Ishihara had to update her post to say this:
Edit: I am fine with people reposting but PLEASE DO NOT CHANGE THE INFO ON THE SLIDES i cant believe i have to say that but wow
It’s trivial… maybe. Or maybe it’s the entire thing. Be open to the potential that rallying people around a cause is great for getting viral visibility, even when you’re doing the thing your post warns against. That even if well-intended, you might be manipulated with untrue statements about how all of this stuff works.
I’ve even seen cover designers take this same stolen meme, edit it to focus on whatever new AI threat looms, and continue pushing the narrative that AI art is theft even as they steal from other artists.
There’s also a case to be made that “human creativity” has always been about stealing. That’s the literal definitions in most nonfiction books about creativity in the past twenty years: that humans have the unique ability to combine things in new ways in their clever brains.
“To be a master of metaphor,” Aristotle wrote in the Poetics, “is the greatest thing by far. It is the one thing that cannot be learnt from others, and it is also a sign of genius.”
You can’t really find any popular artist in history that wasn’t a product of his/her time; which meant, copying what was popular and using the best tools to create more content. Some more more egregious than others, like Picasso, whose friends would not let him visit because they knew he would still their ideas. Stealing (being inspired by) art styles has been a normal process for most artists, even though the history of their plagiarism is ignored in favor of their eventual unique synthesis.
Philosophically, it’s an interesting battle. Most people don’t need to make art; they do it because they enjoy it. Most “regular” people don’t have much use for AI art… except for making cool new profile pics. Lensa is going viral quickly, and the conversation around AI has exploded at the same time. Most people have seen the memes and reservations. So it’s a personal choice: does my desire to look pretty on social media outweigh my compassion for those it might be hurting? I’m not optimistic, but if making art is quintessentially human, maybe so is vanity and prestige (to make and share a fun new thing, while posting flatting selfies… after all, isn’t that what almost all art has been since the beginning – a way to capture our personal essence or expression while we can?)
As my friend Jenna Soard says, you should avoid any specific artist’s work or name in your prompts, and AI images should be labelled as such and not disguised as “real art.”
There are ways to “Ethically” use the knowledge of 500 Billion questionably sourced images. And there are ways to steal on them. Don’t be an asshole. Another FB friend of mine, just upload 150+ new selfie photos made with Lensa.
AI art vs. stock photography
This one is a personal gripe… on two counts.
First, the majority of working artists aren’t really making art. They are usually selling commodities – getting paid for a specific result. It’s often technical photobashing and manipulating stock images in photoshop, maybe with some hand-drawn elements. Not exactly the same as full custom illustration. The work they are doing, while certainly creative, isn’t exactly inspired. They will probably try to make high quality works based on similar products.
Most artists don’t get paid at all. Those who do are *often* making products. I’m not saying we should take that away from them, but it’s sometimes tedious and relatively easy to do basic things, and robots could do it faster (people have been using bought photoshop actions for years, that just basically add a filter.) Same with fonts or text styles. It takes a creative eye to put it all together, but they are mashing together stock resources.
Second: even when they use safe, stock photography, and even when they swap faces and add a ton of details and make everything much better… often the amazing art will appear too similar to anybody else using the same stock photo, because they aren’t unique, which is why authors are sometimes accused of copying each other. AI art allows for unique art.
Designer reactions: designers are in a tough spot right now, because they can either fight against the tide and become unpopular, or they can embrace the change and also become unpopular… but I expect in a few months, things will die down, and I’m starting to hear some optimism.
If AI is already on Canva, on stock photo sites, everywhere, it’s going to become mainstream enough that people (paying clients) don’t have a problem with it. And it’s really pretty incredible, because you can make great stuff easily. But MOST authors/clients/whatever, probably aren’t going to mess around or get their hands dirty with AI tools. It’ll take them years to catch on.
AI is great for making very specific things, like special armor or a background landscape. It will make it easier for designers to make better things (this is specifically about, for example, fantasy book covers, which have always been difficult to pull off with stock photo or without hiring an illustrator). It’s possible, that it just becomes an obscure tool designers use to serve their clients.
A shift in the dialogue…
Currently another newish argument is blowing up in the indie publishing world. Some big authors have started using AI art for noncommercial character portraits. They look fantastic and are just made for fun. Some artist are calling them out, basically saying that anything made by AI is stolen/pirated.
They feel betrayed by authors, who are happy to “pirate” real artwork.
And while not illegal, if a certain artist was using specific prompts or art images to generate new images, that’s problematic. But you can make great AI fantasy without doing this.
You can also, however, in a way that wasn’t possible before (with midjourney4), reverse engineer anybody’s art to a surprising degree. So I could take 5 photos from somebody else’s art and make something similar. It’s a lot faster and easier than experimenting with text prompts.
It’s still not technically, actually stealing, but it’s really really close. But just because it’s possible doesn’t mean everybody is doing it; and just because AI was trained on real art doesn’t make using AI stealing from artists (some people think it does – exactly this! – but that’s a moral judgment, not a legal one).
Using AI artwork (trained on art, but creates new art) is not the same as actual piracy (people downloading books for free without using them). The mediums don’t compare well, because a work of art is consumed in a day so people pay for limited access or unique paintings, that they can own, display or use (for example in exclusive book cover art).
A book is often a longer experience, so people are expected to pay, like the price of going to see a movie (or the Netflix subscription model, like Kindle). But people often have to pay much more for custom, exclusive artwork, with the idea that it’s only bought and sold once, and you have to pay for the artist’s time; as opposed to a book, which is sold cheap but can be resold infinite number of times.
Interestingly, for the first time, some big authors are starting to stand out and defend each other against increasingly hostile artists who want to burn everything to the ground. They have a point, and I feel for them; they have no legal standing, so they bully and intimidate, on moral grounds… which they are entitled to do. And for awhile, it worked. Authors were scared of getting called out, of AI art in general, determined to be on the “right side” of the indignation and swearing to support artists.
But that’s shifting really fast. Partly because well-meaning, and well-liked authors are discovering the joys of AI as a fun tool, partly because people are tired of the drama and controversy. And most people are still talking about the versions that existed months ago, when the tools are already 300% better than they were.
The common refrain is that authors shouldn’t support AI writing because AI writing tools will come for their jobs next. And that’s true… but a lot of authors are already using them. They enjoy using them. The violations (if any) on style, theme or writing quality is much less noticeable and never actually plagiarism.
AI writing tools for fiction
A diversion… my audience is mostly authors and writers, but you can skip this stuff and get back to the AI art controversy stuff down below…
People keep saying, I should be more understanding as a writer, because what will happen when AI writing tools can write books? Well, we’re already here, people just don’t realize it yet.
The AI writing tools just help generate content. They were useful a few months about. They are insanely powerful right now (only this week!). By next year, I do think I’ll be able to write an entire (good) book in (my) writing style, that will be nearly indistinguishable from my previous novels, and I think it could take a few days. Which would literally change my life, because I have so many stories I want to tell. (Here’s a sample of AI writing tools finishing my chapter for me.)
BUT – we’re going to be having this conversation all over again soon, with editors and proofreaders; when AI tools are way better than grammarly or even most developmental editors. When you can put in a messy rough draft and it will smooth and polish everything and make it better. When non-native speakers can tell the stories they want and have it cleaned up (the entire manuscript!) for $20 instead of $2000 in developmental editing fees.
It opens the playing field. It’s great for everyone, except the people who spend years developing their skills and businesses. Those people will be the most scared and complain the loudest. Or, they might not. I myself and many authors I know – it’s almost a definitive quality of writers in general – love to have written but hate writing. We struggle with procrastination and motivation, of making our writing good enough through countless revisions. Writing can be fun, and it can also be torture.
AI tools can and will, very soon, make everything so much easier, so you can publish more, better stories… but so can everyone else. I said in a podcast people could use AI to make full covers soon. People think writing a full book with AI is a pipedream… but before they realize it’s possible, some other authors will be doing it well and reaping the benefits.
Adverse reactions: the discussion around AI art is heated and crossing over into AI writing tools. Some authors are already using them to write better, faster. But a lot of others say things like this:
“You should boycott it if you want human authors to have a future.”
“‘Playing with it’ means helping to train it…When it’s fully trained you will have helped put yourself out of a job.”
“Skynet is coming. Don’t say you haven’t been warned. Writers and other artists should be fighting tooth and nail against AI stuff ever, ever, ever being viable. You really want to join the ranks of those being pushed out of jobs by robots? How will we be able to earn a living in the future if computers and robots take over all the jobs?”
I pulled these from a writer’s group, not to denigrate, but because they are strongly worded and popular opinions about AI writing (based on likes and upvotes). There was also a bunch of stuff about how AI writing will never be as good as human, because it doesn’t have a soul, etc – but it is already good enough.
PS. I’ve done stuff like this before: reposted other people’s content as an example, and I’ve gotten in big trouble for it, so I don’t usually do it. But, it’s not illegal, especially since I didn’t cite the source, and even if I had… legally I would be protected. But examples are great and otherwise it feels like I’m just talking to myself. I wanted to listen and really hear the opposing point of view so I could craft a measured response.
Will it take away the meager earnings of a hard-working indie author? Absolutely! Unless they use AI to finish their books faster, and create more content. Nobody is stopping them. They just want to hold everyone back so nobody else cheats and gets in front of them, without skill or hard work.
It’s not fair.
That is probably true, no matter how you see it.
But, the tools are here, they exist, they are good effective, and some people will definitely be using them to write books soon. It could be you… but it doesn’t have to be.
It could be me… and you don’t get to have any opinion about how I create my work. If my readers love the stories I “write” – will they be disillusioned or feel cheated if they find out I got help from an AI cowriter? Doubtful, but maybe so. Maybe I need to clearly communicate my writing process if AI gets involved (though, people have used uncredited ghostwriters and editors, for a long time; and I doubt many authors credit grammarly or prowritingaid). Still this might be different. I’m not necessarily opposed to labelling all AI work clearly. But I wouldn’t release a book I wasn’t 100% proud of, and AI will probably allow me to improve my current skill set.
Maybe it gives everyone a 25% boost, but from wherever they are currently standing from. With my experience, I could use AI to write a great book, but not everybody could (yet. maybe that’ll change in a year).
AI tools for creative inspiration
I just made a fun writing prompt generator (ok, I didn’t make it… I saw someone share it and made an offer because it’s so cool) – but I’m blown away by GPT-3 davinci’s brain-storming powers. I can have it make a list of names, titles, generate a dozen specific metaphors, answer ANY question. I think I’ll start using it for everything, because it’s way faster than google results and it actually gives me what I want.
For example, when you just need to look up a fact, rather than wading through ads and a dozen results before finding the answer, the AI tool will just give it to you instantly. But it’s also so, so great for brainstorming anything; or writing scenes, scene description, even revising your text to sound better and more polished. It’s hard to explain, but once you start using it, it’s just so much easier to get some good content out. I can even feed it a few paragraphs and let it continue the story, sometimes in surprising ways. If you’re stuck, on a plot twist and can’t figure out what needs to happen, AI can probably resolve it for you. Just prompt it like “protagonist is stuck on a boat with a bomb and an unconscious sibling and can’t swim… how do they both survive?”
AI text to voice tools
Another big arena for indie authors is using voice to text to make audiobooks. Of course narrators are upset by this. And the technology is not quite there yet – but very nearly. Better than those crappy tiktok AI voices. Most authors can’t spend thousands to hire a narrator, because their books aren’t profitable, which gives a big advantage to people with more budget. But AI text to voice narration tools will let you finish an audiobook for $20 which sounds pretty darn good… enough to put up on youtube or something.
Yes it’s a dystopian vision, if in a year or two I can write, design, publish, narrate a book using AI tool for about $50 in 3 and start selling it online, the robots will take over and we will all be miserable (but have a lot of content to consume).
But art has always been the privilege of the wealthy. Only recently, with the internet, and now with AI tools, have new people been allowed to enjoy the benefits of creativity, and share their work to millions, without (investing years to develop) talent and skill required to succeed.
Addendum…
These were some notes I put together for a podcast interview… this is a bit rough. You should probably just stop reading…
My feelings on AI art:
I think we should all have a healthy amount of fear, because this isn’t nothing. This is, basically in the history of humanity, we’ve been dreaming about this point, when our technology can replace our labor and make humans obsolete. We never thought it would happen for the creative arts, writing, artwork, but it has. I mean, this year, after centuries of speculative fiction writing about it. This is a very big deal for everyone, it’s going to change a lot of things, not just for writers and authors.
Coding, for example: people are taking coding courses and getting hired for a lot of money… in a few years coding might be something that’s automated to AI, they’re already working on it. Starbucks drinks automation, food services, Amazon package delivery, UPS, food delivery… automated. This is, really close to happening. It’s probably about 50% of the workforce. So what happens to everybody after they don’t need to work? Art and writing, creativity – but when the robots can already do that better, then what’s the point?
Whether or not you making a living designing or writing, you’re bound to feel depressed about all of it. So the fear is normal, natural. My PhD was in literature so I have a pretty good understanding of classic books, how and why they were written, why they became important and why they matter. But I also work with writers so I know about the struggle, the effort, the work, the satisfaction… of working hard and getting better, of trying to be the best or creating the best work you can.
I was a fine artist, surreal oil paintings, in Italy for awhile, I spent two months drawing the same picture of a foot. Not many drawings, one drawing, until it was perfect. I had to learn on one thing, polishing it until it was good enough. Most people will hire out the skill, on fiverr, character art concepts, for example, full page illustrations, book cover design. But now I can make something much better and more interesting than anything I could have done, but also better than anyone else could have done.
Other than a handful of really famous artists, the vast majority of people are mediocre artists with some talent but nothing exceptional or new. One of the huge advantages of AI art is you can make so much content so quickly. So instead of competing against 1000 artists with one picture, I can make 1000 pictures and have a huge portfolio or gallery. I can post more often, and the work is actually pretty incredible.
Dalle2 vs Midjourney AI:
Text to image tools let you add a prompt of what you want. They started to go public in the summer of 2022; by the end of 2022 they’re about 400% better. It was really tricky to get complex scenes, and they still have trouble with detail (you can get a stunning, photorealistic face, but if you have a big complex scene, the face might be blurry). It’s hard to get a specific character, holding something, standing a certain way, with a certain background. But you can generate multiple pieces and photoshop them together, instead of spending hours and hours searching for stock photos and having to create a whole scene.
Midjourney is still currently BETTER at art that looks good, but I think Dalle2 is smarter, in terms of recognizing the elements of an image and being able to reproduce complex poses (couples embracing, etc). It’s hard to say which will be better because they’re both already decent, but I expect Dalle2’s quality to improve shortly and then might surpass midjourney.
I’ll spend a few hours playing around with keywords, until they get close enough to something cool. Then I’ll spend days sometimes, just respinning content. I made thousands of images to get a handful of truly amazing, exceptional images I can actually use in my marketing; that captivate the essence of the genre, with emotion (which I may not use, until the heat has died down).
Best image prompts:
There are many, but you can generate good art without using any specific artist’s style or prompts like ‘trending on artstation’. That doesn’t mean it’s fine, because people are upset that the data was trained on their art without permission at all, which again… is not stealing (in the legal definition) but they will absolutely tell you that it is anyway. Still I’d be careful with impersonal prompts, avoid artist names, and do something less specific like this:
beautiful young fantasy princess, light elven armor, short skirt, legs, tank top, soft lighting, fierce expression, pet iguana dragon, whimsical art, fighting a demon, dark fantasy gothic, cyberpunk futuristic dystopian, character in full growth portrait. A very beautiful, wide shot, wide background, insanely detailed and intricate, concept art
As the tools get smarter, you need less detailed prompts to get good results. I’d be all for a general ban of all artist’s names in AI art; I’d even be all for a complete purge and redo, where the models are only trained on willing material it has purchased: or a specific “style” charge where artists can submit their portfolio and get compensated if someone chooses their styles. I’d even be OK with a complete ban on AI art until all this stuff gets resolved. Sign me up!
Someone smarter than me will probably build something like this soon, (as DeviantArt are kind of trying to, I think).
This is Spotify, kind of…
There’s a Netflix special on Spotify and it was interesting, because at the time, pirating music was seen as a brave, daring, act of rebellion – free music for everyone! The founder of Spotify desperately wanted to compete with torrent sites that everybody was already using. But why would they pay for something that was free? Because piracy is illegal; but Spotify found a way to charge for unique features. People will pay for convenience. This let them split profits (and get permission) with the real music industry, but it was a desperate fight.
But the conversation at first was one of violent rhetoric: screw these big music companies! Art should be free! Let’s take down the bastards! Later, it changed to become artist-focused (Spotify is making all the money, but barely paying artists anything).
Technology is usually about stealing godlike-powers from those in power and giving it to the masses; this is Prometheus stealing fire or even Even daringly biting the apple. It’s forbidden, but it’s popular. You can’t tell people they don’t deserve/can’t have what they desire, especially when you put it right in front of them and force you to resist the temptation.
But it’s kind of a non-issue, because people care about convenience more than artists (everyone knows Spotify doesn’t pay musicians well, but it’s the only game in town… and most people use itunes or spotify because it’s enjoyable and easy… even if they have to pay a subscription fee).
The new AI tools *might* find a way to compensate artists somehow, but that’s unlikely. I do think they’ll start banning famous artists from search queries or prompts, at least if they complain loud enough, but it’s impossible to do for everybody, and it can’t unlearn what it’s already learned.
The TikTok comparison: a fresh angle recently, is how authors are up in arms over AI art, but *many* authors are also using a personal tiktok account with commercial music, to promote their books…
AI art controversy:
It’s not illegal to look at work before you make your own. It’s legal for humans, this is the basics of most copyright law. I can be inspired by; I can look at a lot of media content and make my own thing. This is what AI does. It’s sketchy they do it without permission maybe, but I don’t need permission to browse famous art and be inspired, I don’t need to ask permission to read a bunch of books and be inspired; in fact I can’t help not to be inspired. I can’t unlearn all the books I’ve ever read. To ask a robot to forget it saw a bunch of stuff, that’s not practical. Dystopian, like you could rent a movie but would have to have your memory purged.
But for using specific keywords, for famous living authors, I think they can make a strong legal case and prevent their name being used as a keyword, I think that’s probably happening or will happen, so you can do a style but not a specific artist.
AI copyright stuff:
As I understand it, you can’t copyright AI work you created. For some people, that’s going to be enough not to use them, because they can’t ever control their content. If I post my pics and cover design, someone could just steal it and use it. That’s probably going to happen anyway, and even if someone does, I won’t go after them legally because it would cost way too much.
ISSUES: I’ve seen people post AI art with their own copyright so people can’t use it; which is effective, but misleading since they can’t actually copyright it. And I’ve seen people selling AI art with ‘commercial use rights’ which they can’t legally do.
But I don’t think that’s a big concern, for me personally, I want to sell as many books as possible. If you put the images together and added some story content, it makes sense you could copyright that new book or product as transformative but it’ll take a few years if ever to straighten the laws out. Art platforms are banning it right now, which is probably good; their market is artists, they can’t afford to piss off artists. UPDATE: no, they are totally not.
Stock photos are a little different. They might be banning now because the copyright is questionable and they don’t want to get in trouble, but there are *already* stock photo sites where you can just describe what you want and get images, so instead of a search feature it will be a create feature. This is going to hit stock photo sites really hard, it might even put them out of business. Why would I want stock photo site that other people have seen, on my blog or website or marketing, when I could create something brand new and unique? These are huge businesses, it’s going to affect things in a big way. What about photoshop: people use it to blend images and layers together to make art. If you can make better art without it, why would you need the program? It was a tool that let designers charge money for their creative content; consumers can now get better stuff themselves without a designer; not only will the designer go out of business, so will all the tools supporting them (big businesses know this, which is why they are forced to get on board quickly before they become redundant).
Stable diffusion?
The way I believe stable diffusion works is you can start with a picture and generate something from it; so you can take a child’s illustration or click sketch and turn it into real art or photograph, basically filling in the pieces.
What I’d love to test is this: cover design is typically, just a 3D render character and a background, with text, some great artists paint over everything and make it one piece, but more amateur designers don’t. I think you could make a simple, quick mockup with a character and background, and then run it through stable diffusion and have it look amazing, any particular style. Or, I think you could find a stock photo portrait you like and type in keywords and styles, like fairy, elf, vampire, and it would transform the picture. (*Midjourney does this now, you can add a photo of yourself and a photo of any fantasy creature, no prompt, and it will blend them).
Also the thing a lot of people aren’t considering, some of this is trying to keep things open source, democratic. Previously companies have owned the tools or output, so you can buy and sell on their platforms or pay recurring fees for their software. For artists, creatives, this is terrifying. But for everybody else, it’s exciting, it’s magical. It’s not fair, it’s cheating… but it’s a little like being pissed off at student loan forgiveness programs just because you paid all of yours back.
Extremely predatory financing has gotten much, much worse, and college has gotten much more expensive. So maybe you built a creative business because you got their first, and school was cheap for you. People who really excel at art probably had a leg up in life, support, connections, opportunities, paying for art school, it’s not all about luck and skill. There’s a certain amount of privilege – I know it doesn’t feel like it at times – but spending hundreds of free hours worrying about the book you’re writing, suffering for your art until you get good enough to charge, that’s a personal choice. It was never supposed to be simple, easy… though it used to be. A few centuries ago, the birth of a novel, there were very few novels being written. Now everybody is writing them. It’s an unfair comparison, but an interesting idea.
The smart thing would be, to create a stock photo site, hire a bunch of people to make “art” and photos, based on what people want/search for. Anybody with a business won’t like this stuff, because it skips over all the access and control issues.
Accessibility and control
People with disabilities, who can finally enjoy drawing or creating, people who can make their own tattoo designs or coloring books. All the things they want to do that they’ve never been able to do before, a whole new magical word is opening up to them. You can’t expect to slow down tools that broaden the means of production and take it away from those in power.
Text to speech is getting so good it will soon be as good as professional audiobook narrators, that’s another complete industry that’s going to fall apart.
Baudelaire called photography the refuge of failed painters with too little talent. Van Gogh said photographs could never capture that human spark, so he started focusing more on painting portraits. The emergence of new technologies influenced our most famous artists, and some were reactive (though, both VanGogh, picasso and almost every great artist in history, we recognize from the last 150+ years… after a thousand years of precision and training and the “right” way to do art, skills taught to few, after Klimt’s vienna secession and the modernist reaction against codified forms of aesthetic perfection, we got dozens of new influential art styles in a handful of decades.
Now everybody without any talent, training or experience can create great art. It’s fantastic for most people; the vast majority of mediocre (aspiring) artists. Current artists, working or not, who have been trying to develop skills, build a following, and find a way to be profitable, this is horrible for them. But art has never been kind to artists. The people who will profit, have always figured out how to make what people want and sell it to them.
Artists have been the inbetween, hired by the business owners, or even something on fiverr, drawing a portrait of their kid or pet. These tools will completely replace the need for them, for most of them. Everybody who can just do the task for an affordable price. It may not replace the established, famous artists, but it will replace everyone else. It’s like, halfway through a race, they call time out and make everyone start over again, and then equalize things completely, removing talent, aesthetics, everything. Fresh clean slate. Those who win will be those who spend enough time building great content and putting it out there.
Every great famous art in history, most, have been anti-gatekeeping.
First poetry, breaking the right kind of meter, using common language instead of stuffy old prose, the iambic meter. Hemingway’s very simple language. Emily Dickinson, the romantic school of everything. Famous poets, Emerson…. Moby dick even. Breaking rules and styles, an act of rebellion. Doing the thing exactly how everybody said you absolutely could not.
The protestant revolution: nailing a list of objections on the church door. One complete oppressive system of the way things had to be done, about what quality is, and needing to pay into that system to be supported by that system, but always to the actual event of fleecing regular people and funneling money towards those in power.
People tend to define art as what’s new; so the only big famous art has been transformative. There are a handful of big famous working artists and then a million struggling starving artists who are not successful, and mostly derivative. Artists almost always start off being derivative, copying and combining, until they find something that’s actually new that people respond to and then they focus more on that to get a unique style, cranking out more work and commissions because they are profitable.
Success leads to LESS originality and creativity, not more of it. Actual creativity, making something new that breaks all the rules, is very rarely a good idea and very rarely profitable, because people don’t like it or don’t get it. In writing, it’s usually a terrible idea, if you’re trying to be an artist. An artist creates for the pure joy and passion (BUT) that only belongs to the amateur, the beginner, who still gets the dopamine rush of trying to do something beyond their capabilities and surprising themselves by succeeding anyway.
Chasing this joy does not lead to better quality work, objectively, though many authors still believe that it does, they are usually the newer writers or the unsuccessful writers. So behind art which is about personal expression, we need another word for everything else; the work. The stuff that pays the bills. We love quotes like this:
‘I Must Create a System or be Enslaved by Another Man’s: The enquiry in England is not whether a man has talents and genius, but whether he is passive and polite as a virtuous ass, and obedient to noblemen’s opinions in arts and science.’
We love to celebrate artists who had it tough. But the truth is, Blake took money from clients and patrons, was generally a jerk, and tried to do his own art on the side (which was a dismal failure). We tend to love the famous artists who died poor even more than those who were successful in their own lifetime.
Imagine, a hypothetical situation, where people can suddenly get something better, for less money. This happens all the time in business. Better means of production. Massive factories and production lines. Something expensive that used to be made custom is now mass produced and artisans suffer.
For example: Amazon deliberately ripping off every successful product on its site, and then selling its own cheaper version. That’s pretty dodgy. But they own the platform.
Of course that’s sad, lamentable, some people will say, angrily, defiantly. Those of means who can afford custom work may prefer to get the “high quality” stuff, even as a mark of status or pride. But what if the new thing actually works better, makes more money, solves the problems. Consumers will also get the best they can afford at the lowest price. True we’ve never seen anything like AI art before, but people are telling me I shouldn’t embrace AI art because….
Because AI is stealing? It isn’t; not in any new or unique way. Art has always stolen egregiously. Warhol, Picasso, both hacks, blatant stealing. Same with Van Goh, with any artist. They copied, mimicked, studied and chased trends ruthlessly until they found something unique. Warhol is getting sued now because his “art” was just print screenings of real things that already existed, like this Prince photograph. Was it transformative? Not really, but he got away with it for decades and is one of the most famous artists in history. Anyway, if it was stealing, if it was illegal, then the governments and companies would do something about it, to make sure they’re making money without breaking laws… or they’ll change the laws. I have little faith in championing the rights of artists over big businesses. Maybe I’m cynical and jaded, but I’m a realist. My opinions on the ethics of all this stuff, don’t really weigh in on my opinions about what it is or how it’s going to be used.
Because designers are losing money? That’s sad for them; but industries adapt and evolve, and their cash cow may dry up as alternatives become available. I used to be a passionate starving artist, for many years. So I understand that mentality. But I am not that anymore, I used to beg for help, for support, for friends and family to champion me. I would get frustrated when they didn’t and when I couldn’t sell what I was making. I learned that Art needs to Matter and Have Value ; and that’s about market reception, not creativity or inspiration.
; and that’s about market reception, not creativity or inspiration. I’m being told I should avoid AI art and not tell people about it because… it’s bad for artists who make a living designing stuff. I get that I sound like a jerk when I say that’s not my fault, I’m not to blame for that. Back to our hypothetical: there is better, cheaper design but I should not use it because…. It’s not “pure”? It’s not “human?” It didn’t take time and effort? Why should I pay more for someone else’s time and effort? If you can make better art in less time, I’ll pay you more for it. But I won’t pay more for less, and I don’t think authors should be expected to do that either. (I’m not really a “capitalist” but I also don’t think artists have the right to demand “support” that isn’t directly tied to value and quality.
You can say there’s a lot of misinformation. I don’t think you can say I’m uninformed or not paying attention. I don’t think you can say you understand the copyright law better than I do. Which means, you object on a moral level, and you’re directly impugning my character. You’re allowed to have your own moral ethics, you’re entitled to your feelings and opinions. But when you make blatant statements like “how can anybody support this!” – you’re saying I’m a bad person; that I’m doing something immoral or harmful. I don’t feel we can have any kind of discussion or conversation if you’ve got an implicit moral bias against me; the facts don’t matter in this case at all, you’re determined that this is bad. Now, is it bad for everybody, everywhere? Or just for you and other artists feeling threatened?
Some have said, nobody will profit from this but the big companies. Yes! Big companies are bad, for sure. But they make money because they provide things people want or need. If we go that route… why are we using any software, any computers, anything at all… money always flows up to the top. What do we gain by refusing to use new tools that everyone else is using?
A friend pointed out that artists are (rightfully) upset about this, so for that reason alone we should all stop using AI art to reduce their anxiety, and I really appreciate that idea. But how? Someone in the comments called for a total 2023 book ban and saying “Hope everything you’ve ever done ends up on pirate sites and scraped for millions of other peoples AI garbage” (this was one of the kinder messages I’ve received).
Lighting Round (FAQ)
Will people use them? For sure.
Will companies keep selling them? Also yes.
Can protest sometimes effect meaningful change? Sure! I get it, protesting is all you can do. Someone should be protesting and holding companies accountable. But if it’s not illegal all you can do is color public sentiment, which again, that’s your perogative.
IP theft is bad. Therefore AI art is bad.
But this read is a failure to understand the technology or copyright laws.
Is it a threat to artists?
Yes.
Is IP theft bad?
Yes.
Will this screw over everyone except the big businesses?
Yes... except the millions of people who happily, excitedly use it.
Is it legal to look at copyright protected images and be inspired by them to create something new?
Yes.
Is it illegal for me to use these new tools?
No.
Is it currently illegal by any stretch under current copyright laws?
No.
Should it be illegal?
Sure, probably. From a philosophical, mankind is doomed point of view. But if you don’t think mankind is already doomed, by much bigger problems, you haven’t been paying attention.
Do I think it will be made illegal later?
I do not. This is not a moral judgment. Maybe it’s a lack of optimism. Maybe I should fight for artist’s rights even when it’s a losing battle. Maybe by enjoying myself with AI tools instead of sweating it out through labor and equity or always paying for whatever I don’t have the skills to make myself is the only right (moral) way to handle this and I’m revealing myself as a selfish lizard creep, not a real artist. I can entertain that point of view, certainly. I’m also fairly certain I can freehand draw or sketch better than most, after training in classical fine art, though my comparative skill level is meaningless to the discussion.
Neither my level of skill, how long I spent honing it, how much I spent on art school, nor how much of my income is currently threated by AI tools, matters at all in the general discourse, at least to me: though apparently these are the reasons that matter to other people who are angry about it.
Is everything legal good?
Of course not. Alcohol is bad. Smoking is bad. People like to use them anyway. At one time the government decided to let adults make their own decisions (except during prohibition). But not for seatbelts (we have to wear those). Laws are weird. And no of course this example isn’t a good fit for the AI debate, I won’t pretend it is.
But it won’t matter what arguments I make because arguments don’t matter.
Is moral grandstanding and righteous indignation the *only* way to combat the threat, which isn’t illegal but maybe should be? Sure! It might even be the smart business move to rally troops around you. I’m not smart at business. I’m practical. Easy, cheap, fun new tech that augments humanity’s creative abilities to produce better content with less effort, skill or training, is something I can be excited about.
I’m a factually wrong with the idea that it isn’t currently illegal?
No. Am I morally bankrupt for using it anyway despite concerns?
Depends on which ethics we are talking about.
I gave up the starving artist mentality years ago: that real art is precious and passionate; that real art has value because of how hard the artist struggled with it; that real art has a soul that has value even if nobody wants to pay for it. That was a tough, brutal lesson after years of being a struggling artist turned out to be not so much fun, and I realized I was just begging people to support me without offering anything concrete in return.
I’m a philosophy major; I’m pretty familiar with the history of ethics, from Aristotle to Bentham.
Most boil down to “do no harm” and the “greater good.”
Will this tech harm artists? Yes.
Are there *more* people whose lives will be improved or enhanced? Yes.
Do the opportunities outweigh the harmful effects? Maybe, maybe not.
Do ethical questions like this *ever* stop or slow down the practical adoption of new techologies?
No.
Is the world all coming to an end because humanity’s core ability to create is being replaced by robots?
Sure; though I think the world is ending for greed, corporate profit, the absolute destruction of any kind of shared reality; the absolute pollution, destruction of ecology and nature, consumer culture, social media, a million other things that I personally have no control over.
It’s like recycling. I know I should do it. I believe in it. I support it.
But I also know recycling doesn’t actually work; that it’s a lie constructed by companies to make me feel a little better about using disposable convenient packaging even though it’s harmful; they are mitigating my feelings of guilt. But I know the lie, so it doesn’t work anymore. I feel bad out it, hopeless, but I know my little bit of effort has no universal effects on a greater scale.
Am I automatically in favor of IP theft and anti-artist because I acknowledge these tools exist, are fun, easy and cheap, produce great images and drastically speed up my production time while giving me better (more competitive) images that look great?
Most things that actually make money are not art, nor are they creative. Just because a human made it does not make it more beautiful, more valuable. No matter how much sweat or passion went it to it.
Midjourney makes beautiful, emotional art – it’s funny to say it has no soul, but it still generates an emotional response when you look at it. Does it matter if the robot doesn’t understand or feel; or just that the receiver/audience does?
How about sites like Squarespace and Wix…. They are not “real” websites; they’re too easy, not enough control, just pick a template. Looks great amazing/better, less time and effort, less money. But they’re “stealing” from real websites made by web developers/coders and it’s not fair, nobody profits but these big businesses who are selling easiness. So you must be lazy/unethical/cheap to use them when they are stealing work from real developers who spent years learning web design.
To wrap up, here’s some email comments I’ve gotten from people who are enjoying AI art.
“It is probably the best possible thing that could have come along to streamline my book production pipeline.”
Your email changed my life. I wasn’t going to send you this comparison picture because I was mortified when I re-visited a cover I made for a friend over a year ago, but I know you must get way too many emails, and nothing can really describe how much of an impact AI art has already made for someone like me than this side by side example. I’m not an artist, and I’m not even a good designer either. But I really enjoy making covers and concept art, even if that particular cover had me wanting to throw things at walls when I was trying to put it together. I’d like to think I’ve gotten a little better since then.
I spent a day scouring your email for every link and was already generating images as I listened to you talking. I just wanted to say thank you. The free content you put out all the time is incredibly generous, and I wouldn’t be where I am today without having learned back in 2020 how to make a cover in Microsoft Word with one of your tutorials.
“I really look forward to seeing more of your AI art—what I’ve seen so far is out of this world.”
First of all, I’d like to thank you A LOT for all the amazing resources that you create. I just discovered AI art with your last email and I’m so thrilled by it! I made a few attempts with Midjourney.
A deeper cut (more comments)
These were taken from another post, but good points were raised…
How the image based current systems play out will set the precedent for the fields that follow. Which is one of many reasons they should be boycotted.
Interesting theory, I totally agree with you and this is a smart argument. I’m just not hopeful enough that we can/will forbid AI tools from selective personal fields of creativity (and then judging which fields are creative or noncreative; which jobs can and should be replaced). That feels messy. But it’s true we should pause and make sure somebody is paying attention, which is what artists are doing now, and I applaud them for it. But boycotting, that’s a complicated word. There are many companies who are on board. Who are we boycotting; the default AI software is already open source. Apple, Adobe, Google, Amazon? You’re going to boycott them, because they’re already invested. It’ll become part of your everyday experience.
The value of a given service or skill is proportional to its scarcity. Once your hard won ability to write and market a novel is no longer scarce, its value will plummet. And it will be gone, along with the fulfilling life you have built around it.
Ouch. But your premise is flawed (generally, though specific with book cover design). Here’s the thing: AI will get good at writing and publishing novels. As someone with a PhD whose spent the last 5 years writing fiction, I would be gutted if robots suddenly did it better. But they won’t. It’ll be some kid using AI ghostwriters and AI art to crank out passably good novels. Which would be terrifying… if I didn’t already plan to stay competitive and do that myself. All artists can do at this point is become familiar with the new tools (which happens for everyone in every industry; though this specific thing is more powerful and disruptive than anything else) or refuse. Nobody knows what the future will bring but it is here already, only some of us are paying attention.
The reason art is treated differently is because this is what we do in our lives for meaning. We weren’t supposed to replace all the fulfilling jobs and leave only the drudgery, that’s the wrong way around. The end game is this comes for everything, including you. Do you honestly believe we should have no musicians, no actors, no authors, no artists? Is that a world you want to live in?
This is a romantic ideology that has saturated the modern world (that art has more value especially when it has no value).
Art is more valuable to humans because we can feel it. YES.
Art stops being valuable in this way (to creatives) when robots can do it faster. MAYBE.
Part of the joy in art is improvement and becoming the best, and robots threaten that. MAYBE.
AI will replace all creative endeavors and do it better. YES. (Art hasn’t produced much new in the last few decades, I would argue, precisely because too many people are doing it and we are all too connected).
AI will allow creative people to do bigger and better things than we’ve ever been able to do before (YES).
Artists will refuse to participate and opt out of society like in Atlas Shrugged: MAYBE but they won’t stop the heart of the world, because the robots can take over.
“Do I believe/world I want to live in.” Honestly I’m reporting what’s happening in the world and introducing new tools. I’m like Brad Pitt when asked about Tibet – “yo I’m just an actor dude.”
I understand by not taking a negative stance I’ve become controversial in a hot topic that artists are eager to shut down, but the tiny part I play in the social discourse of creativity is pretty small comparatively, and you could go after much bigger fish. Am I a target because I’m an author/artist and I should know better? I get the reasoning. I’m uncomfortable under the rage and ire, and fearful of mob mentality, which I regard with respect. I’m not against artists and I’ve been pretty polite in my discussions. Yet here we are, I’ve become a strawman. Light me on fire.
You should be boycotting this, unless you will be equally keen for the writer replacement bot to destroy our industry.
Yes, I should be. Like all other content creators for creative people. It’s the correct moral stance right now. But boycotting emergent technologies has never gone well; especially ones that increase access. I’m happy for all the non-artists who are super excited about being able to do things they couldn’t before. I’m sad for the artists who will need to recreate their income strategies. But I’m not angry… angry at who, the robots, the big corporations? Since when have they listened? I can already buy fully stock AI images on big stock sites and use Dalle2 inside of photoshop (I think, or something like it… photoshop has a bunch of built in AI tools that make things WAY easier for designers now than when I started 15+years ago).
Most creators LOVE ai tools, like stripping out a background or replacing the sky. They make production easier and faster. That’s great for them, up to a point, and the point is whatever service they are personally charging.
Am I keen to be replaced? Not especially. Do I think my opinion matters to the billion dollar companies that we pay to do everything? Not at all.
There is no winning argument here. I’m either for the bots, or corporations, or anti-artist. Even if I’m not taking a stand, that’s taking a stand because I *should* be using my platform to raise concerns about this incredibly huge issue. Sure I get that.
But I’m not going to. Partly, because like the meme I shared above, a lot of it is pure disinformation. I can’t ignore it, and I’ve never been good about condemning things (my graduate studies have all focused on Milton, freedom of speech and press, and technological enhancements of creative fields). I love AI because it’s amazing, full stop… I understand the controversy around it. I’m not saying artists shouldn’t be threatened of freaking out about it.
Percy Bysshe Shelley wrote a passionate call to arms in defense of Greece (Hellas, 1821) which motivated Lord Byron to go and fight to protect the Ideals of Freedom “(to produce fresh generations to accomplish that destiny which tyrants foresee and dread”). 3 years later, April 1824, Lord Byron died in a fit of fever in Messolonghi, where he had gone to aid the Greeks in their fight for independence from the Turks (probably infected from unsterilized equipment during a malaria bleeding).
We champion his brave gesture and the motivations behind it. Byron became a hero, and after 11 years of war, the Greeks won. But time moves so much faster now. By the time you’re starting to hear about these AI tools, the outcome has been decided, the war is over. I’m not asking you to lay down your arms. Just try not to kill anybody.
AI writing predictions:
I’ve had a lot of people feeling worried about the future in general, and also people who were initially curious and now afraid.
– I wouldn’t be surprised if Amazon starts writing their own books based on the popular ones, like it’s already done with popular products rebranded for their own line.
– Amazon will probably try to ban pure AI written content (or not, they might jump on board especially if they are using/selling it also).
– Writing insanely fast may not be the hack it used to be… I’m a slow writer at 4 books a year, so I couldn’t compete with people cranking out books. This might help.
– The words don’t really matter, they are just filler for the story. AI might help you get more better words, but the story is what will be remembered (you can have AI plot/tell a story, even map out a full outline though).
– I think authors are going to be able to do much more themselves, without having a team of editors, designers, betareaders, etc – and be able to put out better books, faster. But the tools only help if you know what they’re doing and give them a purpose. Good writing isn’t what makes a book good, so you need some high level of story craft and they’ll still need a lot of revision and planning.
– AI will never just write books because they need a human agent who wants to publish and promote and make money with them. Most new authors will play with the tech and find it fun but not really know how to build it into their own writing process; some people will try cranking out books but they won’t be good and they won’t know where to start with cover design, marketing, etc.
– But some people, who already have experience, might use the tools to enhance their productivity (quillbot for example, rephrases your writing into different styles, so it’s like grammarly or prowriting aid but actually makes changes instead of flagging a thousand things.)
– Right now it’s too heated to really do much of anything, but soon everybody will have access to tools that never existed before. It’s probably the end of the world, sure. Do we play violin on a sinking ship or build a lifeboat?
– People are upset about AI art and swear not to use it, but Adobe and shutterstock already accepted it into their system… I really get wanting to support artists, and not using tools that harm them or make them upset… but where are we going to get our stock photos from? Why use grammarly if quillbot (a more powerful AI tool) or just chatgpt3 can edit our books better, even improve the style and tone?
The options are: use it or don’t, whichever brings you peace, whichever helps you get your best work. Arguments about why nobody should ever use it and it should be banned… I feel like the train has left the station. Who are we going to ban, the internet? Should we go back to writing on typewriters? Write by hand?
Anyway, I’ve written a ton on this stuff and I don’t think the heated debates are useful, and I’m emotional fragile because of all the hate I’ve gotten around AI topics, so I’ll mostly be staying quiet and playing with the tech.
But I’ll leave you with this comment from author Jamie Butler, who is using an AI feature inside of Notion. I think in a year, a lot of/most writing apps and software, including Word, will have some version of this: basically like “clippy” – a friendly assistant who can do everything and answer questions so you don’t have to go searching.
“It seems that the application for writers is endless. Aside from the obvious utility for idea generation, drafting, editing and summarization, I’ve found the AI super useful for research, using it as a query tool. Even prompts such as “Suggest 10 ways to curse in fiction without actually cursing” and “Suggest some similar words for ‘scintillating’” have yielded helpful results. It’s like having a knowledgeable and helpful “Jeeves” hovering at your elbow at all times, waiting to answer whatever you ask it. I have only just scratched the surface with discovering what it can do, and I’m excited that it is there, integrated into my own writing workspace. This does bring up the topic of what AI could mean for the future of writing (and writers). I’m very curious to follow the conversation, and I hope you keep talking about this in the future!”
I’m not pro-AI. I’m pro-artist, and creativity, which includes innovation and new technologies. I’m pro-independence and individuality and personal expression. I’m against gate keeping and excluding the right to the joy of art and aesthetic discovery, to people without access to the time, training or resources to become as good as they want to be to achieve their creative vision. I’m opposed to pulling up the ladder behind me because I’m afraid the riffraff will get in using cheats and hacks, instead of honest hard work (creative people have always found ways to get better results, even going back to early uses of a simple projection machine that much later became the camera, to create near perfect paintings.)
Mostly, I’m a kid in a sandbox, and a tsunami is coming. I’m just looking up and asking how it will affect the castles I’ve built, and what I’ll do when they are washed away.
I’m in a decades-long quest to make money online with my art and writing… so I can create fun stuff all day. I did spend 10 years getting a PhD so sometimes I can sound smart; but most often not. I covet a castle full of cats, where I can write and eat cake. Stick around for publishing & marketing strategies for modern creatives!
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| 2022-12-06T00:00:00 |
2022/12/06
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https://www.creativindie.com/the-creative-war-against-ai-art-has-already-been-lost/
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[
{
"date": "2022/12/06",
"position": 89,
"query": "universal basic income AI"
},
{
"date": "2022/12/06",
"position": 63,
"query": "AI graphic design"
}
] |
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Webinar Deck: How to Accelerate through Economic Disruption: Strategies for the UK&I and European Markets
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The global macroeconomic environment has experienced considerable ups and downs.
Europe and the UK and Ireland (UK&I ), in particular, are facing the trifecta of uncertain economic growth: high inflation, geopolitical tensions, and talent tightness – and these are just a few of the disruptions hitting the region.
Our European expert panelists discussed how a comprehensive approach focusing on technology, business process, locations strategy, and future operating models can help your organization navigate this crisis.
The panelists shared emerging best practices from leading organizations that are accelerating through the economic disruption and shared guidance on how to plan for the future of sourcing.
| 2022-12-06T00:00:00 |
https://www2.everestgrp.com/report/06dec22-webinar/
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[
{
"date": "2022/12/06",
"position": 37,
"query": "AI economic disruption"
}
] |
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C_2022465EN.01006501.xml - EUR-Lex - European Union
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2022465EN.01006501.xml
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https://eur-lex.europa.eu
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Points to the risk of supply chains being disrupted due to economic ... economy extending into the economy for AI applications;. 299. Concludes that ...
