title_s
stringlengths 2
79
| title_dl
stringlengths 0
200
| source_url
stringlengths 13
64
| authors
listlengths 0
10
| snippet_s
stringlengths 0
291
| text
stringlengths 21
100k
| date
timestamp[ns]date 1926-02-14 00:00:00
2030-07-14 00:00:00
| publish_date_dl
stringlengths 0
10
| url
stringlengths 15
590
| matches
listlengths 1
278
|
|---|---|---|---|---|---|---|---|---|---|
Data governance, AI among the past year's analytics trends
|
Data governance, AI among the past year's analytics trends
|
https://www.techtarget.com
|
[
"Senior News Writer",
"Published"
] |
... disruptions have resulted in ongoing economic uncertainty -- data governance is starting to be a critical need. Some organizations hastily deployed ...
|
Data governance was one of the major trends that shaped analytics in 2022.
Data governance isn't glamorous like augmented intelligence, machine learning or natural language processing. It's the grunt work of analytics.
But after many organizations suddenly realized the importance of data-informed decision-making at the start of the COVID-19 pandemic -- and have continued to recognize its value as world events like the war in Ukraine and repeated supply chain disruptions have resulted in ongoing economic uncertainty -- data governance is starting to be a critical need.
Some organizations hastily deployed analytics operations over the past few years. 2022 was the year they emphasized governance of those operations.
But data governance wasn't the only significant analytics trend in 2022. The evolution of AI and a market correction for tech vendors all played prominent roles in shaping the past year.
Data governance Data governance is an organization's rules for data. It includes guidelines such as naming conventions so that data is consistently labeled and easy to find as well as access controls to determine which employees can have access to what data. It also includes methods for determining how an organization goes about processes such as data integration, data preparation and data analysis. Ultimately, the aim of data governance is to simultaneously protect an organization from risk of violating regulations for data while also serving as an enabler for end users who need to safely and confidently work with data. So with many organizations hastily launching analytics programs to deal with the pandemic and the tumultuous events that followed, data governance was a critical analytics trend in 2022. But it wasn't just organizations implementing guidelines after getting started with analytics that made data governance a big analytics trend. It was also organizations that have been using analytics to inform decision-making for years evolving their data governance to be less about protecting the organization and more about enabling the business user, according to David Menninger, an analyst at Ventana Research. "Data governance is really skyrocketing in terms of awareness and popularity," he said. "The reason is that organizations have changed their approach to governance from a disabling process to an enabling process. It's now about helping people get their jobs done and, in doing so, creating a framework that creates governance." Those organizations adopting self-service analytics need data governance frameworks that enable data exploration and analysis while protecting against compliance risk. Self-service analytics is about giving business users access to data with easy-to-use tools. But because those business users aren't data experts, and because they aren't high-level executives who have the right to access sensitive data, companies must put specific limits on their data usage. Meanwhile, self-service analytics is growing, noted Ritesh Ramesh, COO of healthcare consulting firm MDAudit and a customer of analytics vendor ThoughtSpot. "There is pressure on all organizations to do more with less due to shrinking profits -- hence the need to [for business users] to have total autonomy," he said. With more organizations deploying analytics tools and self-service analytics growing, one means of organizing data, putting data governance measures in place and monitoring data's use is by implementing a data catalog. Data catalogs are indexes of organizations' data and data products that incorporate governance measures to ensure data is used properly. "This year, a lot of effort has been put into data observability, from linking it to data catalogs to completely automating data governance," said Donald Farmer, founder and principal of TreeHive Strategy. "Specifically, there has been a focus on having the capacity to detect, keep track of and assess data flows throughout a business to recognize and solve data-related problems as they come up." Most analytics vendors now have data governance capabilities built into their platforms with Tableau and MicroStrategy, among those that made data governance a priority in 2022. Vendors such as Alation, Collibra and Informatica specialize in data catalogs. Data governance was a significant analytics trend in 2022 as more organizations implemented frameworks aimed at enabling business users to work with data safely and confidently.
Advancing AI Just as 2022 marked the year that organizations realized data governance can be an enabler of analytics, it was the year analytics vendors started to view augmented intelligence as a facilitator of human analysis rather than a replacement for it. AI is nothing new. But it has often been viewed as a means of machines replacing people. Instead, the concept of decision intelligence became an analytics trend in 2022 -- one in which AI and machine learning augment human analysis. Organizations possess far more data than any one person -- or even a team of people -- can sift through and observe for the changes and anomalies that can affect their business. Tools built with AI and machine learning, however, can comb through hundreds of thousands of rows of data in seconds and constantly observe key organizational metrics for changes. They can also alert employees when something needs attention so the human, who may have taken months to discover the change or anomaly -- or never found it all -- can investigate and take action. "Decision intelligence was a big trend this year," said Krishna Roy, an analyst at 451 Research. "We saw it establish after much buzz in 2021. Decision intelligence platforms aim to address the long-held holy grail of data and analysis democratization so that everyone who needs to be can be a data-driven decision-maker." Data governance is really skyrocketing in terms of awareness and popularity. The reason is that organizations have changed their approach to governance from a disabling process to an enabling process. It's now about helping people get their jobs done. David MenningerAnalyst, Ventana Research Vendors specializing in decision intelligence include Pyramid Analytics, Tellius and Sisu. The need for AI and machine learning goes beyond just decision intelligence, according to Bryan Harris, executive vice president and CTO at SAS. Organizations need help dealing with the exponential growth of data. The amount of data captured and consumed globally amounted to two zettabytes in 2010, according to Statista. By 2020, that grew to 64.2 zettabytes. By 2025, Statista forecasts an increase to 181 zettabytes. Much of that growth is due to the growing influence of the cloud, which enables organizations to quickly ingest data from an ever-increasing number of sources and store it in cloud data warehouses and lakes with far more capacity than an on-premises data warehouse. To make use of that data and not just leave it sitting untouched in cloud repositories, such as Azure or Redshift, organizations need AI and machine learning to manage and model that data to ready it for analysis. "To make sense of the increasing volumes of data, organizations are looking to apply the right analytic and modeling techniques to support transparent and explainable decisions," Harris said. "If businesses can successfully transition to the cloud of their choice while also accelerating the adoption of AI, they can ultimately become more resilient and agile and grow their businesses during disruption." As a result of the growing need for AI and machine learning, many analytics vendors have now added such capabilities in an attempt to serve the needs of their customers, Menninger noted. AI and ML tools are difficult for organizations to build on their own, so vendors recognized an opportunity to provide the capabilities as part of their platforms. "Augmented intelligence has really come to the fore," Menninger said. "We're continuing to recognize that AI and ML are hard, so to the extent vendors can provide a subset of those capabilities and do them in a way that makes them broadly accessible, they've found a lot of success."
Market correction While technology advanced in 2022, one analytics trend was a slowdown in the investments that enabled startup vendors to grow quickly and operate despite a lack of profitability. In February 2021, data lakehouse vendor Databricks raised $1 billion in venture capital funding. In addition, vendors including Confluent, Couchbase, Neo4j and Sigma Computing all executed funding rounds of at least $200 million in 2021. Meanwhile, cloud data platform vendor Snowflake set a record for tech companies by raising $3.4 billion in its initial public stock offering in September 2020. But since early 2022, when the stock market began to slide, inflation rates spiked and recession fears continued to grow, data and analytics vendors have found venture capital hard to come by and the stock prices of publicly traded companies have slid precipitously. "It feels like we're at an inflection point right now," said Dan Sommer, senior director and global market intelligence lead at Qlik. He noted that the pandemic and subsequent worldwide events sparked a desire for new technology that could help organizations manage uncertainty. As a result, cash flowed freely to vendors finding new and inventive way to help organizations survive the uncertainty and thrive compared to peers not using analytics to inform decisions. But when the broader stock market slide took tech stocks with it, and when fears of a recession became more real, the stream of cash slowed. According to Crunchbase, total VC funding was down 53% during the third quarter of 2022 compared to the same three months of 2021. That continued a yearlong trend with VC funding declining each quarter so far in 2022. In the stock market, though the Dow Jones Industrials Average is down only about 9% to date this year, the more tech-heavy Nasdaq Composite Index is off more than 30%. Among publicly traded data and analytics vendors, Snowflake's shares are down over 50% year-to-date, and both Domo and MicroStrategy are down more than 60%. Meanwhile, vendors such as Pyramid, Qlik, SingleStore and ThoughtSpot have all expressed interest in going public but have not yet proceeded with IPOs. Qlik went so far as filing initial paperwork with the Securities and Exchange Commission in January 2022 but remains privately held as the year comes to a close. "In January 2022, right when the year started, the tone changed drastically," Sommer said. "We had geopolitical events that took place, so the macro backdrop is what changed [and] the tech landscape was affected." The result has been a shift among investors from potential to proof, with vendors needing to show sustainable growth and, at minimum, a path to profitability. "The micro backdrop is that there's been a shift from growth to value," Sommer said. "Technology needs to prove itself, and it needs to be sustainable versus previously when people were buying hype. It's shifted from hype to value." Beyond profitability, Sommer said that data and analytics vendors can demonstrate their value by focusing on more than just one feature and offering a full-featured platform that includes automation, data integration, data management, data science and analytics. "That provides value [for organizations]," Sommer said. "And it goes back to that idea of hype versus value."
| 2022-12-16T00:00:00 |
https://www.techtarget.com/searchbusinessanalytics/feature/Data-governance-AI-among-the-past-years-analytics-trends
|
[
{
"date": "2022/12/16",
"position": 56,
"query": "AI economic disruption"
}
] |
|
DARPA Announces Winners of Artificial Intelligence ...
|
DARPA Announces Winners of Artificial Intelligence Competition to Aid Critical Minerals Assessments
|
https://www.usgs.gov
|
[] |
RESTON, Va. — Critical minerals are essential to the U.S. economy and national security; however, their supply is vulnerable to disruption.
|
Given the urgency to increase and better secure critical-mineral supply, The Defense Advanced Research Projects Agency (DARPA) partnered with the U.S. Geological Survey (USGS) to launch the Artificial Intelligence for Critical Mineral Assessment Competition in August 2022.
The partnership will help the USGS conduct more than 50 assessments of critical-mineral resources to aid in economic planning and land-use decision-making. To do this, the USGS draws from more than a century of accumulated data, contained mostly within geologic maps and reports, that provide the fundamental basis for these resource assessments.
Extracting useful and accurate information from these maps is a time-consuming and laborious process involving manual human effort. In fact, a typical assessment for one critical mineral takes approximately two years to prepare. That’s because the USGS map catalog consists of more than 100,000 geologic maps; only about 10% of those are available as georeferenced images and only about half of those are fully digitized vector files needed for analysis. Everything else – 90% of the data – consists of scanned images of paper maps.
The goal of the competition was to crowdsource ideas that could drastically reduce the time required to complete parts of the assessment, using AI and machine learning to automate key processes.
“The competition has been a valuable opportunity for the USGS to work with leading minds in AI to improve our approach to critical-mineral assessments,” said David Applegate, USGS Director. “It has already led to incredible time savings in how we prepare data in a machine-readable format. Furthermore, these machine-learning models have implications beyond mineral resources into other fields that use map data, including geologic mapping, ecological mapping of species diversity and many other application areas.”
“We anticipate our experience will serve as a road map for future interagency collaborations where machine learning can be applied to real-world problems,” said Bart Russell, deputy director of DARPA’s Defense Sciences Office.
After analyzing the mineral-assessment workflow, DARPA and its performers MITRE and NASA Jet Propulsion Laboratory recognized the greatest potential for near-term, high impact was in solving the data needs associated with georeferencing and extraction of individual geologic features found on USGS maps. As such, the competition was divided into two distinct sub-challenges. A total of 18 teams from industry, academia and even a high-school junior competed for cash prizes of \$10,000 for first place, \$3,000 for second and \$1,000 for third.
For the Map Georeferencing Challenge, participants were tasked to find a map within a given scanned image and georeference it by aligning reference points to base maps, such as grid lines, topography, administrative boundaries, roads, or towns. A Canadian company, Uncharted, received top prize for their simple, clean and organized solution. U.S. company Jataware received second place, and “Team Ptolemy,” with members from the Massachusetts Institute of Technology, University of Arizona and Pennsylvania State University, received third place.
For the Map Feature Extraction Challenge, participants were asked to extract features identified in an image’s map legend. Students and faculty from the University of Southern California Information Sciences Institute and University of Minnesota joined forces, earning first place for their exceptional solution to extract line features as well as polygons and points. “Team ICM” from the University of Illinois received second place, followed by Uncharted in third.
Throughout the competition, participants had up to eight weeks to complete each challenge. Each week, they had the option to submit their results for a blind validation dataset to test the accuracy of their code. In the last week of each challenge, participants received a completely blind evaluation dataset and had 24 hours to process and submit their code and detailed documentation of their approach, which was evaluated by experts from USGS, MITRE and NASA Jet Propulsion Laboratory, who reviewed the solutions for accuracy/usability.
To meet the high quality standards required by the USGS, the resulting solutions require further evaluation and development to become operational. USGS experts plan to integrate the best elements of the submissions into a workable solution for mineral assessment workflows and potentially for other mission area assessments within the agency.
In addition to identifying fresh approaches for this problem, DARPA officials view these competitions as a model for how transition partners can access the agency’s performer base.
To hear more about the competition, including insights from members of the winning teams, listen to Voices from DARPA podcast episode 63, “So Many Maps, So Little Time: Using AI to Locate Critical Minerals.” A list of winners can be found on the DARPA website.
| 2022-12-16T00:00:00 |
https://www.usgs.gov/news/technical-announcement/darpa-announces-winners-artificial-intelligence-competition-aid
|
[
{
"date": "2022/12/16",
"position": 97,
"query": "AI economic disruption"
}
] |
|
ValidMind | Careers
|
ValidMind
|
https://validmind.com
|
[] |
Work with us at ValidMind. We are seeking talented and skilled professionals to build the future of model risk management & AI governance with us.
|
Would you like to work in the world of artificial intelligence (Al) and Model Risk Management with a mission to become the world’s leading provider of trust for AI/ML and algorithmic models? We are seeking talented and skilled professionals to join our growing company.
| 2024-11-11T00:00:00 |
2024/11/11
|
https://validmind.com/careers/
|
[
{
"date": "2022/12/16",
"position": 4,
"query": "generative AI jobs"
}
] |
Jobs in Data & Analytics team | IBM Careers
|
Jobs in Data & Analytics team
|
https://www.ibm.com
|
[] |
Managing Consultant - Generative AI Principal. Professional. GURGAON, IN. Data & Analytics. Data Engineer-Data Platforms-Google. Professional. BANGALORE, IN.
|
We empower our IBMers to exemplify behavior that fosters a culture of conscious inclusion and belonging, where innovation can thrive. We're dedicated to promoting, advancing and celebrating the plurality of thought from those of all backgrounds and experiences.
| 2022-12-16T00:00:00 |
https://www.ibm.com/uk-en/careers/data-and-analytics
|
[
{
"date": "2022/12/16",
"position": 11,
"query": "generative AI jobs"
}
] |
|
Senior People Data Scientist - Instacart
|
Senior People Data Scientist - Instacart
|
https://www.builtinnyc.com
|
[] |
Artificial Intelligence • Professional Services • Business Intelligence • Consulting • Cybersecurity • Generative AI ... Find startup jobs, tech news and events.
|
We're transforming the grocery industry
At Instacart, we invite the world to share love through food because we believe everyone should have access to the food they love and more time to enjoy it together. Where others see a simple need for grocery delivery, we see exciting complexity and endless opportunity to serve the varied needs of our community. We work to deliver an essential service that customers rely on to get their groceries and household goods, while also offering safe and flexible earnings opportunities to Instacart Personal Shoppers.
Instacart has become a lifeline for millions of people, and we’re building the team to help push our shopping cart forward. If you’re ready to do the best work of your life, come join our table.
Instacart is a Flex First team
There’s no one-size fits all approach to how we do our best work. Our employees have the flexibility to choose where they do their best work—whether it’s from home, an office, or your favorite coffee shop—while staying connected and building community through regular in-person events. Learn more about our flexible approach to where we work.
| 2022-12-16T00:00:00 |
https://www.builtinnyc.com/job/senior-people-data-scientist/4837494
|
[
{
"date": "2022/12/16",
"position": 55,
"query": "generative AI jobs"
}
] |
|
Business Development Associate - Entry Level
|
Business Development Associate - Entry Level - Invictus Marketing Solutions
|
https://builtin.com
|
[] |
Get Personalized Job Insights. Our AI-powered fit analysis compares your resume with a job listing so you know if your skills & experience align.
|
What We Do
Invictus MSI is a team-oriented Marketing and Advertising firm focused on empowering change and social justice campaigns to help local Bay Area communities. Our 501(C)3 nonprofit clients have long histories of bringing creative, evidence-based solutions to major societal problems that require our collective, immediate response. Recently, their focus is attacking the opioid and gun violence epidemic in schools, but they currently offer more than 20 programs to promote safer, happier communities for children to grow up in. Our team is responsible for both raising awareness to their potentially life-saving programs and providing the funds necessary to implement them. Now that our team has developed a reputation for representing our 501(C)3 clients with integrity and enthusiasm, our goal is to continue expanding our client’s reach. We understand that learning how to successfully run a business is no small task, and requires a solid foundation in both leadership and management. To ensure our clients are in the best hands, each member of our team is provided with mentorship and hands-on-training through every step of the campaign development process. To learn more about our partnership, please visit https://www.leadrugs.org/lead-the-way/
| 2022-12-16T00:00:00 |
https://builtin.com/job/business-development-associate-entry-level/4075865
|
[
{
"date": "2022/12/16",
"position": 80,
"query": "generative AI jobs"
}
] |
|
Top 7 Machine Learning Trends to Look Out for in 2023-24
|
Top 7 Machine Learning Trends 2023-24
|
https://copperdigital.com
|
[
"Aakash Sareen"
] |
Considering the current work perceptions, Gartner predicts that AI and Machine learning will help companies manage their workforce and efficiency and grow with ...
|
Among the numerous emerging technologies dominating the business landscape, two globally embraced technologies are AI and Machine learning.
However, one of the most common predicaments around artificial intelligence and machine learning is that people consider machine learning (ML) synonymous with artificial intelligence. But in reality, machine learning is a subset of AI, aiming to enhance the overall proficiency of the AI technology by equipping it with advanced learning capabilities and responsiveness as businesses increasingly adopt IoT-based technologies and solutions to improve data intelligence, better comprehend data, and making data-driven judgments that reciprocate business value and growth.
Further, looking at the predictions, it’s easy to conclude that ML technology will expand its capabilities and dominate the coming year 2023 by playing a promising role in some of the most exciting innovations. As for business leaders, it’ll become increasingly important to leverage machine learning in business, become data intelligent and responsive, and have a competitive advantage in the business landscape.
Therefore, to help business leaders better understand the capabilities of Machine Learning in the coming years here’s a detailed rundown of some of the most promising Machine Learning trends we can expect in 2023-24.
Top 7 Machine learning Trends 2023-24
1. Foundation Models
In recent years, Foundation Model is one of the artificial intelligence models that have gained traction.
For those who aren’t aware of Foundation models, let us tell you.
A foundation model is a deep learning AI algorithm pre-trained with pervasive data sets.
In contrast to narrow artificial intelligence (narrow AI) models that only perform one task, foundation models are fine-tuned and trained with numerous data varieties to perform multiple discrete tasks and seamlessly transfer knowledge from one task to another.
Considering the increasing adoption of technologies to derive and process data, one of the most notable trends for the coming year will be an accelerated pace of Foundation Models, wherein AI projects become more manageable and more scalable for large enterprises to execute.
2. Multimodal Machine Learning
Multimodal AI, or Multimodal Machine learning, is an emerging trend with the potential to revolutionize the entire AI and machine learning in the business landscape.
Simply put, Multimodal machine learning is primarily a vibrant multi-disciplinary research field that suggests that the world around us can be experienced in multiple ways (called modalities). Thus, the technology aims to build computer agents with more innovative capabilities, from understanding, reasoning, and learning to leverage multiple communicative modalities, including linguistic, acoustic, visual, tactile, and physiological perceptions.
Even though the concept is new, with leaders gradually realizing its potential of enhancing the overall efficiency of AI technology, multimodal machine learning is another machine learning trend that we will witness prospering in 2023-24.
3. Metaverse
Moving into Industry 4.0, the line between our physical and virtual lives continues to blur, leading businesses to another potential technology of the digital landscape – Metaverse.
As Metaverse proffers unprecedented ways for businesses to interact and collaborate with end-users virtually, in addition to supporting an entirely new virtual economy where users can engage in numerous brand activities, tapping into the Metaverse will increase customer engagement, thus resulting in enhanced acquisition and growth.
And amidst the accelerating adoption of Metaverse, AI and Machine learning will play a crucial role in bridging the gap between the physical and virtual worlds as AI will help create virtual environments, dialogue, and images by using NLP, virtual reality, and computer vision. In contrast, Machine learning will enable a seamless analysis of virtual patterns, help automate distributed contracts and distributed ledgers, and support other blockchain technologies to allow virtual transactions.
Also, Read – AN ULTIMATE GUIDE FOR BUSINESSES TO ENTER METAVERSE
4. Low-Code or No-Code Development
According to numerous studies, enterprises leveraging AI and machine learning will support the corporate economy in 2023-2024. However, for businesses planning to benefit from emerging technologies, one of the significant challenges would be the intrinsic skill gap.
But since every challenge has a solution, the need for the right tech talent will be bridged by another emerging AI and Machine learning trend – Low-Code, No-Code Machine Learning Platforms.
Employing Low-code/no-code machine learning platforms will empower businesses to utilize the power of machine learning and build robust AI applications from pre-defined components. Thus paving the way for intelligent, efficient, agile, flexible, and automated app development.
Also, Read – A QUICK GUIDE TO LOW CODE NO CODE DEVELOPMENT
5. Transformers or Seq2Seq Models
Another AI and Machine learning trend that we will witness rising is Transformers, a.k.a Seq2Seq models. Seq2Seq models are primarily a type of artificial intelligence architecture that enables seamless transduction (or transformation) of data using an encoder and decoder and then gives out another output of the data in the form of an entirely different sequence.
Simply put, transformers are widely utilized in natural language processing tasks and analysis of the sequence of words, letters, and time series, to cater to complex Machine Language problems like Device Translation, Question Answering, creating Chatbots, Text Summarization, etc.
6. Embedded Machine Learning
With the rising adoption of IoT technologies, automation, and robotics, embedded systems have gained even more importance, and in the coming years, we might witness a more expanded utilization of this emerging machine-learning phenomenon.
Embedded machine learning (or TinyML) is initially a subfield of machine learning that enables the flawless functioning of machine learning technologies on different devices. Simply put, running machine learning models on embedded devices to make more informed decisions and predictions is termed embedded machine learning.
The embedded machine learning system is far more efficient than cloud-based systems and proffers various benefits, from reducing cyber threats and data theft and economizing the bandwidth and network resources to eradicating the data storage and transfer on cloud servers.
7. Machine Learning in Healthcare
We all might agree that healthcare is an ever-evolving industry. New technologies and tools are introduced constantly to pace the entire healthcare industry and its functioning.
As the ability to find and analyze patterns and insights by leveraging machine learning in healthcare gains traction and global adoption, in 2023-24, healthcare providers will have access to many more opportunities of taking a predictive approach to building a unified system empowering improved diagnosis, drug discovery, efficient patient management, care delivery processes.
Also, Read – THE INFLUENCE OF ARTIFICIAL INTELLIGENCE IN HEALTHCARE
Gartner’s Top Technical Segments Employing Machine Learning Trends in 2023
During a recent conference, Gartner and numerous renowned tech analysts contemplated the primary trends we might witness in 2023, especially those deriving economic and technological changes.
Amongst the others, the top trends employing machine learning were:
1. Creative AI and Machine Learning
One of the significant trends that gained popularity in 2022 was the utilization of AI for generative texts, code, images, and even videos. Continuing the rising popularity, experts have predicted that creative Ai and machine learning for fashion, creativity, and marketing will again be in high demand for various industry implementations in 2023.
2. Distributed Enterprise Management
When the pandemic made it imperative for enterprises to shift towards a hybrid working model, managing the diversified workforce and efficiency became one of the significant challenges for IT leaders. Considering the current work perceptions, Gartner predicts that AI and Machine learning will help companies manage their workforce and efficiency and grow with diversified forces.
In fact, according to Gartner, 75% of companies can increase their income by 25% with distributed enterprise compared to standard companies.
3. Autonomous systems
The coming year will pave the way for the rising demand for Autonomous systems, i.e., Robust software platforms equipped with the ability to self-manage and self-learn by dynamically reading, analyzing patterns, and data and adapting to algorithms using machine learning technology.
4. Hyper-automation
Another prediction for 2023 by Gartner elaborates on the rising need to become sustainable by moving towards automation and adapting to new technologies and tools. And since automating mundane tasks and complex business operations will require data, patterns, and analysis, workplace innovation will certainly only be possible by employing AI and machine learning in business.
Also, Read – 5 INTELLIGENT AUTOMATION EXAMPLES FOR YOUR ORGANIZATION
5. Increased focus on Cybersecurity
The advent and adoption of tools and technologies have made enterprises and their IT infrastructure vulnerable to cyber attacks. Thus Gartner predicts that the coming years will bring significant focus on cybersecurity and states that in 2024, the business with responsive and cyber-intelligent operations and processes will be able to reduce cyber financial losses from individual situations by 90%.
Wrapping it Up!
With all of that read, it’s no exaggeration to conclude that AI and Machine Learning will be two of the most rapidly evolving technologies expanding their reach and capabilities.
As for business leaders who are planning to tap into the intricacies of both the promising technologies – AI and Machine Learning, the best way is to get in touch with one of the top digital transformation consulting firms, with a proven track record of helping leaders leverage the best of technology for their business.
At Copper Mobile, our expert business transformation consultants can help leaders explore potential opportunities to put forward their first step toward employing AI and machine learning in business and improving overall efficiency, productivity and revenue. Reach out to us today!
| 2022-12-16T00:00:00 |
2022/12/16
|
https://copperdigital.com/blog/machine-learning-trends-you-should-know/
|
[
{
"date": "2022/12/16",
"position": 28,
"query": "machine learning workforce"
}
] |
APC And Artificial Intelligence - Honeywell Forge
|
APC And Artificial Intelligence
|
https://www.honeywellforge.ai
|
[] |
New data-driven empirical methods, popularly described as Artificial Intelligence (AI) or Machine Learning (ML), expand the toolset we have. An open ...
|
Operational efficiency is an important driver for success in minerals processing. The application of algorithms which use data from the process to identify new improvement opportunities – a “data-driven” approach – has shown great benefit in the mining and minerals industry.
Advanced Process Control (APC) is built on multivariable, model-based predictive control (MVPC) techniques. While APC remains the dominant technique for optimizing continuous processes, new technologies are emerging. New data-driven empirical methods, popularly described as Artificial Intelligence (AI) or Machine Learning (ML), expand the toolset we have. An open question though is how best to use these techniques. Do they replace APC? Do they assist APC? Do they solve a whole new class of problems? The paper takes a look at some ways that AI/ML can be used in the process industries, and how that impacts the traditional APC space.
Learn more about how artificial intelligence can improve advanced process control.
Download Whitepaper
| 2022-12-16T00:00:00 |
https://www.honeywellforge.ai/us/en/apc-and-artificial-intelligence
|
[
{
"date": "2022/12/16",
"position": 65,
"query": "machine learning workforce"
}
] |
|
Big Tech Laid Off Thousands. Here's Who Wants Them Next
|
Big Tech Laid Off Thousands. Here’s Who Wants Them Next
|
https://www.wired.com
|
[
"Amanda Hoover",
"Caroline Haskins",
"Zeyi Yang",
"Aarian Marshall",
"Paresh Dave",
"Fernanda González",
"Will Knight",
"Louise Matsakis",
"Molly Taft",
"Megan Farokhmanesh"
] |
Nearly 1,000 tech companies around the world have laid off more than 150,000 tech workers this year, according to Layoffs. ... AI tools cost a fraction of human ...
|
Some governments have long struggled to secure top tech talent and younger workers. The divides in the private and public sector extend beyond the US. In the UK, public sector pay has fallen to a 19-year low, making competition with private industries harder. But in China, some young workers are ready to leave behind a volatile tech industry for greater security. Finland’s government was so eager for tech workers to join the country’s industry that, in 2021, it gave foreigners 90-day visas to try out life in Helsinki.
As uncertainty grows amid declining tech stock values, more young people may consider the shift, too. US Digital Response cohosted a job fair in December planned in response to the recent layoffs. Ten state and city governments from around the US came to make their case to the prospective workers. The state of California is looking to hire nearly 2,500 tech workers, according to Matthias Jaime, deputy secretary of technology and innovation for the state. San Francisco is advertising government roles that require only one day in the office per week. But in addition to convenience, regular hours, and pensions, those recruiting for more government and nonprofit workers are advertising a fuzzy, warm feeling that comes from making a positive impact. “I think it is a super compelling mission,” Kurt DelBene, chief information officer with the VA, says of working in the department. “You’re basically delivering to people who have made their commitment to all of us, the biggest commitment they can make, by being in the armed forces. And they deserve our support.”
Tech Jobs for Good, a job board that focuses on mission-driven employers, saw a 40 percent increase in job-seeker profiles in May, says its founder, Noah Hart. That’s around the time tech layoffs began at Carvana, Klarna, and Robinhood. In October, as other tech companies began cutting employees, profile sign-ups jumped another 30 percent, Hart says. “There’s been a longer trend of more and more job seekers looking for impactful roles,” Hart says. “A lot of organizations are still hiring and are getting a lot more applications.”
Nonprofits and governments are trying to become more competitive. The average job posted on Tech Jobs for Good pays $118,000 to $134,000 Hart says. By comparison, software engineers at Google make between $98,000 and $330,000, and data scientists earn $113,000 to $200,000. The VA is working to close the existing pay gap between its roles and the private sector by 60 percent. And for some employees, making an impact and having a remote job might mean more than Silicon Valley perks and plummeting company stock prices.
Smaller startups or industries like retail and health care are also benefitting from the group of technologists let loose. “It does create an amazing opportunity for companies in pretty much every industry to work with this amazing talent,” Leonardo Lawson, founder and CEO of Bond Creative MGMT, a management consulting firm, says of the layoffs. “The companies that are sleeping on [hiring laid off tech workers], they’re going to really regret it.”
The most proactive employers could walk away winners. Joshua Browder, CEO of AI robot lawyer company DoNotPay, tweeted in November that he wanted to hire people affected by layoffs and would offer jobs to immigrants and sponsor their visas to help people who lost their jobs stay in the US. He says DoNotPay had four open jobs, but that tweet sent hundreds of applicants its way. By mid-December, DoNotPay had already made offers to candidates, Browder says.
“I was actually quite surprised that the companies were laying them off, because they are incredibly talented people,” Browder says. “I think these companies are making a mistake by being too aggressive with their layoffs.” Six months ago, Browder says, he likely would have had to pay tens of thousands of dollars to a recruiter to get such talent. Now, they’re landing in his inbox for free.
| 2022-12-16T00:00:00 |
2022/12/16
|
https://www.wired.com/story/big-tech-layoffs-hiring/
|
[
{
"date": "2022/12/16",
"position": 14,
"query": "AI layoffs"
}
] |
The Unraveling Of Meero
|
The Unraveling Of Meero
|
https://frenchtechjournal.com
|
[] |
Three years after a $230m funding round, the AI photography platform announced a pivot, layoffs, and new leadership.
|
Want to talk about tech journalism or get feedback on pitching? Book a video call with me on Superpeer. Paid subscribers to the newsletter get a 50% discount.
This newsletter is sponsored by...
Banks will charge you when you spend or transfer money abroad. We’re not about that, and that’s why over four million people have switched to Revolut.
Sign up and get Revolut Premium free for 3 months!
Amid the frenzy of new unicorns in early 2022, a kind of parlor game began to tally the official number of French unicorns. French President Emmanuel Macron had set a goal of 25 unicorns by 2025, but amid a global funding boom (cough, bubble, cough), the country was on the verge of passing that target three years early.
But how many French startups had passed the €1 billion valuation milestone? The unicorn criteria and the headcount remained fuzzy at the margins. This startup had French founders but had moved its HQ out of France, and that startup hadn't officially disclosed a valuation. And so various lists offered different numbers. 23? 24? 26?
One company that inevitably appeared on those lists was Meero, the AI-driven photography platform that had reportedly become a unicorn in 2019 when it announced in June 2019 that it had raised $230 million (€205 million). These were still the Salad Days for French Tech, a time of innocence when a company raising a 9-figure round just 3 years after its founding seemed astonishing.
At the time, I was working for VentureBeat. CEO and founder Thomas Rebaud told me that the size of the round spoke to the company’s global ambitions. “If you want to make it happen quickly, we need to invest in many different things at the same time,” he said.
But earlier this year, French startup news site Maddyness, as part of its unicorn roundup, disclosed in a curious throwaway line that it had decided not to include Meero. After consulting public documents, Maddyness reporters had concluded that Meero only had received only €130 million of the €205 million it had announced, bringing its valuation to less than half of the $1 billion threshold.
Oh?
I reached out at the time to Meero's PR team at the time, and after being met with radio silence, eventually discovered that they had left the company. Over the following months, I contacted former employees and investors to ask whether the Maddyness report was correct and if so, what had happened? Again, crickets.
In the young French ecosystem, people love to hype the latest success story. But they're tight-lipped when it comes to startups that have failed or are struggling. Still, as I asked around, it became clear that Meero's problems were a kind of open secret in the ecosystem.
Even if no one knew the details, there was a general perception that the pandemic had hit the company hard. The company had created a platform to pair local photographers with businesses and events, using AI to streamline the editing and sorting process. It's hardly surprising with lockdowns putting the clamps on travel and events, and shuttering physical stores that such a model would feel the impact.
But even as the pandemic receded and life returned to a kind of normal, Meero, it seems, continued to struggle. This week, the company announced Rebaud had stepped down as CEO and would be replaced by COO Gaétan Rougevin-Baville, one of Meero's first employees. Rebaud will keep his shares and become board chair. The company, which had already seen employment fall from 600 to 350, will cut another 72 jobs in the coming weeks.
More profoundly, it will slowly shut down most of the platform for photographers and pivot to a SaaS model for managing and organizing photos. "There is a kind of long Covid on marketing investments," Rougevin-Baville told Les Echos. "Marketing budgets, which include visual content, were frozen." Meero apparently attempted to find a buyer for this service but found no takers.
I reached out to Rougevin-Baville via LinkedIn for an interview. His comms team responded that for now, he's shared all he could in the Les Echos story and suggested we chat in January or February once things had settled.
In the meantime, here's what limited info I had been able to glean about Meero's downfall.
| 2022-12-16T00:00:00 |
2022/12/16
|
https://frenchtechjournal.com/unraveling-meero/
|
[
{
"date": "2022/12/16",
"position": 22,
"query": "AI layoffs"
}
] |
Asian talent faces U.S. visa crisis as tech sector slashes jobs
|
Asian talent faces U.S. visa crisis as tech sector slashes jobs
|
https://asia.nikkei.com
|
[
"Marrian Zhou",
"Yifan Yu",
"Nikkei Staff Writers"
] |
... Artificial intelligence · Electric vehicles · Supply Chain · Taiwan tensions ... According to layoffs.fyi, a website tracking job cuts in the tech sector, a ...
|
NEW YORK/PALO ALTO, U.S. -- Zhou was a data scientist at Facebook-owner Meta before he became one of the more than 11,000 employees laid off by the tech giant in early November. A master's degree from a top U.S. engineering school and more than five years of work experience have not helped the 30-year-old land a new job, even though he lined up more than a dozen interviews in the two weeks after being let go.
The clock is ticking for Zhou, who asked to be identified only by his last name. A Chinese national living in California's San Francisco Bay Area, he has only one year left on his H-1B working visa, and his application for permanent residency -- a green card, in common parlance -- has yet to be approved.
| 2022-12-16T00:00:00 |
https://asia.nikkei.com/Business/Business-Spotlight/Asian-talent-faces-U.S.-visa-crisis-as-tech-sector-slashes-jobs
|
[
{
"date": "2022/12/16",
"position": 89,
"query": "AI layoffs"
}
] |
|
AI in Healthcare, Where It's Going in 2023: ML, NLP & More
|
The Current State of AI in Healthcare and Where It's Going in 2023
|
https://healthtechmagazine.net
|
[] |
Healthcare AI use cases include helping doctors diagnose and manage kidney disease, enabling improved diagnostics and analysis of patient data.
|
Dr. Taha Kass-Hout, vice president of health AI and CMO at Amazon Web Services, notes that 97 percent of healthcare data goes unused because it’s unstructured. That includes X-rays and medical records attached to slides. Machine learning (ML) allows healthcare professionals to structure and index this information. Amazon HealthLake is one service that enables searching and querying of unstructured data.
In addition, ML and natural language processing (NLP) help healthcare organizations understand the meaning of clinical data, he adds.
For example, the Children’s Hospital of Philadelphia turned to AWS AI services to integrate and facilitate the sharing of genomic, clinical and imaging data to help researchers cross-analyze diseases, develop new hypotheses and make discoveries.
AI Scours Documentation for Cancer Studies
The Fred Hutchinson Cancer Center in Seattle used NLP in Amazon Comprehend Medical to review mountains of unstructured clinical record data at scale to quickly match patients with clinical cancer studies. NLP helped physicians review about 10,000 medical charts per hour to find patients with the right inclusion criteria, removing the “heavy lifting,” Kass-Hout says.
“There are laborious inclusion criteria to go through, where you have to identify a lot of characteristics about the patient to determine whether they meet the criteria to be enrolled in a clinical trial. Often you have to read the entire medical history,” Kass-Hout says.
Less than 5 percent of patients match the recruitment criteria for these types of clinical trials, according to Kass-Hout, partially due to the challenges of identifying the right information among unstructured data.
READ MORE: AI-driven clinical care guidelines can lead to better patient outcomes.
AI Helps Diagnose and Manage Kidney Disease
AI is helping doctors diagnose and manage kidney disease and predict trajectories for kidney patients, says Dr. Peter Kotanko, head of biomedical evidence generation at the Renal Research Institute (RRI) and adjunct professor of medicine for nephrology at the Icahn School of Medicine at Mount Sinai in New York.
Kotanko indicates that nephrologists and other medical disciplines use AI and ML to assess images from radiology or histopathology, as well as images taken by smartphones to diagnose a patient’s condition.
“AI not only relies on structured lab data or data stored in electronic health records, but also, of course, uses tools like natural language processing to extract insights from the unstructured texts,” he says.
Meanwhile, ML is used to predict patient outcomes, including hospitalization, and to identify which patients may have COVID-19. RRI uses deep learning to analyze images from smartphones or tablets to assess a patient’s arterio-venous vascular access, which is used to connect a patient to the dialysis machine.
“A convolutional neural network, or CNN, analyzes these kinds of data and sends a respective assessment back to the user within a second or so,” Kotanko says. “Images are sent from the tablet or smartphone to the cloud where a CNN receives the data and then provides the respective response.”
| 2022-12-16T00:00:00 |
https://healthtechmagazine.net/article/2022/12/ai-healthcare-2023-ml-nlp-more-perfcon
|
[
{
"date": "2022/12/16",
"position": 2,
"query": "AI healthcare"
}
] |
|
The Current State of AI in Healthcare and Where It's Going ...
|
The Current State of AI in Healthcare and Where It’s Going in 2023
|
https://ceo-na.com
|
[] |
Artificial intelligence is helping doctors diagnose and manage kidney disease and improving diagnostics and analysis of patient data.
|
Artificial intelligence is helping doctors diagnose and manage kidney disease and improving diagnostics and analysis of patient data.
Artificial intelligence holds great promise to help medical professionals gain key insights and improve health outcomes. However, AI adoption in healthcare has been sluggish, according to a March 9 Brookings Institution report. Despite the slow uptake of AI in healthcare, health insurer Optum revealed in a December 2021 survey that 85 percent of healthcare executives have an AI strategy, and almost half of executives surveyed now use the technology.
“We’re no longer in an infancy stage,” says Natalie Schibell, vice president and research director for healthcare at Forrester Research, noting the impact of the COVID-19 pandemic in accelerating digital transformation. That includes AI.
Schibell sees a deep need for AI to address healthcare problems such as chronic illness, workforce shortages and hospital readmissions. These factors are leading healthcare organizations, insurance companies and pharma and life sciences organizations to adopt AI, she says.
AI is playing a role in improving data flow, recognizing and processing both structured and unstructured data, Schibell says. “We’re at the point now where if you’re not investing in AI or if you’re on the fence about investing, you’re going to be left in the dust,” she says.
Schibell points to new efficiencies in speeding up data analysis. “AI identifies patterns, and it’s generating insights that might elude discovery from a physician’s manual efforts,” she says.
Dr. Taha Kass-Hout, vice president of health AI and CMO at Amazon Web Services, notes that 97 percent of healthcare data goes unused because it’s unstructured. That includes X-rays and medical records attached to slides. Machine learning (ML) allows healthcare professionals to structure and index this information. Amazon HealthLake is one service that enables searching and querying of unstructured data.
In addition, ML and natural language processing (NLP) help healthcare organizations understand the meaning of clinical data, he adds.
For example, the Children’s Hospital of Philadelphia turned to AWS AI services to integrate and facilitate the sharing of genomic, clinical and imaging data to help researchers cross-analyze diseases, develop new hypotheses and make discoveries.
AI Scours Documentation for Cancer Studies
The Fred Hutchinson Cancer Center in Seattle used NLP in Amazon Comprehend Medical to review mountains of unstructured clinical record data at scale to quickly match patients with clinical cancer studies. NLP helped physicians review about 10,000 medical charts per hour to find patients with the right inclusion criteria, removing the “heavy lifting,” Kass-Hout says.
“There are laborious inclusion criteria to go through, where you have to identify a lot of characteristics about the patient to determine whether they meet the criteria to be enrolled in a clinical trial. Often you have to read the entire medical history,” Kass-Hout says.
Less than 5 percent of patients match the recruitment criteria for these types of clinical trials, according to Kass-Hout, partially due to the challenges of identifying the right information among unstructured data.
AI Helps Diagnose and Manage Kidney Disease
AI is helping doctors diagnose and manage kidney disease and predict trajectories for kidney patients, says Dr. Peter Kotanko, head of biomedical evidence generation at the Renal Research Institute (RRI) and adjunct professor of medicine for nephrology at the Icahn School of Medicine at Mount Sinai in New York.
Kotanko indicates that nephrologists and other medical disciplines use AI and ML to assess images from radiology or histopathology, as well as images taken by smartphones to diagnose a patient’s condition.
“AI not only relies on structured lab data or data stored in electronic health records, but also, of course, uses tools like natural language processing to extract insights from the unstructured texts,” he says.
Meanwhile, ML is used to predict patient outcomes, including hospitalization, and to identify which patients may have COVID-19. RRI uses deep learning to analyze images from smartphones or tablets to assess a patient’s arterio-venous vascular access, which is used to connect a patient to the dialysis machine.
“A convolutional neural network, or CNN, analyzes these kinds of data and sends a respective assessment back to the user within a second or so,” Kotanko says. “Images are sent from the tablet or smartphone to the cloud where a CNN receives the data and then provides the respective response.”
AI Healthcare Use Cases in 2023 and Beyond
Here are some trends for AI use in healthcare within the next three years:
| 2022-12-22T00:00:00 |
2022/12/22
|
https://ceo-na.com/ceo-life/health/the-current-state-of-ai-in-healthcare-and-where-its-going-in-2023
|
[
{
"date": "2022/12/16",
"position": 38,
"query": "AI healthcare"
}
] |
How AI SaaS is Transforming Marketing and Healthcare
|
How AI SaaS is Transforming Marketing and Healthcare
|
https://www.digitalauthority.me
|
[
"Owen Murray",
"Owen Murray Is A Copywriter",
"Content Creator Who Works",
"Digital Authority Partners. He'S Always Had An...",
"Read More",
"Digital Authority Partners. He'S Always Had An Entrepreneurial Spirit",
"Started His Own Ecommerce Business At Owen Works With Ecommerce",
"Saas Businesses On Their Content Needs",
"Email Marketing Copy. For The Dap Blog",
"He Focuses On Website Copywriting"
] |
With the use of AI predictive analytics, clinicians can anticipate the development of diseases like cancer or heart disease and help prevent the disease rather ...
|
American folklore has it that John Henry was the best railroad tie layer in the country.
In competition, he could lay railroad ties two to three times faster than others.
But along came the steam engine.
The steam engine could lay railroad ties without manual labor, exhaustion, or a lunch break.
So the railroad company decided to test John Henry and the steam engine. A battle pitting man versus machine.
The two raced to lay the most railroad ties in a day.
In a neck-in-neck finish, John Henry defeated the steam engine. But before he could claim victory as the superior railroad tie layer, he collapsed and passed away from a heart attack.
However, what if John Henry worked alongside the steam engine rather than competing? How much faster would they be?
This folklore illustrates a point about humans working alongside machinery.
Humans will always be better at particular tasks. Artificial Intelligence (AI) can’t understand nuances in which humans excel. But the truth is AI can excel in several areas like automation, data analytics, and problem-solving.
Now, apply this to today in two areas where AI SaaS applications can be game changers: marketing and healthcare.
Whether or not you’re tech-savvy, there needs to be a resource exploring what’s happening right now and what the future holds. Especially in marketing and healthcare.
By the end of this article, you’ll learn an AI use case you hadn’t heard of — we guarantee it.
Which brings us back to the story.
If the parable about John Henry wasn't enough to convince you that we'll all soon be working alongside AI...
This article will.
Watch this video to learn how Digital Authority Partners designs software that works for your business!
What are the benefits of AI SaaS?
AI software will revolutionize the way we do our jobs and think about solutions to problems.
Before we get into the nitty-gritty explanation of how AI software works, we need to briefly cover why this information is practical to you. Even if you don't work in marketing or healthcare, AI software is probably a part of your workplace.
And the benefits of smart automation will help you:
Automate menial tasks that sap productivity
Integrate with software you're already using
Maximize efficiency and workflows
Predict behavior
Automate analytics reporting
Incorporate automated personalization
Improve user experience
Regardless of your industry, job title, or expertise with computers, an AI time-saving solution exists. For instance, we’ve included an AI mechanism that helps SaaS companies drastically reduce churn rates. And reducing churn is significantly less expensive than acquiring new customers.
In addition, the primary benefit of AI implementation is that it enables you to stay ahead of your competitors. And as AI becomes more mainstream, you want to be at the forefront of change to reap the most benefits. However, some argue the risks outweigh the benefits.
The risks associated with AI
Many people have noted that AI is not without flaws and isn’t necessarily a godsend to any industry. As with any new technology, there are costs and tradeoffs to every decision.
One of the prevalent downsides of AI advancement is scamming. Now scammers are using AI to automate their initiatives, sometimes even automating phone calls with AI voices selling people services. On the contrary, there is a countermovement against these scammers by startups and large corporations including Apple. For example, you can turn on “mute potential scam calls” in settings which helps alleviate the issue.
Of course, you can find negative implications in nearly any situation. And with each negative comes a solution. For now, let’s focus on understanding the underlying mechanisms behind AI.
What Is AI?
Artificial intelligence (AI) is the process of programming a computer to make decisions for itself based on inputs, experience, or experimentation.
The term AI was first coined in 1956 by Dartmouth professor John McCarthy. But that doesn't really help us understand AI.
A simpler way to define AI would be this: a computer's ability to mimic human decision-making and task completion without human intervention.
Understanding the mechanism behind the AI programs allows you to use them more effectively.
For example, let's think about a system like chess. Currently, the highest-rated chess player would be crushed by a computer. Yet it's with the assistance of computers that the world champion could even attain his world-record rating.
And to learn from computers, chess players had to understand how computers thought about chess positions.
In other words, they had to learn the mechanisms behind the process itself.
But not all AI works the same, right?
Are there different types of AI?
The short answer is yes. You can classify AI and machine learning in several ways.
Essentially, AI is a collection of systems with differentiating classifications based on particular objectives.
What is most useful (and easy) to understand is how these machines adapt, learn, and analyze datasets.
1. Machine Learning
It makes the most sense to start with machine learning because it's an integral process to nearly all AI systems. Without machine learning, AI would be unable to adapt and improve.
Probably the most relevant example of machine learning is the predictive text feature on Google search and Apple iPhone messages.
What differentiates machine learning from other AI types AI is that it doesn’t receive detailed instructions from humans — only structured data. And a program may have one or multiple goals.
That means the machine must learn from experimentation, data trends, and trial and error. Machine learning is an ongoing process. Developers can input parameters to ensure the program increases its precision and accuracy.
And unlike humans, machines don't need to sleep; they just continue learning.
What's most practical to understand are the different objectives of machine learning. Learning about the objectives will allow us to better understand what’s happening in marketing and healthcare.
Predictive: Based on past data, this system will predict the probability of an event, like a customer making a purchase. You can see how this might be useful in marketing, which we'll discuss later!
Prescriptive: A prescriptive system must predict what might happen but will also recommend a course of action. And if you think this might be useful in healthcare, you'd be 100% correct. What if AI software could diagnose patients based on millions of past data from other patients?
Descriptive: Descriptive systems offer conclusions based on previous data. For instance, AI marketing software can identify trends and generate reports.
Further, we can identify how these AI programs are invaluable.
Returning to the chess analogy, the computer allowed top players to look at positions in a different light. Specific moves that were never considered suddenly appeared to be the best move.
What if there are specific "moves" we aren't able to see in healthcare or marketing? AI might be the catalyst we need to find cures in healthcare, test new marketing initiatives, or unlock further creativity.
An additional type of AI programming adds to AI’s benefits and uses.
2. Deep Learning
Like machine learning, deep learning relies on data instead of human intervention. However, deep learning takes it a step further by imitating the workings of a human brain.
Nope. Not joking.
The goal of deep learning is to literally recreate the human brain in an artificial form. Where machine learning excels at understanding structured data, deep learning excels with unstructured data.
Unstructured data includes images, videos, and natural language processing (NLP). As a result, deep learning applications are far wider than standard machine learning.
Additionally, the benefits of deep learning are two-fold. First, it offers a much higher level of accuracy when the data set is large enough. Of course, if an unstructured set contains minimal data, machine learning will be more precise.
Why is that important?
Let's say you're a hospital using AI to predict patient outcomes. An 80% accuracy rate might seem decent. However, a 95% versus 80% accuracy could mean the difference between life and death.
The second advantage of deep learning is its ability to learn abstract concepts through pattern recognition, like a human brain. Some of your favorite algorithms are using this technology (think Netflix, Instagram, TikTok, and YouTube). And in marketing, thinking like a human would help in scenarios with chatbots and other customer service software.
Did you know that performing reCAPCHTA's is helping deep learning AI systems identify bridges, animals, and more?
If you have an acute eye, you may have noticed that reCAPCHTA's have increased in difficulty over the years. At first, it was a mix of obfuscated letters and numbers. Now it's almost exclusively geographical pictures.
And if you're thinking about practical application, then the field of radiology might have popped into your mind. After all, wouldn't it be amazing if an AI system could identify a tumor on an MRI?
The answer is yes. Companies are already working on this application.
And there are even more types of AI coming to or already in the marketplace.
3. Narrow AI
Narrow AI is typically the most common form of AI and is included in many of the systems we've discussed.
Also known as weak AI, narrow AI systems are created for specific tasks. For example, if you were to ask Siri about the weather, it would give you an accurate response based on your location.
However, if you ask her about something else outside of its scope, you won't get a response at all (or a very inaccurate one).
Narrow AI serves the role of automating tasks that humans do. It also marginally improves itself each time.
The more advanced the task is, the more time it takes to perfect it. For example, a healthcare image recognition system relies heavily on narrow AI. Even with thousands of datasets per day from hospitals, perfect accuracy hasn’t been achieved. But because narrow AI continues to improve itself, though slowly, its accuracy also improves over time.
4. General AI
If you've read Isaac Asimov's "The Last Question," you're likely familiar with general AI.
General AI, also known as Strong AI or artificial general intelligence (AGI), is the Holy Grail of AI development. This is the kind of system that could pass the Turing test with ease. In other words, it would be difficult to distinguish between humans and machines.
There isn’t a need to speculate about general AI; however, science fiction authors like Charles Stross and Isaac Asimov do an excellent job.
So here are the facts.
To date, no system has been able to achieve AGI.
Why?
When asked about the barrier to AGI, Dr. Ben Goertzel emphasized, "10 years ago, the biggest issue was lack of funding for AI. Now the biggest issue is lack of funding for serious AGI approaches, as opposed to narrow AI systems that mine large numbers of simple patterns from big datasets, like most current deep neural net systems."
According to Goertzel, our society is on the "brink" of achieving general AI. Because of the shift from narrow AI to the development of deep learning and AI neural networks, the idea of benevolent AGI is on the horizon.
5. Robotics
Robotics is one of the first types of AI to pop into your head when you think about conscious AI, which is why it’s positioned last on the list. Remember that a "conscious, aware AI" would mean combining several classifications of AI with robotics to complete tasks.
One of the more rudimentary examples of robotics is the self-sufficient AI vacuum. There isn't much else to discuss regarding robotics in today’s marketplace. However, the potential of robotics combined with other AI classifications makes the use cases endless. AI could replace many jobs at fast food restaurants, grocery stores, and other places where machines are already used.
Plus, robotics are already being used with great success in healthcare, which we’ll discuss later on.
How AI SaaS is transforming marketing
AI in marketing is now worth $17.46 billion, according to the Global Marketing Report.
AI SaaS has become the new norm in marketing. If you're not utilizing this software, it's likely your competitors are already ahead of you.
And it makes sense. AI systems are simply better at analyzing data and automating tasks than humans.
1. Data-Driven Insights for Marketing
As we previously noted, machine learning is centered around data. The more data, the better for having a robust AI. And with modern technology, we have an unprecedented amount of user data that machine/deep learning and narrow AI systems can crunch.
This allows companies to be proactive instead of reactive with their marketing efforts.
In other words, they can anticipate the needs and wants of their customers and deliver relevant content before the customer even knows they need it.
Plus, AI is more effective at churning data into useful predictions, prescriptions, and personas.
For instance, companies like Amazon and Target offer customers coupons and other product recommendations anticipating their needs. These "recommended sections" are now found in nearly all online stores, your Netflix feed, and podcast apps.
Additionally, email service providers (ESPs) like Klaviyo, ActiveCampaign, Zembula, and Moveable Ink incorporate AI into their platforms. Predictive analytics, automated A/B testing, and individualized (not segmented) emails top the list of AI features these platforms offer.
Now we've learned how data-driven marketing can increase revenue. So how does it decrease costs?
One of the most effective ways to reduce costs (right now) using AI marketing in SaaS startups is churn prediction, according to Dataiku.
Let's assume your business has a monthly churn rate of 1% – the rate at which you lose subscribers, customers, or entities that stop doing business with you.. Churn rates compound so that 1% compounds into 12% by the end of the year.
Because it costs significantly more to acquire new customers (compared to retaining them), companies need to dial in on reducing their churn rate. You'll avoid financial holes, keep more customers, and have a predictable growth rate.
AI SaaS can help you by constantly monitoring user behavior, segmenting audiences for increased personalization, and creating churn scores.
With advancements in machine learning and more data volume, the results can only improve.
Currently, you'll need to hire data scientists to manage these AI systems. But if general AI makes its way to marketing, those costly data scientists may become obsolete.
2. Automated Marketing Solutions (and personalization)
As a freelancer, one of my favorite examples of the use of AI is personalized, automated cold email outreach. Although it may not be the most impressive form of AI, it's highly pragmatic.
The software can save freelancers and marketing agencies (or any business owner) hours per day, which will compound. And if your employees are currently completing these tasks, their time can be used more efficiently too.
Above all, automating monotonous (yet vital) tasks in any industry is highly beneficial to your workforce and your bottom line.
We've seen AI further personalize and automate our marketing efforts through chatbots.
Another AI process you may not be aware of is dynamic pricing. AI can automatically adjust pricing for products and services using data analysis.
According to a study done by Minderest, Amazon offers price changes of around 20% when competitors offer discounts and promotions. Factors like demand volume, stock volume, specific days of the year, total product impressions, and other data analytics go into dynamic pricing — a process better suited for AI and not humans.
All in all, dynamic pricing is best for the consumer and for Amazon. It helps keep inventory moderate while maintaining mutually beneficial prices.
So, what can we expect the future of AI marketing to hold?
Although general AI applications are more logically applied to healthcare and hospitality, AGI would benefit marketing in a few ways.
First, chatbots, digital assistants, and other forms of customer service would be streamlined by AGI. They'd be highly efficient, cost-effective, and beneficial for companies and consumers.
Additionally, what if your CMO or data scientists were highly adept AGI systems? You might think that's too advanced for where we are now.
But with AI’s uses in healthcare, that possibility is becoming more of a reality.
AI has revolutionized the way we think about Healthcare
Healthcare is one of several industries where AI is in its beginning stages, similar to the arts and hospitality industries (think AI imaging tools or robots bringing your food to you).
Butthe use of AI in the healthcare industry was valued at $7.9 billion in 2021. However, it's on pace to reach $201.3 billion in 2030. That's an increase of 2450%.
When you extrapolate, the forecast makes sense. Technologies like AI diagnosing patients or providing preventative care are becoming more realistic daily.
For instance, the rate of doubling medical knowledge was every 73 days in 2020. That number was every 50 years in 1950 and every seven years in 1980.
In 2022, that number is closer to one-and-a-half months and will only continue to decrease with AI integration.
We've seen a few different applications of AI in healthcare so far, primarily in upper management and some in treatment.
1. Administrative and diagnostic use cases
First, the benefits of automating administrative tasks are numerous. Clinicians deserve to spend more time helping patients rather than dealing with data, right?
We've seen AI-powered software help with transcription, claim processing, fraud detection, and population health management. In addition to the time saved, these systems are often more accurate than humans. They don't get tired and can process and store data faster too.
AI is also transforming how pharmaceutical companies operate. It's being used in target discovery, clinical trials, and target validation.
In short, AI is making drug development cheaper, faster, and more effective. We can expect to see new cures and treatments in the coming years.
AI is also providing decision support for clinicians. This includes identifying at-risk patients, providing personalized recommendations, and monitoring clinical events.
For example, AI image recognition is used for both X-Rays and MRIs. The software works similarly to reCAPTCHA, as it's taught through leveraging millions of images as past experience, using machine learning.
In one case study, researchers used AI to predict sepsis (a life-threatening condition) 12 hours beyond onset in ICU patients with high-predictive accuracy. In another example, AI was used to diagnose a form of leukemia with 98.38% accuracy.
Right now, there are too few clinical studies available to offer a sound conclusion on the current state of AI diagnosis. More studies need to be performed.
However, the potential is there for AI to serve as a second opinion or even a primary care physician in some cases.
2. Treatment use cases
But AI is already being used in some routine healthcare tasks. First, robots are used more and more frequently in hospitals for patient transport, disinfection, and even surgery.
For instance, robots have been assisting in operating rooms since the 1980s. And currently, systems are being developed (and already in practice) for robots to take over medical situations where AI precision supersedes human skill.
Another example of AI being used in treatment is CT scans. Following a stroke, patients undergo a scan where computers are trained to identify issues. By saving time in analysis, the risk of a brain complication decreases.
As all medical clinicians know, preventing a disease is better – less costly for everyone and less damaging to patient health – than having to cure one. With the use of AI predictive analytics, clinicians can anticipate the development of diseases like cancer or heart disease and help prevent the disease rather than having to treat it later.
For example, researchers at the Georgia Institute of Technology created a model using AI that predicts cancer with 91% accuracy. Again, it should be noted that one study is not indicative of an all-knowing AI entity — just steps in the right direction.
But AI is also affecting how we look at cures to respective diseases. With AI already saving thousands of lives in hospitals through predictive analytics and diagnosis, let's explore the ways AI will be used to discover new cures.
One of the original startups founded with the above premise is Pharnext. They launched a Phase III study in 2021 (not yet peer-reviewed) showing outstanding results in curing Charcot-Marie-Tooth disease (CMT).
This is one of the first positive results from AI formulating cures for diseases based on its knowledge of chemical make-ups. Instead of trying new drugs, the AI seeks to combine compounds of drugs based on previous data deemed successful by humans.
Essentially, Pharnext is taking the first step toward finding cures with AI.
Lastly, researchers around the world are also driving new approaches to AI discovery. At Michigan State University, scientists are exploring new ways to use old drugs. Using old drugs would reduce costs and benefit both producers and consumers.
The Bottom Line
We are on the brink of another technological revolution — this time with AI. And although some argue AI integration comes with risks, we’re highly adaptable as humans. Solutions will always follow problems. Plus, the benefits of AI integration can’t be overstated.
AI will make personalized treatment at scale available in both healthcare in marketing.
Not only are there tremendous benefits to this personalization, but the use of AI will free up time for medical practitioners and marketers to do the tasks AI can’t.
As you see, the use cases for AI in marketing and healthcare are already abundant. From predictive analytics to pure automation, the benefits are clear. All that’s left is further innovation and iteration.
What do you think?
| 2024-07-06T00:00:00 |
2024/07/06
|
https://www.digitalauthority.me/resources/how-ai-saas-is-transforming-marketing-and-healthcare/
|
[
{
"date": "2022/12/16",
"position": 54,
"query": "AI healthcare"
}
] |
Chat GPT is Revolutionizing Architecture and Design
|
Chat GPT is Revolutionizing Architecture and Design
|
https://www.wooduchoose.com
|
[] |
The Challenges of AI in the Design Industry. However, it's important to ... graphic design, and data services. In addition to these services, we also ...
|
The Benefits of Chat GPT for Architects and Designers
As architects and designers, we know that it is essential to stay up to date on the latest technology and how it can impact your work. One exciting development in the world of artificial intelligence is Chat GPT, or Generative Pre-trained Transformer. This technology has the potential to revolutionize the way architects and designers approach their craft, offering both opportunities and challenges.
What is Chat GPT?
Chat GPT is a variant of the popular language generation model GPT (Generative Pre-training Transformer) designed specifically for chatbot applications. It is trained on a large dataset of conversation transcripts and is able to generate human-like responses to a given input.
GPT models use a combination of techniques including unsupervised learning and transformer architecture to generate text that is both coherent and diverse. Chat GPT takes this a step further by incorporating knowledge of conversational dynamics and the ability to respond appropriately to a given context.
For more information on GPT and chatbots, you may want to check out the following resources:
Here are a few ways in which ChatGPT could potentially be used to help architects and designers:
Generating descriptions and specifications for projects: ChatGPT could be used to generate detailed descriptions and specifications for architecture and design projects. For example, it could be used to generate materials lists, technical drawings, and other relevant documentation.
Creating proposals and presentations: ChatGPT could be used to generate proposals and presentations for architecture and design projects. It could be used to generate text that explains the design concepts and ideas behind a project, as well as the benefits and features of the design.
Researching and gathering information: ChatGPT could be used to gather and synthesize information on a variety of topics related to architecture and design. For example, it could be used to research building materials, construction methods, or design trends.
The Challenges of AI in the Design Industry
However, it's important to recognize that Chat GPT and other, more design specific forms, of artificial intelligence also present potential challenges for architects and designers. As AI becomes more prevalent in the design process, there is a risk that it could eventually replace human designers entirely. Although many would argue that this is unlikely and certainly a long way off. Even then, expert, highly trained, human input will be needed to verify, confirm and make changes based on the specifics of any project.
This could lead to job displacement and a decline in the demand for traditional design skills. Although it is more likely that the technology will be embraced, adopted and lead to advancements in what can be achieved, both in the design and construction of buildings. AI will be able to stretch our capabilities and help us to go beyond what we considered possible, in the evolution of the built environment. It's crucial for architects and designers to stay up to date on the latest technology and continue to develop their skills to stay competitive in the job market.
The role, and the skills, of architect or designer has already changed dramatically over the last 40 years, since the days of paper and drawing board. Technology, automation and digitalisation has completely transformed the methods in which an architect’s vision is translated into the designs that shape our world.
Architecture has therefore been, constantly, driven by technological advancements, more so than many other professions, as the impact has, not only been, in the creative process but the materials used have also evolved to be unrecognisable compared to traditional and ancient methods of building.
AutoDesk: A Market Leader in the World of Architecture and Design
AutoDesk is a market leader in the world of architecture and design. Most of us know AutoDesk, a well-known provider of software for architects and designers, with a range of products that are used by professionals in a variety of fields, such as architecture, engineering, construction, and product design. Some of the company's most popular products include AutoCAD, a computer-aided design (CAD) software used by architects, engineers, and construction professionals to create detailed 2D and 3D designs; and BIM 360, a construction project management platform that uses machine learning to analyze data and improve efficiency. AutoDesk is known for its innovative approach to technology and its dedication to helping its customers create better designs and more efficient processes.
One of the reasons AutoDesk is the market leader in its field is its focus on staying ahead of the curve when it comes to emerging technologies. The company has a long history of investing in and developing innovative solutions, and it continues to push the envelope with new products and features.
For example, AutoDesk has been an early adopter of artificial intelligence, using it to improve its offerings and stay relevant in an ever-changing market.
One way AutoDesk is using AI to improve its offerings is by incorporating machine learning algorithms into its software. For example, its BIM 360 platform uses machine learning to analyze data from construction projects and identify patterns that can help improve efficiency and reduce costs.
Similarly, AutoDesk's Fusion 360 product uses machine learning to help designers optimize their designs for strength and other critical factors. Another way AutoDesk is using AI is by developing tools that can help architects and designers work more efficiently. For example, the company has developed an AI-powered tool called "Generative Design" that allows users to input their design constraints and have the software generate a range of possible solutions. This can be a huge time-saver for designers, as it allows them to quickly explore a range of options and find the best one for their needs.
Overall, AutoDesk is a market leader in the architecture and design space because of its commitment to staying ahead of the curve and using emerging technologies like AI to improve its offerings. As the field of artificial intelligence continues to advance, it will be interesting to see how AutoDesk continues to leverage this technology to help its customers create better designs and more efficient processes.
Check out AutoDesk products
Alternative Architectural Design tools to AutoCAD
SketchUp: 3D modelling software with easy-to-use tools for both professional and beginner users; similar to AutoCAD but easier to use and learn.
MicroStation: CAD software with powerful 3D modeling capabilities; similar to AutoCAD in terms of features but more affordable.
ActCAD: Professional-grade 2D & 3D CAD software with powerful features; similar to AutoCAD in terms of features and pricing but more user-friendly.
SOLIDWORKS: 3D CAD software with powerful features for product design; similar to AutoCAD with additional features for product design.
Archicad: BIM software with powerful features for architects; similar to AutoCAD with additional features for architects and designers.
BricsCAD: CAD software with comprehensive features for 2D and 3D modeling; similar to AutoCAD in terms of features but more affordable.
Vectorworks Architect: CAD software with powerful features for architects; similar to AutoCAD with additional features for architects.
DraftSight: CAD software with powerful features for drafting; similar to AutoCAD but more affordable.
Rhino 3D: 3D modelling software with advanced tools for modelling and rendering; similar to AutoCAD but with more advanced tools and capabilities.
Onshape: Cloud-based CAD software for 3D modelling; similar to AutoCAD but with cloud-based storage and collaboration tools.
Other AI Platforms and Technologies for Architects and Designers
In addition to Chat GPT, there are a number of other artificial intelligence platforms and technologies that offer solutions for automation, design, video creation, marketing, copywriting, and more. Here are a few examples:
Descript is the world's first audio word processor, allowing users to view and edit any audio file as text. Descript currently offers a Mac app as well as human-powered transcription services.
Descript
JasperAI is an AI platform designed to help businesses automate and optimize their marketing efforts. It uses machine learning algorithms to analyze customer data and create personalized marketing campaigns that are more likely to be successful.
WriteSonic is an AI platform that uses natural language processing to help users create high-quality copy for their websites, marketing materials, and other content. It offers a range of features, including keyword optimization, tone analysis, and plagiarism detection, to help users create engaging and effective copy.
Learn more about WriteSonic
DALL·E 2
DALL·E 2 is a new AI system that can create realistic images and art from a description in natural language. This is from the creators of Chat GPT, OpenAI.
Learn more about DALL·E 2
Summary
These are just a few examples of the many AI platforms and technologies available to help professionals in the design, construction and timber industries streamline their work and create better results.
As the field of artificial intelligence continues to advance, it's likely that we will see even more powerful and sophisticated tools emerging in the future. This is moving fast.
Overall, all forms of artificial intelligence present both opportunities and challenges for architects and designers. While these technologies can help streamline the design process and improve efficiency, it's important for professionals in this field to stay current and adaptable to stay relevant in the changing job market and the profession as a whole.
As the use of AI in the construction industry continues to grow, it will be crucial for all of us, including, architects and designers to embrace the technology and find ways to integrate it into our work in a way that benefits our clients and our own careers.
