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World Bank Group (WBG) - AI for Good - ITU
|
World Bank Group (WBG)
|
https://aiforgood.itu.int
|
[] |
... Union, and in cooperation with the European Commission's DG REFORM. The work involved piloting an innovative approach for mapping business administrative ...
|
Household surveys give a precise estimate of poverty; however, surveys are costly and can only be fielded infrequently. This project aims at comparing the predictive performance of models based on globally available, spatially referenced public and private sector data sources that have been used to estimate poverty. We include daytime and nighttime satellite imagery, Facebook marketing data, OpenStreetMap data, among other sources. The project trains a machine learning model to predict levels and changes in poverty relying on ground truth poverty data across 82,000 villages and 59 countries, spanning Africa, Asia, the Americas, and Europe. Globally, the model explains over 60% of the variation of an asset-based poverty index at the village level and over 70% of the variation at the district level; in some countries, the model explains over 90% of the variation in poverty at the district level. Features from OpenStreetMaps, nighttime lights, and daytime imagery are most important in explaining poverty, where some features from Facebook Marketing data—such as the proportion of active Facebook users with interests in restaurants and luxury goods—are highly (negatively) correlated with poverty across most countries. Accuracy for predicting changes in poverty is lower, but the model explains above 25% of the variation in poverty in some countries. The model performs best in lower income countries and in countries with more variation in levels/changes in poverty.
| 2023-03-25T00:00:00 |
https://aiforgood.itu.int/about-us/un-ai-actions/wbg/
|
[
{
"date": "2023/03/25",
"position": 82,
"query": "AI labor union"
}
] |
|
Generative AI for Business Sprint - AI course
|
Generative AI for Business Sprint
|
https://www.imd.org
|
[] |
Gain the insights you need to demystify generative AI and explore its application in business through multiple real-world examples.
|
Amit Joshi
Professor of AI, Analytics, and Marketing Strategy
Amit Joshi is Professor of AI, Analytics and Marketing Strategy. He specializes in helping organizations use artificial intelligence and develop their big data, analytics, and AI capabilities. An award-winning professor and researcher, he has extensive experience of AI and analytics-driven transformations in industries such as banking, fintech, retail, automotive, telecoms, and pharma.
Joshi believes that no organization’s digital transformation is complete until they really understand their…
| 2023-03-25T00:00:00 |
https://www.imd.org/sprint/generative-ai/
|
[
{
"date": "2023/03/25",
"position": 38,
"query": "artificial intelligence business leaders"
}
] |
|
Browse products - The Algorithmic Bias Lab - BABL AI
|
The Algorithmic Bias Lab
|
https://courses.babl.ai
|
[
"Shea Brown",
"Michael Paik",
"Jeffery Recker",
"Dinah Rabe"
] |
The AI Governance for Business Professionals certification program provides business leaders, managers, and non-technical professionals with the ...
|
Introduction to Responsible AI
In an era where artificial intelligence is transforming industries and societies, understanding the principles of responsible AI has never been more crucial. This course provides a comprehensive introduction to the ethical, social, and legal considerations in AI development and deployment. You’ll gain practical knowledge of frameworks and tools to assess and promote ethical AI practices, while also learning about current regulations and future trends. By the end of the course, you’ll be equipped with the skills and insights needed to navigate the challenges and opportunities of creating AI systems that align with fairness, accountability, and sustainability. Whether you’re a student, professional, or policymaker, this course offers essential guidance for anyone passionate about shaping the future of responsible AI.
| 2023-03-25T00:00:00 |
https://courses.babl.ai/courses
|
[
{
"date": "2023/03/25",
"position": 54,
"query": "artificial intelligence business leaders"
}
] |
|
UNESCO is the United Nations Educational, Scientific and ...
|
United Nations Educational, Scientific and Cultural Organization (UNESCO)
|
https://aiforgood.itu.int
|
[] |
... Learning project (based on the Teaching Artificial Intelligence at School project). To support Member States to harness AI for education, with equity and ...
|
Based on the findings of the AI Needs Assessment Survey in Africa and another survey of over 1200 judicial operators in 100 countries, UNESCO has launched a global MOOC on AI and the Rule of Law to strengthen capacities of judicial operators in the use of AI in the administration of justice, while addressing the human rights and legal implications of the use of AI with respect to bias, discrimination, privacy, freedom of expression among others. The course is structured around six introductory modules that unpack AI’s application and impact in the judiciary.
The course is available in 7 languages and taught by 20 speakers – including sitting judges from Supreme Courts, Human Rights Courts, Legal Experts and Technology experts. The experts teaching the course come from India, Senegal, Kenya, Netherlands, United States, Chile, Brazil to China.