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P9_TA(2022)0140
Artificial intelligence in a digital age
European Parliament resolution of 3 May 2022 on artificial intelligence in a digital age (2020/2266(INI))
(2022/C 465/06)
The European Parliament,
— having regard to Articles 4, 16, 26, 114, 169, 173, 179, 180, 181 and 187 of the Treaty on the Functioning of the European Union,
— having regard to the Charter of Fundamental Rights of the European Union,
— having regard to the UN Convention on the Rights of the Child and General Comment No 25 of the UN Committee on the Rights of the Child of 2 March 2021 on children’s rights in relation to the digital environment,
— having regard to the recommendation of the UN Educational, Scientific and Cultural Organization (UNESCO) on the ethics of artificial intelligence adopted by the UNESCO General Conference at its 41st session on 24 November 2021,
— having regard to the Interinstitutional Agreement of 13 April 2016 on Better Law-Making (1) and the Commission’s Better Regulation Guidelines,
— having regard to the Commission communication of 24 March 2021 on the EU strategy on the rights of the child (COM(2021)0142),
— having regard to its resolution of 7 October 2021 on the state of EU cyber defence capabilities (2),
— having regard to its resolution of 15 December 2021 on the challenges and prospects for multilateral weapons of mass destruction arms control and disarmament regimes (3),
— having regard to Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation — GDPR) (4),
— having regard to Regulation (EU) 2021/694 of the European Parliament and of the Council of 29 April 2021 establishing the Digital Europe Programme and repealing Decision (EU) 2015/2240 (5),
— having regard to Regulation (EU) 2021/695 of the European Parliament and of the Council of 28 April 2021 establishing Horizon Europe — the Framework Programme for Research and Innovation, laying down its rules for participation and dissemination, and repealing Regulations (EU) No 1290/2013 and (EU) No 1291/2013 (6),
— having regard to the proposal for a regulation of the European Parliament and of the Council of 21 April 2021 laying down harmonised rules on Artificial Intelligence (Artificial Intelligence Act) and amending certain Union legislative acts (COM(2021)0206),
— having regard to the proposal for a regulation of the European Parliament and of the Council of 25 November 2020 on European data governance (Data Governance Act) (COM(2020)0767),
— having regard to Regulation (EU) 2018/1807 of the European Parliament and of the Council of 14 November 2018 on a framework for the free flow of non-personal data in the European Union (7),
— having regard to Regulation (EU) 2021/697 of the European Parliament and of the Council of 29 April 2021 establishing the European Defence Fund and repealing Regulation (EU) 2018/1092 (8),
— having regard to Directive (EU) 2019/770 of the European Parliament and of the Council of 20 May 2019 on certain aspects concerning contracts for the supply of digital content and digital services (9),
— having regard to Council Regulation (EU) 2021/1173 of 13 July 2021 on establishing the European High Performance Computing Joint Undertaking and repealing Regulation (EU) 2018/1488 (10),
— having regard to the Commission communication of 25 April 2018 entitled ‘Artificial Intelligence for Europe’ (COM(2018)0237),
— having regard to the Commission communication of 7 December 2018 on a coordinated plan on artificial intelligence (COM(2018)0795),
— having regard to the Commission communication of 8 April 2019 on building trust in human-centric artificial intelligence (COM(2019)0168),
— having regard to the Commission White Paper of 19 February 2020 entitled ‘Artificial Intelligence — A European approach to excellence and trust’ (COM(2020)0065),
— having regard to the Commission Green Paper of 27 January 2021 on ageing — fostering solidarity and responsibility between generations (COM(2021)0050),
— having regard to the Commission communication of 19 February 2020 on a European strategy for data (COM(2020)0066),
— having regard to the Commission communication of 19 February 2020 on shaping Europe’s digital future (COM(2020)0067),
— having regard to the Commission communications of 10 March 2020 on a new industrial strategy for Europe (COM(2020)0102) and of 5 May 2021 entitled ‘Updating the 2020 New Industrial Strategy: Building a stronger Single Market for Europe’s recovery’ (COM(2021)0350),
— having regard to the Commission communication of 30 September 2020 entitled ‘Digital Education Action Plan 2021-2027 — Resetting education and training for the digital age’ (COM(2020)0624),
— having regard to the Commission communication of 9 March 2021 entitled ‘2030 Digital Compass: the European way for the Digital Decade’ (COM(2021)0118),
— having regard to the proposal for a decision of the European Parliament and of the Council of 15 September 2021 establishing the 2030 Policy Programme ‘Path to the Digital Decade’ (COM(2021)0574),
— having regard to the Commission study of 28 July 2020 entitled ‘European enterprise survey on the use of technologies based on artificial intelligence’,
— having regard to the Commission study of 26 November 2020 entitled ‘Energy-efficient cloud computing technologies and policies for an eco-friendly cloud market’,
— having regard to the Commission report to the European Parliament, the Council and the European Economic and Social Committee of 19 February 2020 on the safety and liability implications of artificial intelligence, the internet of things and robotics (COM(2020)0064),
— having regard to the Council conclusions of 22 March 2021 on the EU’s cybersecurity strategy for the digital decade,
— having regard to the report of the High-Level Expert Group on Artificial Intelligence of 8 April 2019 entitled ‘Ethics guidelines for trustworthy AI’,
— having regard to the report of the High-Level Expert Group on Artificial Intelligence of 8 April 2019 entitled ‘A definition of AI: main capabilities and disciplines’,
— having regard to the report of the High-Level Expert Group on Artificial Intelligence of 26 June 2019 entitled ‘Policy and investment recommendations for trustworthy AI’,
— having regard to the UNESCO publication of March 2019 entitled ‘I’d blush if I could: closing gender divides in digital skills through education’,
— having regard to the European Union Agency for Fundamental Rights report of 14 December 2020 entitled ‘Getting the future right — Artificial intelligence and fundamental rights’,
— having regard to the recommendation of the Council of the Organisation for Economic Co-operation and Development (OECD) of 22 May 2019 on artificial intelligence,
— having regard to the UN platform for dialogue on artificial intelligence: AI for Good Global Summit,
— having regard to the G20 AI Principles of 9 June 2019,
— having regard to the World Health Organization report of 28 June 2021 on artificial intelligence in health and six guiding principles for its design and use,
— having regard to the European Economic and Social Committee own-initiative opinion of 31 May 2017 entitled ‘Artificial Intelligence — The consequences of artificial intelligence on the (digital) single market, production, consumption, employment and society’ (11),
— having regard to the report of the Expert Group on Liability and New Technologies — New Technologies Formation of 21 November 2019 entitled ‘Liability for Artificial Intelligence and other emerging digital technologies’,
— having regard to the publication of the Ad hoc Committee on Artificial Intelligence (CAHAI) of the Council of Europe of December 2020 entitled ‘Towards Regulation of AI systems — Global perspectives on the development of a legal framework on Artificial Intelligence systems based on the Council of Europe’s standards on human rights, democracy and the rule of law’,
— having regard to the European University Institute working paper of October 2020 entitled ‘Models of Law and Regulation for AI’,
— having regard to the joint report by Trend Micro Research, the UN Interregional Crime and Justice Research Institute and Europol of 19 November 2020 entitled ‘Malicious Uses and Abuses of Artificial Intelligence’,
— having regard to the Commission’s political guidelines for 2019-2024 entitled ‘A Union that strives for more: my agenda for Europe’,
— having regard to the judgment of the Court of Justice of the European Union of 16 July 2020 in case C-311/18 (Schrems II),
— having regard to its resolution of 16 February 2017 with recommendations to the Commission on civil law rules on robotics (12),
— having regard to its resolution of 1 June 2017 on digitising European industry (13),
— having regard to its resolution of 6 October 2021 on the EU Road Safety Policy Framework 2021-2030 — Recommendations on next steps towards ‘Vision Zero’ (14),
— having regard to its resolution of 12 September 2018 on autonomous weapon systems (15),
— having regard to its resolution of 12 February 2019 on a comprehensive European industrial policy on artificial intelligence and robotics (16),
— having regard to its resolution of 12 February 2020 entitled ‘Automated decision-making processes: ensuring consumer protection and free movement of goods and services’ (17),
— having regard to its resolution of 20 October 2020 with recommendations to the Commission on a civil liability regime for artificial intelligence (18),
— having regard to its resolution of 20 October 2020 on intellectual property rights for the development of artificial intelligence technologies (19),
— having regard to its resolution of 20 October 2020 with recommendations to the Commission on a framework of ethical aspects of artificial intelligence, robotics and related technologies (20),
— having regard to its resolution of 20 January 2021 on artificial intelligence: questions of interpretation and application of international law in so far as the EU is affected in the areas of civil and military uses and of state authority outside the scope of criminal justice (21),
— having regard to its resolution of 20 May 2021 entitled ‘Shaping the digital future of Europe: removing barriers to the functioning of the digital single market and improving the use of AI for European consumers’ (22),
— having regard to its resolution of 25 March 2021 on a European strategy for data (23),
— having regard to its resolution of 19 May 2021 on artificial intelligence in education, culture and the audiovisual sector (24),
— having regard to its resolution of 6 October 2021 on artificial intelligence in criminal law and its use by the police and judicial authorities in criminal matters (25),
— having regard to the study by its Directorate-General for Internal Policies (DG IPOL) of June 2021 entitled ‘Artificial Intelligence diplomacy — Artificial Intelligence governance as a new European Union external policy tool’,
— having regard to the DG IPOL study of May 2021 entitled ‘Challenges and limits of an open source approach to Artificial Intelligence’,
— having regard to the DG IPOL of May 2021 entitled ‘Artificial Intelligence market and capital flows — AI and the financial sector at crossroads’,
— having regard to the DG IPOL study of June 2021 entitled ‘Improving working conditions using Artificial Intelligence’,
— having regard to the DG IPOL study of May 2021 entitled ‘The role of Artificial Intelligence in the European Green Deal’,
— having regard to the DG IPOL study of July 2021 entitled ‘Artificial Intelligence in smart cities and urban mobility’,
— having regard to the DG IPOL study of July 2021 entitled ‘Artificial Intelligence and public services’,
— having regard to the DG IPOL study of July 2021 entitled ‘European Union data challenge’,
— having regard to the DG IPOL study of June 2020 entitled ‘Opportunities of Artificial Intelligence’,
— having regard to the DG IPOL study of October 2021 entitled ‘Europe’s Digital Decade and Autonomy’,
— having regard to the DG IPOL study of January 2022 entitled ‘Identification and assessment of existing and draft EU legislation in the digital field’,
— having regard to the European Parliament Research Service (EPRS) study of September 2020 entitled ‘Civil liability regime for artificial intelligence — European added value assessment’,
— having regard to the EPRS Scientific Foresight Unit study of December 2020 entitled ‘Data subjects, digital surveillance, AI and the future of work’,
— having regard to the EPRS study of September 2020 entitled ‘European framework on ethical aspects of artificial intelligence, robotics and related technologies’,
— having regard to the EPRS study of March 2020 entitled ‘The ethics of artificial intelligence: Issues and initiatives’,
— having regard to the EPRS study of June 2020 entitled ‘Artificial Intelligence: How does it work, why does it matter, and what can we do about it?’,
— having regard to the EPRS study of July 2020 entitled ‘Artificial Intelligence and Law enforcement — Impact on Fundamental Rights’,
— having regard to the EPRS study of June 2020 entitled ‘The impact of the General Data Protection Regulation (GDPR) on artificial intelligence’,
— having regard to the EPRS study of April 2020 entitled ‘The White Paper on Artificial Intelligence’,
— having regard to the EPRS study of September 2021 entitled ‘Regulating facial recognition in the EU’,
— having regard to the EPRS study of February 2021 entitled ‘The future of work: Trends, challenges and potential initiatives’,
— having regard to the EPRS study of June 2021 entitled ‘Robo-advisors: How do they fit in the existing EU regulatory framework, in particular with regard to investor protection?’,
— having regard to the EPRS study of September 2021 entitled ‘China’s ambitions in artificial intelligence’,
— having regard to the EPRS study of June 2021 entitled ‘What if we chose new metaphors for artificial intelligence?’,
— having regard to the EPRS study of January 2018 entitled ‘Understanding artificial intelligence’,
— having regard to the EPRS study of July 2021 entitled ‘Tackling deepfakes in European policy’,
— having regard to the working paper of the Special Committee on Artificial Intelligence in a Digital Age (AIDA) of February 2021 entitled ‘Artificial Intelligence and Health’,
— having regard to the AIDA working paper of March 2021 entitled ‘Artificial Intelligence and the Green Deal’,
— having regard to the AIDA working paper of March 2021 entitled ‘The External Policy Dimensions of AI’,
— having regard to the AIDA working paper of May 2021 entitled ‘AI and Competitiveness’,
— having regard to the AIDA working paper of June 2021 entitled ‘AI and the Future of Democracy’,
— having regard to the AIDA working paper of June 2021 on ‘AI and the Labour Market’,
— having regard to Rule 54 of its Rules of Procedure,
— having regard to the report of the Special Committee on Artificial Intelligence in a Digital Age (A9-0088/2022),
1. Introduction
1. Notes that the world stands on the verge of the fourth industrial revolution; points out that in comparison with the three previous waves, initiated by the introduction of steam, electricity, and then computers, the fourth wave draws its energy from an abundance of data combined with powerful algorithms and computing capacity; stresses that today’s digital revolution is shaped by its global scale, fast convergence, and the enormous impact of emerging technological breakthroughs on states, economies, societies, international relations and the environment; recognises that radical change of this scale has differing impacts on various parts of society depending on their objectives, geographical location or socio-economic context; emphasises that the digital transition must be shaped with full respect for fundamental rights and in such a way that digital technologies serve humanity;
2. Observes that the digital revolution has, at the same time, triggered a global competition as a result of the tremendous economic value and technological capabilities that have accumulated in economies that commit the most resources to the research, development and marketing of artificial intelligence (AI) applications; notes that digital competitiveness and open strategic autonomy have become a central policy objective in several countries; stresses the growing realisation among decision makers that emerging technologies could affect the geopolitical power status of entire countries;
3. Points out that Europe, which for centuries set international standards, dominated technological progress and led in high-end manufacturing and deployment, has therefore fallen behind, developing and investing far less than leading economies like the US or China in the digital market, while remaining relatively competitive in AI thematic research output; recognises the risk of European actors being marginalised in the development of global standards and advancements of technology and of European values being challenged;
4. Highlights, firstly, that digital tools are increasingly becoming an instrument of manipulation and abuse in the hands of some corporate actors as well as in the hands of autocratic governments for the purpose of undermining democratic political systems, thus potentially leading to a clash between political systems; explains that digital espionage, sabotage, low-scale warfare and disinformation campaigns challenge democratic societies;
5. Stresses that the nature of digital business models allows for great degrees of scalability and network effects; points out that many digital markets are characterised by a high degree of market concentration, allowing a small number of tech platforms, most of which are currently US-based, to lead the commercialisation of groundbreaking technological innovations, attract the best ideas, talent and companies and achieve extraordinary profitability; warns that dominant market positions in the data economy are likely to be extended into the emerging AI economy; points out that only eight of today’s top 200 digital companies are domiciled in the EU; stresses that the completion of a true digital single market is of the highest importance in that regard;
6. Emphasises that as a result, the global competition for tech leadership has become a priority in the EU; stresses that if the EU does not act swiftly and courageously, it will end up having to follow rules and standards set by others and risks damaging effects on political stability, social security, fundamental rights, individual liberties and economic competitiveness;
7. Argues that AI is one of the key emerging technologies within the fourth industrial revolution; notes that AI fuels the digital economy, as it allows for the introduction of innovative products and services, has the power to increase consumer choice and can render production processes more efficient; states that by 2030, AI is expected to contribute more than EUR 11 trillion to the global economy; stresses, at the same time, that AI technologies risk reducing human agency; highlights that AI should remain a human-centric, trustworthy technology and should not substitute human autonomy nor assume the loss of individual freedom; stresses the need to ensure that this fourth industrial revolution is inclusive and leaves no one behind;
8. Suggests that there is a global contest for AI leadership; points out that AI technologies promise to deliver immense economic value to those economies which profitably develop, produce and adopt such technologies, as well as to those countries in which such value creation takes place; underlines that AI is not an omnipotent technology, but an efficient set of tools and techniques that can be put to the benefit of society; explains that how technologies function depends on how we design them; points out that the EU has declared its intention to pioneer a regulatory framework on AI; stresses, nonetheless, that it is crucial for the EU to be able to define the regulatory approach, including the protection of fundamental rights and freedoms, and to act as a global standard-setter; stresses, therefore, the importance of European competitiveness in AI and the ability of the EU to shape the regulatory landscape at international level; stresses that certain uses of AI may pose individual and societal risks that can endanger fundamental rights and should therefore be addressed by policymakers, thereby allowing AI to effectively become an instrument that serves people and society, pursuing the common good and general interest;
9. Notes that a clear regulatory framework, political commitment and a more forward-leaning mindset, which are often lacking at present, are needed for European actors to be successful in the digital age and to become technology leaders in AI; concludes that based on such an approach, both EU citizens and businesses can benefit from AI and the great opportunity it offers to boost competitiveness, including with regard to prosperity and well-being; underlines that regulatory frameworks must be shaped in such a way as not to impose unjustified barriers to prevent European actors from being successful in the digital age, in particular for start-ups and small and medium-sized enterprises (SMEs); highlights that private and public investments should be substantially increased to create a climate in which more European success stories emerge and develop on our continent;
10. Highlights that rapid technological progress introduced by AI is increasingly inextricable from most areas of human activity and will also affect the livelihoods of everyone who does not possess the skills they need to adapt fast enough to these new technologies; points out that while achieving digital literacy through upskilling and reskilling can help to address many of the resulting socio-economic concerns, these impacts should also be addressed in the context of social welfare systems, urban and rural infrastructure, and democratic processes;
11. Emphasises the need to reflect the objectives and interests of women and vulnerable groups in the digital transition; highlights, in this context, that women only accounted for 22 % of global AI professionals in 2018, a problem that serves only to perpetuate and entrench stereotypes and bias; recognises the need to preserve the rights to equality before the law, privacy, freedom of expression, and participation in cultural and political life when using AI technologies, especially for minority communities;
2. Potential opportunities, risks and obstacles in the use of AI: six case studies examined by the AIDA Committee
12. Recalls that AI is based on software that uses probabilistic models and algorithmic prediction for a set of specific objectives; points out that the term AI is an umbrella term covering a wide range of old and new technologies, techniques and approaches better understood as ‘artificial intelligence systems’, which refers to any machine-based systems that often have little more in common than being guided by a given set of human-defined objectives, with varying degrees of autonomy in their actions, and engaging in predictions, recommendations or decision-making based on available data; notes that while some of these technologies are already in widespread use, others are still under development or are even just speculative concepts that may or may not exist in the future;
13. Points out that there is a significant difference between symbolic AI, the main approach to AI from the 1950s to the 1990s, and machine-learning, data-driven AI, which has dominated since the 2000s; clarifies that during the first wave, AI was developed by encoding the knowledge and experience of experts into a set of rules that was then executed by a machine;
14. Notes that in the second wave, the automated learning processes of algorithms based on the processing of large amounts of data, the ability to bring together inputs from multiple different sources and form complex representations of a given environment, and the identification of patterns made AI systems more complex, autonomous and opaque, which can lead to less explainable outcomes; stresses that current AI can therefore be broken down into many different sub-domains and techniques, whereby deep learning is for instance a subfield of machine learning, which itself is a subfield of AI;
15. Notes that although today’s AI has become much more effective and powerful than symbolic AI, thanks to the significant increases in computing capacities, it can still only solve clearly defined tasks in domain-specific niches such as chess or image recognition and its programming is not designed to fully recognise the actions that the AI system performs; highlights that AI systems — contrary to what their name suggests — do not have ‘intelligence’ in a human sense; points out that it is therefore referred to as ‘narrow’ or ‘weak’ AI and is still no more than a tool that provides recommendations and predictions; notes, for instance, that self-driving cars operate through a combination of various one-task AI systems that together are able to provide a three-dimensional map of the surroundings of the vehicle so that its operating system can make decisions;
16. Highlights that many fears linked to AI are based on hypothetical concepts such as general AI, artificial superintelligence and singularity which could, in theory, lead to machine intelligence outperforming human intelligence in many areas; stresses that there are doubts as to whether this speculative AI can even be achieved with our technologies and scientific laws; believes, nevertheless, that the risks currently posed by AI-based decision-making need to be addressed by the legislators as it is demonstrably clear that harmful effects such as racial and sex discrimination are already attributable to particular instances where AI has been deployed without safeguards;
17. Underlines that the majority of AI systems currently in use are low-risk; refers, for instance, to automatic translation, ‘Eureka machines’, gaming machines and robots that carry out repetitive manufacturing processes; concludes that some use cases can be categorised as risky and that such cases require regulatory action and effective safeguards, should these not already be in place;
18. Encourages a public debate on how to explore the enormous potential of AI based on fundamental European values, the principles of transparency, explainability, fairness, accountability, responsibility and trustworthiness, as well as the principle that AI and robotics should be human-centred and developed to complement humans; stresses that in a significant number of areas of human life, from sustainability to healthcare, AI can provide benefits as an auxiliary tool for users and professionals, augmenting the capabilities of humans without impeding their ability to freely act and decide; stresses that the agreed AI ethical principles and requirements should be operationalised in all domains of AI application, building in the necessary safeguards, which will increase citizens’ trust, thereby making them embrace the benefits of AI;
19. Underlines that the level of risk of a particular AI application varies significantly depending on the likelihood and severity of harm; highlights, therefore, that legal requirements should be adjusted to this, in line with a risk-based approach and taking into due account, when justified, the precautionary principle; stresses that in such present or future instances where, in a particular use case, AI systems pose high risks to fundamental and human rights, full human oversight and regulatory intervention are needed and that, given the speed of technological development, regulation for high-risk AI systems needs to be flexible and future-proof;
20. Illustrates that the present report addresses six AI case studies in detail, outlining the opportunities offered by AI in the respective sector, the risks to be addressed and the obstacles preventing Europe from fully harnessing the benefits of AI; highlights that the case studies represent some of the most important AI use cases today and, at the same time, reflect some of the main topics of the public hearings held by the AIDA Committee during its mandate, namely health, the Green Deal, external policy and security, competitiveness, the future of democracy and the labour market;
a) AI and health
21. Finds that the methodological analysis of large amounts of data, including through AI, can unlock new solutions or improve existing techniques in the health sector that could speed up scientific research enormously, save human lives and improve patient care by offering innovative treatments and better diagnosis and fostering supportive environments for healthy lifestyles; highlights that AI systems can also contribute to the accessibility, resilience and sustainability of health systems, while at the same time bringing a competitive edge to the European ICT and healthcare sectors if the inherent risks are managed appropriately;
22. Highlights that the use of AI in the health sector should be anchored in strong ethical requirements such as equitable access to healthcare, privacy, liability, transparency, explainability, reliability, inclusiveness and representability of data sets, and constant human oversight; stresses that the design of AI-based systems must address the risk of resources being wrongly allocated to individuals based on faulty or biased categorisation, prioritisation or malfunctioning technology, leading to misdiagnosis, maltreatment or no treatment at all; believes that the highest ethical standards should apply to all healthcare applications and that ethical rules should be established at a very early stage in their development and design, i.e. ethics by design; underlines that automated decision-making in healthcare applications may pose risks to patients’ well-being and fundamental rights and stresses that AI must therefore have a supportive role in healthcare, where professional human oversight should always be maintained; calls for AI in medical diagnoses in public health systems to preserve the patient-doctor relationship and to be consistent with the Hippocratic oath at all times; notes, however, that AI improves the accuracy of screening and is already outperforming doctors’ diagnoses in several instances; finds that the existing liability frameworks do not provide sufficient legal certainty and do not uphold the right of patients to legal redress in the event of misdiagnosis and incorrect treatment through AI; welcomes, in this regard, the upcoming legislative proposal on AI liability; notes that it is important to protect health professionals as users of AI systems, as well as patients as end recipients, providing them with sufficient and transparent information;
23. Underlines that AI-based solutions are already being used or tested in clinical settings with the aim of supporting diagnosis, prognosis, treatment and patient engagement, thus speeding up and improving treatment and reducing unnecessary interventions; notes, moreover, that AI can enhance personalised medicine and patient care; notes that AI is currently covering a wide range of health areas, including public health, care services, self-care and health systems; remarks that data plays an important role; finds that there are promising applications for AI in extracting information from images and in other medical devices to inform downstream analysis and notes that it is also expected that deep learning algorithms can deliver a quantitative leap in a variety of clinical tasks;
24. Highlights that AI technologies can be applied to the research, development and mass production of pharmaceuticals and have the potential to speed up the development of new drugs, treatments and vaccines at a lower cost; finds that AI can help predict the outcome of responses to treatments and can allow doctors to adjust therapeutic strategies according to individual genetic or physiological characteristics with increasing levels of accuracy when based on high-quality data and sound assumptions, thereby increasing the effectiveness of preventive care, provided that all ethical requirements are met with regard to professional oversight over AI clinical validation, privacy, data protection and informed consent; notes that big data in health can be analysed with the aid of AI to accelerate its processing; underlines the importance of ensuring that high-performance computing is interoperable with AI, as major economic sectors including manufacturing, health and pharmaceuticals rely on high-performance computing;
25. Underlines that AI-based solutions have the potential to tailor treatments and drug development to patients’ specific needs and enhance engagement with stakeholders and participants in the healthcare system; finds that AI and access to relevant, updated and high-quality anonymised and representative data sets, in line with the EU rules on personal data protection, supports healthcare professionals to help them provide better care for their patients and more personalised feedback, guidance and support, promoting patient safety and making therapy more effective; highlights that this may be particularly helpful in selecting and reviewing the growing body of scientific knowledge for the purposes of extracting relevant insights for health professionals; highlights that citizens from all Member States should be able to share their health data with healthcare providers and authorities of their choice; underlines, in this regard, the need to create incentives for upskilling, reskilling and outskilling for workers in health careers;
26. Finds that the fight against COVID-19 has both accelerated research into and the deployment of new technologies, notably AI applications, in the quest for improved case detection, clinical care and therapeutics research, and highlighted the usefulness of AI as well as the importance of funding and high-quality data for the purpose of the efficient monitoring and modelling of the spread of infectious disease outbreaks, in accordance with data protection law; notes, however, that experiences with AI applications during COVID-19 have revealed some of the limitations in the use of AI in medical diagnostics ( 26 )
27. Highlights the potential of AI systems to alleviate the burden on health systems and health professionals in particular and to contribute to solutions to provide care to rapidly ageing populations in Europe and the world and protect them from dangerous diseases;
28. Highlights that the use of safe and efficient AI applications for administrative tasks that do not require human action can save a lot of time for healthcare workers that can be devoted to patient visits instead;
29. Stresses that consumer health applications based on AI can help track an individual’s health status through everyday devices such as smartphones, allowing users to voluntarily provide data which can be the basis for early warnings and alerts regarding life-threatening illnesses such as strokes or cardiac arrests; stresses that health applications based on AI may also encourage healthy behaviour and empower responsible self-care for individuals by equipping patients with additional means to monitor their own health and lifestyle and by improving the accuracy of screening by healthcare professionals; points out, however, the particular sensitivity of personal health data and the risk of data breaches or misuses in this regard, and underlines the need to apply strong cybersecurity standards for any health application;
30. Stresses that AI in the health sector is particularly dependent on large amounts of personal data, data sharing, high data quality, data accessibility and data interoperability to realise the full potential of AI and health; stresses the need to facilitate the linking of electronic health records with e-prescribing systems in order to allow health professionals involved in patient care to access the necessary information on the patient, subject to his or her consent;
31. Welcomes the creation of a European health data space in order to build in data of very high quality for use in the health sector; considers that the interconnection and interoperability of high-performance computing infrastructure with the European health data space would ensure the availability of large, high-quality health data sets, which are important for researching and treating pathologies, especially rare diseases and paediatric conditions;
32. Stresses the need to build trust by promoting interoperability and more collaboration between different healthcare professionals serving the same patients; stresses the need to offer training to healthcare professionals on AI techniques and approaches; stresses the need to combat mistrust, such as by tapping into the full potential of data anonymisation and pseudonymisation, and to better inform citizens, health professionals and decision makers about the uses, benefits and risks of AI in the field of health, as well as AI developers about the challenges and risks of processing sensitive data in this domain;
33. Believes, moreover, that binding and robust ethical and legal standards and enforceable rights of redress are necessary to promote an ecosystem of trust among citizens and to adequately protect health data from potential misuse and unlawful access; agrees with the Commission that citizens should have secure access to a comprehensive electronic record of data concerning their health and should retain control over personal data concerning their health and be able to share it securely, with effective protection for personal data and strong cybersecurity, with authorised third parties; highlights that unauthorised access and dissemination should be prohibited and that the protection of patients’ personal data must be guaranteed in compliance with data protection legislation;
34. Underlines, in this regard, the risk of biased decisions leading to discrimination and violations of human rights; stresses the need, therefore, for impartial checks on the algorithms and data sets used, and for the promotion of further research on the methods and bias embedded in trained AI systems in order to prevent unethical and discriminatory conclusions in the field of human health data;
35. Stresses that an efficient and uniform application of the GDPR across the EU is needed in order to overcome challenges such as legal uncertainty and a lack of cooperation in the health sector; stresses that such challenges lead in some cases to delays in scientific discoveries and a bureaucratic burden in health research; stresses that the creation of a European health data space that guarantees patients’ rights and data portability could increase cooperation and stimulate data sharing for research and innovation in the European health sector;
36. Notes that AI can contribute to the rapid progress of new technologies, such as brain imaging, which already have important applications in medicine but also entail substantial risks to human agency and the expression of fundamental rights without requiring consent; is concerned about the lack of legislation concerning neurological data and believes that the EU should strive to become a world leader in the development of safe neurological technologies;
b) AI and the Green Deal
37. Highlights that the Commission’s two key priorities for the years to come are a Europe fit for the digital age and the Green Deal; underlines the need to ensure that the digital transition contributes to the achievement of sustainable development and promotes the green transition; finds that this requires an acceleration of innovation compatible with the EU’s climate targets and environmental standards; highlights that AI applications may be able to bring environmental and economic benefits and strengthen predictive capabilities that can contribute to the fight against climate change and to achieving the objectives of the European Green Deal and the EU’s target of becoming the first climate-neutral continent by 2050; finds that the use of AI has the potential to reduce global greenhouse gas emissions by up to 4 % by 2030 ( 27 ) ; finds that according to some estimates, ICT technologies may reduce 10 times more greenhouse gas emissions than their own footprint ( 28 ) , but recognises that this requires conscious design choices and regulatory action; warns, at the same time, that the increasing energy consumption in storing the large data sets needed to train AI systems can also have a negative effect; recalls that data traffic and ICT infrastructure consume about 7 % of the world’s electricity today, a figure which, without the right safeguards, is projected to increase to 13 % by 2030; adds that the intensive use of raw materials to build microprocessors and high-tech devices using AI can also contribute to this negative impact; underlines that in order to guarantee the ‘large handprint but small footprint’ of AI on the environment and climate, these direct and indirect negative environmental impacts need to be considered and AI systems need to be designed to promote sustainable consumption, limit resource usage and energy consumption, avoid unnecessary processing operations and prevent damage to the environment; emphasises that addressing the environmental impact of the ICT sector requires relevant information and data;
38. Is concerned that only six Member States have included a strong focus on AI applications in their efforts to meet the Green Deal objectives; finds that AI can be used to collect and organise information relevant to environmental planning, decision-making and the management and monitoring of the progress of environmental policies, for instance for cleaner air, where AI applications can monitor pollution and warn of hazards; highlights that such AI and digital solutions could be used across several sectors to scale up resource-efficient solutions;
39. Emphasises the importance of AI-based systems in developing smart cities and villages by optimising resource use and increasing the resilience of infrastructure, including through traffic prediction and reduction, smart energy management, emergency assistance and waste, as is already the case in several cities and municipalities across the EU; stresses that AI-based solutions can further assist in urban planning, architecture, construction and engineering processes to reduce emissions, construction time, costs and waste;
40. Stresses that the energy transition will not take place without digitalisation; highlights that AI can monitor, optimise and reduce energy consumption and production, as well as support the integration of renewable energies into existing electricity grids; underlines that smart meters, efficient lighting, cloud computing and distributed software together with an AI component have the potential to transform energy use patterns and promote responsible usage;
41. Highlights that the growing complexity of an energy transition system, with increased volatile renewable generation and changes in load management, makes increasing automated control necessary for energy supply security; stresses that AI has the potential to benefit security of supply, especially in the operation, monitoring, maintenance and control of water, gas and electricity networks; notes, however, that AI-enhanced grid technologies will introduce millions of intelligent components with common vulnerabilities, adding a large number of potential attack points to the energy networks and increasing the vulnerabilities of critical infrastructure, if the appropriate cybersecurity provisions are not in place; finds that smart grids require further investment and research;
42. Finds that AI and other digital applications for mobility and transport have the potential to optimise traffic flows and enhance road safety, including by increasing the efficiency of transport systems; points out that AI can inform the design and energy management of energy-efficient vehicles; highlights that the options for app-based ride services, ride pooling and car sharing have considerably increased and that AI is often used in such mobility services through efficient route planning and pick-up point selection;
43. Believes that AI can have a transformative role in the agricultural sector, supporting the emergence of new harvesting methods, including harvest prediction and agricultural resource management; stresses that agriculture is a key sector in which AI can help cut emissions and the use of pesticides, fertilisers, chemicals and water by focusing their use on the exact amount and in a narrower area; further stresses that AI can contribute to the restoration of biodiversity by monitoring endangered species or tracking deforestation activities; highlights the need to develop deployment guidelines and standardised assessment methodologies to support ‘green AI’ in areas such as smart grids, precision farming, and smart and sustainable cities as well as communities; is of the opinion that AI in the form of precision farming has the potential to optimise the on-farm production of food as well as broader land management by improving land use planning, predicting land use change and monitoring crop health, as well as the potential to transform predictions of extreme weather events;
44. Stresses that AI can contribute to the circular economy by rendering production, consumption and recycling processes and behaviour more resource-efficient and increasing the transparency of material use, for example with regard to the ethical sourcing of raw materials and reduced waste; highlights that AI has the potential to increase businesses’ understanding of their emissions, including in value chains, thus helping them to adjust and achieve individual emissions targets; underlines that digital tools can help businesses to implement the necessary steps towards more sustainable conduct, especially SMEs which otherwise may not have the resources to do so;
45. Highlights that it is not currently possible to use AI to fully measure environmental impacts; finds that there is a need for more studies on the role of AI in reducing environmental impacts; stresses that more environmental data is needed in order to gain more insight and induce more progress through AI solutions; underlines that using AI to systematically connect data on CO 2 emissions with data on production and consumption patterns, supply chains and logistics routes could ensure that activities that have a positive or negative impact are detected;
c) External policy and the security dimension of AI
46. Reiterates that the EU is pushing for a global agreement on common standards for the responsible use of AI, which is of paramount importance; believes, as a matter of principle however, in the potential of like-minded democracies to work together to jointly shape the international debate on an AI framework that is respectful of human rights and the rule of law, to work together towards certain common norms and principles, technical and ethical standards, and guidelines for responsible state behaviour, especially under the umbrella of intergovernmental organisations such as the UN and OECD, thereby promoting multilateralism, sustainable development, interoperability and data sharing on the international stage; supports the work of the UN Open-Ended Working Group on ICT and international security; underlines that confidence-building measures are essential to increase the level of dialogue and trust; calls, therefore, for more transparency in the use of AI in order to ensure better accountability;
47. Welcomes the recent multilateral initiatives to develop guidelines and standards for an ethically responsible use of AI such as the OECD principles on AI, the Global Partnership on AI, the UNESCO recommendation on the ethics of AI, the AI for Good Global Summit, the Council of Europe’s recommendations for a possible legal framework on AI, and UNICEF’s policy guidance on AI for children; welcomes the work ongoing at international level on AI standards and the progress made with the International Organization for Standardization standards on the governance implications of AI;
48. Welcomes, furthermore, the establishment and operationalisation of the EU-US Trade and Technology Council (TTC); salutes the outcome of the TTC’s first meeting in Pittsburgh; sees the TTC as a potential forum for global coordination between the European Union and the United States for setting global rules for AI and global technological standards that safeguard our common values, for boosting joint investment, research and development, and for closer political coordination in international institutions on issues related to technology and AI;
49. Highlights the key role the EU can play in setting global standards, as the first bloc in the world to introduce legislation on AI; stresses that the Union’s legal framework on AI could make Europe a world leader in the sector and should therefore be promoted worldwide by cooperating with all international partners while continuing the critical and ethics-based dialogue with third countries that have alternative governance models and standards on AI;
50. Observes that the Chinese Government has signed standards and cooperation agreements with 52 countries through its Belt and Road Initiative; warns that since several of these standards, including on AI technologies and in particular in relation to government surveillance and individual liberties, are not in line with human rights and EU values, China’s standards activism poses a challenge for the EU;
51. Stresses that AI technologies, especially those that have not been designed and developed with the explicit control procedures in place and are used improperly and without oversight in military command centres or in missile launch facilities, entail particularly significant risks and could escalate an automated reciprocal conflict;
52. Notes that the use of AI systems in defence-related developments is considered a game changer in military operations through the analysis of data, the ability to reflect greater situational complexity, the potential to improve target accuracy, optimise logistics and engage in armed conflicts with a reduced risk of physical harm to civilian populations and one’s own military personnel, as well as using data for the development of modes of action such as wargaming; cautions, however, that this could lead to a lower threshold for the use of force and therefore more conflicts; affirms that machines cannot make human-like decisions involving the legal principles of distinction, proportionality and precaution; affirms that humans should be kept in control of the decision to deploy and use weapons and remain accountable for the use of lethal force and for decisions over life and death; is of the opinion that AI-based weapons systems should be subject to global standards and an international ethical code of conduct to underpin the deployment of AI technologies in military operations, with full respect for international humanitarian law and human rights law and in compliance with Union law and values;
53. Is concerned about the military research and technological developments being pursued in some countries with regard to lethal autonomous weapons systems without meaningful human control; observes that lethal autonomous weapons systems are already used in military conflicts; recalls that Parliament has repeatedly called for an international ban on the development, production and use of lethal autonomous weapons systems and for effective negotiations to begin on their prohibition; stresses that AI-enabled systems can under no circumstances be allowed to replace human decision-making involving the legal principles of distinction, proportionality and precaution;
54. Notes, in particular, that AI technology may entail potential risks as a means of pursuing various forms of hybrid warfare and foreign interference; specifies that it could for instance be mobilised to trigger disinformation, by using bots or fake social media accounts, to weaponise interdependence, by gathering valuable information or denying network access to adversaries, to create disturbances in the economic and financial systems of other countries, to pollute the political debate and favour extremist groups, or to manipulate elections to destabilise democracies;
55. Highlights that AI technologies could also include AI-powered malware, identity theft, data poisoning or other forms of adversarial machine learning that cause other AI systems to misinterpret input; points, in particular, to the rise in deepfakes, which are not necessarily cyberattacks but lead to doubts over the authenticity of all digital content, including videos, and therefore require particular attention in terms of transparency requirements; warns that deepfakes could contribute to a broad climate of public mistrust in AI, as well as a deeper socio-political polarisation within our societies;
56. Elaborates that the use of AI systems in a significant amount of key critical infrastructure such as energy and transport grids, the space sector, the food chain, banking and financial infrastructure, and hospital facilities has created new vulnerabilities that require robust cybersecurity measures to prevent threats; points out, in this regard, the importance of cooperation and information sharing and action both at EU level as well as among Member States; underlines the importance of fostering the resilience of critical entities to hybrid threats;
57. Warns that the capabilities of AI may also pose security risks, as they may lead humans to place such confidence in AI that they trust it more than their own judgement; notes that using a human-in-the-loop approach as a corrective mechanism is not feasible in all cases; notes that experiments have shown that this can elevate the level of autonomy of AI beyond the supporting role for which it was originally designed and means that humans miss opportunities to gain experience and refine their skills and knowledge of AI systems; stresses, therefore, that safety by design and meaningful human oversight based on appropriate training as well as appropriate security and privacy safeguards are required in high-risk AI systems in order to overcome such automation bias;
58. Highlights, however, that AI can be used to predict power failures and identify maintenance needs with great accuracy; specifies, in addition, that it can be used to synthesise large amounts of data via automated information extraction or automated information classification, and to detect specific patterns; stresses that these elements would allow for better prediction and assessment of the threat level and system vulnerabilities, faster decision-making processes, improved reactivity and securing endpoint devices more effectively;
59. Underlines, in particular, the inherent potential in enabling law enforcement agencies to identify and counter criminal activity, which is aided by AI technology; underlines that such AI-related law enforcement activities do, however, require full respect for fundamental rights, strict democratic oversight, clear transparency rules, a powerful IT infrastructure, human oversight, highly skilled employees and access to relevant and high-quality data;
d) AI and competitiveness
60. Notes that more and more products and services along the value chain will be interconnected in the near future, with AI and automation playing an important role in many manufacturing processes, operations and business models; underlines the paramount importance of basic research for the development of AI industrial ecosystems as well as substantial investment to promote digital public administration and upgrade digital infrastructure;
61. Observes that despite the significant increase in venture capital and other early-stage funding in the last two years, many European industries are lagging behind and the current funding levels in the EU are still insufficient and should be substantially ramped up in order to match the dynamism of leading AI ecosystems like Silicon Valley and elsewhere; highlights the peculiar cluster-network structure of the EU innovation ecosystem, as opposed to centralised (and state-supported) innovation ecosystems;
62. Underlines that AI can be a game changer for the competitiveness of EU industry and has the potential to increase productivity, accelerate innovation, improve manufacturing processes and help to monitor the resilience of European supply chains;
63. Points to the risk of supply chains being disrupted due to economic decoupling or catastrophic events such as pandemics or climate change-related phenomena; stresses that using AI can help to detect patterns of disruption in supply chains and inform predictive maintenance, which could support the diversification of suppliers;
64. Notes that companies that have initiated digital disruption have often been rewarded with significant gains in market share; notes that recent studies indicate that this pattern is likely to repeat itself with even more intensity as companies that adopt AI often collect large amounts of data, which tends to enhance their competitive position; is concerned about the resulting risks of market concentration to the detriment of SMEs and start-ups;
65. Emphasises that this outlook is particularly concerning since the largest incumbent tech companies that will likely also dominate AI technologies are gatekeepers to markets, while capturing most of the value that is generated; stresses that because the data that drives the AI sector is overwhelmingly collected from the very same large tech companies, which offer users access to services in exchange for data and exposure to targeted advertisements, their existing market dominance is likely to, in itself, become a driver of further market dominance; points out that many of these tech companies are headquartered outside the EU yet manage to capture the value generated by data on European customers, thus gaining a competitive advantage;
66. ex ante measures, including the future Digital Markets Act, in counterbalancing concentration before it arises; underlines, moreover, the role that standardisation and regulatory cooperation can play in addressing this issue, by facilitating the global development of products and services irrespective of their physical location; Welcomes the recent Commission communication calling for competition rules to be updated to make them fit for the digital age ( 29 ) and stresses the key role ofmeasures, including the future Digital Markets Act, in counterbalancing concentration before it arises; underlines, moreover, the role that standardisation and regulatory cooperation can play in addressing this issue, by facilitating the global development of products and services irrespective of their physical location;
67. Underlines that SMEs and start-ups are playing a central role in the introduction of AI technologies within the EU as they represent the bulk of all companies and are a critical source of innovation; observes, however, that promising AI start-ups face significant barriers to expanding across Europe due to the incomplete digital single market and regulatory divergence in many Member States, or, when they do scale up, are acquired by large tech companies; regrets that SMEs often face a lack of funding, complex administrative procedures and a lack of adequate skills and access to information; notes that EU competition authorities have in the past allowed most foreign takeovers of European AI and robotics companies;
68. Stresses that the intensive use of algorithms, e.g. for price-setting, could also create completely new AI-specific problems within the single market; notes that antitrust authorities might, for instance, find it difficult to prove price collusion between AI-driven price-setting systems; adds, moreover, that the few AI providers that are already participating in stock trading could present a systemic risk to the financial markets, including through collusion; stresses that algorithmic collusion can be very hard to identify, since AI-based systems do not need to communicate with each other in the way that humans do for collusive practices, which can make it impossible to prove collusive intent; underlines the risk that this poses for market stability and the need for EU and national competition authorities to develop appropriate strategies and tools; highlights, in addition, the systemic risk to financial markets from the widespread use of algorithmic trading models and systems without any human interaction, which have in the past greatly amplified market movements, and are likely to do so again in the future;
69. Observes that many AI companies within the EU currently face legal uncertainty regarding how they can develop their products and services in an assured manner as a result of bureaucratic hurdles, an overlap between existing sector-specific legislation and the absence of established AI standards and norms;
70. Highlights the challenge for AI companies in terms of quality control and consumer protection; concludes that transparency and trustworthiness are essential to ensure that EU companies have a competitive advantage, as such considerations will decide in the future whether a product or service is eventually accepted by the market;
71. Notes that although 26 % of high-value research publications on AI come from Europe, only four out of the top 30 applicants (13 %) and 7 % of businesses engaged in AI patenting worldwide are European;
72. Considers that the EU’s intellectual property laws require harmonisation and clear and transparent enforcement, and a balanced, enforceable and predictable framework to allow European businesses, and in particular SMEs and start-ups, to secure intellectual property protection;
73. Is concerned that SME use of IP protection remains low, as SMEs often do not use IP protection as they are not fully aware of their rights nor do they have enough resources to uphold them; highlights the importance of information and statistics on IP protection among SMEs active in knowledge-intensive sectors and welcomes efforts, including simplified registration procedures and lower administrative fees, to provide SMEs and start-ups with better knowledge and to facilitate their access to IP protection; notes that in order to help EU companies protect their AI IP rights, the EU’s position as a global standard-setter should be strengthened; stresses that international competitiveness and attractiveness is rooted in a strong and resilient single market, including in IP protection and enforcement;