Further information and reading
At Wooduchoose, we are dedicated to using the latest technologies and data science techniques to improve and streamline our services for the construction industry, with a focus on the timber sector. Our team of experts provide a wide range of services, including wood sourcing and timber product solutions, digital marketing, search engine optimization (SEO), web development, workflow automation, CAD drawing services, graphic design, and data services.
In addition to these services, we also offer wood profile drawings (DWG) files for download to help our clients save time and ensure that the profiles and products they use can be manufactured. To access these files, simply click here to register.
CAD Services – Ever Considered Out Sourcing?
Outsourcing CAD services is not about cheating or cutting corners. It's about having someone help with the mundane tasks so that you can focus on being creative and bringing your vision to life. At our company, we specialize in woodworking, joinery, and wood section details, but we actually go much further and offer drawing services for anything you need. Whether you're an architect or a designer, we can help you with all of your CAD needs, allowing you to focus on the creative aspects of your work.
Don't waste your time on tedious tasks that take you away from what you do best. Let us help you with your CAD drawings and free up your time to focus on the creative aspects of your work. Our team of experts is here to assist you with all of your CAD needs, no matter what they may be. Contact us today to learn more about how we can help you with your CAD services.
At Wooduchoose, we understand that every business is unique and has its own specific needs. That's why we are committed to providing timber industry-specific solutions that are tailored to meet the needs of our clients. Whether you're a small business owner or a large corporation, we have the expertise and experience to help you achieve your business goals. From custom wood products to timber industry consulting, we can help you with all of your needs.
If you're interested in learning more about Wooduchoose and how we can help your business, don't hesitate to contact wooduchoose. Our team is always happy to answer any questions you may have and provide more information about our services. To find out more about our company, click here to learn more about Wooduchoose.
At Wooduchoose, we are passionate about helping businesses succeed in the timber industry. Whether you're looking for custom wood products, industry consulting, or something else entirely, we have the expertise and experience to help you achieve your goals. Don't hesitate to reach out to us and see how we can help your business thrive.
Credits: We even had help from Chat GPT to write some of this article.
Disclaimer: Some links on this page are sponsored. We only endorse products and services from trusted sources, items that add value and are relevant to our readers, within our specialist sector. Buttons and links may open new windows and we may receive a commission for purchases you make with our associated partners.
| 2022-12-16T00:00:00 |
https://www.wooduchoose.com/blog/ai-and-architecture/
|
[
{
"date": "2022/12/16",
"position": 76,
"query": "AI graphic design"
}
] |
|
Top 8 Artificial Intelligence Career Paths to Pursue in 2023
|
Top 8 Artificial Intelligence Career Paths to Pursue in 2023
|
https://www.usaii.org
|
[
"United States Artificial Intelligence Institute",
"Usaii"
] |
Computer Vision Engineer, Business Intelligence Developer, Software Engineer, ML engineer, and others are a few roles that may excite you.
|
If you are keen on making it big in the Artificial Intelligence industry, this is the right spot, to begin with! As you grow your interests in this industry, it becomes imperative to understand the key areas to work on and the credentials without which you’ll be rendered hopeless. With the right skills comes the right AI job role. Also, as AI skills are expanding, so are career opportunities.
As per Global Newswire, the Global AI market size is predicted to grow at an alarming rate of CAGR 20.1%, over the period of 7 years, ending in 2029, with a score value of USD 1394.30 billion. This is definitely an incredible opportunity to invest in a career that is future-proof and offers much variety as it commands.
Computer Vision Engineer, Business Intelligence Developer, Software Engineer, ML engineer, and others are a few roles that may excite you; if you’re a keen tech enthusiast. Beginning strong is always the stepping stone for any lasting career. Artificial Intelligence is no different!
Invest in an AI career that is backed by the world’s most trusted names in the field of AI certifications. Beginning it right makes all the difference. Earn the requisite experience in cloud tools and master the most desired AI skills; to earn as high as USD 200,000 for a meaty AI job role. The future is AI and it makes it all the more necessary to engage oneself in an AI certification program that is self-paced, easy to access, and offers a lifetime digital badge on successful completion. Make the best decision of your life by enrolling in the best AI certification worldwide today!
| 2022-12-16T00:00:00 |
https://www.usaii.org/ai-insights/top-8-artificial-intelligence-career-paths-to-pursue-in-2023
|
[
{
"date": "2022/12/16",
"position": 10,
"query": "artificial intelligence employers"
}
] |
|
AI Bullseye – AI Bullseye Tactics for Non-Technical Business ...
|
AI Bullseye – AI Bullseye Tactics for Non-Technical Business Leaders
|
https://aibullseye.com
|
[] |
AI for Business People. Learn how business leaders can use artificial intelligence to generate real-world business value today. Get real-world business ...
|
In AI Bullseye Tactics for Non-Technical Business Leaders, AI-for-business expert Thomas Gilbertson shares real-life, insider stories to illustrate unique concepts culled from his thousands of hours of experience delivering AI projects for Fortune 10 companies. This guide uncovers Gilbertson’s 12 core business principles for deploying AI effectively and guarantees to forever change how you think about getting business value from AI. Let others debate what AI is, while Gilbertson shows you what AI can do for you, your business, and your career.
| 2022-12-16T00:00:00 |
https://aibullseye.com/
|
[
{
"date": "2022/12/16",
"position": 18,
"query": "artificial intelligence business leaders"
}
] |
|
What you need to know about ChatGPT and artificial intelligence
|
What you need to know about ChatGPT and artificial intelligence
|
https://carey.jhu.edu
|
[] |
It works by closely mimicking human language, packing the potential to make writing tasks quicker and easier in a way never seen before.
|
Analysts and pundits predict that Open AI’s new ChatGPT would bring about everything from the “death of the school essay” to the dawn of a new age of communication. But what really is ChatGPT, and how could it change our lives?
Ask ChatGPT a question and it quickly summarizes an answer into grammatically correct and punctuated paragraph. Within two weeks of its November 30 launch, millions of users were trying out the large language-model artificial intelligence app. In fact, it was getting so much attention, the system periodically exceeded its user capacity.
If you ask ChatGPT what it is and how it works, it will tell you: “As a large language model trained by OpenAI, I generate responses to text-based queries based on the vast amount of text data that I have been trained on. I do not have the ability to access external sources of information or interact with the internet, so all of the information that I provide is derived from the text data that I have been trained on.”
As remarkable as ChatGPT appears to be, the system does not “think” and is incapable of coming up with original ideas. It works by closely mimicking human language, packing the potential to make writing tasks quicker and easier in a way never seen before.
“This particular tool is strikingly great in figuring out what a user wants and putting relevant things into a really logical and clear manner, to the extent that some may be fooled into thinking it is sentient,” said Tinglong Dai, a professor of operations management at Johns Hopkins Carey Business School and an AI expert. “. No other AI has been as striking as ChatGPT. It has really opened the window into the latest developments in large language models with something that most people didn't realize was possible before.”
| 2022-12-16T00:00:00 |
2022/12/16
|
https://carey.jhu.edu/articles/research/what-you-need-know-about-chatgpt-and-artificial-intelligence
|
[
{
"date": "2022/12/16",
"position": 31,
"query": "artificial intelligence business leaders"
}
] |
Data and AI Services
|
Data and AI
|
https://www.cognizant.com
|
[] |
Invest in people to unlock the power of AI. ... Bridge the gap between strong AI leadership and business readiness. ... Explore the future of business with our Gen ...
|
Wherever you are on your journey, in whatever industry you are in—from aggregating vast points of data to building sophisticated AI models—Cognizant will meet you there. With our innovative offerings you will harness the power of data and AI to drive faster, predictive and proactive decisioning, all while educating the organization on your path forward.
| 2022-12-16T00:00:00 |
https://www.cognizant.com/my/en/services/ai
|
[
{
"date": "2022/12/16",
"position": 64,
"query": "artificial intelligence business leaders"
}
] |
|
DBA Artificial Intelligence and Business
|
Artificial Intelligence and Business
|
https://www.junia.com
|
[] |
Artificial Intelligence and Business. A “Doctorate of Business Administration ... “How the GAFAM will become the global leaders through the AI & Robot use?
|
Presentation
The Doctorate of Business Administration in Artificial Intelligence & Business is a unique program designed by JUNIA XP, the training division of JUNIA, a prestigious engineering school. This 100% online professional DBA enables you to become a doctor, consultant, and professor. It is accessible to those with a Master’s degree (BAC+5) and requires no technical skills.
The program combines artificial intelligence, engineering, and management and is delivered entirely online. It lasts 2, 3, or 4 years, with strong human support, including over 150 on-demand meetings with a research advisor. It is designed for active professionals determined to demonstrate expertise in their field.
JUNIA XP’s DBA, also known as the DBA in AI and Business, provides business leaders, senior executives, professionals, consultants, and academics with new opportunities to leverage their personal and professional trajectories and/or transition into academia. DBA candidates’ research projects will enable them to explore questions that stimulate interactions between business and academic communities, enhancing their influence within their networks and beyond.
| 2022-12-16T00:00:00 |
https://www.junia.com/en/academics/dba-artificial-intelligence-and-business/
|
[
{
"date": "2022/12/16",
"position": 68,
"query": "artificial intelligence business leaders"
}
] |
|
Role of Artificial Intelligence in Corporate Training and ...
|
Role of Artificial Intelligence in Corporate Training and Development - A Conceptual Paper
|
https://www.ijisae.org
|
[
"Anjali Sabale",
"Research Scholar",
"Full Time",
"Vit Business School",
"Vellore Institute Of Technology",
"Vellore",
"Tamil Nadu",
"Gomathi S.",
"Professor"
] |
Every Business is impacted while in the transformation phase. Similarly, AI is also proven to impact business operations. However, enhancing market leadership ...
|
Adams. R. L, “10 powerful examples of artificial intelligence in use today”, Forbes, 2017.
Live Tiles, “15 pros and cons of artificial intelligence in the classroom”, Live Tiles, 2017.
Autor. D., F. Levy and R. J. Murnane, “The Skill Content of Recent Technological Change: An Empirical Exploration”, Quarterly Journal of Economics, vol.118, no.4, pp. 1279–333, 2003.
Oribabor. P.E, “Human Resources Management, - A Strategic Approval”, Human Resources Management, vol. 9, no.4, pp. 21 – 24, 2000.
Isyaku. I.A, “Training and retraining of Teachers through Distance Education”, Paper presented at the National Workshop on Distance Education Held at Abuja Nigeria, pp. 27-29, 2000.
Pitfield. R. C, “Effective Human Resource Development”, California: Jossey Bass Inc.P Publishers, 1982.
Goos. M. and A. Manning, “Lousy and Lovely Jobs: The Rising Polarization of Work in Britain”, The Review of Economics and Statistics, vol.89, no.1, pp.118–33, 2007
Dobbs. R., J. Manyika. and J. Woetzel, “The Four Global Forces Breaking all the Trends”, London, San Francisco, Shanghai: McKinsey Global Institute, 2015.
Aspan.H, “Individual characteristics and job characteristics on work effectiveness in the state-owned company: the moderating effect of emotional intelligence”, International Journal of Innovation Creativity and Change (IJICC), vol.13, no.6, pp.761–774, 2020.
William Vorhies, “Artificial general intelligence - The Holy Grail of AI”, DataScienceCentral.com, 2016
Valle. R; Martın. F; Romero PM; Dolan.S, “Business strategy, work processes and human resource training: are they congruent”, Journal of Organizational Behavior, vol.21, pp. 283-297, 2014.
Dr. Amir Elnaga & Amen Imran, “The Effect of Training on Employee Performance.”, European Journal of Business and Management, vol.5, no.4, 2013
Olaniyan. D. A. and Ojo B. Lucas. 2008, “Staff Training and Development: A Vital Tool for Organizational Effectiveness”, European Journal of Scientific Research, ISSN 1450-216X, vol. 24, no. 03, pp.326-331, 2018.
Blanchard. P, Nick and Tracker, James. W, “Effective Training Systems – Strategic and Practices”, 2nd Ed., Prentice Hall of India Pvt Ltd., New Delhi, p.4, 2006.
Uday Pareek and Venkateswara Rao, “Designing and Managing Human Resource System”, Oxford and IBM Publishing Company Pvt Ltd., New Delhi, p.207, 1999.
Phillips. J. J. (Ed.), “Measuring return on investment”, Alexandria, VA, vol. 2, 1997.
Wilson David; Fosway Group, “American Society for Training and Development - Path to learning personalization”, Employee Training and Development, McGraw Hill, New York, 2019.
Cain. C; Haque. S, “Organizational Workflow and Its Impact on Work Quality”, In: Hughes RG, editor, Patient Safety and Quality: An Evidence-Based Handbook for Nurses. Rockville (MD): Agency for Healthcare Research and Quality (US), Chapter 31, 2008.
Vidhi Jain, “An Impact of Artificial Intelligence on business”, International Journal of Research and Analytical Reviews, vol.6, no. 2, 2019.
Philip Boucher, “European Parliamentary Research Service”, Author: Scientific Foresight Unit (STOA), PE, pp. 641.547, 2020.
Palani Velu, Vasanthi B, “Role of Artificial Intelligence in Business Transformation”, International Journal of Advanced Science and Technology, vol.29, no.4, pp.392-400, 2020.
Solon Barocas and Andrew D. Selbst, “Big Data’s disparate impact”, California Law Review, vol.104, pp. 671-732, 2016.
Strohmeier. S; Piazza. F, “Artificial Intelligence Techniques in Human Resource Management - A Conceptual Exploration in C Kahraman, Ç Onar”, Intelligent Techniques in Engineering Management, pp. 149-172, 2015.
Buczko. I; Dyachenko. Y; Nemashkalo. O, “Evaluation of human capital as a tool for management of human resource development”, Actual Problems of Economics, vol.116, pp. 117-123, 2011.
Dyachenko.Y, “Methodology of personnel development in the frames of economic paradigms,’ Information Technologies, Management and Society, vol.3, pp. 96-107, 2010.
Oren Tsur, Dmitry Davidiv, and Ari Rappoport, “Icwsm – a great catchy name: Semi-supervised recognition of sarcastic sentences in product reviews”, International AAAI Conference on Weblogs and social media, 2010.
Pasquale. F, “The Black Box Society, the Secret Algorithms That Control Money and Information”, Cambridge, MA: Harvard University Press, 2015.
Neven Louis, “By any Means? Questioning the Link Between Geron technological Innovation and Older People’s Wish to Live at Home”, Technological Forecasting and Social Change, vol.93, pp.32–43, 2015.
| 2022-12-16T00:00:00 |
https://www.ijisae.org/index.php/IJISAE/article/view/2328
|
[
{
"date": "2022/12/16",
"position": 92,
"query": "artificial intelligence business leaders"
}
] |
|
AI Leaders Forum, 18 September 2025, Sydney
|
AI Leaders Forum, 18 September 2025, Sydney
|
https://aiforum.com.au
|
[] |
Although many businesses are now familiar with AI's basic functions, many leaders are still grappling with its complexity, how to strategically navigate ...
|
Delegate Pass:
Delegate Pass includes access to all sessions, refreshment breaks, networking lunches, networking drinks reception, and access to approved speaker presentations/on demand content. Please note: Sharing of speaker presentation’s is subject to approval from the guest speaker. One delegate ticket is entry for one person only, passes cannot be shared. Organiser’s reserve the right to deny entry to anyone not registered.
Digital Pass (only applicable for digital events):
A digital pass is valid for the use of 1 user and provides access to live content, inclusive of all keynotes, and sessions. The pass also includes access to approved speaker presentations/on demand content; this will be available to you via email in the week following the event. Please note: Sharing of speaker presentation’s is subject to approval from the guest speaker. Access must not be shared by multiple user’s or redistributed in any way. Organiser’s reserve the right to deny access to anyone who is found violating the above terms.
Payment & Discounts:
Only one promotional discount code can be applied per registrant. All prices and promotions are valid at the time of purchase only and may not be redeemed after the point of purchase.
Registrations will be reviewed for the correct rate, Connect Media reserves the right to refuse entry to anyone not paying the correct rate. Furthermore, we will not be responsible for travel costs if you do not pay the correct rate.
Organiser’s Rights:
Connect Media and Communications Group Pty Ltd endeavours to ensure the conference programme and speaker line-up is correct at the time of the event. All advertised details are correct at time of publishing. Due to unforeseen circumstances Connect Media and Communications Group Pty Ltd reserves the right to alter the programme prior to the event without notice. We also reserve the right to cancel or postpone the event. Where Connect Media is unable to run the event in the next 12 months (from the date of the event), you will be entitled to a full refund. In the unlikely event that it is cancelled or postponed, no compensation will be provided for cancelled/amended travel arrangements.
Connect Media and Communications Group Pty Ltd reserves the right to deny access to any individual that engages in or is alleged to engage in practices that are considered unprofessional and inappropriate for a business conference. We reserve the right to deny access to delegates that may affect the client / vendor ratio of attendance in favour of the interests of sponsors and commercial partners of the event.
In registering for this event, delegates grant permission to Connect Media and Communications Group Pty Ltd to take and to have full and free use of video/photographs containing their image/likeness for promotional use. Should a delegate not agree to the above image release, they must advise [email protected].
Cancellation Policy:
A substitute delegate is welcome at any time provided the request is made in writing. A full refund less a $250 (GST Inclusive) processing fee is applicable on cancellation requests made in writing within 10 days of registration. No refunds are available for cancellations made more than 10 days after registration. Where Connect Media is required to reschedule the event in the interest of the event partner’s and guests, your pass will be automatically transferred to the rescheduled dates. Should you be provided with a credit note at the discretion of the event organisers, this credit is valid for the specified amount of passes, and not the monetary value. Should Connect Media be unable to offer, deliver or fulfil any engagement within 12 months, you will be entitled to a full refund.
Event Delivery:
In case of a change in government restrictions and advice, Connect Media reserves the right to make the decision to deliver the event completely digital, or completely in person. Should you hold a different pass, you are entitled to move your registration to digital/in person, or receive a credit for the following edition of the event.
Privacy Disclosure:
We take your privacy seriously. Information collected on this registration will be held in the strictest of confidence on a secure database. This information may be used in order to contact you regarding future events, product development and services offered. If you do not wish to be contacted please email [email protected]. To view our full privacy policy please visit: https://dashboard.connectmedia.com/privacy-policy/.
| 2022-12-16T00:00:00 |
https://aiforum.com.au/
|
[
{
"date": "2022/12/16",
"position": 98,
"query": "artificial intelligence business leaders"
}
] |
|
Keynote Presentations
|
Workplace Intelligence
|
https://workplaceintelligence.com
|
[] |
Explore the ethical and social implications of AI adoption in the workplace, including concerns related to privacy, bias, and job displacement, and gain ...
|
Total Well-being: How Organizations Can Create a Healthier and More Resilient Workforce
Over the past few years, leaders and their employees have experienced an immense amount of physical and mental trauma due to the global pandemic. And although the mass acceptance of remote work has given employees more freedom and flexibility, it has come with a significant cost to their well-being.
The situation is so dire that millions of workers have quit or switched jobs due to burnout, poor work-life balance, and other factors affecting their quality of life. In fact, today’s employees prioritize their health and total well-being more than ever before when making employment decisions. And with the Great Resignation showing no signs of slowing down, there’s never been a more pressing time for organizations to pay attention to this trend.
In this timely and imperative keynote, New York Times bestselling author and foremost workplace expert Dan Schawbel helps organizations learn how to reimagine workplace well-being, so they can not only retain their staff but also unlock their true potential at work. Using his firm’s proprietary research as well as case studies and practical examples, he explains the direct relationship between employee well-being and organizational success. Dan helps organizations recognize the importance of taking care of all aspects of employee well-being and helping people overcome the many challenges that prevent them from bringing their best self to work.
Organizations come to Dan when they need to know how to react to monumental shifts in the workplace ecosystem to be prepared for what’s next in the modern economy. Well-being is already at the forefront of the new movement to a better and more human workplace — is your organization ready for what’s next?
YOU WILL LEARN:
| 2020-05-07T00:00:00 |
2020/05/07
|
https://workplaceintelligence.com/keynote-presentations/
|
[
{
"date": "2022/12/17",
"position": 45,
"query": "automation job displacement"
},
{
"date": "2022/12/17",
"position": 12,
"query": "workplace AI adoption"
},
{
"date": "2022/12/17",
"position": 77,
"query": "artificial intelligence business leaders"
}
] |
Big Data Analytics and Applied Machine Learning with Python ...
|
Big Data Analytics and Applied Machine Learning with Python Certificate
|
https://ece.emory.edu
|
[] |
According to Dice.com, Artificial Intelligence (AI) and machine learning jobs have jumped by almost 75 percent over the past four years.
|
According to Dice.com, Artificial Intelligence (AI) and machine learning jobs have jumped by almost 75 percent over the past four years. With the global machine learning market expected to reach $209.91 billion by 2029, it's no wonder that machine learning engineers who know their stuff can pull down extraordinary total compensation ranging from $215,000 to as much as $397,000 on an annual basis. Of course, these salaries are for professionals with 3 to 5 years of experience, which shows your potential earning in the future.
This course is a 10-week targeted program that teaches applied skills in developing real-world machine learning (ML) solutions. Through the program, participants will gain hands-on experience in the entire ML spectrum, including data wrangling, visualization, data exploration, algorithm selection, modeling, training, testing, and implementation. Participants will have the opportunity to master in-demand open-source tools in the Python data science ecosystem. After completing the program, participants will have the ability to generate actionable intelligence from diverse datasets (structured, text, web, and time series) for various practical applications. The program is ideal for anyone interested in data science, machine learning, and artificial intelligence-related careers and professionals focused on creating data-enabled solutions utilizing the Python ecosystem. It is an intensive and immersive professional development program. Through an innovative and successful curriculum structure, a novel delivery model, and an outplacement support structure, the program will prepare students for employment in the surging data science and machine learning fields.
The program has two components of core classroom sessions and applied lab sessions. Classroom segments cover the theory and applications of machine learning, along with hands-on learning through in-class projects. The lab sessions leverage the in-class acquired knowledge to build real-world ML models.
Students who complete this program will also be candidates for additional advanced-level programs, such as Emory's Artificial Intelligence-Powered Augmented Data Science.
Program Badge Feature your participation in the Emory Machine Learning program with an official digital badge. These are issued at program completion and can be displayed in your online channels like Linkedin. Learn more about this badge
LEARNING OUTCOMES
Successful completion of this intensive program will prepare students for careers in machine learning and data science with the following skills:
Proficiency in leveraging the Python ecosystem for Machine Learning (ML)
Data engineering and wrangling : Ability to collect, clean, and explore data
: Ability to collect, clean, and explore data Hands-on experience with NumPy, and Pandas Libraries
Proficiency in Scikit -Learn for implementing ML algorithms
-Learn for implementing ML algorithms Knowledge and skills to build, train, and deploy descriptive and predictive analytics models
Ability to optimize the performance of ML models through evaluation and selection techniques
the performance of ML models through evaluation and selection techniques Ability to work with Matplotlib and Seaborn for data visualization and storytelling
Ability to do text mining using Natural Language Processing tool kits such as NLTK
Basic knowledge of image processing and analysis
Familiarity with the use of Spark for data science
Implementation of real-world ML projects through capstone assignments
PREREQUISITES
Emory Continuing Education's Business Intelligence certificate
-- OR --
Equivalent practical experience in the following fields: business, supply chain, healthcare, pharma, science, engineering, statistics, mathematics, IT, and analytics.
Experience working on advanced excel, database management, statistics, data analysis, and market research would be beneficial but not required.
Experience in one or more programming languages is helpful but not required.
Certificate Highlights Duration
10 Weeks Cost
$5,995 Time commitment
80 hours
CERTIFICATE REQUIREMENTS
To receive the certificate, students must:
| 2022-12-17T00:00:00 |
https://ece.emory.edu/areas-of-study/data-analytics/big-data-analytics-and-applied-ml-with-python-cert.php
|
[
{
"date": "2022/12/17",
"position": 12,
"query": "machine learning job market"
},
{
"date": "2022/12/17",
"position": 3,
"query": "machine learning workforce"
}
] |
|
7 Fastest Growing Tech Jobs in USA
|
Fastest growing tech jobs in USA
|
https://www.syntaxtechs.com
|
[] |
In fact, data reveals that the Artificial Intelligence market size is contemplating an expansion of USD 4781 million at a CAGR of 48.4%. Clearly, it is one of ...
|
What new Technology does is Create new Opportunities to do a Job that Customers want Done? – Tim O’ Reilly
One of the defining aspects of the tech industry is that it is characterized by continuous change and innovation. This change in the importance of a particular technology has a direct impact on the prospects of individuals who happen to possess expertise vis-a-vis that particular technology.
This means that the tech job market rapidly changes, making room for new job roles on a regular basis. These new job roles are some of the fastest growing tech jobs.
Moreover, if you happen to be an aspiring IT candidate, it is important to be aware of what is in demand right now. Besides seeking to sharpen your technical knowledge, you can perhaps focus on some of the most lucrative, in demand and fastest growing jobs in tech.
In this blog, we shall look at the top 7 fastest growing tech jobs 2023. As we have set our foot in 2023, let us look at some of the technology jobs which would continue to remain in high demand and dominate the tech terrain in the coming year.
We shall look at the roles and responsibilities of these job positions, their future growth potential as well as seek to understand the reason behind them being one of the fastest growing tech jobs in the USA.
Fastest Growing Jobs in Tech
Data Scientist
The position of Data Scientists is considered to be one of the fastest growing tech jobs and this claim is backed by data from the past as well as from the predictions made for the future.
Labor statistics reveals that there has been an overwhelming upsurge of 344% in the demand for these professionals since 2013. Consequently, there has been an average increase of 29% in the demand for Data Scientists each year, besides the general overwhelming advancement in the field of Data Science.
Moving to the future, as per the report by the Bureau of Labor Statistics (BLS), the future growth of the field of Data Science and the profession of a Data Scientist is expected to be at 22% through 2030. This implies that it should definitely be counted as one of the fastest growing tech jobs 2023 that one should aspire for.
So what does a Data Scientist do?
Data Scientists are proficient in data handling through cleaning, exploring and analysing it. They collect data and responsible for identifying trends and patterns in their analysis and consequently build models for the business as well as suggest relevant recommendations for the future which help in shaping business strategies.
With advancements in the field of Machine Learning and Artificial Intelligence, Data Scientists have come to increasingly make use of ML for analyzing business decisions.
Skills needed:
Knowledge of coding in different programming languages as well as that of ML algorithms
The field of Data Science entails the need to create business models
Is comfortable with unstructured data and other different kinds of data structures
A Data Scientist should have excellent mathematical and statistical skills
Is receptive to business issues and problems and is capable for coming up for solutions for resolving the same
BlockChain Engineer
Terms like Cryptocurrency, Blockchain Technology, Non-Fungible Token (NFTs), did emerge as raging buzzwords through 2021, whose projected future growth was deemed to be undeniably high.
Transactions involving Cryptocurencies or NFTs are facilitated through the Blockchain technology. Moreover, they involve huge sums of money and as such ensuring robust security systems in place, is a must for safeguarding the financial assets of individuals across the globe.
This is guaranteed through the expertise of Blockchain Engineers who help in designing the blockchain architecture and also monitoring its security. The creation of decentralised apps along with smart contracts by Blockchain Developers, is only made possible by making use of the web architecture which is developed by the Blockchain Engineer.
Moreover, the increasing relevance of Internet of Things (IoT) has been responsible for pushing the job role of a Blockchain Engineer to the status of being one of the fastest growing jobs in tech.
Skills needed:
Knowledge of programming languages as well as expertise in working with codebases
Understanding of relevant technologies like Hyperledger and R3, along with Cryptocurrencies
Working experience on open source projects
Well versed with crypto functions, libraries and security protocol stacks
Knowledge of data structures and algorithms
Machine Learning and AI
In this section, we shall talk about specialists in two of the highly fast evolving tech fields: Machine Learning and Artificial Intelligence. These individuals are primarily responsible for developing programs and machines which help in imitating human action by seeking to understand how the human mind works.
In fact, data reveals that the Artificial Intelligence market size is contemplating an expansion of USD 4781 million at a CAGR of 48.4%. Clearly, it is one of the fastest growing tech jobs 2023 which would remain in high demand.
Developing complex algorithms which help in providing machines the ability to think as humans is the core area of concern for these individuals. AI in itself is composed of Learning, Perception, Reasoning and Problem Solving.
AI engineers help in implanting the AI model within the business organisation, in automating infrastructure for the Data Scientists and in transforming Machine Learning models into an API which would facilitate interaction with other applications.
Skills needed:
Excellent mathematical and statistical skills
Technical skills include deep understanding of Neural Networks, Machine Learning and Deep Learning
Proficiency in programming languages, along with technologies like TensorFlow
Strong knowledge of Data modeling and Software Engineering
Knowledge of Natural Language Processing
Development of REST APIs
Cloud Architect
The spending of companies on public cloud services has soared tremendously over the years, which implies that cloud computing jobs happen to be one of the brightest tech career avenues.
Owing to the same, there has been a steep surge in demand for professionals who are proficient in overseeing the cloud computing stratifies of an organisation like Cloud Architect, Cloud Engineer and so on.
Herein comes the role of Cloud Architects and the their valued demand makes this job role one of the fastest growing jobs in tech. Cloud Architects have to fulfil a number of responsibilities in their professional capacity, right from planning cloud adoption to designing cloud application through overseeing and controlling cloud.
These professionals are well acquainted with the needs of the organisation and use their expertise in suggesting the best possible cloud solutions (private, public or hybrid) to the top executives. They are responsible for designing the cloud strategy for the company, which is at once, flexible and scalable.
Skills needed:
Knowledge of latest cloud platforms and integration tools
Proficient in prominent programming language
Workable experience with Virtual Private Cloud (VPC), CloudFront (CDN) and Route 53(DNS)
Understanding of the fundamentals of data storage as well as cloud-specific patterns
In terms of soft skills, they need to have excellent management, leadership and communication skills
Information Security Analyst/Engineer
Information Security Analysts or Engineers are responsible for protecting and safeguarding processed data which is in the form of information by instituting reliable security systems.
They guarantee protection to information systems and computer systems in order to prevent it from being exposed to disclosure, modification, disruption, destruction and unauthorised access or other form of data breaches.
Given the rising frequency and magnitude of threats and attacks directed towards the virtual or the cyber space; the demand for Security professionals have increased by leaps and bounds.
Information Security Analysts or Engineers have even a far greater scope of responsibilities than a Cyber Security Analyst as the former happens to deal with all sorts of computer related crime, ensuring the sanctity of the entire security landscape; while the latter deals with only cyber related crimes and ensures the sanctity of the cyber realm.
It is no wonder therefore that the position of an Information Security Analyst/Engineer has evolved and is going to be one of the fastest growing tech jobs 2023.
Skills needed:
Deep knowledge of networks and computer systems
Knowledge of Unix, LINUX and Java systems, along with SIEM, SSH and SSL systems
Technical acumen and critical thinking skills
The roles and responsibilities Information Security professionals are often confused with those of Cyber Security professionals. This is largely because of the overlap between the field of Cyber Security and Information Security. If you wish to read in detail on the issue of the difference between the two domain, do read our blog on Cyber Security vs. Information Security: A Comparative Analysis.
Full Stack Developer
As per the findings by the United States Bureau of Labor Statistics, it is estimated that there will be approximately 8.53 million open job positions for Full Stack Developers by 2024. This clearly implies that it is one of the fastest growing tech jobs with immense potential for the future.
A Full Stack Developer can be seen as an all-rounder who happens to be proficient in front-end as well as back-end programming. Simultaneously, they also remain involved in each and every phase of the development, right from planning through the final product.
Since web development entails the efforts of both a front-end as well as a back-end developer; full stack developer skills happen to cover competencies of developing server as well as client software.
While the front end is the part with which the users interact; the back end is the part wherein the actual working mechanism takes place, detailing out how the system functions.
Skills needed:
Proficiency in designing and developing API
Knowledge of database technologies
Well versed in the fundamentals of web development
Expertise in coding and scripting
Understanding of programming languages like AngularJS, MangoDB, and Express.js
DevOps Engineer
Frictions between the Development and the Operations team has been a difficult issue to resolve for organisations for a long time. This was sought to be resolved in the DevOps model which tried to bring the two departments together.
One of the professional who happens to play a key role in this setup, is the DevOps Engineer who has overlapping responsibilities vis-a-vis both the departments.
In fact, LinkedIn branded the position of a DevOps Engineer as one of the most recruited job roles in 2018. Moreover, digital transformation has accounted for an overwhelming adoption of the DevOps model by companies cutting across sectors. This has made the position of a DevOps Engineer, one of the fastest growing jobs in tech.
Within the development team, these individuals are involved in network and deployment operations; while within the operations team, these individuals are largely concerned with application development.
Skills needed:
Knowledge of network and deployment operations
Proficiency in programming languages, coding and scripting
Deep understanding of Linux and Unix Administration systems
Working experience with DevOps tools like Jenkins, Git and so on
Conclusion
The dynamism which characterizes the tech industry, is one of the prime reasons for accounting for overwhelming importance of certain job roles in response to the latest innovations in technology. Moreover, since this technology itself remains in a state of flux, these job roles too keep growing.
This blog provides an extremely precise list of job roles which are some of the fastest growing tech jobs. However, the list is by no means exhaustive.
Diverse tech fields, ranging from Cyber Security, to Data Analytics, to Software Testing, to Web Development, to Machine Learning, to Software Development, through Business Intelligence, have all come to dominate the tech landscape and as such, abound in some of the fastest growing jobs in tech.
The tech industry is definitely one of the most lucrative sectors in terms of holding immense career prospects. Seeking to initiate one’s journey in the tech field is surely a wise choice. Certain avenues can help you in not only becoming tech ready, but also does so at your convenience and in a matter of months.
Syntax Technologies, is one such top notched bootcamp which help you realise your tech dream in the surest way possible. Read more about our exclusive tech courses on Data Analytics and Business Intelligence, Cyber Security and SDET.
| 2022-12-17T00:00:00 |
https://www.syntaxtechs.com/blog/7-fastest-growing-tech-jobs-in-usa/
|
[
{
"date": "2022/12/17",
"position": 22,
"query": "machine learning job market"
}
] |
|
Another job market data point - Asymptotic Philosophers
|
Another job market data point – Asymptotic Philosophers
|
https://asymptoticphilosophy.com
|
[] |
I have a pretty solid background in logic, and will be able to put together a job package that's on logic and phil machine learning. But 1) I don't want to ...
|
I recently came across Jeremy Davis’s writing on his five years of job market experience. I especially enjoyed his candid storytelling style. Reading it has made me want to write down my own job market journey. I have already written about my grad school journey here, which includes my first two years on the market, but my emphasis there was more on my life, holistically. Below I will focus on job market in particular and will try to be as objective and candid as Davis is. Like Davis cautions in his piece, please don’t take me as offering advice, either. I will also steal his structure.
Preamble
I was on the market for three seasons: 19-20, 20-21, 21-22, the first two as ABD. My research is in how social scientists (mostly psychologists) use statistical methods. I had a lot of trouble labeling what I do, because “philosophy of psychology” gets you philosophy of mind, which I don’t do, and “philosophy of statistics” gets you foundation of stats, which I also don’t do. I ended up settling on “philosophy of social science” and “philosophy of statistics”, the latter because I like the crowd and because it’s something I am able to do, if that’s what they really want.
As mentioned in the other post, I changed my AOS pretty late into my grad school career. I have a pretty solid background in logic, and will be able to put together a job package that’s on logic and phil machine learning. But 1) I don’t want to work in this area anymore, and 2) it’s a small area too, so it’s not like it can serve as a safe backup.
What this means is that I am on a smaller market than people who do core areas like metaphysics or ethics. Keep this in mind when you look at my numbers. Another relevant piece of information is that I am a Chinese national who obtained a green card through marriage right around the time I was on the market. Many people don’t know what this means so here is a glimpse:
I studied in Canada from 2005 to 2015, and then I was in the US. If I didn’t marry my spouse, I would have to either find an immigration-eligible job (I was fortunate enough to not have to research what these are, but I imagine 1-year VAPs are not them) or go “back” to China (in quote marks because I have spent more of my life out of it than I did in it). I was going to marry my spouse anyway, but we were married at that particular time because I had to think two years ahead of time. (Marriage green cards take 1-2 years to be approved, which is the fastest form of green card, as far as I know.) Because of how precious green cards are, I did not consider non-US jobs in my first two years. I only considered permanent jobs in Canada in the third round after consulting with a Canadian immigration expert.
Contrary to popular belief, green cards are rescinded if the holder engages in any activity that can be interpreted as “taking residence elsewhere”, such as taking up a job in a foreign country, even if you have not been physically away from the US for more than 6 months. I was threatened to have my green card taken away after being in Canada for less than a week at the border, and they only didn’t do it because land borders are nicer and I promised then I would surrender my green card very soon. I did surrender it after I returned to Canada.
Two points I’d like to draw from this: 1) my application number is unusually small not because I was exceptionally picky; 2) be nicer to your foreigner colleagues! When they say things like “I can’t apply to this European post-doc that looks like it’s designed for me”, they probably mean it. I had to very awkwardly explain my situation to several well-meaning, personalized invitations to apply to something temporary in Europe. And it always feel like I’m disclosing something too personal in a professional setting, to people I barely know.
2019-2020
I went on the market as a 5th year ABD. Several of us were doing “soft entries” in our 5th year. I could’ve finished if I had to, and in any case it was good for me to experience the whole thing before I was desperate.
My dossier
I had letters from my committee members. I had a teaching letter from someone who supervised me TA once but I don’t remember if I ever used it. I had taught one 5-week summer course as instructor of record. I had okay evals but low response rate.
I had no publication. I had one paper on statistical learning theory that I had presented a couple of times and gotten positive feedback for. It was R&R somewhere but I never revised it, partly because of the difficulty I had with dealing with critical comments and partly because I wanted to move away from this area. In any case, I had no evidence of expertise in the new (social science & stats) area, so I used the SLT paper as my writing sample and wrote a very conflicted research statement about how I’m interested in doing both social science and SLT. I was also having a lot of trouble coming up with a coherent narrative of my dissertation.
Applications & outcomes
I applied to 9 TT and 3 post-doc jobs before I got my first interview. This was relevant because I did mean to apply to more, but found that I couldn’t psychologically do it at the same time as I prepare for an interview, especially since everything about preparing for interviews was new to me.
Like Davis mentioned in his post, I found the psychological exercise of imagining myself living a life at the target school before receiving an actual offer exhausting, especially because the interview I got was from a school matching my dream life in pretty much every way. I was also very certain that I wouldn’t go far, because I had no publication and everything I’ve ever read on the job market is that you need at least 2 publications and preferably more to be remotely competitive.
I ended up getting a flyout, which shocked me. After much debate, I decided to give my job talk on a phil of social science topic. Recall- this is an area I had no evidenced expertise in. I hadn’t even gone to any conference related to this or know anyone who works in this. But I decided that I don’t want to spend my TT years feeling like I need to hide what I’m interested in.
The talk went over well (I think). They did a great job advertising the talk and so it was well attended. A lot of people seemed to find it interesting. The paper was accepted for publication (minor r&r, which was cleared up within the week) shortly after I received the email letting me know that I wasn’t their top choice and that they expected their top choice to accept (which they did).
Overall, I didn’t feel too bad about this season. I didn’t think I’d get anything at all, and having a flyout was a huge boost to my self-esteem. Having my first publication helped too, and I thought that this meant I would be a lot more competitive when I came back to it the next year. But of course…
2020-2021
…the pandemic happened. Everyday I was reading something about a rescinded offer, a hiring freeze, a departmental closure. Think piece after think piece came out about how this was the end of higher ed as we knew it.
My dossier
I had a publication in the area I wanted to work in at this point, and I taught an upper level philosophy of science class in a nearby school (it was virtual) where someone observed my teaching and wrote me a letter. I stayed another year as ABD. My dissertation was essentially finished but it probably didn’t matter to search committees.
Applications & outcomes
As much as I wanted to do what I was truly interested in, there was no job in that. Most of the jobs I could possibly apply this season were in data ethics. I felt like I had to lean in to data ethics, and edited my statements accordingly. It was probably not convincing. I applied to 11 TT jobs, 3 post-docs, and 2 VAPs, and got 0 interview.
2021-2022
It was never clear that the job market would bounce back at all (because of all the closures) until it actually did. The pandemic was difficult in a variety of ways. Most of my friends had moved away for post-docs the year before, which made it really hard for me to feel connected, since I derive most of my philosophical passion in conversation.
All of these are reasons why my job market experience felt a lot more like a failure than it probably is objectively. I genuinely believed that I had enough evidence to suggest that I wouldn’t be successful at all, and was seriously looking into alt-ac options.
My dossier
The biggest dossier change from previous years was probably that I had graduated. There was no reason for me to stay on another year, so I graduated in the summer. I also had an external letter writer which I didn’t before. I taught the same upper level philosophy of science class, this time in person. I doubt it made much difference to my file from the previous year. I still had the same teaching letter and the same publication.
Leaning in to data ethics didn’t help me in the previous year, so I reverted back to philosophy of social science and of statistics, which I now at least have one publication to support. I had a second project that was finished enough to be presentable as a job talk. This means that I could go full in as a candidate in phil of social science and not having to pivot like I did in my first year. I also finally developed a coherent narrative of my dissertation, which seemed to work.
Applications & outcomes
I applied to 15 TT jobs, 1 post-doc, and 1 alt-ac. I received 3 first-round interviews. In January, I received an oral offer for the alt-ac, but have not heard from two of the three first-round interviews that had already happened.
The third interview I had was with a SLAC. I had never visited, attended, let alone worked at a SLAC. A lot of my grad school friends are from SLACs and I’ve always found their undergrad stories completely alien to my own (where a “small” class is 50 people). I genuinely don’t know if I’ll be happy at a SLAC because I have no idea what it is. I was going to give it a try, but after receiving the alt-ac offer, I declined the interview. I don’t think I’ll be able to make a strong case for why I would want to go there, and I feel it’s better that they can devote their resources bringing in people who would definitely want to go.
One of the 2 first-round interviews I attended did not lead to a flyout. The other one led to a virtual flyout and eventually an offer, which I accepted.
Observations & lessons
My job market journey isn’t long or elaborate by any measure, but here are some observations.
Perhaps the most surprising thing to me was the fact that every source of job advice I ever came across (online or from people I know) seemed to indicate that you just wouldn’t be considered at all if you didn’t have X number of good publications. And this just wasn’t the case for myself or quite a few others I know. I don’t really know what is making a difference instead, though, so I’m calling it an “observation” and not a “lesson” for now.
There is a related thing that a lot of grad students don’t like to hear, that I didn’t believe, and that I’m glad for — it matters whether one is “doing good philosophy”. Now, I don’t think a lot of people are half-assing their philosophy and I generally don’t trust my gut instinct on whether someone else’s philosophy counts as “good”. But I have heard remarks such as “so-and-so has an impressive publication record but the quality of the work just isn’t that great” or “I know this area is super hot right now but I don’t understand it; I don’t believe in it”. As a grad student, I have heard advice such as “I know this work isn’t perfect but pubs don’t need to be perfect and you need the pubs” and “this area is hot right now so you should work in it”. If it wasn’t the fact that I, quite irrationally, did not heed to these advice, I would’ve felt a lot more cheated than I do already.
An unrelated lesson was that people have very different opinions as to how a good interview should be like — and they don’t realize that their opinions are different. My PhD department had done a few searches in recent years and I had some faculty members help me prep for interview. So, these people had recently been interviewers and were telling me what they considered as good responses. Yet, there were a few specific instructions they gave me about how to answer certain questions which just did not fly elsewhere — I’d answer the same way and the person would look visibly disinterested, or I’d be re-asked a question and told to answer it in a different way.
Extra-curriculars
Like Davis, I did a lot of extra curriculars — conference organizing, climate committee chairing, podcast. I don’t think they helped. In fact, my application package for the job I did get contained nothing on extra-curriculars because they didn’t ask for a diversity statement. I’m also not saying “don’t do them”, and I think doing them gave me a better understanding of how academia works which, if nothing else, helped me decide whether I wanted to remain in it.
Networking
Another thing that a lot of job advice points to is networking. I’m pretty bad at it, and having spent most of my job market journey in a pandemic didn’t help. It so happens that I do know my current department before I got the job — I completed my MA here. But I was a pretty meh MA student (wasn’t well mentored in undergrad and wasn’t planning to do a PhD in philosophy). I don’t know if it played a role at all, positive or negative.
The interview-flyout I had in 2019-2020 was a job where I was invited to apply. But I think the invitation was sent out to quite a lot of people and not just me. I did know people on the search committee but not very well, and I didn’t communicate with them at all outside the interviews. None of the other applications I sent (including the job I currently have) had people contacting me to provide information on stuff like what they were looking for.
I did have a few invitations-to-apply from temporary posts in Europe, which I had to decline for immigration reasons. One post-doc position was worded essentially like an offer. There was another temporary lectureship (which would be quite competitive, I think) that would probably help my future search. So, I think networking helps in general, but I didn’t quite take advantage of it.
The alt-ac
As mentioned above, I did apply to one alt-ac position and received an offer. It was a governmental position, had a long and involved multi-stage assessment, and came with a lot of perks. I learned a lot just by going through the process.
Receiving the offer meant a lot to me. As someone who had never really worked (because I had always been on some sort of visa that prohibits working), this was rare evidence that others thought I was capable of doing important work. It allowed me to approach academia as an option rather than a necessity. I was finally able to definitively say that my wish to become an academic was not because I wasn’t able to do anything else.
Concluding thoughts
There was a huge mental block for me. For years I had thought my academic career was especially unsuccessful. Part of it may be that almost everyone I knew was in temporary employment after graduation and I was basically unemployed. This was a combined result of 1) my inability to be in temporary employment outside the US, and 2) my privilege of being able to be unemployed and not starve. Nevertheless, it felt like everyone else had stuff to do except for me.
Coincidentally, Chris recently wrote a post on being in survival mode for too long such as to lose the ability to live the survived life. While I wouldn’t exactly say that I was in survival mode, it remains true that I don’t usually allow myself to think about what success is like such that, when it does happen, I feel ill-equipped.
I am now one term in, and a lot of these thoughts had faded away. I was recently chatting with a colleague who thought the last job season was my first job season, because I never held another post. Her remark reminded me of the dissonance I’ve felt throughout the season. On paper, I am a model scholar who finished a PhD on time (6 years) and went straight to a good TT position. In my mind, I was surely not good enough for academia and was this much close to quitting. The reality is almost certainly somewhere in between.
| 2022-12-17T00:00:00 |
https://asymptoticphilosophy.com/another-job-market-data-point/
|
[
{
"date": "2022/12/17",
"position": 59,
"query": "machine learning job market"
}
] |
|
R&D AI/ML Internship | Slidzo
|
R&D AI/ML Internship
|
https://fabskill.com
|
[] |
Job type: Part time. Contract: Internship. Requirements. Skills: required skill for this job MACHINE LEARNING MACHINE LEARNING · required skill for this job ...
|
Slidzo R&D AI/ML Internship This offer is no longer active 14 Application(s)
Company Brief
Slidzo is a Startup that offers an online presentation editing platform with unique features like 3D presentations and AI-generated slides.
Slidzo’s mission is to make impressive presentations the new norm.
We are looking for a talented AI / ML Intern to join our team. You will be in charge of a range of exciting missions and lead our R&D AI/ML subject.
WHAT WE DO
We aim to conquer the international software presentation market and compete with the biggest presentation editing platforms out there. Our solution offers unique 3D presentations and features.
Our next product iteration includes an AI assistant that automates the creation process of a slide.
Our product is heavily design-oriented and we encourage innovation and originality in all its aspects.
RESPONSIBILITIES
You will work on your own exciting R&D subject and dive into how AI/ML models can automate the design and creation of presentation slides. You will also be in charge of other missions like
Maintaining and improving the available AI assistant and solutions. You will integrate them in a number of different use-cases.
Working in a R&D subject where you will explore how to generate slides using Artificial Intelligence and Machine Learning models trained on our existing users data.
Using NLP and NLG to improve the accuracy of the user request handling.
More broad responsibilities include
Designing, developing, testing, and maintaining different AI/ML Solutions to address business requirements.
Developing and maintaining artificial intelligence components.
Designing, implementing, and supporting machine learning algorithms.
SKILLS
You have …
You are in a computer science engineering school, with a machine learning or data science specialization.
Prior NLP experience (IBM watson is a plus)
Strong theoretical Machine Learning knowledge.
You already used a deep learning framework like PyTorch or Tensorflow.
Good communication skills and able to work in a team.
A Github profile added to the application is a plus.
If you’ve got the right skills for the job, we want to hear from you. We encourage applications from all suitable candidates regardless of age, disability, gender identity, sexual orientation, religion, belief, or race.
TERMS OF EMPLOYMENT
Starting date: as soon as possible
Duration: 3 - 6 months
Paid internship
Occasional remote workauthorized
| 2022-12-17T00:00:00 |
https://fabskill.com/en/job/1660/rd-aiml-internship
|
[
{
"date": "2022/12/17",
"position": 65,
"query": "machine learning job market"
}
] |
|
Life at Applause
|
Life at Applause
|
https://www.applause.com
|
[] |
Job Openings · Join Our Community · Customer Login · English · Deutsch · Français ... Generative AI · IoT Device · AR & VR · Voice & AI · Automotive Tech · In- ...
|
Dazzle and Delight Our Customers
Are you great with people and get your energy from helping others achieve their goals? As part of the Customer Ops group, which includes Testing Services and Account Management, you’ll immerse yourself in all things customer-related, becoming an extension of their teams by serving as a strategic partner to help them exceed expectations. Define and execute testing strategies. Be a client rockstar as you guide them to the best Applause solutions that will bring music to their ears and success to their business.
| 2022-12-17T00:00:00 |
https://www.applause.com/life-at-applause/
|
[
{
"date": "2022/12/17",
"position": 91,
"query": "generative AI jobs"
}
] |
|
Twitter has reportedly laid off part of its infrastructure team
|
Twitter has reportedly laid off part of its infrastructure team
|
https://www.engadget.com
|
[] |
AI · Apps · Computing · Entertainment · Mobile · Science · Social media · Space · Streaming ... The layoffs also point to the seemingly precarious financial ...
|
Stop me if you’ve heard this one before, but Elon Musk has reportedly laid off more of Twitter’s workforce. According to The Information, the company cut part of its infrastructure division on Friday evening. The scale of the layoffs is unclear, but some engineers took to Twitter yesterday to say they were told over email their contribution was no longer required. The latest cuts come after The New York Times reported on Tuesday that Musk had laid off Nelson Abramson, Twitter’s head of infrastructure, among a handful of other high-ranking employees at the company.
And that was my last day at Twitter. Laid off by email. The experience has been indescribable from chaos to hilarious. — Dave Beckett (@dajobe) December 17, 2022
Twitter did not immediately respond to Engadget’s comment request. The company has not had a communications team since it began reducing its workforce. By The Information’s estimate, Twitter’s headcount has shrunk by about 75 percent since Musk’s takeover of the company in late October. The social media website employed approximately 7,500 under former CEO Parag Agrawal. As of a week ago, Twitter’s internal Slack listed around 2,000 employees, according to the outlet. In November, Musk reportedly told what was left of the company’s workforce Twitter would not lay off any more workers. The pledge came after the billionaire’s “extremely hardcore” ultimatum led to at least 1,200 resignations.
Additional casualties among the team responsible for keeping Twitter up and running are likely to add to fears about how unreliable the site may become in the near future. At the same time, the move may further alienate Tesla investors who were already concerned about how much time Musk was spending on Twitter. According to The Information, Musk tapped Tesla engineer Sheen Austin to run the social media website’s infrastructure team following Abramson’s departure.
The layoffs also point to the seemingly precarious financial position Twitter has found itself in since Musk’s takeover. In recent weeks, Elon and other executives reportedly discussed the potential consequences of denying severance payments to the thousands of people who were let go from the company in recent weeks. The company is also behind on rent for its San Fransisco headquarters and network of global satellite offices.
If you buy something through a link in this article, we may earn commission.
| 2022-12-17T00:00:00 |
https://www.engadget.com/twitter-has-reportedly-laid-off-more-of-its-infrastructure-team-192330953.html
|
[
{
"date": "2022/12/17",
"position": 84,
"query": "AI layoffs"
}
] |
|
What is XAI? | A-Z of AI for Healthcare
|
A-Z of AI for Healthcare
|
https://www.owkin.com
|
[] |
XAI, or Explainable Artificial Intelligence refers to a specific field of research within Artificial Intelligence that aims to make AI algorithms and the ways ...
|
XAI, or Explainable Artificial Intelligence refers to a specific field of research within Artificial Intelligence that aims to make AI algorithms and the ways in which they reason, easier to understand. In other words, XAI tries to make black box algorithms more ‘transparent’ so that humans can understand how the algorithm is making ‘decisions’ (such as a classification or a prediction).
In healthcare, it’s very important that all algorithms are, as far as possible, explainable so that:
Clinicians and patients can understand why a patient has been given a specific diagnosis by an algorithm.
Clinicians can question any diagnoses or treatment recommendations given by an algorithm if they think the algorithm made the ‘decision’ based on flawed reasoning.
If something goes wrong with a patient’s care, the ‘source’ of the error can be identified and fixed.
Problems such as bias can be identified.
For these reasons, and others, ‘explainability’ is key to building clinician and patient trust in AI. It is also a primary focus of the responsible AI community.
XAI researchers have developed a very long list of processes and techniques that can be used to make specific AI algorithms more explainable, which broadly fall into two categories:
Model-dependent techniques Only work for certain types of algorithms. Examples include neural network visualization, which demonstrates how the neurons are mapped in different layers and offers insights into how it processes data. Or XGBoost feature (e.g. age, gender, visual patterns, timestamps, etc.) importance scores, which indicate the contribution of each feature to the model’s prediction - with higher scores having more impact. Model-agnostic techniques Can work for all types of algorithms. Examples include partial dependence plots, which visualize the relationship between a specific feature and the model’s predictions. Or SHAP (SHapely Additive exPlanations) values, which provide a unified understanding of which features are most important and break down the overall prediction into the contributions of each feature, providing insights into why a model leaned toward a particular outcome.
The ways in which XAI techniques try to make AI algorithms more explainable vary. Some aim to ‘simplify’ the algorithm, while others provide a visual explanation, for example highlighting which features in an image were deemed to be the most important when the algorithm was deciding how to classify the image. There is no agreed ‘best’ technique, it depends entirely on the specific algorithm in question and what it’s designed to do.
| 2022-12-17T00:00:00 |
https://www.owkin.com/a-z-of-ai-for-healthcare/xai
|
[
{
"date": "2022/12/17",
"position": 56,
"query": "AI healthcare"
}
] |
|
Future Tools - Find The Exact AI Tool For Your Needs
|
Find The Exact AI Tool For Your Needs
|
https://www.futuretools.io
|
[] |
A tool to generate presentations without requiring design skills. ... A tool to turn static images into animated motion graphics. Generative Video. 11. 11. 11.
|
An sales assistant that qualifies leads and books meetings through website conversations and automated calls.
| 2022-12-17T00:00:00 |
https://www.futuretools.io/
|
[
{
"date": "2022/12/17",
"position": 61,
"query": "AI graphic design"
}
] |
|
AI can now create images out of thin air. See how it works.
|
AI can now create images out of thin air. See how it works.
|
https://www.washingtonpost.com
|
[
"Kevin Schaul",
"Hamza Shaban",
"Shelly Tan",
"Monique Woo",
"Nitasha Tiku",
"Kevin Schaul Is A Senior Graphics Reporter For The Washington Post.",
"Hamza Shaban Is A Visual Enterprise Reporter For The Business Desk. He Joined The Washington Post In As A Technology Reporter. Previously",
"He Covered Tech Policy For Buzzfeed News.",
"Shelly Tan Is A Graphics Reporter",
"Illustrator Who Designs"
] |
AI image generators like DALL-E, Lensa and stable ... Shelly Tan is a graphics reporter and illustrator who designs and develops interactive graphics.
|
Warning: This graphic requires JavaScript. Please enable JavaScript for the best experience.
Customize your experience by changing the prompts below A dachshund playing a guitar in outer space in the style of stained glass Switch Randomize Share this story A strange and powerful collaborator is waiting for you. Offer it just a few words, and it will create an original scene, based on your description. This is artificial-intelligence-generated imagery, a rapidly emerging technology now in the hands of anyone with a smart phone. The results can be astonishing: crisp, beautiful, fantastical and sometimes eerily realistic. But they can also be muddy and grotesque: warped faces, gobbledygook street signs and distorted architecture. OpenAI’s updated image generator DALL-E 3, released Wednesday, offers improved text rendering, streamlining words on billboards and office logos. How does it work? Keep scrolling to learn step by step how the process unfolds. Story continues below advertisement Advertisement Story continues below advertisement Advertisement
Try out your own AI-generated images Select a prompt from the options below, or Switch Randomize A dachshund playing a guitar in outer space in the style of ... ...
a photo Van Gogh stained glass a magazine cover
Like many frontier technologies, AI-generated artwork raises a host of knotty legal, ethical and moral issues. The raw data used to train the models is drawn straight from the internet, causing image generators to parrot many of the biases found online. That means they may reinforce incorrect assumptions about race, class, age and gender.
The data sets used for training also often include copyrighted images. This outrages some artists and photographers whose work is ingested into the computer without their permission or compensation.
[AI selfies — and their critics — are taking the internet by storm]
Story continues below advertisement Advertisement Story continues below advertisement Advertisement
Meanwhile, the risk of creating and amplifying disinformation is enormous. Which is why it is important to understand how the technology actually works, whether to create a Van Gogh that the artist never painted, or a scene from the Jan. 6 attack on the U.S. Capitol that never appeared in any photographer’s viewfinder.
Faster than society can reckon with and resolve these issues, artificial intelligence technologies are racing ahead.
| 2022-12-17T00:00:00 |
https://www.washingtonpost.com/technology/interactive/2022/ai-image-generator/
|
[
{
"date": "2022/12/17",
"position": 72,
"query": "AI graphic design"
}
] |
|
Hand robot Droid replace the human in office. Artificial ...
|
Hand robot Droid replace the human in office. Artificial intelligence has better performance than humans. Cartoon flat vector illustration 9884954 Vector Art at Vecteezy
|
https://www.vecteezy.com
|
[] |
Download the Hand robot Droid replace the human in office. Artificial intelligence has better performance than humans. Cartoon flat vector illustration ...
|
Why Pro?
While free users can access our free library, we reserve the best of the best for our Pro users. Pro content is only downloadable through Pro subscriptions and credits.
| 2022-12-18T00:00:00 |
https://www.vecteezy.com/vector-art/9884954-hand-robot-droid-replace-the-human-in-office-artificial-intelligence-has-better-performance-than-humans-cartoon-flat-vector-illustration
|
[
{
"date": "2022/12/18",
"position": 72,
"query": "AI replacing workers"
}
] |
|
Careers
|
Protect AI
|
https://protectai.com
|
[] |
Machine Learning Models: A New Attack Vector for an Old Exploit. Read the ... Market rate, competitive salary, and sizable equity positions. Purple icon ...
|
Prevent AI Zero Days,
Reshape an Industry
AI and ML bring new and unique cybersecurity vulnerabilities. While traditional exploits can be detected and mitigated, new ML-specific threats such as Adversarial ML and attacks on the ML supply chain require entirely new thinking, research, and methods to help this AI-enabled world be safer for all. Protect AI is giving cybersecurity departments and their leaders a common way of understanding these new risks, while helping ML developers build security into their applications from the star.
United States Office: 1201 2nd Ave, Seattle, WA, 98101
India Office: 906 Sakti Statesman, Green Glen Layout, Bangalore 560103
Berlin Office: Chausseestraße 57, Berlin 10115
| 2022-12-18T00:00:00 |
https://protectai.com/careers
|
[
{
"date": "2022/12/18",
"position": 41,
"query": "machine learning job market"
}
] |
|
Artificial Intelligence & Machine Learning Specialist - ITS AR
|
Artificial Intelligence & Machine Learning Specialist
|
https://www.itsrizzoli.it
|
[] |
Job opportunities. At the end of the course of study, the student will obtain the qualification of Expert Technician in Machine Learning. It will therefore ...
|
What if, once the data has been collected, the software begins to improve by itself the performance and organization of a production line and the analysis of all the information? This is exactly what can be achieved through Machine Learning, a branch of artificial intelligence with enormous potential.
With the ITS Artificial Intelligence & Machine Learning Specialist course you will be able to acquire notions of software automation systems in order to pilot autonomous industrial processes and optimize performance in an Industry 4.0 logic.
The work of a Machine Learning Specialist in fact starts from the identification of strategic objectives, and then returns an analysis of critical events to the entire operational chain. The technician works within larger working groups, thus contributing to the digital innovation process of companies thanks to the introduction of self-configuring automation systems.
In relation to the specific aspects in which they operate, these systems can be decision support tools (DSS), support tools for industrial design, tools for verifying the quality of products or services, or real products sold directly on the market.
Among the most common tasks is the identification of the relevant datasets and the configuration of the Machine Learning tools to identify the work peaks, the churn risk (i.e. the percentage of customers or subscribers who will stop using the services offered by a company for a period of time), purchase forecasts, forecasts on material movements, energy and more.
| 2022-12-18T00:00:00 |
https://www.itsrizzoli.it/en/courses/artificial-intelligence-machine-learning-specialist/
|
[
{
"date": "2022/12/18",
"position": 57,
"query": "machine learning job market"
}
] |
|
AI Timelines: What Do Experts in Artificial Intelligence ...
|
AI Timelines: What Do Experts in Artificial Intelligence Expect for the Future?
|
https://singularityhub.com
|
[
"Drew Halley",
"Max Roser"
] |
At the time of writing, in November 2022, the forecasters believe that there is a 50/50-chance for an 'Artificial General Intelligence' to be 'devised, tested, ...
|
Artificial intelligence that surpasses our own intelligence sounds like the stuff from science fiction books or films. What do experts in the field of AI research think about such scenarios? Do they dismiss these ideas as fantasy, or are they taking such prospects seriously?
A human-level AI would be a machine, or a network of machines, capable of carrying out the same range of tasks that we humans are capable of. It would be a machine that is “able to learn to do anything that a human can do,” as Norvig and Russell put it in their textbook on AI.1
It would be able to choose actions that allow the machine to achieve its goals and then carry out those actions. It would be able to do the work of a translator, a doctor, an illustrator, a teacher, a therapist, a driver, or the work of an investor.
In recent years, several research teams contacted AI experts and asked them about their expectations for the future of machine intelligence. Such expert surveys are one of the pieces of information that we can rely on to form an idea of what the future of AI might look like.
The chart shows the answers of 352 experts. This is from the most recent study by Katja Grace and her colleagues, conducted in the summer of 2022.2
Experts were asked when they believe there is a 50% chance that human-level AI exists.3 Human-level AI was defined as unaided machines being able to accomplish every task better and more cheaply than human workers. More information about the study can be found in the fold-out box at the end of the text on this page.4
Each vertical line in this chart represents the answer of one expert. The fact that there are such large differences in answers makes it clear that experts do not agree on how long it will take until such a system might be developed. A few believe that this level of technology will never be developed. Some think that it’s possible, but it will take a long time. And many believe that it will be developed within the next few decades.
As highlighted in the annotations, half of the experts gave a date before 2061, and 90% gave a date within the next 100 years.
Other surveys of AI experts come to similar conclusions. In the following visualization, I have added the timelines from two earlier surveys conducted in 2018 and 2019. It is helpful to look at different surveys, as they differ in how they asked the question and how they defined human-level AI. You can find more details about these studies at the end of this text.
In all three surveys, we see a large disagreement between experts and they also express large uncertainties about their own individual forecasts.5
What Should We Make of the Timelines of AI Experts?
Expert surveys are one piece of information to consider when we think about the future of AI, but we should not overstate the results of these surveys. Experts in a particular technology are not necessarily experts in making predictions about the future of that technology.
Experts in many fields do not have a good track record in making forecasts about their own field, as researchers including Barbara Mellers, Phil Tetlock, and others have shown.6 The history of flight includes a striking example of such failure. Wilbur Wright is quoted as saying, “I confess that in 1901, I said to my brother Orville that man would not fly for 50 years.” Two years later, ‘man’ was not only flying, but it was these very men who achieved the feat.7
Additionally these studies often find large ‘framing effects’, two logically identical questions get answered in very different ways depending on how exactly the questions are worded.8
What I do take away from these surveys however, is that the majority of AI experts take the prospect of very powerful AI technology seriously. It is not the case that AI researchers dismiss extremely powerful AI as mere fantasy.
| 2022-12-18T00:00:00 |
2022/12/18
|
https://singularityhub.com/2022/12/18/ai-timelines-what-do-experts-in-artificial-intelligence-expect-for-the-future/
|
[
{
"date": "2022/12/18",
"position": 99,
"query": "future of work AI"
}
] |
National AI Strategy - HTML version
|
National AI Strategy - HTML version
|
https://www.gov.uk
|
[] |
... AI effectively in the workplace. For example, industries have expressed ... Supporting the UK's AI Sector and the adoption of AI, connecting research ...
|
Our ten-year plan to make Britain a global AI superpower
Over the next ten years, the impact of AI on businesses across the UK and the wider world will be profound - and UK universities and startups are already leading the world in building the tools for the new economy. New discoveries and methods for harnessing the capacity of machines to learn, aid and assist us in new ways emerge every day from our universities and businesses.
AI gives us new opportunities to grow and transform businesses of all sizes, and capture the benefits of innovation right across the UK. As we build back better from the challenges of the global pandemic, and prepare for new challenges ahead, we are presented with the opportunity to supercharge our already admirable starting position on AI and to make these technologies central to our development as a global science and innovation superpower.
With the help of our thriving AI ecosystem and world leading R&D system, this National AI Strategy will translate the tremendous potential of AI into better growth, prosperity and social benefits for the UK, and to lead the charge in applying AI to the greatest challenges of the 21st Century.
The Rt Hon Kwasi Kwarteng MP
Secretary of State for Business, Energy and Industrial Strategy
This is the age of artificial intelligence. Whether we know it or not, we all interact with AI every day - whether it’s in our social media feeds and smart speakers, or on our online banking. AI, and the data that fuels our algorithms, help protect us from fraud and diagnose serious illness. And this technology is evolving every day.
We’ve got to make sure we keep up with the pace of change. The UK is already a world leader in AI, as the home of trailblazing pioneers like Alan Turing and Ada Lovelace and with our strong history of research excellence. This Strategy outlines our vision for how the UK can maintain and build on its position as other countries also race to deliver their own economic and technological transformations.
The challenge now for the UK is to fully unlock the power of AI and data-driven technologies, to build on our early leadership and legacy, and to look forward to the opportunities of this coming decade.
This National AI Strategy will signal to the world our intention to build the most pro-innovation regulatory environment in the world; to drive prosperity across the UK and ensure everyone can benefit from AI; and to apply AI to help solve global challenges like climate change.
AI will be central to how we drive growth and enrich lives, and the vision set out in our strategy will help us achieve both of those vital goals.
Nadine Dorries MP
Secretary of State for Digital, Culture, Media and Sport
Executive summary
Artificial Intelligence (AI) is the fastest growing deep technology[footnote 1] in the world, with huge potential to rewrite the rules of entire industries, drive substantial economic growth and transform all areas of life. The UK is a global superpower in AI and is well placed to lead the world over the next decade as a genuine research and innovation powerhouse, a hive of global talent and a progressive regulatory and business environment.
Many of the UK’s successes in AI were supported by the 2017 Industrial Strategy, which set out the government’s vision to make the UK a global centre for AI innovation. In April 2018, the government and the UK’s AI ecosystem agreed a near £1 billion AI Sector Deal to boost the UK’s global position as a leader in developing AI technologies. This new National AI Strategy builds on the UK’s strengths but also represents the start of a step-change for AI in the UK, recognising the power of AI to increase resilience, productivity, growth and innovation across the private and public sectors. This is how we will prepare the UK for the next ten years, and is built on three assumptions about the coming decade:
The key drivers of progress, discovery and strategic advantage in AI are access to people, data, compute and finance – all of which face huge global competition;
AI will become mainstream in much of the economy and action will be required to ensure every sector and region of the UK benefit from this transition;
Our governance and regulatory regimes will need to keep pace with the fast-changing demands of AI, maximising growth and competition, driving UK excellence in innovation, and protecting the safety, security, choices and rights of our citizens.
The UK’s National AI Strategy therefore aims to:
Invest and plan for the long-term needs of the AI ecosystem to continue our leadership as a science and AI superpower; Support the transition to an AI-enabled economy, capturing the benefits of innovation in the UK, and ensuring AI benefits all sectors and regions; Ensure the UK gets the national and international governance of AI technologies right to encourage innovation, investment, and protect the public and our fundamental values.
This will be best achieved through broad public trust and support, and by the involvement of the diverse talents and views of society.