Over 4500 judicial operators from 138 countries have enrolled in the course in 2022.
The project continues to support capacity building for judicial operators through online and in person trainings on AI and the Rule of Law.
| 2023-03-25T00:00:00 |
https://aiforgood.itu.int/about-us/un-ai-actions/unesco/
|
[
{
"date": "2023/03/25",
"position": 92,
"query": "artificial intelligence education"
}
] |
|
The Potentially Large Effects of Artificial Intelligence on ...
|
The Potentially Large Effects of Artificial Intelligence on Economic Growth
|
https://www.lewissilkin.com
|
[
"External Authors",
"Goldman Sachs"
] |
This analysis from Goldman Sachs estimates that 300 million jobs globally could be exposed to automation from ChatGPT. The report notes that worker ...
|
This analysis from Goldman Sachs estimates that 300 million jobs globally could be exposed to automation from ChatGPT. The report notes that worker displacement from automation has historically been offset by the creation of new jobs, and the emergence of new occupations following technological innovations accounts for the vast majority of long-run employment growth. The research finds that the combination of significant labour cost savings, new job creation, and higher productivity for non-displaced workers raises the possibility of a productivity boom that raises economic growth substantially, with AI predicted to eventually increase annual global GDP by 7%.
| 2023-03-26T00:00:00 |
2023/03/26
|
https://www.lewissilkin.com/en/our-thinking/future-of-work-hub/insights/2023/03/26/the-potentially-large-effects-of-artificial-intelligence-on-econo
|
[
{
"date": "2023/03/26",
"position": 81,
"query": "automation job displacement"
}
] |
Artificial Intelligence and Unemployment | Free Essay ...
|
Artificial Intelligence and Unemployment
|
https://studycorgi.com
|
[] |
This paper argues that while artificial intelligence is an effective measure to be applied for better quality and practical work, people may lose jobs as a ...
|
Introduction
Fast technological development and automatization have caused many changes in people’s lives. As soon as the industrial revolution impacted the manufacturing process, productivity skyrocketed, and corporations became major societal driving factors. However, a downside of the industrial revolution was the fast improvement of equipment that worked more efficiently, cheaply, and faster than any person could. From this perspective, the jobs that were previously executed by people were quickly replaced with machines. A similar concern correlates with the current technological development, namely artificial intelligence. Artificial intelligence (AI) is a machine’s ability to learn through certain algorithms, which creates an environment in which it can become fully autonomous and used in any industry while performing any task. There are multiple uses in which AI can be applied, including medicine, physics, the economy, and other essential areas of life. However, similarly to the development of automatization during the industrial revolution, AI may correlate with increased unemployment and further issues related to this concept. This paper argues that while artificial intelligence is an effective measure to be applied for better quality and practical work, people may lose jobs as a result of its global spread.
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AI and Automatization
As mentioned prior, the current state of AI may be compared with the revolutionary automatization that has made mass production and fast and effective manufacturing possible in the previous century. Certain phenomena are relatively similar, including the fear of the unknown when it comes to the process’ impact on humankind. Yet, the perspectives to shape industries and improve them overshadows risks. It is essential to point out that, at the time, machines have partially replaced human beings since manufacturers found them to be cheaper to sustain, more proficient in operating without mistakes, and faster when it comes to production. An example is an automatization in the last half of the 21st century in the US. Researchers mention that more than 50,00 people have lost their jobs in 5 years in Youngstown, Ohio, resulting in an unprecedented number of divorces and criminal activity in the city (Su 38). This exemplifies that while unemployment caused by automatization is the driving factor, the correlating societal processes caused by a lack of jobs also have a significant impact on the area in which the incident occurs.
The unemployment caused by automatization is a documented phenomenon, and while the current job market has multiple options for individuals looking for an income, lower-skilled workers find it harder to be integrated. The same circumstances may occur due to the fast development of artificial intelligence and its wide use in all areas of life. Similar to the industrial revolution and the automatization in the last century, specific jobs are becoming less marketable. Corporations would instead invest in an automatic system with better skills and no limitations that people have in terms of lower productivity, wages, and time off. Thus, the two changes in the technological reals, the first being the industrialization through machines and the second being the revolutionary AI, may be similar in terms of effects on unemployment.
Autonomous AI vs. Limited Human Abilities
AI is a threat not only because of its limitless possibilities and ability to apply to any area and industry but also because people have certain characteristics that make it impossible for them to keep up with the power of the artificial brain. Since artificial intelligence is based on self-learning, it perceives reality, examines data, and determines solutions based on the algorithms that are input into the system. The current state of the technology industry does not allow this area to be fully integrated into every area of life, but since it is improving at a fast pace, this factor cannot be omitted. On the other hand, human limits make people more unreliable and less proficient compared to machines. People are not only limited in terms of how much they can operate but also their skills and knowledge. Even the best specialist with extensive experience in a specific area would not be able to compete with AI due to the limited abilities of the brain.