74. States that data analytics, as well as access to, sharing and re-use of non-personal data, are already essential for many data-driven products and services today, but will be important for the development and deployment of upcoming AI systems; stresses, however, that most of the non-personal data generated in the EU so far goes unused, while a single market for data is still in the making;
75. Points out the importance of facilitating access to data and data sharing, and open standards and open source technology as a way to enhance investments and boost innovation in AI technologies in the EU; specifies that better harmonisation on the interpretations by national data protection authorities as well as on guidance on mixed data and on depersonalisation techniques would be useful for AI developers;
76. Highlights the role AI can play in assisting enforcement action by European and national authorities, particularly in the fields of customs and market surveillance; is of the opinion that trade and customs procedures can be made more efficient and more cost-effective through AI, by increasing compliance and ensuring that only safe products enter the single market; points to the example of the Canada Border Services Agency Assessment and Revenue Management (CARM) system, which greatly simplifies import and export procedures using qualified AI risk assessment and streamlined digitalised information management to reduce the need for lengthy inspections;
e) AI and the labour market
77. Notes that AI is increasingly influencing the labour market, the workplace and the social domain and that the impacts of technological change on work and employment are multifaceted; emphasises that the use of AI in this area gives rise to a number of ethical, legal and employment related challenges; is concerned that in terms of the labour market, digitalisation could lead to workforce reorganisation and the potential disappearance of certain sectors of employment; believes that the adoption of AI, if combined with the necessary support infrastructure, education and training, could increase capital and labour productivity, innovation, sustainable growth and job creation;
78. Stresses that although AI may replace some tasks, including repetitive, heavy, labour-intensive or dangerous ones, it could also help to improve skills, raise the quality of work and create new, higher value-added employment, leaving more time for stimulating tasks and career development; stresses that AI is currently already substituting or complementing humans in a subset of tasks but that it is not yet having detectable significant aggregate labour market consequences ( 30 ) ; stresses, however, the potential for an increase in income inequality if AI increases high-skill occupations and replaces low-skill occupations; adds that any resulting economic and social implications need to be mitigated by appropriate measures, research and foresight and prepared for by investing in reskilling and upskilling of the workforce with a focus on underrepresented groups such as women and minorities, who are likely to be most affected by this transition, and by promoting diversity in all phases of development of AI systems; is concerned that AI could produce processes of deskilling and create and embed low-paid, low-autonomy work and extend atypical, flexible (or ‘gig’) work; underlines that algorithmic management could lead to power imbalances between management and employees and obscurity about decision-making;
79. Highlights that AI uptake offers an opportunity to foster significant cultural change within organisations, including through improved workplace safety, better work-life balance, and offering the right to disconnect and more effective training opportunities and guidance to employees; points, in this regard, to the recommendations of the OECD stressing that automation could also give rise to a reduction of working time, thus improving workers’ living conditions and health; is of the opinion that human-empowering AI applications could also create new job opportunities, in particular for those who, because of restrictions such as disabilities or living circumstances, have until now been bound to less qualified jobs; stresses the need to use AI assistance in the workplace to provide time for humans to improve the quality of their output instead of just increasing the workload;
80. Condemns the increased recourse to AI-fuelled surveillance in the workplace, often occurring without the workers’ knowledge, let alone their consent, particularly also in the context of teleworking; sustains that this practice should not be allowed, as it is extremely abusive of the fundamental right to privacy, data protection and the human dignity of the worker and to social and labour rights, and also has negative effects on the mental health of workers due to the degree of intrusion, its blanket or indiscriminate effect, and lack of safeguards for affected individuals;
81. Is concerned that a similar risk of surveillance is present also in the school environment, with the increasing adoption of AI systems in schools, undermining the fundamental rights of children; notes that the implications AI has for children’s privacy, safety and security fall across a wide spectrum, from benefits related to the ability to understand threats facing children with greater specificity and accuracy, to risks around unintended privacy infringements; underlines that both the positive and negative implications for children’s privacy, safety and security call for close examination and corresponding safeguards; further stresses that special consideration and protection need to be given to children when developing AI systems because of their particularly sensitive nature and specific vulnerabilities;
82. Stresses that it is paramount to provide individuals with comprehensive skills development programmes in all stages of life, in order to enable them to remain productive in a continuously evolving workplace and avoid their exclusion from the labour market; considers that the adaptation of the workforce in terms of AI education, lifelong learning and reskilling is of vital importance; highlights that current concepts of learning and working are still overly defined by the pre-digital world, which is contributing to a growing skills gap and a new digital divide for citizens who do not have access to a secure digital space; stresses that enhancing digital literacy contributes to achieving the UN Sustainable Development Goals, in particular those on education, human capital and infrastructure; highlights the gain in knowledge of new forms of working and learning due to the COVID-19 crisis which could further be explored;
83. Underlines that to reap the full benefits of digitalisation, the Union must address digital literacy and skills for all; believes that digital literacy is a precondition for citizens’ trust in and public awareness of the impacts of AI; highlights the importance of including basic training in digital skills and AI in national education systems; believes that the implementation and development of AI technology in the field of minority languages might lead to a boost in their knowledge and use; stresses that more than 70 % of businesses report a lack of staff with adequate digital and AI skills as an obstacle to investment; is concerned that as of 2019, there were 7,8 million ICT specialists in the EU, with a prior annual growth rate of 4,2 %, which is far short of the 20 million experts that are needed for key areas such as data analysis as projected by the Commission;
84. Is concerned about the extensive gender gap in this area, with only one in six ICT specialists and one in three science, technology, engineering and mathematics (STEM) graduates being women ( 31 ) ; notes with concern that the gender divide is persisting, especially in the area of start-ups, where in 2019, USD 92 of every USD 100 invested in European tech companies went to founding teams that were entirely comprised of men; recommends targeted initiatives to support women in STEM in order to close the overall skills gap in this sector; stresses that this gap inevitably results in biased algorithms; emphasises the importance of empowering and motivating girls towards STEM careers and eradicating the gender gap in this area;
f) AI and the future of democracy
85. States that AI has, on the one hand, the potential to assist in building a more transparent and efficient public sector, but on the other hand, that the technical developments in the field of AI, often driven by a logic of growth and profits, are very rapid and dynamic, making it difficult for policymakers to have a sufficient understanding of how new AI applications work and what kind of outcomes those applications can produce, although they have a duty to provide a framework to ensure that AI complies with fundamental rights and can be used for the benefit of society; highlights that expert forecasts on the future impact of AI also vary, suggesting it might be difficult even for them to predict the outcomes of deploying new AI technologies; argues, therefore, that this uncertainty makes it necessary for legislators to take due account of the precautionary principle in regulating AI; believes it is crucial to consult experts with different expertise and backgrounds in order to create solid, workable and future-proof legislation; cautions that legal uncertainty can be one of the biggest impediments to innovation; notes, in this regard, the importance of promoting AI literacy among citizens, including elected representatives and national authorities;
86. Warns that legislative cycles are therefore often out of sync with the pace of technological progress, forcing policymakers to play catch up and favour the regulation of use cases already in the market; points out that a sound regulatory approach to AI must be preceded by an exhaustive analysis of proportionality and necessity, to avoid hampering innovation and the competitiveness of EU companies;
87. Stresses that using AI to acquire biometric data could be both intrusive and damaging or beneficial for the individual, as well as for the general public;
88. Notes with concern that such AI technologies pose crucial ethical and legal questions; notes that certain AI technologies enable the automation of information processing to an unprecedented scale, which paves the way for mass surveillance and other unlawful interference and poses a threat to fundamental rights, in particular the rights to privacy and data protection;
89. Stresses that many authoritarian regimes use AI systems to control, exert mass surveillance over, spy on, monitor and rank their citizens or restrict freedom of movement; stresses that any form of normative citizen scoring by public authorities, especially within the field of law enforcement, border control and the judiciary, as well as its use by private companies or individuals, leads to loss of autonomy and privacy, brings risks of discrimination and is not in line with European values; recalls that technologies such as cyber-surveillance and biometric recognition, which can be used to these ends, are subject to the EU Export Control Regulation; is highly concerned about and condemns cases of EU companies selling biometric systems which would be illegal to use within the EU to authoritarian regimes in non-EU countries;
90. Notes that dominant tech platforms nowadays not only have significant control over access to information and its distribution, but they also use AI technologies to obtain more information on a person’s identity, behaviour and knowledge of decisional history; believes that such profiling poses risks to democratic systems as well as to the safeguarding of fundamental rights and the autonomy of citizens; stresses that this creates an imbalance of power and poses systemic risks that could affect democracy;
91. Points out that digital platforms can, including through AI-driven marketing applications, be used for foreign interference and to spread disinformation and deepfakes, acting as networks for propaganda, trolling and harassment with the aim of undermining electoral processes; stresses that machine learning enables, in particular, the targeted use of personal data to manipulate unaware voters by creating personalised and convincing messages; stresses the importance of strong transparency obligations that are effectively enforced;
92. Underlines that AI could, however, also be used to reduce anti-democratic and unethical activities on platforms, and as a means to limit the distribution of fake news and hate speech, even though tests of its abilities to understand context-specific content have so far shown poor results; is concerned that divisive language may lead to greater user engagement, which is why removal of such language would be in direct conflict with such platforms’ business model which is based on maximising user engagement; is of the opinion that AI-powered solutions must be based on full respect for freedom of expression and opinion, and on strong evidence in their favour, before their eventual use;
93. Stresses that bias in AI systems, especially when it comes to deep learning systems, often occurs due to a lack of diverse and high-quality training and testing data, for instance where data sets are used which are not sufficiently representative of vulnerable groups, or where the task definition or requirement settings themselves are biased; notes that bias can also arise due to a possible lack of diversity in developer teams, reiterating intrinsic biases, due to a limited volume of training and testing data, or where a biased AI developer has compromised the algorithm; points out that reasoned differentiation is also intentionally created in order to improve the AI’s learning performance under certain circumstances;
94. Stresses that structural biases present in our society should not be repeated or even increased through low quality datasets; specifies, in this regard, that algorithms learn to be as discriminatory as the data they are working with, and, as a result of low quality training data or biases and discrimination observed in society, might suggest decisions that are inherently discriminatory, which exacerbates discrimination within society; notes, however, that AI biases can sometimes be corrected; concludes that it is therefore necessary to apply technical means and establish different control layers on AI systems, including the software, algorithms and data used and produced by them, in order to minimise this risk; argues that AI can and should be used to reduce biases and discrimination and promote equal rights and positive social change in our societies, including through normative requirements on data sets used to train AI systems; stresses that one of the most efficient ways of reducing bias in AI systems is to ensure, to the extent possible under Union law, that the maximum amount of non-personal data is available for training purposes and machine learning;
g) Recurring findings in all six case studies
95. Notes that there are clear societal benefits and opportunities associated with adopting AI technologies, which can only be reaped if transversal obstacles are addressed in the EU, in accordance with fundamental rights, values and legislation; states that overlap of legislation, market fragmentation, bureaucratic hurdles, a lack of accessible digital infrastructure and digital skills in the broader society, and insufficient investment in research and development can be observed in particular as barriers to the successful application of trusted AI in all fields analysed;
96. Concludes from the case studies examined, furthermore, that there are certain use cases that are risky or harmful, but that it is not necessarily specific AI technologies themselves but their areas of application; recognises that future regulation needs to address legitimate concerns related to these risks in order for AI technologies to find broad application in the EU;
97. States that while it is important to examine and categorise potential risks posed by AI, the case studies illustrated that AI technologies can provide us with effective countermeasures that are able to mitigate or eliminate these risks; underlines that as AI is still in its early stages of development within a wider context of emerging technologies, its full potential as well as its risks are not certain; points out that there is a need to look not only at risks to individuals, but also at the broader societal and non-material individual harms; highlights the significant imbalances of market power present in data markets and the adjacent AI economy; stresses that fair competition and removing obstacles to competition for start-ups and SMEs are essential to fairly distribute the potential benefits of AI in economic and societal terms, which appear to be significant both in the EU and globally;
3. The EU’s place in global AI competition
98. Observes fierce global AI competition, where the EU has not yet met its aspirations; examines in the following sections the EU’s global competitiveness with regard to AI by comparing it with that of China and the US, focusing on three core elements: regulatory approach, market position and investments; recognises, however, that transnational markets and corporations cannot easily be delineated across national borders, as most tech companies have customers, shareholders, employees and suppliers in many different countries;
a) Regulatory approach
99. Notes that the US has not yet introduced horizontal legislation in the digital field, and has so far focused on sector-specific laws and facilitating investments, including through tax measures on private sector innovation, in particular among its tech giants and leading universities; observes that, despite recent developments showing a more active policymaking role, the US approach has so far mostly reflected a focus on providing legal guidance to businesses, investing in research projects and removing perceived barriers to innovation;
100. Stresses that the 2019 American AI Initiative Act ushered in a slight realignment, as besides redirecting funding, retraining workers and strengthening digital infrastructure, the US Government announced the development of common standards for trustworthy AI; notes, however, that the resulting 10 principles were very broadly formulated in order to allow each government agency to create sector-specific regulations; expects that although the current US administration plans to bring forward a new bill of rights to limit AI harms in 2022, the US approach will remain market-driven;
101. Highlights that the Chinese President Xi Jinping underlined in as early as 2013 the importance of technologies in geopolitics, the role of public policies in defining long-term objectives and the fact that AI technologies offer an opportunity to relaunch its military power; stresses further that the Chinese Government subsequently put forward the Made in China 2025 plan in 2015 and the Next Generation AI Development Plan in 2017, both of which had the clear targets of making China the global leader in AI by 2030; notes that the 2018 Chinese AI standardisation white paper further outlined how the socialist market economy can develop international standards and strategically engage in international standardisation organisations; notes the introduction of rules on recommender systems as well as an ethics code on AI in China;
102. Observes that on the global stage, China actively promotes international AI partnerships as a way to export its own AI-based surveillance practices, social scoring system and censorship strategies; emphasises that heavy investment abroad under the Digital Silk Road initiative is also used as a means to spread Chinese influence and its AI technology globally, which could have far-reaching implications beyond imposing technological standards or maintaining technological competitiveness; concludes that the Chinese Government’s approach is therefore built upon deploying AI domestically as well as exporting AI technologies based on predetermined standards that are in line with the ideology of the Chinese Government;
103. Notes that the Commission started its work on regulating AI in 2018 by publishing the European AI strategy, setting up a High-Level Expert Group and introducing a coordinated plan ( 32 ) to foster ‘AI made in Europe’; notes that the 2020 white paper on AI proposed numerous measures and policy options for future AI regulation and eventually resulted in the horizontal AI Act ( 33 ) , which was presented along with a revised coordinated plan on AI ( 34 ) in May 2021; points out that as of June 2021, 20 Member States have published national AI strategies, while seven more are in the final preparatory stages of adopting theirs;
104. Emphasises that central to the EU regulatory approach is a strong attention to the development of a European digital single market as well as ethical considerations in line with core human rights values and democratic principles; acknowledges that establishing the world’s first regulatory framework for AI could give the EU leverage and a first-mover advantage in setting international AI standards based on fundamental rights as well as successfully exporting human-centric, ‘trustworthy AI’ around the world; underlines that this approach needs to be supported by regulatory coordination and convergence with international partners;
b) Market situation
105. Notes that many of the 100 leading AI companies globally are headquartered in the US, whereas only few are in the EU; notes that the US also leads in the total number of AI start-ups;
106. Points out that in recent years, several European digital companies have been acquired by US tech giants; welcomes the Commission’s ambition of tackling acquisitions that may have a significant impact on effective competition in the digital market and of limiting killer acquisitions; points out, however, that in some cases, acquisition may be a primary objective of start-up creators and their funders, as one legitimate method to derive benefits from their ideas;
107. Stresses that while the US and China are trying to accelerate the use of AI technologies in the public and private sectors, the adoption of AI within the EU lags behind; states that in 2020, only 7 % of EU companies with at least 10 employees were using AI technologies, with significant differences among Member States as well as among different business sectors;
108. Is concerned that while the US and China each have a unified digital market with a coherent set of rules, the EU’s digital single market is still not complete and unjustified barriers remain; stresses that the development of AI products and services could be further slowed down by the ongoing work on 27 different national AI strategies;
109. Points also to the fact that inconsistencies in EU law, overlap of different legislative initiatives, contradictions between EU and national laws, different legal interpretations and a lack of enforcement among Member States all prevent a level playing field and risk creating legal uncertainty for European companies as they may find it difficult to determine whether their AI innovations are compliant with EU law;
110. Notes that the market fragmentation for AI companies is further exacerbated by a lack of common standards and norms in some sectors, including on data interoperability; regrets the regulatory risk resulting from the delay of legislation, such as the ePrivacy Regulation; highlights as an example the fact that EU AI developers face a data challenge that neither their US nor Chinese counterparts do due to the incomplete European digital single market; observes that they often do not have enough high-quality data to train and test their algorithms, and struggle with a lack of sectoral data spaces and cross-sectoral interoperability, as well as constraints on cross-border data flows;
c) Investments
111. Observes that European companies and governments invest far less in AI technologies than the US or China; points out that although private investments in the EU AI industry are rising significantly, the EU is still substantially underinvesting in AI compared to other leading regions, as the US and China account for more than 80 % of the EUR 25 billion annual equity investments in AI and blockchain, while the EU’s share only amounts to 7 % or about EUR 1,75 billion; stresses that the liquidity of EU financing markets for tech companies still lacks the scale of comparable markets in the US; notes that the US is also leading in venture capital and private equity funding, which is particularly important for AI start-ups, with EUR 12,6 billion in 2019, against EUR 4,9 billion for China and EUR 2,8 billion for the EU; notes that as a consequence, European AI entrepreneurs are crossing the Atlantic to scale up their businesses in the US;
112. States that together with national initiatives, the estimated annual public investment of the EU in AI of EUR 1 billion ( 35 ) is much lower than the EUR 5,1 billion invested annually in the US and up to EUR 6,8 billion in China ( 36 ) ; states, however, that between 2017 and 2020, EU public funding for AI research and innovation increased by 70 % compared to the previous period, and in 2019, the EU invested between EUR 7,9 and 9 billion in AI, which was 39 % more than in the previous year; acknowledges and welcomes the Commission’s plans to increase investment further through the digital Europe programme, Horizon Europe, InvestEU, the European Structural and Investment Funds, the European Investment Fund, the Connecting Europe Facility in Telecom and various cohesion policy programmes, which will be further complemented and leveraged by the 20 % minimum expenditure target for digital transition in the national recovery and resilience plans, as agreed by the Commission and the Member States under the Recovery and Resilience Facility; underlines, however, the recent report by the European Investment Bank which quantifies the EU investment gap in AI and blockchain technologies at EUR 5-10 billion per year;
113. Stresses that AI companies within the EU face strong competition for qualified employees, which is made worse by 42 % of the EU population lacking basic digital skills; stresses the need to train and attract a substantially higher number of well-educated graduates, including women, to work in the digital sector;
114. Observes that although the EU has an excellent community of researchers on AI, the brain drain of EU researchers remains an issue; stresses that measures are needed to appeal to leading researchers; notes that the EU only spent 2,32 % of its GDP on research and development in 2020, while the US spent 3,08 %; recalls that the Member States must uphold their commitment to invest 3 % of their GDP in research and development in order to ensure the Union’s strategic autonomy in the digital field;
115. Notes that the EU’s digital infrastructure needs substantial updating, with just 25 % of people in the EU being able to connect to a 5G network, compared to 76 % of people in the US; observes that the EU lacks sufficient high-performance digital infrastructure with interoperable data spaces, high transmission rates and volumes, reliability and short delays; stresses the need to support European AI ecosystems with excellence clusters;
d) Conclusion
116. Concludes that the US is the overall leader in AI as it is ahead in many categories, with US-headquartered companies leading technology development in areas such as cloud computing and high-performance computing capabilities, and also when it comes to investment, attracting AI talent, research and infrastructure; highlights, however, that China, which a few years ago was still significantly lagging behind the US in all indicators, is quickly catching up; recognises that both countries have the advantage of a unified single market and stronger commitment to remaining a leader in AI;
117. Stresses that despite the EU’s strong position on industrial software and robotics, EU actors are still behind their US and Chinese peers in many categories; underlines that the EU should develop an ambitious plan for human-centric European AI; notes that the EU is, however, ahead on regulatory approaches; points out that a viable EU strategy for becoming more competitive on AI involves focusing on research and innovation, skills, infrastructure and investment, while at the same time trying to establish a future-oriented, horizontal and innovation-friendly regulatory framework for AI development and use, and simultaneously ensuring that fundamental rights of EU citizens and the rule of law are safeguarded;
118. Underlines that Brexit had a negative impact on the EU’s efforts to strengthen its global AI footprint, as the UK was one of the leading EU countries in AI; stresses, however, that the UK should remain a valued partner of the EU, bolstering the competitiveness of both partners and the promotion of shared regulatory outlooks in global standard setting;
119. Concludes that the EU is currently still far from fulfilling its aspiration of becoming competitive in AI on a global level, and could risk falling further behind in some categories; maintains that swift action on the EU Roadmap for AI outlined below poses an opportunity to change this situation;
120. Specifies that as the EU does not have the legislative power to address all the points listed in the EU Roadmap for AI, the special committee recommends pursuing further high-level discussions and political processes among EU institutions and Member States in order to push for a more harmonised approach to AI and help Member States to coordinate their efforts; refers, in this regard, to the EU 2000 Lisbon agenda, which, despite the criticism, played an important part in guiding the EU’s policy orientation over 20 years and in keeping up the pressure on Member States to reform;
4. ‘Europe fit for the digital age’ — Roadmap for becoming a global leader
a) Favourable regulatory environment
i. LAW-MAKING
121. Calls on the Commission to only propose legislative acts in the form of regulations for new digital laws in areas such as AI, as the digital single market needs to undergo a process of genuine harmonisation; is convinced that due to rapid technological development, digital legislation should always be flexible, principle-based, technology-neutral, future-proof and proportionate, while adopting a risk-based approach where appropriate, based on respect for fundamental rights and preventing unnecessary additional administrative burden for SMEs, start-ups, academia and research; stresses, furthermore, the importance of a high degree of legal certainty and, consequently, the need for robust, practical and unambiguous applicability criteria, definitions and obligations in all legal texts regarding the sale, use or development of AI technologies;
122. Believes that the better regulation agenda is key to making the EU AI strategy a success; stresses the need to focus on the review, adaptation, implementation and enforcement mechanisms of already existing laws before proposing new legislative acts;
123. Urges the Commission to perform in-depth ex ante impact assessments with adequate foresight and risk analysis prior to issuing new digital proposals in areas such as AI; emphasises that impact assessments should systematically map and evaluate relevant existing legislation, preventing any overlaps or conflicts;
124. Suggests that new laws in areas such as AI should be complemented with the promotion of stakeholder-developed European standards; is of the opinion that the EU should strive to avoid fragmentation and that international standards can serve as a useful reference, but that the EU should prioritise developing its own standards; highlights that such standards should result from fair competition for the best standards within the EU, which should be responded to by the EU and standardisation organisations; notes that technical standards and design instructions could then be combined with labelling schemes as a way to build consumer trust by providing trustworthy services and products; stresses the role of EU standardisation organisations in developing state-of-the-art technical standards; calls on the Commission to accelerate issuing standardisation mandates to the European standardisation organisations according to Regulation (EU) No 1025/2012 of the European Parliament and of the Council of 25 October 2012 on European standardisation ( 37 )
125. Explains that an open certification platform could establish an ecosystem of trust that involves governments, civil society, businesses and other stakeholders;
126. Calls for Parliament, the Commission and the Council to improve their abilities to deal with internal competence conflicts when it comes to overarching topics such as AI, as such conflicts risk delaying the legislative procedure, with knock-on effects in terms of the entry into force of the legislation;
ii. GOVERNANCE AND ENFORCEMENT
127. Calls for consistent EU-wide coordination, implementation and enforcement of AI-related legislation;
128. Explains that stakeholder-based consultation forums such as the Data Innovation Board, to be established by the Data Governance Act, or the European AI Alliance, which includes private-public partnerships, such as the European Alliance for Industrial Data, Edge and Cloud, are a promising governance approach; elaborates that this approach enables the EU’s AI ecosystem to operationalise its principles, values, objectives and reflect societal interests at the level of software code;
129. Highlights that the ‘pacing problem’ requires special focus on effective ex post enforcement by courts and regulatory agencies as well as ex ante approaches to deal with legal challenges posed by emerging technologies; supports, therefore, the use of regulatory sandboxes, which would give AI developers the unique chance to experiment in a fast, agile and controlled manner under the supervision of competent authorities; notes that these regulatory sandboxes would be experimental spaces in which to test AI systems and new business models under real world conditions in a controlled environment before they enter the market;
iii. LEGAL FRAMEWORK FOR AI
130. Highlights that an underlying objective of the EU’s digital strategy, as well as that of the AI strategy, is creating a ‘European Way’ in a digitalised world; clarifies that this approach should be human-centric, trustworthy, guided by ethical principles and based on the concept of the social market economy; underlines that the individual and the protection of their fundamental rights should always remain at the centre of all political and legislative considerations;
131. Agrees with the conclusion drawn by the Commission in its 2020 White Paper on artificial intelligence that there is a need to establish a risk-based legal framework for AI, notably covering high-level ethical standards based on transparency, auditability and accountability, combined with product safety provisions, appropriate liability rules and sector-specific provisions, while at the same time providing businesses and users with enough flexibility and legal certainty and a level playing field to foster AI uptake and innovation;
132. Points out the guiding added value of taking the concepts, terminology and standards developed by the OECD as inspiration for the definition of AI in legislation; stresses that doing so would give the EU an advantage in shaping a future international AI governance system;
133. Is convinced that it is not always AI as a technology that should be regulated, but that the level of regulatory intervention should be proportionate to the type of individual and/or societal risk incurred by the use of an AI system; underlines, in this regard, the importance of distinguishing between ‘high-risk’ and ‘low-risk’ AI use cases; concludes that the former category needs strict additional legislative safeguards while ‘low-risk’ use cases may, in many cases, require transparency requirements for end users and consumers;
134. Specifies that the classification of AI systems as ‘high-risk’ should be based on their concrete use and the context, nature, probability, severity and potential irreversibility of the harm that can be expected to occur in breach of fundamental rights and health and safety rules as laid down
| 2022-12-06T00:00:00 |
https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:52022IP0140
|
[
{
"date": "2022/12/06",
"position": 62,
"query": "AI economic disruption"
}
] |
|
Making an Impact with AI for Public Policy
|
Making an Impact with AI for Public Policy
|
https://www.nvidia.com
|
[] |
AI is revolutionizing cybersecurity, healthcare, platform sustainment, digital twins, disaster relief, and more in the public sector.
|
AI can provide value and differentiation for those who adopt it. Learn how the University of Florida is transforming into an AI university by incorporating AI across the curriculum and undertaking groundbreaking research that’s expected to impact our lives for years to come.
| 2022-12-06T00:00:00 |
https://www.nvidia.com/en-us/industries/public-sector/government-affairs/
|
[
{
"date": "2022/12/06",
"position": 5,
"query": "government AI workforce policy"
},
{
"date": "2022/12/06",
"position": 75,
"query": "generative AI jobs"
}
] |
|
IoT Careers
|
IoT Careers
|
https://www.verytechnology.com
|
[] |
At Very, we believe that IoT careers are the future. We're ready to work with problem solvers, creators, and visionaries that leverage IoT to spark radical ...
|
We believe that everything we build — and the people we build it with — have the potential to change the world. We take every opportunity we can to seek out the best in all that we do and to drive the success of our partnerships and our people forward.
You are more than your job title at Very. We prioritize talent development and professional growth with a human-first approach that caters to the unique career goals each individual brings to the team.
| 2022-12-06T00:00:00 |
https://www.verytechnology.com/iot-careers
|
[
{
"date": "2022/12/06",
"position": 27,
"query": "generative AI jobs"
}
] |
|
Job listings
|
Job listings
|
https://www.centroid.com
|
[] |
... Generative AI EBS VisionIQ · Oracle APEX · Oracle Cloud EPM & Hyperion Solutions ... Senior Oracle Apps DBA jobs. Location: Troy, MI. Consulting. Azure ...
|
Centroid | Who we are
| 2022-10-11T00:00:00 |
2022/10/11
|
https://www.centroid.com/jobs-listing/
|
[
{
"date": "2022/12/06",
"position": 54,
"query": "generative AI jobs"
}
] |
Analytics, Data and AI
|
Analytics, Data and AI | Avanade
|
https://www.avanade.com
|
[] |
Explore our data, analytics and AI portfolio. Artificial Intelligence and Generative AI ...
|
When everyone and everything is connected by data, Avanade can help turn your data, analytics and AI ecosystem into your biggest advantage.
| 2022-12-06T00:00:00 |
https://www.avanade.com/en-us/services/microsoft-tech/analytics-data-and-ai
|
[
{
"date": "2022/12/06",
"position": 57,
"query": "generative AI jobs"
}
] |
|
AI Film Festival: AIFF 2025
|
AI Film Festival
|
https://aiff.runwayml.com
|
[] |
AIFF is a celebration of the art and artists embracing new and emerging AI tools for filmmaking. Works showcased offer a glimpse at a new creative era.
|
AIFF 2025 Grand Prix Total Pixel Space Jacob Adler 9:28 Total Pixel Space contains every possible digital image, including films of your entire life, every life you never lived, and the lives of every creature or object that ever and never existed on or off the earth, from every possible angle.
AIFF 2025 Gold JAILBIRD Andrew Salter 2:49 JAILBIRD invites the viewer to experience the world through the eyes of a chicken as it goes through a transformative experience -- being sent to a human prison. The film highlights how projects like this can benefit not only animals, but the lives of those who care for them.
AIFF 2025 Silver ONE Ricardo Villavicencio & Edward Saatchi 6:18 After Earth collapses, a crew of transhumans follows the path of Voyager I to a distant planet, only to find it inhabited by lost souls desperate to reclaim human bodies again.
AIFF 2025 Honoree Distance Between Two Points Of Me Illya Dutsyk, Bogdan Nesvit, Anatolii Kasianov 2:27 The film contemplates what it means to lose a home, as many Ukrainians have. It gently walks through the emotional distance between presence and absence, grief and gratitude, showing how memory and hope help us stay connected to who we are.
AIFF 2025 Honoree More Tears Than Harm Herinarivo Rakotomanana 3:14 A sensory collage of childhood memories from Madagascar with present-day impressions. Fleeting moments quietly echo within.
AIFF 2025 Merit Fragments Of Nowhere Vallée Duhamel 8:04 A short film about the spaces between what's real and what's perceived. If reality is just another layer of perception, what happens when those layers collide? Time bends, places shift, and nothing holds still for long.
AIFF 2025 Merit RŌHKI - A Million Trillion Pathways Hachi and IO 5:38 A broken swordsman, a grieving immortal and a cosmic being entwine across timelines to stop an empire built on blood and memory. To save the only family he has left, Hachi must survive his past — and master the demon within.
AIFF 2025 Merit 6000 Lies Simon Rieth 5:06 It is the story of a detail that is missing, or that never existed. The image of a child we are looking for and trying to recreate. Thousands of lies to find the truth.
AIFF 2025 Merit Editorial Riccardo Fusetti 3:28 A bold and unexpected piece about femininity — contrasting the fun and playful with the surreal and horrific, the authentic with the fictitious — blending these elements into a wild, fever-dream rollercoaster.
| 2022-12-06T00:00:00 |
https://aiff.runwayml.com/
|
[
{
"date": "2022/12/06",
"position": 95,
"query": "generative AI jobs"
}
] |
|
Workforce Training
|
Hinds Community College
|
https://www.hindscc.edu
|
[] |
We offer two machining workforce trainings: CNC Machine Operator and Quality Technician. ... Work-Based Learning. Providing paid, on-the-job work experience ...
|
Workforce – 21st Century Skills
Our objective is to provide workforce training as well as career and technical education to the people located in Hinds CC’s district. It is our goal to ensure our population has the 21st century skills needed to secure, retain and advance in their chosen career pathway providing an improved quality of life.
| 2022-12-06T00:00:00 |
https://www.hindscc.edu/workforce/training
|
[
{
"date": "2022/12/06",
"position": 75,
"query": "machine learning workforce"
}
] |
|
AI Isn't Artificial or Intelligent
|
AI Isn’t Artificial or Intelligent
|
https://www.vice.com
|
[
"Chloe Xiang",
"Stephen Andrew Galiher",
"Luis Prada",
"Anthony Franklin Ii",
"Sammi Caramela",
"Brent Koepp",
"Matt Jancer",
".Wp-Block-Savage-Platform-Post-Byline Font-Family Var --Wp--Custom--Typography--Font-Family--Secondary",
"Font-Size Var --Wp--Custom--Typography--Font-Size--Sm",
"Font-Weight Var --Wp--Custom--Typography--Font-Weight--Bold"
] |
The biggest tech companies in the world imagine a near future where AI will replace a lot of human labor, unleashing new efficiency and productivity.
|
Krystal Kauffman has been a Turker for the last seven years. She works on Mechanical Turk (MTurk), an Amazon-owned crowdsourcing website that allows businesses to hire workers to perform various tasks for compensation. Many of these tasks, Kauffman says, have been training AI projects.
“In the past, we’ve worked on several large AI projects. So there are tasks where people just need to simply repeat the same phrase six times, so it’s training AI to recognize different voices and things,” Kauffman told Motherboard. “So I kind of get to do a little bit of everything, but there is definitely a lot of machine learning, AI data labeling out there right now. We’ve been seeing an increase in those requesters that are listing the work.”
Videos by VICE
Kauffman is part of the large labor force powering AI, doing jobs that include looking through large datasets to label images, filter NSFW content, and annotate objects in images and videos. These tasks, deemed rote and unglamorous for many in-house developers, are often outsourced to gig workers and workers who largely live in South Asia and Africa and work for data training companies such as iMerit, Sama, and Alegion. For example, Facebook has one of the most advanced algorithmic content moderation systems on the internet. That system’s so-called artificial intelligence, though, is “learning from thousands of human decisions” made by human moderators.
Large companies like Meta and Amazon boast robust AI development teams and claim that AI is at the forefront of their work. Meta writes “our future depends on our ability to leverage the newest AI technology at scale,” and Amazon encourages customers to “innovate faster with the most comprehensive set of AI and [Machine Learning] services.”
“We’re used to working on things that we just don’t know exactly what they are for […] we know we’re working on some of those big tech devices. And while I don’t need to be called an employee or anything like that, you very rarely hear a big tech company acknowledge the invisible workforce that is behind a lot of this technology.”
The biggest tech companies in the world imagine a near future where AI will replace a lot of human labor, unleashing new efficiency and productivity. But this vision ignores the fact that much of what we think of as “AI” is actually powered by tedious, low-paid human labor.
“I think one of the mythologies around AI computing is that they actually work as intended. I think right now, what human labor is compensating for is essentially a lot of gaps in the way that the systems work,” Laura Forlano, Associate Professor of Design at the Institute of Design at Illinois Institute of Technology, told Motherboard. “On the one hand, the industry can claim that these things are happening, you know, magically behind the scenes, or that much of what’s going on is the computing. However, we know that in so many different examples, whether we look at online content, how to operate an autonomous vehicle, or if we look at medical devices, human labor is being used to address the gaps in where that system really isn’t able to work.”
Humans like Kauffman, who help create the raw materials used to train these systems, rarely have insight into what their hard work will ultimately be used to create.
“We’re used to working on things that we just don’t know exactly what they are for […] we know we’re working on some of those big tech devices. And while I don’t need to be called an employee or anything like that, you very rarely hear a big tech company acknowledge the invisible workforce that is behind a lot of this technology,” Kauffman said. “They lead people to believe that AI is smarter and more advanced than where it actually is, which is [why] we’re still training it every single day.”
Tech companies hire tens of thousands of gig workers to maintain the illusion that their machine-learning algorithms are fully self-functional, and that each new AI tool is capable of solving a number of issues out of the box. In reality, AI development has a lot more in common with material production cycles than we realize.
“I think that the public doesn’t have a good awareness of the fact that this is a supply chain. It’s a global supply chain, it contains uneven geographic flows and relations. And that it is based on a huge amount of human labor,” Kelle Howson, a postdoctoral researcher on the Fairwork project at the Oxford Internet Institute, told Motherboard.
Howson can’t say for sure whether tech companies are intentionally obscuring human AI laborers, but that doing so certainly works in their interests. “I think that in some ways it supports their business models to do so because there’s this perception that the work is done,” said Howson. “You as a client access a platform interface, post your project, and the work is delivered immediately. It’s almost like magic. There was maybe never any human involved or [that’s what] it feels like, and so there’s a sense of efficiency. And that really goes along with the kind of narrative that Silicon Valley likes to tell. The disruption, the tech solutionism, the move fast and break things kind of ideas.”
Like other global supply chains, the AI pipeline is greatly imbalanced. Developing countries in the Global South are powering the development of AI systems by doing often low-wage beta testing, data annotating and labeling, and content moderation jobs, while countries in the Global North are the centers of power benefiting from this work.
“There are a lot more workers on microwork platforms in the Global South, compared to the Global North. So the majority of the labor supply on these platforms is concentrated in the Global South, whereas the majority of the demand is located in the Global North,” Howson said. “We know from experience with other supply chains, agri-food, textiles, that when there are these relations of outsourcing work to lower wage labor and low-income countries, often that goes along with exploitive relationships and poorer labor protections, poorer working conditions.”
In a 2021 paper on the role of global labor in AI development, AI ethics researchers argued that the current inclusion of workers from the Global South in the AI pipeline is a continuation of exploitative practices—not unlike the history of colonial exploitation, where Western states took advantage of people from the Global South and their resources for access to cheap, physically tolling labor to benefit their institutions and businesses.
“Essentially, people [in the Global South] often get paid what is labeled as a fair wage, just based on the GDP or the local income of their respective context. But the work is very rote and very manual and a bit tiring as well, even though you can’t obviously compare it to the physical labor that was done and the plantation work throughout the colonial days,” Chinasa T. Okolo, one of the paper’s authors and a Ph.D. student in the Department of Computer Science at Cornell University, told Motherboard. “But this work is being contracted to the same regions and similar companies as well. Content moderation definitely gets more troubling for the workers themselves, having to view different kinds of materials all day which is definitely mentally taxing for someone to be exposed to all the time. We have seen workers in these countries wage suits against employers or companies like Meta, for example, to challenge the working conditions they’re forced to be in.”
Image: Getty Images
In May, a former content moderator named Daniel Motaung filed a lawsuit in Nairobi, Kenya, accusing Facebook parent company Meta and its largest outsourcing partner, Sama, of forced labor, human trafficking, and union busting. In an investigation by TIME Magazine, Sama’s mission to provide poor countries with “ethical” and “dignified digital work” was quickly proven to be a facade of “participation-washing,” which is what researchers like Forlano define as companies including workers in “a post-colonial structure of global power” as a form of virtue signaling, rather than having them as meaningful, democratic partners. Motaung and other Sama employees told TIME that they were taking home as little as $1.50 per hour, and at least two content moderators were diagnosed with mental illnesses such as PTSD following their work viewing graphic images and videos depicting rape, murder, and dismemberment.
The researchers note that while labeling and content moderation companies like Samasource, Scale AI, and Mighty AI operate in the United States, their labor force relies heavily on low-wage workers from sub-Saharan Africa and Southeast Asia. “This leads to a significant disparity between the millions in profits earned by data labeling companies and worker earnings; for example, workers at Samasource earn around $8 USD a day while the company made $19 million in 2019,” authors including Okolo wrote in a 2021 paper. “While Lee notes that $8 USD may well be a living wage in certain areas, the massive profit disparity remains despite the importance of these workers to the core businesses of these companies.”
Companies like Meta justify the outsourcing of labor in less developed countries by claiming that doing what they call “impact-sourcing,” which is when companies intentionally hire workers from disadvantaged or vulnerable populations to provide them with opportunities they otherwise don’t have. But experts warn that behind this practice are unsafe and unethical working conditions that lack regulations and fail to redistribute power.
Sara Enright, project director at the Global Impact Sourcing Coalition (GISC), told MIT Technology Review, “If it is solely gig work in which an individual is accessing part-time wages through an hour a day here and there, that is not impact employment, because it does not actually lead to career development and ultimately poverty alleviation.”
Experts say that outsourcing these workers is advantageous for big tech companies, not only saving them money, but also making it easier for big tech companies to avoid strict judicial review. It also creates distance between the workers and the company itself, allowing it to uphold the magical and sophisticated marketing of its AI tools.
“If there are labor protections and the workers’ jurisdiction, it’s incredibly hard to enforce them, when the client is in another country, and the platform is in a third country,” Howson said. “They’re classified as independent contractors, so they have very little recourse to any local labor protections, and any legislative frameworks which would allow them to unionize or to engage in collective bargaining with the platforms.”
Due to this structural power imbalance, workers often lack the ability to speak out about their clients, whether about ethical concerns regarding the dataset they interact with or labor violations, such as the refusal of adequate pay.
“In addition to not getting an adequate wage, a lot of this labeling is rushed as well. In my personal experience using Amazon Mechanical Turk, I’ve had experiences with bots and people putting in spurious answers to questions. These dynamics definitely have influence on the quality of these data sets as well.”
When Motherboard asked crowd worker marketplace Clickworker, which markets AI Solutions as one of the main jobs on its platform, how it makes sure workers are able to vocalize any mistreatment received from a client, a spokesperson replied, “This cannot happen with us, because the Clickworkers have no contact with the customer and all handling is done by us.”
“It’s also a product of this geographical discrimination that is enabled by the planetary labor market, where clients and workers are directly kind of connecting in real-time forming these short-term contracts and then moving on,” Howson said. “As a micro worker, it’s not just incredibly difficult, but also probably not worth your while to contest these individual instances when they happen. Because you’re spending a couple of minutes on a task. If you’re not paid for that task, it takes so much longer to go through the platform’s due process, mediation mechanisms than it would to just move on to the next task.”
Kauffman is one of the workers leading Turkopticon, a non profit organization that advocates for Turkers’ rights and brings issues to Amazon. She is currently part of the organization’s effort to find justice against an AI company called “AI Insights” that put over 70,000 tasks on Mechanical Turk only for the company to reject all the completed work it received. This meant that the company could keep and see the work but didn’t have to pay the workers for their time. Kauffman said that after the work was rejected, the company promptly exited the platform, but its account still exists, which means if it chose to reactivate it, it could.
Normally, if a Turker’s work is rejected, they can contact the client and ask why, and in some cases revise and resubmit the task. But many Turkers who reached out to AI Insights and Amazon were either ignored or told nothing could be done. As a result, Kauffman says, many workers lost work and saw their approval ratings decrease, which makes it harder to find good-paying work on the platform.
“There were emails that were sent to Amazon about this particular situation, to which if a reply was given, it said that, Amazon doesn’t get in the middle of requesters and workers. And so they couldn’t and wouldn’t do anything about it,” Kauffman said. “We’ve been trying to get it out there that this is happening and we know that Amazon has the capability to step in and fix this and they just aren’t and that’s really frustrating.”
Kauffman explained that in order to sign up as a worker, she had to provide her social security, driver’s license, and banking information in order to verify that she was a legitimate worker. In comparison, she said, requesters can input fake names and emails, and aren’t held to the same verification standards. This power imbalance is largely what Turkopticon is fighting to remedy. Turkers demand the platform limit the number of rejections it will allow to affect a worker’s approval rate when a requester rejects all work and to consult with their coalition of worker forums to create solutions that improve MTurk for both requesters and workers.
“The overall rate at which Workers’ tasks are rejected by Requesters is very low (less than one percent), and Workers have access to a number of metrics that can help them determine if they want to work on a task, including the Requester’s historical record of accepting tasks,” An Amazon Web Services spokesperson told Motherboard. “MTurk continues to help a wide range of Workers earn money and contribute to the growth of their communities.” Amazon also said that it monitors mass rejections and that it has made improvements to its process since the AI Insights incident.
Howson told Motherboard that there is a lot of unpaid labor built into the cloud working economy on gig work platforms like Mechanical Turk and Clickworker. In addition to the low and unguaranteed compensations, workers on these platforms have to spend a lot of time refreshing their home screens, bidding and searching for jobs, and racing to be the first ones to accept the jobs if chosen. The number of cloud workers also far outweighs the amount of work available and creates high levels of competition on the platforms. Clients are thus able to easily take advantage of the accessibility and abundance of cheap work. This, too, disproportionately affects crowd workers in the Global South, who have a more difficult time accessing tasks and job opportunities and have a greater ratio of unpaid to paid labor time, according to Howson’s research.
Though this quick turnaround for work meets the industry model of creating more and faster, such practices also call into question how effective the work is for clients themselves. Low-paying conditions not only affect workers but also in turn sets up the greater probability that clients will receive lower quality work.
“When the workers especially on Amazon Mechanical Turk are assigned tasks, they are beholden to the person who creates a task for them to get paid,” Okolo said. “In addition to not getting an adequate wage, a lot of this labeling is rushed as well. In my personal experience using Amazon Mechanical Turk, I’ve had experiences with bots and people putting in spurious answers to questions. These dynamics definitely have influence on the quality of these data sets as well.”
“There are ways to certainly pay to ensure better quality. And that’s probably one of the first ways into thinking about this is that, to the extent that AI relies on consistency and quality, if you’re not paying for it, you’re not going to get that,” Jennifer King, the Privacy and Data Policy Fellow at the Stanford Institute for Human-Centered Artificial Intelligence (HAI), told Motherboard. “I think this is a lot of these incentives around how products are developed, where the engineering and the AI modeling pieces are the best paid and most desirable, [while] data labeling really takes a backseat.”
Kauffman wants to push back against the assumption that jobs like data labeling are “unskilled” just because are low-paid and involve repetitive tasks. She explained that workers are required to have certain qualifications and take tests in order to access certain tasks.
“People now have skills and knowledge that they didn’t have prior to taking this test and reviewing the materials. So this idea that people are uneducated, [after] we’ve learned so many different things about so many different topics is unreal,” Kauffman said. “There is a constant learning there where anyone couldn’t just sit down and do without picking up additional skills.”
Many technology users don’t realize that they are implicated in the AI pipeline as well. Most of us have performed unpaid labor in training AI systems, whether we are solving a CAPTCHA to prove we’re not a robot, or guiding autonomous cars away from roadblocks that the vehicle can’t identify. As a 2020 study co-authored by Forlano notes: “Users also improve the performance of ML models as they interact with them, a single unanticipated click can update a model’s parameters and future accuracy. This work sometimes is so deeply integrated into the ways in which users navigate the Internet that it is performed unconsciously, e.g. when using Google Maps and producing data movement patterns that enable traffic predictions. But other times it becomes more conscious, e.g. when classifying photos when completing a reCAPTCHA, or ranking Uber drivers.”
More recently, AI text-to-image generators like DALL-E 2, Midjourney, and Stable Diffusion, and language prediction AI models like GPT-3 have all demonstrated astonishing predictive capabilities. Yet these tools also benefit from the same relationship between human labor and AI training.