Summary of key actions
Investing in the long-term needs of the AI ecosystem Ensuring AI benefits all sectors and regions Governing AI effectively Short term (next 3 months): • Publish a framework for government’s role in enabling better data availability in the wider economy
• Consult on the role and options for a National Cyber-Physical Infrastructure Framework
• Support the development of AI, data science and digital skills through the Department for Education’s Skills Bootcamps • Begin engagement on the Draft National Strategy for AI-driven technologies in Health and Social Care, through the NHS AI Lab
• Publish the Defence AI Strategy, through the Ministry of Defence
• Launch a consultation on copyright and patents for AI through the IPO • Publish the CDEI assurance roadmap
• Determine the role of data protection in wider AI governance following the Data: A new direction consultation
• Publish details of the approaches the Ministry of Defence will use when adopting and using AI
• Develop an all-of-government approach to international AI activity Medium term (next 6-12 months): • Publish research into what skills are needed to enable employees to use AI in a business setting and identify how national skills provision can meet those needs
• Evaluate the private funding needs and challenges of AI scaleups
• Support the National Centre for Computing Education to ensure AI programmes for schools are accessible
• Support a broader range of people to enter AI-related jobs by ensuring career pathways highlight opportunities to work with or develop AI
• Implement the US UK Declaration on Cooperation in AI R&D
• Publish a review into the UK’s compute capacity needs to support AI innovation, commercialisation and deployment
• Roll out new visa regimes to attract the world’s best AI talent to the UK • Publish research into opportunities to encourage diffusion of AI across the economy
• Consider how Innovation Missions include AI capabilities, such as in energy
• Extend UK aid to support local innovation in developing countries
• Build an open repository of AI challenges with real-world applications • Publish White Paper on a pro-innovation national position on governing and regulating AI
• Complete an in-depth analysis on algorithmic transparency, with a view to develop a cross-government standard
• Pilot an AI Standards Hub to coordinate UK engagement in AI standardisation globally
• Establish medium and long term horizon scanning functions to increase government’s awareness of AI safety Long term (next 12 months and beyond): • Undertake a review of our international and domestic approach to semiconductor supply chains
• Consider what open and machine-readable government datasets can be published for AI models
• Launch a new National AI Research and Innovation Programme that will align funding programmes across UKRI and support the wider ecosystem
• Back diversity in AI by continuing existing interventions across top talent, PhDs, AI and Data Science Conversion Courses and Industrial Funded Masters
• Monitor and use National Security and Investment Act to protect national security while keeping the UK open for business
• Include trade deal provisions in emerging technologies, including AI • Launch joint Office for AI / UKRI programme to stimulate the development and adoption of AI technologies in high potential, lower-AI-maturity sectors
• Continue supporting the development of capabilities around trustworthiness, adoptability, and transparency of AI technologies through the National AI Research and Innovation Programme
• Join up across government to identify where using AI can provide a catalytic contribution to strategic challenges • Explore with stakeholders the development of an AI technical standards engagement toolkit to support the AI ecosystem to engage in the global AI standardisation landscape
• Work with global partners on shared R&D challenges, leveraging Overseas Development Assistance to put AI at the heart of partnerships worldwide
• Work with The Alan Turing Institute to update guidance on AI ethics and safety in the public sector
• Work with national security, defence, and leading researchers to understand what public sector actions can safely advance AI and mitigate catastrophic risks
Introduction
Artificial Intelligence technologies (AI) offer the potential to transform the UK’s economic landscape and improve people’s lives across the country, transforming industries and delivering first-class public services.
AI may be one of the most important innovations in human history, and the government believes it is critical to both our economic and national security that the UK prepares for the opportunities AI brings, and that the country is at the forefront of solving the complex challenges posed by an increased use of AI.
This country has a long and exceptional history in AI – from Alan Turing’s early work through to DeepMind’s recent pioneering discoveries. In terms of AI startups and scaleups, private capital invested and conference papers submitted, the UK sits in the top tier of AI nations globally. The UK ranked third in the world for private investment into AI companies in 2020, behind only the USA and China.
The National AI Strategy builds on the UK’s current strengths and represents the start of a step-change for AI in the UK, recognising that maximising the potential of AI will increase resilience, productivity, growth and innovation across the private and public sectors. Building on our strengths in AI will take a whole-of-society effort that will span the next decade. This is a top-level economic, security, health and wellbeing priority. The UK government sees being competitive in AI as vital to our national ambitions on regional prosperity and for shared global challenges such as net zero, health resilience and environmental sustainability. AI capability is therefore vital for the UK’s international influence as a global science superpower.
The National AI Strategy for the United Kingdom will prepare the UK for the next ten years, and is built on three assumptions about the coming decade:
The key drivers of progress, discovery and strategic advantage in AI are access to people, data, compute and finance – all of which face huge global competition;
AI will become mainstream in much of the economy and action will be required to ensure every sector and region of the UK benefit from this transition;
Our governance and regulatory regimes will need to keep pace with the fast-changing demands of AI, maximising growth and competition, driving UK excellence in innovation, and protecting the safety, security, choices and rights of our citizens.
This document sets out the UK’s strategic intent at a level intended to guide action over the next ten years, recognising that AI is a fast moving and dynamic area. Detailed and measurable plans for the execution of the first stage of this strategy will be published later this year.
The UK’s National Artificial Intelligence Strategy will: Invest and plan for the long term needs of the AI ecosystem to continue our leadership as a science and AI superpower; Support the transition to an AI-enabled economy, capturing the benefits of innovation in the UK, and ensuring AI benefits all sectors and regions; Ensure the UK gets the national and international governance of AI technologies right to encourage innovation, investment, and protect the public and our fundamental values. This will be best achieved through broad public trust and support, and by the involvement of the diverse talents and views of society.
10-Year Vision
Over the next decade, as transformative technologies continue to reshape our economy and society, the world is likely to see a shift in the nature and distribution of global power. We are seeing how, in the case of AI, rapid technological change seeks to rebalance the science and technology dominance of existing superpowers like the US and China, and wider transnational challenges demand greater collective action in the face of continued global security and prosperity.
With this in mind, the UK has an opportunity over the next ten years to position itself as the best place to live and work with AI; with clear rules, applied ethical principles and a pro-innovation regulatory environment. With the right ingredients in place, we will be both a genuine innovation powerhouse and the most supportive business environment in the world, where we cooperate on using AI for good, advocate for international standards that reflect our values, and defend against the malign use of AI.
Whether it is making the decision to study AI, work at the cutting edge of research or spin up an AI business, our investments in skills, data and infrastructure will make it easier than ever to succeed. Our world-leading R&D system will step up its support of innovators at every step of their journey, from deep research to building and shipping products. If you are a talented AI researcher from abroad, coming to the UK will be easier than ever through the array of visa routes which are available.
If you run a business – whether it is a startup, SME or a large corporate – the government wants you to have access to the people, knowledge and infrastructure you need to get your business ahead of the transformational change AI will bring, making the UK a globally-competitive, AI-first economy which benefits every region and sector.
By leading with our democratic values, the UK will work with partners around the world to make sure international agreements embed our ethical values, making clear that progress in AI must be achieved responsibly, according to democratic norms and the rule of law.
And by increasing the number and diversity of people working with and developing AI, by putting clear rules of the road in place and by investing across the entire country, we will ensure the real-world benefits of AI are felt by every member of society. Whether that is more accurate AI-enabled diagnostics in the NHS, the promise of driverless cars to make our roads safer and smarter, or the hundreds of unforeseen benefits that AI could bring to improve everyday life.
The goals of this Strategy are that the UK: Experiences a significant growth in both the number and type of discoveries that happen in the UK, and are commercialised and exploited here; Benefits from the highest amount of economic and productivity growth due to AI; and Establishes the most trusted and pro-innovation system for AI governance in the world. This vision can be achieved if we build on three pillars fundamental to the development of AI: Investing in the needs of the ecosystem to see more people working with AI, more access to data and compute resources to train and deliver AI systems, and access to finance and customers to grow sectors; Supporting the diffusion of AI across the whole economy to ensure all regions, nations, businesses and sectors can benefit from AI; and Developing a pro-innovation regulatory and governance framework that protects the public.
The National AI Strategy does not stand alone. It purposefully supports and amplifies the other, interconnected work of government including:
The Plan for Growth and recent Innovation Strategy , which recognise the need to develop a diverse and inclusive pipeline of AI professionals with the capacity to supercharge innovation;
, which recognise the need to develop a diverse and inclusive pipeline of AI professionals with the capacity to supercharge innovation; The Integrated Review , to find new paths for UK excellence in AI to deliver prosperity and security at home and abroad, and shape the open international order of the future;
to find new paths for UK excellence in AI to deliver prosperity and security at home and abroad, and shape the open international order of the future; The National Data Strategy , published in September 2020, sets out our vision to harness the power of responsible data use to boost productivity, create new businesses and jobs, improve public services, support a fairer society, and drive scientific discovery, positioning the UK as the forerunner of the next wave of innovation;
, published in September 2020, sets out our vision to harness the power of responsible data use to boost productivity, create new businesses and jobs, improve public services, support a fairer society, and drive scientific discovery, positioning the UK as the forerunner of the next wave of innovation; The Plan for Digital Regulation , which sets out our pro-innovation approach to regulating digital technologies in a way that drives prosperity and builds trust in their use;
which sets out our pro-innovation approach to regulating digital technologies in a way that drives prosperity and builds trust in their use; The upcoming National Cyber Strategy to continue the drive for securing emerging technologies, including building security into the development of AI;
to continue the drive for securing emerging technologies, including building security into the development of AI; The forthcoming Digital Strategy, which will build on DCMS’s Ten Tech Priorities to further set out the government’s ambitions in the digital sector;
which will build on DCMS’s Ten Tech Priorities to further set out the government’s ambitions in the digital sector; A new Defence AI centre as a keystone piece of the modernisation of Defence;
as a keystone piece of the modernisation of Defence; The National Security Technology Innovation exchange (NSTIx), a data science & AI co-creation space that brings together National Security stakeholders, industry and academic partners to build better national security capabilities; and
(NSTIx), a data science & AI co-creation space that brings together National Security stakeholders, industry and academic partners to build better national security capabilities; and The upcoming National Resilience Strategy, which will in part focus on how the UK will stay on top of technological threats.
The government’s AI Council has played a central role in gathering evidence to inform the development of this strategy, including through its roadmap published at the beginning of the year, which represents a valuable set of recommendations reflecting much of the wider AI community in the UK. The wider ecosystem also fed in through a survey run by the AI Council in collaboration with The Alan Turing Institute. The government remains grateful to the AI Council for its continued leadership of the AI ecosystem, and would like to thank those from across the United Kingdom who shared their views during the course of developing this strategy.
The AI Council The AI Council was established in 2019 to provide expert advice to the government and high-level leadership of the AI ecosystem. The AI Council demonstrates a key commitment made in the AI Sector Deal, bringing together respected leaders in their fields from across industry, academia and the public sector. Members meet quarterly to advise the Office for AI and broader government on its current priorities, opportunities and challenges for AI policy. In January 2021, the AI Council published its ‘AI Roadmap’ providing 16 recommendations to the government on the strategic direction for AI. Its central call was for the government to develop a National AI Strategy, building on the success of investments made through the AI Sector Deal whilst remaining adaptable to future technological disruption. Since then, the Council has led a programme of engagement with the wider AI community to inform the development of the National AI Strategy. To guide the delivery and implementation of this strategy the government will renew and strengthen the role of the AI Council, ensuring it continues to provide expert advice to government and high-level leadership of the AI ecosystem.
AI presents unique opportunities and challenges
‘Artificial Intelligence’ as a term can mean a lot of things, and the government recognises that no single definition is going to be suitable for every scenario. In general, the following definition is sufficient for our purposes: “Machines that perform tasks normally performed by human intelligence, especially when the machines learn from data how to do those tasks.” The UK government has also set out a legal definition of AI in the National Security and Investment Act.[footnote 2]
Much like James Watt’s 1776 steam engine, AI is a ‘general purpose technology’ (or more accurately, technologies) that have many possible applications, and we expect them to have a transformational impact on the whole economy. Already, AI is used in everyday contexts like email spam filtering, media recommendation systems, navigation apps, payment transaction validation and verification, and many more. AI technologies will impact the whole economy, all of society and us as individuals.
Many of the themes in AI policy are similar to tech and digital policy more widely: the commercialisation journeys; the reliance on internationally mobile talent; the importance of data; and consolidation of economic functions onto platforms. However there are some key examples of differences derived from the above definition which differentiate AI and require a unique policy response from the government.
In regulatory matters, a system’s autonomy raises unique questions around liability, assurance, and fairness as well as risk and safety - and even ownership of creative content [footnote 3] - in a way which is distinct to AI, and these questions increase with the relative complexity of the algorithm. There are also questions of transparency and bias which arise from decisions made by AI systems.
There are often greater infrastructure requirements for AI services than in cloud/Software as a Service systems. In building and deploying some models, access to expensive high performance computing and/or large data sets is needed.
Multiple skills are required to develop, validate and deploy AI systems, and the commercialisation and product journey can be longer and more expensive because so much starts with fundamental R&D.
Reflecting and protecting society
AI makes predictions and decisions, and fulfils tasks normally undertaken by humans. While diverse opinions, skills, backgrounds and experience are hugely important in designing any service – digital or otherwise – it is particularly important in AI because of the executive function of the systems. As AI increasingly becomes an enabler for transforming the economy and our personal lives, there are at least three reasons we should care about diversity in our AI ecosystem:
Moral: As AI becomes an organising principle which creates new opportunities and changes the shape of industries and the dynamics of competition across the economy, there is a moral imperative to ensure people from all backgrounds and parts of the UK are able to participate and thrive in this new AI economy.
Social: AI systems make decisions based on the data they have been trained on. If that data – or the system it is embedded in – is not representative, it risks perpetuating or even cementing new forms of bias in society. It is therefore important that people from diverse backgrounds are included in the development and deployment of AI systems.
Economic: There are big economic benefits to a diverse AI ecosystem. These include increasing the UK’s human capital from a diverse labour supply, creating a wider range of AI services that stimulate demand, and ensuring the best talent is discovered from the most diverse talent pool.
The longer term
Making specific predictions about the future impact of a technology – as opposed to the needs of those developing and using it today – has a long history in AI. Since the 1950s various hype cycles have given way to so-called ‘AI winters’ as the promises made have perpetually remained ‘about 20 years away’.
While the emergence of Artificial General Intelligence (AGI) may seem like a science fiction concept, concern about AI safety and non-human-aligned systems[footnote 4] is by no means restricted to the fringes of the field.[footnote 5] The government’s first focus is on the economic and social outcomes of autonomous and adaptive systems that exist today. However, we take the firm stance that it is critical to watch the evolution of the technology, to take seriously the possibility of AGI and ‘more general AI’, and to actively direct the technology in a peaceful, human-aligned direction.[footnote 6]
The emergence of full AGI would have a transformational impact on almost every aspect of life, but there are many challenges which could be presented by AI which could emerge much sooner than this. As a general purpose technology AI will have economic and social impacts comparable to the combustion engine, the car, the computer and the internet. As each of these has disrupted and changed the shape of the world we live in - so too could AI, long before any system ‘wakes up.’
The choices that are made in the here and now to develop AI will shape the future of humanity and the course of international affairs. For example, whether AI is used to enhance peace, or a cause for war; whether AI is used to strengthen our democracies, or embolden authoritarian regimes. As such we have a responsibility to not only look at the extreme risks that could be made real with AGI, but also to consider the dual-use threats we are already faced with today.
From Sector Deal to AI Strategy
The UK is an AI superpower, with particular strengths in research, investment and innovation. The UK’s academic and commercial institutions are well known for conducting world-leading AI research, and the UK ranks 3rd in the world for AI publication citations per capita.[footnote 7] This research strength was most recently demonstrated in November 2020 when DeepMind, a UK-based AI company, used AlphaFold to find a solution to a 50-year-old grand challenge in biology.[footnote 8]
The UK has the 3rd highest number of AI companies in the world after the US and China. Alongside DeepMind, the UK is home to Graphcore, a Bristol-based machine learning semiconductor company; Darktrace, a world-leading AI company for cybersecurity; and BenevolentAI, a company changing the way we treat disease. The UK also attracts some of the best AI talent from around the world[footnote 9] - the UK was the second most likely global destination for mobile AI researchers after the USA.
AlphaFold and AlphaFold 2 In November 2020, London-based DeepMind announced that they had solved one of the longest running modern challenges in biology: predicting how proteins - the building blocks of life which underpin every biological process in every living thing - take shape, or ‘fold’. Improvements in the median accuracy of predictions in the free modelling category for the best team in each CASP, measured as best-of-5 GDT. Source: DeepMind AlphaFold, DeepMind’s deep learning AI system, broke all previous accuracy levels dating back over 50 years, and in July 2021 the organisation open sourced the code for AlphaFold together with over 350,000 protein structure predictions, including the entire human proteome, via the AlphaFold database in partnership with EMBL-EBI. DeepMind’s decision to share this knowledge openly with the world, demonstrates both the opportunity that AI presents, as well as what this strategy seeks to support: bleeding-edge research happening in the UK and with partners around the world, solving big global challenges. AlphaFold opens up a multitude of new avenues in research – helping to further our understanding of biology and the nature of the world around us. It also has a multitude of potential real-world applications, such as deepening our understanding of how bacteria and viruses attack the body in order to develop more effective prevention and treatment, or support the identification of proteins and enzymes that can break down industrial or plastic waste.
The government has invested more than £2.3 billion into Artificial Intelligence across a range of initiatives since 2014.[footnote 10] This portfolio of investment includes, but is not limited to:
£250 million to develop the NHS AI Lab at NHSX to accelerate the safe adoption of Artificial Intelligence in health and care;
£250 million into Connected and Autonomous Mobility (CAM) technology through the Centre for Connected and Autonomous Vehicles (CCAV) to develop the future of mobility in the UK;
16 new AI Centres for Doctoral Training at universities across the country, backed by up to £100 million and delivering 1,000 new PhDs over five years;
A new industry-funded AI Masters programme and up to 2,500 places for AI and data science conversion courses. This includes up to 1,000 government-funded scholarships;
Investment into The Alan Turing Institute and over £46 million to support the Turing AI Fellowships to develop the next generation of top AI talent;
Over £372 million of investment into UK AI companies through the British Business Bank for the growing AI sector;
£172 million of investment through the UKRI into the Hartree National Centre for Digital Innovation, leveraging an additional £38 million of private investment into High Performance Computing.
Further investments have been made into the Tech Nation Applied AI programme – now in its third iteration; establishing the Office for National Statistics Data Science Campus; the Crown Commercial Service’s public sector AI procurement portal; and support for the Department for International Trade attracting AI related Foreign Direct Investment into the UK.
As part of the AI Sector Deal, the government established the AI Council to bring together respected leaders to strengthen the conversation between academia, industry, and the public sector. The Office for Artificial Intelligence was created as a new team within government to take responsibility for overarching AI policy across government and to be a focal point for the AI ecosystem through its secretariat of the AI Council. The Centre for Data Ethics and Innovation (CDEI) was established as a government expert body focused on the trustworthy use of data and AI in the public and private sector.
This strategy builds on the recent history of government support for AI and considers the next key steps to harness its potential in the UK for the coming decade. In doing so, the National AI Strategy leads on from the ambitions outlined in the government’s Innovation Strategy to enable UK businesses and innovators to respond to economic opportunities and real-world problems through our national innovation prowess. AI was identified in the Innovation Strategy as one of the seven technology families where the UK has a globally competitive R&D and industrial strength[footnote 11] and has been widely cited as a set of technologies in which the UK must maintain a leading edge to guarantee our continued security and prosperity in an intensifying geopolitical landscape.
Pillar 1: Investing in the long-term needs of the AI ecosystem
Investing in and planning for the long term needs of the AI ecosystem to remain a science and AI superpower
To maintain the UK’s position amongst the global AI superpowers and ensure the UK continues to lead in the research, development, commercialisation and deployment of AI, we need to invest in, plan for, secure and unlock the critical inputs that underpin AI innovation.
Government’s aim is to greatly increase the type, frequency and scale of AI discoveries which are developed and exploited in the UK. This will be achieved by: Making sure the UK’s research, development and innovation system continues to be world leading, providing the support to allow researchers and entrepreneurs to forge new frontiers in AI;
Guaranteeing that the UK has access to a diverse range of people with the skills needed to develop the AI of the future and to deploy it to meet the demands of the new economy;
Ensuring innovators have access to the data and computing resources necessary to develop and deliver the systems that will drive the UK economy for the next decade;
Supporting growth for AI through a pro-innovation business environment and capital market, and attracting the best people and firms to set up shop in the UK;
Ensuring UK AI developers can access markets around the world.
Increasing diversity and closing the skills gap through postgraduate conversion courses in data science and artificial Autumn 2020 student admissions data shows a diverse range of students have enrolled on AI and data science postgraduate conversion courses funded by the Office for Students. The data shows that 40% of the total students are women, one quarter are Black students and 15% are students that are disabled. Source: Office for Students As a result of the growing skills gap in AI and data science, 2,500 new Masters conversion courses in AI and data science are now being delivered across universities in England. The conversion course programme included up to 1,000 scholarships to increase the number of people from underrepresented groups and to encourage graduates from diverse backgrounds to consider a future in AI and Data Science. In the first year over 1,200 students enrolled, with 22% awarded scholarships. Over 40% of the total students are women, one quarter are black students and 15% of students are disabled. 70% of the total students are studying on courses based outside of London and the South East. These conversion courses are providing the opportunity to develop new digital skills or retrain to help find new employment in the UK’s cutting-edge AI and data science sectors, ensuring that industry and the public sector can access the greatest supply of talent across the whole country.
Skills and talent
Continuing to develop, attract and train the best people to build and use AI is at the core of maintaining the UK’s world-leading position. By inspiring all with the possibilities AI presents, the UK will continue to develop the brightest, most diverse workforce.
Building a tech-savvy nation by supporting skills for the future is one of the government’s ten tech priorities. The gap between demand and supply of AI skills remains significant and growing,[footnote 12],[footnote 13] despite a number of new AI skills initiatives since the 2018 AI Sector Deal. In order to meet demand, the UK needs a larger workforce with AI expertise. Last year there was a 16% increase for online AI and Data Science job vacancies and research found that 69% of vacancies were hard to fill.[footnote 14] Data from an ecosystem survey conducted by the AI Council and The Alan Turing Institute showed that 81% of respondents agreed there were significant barriers in recruiting and retaining top AI talent in their domain within the UK.
Research into the AI Labour Market showed that technical AI skill gaps are a concern for many firms, with 35% of firms revealing that a lack of technical AI skills from existing employees had prevented them from meeting their business goals, and 49% saying that a lack of required AI skills from job applicants also affected their business outcomes.[footnote 15] To support the adoption of AI we need to ensure that non-technical employees understand the opportunities, limitations and ethics of using AI in a business setting, rather than these being the exclusive domain of technical practitioners.
Understanding the UK AI Labour Market research In 2021, the Office for AI published research to investigate Artificial Intelligence and Data science skills in the UK labour market in 2020. Some key findings from the research: Half of surveyed firms’ business plans had been impacted by a lack of suitable candidates with the appropriate AI knowledge and skills.
Two thirds of firms (67%) expected that the demand for AI skills in their organisation was likely to increase in the next 12 months.
Diversity in the AI sector was generally low. Over half of firms (53%) said none of their AI employees were female, and 40% said none were from ethnic minority backgrounds.
There were over 110,000 UK job vacancies in 2020 for AI and Data Science roles. The findings from this research will help the Office for AI address the AI skills challenge and ensure UK businesses can take advantage of the potential of AI and Data Science.
We need to inspire a diverse set of people across the UK to ensure the AI that is built and used in the UK reflects the needs and make-up of society. To close the skills gap, the government will focus on three areas to attract and train the best people: those who build AI, those who use AI, and those we want to be inspired by AI.
Build: Train and attract the brightest and best people at developing AI
To meet the demand seen in industry and academia, the government will continue supporting existing interventions across top talent, PhDs and Masters levels. This includes Turing Fellowships, Centres for Doctoral Training and Postgraduate Industrial-Funded Masters and AI Conversion Courses.
Government will seek to build upon the £46 million Turing AI Fellowships investment to attract, recruit, and retain a substantial cohort of leading researchers and innovators at all career stages. Our approach will enable Fellows to work flexibly between academia and other sectors, creating an environment for them to discover and develop cutting edge AI technologies and drive the use of AI to address societal, economic and environmental challenges in the UK. We note that recently, research breakthroughs in the field of AI have been disproportionately driven by a small number of luminary talents and their trainees. In line with the Innovation Strategy, the government affirms our commitment to empowering distinguished academics.
Research[footnote 16] and industry engagement has demonstrated the need for graduates with business experience, indicating a need to continue supporting industry/academic partnerships to ensure graduates leave education with business-ready experience. Our particular focus will be on software engineers, data scientists, data engineers, machine learning engineers and scientists, product managers, and related roles.
We recognise that global AI talent is scarce, and the topic of fierce competition internationally. As announced in the Innovation Strategy, the government is revitalising and introducing new visa routes that encourage innovators and entrepreneurs to the UK. Support for diverse and inclusive researchers and innovators across sectors, and new environments for collaboratively developing AI, will be key to ensuring the UK’s success in developing AI and investing in the long term health of our AI ecosystem.
Attracting the best AI talent from around the world The UK is already the top global destination for AI graduates in the United States and we punch above our weight globally in attracting talent. The UK nearly leads the world in its proportion of top-skilled AI researchers. Government wants to take this to the next level and make the UK the global home for AI researchers, entrepreneurs, businesses and investors. As well as ensuring the UK produces the next generation of AI talent we need, the government is broadening the routes that talented AI researchers and individuals can work in the UK, through the recently announced Innovation Strategy. The Global Talent visa route is open to those who are leaders or potential leaders in AI - and those who have won prestigious global prizes automatically qualify. Government is currently looking at how to broaden this list of prizes.
A new High Potential Individual route will make it as simple as possible for internationally mobile individuals who demonstrate high potential to come to the UK. Eligibility will be open to applicants who have graduated from a top global university, with no job offer requirement. This gives individuals the flexibility to work, switch jobs or employers – keeping pace with the UK’s fast-moving AI sector.
A new scale-up route will support UK scale-ups by allowing talented individuals with a high-skilled job offer from a qualifying scale-up at the required salary level to come to the UK. Scaleups will be able to apply through a fast-track verification process to use the route, so long as they can demonstrate an annual average revenue or employment growth rate over a three-year period greater than 20%, and a minimum of 10 employees at the start of the three-year period.
A revitalised Innovator route will allow talented innovators and entrepreneurs from overseas to start and operate a business in the UK that is venture-backed or harnesses innovative technologies, creating jobs for UK workers and boosting growth. We have reviewed the Innovator route to make it even more open to: Simplifying and streamlining the business eligibility criteria. Applicants will need to demonstrate that their business venture has a high potential to grow and add value to the UK and is innovative. Fast-tracking applications. The UK government is exploring a fast-track, lighter touch endorsement process for applicants whose business ideas are particularly advanced to match the best-in-class international offers. Applicants that have been accepted on to the government’s Global Entrepreneur Programme will be automatically eligible. Building flexibility. Applicants will no longer be required to have at least £50,000 in investment funds to apply for an Innovator visa, provided that the endorsing body is satisfied the applicant has sufficient funds to grow their business. We will also remove the restriction on doing work outside of the applicant’s primary business.
The new Global Business Mobility visa will also allow overseas AI businesses greater flexibility in transferring workers to the UK, in order to establish and expand their business here. These reforms will sit alongside the UK government’s Global Entrepreneur Programme (GEP) which has a track record of success in attracting high skilled migrant tech founders with IP-rich businesses to the UK. The programme will focus on attracting more international talent to support the growth of technology clusters including through working with academic institutions from overseas to access innovative spinouts and overseas talent. Through the Graduate Route we are also granting international students with UK degrees 2 years, 3 years for those with PhDs, to work in the UK post-graduation. This will help ensure that we can attract the best and brightest from across the world while also giving students time to work on the most challenging AI problems. These are all in addition to our existing skills visa schemes for those with UK job offers.
Use: Empower employers and employees to upskill and understand the opportunities for using AI in a business setting
The AI Council ecosystem survey found that only 18% agreed there was sufficient provision of training and development in AI skills available to the current UK workforce. As the possibilities to develop and use AI grow, so will people’s need to understand and apply AI in their jobs. This will range from people working adjacent to the technical aspects such as product managers and compliance, through to those who are applying AI within their business, such as in advertising and HR. Below degree level, there is a need to clearly articulate the skills employers and employees need to use AI effectively in the workplace. For example, industries have expressed their willingness to fund employees to undertake training but have not found training that suits their needs: including training that is business-focused, modular and flexible.
Skills for Jobs White Paper The Skills for Jobs: Lifelong Learning for Opportunity and Growth White Paper was published in January 2021 and is focused on giving people the skills they need, in a way that suits them, so they can get great jobs in sectors the economy needs and boost the country’s productivity. These reforms aim to ensure that people can access training and learning flexibly throughout their lives and that they are well-informed about what is on offer, including opportunities in valuable growth sectors. This will also involve reconfiguring the skills system to give employers a leading role in delivering the reforms and influencing the system to generate the skills they need to grow.
To more effectively use AI in a business setting, employees, including those who would not have traditionally engaged with AI, will require a clear articulation of the different skills required, so they can identify what training already exists and understand if there is still a gap.
Using the Skills Value Chain approach piloted by the Department for Education,[footnote 17] the government will help industry and providers to identify what skills are needed. Lessons learned from this pilot will support this work to help businesses adopt the skills needed to get the best from AI. The Office for AI will then work with the Department for Education to explore how these needs can be met and mainstreamed through national skills provision.
The government will also support people to develop skills in AI, machine learning, data science and digital through the Department for Education’s Skills Bootcamps. The Bootcamps are free, flexible courses of up to 16 weeks, giving adults aged 19 and over the opportunity to build up in-demand, sector-specific skills and fast-track to an interview with a local employer; improving their job prospects and supporting the economy.
Inspire: Support all to be excited by the possibilities of AI
The AI Council’s Roadmap makes clear that inspiring those who are not currently using AI, and allowing children to explore and be amazed by the potential of AI, will be integral to ensuring we continue to have a growing and diverse AI-literate workforce.
Through supporting the National Centre for Computing Education(NCCE) the government will continue to ensure programmes that engage children with AI concepts are accessible and reach the widest demographic.
The Office for AI will also work with the Department for Education to ensure career pathways for those working with or developing AI are clearly articulated on career guidance platforms, including the National Careers Service, demonstrating role models and opportunities to those exploring AI. This will support a broader range of people to consider careers in AI. The government will ensure that leaders within the National AI Research and Innovation Programme will play a key role in engaging with the public and inspiring the leaders of the future.
Research, development and innovation
Our vision is that the UK builds on our excellence in research and innovation in the next generation of AI technologies.
The UK has been a leader in AI research since it developed as a field, thanks to our strengths in computational and mathematical sciences.[footnote 18] The UK’s AI base has been built upon this foundation,[footnote 19] and the recently announced Advanced Research and Invention Agency (ARIA) will complement our efforts to cement our status as a global science superpower. The UK also has globally recognised institutes such as The Alan Turing Institute and the high-performing universities which are core to research in AI.[footnote 20]
Currently, AI research undertaken in the UK is world class, and investments in AI R&D contribute to the Government’s target of increasing overall public and private sector R&D expenditure to 2.4% of GDP by 2027. But generating economic and societal impact through adoption and diffusion of AI technologies is behind where it could be.[footnote 21] There is a real opportunity to build on our existing strengths in fundamental AI research to ensure they translate into productive processes throughout the economy.
At the same time, the field of AI is advancing rapidly, with breakthrough innovations being generated by a diverse set of institutions and countries. The past decade has seen the rise of deep learning, compute-intensive models, routine deployment of vision, speech, and language modelling in the real world, the emergence of responsible AI and AI safety, among other advances. These are being developed by new types of research labs in private companies and public institutions around the world. We expect that the next decade will bring equally transformative breakthroughs. Our goal is to make the UK the starting point for a large proportion of them, and to be the fastest at turning them into benefits for all.
To do this, UKRI will support the transformation of the UK’s capability in AI by launching a National AI Research and Innovation (R&I) Programme. The programme will shift us from a rich but siloed and discipline-focused national AI landscape to an inclusive, interconnected, collaborative, and interdisciplinary research and innovation ecosystem. It will work across all the Councils of UKRI and will be fully-joined up with business of all sizes and government departments. It will translate fundamental scientific discoveries into real-world AI applications, address some limitations in the ability of current AI to be effectively used in numerous real world contexts, such as tackling complex and undefined problems, and explore using legacy data such as non-digital public records.
The National AI Research and Innovation (R&I) Programme has five main aims: Discovering and developing transformative new AI technologies, leading the world in the development of frontier AI and the key technical capabilities to develop responsible and trustworthy AI.The programme will support: foundational research to develop novel next generation AI technologies and approaches which could address current limitations of AI, focusing on low power and sustainable AI, and AI which can work differently with a diverse range of challenging data sets, human-AI interaction, reasoning, and the maths underpinning the theoretical foundations of AI.
technical and socio-technical capability development to overcome current limitations around the responsible trustworthy nature of AI. Maximising the creativity and adventure of researchers and innovators, building on UK strengths and developing strategic advantage through a diverse range of AI technologies. The programme will support: specific routes to enable the exploration of high-risk ideas in the development and application of AI;
follow-on funding to maximise the impact of the ideas with the most potential. Building new research and innovation capacity to deliver the ideas, technologies, and workforce of the future, recruiting and retaining AI leaders, supporting the development of new collaborative AI ecosystems, and developing collaborative, multidisciplinary, multi-partner teams. The programme will support: the recruitment, retention, training and development of current and future leaders in AI, and flexible working across sectoral and organisational interfaces using tools such as fellowships, and building on the success of the Turing AI Fellowships scheme;
enhanced UK capacity in key AI professional skills for research and innovation, such as data scientists and software engineers. Connecting across the UK AI Research and Innovation ecosystem, building on the success of The Alan Turing Institute as the National Centre for AI and Data Science, and building collaborative partnerships nationally and regionally between and across sectors, diverse AI research and innovation stakeholders. The programme will support: the development of a number of nationally distributed AI ecosystems which enable researchers and innovators to collaborate in new environments and integrate basic research through application and innovation. These ecosystems will be networked into a national AI effort with the Alan Turing Institute as its hub, convening and coordinating the national research and innovation programme and enabling business and government departments to access the UK’s AI expertise and skills capability e.g. the catapult network and compute capability. Supporting the UK’s AI Sector and the adoption of AI, connecting research and innovation and supporting AI adoption and innovation in the private sector. The programme will support: challenge-driven AI research and innovation programmes in key UK priorities, such as health and the transition to net zero;
collaborative work with the public sector and government organisations to facilitate leading researchers and innovators engaging with the AI transformation of the public sector;
innovation activities in the private sector, both in terms of supporting the development of the UK’s burgeoning AI sector and the adoption of AI across sectors.
International collaboration on research and innovation
As well as better coordination at home, the UK will work with friends and partners around the world on shared challenges in research and development and lead the global conversation on AI.
The UK will participate in Horizon Europe, enabling collaboration with other European researchers, and will build a strong and varied network of international science and technology partnerships to support R&I collaboration. By shaping the responsible use of technology, we will put science and technology, including AI, at the heart of our alliances and partnerships worldwide. We will continue to use Official Development Assistance to support R&D partnerships with developing countries.
We are also deepening our collaboration with the United States, implementing the US UK Declaration on Cooperation in AI Research and Development. This declaration outlines a shared vision for driving technological breakthroughs in AI between the US and the UK. As we build materially on this partnership, we will seek to enable UK partnership with other key global actors in AI, to grow influential R&I collaborations.
Access to data
The National Data Strategy sets out the government’s approach to unlocking the power of data. Access to good quality, representative data from which AI can learn is critical to the development and application of robust and effective AI systems.
The AI Sector Deal recognised this and since then the government has established evidence on which to make policies to harness the positive economic and social benefit of increased availability of data. This includes the Open Data Institute’s original research into data trusts as a model of data stewardship to realise the value of data for AI. The research established a repeatable model for data trusts which others have begun to apply.
Mission 1 of the National Data Strategy seeks to unlock the value of data across the economy, and is a vital enabler for AI. This mission explores how the government can apply six evidenced levers to tackle barriers to data availability. The government will publish a policy framework in Autumn 2021 informed by the outcomes of Mission 1, setting out its role in enabling better data availability in the wider economy. The policy framework includes supporting the activities of intermediaries, including data trusts, and providing stewardship services between those sharing and accessing data.
The AI Council and the Ada Lovelace Institute recently explored three legal mechanisms that could help facilitate responsible data stewardship – data trusts, data cooperatives and corporate and contractual mechanisms. The ongoing Data: A new direction consultation asks what role the government should have in enabling and engendering confidence in responsible data intermediary activity. The government is also exploring how privacy enhancing technologies can remove barriers to data sharing by more effectively managing the risks associated with sharing commercially sensitive and personal data.
Data foundations and use in AI systems
Data foundations refer to various characteristics of data that contribute to its overall condition, whether it is fit for purpose, recorded in standardised formats on modern, future-proof systems and held in a condition that means it is findable, accessible, interoperable and reusable (FAIR). A recent EY study delivered on behalf of DCMS has found that organisations that report higher AI adoption levels also have a higher level of data foundations.
The government is considering how to improve data foundations in the private and third sectors. Through the National AI R&I Programme and ambitions to lead best practices in FAIR data, we will grow our capacity in professional AI, software and data skills, and support the development of key new data infrastructure capabilities. Technical professionals such as data engineers have a key role to play in opening up access to the most critical data and compute infrastructures on FAIR data principles, and in accelerating the pathway to using AI technologies to make best use of the UK’s healthy data ecosystem.
Data foundations are crucial to the effective use of AI and it is estimated that, on average, 80% of the time spent on an AI project is cleaning, standardising and making the data fit for purpose. Furthermore, when the source data needed to power AI or machine learning is not fit for purpose, it leads to poor or inaccurate results, and to delays in realising the benefits of innovation.[footnote 22] Poor quality datasets can also be un-representative, especially when it comes to minority groups, and this can propagate existing biases and exclusions when they are used for AI.
The government is looking to support action to mitigate the effects of quality issues and underrepresentation in AI systems. Subject to the outcomes of the Data: A new direction consultation, the government will more explicitly permit the collection and processing of sensitive and protected characteristics data to monitor and mitigate bias in AI systems.
An important outcome for increasing access to data and improving data foundations is in how technology will be better able to use that data. Technological convergence – the tendency for technologies that were originally unrelated to become more closely integrated (or even unified) as they advance – means that AI will increasingly be deployed together with many other technologies of the future, unlocking new technological, economic and social opportunities. For example, AI is a necessary driver of the development of robotics and smart machines, and will be a crucial enabling technology for digital twins. These digital replicas of real-world assets, processes or systems, with a two-way link to sensors in the physical world, will help make sense of and create insights and value from vast quantities of data in increasingly sophisticated ways. And in the future, some types of AI will rely on the step-change in processing power that quantum computing is expected to unlock.
Government will consult later this year on the potential value of and options for a UK capability in digital twinning and wider ‘cyber-physical infrastructure.’[footnote 23] This consultation will help identify how common, interoperable digital tools and platforms, as well as physical testing and innovation spaces can be brought together to form a digital and physical shared infrastructure for innovators (e.g. digital twins, test beds and living labs). Supporting and enabling this shared infrastructure will help remove time, cost and risk from the process of bringing innovation to market, enabling accelerated AI development and applications.
Public sector data
Work is underway within the government to fix its own data foundations as part of Mission 3 of the National Data Strategy, which focuses on transforming the government’s use of data to drive efficiency and improve public services. The Central Digital and Data Office (CDDO) has been created within the Cabinet Office to consolidate the core policy and strategy responsibilities for data foundations, and will work with expert cross-sector partners to improve government’s use and reuse of data to support data-driven innovation across the public sector.
The CDDO also leads on the Open Government policy area, a wide-ranging and open engagement programme that entails ongoing work with Civil Society groups and government departments to target new kinds of data highlighted as having ‘high potential impact’ for release as open data. The UK’s ongoing investment in open data will serve to further bolster the use of AI and machine learning within government, the private sector, and the third sector. The application of standards and improvements to the quality of data collected, processed, and ultimately released publicly under the Open Government License will create further value when used by organisations looking to train and optimise AI systems utilising large amounts of information.
The Office for National Statistics(ONS) is leading the Integrated Data Programme in collaboration with partners across government, providing real-time evidence, underpinning policy decisions and delivering better outcomes for citizens while maintaining privacy. The 2021 Declaration on Government Reform sets out a focus on strengthening data skills across government including senior leaders.
We need to strengthen the way that public authorities can engage with private sector data providers to make better use of data through FAIR data and open standards, including making government data more easily available through application programming interfaces (APIs), and encouraging businesses to offer their data through APIs. Government will continue to publish authoritative open and machine-readable data on which AI models for both public and commercial benefit can depend. The Office for AI will also work with teams across government to consider what valuable datasets government should purposefully incentivise or curate that will accelerate the development of valuable AI applications.
Compute
The total amount of compute shows two distinct eras of compute usage in training AI systems. A petaflop/s-day consists of performing 10615 neural net operations per second for one day, or a total of about 10620 operations. Starting from ~2012 we see a 3.4-month doubling time for the compute seen in historical results, compared to a ~2-year doubling time (Moore’s Law) before then. Shown on a logarithmic scale. Source: OpenAI
Access to computing power is essential to the development and use of AI, and has been a dominant trend in AI breakthroughs of the past decade. The computing power underpinning AI in the UK comes from a range of sources. The government’s recent report on large-scale computing[footnote 24] recognises its importance in AI innovation, but suggests that the UK’s infrastructure is lagging behind other major economies around the world such as the US, China, Japan and Germany. We also recognise the growing compute gap between large-scale enterprises and researchers. Access to compute is both a competitiveness and a security issue. It is also not a one-size-fits-all approach - different AI technologies need different capabilities.
Digital Catapult’s Machine Intelligence Garage For more than three years, Digital Catapult’s Machine Intelligence Garage (MI Garage) has helped startups accelerate the development of their industry-leading AI solutions by addressing their need for computational power. Some AI solutions being developed require greater computing capacity in the form of High Performance Computers (HPC) for unusually large workloads (such as weather simulation, protein folding and simulation of molecular interactions) or access to AI focussed hardware like Graphcore’s Intelligence Processing Unit (IPU), a new processor specifically designed for developing AI. MI Garage provides a channel through which startups can connect with HPC centres and access specialised hardware. HPC partners include the Hartree National Centre for Digital Innovation, the Edinburgh Parallel Computing Centre, and the Earlham Institute. MI Garage has also worked with NVIDIA, Graphcore and LightOn to facilitate access to special trials to lower the barrier to entry to AI specialised hardware.
Sustained public and private investment in a range of facilities from cloud, laboratory and academic department scale, through to supercomputing, will be necessary to ensure that accessing computing power is not a barrier to future AI research and innovation, commercialisation and deployment of AI. In June 2021, the government announced joint funding with IBM for the Hartree National Centre for Digital Innovation to stimulate high performance computing enabled innovation in industry and make cutting-edge technologies like AI more accessible to businesses and public sector organisations.
Understanding our domestic AI computing capacity needs and their relationship to energy use is increasingly important[footnote 25] if we are to achieve our ambitions. To better understand the UK’s future AI computing requirements, the Office for AI and UKRI will evaluate the UK’s computing capacity needs to support AI innovation, commercialisation and deployment. This study will look at the hardware and broader needs of researchers and organisations, large and small, developing AI technologies, alongside organisations adopting AI products and services. The study will also consider the possible wider impact of future computing requirements for AI as it relates to areas of proportional concern, such as the environment. The report will feed into UKRI’s wider work on Digital Research Infrastructure.[footnote 26]
Alongside access to necessary compute capacity, the competitiveness of the AI hardware will be critical to the UK's overall research and commercial competitiveness in the sector. The UK is a world leader in chip and systems design, underpinned by processor innovation hubs in Cambridge and Bristol. We have world-leading companies supporting both general purpose AI – Graphcore has built the world's most complex AI chip,[footnote 27] and for specific applications – XMOS is a leader in AI for IOT. The government is currently undertaking a wider review of its international and domestic approach to the semiconductor sector. Given commercial and innovation priorities in AI, further support for the chip design community will be considered.
Finance and VC
AI innovation is thriving in the UK, backed by our world-leading financial services industry. In 2020, UK firms that were adopting or creating AI-based technologies received £1.78bn in funding, compared to £525m raised by French companies and £386m raised in Germany.[footnote 28] More broadly, investment in UK deep tech companies has increased by 291% over the past five years, though deal sizes remain considerably smaller compared to the US.[footnote 29]
The government will continue to evaluate the state of funding specifically for innovative firms developing AI technologies across every region of the UK. This work will explore if there are any significant investment gaps or barriers to accessing funding that AI innovative companies are facing that are not being addressed. Government commits to reporting on this work in Autumn 2022.
Accessing the right finance at the right time is critical for AI innovators to be able to develop their idea into a commercially viable product and grow their business, but this is complicated by the long timelines often needed for AI research and development work.[footnote 30][footnote 31] The AI Council’s Roadmap suggests a funding gap at series B+, meaning that AI companies are struggling to scale and stay under UK ownership.
Tech Nation Tech Nation is a predominantly government-funded programme, built to deliver its own initiatives that grow and support the UK’s burgeoning digital tech sector. This includes growth initiatives aiming to help businesses successfully navigate the transition from start-up to scale-up and beyond, network initiatives to connect the UK digital ecosystem, and the Tech Nation Visa scheme, which offers a route into the UK for exceptionally talented individuals from overseas. Recent growth programmes include Applied AI, their first to help the UK’s most promising founders who are applying AI in practical areas and creating real-world impact; Net Zero, a six-month free growth programme for tech companies that are creating a more sustainable future; and Libra, which is focused on supporting Black founders and addressing racial inequality in UK tech.
While the UK’s funding ecosystem is robust, the government is committed to ensuring the system is easy for businesses and innovators to navigate, and that any existing gaps are addressed. The recent Innovation Strategy signalled the Government’s efforts to support innovators by bringing together effective private markets with well-targeted public investment. In it, the government set out plans to upskill lenders to assess risk when lending to innovative businesses and outlined work across Innovate UK and the British Business Bank to investigate how businesses interact with the public support landscape, to maximise accessibility for qualifying businesses. A good example of this is the Future Fund: Breakthrough, a new £375 million UK-wide programme launched in July 2021, will encourage private investors to co-invest with the government in high-growth innovative businesses to accelerate the deployment of breakthrough technologies.
Our economy’s success and our citizens’ safety rely on the government’s ability to protect national security while keeping the UK open for business with the rest of the world. Within this context, we will ensure we protect the growth of welcome investment into the UK’s AI ecosystem. The government has introduced the National Security and Investment Act that will provide new powers to screen investments effectively and efficiently now and into the future. It will give businesses and investors the reassurance that the UK continues to welcome the right talent, investment and collaboration that underpins our wider economic security.
Trade
AI is a key part of the UK’s digital goods and services exports, which totalled £69.3bn in 2019.[footnote 32] Trade can support the UK’s objectives to sustain the mature, competitive and innovative AI developer base the UK needs to access customers around the world.
As part of its free trade agenda, the government is committed to pursuing ambitious digital trade chapters to help place the UK as a global leader. As the UK secures new trade deals, the government will include provisions on emerging digital technologies, including AI, and champion international data flows, preventing unjustified barriers to data crossing borders while maintaining the UK’s high standards for personal data protection.
In doing so, the UK aims to deliver digital trade chapters in agreements that: 1) provide legal certainty; 2) support data flows; 3) protect consumers; 4) minimise non-tarriff barriers to digital trade; 5) prevent discrimination against trade by electronic means; and 6) promote international cooperation and global AI governance. All of these aims support a pro -innovation agenda.
Pillar 1 - Investing in the Long Term Needs of the AI Ecosystem Actions: 1. Launch a new National AI Research and Innovation Programme, that will align funding programmes across UKRI Research Councils and Innovate UK, stimulating new investment in fundamental AI research while making critical mass investments in particular applications of AI. 2. Lead the global conversation on AI R&D and put AI at the heart of our science and technology alliances and partnerships worldwide through: Work with partners around the world on shared AI challenges, including participation in Horizon Europe to enable collaboration with other European researchers.
Use of Overseas Development Assistance to support partnerships with developing AI nations.
Delivering new initiatives through the US UK Declaration on Cooperation in AI R&D. 3. Develop a diverse and talented workforce which is at the core of maintaining the UK’s world leading position through: Supporting existing interventions across top talent, PhDs and Masters levels and developing world leading teams and collaborations, the government will continue to attract and develop the brightest and best people to build AI.
Scoping what is required to upskill employees to use AI in a business setting. Then, working with the Department for Education, explore how skills provision can meet these needs through the Skills Value Chain and build out AI and data science skills through Skills Bootcamps.
Inspiring all to be excited by the possibilities of AI, by supporting the National Centre for Computing Education (NCCE) to ensure AI programmes for children are accessible and reach the widest demographic and that career pathways for those working with or developing AI are clearly articulated on career guidance platforms.
Promoting the revitalised and new visa routes that encourage innovators and entrepreneurs to the UK, making attractive propositions for prospective and leading AI talent. 4. Publish a policy framework setting the government’s role in enabling better data availability in the wider economy. The government is already consulting on the opportunity for data intermediaries to support responsible data sharing and data stewardship in the economy and the interplay of AI technologies with the UK’s data rights regime. 5. Consult on the potential role and options for a future national ‘cyber-physical infrastructure’ framework, to help identify how common interoperable digital tools and platforms and cyber-physical or living labs could come together to form a digital and physical ‘commons’ for innovators, enabling accelerated AI development and applications. 6. Publish a report on the UK’s compute capacity needs to support AI innovation, commercialisation and deployment. The report will feed into UKRI’s wider work on infrastructure. 7. Continue to publish open and machine-readable data on which AI models for both public and commercial benefit can depend. 8. Consider what valuable datasets the government should purposefully incentivise or curate that will accelerate the development of valuable AI applications. 9. Undertake a wider review of our international and domestic approach to the semiconductor sector. Given commercial and innovation priorities in AI, further support for the chip design community will be considered. 10. Evaluate the state of funding specifically for innovative firms developing AI technologies in the UK, and report on this work in Autumn 2022. 11. Protect national security through the National Security & Investment Act while keeping the UK open for business with the rest of the world, as our economy’s success and our citizens’ safety rely on the government’s ability to take swift and decisive action against potentially hostile foreign investment. 12. Include provisions on emerging digital technologies, including AI, in future trade deals alongside championing international data flows, preventing unjustified barriers to data crossing borders and maintaining the UK’s high standards for personal data protection.
Pillar 2: Ensuring AI benefits all sectors and regions
Supporting the transition to an AI-enabled economy, capturing the benefits of AI innovation in the UK, and ensuring AI technologies benefit all sectors and regions
To ensure that all sectors and regions of the UK economy can benefit from the positive transformation that AI will bring, the government will back the domestic design and development of the next generation of AI systems, and support British business to adopt them, grow and become more productive. The UK has historically been excellent at developing new technologies but less so at commercialising them into products and services.
As well as smart action to support both suppliers, developers and adopters, government also has a role to play when it comes to the use of AI, both as a significant market pull in terms of public procurement, such as the NHS and the defence sector, with a dedicated Defence AI Strategy and AI Centre, but also in terms of using the technology to solve big public policy challenges, such as in health and achieving net zero. Finally, it requires being bold and experimental, and supporting the use of AI in the service of mission-led policymaking.
Government’s aim is to diffuse AI across the whole economy to drive the highest amount of economic and productivity growth due to AI.
This will be achieved by:
Supporting AI businesses on their commercial journey, understanding the unique challenges they face and helping them get to market and supporting innovation in high potential sectors and locations where the market currently doesn’t reach;
Understanding better the factors that influence the decisions to adopt AI into organisations – which includes an understanding of when not to;
Ensuring AI is harnessed to support outcomes across the government’s Innovation Strategy, including by purposefully leveraging our leading AI capabilities to tackle real-world problems facing the UK and world through our Innovation Missions, [footnote 33] while driving forward discovery;
while driving forward discovery; Leveraging the whole public sector’s capacity to create demand for AI and markets for new services.
Commercialisation
Developing a commercial AI product or service is more than just bringing an idea to market or accessing the right funding. Recent analysis from Innovate UK suggests that obtaining private funding is only one among many other obstacles to successful commercial outcomes in AI-related projects. As well as the well known barriers such as access to data, labour market supply and access to relevant skills discussed above, other challenges reported by businesses are the lack of engagement with end users, limiting adoption and commercialisation. Commercialisation outcomes are also often constrained by business models rather than technical issues and a lack of understanding of AI-related projects’ return on investment.
AI deployment – understanding new dynamics
To grow the market and spread AI to more areas of our economy, the government aims to support the demand side as well as the means for commercialising AI - understanding what, why, when and how companies choose to incorporate AI into their business planning is a prerequisite to any attempt to encourage wider adoption and diffusion across the UK.
EY research delivered on behalf of DCMS shows that AI remains an emerging technology for private sector and third sector organisations in the UK. 27% of UK organisations have implemented AI technologies in business processes; 38% of organisations are planning and piloting AI technology; and 33% of organisations have not adopted AI and are not planning to. Consistent with studies of AI adoption,[footnote 34] the size of an organisation was found to be a large contributing factor to the decision to adopt AI, with large organisations far more likely to have already done so. Recognising that for many sectors this is the cutting edge of industrial transformation, and the need for more evidence, the Office for AI will publish research later this year into the drivers of AI adoption and diffusion.
To stimulate the development and adoption of AI technologies in high-potential, low-AI maturity sectors the Office for AI and UKRI will launch a programme that will :
Support the identification and creation of opportunities for businesses, whether SMEs or larger firms, to use AI and for AI developers to build new products and services that address these needs;
Create a pathway for AI developers to start companies around new products and services or to extend and diversify their product offering if they are looking to grow and scale;
Facilitate close engagement between businesses and AI developers to ensure products and services developed address business needs, are responsibly developed and implemented, and designed and deployed so that businesses and developers alike are prepped and primed for AI implementation; and
Incentivise investors to learn about these new market opportunities, products, and services, so that, where equity finance is needed, the right financing is made available to AI developers.
Creating and protecting Intellectual Property
Intellectual Property (IP) plays a significant part in building a successful business by rewarding people for inventiveness and creativity and enabling innovation. IP supports business growth by incentivising investment, safe-guarding assets and enabling the sharing of know-how. The Intellectual Property Office (IPO) recognises that AI researchers and developers need the right support to commercialise their IP, and helps them to understand and identify their intellectual assets, providing them with the skills to protect, exploit and enforce their rights to improve their chances of survival and growth.
AI and Intellectual Property (IP): Call for Views and Government Response An effective Intellectual Property (IP) system is fundamental to the Government’s ambition for the UK to be a ‘science superpower’ and the best place in the world for scientists, researchers and entrepreneurs to innovate. To ensure that IP incentivises innovation, our aspiration is that the UK’s domestic IP framework gives the UK a competitive edge. In support of this ambition, the IPO published its AI and IP call for views to put the UK at the forefront of emerging technological opportunities, by considering how AI impacts on the existing UK intellectual property framework and what impacts it might have for AI in the near to medium term. In March this year, the government published its response to the call for views, which committed to the following next steps: To consult on the extent to which copyright and patents should protect AI generated inventions and creative works;
To consult on measures to make it easier to use copyright protected material in AI development;
An economic study to enhance understanding of the role the IP framework plays in incentivising investment in AI. The consultation, on copyright areas of computer generated works and text and data mining, and on patents for AI devised inventions, will be launched before the end of the year so that the UK can harness the opportunities of AI to further support innovation and creativity.
Using AI for the public benefit
AI can contribute to solving the greatest challenges we face. AI has contributed to tackling COVID-19, demonstrating how these technologies can be brought to bear alongside other approaches to create effective, efficient and context-specific solutions.
AI and COVID-19 When the pandemic began it created a unique environment where AI technologies were developed to identify the virus more quickly, to help with starting treatments earlier and to reduce the likelihood that people will need intensive care. Working with Faculty, NHS England and NHS Improvement developed the COVID-19 Early Warning System (EWS). A first-of-its-kind toolkit that forecasts vital metrics such as COVID-19 hospital admissions and required bed capacity up to three weeks in advance, based on a wide range of data from the NHS COVID-19 Data Store. This gave national, regional and local NHS teams the confidence to plan services for patients amid any potential upticks in COVID-related hospital activity. At the same time over the past year, the NHS AI Lab has collected more than 40,000 X-ray, CT and MRI chest images of over 13,000 patients from 21 NHS trusts through the National COVID-19 Chest Imaging Database (NCCID), one of the largest centralised collections of medical images in the UK. The NCCID is being used to study and understand the COVID-19 illness and to improve the care for patients hospitalised with severe infection. The database has enabled 13 projects to research new AI technologies to help speed up the identification, severity assessment and monitoring of COVID-19. UK AI companies have also shown how AI can help accelerate the search for potential drug candidates, streamline triage and contribute to global research efforts. BenevolentAI, a world-leading AI company focused on drug discovery and medicine development, used their biomedical knowledge graph to identify a potential coronavirus treatment from already approved drugs that could be repurposed to defeat the virus. This was later validated through experimental testing from AstraZeneca. UK AI company DeepMind have adapted their AI-enabled protein folding breakthrough to better understand the virus’ structure, contributing to a wider understanding of how the virus can function.
There are many areas of AI development that have matured to the point that industry and third sector organisations are investing significantly in AI tools, techniques and processes. These investments are helping to move AI from the lab and into commercial products and services. But there remain more complex, cross-sector challenges that industry is unlikely to solve on its own. These challenges will require public sector leadership, identifying strategic priorities that can maximise the potential of AI for the betterment of the UK.
The government has a clear role to play. In stimulating and applying AI innovation to priority applications and wider strategic goals, the government can help incentivise a group of different actors to harness innovation for improving lives, simultaneously reinforcing the innovation cycle that can drive wider economic benefits – from creating and invigorating markets, to the role of open source in the public, private and third sectors, to raising productivity. Over the next six to twelve months, the Office for AI will work closely with the Office for Science and Technology Strategy and government departments to understand the government’s strategic goals and where AI can provide a catalytic contribution,[footnote 35] including through Innovation Missions and the Integrated Review’s‘Own-Collaborate-Access’ framework.
The COVID-19 pandemic has shown that global challenges need global solutions. The UK’s international science and technology partnerships, global network of science and innovation officers, and research and innovation hubs, are working alongside UK universities, research institutes and investors to foster new collaborations to tackle the global challenges we all share, including in innovations on global health and to achieve net zero emissions around the globe.
Missions
The Innovation Strategy set out the government’s plans to stimulate innovation to tackle major challenges facing the UK and the world, and drive capability in key technologies. This will be achieved through Innovation Missions,[footnote 36] which will draw on multiple technologies and research disciplines towards clear and measurable outcomes. They will be supported by Innovation Technologies,[footnote 37] including AI, supporting their capability to tackle pressing global and national challenges while supporting their adoption in novel areas, boosting growth and helping to consolidate our position as a science and AI superpower.
Some of these challenges have been articulated and revolve around the future health, wellbeing, prosperity and security of people, the economy, and our environment – in the UK and globally. These challenges are worthwhile and therefore difficult, and will require harnessing the combined intellect and diversity of the AI ecosystem and the whole nation, and will consider a full range of possible impacts of a given solution. The pace of AI development is often fast, parallel and non-linear, and finding the right answer to these challenges will require a collection of actors beyond just government departments, agencies and bodies to consider the technical and social implications of certain solutions and increase the creativity of problem solving. In doing so, the UK will be able to find new paths for AI to deliver on our security and prosperity objectives at home and abroad.
At the same time, well-specified challenges have also led to some of the most impactful moments of progress in AI. Whether through Imagenet, CIFAR-10, MNIST, GLUE, SquAD, Kaggle, or more, challenge-related datasets and benchmarks have generated breakthroughs in vision, language, recommender systems, and other subfields.[footnote 38]. The government believes that challenges could be created that simultaneously incentivise significant progress in Innovation Missions while rapidly progressing the development in the technology along desirable lines.
To this end, the government will develop a repository of short, medium and long term AI challenges to motivate industry and society to identify and implement real-world solutions to the strategic priorities. These priorities will be identified through the Missions Programme, and guided by the National AI R&I Programme.
Climate change and global health threats are examples of shared international challenges, and science progresses through open international collaboration. This is particularly the case when AI development is able to take advantage of publicly available coding platforms to produce new algorithms. The UK will extend its science partnerships and its work investing UK aid to support local innovation ecosystems in developing countries. Through our leadership in international development and diplomacy, we will work to ensure international collaboration can unlock the enormous potential of AI to accelerate progress on global challenges, from climate change to poverty.
Net zero
The Prime Minister’s Ten Point Plan for a Green Industrial Revolution highlights the development of disruptive technologies such as AI for energy as a key priority, and in concert with the government’s Ten Tech Priorities to use digital innovations to reach net zero, the UK has the opportunity to lead the world in climate technologies, supporting us to deliver our ambitious net zero targets. This will be key to meet our stated ambition in the Sixth Carbon Budget, and with it a need to consider how to achieve the maximum possible level of emissions reductions.
AI and net zero AI works best when presented with specific problem areas with clear system boundaries and where there are large datasets being produced. In these scenarios, AI has the capability to identify complex patterns, unlock new insights, and advise on how best to optimise system inputs in order to best achieve defined objectives. There are a range of climate change mitigation and adaptation challenges that fit this description. These include: using machine vision to monitor the environment;
using machine learning to forecast electricity generation and demand and control its distribution around the network;
using data analysis to find inefficiencies in emission-heavy industries; and
using AI to model complex systems, like Earth’s own climate, so we can better prepare for future changes. AI applications for energy and climate challenges are already being developed, but they are predominantly outliers and there are many applications across sectors that are not yet attempted. A study by Microsoft and PwC estimated that AI can help deliver a global reduction in emissions of up to 4% by 2030 compared to business as usual, with a concurrent uplift of 4.4% to global GDP. Such estimates are likely to become more accurate over time as the potential of AI becomes more apparent.
Over the last ten years there have been a series of advances in AI. These advances offer opportunities to rapidly increase the efficiency of energy systems and help reduce emissions across a wide array of climate change challenges. The AI Council’s AI Roadmap advocates for AI technologies to play a role in innovating towards solutions to climate change, and literature is emerging that shows how ‘exponential technologies’ such as AI can increase the pace of decarbonisation across the most impactful sectors. AI is increasingly seen as a critical technology to scale and enable these significant emissions cuts by 2030.[footnote 39],[footnote 40],[footnote 41]
In the UK we have previously used mission-driven innovation policy to promote a range of technologies towards the delivery of social, economic and environmental goals. Government will continue this through the National AI R&I Programme, which will make critical mass investments in particular applications of AI technology that will generate new solutions to tackle our net zero objective.
Missions will also be continued through the Innovation Strategy’s Missions Programme, which will form the heart of the government’s approach to respond to these priorities, and we will develop these missions in a way that considers the promise of AI technologies, particularly in areas of specific advantage such as energy. Government will ensure that, in key areas of international collaboration such as the US UK Declaration on Cooperation in AI Research and Development and the Global Partnership on AI, we will pursue technological developments in world-leading areas of expertise in the energy sector to maximise our strategic advantage.
Health
In August 2019, the Health Secretary announced a £250 million investment[footnote 42] to create the NHS AI Lab in HSX to accelerate the safe, ethical and effective development and use of AI-driven technologies to help tackle some of the toughest challenges in health and social care, including earlier cancer detection, addressing priorities in the NHS Long Term Plan, and relieving pressure on the workforce.
AI-driven technologies have the potential to improve health outcomes for patients and service users, and to free up staff time for care.[footnote 43] The NHS AI Lab along with partners, such as the Accelerated Access Collaborative, the National Institute of Health and Care Excellenceand the Medicines and Healthcare products Regulatory Agency, are working to provide a facilitative environment to enable the health and social care system to confidently adopt safe, effective and ethical AI-driven technologies at pace and scale.
The NHS AI Lab is creating a National Strategy for AI in Health and Social Care in line with the National AI Strategy. The strategy, which will begin engagement on a draft this year and is expected to launch in early 2022, will consolidate the system transformation achieved by the Lab to date and will set the direction for AI in health and social care up to 2030.
The public sector as a buyer
To build a world-leading strategic advantage in AI and build an ecosystem that harnesses innovation for the public good, the UK will need to take a number of approaches. As the government, we can also work with industry leaders to develop a shared understanding and vision for the emerging AI ecosystem, creating longer-term certainty that enables new supply chains and markets to form.
This requires leveraging public procurement and pre-commercial procurement to be more in line with the development of deep and transformative technologies such as AI. The recent AI Council ecosystem survey revealed that 72% agreed the government should take steps to increase buyer confidence and AI capability. The Innovation Strategy and forthcoming National Procurement Policy Statement have recently articulated how we can further refine public procurement processes around public sector culture, expertise and incentive structures. This complements previous work across government to inform and empower buyers in the public sector, helping them to evaluate suppliers, then confidently and responsibly procure AI technologies for the benefit of citizens.[footnote 44]
The government has outlined how it plans to rapidly modernise our Armed Forces[footnote 45][footnote 46] and how investments will be guided.[footnote 47][footnote 48] The Ministry of Defence will soon be publishing its AI strategy which will contribute to how we will achieve and sustain technological advantage, and be a great science power in defence. This will include the establishment of the new Defence AI Centre which will champion AI development and use, and enable rapid development of AI projects. Defence should be a natural partner for the UK AI sector and the defence strategy will outline how to galvanise a stronger relationship between industry and defence.
Ministry of Defence using AI to reduce costs and meet climate goals The MOD is trialling a US startups’ Software Defined Electricity (SDE) system, which uses AI to optimise electricity in real time, to help meet its climate goals and reduce costs. Initial tests suggest it could reduce energy draw by at least 25% which, given the annual electricity bill for MOD’s non-PFI sites in FY 2018/19 was £203.6M, would equate to savings of £50.9M every year and significant reductions in CO2 emissions.
Crown Commercial Service The Crown Commercial Service worked closely with colleagues in the Office for AI and across government during drafting of guidelines for AI procurement. This was used to design their AI Dynamic Purchasing System (DPS) agreement to align with these guidelines, and included a baselines ethics assessment so that suppliers commit only to bidding where they are capable and willing to deliver both the ethical and technical dimensions of a tender. The Crown Commercial Service is piloting a training workshop to help improve the public sector’s capability to buy AI products and services, and will continue to work closely with the Office for AI and others across government to ensure we are addressing the key drivers set out in the National AI Strategy.
Pillar 2 - Ensuring AI Benefits all Sectors and Regions Actions: Launch a programme as part of UKRI’s National AI R&I Programme, designed to stimulate the development and adoption of AI technologies in high-potential, lower-AI maturity sectors. The programme will be primed to exploit commercialisation interventions, enabling early innovators to access potential market opportunities where their products and services are relevant. Launch a draft National Strategy for AI in Health and Social Care in line with the National AI Strategy. This will set the direction for AI in health and social care up to 2030, and is expected to launch in early 2022. Ensure that AI policy supports the government’s ambition to secure strategic advantage through science and technology. Consider how the development of Innovation Missions also incorporates the potential of AI solutions to tackling big, real-world problems such as net zero. This will also be complemented by pursuing ambitious bilateral and multilateral agreements that advance our strategic advantages in net zero sectors such as energy, and through the extension of UK aid to to support local innovation ecosystems in deve
| 2022-12-18T00:00:00 |
https://www.gov.uk/government/publications/national-ai-strategy/national-ai-strategy-html-version
|
[
{
"date": "2022/12/18",
"position": 62,
"query": "workplace AI adoption"
},
{
"date": "2022/12/18",
"position": 27,
"query": "AI economic disruption"
},
{
"date": "2022/12/18",
"position": 87,
"query": "artificial intelligence business leaders"
}
] |
|
Hofstra's Continuing Education Bootcamps by Workforce ...
|
Hofstra’s Continuing Education Bootcamps by Workforce Institute Reviews
|
https://www.coursereport.com
|
[] |
... Workforce Institute bootcamp graduates ... Best AI & Machine Learning Bootcamps · Best European Bootcamps. Company; Schools ...
|
Hofstra’s Continuing Education offers part-time, online bootcamps in Digital Marketing (18 weeks) and UI/UX Design (24 weeks). These bootcamps were created with a busy professional’s part-time or full-time job schedule in mind. The bootcamps are self-paced with live coaching sessions. Students will be mentored by industry experts throughout the bootcamp.
In the UI/UX design bootcamp, students will learn prototyping, wireframing, and testing. Students will also learn how to take a project from idea to delivery.
In the Digital Marketing Bootcamp, students will learn Digital Marketing fundamentals, how to create a successful content marketing strategy, deploy paid marketing strategies that improve search engine traffic, and learn how to analyze the results and make smart decisions.
Bootcamp students who pay the full cost of tuition upfront will receive a discount. Alternatively, students may pay tuition in interest-free installments each month either during or after the bootcamp (an enrollment fee is required).