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Artificial intelligence is able to constantly learn based on the input data, which already gives it limitless abilities to improve without having barriers, unlike humans. As a result, autonomous AI can succeed any expert and make the job market useless. This, however, requires time since the technology is not yet on a level that would allow every corporation to replace human beings with such autonomous systems. Nonetheless, the possibility of AI improving in the next decade is almost inevitable, which is a big risk in terms of all the jobs that will then be replaced. It is essential to refer to the amount of work one can do in a certain period of time.
An example is the job of a human resource manager, a person who has to go through multiple CVs and choose the characteristics of potential employees a company needs. Thus, this position implies data analysis and determining the most potentially effective employee for a specific organization. However, an automatic system is able to go through thousands of CVs in little time and organize the information in a way that the potential workers will fit the criteria that the company requires. As a result, the position of a human resource manager would be easily replaced by an AI that can examine extensive amounts of information and, based on an algorithm, proficiently pick the choices that align with the initial requests. The same argument may be applied to multiple other jobs, including data analysis, researchers, or even journalists. Having an AI that can examine extensive information and contribute to successful task accomplishments ultimately diminishes the need for humans to do the same job yet take more time, be less efficient, and require a monthly wage for the same amount of work.
Replaceable Skills
While artificial intelligence is not yet developed enough to take over the job market altogether, certain industries appear to be vulnerable to change faster than others. There are multiple examples that portray how AI may have a negative impact on employment and create an environment in which current workers will not be needed anymore. Thus, transportation is one of the industries that is seemingly going to suffer from significant changes that are arguably going to revolutionize supply chains, thus, changing the way corporations operate. For example, one of the biggest employers in the US, Walmart, is already operating with driverless trucks, which have not yet been fully integrated but have been successfully tested (Holland). This implies that drivers will no longer be needed based on the testing results, which is a positive factor for the company but a negative one for the employees. The company benefits since the products will safely and timely be delivered in stores, mitigating the risk of delays or other circumstances that usually correlate with a person executing the same job. However, while it may be cost-effective for the organization, multiple people who are currently working as drivers will no longer be needed.
There are various jobs that investments in AI development can cover. However, this cannot be implemented at the moment since the current technology is not at an adequate level yet. Still, there is a possibility that artificial intelligence can replace people who work as doctors, teachers, shop assistants, pilots, and other essential positions in the near future. An example would be an AI figuring out the correct diagnosis for a patient based on symptoms and tests. While at the moment, this cannot be possible without a person monitoring the process, advancements in the field can improve the situation.
Risks for Low-Skill Workers
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While it is true that the development of AI is potentially risky for people of all professions, certain positions on the market are more vulnerable than others. This also correlates with the aforementioned industrialization in which laborers were replaced with much more proficient machines able to execute the same work faster and without disturbances. As exemplified, drivers are vulnerable since there are big companies already creating automatic systems that do not imply the presence of people in vehicles. Another example is customer service workers who have to either assist customers with their problems, fix an existing problem, or provide different solutions to mitigate negative customer reviews. This is a task that relies on interpersonal communication, but it does not necessarily require a human being on the other side of the line as long as an AI can deal with the difficulty.
Customer service, while practical, is expensive for companies to sustain since multiple specialists are needed if the customer base is extensive. According to researchers, experts in robotics have predicted an extensive list of professions that will not be necessary once artificial intelligence reaches its full potential (Walsh 637). This includes such professions as retail workers, cashiers, couriers, and receptionists. While executing important tasks and being the backbone of the industry where they operate, these workers are considered low-skill workers since the responsibilities do not require extensive knowledge or experience. Thus, an AI can find the rooms that a customer requests at a hotel or scan products at a supermarket. Moreover, couriers can also be replaced through automatic driving vehicles. These positions are significantly more vulnerable to being replaced through automatization compared to ones that require expert skills in a particular area, yet the job market as a whole will change once artificial intelligence is more developed.
Inequality
Now that the employees who are vulnerable to replacement have been discussed, it is essential to mention that such dispersion creates inequality in the job market. The concept also applies to the previously highlighted industrial revolution. The laborers working at factories and specializing in manual work could not find a similar job because most big companies were investing in machines that would do the same tasks yet more efficiently. As a result, the workers were left with positions that they were not familiar with. The situation may repeat itself in terms of the evolution of AI.