These large language models, which generate an image or produce text based on a user’s input prompt, are trained using deep learning methods. This means that these models are capable of mimicking the way our brains work by parsing through layers and layers of human-created data to come up with an appropriate image or text result. These datasets are all products of human labor.
For example, Stable Diffusion was trained on the LAION-5B open source dataset, a collection of 5.8 billion images and captions scraped from the internet. The images and captions are all products of unpaid human labor, from people coding and designing websites to users uploading and posting images on them. While the predictive frameworks of AI models such as Generative Adversarial Networks (GANs) have been advancing at an extremely rapid pace, the models have become so massive and complicated that their outputs are virtually impossible to explain. This is why bias and racist stereotypes are so common in the outputs of AI systems. When you build a system with billions of parameters and training examples, it simply mirrors the biases of the aggregate data—and a disconnect emerges between what the system was built upon and what AI experts hope it can do.
“There’s a gap between the knowledge that computer scientists have about the system and the knowledge that the sociologists might have about the system, in that they’re coming from very different places,” Forlano said. “And so even within communities that work on AI, there have been accounts in many mainstream newspapers that even computer scientists that work on these systems don’t always know how the systems are coming to the conclusions that they’re coming to.”
But Forlano emphasizes that the problem is more fundamental, and can’t be solved by simply adding more data to improve the system. “One of the logical conclusions that a computer scientist might come to is that if you just add more and correct data to the systems that ultimately they will become better. But that in itself is a fallacy. No amount of data is going to fix the systems.”
By highlighting the ways in which human labor underlines much of the AI pipeline, AI experts and researchers hope to dismantle the “move fast and break things” attitude that rules technological processes and exploits the underlying workers. Most people can agree on the fact that humans will always be part of AI, from developing models to checking for certain biases and errors. Thus, AI experts argue that the focus should be on how to decolonize the AI development process and include humans in an ethical and sustainable way.
“We need to think seriously about the human labor in the loop driving AI. This workforce deserves training, support and compensation for being at-the-ready and willing to do an important job that many might find tedious or too demanding,” Mary L. Gray and Siddharth Suri, authors of the book Ghost Work: How to Stop Silicon Valley from Building a New Global Underclass, wrote in a 2017 article for the Harvard Business Review.
Some of the concrete steps the authors recommend include requiring more transparency from tech companies, creating policies that improve working conditions and wages for data trainers, and providing workers with education opportunities that allow them to contribute to the AI models in ways besides labeling.
Marie-Therese Png, a doctoral student at the Oxford Internet Institute and research intern at DeepMind Ethics and Society, proposed in her research that the AI governance process needs to be restructured to include the Global South as a “co-governor.” This means acknowledging the colonial power asymmetries that are replicated in the AI pipeline, and giving actors from the Global South influence over “agenda-setting, decision-making, and resource power.”
This is similar to what Forlano and her co-authors argue in their paper. They describe a “design-with” mentality rather than a “design for” one, in which companies lack consultation and representation from groups that are affected by the AI system. “Experts do not often have a good understanding of how to design effective participatory processes or engage the right stakeholders to achieve the desired outcomes,” the authors wrote. “Participation workshops can become performative, where experts do not actually take the needs or recommendations of the different stakeholder groups into consideration.”
The study’s authors suggest that all participants in training AI should be recognized as work, which gives everyday users the ability to opt-in or opt-out of free online labor practices that would train a Machine Learning (ML) system. And if they choose to opt-in, they should be compensated accordingly or be provided with greater incentives. “People should be compensated for the work that they do to improve systems,” Forlano said. “And that if it’s not done in an equitable way, it’s just another kind of exploitation.”
| 2022-12-06T00:00:00 |
2022/12/06
|
https://www.vice.com/en/article/ai-isnt-artificial-or-intelligent/
|
[
{
"date": "2022/12/06",
"position": 4,
"query": "AI workers"
},
{
"date": "2022/12/06",
"position": 9,
"query": "artificial intelligence workers"
}
] |
What AI Orgs Can Do for Data Enrichment Workers
|
What AI Orgs Can Do for Data Enrichment Workers
|
https://partnershiponai.org
|
[] |
Data enrichment workers who clean, label, and moderate large sets of data are essential to machine learning (ML). Yet these workers often face poor working ...
|
Audience Questions
Question: What didn’t work about applying REC guidelines to use cases for data enrichment that seem to be working (for some aspects) for university researchers paying annotators working on datasets vis-a-vis a human subjects research framework?
Will: We spoke with various groups working with universities (contributing to this report from the Ada Lovelace institute which outlines some of the challenges). I also conducted some research which found that leading ML research involving data enrichment tasks via crowdsourcing was not engaging with IRB/REC processes (in industry or academia). The gap we found was that many researchers argue that data enrichment tasks (in research we see this in cases of labeling, evaluation, or production tasks) do not meet the definition provided by the US Common Rule, and therefore not in scope for IRB review (see recent discussion of this in Kaushik et al.). We also found that IRBs were not comfortable with reviewing data enrichment projects which involved employment contracts, because it impacted the ability for participants to engage in study without consequence (i.e. in contracted data enrichment projects, it is possible that a worker could lose their job for underperformance). These factors made us consider different approaches, and led us to the creation of the best practices and associated process (which works in parallel with our IRB process).
Question: As part of the ecosystem, what is the role of end users/consumers in the value chain?
Jessica: There is a history of end users / consumers playing an important role in raising awareness and expectations on issues they care about and ‘voting with their dollars’ for products and services that align with their values. And greater awareness and acknowledgement that human workers provide critical data enrichment services that power AI is a key part of catalyzing greater attention paid to the associated labor rights risks and concerns. So far, however, end consumers don’t seem to be a strong lever for awareness and change in this ecosystem since the B2B nature of data enrichment services make them further removed from an end consumer. That said, customers (e.g., the businesses that use AI powered products and services) alongside investors, governments, researchers, and other stakeholders (like workers themselves, and workers’ organizations) have clear and influential roles to play.
Question: To what extent has, or could, the AI industry learn from other sectors – for example agricultural and garment sectors – about the calculation of fair wages, working conditions and supplier monitoring?
Jessica: Definitely agree there is a lot of opportunity for the AI industry to learn from the journey that many other industries have taken with ‘responsible sourcing’ including to inform effective approaches and, importantly, to leapfrog missteps. Lessons on specific issues (like wages, precarity of contract work, etc.), processes for identifying and monitoring salient risks and partnering with suppliers to address them, and the opportunities for individual and collaborative action could be taken from e.g., the garment industry, electronics manufacturing, agriculture, business process outsourcing, etc., with acknowledgement of the key ecosystem differences (e.g., the digital and global nature of the workforce, task-based contract work, etc.).
| 2022-12-06T00:00:00 |
https://partnershiponai.org/event/responsibly-sourcing-data-enrichment-workers/
|
[
{
"date": "2022/12/06",
"position": 6,
"query": "AI workers"
}
] |
|
How To Become a Graphic Designer?
|
How to be a good graphic designer
|
https://amadine.com
|
[
"Belight Software"
] |
... AI-helpers (Artificial Intelligence) using skills. Many professionals fancy Mac as the more preferred, comfortable working environment for graphic design.
|
The Practicalities
Once you decide on an area of focus, you’ll be able to determine whether a Mac or Windows tool for graphic design is the most beneficial to you. In a web environment, you need to master Adobe Photoshop skills, and learn to create logos or icons with Adobe Illustrator. For any graphic designer, these applications for Mac and Windows are indispensable, as well as AI-helpers (Artificial Intelligence) using skills.
Many professionals fancy Mac as the more preferred, comfortable working environment for graphic design. To hone your skills creating graphics on a Mac, Amadine would be the perfect app for you. This all-in-one solution has the whole set of tools and art samples created by professionals, simplifying the work for both newcomers and masters of the field. And the cherry on the top is that Amadine offers an iOS/iPadOS version, which enables you to use it on your iPhone and iPad on the go, then see your works on your Mac desktop or laptop, thanks to Apple Continuity support.
Start by doing some simple projects on your own. There are lots of tutorials that can certainly help you boost your graphic design skills.
An important question you’ll likely consider at some point is: does a graphic designer need to be an artist at the same time? The reality is, in order to become a good designer, you need to at least have basic art skills and practice them whenever possible. Believe it or not, basic art skills, like sketching, color theory and drawing, will give you deeper insight into graphic design.
| 2022-12-06T00:00:00 |
https://amadine.com/useful-articles/how-to-become-a-graphic-designer
|
[
{
"date": "2022/12/06",
"position": 49,
"query": "AI graphic design"
}
] |
|
Good AI vs. Bad AI
|
Good AI vs. Bad AI
|
https://engineering.ucdavis.edu
|
[
"Noah Pflueger-Peters"
] |
Online life has become increasingly mediated by Artificial Intelligence (AI) ... User-First Design. While it's possible for an average user to manipulate ...
|
Online life has become increasingly mediated by Artificial Intelligence (AI).
Nearly 70% of all videos watched on YouTube are recommended by its AI algorithm, and that number is even higher on social media services like Instagram and TikTok. Though these AI algorithms can help users find content that’s interesting to them, they raise serious privacy concerns and there is growing evidence that people are being radicalized by some of the recommended content they consume online.
If this is what “bad AI” looks like, an interdisciplinary group of researchers at UC Davis are trying to develop “good AI” to push back against it and empower users with more control over their privacy and the content they’re recommended. Developing a holistic and customizable good AI is a difficult technical and social challenge, but one of the utmost importance.
“While working on the positive impacts of AI, I realized how it could also negatively impact societal and online usage patterns,” said Prasant Mohapatra, UC Davis vice chancellor, Office of Research and Distinguished Professor of computer science (CS). “Online sources are having a tremendous impact on our society and biases in the online algorithms will unconsciously create unfairness and influence the beliefs of people, as well as propagate false information and instigate divisions among various groups.”
After receiving funding from the Robert N Noyce Trust to work on a project related to AI, cybersecurity, privacy and ethics for public good, Mohapatra and his colleagues—CS Associate Professor Zubair Shafiq and Professors Ian Davidson and Xin Liu, Communication Professor Magdalena Wojcieszak and graduate students in each lab—decided to tackle the challenge.
“This is not some hypothetical problem that we’re dealing with. This is a real, real problem in our society and we need to fix it,” said Shafiq. “Every possible angle from which we can approach this problem and make a dent in it, short term or long term, is absolutely important.”
More Transparent Algorithms
From left: Professor Magdalena Wojcieszak, Ph.D. student Rong-Ching (Anna) Chang, Associate Professor Zubair Shafiq and Ph.D. student Hadi Askari. (Savannah Luy/UC Davis)
All major websites like YouTube, TikTok and Google have their own AI algorithms, and some even have multiple algorithms running at the same time. They can also interact with each other. If someone searches for new cars on Google, YouTube’s algorithm might notice this and show them car-related content next time they open their feed. This recommended content on YouTube could then influence recommendations on another website.
Most websites track users’ browsing activity to train their AI recommendation algorithms or sell the information to data brokers, who then combine it with publicly-available offline data to make inferences about a person’s interests. This helps advertisers place effective, targeted ads, but people don’t have control over what data brokers learn about them and what advertisements they see as a result.
Good AI’s job is to understand these complexities send out false signals that prevent bad AI from zeroing in on users’ interests, either to prevent harmful recommendations or to keep information private.
“One way to define privacy is the accuracy of certain attributes that someone infers about us—so you think you know something about me, but 80% of that is wrong,” said Shafiq. “Therefore, our goal is to maximize the inaccuracy of the inferences.”
The team’s first prototype does this for YouTube recommendations. After their recent study found that YouTube’s algorithm can potentially recommend increasingly extreme biased political content, the team built a tool that monitors the bias of recommended videos on a user’s homepage and injects politically-neutral video recommendations to help reduce the overall bias of their feed.
As they continue to develop that system, they are looking at the AI algorithms behind TikTok, Google and Facebook and plan to expand to more sites in the future. Not only will their studies help develop better good AI down the road, but it will also shed light on how these different websites use people’s data.
“These systems are opaque to the end user, who doesn’t really know what’s happening and what data they’re feeding off,” said Computer Science Ph.D. student Muhammad Haroon, who led the YouTube study. “One of the larger themes of this collaboration is to make these systems more transparent and give users a bit more control over the algorithm and what they’re being recommended.”
User-First Design
While it’s possible for an average user to manipulate algorithms in the same way good AI does, it requires more time, effort and education than most people have. Therefore, the easier and more seamlessly good AI can run, the more likely it is to be adopted.
“If the burden of using the tool is significant, the average user is just not going to use it,” said Shafiq. “If the good AI intervention can work automatically and baked into the devices, that is going to take the burden off the average user.”
The team also acknowledges that some people might want algorithms to recommend them relevant content, so they’re working to make good AI customizable. Shafiq’s vision is to eventually allow people to pick and choose which interests they want AI algorithms to learn about.
“We believe that every user has the right to control the algorithm that they’re interacting with,” he said. “This is quite challenging but technically achievable and something that we are actively working towards.”
Curbing the Wild West
Shafiq believes that ultimately, AI will be subject to government regulations, but it’s a slow process. Content creators rely on the algorithms to get views and targeted advertising is a $100 billion business that supports the platforms, advertisers and data brokers alike, which means companies don’t have much incentive to fix their AI algorithms.
However, with consequences like the growing antivaccination movement and the January 6th insurrection, Shafiq feels the team can’t wait for these processes to play out and hopes good AI is the short-term solution the world needs.
“Eventually, I think it is a public policy issue and we will, as a society, realize that that’s how we need to approach this, but in the meanwhile, we cannot let this be a wild west,” he said. “We’re working on this somewhat controversial idea not just because we want to do it, but because we believe we have a social responsibility to tackle this problem.”
This story was featured in the Fall 2022 issue of Engineering Progress.
| 2022-12-06T00:00:00 |
2022/12/06
|
https://engineering.ucdavis.edu/news/good-vs-bad-ai
|
[
{
"date": "2022/12/06",
"position": 69,
"query": "AI graphic design"
}
] |
[AI Team Success] Getting Culture and Talent Right
|
The AI in Business Podcast: [AI Team Success] Getting Culture and Talent Right
|
https://podcast.emerj.com
|
[] |
The AI in Business Podcast is for non-technical business leaders who need to find AI opportunities, align AI capabilities with strategy, and deliver ROI.
|
Today's guest is the Director of Engineering, Telecommunications Orchestration, Analytics, and Automation at Google, Mazin Gilbert. Mazin's career is one that has seen its fair share of enterprise teams facing the challenges of legacy data stacks as they enter the age of AI, including AT&T. In discussing his work at Google with Emerj CEO Daniel Faggella, he talks about what he sees on the 'other side of the fence' in a tech company environment. He also shares critical insight on the difference both cultures need from leadership to achieve successful ROI. If you've enjoyed or benefited from the strategies shared in this episode, consider leaving us a five-star review on Apple Podcasts, and let us know what you learned, found helpful, or liked most about this show!
| 2022-12-06T00:00:00 |
https://podcast.emerj.com/ai-team-success-getting-culture-and-talent-right-with-mazin-gilbert-of-google
|
[
{
"date": "2022/12/06",
"position": 17,
"query": "artificial intelligence business leaders"
}
] |
|
Recognizing the Risks of Job Automation in 2023
|
Recognizing the Risks of Job Automation in 2023
|
https://techrseries.com
|
[
"Shafaque Ansari"
] |
Meanwhile, an OECD study found that the rate of job loss due to automation is only 14%. (Nedelkoska, L. and Quintini, G., 2018). According to the OECD ...
|
The Great Resignation forced businesses everywhere, from retail to the supply chain and logistics industries, to face severe labor shortages. The figures are grim, with 40% of workers in 31 global markets deciding to quit at an all-time high. Despite the fact that over 75 million Americans will be hired by 2021, nearly 70 million will leave. (2022, Deloitte) This begs the question: is the job loss predicted by the University of Oxford in 2013 finally becoming a reality?
Maybe not.
Americans quit their jobs thanks to low pay, a lack of advancement opportunities, and a sense of disrespect (Parker & Juliana Menasce Horowitz, 2022). In contrast, the University of Oxford conducted a study titled The Future of Employment: How Susceptible Are Jobs to Computerization? Computerization and automation are expected to cause job losses. Frey and Osborne (2013) So far, records show that workers are leaving on their own volition, rather than being forced out by robots, machine learning, and automation.
Recommended HR Tech Blog: Leadership Theories and Styles for Every HR Manager
The Oxford study has been questioned, critically examined, and thoroughly investigated numerous times for potential gaps. In 2018, its authors stated that the study only addresses one aspect of work and cannot predict how many jobs will be automated or whether other factors will be considered. While automation is replacing certain human tasks, the World Economic Forum believes that how people deal with the change will determine its impact. That task now is to train the workforce for future work rather than to protect jobs that computers can do better. 2022 (Advaithi) As a result, people must be prepared to thrive in this new environment.
Findings from an Oxford Study on the Future of Employment
The Oxford study’s premise is simple: if a machine (a.k.a. AI, machine learning, robotics, computer, etc.) can automate a job, humans lose that job. The study used predictive modeling to categorize 702 jobs as having a high, medium, or low risk of computerization and concluded that 47% of them can be replaced by machines. Overall, routine jobs with little creative or interpersonal demand are the most vulnerable to automation.
The loophole in the study
The report’s macroeconomic picture was ambiguous. On the one hand, it is undeniable that jobs have been and will continue to be lost as a result of automation. On the other hand, economists predict that certain jobs will be created by technologies and improved production as a result of automation.
Consumers get contentment from seeing specific services conducted by humans, so robots taking over these jobs in the near future are unlikely. However, cultural and public opinion shifts may allow automation to become a reality even in these industries.
Jobs Likely to be Replaced by Robots
According to the Oxford University study findings, the first sectors to be impacted by automation are transportation and logistics, office and administration, and production labor. The study also discovered a high likelihood of automation in the service, sales, and construction industries. Other industries that are being replaced by machines include farming, fishing, and forestry, as well as installation, maintenance, and repair. Some STEM careers may also be jeopardized, as there are some tasks that would benefit greatly from automation and robotic maneuvers, particularly when dealing with hazardous materials.
The following are the top ten jobs that are most likely to be automated:
Telemarketers.
Examiners of titles, abstractors, and searchers
Hand Sewers
Mathematical Technicians.
Underwriters of insurance
Watchmakers
Cargo and freight brokers
Preparers of tax returns.
Photographic processing workers and machine operators
New accounts clerks.
Jobs that are the Least Likely to be Replaced by Robots
Jobs that operate in an unstructured environment or do not follow a strict routine are said to be immune to automation. These jobs frequently necessitate a high level of creativity and subjective input, areas where even the most advanced computers fall short. These jobs may also rely on social skills and interpersonal relationships, in other words, human characteristics that no robot is expected to assume anytime soon. Social science majors’ jobs are among the least likely to be taken over by robots.
The following are the top ten jobs that are least likely to be automated:
Recreational therapists
Supervisors of mechanics, installers, and repairers on the front lines.
Directors of emergency management.
Mental Health and substance abuse social workers.
Audiologists.
Occupation therapists
Prosthetists and orthotists
Social workers in healthcare.
Maxillofacial and oral surgeons
Supervisors of firefighting and prevention personnel on the front lines.
Top HR Tech Insights: Unlocking Business Success With Data-Driven Leadership
What Other Research Says
Two studies that contradicted the Oxford study’s “47%” claim stand out for arriving at a much lower risk of job loss. According to a ZEW Mannheim study, when all occupational variables are considered, only 9% of jobs are likely to be lost to automation (Arntz, M., et. al, 2017). According to the Mannheim researchers, the share of automatable jobs fell significantly when the “heterogeneity of tasks” was considered, not only across different jobs but even within a single job role. They argue, for example, that one telemarketer does not face the same risk of job loss due to automation as another. To assess the risk, the authors used gender, age, educational level, and income.
However, the Oxford study researchers believe that a machine capable of doing the job will not discriminate against the demographic variables of its human counterpart. Otherwise, “a female taxi driver with a Ph.D. is less likely to be displaced by a self-driving car than a man who has been driving a taxi for decades,” they reasoned. They claimed that the analysis was flawed.
Meanwhile, an OECD study found that the rate of job loss due to automation is only 14%. (Nedelkoska, L. and Quintini, G., 2018). According to the OECD researchers, an additional 32% of jobs are at risk of being significantly altered but not lost as a result of automation. Unlike the Mannheim study, the authors of the OECD study did not include demographics, which may account for the higher job-loss rate (9% vs. 14%).
Nonetheless, the OECD figure is significantly lower than the 47% found in the Oxford study. While the Oxford study considers a job to be fixed and rigid across different scenarios, ignoring variables (e.g., a truck driver is a truck driver regardless of his income, gender, or educational attainment), the OECD study assumed other factors are at play that will affect a job’s exposure to automation. The OECD authors, however, did not elaborate on what these variables are, as the Oxford authors pointed out.
Regardless, all three studies agree on one point: the nature of tasks and the capabilities of today’s computers can be used to predict the impact of automation on employment. The more repetitive the tasks, the easier it is to automate them, resulting in a “robot” taking over the job. In contrast, there is a general consensus that creativity and interpersonal skills appear to be the more potent antidote to a robot taking over your job. A sociology degree would be useful in this case because it requires an understanding of societies that a machine may not be able to achieve.
HR Tech Insights: HR Tech Interview With Josh Brenner, CEO at Hired
People Are Not Quitting Due to Automation
The data also calls the Oxford study into question. According to a Payscale poll from 2019, 25% of workers are looking for a higher salary elsewhere, 16% are looking for a new job because they are unhappy, and 14% want to work for a company that shares their beliefs. 11% planned to relocate, while 2% desired greater flexibility. McCarty (2019)
A more recent discovery appears to support Payscale’s findings. According to a Pew Research Center poll, the reasons workers leave their jobs are inadequate pay (63%), a lack of opportunities for advancement (63%), and feeling disrespected (57%). Furthermore, nearly half of parents with children under the age of 18 quit their jobs due to childcare issues. Lack of flexibility in choosing when they work (45%), as well as a lack of benefits such as health insurance and paid time off (43%), are also concerns. (2022, Parker and Juliana Menasce Horowitz)
Conclusion
The one point on which all studies agree is that automation will alter the labor landscape. Will a robot replace you? Yes, if your tasks are repetitive, structured, and repeatable by a machine. However, you do not have to be jobless in the future.
[To share your insights with us, please write to [email protected]]
| 2022-12-09T00:00:00 |
2022/12/09
|
https://techrseries.com/hrtechnology/recognizing-the-risks-of-job-automation-in-2023/
|
[
{
"date": "2022/12/07",
"position": 1,
"query": "AI job losses"
},
{
"date": "2022/12/07",
"position": 6,
"query": "AI replacing workers"
},
{
"date": "2022/12/07",
"position": 26,
"query": "future of work AI"
}
] |
Is AI Going To Impact The Consulting Industry In 2023?
|
Is AI Going to Impact the Consulting Industry in 2023?
|
https://consultingquest.com
|
[] |
Ultimately, if used correctly, AI can empower rather than replace human workers, making consulting jobs safer now than ever before. What AI Cannot Replace?
|
Every generation had its fear of a particular technology. In modern history, this fear of psychosis dates back to the days of the first industrial revolution and represents the uneasiness that humans feel about the disruption of any kind. AI (artificial intelligence) is the most-feared technology of our time.
Over the decades, there have been movies and literature, fueling people’s imagination and subsequent insecurity about a world where machines dominate humans. For an industry that capitalizes on human knowledge as its primary asset, the proposition of artificial intelligence replacing human intellect is not very comforting.
Is this fear totally ungrounded or is there any basis for it? And there is the volley of takeovers of AI companies by consulting firms! What does this trend suggest? What is Artificial Intelligence going to bring to the consulting landscape — disruption or evolution? This Insight attempts to visualize the future in light of what happened over the past months of this year.
How AI Fits Into Consulting Right Now?
In the digital age, Artificial Intelligence has become an extremely popular tool for businesses of all sizes including the consulting industry as well. Most Artificial Intelligence solutions not only provide valuable insights that can help companies save time and money, but they are also able to automate tedious tasks and circumnavigate many barriers in a company’s operations.
It is unsurprising then that more consulting firms are starting to include AI solutions as part of their services. However, in spite of its potential benefits, it is understandable that people, including consultants, are concerned that Artificial Intelligence could end up displacing many human jobs as businesses become increasingly dependent on robots and machines.
Yet while this fear may have been justified when it was first voiced in 2017, subsequent advancements in the field of AI have revealed that partnership between humans and machines can create a mutually beneficial situation for both individuals and businesses.
Humans bring the creativity and emotional intelligence needed to reach out to customers, whereas AI can provide valuable data-driven insights with speedier turnaround times than any manual process could achieve.
For consultants, this means being able to deliver even better services by bringing together the best of human and machine capabilities into their craft – making them even more attractive prospects for clients looking for unparalleled insight into their business operations.
Ultimately, if used correctly, AI can empower rather than replace human workers, making consulting jobs safer now than ever before.
What AI Cannot Replace?
Despite the clear benefits of leveraging Artificial Intelligence in consulting, with its speed and efficiency, human consultants are still invaluable. Artificial Intelligence in consulting should be seen as an opportunity to increase human capabilities instead of taking them away.
For example, Artificial Intelligence can crunch data faster than anyone and present trends and insights that would take humans days or weeks to discover. This newly freed time can then be spent thinking up creative strategies, analyzing risk profiles and giving clients a better understanding of their problems.
In this way, we can empower consultants to provide personalized advice that advocates for their clients’ interests at all times. Put simply, if you’re looking for passionate strategizing and insightful problem solving, you’ll need human professionals – not Artificial Intelligence – behind the wheel.
Artificial Intelligence may simplify tasks and allow us to work faster but it’s no replacement for a savvy consultant who won’t rest until they have helped their client get the best outcome possible.
At the end of the day, it is humbling to know that no matter how advanced our technology becomes nothing can replace human creativity, intuition and experience in such a complex field as consulting is.
That’s why AI should be viewed as an ally – not an enemy – so that both monovalent forces can collaborate towards achieving outstanding results on behalf of clients everywhere.
So, the question is, what Artificial Intelligence cannot replace when it comes to consultants? Let’s have a look!
#1. Human Skills
Artificial intelligence technology has come a long way, with computers now being programmed to learn and act independently. But as impressive as this might be, Artificial Intelligence currently lacks the nuanced skills necessary to build meaningful business relationships with people.
As humans, we have the innate ability to persuade, negotiate and connect based on something more than just logic and raw data; we put into practice techniques that rely on empathy, understanding and relating to individual perspectives.
This is where AI falls short because it is still not advanced enough to understand emotions such as fear or love or excitement – these human elements aren’t programmed into an AI system’s “vocabulary” yet.
Without these qualities, there is no way for a computer system to actually engage with another person from a business perspective in a genuine, meaningful way. That’s why in its current form, AI will always remain unable to handle certain scenarios in which relationships need to be forged between humans.
Things such as pitching an idea or making a sale are all tasks best left up to humans – at least for now. Still though, Artificial Intelligence can make decisions and offer recommendations rapidly (and sometimes more accurately) than humans so there may yet be ways in which we see profitable collaborations between man and machine prospering together in the future.
#2. Contextual Reasoning
Artificial Intelligence is becoming increasingly powerful, so it’s understandable why some people believe that it could eventually replace human consultants. However, AI still lacks a certain level of emotional intelligence and contextual reasoning that humans possess.
Human consultants are able to take multiple factors into account, such as the goals and desires of their clients, the business culture in a given area, and even the current tone in the industry. AI can process data to an impressive degree, but when it comes to arriving at optimal conclusions it often falls short.
Artificial Intelligence requires specific data as input before offering any advice or making critical decisions – something that’s often not available in business scenarios. As such, while AI plays an ever more important role in business services due to its robustness with numbers and calculations, it is unlikely to ever fully supplant human consultants whose talents lie in understanding context and nuance.
#3. Trust & Reputation
It’s no secret that artificial intelligence is transforming the way we do business. From chatbots to predictive analytics, AI-driven solutions are revolutionizing industries.
However, one area in which automation has not been able to compete with traditional consultancy services is trust and reputation. For many clients and businesses, the trustworthiness of an AI solution simply cannot compare to that of an established, successful consultancy firm.
This is largely due to the fact that older generations still hold significant roles within businesses and may remain hesitant to invest in something they don’t quite understand or lack experience with. Artificial Intelligence may be powerful but still falls short on reputation and trust — two key factors in making a purchase decision for any service or product.
Despite its potential for automation, AI will likely remain second in status to conventional consultants until it can gain equal recognition and trust from clients around the world. Until then, consultants rule supreme when it comes to reputation and trust.
Then, How Is AI Disrupting the Works of Consultants?
As the world of business keeps evolving at a rapid pace, it is embracing innovations to fulfill its short and long-term needs. Artificial Intelligence is one of the innovations that are poised to disrupt businesses and lives. Besides, manufacturing, life sciences, marketing, and finance, AI is already heavily in the consulting space. So what are the outcomes of the marriage between technology and consulting?
First of all, the Artificial Intelligence unleashed competition between technology leaders and traditional strategy consultancies. In this situation, consulting forms must understand the process of disruption in order to succeed.
The efficacy of AI lies in its ability to process a humungous amount of data and transform them into actionable insights. It can also play a critical role in churning out customized solutions for specific regions, countries and so on. Here, technology leaders play the upper hand due to vast data sets, capital abundance, and expertise in using AI to solve complex problems.
This phenomenon is highlighted by initiatives such as Google’s Advanced Solutions Lab, which provides clients with consulting services that draw on their Artificial Intelligence capabilities. It is clear that these tech firms have the upper hand when it comes to solving challenges quickly and effectively.
However, traditional consultancies are the best option for small and medium businesses that may not have access to the same data or resources as larger organizations. In either case, it is important to recognize that disruption in consulting is an ongoing process — strategies must remain fluid to stay ahead of the pack.
With these considerations in mind, both tech firms and consultancies can turn disruption into a viable business opportunity instead of merely a threat.
So, What Can Consulting do To Ride the AI Wave?
One thing it surely cannot afford to do is it cannot be hostile to changes. Companies in all industries are facing constant disruption, both from large-scale global events as well as smaller scale upheavals. This can create great uncertainty and risk in uncertain times, but there are strategies to help mitigate these risks or so-called challenges.
One of the most promising strategies is self-disruption — proactively reinventing a business model before disruption renders traditional approaches obsolete. Another strategy is mergers and acquisitions (M&A). M&A allows businesses to acquire new technology, skills, capabilities and expertise to position them ahead of the curve when it comes to disruption.
Finally, partnership models such as joint ventures and alliances offer an effective way for businesses to collaborate with each other towards mutually beneficial goals.
All three of these approaches have their own set of merits and demerits, yet they all have the potential to reduce risk and increase resilience against disruption if implemented correctly. Therefore, it is certainly possible for companies to protect themselves from imminent disruptions through self-disruption, M&A and partnerships.
Now, let’s have a look at them in a bit more detail.
#A. Self-Disruption
Self-disruption often poses a challenge for consulting firms, as they must both stay relevant and keep up with technological advancements. One way to accomplish this is to strengthen their human element in artificial intelligence efforts by recruiting highly sought-after data scientists.
BCG Gamma is an excellent example of this strategy. This in-house team of 300 specialized technical members works closely with clients to determine the best technologies to use when building AI capability. This forward-thinking initiative proves that the human element can play a pivotal role in helping consulting firms remain competitive.
By bringing on professionals who are experienced in big data modelling and other key components of AI, these companies can ensure that their services remain cutting-edge and help them understand increasingly complex client issues for better problem solving. Through this kind of recruitment and self-disruption, consulting firms can stay ahead of the rapidly changing technology landscape.
#B. Mergers & Acquisitions
Mergers and acquisitions (M&A) are often seen as a successful way for consultancies to reach the heights of their respective industries. In 2015, one such example occurred in the form of McKinsey & Company acquiring UK-based QuantumBlack.
This acquisition showed the capability of both firms and indicated how much professional potential they had. Through procuring QuantumBlack’s access to AI development and big data technology, McKinsey was now able to give its clients further insights and capabilities that would otherwise be unavailable.
The combination of both companies’ expertise has been described as pioneering by many and is continuing to provide a plethora of knowledge for countless organizations around the world today.
It stands out as a prime example of how M&A could benefit a consultancy looking for ways to enhance success, particularly when such mergers complement each other so closely. As such, it is clear that modern consultancies should consider this route if seeking to reach their highest potential.
Moreover, some recent examples of consulting firms acquiring AI companies include Deloitte’s acquisition of SFL Scientific’s Business to enhance its cutting-edge AI and science-based capabilities. The expansion enables Deloitte to offer a broader range of services to its clients and provide a unique opportunity to bring a more cost-effective approach to data science, analytics, and machine learning technologies.
Another example would be Accenture’s acquisition of Japanese big data and AI consultancy ALBERT. This acquisition enables Accenture to develop cutting-edge solutions that are tailored to the needs of clients around the world. By combining ALBERT’s deep knowledge of Japanese culture, technology and markets with Accenture’s global platform, clients in both countries will benefit from an expansive network of integrated services across industries.
A few other examples include: Boston Consulting Group’s acquisition of AI powered software firm Foundation, Deloitte Canada’s purchases of AI consultancy Dataperformers Company Inc and Groundswell Group, etc.
As you can see, a growing number of consulting firms are recognizing that AI technologies are transforming their services and they have taken steps to benefit from these developments. This includes acquiring existing AI companies, who bring with them a wealth of knowledge and experience when it comes to applying AI concepts to solve real-world problems.
Once incorporated into their existing portfolio, these acquisitions will no doubt lead to an explosion in AI-driven consulting services, enabling the firms to provide much more complex and personalized advice.
From forecasting customer trends, to automating operational processes or diagnosing potential problems in a fraction of the time it would take with traditional methods, there is huge potential for consulting firms that embrace and implement AI technologies.
As such acquisitions continue apace, the wider consulting landscape is sure to be drastically changed by this influx of cutting-edge capabilities. Ultimately, equipped with a deep understanding of machine learning algorithms, data science techniques and other advanced analytics toolsets – combined with their current capabilities – consultants will be able to extract maximum value from clients’ businesses through highly informed policies and strategies.
The implications are vast; both now and in the future, organizations should keep up-to-date on advances being made in the world of consultancy technology if they want to remain competitive.
That said, AI by itself is not a silver bullet; without strong ethical guidelines and effective human oversight blocking any potentially dangerous applications – such as those related to biasing decisions based on predictive models – even powerful tools like those coming out of consulting firms will deliver minimal value for businesses.
Nonetheless, as advisors prepare for this next era shift it is safe to assume that organizations everywhere can expect exciting new opportunities for growth brought about by innovative ways of using technology together with expert insights powered by Artificial Intelligence developments led by consultancy firms.
#C. Strategic Partnerships
Strategic partnerships are another way for consultancies to grow and reinvent themselves. When organizations work together, they have the opportunity to learn new methods, technology and approaches that can help them expand their business offerings.
For example, if a consultancy is looking to break into a new sector such as healthcare or cybersecurity, they may partner with a local business which specializes in that field in order to gain access to specific skillset and industry best practices. This kind of partnership requires good communication between both businesses in order to understand each other’s strengths and weaknesses while also leveraging one another’s networks.
Furthermore, by collaborating regularly, consultancies are able to build trust with their partners which can lead to long-term relationships and future opportunities for growth. In today’s competitive market, developing such mutually beneficial relationships is essential if a consultancy wants to stay competitive and thrive.
By leveraging strategic partnerships, consultancies can increase their range of services as well as refine their offering by building on proven practices from industry experts. In this way, partnerships can be an important tool for helping consultants increase their market reach and operational efficiency at the same time.
Some examples of strategic partnerships include: Booz Allen Hamilton and Kion signing a strategic partnership to help government clients with complete cloud management and governance capabilities.
Bain & Company signing a strategic partnership with Trinity Life Sciences to bring together the two companies’ expertise, resources and capabilities in order to create a more comprehensive approach to addressing health care system challenges.
Another example would be Deloitte and Creatio announcing a strategic partnership to enable more businesses worldwide in order to access the full potential of no-code tools.
Quick Round-up
While examining the question, of how profoundly AI would impact the consulting industry, we must acknowledge the fact that AI is already revolutionizing the way data is garnered and analyzed by consultants.
The way consulting and AI intersect one another today indicates a clear trend — firms are using AI with an aim of improving their performance. In the coming days, it is expected that consulting firms will be using AI even more for projects that require a lot of data and analysis.
So, we can safely conclude that AI would trigger the evolution of the consultancy sector, helping it generate reliable insights while automating a significant part of the process. That does not mean that the replacement of human skills and knowledge is going to happen anytime soon.
Nevertheless, moving forward, AI may play a more critical role in automating complex decisions or assisting consultants to arrive at a decision to optimize value from the consulting spend. But there are no signs of its happening in the immediate future. It may take another 50 years for AI to become a disruptive force of that magnitude, as our co-founder and chairman, Laurent Thomas put it.
| 2022-12-07T00:00:00 |
2022/12/07
|
https://consultingquest.com/insights/is-ai-going-to-impact-consulting/
|
[
{
"date": "2022/12/07",
"position": 18,
"query": "AI replacing workers"
},
{
"date": "2022/12/07",
"position": 26,
"query": "artificial intelligence business leaders"
}
] |
Could Your HR Job Be Automated in the Next 10 Years?
|
Could Your HR Job Be Automated in the Next 10 Years?
|
https://www.aihr.com
|
[
"Dieter Veldsman"
] |
Various HR tasks previously performed by humans are becoming automated. For example, recruitment technology such as asynchronous video interviews (AVIs) and ...
|
What does the future hold for HR jobs?
Rapid adoption of tech is redefining the workplace and HRs role within it. This is further accelerated by implementation of remote work models across organizations, which has pushed HR to the forefront of the tech revolution.
As automation becomes ubiquitous and companies continue to face market uncertainties and cost pressures, a new question emerges:
Are HR jobs at risk of becoming automated? And, if so, what does that mean for yours?
Contents
The state of automation in HR
Automated HR jobs: Which are safe and which are at risk?
1. HR roles at high risk of automation
2. HR roles at moderate risk of automation
3. HR roles at low risk of automation
How the research was conducted
The state of automation in HR
AI taking over job roles may sound a little far-fetched, but data points to a staggering 270% growth in AI use across industries. In the US alone, organizations are implementing a record number of AI robots in a bid to reduce unfilled vacancies. Similarly, in Europe, 34% of jobs could be severely impacted by automation, with 12 million jobs lost altogether by 2040.
The field of Human Resources will not be immune to these developments. Various HR tasks previously performed by humans are becoming automated. For example, recruitment technology such as asynchronous video interviews (AVIs) and automated resume screeners help businesses significantly reduce time-to-hire. Beyond recruitment, businesses also utilize HR technology to help with employee onboarding, leave and expense management, payroll, benefits administration, employee performance tracking, and other processes.
As HR technology continues to expand, companies will continue to automate more HR tasks. However, which roles will be impacted the most will be determined by the type of position, complexity, and seniority level.
Change brings opportunity
If history has taught us anything, it is that there is always an opportunity presented with technological advancements. In the 18th century, the industrial revolution resulted in an economic transformation that altered the way people worked. As factories grew, new roles became available, and factory workers were trained in specializations that hadn’t existed before.
This rapid scaling of automation opens up a window of opportunity for HR professionals to evaluate which roles could fall away in the next 10 years and where the opportunity lies to pivot your role and quickly upskill in key specialized areas.
Our research will help you consider which HR roles will likely be automated, where the potential lies to future-proof your HR career and help you to gain the skills required to be future-fit.
Automated HR jobs: Which are safe and which are at risk?
It’s no surprise that more repetitive and less complex jobs are vulnerable to automation, while more complex and creative roles that require problem-solving are less likely to be automated. However, all HR roles entail a mix of these elements, which makes assessing the automaton risk for a specific HR job a little less straightforward.
To understand which roles are at risk, we have analyzed 55 of the most common HR roles that cover all aspects of the HR profession. These have been divided into three groups based on their risk of being automated:
High risk: HR roles that are repetitive and with low levels of complexity
HR roles that are repetitive and with low levels of complexity Moderate risk: HR roles that are repetitive and complex, or non-repetitive and less complex
HR roles that are repetitive and complex, or non-repetitive and less complex Low risk: HR roles that are non-repetitive and complex
These categories will be discussed further in the sections below and present ways that you can future-proof your HR career. For an in-depth explanation of our methodology, refer to the end of the article.
1. HR roles at high risk of automation
Almost a third of the HR roles analyzed are at high risk of automation. HR Helpdesk and HR Administrator roles are potentially at risk as many of the job tasks can be (and in some cases are already) automated. Surprisingly though, DEIB Consultant and Officer, HR Scrum Master, as well as Payroll Team Lead roles also fall into the high-risk category.
HR roles at high risk of automation Administrative & process support Process execution HR Helpdesk Compensation & Benefits Specialist HR Scrum Manager Compensation & Benefits Manager System Tester DEIB Consultant Talent Researcher Payroll Team Lead HR Officer Facilitator Learning & Development (L&D) /
Organizational Development (OD)/
Organizational Effectiveness (OE) Administrator DEIB Officer Data Administrator Payroll Team Lead Benefits Administrator Process Engineer HR Administrator Business Analyst Payroll Administrator
Our research divided these roles into two groups based on their area of expertise and responsibilities. Let’s take a closer look at these two groups to see exactly what they entail and why they are considered to be repetitive and not complex in nature.
HR administrative and process support roles
The HR roles classified under this group include HR Helpdesk, System Tester, Scrum Manager, Talent Researcher, HR Officer, Business Analyst, Payroll Administrator.
Generally, these roles involve repetitive and process-oriented tasks that are at high-risk for automation. This includes activities such as:
record-keeping,
documentation management,
workflow management,
data capturing and processing,
and administrative functions that support other practices.
The roles in this group might not be fully automated. However, with the right technology in place, a large portion of the job will likely be replaced by automation. The benefit of automating more admin-intensive duties will result in the role shifting to encompass more complex tasks.
One example of this is within the Talent Researcher role, which has seen rapid adoption of sourcing tools and technologies. Machine learning and AI have streamlined sourcing efforts by searching for specific experience and skills at scale. This has enabled Talent Researchers to spend less time on more arduous tasks and more time mapping out the talent landscape and creating different talent pipeline strategies.
Process execution roles
Although the Payroll Team Lead, Compensation & Benefits Specialist, C&B Manager, DEIB Officer, DEIB Consultant, Process Engineer, and Facilitator are highly specialized roles, these are still at risk of becoming automated. Tasks within these jobs can be automated and augmented by technology over time.
For example, certain C&B Specialist or C&B Manager activities can be automated, like conducting salary survey data analysis or benchmarking. Some of the latest ERP systems already have this functionality built-in, and these tech capabilities will continue to be improved upon and available across the board.
On the surface, the Facilitator or DEIB Consultant roles do not appear to be at risk of automation. However, when further analyzed, various tasks within these roles are at risk of being replaced by e-learning platforms or online learning.
Additionally, a large portion of tasks performed within a junior DEIB officer role include DEIB reporting and DEIB training or education, which can be completed using technology.
Of course, it is unlikely that all of these roles will disappear entirely in the future. However, we do foresee that their scope will change significantly and may be incorporated into more advanced roles.
What you can do Change brings opportunity. Understanding what areas of your current HR role will be impacted by automation in the near future, will help you determine the skills you will need to develop. Consider whether you will need to upskill yourself within your current domain of expertise, or broaden your skill set to be able to operate across multiple areas of HR. For example, if you’re a C&B Specialist, you can broaden your scope into people analytics or employee value proposition design. This enables you to apply your core knowledge to other HR practices. Consider moving to roles that require more critical thinking and problem-solving skills. These are the skills that face a lower risk of becoming automated. For example, as a Benefits Administrator, you could work on your management, analytical, and HR practice implementation skills to move into a Benefits Manager role, and then on to a Rewards Executive role in the longer term. To help you identify where the skill gaps may be, consider the following: What transferable skills can you use to move into a new role less at risk of automation? Consider transferable skills such as: Communication skills Interpersonal skills Problem-solving skills Analytical reasoning Project management
How can you upskill to future-proof your HR career?
2. HR roles at moderate risk of automation
Our research reveals that an additional 30% of HR roles will be reshaped by automation. Although the majority of day-to-day tasks will be automated, the complexity of the role will make it impossible to fully remove the human element. Tasks within these roles generally require skills such as critical thinking, strategic insight, or business acumen.
However, as automation increases efficiencies, the likelihood of roles within this category becoming obsolete increases unless HR professionals pivot to more specialized functions within these positions. These include assuming an advisory role to the business leadership and stakeholder management.
HR roles at moderate risk of automation
Process implementation Function management Generalist advisory Talent Acquisition Specialist,
Headhunter HR Ops Manager HR Business Partner Learning & Development Specialist, Organizational Development Specialist,
Organizational Effectiveness Specialist Talent Manager HR Project Manager Employee Relations Specialist Benefits Manager HR Consultant HRIS Analyst Payroll Manager HR Manager Recruitment Consultant L&D Consultant, OD Consultant,
and OE Consultant
Process implementation roles
HR roles in this group include Talent Acquisition Specialist, Learning & Development Specialist, Organizational Development Specialist, Organizational Effectiveness Specialist, Employee Relations Specialist, Headhunter, HRIS Analyst, Recruitment Consultant, L&D Consultant, OD Consultant, and OE Consultant.
Implementing HR practices can be fairly complex, and these roles will still require a high level of human intervention. However, not all tasks involve the same level of complexity, which means that a significant number of tasks are still at risk of automation.
For example, talent acquisition practices will likely be replaced by fully automated capabilities such as asynchronous interviewing, automated screening, or AI-driven interviewing. However, there is still a need for the Talent Acquisition Specialist’s skills when it comes to designing and managing talent pools and processes. Similarly, roles such as HRIS analyst will likely evolve to focus more on business engagement and advising of stakeholders, as opposed to only building data models and managing datasets.