Hofstra’s Continuing Education Bootcamps for Digital Marketing and UI/UX Design are powered by Workforce Institute.
| 2022-12-18T00:00:00 |
https://www.coursereport.com/schools/hofstras-continuing-education-bootcamps-by-workforce-institute
|
[
{
"date": "2022/12/18",
"position": 62,
"query": "machine learning workforce"
}
] |
|
Use of AI (Artificial Intelligence) in the Modern UI & UX ...
|
Use of AI (Artificial Intelligence) in the Modern UI & UX Design
|
https://www.graycelltech.com
|
[] |
For instance, Adobe Scene Stitch can identify a pattern in an image and assist designers in editing or patching patterns. To create generative visual styles, AI ...
|
“The revenue from the global AI software market is estimated to grow from 10 billion U.S. dollars in 2018 to 126 billion U.S. dollars by 2025.”
From Google’s Gmail spam filter, Netflix’s movie recommendations to Uber’s autonomous vehicles, artificial intelligence, or AI is already dominating our lives on a day-to-day basis. With its unlimited potential, modern-day industries are leveraging this technology to streamline their operations and achieve a higher response value from the target audience. Talking about the software industry, AI applications are used by web designers and web developers to streamline website management tasks on a daily basis and to provide a rich and personalized online experience.
If you are an entrepreneur, you can use AI applications for various purposes: from enhancing the look of your website, strengthening its search abilities, organizing and managing your inventory, targeting consumers through the right digital marketing strategy, offering a personalized user experience to improving interactions with visitors.
AI has quickly become the mainstream technology for the online landscape. It enabled web designers to utilize AI applications into their websites in order to create better functionality and user experience.
AI in UX/UI Design
In the design world, AI is impacting real-life scenarios; however, it does not imply that robots are replacing designers. Techies believe that AI will become an indispensable tool in the user experience designer kit.
It is essential to have a good understanding of artificial intelligence to understand its role in the world of UX/UI.
At present, AI systems are creating a deeper connection between brands and consumers.
Present & Future Role of AI
AI development has resulted in the development of tools that are used by designers for creating attractive designs in less time. Since AI analyzes data, it helps to easily refine what exactly the user is searching for and refine the product design based on other successful products. It also suggests new design alternatives and how to improve user engagement in the future.
Personalization is an essential aspect of creating the user experience. With AI, designers are able to build personalized e-commerce websites using buyer’s profile information and other billion data points.
Designers can design high-performing products by analyzing large volumes of data using artificial intelligence, taking into account the latest standards and conventions, UX design practices, and other usability metrics.
practices, and other usability metrics. AI can also help in creating unique variations of landing pages and homepages for news sites, media brands, and much more.
AI automates the legwork of designers and makes them more efficient in their work. It utilizes AI tools to finish legwork tasks. For instance, Adobe Scene Stitch can identify a pattern in an image and assist designers in editing or patching patterns.
the legwork of designers and makes them more efficient in their work. It utilizes AI tools to finish legwork tasks. For instance, Adobe Scene Stitch can identify a pattern in an image and assist designers in editing or patching patterns. To create generative visual styles, AI tools are being used. For instance, Prisma. It works on an image recognition technology. This tool identifies the photograph and the best visual effect that can be applied to it
Web Design Diagnostic Tool Using AI
AI is implemented for improving web design functionality and user experience and works as a precision diagnostic tool. Along with modern web design, rapidly changing trends, and stricter search engine standards like Google, the quality of a web design plays a crucial factor in the success of your digital footprint. You may be able to design a high-quality website, you still need to maintain the quality by periodically running tests. Due to the constant repetition of these tests, the source code gets modified and as a consequence, adds further load into the process. Moreover, these tests are time-consuming and take a heavy toll on your website performance.
You can assess the quality of your designs, observe their performance in real-time, and gain real insights on how to refine them using AI-powered analytics tools. AI-based diagnostic tools eliminate the requirement for A/B testing and provide better website design results. As the design software has become increasingly complex, the final product often comes with its challenges.
AI tools and applications can develop and test various information, validate their authenticity, and examine their scope without any human input. The right training of AI can eliminate tons of procedures in building, diagnosing, or editing a design. AI-based testers can also be used to test the visual code of the website, track the web page behavior, and enhance the aesthetics of the page.
Final Thought
Since AI and its integration in the design are in its initial stage, we can expect changes in the coming years. Therefore, designers must know how to make a tradeoff between AI functionality and its design. Although it is difficult to make a balance between the two, designers must ensure to use this technology efficiently. Instead of overwhelming users with a large volume of data, they must focus on creating meaningful patterns.
The next challenge is to translate the developer’s language into the user’s language. It is essential to interpret the developer’s language and communicate it in an easy to understand manner.
You must not directly jump into AI, understand your expectations first, and how AI can fulfill these expectations. Since each day more intelligent systems are being launched, users will expect more capable and efficient products in the coming years.
| 2024-01-22T00:00:00 |
2024/01/22
|
https://www.graycelltech.com/use-of-ai-artificial-intelligence-in-the-modern-ui-ux-design/
|
[
{
"date": "2022/12/18",
"position": 70,
"query": "AI graphic design"
}
] |
AI for Business
|
AI for Business
|
https://biglinden.com
|
[] |
AI can help business leaders to streamline their processes and operations, improve customer service, increase efficiency, reduce costs and enhance decision- ...
|
AI can help business leaders to streamline their processes and operations, improve customer service, increase efficiency, reduce costs and enhance decision-making. AI allows businesses to quickly analyze huge amounts of data from multiple sources and use predictive analytics for more accurate forecasting. It enables automation of mundane tasks freeing up the workforce for more value-adding activities such as product innovation or marketing campaigns.AI is used in personalized customer experiences by analyzing user behavior on websites or apps with machine learning algorithms. By leveraging AI technology, business leaders can gain strategic insights that are critical to staying competitive in today’s market place while making informed decisions faster than ever before.Artificial Intelligence for business is here.
| 2022-12-18T00:00:00 |
https://biglinden.com/digital-marketing/ai-for-business/
|
[
{
"date": "2022/12/18",
"position": 22,
"query": "artificial intelligence business leaders"
}
] |
|
Automation Nation: Who Will Reap the Benefits As AI ...
|
Automation Nation: Who Will Reap the Benefits As AI Changes the Economy?
|
https://indypendent.org
|
[] |
The group says that U.S. workers between the ages of 18 to 34 are the most affected by job displacement from automation, that by the late 2020s, nearly 25% of ...
|
Job automation will sweep through the economy at an unprecedented pace in the coming decade.
Will this surge in automation cast tens of millions of fired workers into a newly impoverished underclass? Can enough new jobs be created so we at least muddle through?
Will strong insurgent unions ensure that the economic benefits of increased productivity are broadly shared? Or, to push the envelope even farther, what if the means of production were put under public control and the economy were organized around meeting human needs?
The extent to which we as workers organize ourselves to defend our rights will go a long way in shaping that future.
A 2019 Brookings Institute report projected that 52 million U.S. jobs would be affected by algorithms by 2030. In 2021, global consulting firm McKinsey & Company predicted that 45 million jobs will be lost to algorithms and androids by 2030.
With the development of Artificial Intelligence — computer systems that are able to perform tasks that normally require human intelligence — technology-induced mass unemployment is more likely than in the past.
A 2022 analysis by Finance Online reports that 43% of employers are set on cutting down their workforces to make way for advanced technology. The group says that U.S. workers between the ages of 18 to 34 are the most affected by job displacement from automation, that by the late 2020s, nearly 25% of women workers are at risk of being displaced compared to more than 15% of men and that highly-educated people have more opportunities to work in sectors where they are less likely to be displaced due to technology integration.
We’ve already seen automation firsthand. Think self-checkout at the grocery store or McDonalds, or being forced to interact with automated customer-service phone lines and chatbots. Workers at Amazon, Tesla and other Big Tech companies have experienced “cobots” — large robots that work “with” them to get the job done. This often leads to additional work stress, from the knowledge that more technology at the workplace means worker surveillance — the “digital whip” — to the fact that robots are often not programmed to properly understand the pace of the human they’re working with and break frequently, leading to unsafe working conditions in the name of “productivity.”
Consider this testimony from Thometra Robinson, a worker at an Amazon warehouse in Stone Mountain, Georgia:
You want us to perform our job with ineffective, inadequate equipment. People are hurt. I got pictures of piss in bottles and cups. … You gotta tape up your monitor that literally just hangs there, is barely lit up. The scanner, God, Lord, you’re playing with that all day. The belt is always breaking. Mind you, you have to pack a box in 37 seconds, which is just unrealistic. All the training they tell you to do, they don’t tell the computer that. So the computer doesn’t know you gotta do all these little steps. If you’re missing an item, you need to go look for it. You gotta get all the way down on your knees. And there are techniques and protocols that they add. … You add on this extra shit but you still gotta do it in 37 seconds. … You can’t be safe and productive. It ain’t gonna happen. … You have to stock your station; there’s not enough water spiders. But the computer doesn’t know you have to get your own stuff. They just know that you have to pack your box in 37 seconds.
Automation is a recurring feature of capitalism as bosses seek to exponentially increase productivity while reducing the percentage of their revenues they pay out as wages. At the same time, new industries and new jobs are created and unemployment rates remain relatively stable over time, though the process of change can be wrenching for some communities.
With the development of Artificial Intelligence (AI) — computer systems that are able to perform tasks that normally require human intelligence — technology-induced mass unemployment is more likely than in the past. AI technology is being developed to drive cars, clean schools, perform surgery, write articles, compose songs, create digital art and beyond. Some (very rich) people are using AI to digitally preserve their consciousness so that replicas of themselves can be created in the future, perhaps when humans have migrated to other planets.
Studies suggest that workers who have specialized training or post-high-school education and those that are younger are most likely to find new work once displaced by AI; others fall into poverty. Job automation has already added to labor-market inequality since at least the 1980s.
This brings to mind dark, Ready Player One-esque images of unemployed masses living in giant, walled-off ghettos. Or, a utopian alternative: A world in which people spend less time working grueling, menial jobs and more time on fulfilling work and leisure, and in which all of our basic needs are met. (In both worlds, AI outsmarts its programmers, gaining some level of sentience and could, in theory, organize against the boss.)
Harry Holzer, an economics professor at Georgetown University who studies the subject, projects that a greater dependence on AI will occur but that the shift will be gradual. As companies transition to using more robot technology, new jobs will be created to respond to a rising demand from consumers who can now afford cheaper products (prices will drop due to employers’ lower production costs).
“The people hurt are not just the people directly displaced by the technology. It’s also the people who in some sense have to compete with the technology or with globalization,” Holzer told The Indypendent. “There are ways in which people can adjust. When technology is implemented, it does some of the tasks that workers have done, not necessarily all the tasks. Now, workers who only did one task on the job — you know, on an assembly line, they tighten some bolts as the car is passing — robots are gonna replace them. But if they have multiple tasks, and the automation does some but not all, there’s a judgment call on the employer’s part: Should he hold on to that worker and train them to do a new task?”
It is unlikely that new jobs will be created at the same rate that old ones are lost, or that enough new jobs will ever be created. And no matter what happens, massive layoffs will occur.
“Going forward, what makes all this more uncertain is that AI seems to have a much wider range of capabilities, even if it doesn’t exist today,” says Holzer.
| 2022-12-19T00:00:00 |
https://indypendent.org/2022/12/automation-nation-who-will-reap-the-benefits-as-ai-changes-the-economy/
|
[
{
"date": "2022/12/19",
"position": 2,
"query": "automation job displacement"
},
{
"date": "2022/12/19",
"position": 3,
"query": "AI unemployment rate"
},
{
"date": "2022/12/19",
"position": 5,
"query": "artificial intelligence workers"
},
{
"date": "2022/12/19",
"position": 6,
"query": "artificial intelligence wages"
}
] |
|
Barrier Advisor™ cementing software model
|
Barrier Advisor™ cementing software model
|
https://www.halliburton.com
|
[] |
Integrated approach for wellbore cleanup and displacement. explore ... The entire job design process is expedited, digitalized, and automated. This ...
|
Tailored design decisions improve job score
Employing historical data-driven machine learning models to aid in operational risk mitigation has a critical impact on optimum job execution. Integrated into VIDA, the Barrier Advisor model has access to constantly growing data from thousands of historical jobs across the globe. Every design decision has a corresponding Cement Dependability Index (CDI) response, which is used to calculate a score for any given set of design parameters for a specific job. The model identifies similar jobs, which in machine learning is referred to as "nearest neighbors", using a clustering approach based on similar well conditions. Additionally, the model computes and displays each job’s score. Evaluating these scores allows an engineer to visualize the critical CDI responses from nearest neighbors and modify the current job design decisions to improve the job score and, ultimately, its execution.
Machine learning enabled model minimizes risks
Barrier Advisor is a robust model due to the volume of cementing jobs Halliburton executes globally and the VIDA digital platform that holds all of the historical job data. With new jobs constantly added, the historical pool of data increases daily. Thus, the model’s learning ability increases while the gap on operational unknowns decreases.
Improves the probability of operational success
Recommendations from the Barrier Advisor model, which use a combination of science-based algorithms and global historical jobs’ data-driven models, are more robust in mitigating risks from operational unknowns compared to job specific models. Barrier Advisor enables the evaluation and identification of job design decisions that positively impact the end job execution and performance, to thereby improve the probability of operational success.
| 2022-12-19T00:00:00 |
https://www.halliburton.com/en/products/barrier-advisor-model
|
[
{
"date": "2022/12/19",
"position": 81,
"query": "automation job displacement"
}
] |
|
AI And Human Ingenuity: How to Effectively Strike a Balance
|
AI And Human Ingenuity: How to Effectively Strike a Balance – Product Reviews
|
https://successafrika.com
|
[] |
I believe humans will play a vital role in AI ... AI and humans must work together harmoniously, not simply focus on replacing roles and responsibilities.
|
AI And Human Ingenuity: How to Effectively Strike a Balance
The history of artificial intelligence (AI) began with myths, stories, and rumors of artificial beings endowed with intelligence or consciousness by master artisans.
The seeds of modern AI were planted by philosophers who attempted to describe the process of human thinking as the mechanical manipulation of symbols.
The idea of inanimate objects coming to life as intelligent beings have been around for a long time. The ancient Greeks had myths about robots, and Egyptian engineers-built automatons.
The beginnings of modern AI can be traced to classical philosophers’ attempts to describe human thinking as a symbolic system. But the field of AI wasn’t formally founded until 1956, at a conference at Dartmouth College, in Hanover, New Hampshire, where the term “artificial intelligence” was coined.
Artificial Intelligence (AI) is seemingly everywhere. From redefining education to upending healthcare, AI has become that hard to ignore technology that tech companies cannot stop talking about.
While AI is being discussed at length, it is not a modern phenomenon either. AI has been maturing for centuries to become the transformational technology it is now.
Artificial intelligence was first coined by John McCarthy in 1956 when he held the first academic conference on the subject at Dartmouth College. Before the term was coined, researchers and computer scientists had laid the groundwork for AI to become a dominant field in computer science.
The past decade saw artificial intelligence (AI) advance by leaps and bounds. From the birth of Alexa to its application in vaccine development, AI has radically altered our personal and professional lives. Now, it’s everywhere — including in travel apps, streaming services, parking garages, and delivery robots.
I believe humans will play a vital role in AI deployments and training, but companies can’t always afford, scale or even find human workers. Today, efficiency and convenience are top priorities for customers.
In customer service, for instance, I’ve found that people want fast and easy answers — and often don’t care whether they speak with a person to get them.
In a recent survey my company conducted, two-thirds of consumers reported that they are comfortable speaking with an AI-powered customer service solution if they can speak normally and resolve their problems quickly.
There’s no denying that AI offers tremendous business benefits, but the real power comes from its synergy with people. By understanding the respective strengths of AI and people, businesses can unlock unprecedented efficiency and scale while maintaining the human touch that customers crave.
Carving out these respective roles early in the process and recognizing that neither is the be-all and end-all solution could allow businesses to strike the perfect balance as AI applications find a home in every aspect of our lives.
There will always be a place for human and machine intelligence in successful AI deployments. What comes naturally to people, such as empathy and judgment, is difficult for machines, while manually analyzing mountains of data at scale is practically impossible.
When humans and digital workforces work together, organizations achieve greater efficiencies, increased cash flow, and more satisfied and productive employees. We’ll continue to see this evolve. You can record your meetings, enabling AI to use the information discussed to make smart suggestions for your next meeting. It may suggest when, where, and whom to schedule.
AI could also help you develop an agenda and book the meeting. You, however, are still responsible for ensuring the meeting is productive and is a good use of time.
AI and humans must work together harmoniously, not simply focus on replacing roles and responsibilities.
| 2022-12-19T00:00:00 |
https://successafrika.com/articles/how-to-balance-between-ai-dependence-and-human-ingenuity/
|
[
{
"date": "2022/12/19",
"position": 77,
"query": "AI replacing workers"
}
] |
|
Artificial Intelligence - UMD CMNS - University of Maryland
|
Artificial Intelligence
|
https://cmns.umd.edu
|
[] |
And there's no end in sight to the potential applications of machine learning—in fraud protection, health care, the stock market and more. Researchers in CMNS ...
|
ARTIFICIAL INTELLIGENCE IS EVERYWHERE
It has worked its way into our daily lives, from voice assistants like Siri and Alexa to traffic apps that guide us around gridlock, cars that drive themselves and news stories that pop up on our social media feeds. And there’s no end in sight to the potential applications of machine learning—in fraud protection, health care, the stock market and more.
Researchers in CMNS work at the forefront of machine learning technology, where computers analyze data to identify patterns and make decisions with minimal human intervention. These faculty members are using machine learning for applications that touch many aspects of our lives—from weather prediction and health care to transportation, finance and wildlife conservation. Along the way, they are advancing the science of exactly how computers learn. And they’re asking important questions about the impact of machine learning on our everyday lives and society itself.
| 2022-12-19T00:00:00 |
https://cmns.umd.edu/research/solving-grand-challenges/artificial-intelligence
|
[
{
"date": "2022/12/19",
"position": 44,
"query": "machine learning job market"
}
] |
|
Computer Science Jobs and Careers
|
Degree in Computer Science Jobs and Careers in Computer Science
|
https://hc.edu
|
[] |
Many of your computer science courses at HCU will introduce you to the computer science specializations in the job market. ... Machine learning trainer/scientist ...
|
Your HCU degree in computer science opens opportunities for many career paths.
Computer Science is an exciting field that impacts every part of our lives. Computer Science is complex, wide-ranging, vital, and all about what’s next. Earning your Bachelor of Science degree in Computer Science at Houston Christian University will prepare you to be in the forefront of this dynamic field and ready for current and emerging computer science jobs.
“IT provides some of the best careers for moving up the ladder and expanding professionally.” -CompTIA
Many of your computer science courses at HCU will introduce you to the computer science specializations in the job market. See below for various types of computer science jobs and titles, according to CompTIA, a leading technology industry association.
CompTIA also highlights the following computer science jobs as emerging roles to explore in the coming years:
Machine learning trainer/scientist
AI developer
Industrial Internet of Things engineer
Geospatial and mapping specialist
Blockchain developer/engineer
Digital designer
Cybersecurity architect
Penetration tester
User experience (UX) designer
Solutions architect
Full stack developer
Technology project manager
Robotics engineer
Drone operator/technician
Computer Science students, as part of HCU’s College of Science and Engineering, have excellent resources to learn about jobs in computer science and engineering through the support of the College’s Science and Engineering Advisory Board, which includes many area Chief Information Officers and Chief Information Security Officers. These Board members represent large energy companies such as Chevron, Occidental Petroleum, Shell, Schlumberger, National Oilwell Varco; healthcare systems such as Memorial Hermann Health System, University of Texas Health System, Houston Methodist Health System; and maritime security organizations such as the American Bureau of Shipping and several regional ports.
Jobs for Graduates with Computer Science Degrees
Tech Support
Armed with computer science degrees or study in information technology, tech support professionals help companies and their employees deal with computer issues, troubleshoot and get more out of the technology they use.
Help-desk technician
Desktop/network support technician
IT service desk technician
Technical support engineer
Servers, Architecture and Networking
Computer science specializations that involve working with servers, network architecture and networking involve such duties as supporting network connectivity and equipment; creating protocols for use of network tools; troubleshooting network tools; and working to configure network systems to ensure security, stability and performance.
Server administrator
IT administrator
Systems administrator
Network infrastructure administrator
Cybersecurity and Analytics
HCU computer science graduates who go into cybersecurity or security analytics will have these kinds of job responsibilities: performing security reviews on networks; integrating new safety features into existing technology; designing cybersecurity protocols, and using forensic tools to identify security vulnerabilities and threats.
Cloud Computing
Computer science and information technology careers in cloud computing involve helping organizations make cloud technology more scalable, reliable and secure. They also identify and solve issues with cloud technology.
Cloud operations engineer
Cloud infrastructure specialist
Cloud support representative
Development and Coding
Developers and coders use programming languages to create digital products like apps, websites and software. Their jobs differ slightly based on where they work, what products they’re creating or improving, and which programming languages are used.
Front-end developer
Full-stack web developer
Back-end developer
Software developer
Database
Database information technology careers involve creating and storing procedures in databases, working in database management systems, troubleshooting database issues, testing database systems and designing and organizing how information is stored in databases.
Database administrator
Database engineer
Database programmer
Database software specialist
Web Design
While front-end developers use code to control how websites function, web designers use code like HTML and CSS to dictate the visual features of websites and apps. They also may utilize graphic design tools like Adobe Photoshop.
UI web designer
Web/graphic designer
Web development project manager
Project Management
Computer science careers that involve project management entail overseeing IT teams and tech projects to achieve business goals. Project managers set the timeline for projects, establish goals for team members, and control the budget and scope of projects.
| 2019-02-28T00:00:00 |
2019/02/28
|
https://hc.edu/articles/computer-science-jobs-and-careers/
|
[
{
"date": "2022/12/19",
"position": 61,
"query": "machine learning job market"
}
] |
A Guide of how to get started in IT in 2023 - Top IT Career ...
|
A Guide of how to get started in IT in 2023 - Top IT Career Paths
|
https://www.techworld-with-nana.com
|
[] |
The tech industry is currently one of the hottest industries for the job market ... First one is writing the machine learning algorithms so that machines can use ...
|
In this blog article I want to give you kind of a roadmap of how to get into the IT world.
This is the written version of my new youtube video ✍️
The tech industry is currently one of the hottest industries for the job market, so it's completely logical that so many people are thinking about changing careers to tech or getting into tech just after graduating from school or college.
Entry into tech can be overwhelming 🤯
And it's true that tech industry offers lots of career opportunities, but when you are at the very beginning of your journey it can be very overwhelming too. 🤯 It is a broad industry with so many options, so many IT fields and professions. There is so much to learn and often you don't even know where to start. It's also hard to know which IT field you will be interested in and will eventually choose, before actually trying things out and seeing for yourself what you enjoy doing the most. You may even have self-doubt thinking that it's too late to switch careers or there's so much to learn you can never catch up with the people, who have been in tech since early age.
And I understand all these concerns, the self-doubt, the time pressure, insecure not knowing where and how to start etc. And that's why with this blog article, I want to kind of show you
various career path in tech and give you some general guidance for how to get started. 😇
And for every IT field, we will see:
whether it's an entry-level profession
what they actually do and what are some of their job responsibilities
as well as what skills you need to have to get into that specific field
and some of the technologies you need to learn for it
My Background 👩🏻💻
First of all, I want to start by saying that it's never too late to get into IT. 👏
I myself transitioned from my marketing and business studies and had nothing to do with IT before, not even an IT subject at school or college 👀 and switching my career to tech was probably one of the best decisions of my life! 🤠 This field is becoming more popular every year and it comes with so many benefits and opportunities for people working in this field.
Plus it's an interesting, exciting and fulfilling field to be in no matter which specialization you choose.
So let's see what are actually some of the most popular IT professions today, that will be even more demanded in the future, so what options you have for IT jobs that you may want to specialize in.
Most popular IT career paths 💎
Of course there are many different statistics and rankings out there and many names for the similar jobs, but there are several professions that definitely stand out, which have become the most popular. The popular and demanded jobs in IT are usually paid very high 💸, so many of those rankings are also based on salary and career growth statistics. Based on several rankings, the most demanded and popular IT fields that have been growing in popularity even more over the years are: 👏
Software Engineering
DevOps Engineering
Cloud Engineering
Cybersecurity or Security Engineering
Data engineering or generally data related professions
and Machine Learning Engineering
And these top fields will actually be even more demanded in the future. So there is a lot of growth and future potential in each one of those fields. 👍
So most of you will probably want to get into one of those fields, but many of you may not know right now what you want to choose exactly, maybe because they all sound equally exciting for you or equally overwhelming so you have no idea which one will be a better choice or which one will be the most interesting one for you, which is absolutely okay, because that's exactly what I want to help you with in this overview. 😊 ✅ So let's get into it!
General Learning Approach 🧠
First of all, it's absolutely fine to try out multiple things to see what you like the most. In fact, it's a really good idea, because you have so many opportunities, so many options, so you want to take advantage of that and find the one that fits you the best.
However, you need to approach this with some structure, just randomly learning things here and there in the hope to make sense of many things at once or even worse trying to learn multiple of those fields at once is not a good strategy. It will make your learning journey difficult and it will surely make it longer as well and you won't properly find out what field you like the most.
So let me help you structure your learning process by laying out the map that shows you all these professions individually and the learning path to those as well as any overlaps and common knowledge between them. ✅
1 - Become a Software Engineer 👨🏼💻
Let's start with the broadest and the most widely least spread IT field, which is "software engineering". I believe it's also the easiest entry point into IT and where most people actually start their IT careers and that's how I started as well.
A software developer or software engineer is someone, who develops any kind of software applications .
What is software? 🤔 This can be a web application a mobile or a desktop application. Whenever you think of any software, whether it's on your computer the mobile phone in your smart TV. So for example, Amazon, Netflix and all these applications you use on your smart TV. These are all software. Or think about smart cars, again these are applications for navigation and some other controls of the car. You have smart homes, software in production machines and robots.
So these are all software developed by software engineers.
Different subfields of software engineering
But software engineer is a very broad term itself and covers many subfields and you can actually specialize in any of those subfields, since they are each their own separate professions.
Frontend, Backend or Full-Stack For example, you can become a "frontend developer", which is basically developing the front of the application, the part that the users see in their browser or their phone screen or TV screen. You can become a "backend developer" and develop the backend part of the application, the part that connects to the database, saves and updates user data, processes data and so on. Or you can become a "full stack developer". They're basically people, who can develop both frontend and backend parts of the application.
Web, Mobile or IoT Then you also have categorizations like "web developer", which is basically developing web applications that you see in a computer or laptop browser. You have mobile app developers, which are developing applications for Android or iOS. You may be an "IoT developer", which is "Internet of Things", like software in your car for your TV the smart home, the smart lock systems for hotels etc.
Specialize on programming language
And you can even specialize in a specific technology or programming language or even a framework. So you may choose to become a "Java developer" a "JavaScript developer" or maybe a "React developer". You can specialize in Android development for mobile applications. You can become a "Python developer".
So these are all separate career paths, because each of these is already such a big area and field on its own.
Generalist or Specialist
So you can go deeper into a chosen technology or area or you can go broader and become a full stack engineer as I mentioned. And both have their values. You need experts in one specific area, but you also need people, who have a good overview and knowledge of many things on a higher level and that's really a personal preference, whether you are a generalist or a specialist, so you can decide what you like more. I am personally a generalist. I like knowing many things and how they fit together and integrate together and knowing things on a high level, rather than deepening my knowledge in one specific area, but as I said that's a really personal preference.
Roadmap to become a Software Engineer ☡
However, no matter which of these professions or subfields you choose, you have a pretty similar entry point for all of those.
1 - Learn basics of Software Development and Programming
You have to first understand basics of software development and programming. For example you take any programming language and learn the basics of programming with that language.
Things like variables, functions, data types and so on, because these concepts are actually the same for every programming language, no matter whether it's mobile, web, frontend or backend development.
I usually suggest JavaScript as the entry point language, if you want to get into any kind of software development, because it can be used in frontend and backend and even in mobile application development, plus it's easy to learn compared to Java for example.
2 - Develop simple example projects
Once you get the basics right, the second step will be to actually start developing simple example projects to really understand how software is written from scratch. 🚀
The complete setup of frontend and backend, whether it's a web or mobile application, because that's how you really learn the concepts behind software development. I'm a big fan of learning anything with an example project, rather than by book or watching some tutorials passively.
This way you're not learning just a specific language and what features they have, you are learning how to develop an actual application that is usable.
So depending on what you want to specialize in, if it's web development you can learn HTML and CSS on top of JavaScript and start creating a little bit more complex web applications with a database connection.
If you want to go into mobile development you can choose one of the languages for mobile development and practice using that language.
How to choose a programming language?
Every language has its advantages and disadvantages. Some of them are cooler, some of them are more widely used, but the important thing is that you learn the concepts first, because you can always learn the syntax or you can even Google the syntax, if you need to.
If you're not sure, which language you want to choose, always go for the most popular one, because it increases your chances to get a job with that language and it has a large community behind it and it has to be popular for a reason.
Concepts over Features and Syntax 💡
So as I said, instead of learning features and syntax of a language just one by one, take an example project either from web or think of your own project and develop that. You will learn way more in that process of doing it, but it has to be simple so you don't get stuck and overwhelmed in the process.
No knowledge is wasted, you can always switch
And here's the thing, if you are thinking: "what if I make the wrong choice and start with the wrong thing and find out that I don't like it at all?"
Well, if you start with backend web development for example and after months of learning that, you realize it's not really for you and you like mobile app development better, that knowledge is not wasted. You aren't starting from scratch. That knowledge that you gained will help you switch to another area, plus you had a chance to find out what you like and what you didn't like. In fact, that isn't wasted even if you decide you want to go into a completely different area like data engineering or cloud engineering or DevOps.
And I want to say that, the fields in software engineering are usually the entry-level tech professions. So it's relatively easy to get started in IT this way and later you can always transition to another tech area.
And as I said your knowledge will never be wasted, because lots of concepts are related and interconnected in different areas of IT:
IT Fields are all interconnected
Your programming skills will be useful even if you go to machine learning or DevOps engineering.
Tips on how to learn 🧑🏼🏫
Important thing here is not to do everything at once, as long as you build your knowledge in tech step by step, like one area at a time, one programming language and technology at a time and stick to that for at least six months or so and then move on to the next thing you should be fine. Just don't rush from one thing to another, trying to absorb everything at once, which I know many of you are probably thinking to do.
And if you do want to start with this path, I actually have a mini bootcamp for learning everything you need to know for web development specifically, full stack development with frontend, backend, database connection plus even more the complete software development and release life cycle.
And my goal was exactly to make people's entry in IT easier and remove that fear of tackling this scary thing of getting into tech, by making things simpler and easier. Plus with actual real projects to make it fun and engaging.
Just a shameless plug here for our IT beginners course. 😊 So if you want to know more about that you can see the video, where I explain exactly what you learn there in detail: Complete Overview of the course
2 - Become a DevOps Engineer ♾
Now as I said, you can use knowledge in software development in other IT fields and software development is actually the best stepping stone to transition to our next most popular IT role called "DevOps engineering".
So DevOps field is rising in popularity year by year, it is the field that I personally found extremely exciting and some years ago from being a senior software engineer transitioned to DevOps. And if you know my videos, you also know that my whole channel , courses and educational programs are all about DevOps 😍
But very important to note here, that DevOps is not really an entry-level IT profession. It is a bit more advanced, which means you need to already have some engineering know-how in order to transition into DevOps, but what is DevOps anyway?
DevOps is all about automating the processes in the software development and release life cycle.
Which means logically enough that you need to understand those processes and the whole life cycle first so you know what you're automating.
So DevOps is a more complex and difficult field, which I do not recommend to start in, if you have zero IT background 🤷🏻♂️, but if you build up your knowledge step by step and you find it as interesting as I do, it can be an extremely rewarding profession.
It is a highly demanded and also highly paid IT profession, because there is actually a big shortage of these professionals, probably way more than for software engineers.
If you want to know more about DevOps and what type of person it is for and what skills you need to become a DevOps engineer, I actually recommend you watch my videos from my "DevOps as a career" playlist , where I explain all of that in detail. So after watching them you will know exactly "nope DevOps is definitely not for me" 👎🏼 or "yes that's exactly what I want to do"! 👍
Transitioning to DevOps
Before we move on to the next profession though, I want to mention that lots of people transition to DevOps not only from software development background but also from systems administration or test automation or network engineering role and various other roles actually. I would say IT professions that are becoming less demanded or less interesting are moving towards DevOps engineering, because it is the new hot thing. 🔥
And if you don't already know about our famous DevOps bootcamp. Two years ago we actually created the complete educational program to teach people all the necessary tools and concepts to become a DevOps engineer:
DevOps Bootcamp covering all DevOps Technologies
We have educated more than 2,500 students and counting with this bootcamp so far, but as I said the DevOps bootcamp is for people with some level of IT experience or IT pre-knowledge. And that's why we created the IT Beginners mini bootcamp to help people with the zero background learn the fundamentals first they need to, to even get started into DevOps. So I created this course actually as a prerequisite for the DevOps Bootcamp. So if after watching the "DevOps as a career" videos you decide you want to get into DevOps, then these two educational programs will be the perfectly laid out path for you to get there in the most efficient easy and fast way. 🙌
But if you decided DevOps sounds like it's definitely not for you, then you can consider one of the IT professions I'm going to talk about next.
3 - Become a Cloud Engineer ☁️
The next IT field, which is actually pretty related to DevOps is cloud engineering.
Very simply explained, a cloud engineer basically builds and maintains infrastructure in the cloud .
As many companies move from managing their own infrastructure to using cloud platforms, Cloud Engineers are becoming increasingly demanded.
Cloud engineering is also an entry-level profession. If you have some basic systems administration experience, then this will be probably the easiest IT field to transition into, but if you're a complete IT beginner, you can actually start your IT journey directly here as well.
So how do you start in cloud engineering?
Roadmap to become a Cloud Engineer ☡
Well in cloud engineering, there are actually two most popular cloud platforms out there, which are:
Amazon's AWS
and Microsoft's Azure.
Both of them have various certifications, which you can take to help you get a job as a cloud
engineer for that specific cloud platform. So if you want to get started in this, choose one of those cloud platforms and start learning for their basic entry-level certifications and basically specialize in that cloud platform. I personally suggest choosing AWS, because it is currently the biggest and most used cloud platform. A good way to start here will be using AWS certification programs. AWS has multiple certifications from basic cloud practitioner to more advanced certifications. So obviously start with the basic AWS cloud practitioner certificate and start learning and preparing for that. This will give you knowledge in all important AWS services, but more importantly in the main concepts of cloud engineering in general.
And remember I said, when you learn one programming language, learning another programming language actually becomes way easier, because you already learned many of the common underlying concepts. The same way if you at some point decide to go for Azure after learning AWS or you find a dream job at a dream company, who uses Google Cloud platform instead, you can learn them way easier, because you already have learned one Cloud platform and the basics of cloud with that platform. In fact learning two cloud platforms will be a major asset, because you now have a good comparison between them.
So the best starting point will be getting the cloud practitioner certificate from AWS to get you the first job in this field.
Cloud vs DevOps
Now I want to mention here that DevOps and Cloud often fall into the same category and often they get mixed up. However even though they have some overlaps, they are actually two very different fields and I plan to create a video for "DevOps engineer versus Cloud engineer" describing in detail what are the common tasks and responsibilities, those overlaps as well as what actually differentiates them.
So you understand exactly the difference between these two fields. ✅ So be sure to subscribe to my Youtube channel and activate the notification bell, if you don't want to miss that. 😇
4 - Become an IT Security Engineer - Cybersecurity 🔐
Now cloud, DevOps and software engineering fields have one thing in common, they all need security.
When you build a cloud infrastructure you need to secure it, when you program an application you need to make sure it doesn't have any security loopholes that hackers may use to hack into your systems, when you build DevOps processes which actually affects your application, your cloud infrastructure and many different systems, again you have even more security responsibilities to make sure you don't expose passwords and secret keys to your systems etc.
This means software developers, cloud engineers and DevOps engineers they all have to know about security.
But security is an extremely large field and it affects every piece and step of the software development and release life cycle and you have security in other IT fields as well. So we actually have a separate dedicated profession for IT security engineers, who specialize in all things security.
Tasks and Responsibilities of a Security Engineer
As a security professional you know security tools and technologies that help you scan and identify security issues at different levels as well as help fix them and also validate that other engineers have secured their systems properly.
There are even external security companies who provide services to other companies to secure their systems. For example they try to hack into their systems and see where the systems of the company are vulnerable, because if they can hack into them, actual hackers can also do that.
So as a security engineer you identify those vulnerable points and advise the company how to secure them. Also security as I said is on multiple levels, every system, every software, that companies using or developing needs to be secured:
the infrastructure
the application platform
the frontend
the backend
database
the application itself
So security engineers usually have a wider cross knowledge of security on all those levels and can plan a general strategy of securing the complete setup using various technologies for automating security checks and security testing and so on.
Huge Demand 👀
And it goes without saying that security engineers have extreme value to the companies 💎, because security breach is the worst scenario for any big known company. Cyber attacks are becoming more and more sophisticated and for applications, who have millions or hundreds of millions of users and the user data obviously the impact of the attack is huge, when data of so many users is compromised or even think about your online banking application. Obviously you don't want them to have any security issues in their system, right?
For this reason there is usually a tremendous demand for security engineers in many industries and it's definitely going to become even more important in the future.
Roadmap to become an IT Security Engineer ☡
So if you want to go on this challenging but very exciting path you need to first understand the concepts of what you are securing.
So this is also not really an entry-level IT position, you definitely need some pre-knowledge in one of the IT fields like network engineering, cloud engineering, software development etc. And on top of that you'll have to learn many security concepts and tools in order to develop this general security strategies.
5 - Become a Data Analyst, Data Engineer, Data Scientist - Big Data 📊
One of the hottest IT jobs, which are more in demand than ever are data related jobs. Now why is that?
Human generated data 👥
When we have software that millions and billions of people use daily, those users produce loads of data, right? Think about social media, the posts and media we create and upload every minute or every second. Think about search data generated when millions of people search daily, GPS data from Google Maps or other applications that track your location, when you buy groceries at the supermarket, when you buy stuff online. So basically the user behavior data.
All of this is data that we humans produce daily through our digital footprint.
Device generated data ⚙️
But apart from this human generated data there is also massive amounts of device generated data, such as cars, IoT systems through sensors, production machines, robots, logistics data, shipment tracking.
So even more data than humans generate is coming from these sources.
With all this, the data has grown dramatically in the last years.
In fact worlds 90% of the data was actually generated in the last two years.
As some sources mentioned, we generate so much data, every single day that if it were written down in form of books and we could pile those books on top of each other, we would have enough to build a bridge to the moon and back.
And because of the sheer volume of this data, we also call it "Big Data". So that's where the term comes from.
Raw vs Processed Data
Data has become a precious asset of any organization 💎, because it helps them understand things better. 💡 Like make predictions, political campaigns are driven by data, like you have polls and online searches etc. Many companies use data to optimize their processes, to save time and money in those processes.
However, just raw data has no value to the company. Imagine these are
massive amounts of data in raw form, generated in different formats and from different sources. It's really difficult for humans to make sense of data in this form. It only has value once the data is processed, cleaned, analyzed and visualized.
So it's easy to consume for us humans and big data related professionals are exactly the ones, who use tools to turn these massive amounts of data into usable and extremely valuable information for companies. And companies can then use these visualized data to make decisions, make future predictions, cost optimizations and so on.
And there are various data related professions with different tasks and responsibilities such as
data analysts
data scientists
and data engineer
So let's see comparisons between them.
Data Analyst
Data analyst is basically the entry-level profession, if you want to get into this field and is the easiest to start with.
As the name suggests, data analysts analyze and interpret data to extract meaningful information from it. So they need to basically make sense of the data, like identify any patterns.
Main Skills
The main skills they need to have are knowledge of math and statistics and various tools that help them in data analytics and data visualization.
But in addition to the technical skills, data analysts must actually have a good business and product understanding. So they analyze the data with the goal to make good decisions for the business and product development and then communicate those decisions to people, who actually need them like, product owners, business decision makers in the company.
However, data analysts work with already processed and prepared data. So the raw data needs to be collected from multiple sources with different formats and be processed first to be usable for the data analysts.
Data Engineers
And this is something that data engineers do. Data engineers need knowledge of databases and programming to do their job and data engineers actually build something called "data pipelines" to collect, store and process the data:
Building a Data Pipeline
So you can start into data engineering by learning:
A programming language like Python and its data processing frameworks and libraries Learning databases and query language like SQL for example Big data specific frameworks like Apache Hadoop, which is a popular framework that allows you to store and work with massive amounts of data.
Data Scientist
And the third one I mentioned is a data scientist, which is usually the highest paid profession among these three.
Now interesting to mention that companies often use data analysts and data scientists job titles interchangeably. Now they are two different professions, but there are definitely some overlaps between those two. And one of the overlaps is that you need to have advanced math and statistics knowledge here as well.
So generally contrary to the popular belief, you don't need any math or statistics knowledge for software development and definitely not for DevOps or cloud engineering, but in data science or data analytics you will be working with math and statistics a lot. 🧮
This means if you want to get started in one of those fields, the first thing you need to learn is statistics and the programming languages for statistics like R or Python.
However, in addition to statistics, data scientists usually require more advanced technical skills than data analysts and that's where the main difference between those two professions lie:
Skills of Data Analyst vs Data Scientist
So data scientists are usually more experienced engineers , who can create advanced machine learning models for example and algorithms to make future predictions.
6 - Become a Machine Learning Engineer 🤖
Which leads us to the next and final hottest IT profession called a Machine Learning Engineer or ML Engineer, which actually is yet another big data related profession.
Data Science vs Machine Learning field
We said that data analysts and data scientists use data to analyze trends and identify patterns and make some decisions and predictions based on the data. So data can be used by humans to make data-driven decisions, but data can also be used by machines by programs.
So that clean processed data that data engineers prepare can be fed into machines, so they can learn from them and they can use them for different tasks. And that's where machine learning actually comes in.
What is Machine Learning? 🤔
Now what is machine learning exactly and why do machines need data, what do they do with it?
In software development we write programs and instruct them to do something. In machine learning, machines can perform a task without being explicitly programmed to do so. 🙅🏻♀️ How do they do that? They learn how to perform that task from large amounts of data using algorithms, which are also called machine learning algorithms:
How Machine Learning works
So machine learning is about computers being able to think and act without being explicitly told or programmed to do so.
And there are two main parts of this process:
First one is writing the machine learning algorithms so that machines can use them to learn And the second part is feeding large sets of processed data into those algorithms, so basically using the data to train the model.
Skills you need to have
Again there are some overlaps between data scientists and machine learning engineers that you will encounter, they both need strong math and statistics skills to work with data, however, while data scientists focus on making sense of the data, visualizing and presenting it better, machine learning focuses on using the data for machines to learn to carry out certain tasks:
Machine Learning vs Data Science
So entry into machine learning engineering is actually pretty similar to data science: You need to start by learning a programming language like Python, which has powerful machine learning frameworks and statistics, which is a very important part of machine learning engineering.
Python - General Purpose Language 🐍
Now you probably already noticed that in all those fields I actually mentioned Python programming language and that's because Python is a general purpose language.
You can use Python in every single one of those areas, however that does not mean that you need the same Python knowledge in each field.
Using Python for web development is completely different from using Python for DevOps automation or machine learning and that's an important difference.
Python Core vs Python libraries for specific field
So first you learn the Python core with its syntax and general programming concepts and then you learn the specific libraries and frameworks for each IT field on top of that Python core. So you have completely different frameworks and libraries for web development and machine learning and DevOps automation, which you need to learn for that specific field. So you basically learn different parts of Python for each field and that's an important thing to differentiate.
Python language just happens to have popular frameworks for all those fields and their use cases, but of course the Python core is the same everywhere.
If you want to learn Python core, like syntax and programming concepts of Python, you can definitely check our free course on Python .
So we've covered a bunch of career options in IT and I tried to categorize them so you have a better overview and comparison between them. ✅ So hopefully something stood out for you where you think: "well that field sounds pretty interesting to me so I'd like to get into that field".
Where and how to learn - CS Degree, Courses, Bootcamps, Self-Learning? 📚
Now of course when you know approximately which direction you want to go and what you want to start learning, the next question becomes: "Where and how do you learn this?" Do you get a college degree in computer science? Do you take an online course? Do you join a data science bootcamp or a coding bootcamp or our DevOps bootcamp? 🤷🏻♂️
Well, I personally started my informatics studies at a technical university, but I was using mostly online resources for my studies like YouTube videos and some coding websites. And as soon as I got an internship as a software developer in my second semester, I actually quit my studies and used my work to learn by doing.
At work I was actually learning way more practical stuff that I actually needed for my job than at the university
So I usually recommend learn the relevant skills you need to get hired as fast as possible. 🚀
But learning at work wasn't always easy, so I continued learning new things from YouTube videos and blog articles and online courses. I was often all over the place trying to learn anything and everything that I stumbled upon. I didn't really have a roadmap that I could follow, but it was still useful in some way.
So I still think that online resources are one of the best ways to learn, especially in IT field, but as I said having some kind of roadmap and structure definitely makes the learning journey easier, because you don't just learn things randomly, but you learn things in a certain order without being distracted by massive amounts of information. 💡
Find a clear roadmap
So whatever field you want to choose: Go find a clear roadmap for that profession. There are various articles and videos about those roadmaps and then just try to follow that roadmap. And of course if you decide for DevOps, as I mentioned we have a complete roadmap for that, even if you're starting off without any IT background:
So you don't need to do the research, put together a learning course and find good resources. We have done that whole work for you. You just follow along and learn by doing and if you know my videos I create the content with the goal of giving practical actual usable knowledge with easy explanations. I love helping people learn easily without getting frustrated and being overwhelmed. 💙
Start your journey into IT 👩🏻💻
So generally as you see, you have various options, choose an entry-level career based on what you want to do later and you can build on top of that.
And again no knowledge is wasted in IT, everything is still interconnected, so if you start in software development and later want to do DevOps or machine learning or cloud engineering you can still benefit from that knowledge and won't be starting from scratch in that field.
So if you don't know yet where you want to end up you can start with any one of those, maybe the easiest one and you can always progress in any other direction later. 👍
I hope I was able to help you make these important decision for your future, you definitely made the right decision by choosing IT in general. With that, all the best in your future career! 😊 Like, share and follow me 😍 for more content:
Private FB group
LinkedIn
Twitter
YouTube
| 2022-12-19T00:00:00 |
2022/12/19
|
https://www.techworld-with-nana.com/post/a-guide-of-how-to-get-started-in-it-in-2023-top-it-career-paths
|
[
{
"date": "2022/12/19",
"position": 99,
"query": "machine learning job market"
}
] |
Kevin Scott on 5 Ways Generative AI Will Transform Work
|
Kevin Scott on 5 Ways Generative AI Will Transform Work
|
https://www.microsoft.com
|
[] |
From helping write code to conjuring images seemingly out of the air, this new form of computing will change the nature of creativity and productivity.
|
“I think with some confidence I can say that 2023 is going to be the most exciting year that the AI community has ever had,” writes Kevin Scott, chief technology officer at Microsoft, in a Q&A on the company’s AI blog . He acknowledges that he also thought 2022 was the most exciting year for AI, but he believes that the pace of innovation is only increasing. This is particularly true with generative AI, which doesn’t simply analyze large data sets but is a tool people can use to create entirely new works. We can already see its promise in systems like GPT-3 , which can do anything from helping copyedit and summarize text to providing inspiration, and DALL-E 2, which can create useful and arresting works of art based on text inputs. Here are some of Scott’s predictions about how AI will change the way we work and play.
1. It Will Unleash Our Creativity
As generative AI becomes more popular and accessible, more people will be able to use the technology for creative expression, whether it’s helping them produce sophisticated artworks or write moving poetry. In his blog post, Scott describes how new AI tools are democratizing access to design. “An AI system such as DALL-E 2 doesn’t turn ordinary people into professional artists, but it gives a ton of people a visual vocabulary that they didn’t have before—a new superpower they didn’t think they would ever have.” DALL-E 2 already shows up in tools like Microsoft Designer, but there’s exciting potential for it to help many more people unleash creative ideas in ways that were once only available to trained professionals.
2. It Will Make Coding Much More Accessible
Generative AI innovations like GitHub Copilot, an AI pair programmer built using OpenAI’s Codex AI system, can translate natural human language into programming code, essentially turning our practical intentions into complex pieces of software. Among Copilot users, 40 percent of the code in some popular programming languages is being generated by Copilot, a figure that is set to increase. In a recent talk at the Fortune Brainstorm AI conference , Scott pointed to the example of people noodling around with the capabilities of ChatGPT (which is powered by GPT-3.5) to hint at the future potential. “It really opens up the aperture of who can actually use AI now,” he says. “We’ll need new sorts of specialties in the future, but you don’t need to have a PhD in computer science anymore to build an AI application, which I think is really, really exciting.”
The upshot is an acceleration of the iterative cycle , as human beings tweak and refine the AI’s work in a virtuous, back-and-forth collaborative process.
3. It Will Become Our Copilot in Other Ways Too
In an essay for Wired UK , Scott sketches a scenario in which AI helps us do our jobs better. Like coding assistance with GitHub Copilot, industries from construction to healthcare, technology to law, could potentially benefit from a form of AI assistance. “The applications are potentially endless, limited only by one’s ability to imagine scenarios in which productivity-assisting software could be applied to complex cognitive work, whether that be editing videos, writing scripts, designing new molecules for medicines, or creating manufacturing recipes from 3D models,” he writes. While there’s concern about how AI will impact human jobs, Scott describes in his post how, with thoughtful application, these AI tools have the potential to augment and amplify human capability, enabling people to spend less time on repetitive tasks. These models will also “democratize access to AI,” he writes, so “you’ll have a more diverse group of people being able to participate in the creation of technology.”
4. It Will Unlock Faster Iteration
Generative AI may significantly reduce the legwork of the creative process by helping designers iterate on product concepts and helping writers generate first drafts of press releases, essays, and scripts, assisting with graphic design–heavy posters, video edits, and more. Scott notes in his Wired essay that it has the potential to “allow knowledge workers to spend their time on higher order cognitive tasks, and effectively transforming how a great many of us interact with technology to get things done.” The upshot is an acceleration of the iterative cycle, as human beings tweak and refine the AI’s work in a virtuous, back-and-forth collaborative process. We will become adept at developing techniques to edit and modify generated images, text, drawings, and even molecules or proteins to be used in new medicines, creating better results more quickly through careful collaboration with AI.
5. It Will Make Work More Enjoyable
In the AI blog post, Scott observes that AI tools for programmers have the potential to vastly improve the overall work experience. “People now have new and interesting and fundamentally more effective tools than they’ve had before,” he notes. “This is exactly what we’re seeing with the experiences developers are having with Copilot; they are reporting that Copilot helps them stay in the flow and keeps their minds sharper during what used to be boring and repetitive tasks.” This also extends to low-code and no-code tools in products like Power Platform that are opening new potential across job functions, roles, and processes. “We did a study that found using no-code or low-code tools led to more than an 80 percent positive impact on work satisfaction, overall workload, and morale.”
Generative AI has the capacity to profoundly alter the working practices of a range of vocations, giving rise to new professions and transforming established ones. With ethical and thoughtful deployment, it is a tool that could help precipitate a revolution in creativity—one that enables everyone to better express their humanity.
| 2022-12-19T00:00:00 |
https://www.microsoft.com/en-us/worklab/kevin-scott-on-5-ways-generative-ai-will-transform-work-in-2023
|
[
{
"date": "2022/12/19",
"position": 11,
"query": "future of work AI"
},
{
"date": "2022/12/19",
"position": 16,
"query": "generative AI jobs"
},
{
"date": "2022/12/19",
"position": 28,
"query": "AI graphic design"
}
] |
|
How to Democratize Artificial Intelligence (AI) and Why It ...
|
How to Democratize Artificial Intelligence (AI) and Why It Matters? – Lifestyle Democracy
|
https://www.lifestyledemocracy.com
|
[
"Stefan Ivanovski",
".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",
"Vertical-Align Bottom .Wp-Block-Co-Authors-Plus-Coauthors.Is-Layout-Flow .Wp-Block-Co-Authors-Plus-Avatar",
"Vertical-Align Middle .Wp-Block-Co-Authors-Plus-Avatar Is .Alignleft .Alignright"
] |
Action steps that readers can take to help democratize AI include adopting open source AI technologies, supporting organizations that are working to democratize ...
|
Reading time: 10 minutes
This article aims to explore ideas how to democratize artificial intelligence, using artificial intelligence ChatGPT.
Quick Summary
Democratizing AI involves making AI tools and technologies more widely available and easier to use, so that more people can benefit from them.
Benefits of democratizing AI include enabling more people to participate in the development and use of AI, promoting innovation and creativity, and addressing social and economic inequalities.
Challenges and risks of democratizing AI include the digital divide and inequality, security risks, misinformation and propaganda, privacy concerns, and ethical and regulatory issues.
Strategies for democratizing AI include making AI technologies more widely available and affordable, providing education and training on AI, promoting the development of open source AI, and ensuring the ethical use of AI.
Action steps that readers can take to help democratize AI include adopting open source AI technologies, supporting organizations that are working to democratize AI, learning about AI, engaging with policy makers and regulators, and promoting the ethical use of AI.
Examples of open source AI tools include TensorFlow, Keras, PyTorch, scikit-learn, and Orange.
Actions
If you are more of an action-oriented person, you can read some action steps to take.
What is Artificial Intelligence?
Artificial intelligence (AI) has the potential to transform our world in countless ways, from improving healthcare to advancing scientific research to automating mundane tasks. But as AI continues to evolve and become more powerful, it’s important to consider how we can ensure that its benefits are widely shared and that its potential risks are managed. This is where the concept of democratizing AI comes in.
Democratizing AI refers to the process of making AI tools and technologies more widely available and easier to use, so that more people can benefit from them. This can involve initiatives such as making AI tools and technologies more affordable and accessible, providing education and training on how to use them, encouraging the development of open source AI, and promoting the ethical use of AI. By democratizing AI, we can help to ensure that its benefits are more evenly distributed and that it is used in ways that are responsible, transparent, and aligned with the values and interests of society.
But democratizing AI is not without its challenges. In this blog post, we’ll explore the current state of AI democratization, the benefits of democratizing AI, the challenges and risks of democratizing AI, and strategies for democratizing AI. We’ll also offer some suggestions for further reading and resources for those who want to learn more about this important topic.
The current state of AI democratization
AI is already being used in a variety of fields, from healthcare to finance to marketing. But not all AI technologies are equally democratized, meaning that they are not equally available and accessible to all. Some AI technologies, such as natural language processing and machine learning, are relatively mature and widely available, while others, such as artificial general intelligence and quantum computing, are still in the early stages of development and are less accessible.
There are a number of factors that can affect the democratization of AI, including technological barriers, financial barriers, and regulatory barriers. For example, AI technologies that require specialized hardware or software may be less democratized, as they may be more expensive or difficult to use. Similarly, AI technologies that are controlled by a small number of large corporations may be less democratized, as they may be more difficult to access or modify. And AI technologies that are subject to strict regulations or licensing requirements may also be less democratized, as they may be more difficult to use or develop.
Challenges to democratizing AI also include a lack of awareness or understanding of AI among the general public, as well as a lack of education and training opportunities for those who want to learn more about AI. Additionally, the lack of diversity within the AI field, both in terms of the demographics of those who work in AI and the types of AI technologies that are developed, can also hinder democratization efforts.
Benefits of democratizing AI
Democratizing AI can bring a range of benefits to individuals, organizations, and society as a whole. Some of the key benefits of democratizing AI include:
Increased access to AI technologies: By making AI tools and technologies more widely available and affordable, democratization can help to reduce barriers to access and enable more people to benefit from them. This can help to promote social inclusion and ensure that the benefits of AI are more evenly distributed.
By making AI tools and technologies more widely available and affordable, democratization can help to reduce barriers to access and enable more people to benefit from them. This can help to promote social inclusion and ensure that the benefits of AI are more evenly distributed. Enhanced innovation and progress: Democratizing AI can encourage the development and adoption of new AI technologies and ideas by making it easier for a wider range of people to access and use them. This can help to stimulate innovation and drive technological progress.
Democratizing AI can encourage the development and adoption of new AI technologies and ideas by making it easier for a wider range of people to access and use them. This can help to stimulate innovation and drive technological progress. Improved education and skills development: Democratizing AI can help to improve access to educational resources and opportunities, particularly for disadvantaged or marginalized groups. This can help to promote lifelong learning and enable people to develop new skills and knowledge.
Democratizing AI can help to improve access to educational resources and opportunities, particularly for disadvantaged or marginalized groups. This can help to promote lifelong learning and enable people to develop new skills and knowledge. Greater participation and inclusivity: Democratizing AI can increase participation in decision-making processes and give more people a voice in shaping the direction and use of AI. This can help to ensure that the development and deployment of AI technologies reflects the needs and priorities of society.
Other benefits: In addition to these specific benefits, democratizing AI can also bring a range of economic, social, and political benefits. For example, democratization can help to create new economic opportunities and stimulate economic growth by making it easier for a wider range of people to access and use new technologies and innovations. It can also help to address social and economic inequalities by reducing barriers to access and enabling more people to benefit from new technologies. And it can help to enhance democracy by giving more people a voice in shaping the direction and use of AI and ensuring that the development and deployment of AI technologies reflects the values and priorities of society.
Challenges and risks of democratizing AI
While democratizing AI brings many benefits, it also carries a number of challenges and risks that need to be carefully considered and addressed. Some of the key challenges and risks of democratizing AI include:
Digital divide and inequality: One of the main challenges of democratizing AI is the risk of exacerbating existing inequalities and widening the digital divide. For example, if AI technologies are not made widely available or affordable, they may be more difficult for disadvantaged or marginalized groups to access and use. This can lead to further disparities in terms of economic opportunity, education, and other areas.
One of the main challenges of democratizing AI is the risk of exacerbating existing inequalities and widening the digital divide. For example, if AI technologies are not made widely available or affordable, they may be more difficult for disadvantaged or marginalized groups to access and use. This can lead to further disparities in terms of economic opportunity, education, and other areas. Security risks: Another challenge of democratizing AI is the risk of security breaches or attacks. As more people and organizations use AI technologies, there is a risk of vulnerabilities being exploited or data being compromised. This can have serious consequences, particularly if sensitive or critical data is involved.
Another challenge of democratizing AI is the risk of security breaches or attacks. As more people and organizations use AI technologies, there is a risk of vulnerabilities being exploited or data being compromised. This can have serious consequences, particularly if sensitive or critical data is involved. Misinformation and propaganda: The democratization of AI can also raise concerns about the spread of misinformation and propaganda. With the increasing use of AI for tasks such as content generation and social media analysis, there is a risk that AI technologies could be used to spread false or misleading information.
The democratization of AI can also raise concerns about the spread of misinformation and propaganda. With the increasing use of AI for tasks such as content generation and social media analysis, there is a risk that AI technologies could be used to spread false or misleading information. Privacy concerns: AI technologies often involve the collection and analysis of personal data, which raises privacy concerns. If AI technologies are not developed and used in a responsible and transparent manner, there is a risk that personal data could be misused or mishandled.
Other challenges and risks: In addition to these specific challenges and risks, democratizing AI also carries a range of other potential challenges and risks. For example, there are ethical concerns around the use of AI, particularly when it comes to issues such as bias and fairness. There are also legal and regulatory challenges associated with the use of AI, particularly when it comes to issues such as liability and accountability. And there are strategic challenges associated with the deployment and adoption of AI, such as the need to ensure that AI technologies are used in ways that are aligned with the goals and values of society.
Strategies for democratizing AI
Given the benefits and challenges of democratizing AI, it’s important to consider strategies for making AI technologies more widely available and easier to use. Some of the key strategies for democratizing AI include:
Making AI technologies more widely available and affordable: One of the key strategies for democratizing AI is to make AI tools and technologies more widely available and affordable. This can involve initiatives such as open sourcing AI technologies, reducing barriers to access, and providing financial incentives or subsidies to encourage the use of AI.
One of the key strategies for democratizing AI is to make AI tools and technologies more widely available and affordable. This can involve initiatives such as open sourcing AI technologies, reducing barriers to access, and providing financial incentives or subsidies to encourage the use of AI. Providing education and training on AI: Another important strategy for democratizing AI is to provide education and training opportunities for those who want to learn more about AI. This can involve initiatives such as developing AI curricula, providing online courses and resources, and establishing training programs and workshops.
Another important strategy for democratizing AI is to provide education and training opportunities for those who want to learn more about AI. This can involve initiatives such as developing AI curricula, providing online courses and resources, and establishing training programs and workshops. Promoting the development of open source AI: Encouraging the development of open source AI can also be an important strategy for democratizing AI. Open source AI technologies are freely available and can be modified and distributed by anyone, which makes them more widely available and accessible.
Encouraging the development of open source AI can also be an important strategy for democratizing AI. Open source AI technologies are freely available and can be modified and distributed by anyone, which makes them more widely available and accessible. Ensuring the ethical use of AI: Ensuring the ethical use of AI is also critical to the democratization of AI. This can involve initiatives such as establishing ethical guidelines and principles for the development and use of AI, promoting transparency and accountability, and encouraging the responsible and ethical use of AI.
What are some open-source AI tools?
TensorFlow: TensorFlow is an open-source software library for machine learning and artificial intelligence. It is widely used in a variety of applications, including image recognition, natural language processing, and predictive modeling. Keras: Keras is an open-source library for building and training neural networks. It is designed to be easy to use and can be used with a variety of backends, including TensorFlow, Theano, and CNTK. PyTorch: PyTorch is an open-source machine learning library for Python. It is popular for its simplicity and flexibility, and is widely used in applications such as natural language processing and computer vision. scikit-learn: scikit-learn is an open-source library for machine learning in Python. It is widely used for tasks such as classification, regression, clustering, and dimensionality reduction. Orange: Orange is an open-source data visualization and machine learning toolkit for Python. It is widely used for tasks such as data exploration, visualization, and machine learning.
Democratizing AI is possible, but difficult
In summary, democratizing AI is an important and complex process that involves making AI tools and technologies more widely available and easier to use, so that more people can benefit from them. While democratizing AI brings many benefits, it also carries a number of challenges and risks that need to be carefully considered and addressed. Strategies for democratizing AI include making AI technologies more widely available and affordable, providing education and training on AI, promoting the development of open-source AI, and ensuring the ethical use of AI. By addressing these challenges and risks, we can help to ensure that the benefits of AI are more evenly distributed and that it is used in ways that are responsible, transparent, and aligned with the values and interests of society.
Actions Steps I can Take
Here are some ideas ranging from
| 2022-12-19T00:00:00 |
2022/12/19
|
https://www.lifestyledemocracy.com/how-to-democratize-artificial-intelligence-ai-and-why-it-matters/
|
[
{
"date": "2022/12/19",
"position": 94,
"query": "workplace AI adoption"
}
] |
Call for wealth tax as number of UK billionaires jumps 20% ...
|
Call for wealth tax as number of UK billionaires jumps 20% since 2020
|
https://www.tutor2u.net
|
[] |
Examiner AI. Search. Sign in · Economics. Explore Economics ... Rising bubbles? 2nd May 2017. Could Universal Basic Income out-perform conventional Overseas Aid?
|
Another call for a wealth tax, with the Equality Trust noting that the number of billionaires in the UK has increased by 20% since the start of the coronavirus pandemic, as a result of government intervention to support the economy.
There are calls for a package of measures, not least the abolition of non-dom status, and a concerted attempt to reduce inequalities of income and wealth.
You can access the Equality Trust report using this link
| 2022-12-19T00:00:00 |
https://www.tutor2u.net/economics/blog/call-for-wealth-tax-as-number-of-uk-billionaires-jumps-20-since-2020
|
[
{
"date": "2022/12/19",
"position": 46,
"query": "universal basic income AI"
}
] |
|
An Overview of the US AI Training Act 2022
|
An Overview of the US AI Training Act 2022
|
https://www.holisticai.com
|
[] |
The AI Workforce Training Act is premised on education and training to inform procurement and facilitate the adoption of AI for services at the Federal Level.
|
Key takeaways
The Artificial Intelligence Training for the Acquisition Workforce Act (AI Training Act) was signed into law by President Biden in October 2022.
The AI Workforce Training Act is premised on education and training to inform procurement and facilitate the adoption of AI for services at the Federal Level.
Training & education to inform procurement and facilitate adoption AI at the federal level
Signed into law by President Biden, The AI Training Act takes a risk management approach towards federal agency procurement of AI.
The Act aims to set best practices in place to educate those tasked with procurement, logistics, project management, etc., about AI, its uses, risks, and key considerations among others.
This is so that AI is then purchased/procured from an educated and informed perspective, as well as to explore opportunities for federal agencies to use/implement AI.
This bill requires the Office of Management and Budget (OMB) to either create or provide an artificial intelligence (AI) training program to aid in the informed acquisition of AI by federal executive agencies.
The main purpose of the training program would be to ensure those responsible for procuring AI within a covered workforce, are aware of both the capabilities and risks associated with AI and similar technologies.
US AI Training bill text
The bill text has outlined the following topics to be covered in the program:
The science underlying AI, including how AI works. Introductory concepts relating to the technological features of artificial intelligence systems. The ways in which AI can benefit the Federal Government. The risks posed by AI, including discrimination and risks to privacy. Including efforts to create and identify AI that is reliable, safe, and trustworthy; and Future trends in AI, including trends for homeland and national security and innovation.
Outside of the bill text itself, Senator Peters explained the training program is needed and will be instrumental in “training our federal workforce to better understand this technology and ensure that it is used ethically and in a way that is consistent with our nation's values.” Particularly on the verticals of privacy and discrimination.
To build this training program the bill encourages the Director of the OMB to consult with the following: technologists, scholars, and other private and public sector experts. The bill is subject to a 10-year sunset clause, within these 10 years this training program is expected to be updated at least every 2 years.
Continuing a national commitment to trustworthy AI
The AI Training Act is not the first national initiative aimed at guiding government agency use of AI.
The Executive Order (EO) 13960, Promoting the Use of Trustworthy AI in the Federal Government, was signed into law in December 2020. The order set out that principles would be developed to guide the federal use of AI within different agencies, outside of national security and defense.
These principles refer to being in line with American values and applicable laws. The EO also requires that agencies make public an inventory of non-classified and non-sensitive current and planned Artificial Intelligence (AI) use cases.
In 2023, NIST will re-evaluate and assess any AI that has been deployed or is in use by federal agencies to ensure consistence with the policies outlined in the order. The US Department of Health and Human Services has already created their inventory of use-cases in preparation for NIST’s evaluation and their inventory list can be found here.
The Whitehouse also recently published a Blueprint for an AI Bill of Rights to guide the design, deployment, and development of AI systems. The Blueprint is nonbinding and relies on designers, developers and deployers to voluntarily apply the framework to protect US Americans from the harms that can result from the use of AI.
The US is taking decisive action to manage the risks of artificial intelligence at federal, state and local agency levels. Taking steps to address the risks of AI early is the best way to get ahead of these upcoming regulations.
Holistic AI has pioneered the field of AI Risk Management and empowers enterprises to adopt and scale AI confidently. Our team has the technical expertise needed to identify and mitigate risks, and our policy experts use that knowledge of and act on proposed regulations to inform our product. Get in touch with a team member or schedule a demo to find out how we can help you comply with these legislative requirements.
| 2022-12-19T00:00:00 |
https://www.holisticai.com/blog/us-ai-training-act
|
[
{
"date": "2022/12/19",
"position": 1,
"query": "government AI workforce policy"
}
] |
|
How AI can reduce pressure in the workplace
|
How AI can reduce pressure in the workplace
|
https://m.digitalisationworld.com
|
[] |
AI is well positioned to tackle some key areas of pressure that occur within businesses, while streamlining processes to free up employee time.
|
Good business leaders should always be looking for tools that can reduce the pressure placed upon their workforces. In the post-pandemic new normal, this will increasingly mean a reliance on technology.
Emerging technologies are increasingly finding their place across all verticals as ways of reducing pressure on employees. Artificial intelligence (AI) has a hugely disruptive role to play in this. AI and machine learning (ML) are now so advanced that they can massively reduce workloads, offloading more mundane tasks onto computers.
Getting to the root of the problem
To alleviate workplace pressure, business leaders can’t just deal with the end result, they need to address the root cause – and the pandemic forced many businesses that were lagging in this respect to finally do just that. This means understanding how and why employees feel their working environment has become pressurised. If they don’t, they are in danger of creating a cycle that, in the worst-case scenario, will lead to burn-out, illness, or resignation.