It may be argued that automatization in all industries will create job opportunities for individuals willing to work in IT and similar areas. However, it is reasonable to consider that multiple individuals do not have the right skills, expertise, or knowledge to acquire a position that implies proficiency in technology (Ernst et al. 6). As a result, those hired as a result of automatization will be the current experts operating in the same field. On the other hand, the low-skilled workers who used to work as drivers, cashiers, or couriers will not have an accessible job market that would align with their personal expertise. In this case, multiple individuals who, based on the new job opportunities, will have the choice to join the market by working in IT will not be qualified to do so. Thus, the argument that AI creates jobs rather than replaces them is redundant due to the fact that the jobs that it creates can merely be executed by experts with high skills and experience in the area.
Conclusion
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The arguments exemplified prior are evidence that the development of artificial intelligence and its spread among the different industries offering people jobs will facilitate unemployment. Since further automatization creates an environment in which low-skilled workers are vulnerable, the changes correlate with a reform in the job market, which may result in people losing their current positions. Moreover, the industrialization of the last century, as well as the endless possibilities of AI, are direct illustrations of the need for human assistance becoming less significant. Based on these illustrations of the possibilities for negative change facilitated by artificial intelligence, it is evident that the job market that exists today with change drastically, and certain jobs will no longer require human involvement.
Works Cited
Ernst, Ekkehardt, et al. “Economics of Artificial Intelligence: Implications for the Future of Work.” IZA Journal of Labor Policy, vol. 9, no. 1, 2019, Web.
Holland, Frank. “Walmart Is Using Fully Driverless Trucks to Ramp up Its Online Grocery Business.” CNBC, CNBC, 2021, Web.
Su, Grace. “Unemployment in the AI Age.” AI Matters, vol. 3, no. 4, 2018, pp. 35–43., Web.
Walsh, Toby. “Expert and Non-Expert Opinion About Technological Unemployment.” International Journal of Automation and Computing, vol. 15, no. 5, 2018, pp. 637–642., Web.
| 2023-03-26T00:00:00 |
https://studycorgi.com/artificial-intelligence-and-unemployment/
|
[
{
"date": "2023/03/26",
"position": 20,
"query": "AI unemployment rate"
}
] |
|
Addressing algorithmic bias in hiring
|
Addressing algorithmic bias in hiring
|
https://www.thehrdirector.com
|
[] |
This blog explores AI's current use in HR, emerging regulations in the US and EU, and how to safeguard against potential risks with AI risk management ...
|
Do hiring algorithms prevent bias or amplify it?
As the use of artificial intelligence (AI) continues to expand across all industries, human resources (HR) departments have been among the first to embrace its capabilities. AI provides HR departments with new tools to streamline the job application process for both candidates and employers, automate repetitive tasks in the onboarding process and recognise patterns indicative of employee turnover intention. While these technologies offer a range of benefits, such as improving the candidate experience and reducing time and cost, they also pose risks, such as perpetuating existing biases. As a result, it is essential to manage these risks to ensure that AI is used to its full potential without causing harm.
This blog provides an overview of how AI is being used in HR, examines regulations that are emerging in the US and EU to shape the use of HR technology, and outlines steps that employers and businesses can take to protect against unintended harm caused by AI systems through AI risk management.
Shaping the candidate pool and narrowing the funnel
The applications of AI in HR are vast, including targeting job postings, screening applications, and evaluating assessment and interview performance. Although automated tools have benefits for both candidates and HR staff, they can pose serious risks. As recruiters use algorithms at more steps of the hiring process, bias can enter hiring decisions in several ways. Algorithms can influence hiring decisions by targeting job postings based on factors such as age or gender, limiting the pool of potential applicants. Programs that screen resumes can lead to discrimination if resumes contain the word “women’s” or disclose a disability. Models that analyse video interviews may have difficulty with accurately recognizing facial features for applicants with darker skin tones or penalize non-native speakers. In short, HR practices are becoming less human-led and more automated each year, and AI systems are increasingly being used to assist or even replace human decision-making.
Regulation ahead
The passing of laws aimed at regulating HR technology signals that managing the risks of automated tools is becoming an increasing priority across the industry. The first legislation of its kind, Illinois’ Artificial Intelligence Video Interview Act, came into effect on 1st January 2020, seeking to increase transparency around the use of AI to evaluate video interviews. Under this law, employers using AI in their video interviews must disclose this to candidates, in addition to the characteristics that will be considered by the tool and how it works. Candidates must also consent to the use of the tool prior to it being used to evaluate them. Maryland has taken similar action, introducing legislation to require employers to obtain a signed waiver from candidates before using AI-driven video interviews.