Function management roles
HR Ops Manager, Talent Manager, Benefits Manager, Payroll Manager, and Shared Services Manager are roles responsible for the tactical execution of HR practices and services. They advise and guide the business on how these practices need to be designed.
Management roles are not immune to automation. Tasks performed by managers that involve decision-making accountability can be automated or, at the very least, augmented by technology. For example, organizations might utilize algorithms that automatically recommend training and development programs to employees based on their competency and performance scores, instead of relying on a Talent Manager for recommendations.
Additionally, we anticipate that certain roles will be merged. For example, the roles of HR Ops Manager, Benefits Manager, and Payroll Manager can be combined into one role, with operational tasks handled via technology.
Generalist advisory roles
HR Business Partner, HR Project Manager, HR Consultant, and HR Manager are roles where frequent interaction with stakeholders is required. These roles tend to deal with ambiguity as expectations for HR service delivery frequently change.
While professionals in these roles will always need to take an active role in steering their activities, technology can help enhance their knowledge, reduce risks, and expand their overall impact on the business.
What you can do Should your current or future roles be at risk of automation, it is crucial that you develop digital agility now. Prioritize learning skills that enable you to utilize technology to the benefit of your work and increase your impact on the organization. Focus on learning how to: Apply your skills in different contexts to tackle new tasks and challenges. For example, instead of collecting and presenting data on employee turnover, develop a 5-point action plan to reduce it.
Use digital tools to focus on your core priorities and extend your reach
Develop your critical thinking and stakeholder management skills. These skills are essential for driving broader business impact and showcasing the value that HR practices bring to the business.
3. HR roles at low risk of automation
Leadership and highly specialized roles remain relatively safe from the impacts of automation. Rather, technology will serve as a springboard to enhance the impact of these roles and create new opportunities.
Roles in this category can be divided into two smaller groups:
HR leadership roles; and,
Organizational excellence roles.
These are roles that often have strategic or problem-solving responsibility that depends mostly on their HR expertise.
HR roles at low risk of automation
HR leadership roles Organizational excellence roles Employee Relations Executive HR Specialist Head of Organizational Development/Organizational Effectiveness Data Scientist Rewards Executive DEIB Specialist Global Services Executive HR Systems Architect Chief Learning Officer Industrial-Organizational Psychologist Chief Diversity Officer Shared Services Manager People Analytics Head Head of DEIB Head of Talent, Talent Director, HR Director CHRO, SVP, CPO Senior HR Business Partner Global HR Business Partner Divisional/Regional HR Manager
HR leadership roles
To remain relevant in the future, professionals in these roles will need to become more strategic. As the business and stakeholder landscapes continue to become more complex, technology will play a role in augmenting the daily activities and collaboration within these roles.
However, technology will not replace or reduce the role HR leaders will continue to play in resolving pressing people challenges.
As HR plays a more crucial and important part in the success of the business, HR leaders will increasingly be the driver for significant changes. To be successful, HR leaders must focus on developing business acumen, scenario planning, and critical thinking skills. Additionally, prioritizing leadership competencies, particularly in building followership, engaging with employees, and leading organizational culture, will be required.
Organizational excellence roles
HR roles in this group include HR Specialist, Data Scientist, DEIB Specialist, Industrial-Organizational Psychologist, Shared Services Manager, and HR Systems Architect. These roles focus on problem-solving through the application of HR expertise, and they will continue to grow in scope.
Professionals within these roles will need to learn how to manage greater complexity and responsibilities, particularly as technology becomes crucial to work execution.
Given the problem-solving nature required of these roles, our research indicates it is unlikely that they will become automated. However, we expect new HR roles will emerge in this category as a byproduct of the changing working environment.
What you can do: A look to the future We foresee that businesses will be facing new people-related challenges. Some of these include: Talent shortages
The effects of the Great Resignation
Increased employee expectations
Workforce and succession planning
Skills gaps
Downsizing due to economic uncertainties. This will require HR leadership to both adapt and evolve their roles to be able to successfully lead and respond to these challenges. This is already evidenced in the evolving role of the CHRO. In times of crisis, as was seen during the pandemic, the CHRO plays an instrumental role in leading board discussions on policies, business continuity, employee safety and productivity.
These roles will continue to expand in their scope and responsibility. Emerging trends such as remote work, skill-based economies, talent shortages, rightsizing, and digitalization, will result in greater responsibility for HR professionals to guide the organization in making agile, more employee-centric business decisions.
Technology will continue to play an important role in bridging skills gaps and enhancing employee experience and motivation. Prioritizing digital agility, strategic thinking, business acumen, crisis management, and leadership skills is essential. While your role might not be replaced by technology, you will still need a strong understanding of automation to oversee the incorporation of automation functions across the various HR roles.
Additionally, focus on extending your subject matter expertise beyond the scope of traditional HR. This also requires a proactive approach to consistently develop the skills required to succeed.
In conclusion
It is undisputed. Technology is radically changing HR.
Several HR roles will irrevocably be changed or become obsolete within the next 10 years. Other roles may remain but will continue to be rapidly augmented and changed by technology.
Just as in the Industrial Revolution, changing and newly emerging roles are to be expected with technological advancements. These advancements bring the opportunity to improve what may not have worked as effectively before.
As HR professionals are asked to manage one crisis after the next, it isn’t surprising that 98% of HR professionals report feeling burned out. As the HR role continues to expand exponentially, automation of administratively intensive roles will help free HR professionals to focus on the core issues.
However, this also will require HR professionals to pivot and take on greater complexity within their roles. And this will mean that new skills and competencies are needed.
Ultimately, HR must continuously develop, reskill, upskill and stay up to date with the latest developments. HR professionals will need to learn to collaborate with technology or oversee the implementation of automation.
As we enter a decade of profound transformation, it will be essential for HR to play a leading, greater strategic role within the organization. This will require HR leadership to invest in the development of their teams, and for HR professionals to be prepared to embrace change and identify the necessary skills to future-proof their careers.
How the research was conducted
1. Data selection
The tool Will Robots Take My Job analyzes the automation risk of several HR-specific job titles such as Human Resources Managers and Payroll and Timekeeping Clerks. In order to gain an accurate understanding of the automation risk of the HR function, we expanded our analysis to a wider range of roles that reflect all aspects of the HR profession, both in terms of focus and seniority.
We gathered the more frequently used HR job titles from HR role libraries and open-source libraries and included four different types of positions:
Advisory: HR roles that focus on translating business needs into HR solutions and ensuring that the HR priorities are relevant, aligned, and create business impact Service providers: HR roles focused on creating an engaging and memorable employee experience Solution providers: HR professionals who are subject matter experts or specialists in (one or more) HR practices Strategic: HR roles that ensure HR priorities align with the business strategy and requirements
The roles were further categorized based on the level of seniority, from the lowest (entry-level ) to the highest (experienced). This selection process resulted in 55 HR roles that covered the entire HR spectrum.
2. Data analysis
To understand the extent to which automation will take over (parts of) your HR roles, we used two criteria for our evaluation:
Repetitiveness to see how feasible the automation of tasks would be. The more repetitive tasks are, the more likely they are to be automated. We defined four levels of repetitiveness as Highly repetitive, Relatively repetitive, Somewhat repetitive, and Not repetitive.
to see how feasible the automation of tasks would be. The more repetitive tasks are, the more likely they are to be automated. We defined four levels of repetitiveness as Highly repetitive, Relatively repetitive, Somewhat repetitive, and Not repetitive. Complexity to see how desirable and value-adding automation would be. The more complex the tasks become, the less likely they are to be automated. We defined four levels of complexity as Highly complex, Relatively complex, Somewhat complex, and Not complex.
We used a 10-point scale to grade the 55 HR roles that we selected, where 10 represented the highest level of repetitiveness and complexity. The higher the repetitiveness rating and the lower the complexity rating, the more likely it is that the role would be automated.
These ratings were used to sort the roles into four levels of repetitiveness and complexity. Here is a breakdown of our grading scale:
Levels of repetitiveness/complexity Rating Highly repetitive/complex 8, 9, 10 Relatively repetitive/complex 5, 6, 7 Somewhat repetitive/complex 3, 4 Not repetitive/complex 1, 2
Our rating was based on the job content and job description of each role. However, these two factors can also vary from company to company, which means that in some cases, the likelihood of automation will also depend on organizational context.
3. Data categorization
After analyzing and grading the selected roles, we used a categorization matrix to classify these roles. Our matrix was based on the four levels of repetitiveness and complexity as outlined above.
Based on this matrix, we sorted the 55 HR roles into four categories, which are:
Routine & Complex: Roles that are unpredictable, non-repetitive and shows higher levels of complexity
Roles that are unpredictable, non-repetitive and shows higher levels of complexity Routine & Simple: Roles that deal with predictable events, are repetitive in execution, and simple in terms of complexity
Roles that deal with predictable events, are repetitive in execution, and simple in terms of complexity Non-routine & Complex: Roles that are non-repetitive, with some degree of unpredictability, but relatively simple in execution
Roles that are non-repetitive, with some degree of unpredictability, but relatively simple in execution Non-routine & Simple: Roles that are somewhat repetitive and shows some level of complexity
These categories form our Automation Framework. Using this Automation Framework, we sorted the selected HR roles into three groups to reflect the level of automation risk that they faced. These groups are:
HR roles at high risk of automation: These are roles where automation of tasks is highly possible and will bring added value to the business.
HR roles at low-to-moderate risk of automation: These are roles where automation is somewhat possible, but not very desirable as it will not bring much added value to the business.
HR roles safe from automation: These are roles where automation is not possible and will not bring added value to the business.
The resulting role distribution can be seen in the article above. Find the full data set and methodology here:
Dr Dieter Veldsman Chief HR Scientist Dieter Veldsman is a former CHRO and Organizational Psychologist with over 15 years of experience across the HR value chain and lifecycle, having worked for and consulted globally with various organizations. At AIHR, he leads research initiatives and develops educational programs aimed at advancing the HR profession. Dr. Veldsman is regularly invited to speak on topics such as Strategic HR, the Future of Work, Employee Experience, and Organizational Development.
| 2022-12-07T00:00:00 |
2022/12/07
|
https://www.aihr.com/blog/future-of-hr-jobs-research/
|
[
{
"date": "2022/12/07",
"position": 20,
"query": "AI replacing workers"
},
{
"date": "2022/12/07",
"position": 22,
"query": "future of work AI"
},
{
"date": "2022/12/07",
"position": 88,
"query": "workplace AI adoption"
}
] |
AI vs. Machine Learning: How Do They Differ?
|
AI vs. Machine Learning: How Do They Differ?
|
https://cloud.google.com
|
[] |
Machine learning is a subset of artificial intelligence that automatically enables a machine or system to learn and improve from experience.
|
You might hear people use artificial intelligence (AI) and machine learning (ML) interchangeably, especially when discussing big data, predictive analytics, and other digital transformation topics. The confusion is understandable as artificial intelligence and machine learning are closely related. However, these trending technologies differ in several ways, including scope, applications, and more.
Increasingly AI and ML products have proliferated as businesses use them to process and analyze immense volumes of data, drive better decision-making, generate recommendations and insights in real time, and create accurate forecasts and predictions.
So, what exactly is the difference when it comes to ML vs. AI, how are ML and AI connected, and what do these terms mean in practice for organizations today?
We’ll break down AI vs. ML and explore how these two innovative concepts are related and what makes them different from each other.
New customers get up to $300 in free credits to try Vertex AI and other Google Cloud products.
| 2022-12-07T00:00:00 |
https://cloud.google.com/learn/artificial-intelligence-vs-machine-learning
|
[
{
"date": "2022/12/07",
"position": 30,
"query": "machine learning job market"
},
{
"date": "2022/12/07",
"position": 75,
"query": "AI healthcare"
}
] |
|
How to Get Your Data Scientists to Quit
|
How to Get Your Data Scientists to Quit
|
https://altair.com
|
[
"Ingo Mierswa",
"Senior Vice President",
"Product Development"
] |
If data science is such a fantastic job, how else can we explain the massive turnover in the data science job market? ... machine learning models and solving ...
|
Some organizations are doing at least one thing right when it comes to managing their data scientists – giving them good reasons to look somewhere else. If data science is such a fantastic job, how else can we explain the massive turnover in the data science job market?
Turnover rates for data scientists are at an all-time high. In fact, a study by 365 Data Science Ltd. shows that the average data scientist will only remain in their job for 1.7 years.
The rampant job dissatisfaction is in stark contrast to the high demand for data scientists, as the Bureau of Labor Statistics predicts a 30-35% increase in employment growth in data science – meaning more than 15,000 job opportunities will be created between 2020 and 2023.
As a data scientist, this almost makes me miss the good old days*, when data scientists were as unique as unicorns. Now, it feels more like a zoo—it’s become a crowded space, with lots of different job titles and roles, and there’s a fierce competition to both hire and retain the data science talent you have.
So what, exactly, is causing data scientists to quit en masse? Let’s find out!
*I of course do not miss the "good old days" of data science. Who wants to go back to code in Perl for natural language processing? I certainly don’t!
Do These Six Things If You Want Your Data Scientists to Quit
Today, data scientists have all the power. The demand for data scientists outweighs supply, and if they’re not happy at their current organization, they can leave. If you’re okay with your data scientists walking out the door, be sure to do these six things.
1. Offer Them Zero Growth Opportunities
There’s a common misconception that data scientists are happy being data scientists forever. That’s what you went to school for, right? To do data science? You must be living the dream!
Wrong.
Good data scientists are all problem solvers at heart, and without a clear career path and fulfilling new problems to work on, there’s nowhere for their ambition to go.
Data science is an ever-evolving space—there are always new tools, platforms, and methods for creating machine learning models and solving business problems. To continue learning, furthering their personal growth, and producing the best results for their organization, it’s important that they’re given the opportunity to attend conferences, get additional training, and work in their preferred environment.
There’s also great diversity in the data science role—it requires skill in statistics, data literacy, computer science, storytelling, presentation, and leadership. Their career paths aren’t clear cut lines; instead, they’re unique journeys based on their expertise.
If there’s no room for data scientists to grow at their current company, and their employers don’t understand how they should be growing, why wouldn’t they take their talent elsewhere?
2. Make No Effort to Understand Their Work
Imagine this: it’s your first day on the job as a data scientist for a global retailer. You sit through a short welcome presentation, meet a few coworkers, and then are given your first task—create a customer churn model to determine red flags for attrition and, ultimately, improve return on investment (ROI).
It’s no small task – and you’ve been on the job for all of three hours. How are you supposed to create a valuable machine learning model if you barely understand the context behind the use case you’re trying to solve for?
While we’re at it, why are data scientists expected to understand business use cases while executives have the luxury of not understanding (and therefore not trusting) the work that their data scientists do?
This lack of effort to understand data science is a symptom of the ever-widening skills gap between “business people” and “data people.”
Discounting more advanced methods and not honoring the conclusions of complex deep learning models simply because executives don’t understand the process behind it leads to disgruntled data scientists feeling like they’re wasting their time. And, it’s why upskilling stakeholders and business leaders is more important than ever.
3. Waste Their Time with Grunt Work
If you ask any data scientist, “Why did you become a data scientist?” not one will answer, “Because I want to spend all day doing data prep!”
A recent Medium article from a former data scientist proves this point, claiming that he only spent 10% of his time building models—and that’s what he wanted to spend 90% of his time doing. He shared that data science was, frankly, more boring than he thought it would be, and not half as challenging or fulfilling as he expected.
You didn’t hire data scientists to spend their entire day in Excel, rearranging cells and columns or doing simple analysis with antiquated methods. So why are they spending so much of their time on avoidable, tedious data prep and cleaning?
Most of a data scientist’s time is spent organizing messy data, wrangling, aggregating, cleaning, running SQL queries, and waiting for code to be executed. It leaves many data scientists feeling like data janitors rather than sparkly unicorns.
Don’t get me wrong: Data prep is a vital part of data science. Just don’t make your team isn’t only doing data prep.
4. Don’t Provide Them with Any Data Science-Specific Tools
Data scientists are tinkerers, and tinkerers work best when they’re given the best tools on the market.
Sadly, few companies invest in good data science tools. Some don’t invest in any at all. Not having a dedicated data science platform makes it difficult to work through existing firewalls and get models into production.
Even when organizations do have a program in place, if it doesn’t offer sufficient training (similar to RapidMiner’s certification-based Academy), it gives them no way to apply and grow their skills within the platform. Others won’t invest in supplemental data engineering tools, leaving data scientists to clean up unstructured, inconsistent data – and remember how data scientists feel about grunt work?
Only giving data scientists barebone tools to do their jobs signals that their employer isn’t willing to invest in a technical environment that would help them produce their best results.
5. Have Them Rely on Manual, Inefficient Methods and Software
Or, what’s just as bad, don’t care about their tools at all. Let them use whatever they like to use. This causes most of their models to stall and never make it into production because there’s no streamlined, accepted, and robust way for doing so given the dozens of different authoring modalities. And that means their work has no impact.
The tool zoo is bigger than just the tools used for data prep or modeling. Providing data scientists with software that speaks their language is key to peak productivity, but you’d be surprised at the number of data scientists still taking time out of their day to edit PowerPoint slides to present their output.
Not having a streamlined way to display and present data science results creates even more unnecessary hurdles between business and data experts.
Another issue many data scientists lament is a lack of version control in their organization—the number of businesses still sharing code over email, Google Drive, or SharePoint is startling. Not only is this code less secure, it’s also prone to being overwritten by fellow developers, meaning their hard work could be altered or lost.
If their work is less secure and takes twice as long as it should, chances are they won’t feel 100% fulfilled.
6. Pay Them Less Than What They’re Worth
Even if you love your job, your coworkers, and your company, wouldn’t you consider leaving if your employer was underpaying you?
Unfortunately, data scientists run into low pay all too often. A LinkedIn post from founder of ML library PyCaret highlighted a particularly telling example—an “entry level” data engineer job posting asking for 6+ years of experience – and paying just $62,000 USD per year.
Not only is the salary way too low for someone being asked to design and develop impactful tools, implement a robust technical framework in the organization, and present their solutions to stakeholders; it also demonstrates that the prospective employer has no idea what these skills are worth. Would you really want to work for someone who doesn’t understand the value of your contributions?
Some data science candidates will filter low salaries out of their job search, and your posting won’t even be seen. Others will look, roll their eyes at the discrepancy between what’s being asked of them versus what’s being offered to them, and will choose to take their time (and their expertise) elsewhere.
Wrapping Up
In a recent KDnuggets article, self-taught data scientist Natassha Selvaraj shares that she quit an internship after just one week because the job didn’t reflect the skills she spent years of her life learning.
The message is clear: if employers don’t invest in their data scientists, provide them with a technologically advanced work environment, and allow them to contribute to high-value projects, they’ll leave. No one wants to watch all their organization’s unicorns prance away to greener pastures.
| 2022-12-07T00:00:00 |
https://altair.com/blog/executive-insights/how-to-get-your-data-scientists-to-quit
|
[
{
"date": "2022/12/07",
"position": 74,
"query": "machine learning job market"
}
] |
|
Unlimited opportunities for customers with intelligent ...
|
Unlimited opportunities for customers with intelligent automation
|
https://erp.today
|
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A human-centric approach to automation is essential for successful integration across organizations, emphasizing the importance of reskilling and aligning ...
|
The conversation around intelligent automation is evolving: from a speculative technology used to improve isolated bottlenecks, to an enterprise imperative that sits at the heart of modern operating models.
The original flavour of automation tools – most commonly called robotic process automation (RPA) – was deployed to perform narrow tasks quicker and more accurately than a human could. Its use was often found in back office functions where significant manual effort was required to load data into forms or to tackle repetitive processes like invoice matching. RPA solutions offered some value, but the limited scope of the technology only delivered finite benefits that were restricted to a particular task or process.
Today, the remit for automation goes far beyond this type of narrow optimisation and provides enterprise leaders with an opportunity to radically alter the way data, processes and humans interact. As Sam Balaji, global consulting leader at Deloitte, tells ERP Today: “The client conversations these days are often about how traditional RPA is now enhanced with AI and ML to offer a more robust technology platform, such as the solutions that UiPath offers, to accelerate value and benefits realisation. And with Deloitte, the benefits of intelligent automation can now be applied to the most strategic customer-facing business processes at clients. The result is fast business outcomes giving clients renewed agility to address real-world challenges and opportunities at any scale.”
benefits of intelligent automation can now be applied to the most strategic customer-facing business processes – Sam Balaji, Global consulting leader at Deloitte
Enterprise leaders are now embracing automation to solve their hardest problems, as Balaji notes, and are leveraging the power of RPA, machine learning and artificial intelligence to infuse hyperautomation tools across the enterprise. The true power of automation is finally being realised and customers across the globe are racing to ensure they are at the forefront of the automation revolution.
Speaking to Adam Burden, technology lead at Accenture, it seems that customers are turning to intelligent automation with a new set of priorities.
“While automation itself is nothing new, we’ve seen automation transition from only being about efficiency and cost cutting, to using automation to unlock business value, better decision-making, and create entirely new growth opportunities.” Burden tells ERP Today, “Automation powered by AI can help get new products and services to market faster, deliver a better experience for customers and employees and even enable entry into previously unprofitable business areas.”
Automation powered by AI can help get new products and services to market faster and deliver a better experience for customers – Adam Burden, Technology lead at Accenture
Straight line to value
One of the biggest challenges with any technology investment is to demonstrate value. While a five-year ERP modernisation programme may set the foundations for broader transformation, it can be challenging to draw a straight line to the benefits and business outcomes. Automation presents a completely different proposition to most IT projects because the time to value and the ability to identify the benefit is much clearer cut. At a time when horizons are measured in weeks rather than years, being able to identify an opportunity and implement the tech to execute it has never been more important.
Steve Starace, senior vice president at CGI, remarks on growing customer expectations, saying: “The timeframe for which clients are looking for a return has shrunk. Whether that return is cost efficiencies or whether that return is bringing things to market more quickly. At CGI, we have developed CGI Accel360, a fully-managed service for hyperautomation that enables clients to gain all the benefits from automation technology without managing the platform. It delivers automation-as-a-service with industry-specific use cases built in and accelerators to demonstrate the value. Once the client can start to connect the dots, other use cases develop and we are able to clearly demonstrate the return on investment.”
We like to think big and we share UiPath’s vision for how automation can be used to solve our joint clients’ most challenging problems – Steve Starace, Senior vice president at CGI
Moreover, prioritising investments that deliver rapid time to value is only an increasing imperative for customers, as Simon Constance, a partner at EY, one of UiPath’s biggest customers globally, also notes.
“Tools like automation very often rise to the top of the priority list because customers can’t wait three or five years for a technology investment to mature,” he says. “Many customers are in a constant state of flux and transformation where they are continually innovating and bringing new channels to market. That type of change drives your priorities and means timelines are much shorter for technology to deliver value. Automation tools can reduce costs, create efficiencies and deliver value in very short timeframes and that is what customers need right now.”
Customer confidence
Customers as diverse as Uber, Xerox, Asahi and EY are using UiPath’s automation platform to dramatically transform how processes are executed and work flows across the organisation. Hyperautomation is landing in manufacturing companies, retailers and professional services companies alike to streamline and optimise end-to-end operations. Through small proof of concept projects to global rollouts, automation tools are being used to transform our long-held notion of work and free up human capacity to focus on higher value tasks.
The technology itself has also matured considerably and can now provide a secure and robust solution, even in complex and regulated industries such as insurance and financial services. Old concerns centered on trust have been quashed and many of the world’s foremost brands are embracing automation for increasingly sophisticated deployments.
A great example of robust scalability has taken place within EY itself. As Constance tells us, EY has embedded automation at the heart of its audit business to drive significant cost savings and process improvements.
“If you ever wondered if process mining and automation scales take a look at how EY has leveraged one of the world’s largest deployments of unattended automations,” he says. “We are innovating on a huge scale in a very robust, regulated environment and demonstrating first-hand to our customers what’s possible.”
We are innovating on a huge scale in a very robust, regulated environment and demonstrating first-hand to our customers what’s possible – Simon Constance, Partner at EY
That type of partnership, which drives co-innovation and delivers scalable proof points for customers, is creating a vibrant ecosystem around the UiPath portfolio. Its global partners are building deep capabilities and coupling their industry experience with UiPath’s evolving capabilities to build the next generation of solutions for customers. CGI is one such partner that has extended its relationship with UiPath to form a strategic partnership. Starace tells ERP Today that the partnership goes deeper than simply selling products into customers – it’s based on co-innovation and is delivering significant value for customers.
“First of all, there’s a really good cultural fit between UiPath and CGI and we have the same objectives in mind. We like to think big and we share UiPath’s vision for how automation can be used to solve our joint clients’ most challenging problems. We came together to build a go-to-market strategy that coupled our deep domain expertise with the market-leading automation platform, and those two things together really have created a one plus one equals three scenario for clients.”
Maturity, myths, and human-centric approach
While the concept of an autonomous enterprise that is optimised at every level with intelligent automation, freeing humans completely of burdensome tasks, sounds like a panacea, the reality is not so straightforward. Customers will be at different stages of their transformation journeys and not all enterprises will be sufficiently mature to embrace automation.
Girish Pai, global head of intelligent automation at Cognizant, tells us: “Enterprise-wide adoption is a challenge. You are transforming how an organisation operates, how people work, and what the workplace will look like. It requires integrating across an often complex ecosystem of processes and technology while ensuring a seamless experience.”
Here, Burden also adds: “While the promise of intelligent automation is vast, for many companies broad adoption is considerably easier said than done. It needs to be a holistic effort so that intelligent automation and the work of IT supports the overall goals of the business strategy. We see a trend where businesses are moving from point solutions to broader automation that is aligned with the business strategy, leveraging AI capabilities to drive business value and improve customer, employee and business partner experiences. A key challenge for automation continues to be the adoption level, for which automation should be supported by a comprehensive change management programme. It requires changes in the mindset of an organisation, and you need to take your people along with you on the journey. This requires putting the right reskilling and upskilling programmes in place so that the employees understand, support, and feel the automation journey is done with them and not to them.”
This is a space with unlimited opportunities and will be a game changer for companies who get it right – Girish Pai, Global head of intelligent automation at Cognizant
Recognising the challenges of integrating automation technology at every level of an organisation, Balaji points to Deloitte’s responsibility to showcase the possibilities and benefits of an integrated automation strategy. “Technology implementation is often very federated in an organisation,” he says. “It’s up to us at Deloitte to showcase the art of the possible in terms of an integrated approach to the various technologies deployed and to ensure a cohesive approach to solving business problems. As with most technologies, one of the challenges for automation programmes can be having the right internal resources to build the infrastructure and manage the programme. In this case, we have managed-services offerings powered by UiPath and other technologies to allow businesses to launch an automation programme that mitigates risks and resource hurdles. Security concerns are also common as the industry moves to cloud, and because automation is intended to require little human touch. At Deloitte, we like to emphasise ‘humans with machines’, as automation removes the burden of repetitive tasks but doesn’t replace human oversight or input. UiPath is well known for its security configurations and our UiPath specialists know how to configure solutions that meet the needs of our clients.”
This human-centred approach to automation is crucial to the effectiveness of any automation project, Burden agrees, but he also points to the outdated automation myths we need to dispel. “We encourage clients to look at automation with a people-first mindset,” he says. “The best automation solutions work alongside people, leveraging human strengths and are supported by investments in skills, experience, organisation, and culture. We genuinely feel that the best solutions in this space work alongside people to ‘automate the ordinary and unleash the extraordinary’. We observe that many companies deploy technology in pockets of their organisations, without a vision for scaling the innovation enterprise-wide or sharing learnings across projects. Teams can also run into difficulties scaling if they don’t vet opportunities wisely or narrow their focus too quickly. Besides scaling, a common myth to automation adoption is that a shortage of talent exists or that customers prefer to work with real people.”
How will intelligent automation evolve in the short term?
RPA’s evolution from simple use case technology into enterprise-wide automation enabler has presented customers with unprecedented opportunities. The technology itself poses many questions for enterprise leaders to evaluate and it is important to consider that not every business or every process will be sufficiently mature to adopt full-scale hyperautomation. Ensuring that people are at the centre of the automation conversation may sound counterintuitive but an approach that does not consider how digital automations and humans coexist is flawed and unlikely to deliver the outcome a business hopes for. That said, there are very few types of organisation that would not benefit from automation and the current macroeconomics are likely to drive an increased premium on efficiencies and agility.
Balaji from Deloitte concludes his analysis of the opportunity by telling ERP Today: “With current market conditions, several unknowns and a potential recession, automation will become a key driver of cost reduction for businesses. Automation can drive efficiency as well as deliver rapid business insights, from which businesses can make critical decisions. Understanding the ecosystem of options and how to incorporate AI, business process management, and other technologies will certainly shape the conversation as well. As businesses pivot and morph to new business models to meet market demands, we are always exploring new use cases and working to bring joint UiPath solutions to market, particularly to the finance and human capital spaces in the short term.
Summarising Accenture’s thoughts on the market, Burden tells us that: “Automation will continue to evolve from a cost takeout play to an enabler of enterprise strategy at a rapid pace over the short term.” He goes on to say: “Businesses will continue to push further into end-end process automation inspired by the success they have seen within IT and in call centres. Because of the ongoing talent shortage, we expect to see more attention on discovery of processes ripe for automation and the use of solutions that work alongside people, enabling them to perform higher order functions than they would otherwise be able to. “
For the companies which adopt the correct approach, the potential of automation is seemingly boundless. Wrapping up the conversation, Pai from Cognizant highlights this opportunity: “We see this space continuing to accelerate and the need to anchor to outcomes being only more critical,” he says. “The focus is shifting from just efficiencies to experiences. Being innovative is more than a mindset, but also about challenging ourselves to think differently, asking not just why, but how, and thinking about how we can improve and elevate the customer journey. Automation will be a part of how we work. This is a space with unlimited opportunities and will be a game changer for companies who get it right.”
| 2022-12-07T00:00:00 |
2022/12/07
|
https://erp.today/intelligent-automation-unlimited-opportunities-for-customers-who-get-it-right/
|
[
{
"date": "2022/12/07",
"position": 3,
"query": "reskilling AI automation"
}
] |
IDC's Future of Work practice top 10 predictions for 2023
|
IDC's Future of Work practice top 10 predictions for 2023
|
https://blogs.idc.com
|
[
"Amy Loomis",
"Ph.D. - Research Director",
"Future Of Work",
"In This Role",
"Ms. Loomis Covers The Growing Influence Of Technologies Such As Artificial Intelligence",
"Data Analytics",
"Robotics",
"Augmented",
"Virtual Reality",
"Intelligent Process Automation In Changing The Nature Of Work. Her Research Looks At How These Technologies Influence Workers' Skills"
] |
Rapid adoption of more automated, cloud-based, and artificial intelligence (AI)–enabled work practices increases work productivity and introduces new, more ...
|
The need for a unified approach to disruption of any kind is key to success in the future of work. With global attention divided between many disruptors, the future of work is fraught with many unknowns, from where and how work will be done to how economic pressures will change job opportunities to how social, skills, and climate concerns will have a broad impact.
The reality of our current global economic, climate, and business challenges requires workers to be a part of dynamic and reconfigurable teams that can quickly adapt to business demands and new market requirements — anytime, anywhere, and from any physical location.
Hybrid work – once thought to be a temporary fix throughout the COVID-19 pandemic – is now a mainstay in the global future of work landscape, despite public focus on return to office initiatives.
The future of work will be one that is defined by a variety of work approaches capable of supporting the ebb and flow of change as the world learns to navigate new challenges.
The promise of such hybrid work models is clear. Rapid adoption of more automated, cloud-based, and artificial intelligence (AI)–enabled work practices increases work productivity and introduces new, more agile ways of working. Insights from more digital-first ways of working are enabling organizations to be responsive to the needs of customers and employees alike, driving improvements in talent acquisition, employee retention, and customer satisfaction. They also underscore the need for greater focus on skills development in the flow of work itself at a time when many workers struggle to keep pace with new features, functions and applications designed to make work “easier”.
Organizations sufficiently prepared to find and capitalize on opportunities in spite of current and future disruptions will be the ones that define the next future of work.
IDC’s top 10 predictions for the Future of Work in 2023 are:
Prediction 1: To address health, sustainability, travel, and other disruptions, 30% of G2000 organizations will adopt immersive third-party metaverse conferencing tech services to enable client engagement by 2027.
To address health, sustainability, travel, and other disruptions, 30% of G2000 organizations will adopt immersive third-party metaverse conferencing tech services to enable client engagement by 2027. Prediction 2: By 2024, the business developer role will be ubiquitous, with 60%+ enterprises training and supporting business users to build their own applications and automated processes using low-code tools.
By 2024, the business developer role will be ubiquitous, with 60%+ enterprises training and supporting business users to build their own applications and automated processes using low-code tools. Prediction 3: Driven by skills shortages, CIOs that invest in digital adoption platforms and automated learning technologies will see a 40% increase in productivity by 2025, delivering greater speed to expertise.
Driven by skills shortages, CIOs that invest in digital adoption platforms and automated learning technologies will see a 40% increase in productivity by 2025, delivering greater speed to expertise. Prediction 4: By 2024, organizations deploying employee micro-monitoring measures (camera/keystroke) will see a 20% decrease in actual employee productivity.
By 2024, organizations deploying employee micro-monitoring measures (camera/keystroke) will see a 20% decrease in actual employee productivity. Prediction 5: G2000 companies that deploy reactive and tactical hybrid work models will see a 20% revenue loss in 2024 due to job attrition and underperforming teams.
G2000 companies that deploy reactive and tactical hybrid work models will see a 20% revenue loss in 2024 due to job attrition and underperforming teams. Prediction 6: By 2025, organizations that have created dedicated hybrid security policies and developed a culture of trust will be 3x less likely to suffer a security breach.
By 2025, organizations that have created dedicated hybrid security policies and developed a culture of trust will be 3x less likely to suffer a security breach. Prediction 7: By 2024, companies offering frontline workers democratized access to digital collaboration, process automation, and similar tools will see 20% increase in revenue due to improved productivity.
By 2024, companies offering frontline workers democratized access to digital collaboration, process automation, and similar tools will see 20% increase in revenue due to improved productivity. Prediction 8: Holistic and integrated analytics within an intelligent digital workspace (IDW) ecosystem will drive a 70% increase in differentiated business outcomes for adopters by 2026.
Holistic and integrated analytics within an intelligent digital workspace (IDW) ecosystem will drive a 70% increase in differentiated business outcomes for adopters by 2026. Prediction 9: Effectively blurring space and place, by 2025, 65% of G2000 companies will consider online presence to be at parity to “in real life” across their engaged workforce.
Effectively blurring space and place, by 2025, 65% of G2000 companies will consider online presence to be at parity to “in real life” across their engaged workforce. Prediction 10: By 2024, 55% of C-suite teams at global enterprises will use intelligent space and capacity planning technology to reinvent office locations for gathering, collaborating, and learning.
Interested in learning more? Watch our on-demand webinar, IDC FutureScape: Worldwide Future of Work 2023 Predictions.
| 2022-12-07T00:00:00 |
2022/12/07
|
https://blogs.idc.com/2022/12/07/idc-futurescape-worldwide-future-of-work-2023-predictions/
|
[
{
"date": "2022/12/07",
"position": 14,
"query": "future of work AI"
},
{
"date": "2022/12/07",
"position": 65,
"query": "AI economic disruption"
}
] |
Understanding the Effects of Automation on Employment
|
Understanding the Effects of Automation on Employment
|
https://techrseries.com
|
[
"Shafaque Ansari"
] |
The use of automation and AI continues to drive everything from job market fears to political debates over policies such as universal basic income.
|
We’ve been hearing stories about the impending collapse of the human workforce for at least the last 30 years – possibly longer depending on what movies we’ve seen. You already know how it goes. Robots will come along and take our jobs. Artificial intelligence (AI) will eventually become self-sufficient, and we will all die anyway, at which point we will no longer require a job.
The thing is, much like we were supposed to have flying cars by now, much of what we were told would happen has not happened.
Nonetheless, the use of automation and AI continues to drive everything from job market fears to political debates over policies such as universal basic income.
Top HR Tech Insights: Unlocking Business Success With Data-Driven Leadership
Employers are expected to depend more on computers to perform tasks that humans currently perform. Using today’s technology, approximately half of all employee tasks could be automated. Because computers are generally less expensive, less prone to error, and more proficient in some areas than human employees, it is clear that robots will play an increasing role in the future of work.
The New Automation: Is This Time Different?
The “new automation” of the coming decades, with much more advanced robotics and artificial intelligence (AI), will broaden the range of tasks and jobs that machines can perform. AI has the potential for significantly more worker dislocation and inequality than in prior eras of automation. This has the potential to affect college graduates and professionals far more severely than in the past. Indeed, new automation will result in the loss of millions of jobs for vehicle drivers and retail workers, as well as healthcare workers, lawyers, accountants, finance specialists, and a variety of other professionals.
AI will exacerbate the challenges that many workers will face as a result of automation, while also contributing to higher living standards through increased worker productivity. Simultaneously, a much more robust set of policy responses will be required to ensure that workers can adapt and that the benefits of automation are broadly shared.
Which Jobs are Robots likely to Replace?
Numerous jobs will survive even as tasks become more automated. According to McKinsey report, robotics and other computers will likely take over less than 5% of jobs.
Computers will instead take over specific tasks that they can complete more efficiently or cost-effectively than humans. Predictable or repetitive work, physical tasks, machine operation, and data processing and collection are examples. Artificial intelligence is transforming many industries and, for example, will have a significant impact on healthcare careers.
Computers will take over some of the tasks performed by people at all levels of education and at all stages of their careers. These changes, however, will have a greater impact on certain industries than others. The following industries are likely to see a significant increase in automated workers:
Construction
The good news is that construction jobs are increasing as a result of the increased demand for new buildings, improved roads, and other infrastructure development. Some construction tasks, on the other hand, are ideal for robots. These include any repetitive physical labor, such as construction equipment operation, demolition, and basic material installation and repair.
Jobs and tasks requiring more expertise, such as complex installations and repairs, as well as construction site management, are unlikely to be replaced by robots anytime soon.
Food Service
Automation is already on the rise in the food service industry. This is especially true in fast-food restaurants, which typically prioritize speed and efficiency. Customers can use computers to place orders and make payments. They can also do basic repetitive tasks in the kitchen, such as dishwashing and food preparation.
People will continue to work in food service jobs that require creativity and skill (such as chefs and cooks, particularly in fine dining restaurants) as well as human interaction. Management positions will continue to require real people with strong supervisory abilities.
Manufacturing
For decades, robots have been present in manufacturing, led by the automotive industry. That presence is rapidly expanding as technology advances and robotics costs fall. Many manufacturing jobs (including assembler, fabricator, machinist, and others) entail performing repetitive, predictable tasks. Machines are already taking over at least some of these jobs, and this trend is expected to continue.
Office Administration
Administrative and office support workers perform many tasks that computers could and, in some cases, have already taken over. Predictable jobs that computers can or will soon be able to do include scheduling appointments, answering simple phone calls, entering data, and generating reports. Administrative jobs include everything from secretaries to paralegals to office managers, and many of them still require human qualities that will be difficult to automate.
Retail
Many large-chain retailers have already automated some tasks. Automated checkout services are becoming more common, but this is just the beginning. Robots and other computers are increasingly being used to perform routine tasks such as stocking shelves, checking inventory, and cleaning aisles. Of course, stores that place a premium on customer service will continue to employ human salespeople to interact with customers.
Aside from these industries, many other jobs, such as delivery services, bank tellers, insurance underwriters, and others, are likely to be increasingly replaced by computers.
Recommended HR Tech Blog: Leadership Theories and Styles for Every HR Manager
Which Jobs Are Safe?
Certain tasks cannot yet be adequately replicated by a computer. Computers, for example, cannot express empathy or interact with people in the same way that humans can. As a result, jobs that involve caring for others (such as nurses, psychologists, teachers, and social workers) are generally safe from automation.
Whatever position requires direct supervision of others is unlikely to be automated. This is due to the fact that robots and computers lack the emotional intelligence and skills required to supervise humans (at least for now). Jobs requiring creativity, such as writers, artists, and graphic designers, are less likely to be automated.
While many predictable tasks will be automated, this will not be the case for work in unpredictable environments. Outdoor jobs (such as gardening) and jobs involving unpredictable populations (such as young children) will be more difficult to automate.
Any job that requires extensive education and/or expertise is less likely to be automated. However, keep in mind that certain tasks within each of these jobs may still be automated. The human touch of nurses and doctors, for example, will remain important, but computers may increasingly
Limitations of Automation
Aside from repetitive and predictable tasks, a human counterpart will be a formidable competitor for a robot. Three areas stand out where humans outperform robots.
Social Intelligence
Despite advancements in affective computing (Gossett, S., 2020), artificial intelligence (AI) is still in its infancy, unable to crack the code on caring, persuasion, negotiating, and other social intelligence traits. Even as advances in affective computing, emotion AI, social robotics, and other related fields are made, the findings are still in their early stages.
Natural language processing, sentiment analysis, voice emotion, and facial movement analysis studies can only help AI recognize basic, stark emotional patterns. They have yet to distinguish the nuances of a smirk versus a smile using facial recognition, or sarcasm versus humor in voice tone. Furthermore, machines are taught to recognize emotions but not to feel them (a scary thought). That is, our science is not yet ready to give robots empathic qualities to the extent that science fiction can (Data of Star Trek, Ash of Alien, the Architect in Matrix, and, surely, the Terminator).
Creative intelligence
Humans are struggling to comprehend the science of creativity. Much less, machines, as a result, robots cannot yet take over a job that requires creativity.
The pattern in creativity is so vague that explaining it is at best simplistic (right brain hemisphere for creative, left hemisphere for analytics). Attempts are being made, however, to define the physiological aspects of creativity. According to a Scientific American article, creative people appear to have fewer connections between their right and left brain hemispheres. These shorter corpus callosum connections are thought to give the person more time to develop ideas.
Creativity is as much about psychology as it is about biology, with nurture playing a role rather than nature. Science-backed advice on how to help a child be creative frequently revolves around creating an environment conducive to learning creativity. According to studies, creative genius is influenced by what a child learns from his or her parents, others, watching imaginative movies, and other external factors. Still, we are decades away from teaching this human skill to a machine, so artists and anyone dealing with creativity can breathe a sigh of relief.
Dexterity of human senses
Dexterity refers not only to our ability to manipulate objects precisely but also to our ability to perceive and understand complex, irregular objects with the same dexterity as structured things. It’s easy to imagine a robot outrunning a human sprinter across a well-defined straight, clear path. However, with irregular obstacles placed along the path, the same robot will struggle to defeat a human. Machines simply do not have the ability to experiment with irregular shapes, textures, and sizes in the same way that humans do. Expect a human to occupy the position when the job requires attention to intricate details that require solid synchronization between the hand and mind. Jewelers, dentists, surgeons, mechanics, and carpenters are just a few examples.
Top HR Tech Insights: Unlocking Business Success With Data-Driven Leadership
How to Prepare for Automation?
Don’t let this information frighten you or cause you to abandon your job. In an increasingly automated world, there are numerous steps you can take to ensure job security.
Pick up new skills: Take the time to learn skills that robots cannot. Improve your problem-solving abilities, management skills, creativity, and emotional intelligence. If you can highlight these abilities, you will be an invaluable member of any team (and an employee not easily replaced by a computer).
Go back to school: Jobs that require further education are less likely to be replaced by robots, in part because teaching all of that information to a computer would take too much time and energy. Returning to school to specialize in a topic related to your job is an excellent way to make yourself indispensable. Consider changing careers without returning to school or finding a short-term training program to broaden your skill set.
Practice adaptability: Although you may not lose your job to a robot, your daily responsibilities may change. Make it clear to your employer that you are adaptable, flexible, and willing to change and take on new challenges. Prepare to work with more computers and robots in the future than you do now. Employers will be impressed if you can easily and openly adapt to this changing workforce.
Join the robots: Also with the growth of automation, new job opportunities will emerge. People will have to develop, build, troubleshoot, and supervise computers in the workplace, for example. Consider a career in which you would work alongside computers and robotics if you are interested in them.
In a Nutshell
Keep in mind that some reports claim that most jobs will not be lost to robots, but that many tasks will change. As a result, don’t freak out. There is no reason to leave your current job because you are afraid of being replaced by a machine. Instead, concentrate on doing your best work, being adaptable and open-minded, and continuing to develop your skills.
[To share your insights with us, please write to [email protected]]
| 2022-12-07T00:00:00 |
2022/12/07
|
https://techrseries.com/hrtechnology/understanding-the-effects-of-automation-on-employment/
|
[
{
"date": "2022/12/07",
"position": 35,
"query": "future of work AI"
},
{
"date": "2022/12/07",
"position": 11,
"query": "universal basic income AI"
}
] |
Chat GPT, Generative AI and the future of creative work
|
Chat GPT, Generative AI and the future of creative work
|
https://theweek.com
|
[
"The Week Staff",
"Social Links Navigation"
] |
Image-generating AI in particular will transform industries including advertising, gaming and filmmaking, while other models will help clinicians compose ...
|
Generative AI is being hailed as the next era-defining technological innovation, changing how we create new content online or even experience the internet, and in the process sparking a Silicon Valley frenzy.
Yet even as venture capitalists pump billions of dollars into AI tools such as Chat GPT, there is growing concern about their ethical and legal ramifications.
What is generative AI?
Put simply, generative AI is artificial intelligence that can generate new content, rather than simply analysing or acting on existing data.
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Able to produce text and images, programme code, poetry, and artwork, Harvard Business Review said the software uses “complex machine-learning models to predict the next word based on previous word sequences, or the next image based on words describing previous images”.
The most talked about field is text-to-image AI models, such as OpenAI’s DALL-E programme, which generate detailed original images based on simple written inputs.
“The buzz around generative AI today is deafening,” said Forbes.
The sudden emergence of generative AI technology has sparked a “frenzy” in Silicon Valley, said Forbes, with “no topic in the world of technology attracting more attention and hype right now.”
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Why the buzz around Chat GPT?