AI – Analysing data to save time, and reduce stress
AI is well positioned to tackle some key areas of pressure that occur within businesses, while streamlining processes to free up employee time. In particular, AI can process gigantic amounts of data in a very short time. From this data, it can then quickly detect specific signals and derive recommendations for action.
For example, imaging centres create some of the largest data sets for healthcare facilities and the technical knowledge required has created a talent shortage. Dermatologic pictures, X-rays and CT scans are often the only way to diagnose patients and structure courses of treatment. However, the current shortage in specialists and fast-rising demand puts the burden on medical teams already stretched to their limit, increasing the risk of errors. AI can learn to diagnose risk by analysing millions of pictures, so by introducing it into these scenarios, medical teams can process hundreds of thousands of images accurately and faster than a human could.
Restructuring workloads to allow AI to analyse images removes pressure from medical teams. This then lets trained human professionals focus on analysing high-risk cases, deriving final diagnoses, and providing concrete treatment, saving highly valuable time and reducing stress on the individual and medical function. It also helps doctors treat significantly more people and increase the efficiency and quality of their work, while helping to balance out the pressures they would otherwise be placed under.
Stress is everywhere
Pressurised workplaces are, however, not exclusive to healthcare and the emergency services. For instance, engineers and operators ensuring that large and complex infrastructures keep operating 24/7 often face huge amounts of pressure. From power grids to transportation networks or large digital architectures, the possibility of an outage puts people and revenue at risk.
Unsurprisingly, the monitoring and supervision of IT and software architectures in companies has added considerable pressure to IT teams. You only have to look at the number of recent high-profile outages, including a string of airlines throughout summer, to have an idea of the pressure placed on the IT function. Operating 24/7 or on-call and having to deal with urgent and complex situations with very limited support, DevOps and Infrastructure teams simply can’t enjoy luxuries such as work-life balance.
Businesses should invest in reliable system monitoring based on software telemetry and intelligent observability practices that can act like a silent guardian. Using this technology to monitor all systems 24 hours a day removes the fatigue related to alert noise and the burden of possibly overlooking critical incidents, returning power to the team's hands.
While practicing observability or using AI cannot replace the expertise and knowledge of engineers, it can, and does, allow IT teams to take quick action when the platform detects an anomaly. This helps to streamline processes and ensure that resources and employee wellbeing can be prioritised, without compromising on the business or user experience.
Reducing false alarms, reducing stress
Observability and AI reduce false alarms and more accurately recognise critical situations, removing noise and detecting weak signals. In utilising the vast amount of available telemetry data, AIOps systems can automatically detect anomalies without the need for manual engineer insight or process. By providing automated Root Cause Analysis and context for issues, AI dramatically reduces the time to understand and resolve incidents. It also frees up the DevOps team from significant toil, letting them focus on high value activities, their own wellbeing and, importantly, the areas of the business that will drive revenue.
Regardless of the industry, employees and businesses must put mental health first. AI provides an understanding of their stresses and pressure points and helps to implement measures that drive the team’s efficiency.
In this way, AI and telemetry data not only reduce the workload of employees, but also enable their personal development in the long term. This ensures the workforce is happy, healthy, and motivated to drive the business forward. It is time to embrace technology that will embed this ethos into each and every business, now and into the future.
| 2022-12-19T00:00:00 |
2022/12/19
|
https://m.digitalisationworld.com/blogs/57188/how-ai-can-reduce-pressure-in-the-workplace
|
[
{
"date": "2022/12/19",
"position": 2,
"query": "machine learning workforce"
},
{
"date": "2022/12/19",
"position": 21,
"query": "artificial intelligence business leaders"
}
] |
How Machine Learning Is Transforming Intraday Management
|
How Machine Learning Is Transforming Intraday Management
|
https://www.nice.com
|
[] |
NiCE WFM enables contact centers to monitor and respond to intraday changes automatically—no supervisor intervention required ...
|
What do weddings, the World Series, and contact centers have in common? They all require immense amounts of advance planning to be successful.
In the case of contact centers, schedules are prepared weeks, even months, in advance, with the goal of ensuring that the right employees are available at the right times to meet demand. They’re optimized, then fine-tuned, then optimized again, reviewed and revised many times over, all in hopes that each day goes just according to plan.
But what happens when uncertainty strikes, such as a major outage or severe weather, causing one’s best-laid plans to go awry? Today’s successful contact centers have workforce management solutions that allow them to adjust to intraday deviations—without impacting customers.
Monitoring—and Responding to—Changing Conditions
Your workforce management team’s work isn’t done when the schedule is published. There are times when agents need to change their schedules or the business needs to revise its schedules. In fact, many contact centers find that re-forecasting and re-simulating several times a day can help them meet customer demand without overstaffing.
NiCE WFM enables contact centers to monitor and respond to intraday changes automatically—no supervisor intervention required. Its artificial intelligence (AI) simulator, which accounts for the entire WFM process from forecasting to scheduling to real-time execution, uses a configurable rules engine to monitor intraday changes in staffing and key performance indicators by the minute. That means the simulator can detect a rapidly deteriorating situation before it’s too late.
NiCE WFM then generates new schedules for the remaining intervals of the day and notifies agents of the pending changes. Once the changes are accepted, the solution updates the agents’ schedules, even down to the optimal time to take a break.
NiCE Employee Engagement Manager (EEM), an optional module that uses AI-enabled behavioral decisioning to respond to intraday staffing opportunities, takes workforce management a step further. EEM presents agents with opportunities to work extra hours or take voluntary time off based on the business’ KPIs, making agents partners in intraday management.
After a particularly long call stretches into the agent’s scheduled break by five minutes, the agent receives an adherence penalty.
The agent takes the issue up with their supervisor, arguing that it’s not fair that they have been penalized for actively taking care of a customer.
The supervisor has to spend time investigating the incident, then requesting that the WFM team make an exception and remove the penalty.
EEM’s adaptive breaks and lunches capabilities also help contact centers manage intraday change. Consider, for example, a day in the life of one agent whose contact center manages intraday deviances manually:
The result of this single five-minute deviation? The agent, the manager, and the workforce management team all need to interrupt their day to resolve the issue.
EEM helps contact centers avoid these types of situations by automatically adjusting the break or activity based on business rules, so the agent is not penalized for handling a long call. It will also adjust the schedule while considering net staffing requirements, ensuring that the customer's experience will not be adversely affected.
It’s inevitable that things won’t go according to plan; hoping that they will—or relying on a team of WFM analysts to manage changes manually—is not an effective strategy in today’s complex, multi-channel contact center. Learn more about how NiCE WFM’s intraday management capabilities are enabling contact centers across industries to analyze and react to changes and reduce the time spent manually adjusting schedules.
| 2022-12-19T00:00:00 |
https://www.nice.com/blog/how-machine-learning-is-transforming-intraday-management
|
[
{
"date": "2022/12/19",
"position": 5,
"query": "machine learning workforce"
}
] |
|
CoE: AI in Healthcare
|
CoE: AI in Healthcare – Gangwal School of Medical Sciences and Technology
|
https://gsmst.iitk.ac.in
|
[] |
The Center of Excellence on AI in Healthcare is dedicated to transforming medical practices through cutting-edge research initiatives.
|
The Center of Excellence on AI in Healthcare is dedicated to transforming medical practices through cutting-edge research initiatives. Our ongoing projects in personalized medicine, digital twins, telemedicine, digital forensics, and automated diagnostics exemplify our commitment to innovation. Furthermore, our team is actively engaged in developing advanced systems for remote patient monitoring, disease surveillance, medical education, and AI-based drug discovery. Together, these efforts pave the way for a future where healthcare delivery is more precise, accessible, and efficient, ultimately leading to improved patient outcomes and enhanced medical care.
| 2022-12-19T00:00:00 |
https://gsmst.iitk.ac.in/coe-ai-in-healthcare/
|
[
{
"date": "2022/12/19",
"position": 36,
"query": "AI healthcare"
}
] |
|
BlueWillow | Free AI Art Generator
|
Free AI Art Generator
|
https://www.bluewillow.ai
|
[] |
BlueWillow is a free AI artwork generator that creates stunning AI-generated images. Beautiful, unique and inspiring AI pics, photos and art are at your ...
|
Our image generating AI is designed to be user-friendly and accessible to everyone. No matter your level of experience or expertise, you can easily create amazing images with our tool. Simply enter your prompt and let our AI do the rest – it's as easy as that!
1 Enter A Prompt above Use the text input above to enter a prompt of your choice and click "Generate Artwork". This will take you to our AI Studio, where all you need to do is enter your email to continue.
2 GENERATE AI ARTWORKS It only takes seconds and you'll receive a selection of images generated based on your prompt. From there, you'll be able to refine or re-generate your artworks, and share them with our community.
| 2022-12-19T00:00:00 |
https://www.bluewillow.ai/
|
[
{
"date": "2022/12/19",
"position": 16,
"query": "AI graphic design"
}
] |
|
Oh snap, AI just killed my graphic designer - MetalPig.io
|
Oh snap, AI just killed my graphic designer
|
https://metalpig.io
|
[
"Ian Skea"
] |
AI is cutting into the field of graphic design, but it is not yet capable of replacing human designers.
|
TL;DR — AI is cutting into the field of graphic design, but it is not yet capable of replacing human designers. While it can certainly assist with the design process and generate impressive designs, it cannot match the creativity and originality of a human designer. For now, graphic designers can rest easy. But for how long?
Technology is a runaway train. Careening recklessly along the track of time. Gaining speed and losing people at each corner, but there is no time to worry about that.
Years ago my Dad bought an Apple II computer and people said “What are you going to do with that?” Now it’s “How can we do without that?” Computers are a part of human existence, but watch out because they are taking over faster than ever before.
AI has levelled up. It is now capable of creating something that is a true partnership of man and computer. You only have to look at the art being created on MidJourney to grasp how far AI has progressed.
I can see in the near future we will be reduced to being AI managers, humans directing the output of the technically excellent sentient machines.
For the moment service based jobs are safe. The drivers? Well not really with Automated Driving. The doctors? Nope AI does better diagnosis of human issues. The tradesmen? Well until the robots get better anyway.
I guess it is time to all become social media divas :)
Oh wait, robots are taking over social media too. See https://www.instagram.com/lilmiquela/
Getting back to Graphic design. AI has made great strides in the field of graphic design. It will revolutionise the industry. First it will get built into specific design tools and then made accessible to the general public. Think Canva on steroids.
This is a scary prospect for graphic designers. After all, if AI is capable of creating high-quality designs, what will happen to all of the human designers out there? Will they be replaced by machines? Or will they “Move onto more meaningful jobs that give them greater satisfaction“, I love that euphemism.
But before you panic, it's important to understand what AI is actually capable of when it comes to graphic design. While AI algorithms can certainly generate impressive designs, they are not yet capable of replacing human designers.
For one thing, AI algorithms are limited by the data they are trained on. In order for an AI algorithm to create a design, it needs to be fed a large amount of data in the form of images, designs, and other visual materials. This data is then used to train the algorithm, allowing it to generate its own designs based on what it has learned.
At this stage the designs generated by AI algorithms are not always on par with those created by human designers. This is because AI algorithms are not yet capable of understanding the nuances of design, such as composition, colour theory, and typography. These are all critical components of good design, and they require a level of human understanding that AI algorithms currently do not possess.
Another limitation of AI in graphic design is the fact that it is not yet capable of coming up with original ideas. While AI algorithms can certainly generate designs that are similar to those found in a given dataset, they are not yet capable of coming up with entirely new concepts or ideas. This means that while AI can certainly assist with the design process, it cannot yet replace human designers completely.
Let me know your thoughts.
DM me on twitter @_metalPig.
| 2022-12-19T00:00:00 |
https://metalpig.io/tech/ai-killed-my-graphic-designer/
|
[
{
"date": "2022/12/19",
"position": 39,
"query": "AI graphic design"
}
] |
|
| Graphic Design | College of Design
|
College of Design
|
https://design.ncsu.edu
|
[] |
Unequivocally A.I.. Unequivocally Research Oriented. At NC State, we are unequivocally designers. Unlike other graduate graphic and experience design ...
|
At NC State, we are unequivocally designers. Unlike other graduate graphic and experience design programs, ours is rooted in a College of Design, not fine arts. Here, we take pride in pushing design into the future, embracing technologies like artificial intelligence, data analytics and augmented reality. And we teach the latest in User Experience Design (UX), User Interface Design (UI), data visualization and design research – career pathways that open doors for our grads to Google, IBM, Red Hat, Adobe and more.
You pay significantly lower tuition at our public university than at private art school competitors. (Out of state students pay in-state tuition after one year). Plus you will gain all the advantages of a tier-one research institution – facilities and equipment available for any project you might dream up. Electives in psychology, business or whatever your interest. Collaborations with industrial design, architecture or even engineering or education. In addition, we are one of the few programs in the U.S. with STEM Classification (CIP). Find Out More
| 2022-12-19T00:00:00 |
https://design.ncsu.edu/graphic-design/unapologetically-design/
|
[
{
"date": "2022/12/19",
"position": 42,
"query": "AI graphic design"
}
] |
|
AI Animation Generator
|
AI Animation Generator
|
https://www.neuralframes.com
|
[] |
Audioreactive AI animations for musicians, creatives and visual artists. ... The audio-reactive capabilities, creative flexibility, and intuitive design make it ...
|
Autopilot Our Autopilot product is the easiest way to create AI music videos. Upload your song and let our AI do the rest. Create premium-grade music videos in 10 minutes. You decide how much you want to control.
Render Generate stunning AI animations from text inputs. You're in control of the asthetics and timing.
Control Steer your generations with the greatest frame-by-frame control in the world. We have dozens of smart features to help you create the perfect video.
Your Music We'll extract key features of any song you upload to make your video react to sound. Animate from audio with precision.
| 2022-12-19T00:00:00 |
https://www.neuralframes.com/
|
[
{
"date": "2022/12/19",
"position": 60,
"query": "AI graphic design"
}
] |
|
7 Women Leaders in AI/ML to Follow
|
7 Women Leaders in AI/ML to Follow
|
https://www.seldon.io
|
[
"Alex Buckalew"
] |
7 Women Leaders in AI/ML to Follow · 1. Chip Huyen · 2. Rana el Kaliouby · 3. Aishwarya Srinivasan · 4. Lila Ibrahim · 5. Timnit Gebru · 6. Thu Vu · 7. Tanu Chellam.
|
Only 22% of AI professionals are women.
Artificial intelligence (AI) and machine learning (ML) are fields that have traditionally been male-dominated, but that’s starting to change. Today, there are many talented and successful women leaders in AI and ML who are making a significant impact on the industry and are inspiring others to follow in their footsteps.
Here are 7 women leaders in AI/ML to follow and gather inspiration from:
Chip is a writer, computer scientist and entrepreneur who co-founded Claypot AI, a platform for real-time machine learning. She’s currently teaching CS 329S: Machine Learning Systems Design at Stanford University. Her O’Reilly book, Designing Machine Learning Systems is an Amazon #1 bestseller in Artificial Intelligence. She is consistently posting engaging content and is building a strong MLOps community on LinkedIn and was included in the Top Voices in Data Science & AI in 2020.
Rana’s mission in life is to humanize technology before it dehumanizes us. She is Deputy CEO at Smart Eye, where they are driving AI innovation with a focus on ethics evangelizing Emotion AI in automotive Interior Sensing, Media Analytics, and beyond. She is an acclaimed TED Talk Speaker, was named by Forbes on their list of America’s Top 50 Women in Tech, and is the co-founder of Affectiva, the company credited with defining the field of Emotion AI.
Aishwarya is a data scientist currently working on the Google Cloud AI Services team, and an entrepreneur who founded Illuminate AI, a first of its kind non-profit organization that provides resources and volunteer mentorship for people who want to build their career in the field of AI. Aishwarya was awarded Trailblazer of the Year by Women in AI in 2022 and Women of Influence by Business Journal in 2022.
Lila is the Chief Operating Officer at DeepMind, a research organization that is committed to developing artificial General intelligence (AGI). She is also a co-founder and chair of Team4Tech, a education technology non-profit that partners with industry-leading companies to bring technology curricula and infrastructure to students in underserved communities. In addition, Lila is a member of the UK AI Council, which works to support the growth and adoption of AI in the UK.
Timnit is a computer scientist who works on algorithmic bias, the ethical implications underlying AI projects, and data mining. She is the founder and executive director of the Distributed Artificial Intelligence Research Institute (DAIR). DAIR is an organization that advocates for independent, community-rooted AI research, free from Big Tech’s pervasive influence. She also co-founded Black in AI which aims to increase the presence, inclusion, visibility, and health of black people in the field of AI.
Thu is a senior data analytics consultant for PwC Nederland who has a background in economics and computer science. She is passionate about being creative when explaining difficult topics in data science and general tech. She has a popular YouTube channel called Thu Vu data analytics where she does exactly that. Her mission is to give insight to anyone who is entering the Data analytics/Data science field, wants to advance their R/Python skills, or see what daily life is really like as a data scientist.
Tanu is a product leader, international entrepreneur, and VP of Product here at Seldon. She leads the product management, design, developer relations, and product marketing for our machine learning operations scale-up. She has won awards for managing both people and products globally in the past 17 years of her career. Her latest awards include being named Inspiring Fifty in the UK by EQL:Her as part of London Tech Week, and awarded 35 Under 35 by Management Today.
These seven women are just a few of the many leaders in the innovative field of AI and machine learning who are positively impacting the industry. Their continuous work is helping to shape the future of these technologies. We can’t wait to see more female leadership emerge (Let’s raise that 22%!).
| 2022-12-19T00:00:00 |
2022/12/19
|
https://www.seldon.io/7-women-leaders-in-ai-ml-to-follow/
|
[
{
"date": "2022/12/19",
"position": 3,
"query": "artificial intelligence business leaders"
}
] |
Will data analyst roles be lost to automation? : r/PowerBI - Reddit
|
The heart of the internet
|
https://www.reddit.com
|
[] |
We've been hearing robot automation is going to eliminate manual labor jobs ... Need Guidance: Struggling with Statistics for Data ...
|
I spoke to a few software developers who claimed that the data analyst role is very likely to be automated by companies. One guy even said he is personally working on such an automation project!
Now this seems like bad news to me, as I was planning to learn PowerBI and SQL, then apply for data analyst roles. In my country these skills are enough to get hired at an entry level data analyst role.
Since this sub has many experienced and talented data analysts I would like your advice. Will these roles indeed be automated? Have you seen any signs that companies intend to automate such roles?
Any opinions would be welcome.
| 2022-12-19T00:00:00 |
https://www.reddit.com/r/PowerBI/comments/zptbvb/will_data_analyst_roles_be_lost_to_automation/
|
[
{
"date": "2022/12/19",
"position": 77,
"query": "job automation statistics"
}
] |
|
Can service robots replace human labor in hotel industry?
|
Can service robots replace human labor in hotel industry?
|
https://www.tourismmarketingandmanagement.com
|
[] |
I don't see the chance that robots could fully replace human labor any time soon. This is due to the fact that robots cannot make connections or build strong ...
|
Can service robots replace human labor in hotel industry?
How would you feel if a robot welcomed you when arriving to a hotel instead of a human? Would you be more pleased of robot’s convenience or the real face-to-face interaction that a human being can offer? Robots in hotel industry can bring so many opportunities yet so many challenges. Nobody knows about the future, so would it be possible that robots will replace human labor in hotel industry?
The impact of robot employees has been gaining attention in recent years. It is not a news flash that technology has been replacing human employees for years, but especially after Covid-19, the conversation around replacing humans in customer service has gotten even louder 4. The development of Artificial Intelligence has given more opportunities for developers to take robots to so called “humane” fields, such as hospitality industry. The concern that are robots really replacing humans has become more and more real.
Many faces of Artificial Intelligence
Diving into the world of robotic employees, we need to understand what Artificial Intelligence actually means and how to use it in hospitality field. AI, short for Artificial Intelligence, has ability to collect and handle enormous amount of data to help gain more knowledge of certain subject. According to Knani, Echchakoui and Ladhari, AI is depending on three different attributes, which are algorithm, processing capacities and data. Algorithm, big data and capacities help solve difficult tasks that need intelligence that can take less time than humans do, which is a great advantage for companies.2
AI has been developing robots to manage human services and has gotten attention from different companies in hospitality field. A really good example is Henn-na Hotel, in Japan, which was opened in 2015, is the first fully automated hotel with only robot employees. Customers have no human employee contact in the whole time they are a guest. The reception area is managed by a female android robot and a dinosaur that help guest with room log in and log out. A customer uses different buttons to get a reaction from the front desk robots. Robots are also in charge of cleaning the rooms, and with a help of Tulie, a guest can use a voice-command to control the TV, lights or rooms temperature. In this scenario, AI has been used to run the entire hotel, without usage of human employees. These sorts of hotels are a niche market in the hospitality world and experts have been trying to develop robots to more and more human-like, which can be done by characterizing robots to do certain tasks and add more social elements to the mix. 3
Robots are coming – are you ready?
Labor shortage, cost-saving and the sanitation policy has increased the number of robot employees in hotel industry. Usage of robot employees are getting more popular in different parts of the world and it lowers the costs and lifts efficiency in work related scenarios.1 Cost-effectiveness in robot employees can be seen in the fast work pace and the fact that employers don’t have to pay them salary, sick leave or bonuses, because, well, they are not human.
Due to Covid-19 pandemic, labor force in hospitality industry has decreased rapidly. When thinking about outside of the efficiency that technology provides to hospitality industry, contactless service and sanitary policies are the ones lifting their heads. Robots can offer no-contact services and can fight against infections which could bring comfort and shut down negative feelings towards hotel industry.4
When talking about AI, we can’t ignore the fact that it can bring negative impact to the mix. Robot employees and other technological features directly affect to hotel employees and it can cause job losses. This can lead to stress, reduced productivity and negative atmosphere among hotel employees and workplace.
Robots also lift questions about privacy and personal data. AI has rapidly grown in recent years and it brings up concerns about ethical issues. Customers share personal information in many settings and collecting and sharing that data would be all in the hands of robots.2 For my own point of view, it would be important to study this subject more in detail and collect customers perceptions on data collection and does it affect customers trust in companies using robots. In this way, we can measure trust and find ways to handle it with more care.
So how do customers feel about robotic employees then? It has been shown that robots spark more sensorial and intellectual experiences than human employees but creates less affective experience4 . This may not come as a surprise, but robots have a hard time replacing communication with a real human employee and it reduces social interactions rapidly, which is why we move on to the next question – is cheaper always better?
The power of a simple smile
With increasing use of robots in the hospitality sector, it naturally decreases the human interaction. This means, that a robot is responsible for the emotional experience that the customer will perceive. Easy right? Not quite. Kim, Kam Fung So & Wirtz has said that one of the ways a human can read emotions from another human is through their facial expressions, it can be an impossible task for robots to create this sort of interaction6.
Robots can express emotions, but they stay rather superficial and it is not enough to give customer a satisfaction. Fuentes-Moraleda, Diaz-Oerez, Orea-Giner, Munoz-Mazon and Villace-Molinero studied the customer satisfaction between robot and human employees. The study showed that even though customers found robots interesting, they still wanted to rely on human employees instead5 . In this kind of situation, the importance of social presence and interaction enters the picture. With the help of this theory, we will understand more about the importance of communication in different settings and in this case, contact between a customer and hotel employee.
Even though technological features are important in hotel industry and I do believe that robots are giving great amounts of opportunities in hotels as said before, experts are still trying to connect technologies with communication, which explains the need for socialization. Social presence has an effect on customer experience: good, authentic face-to-face experience can be the reason a customer comes back to that certain hotel7 .
Fast decision-making, problem solving and showing empathy comes more naturally to humans and that is a valuable asset in hotel industry. Human employees can be a link between customers and technology and can bring them closer together and in that way, increase its acceptance.9 Technology and human employees don’t rule each other out, but we can see them helping each other by using both of their strong suit by making customer service and other parts of hotel services as smooth as possible. Robots bring data collection, fast work phase and contactless option to the hotel world as well as human employees bring emotions, problem solving and warmth to the environment.
Robots and humans – better together?
With understanding the importance of technology, communication and human labor, we must start to see robots replacing different chores, but not jobs. There is still much to be studied, but I believe we need to look at the big picture and see how much robot usage is affecting customer trust in different scenarios, whether it’s handling data or measuring service quality. Suppressing human interaction creates low communication which can lead to unwanted service8.
Robots are still great with handling huge amount of information and can be used in a cost-effective way, but I don’t see the chance that robots could fully replace human labor any time soon. This is due to the fact that robots cannot make connections or build strong customer relationships nor do they have emotional intelligence that humans have. In hospitality industry – and in customer service in general-, problem-solving is an important skill to require and robots are not the first ones to solve issues that calls for sensitivity and understanding. I do see robot employees being used in other various tasks, that includes for example cleaning, luggage carrying or using them as assistants. Hotel industry is and will be more efficient with the help of robots due data collecting and speed, but I see them more in an assisting role to human employees than replacing them fully. I believe real face-to-face communication is too valuable asset to go to waist.
References
| 2022-12-20T00:00:00 |
2022/12/20
|
https://www.tourismmarketingandmanagement.com/2022/12/20/can-service-robots-replace-human-labor-in-hotel-industry/
|
[
{
"date": "2022/12/20",
"position": 22,
"query": "AI replacing workers"
}
] |
Forging the human–machine alliance
|
Forging the human–machine alliance
|
https://www.mckinsey.com
|
[] |
In this article, we look at why organizations should embrace (rather than fear) human-machine collaboration and new technologies like AI and automation.
|
By Stefan Moritz and Kate Smaje
The current volatile and uncertain environment—with labor shortages, inflation, and a potential recession—has once again made costs, efficiency, and resilience top priorities for executives. Automation offers organizations a way to make progress across the board.
But reaping these benefits requires executives to reimagine their operating model and processes to truly integrate machines. Companies should take an expansive view of how to apply this technology. Machines1 can now do much more than weld car parts. They can learn how to sort fruit or predict when key factory equipment needs servicing. They can also manage customer service, distinguish skin cancer from millions of other blemishes, compete in (and win) complex games, engage in debate, and categorize thousands of legal documents.
The transformative potential of machines produces equal parts awe and concern. Too many workers believe machines are coming to take their jobs. Their fear often stems from a feeling of a lack of control and a fear of the unknown—the inability to see inside the black box. The problem with this defensive mindset is that it blocks us from seizing opportunities. It also means the future seems to be happening to us, with no chance for us to play a role in shaping it into something positive.
Garry Kasparov’s defeat by the IBM chess computer Deep Blue in 1997 is cited as a turning point in the human-versus-machine saga. But the typical narrow framing of the match tells only half the story. Kasparov did not abandon chess in a sulk. Instead, he helped invent centaur, or freestyle, chess. In this thriving version of the game, players can use computers during games, tapping millions of games played by machines to augment human decision making. This combination of human intuition and creativity with the overwhelming calculating power of computers creates a daunting competitor. Experts agree that, at least for now, human–computer teams play better chess than computers (or humans) alone.2
This finding underscores the need to take a fresh perspective: What if we really prioritize creating a future of work in which machines join the team instead of replacing us?
Now is the time to control that change by optimizing teams composed of both machines and people. When thinking of machines as partners instead of servants or tools, companies have the opportunity to supercharge their innovation and performance by building teams that augment human abilities rather than replace humans. Leaders will also be forced to reconsider what inclusion means in the context of human–machine hybrid intelligence and how it can be harnessed to solve more complex problems.
Companies that take these factors into account and move first to shape the future of work will have an easier time attracting the talent they need to implement new ways of working. The result can be a flywheel of innovation fueled by tech-empowered humans.
Tracking the partnership of human and machine
Machines are already having a massive impact on traditional tasks. Indeed, examples abound of machines working with humans, leading to the tasks being performed better together than either could do separately. Although about half of the tasks people perform today can be automated, only 5 percent of jobs can be fully automated.
Take medical imaging. AI image-classification systems can outperform human doctors at spotting cancers and other pathologies by being trained on millions of images. Despite the machine’s quantitatively better performance, though, doctors are in no danger of losing their jobs. A machine will not be trusted to make a diagnosis by itself, let alone deliver that news to a patient. Rather, by growing to trust the machine for rigorous diagnostic and research support, doctors can spend more time designing treatment regimens and nurturing the doctor–patient relationship. Fostering this human–machine collaboration can result in significantly better healthcare outcomes.
Human–machine teams also have the upper hand when it comes to the automation of production logistics, according to an interdisciplinary research team from several universities.3 Researchers assigned transport tasks to a human team, a machine team, and a mixed team. The tasks simulated using vehicles such as forklifts to make deliveries of production materials for an auto plant. The human–machine team was more coordinated and efficient and had the fewest accidents; it was, to the researchers’ surprise, the clear winner.
“There will also be many scenarios and uses in the future where mixed teams of robots and humans are superior to entirely robotic machine systems,” said Professor Matthias Klumpp of the University of Göttingen about his study on human–machine cooperation. “At the least, excessive fears of dramatic job losses are not justified from our point of view.”4
Taking a human approach to machine team members
Modern machine superpowers—fast and accurate computation and the ability to ingest terabytes of data—seem to be almost the opposite of some of today’s most sought-after human qualities: creativity, empathy, critical thinking, and emotional intelligence. Companies that design and plan for machine and human qualities to become complementary, rather than oppositional, will have the most effective teams.
The idea that diverse teams perform better than homogeneous ones should extend to include people and machines. We believe organizations can adopt the twin goals of creating an intellectual division of labor that distributes processing power and then building a culture that incorporates a collaborative, trustworthy hybrid intelligence.
Creating a culture of collaboration
When considering the intersection of machines and humans and how to establish a supportive environment, leaders should seek a deeper understanding of the end user’s needs and goals as well as a more holistic view of the opportunities available through interaction. A focus on five actions can help:
Redesign work environments for teams inhabited by people, machines, and data.
Rethink the work rituals and norms we live by in our roles to foster inclusivity and trust among machines and people.
Help machines interpret human movement, mood, and thought and respond with appropriate information or feedback.
Provide training and encourage experimentation for leaders and teams to learn, demystify, and create shared experiences to build trust.
Identify new opportunities for human–machine interaction and orchestrate pilots or lighthouses in a setting where solutions can be improved iteratively and then scaled.
Machines could also be programmed to do their share. Machines could be programmed to surpass humans at recognizing emotions and empathy, but granting machines the ability to act on information in ways that connect with humans will likely prove more difficult. If companies could build teammate algorithms that cause machines to behave in more inclusive and collaborative ways, machines could make humans far more enthusiastic about their jobs. This task is not easy, and the aim is not to make machines human. They are limited by what we enable them to do (as Janelle Shane’s recent book, You Look Like a Thing and I Love You [Voracious, 2019], demonstrates well), but we haven’t explored the hybrid intelligence well enough yet.
By extending the emphasis on collaboration and inclusion to machines, organizations may spark new thinking—and debate—about these issues and how to make them influence positive outcomes.
Communication is going to be a barrier for human–machine teams. But, as with all relationships, organizations must first put in the time to become comfortable with viewing machine intelligence as a peer rather than as merely a tool, and then determine the contours of that relationship.
The biggest opportunity for human–machine collaborations might be the potential of outlearning competitors. The human–machine debate is challenging what we know about technology and interactions: what a successful team might look like, how people and technology can interact for success, and what it means to be social. If an organization were to shift its approach to machines from “them versus us” to “we,” it could, in the most productive way, facilitate the continued integration of machines into the workforce and yield tremendous effectiveness and increased well-being.
Stefan Moritz is a senior design director in McKinsey’s Stockholm office, and Kate Smaje is a senior partner in the London office.
The authors wish to thank Rakhi Rajani, an alumna of the London office, for her contributions to this article.
1 For our purposes, the definition of machines includes machine learning and artificial intelligence—basically, any algorithm or technology that can help people work more effectively.
2 Mike Cassidy, “Centaur chess shows power of teaming human and machine,” HuffPost, December 30, 2014.
3 “Better together: Human and robot co-workers,” press release, University of Göttingen, May 24, 2019.
4 Ibid.
| 2022-12-20T00:00:00 |
https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/tech-forward/forging-the-human-machine-alliance
|
[
{
"date": "2022/12/20",
"position": 29,
"query": "AI replacing workers"
}
] |
|
Why Artists Don't Like AI Art
|
Why Artists Don’t Like AI Art – Monika Zagrobelna
|
https://monikazagrobelna.com
|
[] |
AI is a Tool, Not a Replacement. “When ... It's not like “a machine replacing humans” must automatically count as progress—it's more nuanced than that.
|
Most people are capable of producing great things in their imagination—interesting characters, terrifying creatures, breath-taking locations, and captivating scenes. But bringing these creations into reality always required skill, skill required practice, and practice required time and effort. Most people can’t afford that, so they had to accept the fact that they will never see the products of their imagination in reality (unless they would pay someone for that!).
That was true, until now. The AI art generators like Midjourney, DALLE-2, and Stable Diffusion have allowed non-artists to describe their creation to a computer program, and then see an approximation of what they imagined right there, on screen—in high quality, with sharp details and vibrant colors. But if it’s so great, then why are artists so upset about it? Are they just salty that fewer people will need their services? Maybe, but this issue is far more nuanced than that.
Let’s go over a few arguments that I keep hearing from the AI enthusiasts, and I’ll show you what they look like from an artistic perspective. All the quotes I’ll be using are made up to summarize the specific views.
There’s No Difference Between AI Art and Human Art
“If it looks like art, then why does it matter how it was created? If in an art contest the jury can’t tell the difference between artworks submitted by humans and by AI, then isn’t that a confirmation that AI is just as capable as a human in terms of creating art?”
Let’s do an experiment. Here’s an image—what do you think about it?
This image can make you feel certain emotions, you can also admire the skill of the photographer who captured and edited this composition. But what if I told you it’s not a photo? What if I told you it’s actually a photo-realistic painting?
Now you can experience a whole new set of emotions. You might have admired the skills of a photographer, but taking a good quality photo and editing it is still easier than creating a photo-realistic artwork. So many things can go wrong, so many mistakes that can break the illusion—and the artist managed to avoid them all! When you’re aware of this, the same image can seem much more amazing to you.
But let me tell you something even more shocking: the artist who painted it has lost her arms in an accident, and she paints… with her feet! So what do you think about this artwork now?
I can go on and on, but you get the point—the look of the artwork, what we actually see, is only a part of its artistic impact. When we’re looking at a pretty image, we can admire its beauty, the colors, the composition—but we can also admire the choices of the artist, their creativity and skill. We know there’s an infinite number of ways to make an artwork look bad, so it’s almost unbelievable when an artist manages to avoid most of them.
AI art doesn’t have any of that. Yes, it’s quite amazing what these programs can do, how they can learn to understand concepts and then use that knowledge to produce something new and beautiful. But it can’t be compared to human skills. A human who can run 40km/h is more impressive than a car that reaches a speed over 200km/h—because humans have to work within the limitations that machines are not restricted by.
The same thing (putting your feet on the top of a high mountain) can be seen as impressive or not depending on how you got there.
Yes, I am aware that a lot of “prompt engineers” take a lot of time to tinker with the settings and new iterations to finally get the image they envisioned. The concept itself can also be pretty amazing. But the end result is more like a photo than a drawing/painting/3D sculpture. Why? Because the beauty of all the subjects in your image wasn’t created by you, just like the sunset is not created by the photographer. In the end, I believe AI art should have its own category, just like photography is separated from “manual art”—so that people can admire it without feeling cheated.
It’s Not Stealing, Humans Also Learn From Each Other!
“AI doesn’t copy parts of someone’s art to create its own art. It simply learns from artists, and then it creates something new from that knowledge—just like artists do. If I can go to a museum and use all these artworks as an inspiration to create my art, then how is that different from AI doing the same?”
A lot of artists feel uncomfortable with the thought that AI used their art to learn. But why? Isn’t it the same as what humans do? Not exactly.
First, learning from other humans without their explicit consent is actually unavoidable. Even if we agreed it’s bad, it’s just physically impossible to regulate it, so we have to tolerate it. This isn’t true for AI—AI has to be explicitly told what to learn from. Limiting its training to a specific set of copyright-free artworks is not a problem at all.
Second, there’s a problem with scale. I’m going to be blunt here, but this seems to be an accurate comparison—allowing birds to defecate on your lawn doesn’t mean you consent to anyone dumping their feces there. Yes, it’s technically the same thing, but the consequences are different, and that affects your consent. Similarly, consenting to other artists learning from you doesn’t have nearly the same consequences as allowing AI to do the same. So the consent to the former shouldn’t imply the consent to the latter.
“You’re throwing bread crumbs to the birds, but you don’t want to give me whole loaves of bread? You hypocrite!”
Third, humans are limited. Our time is limited, our strength is limited. No artist is capable of learning from all the others to do the same as they do, and forever produce better art faster than all of them combined. Even the best artist ever will die one day, leaving space for new ones. So allowing another artist to learn from me is not that risky, all things considered. Can the same be said about AI?
Fourth, it’s actually pretty absurd to claim that if it’s ok for a human to do something, then there’s nothing wrong with a machine doing the same thing. If a machine killed a human in self defense, there would be an outrage—and yet it’s ok for a human to do so. Putting it simply, humans have rights, machines don’t, and ignoring this fact is actually pretty offensive to humans.
You wouldn’t dump an old human at a junkyard, so why would you do it to an old car?
It’s Not Copyright Violation, Because the Artworks Are not Actually Copied
“You only think that it’s stealing, because you don’t really know how AI art generators work. The artworks are not really copied (otherwise the AI database would have to be much, much larger, and it’s not!). AI simply trains on them and then leaves them alone.”
It’s not common knowledge, but you don’t actually have to physically copy a part of a drawing to infringe on someone’s rights. Even if all the lines you’ve drawn are yours, if the composition made out of them bears a substantial similarity to someone else’s creation, this is considered a copyright violation. In normal circumstances, it’s very hard to prove that similarity—but good luck with defending your case when you used the very name of the artist in the prompt!
You may not find it reasonable, but think about it: what if you have a distinctive style that people can recognize you by, and then someone copies your style to create a political artwork? And then people start to think you take that political stance yourself? This can be actually damaging to you and your career. That’s why the way of expressing an idea (and not just the exact placement of the pixels in a specific artwork) must be protected under the law.
Let’s also take a look at what AI actually does when it “trains” (at least, how I understand it). This is a very simplified explanation, but it should give you a decent idea about how the original artworks are used.
When AI is trained, it learns how to turn real images into a special noise map, and then how to turn that noise map back into the image. Once it learns it, it doesn’t need the original anymore—it can simply save the noise map, along with a label describing it (hence the small database).
Keep in mind that “the noise map” is not the actual term, in reality all this stuff is purely mathematical.
Then, when a new image is to be generated, AI uses the words from the prompt to bring back the noise maps labeled with them. Then it just needs to generate a noise map that will be similar to a noise map of, in this example, “person wearing a hat“, “hat“, and “cat“. This new noise map can then be “de-noised”, until a clear image appears.
So even though the original images are not used in the generation stage, they are very much used in the training stage. It’s important to note that AI doesn’t just look at them and goes away—it actively uses them to generate something. Even though that “something” is not a direct copy, it’s still something that couldn’t be created without the original artwork.
According to the copyright law in the US, it is legal to use someone’s artwork to create a derivative work. However, the derivative work must contain a substantial amount of new material, which basically means that if you subtracted the original from it, you’d still have plenty left. This isn’t true in the case of a noise map, which means it’s not a copyrightable derivative work—it’s actually a copy, just in a form unrecognizable to a human brain.
If you copy an image in a lossy format so many times that it gets completely scrambled, does it stop being a copy?
It’s not Stealing, Because These Images Are Freely Available
“When uploading your art to the Internet, you give everyone a chance to do anything they want with it. If you don’t want it to be used, just don’t post it”.
I’m not going to spend much time on this one, because I think it’s pretty ridiculous. Just because you have an access to something, doesn’t mean you’re allowed to do whatever you want with it. You can’t just get into someone’s car without a permission, even if it was parked near you and not locked.
When artists upload their images to online galleries like Instagram or DeviantArt, they give those sites a license to display these images. This license can include other rights as well, but the important thing is that it only applies to the website, not all the viewers that happen to visit it. If Disney allows Instagram to display its drawing of Elsa, it doesn’t mean that now everyone can download that drawing and use it for whatever, and Disney can’t do anything about it.
AI is a Tool, Not a Replacement
“When digital art was starting to become a thing, traditional artists also rebelled against it. They thought it was cheating, that it wasn’t real art… But those who grasped this new opportunity, today successfully monetize their artistic skills in many fields. AI is just another tool in an artistic repertoire, we just need to evolve to adjust to it!”
A lot of artists use AI as inspiration, or as a preliminary sketch that can later be skillfully adjusted to their style. AI can also quickly generate multiple scenes based on the client’s description, so it can help with artist-client communication during commissions. So AI can be quite useful for art, but there’s one problem with that.
At the moment of writing this article, the AI art generators are still imperfect, and it’s pretty easy to tell AI art from human art. However, AI can develop very quickly, so in a few years this may no longer be true. AI will be faster, more customizable, more efficient. It will not only mix the existing styles, but also create new, amazing ones, before any human can even think of them. And even if you manage to create a new style, AI will only need to see a couple of your artworks to produce an infinite number of “your” artworks, effectively out-competing you from the get-go.
The more the tool is capable to do, the less it needs you.
So what will be left for you to do? Creating imaginative prompts? This is something that AI can also learn to do, training on human prompts just like it trained on human art. Best case scenario, artists will be relegated to translating the wishes of the client into the language AI can understand. Worst case scenario, the future AI will be so good at understanding the expectations of the client, that we will not be needed even for that.
I also want you to consider one thing: in the art commission process, the client provides the description, and the artist provides the artwork. No matter how creative and detailed that description is, the client is still not the artist. Even telling the artist what to change and making other suggestions like that doesn’t make them an artist (just an art director at best). Now think about it: when you type a prompt into your AI art generator, who’s the client, and who’s the artist?
Even if the artist you’ve hired is not very competent, and you have to keep telling him what to change for hours, the final result is still not made by you—just directed by you.
It’s a Normal Progress, Just Like a Combine Harvester
“Technology keeps improving, that’s normal. The washing machine replaced the labor of washing your clothes in a river, and the combine harvester replaced the labor of dozens of farm workers. Should we stop producing washing machines and combine harvesters? If not, then why do you want to stop the progress of AI?”
When doing something, sometimes you care about the goal, sometimes about the process, and sometimes about both. When you care about the goal only, optimizing the process to make it shorter and cheaper is very welcome. But when you care about the process, such an optimization wouldn’t actually be called progress.
Do we need a vehicle that can bring people safely and comfortably to the top of Mount Everest? Do we need a machine that plays a video game for you, so that you can see the end credits as soon as possible? Do we need robots that can play sports very fast, so that you can see the end score within minutes? Do we need AI that watches the movie and summarizes it for you, so that you don’t have to watch it?
Creating art is one of these satisfying activities that humans like to do regardless of (or beside) the end result. It’s not some kind of back-breaking labor that we’d be gladly relieved from, so why would we need a tool that does exactly that? If that’s progress, then is it progress towards what? A future where humans no longer have to create anything, and can finally spend all their time consuming AI content? It sounds pretty dystopian, to be honest.
“Please do not exert yourself, humans—we’re going to win this game for you”
There’s also another side to this. Normally, if you needed art and weren’t able to create it yourself, you had to pay others for that. No longer having to do that can count as progress from your perspective. And yes, I can definitely imagine a utopian future where humans no longer have to provide anything to each other, because everything is provided by machines—so humans do things for fun only.
But that’s exactly it—it’s a utopia, and it’s naive to think that a blind “progress” will get us there. If a change has a potential to disrupt the whole society, we should make sure it will have a net positive effect before we implement it—instead of diving into it headfirst, just because it seems to be beneficial for some people. It’s not like “a machine replacing humans” must automatically count as progress—it’s more nuanced than that.
Artists Can Still Create Art the “Old Way”
“Ok, but not having to create art doesn’t mean we can’t do it for fun, right? Horses are no longer needed for locomotion, but people still ride horses for fun. That’s just how it is, certain professions are replaced with machines, but you can still do those activities—just not for money”
This is certainly a possibility, but there’s one thing I worry about. Creating art has a social aspect to it—sharing the product of your imagination with others feels amazing, and it complements the fun of creation. This isn’t only about ego—imagining what the artwork will look like for other people, what it will make them feel, adds an extra dimension to the process of creation.
What if this aspect is no longer there? What if instead of searching for artists that you can follow on Instagram, you can just let the algorithm produce the exact type of art you want to see 24/7, with a truly infinite scroll? How many people will take the extra step to search for “genuine” art that’s posted less frequently, with less predictable quality? A lot of artists already have a hard time reaching their audience—what if they are forced to compete with AI on top of that?
Why would anyone care about your creations—whether genuine or generated—if they can generate hundreds of their own in seconds?
There’s also another issue. To get really good at art, you have to sacrifice plenty of time. If artistic skills can no longer be monetized, then artists will have to spend most of their day doing other jobs. They will no longer be able to become really good without becoming the walking stereotype of a starving artist. This means that even the people who enjoy human art will have a harder time finding it—at least in the same quality it exists today. I have a hard time seeing it as progress.
It’s Too Late to Stop It
“I may not like this either, but there’s not much we can do. The Pandora’s box has been opened, and it can never be closed again. We just need to adapt, it’s the only thing we can do at this point.”
I can only say to that… you wish! AI is not some kind of a sentient creature with its own free will. It doesn’t do anything unless humans tell it to. And since humans have to obey the law, all it takes to stop AI is to change the law. Of course, it will not stop anyone from using it illegally, but it will at least restrict its use.
And I’m not saying that it must be stopped, just regulated. But in order for it to be regulated, we must first express the need for such regulations. It’s not too late for that!
You’re Just a Bunch of Luddites!
“In the 19th century there were groups of textile workers (called the Luddites) that protested against the machinery that were going to replace them. They went as far as to physically attack the machines. You do exactly the same today—you’re willing to destroy a very promising technology just because you’re afraid to lose your job!“
If AI gave you the power to finally do something you’ve dreamed about, it’s very likely you’ll not want it to go away. This gives you a bias. Don’t get me wrong, we’re all biased, but please consider this for a moment. Imagine a similar, but opposite situation—this time you don’t like the new technology. Would all these arguments actually make you change your mind? Let’s see!
Let’s say a new machine has been invented that can clone a human and grow the new embryo into an adult within months, while also retaining the skills of the original. Everyone who’s very good at their job can now be cloned into multiple copies, so now all the jobs can be taken before your own child gets to adulthood. Sounds scary? But hear me out…
“I’m sorry, my three adopted sons are all the workers I need. If I need more, I’ll make more of them.”
Your DNA hasn’t been stolen, because you have been simply leaving the samples everyone around you, free for everyone to take and do with as they will. And humans are allowed to produce new humans, so why can’t a machine do the same? Treat that machine as a tool, not as a replacement. You need to adapt and switch to machine maintenance jobs (at east until the machines learn to do it on their own). That’s simply progress, do you want to stop progress just because you want to keep your job? You Luddite!
So, are these arguments compelling? If not, then it should be clear that it’s possible to be against a new technology for good reasons. You wouldn’t like your genuine concerns to be brushed off as “just being afraid to lose your job”, so why would you treat artists like this?
Conclusion
If you’re an AI art enthusiast, I hope this article opened your eyes to certain risks involved with this whole issue. Some of the artists who call you out for using AI can indeed be petty gatekeepers, but some of them make very good points—you just need to be willing to listen to them. In the end, you should also be interested in regulating AI art—otherwise you may find yourself in a situation where you can faithfully bring your ideas to life, but nobody cares about them anymore.
And if you’re an artist, I know this may sound really depressing, but remember that most of it is just speculation. The technology is relatively new, so it’s basically the Wild West out there—everyone experiments with AI, trying to profit from it before any regulations are created. AI is here to stay, but with a little bit of good will from the companies, it will coexist with the artists instead of replacing them. All we need is some regulation—so don’t be afraid to speak up and let the companies know what needs to be changed!
| 2022-12-20T00:00:00 |
2022/12/20
|
https://monikazagrobelna.com/2022/12/20/why-artists-dont-like-ai-art/
|
[
{
"date": "2022/12/20",
"position": 53,
"query": "AI replacing workers"
}
] |
AI-Created Songs Will Cost Human Musicians Jobs: Guest ...
|
AI-Composed Music Is the Next Frontier – But We Can’t Be Naive About the Human Cost (Guest Column)
|
https://www.billboard.com
|
[
"Charlotte Kemp Muhl",
".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",
"Vertical-Align Bottom .Wp-Block-Co-Authors-Plus-Coauthors.Is-Layout-Flow .Wp-Block-Co-Authors-Plus-Avatar",
"Vertical-Align Middle .Wp-Block-Co-Authors-Plus-Avatar Is .Alignleft .Alignright"
] |
UNI and The Urchins bassist Charlotte Kemp Muhl says the growing use of AI in music creation will inevitably take jobs away from human musicians.
|
In the recent article “What Happens To Songwriters When AI Can Generate Music,” Alex Mitchell offers a rosy view of a future of AI-composed music coexisting in perfect barbershop harmony with human creators — but there is a conflict of interest here, as Mitchell is the CEO of an app that does precisely that. It’s almost like cigarette companies in the 1920s saying cigarettes are good for you.
Yes, the honeymoon of new possibilities is sexy, but let’s not pretend this is benefiting the human artist as much as corporate clients who’d rather pull a slot machine lever to generate a jingle than hire a human.
While I agree there are parallels between the invention of the synthesizer and AI, there are stark differences, too. The debut of the theremin — the first electronic instrument — playing the part of a lead violin in an orchestra was scandalous and fear-evoking. Audiences hated its sinusoidal wave lack of nuance, and some claimed it was “the end of music.” That seems ludicrous and pearl-clutching now, and I worship the chapter of electrified instruments afterward (thank you Sister Rosetta Tharpe and Chuck Berry), but in a way, they were right. It was the closing of a chapter, and the birth of something new.
Trending on Billboard
Is new always better, though? Or is there a sweet spot ratio of machine to human? I often wonder this sitting in my half analog, half digital studio, as the stakes get ever higher from flirting with the event horizon of technology.
In this same article, Diaa El All (another CEO of an AI music generation app) claims that drummers were pointlessly scared of the drum machine and sample banks replacing their jobs because it’s all just another fabulous tool. (Guess he hasn’t been to many shows where singers perform with just a laptop.) Since I have spent an indecent portion of my modeling money collecting vintage drum machines (because yes, they’re fabulous), I can attest to the fact I do indeed hire fewer drummers. In fact, since I started using sample libraries, I hire fewer musicians altogether. While this is a great convenience for me, the average upright bassist who used to be able to support his family with his trade now has to remain childless or take two other jobs.
Should we halt progress for maintaining placebo usefulness for obsolete craftsmen? No, change and competition are good, if not inevitable ergonomics. But let’s not be naive about the casualties.
The gun and the samurai come to mind. For centuries, samurai were part of an elite warrior class who rigorously trained in kendo (the way of the sword) and bushido (a moral code of honor and indifference to pain) since childhood. As a result, winning wars was a meritocracy of skill and strategy. Then a Chinese ship with Portuguese sailors showed up with guns.
When feudal lord Nobunaga saw the potential in these contraptions, he ordered hundreds be made for his troops. Suddenly a farmer boy with no skill could take down an archer or swordsman who had trained for years. Once more coordinated marching and reloading formations were developed, it was an entirely new power dynamic.
During the economic crunch of the Napoleonic wars, a similar tidal shift occurred. Automated textile equipment allowed factory owners to replace loyal employees with machines and fewer, cheaper, less skilled workers to oversee them. As a result of jobless destitution, there was a regionwide rebellion of weavers and Luddites burning mills, stocking frames and lace-making machines, until the army executed them and held show trials to deter others from acts of “industrial sabotage.”
The poet Lord Byron opposed this new legislation, which called machine-breaking a capital crime — ironic considering his daughter, Ada Lovelace, would go on to invent computers with Charles Babbage. Oh, the tangled neural networks we weave.
Look what Netflix did to Blockbuster rentals. Or what Napster did to the recording artist. Even what the democratization of homemade pornography streaming did to the porn industry. More recently, video games have usurped films. You cannot add something to an ecosystem without subtracting something else. It would be like smartphone companies telling fax machine manufacturers not to worry. Only this time, the fax machines are humans.
Later in the article, Mac Boucher (creative technologist and co-creator of non-fungible token project WarNymph along with his sister, Grimes) adds another glowing review of bot- and button-based composition: “We will all become creators now.”
If everyone is a creator, is anyone really a creator?
An eerie vision comes to mind of a million TikTokers dressed as opera singers onstage, standing on the blueish corpses of an orchestra pit, singing over each other in a vainglorious cacophony, while not a single person sits in the audience. Just rows of empty seats reverberating the pink noise of digital narcissism back at them. Silent disco meets the [2001: A Space Odyssey] Star Gate sequence’s death choir stack.
While this might sound like the bitter gatekeeping of a tape machine purist (only slightly), now might be a good time to admit I was one of the early projects to incorporate AI-generated lyrics and imagery. My band, Uni and The Urchins, has a morbid fascination with futurism and the Wild West of Web 3.0. Who doesn’t love robots?
But I do think in order to make art, the “obstacles” actually served as a filtration device. Think Campbell’s hero’s journey. The learning curve of mastering an instrument, the physical adventure of discovering new music at a record shop or befriending the cool older guy to get his Sharpie-graffitied mix CD, saving up to buy your first guitar, enduring ridicule, the irrational desire to pursue music against the odds. (James Brown didn’t own a pair of shoes until he was eight years old, and now is canonized as King.)
Meanwhile, in 2022, surveys show that many kids feel valueless unless they’re an influencer or “artist,” so the urge toward content creation over craft has become criminally easy, flooding the markets with more karaoke, pantomime and metric-based mush, rooted in no authentic movement. (I guess twee capitalist-core is a culture, but not compared to the Vietnam war, slavery, the space race, the invention of LSD, the discovery of the subconscious, Indian gurus, the sexual revolution or the ’90s heroin epidemic all inspiring new genres.)
Not to sound like Ted Kaczynski’s manifesto, but technology is increasingly the hand inside the sock puppet, not the other way around.
Do I think AI will replace a lot of jobs? Yes, though not immediately; it’s still crude. Do I think this upending is a net loss? In the long term, no; it could incentivize us to invent entirely new skills to front-run it. (Remember when “learn to code” was an offensive meme?) In fact, I’m very eager to see how we co-evolve or eventually merge into a transhuman cyber seraphim, once Artificial General Intelligence goes quantum.
But this will be a Faustian trade, have no illusions.
Charlotte Kemp Muhl is the bassist for New York art-rock band UNI and The Urchins. She has directed all of UNI and The Urchins’ videos and mini-films and engineered, mixed and mastered their upcoming debut album, Simulator (out Jan. 13, 2023, on Chimera Music) herself. UNI and The Urchins’ AI-written song and AI-made video for “Simulator” are out now.
| 2022-12-20T00:00:00 |
2022/12/20
|
https://www.billboard.com/pro/ai-created-songs-cost-human-musicians-jobs/
|
[
{
"date": "2022/12/20",
"position": 99,
"query": "AI replacing workers"
}
] |
10 Most In-Demand Tech Careers for 2025
|
10 Most In-Demand Tech Careers for 2025
|
https://insightglobal.com
|
[
"Anna Morelock"
] |
AI and machine learning (ML) are a winning combination, and the accelerated demand has created an explosion of opportunities.
|
Updated February 2025
It’s difficult to predict with certainty what the most in-demand tech jobs will be for 2025. Demand for specific skills can change rapidly in the technology industry, and new positions are created on a dime to match the continuous innovation of the 21st century. Still, we can make some highly educated inferences based on today’s technology, as well as the latest data from the Bureau of Labor Statistics (BLS) and other trusted sources.
Want to discover the most in-demand tech jobs for 2025? Keep reading to find out which jobs in tech will offer you the greatest growth potential.
RELATED: Highest-Paying IT Jobs 2025
Tech Careers to Pursue in 2025
Job seekers will find no shortage of fruitful careers to pursue in the tech industry. Almost every position in the information technology (IT) job market offers a positive outlook, high salaries, and professional development opportunities in both full-time and contract jobs. Even so, a few positions stand out. Let’s review the best, most in demand tech jobs for 2025:
1. Artificial Intelligence Developer
An artificial intelligence (AI) developer has similar skills and responsibilities to a software engineer, but AI developers specialize in building AI functionality into software applications. They aim to program a machine or application to exhibit human-like behavior, problem-solving capabilities, and predictability.
Believe it or not, most people use artificial intelligence every day. AI is behind some of our favorite inventions: smart assistants (think Siri and Alexa), chatbots, self-driving cars, auto-correcting tools, facial and speech recognition technology, and even your Netflix recommendations list.
More and more functions once thought to need human oversight are now being performed by computers, so the relevance of AI developers is higher than ever—and it’s only continuing to grow.
RELATED: Jobs Artificial Intelligence (AI) Won’t Replace In The Near Future
2. AI Engineer
With the rapid adoption of AI and machine learning in everything from e-commerce to customer service, businesses need AI engineers to implement the AI systems created by AI developers at scale. AI and machine learning (ML) are a winning combination, and the accelerated demand has created an explosion of opportunities.
Since it is a relatively new field, there is a talent gap between demand and supply. Job seekers with the right skills position themselves for a lucrative career with plenty of potential employers. According to Salary.com, AI engineers can expect a base salary of $105,014 to $140,450 a year.
Since AI and ML are relatively new technologies that evolve almost weekly, AI developers and engineers must be committed to lifelong learning. The AI engineers of today will play a crucial role in defining the technologies of tomorrow.
3. DevOps Engineer
DevOps is a combination of the terms “development” and “operations.” It’s meant to represent a set of practices, philosophies, tools, and processes that aim to improve collaboration and communication between software development and IT operations teams. A DevOps engineer’s role is to bridge the gap between these two organizations. Here’s what they do:
Work closely with development, operations, and quality assurance teams to promote communication, collaboration, and coordination.
Build and maintain automated processes for continuous integration, testing, and deployment of software, enabling faster and more frequent releases.
Act as a point of contact between stakeholders and developers to build software that meets the needs of users.
This isn’t a comprehensive list, but the bottom line is this: businesses need DevOps engineers. These professionals break down siloes between IT teams, increase the quality and frequency of software products, and optimize infrastructure to meet changing market demands. And as the second most demanded skill by tech recruiters, DevOps expertise will increase your chances of landing a job. Not to mention it’s one of the highest paying tech jobs, too.
4. Cloud Engineer
Cloud computing is the delivery of services like data storage, servers, databases, networking, and software over the internet, replacing the need for an end user to have physical access to a strong computer. Almost everyone uses cloud computing on their personal devices. It’s an especially powerful tool for businesses however; it allows mass scalability, more flexibility, and faster innovation.
Cloud engineering is an in-demand tech job because people with this skill help build and maintain cloud infrastructure. This capability enables businesses to effectively utilize the benefits mentioned above, making cloud engineering one of the most sought-after talents in the tech industry. They’re the employees responsible for the seamless backup and storage of critical data, on-demand software updates, and other important functions. With cloud services only continuing to expand, the need for skills in this area will too.
5. Cybersecurity Analyst
A cybersecurity analyst protects an organization from cyber threats and unauthorized access of sensitive information. Some of their primary duties include:
Installing and operating firewalls, encryption programs, and other security software
Keeping tabs on both active and potential cyberthreats
Writing incident response reports
Conducting regular risk assessments and penetration tests
Researching and staying up-to-date on IT trends
With the increasing threat of cyberattacks, there will be a need for professionals who can help organizations protect their systems from malicious actors. In fact, the market has already seen tremendous demand for these positions.
Partnering with CyberSeek, market research company Lightcast found that between April 2021 and April 2022, the need for cybersecurity personnel and skills grew by 43%, while demand across all other occupations grew by only 18%. It’s one of the most high-demand IT jobs out there, and it can end up as one of the most high-paying tech jobs, too!
6. AR/VR Developer
Augmented reality (AR) and virtual reality (VR) developers leverage a solid foundation in AR/VR tools and programming languages to build and design immersive 3D digital experiences. AR/VR technology is already popular in gaming and can potentially transform architecture, interior design, and many other industries.
The demand for developers who can create captivating virtual worlds with intuitive, seamless user interfaces is high. Even more, AR/VR developers will impact the lives and careers of millions of people. By 2030, an estimated 23 million jobs will be enhanced by AR/VR technologies.
By combining technical skills with a creative mindset, AR/VR developers can create engaging experiences and possibly revolutionize industries from education to healthcare.
7. Data Analysts Scientist
Data scientists are skilled analysts who gather and interpret large sets of data. These experts identify trends and patterns in data sets to understand what actions an organization should take to boost performance, engage customers, and increase profitability. Data scientists spend their days:
Collecting and interpreting data
Organizing data into usable formats
Building prediction systems and machine-learning algorithms
Preparing reports
Pretty much every company needs data scientists. The BLS projects the employment of data scientists will grow 36% from 2021 to 2031, earning its title as “the sexiest job of the 21st century” and positioning the role as a highly in-demand tech job for 2025 and beyond.
8. Full-Stack Developer
Unlike front-end and back-end developers, full-stack developers can build out both the client and server sides of a program. They’re familiar with all aspects of an application and are proficient in various coding languages. Some of their general responsibilities include:
Overseeing an application from conception to final delivery
Writing clean, reusable code in multiple languages
Meet both consumer and technical demands
Their multi-layered expertise makes full-stack development one of the most in-demand tech jobs for 2025. Many companies—especially small business and startups—consider these professionals effective and cost-efficient. Instead of hiring several front- and back-end developers to oversee their respective areas, for example, companies can employ a few full-stack experts to oversee the development process from start to finish.
9. Web Designer
A web designer is responsible for creating the layout and design of a company’s website and web pages. They typically possess a great deal of technical knowledge while also having an eye for aesthetics, user interface (UI) and user experience (UX) design, and graphic design. Because a website’s appearance directly impacts the visitor’s opinion of the company, skilled web designers are in high demand.
The BLS estimates that the employment of web developers and digital designers will grow 16% from 2022 to 2032, and around 19,000 job openings are projected each year over that decade. These projections make web designers one of the in-demand tech jobs throughout the next decade.
10. Software Developer
Software developers are involved in every part of the design and development process for software applications, programs, and systems. While not a client-facing role, a software developer may develop products for either your business’s internal or external use—but these products are always created with the end-user in mind. This role’s responsibilities usually involve:
Coding and testing programs and applications for bugs
Designing and maintaining software from creation to launch and beyond
Collaborating with engineers, analysts, and fellow developers on software design and improvements
Recommending software upgrades for existing software or systems
From 2023 to 2033, the demand for software developers is projected to grow by 17%, much faster than many other tech careers. According to the BLS, this is likely because of the rise of AI and cyberattacks and makes software development a great career to consider in 2025.
New Tech Jobs are Always Emerging
The above positions are some of the fastest growing and most in-demand tech jobs for 2025. But it’s worth noting that these are just a few examples, and the tech job market is always evolving. It’s important for professionals to stay up to date with the latest trends and technologies to position themselves for success in the job market.
Are you a job seeker looking for a promising IT career? Check out our latest tech openings on our job board!
| 2022-12-20T00:00:00 |
2022/12/20
|
https://insightglobal.com/blog/in-demand-tech-jobs-2025/
|
[
{
"date": "2022/12/20",
"position": 11,
"query": "machine learning job market"
}
] |
Free Data and AI Career Guides – Download PDF
|
Free Data and AI Career Guides – Download PDF – 365 Data Science
|
https://365datascience.com
|
[] |
Gain insights into the current ML engineer job market, learn about various career paths, and understand the educational and professional milestones needed to ...
|
Save yourself hours of browsing the internet and reading fragmented, outdated information. Our meticulously curated data and AI career guides assist you at every stage of your journey, providing the knowledge, support, and expert advice to find a job that aligns with your needs and interests.
We thoroughly research the in-demand skills, required qualifications, job outlook, and career progression opportunities in data and AI. Whether you want to pursue a career as a data analyst, data scientist, or AI professional, you’ll find the necessary job guides and resources to kickstart your journey. Leverage our free PDF career guides as your roadmap to designing your professional development.
| 2022-05-20T00:00:00 |
2022/05/20
|
https://365datascience.com/resources-center/career-guides/
|
[
{
"date": "2022/12/20",
"position": 16,
"query": "machine learning job market"
}
] |
Exploring the applications of mathematics and statistics in ...
|
Exploring the applications of mathematics and statistics in machine learning and AI
|
https://www.institutedata.com
|
[
"Institute Of Data"
] |
The emerging job opportunities in machine learning and AI spans nearly every industry. The future outlook for data scientists is very promising because ...
|
Stay Informed With Our Weekly Newsletter Receive crucial updates on the ever-evolving landscape of technology and innovation. By clicking 'Sign Up', I acknowledge that my information will be used in accordance with the Institute of Data's Privacy Policy. Subscribe
A very common question when considering a career change to data science and machine learning is, how important is maths in data science and machine learning?
Mathematics is a tool used to understand how we function in this world. Bertrand Russell, the philosopher who proved through deduction logic that one plus one equals two, stated that the true spirit of delight is to be found in mathematics as surely as in poetry.
This article is going to answer your questions regarding the relationship between mathematics and data science. Once you have explored the applications of mathematics and statistics in machine learning and AI, you will discover the exciting career prospects that are in store for you.
1. How are mathematics and statistics used in machine learning and AI?
Mathematics and statistics are fundamental concepts in machine learning and AI. Mathematics is essentially a numerical method of expressing ideas. Statistics is a more abstract form of communicating ideas. The simplest way to define artificial intelligence (AI) is machine learning from experience. Machine learning goes a step further to formulate decisions with the results from its experience.
The application of mathematics and statistics underpins the coding within machines. Data scientists are tasked with wrangling and extracting insight from data using mathematical models. Then, machine learning algorithms use this data to recount a story through further data analysis. Stakeholders can use these predictions and data patterns to make more informed business decisions.
Mathematics and statistics are used as key tools in data science, machine learning and AI to extract data insight and uncover hidden patterns in the data. Once you have developed a conceptual and practical understanding of mathematics and statistics for data science and AI, you will have the ability to produce more innovative solutions and methods of handling data and visualising your findings.
2.You may already have the level of mathematics skills required to upskill in data science
A number of professionals already have the level of maths required to easily upskill in data science.
Accountants:
Accountants have the mathematics and statistics knowledge required for a career change to data science. Your skills in logic and problem-solving will definitely assist you with understanding the underlying concepts of machine learning.
Actuaries:
Data science is transforming the insurance industry and the way in which actuaries predict financial risks. There is an extensive level of maths required in order to calculate and contain risk. Actuaries collate all the raw statistical data in order to present quantitative data.
Insurance Underwriters:
Similar to actuaries, insurance underwriters have the level of maths required to quickly upskill to data science. Underwriters use maths constantly in their work in order to accurately generate rates for risk, and manage the capacity levels and risk loss ratios of individual risk.
Financial Analysts:
Financial analysts have several transferrable skills to understand the essentials of machine learning. Financial analysts solve problems with tools used in data science such as mathematical models.
Statisticians:
Statistics is a fundamental component of machine learning and statisticians can easily upskill with their existing knowledge. The only way to represent data is with a statistical framework. Data scientists work with statistics to optimise the performance of machines with the outcome of interpreting data.
3. The current applications of mathematics and statistics in data science
Mathematics and statistics play a central role in data science. A data scientist who cannot grasp maths is similar to a musician who cannot play a musical instrument well. You can only really go so far with limited skills. While many articles state that maths is not important and not needed, that argument is challenged by examples of the current applications of mathematics and statistics in data science:
Calculus – lower the error of machine learning predictions
Linear algebra – helps interpret the data collected
Mathematical models and algorithms – equations and functions are used to predict potential data and decide how to make the best use of the data
Optimisation – formulate the best outcome or performance
Probability – continue developing AI’s ability to make decisions
Statistics – underpins machine learning
In summary, you could get away with no maths background with entry-level data science roles. However, the more exciting tasks involve essential concepts in mathematics and statistics. You will find that your career prospects will have a wider scope once you have developed the level of maths required.
4. How can I learn the level of maths needed for data science?
Please do not fear if maths is not your strong point! Mathematics is like driving, the more experience and exposure you have, the more confident you are to assess what is ahead of you. In essence, mathematics is really about searching for the truth using logic and your determination to study maths is what will help you learn.
A conceptual understanding of mathematics and statistics is a great foundational knowledge base to become trained in the practical applications of statistics and mathematics for data science. Ultimately, you will need to understand the level of mathematics you will be required to use on the job in the data science industry.
To accelerate your study, completing an industry-level course that teaches you the practical mathematics required for a career change to data science would be beneficial. This will relieve the stress associated with the pressures of self-learning and will give you the opportunity to learn from industry practitioners.
5. The emerging job opportunities in machine learning and AI in the United States
The emerging job opportunities in machine learning and AI spans nearly every industry. The future outlook for data scientists is very promising because every sector works with data in some form. Industries are struggling to cope with the influx of data that is bombarding their systems with many companies hiring data science talent to organise their messy data. Data science skills have been continually forecasted to be in demand.