The New York City Council has passed legislation that mandates bias audits of automated employment decision tools (AEDTs). Local Law 144, colloquially known as the NYC Bias Audit law, requires employers to commission independent, impartial bias audits of their AEDTs before using them to evaluate candidates for employment or employees for promotion within New York City. The law stipulates that employers and employment agencies must notify candidates or employees at least 10 business days before an AEDT tool is used. Notice can be given on the careers section of the employer’s website, in a job posting or sent via email. As part of this notification, employers are required to inform candidates of the factors and variables that the automated tool uses and considers to make its decision and provide the AEDT data retention policy, and candidates can also request information about the type and source of data being used. This enables candidates to give more informed consent about their interaction with the automated tool and gives them a generous amount of time to research and consider the tool that they will interact with, the implications this might have for them and whether they object to it.
Outside of the US, the European Commission’s EU AI Act seeks to regulate AI systems available on the EU market. Taking a risk-based approach, obligations and corresponding penalties for failing to comply are proportional to the risk presented by the system, with risk being categorized as minimal, limited, high, or unacceptable. Since AI systems used for employment and talent management are deemed high-risk, employers utilizing automated HR solutions must take action to reduce the potential dangers of their employment systems.
Towards equitable hiring
Although U.S. and EU laws impose some limitations on employers utilizing predictive hiring technologies, they are inadequate for addressing the evolving risks associated with machine learning-enhanced tools. For example, the Civil Rights Act of 1964 prohibits discrimination on the basis of sex or race when making hiring, promoting, and firing decisions. Similarly, the Equal Employment Opportunity Commission’s Uniform Guidelines on Employee Selection Procedures state that the four-fifths rule should be used to identify any potential adverse effects or prejudice in hiring practices based on selection rates, yet this, is often insufficient to address the novel harms that are posed by the use of AI.
So, how can we ensure hiring algorithms promote equity? Even though the process of regulation is slow and the implementation of industry-wide best practices is still in its early stages, they certainly have roles to play. Vendors constructing automated hiring tools, as well as employers utilizing them, should consider more than just the bare minimum of compliance regulations in the interim.
Before deploying any predictive tool, vendors must thoroughly evaluate whether their algorithms can create equitable hiring outcomes. They must also assess how subjective measures of success might affect the tool’s predictions over time. Moreover, employers should not only look for any evidence of adverse impact at the selection phase but also monitor their entire recruitment pipeline to identify any places where bias may be present or arise.
The need for AI Risk Management in HR Tech
One approach to ensure responsible AI is risk management: the process of identifying, assessing, and managing risks. By adopting an AI risk management framework, when bias is identified, it can be addressed by debiasing the data the model is trained on, modifying the model to make it more equitable across groups, or amending the model’s outputs to make the predictions more equitable, depending on the source of the bias. There are a number of ways that vendors and employers can support these efforts, including being transparent about the types of technology and model used by the algorithms, any efforts to test the model for bias, and how the model performs in terms of accuracy, for example.
Minimizing the risks, particularly bias, will soon become a legal requirement. Employers will be expected to be more transparent about how their AI is supporting their employment decisions. To decrease bias in hiring, organizations must actively build and examine their tools with intention. Without AI risk management, the technology will most likely not be able to meet that guarantee — and may even make it worse.
| 2023-03-26T00:00:00 |
2023/03/26
|
https://www.thehrdirector.com/addressing-algorithmic-bias-hiring/
|
[
{
"date": "2023/03/26",
"position": 18,
"query": "AI regulation employment"
},
{
"date": "2023/03/26",
"position": 40,
"query": "artificial intelligence hiring"
}
] |
Workforce Exchange
|
Workforce Exchange
|
https://eightfold.ai
|
[] |
AI-powered career center. Match job seekers to roles and learning opportunities. Deep-learning AI efficiently matches people with employers, helping ...
|
What every leader needs to know about responsible AI
Responsible AI is more than a compliance checkbox. It’s a strategic imperative for building trust, driving innovation, and shaping the future of work.
Why responsible AI is your competitive advantage →
| 2023-03-26T00:00:00 |
https://eightfold.ai/products/workforce-exchange/
|
[
{
"date": "2023/03/26",
"position": 36,
"query": "machine learning workforce"
}
] |
|
Up to 80 percent of workers could see jobs impacted by AI
|
Up to 80 percent of workers could see jobs impacted by AI
|
https://nypost.com
|
[] |
A new research paper claims that a staggering amount of employees could see their careers impacted by the rise of ChatGPT, a shockingly intelligent chatbot.
|
The chatbots are coming for your job.
A new research paper claims that a staggering amount of employees could see their careers impacted by the rise of ChatGPT, a shockingly intelligent chatbot released in November.
Researchers from OpenAI and the University of Pennsylvania argued in a new research paper that 80 percent of the US workforce could have at least 10 percent of their work tasks affected by the introduction of ChatGPT.