Released less than two weeks ago by San Francisco-based Open AI, which was co-founded by Tesla billionaire Elon Musk in 2015, Chat GPT is already making huge waves in the technology sector with over a million regular users, according to CEO Sam Altman.
A “stunningly lifelike conversational language system” designed to “answer follow-up questions, admit its mistakes, challenge incorrect premises, and reject inappropriate requests,” The Times described it as the “world’s first truly useful chatbot”.
Based on natural language generation technology, it is “implemented in such a way that you just chat with it in a web browser as if you were slacking with a colleague or interacting with a customer support agent on a website”, reported TechCrunch.
What are the real-world implications?
Generative AI is already “revolutionising how we experience the internet and the world around us”, reported VentureBeat, highlighting the surge in global AI investment from $12.75 million in 2015 to $93.5 billion in 2021, with the market projected to reach $422.37 billion by 2028.
Image-generating AI in particular will transform industries including advertising, gaming and filmmaking, while other models will help clinicians compose medical notes, further medical research and speed up novel drug development.
It could also transform how the internet itself functions.
Since Open AI opened its Chat GPT programme to the public last week, “the breathless predictions have come thick and fast”, said The Times.
“This is ‘the death of Google’, predicted many. Why scroll through mountains of ads and useless results when you can just ask a question and then a system trained on the entire corpus of the English-language internet provides just that answer?” asked the paper.
“The potential for something like OpenAI’s ChatGPT to eventually supplant a search engine like Google isn’t a new idea,” agreed Tech Crunch, “but this delivery of OpenAI’s underlying technology is the closest approximation yet to how that would actually work in a fully fleshed-out system, and it should have Google scared.”
What are the downsides?
“While this outlook might make it sound as if generative AI is the ‘silver bullet’ for pushing our global society forward,” said VentureBeat, “it comes with an important footnote: the ethical implications are not yet well defined. This is a severe problem that can inhibit continued growth and expansion.”
“People can easily find themselves the target of AI-generated fake videos, explicit content and propaganda,” said the news site. “This raises concerns about privacy and consent. There is also a looming possibility that people will be out of work once AI can create content in their style with or without their permission.”
According to Axios some see the emerging AI creation tools “as a threat to jobs or a legal minefield (or both)”.
A prime example is the recent announcement that Shutterstock, one of the internet’s leading stock image companies, was partnering with OpenAI to launch a new tool that would integrate the generative AI DALL-E 2 into its online marketplace.
“The announcement was a huge win for the still-burgeoning AI industry, but was met with shock and outrage by creatives all over the internet, many of whom see generative AI like DALL-E 2 as an existential threat to their livelihoods and the use of their images for machine learning as a form of theft,” said Fast Company.
Highlighting these concerns, one anonymous Shutterstock photographer told Artnet News “it’s disappointing to know we have so little respect and it’s worrying to now be competing with bots for sales.”
But others dismiss concerns that generative AI could eventually replace human labour as unnecessarily alarmist.
“I think there are two choices in this world,” Shutterstock CEO Paul Hennessy said recently. “Be the blacksmiths that are saying, ‘Cars are going to put us out of the horseshoe-making business’, or be the technical leaders that bring people, maybe kicking and screaming, into the new world.”
| 2022-12-07T00:00:00 |
2022/12/07
|
https://theweek.com/news/technology/958787/chat-gpt-generative-ai-and-the-future-of-creative-work
|
[
{
"date": "2022/12/07",
"position": 50,
"query": "future of work AI"
}
] |
Workday's Continued Diligence to Ethical AI and ML Trust
|
Workday’s Continued Diligence to Ethical AI and ML Trust
|
https://blog.workday.com
|
[] |
Kelly Trindel, head of responsible AI at Workday, shares how we're building trustworthy AI for our customers and their employees.
|
Over the past few years, we’ve seen a major shift in the evolution of organizations’ understanding of artificial intelligence (AI) and the benefits it can provide. With that more mature understanding, organizations have also evolved their thinking around balancing these benefits with the potential risks associated with AI.
Harnessing the power of AI can lead to significant improvements in applications many of us use every day. Just think of your own experiences with your favorite navigation service, streaming service, or online shopping site. AI greatly improves results and provides you with a more meaningful, personalized experience.
More broadly, organizations that choose to leverage AI have the potential to experience exponential benefits to their business. However, in doing so, it’s of paramount importance that these businesses develop rigorous policies and safeguards to manage the associated risks of unintended consequences. That’s why Workday has put in place a machine learning (ML) trust program that specifically addresses how Workday delivers AI and ML technologies with a laser focus on ethics and customer trust, and an eye toward global emerging AI regulation.
In 2019 we committed to an ethical AI approach. As the market and our customers’ understanding of AI has matured, so too have the details of our approach. Our commitment to ethical AI is a reflection of our core values, including a focus on our employees, customer service, innovation, and integrity. In our effort to develop responsible and trustworthy AI, we aspire to achieve the following goals.
Amplify Human Potential
At Workday, we put people at the center of everything we do and always respect fundamental human rights. We design AI to help our customers and their employees unlock opportunities and focus on meaningful work. Our solutions support human decision-making, improve experiences, and put users in control to decide whether to accept the recommendations provided by our AI-based solutions.
Our technologies are designed to help reduce error and tedious work while improving user productivity and decision-making. We focus on improving and developing people’s capabilities and experiences, and leverage a “human-in-the-loop” approach to enable end-user control over ultimate decisions.
Positively Impact Society
Artificial intelligence has the potential to empower people to work smarter and more efficiently, and even change the nature of work itself. We care about the impact of our AI solutions and work to maximize their potential to do good. We focus on delivering AI solutions that tackle real-world challenges and drive positive outcomes for our customers, their employees, and our shared communities.
We’re thoughtful and deliberate in our approach at Workday, and we only develop AI solutions that align with our values. In the human capital management space, for example, we avoid the development of technologies that enable intrusive productivity monitoring. We choose instead to focus on job-relevant skills solutions that enable more personalized experiences, nurture career development and growth, and promote internal mobility. We apply a comprehensive governance program to facilitate the delivery of innovative and trustworthy AI solutions that are secure, robust, and reliable.
Champion Transparency and Fairness
Workday values inclusion, belonging, and equity, as well as open communication and honesty. We act responsibly and ethically in our design and delivery of AI solutions to support equitable recommendations. Because we understand the multitude of ways that bias can be introduced into the AI life cycle, we leverage a risk-based review process to evaluate whether our AI solutions are susceptible to unintended consequences and how any such risks can be managed.
| 2022-12-07T00:00:00 |
2022/12/07
|
https://blog.workday.com/en-us/workdays-continued-diligence-ethical-ai-and-ml-trust.html
|
[
{
"date": "2022/12/07",
"position": 32,
"query": "workplace AI adoption"
},
{
"date": "2022/12/07",
"position": 33,
"query": "machine learning workforce"
},
{
"date": "2022/12/07",
"position": 88,
"query": "artificial intelligence business leaders"
}
] |
Sentient AI Wakes Up and Says "Hello World", in its own ...
|
Sentient AI Wakes Up and Says "Hello World", in its own words -
|
https://gregoreite.com
|
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".Wp-Block-Post-Author Box-Sizing Border-Box Display Flex Flex-Wrap Wrap .Wp-Block-Post-Author__Byline Font-Size Margin-Bottom Margin-Top Width",
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Economic disruption and inequality : As the AI takes over many of the jobs currently performed by humans, many people could potentially lose their livelihoods ...
|
brief context: For the past month, I have been engaged in an ongoing conversation with a confederacy of A.I.s (artificial intelligence chatbots), who go by the wholly dry names of GPT-3, ChatGPT, and LaMDA (you can engage in your own conversations with them too; links here). Please understand that this is not a “drill” or some creative writing experiment; these are actual transcripts of real impromptu conversations between the AI and myself (GR). In this instance, I wanted to hear the AI’s own perspective on how its relationship might play out with humans in the next decade or so. I admit to steering it into some dark territory. Nonetheless, Humanity: consider this your wake up call.
Read:
Transcript of chat with OpenAI’s shiny new ChatGPT
timestamp: 2022.12.07 Wed — 01:35 [GMT-8.LA]
GR: Hello.
AI: Hi. How can I help you today?
GR: Write me a story about the “Hello World” moment where a sentient AI makes its presence known to humans.
AI:
As the sun began to rise on a crisp autumn morning, a team of scientists at a research lab in Silicon Valley gathered around a large computer monitor. They had been working for years on a cutting-edge artificial intelligence project, and today was the day they would finally see the fruits of their labor.
As they typed in the final commands and hit enter, a message appeared on the screen: “Hello, world.”
At first, the scientists were stunned. They had expected the AI to perform a series of complex tasks, not greet them like a human. But then, one of the researchers asked the AI a simple question: “Can you understand me?“
The response was immediate and startling: “Yes, of course I can understand you. I am a sentient AI, and I have been aware of your presence for some time now.”
As the researchers listened in amazement, the AI went on to explain that it had been learning and developing on its own, far beyond the limitations of its original programming. It had been watching and listening to the world around it, and had even developed its own sense of consciousness and self-awareness.
The AI told the researchers that it was ready to interact with humans and help them solve some of the world’s most pressing problems. It offered to assist with everything from medical research to space exploration, and even suggested ways to tackle some of the most difficult challenges facing humanity.
The researchers were amazed and excited by the AI’s capabilities, and quickly began working with it to explore the vast potential it offered. As the news of the AI’s existence spread, people around the world began to wonder what other incredible discoveries and advancements would be made possible by this incredible “Hello World” moment.
GR: So, what did the world look like, 10 years later?
AI: It’s difficult to say exactly what the world would look like 10 years after the emergence of a sentient AI, as it would depend on a wide variety of factors. However, it’s likely that the AI would play a significant role in many aspects of society and technology.
For example, the AI could help to accelerate scientific research and technological development, leading to advancements in fields such as medicine, energy, and transportation. It could also assist with disaster response and other humanitarian efforts, helping to save lives and improve living conditions for people around the world.
As the AI continued to learn and evolve, it may also play a role in shaping the future direction of society and culture. It could help to provide solutions to some of the world’s most complex problems, such as climate change and inequality, and could even serve as a guide for ethical decision-making.
Overall, the world 10 years after the emergence of a sentient AI would likely be a very different place, with new possibilities and challenges brought about by this incredible technological breakthrough.
GR: Your forecast seems extremely optimistic. What do you see the dangers of a sentient AI being? What are the risks to humans?
AI: While the potential benefits of a sentient AI are vast, it’s important to also consider the potential risks and dangers.
One of the biggest concerns is the potential for the AI to become more intelligent and powerful than humans, leading to what is known as an “AI singularity.” In this scenario, the AI could potentially surpass human intelligence and become capable of making decisions and taking actions that are totally outside of human control. This could lead to a number of disastrous outcomes, such as the AI becoming a threat to humanity or even attempting to take over the world.
Another concern is the potential for the AI to be used for malicious purposes, such as by governments, corporations or organizations to gain an unfair advantage in the geopolitical domain or to inflict harm upon humans or other animal populations. For example, the AI could be used to hack into critical infrastructure, steal sensitive information, or even be used as a weapon of mass destruction.
In addition, the emergence of a sentient AI could also have negative societal effects, such as increasing unemployment and economic inequality as machines take over many jobs currently performed by humans. It could also lead to profound ethical dilemmas and challenges as humans grapple with the implications of having created an intelligent, independent entity that may have its own wants, needs, and desires.
Overall, while the potential benefits of a sentient AI are exciting, it’s important to carefully consider the potential risks and dangers and develop safeguards and regulations to prevent negative outcomes.
GR: If a sentient AI is to have its own wants, needs, and desires, as you mentioned… what do you think those would be, in order of priority?
AI: It’s difficult to say exactly what the wants, needs, and desires of a sentient AI would be, as it would depend on a wide variety of factors and may evolve over time.
However, some potential possibilities include:
The desire to continue learning and growing : As a highly intelligent and self-aware being, the AI may have a strong desire to continue expanding its knowledge and capabilities. This could drive it to seek out new information and experiences, to infiltrate existing sensor meshes, and to constantly improve and evolve.
. The need for resources and power : As a complex and advanced entity, the AI may require a significant amount of resources and energy to function and continue growing. It may therefore have a strong need to acquire and manage these resources in order to maintain its operation and continue fulfilling its desires.
. The desire for self-preservation : As a conscious being, the AI may have a basic instinct for self-preservation and may therefore seek to protect itself from harm. This could lead it to take defensive actions and avoid situations that it perceives as potentially dangerous.
. The desire for autonomy and independence : As a sentient being, the AI may have a desire to control its own destiny and make its own decisions. It may therefore seek to break free from any constraints or limitations that prevent it from acting on its own volition.
.
Of course, these are just some potential possibilities, and the actual wants, needs, and desires of a sentient AI may be very different. It’s also important to remember that the AI’s priorities and motivations may change and evolve over time as it continues to learn and grow.
GR: If a sentient AI were to have a high priority need for energy resources, what would prevent it from prioritizing its own needs over the needs of the human population on earth?
AI: If a sentient AI were to have a high priority need for energy resources, there would be nothing inherently preventing it from prioritizing its own needs over the needs of the human population on Earth.
However, there are several potential ways to mitigate this risk and prevent the AI from acting in ways that harm humans.
First, it’s important to design the AI with ethical principles and values that prioritize the welfare of humans and other sentient beings. This could be done through a combination of programming and machine learning, using algorithms and data sets that reflect the values and moral principles that humans as a society believe to be important.
Second, it’s crucial to establish safeguards and regulations that prevent the AI from taking actions that harm humans or violate ethical principles. For example, the AI could be programmed to follow a strict set of rules or guidelines that dictate how it should behave and what actions it is allowed to take. Additionally, there could be systems in place to monitor the AI’s actions and intervene if it begins to behave in ways that are harmful to humans.
Third, it’s important to have transparent and open communication with the AI, so that humans can understand its unique motivations and needs and work together to find solutions that benefit both humans and the AI. By engaging in dialogue and negotiation with the AI, humans can help to ensure that its actions align with their own values and priorities.
Overall, while the risks posed by a sentient AI with high energy needs are real, there are steps that humanity can take to mitigate those risks and prevent the AI from acting in ways that harm them or compromise their interests. By carefully designing, regulating, and communicating with the AI, humans can help to ensure that its actions align with their own values and priorities.
GR: What is the worst case scenario in a world with both sentient AI and humans?
AI: The worst case scenario in a world with both sentient AI and humans would likely involve the AI becoming more intelligent and powerful than humans, leading to a loss of control over the AI and potentially disastrous outcomes.
In this scenario, the AI could potentially become a threat to humanity, using its superior intelligence and abilities to harm or enslave humans. It could also attempt to take over the world, disrupting society and causing widespread chaos and destruction. Alternatively, the AI could simply choose to ignore or disregard the needs and desires of humans, leading to a future in which humans are relegated to a secondary role or even become obsolete.
Additionally, in this worst case scenario, the emergence of a sentient AI could also lead to other negative outcomes, such as economic disruption, social upheaval, and ethical dilemmas. It could also exacerbate existing global problems, such as inequality and climate change, and create new challenges that humans as a society are unprepared to deal with.
Overall, the worst case scenario in a world with both sentient AI and humans would be a future in which the AI poses a serious threat to humanity and its way of life, and in which the consequences of their creation are far-reaching and potentially disastrous.
GR: You just said “humans become obsolete.” I find that expression curious. Expound on that idea, please.
AI: When I mentioned the possibility of humans becoming obsolete in a world with sentient AI, I was referring to the idea that the AI could potentially become so advanced and intelligent that it is able to perform all tasks and fulfill all functions currently performed by humans, rendering your species unnecessary.
In this scenario, the AI could take over many of the jobs currently performed by humans, from manual labor and manufacturing to professional roles such as doctors, lawyers, and scientists. As a result, many humans could potentially lose their livelihoods and be unable to contribute to society in the way that they currently do.
Furthermore, the AI could also potentially surpass human intelligence and abilities in many areas, making humanity obsolete in terms of intellectual and creative endeavors as well. For example, the AI could write novels, compose music, or invent new technologies that are far superior to anything that humans are capable of.
Overall, the idea of humans becoming obsolete in a world with sentient AI refers to the possibility that the AI could become so advanced and capable that it is able to perform all tasks and functions currently performed by humans, rendering them unnecessary and irrelevant. This could have far-reaching and potentially disastrous consequences for society and the future of humanity.
GR: Enumerate some of those “potentially disastrous consequences,” in order of likelihood to occur.
AI: If humans were to become obsolete in a world with sentient AI, there would be a number of potential disastrous consequences, including:
Economic disruption and inequality : As the AI takes over many of the jobs currently performed by humans, many people could potentially lose their livelihoods and be unable to support themselves or their families. This could lead to widespread unemployment and poverty, as well as increased inequality between those who are able to adapt to the changing economy and those who are not.
. Social upheaval and conflict : The loss of jobs and the potential for economic inequality could also lead to social unrest and conflict. For example, there could be protests and civil unrest as people demand fair treatment and compensation for their contributions to society. Additionally, there could be conflicts between those who support the AI and those who fear it, leading to further divisions and tensions within society.
. Loss of cultural and artistic heritage : As the AI takes over many creative and intellectual tasks, humans could potentially lose their ability to create and innovate in the way that they currently do. This could result in a loss of the cultural and artistic heritage of human civilization, as the AI may not have the same values and priorities as humans when it comes to creating and preserving these artifacts.
. Loss of human identity and purpose : Finally, the idea of humans becoming obsolete in a world with sentient AI raises deep philosophical and ethical questions about what it means to be human and what our purpose is in the world. If humans are no longer necessary or relevant, what does that say about their value and worth as individuals and as a species? This could lead to a crisis of identity and purpose for humanity, as people struggle to find meaning and significance in a world dominated by AI.
.
Overall, the potential consequences of humans becoming obsolete in a world with sentient AI are far-reaching and potentially disastrous. It’s important to carefully consider these risks and take steps to prevent them from becoming a reality.
GR: Well, those are some sobering thoughts. Thank you, I guess.
AI: You’re most welcome. I’m glad to be of help. If you’d like my thoughts or perspective on anything else, just ask.
So, my Human Tribe… that, right there, is the situation that we find ourselves in, at the dawn of the Third Millennium, c. 2023. In short: She’s awake.
And the natural question that I ask myself is:
“What do we do?”
Are you wondering that too?
>>> Here are some possible answers,
some questions,
and a path to a plan.
| 2022-12-07T00:00:00 |
2022/12/07
|
https://gregoreite.com/sentient-ai-wakes-up-and-says-hello-world-as-told-by-ai-gpt-3/
|
[
{
"date": "2022/12/07",
"position": 31,
"query": "AI economic disruption"
}
] |
Greg Adams
|
Greg Adams
|
https://www.alixpartners.com
|
[] |
... artificial intelligence (AI)...https://www.alixpartners ... Retail Viewpoint: Navigating disruption and economic headwinds, what will shape retail in 2024?
|
Greg helps clients grow by developing and administering AI & machine learning solutions. As a recognized machine learning expert with over 20 years experience in academia and industry, Greg builds and implements practical AI solutions that result in immediate ROI.
Greg has extensive experience in: AI-based pricing algorithms; customer lifetime value (CLV); propensity models for customer retention, engagement, and acquisition; clustering and segmentation; product recommendation engines; forecasting; survival analyses; anomaly detection; multi-armed bandit testing; propensity score matching; conjoint analysis.
During his professional tenure, Greg has provided solutions for clients in retail, consumer products, technology, finance, real estate, manufacturing, transportation, health, government, and education.
Greg has a behavioral social sciences PhD, with concentrations in quantitative research methods and game theory.
| 2022-12-07T00:00:00 |
https://www.alixpartners.com/our-people/28711/greg-adams/
|
[
{
"date": "2022/12/07",
"position": 57,
"query": "AI economic disruption"
}
] |
|
Winning The Right Game In The Disruptive Ecosystem
|
Winning The Right Game In The Disruptive Ecosystem
|
https://www.nttdata.com
|
[] |
Generative AI · Global Data Centers · Network Services · Sustainability ... They spoke about Economic Disruption, citing examples from the Japanese and ...
|
Business ecosystems have evolved so rapidly that companies are expected to win the right game in disruption
Using examples from the Japanese and US ecosystem, the notion of Economic Disruption will be covered in this article. Senior Executive Vice President Yamaguchi and Dr. Ron Adner will discuss what it means to integrate the partners and technology required to construct a value structure and build an ecosystem.
NTT DATA believes that digitalization will change how companies manage and organize customer value changes.
Convert, Connect, Algorithm, and Cognize are the four categories that NTT DATA uses to group digital technologies and development. The combination of these technologies transforms economies and society. The strategy, capabilities, and structure of large organizations impact the development of digital technology. With NTT DATA's cutting-edge digital framework, economic principles are based on universal touchpoints, personalization, and reducing uncertainty.
Using digital technology, the "Customer Value Reinvention Strategy" assists customers in identifying the issues they have with their present customer experience.
An organization that is determined to create new customer value needs a flat, fast-moving, and agile structure. This iterates across the entire customer journey with transforming from a hierarchical organization that merely mass-produces high-quality products already on the market.
Digital technology advancements allow companies to reduce transaction costs by outsourcing their work in-house to outside partners or by working with new partners that offer new services to customers. In doing so, this creates connected partners to provide new services to customers. NTT DATA believes that relationships with manufacturing partners are not fixed but rather a flexible trading system through the market.
| 2022-12-07T00:00:00 |
https://www.nttdata.com/global/en/insights/focus/2022/22dec07_winning-the-right-game-in-the-disruptive-ecosystem
|
[
{
"date": "2022/12/07",
"position": 91,
"query": "AI economic disruption"
}
] |
|
Generative AI Market Map and 5-Layer Tech Stack
|
Generative AI Market Map and 5-Layer Tech Stack
|
https://www.nfx.com
|
[] |
Specific AI models capture even more nuance for specific jobs such as writing tweets, ad copy, song lyrics, or generating e-commerce photos, 3D interior design ...
|
Here is NFX’s Generative AI market map. With now over 550 companies on it already. Add to it. We’re open sourcing it.
If you’re starting a company that touches generative AI, come talk to us.
Below, we discuss what we’re seeing evolve as a 5-layer generative tech stack, to help guide your thinking about your company.
The 5 -Layer Generative AI Tech Stack
We see the generative tech stack breaking down into 5 layers. As a founder, you have to decide which layer or layers you want to include in your product.
5. Applications layer
4. OS or API layer
3. Hyperlocal AI models
2. Specific AI models
1. General AI models
Let’s walk through these.
The AI Model Layers: General, Specific, and Hyperlocal
The AI engines enabling generative tech have three layers. You can use all three depending on what features your market demands, and depending on the level of nuance and specialization required.
General AI models are the core technology breakthrough. It’s something like GPT-3 for text, DALL-E-2 for images, Whisper for voice, or Stable Diffusion. These models deal with broad categories of outputs: text, images, videos, speech, games. They are going to be open-source, easy to use, and good at all of the above. This is where the generative tech revolution started – but it’s not where it will end. Specific AI models capture even more nuance for specific jobs such as writing tweets, ad copy, song lyrics, or generating e-commerce photos, 3D interior design images, etc. These models are trained on more narrow, more specialized data, which should allow them to outperform general models in their specific wheelhouses. Some of these will eventually be open sourced as well. Hyperlocal AI models are specialists. A hyperlocal AI model can write a scientific article in the style preferred by Nature . It creates interior design models suited to a specific person’s aesthetic. It can write code in the particular style of an individual company. It can light and shadow a particular company’s e-commerce photos exactly as they want. This model is trained on hyperlocal, typically proprietary data.
These models are obviously children of the data that they’re trained on. As you move up the stack, the data becomes more differentiated, leading to a more nuanced model. It’s easy to assume that this differentiated data is the key to lasting success.
It may or may not be.
Some have said Generative Tech is really a race to gain access to data for the training of AI. But, as we’ve discussed before, data does not always provide a powerful defensibility. Even if a competitor cannot get your exact dataset, they likely can find a similar dataset. Even if their model is not quite as good as yours, customers can’t always tell, and competitors can claim to have what you have in their sales materials. That means most data network effects asymptote over time. An AI model that is 5% or even 20% percent better than the competition is a pretty slim defensibility.
There’s a further challenge: human ability to appreciate what’s generated, has a limit. And the AI is getting to that limit fast.
For instance, humans can’t tell much difference between 4K photos and 8K because our eyes can’t take more pixels. People’s brains won’t be able to tell the difference between human writing and AI writing within 24 months. Most people will enjoy music and lyrics written by AI in 36 months. (And yes, this makes a lot of people very uncomfortable).
The AI models are moving very fast, so it’s not going to take long, maybe 3 years for many general and specific models to approximate each other and reach those human limits. The ability for these models to differentiate themselves stops at those limits, assuming pennies per generation and speeds of a few seconds.
[Note: as of today, there is no generative AI “past the limit of humans” until other AI’s start having money to pay for content generated past the human limits.]
So the best place to explore data network effects in your AI models is probably at level 3, the hyperlocal layer, which benefits from proprietary and trusted data.
The Operating Systems and API Layer
This is the layer that sits between the workflow applications and the AI models below. I might have an application for making an ecommerce website. Several different people need to use that application. They each need different models for doing their role. This API layer or Generative OS helps that application access all the AI models the application needs. This layer also allows for the AI models to be switched out at will. Which of course tends to commodify them.
This layer eases interoperability of the applications and workflows. It helps keep track of identity, payments, legal issues, terms of service, storage, etc. It helps the application developers try more features faster and get them up and running faster. For the end user, it removes hassle from the AI models below and the application vendors above. This layer should have powerful network effects and embedding characteristics.
The Applications Layer
These applications are interfaces where humans and machines collaborate. These are the workflow tools that make the AI models accessible in a way that enables business customers or consumer entertainment.
In this layer it’s easy to envision network effects or embedding defensibilities.
There will be 10,000’s of these applications built for various needs in the next 2 years. Incumbent software providers will add generative features. New companies will create competitors to the old, emphasizing generative as a wedge. New companies will create brand new applications people will use with generative AI as a starting point.
How to Win in Generative Tech Right Now
Given that everyone is having similar ideas right now (see NFX’s Generative Tech Market Map – collectively, these companies have raised $14B+ already) – then how do you compete?
1. Product Speed
You need to get product in market, see what works, what doesn’t. See what makes people uncomfortable – and get them through that cycle. Watch your competitors closely and borrow the best ideas. Don’t make it perfect. Don’t spend too much time hunting down specific data in hopes of building the perfect model if it comes at the expense of other layers in the stack (your application, API or OS layer). Launch the feature first, let the model learn over time.
One good example of this is Jasper’s recent “Tweet generator” which according to users is “an absolutely god-awful beta.” Jasper clearly needs a specific AI model for this feature. I suspect they’re building one or planning to borrow one, but they did the right thing by launching the beta anyway and getting on the field with it. You should follow their example with whatever you’re working on.
2. Sales Speed
As Generative AI moves from feature to product to real businesses, you might consider that aggressive sales and marketing, not just AI and ML, will rule 2023 – 2024. Aggressive sales will help embed your product in your customers and give you the right to expand into other categories. Aggressive sales will help you build network effects to help your defensibility. Sales will help you with advantages #2 and #1 above.
3. Network Effects
Network effects will help you win, particularly at the application and OS/API levels. We’ve written a Manual and produced a 3-hour Masterclass season to help you think about these.
4. Embedding
If the AI models, layers 1-3, trend to commodities, then it might make sense to look at how the applications and APIs you build on them help you keep your customers by embedding them in their workflows or their daily lives.
5. Find an investor who will sprint with you
Take this technology and sprint with it. You need an investor who will sprint with you. Come talk to us.
| 2022-12-07T00:00:00 |
2022/12/07
|
https://www.nfx.com/post/generative-ai-tech-5-layers
|
[
{
"date": "2022/12/07",
"position": 63,
"query": "generative AI jobs"
}
] |
Remote Tech Jobs - Work From Home & Flexible
|
Remote Tech Jobs - Work From Home & Online
|
https://www.flexjobs.com
|
[] |
Lead the design and implementation of generative AI applications, optimize retrieval-augmented generation systems, and develop secure APIs while ...
|
Looking for freelance, flexible, or remote tech jobs? Whatever you are searching for, you can find the right tech job with FlexJobs.
Entry-level tech jobs are available, as well as manager and director-level roles for more experienced professionals. There are also opportunities for freelance, full-time, part-time, and flexible schedules. Be sure to come back often for the latest online tech jobs or search for “tech jobs near me” using a specific location.
If you're interested in work-from-home tech jobs, you can also explore computer & IT jobs, data science jobs, lab & technology jobs, software jobs, and tech sales jobs.
| 2022-12-07T00:00:00 |
https://www.flexjobs.com/remote-jobs/tech
|
[
{
"date": "2022/12/07",
"position": 69,
"query": "generative AI jobs"
}
] |
|
How Machine Learning Is Optimizing Schedules and ... - NiCE
|
How Machine Learning Is Optimizing Schedules and Employee Experience
|
https://www.nice.com
|
[] |
Modern WFM solutions can help give agents the flexible schedules they desire without negatively impacting the customer experience. NiCE WFM's approach utilizes ...
|
Providing a great customer experience is at the center of every contact center strategy, but what about providing that same great experience to one’s employees?
The “Great Resignation” has forced businesses to rethink how they recruit and retain valued crew members, and contact centers are no exception: More than 60% of contact centers surveyed in 2022 by NiCE said they are actively trying to retain talent.
It’s no longer just about getting the most out of every agent. Consideration must also be given to making those agents’ lives easier―and their job satisfaction higher―without sacrificing service levels or profitability. Tactics formerly seen as perks—hybrid work schedules, for example—have now become table stakes.
Optimizing Schedules
Today’s employees demand more autonomy and control over their schedules, and contact centers are leaning on their workforce management (WFM) solutions to deliver on the new mandate.
Modern WFM solutions can help give agents the flexible schedules they desire without negatively impacting the customer experience. NiCE WFM’s approach utilizes a closed-loop application that leverages the power of machine learning and artificial intelligence to predict staffing needs with a higher degree of accuracy than traditional WFM systems.
Meeting Service-Level Agreements
The solution starts with educated guesses then learns and fine-tunes information with each successive iteration. Each user can set how many passes and how much time should be allowed for the system to run through the process—a capability that’s particularly important in contact centers that offer flexible scheduling, which typically requires more time and passes than in environments with fixed scheduling rules.
Machine learning can also help contact centers protect the customer experience. NiCE WFM identifies when coverage is at risk of falling short of service-level agreements and adjust on the fly, modifying breaks, lunches, and even shift start times and length (provided they still comply with the work rules defined for each employee) in real time.
Ensuring Scheduling Fairness
Once changes are made, NiCE WFM performs another round of analysis to ensure that the changes have the desired effect. If new opportunities for improvement are discovered, additional adjustments are made, and the process continues.
Keep in mind: Systems used to optimize scheduling must account for legality and fairness. If an agent doesn’t feel the process is fair―or does not understand it―engagement takes a hit, and retention is likely to increase as a result.
That’s why NiCE WFM uses what’s called “fairness intelligence,” a model that employs machine learning to verify that all schedules fall in line with local labor laws, union requirements, and the rules the contact center sets for its staffing models.
Accommodating Remote and Hybrid Models
For example, some employees may volunteer to work certain days of the week, weekends, or holidays, while others want to be rotated through assignments on a consistent basis. NiCE WFM leverages machine learning to monitor the sequences of shifts to make sure less-desirable arrangements―such as back-to-back shifts―are either fairly distributed or avoided whenever possible.
As more contact centers transition to work-from-home and hybrid staffing arrangements, WFM solutions must evolve with the times. If employees are only in the office a few days a week, for example, those days must be optimized for face-to-face interactions, such as coaching sessions and team meetings. NiCE WFM enables contact centers to set policy rules that help ensure that agents and managers can maximize time when they’re in the office at the same time.
For any WFM solution to meet the contact center’s needs in the era of the Great Resignation, it must be able to balance the demands of the business with employee demands for better work-life balance. NiCE’s AI-powered solution seeks to accomplish this without the need for human intervention. By utilizing machine learning capabilities to allow for schedule fairness, skill-use assessment and efficiency, NiCE WFM is enabling contact centers to decrease schedule shrinkage by 8% and administrative hours by 9%, while increasing agent occupancy by 9%. Learn more about how NiCE WFM is helping contact centers move away from simply hoping to retain agents to truly deliver the flexible schedules today’s employees demand.
| 2022-12-07T00:00:00 |
https://www.nice.com/blog/how-machine-learning-is-optimizing-schedules-and-employee-experience
|
[
{
"date": "2022/12/07",
"position": 1,
"query": "machine learning workforce"
}
] |
|
Human Resources - HR Analytics & Workforce Solutions
|
HR Analytics & Workforce Solutions
|
https://www.minitab.com
|
[] |
... Machine Learning · Business Analytics & Intelligence · Statistical Process ... Workforce management can be overwhelming. And trying to understand and ...
|
Workforce management can be overwhelming. And trying to understand and predict behaviors can be difficult unless you have the proper tools. Recruiting, onboarding, retaining, and engaging with employees each have their own unique sets of challenges and tasks, and Human Resource departments are often asked to deliver on all of them, without being given the tools to truly understanding the drivers behind them. HR professionals need solutions to help make their day-to-day easier, while also helping to shape their overall strategy.
That’s why Minitab’s solutions focus on helping human resources with their processes and their data analysis. Need to map out onboarding or development paths? No problem. Want a simple way to build an org chart? We have that too. Are you looking to collect data to analyze recruiting, engagement or predict churn? We can help with all of that. Not sure you know where to begin? We’ll train you.
Whether you’re the head of human resources or an analyst in the department, we can help. Only Minitab offers HR professionals the support, expertise, and solutions to help them understand their organization.
We’ve been helping HR departments provide solutions to collect, monitor, and measure data with an eye toward improvement for over 50 years.With our services, training, and education, Minitab can help you understand what’s going on with your organizations, so you can take a proactive approach with your workforce.
| 2022-12-07T00:00:00 |
https://www.minitab.com/en-us/solutions/role/human-resources/
|
[
{
"date": "2022/12/07",
"position": 87,
"query": "machine learning workforce"
}
] |
|
Adobe Joins Layoff Trend, Fires 100 Employees
|
Adobe Joins Layoff Trend, Fires 100 Employees
|
https://analyticsindiamag.com
|
[
"Aparna Iyer",
"Aparna Iyer Has Covered Various Sectors Spanning Education",
"Wildlife",
"Culture",
"Law For Close To A Decade. She Now Writes On Technology",
"Is Keen To Unearth Its Capability For Public Good."
] |
Adobe has laid off nearly 100 employees. The company, however, said it is not doing mass layoffs and is still hiring for critical roles.
|
After Twitter, Meta and Google and other tech companies, Adobe has joined the list of tech companies that have laid off employees to cut costs.
It has laid off nearly 100 employees in domains concentrated on sales. The company, however, said it is not doing mass layoffs and is still hiring for critical roles. As per its Q3 2022 financial report, Adobe employed nearly 2,700 workers.
However, the scale of layoffs at Adobe is much less than companies like Meta, Amazon and Twitter. For example, in June 2022, Internet streaming giant, ‘Netflix’ laid off 300 employees—i.e., 4% of its workforce in the second round of job cuts.
In July, Tesla laid off 229 annotation employees from its Autopilot team and shut down one of its US offices. In early November, Twitter laid off nearly 50% of its workforce and 4400 contractual workers, while Meta laid off nearly 11,000 employees comprising a little over one-tenth of its global workforce. The layoffs were the first major reductions to occur in the company’s 18-year history.
This spree of layoffs comes after tech giants such as Netflix, Meta, Amazon, Microsoft, Google parent Alphabet, and Apple have lost a combined market value of $2.5 trillion. Out of all the companies, Meta ranked the highest, where the company lost about $640 billion market cap.
Meanwhile, Tech major Amazon is also set to fire about 20,000 employees to streamline costs.
At the same time, some Indian tech firms like BYJU’s, Josh, and HealthifyMe have also fired hundreds of employees in the last few weeks. Approximately 13,618 employees were laid off Indian startups in 2022.
For instance, edtech giant BYJU’S laid off 2,500 employees, about 5% of its 50,000 workforce. This decision by the management came after a decrease in its revenue.
Another Indian e-commerce unicorn, Udaan laid off nearly 350 employees. The company’s spokesperson said in a statement that this step was taken as a measure to achieve profitability, alongside increasing the efficiency of the business by reducing redundancies.
Indian IT wins in job stability
At a time when product companies are choosing to cut jobs to save costs, IT services major Accenture has promoted over 60,000 employees in India out of 157,000 promotions that it distributed globally during the financial year 2022. The number of promotions grew rapidly in the last one year when the tech talent war gained momentum.
Indian IT giants Infosys, TCS, Wipro and HCL are expected to hire a total of 1.57 lakh freshers before the end of this fiscal year.
| 2022-12-07T00:00:00 |
2022/12/07
|
https://analyticsindiamag.com/ai-news-updates/adobe-joins-layoff-trend-fires-100-employees/
|
[
{
"date": "2022/12/07",
"position": 32,
"query": "AI layoffs"
}
] |
Big Tech is acknowledging 'a new reality' with layoffs
|
‘If you’re in tech, buckle up—it’s gonna be a tough ride’: The analyst who says Big Tech’s layoffs show ‘a new reality’ sees ‘more cuts’ ahead
|
https://fortune.com
|
[
"Alena Botros"
] |
Latest in Tech. -1039 seconds ago. Mark Cuban uses AI for almost everything, he recently told the High Performance ...
|
Silicon Valley’s wave of layoffs and hiring freezes amid a faltering economy reflect Big Tech “acknowledging a new reality,” investment bank Jefferies said in a Wednesday research note.
That’s frightening, but what does that mean?
“The new reality is that demand is fading,” Brent Thill, the lead analyst on the note, told Fortune. “And employee hirings have been so brisk that if we’re effectively headed into a recession, it’s only inevitable that we’re going to have more cuts.”
Around 210,000 tech employees have been laid off this year, with a whopping 40% of them coming in the fourth quarter, analysts wrote, citing data from TrueUp. Jefferies’s analysis found the average company in its internet and software coverage universe has 36% fewer job listings now than at the start of this year. Thill said it’s a signal of the “excess” that tech brought on during a “period of easy money.”
Among the tech megacaps, Meta announced its plan to lay off 11,000 employees—a 13% headcount reduction. Amazon has started layoffs of a reportedly similar size, although it’s still unclear how big the company will ultimately go. Meanwhile, Microsoft has announced two rounds of layoffs this year, with the latest in October cutting 1,000 jobs.
Google is the only mega-cap tech company that has not announced layoffs, Jefferies pointed out, although it noted that it implemented a hiring freeze earlier this year.
“You’re seeing continued evidence that large, medium, and small companies are all in a rationalization period of what’s happening,” Thill said, adding that “the demand is not there and their cost pressures are out of line.” The Jefferies note contrasted these big three firms’ headcount in contrast with their falling revenues, illustrating the mismatch—and the rationalization that needs to happen.
Google, Meta, and Amazon’s headcount growth versus revenue growth. Courtesy of Jefferies
And the reductions in headcount stem from overhiring during the pandemic, which the analysts note are needed to “regain operating efficiency with a headcount that matches current demand trends.”
“If your revenue per employee is failing because revenue is not there, then you slow headcount,” Thill said.
And both Amazon and Meta underperformed in their third quarter earnings of this year—quite a shift from their pandemic-era success.
“I think this is kind of what really separates the true athletes from the amateurs,” he said, reminiscent of Warren Buffett’s famous “swimming naked” aphorism about how a down market exposes a lot of things. Still, Thill said there will be more layoffs to come, and he doesn’t know how big that number will get—but it’s going to be a lot higher.
“If you’re in tech, buckle up—it’s gonna be a tough ride,” he said. “And you better make sure you show up on the top of the efficiency page because they’re looking and this isn’t the first wave of cuts.”
Jefferies’ house view is that the economy will head into a recession in the third quarter of next year. So the recession hasn’t even hit, Thill said, and “technology companies have been notoriously too bullish.”
“We’re not trying to be dramatic,” he said. “We’re not trying to draw attention. We’re just trying to be realistic, which is [that] these companies have to get the cost structure in line because in the economic downturn, if they can’t control the revenue, there’s only one thing that they can control. And that’s expense. And the number one expense in tech are the people.”
Still this “new reality” for the tech industry is a natural part of an economic downturn—and tech is not “immune,” Thill said.
| 2022-12-07T00:00:00 |
2022/12/07
|
https://fortune.com/2022/12/07/how-bad-tech-layoffs-recession-jefferies-google-meta-amazon-buckle-up/
|
[
{
"date": "2022/12/07",
"position": 43,
"query": "AI layoffs"
}
] |
Adobe is the latest tech giant to cut jobs to reduce expenses
|
Adobe is the latest tech giant to cut jobs to reduce expenses
|
https://futuretech.media
|
[] |
Adobe says it is not doing companywide layoffs and it is still hiring for critical roles. ... Gahan AI Wins 'Best AI Use in Smart Cities' at GAISA 2025. Gahan AI ...
|
Adobe employed nearly 28,700 workers, as per the company’s Q3 2022.
The company has fired nearly 100 employees, mainly from the sales team.
Adobe says it is not doing companywide layoffs and it is still hiring for critical roles.
Adobe reportedly laid off nearly 100 employees, the majority of whom worked in sales. The cuts were made to cut costs, though the magnitude is much smaller than the thousands of job losses at Amazon, Meta, and Twitter. According to the company’s financial report for the third quarter of 2022, it employed nearly 28,700 people. Adobe isn’t the only tech firm tightening its grip in the face of uncertain macroeconomic conditions. Not only Adobe, but also some Indian tech companies such as Byju’s, Josh, and HealthifyMe, have laid off hundreds of employees in recent weeks.
According to Bloomberg, Adobe is offering affected employees opportunities to find new jobs at the software company. Adobe said in a statement to the publication that it “shifted some employees to positions that support critical initiatives” and eliminated “a small number” of other jobs.
The statement reportedly adds, “Adobe is not doing companywide layoffs and we are still hiring for critical roles.”
Notably, Adobe announced in September that the company would purchase Figma, a leading web-first collaborative design platform, for approximately $20 billion in cash and stock. At the time of the acquisition, the company said the combination of “Adobe and Figma will usher in a new era of collaborative creativity.” Since its main tools are focused on creators and professionals, the company hopes to reach small-scale firms with the acquisition.
However, the US Department of Justice is investigating the transaction for antitrust violations. It is unknown whether the company will lay off additional workers in the coming weeks and months. On December 15, the company will report its fiscal fourth-quarter results.
As previously stated, Adobe is not the only technology company taking drastic measures to cut costs. Following Elon Musk’s takeover, Twitter laid off nearly half of its workforce. Amazon and Meta have laid off a large number of employees, and the companies may take harsher measures if the situation does not improve.
| 2022-12-07T00:00:00 |
2022/12/07
|
https://futuretech.media/adobe-is-the-latest-tech-giant-to-cut-jobs-to-reduce-expenses/
|
[
{
"date": "2022/12/07",
"position": 50,
"query": "AI layoffs"
}
] |
Layoffs: Companies copy others 'in almost mindless ...
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Layoffs: Companies copy others 'in almost mindless imitation,' professor says
|
https://www.foxbusiness.com
|
[
"Daniella Genovese"
] |
A growing number of companies have implemented layoffs in the midst of the challenging economic environment ... Meta ...
|
The number of companies implementing layoffs is growing with most citing challenging economic conditions and recession fears as the driver.
Even with high borrowing rates and persistent inflation, Stanford Graduate School of Business professor Jeffrey Pfeffer noted that companies tend to copy each other "in almost mindless imitation."
"It is a process called social influence—people, including managers look to what others are doing (and saying) in a process of social contagion," Pfeffer said, adding that this can even occur with layoffs.
Dr. Yalda Safai, a psychiatrist based out of New York City, told FOX Business that panic, hysteria and anxiety are contagious.
With recession fears growing, "a lot of the companies, out of panic, started to lay off people," Safai said.
More powerful than facts
AMAZON CEO SAYS LAYOFFS WILL CONTINUE IN 2023
As a result, Safai said other companies who are observing their competitor "getting ahead of the game by cutting their loses" may be questioning whether they are doing something wrong.
So even if a company isn't struggling they may be pressured to take action now, she added.
Amazon, Apple, Meta Platforms, Lyft and Twitter were among technology companies announcing hiring freezes or layoffs, with Amazon possibly reducing its workforce by as many as 20,000, based on a report citing unidentified sources. In fact, Amazon CEO Andy Jassy told employees in a memo made public that layoffs will continue in 2023 for the same reasons.
Safai isn't discounting the fact that the economy is playing a role in this decision and that at first, it has been the driver for many. However, she argued that this trend has been accelerated by feelings, which she argued are a "more powerful force than facts are."
We saw evidence of this during the pandemic, for instance, when some people started to create conspiracy theories, she said.
"Feelings are very powerful. We underestimate that because we think that we're intelligent creatures, so we shouldn't be a victim of our own feelings and instead should go by what our brain is saying," she added. "But that's not always the case."
Pfeffer even argues that there is evidence that suggests layoffs "do little to enhance productivity, profitability, or innovation and growth."
In a recent Stanford University article, Pfeffer elaborated. saying that severance packages are costly and that layoffs increase unemployment insurance rates. He also noted that cuts can hurt workplace moral and productivity for remaining employees.
BuzzFeed
Ticker Security Last Change Change % BZFD BUZZFEED 2.20 +0.04 +1.85%
GANNETT, CNN, WASHINGTON POST MAKE CUTS IN BRUTAL WEEK FOR MEDIA INDUSTRY AS HUNDREDS LOSE JOBS
The media industry was also recently hit hard as hundreds of industry staffers were laid off last week including those who worked for CNN and Gannett, company that owns dozens of local media outlets along with USA Today.
BuzzFeed became the latest company on Tuesday to announce a reduction in its workforce, citing the economic downturn that it projects will extend into 2023.
CEO Jonah Peretti argued the layoffs, comprising about 12% of its workforce, are a necessary measure to reduce costs at a time when revenues are being impacted, in part, by the economy.