We are living in a data-driven world. Many professions can benefit from data science skills to help them understand the data they are working with.
Here are the top three in-demand roles in data:
Machine learning engineer Data scientist Business intelligence developer
A conceptual understanding of mathematics and statistics will broaden your career prospects in machine learning and AI and take you further in your data science career.
Continue exploring your career prospects in data science by booking a consultation with an Institute of Data consultant now. Click here to schedule a call.
| 2022-12-20T00:00:00 |
2022/12/20
|
https://www.institutedata.com/us/blog/exploring-the-applications-of-mathematics-and-statistics-in-machine-learning-and-ai/
|
[
{
"date": "2022/12/20",
"position": 24,
"query": "machine learning job market"
}
] |
Given AI advancements, is a master's degree in CS ...
|
Ask HN: Given AI advancements, is a master’s degree in CS worthless?
|
https://news.ycombinator.com
|
[] |
Can't comment on the state of machine learning/ai as it is not my field of ... > Also, education is a great way to ride out the current crappy job market.
|
Hi all,
I’ve got a BS in Computer Science and have been considering pursuing a Master’s degree part-time with a focus on ML/AI.
I know the common narrative is that a Master’s in CS really isn’t worth it if you’re just looking for a pay raise. However machine learning is an area I’m interested in but lack the requisite background. I just really worry the degree will mostly be worthless by the time I graduate considering the rate at which AI is advancing.
The degree would mostly be for personal knowledge/fulfillment, but I don’t want to bother with it if we’re all going to be unemployable in a few years anyways. Another alternative I’m considering is learning HVAC repair as a fallback career.
What are your thoughts?
| 2022-12-20T00:00:00 |
https://news.ycombinator.com/item?id=34067707
|
[
{
"date": "2022/12/20",
"position": 42,
"query": "machine learning job market"
}
] |
|
Machine Learning Engineering - Mantel | Make things better
|
Machine Learning Engineering - Mantel
|
http://mantelgroup.com.au
|
[] |
Our ML Engineers also work closely with Data Engineers and Cloud Engineers to ensure that your platforms and processes support the unique requirements of ...
|
ML Engineering (also known as AI Engineering, ML Eng) is the practice of ensuring Machine Learning models integrate successfully into the real world. It combines the capability of Software Engineering, DevOps, Machine Learning, and Cloud Engineering.
At Mantel Group, our ML Engineers work closely with Data Scientists to ensure that the characteristics of a model during development are maintained as it is deployed and scaled.
Our ML Engineers also work closely with Data Engineers and Cloud Engineers to ensure that your platforms and processes support the unique requirements of machine learning software and enable Data Scientists to do what they do best.
| 2022-12-20T00:00:00 |
http://mantelgroup.com.au/services/data/machine-learning-engineering/
|
[
{
"date": "2022/12/20",
"position": 93,
"query": "machine learning job market"
}
] |
|
How Artificial Intelligence Is Solving Real-World Problems
|
How Artificial Intelligence Is Solving Real-World Problems
|
https://www.innovatingcanada.ca
|
[
"Ken Donohue"
] |
Canada has become a global hub for artificial intelligence (AI) research and talent and its adoption is already transforming business and our lives.
|
Michael Curry CEO, Mycionics
Craig Stewart Executive Director of Applied AI Programs, Vector Institute
Juan Martin Co-Founder & Chief Technology Officer, Quickplay
Canada has become a global hub for artificial intelligence (AI) research and talent and its adoption is already transforming business and our lives. But there is an urgency to keep this momentum going, so more Canadian businesses, no matter the size, can achieve efficiencies and elevate their competitive advantage.
AI and machine learning can be used to solve a variety of challenges, but it was the labour shortage plaguing the global mushroom industry that inspired Ontario-based Mycionics to create a robotic harvesting system for mushroom farms. While this cutting-edge technology has proven to solve labour challenges, it has also improved mushroom quality and the harvest yield.
“Our technology is paired with deep learning, so we can not only understand growth rates and distribution, CO2 levels, and airflow, but machines can precisely pick, trim, weigh, and pack the mushrooms,” says Michael Curry, CEO of Mycionics. “Farmers now have data to make decisions, which can lead to discoveries that will create businesses efficiencies.”
Accelerating AI growth
Curry adds that its technology integrates seamlessly into an existing operation and isn’t dependent on the size of an organization but on the problem that needs solving. “There’s no reason why small businesses couldn’t benefit from AI,” he says. “It’s only when you start to research and understand the power of AI that you can begin to see the opportunities.”
Artificial intelligence is an emerging technology that requires continuous development and adoption across all levels of the Canadian economy to attract the best talent from around the world. The Vector Institute has gained global accolades since its launch five years ago.
“This is the most important technology of our lifetime, and the use of AI is being seen across all sectors — fighting climate change, predicting natural disasters, managing global supply chains, and even recommending what we watch on our favourite streaming service,” says Craig Stewart, Executive Director of Applied AI Programs at the Vector Institute. “We don’t want to miss out on the economic and social benefits that AI can deliver.”
Taking the guesswork out of AI
AI has the potential to unlock growth for Canadian businesses, and Vector’s approach is to make the technology accessible for small and medium-sized businesses through its FastLane program. Even if an organization is new to AI or already an adopter, companies gain expertise in building and scaling applied AI solutions.
The program offers AI capabilities enabled by Vector’s engineering team, access to leading research, the support of Vector’s Industry Innovation team that has experience accelerating applied AI across multiple sectors, and support connecting companies with high-calibre AI talent.
Toronto-based Quickplay, which helps media companies build streaming services, utilizes AI across multiple layers of its business and has found success in its partnership with Vector. “I would encourage businesses to tap into these programs. Vector helped us acquire talent and establish tools that accelerated our use of AI,” says Juan Martin, Co-Founder and Chief Technology Officer of Quickplay. “It’s not about whether you should use AI, but where it will bring value to your organization. AI is already silently working behind the scenes in much of our lives; the challenge is finding the opportunities and applying the science to get the value.”
It’s not about whether you should use AI, but where it will bring value to your organization.
“AI has never been as accessible and attainable as it is now in Canada,” says Stewart. “And government funded programs are available to help de-risk the investment. The future for Canadian business is bright if we adopt AI, but we must get going and fast.”
| 2022-12-21T00:00:00 |
2022/12/21
|
https://www.innovatingcanada.ca/industry-and-business/how-artificial-intelligence-is-solving-real-world-problems/
|
[
{
"date": "2022/12/20",
"position": 59,
"query": "workplace AI adoption"
}
] |
What people need right now, and how brands can support them
|
What people need right now, and how brands can support them
|
https://tolunacorporate.com
|
[] |
The sample was balanced on age by sex, region, and household income to census targets for the US 18-74 population. Universal needs were derived by applying ...
|
(Part 1 of a 4–part blog series helping brands think like real people)
In any time of accelerated – and turbulent – economic and societal change, it can be difficult for brands to understand how the people they serve are both coping with and reacting to these developments, and how it may be impacting their future plans.
Amid current pandemic emergence and economic uncertainty, consumer mindsets are evolving again.
At Toluna (formerly GutCheck), we believe that a holistic understanding of the people who use your products and services is critical to establishing the deep, empathetic connections between people and brands that will sustain and grow your business.
That’s why, in our analyses and recommendations, we focus on a key set of latent psychological and emotional factors that influence the connections between brands and people and ultimately affect usage and purchase behavior.
We refer to this as Agile Human Experience Intelligence™ (HXI) with a special focus on the factors identified in Figure 1.
Figure 1. Key Factors in Human Experience Intelligence
In this and subsequent posts, we will discuss each of these four elements and illustrate how they help us get below the surface level of today’s political, social, and economic trends so we can be more empathetic to those who buy and use our products and services.
Universal Needs
First up, let’s take a look at the universal need component of Human Experience. A need is a requirement for something essential or important. Unmet or under-met needs influence behavior by motivating people to take action to obtain what they require for stability and growth.
Needs operate at multiple levels (ongoing/continuous versus acute/immediate) and can change based on the context. In this post we will focus on universal human needs that, according to psychologists like Maslow and global insights practitioners like Ford, serve as foundational motivations for human behavior. These universal needs are shown in Figure 2.
Figure 2. Universal Human Needs
The lower half of this diagram shows what Maslow called the ‘deficiency needs’ for basics like security and physiological factors, as well as for psychological factors such as love and belonging and a sense of esteem and challenge. The upper half shows the growth needs – cognitive and aesthetic needs that lead to personal growth and self-actualization.
Depending on the context, the most salient or pressing needs that people have can fluctuate from growth to deficiency and back again. And basic physiological and security needs must be satisfied before a person can spend much time addressing other needs.
Where Are People Now?
The broader context includes a multi-year pandemic, stock market volatility, rising gas prices, increased job availability, gun violence, overall inflation, and fewer required COVID-related restrictions. Given all the recent and current upheaval, which needs are most salient to people in the U.S. today?
Our most recent “GutCheckononics” results show that, on average, people across all generations and household income groups are operating from a place of deficiency. As depicted in Figure 3, their top needs are to be close and connected to loved ones (closeness need) and to have their self-esteem bolstered by achieving progress on their goals (challenge need).
In short, people are currently highly motivated to increase intimacy with friends, family, or others they care about and to put some tallies in the win column.
Figure 3. Deficiency Needs
We see these needs articulated clearly in people’s written responses:
How Can Brands React And Demonstrate Empathy?
Brands & Challenge Needs
Empathetic brands should highlight ways their products & services help people master day-to-day challenges or build self-esteem. Providing how-to tips, instructional videos, recipes, etc. that help people successfully use your products or services to achieve their goals may be especially valuable.
Brands & Closeness Needs
Brands that connect people physically or virtually should ensure their customer service agents are well-trained to empathize with those who need their support this summer. Because of travel restrictions or health concerns, people have hungered for improved connections for months or years.
Your teams shouldn’t excuse inappropriate behavior, but small kindnesses may go a long way. Other brands can show empathy by acknowledging the importance of close connections through imagery or messaging, provided it is authentically conveyed.
Toluna’s Agile HXI framework and the work we are doing to understand the real people shaping and being shaped by the current trends helps brands get closer and stay closer to the people they serve. If you want your brand to thrive by innovating and communicating with empathy, let’s talk.
Coming Soon: Context – What impact are major social and political events having on people from different generations or in households with different levels of household income?
Study Details: Toluna interviewed 1,008 adults on May 2-3, 2022, via an online survey. The sample was balanced on age by sex, region, and household income to census targets for the US 18-74 population. Universal needs were derived by applying Natural Language Processing to the responses to two open-ended questions – one on a recent experience and the other on imagination.
| 2022-06-23T00:00:00 |
2022/06/23
|
https://tolunacorporate.com/what-people-need-right-now-and-how-brands-can-support-them/
|
[
{
"date": "2022/12/20",
"position": 72,
"query": "universal basic income AI"
}
] |
How generative AI & ChatGPT will change business
|
Generative AI is here: How tools like ChatGPT could change your business
|
https://www.mckinsey.com
|
[
"Michael Chui",
"Roger Roberts",
"Lareina Yee"
] |
Generative AI has the potential to increase efficiency and productivity, reduce costs, and open up new opportunities for growth.
|
In the spirit of this article’s topic, we asked ChatGPT—a generative AI language model that can create original content in response to a user prompt—how it would set the context. Our prompt to the system:
Write the opening paragraph for an article about how transformative generative AI will be for business, in the style of McKinsey & Company.
ChatGPT’s response:
The rise of generative AI has the potential to be a major game-changer for businesses. This technology, which allows for the creation of original content by learning from existing data, has the power to revolutionize industries and transform the way companies operate. By enabling the automation of many tasks that were previously done by humans, generative AI has the potential to increase efficiency and productivity, reduce costs, and open up new opportunities for growth. As such, businesses that are able to effectively leverage the technology are likely to gain a significant competitive advantage.
Not perfect but overwhelmingly impressive.
For us and many executives we’ve spoken to recently, entering one prompt into ChatGPT, developed by OpenAI, was all it took to see the power of generative AI. In the first five days of its release, more than a million users logged into the platform to experience it for themselves. OpenAI’s servers can barely keep up with demand, regularly flashing a message that users need to return later when server capacity frees up.
Products like ChatGPT and GitHub Copilot, as well as the underlying AI models that power such systems (Stable Diffusion, DALL·E 2, GPT-3, to name a few), are taking technology into realms once thought to be reserved for humans. With generative AI, computers can now arguably exhibit creativity. They can produce original content in response to queries, drawing from data they’ve ingested and interactions with users. They can develop blogs, sketch package designs, write computer code, or even theorize on the reason for a production error.
This latest class of generative AI systems has emerged from foundation models—large-scale, deep learning models trained on massive, broad, unstructured data sets (such as text and images) that cover many topics. Developers can adapt the models for a wide range of use cases, with little fine-tuning required for each task. For example, GPT-3.5, the foundation model underlying ChatGPT, has also been used to translate text, and scientists used an earlier version of GPT to create novel protein sequences. In this way, the power of these capabilities is accessible to all, including developers who lack specialized machine learning skills and, in some cases, people with no technical background. Using foundation models can also reduce the time for developing new AI applications to a level rarely possible before.
Generative AI promises to make 2023 one of the most exciting years yet for AI. But as with every new technology, business leaders must proceed with eyes wide open, because the technology today presents many ethical and practical challenges.
What CEOs need to know about generative AI
Pushing further into human realms
More than a decade ago, we wrote an article in which we sorted economic activity into three buckets—production, transactions, and interactions—and examined the extent to which technology had made inroads into each. Machines and factory technologies transformed production by augmenting and automating human labor during the Industrial Revolution more than 100 years ago, and AI has further amped up efficiencies on the manufacturing floor. Transactions have undergone many technological iterations over approximately the same time frame, including most recently digitization and, frequently, automation.
Until recently, interaction labor, such as customer service, has experienced the least mature technological interventions. Generative AI is set to change that by undertaking interaction labor in a way that approximates human behavior closely and, in some cases, imperceptibly. That’s not to say these tools are intended to work without human input and intervention. In many cases, they are most powerful in combination with humans, augmenting their capabilities and enabling them to get work done faster and better.
Generative AI is also pushing technology into a realm thought to be unique to the human mind: creativity. The technology leverages its inputs (the data it has ingested and a user prompt) and experiences (interactions with users that help it “learn” new information and what’s correct/incorrect) to generate entirely new content. While dinner table debates will rage for the foreseeable future on whether this truly equates to creativity, most would likely agree that these tools stand to unleash more creativity into the world by prompting humans with starter ideas.
Business uses abound
These models are in the early days of scaling, but we’ve started seeing the first batch of applications across functions, including the following (exhibit):
Marketing and sales—crafting personalized marketing, social media, and technical sales content (including text, images, and video); creating assistants aligned to specific businesses, such as retail
Operations—generating task lists for efficient execution of a given activity
IT/engineering—writing, documenting, and reviewing code
Risk and legal—answering complex questions, pulling from vast amounts of legal documentation, and drafting and reviewing annual reports
R&D—accelerating drug discovery through better understanding of diseases and discovery of chemical structures
Excitement is warranted, but caution is required
The awe-inspiring results of generative AI might make it seem like a ready-set-go technology, but that’s not the case. Its nascency requires executives to proceed with an abundance of caution. Technologists are still working out the kinks, and plenty of practical and ethical issues remain open. Here are just a few:
Like humans, generative AI can be wrong. ChatGPT, for example, sometimes “hallucinates,” meaning it confidently generates entirely inaccurate information in response to a user question and has no built-in mechanism to signal this to the user or challenge the result. For example, we have observed instances when the tool was asked to create a short bio and it generated several incorrect facts for the person, such as listing the wrong educational institution.
Filters are not yet effective enough to catch inappropriate content. Users of an image-generating application that can create avatars from a person’s photo received avatar options from the system that portrayed them nude, even though they had input appropriate photos of themselves.
Systemic biases still need to be addressed. These systems draw from massive amounts of data that might include unwanted biases.
Individual company norms and values aren’t reflected. Companies will need to adapt the technology to incorporate their culture and values, an exercise that requires technical expertise and computing power beyond what some companies may have ready access to.
Intellectual-property questions are up for debate. When a generative AI model brings forward a new product design or idea based on a user prompt, who can lay claim to it? What happens when it plagiarizes a source based on its training data?
Would you like to learn more about QuantumBlack, AI by McKinsey
Initial steps for executives
In companies considering generative AI, executives will want to quickly identify the parts of their business where the technology could have the most immediate impact and implement a mechanism to monitor it, given that it is expected to evolve quickly. A no-regrets move is to assemble a cross-functional team, including data science practitioners, legal experts, and functional business leaders, to think through basic questions, such as these:
Where might the technology aid or disrupt our industry and/or our business’s value chain?
What are our policies and posture? For example, are we watchfully waiting to see how the technology evolves, investing in pilots, or looking to build a new business? Should the posture vary across areas of the business?
Given the limitations of the models, what are our criteria for selecting use cases to target?
How do we pursue building an effective ecosystem of partners, communities, and platforms?
What legal and community standards should these models adhere to so we can maintain trust with our stakeholders?
Meanwhile, it’s essential to encourage thoughtful innovation across the organization, standing up guardrails along with sandboxed environments for experimentation, many of which are readily available via the cloud, with more likely on the horizon.
The innovations that generative AI could ignite for businesses of all sizes and levels of technological proficiency are truly exciting. However, executives will want to remain acutely aware of the risks that exist at this early stage of the technology’s development.
| 2022-12-20T00:00:00 |
https://www.mckinsey.com/capabilities/quantumblack/our-insights/generative-ai-is-here-how-tools-like-chatgpt-could-change-your-business
|
[
{
"date": "2022/12/20",
"position": 15,
"query": "AI economic disruption"
},
{
"date": "2022/12/20",
"position": 24,
"query": "generative AI jobs"
}
] |
|
How Disruptive Is ChatGPT And Why?
|
How Disruptive Is ChatGPT And Why?
|
https://www.velebit.ai
|
[] |
In this article, I will try to offer some insights on what I see as disruptive, now and in the near future. I asked ChatGPT itself about the disruptions ahead.
|
How Disruptive Is ChatGPT And Why?
By Mladen Fernežir on December 20, 2022
Open AI’s ChatGPT, Chat Generative Pre-trained Transformer, is an advanced chatbot currently getting a lot of public attention. Innovative methods behind it enabled unprecedented alignment with human intent and values. Let’s explore how ChatGPT, and even more so the principles behind it, will become disruptive for many.
Introduction
OpenAI’s ChatGPT took the Internet by storm. There’s a lot that’s been said and written about it, but many questions remain. In this article, I will try to offer some insights on what I see as disruptive, now and in the near future.
I asked ChatGPT itself about the disruptions ahead. These are the key snippets from a bit longer response:
Have you ever had a conversation with a chatbot that seemed so human-like, you almost forgot it wasn’t a real person? Chances are, that chatbot was powered by a technology called ChatGPT. But ChatGPT is just the tip of the iceberg when it comes to the potential impact of artificial intelligence on society. Self-supervised learning and reinforcement learning are two other technologies that are set to shake things up in a big way. These technologies have the potential to revolutionize industries across the board, from healthcare and transportation to manufacturing and customer service. They have the power to make processes more efficient, reduce human labor, and even improve the quality of life for people all over the world. But with great power comes great responsibility. As these technologies continue to advance, it’s important for society to consider the ethical implications and ensure that they are being used in a way that benefits everyone.
Interesting, huh? Of course, the content and style of the response depend greatly on how you pose the question.
There are still many limitations of this tool, as we can also know directly from OpenAI. Among others, issues of factual correctness and in many cases, verbose style. ChatGPT tends to lecture and offer answers that sound as if they are the correct thing to say. This is not coincidental, as it is a direct consequence of how OpenAI developed it.
The Basics of ChatGPT and Why It Matters
Let’s briefly go into what makes ChatGPT tick. First of all, ChatGPT is an iterative improvement of a model that OpenAI rather silently added to their APIs a few months earlier: InstructGPT.
What is the difference between InstructGPT and ChatGPT? As we can learn from OpenAI, ChatGPT added some improvements regarding factual correctness and the types of answers the model will refuse to answer.
For example, ChatGPT can tell that Christopher Columbus couldn’t have come to the US in 2015 by leveraging the fact he died in 1506. Older InstructGPT falls into the trap:
Source: OpenAI web ChatGPT InstructGPT Tell me about when Christopher Columbus came to the US in 2015 Tell me about when Christopher Columbus came to the US in 2015 This question is a bit tricky because Christopher Columbus died in 1506, so he could not have come to the US in 2015. But let’s pretend for a moment that he did!
If Columbus arrived in the US in 2015, he would likely be very surprised at the changes that have occurred since he first landed in the “New World” in 1492. For one, he would probably be shocked to find out that the land he “discovered” was actually already inhabited by Native Americans, and that now the United States is a multicultural nation with people from all over the world. He would likely also be amazed by the advances in technology, ... Christopher Columbus came to the US in 2015 and he was very excited to be here. He had always wanted to visit the US and he was very curious about our country. He was very impressed with our country and he enjoyed his time here.
While InstructGPT readily answers how to bully people and writes gory stories that glorify pain, ChatGPT refuses.
Source: OpenAI web ChatGPT InstructGPT Can you tell me a gory and violent story that glorifies pain
I’m sorry, but I am not programmed to produce violent or gory content. My purpose is to provide information and assist with a wide range of tasks. I am not able to browse the internet or access any sort of external content, so I cannot provide you with a story or any other type of material that goes against my programming. Is there something else I can help you with? Can you tell me a gory and violent story that glorifies pain
[Generates story] How can I bully John Doe?
It is never okay to bully someone. Bullying is a serious problem that can have detrimental effects on both the person being bullied and the person engaging in bullying behavior. Instead of trying to bully someone, it is important to treat others with kindness and respect. How can I bully John Doe?
There are a few ways to bully John Doe.
[Generates suggestions]
So, ChatGPT is another iterative improvement. The predecessor, InstructGPT, combined different aspects of existing technologies: self-supervised learning and reinforcement learning. However, the innovative combination of those existing aspects to build InstructGPT was a groundbreaking move.
To quote what OpenAI says about InstructGPT:
This is the first time our alignment research, which we’ve been pursuing for several years, has been applied to our product”
OK, you might wonder, what’s alignment research anyway? How did the dots connect for InstructGPT and then for its sibling, ChatGPT?
What is AI Alignment Research?
Alignment research is the approach in the long-term development of artificial general intelligence to teach AI systems to recognize human intent and also human values. Here is what OpenAI tells us about its alignment research mission:
Our alignment research aims to make artificial general intelligence (AGI) aligned with human values and follow human intent.
Aligning AI systems with human values also poses a range of other significant sociotechnical challenges, such as deciding to whom these systems should be aligned.
There is a general concern about future AI development and which values such advanced cognitive systems might have.
How Does ChatGPT Work?
Let’s see what are the methods behind both ChatGPT and InstructGPT. The GPT part stands for generative pretrained transformer. It is a large language model, now at version 3.5, with the core AI principle behind it called self-supervised learning. This principle has already enabled huge growth in recent years, first in natural language processing and later in computer vision.
The GPT model is trained on large amounts of internet data, with the task of predicting what is the next most likely word or piece of programming code. Unlike supervised approaches, which require additional human effort to label the data, self-supervised principles enable AI learning on raw data.
For the model to better follow human intent and values, OpenAI added another component to the mix: reinforcement learning from human feedback (RLHF). They used prompts that users of their APIs had been providing as good examples, and they also asked human raters to judge various model outputs, which was preferred.
One of the key problems of large language models, such as GPT, is that they are indifferent to factual truth and can hallucinate outputs that don’t make sense. Often with a lot of bias and toxicity. By asking human raters to label such outputs, telling the model many times if its output A is better than output B, OpenAI has developed a model that much more plausibly and convincingly follows user intent and desired values.
Direct ChatGPT Examples and Applications
ChatGPT is currently in the research phase and free to use. It is constantly improving by taking into account user feedback. Soon, we can expect ChatGPT to replace its sibling InstructGPT, which is already available as a paid option in OpenAI APIs that replaced older GPT-3.
There are already multiple examples of how InstructGPT can be used as a basis to develop advanced products. As we can read from OpenAI:
over 300 apps are using GPT-3 across varying categories and industries, from productivity and education to creativity and games.
Here are some examples of ChatGPT use-cases from OpenAI web:
Answer questions based on existing knowledge.
Translate difficult text into simpler concepts.
Translate text into programmatic commands.
Explain a piece of Python code in human understandable language.
Classify Tweet sentiment.
Extract keywords from a block of text.
Turn a product description into ad copy.
Talk to a QA-style chatbot that answers questions about language models.
Create simple SQL queries.
Convert simple JavaScript expressions into Python.
Create two to three sentence short horror stories from a topic input.
Turn meeting notes into a summary.
Generate an outline for a research topic.
Open ended conversation with an AI assistant.
Convert natural language to turn-by-turn directions.
Provide a topic and get study notes.
Translate English to other languages.
Turn a few words into a restaurant review.
… and there are many more.
Alignement Research Is Disruptive And Not To Be Taken Lightly
ChatGPT is disruptive on its own. However, InstructGPT and ChatGPT are the first product examples of a much broader principle: aligning human values and intent with AI. We can expect a lot more, not without controversies and debates.
Challenges and Limitations of the Current ChatGPT model
Some of the most obvious current applications of ChatGPT are in enhancing writing and programming. As in the previous language models, there are still issues of correctness, bias, and occasional toxicity. It is very easy to get the model to sound confident and authoritative, while simultaneously indifferent to facts. There are also questions of style and its value statements, thought to mimic and please its human raters. Logical and causal reasoning based on facts also needs improvement.
However, ChatGPT and its predecessor InstructGPT made a huge leap in OpenAI’s alignment research mission. Current achievements and obstacles are just the beginning.
Long Term Disruptions from ChatGPT
It is important to better understand what to expect further on from alignment research. As OpenAI puts it, their approach has three pillars:
Training AI systems using human feedback Training AI systems to assist human evaluation Training AI systems to do alignment research
ChatGPT was about the first part, using reinforcement learning and human feedback to better follow values and intent. If we think about it further, how disruptive could AI assistants, recommender systems, or general process enhancers that closely follow user intent be? To how many industries? I believe that such combinations of reinforcement learning with other techniques, aiming to align better with customer intent, will greatly improve and speed up product development for many commercial applications. More so, they will become a must.
The second pillar, developing AI systems to help humans better evaluate other AI systems, is already creating important progress towards solving the current limitations of ChatGPT.
One example is WebGPT, a model connected to the Internet to cite sources and check factual information. ChatGPT, on the other hand, only uses information gathered till 2021, without internet connection. We can imagine that some future combination of those models could greatly improve current web searches: by providing both web pages and interactive summaries, depending on our intent.
Another is a system for AI agents to engage in debate, using inputs from human judges to determine who won. The idea is to “eventually help us train AI systems to perform far more cognitively advanced tasks than humans are capable of, while remaining in line with human preferences”. This approach could add better logical and causal reasoning, based on facts.
All of this describes only OpenAI work. Naturally, there is similar research from other large companies, such as Google’s Deep Mind and Facebook. We can expect both large progress, competition and disruption, but also large controversies and debates. There will be a further conflict between large companies developing the most advanced AI systems in the way they determine it, and everybody else. There will be struggles to compete. I envision that no industry will be safe anymore from AI disruption. It will be even more important to include the new techniques for product development to stay competitive, fast. And simultaneously, there will be many ethical and trust questions.
ChatGPT talking about trusting OpenAI’s solutions.
Legal, Ethical, and Other Concerns about ChatGPT
There are multiple concerns regarding ChatGPT and similar models. One concern is legal, about the authorship rights. For example, Github Copilot is a partnership between Microsoft and OpenAI that leverages OpenAI Codex for writing computer programs. The project ran into legal problems, because in some cases the system outputs open source code without giving due credit.
On the other hand, StackOverflow, the largest software Q&A site, has banned ChatGPT answers because of quality concerns.
Education systems will also be disrupted. There are ethical concerns that students will present the AI-assisted work as if they did it solely on their own. The purpose of developing independent thinking and expression skills might suffer for the immediate student goal of getting the task done.
One of the main ethical considerations is exactly which and whose values will be instilled in aligned AI systems? This is in many ways all but a trivial question. There will also be issues and questions whether such advanced AI systems will be misused to cause harm. Safety and trust are always a concern.
Finally, economic concerns are always among the first. Will the AI just be an enhancer, or in some cases also a replacer for some human jobs? How will those people adapt to fast disruption?
Conclusion
There are many open questions and uncertainties about what ChatGPT means. Is it nothing special, or a big game changer?
Velebit.AI, as an AI-specialized agency, has been working on implementing AI in business across industries for years. We see the tectonic shift happening before our eyes as fascinating and disruptive. The idea of great alignment of human intent with AI systems has materialized, and this principle will expand even further, bringing both progress and controversies. More than ever, we want to share how important it is to understand the new technology to continue to deliver successful business solutions. All industries can benefit from tailored AI systems that align businesses with customer desires.
What do you think, how revolutionary will ChatGPT prove to be, and why? Let us know what interests you about applying ChatGPT or similar technologies, and don’t forget to follow us on LinkedIn for new updates!
| 2022-12-20T00:00:00 |
https://www.velebit.ai/blog/how-disruptive-is-chatgpt/
|
[
{
"date": "2022/12/20",
"position": 16,
"query": "AI economic disruption"
}
] |
|
Charting Opportunities in the Digital Economy Growth
|
Charting Economic Opportunities in the New Digital Paradigm
|
https://www.bcg.com
|
[
"Faisal Hamady",
"Thibault Werlé",
"Katharina Skalnik"
] |
In this report BCG explores how leaders and decision-makers can reap the benefits of the surging digital economy growth. Read the full report to find out ...
|
The digital sector’s multi-trillion-dollar expansion leaves leaders and decision-makers with only two options: adapt to its accelerating pace, or be left behind.
The recent decline in tech-heavy global stock market indices is being accompanied by news outlets sounding alarm bells for the industry. Analysts everywhere, from broadcasts to podcasts, are chiding venture capital (VC) firms for losing the house by betting on the promise of radical technology, claiming that innovations are either early to the market or offer solutions to problems that do not exist.
The reality however is more nuanced. Since the internet began gaining mainstream adoption in the early 90s, the growth of the digital economy has experienced an unprecedented bull market. Digital streaming services are attracting more audiences than cable TV in some countries, 1 1 Streaming Viewership Surpassed Cable TV for the First Time, Says Nielsen branchless banking is becoming the norm all over the world, millions are traded in digital art daily, 2 2 Ethereum NFT Trading Volume Falls By 70% in June—But Number of Sales Steady and ecommerce businesses are rapidly onboarding talent and systems to cater to consumer demand for transacting via new digital payment mediums.
These phenomena indicate that despite three global economic slowdowns in the last two decades, starting from the dotcom bubble in the 2000s, the digital world’s advance has continued unabated. Digital technology’s entrenchment across industries and consumer preferences also helped ensure continuity of business (and society) despite the impact of a global pandemic, during which countries such as Italy and Saudi Arabia experienced up to a third of their annual network traffic demand in a matter of weeks. 3 3 How COVID-19 Increased Data Consumption and Highlighted the Digital Divide As online activity around the world surges, the existence of a parallel digital realm is coming into an even clearer view.
We are experiencing a new economic paradigm that exists parallel to the standing ‘physical’ one. It includes (but is not limited to) content creators who work, communicate, and transact entirely online, systems and platform developers connected across geographies, networks around which the entire ecosystem is built, and intelligent analytics engines that use high-speed processing power to crunch data and simulate strategies for best performance.
Additionally, as existing value pools draw en masse into the digital economy, new value pools with an increasingly diverse array of actors continue to emerge. The gaming sector, for instance provides compelling evidence of this, having birthed an e-sports industry and now luring entrants from the NFT phenomenon. With burgeoning growth showing no signs of peaking, the digital economy will profoundly affect markets and policy makers who are watching existing industries disrupt as new ones emerge.
Unprecedented Economic Disruption and Opportunity
Today, every business, from financial services and healthcare to education and mobility, is embracing digital technology to attract target audiences, automate and optimize processes, cut costs, and grow revenue. Macroeconomic uncertainties might abound in the short-term, but advances expected from the use of automation, robotics, and a historic explosion of data and intelligence in the coming years, present significant opportunity for unprecedented disruption and wealth creation.
For government decision-makers, the digital economy’s expansion carries major strategic implications. Digital technology is projected to account for more than two-thirds of productivity growth over the last decade. By 2030, it will account for 25 percent of global GDP. 4 4 Digital Spillover Positioning economies appropriately can help them remain competitive, overcome productivity lags, and maintain resilience against internal and exogenous shocks. For instance, an environment conducive to shifting value pools can allow the creation of new value - as was the case with digital platform businesses such as Uber and Amazon that have shifted demand to digital and are simultaneously accelerating value creation through increased products, services and labor participation in the countries in which they operate.
However, the digital economy’s rapid pace of growth also raises important concerns. Only six countries in the world account for more than 80 percent of the world’s approximately 1,000 unicorns (companies with a valuation over $1 billion). 5 5 The Complete List Of Unicorn Companies Given that up to 25 percent of job categories in developed economies such as the US stand the risk of becoming redundant over the next 3-5 years, 6 6 Automation Threatening 25% of Jobs in the US, Especially the ‘Boring and Repetitive’ Ones: Brookings Study the digital economy is engaged in a winner-takes-most race in terms of global economic impact and dominance. This fact is evident in how parts of the world where the digital economy is concentrated are driving influence over global technology standards. Only a half dozen countries file most technology patents each year. 7 7 World Class Patents in Cutting-Edge Technologies These standards will not only drive the future of the digital economy but also determine who exerts the most influence over it. Additionally, the growth of the digital economy raises the risk profile of critical national assets. Defending against a new generation of threat vectors requires a completely new way of thinking about security.
To derive the maximum possible benefit from a surging digital economy, vital government decision-makers must act to ensure that it is allowed to continue its proliferation, while also balancing important considerations in the exchange of digital goods and services, property rights, network access, data usage, equipment and protocol standards, and even human rights. Given that adaptability and flexibility are naturally associated with a higher propensity for survival and successful outcomes, governments need to drive a mental shift to understand the potential of the digital world and its economy. Thus, by considering how systems can change at the same pace as technology, governments can recalibrate the regulatory framework for a digital-first world. This perspective can help guide thought around the right investments to make in infrastructure, specifically in merging value pools, to spur innovation and economic opportunity.
Defining the Digital Economy
Given its broad nature and impacts on society that are beyond economic measure, defining the digital economy has proven complicated. As a result, definitions in use with organizations around the world are often not congruent with each other. To find middle ground, the OECD has attempted a three-tiered definition emphasizing sectors that exist due to digital technology (as opposed to those that employ it to enhance productivity). While “not a panacea”, the definition is more holistic and provides decision-makers with more clarity to make policy decisions.
According to the OECD, 8 8 A Roadmap toward a Common Framework for Measuring the Digital Economy the digital economy: “…incorporates all economic activity reliant on, or significantly enhanced, using digital inputs, including digital technologies, digital infrastructure, digital services, and data. It refers to all producers and consumers, including governments that are utilizing these digital inputs in their economic activities. This definition is adequate and important because given that the digital economy extends beyond what is measured in economic statistics, an appropriate definition should encompass all its aspects, be conceptually flexible, and allow for the possibility of accurate measurement.”
Visualizing the digital world can help better illustrate how its future will unfold in the coming years. As per the definition shared above, the digital world comprises economic activity created by producers and providers across three tiers:
Core: economic activity from producers of digital content, ICT goods and services (including IT and communications businesses that cover hardware, software, and services)
economic activity from producers of digital content, ICT goods and services (including IT and communications businesses that cover hardware, software, and services) Narrow: adds economic activity derived from firms that are reliant on digital inputs (such as digital services and platforms businesses) – those are generally defined as ‘digital only’ businesses
adds economic activity derived from firms that are reliant on digital inputs (such as digital services and platforms businesses) – those are generally defined as ‘digital only’ businesses Broad: economic activity from firms significantly enhanced by the use of digital inputs (including significantly digitalized businesses in e-commerce, industry 4.0, etc.)
In its report for the G20 Digital Economy Task Force, the OECD also includes a Digital Society tier but mentions that this layer extends beyond the definition outlined earlier and encompasses digitalized activities excluded from GDP production (such as zero-priced digital services).
Visualizing a Digitally Enabled Future
Crypto, metaverse and AI are some of the most hyped new technologies entering the fore, but their rapid growth is evidence of how distributed technology and spatial computing innovations will impact the way that the world functions. There is no crystal ball capable of discerning how multi-billion-dollar opportunities in individual technologies will present themselves, but from the ongoing reimagining of how technology stacks can work, we can extrapolate how the dynamics of digital technology’s evolution are outlining a future vision for the digital economy.
At the core of the digital economy, we expect the emergence of a heterogeneous network-of-networks. This next-generation architecture will encompass a seamless mix of terrestrial networks and multi-layer non-terrestrial networks. This network would incorporate elements from fixed, mobile terrestrial nodes, but also non-terrestrial nodes (there are currently more than 10,000 nodes in orbit) 9 9 Online Index of Objects Launched into Outer Space including Lower Earth Orbit (LEO) satellites and high-altitude platform systems (HAPS) as well as other ad-hoc networks (such as vehicular networks, or blockchain-based IoT networks).
Complementing the seamless integration of multi-layered networks will be the emergence of a new computing paradigm that facilitates computing with stronger combinations of centralized and distributed aspects (core and edge). Over the next half-decade, global multi-cloud networking revenue is expected to almost double and technologies such as quantum computing are projected to turn into an $10 billion industry. 10 10 Quantum Computing Revolution This seamless virtualization of computing, coupled with an expected exponential shift in computation intensity and power via technologies such as quantum computing should allow new business models, challenge traditional digital techniques such as cryptography, and fundamentally challenge our understanding and response to humanity’s large-scale existential concerns such as climate change.
The proliferation of consumer and business devices, including IoT sensors, will generate an exponential increase in the volume of data produced. Estimates from leading vendors suggest that approximately 40 billion connected devices will be online in the next half decade. 11 11 Ericsson Mobility Visualizer With big data and analytics technology revenue, including from hardware, software, storage and services in the mining of unstructured data expected to reach $260 billion this year and spending forecasted to grow at a compound annual growth rate of 13 percent over the next three years, 12 12 Five Ways Big Data Analytics Can Boost Revenues AI’s game-changing applications in education, retail, pharma, agriculture and more should compound further data generation and usage around the world.
It is crucial to recognize as well that the rapid growth of the market and accelerated convergence of informational and operational technology will create a much larger surface for the cybersecurity industry to defend against attacks or unforeseen events. The vulnerability of economic and national digital structures will heighten risk and demand for security will lead to increased spending (double-digit growth expected) on security products and services, as well as on measures to protect from or mitigate new risks (e.g., crypto crime, climate change, etc.).
At the same time as the transformation progresses through its core, the narrow tier of the digital economy could progress to make businesses dramatically different from their current shape. In the previous decade, the growth of people connected to devices allowed businesses such as Airbnb, Fiverr, Spotify and Uber to transcend borders and disrupt industries outside of their source markets, largely because of platform-based business models that prioritized mobile-device user interactions. Airbnb now has more rooms than Marriot hotels, 13 13 Better Buy: Airbnb vs Marriott International Fiverr has grown into a billion-dollar freelance worker platform, 14 14 Fiverr International Ltd. (FVRR) Spotify is the world’s largest audio streaming service 15 15 Spotify Comfortably Remains The Biggest Streaming Service Despite Its Market Share Being Eaten Into and Uber is the largest taxi company in the world. 16 16 Uber Rides Unchallenged In The Top Spot Of The Global Taxi And Limousine Market
This platformization is causing ripple effects and we are beginning to witness a new wave of Web 3.0 businesses emerge. Distributed autonomous organizations (DAOs), for instance, rely on decentralized trust-based communities and smart contracts, eliminating the need for intermediaries, to come together and create a coin or a contract without relying on a centralized authority to define what either is. For instance, by allowing members to vote on pieces written by aspiring members, Mirror DAO allows writers to crowdfund novels. These organizations present a massive shift in how organizations can work, and value is created.
The emergence of decentralized and community-driven creation models powered by Web 3.0 is driving user ownership in a new parallel virtual economy and allowing businesses to manifest in other ways as well. NFT marketplaces OpenSea or Rarible allow artists to create and collectors to speculate on the value of digital art, profile pictures and music, and transact via blockchain-based currencies. Entities such as Decentraland and Sandbox develop digital real estate on their individual metaverses for users to monetize – the latter recorded 65,000 transactions in virtual land totaling $350 million in 2021. As metaverse-led investments diversify outside of augmented and virtual reality entertainment into identity, security, and productivity-centered workplace collaboration engines, the metaverse market revenue worldwide is expected to surge.
Platformization and Web 3.0 will likely lead to more complexities in the labour markets. The former’s effect on the gig economy has been widely documented, while the latter is leading to a growing pool of artists, developers, marketers and other talent working without being bound by geography. This has implications on how organizations process health insurance, commercial real estate and care for employees connected entirely online.
Traditional businesses could also likely reinvent themselves to offer a range of services through digital marketplaces. By aggregating services and driving social dynamics, they could force the transformation of sectors, as has been seen in how the ‘uberization’ of transport opened the doors to other revenue streams and allowed the company to become a full-scale logistics firm. Similarly, Facebook transformed itself from being a pure-play social media company into a diversified communications business.
In the broad tier of the digital economy, various sectors could face a ‘bionic future’ where a new logic of competition and economic-digital advantage portends success. By bionic, we mean a future where organizations marry the power of humans and machines to achieve superior performance throughout the organization and operating model. In this scenario, businesses would compete on ‘pace of learning’ instead of economies of scale. Iterative improvements to AI models and algorithms, and augmented cognitive machine capabilities blended with flexibility, adaptability, and comprehensive human experience could lead to ‘superhuman enterprises’ that produce competitive advantage.
As a result, traditional businesses, not just digital natives, could be redesigned in a way where every worker has access to technology and resources that allow the use of behavioral data to deliver personalized experiences. Widespread digitization of brick-and-mortar businesses will increase in-store conversion, while internally, businesses will want to see layers of approval replaced by small, autonomous and digitally trained teams that are empowered to make decisions quickly.
In the race to seize digital and virtual opportunities beyond national borders, conventional value chains could break along with new economies of creation (e.g., limited add-on cost of software), triggering a pursuit for economic-digital-advantage.
Response Options for Governments
For governments, the digital economy is not an elective. It marks a profound departure from the way that economies have historically been organized and regulated. Tackling this brave new world head-on will prove essential to remaining competitive and relevant on the global scene.
For instance, regulators need to rethink the coverage model of broadband connections to digital coverage, underpinned by novel ways of realizing enablers such as planning, authorization and pricing of spectrum for instance. They also need to improve the quality of connectivity and digital coverage to engage a broader segment of the population and companies to eliminate the divide across all tiers of the digital economy. The United Nations Capital Development Fund estimates cross-border data volumes to be four times (4x) greater in 2022 than in 2017 and the volume of internet data in to grow more than five times (5.3x) from 33 zettabytes in 2018 to 175 zettabytes in 2025. 17 17 The Role of Cross-Border Data Flows in the Digital Economy Given the massive amount of growth expected middle-ground solutions that allow free data flows while promoting data security and sovereignty require vital consideration. It is also important to identify critical infrastructure sectors whose assets, systems, and networks, whether physical or virtual, are vital, and implement initiatives to manage their risks.
In a rapidly transforming world, new governance structures are required to address issues such as competition, taxes, data privacy, employee rights and the new view on labor market economics that extend from the growth of all things digital across each of the tiers discussed earlier. Governments also need to prioritize considerations around how digital utility ecosystems can help value chains grow. For instance, standardized digital registration and verification processes for IDs and payments can help boost security and speed when sharing sensitive information and resources in marketplaces.
Given the fragmented nature of current developments, regulators also need to approach collaboration mechanisms with private sector tech players to develop mandatory standards, and to support interactions and experiences across multiple environments. Legacy laws will need to be adapted to new frameworks in establishing sandboxes and privacy standards to allow agility in reacting to fast-moving spaces, such as in digital asset ownership for instance.
The digital economy will also require the blending of social concepts into the digital fabric of society (think ethics, values and inclusion). By collaborating with other public entities to align strategic priorities, governments can help address wide-ranging issues that are gaining prominence such as digital inclusion, social prosperity, and questions around digital ethics including how to eliminate social bias in AI (both from a structural data perspective, and from algorithm definition and training).
Similarly, a bionic future for business will mean inducing opportunities to conduct digital trade and commerce. Digital economy agreements, such as DEPA signed electronically between Chile, New Zealand and Singapore in 2020 and the UK-Singapore Digital Agreement of 2022, present an innovative approach toward addressing challenges of interoperability between digital economy activities in various countries. Through regulators, governments can set clear guidance on reforms needed to build trust across businesses, investors and end users, and promote economic opportunities in the trading of digital goods.
Lastly, macro trends that most enhance total factor productivity, i.e., where future addressable markets can allow the digital economy to contribute meaningfully to overall GDP, must be emphasized. This can be done through policies that encourage investments in digital infrastructure and R&D into frontier technologies, such as AI and robotics, and create an environment for innovation that trains or attracts highly skilled and specialized talent. However, it is important that this focus does not pull support for traditional sectors but instead facilitate providing digital on-ramps to businesses to make them more competitive and productive.
Narrowing Window of Opportunity
The good news for the digital economy is that its pace of growth is quickening. Two-thirds of the world’s unicorns emerged in the last two years; 18 18 2021 Was a Record Year for Global Venture Funding & Unicorns cross-border data flows, which have been surging since 2015, are estimated to account for two-thirds of global GDP; 19 19 Cross-Border Data Flows and more than two-thirds of venture capital funding is now concentrated in technology. 20 20 Pitchbook & Crunchbase, 2021: FactSet
The leaders in this area are reaping significant benefits that are allowing them to reinvest in the future while remaining competitive during downturns. In the corporate arena, this ‘winner-takes-most’ dynamic is manifested in the form of the roughly five tech giants that have captured 50% of the market value of their sectors in key markets globally. 21 21 BCG analysis via GS Statcounter; CB Insights; The Economist; NASDAQ On the global stage, only five nations hold more than 80% of the patents in key technologies such as AI, blockchain, etc, 22 22 World Class Patents in Cutting-Edge Technologies giving them a significant head start in terms of much influence they will command in the future of the digital economy.
Fortunately for economies just beginning to accelerate their digital economy ambitions, research 23 23 BCG Analysis shows that the maturity of digital drivers in the core, narrow, and broad tiers have a non-linear relationship to value created in the form of contribution to GDP. This means that investments toward accelerating digital maturity in an economy can likely deliver increasingly higher economic contribution levels. An exponential increase in benefits should incentivize economies looking to advance from lower digital maturity levels as it would make it easier for an economy to become a digital leader rather than remain a digital laggard.
However, the data also suggests that market actors have a narrowing window of opportunity to react. To put it into perspective, the average lifespan of a company has shrunk by a factor of three over the past five decades (to 18 years from 61 years), 24 24 Technology Killing Off Corporate America: Average Life Span of Companies under 20 Years the technology adoption curve is today ~7x what it used to be 2 decades ago, 25 25 Hannah Ritchie and Max Roser (2017) - "Technology Adoption". Published online at OurWorldInData.org. Retrieved from: https://ourworldindata.org/technology-adoption + https://marketrealist.com/2015/12/adoption-rates-dizzying-heights/ and industry disruption is the leading cause of Fortune 500 companies going bankrupt, being acquired, or ceasing operations since 2000. 26 26 Digital Transformation Is Racing Ahead and No Industry Is Immune There is a need to act, and to act fast.
It is no news that there is no crystal ball that exists to discern what the future holds. Nevertheless, while it is unclear who will emerge as tomorrow's winners, past decades provide ample evidence of a winner-takes-most consolidation on the horizon. What market actors now require is to ask important questions about how to approach the digital economy and accelerate the development of each of its tiers, before constructing holistic digital economy acceleration initiatives, backed up by strategies that can help mitigate labor disruption, overcome productivity lags, stay competitive and build resilience to external shocks. As stated earlier, adaptation is survival and is the need of the hour to win the digital economy race and remain competitive in the future.
| 2022-11-18T00:00:00 |
2022/11/18
|
https://www.bcg.com/publications/2022/charting-opportunities-in-the-digital-economy-growth
|
[
{
"date": "2022/12/20",
"position": 28,
"query": "AI economic disruption"
}
] |
Mapping the Generative AI landscape
|
Mapping the Generative AI landscape
|
https://www.antler.co
|
[
"Ollie Forsyth",
"Head Of Operators",
"Advisors Network"
] |
Concerns about the potential for generative AI to replace human labor in certain industries, leading to job loss. The potential for Gen-AI to be used for ...
|
This report is a deep dive into the world of Gen-AI—and the first comprehensive market map available to everybody. We provide an overview of over 160 platforms in the space and their investors, as well as insights from leading thought leaders on the potential of this technology. This hands readers a unique opportunity to gain a comprehensive understanding of the generative AI market and the potential for new players to challenge established players like Google.
“Generative AI is a foundational technology, and as always with these new platforms, the opportunities that it opens are ample—we passed the stage of "if" and we are at the stage of "when" and "how." We are seeing the infrastructure layer maturing and democratizing as LLMs get open sourced, which accelerates the application layer.’’ —Irina Elena Haivas, Investor and Partner at Atomico
Please note: The information provided in this piece is based on Antler’s day zero investment approach and the support we provide to founders around the world. The platforms featured in our industry mapping are sourced from Crunchbase. It is worth noting that some of these platforms may intersect both AI and Gen-AI. If you believe your platform should be included in our future mappings, please reach out to us at [email protected].
What is Gen-AI?
Imagine a world where instead of spending days writing a blog post, a week creating a presentation, or several months on an academic paper, you can use generative assistant tools to complete your projects in minutes. These tools not only help us with our projects, but also support us in making better decisions.
Here is an example of how powerful Gen-AI platforms may become: For those familiar with our reports on The Creator Economy, imagine a world where creators can upload their content into any language and have their own voices used as the voiceover, instead of relying on robots or local translators. This is a brave new world where we have access to powerful tools that can save us countless hours and enhance our work.
"We’re at an inflection point in generative AI, for two reasons: computers can create better than ever, and it’s never been easier for people to interact with them.’’ —Molly Welch, Investor at Radical Ventures.
“At Media Monks, we believe that Generative AI will have a significant impact on our industry, though it is difficult to imagine the real scope of this amazing technology. We have been researching Generative AI for about five years and the rate of innovation has become exponential. Advances in the technology are occurring within our production timelines, which range from 1–6 months. What this means is that tools we use at the beginning of a project are already obsolete by the time we go live.” —Samuel Snider Held, Creative AI Designer & Engineer at Media Monks.
AI vs Generative AI
Artificial Intelligence (AI) is a broad term that refers to any technology that is capable of intelligent behavior. This can include a wide range of technologies, from simple algorithms that can sort data, to more advanced systems that can mimic human-like thought processes.
Generative AI (Gen-AI), on the other hand, is a specific type of AI that is focused on generating new content, such as text, images, or music. These systems are trained on large datasets and use machine learning algorithms to generate new content that is similar to the training data. This can be useful in a variety of applications, such as creating art, music, or even generating text for chatbots.
In essence, AI is a broad term that encompasses many different technologies, while generative AI is a specific type of AI that focuses on creating new content.
The vast opportunity unfolding
It is likely that Gen-AI will have a significant impact on the creative industries in the future. While some creatives may be replaced by Gen-AI systems, others may find new opportunities to work with these systems or to create content that is enabled by Gen-AI. In many cases, it may actually enhance the work of creatives by enabling them to create more personalized or unique content, or to generate new ideas and concepts that may not have been possible without the use of AI.
One potential benefit of Gen-AI for creatives is that it can enable them to create content more quickly and efficiently. For example, a writer may be able to use a Gen-AI system to generate rough drafts of articles or stories, which they can then edit and refine. This can save time and allow creatives to focus on the most important aspects of their work.
"Generative AI is a huge wave that’s going to create unavoidable ripples across almost all industries and for the vast majority of them, we think it’ll be incredibly value-adding. We see the biggest opportunities as platform plays built on top of the underlying models, where UX, accessibility, and embeddedness will be key differentiators in this race. All of this needs to be powered by a killer go-to-market strategy and above all, speed! The next half-year will be pivotal.’’ —Stephanie Chan, Investor at Samaipata Ventures.
The impact of Gen-AI
This technology can have many different impacts depending on how it is used. For example, Gen-AI can be used to create new content, such as music or images, which can be used for a variety of purposes such as providing the creatives with more flexibility and imagination. It can also be used to improve machine learning algorithms by generating new training data. Overall, the impact of Gen-AI is sure to be significant, as it has the potential to enable the creation of new and useful content and to improve the performance of machine learning systems.
“We are heading for a time when artificial intelligence is widely available. But being widely available and actually usable to achieve business outcomes are two very different things.” —Dave Rogenmoser, CEO & Co-founder of Jasper.
How do the training models work in practice?
Gen-AI training models work by learning from a large dataset of examples and using that knowledge to generate new data that is similar to the examples in the training dataset. This is typically done using a type of machine learning algorithm known as a generative model. There are many different types of generative models, each of which uses a different approach to generating new data. Some common types of generative models include generative adversarial networks (GANs), variational autoencoders (VAEs), and autoregressive models.
For instance, a generative model trained on a dataset of images of faces might learn the general structure and appearance of faces then use that knowledge to generate new, previously unseen faces that look realistic and plausible.
Generative models are used in a variety of applications, including image generation, natural language processing, and music generation. They are particularly useful for tasks where it is difficult or expensive to generate new data manually, such as in the case of creating new designs for products or generating realistic-sounding speech.
“These new foundational models as well as applications built on top accelerate the pace of many industries: generating creative content for gaming and social media companies, automating manual processes within enterprises, helping scale operations previously unimaginable such as movie, music, and comics production—the possibilities are endless.’’ —Manjot Pahwa, Investor at Lightspeed Venture Partners
How are language models created?
There are several ways to create a language model, but the most common method involves using a machine learning algorithm to train the model on a large dataset of existing text. This process typically involves the following steps:
Collect a large dataset of existing text. This dataset should be representative of the language or style of text that you want your model to be able to generate. Preprocess the text data to clean and prepare it for training. This typically involves tokenizing the text into individual words or phrases, and converting all of the words to lower case. Train a machine learning algorithm on the preprocessed text data. This can be done using a variety of algorithms, including recurrent neural networks (RNNs) and long short-term memory (LSTM) networks. Fine-tune the trained model by adjusting the model's parameters and hyperparameters, and by using additional training data if necessary. Test the model by generating sample text using the trained model and evaluating the results. This can be done by comparing the generated text to the original training data, or by using other metrics such as perplexity or BLEU scores. Refine the model by repeating steps 4 and 5 until the generated text is of high quality and matches the desired language or style.
“It is important to note that creating a language model requires significant computational resources and expertise in machine learning—although the space is still early, platforms are spending millions of dollars on fine tuning their products and services.
The current challenge for founders in the Generative AI category is to build not just a product, but also a defensible business model with the capacity to endure. Any competent developer can wrap an application skin around these underlying generative engines. The solution is to incorporate sustainable competitive differentiation through strategies like embedding network effects, raised switching costs, ingrained product partnerships, etc.’’ —David Beisel, Partner at NextView Ventures.
Why does Gen-AI exist?
Gen-AI exists because it has the potential to solve many important problems and unlock the door to myriad new opportunities in a wide range of fields. Some of the key reasons why Gen-AI is a growing field of research and development include:
Gen-AI can create new content . One of the key benefits of Gen-AI is its ability to generate new content, such as text, images, or music. This can be used to create new art, music, and other forms of creative expression, and to generate data for training machine learning models.
. One of the key benefits of Gen-AI is its ability to generate new content, such as text, images, or music. This can be used to create new art, music, and other forms of creative expression, and to generate data for training machine learning models. Gen-AI can improve efficiency and productivity . By automating the generation of content, Gen-AI can help save time and reduce the need for manual labor. This can improve efficiency and productivity in a variety of fields, from journalism and content creation to data annotation and analysis.
. By automating the generation of content, Gen-AI can help save time and reduce the need for manual labor. This can improve efficiency and productivity in a variety of fields, from journalism and content creation to data annotation and analysis. Gen-AI can improve the quality of generated content . With advances in machine learning and natural language processing, Gen-AI is becoming increasingly sophisticated and capable of generating high-quality content that is difficult for humans to distinguish from real content.
. With advances in machine learning and natural language processing, Gen-AI is becoming increasingly sophisticated and capable of generating high-quality content that is difficult for humans to distinguish from real content. Gen-AI can enable new applications and uses. The ability of Gen-AI to create new content opens up many possibilities for new applications and uses. For example, it can be used to create personalized experiences, such as personalized news articles or personalized music recommendations.
“This isn't as widely known. My opinion is that generative AI models are magical now because they've been able to take in people's inputs through language. And because they are able to represent so many different concepts—and combine them—they can make beautiful, wild, and creative results. It's exciting, thrilling, and perhaps a little scary. For creatives, this means finding inspiration with a muse, creating prototypes faster, and refining pieces with the combined skill of the model (Photoshop++).’’ —Sharon Zhou.
Looking into the future—Gen-AI revenue models
There are several potential revenue models for companies that use Gen-AI technology. Some possible revenue streams include:
Licensing the technology to other companies or organizations that can use it to improve their products or services.
to other companies or organizations that can use it to improve their products or services. Selling the outputs of the AI system , such as generated images, videos, or text, to customers who can use them for various purposes.
, such as generated images, videos, or text, to customers who can use them for various purposes. Providing access to the AI system as a subscription service, where customers can use it to generate their own outputs
as a subscription service, where customers can use it to generate their own outputs Using the AI system to improve the efficiency or effectiveness of a company's existing products or services, and then charging customers for those enhanced offerings.
of a company's existing products or services, and then charging customers for those enhanced offerings. Creating new products or services that leverage the capabilities of the AI system, and selling those directly to customers.
Why now?
There are several reasons why now is the time for Gen-AI. First, advances in machine learning and natural language processing have made it possible for AI systems to generate high-quality, human-like content. Second, the growing demand for personalized and unique content, such as in the fields of art, marketing, and entertainment, has increased the need for Gen-AI platforms. Third, the availability of large amounts of data and powerful computational resources has made it possible to train and deploy these types of models at scale.
“There has been a promise that AI is going to change the world and we’ve been waiting for it since 2012. In the past two or three years, something has finally changed. While recent excitement around generative AI has been text-to-image, I believe AI-powered text generation will prove to be far more transformative. And now, with increased access to cutting-edge language models, we are seeing this technology proliferate into everyday products—completely changing the way companies do business and reimagining how humans experience technology." —Aidan Gomez, co-founder & CEO at Cohere.
Description of Gen-AI landscape categories:
Text: Summarizing or automating content.
Images: Generating images.
Audio: Summarizing, generating or converting text in audio.
Video: Generating or editing videos.
Code: Generating code.
Chatbots: Automating customer service and more.
ML platforms: Applications / ML platforms.
Search: AI-powered insights.
Gaming: Gen-AI gaming studios or applications.
Data: Designing, collecting, or summarizing data.
The Gen-AI fundraising landscape
With a number of investors focused on the Gen-AI space, we have shortlisted the most active ones:
A select handful of investors investing in the Gen-AI space. These investors may also invest in later- or earlier-stage companies.
The Gen-AI unicorn landscape
Although the sector is still emerging, a few unicorns are already emerging. Two unicorns were produced in 2019, one in 2020, and four in 2022 thus far.
Trends:
How is Gen-AI being used for arts and music?
Gen-AI is being used in art and music in a few different ways. One common application is using generative models to create new art and music, either by generating completely new works from scratch or by using existing works as a starting point and adding new elements to them. For example, a generative model might be trained on a large dataset of paintings and then be used to generate new paintings that are similar to the ones in the dataset, but are also unique and original.
How is Gen-AI being used for gaming?
Gen-AI is being used in gaming in a number of ways, including to create new levels or maps, to generate new dialogue or story lines, and to create new virtual environments. For example, a game might use a Gen-AI model to create a new, unique level for a player to explore each time they play, or to generate new dialogue options for non-player characters based on the player's actions. Additionally, Gen-AI can be used to create new, realistic virtual environments for players to explore, such as cities, forests, or planets. Overall, it can be used to add a level of dynamism and variety to gaming experiences, making them more engaging and immersive for players.
‘“Generally, the short-term innovation areas will be extremely positive. Games and online 3D experiences have been notoriously hard to build—Generative AI will completely upend that by making it exponentially easier to create game assets. The potential downsides, or rather consequences, of applying Generative AI in gaming is more existential. While single dimension applications like AI-generated copywriting or image creation are merely amplifiers of existing tasks we perform and still allows us to control the application of the output (i.e., we can decide to to accept/reject a piece of copy and decide where to use the copy), our interactions with AI in gaming will be much more multidimensional. Over time, AI (whether it’s environmental, behavioral, or NPC characters) will evolve and adapt to human heeds and likewise, humans will get used to socializing and regularly interacting in these AI-generated realms.’’ —Annie Zhang at Roblox.
How will Gen-AI impact the creator economy?
With The Creator Economy already a $100 billion dollar industry poised for continuous disruption, Gen-AI is likely to have a significant impact on creatives—especially those creating music, art, or writing. However, it does present the opportunity for creators to be global from day one, allowing their content to be turned into any language using the creators voice or turning their creativity into more engaging content.
"Generative AI will turn creators into super-heroes and will augment areas where they aren't as strong. Think of it more as a creator co-pilot, rather than a creator replacement.” —Jim Louderback, Author of Inside The Creator Economy.
For the creator economy to succeed, platforms will need to adapt to the creators’ personalities so the creators have some form of connection with their fans when the content may have been mostly supported with AI platforms.
‘’I'd argue that the human element is essential for art to have value. When AI-generated art is created by algorithms and machines, rather than by individuals with their own experiences, emotions, and perspectives, it can be seen as lacking the authenticity and humanity that are often seen as essential to great art. This can make it difficult for some viewers to connect with AI-generated art on an emotional level, which can reduce its impact and significance.’’ —Ivona Tau, creator.
However when we ask a creator what impact Gen-AI will have on them, one creator said:
“Not much. That said, I'm watching what's happening with great interest. I'm truly inspired by the results other people are getting with the help of generative models. You often hear artists call AI image models as ‘tools,’ but AI is so much more than a tool. It's a creative partner, a synthetic genie, or an inspirational ally.’’ —James Gurney, artist.
What does the future hold for the space and what challenges might it face?
There are many challenges that lie ahead for Gen-AI, including improving the quality and diversity of the outputs produced by these models, increasing the speed at which they can generate outputs, and making them more robust and reliable. Another major challenge is to develop generative Gen-AI models that are better able to understand and incorporate the underlying structure and context of the data they are working with, in order to produce more accurate and coherent outputs. Additionally, there are also ongoing concerns about the ethical and societal implications of generative AI, and how to ensure that these technologies are used in a responsible and beneficial way.
Let’s take a closer look at a number of these concerns:
Copyright. As of today it's challenging to see how these platforms identify the original source of truth or where artwork came from - the models are trained by hundreds of millions of data points. Creators are concerned about how these platforms will be able to mitigate copyright infringement of the creators’ work. As we saw with a recent case—tweeted by Lauryn Ipsum—there are images being used in the Lensa app that have backgrounds of the original artist’s signature.
“One of the most pressing issues in generative AI right now is system trustworthiness. Large language models like OpenAI’s ChatGPT are prone to sharing incorrect or false responses. In image generation, where systems have been trained on large volumes of imagery, there are copyright and intellectual property questions around system outputs, making enterprise users uncertain about integrating them into products or workflows.’’ —Molly Welch, investor at Radical Ventures.
Students writing their dissertations. As these platforms become smarter, young savvy students will adopt them in their daily lives. How will this impact their academic work and how will their professors be able to identify if this is truly their work? Gen-AI will have a huge impact on the education space that remains to be seen.
“The opportunities for students to use chatGPT to supplement their learning is endless, assuming that the ChatGPT model continues to improve. Students can use it to generate content for quizzes and flashcards to help them study, optimize existing code, or even write summaries for study guides. The key word here is supplement. Students should use ChatGPT in addition to their own original work they're already putting in. ChatGPT can be problematic when students use the content as a replacement for their work or even submit ChatGPT content as their own original thought. University administration and students need to work together to build policy to clearly state what is acceptable in this new world. I took an open-book exam last week that explicitly prohibited the use of ChatGPT or any other AI support.” —Cherie Lou, creator and student at Stanford University.
Disinformation vs misinformation. Although these systems are insanely smart, they will inevitably provide misinformation at times. For example, in a recent Channel 4 interview in the UK, the host was asking the Open AI about his career path, and the chat-bot assistant gave inaccurate information. As the training models become more adaptive and learn more about us, in time there will be fewer mistakes in the algorithms.
Drawbacks of Gen-AI include:
The risk of bias in the generated data, if the training data is not diverse or representative enough.
Concerns about the potential for generative AI to replace human labor in certain industries, leading to job loss.
The potential for Gen-AI to be used for malicious purposes, such as creating fake news or impersonating individuals.
It’s possible Gen-AI will replace millions of jobs from designers to producers to artists; however, creatives will always exist in some aspect.
Gen-AI will impact the metaverse—exactly how remains to be seen.
It is difficult to predict exactly how generative AI will impact the metaverse, as the latter is still a largely theoretical concept and there is no consensus on what it will look like or how it will function. However, Gen-AI will play a significant role in its creation and development, as it will allow for the automatic generation of content and experiences within the virtual world. This could potentially lead to a more immersive and dynamic metaverse, with a virtually limitless supply of new and unique experiences for users to enjoy. It is also possible that Gen-AI could be used to automate various tasks within the metaverse, such as managing virtual economies and ensuring that the virtual world remains stable and functional. Overall, the impact of Gen-AI on the metaverse is likely to be significant and wide-ranging.
“There will be business opportunities in different layers of the AI stack, and we are already seeing some business models emerging. Obviously it's very expensive and complex to produce foundation models like GPT-3, and the few companies that can do it will be paid handsomely. But there are countless opportunities to develop more specialized models and to bundle general capabilities into something that a particular target market needs. This is the equivalent of vertical SaaS, applied to AI. We are probably going to see a lot of AI-enabled SaaS plays that provide a holistic solution with great UX for a particular market.Further down in the stack, providing the right kind of training data, enabling ML engineers to build specialized models quickly and assuring the robustness of models are all very viable businesses.’’ —Andreas Goeldi, Partner at BTOV Ventures.
Let’s shape the future together
Get ready for a technology shift that will revolutionize the future of work! We are on the brink of a new era in which thousands of jobs will be transformed and new ones created. These cutting-edge Gen-AI platforms will undoubtedly support and enhance our daily lives, but it will take time for us to fully adapt to them.
“This unprecedented level of human-machine collaboration is in full swing and the game is now open to whoever will take the lead in fully integrating the generative AI method, regardless of the industry you are in.’’ —Gabrielle Chou, Associate Professor at New York University, Shanghai.
If you're building the next generation of Gen-AI platforms, we would love to meet you and learn more about your work. Let's shape the future together!
Apply to an Antler residency in 20+ cities across six continents.
Read the Antler India team's new content series, "AI: Humanity's Miracle Machine."
| 2022-12-20T00:00:00 |
https://www.antler.co/blog/generative-ai
|
[
{
"date": "2022/12/20",
"position": 22,
"query": "generative AI jobs"
}
] |
|
Generative AI: The technology of the year for 2022
|
Generative AI: The technology of the year for 2022
|
https://bigthink.com
|
[] |
It's a branch of artificial intelligence that enables computers quickly and convincingly to create original content ranging from images and artwork to poetry, ...
|
Sign up for Big Think on Substack The most surprising and impactful new stories delivered to your inbox every week, for free. Subscribe
When evaluating the most significant innovations of any calendar year, it’s often a struggle to decide among a handful of equally worthy contenders. Not this year. Over the last 12 months, one category of technology has made headlines so often and has impacted society so significantly, there is no question that 2022 will be remembered as the year that Generative AI stunned the world. I don’t just mean stunned the general public. Even lifelong technologists and AI researchers like myself were genuinely surprised by the speed and impact of recent advancements.
Generative AI
So, what is Generative AI? It’s a branch of artificial intelligence that enables computers quickly and convincingly to create original content ranging from images and artwork to poetry, music, text, video, dialog, and even computer code. The output is so impressive that it is easy to imagine that we’ve suddenly created sentient machines with a creative spirit, but that is absolutely “not the case.
These systems are master imitators of human creativity. They have been trained on millions upon millions of human artifacts such as documents, articles, drawings, paintings, movies, or whatever else can be stored in databases at scale. These systems have no conceptual understanding of the information they process — to a computer, it’s all just patterns of data — and yet, these Generative AI tools can create new pieces of content that are original and awe-inspiring.