They also found that about 19 percent of workers may find at least 50 percent of their duties impacted by GPT, or General-purpose technologies.
Researchers also found that higher-income jobs will likely have greater exposure to GPT, but that it will span across almost all industries.
Researchers argued that 80 percent of the US workforce could have at least 10 percent of their work tasks affected by the introduction of ChatGPT. Christopher Sadowski
The paper examines “exposure” of work tasks to AI “without distinguishing between labor-augmenting or labor-displacing effects.”
Within the study, researchers defined “exposure” as a measure of whether access to a GPT or GPT-powered system would reduce the time it takes for a human to perform a work task by at least 50 percent.
Researchers stressed that exposure doesn’t equate to tasks being fully automated by GPT, but that the technology could save workers “a significant amount of time completing a large share of their tasks.”
Within the study, researchers defined “exposure” as a measure of whether access to a GPT or GPT-powered system would reduce the time it takes for a human to perform a work task by at least 50 percent. credit is on chart
The study found that mathematicians, interpreters, accountants, legal secretaries, writers and authors are some of the jobs to have the highest levels of exposure.
At the other end of the spectrum, more low-paying jobs like rail maintenance workers, cooks, mechanics, floor-layers, meat-packers and stonemasons had no exposure.
Open AI researcher Pamela Mishkin highlighted the research in a Twitter thread, writing: “Today’s GPTs can do a lot. Over the past few years we’ve seen them get better and better at solving more and more complex tasks with fewer and fewer examples of less and less related tasks.”
She added: “The paper examines this trend not any particular model available today.”
| 2023-03-26T00:00:00 |
2023/03/26
|
https://nypost.com/2023/03/26/up-to-80-percent-of-workers-could-see-jobs-impacted-by-ai/
|
[
{
"date": "2023/03/26",
"position": 19,
"query": "artificial intelligence workers"
}
] |
What Elon Musk, Bill Gates, and 12 other business leaders ...
|
What Elon Musk, Bill Gates, and 12 other business leaders think about AI tools like ChatGPT
|
https://www.businessinsider.com
|
[
"Aaron Mok"
] |
Others are skeptical, unimpressed, and even scared. Insider compiled a list of 14 business leaders' thoughts on AI, from Bill Gates to Elon Musk.
|
AI will be the"biggest thing in this decade," Gates said on a podcast, mentioning that he's "excited" about ChatGPT and finds the chatbot "pretty fantastic" in its potential to teach math and offer medical advice to people with limited access to resources, per Forbes.
He said he uses ChatGPT primarily "for serious purposes" though he admits to using it for "fun things" like writing poetry.
Still, Gates said ChatGPT is "truly imperfect" and has seen it answer math questions "completely wrong."
In terms of jobs, Gates said AI can "change our world" by making work more efficient, he told German newspaper Handelsblatt.
When asked about his thoughts on the Big Tech race to build the best AI chatbot, Gates said he isn't sure there will be a clear winner, per the podcast.
Gates sees AI-integration into search engines as a "personal agent" that studies and understands its users' behavior, he said on the podcast, eliminating the need for separate tech services.
"A decade from now, we won't think of those businesses as separate, because the AI will know you so well that when you're buying gifts or planning trips, it won't care if Amazon has the best price, if someone else has a better price — you won't even need to think about it," Gates said. "So it's a pretty dramatic potential reshuffling of how tech markets look."
In a seven-page letter Gates published on AI, he wrote that building AI is "as fundamental as the creation of the microprocessor, the personal computer, the internet, and the mobile phone." AI, he said, can serve as a "digital personal assistant" for workers, and can help healthcare workers and teachers make their jobs easier.
| 2023-03-26T00:00:00 |
https://www.businessinsider.com/elon-musk-bill-gates-business-leaders-quotes-on-chatgpt-ai-2023-2
|
[
{
"date": "2023/03/26",
"position": 29,
"query": "artificial intelligence business leaders"
}
] |
|
analytics in action | Duke's Fuqua School of Business
|
analytics in action
|
https://www.fuqua.duke.edu
|
[] |
The rise of artificial intelligence is enhancing online platforms' ability to predict customer preferences. Professor Ali Makhdoumi says this may enable ...
|
Improving operations, mitigating risk, forecasting revenue, generating insight, increasing productivity—all now involve understanding emerging applications in data science, AI and machine learning. While data analytics has become a core discipline for the practice of business, its role within different functions and influence on different industries continues to evolve.
Consider this your front row seat to the action.