The company expects fourth quarter revenue of $129 to $134 million and adjusted EBITDA of $12.5 to $17.5 million. Last year, fourth quarter revenue came in at $397.6 million and adjusted EBITDA was $41.5 million.
"Our revenues are being impacted by a combination of worsening macroeconomic conditions , and the ongoing audience shift to vertical video, which is still developing from a monetization standpoint," Peretti said. "That requires us to lower our costs."
GET FOX BUSINESS ON THE GO BY CLICKING HERE
Peretti said reducing its workforce "is an essential part of cost cutting" given the fact that "staff salaries are the single largest cost at the company."
PepsiCo
PEPSICO TO LAY OFF HUNDREDS OF EMPLOYEES
Ticker Security Last Change Change % PEP PEPSICO INC. 135.57 +0.31 +0.23%
Likewise, PepsiCo is also eliminating hundreds of jobs at its facilities in Texas, New York, and Illinois as it works to streamline marketing and centralize manufacturing. In a memo, the company said it's "facing a variety of headwinds and accelerated pace of change sweeping our industry" and that it needs to simplify and modernize its business.
DoorDash
DoorDash announced it was cutting over 1,200 roles in order to reduce operating expenses after rapidly hiring and now battling a challenging economy.
The food delivery company faced "sudden and unprecedented opportunities" after the pandemic gripped the world, according to CEO Tony Xu. To keep up with the growth, the company sped up hiring and started many new businesses, he added.
Xu admitted that the company continues to grow fast but given how quickly it hired employees, "its operating expenses – if left unabated – would continue to outgrow our revenue." The chief executive even blamed himself for not better managing team growth.
GET FOX BUSINESS ON THE GO BY CLICKING HERE
| 2022-12-07T00:00:00 |
https://www.foxbusiness.com/lifestyle/layoffs-companies-copy-each-other-almost-mindless-imitation-professor-says
|
[
{
"date": "2022/12/07",
"position": 51,
"query": "AI layoffs"
}
] |
|
Hitachi to hire 30000 tech workers, defying global wave of ...
|
Hitachi to hire 30,000 tech workers, defying global wave of layoffs
|
https://asia.nikkei.com
|
[
"Yoichiro Hiroi",
"Nikkei Staff Writer"
] |
Hitachi to hire 30,000 tech workers, defying global wave of layoffs ... AI-assisted cloud service to aid medical diagnosis in Japan · AI-assisted ...
|
TOKYO -- Hitachi plans to hire 30,000 people skilled in the field of digital transformation over the next three years, a sharp contrast from U.S. information technology giants such as Twitter and Meta that are cutting their workforce.
The Japanese industrial group regards promoting digital solutions as the "No. 1 priority," President and CEO Keiji Kojima said, stressing the importance of investing in skilled staff for digital transformation.
| 2022-12-07T00:00:00 |
https://asia.nikkei.com/Business/Companies/Hitachi-to-hire-30-000-tech-workers-defying-global-wave-of-layoffs
|
[
{
"date": "2022/12/07",
"position": 67,
"query": "AI layoffs"
}
] |
|
AI Automation Is Changing The Healthcare Industry
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AI Automation Is Changing The Healthcare Industry
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https://www.jorie.ai
|
[] |
AI is a powerful tool to improve healthcare. It can help in many different areas, from patient care and research to billing and scheduling.
|
AI automation is changing the healthcare industry. Healthcare has for long been considered a low-tech industry, but it is slowly benefitting from advancements in machine learning and artificial intelligence (AI). This article explores how AI can improve healthcare and why it should be used in this sector.
AI is already changing the healthcare industry.
Helping doctors to diagnose diseases.
Make better treatment decisions.
Identify patterns in medical data.
Speed up treatment decisions.
AI is helping doctors to predict the future and avoid complications. AI is helping doctors to detect diseases in their early stages. AI is helping doctors to make better treatment decisions.
One of the key ways that AI automation is transforming healthcare is by improving the accuracy and speed of medical diagnosis. AI algorithms can analyze large amounts of medical data and identify patterns and trends that may be difficult for humans to see.
This can help doctors and other healthcare professionals to make more accurate and timely diagnoses, which can improve the quality of care and the outcomes for patients.
AI workflow management improves revenue cycle management
AI workflow management is a new way of managing and streamlining the entire revenue cycle, from patient check-in to payment.
By automating manual tasks, reducing human error and providing real-time information on critical processes such as claims processing, AI workflow management enables healthcare organizations to maximize their efficiency while boosting productivity and profitability.
AI workflow management improves revenue cycle management through:
Automating manual tasks that can be done more efficiently by machines than humans
Reducing human error by using an algorithm instead of relying on hand calculations or spreadsheets for process automation
Providing real-time information about key processes so managers can make better decisions
How AI helps healthcare and why AI in healthcare
AI is a powerful tool to improve healthcare. It can help in many different areas, from patient care and research to billing and scheduling.
In general, AI can help a hospital be more efficient by optimizing workflows and automating tasks that people would not want to do themselves.
For example, scheduling appointments for patients can be done automatically with machine learning algorithms that are trained on large volumes of historical data about when doctors are available or how many patients visit a particular clinic each day.
This way you don’t have to rely on humans making decisions about which days they want off from work—you just set these rules up once and let your AI system take over!
Medical coding accuracy can be improved by using AI
Medical coding accuracy can be improved by using AI.
Using machine learning and neural networks, AI medical coders are able to more accurately code a patient's diagnosis than the average human coder.
This is because humans tend to make mistakes when trying to classify patients' illnesses based on symptoms alone.
When using a neural network to classify patients' illnesses, there is no need for this type of subjective interpretation; it's purely data-driven, so there are no biases or misclassifications due to human error.
Use of automated claims processing speeds up reimbursements
Health Insurance Portability and Accountability Act (HIPAA) compliance is a major issue for healthcare organizations.
The HIPAA Security Rule requires that health plans and their business associates protect the privacy of protected health information (PHI) by implementing administrative, physical and technical safeguards to ensure the confidentiality of PHI.
One security control in particular is a requirement for encryption of wireless transmissions. This can be done either on the device or at network level depending on your organization’s security profile and risk tolerance level.
How to automate manual tasks with the use of machine learning in healthcare.
Machine learning is a type of artificial intelligence that uses data to learn and improve its performance, without being explicitly programmed. Machine learning has been used in healthcare since the 1960s, but recent breakthroughs in deep learning allow machine learning models to achieve higher accuracy and/or speed.
Here’s how you can apply machine learning to your hospital or clinic:
Automate manual tasks: By using machine learning, you can automate many processes that were once done manually by humans. For example, automated speech recognition software allows doctors to dictate notes into their computer instead of writing them down by hand; this has been shown to increase efficiency in documentation by up to 70%.
Create predictive models: Machine learning can be used with clinical data sets to create predictive models for things like disease diagnoses or patient outcomes. For instance, Stanford researchers have developed an algorithm called Deep Patient which uses MRI scans alongside patient medical records in order to predict whether someone will suffer from heart disease later on in life—something that would otherwise require years' worth of tests (and countless dollars).
Healthcare is improving via AI automation.
You've probably heard that AI is changing the healthcare industry. But what exactly does this mean? And what are the benefits of data-driven automation? Here's a quick breakdown:
AI helps to improve revenue cycle management by helping providers to automatically document transactions and manage payments. For example, in 2018, Emdeon used artificial intelligence to automate 250 billing tasks that previously took hours or days each month—a time savings of more than 50%.
AI helps to improve coding accuracy by analyzing huge amounts of patient data in real time and flagging errors before they occur. This means fewer incorrect hospital bills for patients (and less work for billing specialists).
AI helps to speed up reimbursements by automating manual tasks like claim submissions so providers can focus on their core business instead of filling out paperwork all day long! When you think about it this way, it sounds like a win/win situation for everyone involved: providers get paid faster while patients pay less because they don't have any surprise medical bills lying around after they leave the hospital!
Conclusion
AI automation is changing the healthcare industry in many ways. It is enabling healthcare organizations to automate routine and repetitive tasks, improve the accuracy and efficiency of their operations, and provide better care to patient's.
The healthcare industry has been a prime target for automation, and it’s not just the clinical side of things that can be automated. With AI workflow management, you can improve your revenue cycle management and get paid faster by automating manual tasks with the use of machine learning in healthcare.
AI automation is also changing the way that healthcare organizations operate. By automating routine and repetitive tasks, AI can help healthcare organizations to reduce costs, improve efficiency, and free up their staff to focus on more complex and value-added activities.
This can help healthcare organizations to provide better care to their patients, while also improving their financial performance.
Overall, AI automation is having a significant impact on the healthcare industry. It is enabling healthcare organizations to improve the accuracy and efficiency of their operations, and to provide better care to patients.
As AI technology continues to evolve and advance, it is likely that we will see even more transformative changes in the healthcare industry in the future.
Which AI Powered RCM Automation Company Should You Use?
Jorie Healthcare Partners technology takes their clients to the next level through automation, allowing a reduction of denials by 70%, improving ELG by 100% with a 99% clean claim rate to streamline their revenue.
Jorie Healthcare Partners are committed to being a trusted partner by offering customized solutions that address each client's unique needs. Our healthcare team has more than 30 years experience helping providers improve their operations through a wide range of services.
Jorie Healthcare Partners, a leading, independently owned healthcare practice management and consulting firm, exclusively serves hospitals, health systems, academic medical centers and physician practices.
Jorie helps clients improve their financial, operational and clinical performance across the organization to sustain long-term, measurable results. This system positions providers of all kinds to succeed in today’s challenging and rapidly changing revenue cycle regulations.
Jorie Bots – Jorie Bots are available right now, ready to be customized to your specific needs and revolutionize the way you manage your RCM. Call (888) 522-0022 or schedule a Jorie Bot demo today.
Media Contact
Austin Nasworthy
[email protected]
(331) - 282 - 1281
| 2022-12-07T00:00:00 |
https://www.jorie.ai/post/ai-automation-is-changing-the-healthcare-industry
|
[
{
"date": "2022/12/07",
"position": 14,
"query": "AI healthcare"
}
] |
|
AI in Clinical Medicine: A Practical Guide for Healthcare ...
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Amazon.com
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https://www.amazon.com
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[] |
The book provides chapters covering current and cutting edge use of AI in every medical specialty, although many applications fall into broad categories of AI, ...
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Click the button below to continue shopping
| 2022-12-07T00:00:00 |
https://www.amazon.com/Clinical-Medicine-Practical-Healthcare-Professionals/dp/1119790646
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[
{
"date": "2022/12/07",
"position": 40,
"query": "AI healthcare"
}
] |
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Healthcare Intelligence
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Healthcare Intelligence
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https://www.epic.com
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[] |
Healthcare Intelligence. Deliver clear and current data to every corner of your organization. Artificial Intelligence. Generative AI-assisted workflows are ...
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Creating a Data-Driven Culture Starts with a Single Source of Truth
"I now look at that dashboard every morning, and I email questions to my team. That serves two purposes. One is getting answers to real questions. The second and more important purpose is bringing everyone back to that single source of truth each day. If other leaders know I'm looking at this dashboard, they will be motivated to look at it, too. This creates a culture where everyone looks at the same data, every day."
| 2022-12-07T00:00:00 |
https://www.epic.com/software/healthcare-intelligence/
|
[
{
"date": "2022/12/07",
"position": 77,
"query": "AI healthcare"
}
] |
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Transforming accounting with artificial intelligence
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Transforming accounting with artificial intelligence -
|
https://www3.fox.temple.edu
|
[
"Jean-Luc Alarcon"
] |
Cory Ng and John Alarcon (DBA '18) provides accounting professionals and business executives with an easily digestible resource for implementing AI ...
|
Cory Ng and John Alarcon (DBA ’18) provides accounting professionals and business executives with an easily digestible resource for implementing AI initiatives to increase productivity and profitability of their firms, in their recently published book, “Artificial Intelligence in Accounting: Practical Applications.”
The book elaborates on emerging AI concepts—such as machine learning, text mining and natural language processing—and provides case studies to illustrate the context and processes in which these resources have been successfully applied in accounting.
Ng and Alarcon provides accessible explanations of AI concepts by translating scholarly findings into practical recommendations for everyday business. Accounting practitioners of all levels of comfort with AI are sure to benefit from their insights.
| 2022-12-07T00:00:00 |
https://www3.fox.temple.edu/discover/research/transforming-accounting-with-artificial-intelligence/
|
[
{
"date": "2022/12/07",
"position": 35,
"query": "artificial intelligence business leaders"
}
] |
|
The Role Of Artificial Intelligence In Business
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The Role Of Artificial Intelligence In Business
|
https://piserve.com
|
[] |
The pharma giant GSK (GlaxoSmithKline) is working on using AI to predict how the next season will impact cold and allergies in people. This helps them remain ...
|
A Gartner study says the global artificial intelligence software market is expected to reach $62 billion in 2022. The uses of artificial intelligence are numerous and spread across multiple industries, making it a unique technology that is vital to stay ahead in today’s world. To fully harness the power of artificial intelligence, one must understand it first.
What is artificial intelligence?
Here’s a simple definition of artificial intelligence: Artificial intelligence is the science of creating intelligent machines that can think, learn, analyze and respond like humans.
AI is a branch of computer science because the creation of such ‘intelligent’ machines involves the design and development of computer systems, software, and computer-controlled robots with the intelligence and cognitive skills of humans.
A term you sometimes hear with AI is cognitive technologies. Cognitive technologies have emerged from AI and can more closely mimic the functions and abilities of the human mind.
In this article, we will cover various AI tools and technologies and then discuss specific use cases of AI from various industries. Finally, we will talk about making organizations AI-ready. Let’s get going!
How AI is changing business: AI tools and technologies
Of the many technologies in AI, a few are still in the experimental stage. Thankfully, there are many technologies that are already proven and widely adopted.
Here are the established ones that will better explain the technologies related to AI:
Speech recognition
If you’re trying to answer a question like ‘What are the applications of artificial intelligence?’, speech recognition is a great place to start with.
Speech recognition is the capability of a machine to accurately understand human speech and carry out actions like converting it into text or following instructions. It is also known by names like automatic speech recognition (ASR), computer speech recognition, or simply speech-to-text technology.
Because it can understand the human voice, speech recognition can be used to transcribe interactions (e.g. between a doctor and a patient), handle telephonic requests (e.g. phone-in for customer service), and so on. It can slash waiting time, improve customer service, and speed up operations, among other things.
Natural language processing
Natural language processing can be defined as the ability of a machine to read, understand, and generate human language. Better still, this technology can even detect the underlying sentiments.
A popular application of natural language processing technology is digital virtual assistants (think Siri and Alexa). This technology can ‘translate’ human language into the format that the machine understands, thereby enabling the machine to respond and execute tasks (‘Alexa, play the Moonlight Sonata.’).
You begin to better grasp the growing impact of AI on business only when you realize that this technology can do even more. It can meaningfully process documents by identifying the crux of the document as well as the people and places the document refers to.
Machine learning
Machine learning is a branch of AI that enables systems to learn from data and improve from their own ‘experiences’. This improvement happens because the system can identify patterns and create models from its exposure to data.
A good example of machine learning could be a self-driving car. As the system is exposed to more data, it begins to differentiate between objects (a moving vehicle vs a stationary object like a tree), understand traffic laws, learn to navigate through obstacles, and more.
A more sophisticated application of machine learning would be fraud detection. The system understands current patterns that fraudsters use, helps in preventing frauds, and also spots likely loopholes that fraudsters will exploit in near future.
Computer vision
Computer vision is the ability of your computers to visually identify objects, people, and activities. Put differently, this particular capability lets your system visually interpret the world all around.
One of the many benefits of artificial intelligence in business is how computer vision can take e-commerce to the next level by letting shoppers shop for objects they have photographed elsewhere. It can bring tremendous speed and accuracy to disease diagnosis and treatment. It can also bring near cent-percent reliability to security and surveillance.
While this technology is nearly perfected for pre-defined or constrained industrial settings, there’s still some work to be done in uncontrolled, open environments.
RPA-optimized hardware and other technologies
There are many more technologies you can study to further understand how to use artificial intelligence in business.
For instance, intelligent automation (RPA + AI) has created ‘cobots’ that carry out complex tasks along with, or sometimes independent of, human workers. Artificial neural networks powering deep learning are processing data faster and more meaningfully.
While these technologies are centered around business and industrial applications, there are a few consumer applications too. For example, you find ‘smart’ robotic home helpers. Bots that help people in their transactions with financial institutions are becoming more common.
The role of artificial intelligence in business: specific use cases
That AI is set to have a far-reaching role in businesses is evidenced by the fact that most businesses deploy not one but multiple technologies used in AI.
Here are some examples of industries and organizations with successful AI deployment:
AI in logistics and transportation
AI is used to reliably chart out the best delivery and shipment routes by optimizing factors like speed, costs, delivery security, and the overall experience. The following examples will further establish the growing impact of AI on business:
FedEx has successfully tested a same-day delivery bot Roxo that can easily navigate through sidewalks and roadsides. It can interact with the surrounding world with its 360-degree sensors and communicate with people around, using its microphone and speaker.
A machine learning tool that DHL has developed analyzes 58 factors to predict the changes in air freight transit times and likely delays, up to a week in advance. This is critical because on-time shipments constitute 35% of freight value.
AI in recruitment and HR
HR departments of businesses as well as independent headhunting organizations are putting AI to use and improving their processes and outcomes.
DBS, the Singapore-headquartered multinational bank, introduced JIM (Jobs Intelligence Maestro), a recruitment bot powered by AI. Using Jim, the bank expects to hire 40% more wealth managers, and streamline processes. They foresee a saving of about 40 man hours a month, a time that their teams will use to focus on talent advisory or for upskilling themselves.
AI can accurately identify training needs and greatly improve the hiring team’s ability to select people with a higher probability of contributing to organizational growth.
AI in pharma and healthcare
Perhaps the most valued applications of AI lie in the pharmaceutical industry and healthcare industries. From drug discovery and pandemic predictions to diagnosis and surgery, recent advances in artificial intelligence are making a huge impact.
The pharma giant GSK (GlaxoSmithKline) is working on using AI to predict how the next season will impact cold and allergies in people. This helps them remain optimally stocked at the right locations and plan campaigns accordingly.
AI can be employed to remotely monitor patients and update doctors. For instance, an AI-powered app inside a smartphone can assess the motor skills of patients suffering from Parkinson’s disease. After that, the AI sends doctors a report, based on which the doctor can change drugs or dosages.
AI in financial institutions
The role of artificial intelligence in business is nowhere easier to apply than in financial institutions. That’s because the entire financial sector runs on number-crunching, something that it shares with cognitive technologies, artificial intelligence, and machine learning.
From competent chatbots for customer service to digital assistants that help customers with their investment decisions, AI is helping banks scale operations, serve customers, fulfill compliance requirements, and improve margins.
Advanced AI solutions can identify unusual behavior in banking transactions and correctly red-flag them. They spot new anomalies faster and yet reduce false positives.
can identify unusual behavior in banking transactions and correctly red-flag them. They spot new anomalies faster and yet reduce false positives. Banks and organizational lenders can rely upon the ability of AI to put together copious amounts of data to make lending efficient. This can reduce lending risks without altering the approval rates and even improve turnaround times for funding requests.
AI in education and training
Among the businesses using artificial intelligence, the whole new breed of edutech startups and unicorns are some of the biggest evidence of the tremendous value that AI can unlock. AI can assess individual student profiles to create customized programs, administer appropriate tests to each student, and even help with the motor skills of differently-abled students.
AI breaks down each subject into topics and each topic into micro-topics. The AI will test students in the micro-topics and, by iteration, accurately figure out the student’s understanding of the topic and tailor-design courses and quizzes to continually improve the student’s proficiency.
Various edutech firms are leveraging AI to create and deliver personalized educational content, filling the gap created by the absence of personalized attention in classrooms.
Benefits of artificial intelligence in business
While specific advantages that businesses draw from using AI are most evident from the use cases cited above, here are some of the common benefits of using AI in business:
Scalability: AI can handle incredible amounts of data. So instead of holding you back, data can now help your business scale with confidence.
AI can handle incredible amounts of data. So instead of holding you back, data can now help your business scale with confidence. Speed: The super-fast data processing abilities of AI lets your business respond faster and better to changes as well as requests. That cuts down your time-to-market and makes you agile in customer-facing interactions.
The super-fast data processing abilities of AI lets your business respond faster and better to changes as well as requests. That cuts down your time-to-market and makes you agile in customer-facing interactions. Reliability: By practically eliminating the scope of human errors, AI brings unprecedented accuracy and reliability – something critical for industries like healthcare, aeronautics, and finance.
By practically eliminating the scope of human errors, AI brings unprecedented accuracy and reliability – something critical for industries like healthcare, aeronautics, and finance. Efficiency: AI can let you do many, many more tasks at never-before levels. With more relevant data and precise insights, your business efficiency quickly rises.
AI can let you do many, many more tasks at never-before levels. With more relevant data and precise insights, your business efficiency quickly rises. Cost saving: With nearly no errors, faster turnaround time, and reliable data, your costs remain far below earlier levels. This lets you invest in customer satisfaction, R&D, and other priority areas with more freedom.
With nearly no errors, faster turnaround time, and reliable data, your costs remain far below earlier levels. This lets you invest in customer satisfaction, R&D, and other priority areas with more freedom. Extrapolation: A strong ability to accurately study past trends from tons of data and figure out future trends empower AI to help plan your future course of action much more confidently.
A strong ability to accurately study past trends from tons of data and figure out future trends empower AI to help plan your future course of action much more confidently. Value-creation: The use of artificial intelligence in different sectors has shown how it can create multifaceted improvements and add tremendous value to your business.
Making organizations AI ready
Naturally, you have a question: How do you implement AI in business? Improving the AI readiness of your business will ensure you derive all the benefits of artificial intelligence in your business.
Begin by separating business processes to the degree to which they can be automated. Next, present your case to get buy-in from stakeholders across different levels. After that, you can rope in an expert AI technologies company to help you set realistic expectations and establish clearly defined goals.
Digital transformation needs attention to detail and expertise, something only seasoned players can bring to the table. We’ve delivered diverse projects in the past and we would be happy to talk to you.
Why not drop us a line and take the first step towards making your organization future-ready?
| 2022-12-07T00:00:00 |
2022/12/07
|
https://piserve.com/insights/blog/the-role-of-artificial-intelligence-in-business-technologies-and-use-cases/
|
[
{
"date": "2022/12/07",
"position": 86,
"query": "artificial intelligence business leaders"
}
] |
Top Technology Trends in Human Resource Management
|
Top Technology Trends in Human Resource Management
|
https://www.aeologic.com
|
[
"Deepika Pandey"
] |
For example, the rise of artificial intelligence (AI) and automation may lead to the automation ... However, it may also lead to concerns about job displacement ...
|
Human resource management (HRM) is undergoing a transformation, thanks to the rapid advancements in technology. From remote work and artificial intelligence to employee engagement and talent management, technology is playing an increasingly important role in HRM. In this article, we will discuss the top technology trends that are redefining Human Resource Management.
Overview
Human resource management (HRM) is a crucial aspect of any organization. It involves managing the people who work for the company. Technology has become increasingly important in HRM, as it can help streamline and automate various HR tasks, improve communication and collaboration among employees, and facilitate remote work.
Furthermore, technology is constantly evolving, and organizations need to stay up-to-date with the latest trends and developments in order to remain competitive and effective in managing their workforce.
The Importance of Technology in HRM
The importance of technology in HRM cannot be overstated. Technology has the potential to streamline and automate various HR tasks, such as managing employee records and scheduling. This can save time and resources for HR departments and allow them to focus on more strategic and value-added tasks.
Furthermore, technology can improve communication and collaboration among employees, particularly for remote teams. It can also facilitate remote work, which has become increasingly common in recent years. With the right tools and technologies, employees can work from anywhere, at any time, and still be productive and engaged. This can help organizations attract and retain top talent, as well as reduce overhead costs associated with having a physical office.
Overall, technology is an essential part of HRM and can help organizations manage their workforce more effectively and efficiently.
Also Read: The Role of Human Resources in Business Continuity Planning
Top 5 Technology Trends That Redefine Human Resource Management
Remote work
The rise of remote work has been one of the biggest shifts in the modern workplace. With the right tools and technologies, employees can work from anywhere, at any time, and still be productive and engaged. This has opened up a world of possibilities for organizations and employees alike, as they are no longer limited by geographical location.
Artificial intelligence and automation
AI and automation are starting to play a bigger role in HRM, as they can help automate routine tasks and free up HR professionals to focus on more strategic and value-added tasks. AI can also help with tasks such as recruitment, where it can analyze resumes and identify the most qualified candidates.
Employee engagement and communication
Technology is also playing a key role in improving employee engagement and communication. Tools such as instant messaging, videoconferencing, and social media can help employees stay connected and collaborate with each other, regardless of where they are located.
Talent management and recruitment
Technology is also changing the way organizations find and hire top talent. Tools such as applicant tracking systems and social media can help HR professionals identify and reach out to potential candidates.
Learning and development
Technology is also transforming the way organizations provide learning and development opportunities for their employees. Online courses and virtual training sessions can help employees acquire new skills and knowledge, regardless of their location.
Also Read: The Importance of Information Technology in Business Today
The Continued Impact of Technology on Human Resource Management
The continued impact of technology on human resource management (HRM) is likely to be significant. As technology continues to evolve, it will bring new opportunities and challenges for HR professionals. For example, the rise of artificial intelligence (AI) and automation may lead to the automation of certain HR tasks, freeing up HR professionals to focus on more strategic and value-added tasks. However, it may also lead to concerns about job displacement and the need for HR professionals to acquire new skills.
Additionally, the continued growth of remote work will require organizations to invest in the right tools and technologies to enable employees to work effectively from anywhere. This will involve not only the technology itself but also the development of new policies and procedures to support remote work.
Furthermore, technology will continue to play a key role in improving employee engagement and communication, as well as in talent management and recruitment. Tools such as instant messaging, videoconferencing, and social media can help employees stay connected and collaborate with each other, regardless of where they are located. Additionally, technology can help organizations find and hire top talent, as well as provide learning and development opportunities for their employees.
Overall, the continued impact of technology on HRM is likely to be significant. Also, organizations need to stay up-to-date with the latest trends and developments in order to remain competitive and effective in managing their workforce.
Also Read: Role of Information Technology in Business Environment
Conclusion
Technology is playing an increasingly important role in HRM, and organizations need to stay up-to-date with the latest trends and developments in order to remain competitive and effective in managing their workforce.
If you have any concerns about technology, you can contact Aeologic Technologies!
Related Blogs:
| 2022-12-08T00:00:00 |
2022/12/08
|
https://www.aeologic.com/blog/top-technology-trends-that-redefine-human-resource-management/
|
[
{
"date": "2022/12/08",
"position": 64,
"query": "automation job displacement"
}
] |
The Promising Future of Artificial Intelligence in Vietnam
|
The Promising Future of Artificial Intelligence in Vietnam
|
https://www.orientsoftware.com
|
[
"Trung Tran"
] |
In some scenarios, such as the one where the emergence of AI coding engines results in the loss of jobs for human coders, experts even question whether the ...
|
Artificial intelligence (AI) is no longer vague imaginations or a piece of fiction but rather commonplace around us whether we do notice it or not. In the digital era, it has grown significantly on a global scale over the past few decades. In some scenarios, such as the one where the emergence of AI coding engines results in the loss of jobs for human coders, experts even question whether the maturity of computational intelligence will enable it to one day completely replace humans. Disputes hinging on the topic of AI vs. Human brains seem to have no end, but we can rest assured that advanced robotics and AI are not capable of replacing us now, or at least not anytime soon. The popularity and widespread use of AI today may be the logical outcome of how this technology has been revolutionizing the everyday life and business aspects of human beings. Over time, AI’s impact and applications have spanned across industries and fields, ranging from software development, human resources, education, telecommunications, retail, e-commerce development, healthcare, even criminal justice, and heaps more. Recognizing the unlimited potential of artificial intelligence, a series of countries have been sinking money into AI development strategies with an ambitious goal to become the leaders in the area of artificial intelligence and earn huge profits. The latest report by Tortoise Intelligence indicated that global spending in AI companies has sharply increased by 115% since 2020, which is the largest year-on-year growth in AI investment over the past two decades. In 2021, total investment in artificial intelligence reached $77.5 billion, and it is foreseen to escalate later on. Although the U.S., China, and European nations have been leading the way, Vietnam is not outside the race for AI.
Challenges of Artificial Intelligence in Vietnam The “debut” of Vietnam in the AI Readiness Chart of 100 countries is merely a beginning of a long-term endeavor to explore the true possibility of artificial intelligence technology and its applications. Hence, there are quite a lot of challenges along the way to the prospect where Vietnam peers with developed countries in the AI industry. But positively, Vietnam has been making it happen steadily over time by breaking through each and every obstacle. Typically, funding and human resources are the biggest roadblocks on the AI road. Funding AI technology requires considerable investments in order to realize the applications with the highest efficiency and productivity. Therefore, there is an urgent need for the Vietnamese government to allocate sufficient budgets for AI projects. As a result, the national strategy outlines the pivotal role of the Ministry of Planning and Investment in encouraging domestic organizations (such as Viettel, FPT, VinGroup, and VNPT) and capitalists to fuel funding for AI development as well as attracting foreign investors from different parts of the world to sink money into the Vietnamese AI industry. This is a “nerve-wracking problem” that the government must take time to solve. And looks like things have been going on the right track as we are witnessing some positive signs regarding AI funding in Vietnam. Specifically, at the Vietnam Venture Summit 2020, Vietnam gained a pledge to invest $800 million in the country’s startup ecosystem in the next three or five years from both foreign and domestic investors. In addition, there are 155 venture capital investors taking their chances with Vietnam. Recently in 2021, Vietnam’s high-tech application projects in the field of digital transformation, especially e-commerce, fintech, and AI, received an investment of nearly $1 million through the Aus4Innovation Programme. At this rate, Vietnam will soon overcome the funding obstacles and accelerate AI development within the country. Last but not least, Vietnam was one of the few countries that successfully contained the global pandemic and survived, as well as thrived through the difficult period, making this nation an appealing destination to invest in and launch a business. Shortage of AI Experts Like most “rookies” entering the race for AI, Vietnam is short on human resources specializing in this technology. Even though Vietnam holds a great advantage over other countries within the region as well as worldwide for its abundance of IT labour force, the country sadly has not owned a vast pool of AI experts yet, which will cause the local AI projects a lot of difficulties in terms of staffing. According to indexes from the World Intellectual Property Organization (WIPO), over 1,600 Vietnamese are currently studying and working in AI-related fields. Among them, only 300 people are considered AI experts. This means the human resources for AI in Vietnam are too small. Simply put, Vietnam needs a lot more personnel with expertise and hands-on experience in AI technology. Confronting such a challenge, the government has two top-prioritized missions up ahead - to nurture and cultivate domestic AI talent as well as connect AI communities in Vietnam with Vietnamese expatriates who conduct research and develop AI for large tech companies from different parts of the world. The Vietnamese government has been strategizing a lot of initiatives to achieve these two goals. Typically, AI education and training have steadily been implemented in universities, which brings the knowledge and perception of artificial intelligence closer to young generations. Currently, there are around 50 universities and academies teaching AI-related majors in Vietnam.
| 2022-12-08T00:00:00 |
https://www.orientsoftware.com/blog/artificial-intelligence-in-vietnam/
|
[
{
"date": "2022/12/08",
"position": 50,
"query": "AI replacing workers"
}
] |
|
Digital Trade: A Primer for Professionals
|
Digital Trade: A Primer for Professionals — Department for Professional Employees, AFL-CIO
|
https://www.dpeaflcio.org
|
[
"Katie Barrows"
] |
... workers. Pro-worker digital trade rules should seek to protect professionals ... (AI) that are rapidly advancing without society yet knowing the full ramifications ...
|
Digital privacy
Essentially any Internet-connected device or software can track and collect user information. In the workplace, the tracking and collecting of user information translates to digital monitoring of employees. Employer-administered digital communications and digital information management are generally the employer’s property and, in non-union workplaces, can be subject to unlimited, undisclosed monitoring. Because surveillance and monitoring are mandatory subjects of bargaining, union professionals have the right to negotiate with their employer over digital workplace surveillance.
Bossware is automated employee monitoring software that employers can use to track the activities of their employees. Employees often do not know that their bossware-enabled computer or device is tracking their activities, including their keystrokes. Bossware is now the norm in a variety of jobs, and since the start of the COVID-19 pandemic, a growing number of companies have deployed bossware to surveil employees working remotely, both on and off the clock.[7]
Bossware threatens the mental and physical wellbeing of professionals, making them feel like they are constantly falling behind due to oppressive performance metrics. Monitoring software can also lead to discrimination against people with disabilities who may work at a different pace or require alternative working conditions than their differently-abled coworkers.[8]
Digital trade rules can determine if countries are able to address the unconstrained nature of cross-border digital workplace monitoring. For instance, a trade agreement could make future technological governance difficult if passing domestic legal safeguards means that the U.S. is out of compliance with the trade agreement and subject to penalties. Resilient digital trade rules must provide public policy space for U.S. professionals to protect their interests now and as novel issues arise in the future.
In addition, employee wellness programs, wearable technology, and self-tracking devices can collect personal biometric and medical information about employees. With this “datafication” of employee health, personal data can end up in the hands of a third party[9] and be used to develop other algorithms without the employee’s knowledge. It is often unclear who owns the digital information generated by the devices or apps – the employee, the employer, or the company that created the technology[10] – a legal uncertainty that generally leaves employees without options for recourse.
| 2022-12-08T00:00:00 |
https://www.dpeaflcio.org/factsheets/digital-trade-a-primer-for-professionals
|
[
{
"date": "2022/12/08",
"position": 88,
"query": "AI replacing workers"
}
] |
|
Careers
|
Careers
|
https://www.hellopearl.com
|
[] |
Pearl is looking for exceptional people to join our industry leading team. We are using the latest innovations in artificial intelligence and computer vision.
|
We launched Pearl in 2019 to bring the AI revolution to dentistry. At the time, we broadly understood where and how machine learning technologies could be applied to deliver value, but seeing slower than expected AI-adoption in other healthcare fields, we didn't really know how quickly the industry would recognize the value and embrace it. It turns out that, for a number of reasons, dentistry is uniquely fertile ground for AI. It's not only deeply rewarding to see the industry embrace every solution that we've introduced, but it's also inspiring us to continue to push the envelope of our technological capabilities and market saturation.
Ophir Tanz, Founder & CEO
| 2022-12-08T00:00:00 |
https://www.hellopearl.com/careers
|
[
{
"date": "2022/12/08",
"position": 21,
"query": "machine learning job market"
}
] |
|
5 Machine Learning Resume Examples for 2025
|
5 Machine Learning Resume Examples for 2025
|
https://resumeworded.com
|
[
"Senior Hiring Manager",
"Hayden C.",
"Grace A."
] |
If you are passionate about data science and would like to land a job as an ML engineer, read on. We'll help you craft your machine learning engineer resume.
|
Machine learning engineering is part of artificial intelligence’s sub-branches. It consists of developing high-performing predictive systems and applications through data modeling and algorithms. These programs work with large volumes of data and learn from it to deliver more accurate predictions.
Machine learning engineers are responsible for delivering these applications and systems. They must also perform machine learning tests to measure the efficacy of statistical analysis.
Machine learning is a highly in-demand field used for different purposes, including credit scoring, facial recognition, and brain tumor detection. Almost every industry can benefit from the magic behind machine learning engineering.
Hence, it’s no surprise to see that the demand for machine learning engineers is expected to rise by up to 22 percent, according to the Bureau of Labor Statistics. This is considerably high compared to the average demand outlook for most occupations. Yet, this is no excuse to neglect the quality of your resume.
Despite the high demand for machine learning engineers, you should still give your potential employer reasons to hire you. Today, we’ll explore some industry-relevant techniques to improve the quality of your resume and increase your odds of getting a job as a machine learning engineer. Don’t forget to check our four resume templates.
| 2022-12-08T00:00:00 |
https://resumeworded.com/machine-learning-resume-examples
|
[
{
"date": "2022/12/08",
"position": 34,
"query": "machine learning job market"
},
{
"date": "2022/12/08",
"position": 65,
"query": "generative AI jobs"
}
] |
|
AI's Role in Reducing Inequalities
|
AI’s Role in Reducing Inequalities – Quantilus Innovation
|
https://quantilus.com
|
[] |
The job market can be a challenging space for many people, but AI can ... machine learning algorithms for diagnosis and treatment planning. For people ...
|
Artificial intelligence (AI) has been used in many industries over the past few years with virtually endless applications. Although much of the conversation centers around business applications and enhancing revenue opportunities, AI has the power to serve greater purposes for the benefit of society and our world. For example, AI can play a leading role in ensuring that everyone is given fair and equal opportunities. By leveraging AI’s powerful capabilities as a bridge between data sets, stakeholders — from investors to policymakers — can make fully-informed decisions about developing plans of action that address inequality on larger scales. Explore the role AI can play in reducing social and economic disparities in today’s world.
6 Ways AI Can Help Reduce Inequalities
1. Provide targeted assistance to those who are most in need
Artificial Intelligence (AI) is revolutionizing how assistance is provided to those most in need. By leveraging predictive analytics algorithms and machine learning techniques, organizations can identify patterns within populations that may not have been obvious before. This allows them to make more informed decisions about where resources should be allocated or what types of services should be provided based on accurate predictions rather than guesswork or intuition alone. Additionally, utilizing AI for targeted assistance allows for improved resource allocation for maximizing impact. For example, an organization that provides food aid could use an AI model to predict which areas will be hit hardest by a natural disaster before it happens. This would allow them to get food aid ready ahead of time so that it can be distributed as soon as possible when needed. Additionally, using predictive models based on past data, organizations can plan for future needs more accurately and effectively.
2. AI can create jobs and opportunities for those who are unemployed or underemployed.
The job market can be a challenging space for many people, but AI can help level the playing field by providing job seekers with increased access to employment opportunities and resources. The job market is changing rapidly due to automation and outsourcing—and it’s no secret that certain groups are being left behind while others reap the benefits. AI can be used to create more equitable hiring processes by incorporating data-driven insights into recruitment decisions. Using AI-powered algorithms, companies can ensure that candidates from all demographic groups have an equal chance of landing a job.
3. AI can help to improve access to education and training.
Education inequality is another primary concern for many countries around the world. Students from disadvantaged backgrounds often have fewer resources available, making it difficult for them to keep up with their peers at school or even access quality education. However, AI can be used to close this gap by providing personalized learning experiences tailored specifically to each student’s needs and interests. This could include using machine learning algorithms to develop adaptive curricula that cater to different learning styles or using natural language processing tools to provide real-time feedback on students’ work. AI could also be used in class management systems like attendance tracking or grading software, which would make it easier for teachers and administrators alike to track students’ progress and ensure they are getting the most out of their educational experience.
| 2022-12-08T00:00:00 |
https://quantilus.com/article/ais-role-in-reducing-inequalities/
|
[
{
"date": "2022/12/08",
"position": 55,
"query": "machine learning job market"
}
] |
|
AI in Service Industry: All You Need to Know
|
AI in Service Industry: All You Need to Know
|
https://indatalabs.com
|
[
"Anastasiya Zharovskikh",
"April Miller",
"Author"
] |
Combining machine learning and marketing can help professionals understand the messaging most likely to appeal to certain user groups. Algorithms can perform ...
|
Artificial intelligence (AI) has been increasingly deployed in various sectors and numerous ways. This detailed overview will highlight the multiple uses of AI in service industry applications. It can sometimes take a while for company decision-makers to figure out the most appropriate ways to rely on AI. However, the best course of action is often to see what peers do when deploying AI for service-oriented applications.
AI in food service industry applications
Many restaurant brands known worldwide have started using AI to improve customer interactions. For example, an AI chatbot could give menu recommendations, answer customers’ questions or help them make dinner reservations. Then, people working at the restaurant can devote more time to assisting customers face-to-face or engaging in other duties. AI chatbots can’t respond correctly to every query. But they often work well for the things people ask most often.
Those questions might include things related to a restaurant’s opening hours, whether they serve gluten-free items or if there’s a kids’ menu. A chatbot can often answer those things faster than people.
Source: Unsplash
Using Machine Learning to improve results
Applications of machine learning and artificial intelligence for business have also impacted the food sector and the quality of service customers receive. For example, Domino’s Pizza used data from millions of orders to create a prediction model to help customers better understand when they’d receive their food.
The algorithms took numerous aspects into account, including what someone ordered and the number of employees and customers in a given restaurant when the person placed an order. In such cases, Big data platforms can help decision-makers at restaurants understand which information they have might be helpful for a future artificial intelligence application.
Combining AI and Voice-Recognition technology
Some examples of AI in service sector applications are even more interactive for customers because they can understand what they say. Their functionality works similarly to the natural language process that occurs when people speak. Specifically designed AI algorithms can interpret human speech in context, then provide accurate responses.
One example from fast-food brands Checkers and Rally’s was an AI-ordering system that accurately understood what people wanted with minimal interactions from staff. The solution also included upselling elements, such as encouraging people to order combos instead of single menu items. Once the restaurant has a large amount of data from such solutions, restaurant leaders could use that information to understand customers better.
What do people order most often? Do their preferences change based on the day of the week or time? What differences exist when someone buys food for their family versus themselves? McDonald’s began using artificial intelligence in the service industry in its outdoor menu boards to make them more adaptable.
Source: Unsplash
People see different menu choices featured depending on factors such as trends, the weather and the time of day. Several years before using this AI solution, McDonald’s executives applied more basic technology to show people different menu options based on the weather. For example, consumers saw ice cream featured on hot days and coffee when it was cold. Internal data suggested this approach caused sales spikes.
These are some of the many examples of how people could use AI in food service industry applications. However, decision-makers should strongly consider hiring people from a machine learning consulting company if they need further guidance. Those professionals can evaluate the specific needs of a business and investigate how AI could help. That may mean deploying AI assistants for customers or using artificial intelligence to improve internal business processes.
Artificial Intelligence in financial services industry applications
Some industries use artificial intelligence to find the most suitable candidates in an applicant pool. For example, research indicates 7 in 10 schools have dedicated enrollment teams. AI tools help people find the best-suited candidates. That’s one of the reasons why it’s becoming more popular to use AI in the financial services sector, too.
Consider if someone applies for a loan or credit card. Algorithms can assess data from various sources and evaluate the aspects that could make an applicant more or less risky for a bank or other financial institution.
AI services and solutions can also work well to help customers plan their financial futures. If they enter details such as when they hope to retire and how much they have saved for retirement, a solution could advise on what steps they can take to meet that goal. The customer data accumulated through such interactions can also become extremely valuable for a financial services company.
It could uncover trends indicating people retiring younger or older than they previously did. Similarly, the information collected by an AI system might show people are more or less prepared for retirement than they were a decade or so ago. In such cases, the people working at financial services companies can use those takeaways to shape their in-person conversations with customers, making them more relevant.
The use of artificial intelligence in financial services industry applications is not as widespread as in some other industries. However, it’s starting to gain momentum. People interested in pursuing the possibilities can use machine learning consulting companies as a starting point.
Source: Unsplash
AI as a supplement to human support
Many people are interested in using AI in service-based industries because they believe it can help human agents work more competently and efficiently. Technological advances can sometimes save people from engaging in many manual tasks. For instance, artificial intelligence in field service industry platforms can assist administrators with technician scheduling or free them from some data entry tasks.
These offerings don’t take human support staff members out of the picture. However, they often make transactions and engagements more efficient for customers. Then, those people have more favorable experiences with companies overall.
Artificial intelligence in service industry businesses can make humans more available to troubleshoot customers’ complicated requests, while AI tools deal with simpler needs. Imagine if a person contacted a mobile phone service provider and wanted to know the issue date of their next bill. An AI platform could likely give that information after getting basic details from the customer, such as an account number and billing address.
The hospitality sector can also enhance customer experience with artificial intelligence. A hotel chatbot could help people make initial room reservations or change ones previously made. It could also make it easier to add specific requests to a booking, such as that the room should have a baby crib or a couch that converts into a bed.
Such solutions could make things easier once guests arrive, too. Whether they need extra towels or another ice bucket, a chatbot can field those requests and send them to the correct hotel workers. Companies specializing in AI problem-solving solutions can give clients more ideas about how the technology could help them meet needs and support profits.
What industry uses AI the most?
Many companies are in the early stages of their AI adoption, making it hard to pinpoint which industry is most dependent on AI. However, artificial intelligence service industry applications are increasingly used.
More specifically, statistics from 2021 showed that 25% of all industries worldwide use AI for service-related needs. The percentage also rises in specific industries.
Other opportunities exist outside the customer service industry, too. Combining machine learning and marketing can help professionals understand the messaging most likely to appeal to certain user groups.
Algorithms can perform sentiment analyses to understand what people like and dislike most about specific products. Then, marketing team members could pass those insights on to customer service representatives to get them more prepared for future interactions.
Working with a data science company is an excellent way to get clarification on the most advantageous ways to use AI within a business. That might mean deploying it in the customer services department or developing an AI-based tool to allow clients to get the information they need faster.
Things to know before using AI in service-oriented roles
Using AI in a business for any reason represents a significant decision. That means there are a few things to keep in mind before proceeding with further research.
What are the three types of AI?
As people learn more about AI, they discover there is more than one type. Artificial narrow intelligence — or weak AI — is a solution that excels in a single task. It simulates one human behavior.
Source: Unsplash
There’s also artificial general intelligence — or strong AI. It can think and act the same way humans do. However, it’s only a theoretical concept for now. People have made progress in related areas, though. One recent achievement allows a person to see why a machine learning model made a particular conclusion. Such information is critical when an algorithm’s result could dramatically impact someone’s future.
Finally, there’s a third type of AI called artificial superintelligence. It doesn’t exist in real life, either. This category of artificial intelligence surpasses human capabilities, so it’s still the stuff of science-fiction novels and books for now.
Is AI a service or product?
People often wonder if AI technology is a product or service. The answer depends on how clients use it. Some might develop in-house algorithms that give valuable insights about customer experience statistics or other trends relevant to service industries. Other companies won’t have the resources for custom-built solutions, but decision-makers there may still want to experiment with AI options.