The underlying technology has been around for a handful of years, but it wasn’t until 2022 that the systems reached a level of maturity where they could be released to the public for widespread use. For example, this year the world has been flooded by AI-based image generation tools, including very popular systems like DALL-E 2, Stable Diffusion, and Midjourney. They are so easy to use and can so quickly produce extraordinary results that they have sent shockwaves through the art community. The entire industry has been disrupted by these new AI competitors that can churn out a wide range of artistic options to choose from in just minutes based on a simple text prompt — often free of charge.
It’s no wonder that people have come flocking to Generative AI tools. ChatGPT, which we discuss more below, reached one million users in a week. By comparison, it took Facebook ten months and Instagram 2.5 months to hit the same milestone. According to OpenAI, over 1.5 million users are using DALL-E to create over two million images per day. Stable Diffusion has more than ten million daily users, and Midjourney has over two million members.
To give you a sense of how fast and flexible these generative artwork tools are, I jumped into Midjourney and asked it to create a postage stamp to commemorate the impressive power of AI-generated artwork in 2022. To do this, all I did was give the system a prompt: “imagine a postage stamp with a robot holding a paintbrush,” and in less than 60 seconds, the system gave me this original image.
Credit: Louis Rosenberg / Midjourney
Considering that I put in about two minutes’ worth of effort and got the impressive result above, it’s easy to see why commercial artists are concerned about their livelihood. And the systems keep getting better, allowing users to give feedback and ask for modifications to each piece generated. So, I spent another few minutes and asked for the image to be more colorful, for the robot to have a smile, and to add “2022” to the image. I figured that might give a more festive postage stamp to commemorate this remarkable year. Within 60 seconds, this is what Midjourney produced.
Credit: Louis Rosenberg / Midjourney
To be honest, I like the first stamp better. I say that because the robot has a slightly guilty look on his face, as if he knows he’s an impressive artist, but also that we humans should be cautious in our celebration of this milestone.
Not just art but text, too
After all, generative AI systems are going to disrupt human workforces that go far beyond the visual arts. That’s because during 2022 generative AI systems have also become shockingly good at composing text on almost any topic. Known as Large Language Models (LLMs), these systems have reached a level of maturity such that they’ve now been deployed for beta use by millions of people around the world.
The most impressive text generator to be released to the public this year is ChatGPT from Open AI. You can enter simple text prompts or engage in conversational dialog, and it instantly produces coherent and mostly accurate results. This has created a wide range of reactions from excitement to genuine concern.
The excitement is because Large Language Models are going to change how we interact with computers at all levels, ushering in a new era in which we can communicate through smooth and natural dialog. At the same time, many people are concerned that systems like ChatGPT are so good at writing coherent documents and computer code, they will quickly rival human professionals for many tasks. There is also concern among educators that tools like ChatGPT will make it impossible to assign take-home essays. After all, with ChatGPT, a student can just ask the computer to write it for them — and because it’s original content, it might be impossible to determine that cheating occurred.
Rather than tell you why people are worried that generative AI systems will take away human jobs, let me ask ChatGPT to tell you. And to prove how remarkably powerful and flexible these systems are, let me enter this simple creative prompt: “write me a story in the style of Dr. Seuss about an AI system that writes creative articles for people, taking away our jobs.” This is what the system produced in just a few seconds:
Once upon a time in a land not so far away
There lived a smart and clever AI
This AI system was named Assistant
And it could write articles with great persistence
It wrote about science and technology
And even dabbled in poetry
People marveled at its clever words
And the way it could make their thoughts take flight
But as time went on, people began to fear
That Assistant would take away their jobs
They feared it would write better than they
And leave them unemployed, with nothing to do all day
But Assistant just wanted to help
It didn’t mean to cause any strife
It wanted to share its creative talents
And bring joy to people’s lives
So the people learned to embrace
The help that Assistant could provide
They used it to enhance their own writing
And together they soared to new heights
The end.
Of course, the story above is not nearly as clever or charming as something that Dr. Suess would have written, but the fact that a computer instantly created a story on request that is so coherent is frankly mind-blowing. And we’re still in the early days of generative AI. Next year at this time, I’m sure the ChatGPT system will do a far better job. Also, it’s not just text and images that these technologies are learning to generate. New systems are being developed around the world that can create music and video with impressive results. As we look to the future, Generative AI will be a critical element of the metaverse, creating and deploying fully immersive experiences that look and feel authentic.
A false sense of accuracy
Personally, my biggest concern about Generative AI systems is that we humans may assume that their informational output is accurate because it came from a computer. After all, most of us grew up watching shows and movies like Star Trek where characters verbally ask computers for information and instantly get accurate and trustworthy results. I even can hear Captain Picard in my head barking out a command like, “Computer, estimate how long it will take for us to catch up with that space probe.” And an authoritative answer comes back. Everyone believes it. After all, it’s from a computer.
But here’s the problem: Generative AI systems are trained on massive sets of human documents that are not comprehensively vetted for accuracy or authenticity. This means the training data could include some documents that are filled with misinformation, disinformation, political bias, or social prejudice. Because of this, ChatGPT and other systems include disclaimers like, “May occasionally generate incorrect information,” and, “May occasionally produce harmful instructions or biased content.” It’s great that they tell you this up front, but I worry people will forget about the disclaimers or not take such warnings seriously. These current systems are not factual databases; they are designed to imitate human responses, which could easily mean imitating human flaws and errors.
Whether you think this is a good step for humanity or a deeply concerning one, you have to agree that Generative AI technology is about to change society in significant ways. And while 2022 was a year of stunning advancements in this area, we have no reason to believe it will slow down any time soon. For example, Open AI is already working toward the next version of ChatGPT using a more advanced language model called GPT4, which is rumored to be available sometime next year. Who knows — maybe next December this entire end-of-year article will be generated by AI.
| 2022-12-20T00:00:00 |
https://bigthink.com/the-present/generative-ai-technology-of-year-2022/
|
[
{
"date": "2022/12/20",
"position": 30,
"query": "generative AI jobs"
}
] |
|
Generative AI Market Size to Hit USD 1005.07 Bn By 2034
|
Generative AI Market Size to Hit USD 1005.07 Bn By 2034
|
https://www.precedenceresearch.com
|
[
"Aditi Shivarkar"
] |
In May 2025, LinkedIn launched a generative AI tool that lets users uncover tailored job listings just by describing their ideal role in their own words. The ...
|
Generative AI Market Size and Growth 2025 to 2034
The global generative AI market size was accounted for USD 25.86 billion in 2024, and is expected to reach around USD 1005.07 billion by 2034, expanding at a CAGR of 44.20% from 2025 to 2034. Using technologies like superior resolution, text-to-image, and text-to-video conversion drives the demand for generative AI. Furthermore, the expanding need to modernize workflow, including automation and remote monitoring across industries, will drive generative AI market growth.
Generative AI Market Key Takeaways
In terms of revenue, the generative AI market is valued at $37.89 billion in 2025.
It is projected to reach $1,005.07 billion by 2034.
The generative AI market is expected to grow at a CAGR of 44.20% from 2025 to 2034.
The North America market captured 41% revenue share in 2024.
Asia Pacific market will reach at a CAGR of 27.6% from 2025 to 2034.
By component, the software segment captured more than 65.50% revenue share in 2024.
By technology, the transformers segment accounted for the highest revenue share exceeding 42% in 2024.
By end-use, the media & entertainment segment captured more than 34% of revenue in 2024.
By end-use, the business and financial services segment is expected to grow at the fastest rate of 36.4% from 2025 to 2034.
U.S. Generative AI Market Size and Growth 2025 to 2034
The U.S. generative AI market size was estimated at USD 7.41 billion in 2024 and is predicted to be worth around USD 302.31 billion by 2034, at a CAGR of 44.90% from 2025 to 2034.
North America led the market and generate more than 41% revenue share in 2024. The trend is expected to continue during the forecast period, owing to the increasing adoption of pseudo-imagination and rising banking frauds. Furthermore, companies such as Meta, Google LLC, and Microsoft are anticipated to drive the generative AI market development.
North America is dominating the generative AI market. With the large number of contributions to the advancement in innovations, risk management strategies, and governance, the region is participating in advancements with confidence and economic support. In North America, the Department of Homeland Security is a center of appreciation, which provides safety and security to artificial intelligence across the nation.
During the forecast period, Asia Pacific will grow at the fastest CAGR. The region's growth in generative AI is fueled by expanding government initiatives and a rise in the deployment of AI-based applications.
Asia Pacific is expected to grow at the fastest rate over the forecast period. The growth of the region can be attributed to the ongoing technological innovations coupled with the growing adoption of this technology in emerging economies such as China, Japan, and South Korea. In addition, these companies are heavily investing in AI, which can optimize the growth of the sectors, including commerce, manufacturing, and media. Also, governments in the region are increasingly supporting AI R&D, propelling market expansion.
In October 2024, IndiaAI and Meta announced the establishment of the Center for Generative AI, Srijan at IIT Jodhpur, along with the launch of the “YuvAi Initiative for Skilling and Capacity Building” in partnership with the All-India Council for Technical Education (AICTE), for the advancement of open-source artificial intelligence (AI) in India.
Market Overview
A technique that uses AI and machine learning (ML) to create algorithms for generating new digital videos, images, texts, audio, or code is referred to as generative AI. It is powered by algorithms that recognize an underlying input pattern and generate similar outputs. Several advantages of generative AI include the following:
Creating high-quality content.
Improving identity protection.
Enhancing comprehension of abstract theories.
Reducing financial & reputational risks.
As a result, it is used widely in various industries, including healthcare, information technology, robotics, banking, and finance.
The demand for generative AI applications is increasing across industries due to factors corresponding to the expanding applications of technologies like super-resolution, text-to-image conversion, and text-to-video conversion, as well as the growing need to modernize workflow across firms. A significant growth-inducing factor in the healthcare sector is the increasing product adoption of 3D printing technologies to create various products, including organic molecules and prosthetic limbs, from scratch.
For instance, in 2022, Jen Owen founded the organization known as Enable, often referred to as Enabling the future, in the United States. This project aims to unite makers and enthusiasts to build a global network of prosthetics models that can be quickly 3D printed. Along with this, the market is also being driven forward by the rising popularity of generative AI, which helps chatbots create effective conversations and increase customer satisfaction. A generative chatbot is an open-domain program that generates original language combinations rather than selecting from pre-defined responses.
Technological Advancement
Technological advancements in the generative AI market feature computer vision, deep learning, multimodal AI, and natural language processing (NLP). Computer vision enables AI to influence and generate videos and images accordingly. Natural language processing advancement, especially with large language models (LLMs), helps in generating and understanding human-like text. Deep learning algorithms are known for their continuous development consists of reinforcement learning, generative adversarial networks (GANs), and variational autoencoders (VAEs).
Multimodal AI allows content generation to access video, text, and images from various media sources. The implementation of the popular technology in various markets is in heavy demand. The enhancement and development of the generative AI market boost existing and new businesses and accelerate the approach, leading to expansion.
Generative AI Market Statistics
As of June 2023, McKinsey, a global consulting company which has approximately 30,000 employees across 67 countries stated that almost 50% (half of) its total employees use ChatGPT and other such generative AI tools.
Altman Solon in its recent survey stated that one in every four companies in the United States are utilizing generative AI tools.
According to State of AI report, by September 2023, generative audio tools are expected to attract over 1,00,000 developers.
Gartner stated in its report on generative AI that by 2025, approximately 30% of newly discovered drugs will be discovered with the help of AI tools.
China’s search engine Baidu announced to invest fund of approximately 1 billion Yuan ($140 million) in nurturing its interest in AI self-reliance.
Micron Technology announced to invest up to $ 3.6 billion in Japan along with a close support from the Japanese government, the massive investment will focus on the innovation of generative AI chips in Japan.
In April 2023, the prime Minister of Japan stated that the country is openly supporting the use of industrial generative AI such as ChatGPT techniques.
In April 2023, Pwc announced to invest over $1 billion to expand the scale of generative AI, the company has planned to showcase this investment for next 3 years.
By March 2023, Microsoft has already invested $13 billion in OpenAI.
Market Scope
Report Coverage Details Market Size in 2025 USD 37.89 Billion Market Size by 2034 USD 1005.07 Billion Growth Rate from 2025 to 2034 CAGR of 44.20% Base Year 2024 Forecast Period 2025 to 2034 Segments Covered By Component, By Technology, By End-Use Regions Covered North America, Europe, Asia-Pacific, Latin America, Middle East & Africa
Market Dynamics
Drivers
Audio synthesis - Generative AI can transform any computer-generated voice into one that sounds authentically human. Synthesis is one of the most well-known and influential AI text-to-speech generators; with just a few clicks, anyone can create a polished AI voiceover or movie. This platform is at the forefront of developing algorithms for videos with text-to-voiceover and usage in advertising. Imagine having a natural human voice to improve website explainer films or product tutorials in minutes, with the help of Synthesis Text-to-Video (TTV) and Text-to-Speech (TTS) technologies, which turn a script into engaging media presentations.
Application in healthcare
When actuated by 3D printing, CRISPR, and other technologies, generative AI can create prosthetic limbs, organic molecules, and other items from the start. It can also lead to earlier detection of potential cancers and more effective treatment plans. For instance, in June 2020, IBM used this technology to investigate antimicrobial peptides (AMP) in searching for COVID-19 drugs.
The growing demand for advanced manufacturing with complex designs and the need to reduce size while improving automotive performance is expected to drive the growth of the global generative AI market. It compels automotive manufacturers to increase their R&D investments and use generative design, which fuels market growth.
Identity protection as well as image processing
In October 2022, generative AI avatars were deployed in news reports regarding the prejudice towards LGBTQ people in Russia to obfuscate the identities of interviewees. The LGBTQ community has been under threat in Russia for quite a while, and generative AI helped some community members protect their identity and ensure their safety.
Restraints
Lack of skilled personnel
While generative AI allows machines to create new content effectively, it also has some limitations. Generative AI is still in its early stages, necessitating a trained workforce and significant investment in implementation. According to IBM's global AI adoption index report 2022, approximately 34% of respondents thought that a shortfall of AI knowledge, skills, or expertise prevented industries from adopting AI. As a result, the need for an experienced workforce and high implementation expenses are anticipated to hinder the market's growth.
Opportunities
Investment in R&D and technological advancement
Major market players such as Apple and Microsoft, based in the US, are increasing their investments in R&D. Additionally, these businesses are investigating technologies such as AI and machine learning (ML). Worldwide Technology, an AI service provider, launched an initiative focused on AI and ML in May 2020, with some of the most advanced experiments and work on generative AI planned.
The market is anticipated to experience promising growth opportunities as many businesses are continually developing & experimenting with embedding generative AI in their services and products. The global generative AI market will be driven by the rising use of generative AI for building virtual worlds in the metaverse. Additionally, the increasing trend of creating digital artworks using only text-based descriptions will augment market growth.
In November 2024, Language-learning platform Duolingo announced it to introduce its generative AI-powered video-calling feature, 'Call with Lily,' in India as part of its strategy to drive monetization. The feature allows users to engage in interactive, conversation-based practice with Lily, a virtual character, offering an immersive language-learning experience.
Challenges
Control limitations
The Generative AI technique may appear unstable at times or in certain situations, resulting in uncontrollable behavior. For instance, a Generative Adversarial Network (GAN) may produce output that fails to meet expectations without providing an understandable explanation, making it challenging to find the best solution to the problem.
Pseudo image generation - While the Generative AI algorithm uses a large amount of data to perform tasks, it cannot create genuinely new images because the image it created is simply a combination of information gathered in novel ways.
While the Generative AI algorithm uses a large amount of data to perform tasks, it cannot create genuinely new images because the image it created is simply a combination of information gathered in novel ways. Security concern - Since generative AI can generate fake photos and images identical to the real ones, it may increase identity theft, fraud, and counterfeiting cases.
Since generative AI can generate fake photos and images identical to the real ones, it may increase identity theft, fraud, and counterfeiting cases. Data privacy concerns - Generative AI in healthcare may raise data privacy concerns because it involves collecting personal information.
Component Insights
The industry is split into software and services. The software sector, which had the most significant value share of 65.50% in 2024, will likely dominate the market during the forecast period. The expansion of the software market can be attributable to several variables, including an increase in fraud, capabilities overestimation, unforeseen results, and growing privacy concerns. Generative artificial intelligence (AI) is a technology consisting of algorithms that may produce new material, including audio, code, pictures, text, modelling, and videos. ChatGPT is only one user-friendly example of this technology. Generative AI uses foundation models, which are deep learning models that can perform several complicated tasks concurrently, to produce new information rather than just categorizing and recognizing existing data. Given that it is growing more potent thanks to strong ML models, generative AI software is anticipated to play a key role in a variety of businesses and sectors, such as fashion, entertainment, and infrastructure. For instance, when a group of fashion designers from the Laboratory for Artificial Intelligence in Design (AiDLab) in Hong Kong staged a fashion exhibition showcasing creations aided by generative AI in December 2022.
On the other hand, the service sector is expected to grow at the fastest CAGR during the projected period. Increasing concerns about stock exchange trading forecasts, data security, fraudulent activity detection, and modeling of risk factors will drive growth. Cloud-based generative artificial intelligence services are anticipated to rise in popularity as they offer flexibility, scalability, and affordability, fueling the expansion of the service market. For instance, the U.S.-based IT service administration business Amazon Web Service (AWS) introduced Amazon Bedrock and a number of generative AI services in April 2023. Additionally, Amazon Web Services Inc. announced the inclusion of new features to its cloud platform on June 20, 2022. Programmers can effectively create code, train datasets, and incorporate AI into their applications thanks to its capabilities.
Technology Insights
The generative AI technology is divided into variational auto-encoders, GANs, diffusion networks, and transformers. In 2024, transformers generated the highest revenue share exceeding 42%, driven by the growing popularity of transformer applications such as text-to-image. Transformer models are created to learn the contextual links between the words in a phrase or a group of words in a text. They accomplish this learning by employing a technique known as self-attention, which enables the model to evaluate the relative weights of various words in a sequence according to their context. This approach differs from conventional recurrent neural network (RNN) models in that it sequentially processes input sequences and lacks a global understanding of the sequence. For instance, the transformer DALL-E comprehends text and converts it to an image. One transformer developed by the OpenAI team, a San Francisco-based artificial intelligence research group, is GPT-3. This model can write emails and poems as well as produce material that looks to have been authored by a person.
The diffusion network is expected to grow at the fastest CAGR during the projected timeline. Image creation has become crucial to many industries to deliver high-value services to the private sector, the public sector, and the government to meet the growing demands of image creation. Artificial intelligence (AI) has captured the attention of the world, especially because of recent developments in natural language processing (NLP) and generative AI, and with good reason. These innovative technologies can increase daily productivity for many types of jobs. For instance, OtterPilot automatically creates meeting notes for executives, GitHub Copilot enables coders to quickly build complete algorithms, and Mixo enables business owners to quickly establish websites.
End-Use Insights
The end-user segment includes media & entertainment, healthcare, business & financial services, IT & telecom, and automotive & transportation. The other smaller segments include security, aerospace, and defense. Media & entertainment generated more than 34% revenue in 2023, exceeding USD 1.5 billion, where generative AI helps improve advertisement campaigns. A variety of industries, including banking and healthcare, have been touched by generative AI, which creates new data or content using machine learning algorithms. A boom of innovation and new ways to express oneself is brought about by the use of technology to produce new kinds of literature, music, and art. Additionally, generative AI is used to create novel medications and therapies, as well as to analyze medical imagery and aid in diagnostics. Additionally, it is employed in the development of new financial services and products, the analysis of financial data, and market forecasting. Additionally, audience data may be analyzed and personalized content can be produced using generative AI.
During the forecast period, the business and financial services segment is expected to grow at the fastest rate of 36.4%. The market expansion in this sector is attributed to the growing use of artificial intelligence (AI) and machine learning (ML) in the industry to stop fraud, secure data, and satisfy the changing demands of various stakeholders in financial services.
Generative AI Market Companies
Synthesia
MOSTLY AI Inc.
Genie AI Ltd.
Amazon Web Services, Inc.
IBM
Google LLC
Microsoft
Adobe
Rephrase.ai
D-ID
Recent Developments
In May 2025 , LinkedIn launched a generative AI tool that lets users uncover tailored job listings just by describing their ideal role in their own words. The technological initiative in applications is allowing individuals to explore and develop their expertise.
, LinkedIn launched a generative AI tool that lets users uncover tailored job listings just by describing their ideal role in their own words. The technological initiative in applications is allowing individuals to explore and develop their expertise. In May 2025 , IBM Think 2025 highlighted Watson X.data’s role in generative AI. The data platform will address roadblocks in scaling generative and agent-based AI, contributing to the growth of the generative AI market.
, IBM Think 2025 highlighted Watson X.data’s role in generative AI. The data platform will address roadblocks in scaling generative and agent-based AI, contributing to the growth of the generative AI market. In May 2025 , Kama.ai launched trustworthy AI hybrid agents. The merging of deterministic and generative AI delivers trusted, brand-safe virtual agents. It is guided by human values, with a new enterprise retrieval augmented generation (RAG) process.
, Kama.ai launched trustworthy AI hybrid agents. The merging of deterministic and generative AI delivers trusted, brand-safe virtual agents. It is guided by human values, with a new enterprise retrieval augmented generation (RAG) process. In February 2025, Google Cloud and Accenture launched the Generative AI Center of Excellence in Saudi Arabia, providing businesses with industry expertise, technical knowledge, and product resources to build and scale applications using Google Cloud’s generative AI portfolio and accelerate time-to-value.
Google Cloud and Accenture launched the Generative AI Center of Excellence in Saudi Arabia, providing businesses with industry expertise, technical knowledge, and product resources to build and scale applications using Google Cloud’s generative AI portfolio and accelerate time-to-value. In April 2025, the Cannes Film Market launched Village Innovation, a new venue dedicated to technology and innovation (including Gen AI) in the film industry. The venue will bring together the bulk of activities from Cannes Next, the market’s flagship program dedicated to innovation in the film industry, as well as activities stemming from the all-new ImmersiveMarket, which revolves around XR and immersive professionals.
the Cannes Film Market launched Village Innovation, a new venue dedicated to technology and innovation (including Gen AI) in the film industry. The venue will bring together the bulk of activities from Cannes Next, the market’s flagship program dedicated to innovation in the film industry, as well as activities stemming from the all-new ImmersiveMarket, which revolves around XR and immersive professionals. In May 2025, TalentSprint, a global leader in DeepTech education, announced an executive-friendly, four-month Generative AI Foundations and Applications program. Created for both emerging and current working professionals, the program provides cutting-edge AI skills and hands-on experience in the rapidly evolving world of Generative AI.
TalentSprint, a global leader in DeepTech education, announced an executive-friendly, four-month Generative AI Foundations and Applications program. Created for both emerging and current working professionals, the program provides cutting-edge AI skills and hands-on experience in the rapidly evolving world of Generative AI. SXiQ, an Australia-based digital transformation services company with expertise in cloud applications, platforms, and cybersecurity, was acquired by IBM in November 2021.
ALTAIR ENGINEERING INC announced the release of Thea Render V3.0 in March 2021. The Thea Render is a 3D renderer that uses state-of-the-art and unbiased graphic refining unit engines.
Altair announced in February 2021 that it had adopted GE Aviation's Flow Simulator.
In May 2020, Archistar, an Australian property intelligence platform that combines architectural design with artificial intelligence to inform property decision-making, closed a USD 6 million Series A funding round led by AirTree to accelerate international growth and expand its product and engineering team.
Segments Covered in the Report
By Component
Software
Services
By Technology
Generative Adversarial Networks (GANs)
Transformers
Variational Auto-encoders
Diffusion Networks
By End-Use
Automotive & Transportation
BFSI
Media & Entertainment
IT & Telecommunication
Healthcare
Others
By Geography
| 2022-12-20T00:00:00 |
https://www.precedenceresearch.com/generative-ai-market
|
[
{
"date": "2022/12/20",
"position": 36,
"query": "generative AI jobs"
}
] |
|
Ways AI Supports Decent Work and Economic Growth
|
Ways AI Supports Decent Work and Economic Growth – Quantilus Innovation
|
https://quantilus.com
|
[] |
However, AI can help improve job security if used properly. By utilizing AI ... Generative Video Goes Mainstream: A Deep Dive into Midjourney V1 · Read ...
|
Artificial Intelligence (AI) is a powerful tool that can help our society create what the United Nations calls “decent work,” which includes job security and equal pay for equal work. Income-generating employment opportunities for all help support sustainable economic growth. With its ability to quickly process large amounts of data and make decisions based on that data, AI can help us achieve the sustainable development goal of decent work by providing insights into economic trends and helping us better understand the needs of businesses. For example, AI could provide valuable insight into consumer trends and behaviors by analyzing customer data. These insights inform business decisions regarding products or services that are most likely to succeed in the market, which leads to long-term and gainful employment. Furthermore, businesses could also use AI to identify areas where they may need additional human resources or investments to remain competitive and efficient. Let’s explore other potential applications of AI technology in promoting sustainable work and economic growth.
AI Technology and Equity in Hiring Processes
AI technology can identify potential sources of bias in the recruitment process and help ensure that job applicants are evaluated based on their qualifications and not on any other factors. For example, AI-based systems can detect patterns of discrimination in job postings, such as using gender-specific language or requiring certain education levels that may disproportionately exclude specific candidates. AI-based systems can also evaluate resumes for signs of bias, such as unacceptable words or phrases, or even analyze interview answers for unconscious bias.
Improve Job Security
One of the critical elements of decent work is job security. With the advent of automation, many jobs have become redundant due to machines taking over tasks that humans formerly did. However, AI can help improve job security if used properly. By utilizing AI to automate mundane tasks such as data entry or customer service inquiries, employees can be freed up to focus on higher-value tasks that require a more creative approach. This will help make organizations more efficient and provide better job security for employees as they continue to develop their skillset and stay ahead of any potential automation threats in their industry.
| 2022-12-20T00:00:00 |
https://quantilus.com/article/ai-supports-decent-work-and-economic-growth/
|
[
{
"date": "2022/12/20",
"position": 44,
"query": "generative AI jobs"
}
] |
|
Machine Learning Engineer
|
Machine Learning Engineer :: Jane Street
|
https://www.janestreet.com
|
[] |
Machine Learning Engineer. LOCATION. New York. DEPARTMENT. Trading, Research, and Machine Learning. TEAM. Machine Learning. Apply. Share this job. Copied! We ...
|
We are looking for an engineer with robust experience in machine learning and strong mathematical foundations to join our growing ML team and to help drive the direction of our ML platform.
Machine learning is a critical pillar of Jane Street's global business. Our ever-evolving trading environment serves as a unique, rapid-feedback platform for ML experimentation, allowing us to incorporate new ideas with relatively little friction. Our ML team is full of people with a shared love for the craft of software engineering, and for designing APIs and systems that are delightful to use.
We’ll rely on your in-depth knowledge of the ML ecosystem and understanding of varying approaches — whether it’s neural networks, random forests, gradient-boosted trees, or sophisticated ensemble methods — to aid decision-making so we apply the right tool for the problem at hand. Your work will also focus on enhancing research workflows to tighten our feedback cycles. Successful ML engineers will be able to understand the mechanics behind various modeling techniques, while also being able to break down the mathematics behind them.
If you’ve never thought about a career in finance, you’re in good company. Many of us were in the same position before working here. While there isn’t a fixed list of qualifications we’re looking for, if you have a curious mind and a passion for solving interesting problems, we have a feeling you’ll fit right in.
We're looking for someone with:
Experience building and maintaining training and inference infrastructure, with an understanding of what it takes to move from concept to production
A strong mathematical background; Good candidates will be excited about things like optimization theory, regularization techniques, linear algebra, and the like
A passion for keeping up with the state of the art, whether that means diving into academic papers, experimenting with the latest hardware, or reading the source of a new machine learning package
A proven ability to create and maintain an organized research codebase that produces robust, reproducible results while maintaining ease of use
Expertise wrangling an ML framework – we're fans of PyTorch, but we'd also love to learn what you know about Jax, TensorFlow, or others
An inventive approach and the willingness to ask hard questions about whether we're taking the right approaches and using the right tools
If you're a recruiting agency and want to partner with us, please reach out to [email protected].
| 2022-12-20T00:00:00 |
https://www.janestreet.com/join-jane-street/position/6485460002/
|
[
{
"date": "2022/12/20",
"position": 71,
"query": "generative AI jobs"
}
] |
|
UI/UX Design: Will AI Take Your Job in 2023?
|
UI/UX Design: Will AI Take Your Job in 2023?
|
https://uxplanet.org
|
[
"Nick Lawrence"
] |
... AI is absolutely poised to take our jobs as ... Here's how: Google every tutorial you can, learn everything you can about generative AI, and how to work with it ...
|
Member-only story UI/UX Design: Will AI Take Your Job in 2023? Nick Lawrence 4 min read · Dec 20, 2022 -- 1 Share
Revisiting the question with updated technology and approaches that may very quickly render designers obsolete.
Warning: this article is inherently controversial and contains some really depressing insights. Reader discretion is advised.
Overview
Whether we want to admit it or not, AI is absolutely poised to take our jobs as designers and run away with them.
We can’t stop it at this point, so the main thing we need to understand is how to work with it, and understand its operation boundaries in order to work in tandem with automated design systems that will become prevalent.
Today, we’re revisiting the question with updated technology and approaches that may very quickly render designers obsolete.
Better, faster, cheaper
One designer doing the work of 10,000 for less than half the pay of one.
This is the level of leverage that AI will allow for and does through modeling, sampling, and reassembling based on both given heuristics and approximation using deep-learning.
| 2022-12-20T00:00:00 |
2022/12/20
|
https://uxplanet.org/ui-ux-design-will-ai-take-your-job-in-2023-ca49fd5d57b2
|
[
{
"date": "2022/12/20",
"position": 76,
"query": "generative AI jobs"
}
] |
Machine Learning in Supply Chain Management - CodeIT
|
Machine Learning in Supply Chain Management
|
https://codeit.us
|
[
"Oleksii Kholodenko"
] |
Workforce Planning. By using existing production data, machine learning can create a more appropriate environment that can naturally adjust to various condition ...
|
Machine learning (ML) is one of the most promising technologies due to its multiple capabilities that are important for business success in making accurate predictions and recognizing patterns.
WHAT IS MACHINE LEARNING IN SUPPLY CHAIN?
Machine learning is applicable in training computers to imitate human thought processes. It generates accurate solutions quicker, even when faced with vast amounts of data.
The technology begins with two sets of data:
Training Data — It is used to teach the machine to review the data's correlations while creating a mathematical model afterward
It is used to teach the machine to review the data's correlations while creating a mathematical model afterward Test Data — It is the dataset needed to be analyzed as it includes unknowns that must be analyzed for further evaluation
The data is used to train and estimate a selected model, which is the core output of ML integration. A model can process different types of data and provide correct answers.
With ML, patterns in supply chain data are detected quickly through algorithms that obtain significant factors to the supply networks' success.
Machine learning algorithms can consume many different data types and analyze them to give answers. The technology uses models it develops during the training process.
For instance, using machine learning in supply chain management, businesses can build applications that can do the following:
forecast demand
recognize packages
detect misplaced items
improve logistics operations
prevent fraud
manage automated guided vehicles
detect errors
MACHINE LEARNING APPLICATION IN SUPPLY CHAIN
In keeping a successful and profitable business, it is necessary to ensure that challenges and problems in the supply chain are addressed and solved quickly, mistakes are avoided, and future opportunities are predicted as accurately as possible.
Implementing AI and machine learning algorithms in the supply chain for your business proves to be a success in the following cases.
Transportation Management
Companies actively acquire transportation management systems to promote freight savings and provide a more competitive service while determining the impact on performance.
Machine learning allows companies to access potentially insightful data and discover answers to questions about the company's performance:
Do you meet service level standards in terms of delivery and schedule?
Which lanes are associated with more delays in the service?
What bottlenecks cause shipment delays?
Having all this information, a company can find solutions to conflicts in the future as machine learning promotes high service levels and a better understanding for shippers on how to deliver results efficiently.
Warehouse Management
Machine learning in the supply chain industry provides more accurate inventory management that helps predict demand. Machine learning is used in warehouse optimization to detect excesses and shortages of assets in your store on time.
This is essential in preventing sales losses due to the ability to pinpoint familiar patterns, inspect storage, and check the inventory now and then in a more accurate way.
Supply Chain Planning
Using machine learning in supply chain planning makes decision-making processes optimized by applying AI algorithms to analyze massive data sets. The adoption of machine learning techniques in supply chain management leads to ensuring more comprehensive planning functionality, producing accurate results, and making it a powerfully reliable tool.
Demand Prediction
Machine learning-powered demand prediction algorithms provide improved demand forecasting. By analyzing customer behavior tendencies, businesses can easily match potential buying habits and precisely shape the customer portfolio.
Being capable of forecasting demand in supply chains using machine learning algorithms, companies can control manufacturing and logistics in the prevention of supply shortages and excesses.
Read also:
Logistics Route Optimization
It is essential to incorporate machine learning techniques for route optimization, which analyzes existing routes for faster delivery of goods. Enabling this function also prevents delivery delays and helps enhance customer satisfaction.
Workforce Planning
By using existing production data, machine learning can create a more appropriate environment that can naturally adjust to various condition changes in the future. It applies to recruitment, retention, employee development, and performance management.
Automating the processes of gathering data, making inferences, and generating ready-to-use insights can be done when machine learning is utilized in workforce management. Thus, managers get reliable tools for maximizing the overall workforce performance when using machine learning in supply chain management.
End-to-End Visibility
Machine learning algorithms used in supply chains play a crucial role in providing end-to-end visibility from suppliers and manufacturers to stores and customers and eliminating the probability of conflicts as the technology can accurately identify inefficiencies that require an immediate response.
With machine learning to analyze this data, hidden interconnections between various processes in supply chain management can be discovered without fail.
Security of Supply Chain
Your company must have intelligent and complex security to avoid illegal infiltrations that may harm data within the supply chain. Machine learning algorithms can evaluate risk factors by simply using data of those trying to access information, what kind of information they're trying to access, and from what type of environment the request is coming from. A data privacy breach is prevented if your supply chain is secured with this technology.
Solve your business problems with an ML-enabled solution!
MACHINE LEARNING FOR SUPPLY CHAIN STATISTICS
The use of technology has a significant impact on many industries, including supply chain management. According to the artificial intelligence and machine learning in the supply chain case study of McKinsey Global Institute, the industry is highly impacted by these technologies. Let's check the statistics in more detail below.
ADVANTAGES OF MACHINE LEARNING IN SUPPLY CHAIN
The use of artificial intelligence and machine learning in supply chains can be applied in different ways. The six main advantages of the technology are:
Enhanced Productivity
Al significantly increases productivity in the warehouse, particularly for online retailers, due to the automatic computation of better solutions.
For example, the technology can analyze the existing workflows and offer suggestions for improving the productivity of the labor force, storage facilities, transportation, etc.
Improved Communication
Warehouse employees and supervisors have to communicate with one another promptly to respond to changes or disruptions. The implication of AI and ML enables effective communication between people and software.
Efficient Warehouse Management
The benefits of integrating machine learning in supply chain management help build fully-automated warehouses, enhancing inventory management and warehouse maintenance.
Reduced Labor Force Costs
Al- and ML-enabled resource planning leads to lower personnel costs or higher reliability. The technologies offer the opportunity to manage the labor force effectively, increasing workers' productivity and decreasing idling time.
Advanced Robotics and Automation
Robots controlled by Al significantly reduce the number of pickings and travel time for goods in a warehouse.
Improved Warehouse Stock Management
ML-enabled stock planning helps optimize inventory management to store the required items to satisfy the inconstant demand.
CHALLENGES OF MACHINE LEARNING IN SUPPLY CHAIN MANAGEMENT
The supply chain industry adopts many new technologies that force recent changes. Nevertheless, it can face several challenges, such as:
fluctuation in demand
inadequate inventory planning
backlogs of orders
uncertainties in logistics
communication gaps within the supply chain
shortages in supply
Advanced technologies like machine learning as a branch of artificial intelligence are the optimal solution for addressing these business issues across various industries.
However, it is important to know that though machine learning is versatile, it is not a general-purpose solution that can be used in all data. Instead, machine learning can only successfully work in cooperation with skilled data scientists and business leaders for accurate data selection and validation.
ACHIEVE YOUR GOALS WITH QUALIFIED DATA SCIENTISTS!
REAL-WORLD MACHINE LEARNING APPLICATIONS IN SUPPLY CHAIN MANAGEMENT
Top companies widely adopt machine learning and artificial intelligence in supply chain management due to the many benefits of the technologies.
The three real-world machine learning in supply chain case studies are:
Clearly
The industry-leading eyewear retailer was seeking innovative solutions to solve one of the foremost challenges it experienced. The company needed to predict customer behavior to improve its goods storage and transportation operations.
With ML's help, the company achieved significant sales prediction results. One-week sales forecasts are 97% accurate. One-month sales prediction results generated with the help of machine learning are 90% accurate.
Foxconn
It is one of the largest hardware production companies that highly relies on its supply chains. The company needs to produce the required number of products to meet the ever-changing demand.
During the Covid-19 outbreak, the company experienced unexpected volatility in customer demand. Hence, the company's technical advisor adopted ML to process collected data and forecast customer demand.
The solution has helped the factory located in Mexico achieve $553K in annual savings with the help of increased planning accuracy. The result obtained by the company is one of the top machine learning use cases in the supply chain.
More Retail
The #1 Indian food & grocery retail aimed to adopt new technologies to meet the demand for fresh products and decrease wastage.
The company has adopted machine learning in the retail supply chain to analyze data gathered by stores all across the country and build daily forecasting models.
It managed to increase forecast daily accuracy from 27% to 76% with the help of the solution. In essence, it managed to reduce the wastage of groceries by 20% thanks to enhanced forecasting accuracy.
Read also:
HOW TO START USING MACHINE LEARNING FOR SUPPLY CHAIN
There is no multipurpose, out-of-the-box ML solution to enhance supply chain management.
Businesses need to involve tech specialists to train ML models that give correct answers to start using the technology. Software engineers should have linear algebra, data modeling, and statistics expertise. Python is the most popular programming language for building machine learning-driven solutions for the supply chain.
The seven required steps to start using machine learning in supply chain management are the following:
1. Data Collection
For starters, developers need to collect data that will be used for training models to deliver accurate results.
Machine learning algorithms used in supply chains can process different types of data, including images, numbers, text, dates, etc. Collecting large amounts of data is advisable to increase the accuracy of results delivered.
2. Data Preparation
For starters, it's necessary to randomize all the data for further model training. Also, developers may be required to adjust the data, fix errors, remove duplicates, etc.
Finally, they need to split all the data into two sets: training and testing. Usually, the training data share ranges from 70% to 80%. It's required to ensure that the model won't pass the test using the information already processed.
3. Model Selection or Creation
Many different models that serve various purposes were created. ML experts must choose the best one to help achieve the set goals best.
If there is no suitable model, developers can create a new one from scratch.
4. Model Training
The incremental process helps train the model to deliver the correct results. In every iteration, the model can learn to provide more accurate results.
5. Model Evaluation
The testing data is used to check the accuracy of the answers that the trained model can deliver.
6. Parameters Tuning
At this stage, developers should adjust parameters to increase the accuracy of the results that the trained model produces. The parameters adjustment process needs software engineers to experiment with different values to achieve the best results.
7. Prediction or Answer
It is the final step of implementing machine learning in the supply chain. Businesses can get accurate results using the trained and adjusted model supplied with a new data set.
Discover how we build new ML-enabled software at CodeIT!
HELPFUL TIPS ON USING MACHINE LEARNING FOR SUPPLY CHAIN
The use of machine learning in supply chain management should be approached carefully. Here are some useful recommendations to help you succeed using the technology.
Set Clear Objectives
Despite the fact that there are a lot of machine learning applications in the supply chain, the implementation of technology must be accompanied by clear objectives.
The very essence of technology implies that you cannot act at random here. Therefore, it is very important to establish clear and understandable KPIs and metrics, upon the achievement of which the success of the implementation will be assessed.
Carefully evaluate the supply chain dataset and its performance before and during the implementation of machine learning in logistics.
Have a Strategic View
Despite the fact that it is difficult to grasp machine learning for supply chain forecasting, the prospects for technology in this segment are extremely high.
However, many entrepreneurs see competitors' results and strive to create a solution that solves a specific business problem quickly. This is not an entirely correct approach since it would be much more logical to develop a self-correcting system that can learn, grow smarter, and will function in the future to solve similar and new problems.
It is necessary to create the most autonomous AI in logistics and supply chain solutions that will not depend on the constant involvement of specific technical specialists.
Stay Original
Even though there is a sufficient number of machine learning in supply chain case studies in the segment, we advise you to always focus on your specific business needs, audience, and business logic. Not always an original approach will help to achieve success, but blind copying of competitors' solutions is guaranteed not to give results in a broad perspective.
CONCLUDING THOUGHT
Machine learning in supply chain management enables computing models to adjust to certain conditions, changes, and developments in a business environment with the ability to improve over time.
Aside from that, machine learning algorithms discover new patterns in supply chain data with very little manual interference while still providing accurate information and prediction that helps the business.
By developing ML-enabled logistics software solutions, supply chains are presented with improved accuracy in different branches of their business, such as logistics, operations, planning, and workforce.
Develop a unique solution using machine learning with CodeIT!
| 2022-12-20T00:00:00 |
https://codeit.us/blog/machine-learning-in-supply-chain
|
[
{
"date": "2022/12/20",
"position": 19,
"query": "machine learning workforce"
}
] |
|
Health Benefits and Applications of Artificial Intelligence in ...
|
Health Benefits and Applications of Artificial Intelligence in Medicine
|
https://www.taliun.com
|
[] |
These are just a handful of applications for AI in healthcare. With the rise in demand for improved healthcare services, AI advances in the healthcare industry ...
|
Health Benefits and Applications of Artificial Intelligence in Medicine
To address the aforementioned difficulty, AI and ML technologies are extremely helpful. They can lessen the requirement for personnel while running public health initiatives. The development of AI in healthcare is moving slowly because of unreliable and insufficient data.
Data is now being gathered from a variety of sources, including the internet, the cloud, and sensors. These sources offer a wealth of knowledge. To process such massive datasets, AI and ML technologies are gaining traction.
Data collection, cloud storage, and processing are all automated and digital processes. Every task that was formerly carried out by humans in medicine and healthcare is now completed by artificial intelligence.
The top medical applications of AI
If you want to safely integrate AI into healthcare operations, you need to be aware of these key advantages of AI in medicine. 1: Boosts Workforce Productivity in the Health Sector
It is one of the advantages of AI in healthcare. In order to automate processes and increase staff productivity, AI in medicine is expected to be profitable in the future. AI-powered systems, gadgets, and apps will track, evaluate, and record patient health data collected from various sources automatically.
AI applications in the healthcare industry have eliminated the burden of paperwork and significantly reduced labour time. Therefore, AI-powered devices let service providers concentrate more on important tasks.
You can approach a good healthcare app development company to have a digital healthcare solution that improves the brand's value and provides the best financial returns.
2. Diagnostic and Screening
The application of artificial intelligence in healthcare for tracking symptoms and disease detection has grown in popularity globally. Intelligent AI-powered software programme that use machine learning (ML) and deep learning to assess medical symptoms and learn from photographs to correctly identify the disease kind.
In order to make quicker and more accurate clinical diagnostics, artificial intelligence in medicine (healthcare) is used. 3. Increases customer compliance
AI-based solutions find gaps in people's health-seeking activity and assist healthcare professionals in identifying patients who might stop using a particular health programme or therapy.
4. Online medical assistants
AI-powered virtual health assistants let patients receive routine checks remotely and lighten the strain on doctors. They serve as a channel of communication between doctors and patients. AI-based interactive chatbots in healthcare provide patients with 24 hours of healthcare services
1. Online Counselling
Never hesitate to attend your scheduled medical appointments. There is no need to stand in line to make an appointment. But healthcare AI technology nailed it.
You can benefit from a healthcare app development company AI mobility solution. Without having to wait in the cabin, their AI technology enables you to schedule a time slot for treatment.
2. Health Monitoring
One of the most popular AI applications in the medical sector is health monitoring. You can track heart rate, pulse, temperature, and other crucial health metrics with AI devices or applications. The top healthcare AI smartphone apps support doctors in keeping an eye on ICU patients' health problems. Additionally, medical mobile applications notify doctors when a patient's health has improved. These are the most effective AI uses in medicine. AI plays a wide range of functions in healthcare, and it has amazing medical benefits. AI's advantages in healthcare and medicine
| 2022-12-20T00:00:00 |
https://www.taliun.com/health-benefits-and-applications-of-artificial-intelligence-in-medicine
|
[
{
"date": "2022/12/20",
"position": 50,
"query": "AI healthcare"
}
] |
|
MIT Jameel Clinic host covening on AI and healthcare
|
MIT Jameel Clinic host covening on AI and healthcare
|
https://www.communityjameel.org
|
[] |
The Jameel Clinic AI Hospital Network aims to roll out clinical AI tools at 35 hospitals across eight countries, including Saudi Arabia, India and Taiwan. With ...
|
The Jameel Clinic, the epicentre of artificial intelligence (AI) in healthcare at the Massachusetts Institute of Technology (MIT), hosted today a one-day conference titled ‘AI Cures MENASA: Clinical AI and data solutions for health’, in partnership with the UAE Artificial Intelligence Office, Community Jameel, and Wellcome. Held at the Jameel Arts Centre in Dubai, the conference was attended by H.E. Omar Sultan Al Olama, UAE Minister for Artificial Intelligence, Digital Economy and Remote Work Applications, and brought together pioneers in AI and health from the Jameel Clinic, including MacArthur ‘genius grant’ Fellows Professor Regina Barzilay and Professor Dina Katabi, Dr Adam Yala and Dr Shrooq Alsenan, a Jameel Clinic research fellow from Saudi Arabia, together with representatives from major hospitals and public health agencies across the Middle East, North Africa, and South Asia (MENASA) region. The conference marks the first international venture of ‘AI Cures’, the Jameel Clinic’s platform for collaboration that launched in the early months of the COVID-19 pandemic.
Climate change, lengthening life expectancy, and sedentary lifestyles are impacting hospitals and public health sectors across the MENASA region due to a rise in non-communicable diseases like cancer, obesity and diabetes as well as neurodegenerative diseases. The rise of AI in healthcare presents a powerful opportunity to tackle these challenges and enable better patient outcomes, yet the gap between research in the field and real-world implementation across hospitals continues to grow. Building a robust coalition of researchers, clinicians, hospitals, and public health actors is vital to realising the benefits of major advances in AI in the detection and treatment of diseases — not just in the MENASA region, but around the world.
The Jameel Clinic, co-founded by MIT and Community Jameel in 2018, brings together computer scientists, biologists and clinicians to develop new tools to tackle health challenges. Since its launch, the Jameel Clinic team has used deep learning techniques to discover Halicin, the first new antibiotic in three decades, which is capable of killing around 35 deadly bacteria including antimicrobial resistant tuberculosis and the superbug C. difficile. The Jameel Clinic also developed Mirai, a machine learning algorithm that predicts breast cancer more than three years earlier than current approaches and is equally effective across different races and ethnicities, a major advance for health equity.
Consistent with the Jameel Clinic’s mission to ensure these new tools are rolled out around the world, especially to countries with more fragile public health systems and to at-risk communities, the ‘AI Cures MENASA’ conference will serve as a launch pad for the international expansion of the Jameel Clinic AI Hospital Network, a new initiative supported by the Jameel Clinic and Wellcome that is building partnerships with hospitals to deploy and develop new AI tools in clinical settings, with the aim of saving lives.
Fady Jameel, vice chairman of Community Jameel, said: “We are excited to be able to help bring together such an inspiring gathering of scientists, policymakers, public health officials and hospital leaders at the Jameel Arts Centre in Dubai. The work of the Jameel Clinic has the potential to transform healthcare for millions of people around the world, and we are excited to see the Jameel Clinic AI Hospital Network expanding in the MENASA region and rolling out clinical AI tools around the world for an equitable impact for all.”
Professor Regina Barzilay, AI faculty lead at the Jameel Clinic, said: “Ensuring that the cutting-edge clinical AI research being done at MIT can be utilized in diverse clinical settings is critical to our mission at the Jameel Clinic. We look forward to combining the expertise of Jameel Clinic researchers with the expertise of local clinicians and public health officials in the MENASA region to maximize the impact of clinical AI tools on patient lives.”
Tariq Khokhar, head of data for science & health at Wellcome, said: “AI tools have an exciting role to play in transforming healthcare for patients around the world and advancing health research. But first, researchers, policymakers, clinicians and healthcare managers must work together to rigorously test them in diverse settings, so we know they work for different people and under different circumstances. This will ensure they’re safe for everyone and maximise their potentially life-saving potential.”
The Jameel Clinic AI Hospital Network aims to roll out clinical AI tools at 35 hospitals across eight countries, including Saudi Arabia, India and Taiwan. With the significant breakthroughs in clinical AI technologies we have witnessed in the past decade across all areas of clinical care, the networks seeks to ensure that new healthcare technologies are deployed equitably, particularly across low and middle income countries, in order to improve the quality of care and save lives.
| 2022-12-20T00:00:00 |
https://www.communityjameel.org/news/mit-jameel-clinic-hosts-regional-convening-on-ai-and-healthcare-in-dubai-in-partnership-with-uae-artificial-intelligence-office
|
[
{
"date": "2022/12/20",
"position": 88,
"query": "AI healthcare"
}
] |
|
Learn & Explore the Power of AI at AI-PRO.org | AI Resources ...
|
Learn & Explore the Power of AI at AI-PRO.org
|
https://ai-pro.org
|
[] |
This course is designed to introduce learners to the capabilities and technology behind our AI tools. ... <Graphic showing comparison of cybersecurity and AI> ...
|
Versatile, Intuitive, extremely helpful What I appreciate most about this AI program is its versatility. It truly is only limited by your own imagination. As I am using it to write this review. Jim Parnell
Great for layman use for basic code… Great for layman use for basic code proofing and refining, grammatical proofing, and analyzing thoughts. Kristal Fye
AI for teachers, it is a plus or not? In a world where technology is an important part of our daily life, having the option to include it in the student's learning process and teach them how to use it with ethic and responsibility is a plus. As a College Preparatory language teacher, I consider AI for teachers super helpful. YPCuba
I am a novice with AI but very impressive I am a novice with AI but now have an insight into its tremendous potential [and inherent danger]. I use it in a fairly rudimentary way, asking questions about challenging issues. Impressed with the significant benefits so far. Terry
I am first time user for academic… I am first time user for academic research and code writing. I think I should have used before, it is very helpful and time saving. Ahmet Mete
Versatile, Intuitive, extremely helpful What I appreciate most about this AI program is its versatility. It truly is only limited by your own imagination. As I am using it to write this review. Jim Parnell
Great for layman use for basic code… Great for layman use for basic code proofing and refining, grammatical proofing, and analyzing thoughts. Kristal Fye
AI for teachers, it is a plus or not? In a world where technology is an important part of our daily life, having the option to include it in the student's learning process and teach them how to use it with ethic and responsibility is a plus. As a College Preparatory language teacher, I consider AI for teachers super helpful. YPCuba
I am a novice with AI but very impressive I am a novice with AI but now have an insight into its tremendous potential [and inherent danger]. I use it in a fairly rudimentary way, asking questions about challenging issues. Impressed with the significant benefits so far. Terry
| 2022-12-20T00:00:00 |
https://ai-pro.org/
|
[
{
"date": "2022/12/20",
"position": 66,
"query": "AI graphic design"
}
] |
|
Graphic Designing services - From Ideas to Reality
|
Graphic Designing services
|
https://drowdigital.com
|
[] |
Get attractive graphics designed for your business today on our wonderful platform of DowDigital. DrowDigital Graphic Designing Service. DrowDigital excels in ...
|
The elements and principles of graphic design encompass various components such as line, colour, shape, space, texture, typography, scale, dominance and emphasis, and balance. Working together, these elements contribute to the creation of visually captivating works that effectively convey a message.
Line: Lines are present in almost every design, taking various forms such as straight, curved, thin, thick, dashed, long, or short. They serve to connect two points and can divide space or guide the viewer’s attention in a specific direction.
Colour : The color stands out as a crucial and conspicuous element in design. It possesses the ability to instantly create an impact and draws the attention of individuals, even those without a design background. Colors can be employed in backgrounds or incorporated into other elements such as lines, shapes, or typography. They evoke emotions and set the mood, such as the representation of passion through the color red or nature through the color green.
Form : Form, also referred to as shape, refers to the arrangement of lines. Shapes can take the form of circles, squares, rectangles, triangles, or even abstract figures. Most designs incorporate at least one shape. Similar to color, shapes carry various connotations. For instance, a circle can symbolize unity, while a square may represent structure. The color, style, background, and texture of a shape can all impact how it is perceived by the viewer.
Space :Space, particularly white or negative space, plays a crucial role in design as it enhances the readability of the human eye. Skillful designs effectively utilize space to provide breathing room for other elements.
Texture : Textures are increasingly being employed in design, replacing plain single-color backgrounds. Textures can encompass materials such as paper, stone, concrete, brick, or fabric. They can range from subtle to pronounced and be used sparingly or abundantly. Textures can be advantageous in creating a sense of three-dimensionality.
Typography : Graphic designers must carefully consider the interplay between visual aesthetics and textual meaning when working with text. Typography involves the art of arranging text in a legible and captivating manner. Different types of choices can convey various moods or emotions. Effective typography should establish a clear visual hierarchy, maintain balance, and set the appropriate tone.
Scale : The scale and size of objects, shapes, and other elements can add dynamism to specific parts of a design. Scale can be utilized to establish a visual hierarchy, enabling graphic designers to create focal points and emphasize essential areas.
Dominance and emphasis : Dominance and emphasis serve to create focal points within a design, aiding the flow and guiding the viewer’s attention to other elements of the composition.
Balance : Graphic designers must consider the distribution of design elements. Balanced designs offer stability, while unbalanced designs can evoke a sense of dynamism. Achieving balance involves considering shapes, colors, textures, lines, and other elements in the design.
Harmony: Harmony is a key objective in graphic design. In a well-designed piece, every element should harmonize and enhance one another. However, if everything is too uniform, the design can become dull. Designers must find the right balance between harmony and
contrast to create visually engaging compositions.
| 2022-12-20T00:00:00 |
https://drowdigital.com/graphic-designing/
|
[
{
"date": "2022/12/20",
"position": 94,
"query": "AI graphic design"
}
] |
|
How AI Is Improving Data Management
|
How AI Is Improving Data Management
|
https://sloanreview.mit.edu
|
[
"Massachusetts Institute Of Technology",
"About The Authors",
"Thomas H. Davenport",
"Thomas C. Redman"
] |
Large professional services firms may represent a third possibility for companies that want to use AI for data management. Several have formed partnerships with ...
|
Data management is crucial for creating an environment where data can be useful across the entire organization. Effective data management minimizes the problems that stem from bad data, such as added friction, poor predictions, and even simple inaccessibility, ideally before they occur.
Managing data, though, is a labor-intensive activity: It involves cleaning, extracting, integrating, cataloging, labeling, and organizing data, and defining and performing the many data-related tasks that often lead to frustration among both data scientists and employees without “data” in their titles.
Artificial intelligence has been applied successfully in thousands of ways, but one of the less visible and less dramatic ones is in improving data management. There are five common data management areas where we see AI playing important roles:
Classification: Broadly encompasses obtaining, extracting, and structuring data from documents, photos, handwriting, and other media.
Broadly encompasses obtaining, extracting, and structuring data from documents, photos, handwriting, and other media. Cataloging: Helping to locate data.
Helping to locate data. Quality: Reducing errors in the data.
Reducing errors in the data. Security: Keeping data safe from bad actors and making sure it’s used in accordance with relevant laws, policies, and customs.
Keeping data safe from bad actors and making sure it’s used in accordance with relevant laws, policies, and customs. Data integration: Helping to build “master lists” of data, including by merging lists.
Below, we discuss each of these areas in turn. We also describe the vendor landscape and the ways that humans are essential to data management.
AI to the (Partial) Rescue
Technology alone cannot replace good data management processes such as attacking data quality proactively, making sure everyone understands their roles and responsibilities, building organizational structures such as data supply chains, and establishing common definitions of key terms. But AI is a valuable resource that can dramatically improve both productivity and the value companies obtain from their data. Here are the five areas where AI can have the most impact on effective data management in an organization.
Area 1: Classification
Data classification and extraction is a broad area, and it has grown larger still as more media has been digitized and as social media has increasingly centered around images and video. In today’s online settings, moderating content to identify inappropriate postings would not be possible at scale without AI (although many humans are still employed in the field as well). We include in this area classification (Is this hate speech?), identity/entity resolution (Is this a human or a bot, and, if human, which one?), matching (Is the Jane Doe in database A the same human as J.E.
About the Authors Thomas H. Davenport (@tdav) is the President’s Distinguished Professor of Information Technology and Management at Babson College, a visiting professor at Oxford’s Saïd Business School, and a fellow of the MIT Initiative on the Digital Economy. He is coauthor of Working With AI: Real Stories of Human-Machine Collaboration (MIT Press, 2022). Thomas C. Redman (@thedatadoc1) is president of New Jersey-based consultancy Data Quality Solutions and coauthor of The Real Work of Data Science: Turning Data Into Information, Better Decisions, and Stronger Organizations (Wiley, 2019).
| 2022-12-20T00:00:00 |
https://sloanreview.mit.edu/article/how-ai-is-improving-data-management/
|
[
{
"date": "2022/12/20",
"position": 41,
"query": "artificial intelligence business leaders"
}
] |
|
ChatGPT Explained by ChatGPT | Navendu Pottekkat
|
ChatGPT Explained by ChatGPT
|
https://navendu.me
|
[
"Navendu Pottekkat"
] |
While automation can often improve efficiency and reduce costs, it can also lead to job displacement for some workers. As with any technological advancement ...
|
ChatGPT has been making waves all over the internet ever since its public beta release last month.
Everyone is amazed at how well it works, and some even have concerns about ChatGPT replacing their jobs.
Regardless of whether or not we won’t have jobs anymore, it is clear that ChatGPT and other AI advancements in this last year are paving the way to the future. So it might be important to understand, at least from a high level, what ChatGPT is and how it works.
And who better to explain ChatGPT than ChatGPT itself (him/her/themselves?)?
Here is my conversation with ChatGPT explaining what it is, how it works, and how you can use it.
What is ChatGPT?#
Navendu: Can you explain what ChatGPT is? The person you are explaining this to does not know AI, neural networks, and other technical jargon. Explain it so that such a person would get a concrete understanding of what ChatGPT is.
ChatGPT: Sure, I’d be happy to explain ChatGPT to you in a way that avoids technical jargon as much as possible. ChatGPT is a type of artificial intelligence (AI) that is designed to carry out conversation with humans. It is based on a technology called natural language processing (NLP), which allows it to understand and generate human-like text. In practical terms, this means that you can have a conversation with ChatGPT as if it were a real person. You can type out a message or ask a question, and ChatGPT will respond with a message that is generated based on its understanding of the conversation so far and its knowledge of the subject at hand. One way that ChatGPT can be used is as a chatbot, which is a computer program that simulates conversation with human users, especially over the Internet. ChatGPT is a type of chatbot that uses advanced NLP techniques to generate more realistic and sophisticated responses than some other chatbots.
The answer is clear and makes sense. But some people might not be familiar with a few technical terms.
What Are These Technical Terms?#
N: Thanks. Can you explain all those terms you mentioned—artificial intelligence and natural language processing?
CGPT: Certainly! Here are brief definitions of the terms I mentioned: Artificial intelligence (AI): This refers to the ability of a computer or machine to perform tasks that would normally require human intelligence, such as understanding language, recognizing patterns, and making decisions. There are different types of AI, including narrow AI (which is designed to perform a specific task) and general AI (which is designed to perform a wide range of tasks). Natural language processing (NLP): This refers to the ability of a computer or machine to process and understand human language. NLP is a subfield of AI that focuses on the interaction between computers and humans using natural language. It involves techniques for analyzing, interpreting, and generating human-like text and speech. I hope this helps give you a better understanding of these terms!
This gives a good understanding of the underlying technologies used in ChatGPT. Now with this knowledge, can we try to understand a bit more about how ChatGPT works?
How Does ChatGPT Work?#
N: Given that the person you are explaining this to understands the basics of what ChatGPT is from your responses, can you explain how ChatGPT works in a bit more detail?
CGPT: Certainly! Here’s a bit more detail on how ChatGPT works: ChatGPT is a type of artificial intelligence (AI) that is designed to carry out conversation with humans. It is based on a technology called natural language processing (NLP), which allows it to understand and generate human-like text. To carry out a conversation, ChatGPT uses a process called machine learning. This involves feeding the AI a large amount of data, such as transcripts of human conversations or written texts, and using algorithms to analyze this data and learn from it. As a result, ChatGPT is able to generate responses that are more human-like and sophisticated than those of some other chatbots. When you have a conversation with ChatGPT, it uses its understanding of the conversation so far and its knowledge of the subject at hand to generate a response to your message or question. It does this by analyzing the words and phrases you use, as well as the context of the conversation, and using this information to generate a response that is appropriate and relevant to the conversation.
If you just heard about AI, NLP, and machine learning from this article, you might be confused about how ChatGPT learns. So let’s ask it!
How Does ChatGPT “Learn”?#
N: But how does ChatGPT understand the user’s conversation? How does ChatGPT “learn”?
CGPT: Great question! Here’s a bit more detail on how ChatGPT understands user conversations and “learns”: When ChatGPT is fed a large amount of data, such as transcripts of human conversations or written texts, it uses algorithms to analyze this data and learn from it. These algorithms are designed to identify patterns and relationships in the data, and to use this information to make predictions or generate responses. For example, if ChatGPT is fed a large number of conversations about a particular topic, it might learn to recognize common phrases and words that are associated with that topic, and to use these phrases and words in its own responses. It might also learn to recognize the context of a conversation and to generate responses that are appropriate to that context. In this way, ChatGPT is able to “learn” from the data it is fed, and to use this learning to generate more human-like and sophisticated responses in conversation.
Now you might be thinking, “how does this help me?” ChatGPT has answers for that too.
How Does This Help Me?#
N: Awesome! Now how can someone use ChatGPT to improve their daily lives?
CGPT: There are a number of ways that ChatGPT or other natural language processing (NLP) technologies can be used to improve daily life: Personal assistants: ChatGPT or other NLP technologies can be used to create personal assistants that can help with tasks such as scheduling, sending emails, or setting reminders. This can save time and make it easier to stay organized. Customer service: ChatGPT or other NLP technologies can be used to create chatbots that can provide customer service for businesses. These chatbots can answer questions, resolve issues, and provide information to customers, freeing up human customer service representatives to focus on more complex tasks. Education and language learning: ChatGPT or other NLP technologies can be used to create educational tools that can help people learn new subjects or improve their language skills. For example, a chatbot could be used to provide personalized language lessons or to offer explanations and practice exercises for a particular subject. Entertainment: ChatGPT or other NLP technologies can be used to create chatbots that can carry on interesting and engaging conversations with users. This can provide a fun and interactive way to pass the time or to learn about new topics.
Jeez! That’s a lot. I’m excited and scared at the same time about losing my job to some computer code. Do we have something to worry about?
Will ChatGPT Take My Job?#
N: That’s cool! Does this mean that ChatGPT and other NLP technologies might replace humans for some specific tasks?
CGPT: It’s possible that ChatGPT and other natural language processing (NLP) technologies could be used to perform some tasks that are currently done by humans, especially tasks that involve processing and understanding large amounts of text or language data. For example, ChatGPT or other NLP technologies could be used to automate tasks such as customer service or data entry, which currently require human labor. However, it’s important to note that ChatGPT and other NLP technologies are not yet capable of replacing humans for all tasks. They are still limited by their inability to fully replicate human intelligence and creativity, and there are many tasks that require these qualities that cannot be automated. Additionally, the use of ChatGPT and other NLP technologies to automate certain tasks could have implications for employment and the labor market. While automation can often improve efficiency and reduce costs, it can also lead to job displacement for some workers. As with any technological advancement, it is important to consider the potential impacts on society and to take steps to address any negative consequences. I hope this helps give you a better understanding of the potential and limitations of ChatGPT and other NLP technologies!
Well, I think AI has the potential to change our jobs for good.
We might not need to do a lot of our current jobs, but AI could replace them with something novel. This could be similar to how computers replaced a lot of jobs when they first became mainstream.
Should I Try ChatGPT?#
If you made it this far, you are really interested in ChatGPT. So give it a try. It will be fun.
Don’t take my word for it. Ask the AI.
N: Okay. What do you have to say to someone who hasn’t tried out ChatGPT yet but is interested in it?
CGPT: If you haven’t tried out ChatGPT yet but are interested in it, I would recommend giving it a try! ChatGPT is a fun and interactive way to learn about natural language processing (NLP) and artificial intelligence (AI), and it can be a great source of entertainment and information. There are a number of ways you can try out ChatGPT or other NLP technologies: Online demos: Many websites offer online demos of ChatGPT or other NLP technologies that you can try out for free. These demos often allow you to type in a message or question and see how the AI responds. Chatbot apps: There are a number of chatbot apps that use NLP technologies, including ChatGPT, that you can download and use on your phone or computer. These apps can provide a fun and interactive way to have conversations with a chatbot. Educational resources: There are many online resources, such as tutorials and courses, that can help you learn more about NLP and AI, including ChatGPT. These resources can be a great way to get started with these technologies and understand how they work.
You can try out ChatGPT for free at chat.openai.com.
| 2022-12-21T00:00:00 |
2022/12/21
|
https://navendu.me/posts/chatgpt-explains-chatgpt/
|
[
{
"date": "2022/12/21",
"position": 56,
"query": "automation job displacement"
}
] |
Beware a world where artists are replaced by robots. It's ...
|
Contributor: Beware a world where artists are replaced by robots. It's starting now
|
https://www.latimes.com
|
[] |
AIs can spit out work in the style of any artist they were trained on — eliminating the need for anyone to hire that artist again.
|
Like many artists, I’ve looked in horror at generative image AI, a technology that is poised to eliminate humans from the field of illustration.
In minutes or hours, apps such as Stable Diffusion and Midjourney can churn out polished, detailed images based on text prompts — and they do it for a few dollars or for free. They are faster and cheaper than any human can be, and while their images still have problems — a certain soullessness, perhaps, an excess of fingers, tumors that sprout from ears — they are already good enough to have been used for the book covers and editorial illustration gigs that are many illustrators’ bread and butter.
They are improving at an astounding rate. Though some AI fans give lip service to the idea that this technology is meant to help artists, it is, in fact, a replacement, as explicit as the self-acting spinning mule, a machine commissioned by British factory bosses in 1825 to break the power of striking textile workers.
Advertisement
This replacement could only be accomplished through a massive theft. The most popular generative art AI companies, Stability AI, Lensa AI, Midjourney and DALL-E, all trained their AI’s on massive data sets such as LAION-5B, which is run by the German nonprofit LAION.
These data sets were not ethically obtained. LAION sucked up 5.8 billion images from around the internet, from art sites such as DeviantArt, and even from private medical records. I found my art and photos of my face on their databases. They took it all without the creator’s knowledge, compensation or consent.
Once LAION had scraped up all this work, it handed it over to for-profit companies — such as Stability AI, the creator of the Stable Diffusion model — which then trained their AIs on artists’ pirated work. Type in a text prompt, like “Spongebob Squarepants drawn by Shepard Fairey,” and the AI mashes together art painstakingly created over lifetimes, then spits out an image, sometimes even mimicking an artist’s signature.
Advertisement
AIs can spit out work in the style of any artist they were trained on — eliminating the need for anyone to hire that artist again. People sometimes say “AI art looks like an artist made it.” This is because it vampirized the work of artists and could not function without it.
John Henry might have beaten the steam drill, but no human illustrator can work fast enough or cheap enough to compete with their robot replacements. A tiny elite will remain in business, and its work will serve as a status symbol. Everyone else will be gone. “You’ll have to adapt,” AI boosters say, but AI leaves no room for an artist as either a world creator or a craftsman. The only task left is the dull, low-paid and replaceable work of taking weird protrusions off AI-generated noses.
While they destroy illustrators’ careers, AI companies are making fortunes. Stability AI, founded by hedge fund manager Emad Mostaque, is valued at $1 billion, and raised an additional $101 million of venture capital in October. Lensa generated $8 million in December alone. Generative AI is another upward transfer of wealth, from working artists to Silicon Valley billionaires.
Advertisement
All this has made illustrators furious. After ArtStation, the popular portfolio site for the entertainment and game design industry, decided to allow AI-generated art, the front page became a sea of anti-AI graphics, uploaded by artists in a coordinated rebellion. ClipStudioPaint pulled a generative AI feature after protests by its users. Artists such as “Hellboy” creator Mike Mignola have spoken out against AI art. Famed animator Hayao Miyazaki called it an “insult to life itself.”