Here you’ll find research and insights from Fuqua faculty who deeply study AI, machine learning, and how to apply analytics to real business problems—partnering with some of the biggest names in tech. You’ll also learn from Fuqua leaders using data analytics to innovate at their companies now, who share approaches to navigating these issues across a diverse range of industries and functions.
| 2023-03-26T00:00:00 |
https://www.fuqua.duke.edu/data-analytics
|
[
{
"date": "2023/03/26",
"position": 65,
"query": "artificial intelligence business leaders"
}
] |
|
Salary Story: Research Engineer in Artificial Intelligence, making ...
|
The heart of the internet
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https://www.reddit.com
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Current salary, including bonus, benefits, & perks: $260k base, $53k bonus, $260k RSUs (private company), 3 meals/weekday, free car service within SF and to/ ...
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Current location: VHCOL (San Francisco)
Current salary, including bonus, benefits, & perks: $260k base, $53k bonus, $260k RSUs (private company), 3 meals/weekday, free car service within SF and to/from airport
Age and/or years in the workforce: 30 years old, 7.5 years in workforce
Brief description of your current position: I code, write papers, set project direction for my team, give talks at conferences
Degrees/certifications, if any, and whether they're applicable to your current position (Also mention how/how much you paid for these and how they've been helpful.): I have a bachelor's in math and master's in CS. I went to a private college that had good financial aid, so they covered my tuition except for $18k in government student loans which I paid off within a few months at my first job. I paid for my master's degree by being a teaching assistant. I taught undergrad linear algebra for three semesters (2 lectures/week, writing homework, writing exam questions, grading, and holding office hours). This paid me a grad student stipend and covered my tuition.
A complete history of jobs leading up to your current position. Include job title + salary (hourly wage, if applicable) with each step of your salary story, along with job description/duties, and whether you negotiated for pay raises/promotions in that role.
2011: Lab assistant, $9.25/hr. Freshman year of college. This was a campus job where I worked in a wet lab, pipetting stuff. I was extremely bad at it. I would pipette 1 mL instead of 1 μL and ruin everyone's experiments. I once centrifuged some glass test tubes (should have used plastic tubes), and the glass shattered all over this 10k machine. My postdoc tried to fire me by telling me to go home and don't come back. I didn't know what to do when he said this and felt super awkward but also didn't want to go home so I just stayed and continue doing the job, and we just never spoke about it again.
2012: Intern, $4,000/month. Sophomore summer. I honestly didn't know what the hell this internship was going to be like when I applied, and it turned out to be the only internship offer I got that year so I took it. I remember the interview being like some guy calling me on the phone and asking what my favorite class was, and then after the call I got an acceptance email to my inbox. The company was this early stage software ideation company that spins up startups and then sells them to some business school types to build a customer base, hire permanent employees, etc. I worked with two other interns, and for the whole summer we just sat in a room just thinking of ideas we could build. We ended up coming up with an app idea where people could list trash they were going to throw out that day, like furniture, so other people could swing buy and pick it up before trash got collected. Facebook marketplace before that became a thing, I guess. I didn't know how to code so I kind of just played with Python while the other interns made the app. I think it got profiled on TechCrunch a few months later, which caused it to crash and then never recovered.
2013: Finance internship, $6,000/month. Junior year summer. I learned about sales and trading, but I wasn't a very good intern (was chronically late which is apparently! very! important! in finance), so I didn't get a return offer. The internship was very fun though because the company put a lot of effort into making the experience memorable. We played a lot of poker as part of "work," sat front row at a Mets baseball game, and the full timers even brought all the underaged interns to strip clubs but that was totally down low (not a sanctioned event).
2014: Software internship, $9,000/month. Senior year summer. I actually programmed a lot during this internship. I basically learned to program during this internship. I also played a lot of foosball. I did get a return offer this time.
2014-2016: Teaching assistant. $3,000/month + tuition. I taught linear algebra to overachieving undergrads. It was supposed to be a 20 hour/week job because I was *technically* supposed to spend the other 20 hours doing coursework + writing my thesis for my master's degree, but it was easily over 40 hours total between 2x/week lectures, writing homework, grading homework, writing exams, grading exams, holding office hours, replying to emails, and comforting anxious students. It did get easier in subsequent semesters as you get the hang of teaching and have last semester's coursework to fall back on, but it is easily the hardest job I've ever done. I got pretty average to below average student reviews, but they still renewed my contract for three semesters which I was happy about because I didn't want to have to borrow money to finish my master's.
2016-2017: Finance job. $150k/year salary + $28k/year bonus. I tried to negotiate my salary with the argument that "I need to pay off student loans," but they wouldn't budge because "everyone got paid the same." I only stayed for 8 months because I hated it (really toxic all-male environment), but when I left I got a nice 6 months of "garden leave" which is where they pay you your base salary (not including bonus) to not work.