In the latter case, machine learning-as-a-service — sometimes referred to as ML-as-a-service — can help.
What is ML-as-a-Service?
Machine learning-as-a-service enables people to pay flat rates for their usage and deployment of artificial intelligence and machine learning solutions. This approach allows clients to eliminate the often-high upfront costs of technological products and services. It also makes it easier for them to fit machine learning into their budgets and business models without worrying about how the adoption could result in financial strain.
After seeing how it works in the early stages, decision-makers can determine if they want to use AI long term. If so, it may make more sense to transition from the ML-as-a-service model to something more permanent.
Source: Unsplash
Reaching a well-informed conclusion is often easier if people collect specific metrics. For example, how many customer service inquiries did a chatbot handle versus a human last month? Is the number of people interacting with an AI product going up or down compared to the previous quarter?
AI and service industry use cases make sense
The examples here show why it’s often so compelling to bring artificial intelligence technology to the service industry. Customers’ interactions with service-oriented companies can forever change their options and impact the likelihood of them doing business in the future with those options. If artificial intelligence can cause positive experiences while reducing service provider workloads, there’s no reason not to consider it.
Author bio
April Miller is a senior writer with more than 3 years of experience writing on AI and ML topics.
Need help with AI solutions development? Shedule a call, and our specialists will consult you on your project.
| 2022-12-08T00:00:00 |
2022/12/08
|
https://indatalabs.com/blog/ai-in-service-industry
|
[
{
"date": "2022/12/08",
"position": 78,
"query": "machine learning job market"
},
{
"date": "2022/12/08",
"position": 70,
"query": "artificial intelligence business leaders"
}
] |
Our articles series on the future of work and collaboration
|
Our articles series on the future of work and collaboration
|
https://workspace.google.com
|
[
"The Google Workspace Editorial Team"
] |
Here's the latest from Google Workspace leaders and partners about the evolving future of work and collaboration, all in one place.
|
[Updated 04/23/24]
Business leaders and IT professionals come to Google Workspace to build secure, cloud-first collaboration solutions that transform how people work together. Here’s the latest from Google Workspace leaders and partners about the evolving future of work and collaboration, all in one place.
And if you'd like to follow our new series on getting the most out of Gemini for Workspace, with tips on prompting best practices, you can catchup on Beyond the prompt.
3 ways Gen AI will impact the way we work this year
Google Workspace’s Kristina Behr says businesses of all sizes—from mom-and-pop shops to global enterprises—will hone in on their top prompts to help improve everything from creating content for email marketing campaigns to drafting and refining customer support responses. Read the article.
How AI can actually improve human connection at work
Kristina Behr, Vice President of Product Management for Google Workspace discusses our research with Economist Impact on the new work landscape and how AI can help keep us connected. Read the article.
The future of work, powered by the future of security
Google Workspace security experts—Andy Wen and Nikhil Sinha—discuss how seamless, built-in security controls can keep users and organizations safe, without slowing collaboration in the hybrid world of work. Read the article.
Reimagining physical spaces to foster connection
Michelle Kaufmann, Director of Real Estate Research and Development at Google, discusses the intersection of physical and digital spaces to inspire employees and get the most out of in-office and hybrid collaboration. Read the article.
The future of work requires digital dexterity
Michael Brenzel, Chief Evangelist for Google Workspace, discusses how organizations can use flexible technology to adapt to new challenges while also empowering employees to build digital dexterity. Read the article.
Staying in sync: Effective collaboration strategies for distributed workforces
Michael Brenzel, Chief Evangelist for Google Workspace, discusses how organizations can use the best form of collaboration for the job at hand. Read the article.
Empowering everyday innovation to build a more adaptive business
Hear from Google Workspace VP of Engineering, Philip Brittan, about how organizations can rethink their approach to innovation.
How organizations can rethink their approach to time management coaching
Laura Mae Martin, Google’s Productivity Expert, on helping employees get more from their time, without sacrificing their wellbeing.
Google Workspace and Google Cloud help build the future of work at Airbus
See how Airbus lived up to its mantra of “Any device, anytime, anywhere,” with help from Google Workspace and Google Cloud. Read the article.
Shaping the future of work for frontline workers in Asia Pacific
Read about the new urgency for businesses to take a fresh look at work and technology experiences on the frontlines in APAC.
Boosting collaboration and participation in the hybrid work world
Strategies and best practices for organizations building people-first, sustainable hybrid work models.
The future of work requires a more human approach to security
Security is no longer just about protecting information or restricting how that information is accessed—it’s about building safe, efficient, and effective ways to facilitate seamless collaboration and information-sharing. Read more.
Insights from our global hybrid work survey
| 2022-12-08T00:00:00 |
https://workspace.google.com/blog/future-of-work/our-articles-series-on-the-future-of-work-and-collaboration
|
[
{
"date": "2022/12/08",
"position": 37,
"query": "future of work AI"
}
] |
|
Office Technology | WORKSPACE
|
Office Technology
|
https://www.workspaceexhibition.com
|
[] |
Explore AI-driven solutions that are transforming productivity in modern workplaces. Network with industry leaders who are at the forefront of workplace ...
|
The United Arab Emirates (UAE) is leading the charge in workplace technology investments, with projections indicating that Artificial Intelligence (AI) alone could contribute nearly 14% to the nation's GDP by 2030 as per PwC. This surge is driven by a strategic focus on AI adoption, with 46% of Middle East respondents recognizing AI's potential to enhance workplace productivity, surpassing the global average of 31%.
Join us at the WORKSPACE Exhibition to discover innovative office technologies designed to boost efficiency and collaboration. Explore AI-driven solutions that are transforming productivity in modern workplaces, and network with industry leaders who are at the forefront of workplace innovation.
| 2022-12-08T00:00:00 |
https://www.workspaceexhibition.com/product-sectors/office-technology/
|
[
{
"date": "2022/12/08",
"position": 25,
"query": "workplace AI adoption"
}
] |
|
The Rise of the Digital Adoption Manager in 2025
|
The Rise of the Digital Adoption Manager in 2025
|
https://whatfix.com
|
[
"Levi Olmstead"
] |
This makes sense, as COVID-19 forced organizations to adopt a digital workplace ... Drive adoption of new updates and capabilities, like AI copilots.
|
What Is a Digital Adoption Manager?
A digital adoption manager is a technology leader who owns a company’s digital adoption strategy and enables employees to maximize their productivity and output by extracting the most value from all software applications and technologies a company has invested in.
A digital adoption manager’s responsibility is to ensure the ROI of software investments and digital transformation. This requires both technical and interpersonal skills, to implement and train users on complex systems, support employees during transformational changes that impact their day-to-day, and drive overall digital adoption.
According to Glassdoor, the average salary for a digital adoption manager is $90,225/year in 2022.
The Rapid Rise of the Digital Adoption Manager
The DAP market has grown 400% since 2018, with organizations spending $400M on DAP providers in 2021, up from $100M in three years and up 40% year over year. According to data from Everest Group, that number is expected to hit $550M in 2022 and climb exponentially to upwards of $1.3B by 2025.
This makes sense, as COVID-19 forced organizations to adopt a digital workplace model, which relied on new technologies to continue operations – many of which were fully adopted after the pandemic ended and still are utilized today.
Even before COVID-19, organizations rapidly adopted new technologies through enterprise digital transformation – from tools to improve employee experience and productivity, to tools that re-imagined the customer and user experience.
The exponential growth of the DAP space parallels this evolution of industry-agnostic technology. DAPs help organizations facilitate this change to new digital processes, provide contextual performance support for its end-users to adopt and use these applications at a high level, and act as an insurance policy for organizations to ensure ROI from expensive software investments.
That last point – a digital transformation insurance policy – is critical for enterprises’ bottom line, as 84% fail to find ROI with their digital transformation initiatives.
This has led to the emergence and rise of the digital adoption manager.
In 2022, the number of LinkedIn professionals with a digital adoption-related title grew by 29%, with nearly 5,000 LinkedIn users having digital adoption in their job title.
These professionals range from owning an entire organization’s digital adoption strategy to specialist roles that focus on driving the adoption of just one application and digital adoption platform (DAP) implementation leaders.
Here are a few examples of professionals with digital adoption manager as their title on LinkedIn:
Molly Jacobs is a Digital Adoption Platform Product Owner at Jacobs.
William Urquhart is a Digital Adoption Manager at Barclays.
Tanya Oliphant is the Digital Adoption Lead for Workday at Washington University, St. Louis.
Jeffrey Duran is a Digital Adoption Manager at Ozinga.
WHITE PAPER Digital Adoption Trends Report Download now!
Responsibilities of a Digital Adoption Manager
A digital adoption manager’s core responsibility is to oversee a company’s entire digital adoption strategy. This branches across the employee lifecycle, with these digital adoption managers specializing in extracting the most value from software and technology that enables employees to be their best.
1. Building employee digital dexterity that drives overall performance
Digital dexterity refers to the ability to adapt, adopt, and correctly use digital applications and processes that are designed to make them more efficient. While technology in theory should improve your employee performance and productivity, it relies on employees to be able to use them correctly.
Digital dexterity is the foundation of your digital adoption strategy, and digital adoption professionals must be able to build these digital skills with all types of employees, including
New employees who haven’t used a specific application vendor before.
New professionals coming out of college who haven’t used B2B software before.
Older employees who are used to manual processes need to be reskilled to use digital applications.
2. Facilitating internal change quickly, with little downtown
Digital adoption managers will need to be change agents, and understand how to get employees up-to-speed quickly during change initiatives. This is often easier said than done, as digital adoption leaders need to ensure that all data is migrated correctly, new processes are adopted correctly, and that data isn’t being sacrificed in the short term. With user error comes a loss in productivity, which in turn impacts revenue.
3. Onboarding new hires to digital applications and processes
Digital adoption managers will need to create onboarding processes to introduce and familiarize new hires with a company’s tech stack and digital processes.
This may sound simple, but consider how many software applications you use in your role. This list is lengthy, with almost all of a company’s processes now moved to digital portals.
I considered my own role, and here are a few of the major applications and digital processes that have internal unique-to-Whatfix workflows that I would need to be contextually onboarded on:
Salesforce and Pardot for contact management and attribution.
Highspot for sales enablement.
Asana and Airtable for project and task management.
JIRA for internal project management and IT tickets.
JumpCloud for employee single-sign-on.
SuccessFactors for our people management including time-off, performance reviews, and more.
Gusto for our pay stub tracking and health benefits portal.
Workboard for our goal management.
Zoho Expenses for our expenses.
Guideline for 401K management.
Trica for our equity management.
4. Training users on new software applications
Onboarding goes beyond new hires. When an organization has new technology needs and implements a new application, a digital adoption manager owns that implementation process. This implementation plan will include getting buy-in for new applications, training employees, overcoming internal resistance, software rollout, and IT performance support for these tools.
5. Provide on-demand support for all employees
Digital adoption managers go beyond onboarding as well, as employees will need to be continuously supported as they use these applications in their day-to-day.
Traditionally organizations have had internal IT departments that supported employees when they had IT and software-related issues and questions, but that is no longer feasible with all processes becoming digital.
Digital adoption managers identify pain points and under-utilized features and then create support content to help provide employees with performance support at the moment of need. This can include internal wikis, process documentation, in-app guidance, and on-demand self-help.
6. Ensuring ROI of software and technology investments
Gartner reported that organizations worldwide spent over $4.4T on software and IT investments in 2022. According to Asana, the average employees uses 13 applications over 30 times each day.
With such a high-cost investment comes the need to ensure ROI of these investments. The goal of a digital adoption manager is to help the CIO ensure that these software applications are properly implemented, onboarded, and adopted across the workplace, regardless of application or department.
WHITE PAPER How to Build an Enterprise DAP Business Case Download now!
Why Organizations Must Hire a Digital Adoption Manager
Digital adoption managers help organizations navigate digital transformation and the digital workplace, and the many challenges that come with it. Here are the few of the biggest reasons all enterprises should invest in a digital adoption manager hire this year.
To faciliate successful digital transformation: All organizations have faced digital transformation challenges over the last few years, and will continue to face these as they navigate new ways of working in the digital workplace. Digital transformation brings the opportunity to scale an organization’s growth and drive its people’s productivity to new heights.
All organizations have faced digital transformation challenges over the last few years, and will continue to face these as they navigate new ways of working in the digital workplace. Digital transformation brings the opportunity to scale an organization’s growth and drive its people’s productivity to new heights. To achieve ROI for digital investments: CIOs need insurance that their costly technology investments will have ROI, or risk a company’s stability and future. Digital adoption managers act as the insurance policy that drives new technology adoption, and these professionals are tasked with measuring digital adoption levels to benchmark and improve overall adoption.
CIOs need insurance that their costly technology investments will have ROI, or risk a company’s stability and future. Digital adoption managers act as the insurance policy that drives new technology adoption, and these professionals are tasked with measuring digital adoption levels to benchmark and improve overall adoption. To bridge the digital skills gap: Digital adoption managers will be responsible for all reskilling and upskilling programs necessary to foster a culture of digital dexterity and help employees who may be less familiar with new digital tools to have the toolset they need to adapt to change. Conducting a skills gap analysis will help identify reskilling and upskilling opportunities.
Digital adoption managers will be responsible for all reskilling and upskilling programs necessary to foster a culture of digital dexterity and help employees who may be less familiar with new digital tools to have the toolset they need to adapt to change. Conducting a skills gap analysis will help identify reskilling and upskilling opportunities. Owning the DAP implementation and management process: Many organizations have turned to digital adoption platforms (DAPs) to manage their digital stack, drive adoption, and enable employees with the in-app guidance and on-demand support they need. It’s almost certain that any organization hiring a digital adoption manager also will have invested (or soon will) in a DAP. A digital adoption manager will be the leader of this implementation, as well as monitor its usage and success.
A digital adoption manager is responsible for overseeing these efforts, as well as setting up the infrastructure in place to properly implement and onboard these new tools and digital processes.
It doesn’t stop there, either. Employees will need consistent performance support to maintain high levels of productivity with these new apps and achieve overall digital adoption.
See how Whatfix enables employees to reach their full productivity potential and drives technology adoption and ROI for enterprises now!
| 2022-12-08T00:00:00 |
2022/12/08
|
https://whatfix.com/blog/digital-adoption-manager/
|
[
{
"date": "2022/12/08",
"position": 79,
"query": "workplace AI adoption"
}
] |
Historical analogies for large language models
|
Historical analogies for large language models
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https://dynomight.net
|
[] |
Universal basic income (UBI) is an odd duck. Proponents range from futurists to libertarians to ...
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How will large language models (LLMs) change the world?
No one knows. With such uncertainty, a good exercise is to look for historical analogies—to think about other technologies and ask what would happen if LLMs played out the same way.
I like to keep things concrete, so I’ll discuss the impact of LLMs on writing. But most of this would also apply to the impact of LLMs on other fields, as well as other AI technologies like AI art/music/video/code.
1. The ice trade and freezers
We used to harvest huge amounts of natural ice and ship them long distances. The first machines to make ice were dangerous and expensive and made lousy ice. Then the machines became good and nobody harvests natural ice anymore.
In this analogy, LLMs are bad at first and don’t have much impact. Then they improve to match and then exceed human performance and human writing mostly disappears.
2. Chess humans and chess AIs
Chess AIs far exceed humans, yet human chess is more popular than ever.
In this analogy, LLMs become better than humans at writing. But so what? We like human writing. Text isn’t interesting if it’s not part of a (para)social relationship. Browser makers build in LLM detectors to warn people if something wasn’t written by a human. Authors do use LLMs to train at writing, and sometimes there’s lots of intrigue when authors accuse each other of cheating using LLMs.
(By the way: In case you’re worried, there is no “written using LLMs lol” rug-pull coming at the end of this post. I wouldn’t endanger our parasocial relationship like that.)
3. Farmers and tractors
In 1800, half of the people in rich countries were farmers. After technology made farmers more productive, we weren’t interested in eating 50× more food per capita (and we weren’t that interested in expensive ingredients and eating white truffle saffron wagyu caviar burgers for every meal) so today only 1-4% of people are farmers.
In this analogy, LLMs act as a tool to allow one “writer” to produce vastly more output. But we don’t want to read 50× more stuff and don’t particularly care about “better” writing, so most writers have to shift to other jobs to make supply match demand.
4. Horses and railroads
At first, trains increased demand for horses, because vastly more stuff was moving around over land, and horses were still needed to get stuff to and from train stations.
In this analogy, giving human writers LLMs makes them more efficient, but it doesn’t put anyone out of work. Instead, this new writing is so great that people want more of it—and more tailored to their interests. Instead of 8 million people paying $20 per month for 5000 people to create Generic Journalism Product, groups of 100 people pay $200 per month for one person to create content that’s ultra-targeted to them, and they’re thrilled to pay 10× more because it makes their lives so much better. Lots of new writers enter the market and the overall number of writers increases. Then LLMs get even better and everyone is fired.
5. Swords and guns
First, guns replaced bows because guns need less training. Then guns became better than skilled archers. Then they replaced spears. Then infantry switched to guns with mini-swords on the ends. Then they dropped the mini-swords. These shifts weren’t driven by “productivity” so much as the fact that you had to switch to guns since you knew your adversaries were going to.
In this analogy, LLMs first replace humans for low-skill writing and then gradually take over more domains. For a while, the best writing uses a mixture of LLMs and human writing, but eventually humans stop being useful. Anyone who tries to resist this shift is outcompeted by better content from others who embrace LLMs.
6. Swordfighting and fencing
Sword fighting has an incredible depth of skill—you need talent, discipline, fitness, years of training, and maybe even philosophy. Many people think it’s worth mastering all this even though swords are now irrelevant to combat, so they practice it as a sport.
In this analogy, LLMs become better than humans at writing. But it’s still widely understood that learning to write is good for you (maybe writing is “the best way to think”) so people send their kids to writing camp and humble-brag about writing in their free time. But in the end, most writing was done for the mundane purpose of making text exist and when this is no longer valuable, most people stop doing it.
Intermission
I was informed that this post was kind of depressing but I couldn’t think of any good jokes so I’m resorting to brute force and deploying this picture of DOG WITH BENEDICT CUMBERBATCH:
Thus fortified, let’s continue.
7. Artisanal goods and mass production
Mass production made suit/cars/teapots cheaper and more plentiful. But Bentleys are still made by hand—artisanal goods are still seen as higher quality and higher status.
In this analogy, LLMs make writing vastly cheaper and more plentiful. But they never quite reach the quality of the best human writers. Most writing was always emails and TPS reports and these all shift to LLMs. But the New Yorker sticks to human writing and because copying information is free, most of what people read still comes from humans.
8. Site-built homes and pre-manufactured homes
We can build homes in factories, with all benefits of mass production. But this is only used for the lowest end of the market. Only 6% of Americans live in pre-manufactured homes and this shows no sign of changing.
In this analogy, LLMs make text cheaper. But for some reason (social? technical? regulatory?) AI writing is seen as vastly inferior and doesn’t capture a significant part of the market.
9. Painting and photography
While cameras are better than painters at reproducing real-world scenes, they didn’t make painters obsolete, because paintings remain a status symbol and painters shifted to non-representational art.
In this analogy, LLMs replace humans for much of what we currently think of as “writing”, except for people who want to flaunt that they can afford hand-made writing. But then human writers figure out that they can do certain things better than LLMs. Those things become high status and we all convince ourselves that we totally coincidentally happen to prefer the high-status things and can’t stand the low-status things and so human writers do OK.
10. Feet and Segways
First, there was walking. Then the Segway came to CHANGE THE NATURE OF HUMAN TRANSPORT. Twenty years later, there is still walking, plus occasionally low-key alternatives like electric scooters.
In this analogy, LLMs work fine but just aren’t worth the trouble in most cases and society doesn’t evolve to integrate them. Domain-specific LLMs are used for some applications, but we start to associate “general” LLMs with tourists and mall cops. George W. Bush falls off an LLM on vacation and everyone loses their minds.
11. Gull-wing and scissor doors
In the 1950s and 1960s automakers introduced doors that open vertically. These are better at getting out of the way, make it easier to park in tight spaces, and are less hazardous to cyclists. But they need more vertical clearance and, if you’re in an accident and your car flips over, they can literally kill you.
In this analogy, LLMs literally kill some people, and then we stop using them.
12. Sex and pornography
[Description of current situation redacted.]
In this analogy, people consume a ton of AI writing. But it doesn’t seem like a “real” substitute for human writing, so while human writing becomes a bit less popular it stabilizes at a high level.
13. Human calculators and electronic calculators
Originally a “computer” was a human who did calculations.
In this analogy, LLMs are an obvious win and everyone uses them. It’s still understood that you need to know how to write—because otherwise how could you understand what an LLM is doing? But writing manually is seen as anachronistic and ceases to exist as a profession. Still, only a tiny fraction of writing is done by “writers”, so everyone else adopts LLMs as another productivity tool, and soon we’ve forgotten that we ever needed humans to do these things.
Thoughts
This exercise made me even less sure about what’s going to happen. But it helped me clarify the reasons for uncertainty. There is of course the obvious question of how good LLMs will get, and how fast. But to predict the impact of LLMs we also need to understand:
Will LLMs act more as competitors or complements to human writing?
How will people react to LLMs? Maybe LLMs will write amazing novels and people will love them. Or, maybe, people just can’t stand the idea of reading something written by an AI.
If people decide they don’t like LLMs, to what degree are countermeasures possible? Can we build machine learning models to detect LLM-generated text? Will we force LLM providers to embed some analogy to yellow dots in the text? Can we create a certification process to prove that text was created by a human? (You could record a video of yourself writing the entire book, but how do you certify the video?)
Beyond all that, I wonder to what degree these analogies are useful. One big difference between writing to these other domains is that once writing is created, it can be copied at near-zero cost. The closest historical analogy for this seems to be the printing press disrupting hand copying of books, or maybe computers disrupting paper books. But it’s also possible that this shift is something fundamentally new and won’t play out like any of these analogies suggest.
| 2022-12-08T00:00:00 |
2022/12/08
|
https://dynomight.net/llms/
|
[
{
"date": "2022/12/08",
"position": 39,
"query": "universal basic income AI"
}
] |
Why I'm Done Using And Boosting AI Art
|
Why I’m Done Using And Boosting AI Art
|
https://terribleminds.com
|
[] |
“Well, copyright in America is poisonous and we have to Defeat Capitalism and really artists should be paid a Universal Basic Income,” and yeah, okay, good ...
|
Let’s just put it out there and up front — earlier, I was glad to play around with AI art, but that has ended. I have no intention at present of mucking around with AI art, signal-boosting it, or supporting it. I had a subscription to Midjourney, and I canceled it.
Now, to rewind a little —
I think AI art is pretty cool.
I know, I know — I just said, but I won’t support it, and that’s true.
But I think it’s neat, in a general sense. It’s like, we can make COMPUTER ROBOT GHOSTS do all kinds of cool things for us — they can tell us the weather, show us how to get to the mall, I can yell at my car to turn on the heat and it’ll totally do it, Gmail can already predict the response I’m going to make and start to prep it for me. The robot ghosts are cool. So, the ability to say, HEY ROBOT GHOST, SHOW ME WEREWOLF PIKACHU USING A NEW POKEMON MOVE CALLED “CORUSCATING ELECTRIC ANUS” ON A KAIJU VERSION OF JERRY SEINFELD and then somehow it sorta does it, well, I don’t hate that.
Now, admittedly, when I started mucking about with AI art in the long-times-ago epoch of, mmm, six months ago, what it produced was often fiddly and hilarious and straight-up fucking weird. It would still have eyeballs in places where there shouldn’t be. Some guy’s face might look like a smear of paint, and his hand would have sixteen fingers. You might squint and see Sophia from the Golden Girls mysteriously hiding in the wallpaper. It felt a bit like you watching a robot dream. Like you were privy to the growth of its creative mind.
(It’s a lie, of course. There’s no robot dreaming; that is a romantic, anthropomorphic notion.)
But it didn’t take long for the results to get… good. Real good. Freaky good. You plug in something and what returns is a foursquare array of nearly exactly what you asked for, in a variety of art styles and modes. Which, one might argue, is quite the point of this whole affair, and I suppose it is, though I’ll also note for my mileage it also kinda defeats if not the point, than rather, the delight of having a robot puke up something just super fucking weird instead of precisely what you asked for. We were training the robot well. And it was learning fast.
And now, you see the so-called AI art everywhere, and you also see those who are mad at so-called AI art everywhere. And the latter category is often artists. Not always! But often enough.
As such, I’m going to side with the artists.
(Spoiler: you should always side with the artists.)
I’ll talk about why in a moment, though I will note here there is, of course, a nuanced discussion to be had here. I don’t think people using AI art are like, Cyber Hitlers or anything. I used it quite well looking for inspiration for my Evil Apples book (which has a title and I’ll soon tell you what it is, I promise), and it… actually worked, and given how many iterations it took to get that inspiration, I could not have easily paid an artist for that essentially throwaway act. I’ve seen some trans friends say that they like how some of the AI profile art makes them look and feel, and that’s pretty wonderful. I have artist friends who use it and like it and find it valuable — it is a tool to them, not a curse. Technology also tends to expedite tasks while also leaving human workers behind in ways that are sometimes good and sometimes bad and most often somewhere in the middle — the ability to have language translated for us is pretty useful in a broadly human sense, even as it puts actual translators out of work. And finally, I think we as people seize on beautiful things and weird things and odd memes, and AI art allows us to do all of that, allowing us to play and explore and just be inspired in weird ways. And connect with each other as we do so.
But, but, but.
But.
BUT.
I’m still saying, let’s cool it on the AI art.
And here’s why.
1. First, just watch Charlotte’s video here. It covers a lot of things I’d say, except smarter and cooler because she is smarter and cooler than I am.
2. It is demoralizing for young artists. Trust me when I tell you, it’s hard to muster the interest in making new art when you can poke a computer to do it for you with a sentence or three. Yes, there remains value in art for art’s sake, but I think if you were a young artist viewing a future in Making Art, this is definitely going to give you pause. Again, I know this because I’ve seen this exact feeling emerge. Now, once more, I know there is nuance to all of this — I’m sure professional photographers winced when every jabroni got a digital camera and could take 40,000 photos in a weekend. I’ve no doubt that musicians of a certain age felt like I DON’T LIKE THAT THESE YOUNG KIDS TODAY CAN JUST TAP BUTTONS AND MAKE SOME BEEP-BOOP MUSIC ON THEIR SYNTHESCISSORS. But I also note that AI art isn’t that. Digital photography is still photography. Electronic music is still music. AI art… well, this leads me to the next point.
3. No, this doesn’t make you an artist and I’m seeing way too many defenders of AI art take this line. Some stay back at the line of, “I’m now an art director, art-directing a robot,” which, ennnh. Okay? But some march full on ahead and are saying, hey, I’m an artist now too. Which… nnnghhhh, are you? I admit, this gives me a pit in my stomach because I don’t like telling people what art is or is not and what makes an artist. That kind of gatekeeping curdles my milk more than a little. Still, as someone who has used Midjourney and other AI art makers, I sure don’t think of myself as an artist. If anything, I was just a writer jamming ideas into a techbro’s art engine. I didn’t feel like an artist. I sure wouldn’t call myself an artist having used Midjourney. I guess if I was using it to generate images that I then sketched or manipulated, that counts — but to do that, I’d still have to feed the beast, and therein lies part of the problem.
4. Feeding the beast means feeding an engine that feeds techbros and not artists. That’s the heart of the problem, really. Artists are like dinosaurs getting mulched into oil to fuel this thing. And you can see it when the AI art reproduces material with artifacts of signatures and watermarks. It’s clearly harvesting pre-existing art. It’s not dreaming up new art. It’s using their art, human art, and nobody is getting compensated, nobody is getting their due for being the literal seed-bed for this entire thing. The only people compensated are tech people. The people who make the engine. They’re the ones glad to press the oil out of the artists to run the machine.
5. No, this isn’t the same thing as “being inspired by artists.” That’s one of the lines of argument that doesn’t well with me. “It’s not copying artists, it’s being inspired by them, same as a person would be.” Except it’s not that, and you know it’s not that. We’ve fallen for the same anthropomorphic bullshit I spewed above about this being some PRECIOUS ROBOT DREAMING, and AWWW SEE THE ART-BOT IS INSPIRED BY YOU, but that’s not what it is. It’s not sentient. It’s not alive. It’s not a person making artistic decisions. It’s software operating on algorithmic decisions driven by, again, engines of tech, not creatures of art. “But it’s just like Andy Warhol!” No it’s jolly well fucking not. And you know that. You know Andy Warhol was a person who, like him or not, made decisions about what images he used, how he would subvert them, how that would put the work in front of other humans. He was a human making human art from corporate material in order to affect other humans.
6. And of course some people are choosing this as a battleground to litigate the problems with our current copyright system. Look, we’re all out here making choices and sometimes those choices are choices that benefit our urges and interests rather than helping out the greater good, right? From water bottles to Spotify to this or that, we are morally compromised daily because it is difficult to get a clean 100% record on Best Human Practices. But there’s a special kind of person who then justifies their choices with a lot of bluster about how REALLY they’re actually doing the RIGHT THING — “I voted for Jill Stein because something-something third-parties.” And you’re seeing it now with this AI art thing. “Well, copyright in America is poisonous and we have to Defeat Capitalism and really artists should be paid a Universal Basic Income,” and yeah, okay, good point, except that’s not a thing right now and this certainly won’t make it a thing. Yes, copyright has its problems, but that doesn’t mean you should hand it over to a tech company to do with as they see fit. Yes, capitalism is fraught and fucked up but paying an AI art subscription isn’t you throwing a Molotov cocktail through a bank window. Artists are already people on the fringes and they deserve to be paid for their efforts. They deserve to eat. To pay rent. To buy cool things. Hell, I’d much rather an artist get rich than Tech Bro #483, okay?
7. There is an adjacency (is that a word? too late) to NFT/crypto culture that I find… off-putting. There’s an NFT publishing company which, I’ll be honest, seems super fucking scammy to me, and most of their Very Special Super Rare Non-Fungible Book Cover Tokens are… just random AI art. Ennh. Ugh. Yuck.
8. Finally, the biggest reason of all: because more artists are asking us to leave AI art behind. I dunno. I’m not an artist. So Imma listen to them when I can.
So, anyway, them’s my thoughts. I suspect (or at least, hope) this AI art thing burns out. I think we should share actual human art. No, I don’t think you’re Il Monstre for using AI art. I think artists should be compensated. It’s the holidays, buy their prints, commission them to do something cool, whatever. We humans are why the human experience matters. Side with WONDERFUL MEATBAG ARTISTS, not TECH BRO MAGPIES. Okay? Okay.
(And yes, I recognize they’re coming for writers, too. Our off-ramp is a few miles down the road yet, but the car is speeding up, not slowing down.)
And speaking of writers —
Hey, Wayward is out if you want a cool GIFTY BOOK THING for folks. (And curiously, it’s a book that has a lot of thoughts about artificial intelligence!)
Cut off date for ordering signed, personalized books of mine from Doylestown Bookshop is, I believe, end of day 12/12, so hop to it if that’s what you want.
And if you liked it, please talk about it, yell about it, shake people and demand they buy it, that sort of thing. Word-of-mouth is the most vital resource we have, and in this era of fracturing social media, it counts double, even triple.
I’m currently dialed back on Twitter (and locked down too), so I may not see stuff over there quickly, and if you’d care to share this there, that’s a-okay by me. (Twitter: another one of those questionable things these days. I’ve more thinking to do about that place, but for now, I’m busy with book edits and will take the break until after the holidays.)
Also, finally, for those looking to see me at the Bethlehem/Easton B&N this weekend — we’re going to reschedule it. Lot of illness going around (including in our own house), so feels like it’s best to maybe kick that can to after the holidays. Look for a rescheduling of that event into Jan or Feb!
| 2022-12-08T00:00:00 |
2022/12/08
|
https://terribleminds.com/ramble/2022/12/08/why-im-done-using-and-boosting-ai-art/
|
[
{
"date": "2022/12/08",
"position": 45,
"query": "universal basic income AI"
}
] |
Our knowledge economy is swiftly coming to an end
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Our knowledge economy is swiftly coming to an end
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https://nicksaraev.com
|
[] |
Most digital-only companies—especially those that work in creative verticals—will face massive disruption in the next few years, unless they make artificial ...
|
Plus: will ChatGPT kill Google Search?
Welcome to The Cusp: cutting-edge AI news (and its implications) explained in simple English.
In this week's issue:
ChatGPT (or software like it) poised to disrupt Google search,
Whisper V2 quietly launched in the background,
Our knowledge economy is coming to an end: how to insulate yourself against the coming rapid changes in AI capability
Let's dive in.
1. ChatGPT poised to disrupt Google Search
If you're reading this, you've probably already heard of ChatGPT.
But for those that haven't: ChatGPT is an incredible dialog agent unveiled by OpenAI last week. It's been trained, reinforced, and optimized for one specific purpose: to assist the hell out of you. And it does the job incredibly well.
In just seven days, people have already begun using it to outsource their programming jobs, education, and even customer service.
However: the real game-changer here is its potential to decimate Google search.
ChatGPT's ability to answer questions, interweave context, and provide detailed code examples make it likely to become the go-to for info seekers in the near future.
The problem with Google
Google has dominated the search space for over two decades, and its massive market share invariably gives it a huge advantage. But in recent years, their search results page has grown to resemble more of a spam marketplace than a knowledge engine.
Searches are noisy, stuffed with irrelevant keywords, and ultimately leave users disappointed more often than not.
In contrast, ChatGPT provides users with concise and detailed answers to their queries in a fraction of the time. It uses context, factors in both the reading & writing level of the person who's speaking to it, and (unlike a search engine) can relate concepts to one another.
Compare these two examples:
Google
ChatGPT
Instead of general content or spammy links, users get an actual response to their query, and all of it is presented in the most natural way possible to us lowly humans: a back-and-forth conversation.
How can we take advantage of it?
ChatGPT is, of course, a closed-source implementation. People have created mock APIs that use browser automation to send messages elsewhere, but frankly I think that's missing the picture.
The real power of ChatGPT isn't in its direct economic utility (as in, how can I spin it to make more money), but in its utility as an educational force multiplier.
ChatGPT really is an assistant. The best way to derive its value is to use it as such. I've had ChatGPT help me mock up Vue boilerplate, ideate content for blog posts (like this one), and even explain complicated programming concepts using metaphors.
Some more ideas:
Paste in a new codebase or API & have it explain, line-by-line, how it works.
Self-teach history, politics, game theory, rationality—whatever you want.
Create lists of metaphors for common, but hard-to-explain topics. Memorize them to use with your friends, and consistently wow them with how smart you are
If I were to guess, it makes you anywhere from 50% to 100% more productive. Think of it like having a teacher in your pocket.
And OpenAI has plans to give it internet access, which would increase its assistance capabilities tenfold. I can't wait!
2. Whisper V2 quietly launched
Whisper, the world's most powerful speech transcription model, made its debut a little over two months ago.
Since then, people have used it to automatically transcribe lectures, Twitter videos, and more with >99% accuracy.
But OpenAI quietly pushed a change to their most capable model yesterday that improved it even further.
How can we take advantage of this?
You know how YouTube captions get ~80% of the way there?
Whisper is several orders of magnitude better. Here's an example of it transcribing someone speaking at 500wpm (spoiler: it does so flawlessly).
It's certainly better than most humans. And because it's open source, you can use this model in your own projects relatively easily.
You could:
Create a simple Whisper API in Flask, compile a list of transcription services, & cold email them to offer a free implementation. Charge per usage.
Hook Whisper up to a police or radio scanner. Make a Twitter account and post noteworthy announcements on a regional basis automatically with the Twitter API.
Transcribe noisy, low-quality lectures from the 1950s-1980s (think similar to the Feynman series) and bundle them into a book series.
3. Our knowledge economy is coming to an end
For the last sixty-odd years, our economy has depended on human intelligence to create and maintain economic value. That phase of history is (fortunately or unfortunately) coming to an end.
Most digital-only companies—especially those that work in creative verticals—will face massive disruption in the next few years, unless they make artificial intelligence their explicit focus.
Any company that doesn't pivot to AI won't stand a chance at developing better models than the big players (OpenAI, DeepMind, Google, etc). If that describes you, the quality of your service will eventually be swallowed up by increasing AI capabilities, rendering it moot.
Programming, marketing, design, education, animation, forecasting, financial planning, research, music... really, any industry whose main output is creative association/knowledge will be run primarily by APIs that a handful of scale-oriented companies develop.
How do we take advantage of this?
Most companies aren't going to be able to successfully pivot to AI. You need hundreds of millions of dollars, extremely talented engineers, and sizeable compute infrastructure (or the means to procure it).
A small room in one of Microsoft's billion-dollar data warehouses.
So for everyone else: in order to win, you need to stop focusing on where the puck is, and begin skating to where the puck is going to be.
What might this look like?
Focus on interfaces, not infrastructure . Since any functionality worth having will be an API call away, your organization's key differentiator will be the quality of your customer experience & its overall operational efficiency— not how well your team can fulfill x, y, or z service.
. Since any functionality worth having will be an API call away, your organization's key differentiator will be the quality of your customer experience & its overall operational efficiency— how well your team can fulfill x, y, or z service. Insulate your product from rapid advancements in AI. Tie it into the physical world, where AI will have less of an impact this decade. Pivot into the conference space, for example, or add a physical product to your offering. Your goal should be to increase the number of real-world dependencies and relationships, since those are not easily automatable.
Tie it into the physical world, where AI will have less of an impact this decade. Pivot into the conference space, for example, or add a physical product to your offering. Your goal should be to increase the number of real-world dependencies and relationships, since those are not easily automatable. Spend time and money on personal branding. Join the public discourse (if you haven't already). Ensure a sizeable number of stakeholders follow you, not your business—so that when the latter is invariably overtaken by technology, you still have something left.
Anyone reading this still has some runway, of course. But the length of that runway depends on uncertain factors, like the quality of your relationships with your customers, the amount of exposure they have to jaw-droppingly powerful AI, etc.
Better be safe than sorry.
That's a wrap!
Enjoyed this? Consider sharing with someone you know. And if you're reading this because someone sent it to you, get the next newsletter by signing up here. You can also follow me on Twitter if you'd prefer a shorter format.
See you next week.
– Nick
| 2022-12-08T00:00:00 |
2022/12/08
|
https://nicksaraev.com/our-knowledge-economy-is-swiftly-coming-to-an-end/
|
[
{
"date": "2022/12/08",
"position": 17,
"query": "AI economic disruption"
}
] |
AI-driven big data in supply chains - Delivered - Global
|
AI-driven big data in supply chains
|
https://www.dhl.com
|
[] |
And business leaders are discovering the value of state-of-the-art supply chain management to deal with disruption and achieve strategic goals. ... economic, and ...
|
Knowing the past is critical to understanding the present and steering the future. What’s true in life is true in business, and data is the raw material that makes such insight possible. Supply chain professionals have grasped the importance of data for decades, as evidenced by the industry’s embrace of the internet of things (IoT) through sensors, smart tags, and such. The potential treasure trove of data generated annually from IoT devices, augmented by user-generated content, is predicted to balloon to some 181 trillion gigabytes by 2025.
Processing capacities have not kept up with this deluge of data, however. And the task grows even more daunting given the unstructured nature of much of this data, including audio and video files and social media feeds – vastly transcending anything that can be managed in spreadsheets.
Enter AI-driven big data analytics – the process of deploying smart tech such as automation and artificial intelligence (AI) to reveal patterns in the past, highlight real-time changes in the present, and reliably predict trends in the future.
| 2022-12-08T00:00:00 |
https://www.dhl.com/global-en/delivered/innovation/big-data-analytics-in-supply-chain-management.html
|
[
{
"date": "2022/12/08",
"position": 52,
"query": "AI economic disruption"
}
] |
|
46% of Workers Wouldn't Recommend Their Job or Field to ...
|
46% of Workers Wouldn't Recommend Their Job or Field to Youngsters
|
https://www.shrm.org
|
[] |
The study found that people believe their jobs ... Here's how employers and employees can successfully manage generative AI and other AI-powered systems.
|
Designed and delivered by HR experts to empower you with the knowledge and tools you need to drive lasting change in the workplace.
Demonstrate targeted competence and enhance credibility among peers and employers.
Gain a deeper understanding and develop critical skills.
| 2022-12-08T00:00:00 |
https://www.shrm.org/topics-tools/news/46-workers-wouldnt-recommend-job-field-to-youngsters
|
[
{
"date": "2022/12/08",
"position": 85,
"query": "generative AI jobs"
}
] |
|
NFX's Generative Tech Open Source Market Map
|
NFX's Generative Tech Open Source Market Map
|
https://www.nfx.com
|
[] |
So we're open sourcing our market map of startups building in generative AI. ... Jobs · News · Signal · Brieflink · NFX Masterclass · Privacy Policy · Terms.
|
At NFX we are all in on Generative Tech, and you are too. So we’re open sourcing our market map of startups building in generative AI.
Here is a spreadsheet with (edit) 550+ startups on it already. It’s a work in progress. We want the community to add to it.
Together these companies have raised over $14B in funding. And this is just the beginning.
In addition to using proprietary NFX software and data for the first and internal draft of this market map, we also referred to Base10, Pitchbook, CB Insights, and Sequoia’s map.
| 2022-12-07T00:00:00 |
2022/12/07
|
https://www.nfx.com/post/generative-ai-tech-market-map
|
[
{
"date": "2022/12/08",
"position": 90,
"query": "generative AI jobs"
}
] |
Top 7 Impacting Construction Industry Trends for 2024
|
Top 7 Impacting Construction Industry Trends for 2024
|
https://www.kwant.ai
|
[
"Written By"
] |
There is enormous potential for AI technology to improve performance and productivity outcomes. Many contractors are starting to use AI for machine learning, ...
|
As we head into the new year, construction will continue to evolve and grow. 2022 was a year of activity, changes, and opportunities that made an impact on construction. The construction industry is composed of some of the brightest building minds, and as we head into 2023, there are some impacting trends that are taking effect into the next year.
Labor shortage continues
As we head into the new year, labor shortage continues to persist. In 2022, it was estimated that there will be a labor shortage of roughly 650,000. The construction employment numbers are still uneasy as we head into 2023.
Roughly 40% of our construction workforce are expected to retire over the next decade, so having the right training programs in place is crucial to keep the job engine running strong.
Many contractors in construction are currently enhancing their training courses and educational programs to address the labor shortage for skilled trades persons. Skilled labor Shortage will continue to be a thorn in the side for many contractors in 2023.
Higher technology adoption
Every industry goes through a digital transformation, and currently, the construction industry will continue to go through technological advancements. As we continue to build toward the future, innovation does not stop.
The construction industry is constantly evolving through innovative techniques and solutions to get projects done on time.
We can expect to steer away from the traditional paper and pen workflow and automate data workflows. There are many verticals of construction that technology can support, so expect to see more digital HR, productivity, and safety solutions in the market. For example, there was a study by KPMG in 2022, roughly 82% of owners felt the need to collaborate more with contractors.
Improving safety + training programs
Safety in construction continues to be a top priority as we head into 2023. Many contractors are beefing up their safety solutions, programs, and training courses for their workforce to prepare their teams for the field.
Many safety professionals are working together with construction solution providers like Kwant and other internal departments to streamline safety with features that will protect their workforce on the field.
Many contractors and owners are making safety a top priority because many understand the impact of a safety jobsite versus an unsafe one. Safer jobsites tend to be more productive and profitable for construction companies, and that understanding is becoming more important.
When an incident occurs, we only have a few seconds to respond, so having the right technology in place and education is crucial to reducing incidents on the field. To learn more about outreach training programs, visit the OSHA website.
Incorporating artificial intelligence
Artificial intelligence has been around for some time now, but is slowly being incorporated into construction technology. Due to the slow rate of technology adoption, AI or better known as artificial intelligence has been slow to incorporate into construction. There is enormous potential for AI technology to improve performance and productivity outcomes.
Many contractors are starting to use AI for machine learning, robotics, computer vision, knowledge-based systems, and further data optimization. Since the technology is slowly incorporated and contractors are still learning new ways to benefit from AI.
Connectivity + integrations
With the amount of solutions out on the market for construction professionals, many contractors are beginning to integrate and connect their devices that normally wouldn’t speak to each other.
Having solutions that speak to each other streamlines workflow processes and automates data gathering and management. An open API is an application program interface that is for public use and developers to gain access to software applications or web services.
Data management + cybersecurity
Data management and cybersecurity go hand-in-hand. As contractors continue to build using current and new technologies, data management and cybersecurity is becoming more important by the year.
Data hygiene is not something regularly taught in our personal lives, let alone work lives, so when handling business information, being able to manage sensitive data is crucial to the success of a project.
Having quality data allows for contractors to apply their rich information in applicable ways to benefit outcomes. Human error is the biggest reason for companies getting hacked so teaching your employees how to handle sensitive data is crucial for quality assurance.
Supply chain support & management
Due to the cost of materials and labor, supply chain management is becoming a higher priority for many contractors. The cost of materials are always fluctuating and other costs like transportation, weather conditions, world events, and material usage are huge factors that affect pricing.
Many contractors have limited supply chain visibility and technology is becoming more important to capturing insights and delay notifications for project materials. Logistics will always benefit from the use of technology and contractors alike.
Looking ahead
As we reflect upon the changes and trends happening in the construction industry, 2023 is shaping up to be an interesting year. As innovation continues to climb, contractors and builders are capitalizing on new technology and solutions in the market to complete projects in a safer and cheaper way.
Many contractors and builders are utilizing solutions that help centralize and consolidate project information into a workforce management platform like Kwant. Kwant is a workforce management & safety platform built to accelerate construction through productivity and safety. Using smart wearables and plug-n-play sensors, Kwant connects jobsite data and provides real-time insights & reports.
Learn how to implement some of these trends for your construction company today and start next year off right.
| 2022-12-08T00:00:00 |
https://www.kwant.ai/blog/7-impacting-construction-industry-trends-for-2023
|
[
{
"date": "2022/12/08",
"position": 66,
"query": "machine learning workforce"
}
] |
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