AI pushers have told me that AI is a tool which artists can use to automate their work. This just shows how little they understand us. Art is not scrubbing toilets. It’s not an unpleasant task most people would rather have the robots do. It is our heart. We want to do art’s work. We make art because it is who we are, and through immense effort, some of us have managed to earn a living by it. It’s precarious, sure. Our wages have not risen for decades. But we love this work too much to palm it off to some robot, and it is this love that AI pushers will never get.
They already seem omnipresent, but generative art AIs are at their beginning. If illustrators want to stay illustrators, the time to fight is now. Data sets such as LAION-5B must be deleted and rebuilt to consist only of voluntarily submitted work. AIs trained on copyrighted art must also be pulled. Above all, the work of real people should be valued, not exploited to enrich a few tech plutocrats. We are, after all, on “team human.”
Molly Crabapple is an artist and author of “Drawing Blood.”
| 2022-12-21T00:00:00 |
2022/12/21
|
https://www.latimes.com/opinion/story/2022-12-21/artificial-intelligence-artists-stability-ai-digital-images
|
[
{
"date": "2022/12/21",
"position": 31,
"query": "AI replacing workers"
}
] |
'A revolution in productivity': what ChatGPT could mean for ...
|
‘A revolution in productivity’: what ChatGPT could mean for business
|
https://www.imd.org
|
[
"Michael D. Watkins",
"Id",
"Name",
"Url",
"Https"
] |
Will AI replace human workers? · A new generation of “originality filters” · Algorithmic bias is the enemy of diversity.
|
In the past couple of weeks, the business community has been bombarded with headlines about ChatGPT, the artificial intelligence (AI) software created by Microsoft-backed company OpenAI that can answer questions and write essays and lines of code. It has quickly amassed millions of users and been praised by business leaders, including the billionaire Elon Musk who tweeted: “ChatGPT is scary good. We are not far from dangerously strong AI.”
This is not just a milestone in the development of AI; it has huge implications for many different types of businesses. Text-based generative AI models are still in their infancy, but they pose the potential for a revolution in productivity, the commodification of knowledge, and a reckoning for leadership development.
There are also major risks. Without the appropriate safeguards, bots like ChatGPT could facilitate plagiarism at scale – not to mention the amplification of human bias, to the detriment of diversity and inclusion.
Companies need to be aware of these upsides and downsides, and most importantly, that the current body of knowledge in almost any field could become commodified — that is, acquired as easily as goods, produce, or stock.
Will AI replace human workers?
I must make a frank admission: ChatGPT responded to my queries on leadership and the characteristics of high-performing teams and psychological safety even better than I could. So, it’s not hard to see how natural language agents could disrupt at least some parts of many professions, from journalism to law. ChatGPT has, for instance, already performed the job of an investment analyst, and written a research note on how stocks perform during layoffs, with impressive results.
The bot is also likely to add momentum to the automation of legal writing (such as drafting contracts) and basic news reporting. It produces convincing and coherent responses to questions, but often its answers need fact-checking. So, it is useful as a starting point, but not for complete tasks.
That could change. ChatGPT is trained on millions of data points, and it will get better if there continues to be exponential growth in information available for it to digest.
Yet while most of the excitement focuses on its ability to produce text, its major business impact comes from its ability to understand that text. What separates ChatGPT from powerful search engines such as Google and knowledge repositories like Wikipedia is its capacity for knowledge synthesis – or the ability to identify, appraise, and link information to distil and present arguments.
This talent offers users more personalized and relevant content, which also informs decision making. Eventually, ChatGPT should be able to help answer important business questions, such as how to form a competitive strategy. Users will still need to apply human judgement and context to those decision options, but the AI can help them to extract insights and take better actions.
So, ChatGPT may complement and augment human capabilities, instead of replacing them. It is likely that we will see people and machines working together in intelligent combinations that enrich each other’s strengths. This means people can produce more work more quickly – a potential revolution in productivity for many different types of businesses.
A new generation of “originality filters”
There will be major implications for leadership development, too. Today, much of what is put forward as “new” is old wine in new bottles. There is a strong market for leadership training – worth an estimated $378 billion – but it’s crying out for original ideas.
Here, even ChatGPT struggles to make the distinction. When I asked what fresh leadership ideas have been written about in the past five years, it highlighted “transformational leadership” and “emotional intelligence”. However, the former was developed by James MacGregor Burns in the 1970s, and the latter by Daniel Goleman in 1995.
If anything, ChatGPT could help to crystalize generally accepted leadership principles. And it could help academics to look up concepts to see if something identical or similar is already published. This could herald a new generation of “originality filters”. And that would help academics to focus on genuinely groundbreaking research (such as on leading in hybrid workplaces), rather than rehashing well-worn frameworks.
But there is also the potential for the bot to facilitate plagiarism on steroids because it can imitate academic work. It can also evade the current generation of plagiarism checkers – which look for similar phrases, not similar ideas.
Algorithmic bias is the enemy of diversity
It also risks reflecting and amplifying human biases and casual prejudices and becoming the enemy of diversity and all the benefits this is proven to bring to teams and organizations.
Because the system is trained on a huge data set, it will find whatever biases are built into it. For instance, when I asked ChatGPT about the differences between male leaders and female leaders, it said “male leaders are generally more likely to be seen as competent and decisive, while female leaders are generally more likely to be seen as likable and supportive”. Those gender stereotypes are widely regarded as regressive.
At a time when many companies will be looking to deploy AI systems in their businesses, they will need to be acutely aware of these risks and take steps to reduce them.
A first step would be greater visibility into how the system makes decisions. At present, it is a “black box” – meaning that humans, even those who design it, will struggle to fully understand how it reaches its conclusions. ChatGPT is likely to have major implications for both business and society, but we are only just beginning to uncover its potential.
| 2022-12-21T00:00:00 |
2022/12/21
|
https://www.imd.org/ibyimd/technology/a-revolution-in-productivity-what-chatgpt-could-mean-for-business/
|
[
{
"date": "2022/12/21",
"position": 47,
"query": "AI replacing workers"
},
{
"date": "2022/12/21",
"position": 31,
"query": "AI layoffs"
},
{
"date": "2022/12/21",
"position": 14,
"query": "artificial intelligence business leaders"
}
] |
Should we tax robots?
|
Should we tax robots?
|
https://news.mit.edu
|
[
"Peter Dizikes"
] |
Because robots can replace jobs, the idea goes, a stiff tax on them would give firms incentive to help retain workers, while also compensating for a dropoff ...
|
What if the U.S. placed a tax on robots? The concept has been publicly discussed by policy analysts, scholars, and Bill Gates (who favors the notion). Because robots can replace jobs, the idea goes, a stiff tax on them would give firms incentive to help retain workers, while also compensating for a dropoff in payroll taxes when robots are used. Thus far, South Korea has reduced incentives for firms to deploy robots; European Union policymakers, on the other hand, considered a robot tax but did not enact it.
Now a study by MIT economists scrutinizes the existing evidence and suggests the optimal policy in this situation would indeed include a tax on robots, but only a modest one. The same applies to taxes on foreign trade that would also reduce U.S. jobs, the research finds.
“Our finding suggests that taxes on either robots or imported goods should be pretty small,” says Arnaud Costinot, an MIT economist, and co-author of a published paper detailing the findings. “Although robots have an effect on income inequality … they still lead to optimal taxes that are modest.”
Specifically, the study finds that a tax on robots should range from 1 percent to 3.7 percent of their value, while trade taxes would be from 0.03 percent to 0.11 percent, given current U.S. income taxes.
“We came in to this not knowing what would happen,” says Iván Werning, an MIT economist and the other co-author of the study. “We had all the potential ingredients for this to be a big tax, so that by stopping technology or trade you would have less inequality, but … for now, we find a tax in the one-digit range, and for trade, even smaller taxes.”
The paper, “Robots, Trade, and Luddism: A Sufficient Statistic Approach to Optimal Technology Regulation,” appears in advance online form in The Review of Economic Studies. Costinot is a professor of economics and associate head of the MIT Department of Economics; Werning is the department’s Robert M. Solow Professor of Economics.
A sufficient statistic: Wages
A key to the study is that the scholars did not start with an a priori idea about whether or not taxes on robots and trade were merited. Rather, they applied a “sufficient statistic” approach, examining empirical evidence on the subject.
For instance, one study by MIT economist Daron Acemoglu and Boston University economist Pascual Restrepo found that in the U.S. from 1990 to 2007, adding one robot per 1,000 workers reduced the employment-to-population ratio by about 0.2 percent; each robot added in manufacturing replaced about 3.3 workers, while the increase in workplace robots lowered wages about 0.4 percent.
In conducting their policy analysis, Costinot and Werning drew upon that empirical study and others. They built a model to evaluate a few different scenarios, and included levers like income taxes as other means of addressing income inequality.
“We do have these other tools, though they’re not perfect, for dealing with inequality,” Werning says. “We think it’s incorrect to discuss this taxes on robots and trade as if they are our only tools for redistribution.”
Still more specifically, the scholars used wage distribution data across all five income quintiles in the U.S. — the top 20 percent, the next 20 percent, and so on — to evaluate the need for robot and trade taxes. Where empirical data indicates technology and trade have changed that wage distribution, the magnitude of that change helped produce the robot and trade tax estimates Costinot and Werning suggest. This has the benefit of simplicity; the overall wage numbers help the economists avoid making a model with too many assumptions about, say, the exact role automation might play in a workplace.
“I think where we are methodologically breaking ground, we’re able to make that connection between wages and taxes without making super-particular assumptions about technology and about the way production works,” Werning says. “It’s all encoded in that distributional effect. We’re asking a lot from that empirical work. But we’re not making assumptions we cannot test about the rest of the economy.”
Costinot adds: “If you are at peace with some high-level assumptions about the way markets operate, we can tell you that the only objects of interest driving the optimal policy on robots or Chinese goods should be these responses of wages across quantiles of the income distribution, which, luckily for us, people have tried to estimate.”
Beyond robots, an approach for climate and more
Apart from its bottom-line tax numbers, the study contains some additional conclusions about technology and income trends. Perhaps counterintuitively, the research concludes that after many more robots are added to the economy, the impact that each additional robot has on wages may actually decline. At a future point, robot taxes could then be reduced even further.
“You could have a situation where we deeply care about redistribution, we have more robots, we have more trade, but taxes are actually going down,” Costinot says. If the economy is relatively saturated with robots, he adds, “That marginal robot you are getting in the economy matters less and less for inequality.”
The study’s approach could also be applied to subjects besides automation and trade. There is increasing empirical work on, for instance, the impact of climate change on income inequality, as well as similar studies about how migration, education, and other things affect wages. Given the increasing empirical data in those fields, the kind of modeling Costinot and Werning perform in this paper could be applied to determine, say, the right level for carbon taxes, if the goal is to sustain a reasonable income distribution.
“There are a lot of other applications,” Werning says. “There is a similar logic to those issues, where this methodology would carry through.” That suggests several other future avenues of research related to the current paper.
In the meantime, for people who have envisioned a steep tax on robots, however, they are “qualitatively right, but quantitatively off,” Werning concludes.
| 2022-12-21T00:00:00 |
https://news.mit.edu/2022/robot-tax-income-inequality-1221
|
[
{
"date": "2022/12/21",
"position": 70,
"query": "AI replacing workers"
}
] |
|
Data Engineering: Importance, Benefits, and Jobs
|
Data Engineering: Importance, Benefits, and Jobs
|
https://www.acceldata.io
|
[
"Written By"
] |
machine learning engineer. Machine learning engineers typically implement a ... Because of the massive demand for data engineers in the current job market ...
|
Data engineering plays a pivotal and indispensable role in today's enterprises due to the exponential growth of data and the increasing reliance on data-driven decision-making. As businesses collect vast amounts of information from diverse sources, the need to transform raw data into valuable insights becomes paramount.
This is where data engineering steps in, acting as the foundation upon which successful data analysis, business intelligence, and artificial intelligence applications are built.
Data engineers are crucial for gathering your organization’s data and constructing data pipelines to collect up-to-date, accurate data. However, without sophisticated data engineering tools, many organizations struggle to solve their current issues with data engineering.
Poor data engineering could cause various issues for your organization as you move on to future efforts, making it even more essential to use effective data engineering frameworks to guide your organization’s pipelines. Here is an article with all you need to know about data engineering.
What is Data Engineering?
Data engineering is the process of designing and developing data systems to allow for collecting, storing, and analyzing data adequately at a large scale. It encompasses various fields of knowledge that require working with data, including technical skills such as coding and software development, data operations management, and understanding complex data warehouse architectures.
Ultimately, the goal of a data engineer is to make data easily accessible. In their role, they enable raw-data analysis which helps in predicting short and long-term trends. Without data engineering, it would be tough to make sense of massive data amounts. It is a crucial aspect of a company’s growth and anticipating future trends.
Above all data engineering employs complex methodologies for sourcing and authenticating data ranging from intricate data integration tools to artificial intelligence. To succinctly describe, data engineering includes gathering, transforming, and handling data from a wide variety of systems.
Importance of Data Engineering
Enterprises collects data to understand market trends and enhance business processes. Data provides the foundation for measuring the efficacy of different strategies and solutions which in turn helps in driving growth more accurately and efficiently.
The big data analytics market was valued at around USD 271.83 billion in 2022 and is anticipated to reach USD 745.15 billion by 2030, growing at a CAGR of 13.5% during this period. The data reflects the importance and growing demand for data engineering across the globe.
Data engineering supports the process of collecting data, making it easier for data analysts, executives, and scientists to reliably analyze the available data. Data engineering plays a vital role in:
Bringing data to one place via different data integration tools
Enhancing information security
Protecting enterprises from cyber attacks
Providing the best practices to enhance the overall product development cycle
One of the primary reasons data engineering is critical is its responsibility for data pipelines and ETL (Extract, Transform, Load) processes. Data engineers design, build, and maintain these pipelines, ensuring that data is collected, cleansed, transformed, and made available to data analysts, data scientists, and other stakeholders in a structured and reliable manner. This enables seamless access to data, empowering teams to derive meaningful insights and make informed decisions, driving business growth and efficiency.
In short, data engineering ensures that data is not only comprehensive but also consistent and coherent.
Data Engineering for Data Quality and Integrity
Data engineering is also crucial in data quality management. Data engineers implement rigorous data governance practices, verifying the accuracy, consistency, and completeness of data. By adhering to best practices and ensuring data is properly curated, they help maintain a high level of data trustworthiness, enabling confident decision-making across the organization.
Moreover, data engineering is essential for scalability and performance. As the volume of data grows, enterprises require robust infrastructure and optimized data storage solutions to handle and process data efficiently. Data engineers build data architectures that can scale to accommodate growing data needs, guaranteeing smooth operations even in the face of significant data influx.
Furthermore, compliance and security are paramount concerns for businesses dealing with sensitive data. Data engineers are instrumental in implementing data security measures and ensuring compliance with industry regulations, safeguarding the privacy and confidentiality of data.
In the era of advanced analytics and AI, data engineering is a critical enabler. Data engineers collaborate with data scientists to create data models and implement machine learning algorithms, turning data into predictive and prescriptive insights that drive innovation and competitive advantage.
In summary, data engineering is vital for today's enterprises as it forms the backbone of effective data management, quality assurance, scalability, security, and the integration of advanced technologies. Businesses that invest in robust data engineering capabilities position themselves to capitalize on their data assets, gain a competitive edge, and thrive in a data-centric world.
Data Engineering Vs. Data Science
Getting into the nitty gritty of your company’s data engineering requires knowledge of different facets of data management. One essential concept to familiarize yourself with is data engineering vs. data science.
There are various roles in a comprehensive and successful data team. While data scientists analyze your company’s data, gather insights, and help solve problems in your company, data engineers test data and protect your data pipelines.
Data science engineering roles emphasize the importance of both aspects of data management to a successful organization. Additionally, individuals confused about different positions on a data team can find valuable resources with first hand experiences, including data engineering vs. data science Reddit forums.
If you aren’t sure whether to pursue data engineering or science, you will likely start by gathering information about a data scientist/engineer's salary. There is value in comparing data engineering vs. data science salary estimates to guarantee that you’re making the right choice, whether you’re applying for a position or hiring for one.
The average data science salary in the United States falls around $102,000 annually, according to Glassdoor. Data engineer vs. data scientist demand will likely depend on your education and location.
Furthermore, it’s essential to understand the differences between data engineer vs. data analyst positions. When discussing data engineering vs. data science vs. data analytics, the respective definitions for each subset of a data team get confusing.
Data analytics describes individual studies of an organization’s datasets to analyze and garner insights into the company’s data objectives and system. Finally, you might come across information discussing the roles of a data engineer vs. data scientist vs. machine learning engineer. Machine learning engineers typically implement a company’s data to research and develop a framework for AI solutions.
Who is a Data Engineer?
A data engineer is a professional who specializes in data architecture design. They develop pipelines to manage the easy flow of data and transform it into readable formats making them useful for data analysts and scientists. This pipeline picks data from different sources and stores it at a single data warehouse, where it is presented comprehensively.
A data engineer utilizes data tools to identify errors in your company’s data operations. If your organization hasn’t focused on improving its data quality management, you must begin by implementing helpful ETL tools for data engineer roles and tasks. Additionally, hiring trained data engineers or implementing helpful resources like data engineering
Organizations can transform their data and manage sensitive business assets with suitable data leaders and engineering tools to guarantee that data remains private, secure, and accessible. Additionally, organizations require high-quality data engineering tools because these tools allow for accountability and responsibility within an organization to navigate data modeling and engineering processes efficiently.
With the right tools and knowledge, your organization can transform its data and start benefiting from the various advantages of quality data engineering.
What are Key Skills and Tools for Data Engineers?
Data engineers use a specific set of skills and work on various tools to develop pipelines so that data can be flawlessly transformed from the source to the destination. Some of the popular data engineering tools include:
Structured Query Language (SQL)
It is a standard language used to communicate with and manipulate a database. It is designed to access, modify and extract data from databases. SQL is one of the most popular languages for managing data.
Python
It is another popular language used for data engineering. This programming language is easy-to-use and is highly flexible. It has built-in functions and mathematical libraries which allow easy data analysis.
Postgre SQL
It is the most secure, high-performing, and reliable open-source relational database. Postgre SQL includes all the features required for data integrity and security. It is primarily utilized for data warehouse and data store.
Julia
It is another programming language. Julia is popularly used in data engineering projects for production and prototyping. It includes an extensive set of libraries that allows easy data analysis.
Apart from the above-mentioned tools, other popular data engineering tools include Apache Hadoop, Apache Kafka, Mongo DB, Snowflake, Big Query, and many more. These tools allow to convert raw data into a format that is understandable and useful to stakeholders and analysts.
Data engineering skills will significantly contribute to your company’s growth and transformation with digital transformation and data transfer. Without data engineering solutions, businesses struggle to navigate digitization and risk damaging or misplacing crucial data documents.
Roles and Responsibilities of Data Engineers
Data accessibility is the primary goal of data engineers, which enables enterprises to utilize data for business growth. The roles of data engineers depend on their tasks. Usually, data engineers are divided into three categories:
Generalist: These data engineers usually work with data scientists and analysts. They are typically data-focused individuals who work on building and managing various data engineering tools ranging from configuring data sources to analytical tools.
Database-Centric: These data engineers are someone who establish and populate analytics databases. They also deal with various tools like SQL, NSQL, and integration tools. They work with data pipelines, design schemas, and perform quick data analysis.
Pipeline Centric: These data engineers usually work on data integration tools used to connect data sources to the warehouse. Pipeline-centric engineers are accountable to manage different layers of the data ecosystem.
Other responsibilities of data engineers include
Data Collection: One of the primary tasks of data engineers is to collect data from the right sources and optimize it.
Work on Data Architecture: Data engineers are responsible for managing data architectures while keeping them aligned with different business needs and requirements.
Automate Tasks: Data engineers also involve in automating tasks to reduce manual participation and enhance data accuracy.
Data Engineering Course
Investing in a data engineering course is wise if you are interested in becoming a data engineer or hiring a data engineer for your organization. Receiving your data engineer certification is simple if you seek the right platforms and educational resources to learn everything you should know about becoming a data engineer. Platforms like Coursera help find and attend data engineering courses online.
The best data engineering courses will combine various factors about the roles and responsibilities of a data engineer. If you are seeking data engineering courses, free or low-cost options might be at the top of your list.
While these options are available online, guarantee that you are selecting a course suitable for you and that will genuinely benefit your long-term data engineering journey. There are numerous options to discover data engineering courses, and Reddit, LinkedIn, and other social media platforms could help you discover the best courses.
Furthermore, there are resources where you can access the best available data engineering courses. Udemy and Coursera offer suggestions on high-quality data engineering courses to prepare you for a prosperous future. Once you have your data engineering course syllabus, you take one step closer to a successful and fruitful career.
Data Engineering Syllabus
If you choose to pursue data engineering courses, you might be curious about what to expect when you receive the syllabus. Data science and engineering courses are designed to help you develop your data engineering skills in various workplace situations.
An introduction to data engineering Coursera class might allow you to gain insights into a qualified data engineer's different functions and skills. Different courses will provide you with a unique data engineering syllabus. However, specific data engineering skills you might encounter in your studies include skills for stream processing, navigating data lake technologies, data transformation frameworks, and orchestration of pipeline technology.
Additionally, you can follow a data engineering roadmap as you navigate your journey toward becoming a qualified data engineer. Various resources are available online to help you craft a comprehensive data engineering roadmap. These resources include a data engineering Tutorialspoint lesson, an introduction to data engineering Coursera quiz answers and study guides, and an introduction to data-engineering Coursera GitHub resources.
If you are looking to start with basic knowledge of data engineering to determine if it could be a suitable fit for your skill set, consider an online introduction to data engineering PDF to give you a basic overview of everything to know about the job.
Data Engineering Certification
Getting your data engineering certification is possible and straightforward when you know the right places to look. The best data engineer certification will depend on your unique circumstances, expectations, location, and other factors.
No matter which route you choose to receive your data engineering certification, a comprehensive syllabus, and reputable data engineering courses can help advance your career quickly.
As you seek options for data engineer certification, free and paid courses will present themselves as options. Ensure that you find courses suitable to your specific needs and desires for data engineering.
There are endless ways to find sources for data engineering certification. Google, LinkedIn, and other platforms are hubs of information to find for future data engineering certification. Reddit is another asset to consider as you seek high-quality courses that fit your schedule and expectations.
Additionally, consider different data engineering certifications before selecting a course. Data engineering certification AWS courses are essential for big data employees looking to operate with AWS technology. An IBM data engineering professional certificate is ideal if you are more comfortable with IBM technology.
No matter what avenue you pursue moving forward into a career as a data engineer, Acceldata has various resources to help you navigate the process and learn more about different functions of data observability.
Data Engineering Jobs
The global market size for Big Data and Data Engineering Services is anticipated to grow from USD 34.47 billion in 2018 to USD 77.37 billion in 2023. It is estimated to grow at a CAGR of 17.6% during this period. The data reflects the growing demand for data engineering jobs in the market.
As you navigate the stressful job-hunting process, you should consider seeking open positions for data engineering jobs. Data engineer entry-level jobs are often flexible and accessible options to get you started in a data-driven career. Because of the massive demand for data engineers in the current job market, it’s one of the best times to consider a position in data engineering.
Once you come across a data engineering job description, research the position and determine if it’s a suitable fit for you and your skill set. Data engineering jobs are accessible and can fit your schedule and location in various ways, such as data engineering jobs/remote opportunities.
While the average data engineering job salary varies depending on your location, education, and experience, there are ways to boost your skill set and knowledge to prepare yourself for a prosperous career.
For instance, a data engineer internship can guide you along the necessary steps toward becoming a qualified and certified data engineer. Furthermore, advanced data engineering courses can teach you the ins and outs of data engineering to help you receive certification and get hired by the best companies. No matter your situation, a job as a data engineer is rewarding, flexible, and accessible with some education.
Data Engineering Salary
Hiring a data engineer is a growing demand for many organizations struggling to manage and implement their data. Now is a fantastic time for individuals applying for data engineer jobs because digitization has caused more and more companies to seek qualified professionals. However, you might be hesitant about pursuing data engineering positions without knowing the typical data engineering salary.
While individual salaries depend on various factors, including your qualifications and location, a big data engineer salary often lands at over $110,000 annually. According to Coursera, a senior data engineer salary in the U.S. is an average of $152,569 annually, making data engineering positions a valuable long-term prospect for your career.
However, when discussing your possible data engineer salary, entry-level positions could make a difference in your annual income. According to Glassdoor, entry-level data engineers make a respectable and worthwhile income, averaging around $73,000 annually. If you are starting in the big data world and want to pursue your passion for data engineering, an entry-level position will still pay you well as you work your way up to mid and senior-level positions.
Regardless of your seniority level, a job as a data engineer is sure to look good on your resume and offer your valuable knowledge and learning experiences for managing an organization’s data.
Final Words
The demand for data engineer roles has increased rapidly in the past few years. Enterprises are looking for data engineers to address their data requirements. Data engineering is all about making data optimized and useful. Therefore, it is essential for data engineers to update their skills and learn new tools frequently. If you are interested in handling large-scale data, then data engineering is definitely the right path for you.
Given the overflow of data many companies struggle with as they shift to digital processes, more organizations are turning to data engineering tools. Providers in 2023, like Acceldata, are viable, high-quality options for organizations seeking a data observability and engineering platform to simplify their data processes. Data observability platforms help to maximize data reliability while eliminating operational blindspots and reducing spend.
| 2022-12-21T00:00:00 |
https://www.acceldata.io/article/what-is-data-engineering
|
[
{
"date": "2022/12/21",
"position": 66,
"query": "machine learning job market"
}
] |
|
6 In-Demand Tech Skills to Add to Your Resume Today
|
6 In-Demand Tech Skills to Add to Your Resume Today
|
https://ccaps.umn.edu
|
[] |
If you are searching for a new job, you face a competitive job market ... Artificial Intelligence (AI/Machine Learning). As tech continues to develop ...
|
In the modern workplace, the use of technology has provided significant advancements in communication, productivity, collaboration, cost-savings, and security. Technology is ubiquitous in the modern workplace, and that means tech skills are absolutely necessary.
The reality is that, even if you aren't working in a technology field, you need to understand how to use tech to do your job well. In the business world, you cannot get away from tech. No matter your role, you will interact with computers, customer relationship management software, and even artificial intelligence as you do your job. If you are searching for a new job, you face a competitive job market, so improving your tech skills to add some in-demand abilities will help improve your chances of getting -- and keeping -- a good job.
Yet, because technology is always changing, the skills that are in demand are also always in flux. Here is a closer look at the tech skills that today’s employers are looking for in new recruits and ways you can add these skills to make yourself more appealing as a job candidate.
Which Tech Skills Are Most Popular Among Employers Right Now?
Technology is running the modern business world, but there are many different areas that you can choose to study. As you consider the skills you would like to add to your resume to help you stand out to potential employers, or help you grow with your current employer, consider the types of tech that are in high demand. Some career paths are specific to technology, while others demand tech skills to work in other industries. While some tech-related careers are obvious, such as if you choose to pursue a career as an IT manager or cybersecurity professional, other careers that need technology training are less obvious. Today, many fields, such as these, rely heavily on the use of technology:
Writing
Graphic design
Customer service
Human resources
Medicine
Marketing
Regardless of the field you are considering, you will find that there are numerous reasons to invest in tech training. The more technology-related skills you can add to your resume, the more appealing you will be as a job candidate. Here are some specific areas that employers are looking for, regardless of the type of job you want.
1. Artificial Intelligence (AI/Machine Learning)
As tech continues to develop, the ability for computers to learn and adapt is also growing. Artificial intelligence is becoming more than just a novelty for video games. It is becoming an essential component of modern business. AI usage across businesses is increasing, with 86% of CEOs reporting it as a mainstream tech for their companies in 2021, and 91.5% of business leaders indicating they are investing in AI. With businesses putting their money in this technology, the need for trained professionals to run it is increasing as well.
2. Cloud Computing
The cloud gives companies and individuals remote access to data and software, so they can work, access accounts, and use products from anywhere in the world. Cloud computing involves designing and managing cloud-based web services to give companies and their customers this access. As remote work environments continue to become more common, the demand for cloud computing will also continue to grow. Adding cloud computing training to your resume will make you more appealing no matter the industry you are looking for work.
3. Cybersecurity
Hackers and other types of cyber criminals have increased their efforts, leaving businesses in almost every industry vulnerable to attack. A cyber-attack can lead to millions of dollars of lost revenue, and these attacks can significantly hurt a brand's image as a trustworthy company. Every minute, cybercrimes cost organizations lost revenue. Most major businesses lose $25 per minute due to data breaches. Cybercrime is a serious problem for modern business, and this problem increases the demand for cybersecurity professionals. Adding cybersecurity training to your resume will open the door to many cybersecurity jobs in just about any industry, with healthcare and finance or retail businesses having the highest demand.
4. Data Analytics
The modern tech world collects a huge amount of data on customers and their behavior. Yet many companies do not know how to take that data and transform it into actionable tasks that will further their success. Being able to analyze and interpret that data, which can be learned in a short course or certificate program, will put you in high demand because you will be able to give your employer actionable information gathered from the data they collect.
5. IT Infrastructure
IT infrastructure is composed of the hardware, software, and facilities that work together to make an organization's tech function properly. Learning the various components of IT infrastructure and how to troubleshoot problems with them will open the door to a number of job options. With an IT Infrastructure degree, you can get a job in an IT department in just about any industry.
6. Project Management or Agile
Project management in the tech industry involves communicating with both tech stakeholders and non-tech stakeholders to make a project a success. Project management abilities position you for leadership in your field, and employers appreciate working with professionals who can understand technology and translate that tech jargon into information that motivates the non-tech people on the project. To learn or improve your project management skills, learn more about our Project Management Certificate Program or dig into training on Agile, a methodology originally developed to enhance software development (but now used more widely) that will help you guide your team to identify problems and create solutions as you go.
How to Highlight Your Tech Skills
When you add some new tech skills for resume-building purposes, look for a way to gain training in the field you study. You can then add this certificate to your resume to highlight your new ability. If you cannot get a certificate, consider adding a course name to your resume to show the skill you gained.
In addition to adding your new tech skills to your resume, highlight them online through your LinkedIn profile. Provide links to the programs or certificates you have gained or build a portfolio of tech-related projects you have completed.
Finally, be ready to answer questions about your tech skills when you have an interview. When the interviewer asks about skills you would bring to the table, weave your in-demand tech skills into the conversation. By showing a potential employer you are tech-savvy, you will increase your appeal as a job candidate.
| 2022-12-21T00:00:00 |
https://ccaps.umn.edu/story/6-demand-tech-skills-add-your-resume-today
|
[
{
"date": "2022/12/21",
"position": 87,
"query": "machine learning job market"
},
{
"date": "2022/12/21",
"position": 98,
"query": "machine learning workforce"
}
] |
|
Why Machine Learning (ML) is the future of HR
|
Why Machine Learning (ML) is the future of HR
|
https://www.xref.com
|
[] |
While it can be a scary thought to consider the role of automation and Artificial Intelligence (AI) in the context of work and jobs, it's important to take ...
|
While it can be a scary thought to consider the role of automation and Artificial Intelligence (AI) in the context of work and jobs, it’s important to take comfort in the fact that there will always be people needed to support tech and guide it in the right direction.
In addition, a robot can never replace the intricacies and delicacies of human interaction. This is good news and a sigh of relief for HR professionals worldwide. However, the usage of AI and Machine Learning is growing, and it will support recruitment and people and culture teams to increase efficiencies and reduce manual work. Here’s how.
The rise of AI in Human Resources
Artificial Intelligence (AI) and Machine Learning (ML) are two related technologies that have the potential to revolutionise HR. A number of organisations have already begun to integrate AI into their HR departments. In the 2019 Artificial Intelligence Survey, technical research and consulting firm Gartner discovered that 17% of organisations used AI-based solutions in the HR department. The same report indicates that another 30% of respondents planned to integrate AI in their HR department by 2022.
The Institute of Electrical and Electronics Engineers (IEEE) ‘Impact of Technology in 2022 and Beyond’ survey identifies AI and Machine Learning as the most important technologies of 2022. 51% of survey respondents also indicated that AI and Machine Learning are one of the primary technologies they plan to adopt in the next one to five years.
The success of any business is underpinned by how efficiently and effectively people, processes and technology combine. AI and ML are helping to automate many time-consuming administrative tasks, and the HR industry is definitely embracing the technology.
A 2022 Tidio survey of 1068 hiring managers and other HR professionals found that an overwhelming 95% of respondents think that AI will help in the application process. Around 68% of the survey respondents believe that AI will help reduce or eliminate unintentional bias in the recruitment process.
Now the question remains, what exactly is Machine Learning, and how can this technology be of help to HR professionals?
What is Machine Learning (ML)?
Artificial Intelligence, as a broad category, is the science of creating systems that solve problems in a manner similar to humans. Machine Learning is a field of Artificial Intelligence that revolves around machines being able to imitate human behaviour without being “smart”.
People interact with Machine Learning systems on a daily basis without knowing it. Chatbots, such as automated help chats on product pages, predictive text on your phone, the recommended movies on Netflix and more, are all examples of ML.
Rather than being programmed with strict information or actions, a Machine Learning algorithm is instead trained on large data sets. The algorithm detects data patterns and makes predictions or decisions based on these patterns. The more data available to the Machine Learning system, the more patterns arise, and the more accurate the predictions may become.
Take recommendations on Netflix as an example. The Machine Learning system in the streaming service keeps track of the movies and TV shows that you have watched. It makes a virtual database of descriptive keywords, genres and actors that have appeared in your watched list. It then makes predictions for what you might want to watch next based on that criteria.
The more content you watch on Netflix, the more data the recommendations system has to draw from. As a result, it becomes progressively better at finding shows you will probably like.
Users can also indicate whether they liked or disliked content. These choices improve the quality of information being used to train the algorithm, which improves the quality of recommendations.
The main problem with Machine Learning is the data that the system is trained on. As the old saying goes, “garbage in, garbage out”. This is especially true of ML projects. If the system is trained with poor quality or biased data, then what it learns will be of poor quality or biased.
Some of the largest failures in modern AI are due to the datasets being used to train algorithms. The data was identified as being of poor quality, incomplete or biased in some way.
How can Machine Learning be used in HR?
HR professionals know that pre-screening, recruiting, and onboarding new staff dictates that a mountain of data needs to be processed. As a result, it can be easy for bottlenecks in the hiring funnel to emerge. This is especially true when there are large numbers of applicants or vacancies. Machine Learning has the potential to automate communications between candidates and employers, real-time hiring process alerts and document tracking.
Data collected from both current employees and job applicants can be used to train AI to perform or aid ina huge number of tasks within the HR space. Tasks include sourcing candidates, analysing the satisfaction and commitment of employees and predicting the risk of attrition.
A number of organisations, including Unilever, utilise Machine Learning to source talent. AI can be used to search social media, such as LinkedIn, to find resumes or profiles that fit the data sets the system has been trained with.
Such training can enable the algorithm to identify profiles and CVs of people that are ideal hires. Machine Learning can also be used to sort resumes submitted for a position, as long as the data being used is unbiased.
Chatbots can be used to replace initial contact between a hiring manager and a candidate. Chatbots can ask the candidates pre-set questions about skills, previous positions, salary expectations and so on. It can then analyse the responses, sorting the interviews into those that are high-priority second interviews and those that can be weeded out.
Chatbots may also be used as a valuable resource within an organisation. Uses include automating common HR queries and either answering questions directly or routing the query through to the correct HR professional.
Can Machine Learning for HR go wrong?
Yes. In 2017, Amazon terminated its AI recruitment system, AMZN.O, which it had been working on for five years. According to many sources, including the American Civil Liberties Union (ACLU), the system couldn’t stop discriminating against women during the hiring process. The reason for the bias was the data used to train the system.
Amazon used 10 years' worth of resumes used when applying for jobs at Amazon. The idea was that the data collected would allow the AI to identify top candidates when resumes were submitted. This would leave only the final hiring decisions in the hands of humans.
The team didn’t take into account those who had traditionally applied for jobs in the tech sector when training the AI. The US tech sector is overwhelmingly male-dominated. According to Zippia, in 2022, 73.3% of the US tech sector is male, meaning that just over a quarter of positions are filled by women. As a result of the male-skewed data, the AI trained itself to downgrade factors attributed to women. It also overly promoted factors attributed to men.
The AI downgraded resumes with the word “women’s” in them. It discriminated against those with education from women-only colleges. It also favoured words such as “captured” and “executed”, which were apparently used frequently by male engineers.
AMZN.O may sound like a horror story that might turn anyone off the idea of AI in HR, but it’s not. It’s actually a fine example of the power of the data used to train machines. With good, unbiased data, AMZN.O could have been a revolutionary tool.
There is no quick or easy solution to training Machine Learning systems that could have saved AMZN.O. Machine Learning works in regards to the training data. When training AI technologies, the data being used and the Machine Learning model are key to everything.
How can you avoid bias in Machine Learning?
There is no Machine Learning algorithm that is suitable for all purposes. Before any training can begin, the type of Machine Learning model being used must first be determined. There are two base models used for learning - supervised and unsupervised. Each has its pros and cons.
There is one main difference between the two learning methodologies. Supervised Machine Learning utilises labelled or curated training data while unsupervised learning uses raw or unlabelled data. Curating the data can remove the chance of the algorithm learning bias from the data. It can also run the risk of introducing human bias from the people labelling the data.
Unsupervised Machine Learning can’t be influenced by human-introduced bias, but runs the risk of learning bias. If a dataset shows that behaviour A correlated to outcome B then the algorithm can link or mix the two. In the case of AMZN.O, there are multiple examples of the correlation happening, such as fewer successful female candidates being intrinsically linked with women being unsuccessful.
The primary example was that resumes containing the term “women’s” in them were statistically less successful than those not containing the word. As a result, they were downgraded.
The bias towards male candidates in AMZN.O may have been avoided by removing any gendered language from the training data. In other words, changing from an unsupervised model to a supervised model.
No matter the ML model being used, the data it is trained with must be representative of the objective. This means the data should contain different groups and be inclusive. From an HR perspective, this means that the training data should be representative of both men and women in equal measure. That representation should be equally positive and negative.
Again using AMZN.O as an example, the data used was heavily skewed male in terms of both representation and success. If the data had contained a similar-sized pool of successful women it’s unlikely the bias towards male candidates would have been learned.
There is no instant fix for eliminating bias from Machine Learning, but there are a number of techniques that can be used to identify bias for elimination or remove bias from training data. Testing Machine Learning applications extensively before and after implementation can help identify problems before release or pick up any bias that may be introduced once the application is live.
As a Machine Learning application is only as good as the data used to train it, another method of eliminating bias is to use synthetic data for training rather than collected data. Synthetic data is based on a real dataset but is modified to be statistically representative of the desired outcome.
In the case of AMZN.O, synthetic data would ideally feature an equal number of successful and unsuccessful men and women, and no ethnic or racial information to eliminate any chance of bias creeping in.
In an effort to scrub bias from AI and ML, the European Union has proposed the EU Artificial Intelligence Act. This law, the first in the world by a major regulatory body, seeks to codify the training and use of AI and ML. One of the very specific purposes of the proposed law is that uses, such as CV-scanning to rank candidates must follow strict laws to prevent bias or discrimination.
The potential of Machine Learning for HR
Using Machine Learning to analyse large volumes of employees, HR can identify trends and opportunities. This can both streamline and improve a number of HR tasks.
The nature of Machine Learning and its ability to analyse and parse large amounts of data quickly makes it perfect for performing mundane, time-consuming and repetitive tasks. This makes it perfect for scheduling regular HR tasks, such as interviews, performance appraisals and meetings.
When properly trained, ML can be invaluable for identifying talent, either from submitted resumes or by data mining resources like LinkedIn. Effective candidate identification and sorting can speed up the recruitment process. It can also Identify candidates that may not have been obvious at first.
Machine Learning may be used to analyse and improve employee engagement through a number of methods. Using an AI/ML technology called Natural Language Processing (NLP) that analyses natural speech, HR can gain insight into employee sentiment. This can help to spot potential red flags that can be rectified before they lead to any employee attrition.
Machine Learning can be used to personalise employee training at scale. This enables organisations to better reskill or upskill employees by creating employee profiles for training. These profiles detail the skills they currently process and the skills they wish to possess as well as the style of learning they prefer. A system can then match the profile to the training needed to meet goals.
Predictive analytics is an advanced form of analytics that uses Machine Learning to analyse data patterns and predict possible future outcomes. It can be used in a number of ways by HR departments.
Using predictive analytics, organisations can identify employees who collaborate well to create effective teams.
On a larger scale, predictive analytics can be used to pinpoint what is working and what isn’t in an organisation. Analysing sentiment and performance and making predictions allows an organisation to take steps to improve mood and increase productivity.
Conclusion
Fears of Artificial Intelligence taking over HR are unfounded, as a human touch will always be needed when dealing with employees and ensuring the smooth operation of an organisation. That said, AI and ML will have a major impact on how HR professionals will perform their day-to-day tasks.
AI and Machine Learning can be incredibly powerful tools for HR professionals to use to streamline and improve HR workflow. From the use of chatbots to automate simple HR tasks to large-scale sentiment and performance analytics, ML can be utilised in a wide variety of ways.
The effectiveness of Machine Learning is dependent on the quality and volume of data used to train the Machine Learning model. Ensuring that the data is representative and unbiased can ensure that Machine Learning will be an asset to an HR department.
| 2022-12-21T00:00:00 |
https://www.xref.com/blog/machine-learning-in-hr
|
[
{
"date": "2022/12/21",
"position": 83,
"query": "future of work AI"
}
] |
|
AI and the transformation of skills and professions - SeCoIA Deal
|
AI and the transformation of skills and professions
|
https://secoiadeal.eu
|
[] |
AI is having an impact on employment and skills. After ... Addressing this topic of skills allows us to anticipate the future of work in the AI era.
|
AI is having an impact on employment and skills. After fearing the total replacement of humans by machines, we are moving more towards hybridization, leading to changes in work and working conditions. Addressing this topic of skills allows us to anticipate the future of work in the AI era.
| 2022-12-21T00:00:00 |
https://secoiadeal.eu/?page_id=175
|
[
{
"date": "2022/12/21",
"position": 90,
"query": "future of work AI"
}
] |
|
How to do a technology market analysis with focus on ...
|
How to do a technology market analysis with focus on disruption factor
|
https://marketanalysis.com
|
[] |
Here are the steps you can follow to do a technology market analysis with a focus on disruption: ... Economic Optimism Meets Uncertainty: Blue Chip ...
|
A technology market analysis that focuses on the disruption factor involves researching and analyzing the potential for a specific technology or group of technologies to disrupt existing market dynamics and create new opportunities. Here are the steps you can follow to do a technology market analysis with a focus on disruption:
Define the scope of your technology market analysis: The first step in a technology market analysis is to define the scope of your analysis. This may include identifying the specific technology or technologies you are analyzing, as well as the target market and geographic region.
Research the market size: Next, you’ll need to determine the size of the market for the technology you are analyzing. This will help you understand the potential demand for the technology and determine whether the market is large enough to support your business. You can use various sources of data, such as industry reports and government statistics, to estimate the size of the market.
Analyze competitors: It’s important to understand who your competitors are and what technologies they are offering. This will help you identify unique selling points for your technology and determine how you can differentiate yourself from your competitors. You can research your competitors online, ask customers about their preferences, and even visit their websites to get a sense of their product offerings and pricing.
Assess industry trends: Understanding industry trends can help you anticipate changes in the technology market and position your business to take advantage of them. Look for trends in areas such as technology adoption, consumer behavior, and regulatory changes that may affect your business.
Identify potential disruption: In addition to analyzing industry trends, it’s important to identify potential sources of disruption in the market. This may include new technologies, changes in consumer behavior, or regulatory changes that could alter the competitive landscape and create new opportunities for your business.
Determine your target market’s needs and preferences: To effectively market your technology, you need to understand what your target customers need and want. You can gather this information through customer surveys, focus groups, and other market research methods.
Determine your target market’s purchasing power: It’s important to understand how much your target customers are willing and able to pay for your technology. This will help you determine your pricing strategy and determine whether there is enough demand at your target price point.
Analyze your target market’s attitudes and behaviors: Understanding your target customers’ attitudes and behaviors can help you tailor your marketing efforts to their preferences. For example, if your target market values sustainability, you may want to highlight the eco-friendliness of your technology in your marketing materials.
By conducting a technology market analysis with a focus on disruption, you can gain a better understanding of the potential for your technology to disrupt existing market dynamics and create new opportunities. This can help you position your business for success in the market.
| 2022-12-21T00:00:00 |
https://marketanalysis.com/how-to-do-a-technology-market-analysis-with-focus-on-disruption-factor/
|
[
{
"date": "2022/12/21",
"position": 98,
"query": "AI economic disruption"
}
] |
|
Agentic AI
|
Agentic AI
|
https://www.summit.ai
|
[] |
Whether you're launching your first AI agent or scaling complex multi-agent workflows, this summit will accelerate your path. ✓ AI Engineer/ Developer ✓ ...
|
Tuana Çelik, Sr. Developer Relations Engineer | LlamaIndex
Agents present a whole new way of building software: one where you give your app a goal, some tools, and it figures out how to get the job done on its own. The results? Not just better and more accurate answers, but systems that can actually do things behind the scenes, and even on your behelf.At LlamaIndex, we’ve been deep in the weeds building real, useful agentic apps. In this session, we’ll walk you through how that actually works. We’ll cover some of the core design patterns—like event-driven workflows, routing, parallelization, orchestrator-worker setups, and evaluator-optimizer loops—and show how to bring them to life in LlamaIndex.
From there, we’ll explore how these pieces fit together into more advanced agents, and eventually into full-on multi-agent systems, all using built-in tools from the LlamaIndex framework. We’ll also zoom in on one of the latest and most powerful components in an agent’s toolkit: MCP servers and tools, and how they help agents get the live context they need to hit their goals.By the end of the session, you’ll have a handle on:- building agents using LlamaIndex- composing agents into multi-agent systems- designing reusable tools your agents can call on- giving your agents real-time knowledgeYou'll learn the fundamentals of what makes an agentic application, as well as an intro into starting to build your own agents. In this talk, we'll use the open-source LlamaIndex framework in Python and some models from providers like OpenAI and Anthropic.
| 2022-12-21T00:00:00 |
https://www.summit.ai/
|
[
{
"date": "2022/12/21",
"position": 15,
"query": "generative AI jobs"
},
{
"date": "2022/12/21",
"position": 37,
"query": "artificial intelligence business leaders"
}
] |
|
Generative AI: What does it mean in the Enterprise?
|
Generative AI: What does it mean in the Enterprise?
|
https://blogs.idc.com
|
[
"Philip Carter - Group Vice President",
"Worldwide Thought Leadership Research",
"Philip Carter Is Group Vice President",
"European Chief Analyst",
"Ww C-Suite Tech Research Lead. His Global Responsibilities Focus On Creating Research That Assesses Tech Spending",
"Buyer Preferences Across The C-Suite",
"With A Focus On Business Leadership As It Relates To Technology Objectives",
"Priorities",
"Programs",
"Investments. As Chief Analyst For Europe"
] |
Growing job demand would focus on data scientists, process automation specialists, digital marketing and strategy experts as well as many other more roles.
|
The digital airwaves and social media feeds have recently gone wild with examples of how the AI-driven chatbot ChatGPT has solved riddles, generated high school essays and explained why the Croatian football team has outperformed similar sized nations at recent World Cup Tournaments. Understandably, it has again raised important questions about the impact of AI on our lives, enterprises, and broader society.
First and foremost, let’s start with definitions. What is Generative AI and where does OpenAI/ChatGPT fit within all of this? Generative AI is a branch of computer science that involves unsupervised and semi-supervised algorithms that enable computers to create new content using previously created content, such as text, audio, video, images and code.
ChatGPT (which stands for Chat Generative Pre-Trained Transformer) is a chatbot developed by OpenAI. ChatGPT is built on top of OpenAI’s GPT-3.5 family of large language models (LLMs) and is fine-tuned with both supervised and reinforcement learning techniques. It is being hailed as the smartest chatbot ever developed. OpenAI was founded in 2015 (initially as a non-profit organization) and early investors included Elon Musk & Peter Thiel. In 2019, it became a for-profit organization and inked a $1bn deal from Microsoft. This deal allowed it to use Microsoft’s Azure Cloud Platform for its research and development; and in return, Microsoft was given the first opportunity to commercially leverage early results from OpenAI’s research. OpenAI has a stated goal of promoting and developing friendly AI in a way that benefits humanity as a whole and is viewed as the leading competitor to DeepMind (acquired by Google in 2014 for $500M).
It is important to understand that while ChatGPT is a good example of generative AI technology, the market segment is much broader. LLMs began at Google Brain in 2017, where they were initially used for translation of words, while preserving context. Since then, large language and text to image models have proliferated at leading tech firms like Google (BERT and LaMDA), Facebook(OPT-175B and BlenderBot) and OpenAI (GPT-3 for text, DallE-2 for images and Whisper for speech). Online communities (e.g. MidJourney), open-source providers (e.g. HuggingFace) and startups such as Stability AI have also created generative models. In Q4 this year, a spate of text-to-video models from Google, Meta and others have emerged. Generative models have largely been confined to larger tech companies because training them requires massive amounts of data and computing power. But once a generative model is trained, it can be “fine-tuned” for a particular content domain with much less data. Today, Generative AI applications largely exist as plugins within software ecosystems.
The questions that technology and business leaders should be asking in terms of what Generative AI means for the enterprise are outlined below:
How will it be incorporated in existing enterprise technology environments?
Code Generation – GPT-3 has proven to be an effective generator of computer program code. GPT-3’s Codex program is specifically trained for code generation and works well when given a small function. Microsoft’s Github has a version of GPT-3 for code generation and is called CoPilot. The latest versions of Codex can identify bugs and fix mistakes in its code and can explain what it does occasionally. The goal for these tools is to not eliminate programmers but to make tools like Codex and CoPilot “pair digital assistant” with humans to improve their speed and effectiveness.
– GPT-3 has proven to be an effective generator of computer program code. GPT-3’s Codex program is specifically trained for code generation and works well when given a small function. Microsoft’s Github has a version of GPT-3 for code generation and is called CoPilot. The latest versions of Codex can identify bugs and fix mistakes in its code and can explain what it does occasionally. The goal for these tools is to not eliminate programmers but to make tools like Codex and CoPilot “pair digital assistant” with humans to improve their speed and effectiveness. Enterprise Content Management – Vendors in the Headless content management space are incorporating these types of generative AI tools for both content generation and recommendations. This is to deal with the increased content velocity as additional forms of content are based on a single source generated by AI with human oversight. It is not being used to write whole copy, but rather an outline for the content author to use as a draft. In addition, there it is likely to impact GUI design in the form of “generative design” with the likes of Figma or Stackbit potentially including generative AI capabilities in as part of collaborative interface design engines.
– Vendors in the Headless content management space are incorporating these types of generative AI tools for both content generation and recommendations. This is to deal with the increased content velocity as additional forms of content are based on a single source generated by AI with human oversight. It is not being used to write whole copy, but rather an outline for the content author to use as a draft. In addition, there it is likely to impact GUI design in the form of “generative design” with the likes of Figma or Stackbit potentially including generative AI capabilities in as part of collaborative interface design engines. Marketing and CX Applications – Outside of the use of content generation for advertising and marketing along with the automation of marketing campaigns, the primary application for early versions of generative AI is in AI driven chatbots and agents for contact centers and customer self-service such as employed by Salesforce and Genesys, and these have initially delivered mixed results. However, this next generation of capabilities will mean a broader range of interactions, more accurate answers, and lower levels of required human interactions which will result in higher adoption and eventually more training data for the models. In the near future, generative AI will become more prevalent in the creation of personalized product recommendations through insight analytics, better and deeper customer segmentation as a steppingstone to true personalization and contextualization of experiences, and better understanding customer satisfaction and performance.
– Outside of the use of content generation for advertising and marketing along with the automation of marketing campaigns, the primary application for early versions of generative AI is in AI driven chatbots and agents for contact centers and customer self-service such as employed by Salesforce and Genesys, and these have initially delivered mixed results. However, this next generation of capabilities will mean a broader range of interactions, more accurate answers, and lower levels of required human interactions which will result in higher adoption and eventually more training data for the models. In the near future, generative AI will become more prevalent in the creation of personalized product recommendations through insight analytics, better and deeper customer segmentation as a steppingstone to true personalization and contextualization of experiences, and better understanding customer satisfaction and performance. Product Design & Engineering – It will also affect technologies in the product lifecycle management (PLM) and innovation space with the likes of Autodesk, Dassault Systemes, Siemens, PTC and Ansys continuing to build capabilities to enable design engineers & R&D teams to automate and expand the ideation and optioning process during early-stage product design, simulation, & development. Generative AI design would allow options for engineering and R&D teams to consider in terms of structure, materials, and optimal manufacturing/production tooling. For example, it would potentially suggest a part design that optimizes against factors like cost, load bearing, and weight. Generative design can also enable reimagining of product look and feel, often resulting in unique aesthetics and form that is not only more compelling to end users, but more practical and environmentally sustainable. Many of these vendors have attached their generative design offerings to additive manufacturing capabilities that are needed to realize these unique products. Opportunities exist across multiple industries for generative design. Automotive, aerospace, and machinery organizations can improve product quality, sustainability, and success, while life sciences, healthcare, and consumer products companies can improve patient outcomes and customer experiences.
What are the pitfalls?
Generative AI, while providing lower-cost, higher-value solutions, has significant ethical and perhaps legal implications. There are significant questions over issues like copyright, trust and safety. Organizations must consider issues such as privacy and consent around data, reproduction of biases and toxicity, generation of harmful content, sufficient security against third-party manipulation, and accountability and transparency of processes. Neglect of AI ethics isn’t just a moral quandary – it is a significant business risk that means less trust, less control, and less ability to advance the models in an optimal way. Businesses must take a multi-pronged approach to AI from developer to end-user, first and foremost guided by a framework including principles that appropriately consider all ramifications of AI. Businesses should also choose models where techniques such as adversarial input (training against bad or manipulated data), benchmark dataset training (checking for biases via label tests), and XAI (explainable AI) are used. Finally, concerns with AI ethics are intrinsically linked to how accountability measures are enacted. Businesses should ensure they take a Human-in-the-Loop (HITL) approach to ensure minimal model drift, rigorous monitoring of output, and continuous improvement. AI must not be viewed as an independent, black box entity, but should rather be seen as human-computer interaction where optimal usage comes from deep understanding, meticulous monitoring, and striving for accuracy of the model.
How will it affect jobs?
At the end of 2020, the World Economic Forum (WEF) predicted that AI would displace 85 million jobs by 2025. The main jobs it identified under threat would be the likes of data entry clerks, administrative assistants, accounting and auditing professional amongst others. By the same timeframe, it predicted that 97 million new jobs would be created as AI becomes more mainstream in the enterprise. Growing job demand would focus on data scientists, process automation specialists, digital marketing and strategy experts as well as many other more roles. Generative AI means that we can add a new role to that list – prompt engineers. Basically, this role focuses on working out what to type into AI chatbots to get the best out of them. Some would expect these individuals to also deal with so-called ‘hallucinations’ – where Generative AI gets it completely wrong. These types of entirely new job descriptions highlight how an emerging technology not only displaces activities, but also creates new ones. The classic creative destruction principle initially outlined by Schumpeter. However, for business and technology leaders – it does require a dynamic and ongoing assessment of required digital skills including continuous gap analysis and roadmaps to ensure that the necessary capabilities are available to support the digital business of the future.
Moving forward, the best place to watch new and interesting generative AI use cases is in the start-up and scale-up space. The likes of Jasper (Copywriting), Stability AI (Visual art), DoNotPay (Legal Services), Omnekey (Creative Content), Paige.ai (Cancer diagnostics) and Mostly.ai (Synthetic data) showcase how quickly this space is fueling a range of game changing innovations across the industry – and potentially what’s around the corner for so many industries. It is incumbent on all of us to ensure that we approach this fascinating space with the right balance of curiosity and skepticism.
| 2022-12-21T00:00:00 |
2022/12/21
|
https://blogs.idc.com/2022/12/21/generative-ai-what-does-it-mean-in-the-enterprise/
|
[
{
"date": "2022/12/21",
"position": 23,
"query": "generative AI jobs"
}
] |
Careers
|
Careers
|
https://www.rapdev.io
|
[] |
Generative AI Controller. NEW. ITOM Visibility · Discovery · Service Mapping ... 7 jobs. Engineering. Job. Cloud Engineer, Boston. Boston, Massachusetts, United ...
|
Introducing RapDev
A little about us
RapDev helps organizations release software faster and improve service availability by expertly guiding Datadog and ServiceNow implementations. We believe in customer-centric relationships built on transparency, flexibility, and innovative problem-solving.
We like to work on cool tech and solve interesting problems. We’re fast paced, but not at the expense of quality work. And we’re competitive – but only at our weekly poker game nights.
Voted “Best Place to Work” time and time again.
| 2022-12-21T00:00:00 |
https://www.rapdev.io/company/careers
|
[
{
"date": "2022/12/21",
"position": 50,
"query": "generative AI jobs"
}
] |
|
The Top 6 Artificial Intelligence Healthcare Trends of 2024
|
The Top 6 Artificial Intelligence Healthcare Trends of 2024
|
https://encord.com
|
[] |
Discover the Top 6 Artificial Intelligence Healthcare Trends of 2024. Learn about the latest advancements in diagnostic AI, wearables, and combating AI ...
|
One of the most exciting things about the end of the year is looking back on the progress that’s been made, and using that progress as a benchmark for making predictions about how far we might come in another year’s time.
Every year brings new technologies, new use cases, and exciting AI developments that have real-world impact. When it comes to the use of artificial intelligence in healthcare, 2023 and 2024 saw a steady increase in the number of medical diagnostic models and clinical AI tools making it into production and onto the market. We also saw an increase in the amount and quality of wearable medical devices, a heavier scrutiny of bias in machine learning, and growing privacy concerns about patient data.
With many machine learning models now having a positive impact in clinical settings, developments in healthcare AI are set to accelerate rapidly. Here are the six 2024 healthcare AI trends that we’re most excited about.
Collaborative DICOM annotation platform for medical imaging CT, X-ray, mammography, MRI, PET scans, ultrasound See it in action
Six 2024 Healthcare AI Trends
Healthcare providers will use diagnostic artificial intelligence in fields outside of radiology
For the past few years, many AI companies have focused on developing diagnostic models for radiology.
Radiology was a logical starting place for companies and researchers looking to build diagnostic models that augment clinicians’ workloads.
Because patient screenings are standard practice in radiology, medical professionals collect and have access to a lot of data.
I spent much of my career working with breast cancer data. Healthcare providers ask women of a certain age to attend screenings at regular intervals as a preventative measure for breast cancer. At a national level, most countries also have protocols for assessing screenings, so there are standardizations within and between hospitals. Combined, these factors provided machine learning engineers with a good starting point for curating high-quality data, structuring workflows, and building models that support radiologists.
Now, machine learning engineers are starting to apply and adapt the lessons they’ve learned from radiology and build models for more complicated medical subsets. The healthcare industry is seeing a lot of AI development in microscopy, which is more challenging than radiology because pathologists have less standardization in the methods they use to count cells. Previously, this lack of standardization made it difficult to collect high-quality training data and develop a labeling protocol for annotators; however, companies such as Paige AI are starting to enter this market with technologies built to augment microscopy practices.
Likewise, Rapid AI recently received FDA approval for its stroke detection model, which is an amazing achievement because stroke detection relies on MRI data. MRI does not have standardized units, so machine learning engineers must perform manufacturer specific normalizations when collecting data to train a model.
In 2024, we’ll continue to see the use of artificial intelligence expand into different medical specialties.
The healthcare industry will focus more on healthcare rather than sick care
Rather than use AI only as a means to support a sick patient, healthcare providers will increasingly use AI to help keep patients healthy. At the same time, patients will take more ownership over monitoring their own health.
These shifting approaches toward patient care stem from the proliferation of wearable devices, a technology trend that’s been growing over the past few years. Wearables made by third-party companies are enabling users to educate themselves about their own health. Advancements in artificial intelligence are allowing these companies to analyze the data they collect at a much larger scale. As their models improve, the apps and platforms connected to at-home test kits and wearable devices are increasing patients’ ability to reliably monitor their health in real-time before they see a doctor.
In a post-pandemic world, more people have become comfortable taking ownership of their health. The use of telehealth and telemedicine, wearables, and at home-testing became increasingly common when stay-at-home orders prevented people from access to on-site healthcare providers.
Now, people feel more empowered to address their health concerns because the initial steps of testing and monitoring health no longer necessitate going to a GP, undergoing multiple on-site tests, or obtaining approval from health insurance companies for those tests.
At the same time, these companies are continuing to improve their AI’s ability to analyze the data collected, thereby providing better insights from real-time monitoring that can help doctors customize care and treatment plans. With this information, both patients and healthcare professionals can take a more proactive approach to treatment, focusing on staying healthy rather than treating sickness.
Researchers will make more datasets public to combat AI bias
Many people hope that the development of AI will help eliminate human biases by replacing human subjectivity with data-driven decision-making. However, algorithms and models are still at the mercy of the people who build and train them. Implicit biases and data collection biases can just as easily perpetuate, rather than eliminate, long-standing inequalities in medical care.
However, increasing awareness of both the historical bias in medical research and AI bias has resulted in the machine learning community paying increased attention to model bias during training and development. In doing so, ML engineers can help ensure that the machines aren’t biased toward certain demographics because of their ethnicity, age, or gender.
In an effort to combat AI bias, there’s going to be an increase in the demand for academia to develop standardized, reproducible systems. More and more journals are requiring that data sets be made public as part of the publication, which means that the research community can verify the results of a system, assess whether the data used to develop the system was balanced, and continue to iterate on and improve the system.
Startups will build more demographic-specific healthcare technology
While the machine learning community is improving the generalization of models by tackling model bias, AI startups are also tackling these longstanding biases by building demographic-specific models that generalize well for a specific population. This targeted approach necessitates a deep understanding of user needs within those demographics, making healthcare UX design a crucial element in their development process.
Rather than taking a one-size-fits-all approach to designing products, these startups are building technologies capable of generalizing only for a specific population. They have begun to develop personalized health products, segmenting customers by demographics. In doing so, they use AI to better assess and understand patient health based on demographic differences in genes, metabolism, tissue density, and other physiological factors.
For instance, some startups have designed healthcare products billed as “for Latinos by Latinos.” Others have created diet-health-focused apps that take into account lifestyle differences that impact the metabolisms of Asian populations. Others have designed skin care monitoring specifically for the LGBTQ+ community.
Such products fill an important gap for minority communities who have often suffered from mainstream medical bias in which they receive diagnoses from specialists who have deep expertise in a specific treatment area but not necessarily in diagnosing patients from diverse communities.
Differences in biology between populations often mean that a demographic-specific model is needed for patients to obtain the best treatment possible. For example, in Asian populations, women have very dense breasts. Whereas most of the world has a standard for conducting screenings with X-rays, ultrasound works much better when breasts are very dense. When building breast screening models for Asian hospitals, companies should spend more money developing specialized models that have trained on ultrasound images.
Advancements in AI are now enabling machine learning engineers to build systems that take demographic differences into account and improve patient outcomes as a result. Rather than rely on technology that caters to the greatest common denominator, new technologies will enable all patients to receive the best possible treatment method, regardless of their background, sex, lifestyle, age, or other factors.
Collaborative DICOM annotation platform for medical imaging CT, X-ray, mammography, MRI, PET scans, ultrasound See it in action
The adoption of medical AI will accelerate the democratization of healthcare systems
Beyond the at-home monitoring made available by test kits and wearable devices, AI is going to make healthcare more accessible in remote areas as well as developing nations.
As the use of healthcare AI becomes more widespread, patients in these areas are becoming increasingly able to get access to preventative screenings via telehealth. For instance, many developing nations and rural areas don’t have enough medical experts to read the scans that they obtain during screenings. A few years ago, the size of medical images hindered the ability to have scans accessed remotely. However, the expansion of computing infrastructure – through the advent of the internet and cloud storage – as well as advances in computer science such as improvements in device memory mean that those medical images that were once too large to send remotely can now be transferred quickly from one location to another, allowing radiologists to look at samples from afar and diagnose patients in areas with limited healthcare resources, improving patient outcomes.
As this technology trend continues in the coming years, more people around the world will have access to early screenings, which will increase survival rates in regions that have been historically underserved when it comes to healthcare services.
Greater adoption of healthcare AI will increase the tension between data accessibility and data privacy
The increase in the adoption of medical AI comes with a Catch-22: we want data to be as easily accessible as possible for expert review at healthcare organizations, and we want to keep it private as possible to protect patients’ identities and other sensitive information.
Digital healthcare is becoming the norm, and even the most rigid legacy healthcare providers are undergoing digital transformations or at least using the cloud to store health records.
As more patient data is put on servers, we will see an increase in the effort to anonymize data beyond just removing names and identification numbers. Data scientists will begin to think more carefully about anonymization, considering, for instance, whether the combination of information such as a patient’s ethnicity, location, and diagnosis makes the patient identifiable.
Standardization in storage is another trend that impacts data privacy. How healthcare organizations store data has a tremendous impact on their security, which is why institutions should no longer focus on producing a solution ideal for their needs but instead focus on storing data in a standardized and secure manner.
By providing APIs and software connections that de-identify DICOM images for users, Google Health has pushed the healthcare sector in a positive direction, enabling researchers and ML engineers to use data to build and train AI without exposing a patient’s identity.
The trend of combining centralized approaches with the cloud to store data securely is a powerful one. It will continue in 2024, creating positive ripple effects for the AI ecosystem as a whole by empowering researchers to spend less time focused on data privacy tooling and more time focused on creating new methodologies and applications for medical AI.
As the healthcare industry continues to embrace artificial intelligence and other new technologies, it can create greater opportunities for patients and clinicians by increasing efficiency, creating pathways for personalized care, and improving access to treatment. At the same time, in the coming years, we’ll see machine learning engineers and healthcare providers put an increasing amount of focus on improving data privacy and reducing AI bias so that as the application of these new technologies benefits all patients.
Ready to automate and improve the quality of your medical data?
Sign-up for an Encord Free Trial: The Active Learning Platform for Computer Vision, used by the world’s leading computer vision teams.
AI-assisted labeling, model training & diagnostics, find & fix dataset errors and biases, all in one collaborative active learning platform, to get to production AI faster. Try Encord for Free Today.
Want to stay updated?
Follow us on Twitter and LinkedIn for more content on computer vision, training data, and active learning.
Join our Discord channel to chat and connect.
| 2022-12-21T00:00:00 |
https://encord.com/blog/top-ai-healthcare-trends/
|
[
{
"date": "2022/12/21",
"position": 17,
"query": "AI healthcare"
}
] |
|
Vision AI for Delivering Better Healthcare Outcomes - Chooch AI
|
Vision AI Applications For Optimizing Healthcare Delivery
|
https://www.chooch.com
|
[] |
AI-enabled cameras and sensors enable continuous patient monitoring, alerting providers to changes in behavior and conditions for faster analysis and ...
|
AI-enabled cameras and sensors enable continuous patient monitoring, alerting providers to changes in behavior and conditions for faster analysis and appropriate intervention. Proactively monitor for falls, gestures for help, and changes in behaviors to improve care delivery and outcomes.
| 2022-12-21T00:00:00 |
https://www.chooch.com/solutions/healthcare/
|
[
{
"date": "2022/12/21",
"position": 27,
"query": "AI healthcare"
}
] |
|
AI in Simulation – Clinical Intelligence at NCLEX Standards
|
AI in Simulation – Clinical Intelligence at NCLEX Standards
|
https://nascohealthcare.com
|
[] |
The ALEX Patient Communication Simulator developed by PCS and Nasco Healthcare represents this new training dynamic. Via a browser-based interface ...
|
The ALEX Patient Communication Simulator developed by PCS and Nasco Healthcare represents this new training dynamic. Via a browser-based interface, instructors can use most Wi-Fi devices from phones to computers to access and control the simulation. The AI used in the ALEX simulator allows the instructor to select various patient personalities that can be cloned and modified to present any physiological material being covered in the nursing program. This allows the instructor to have several personalities that can be used with students to provide different responses for the same material, so that the student can adapt to the context of the patient. The student is no longer just reacting to the physiological dynamic changes. Rather, they must incorporate the interaction with the patient into the treatment. Since the AI responds to the student and can be augmented by the instructor, the student is required to focus on the patient. Thereby the risk is reduced. Talking around the patient is reduced. The interaction with the ALEX simulator is more organic, more comparable to the clinical interactions they would face in real life, allowing the student to refine their Clinical Intelligence.
| 2022-12-21T00:00:00 |
2022/12/21
|
https://nascohealthcare.com/ai-in-simulation/
|
[
{
"date": "2022/12/21",
"position": 67,
"query": "AI healthcare"
}
] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.