2017-2020: Statistician. $130k/year base salary (2017) which increased to $150k/year base salary (2020) + $150k/year stock + $10k/year bonus. I did negotiate my salary on this, from $115k/year base salary -> $130k/year base salary and $120k/year stock -> $150k/year stock. The stock was private when I joined but went public sometime in the last 5 years, so it wasn't completely paper money. This job included other perks like lunch and snacks, and if I stayed past 7pm I could order DoorDash and expense it. I did get small inflation raises but the stock stayed more or less constant. I got laid off in 2020, so I got 3 months of severance + 1 month of stock and also claimed unemployment for as long as I could.
2020-2021: Research engineer. $200k/year base salary + $150k/year stock + $30k signing bonus + 18% pro rata bonus. The negotiation process for this company was super weird. The initial offer was $200k/year + $100k/year stock + NO signing bonus. I asked if this was negotiable and the recruiter said this was "the highest offer she could make" and anything else was "impossible and needs to be escalated to a VP which could take several days blah blah," but if they could get $130k/year stock would I sign immediately? They basically made me pre-sign a contract saying if they got that offer, I would agree to join the company. Then a couple hours later they replied saying not only did they get approval for my request, they bumped it up to $200k/year base salary + $150k/year stock + $30k signing bonus. At this point I felt rather manipulated (like clearly it wasn't such a big deal after all), and my (male) friend got SUPER annoyed and said the recruiter tricked me to not negotiate when I could have. Anyway, I ended up joining anyway because I really liked the company.
Other perks: 3 meals on weekdays, barista during working hours, free car service around city limits, and 50% 401k matching. You have the option of going fully remote without any salary adjustment, but I like going to the office. The stock is private, but they do have a buyback program every few years where you can sell your RSUs to them for face value. However, I don't participate in that and even participate in RSU purchase programs so I can put more money into the same company, because I hate diversification apparently.
2022: Same research job, got a raise. $215k/year + $200k/year stock + $40k bonus.
2023: Same research job. Got promoted, so my salary jumped to $260k/year + $260k/year stock + $53k bonus.
Optional: In the spirit of transparency and helpfulness include any of your supporters (family, spouse, network, other women - anonymously of course), things and people that kept you going, or inspired you, books, boot camps, podcasts, networking groups, etc. Also share your struggles, if you ever felt like giving up, if you were underpaid or are still underpaid, if you had to reskill, were laid off, or struggled to find work. Tell us about your work/life balance over time and challenges or changes in priorities.
Definitely my parents. If I ever lost my job or my money, I know I can move in with them into my old room indefinitely, and I know they would be really happy to have me. Also, my parents would have supported me through college if they had to. Thankfully they didn't have to (because my college had great financial aid), but they were prepared to sell their house and move to an apartment to pay for my tuition.
After that, various men I've dated throughout the years also supported me I guess? Like in college I had a bit of a spending problem and once had to "borrow" $5,000 from my then-boyfriend to pay off credit cards 😬 I still spend a LOT (almost all of my paycheck not including stock/bonus). I put borrow in quotes because I never ended up paying it back, I let him live in my apartment for a few months when he was between apartments and just said "well I guess we'll just write that off as the rent you owe me" lol.
I don't think I'm underpaid. I'm overpaid compared to how hard everybody else in the world works. My work life balance is fine, albeit a little all over the place. For example, on Weds-Fri I usually work between 10am to 3pm, then I go exercise for 2 hours, have dinner, and maybe work 2-3 more hours after that, but not always. Recently my job has been super stressful though, because I've being pushed more and more into a product management role. As an introvert, I prefer to just...code and do technical work rather than talk to people. I keep wavering between quitting or trying to find a new job first.
During the pandemic I worked a LOT, easily 12 hours/day, because there was nothing else to do and nowhere to go.
Feel free to include as much or little as you like to tell your Salary Story.
**Please use the "Salary Stories" flair when posting.
| 2023-03-26T00:00:00 |
https://www.reddit.com/r/MoneyDiariesACTIVE/comments/122cklu/salary_story_research_engineer_in_artificial/
|
[
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"date": "2023/03/26",
"position": 32,
"query": "artificial intelligence wages"
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"date": "2023/03/26",
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"query": "artificial intelligence wages"
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"date": "2023/03/26",
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"query": "artificial intelligence wages"
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"date": "2023/03/26",
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"query": "artificial intelligence wages"
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|
Artificial Intelligence Salary Expectations - Noodle.com
|
Artificial Intelligence Salary Expectations
|
https://resources.noodle.com
|
[
"Ginny Bartolone",
"March"
] |
AI engineer: PayScale lists an AI engineer salary at $131,000 a year. You'll find a higher salary range for those with multiple years of ...
|
Earn your master’s degree in information management online. The University of Washington Information School’s Master of Science in Information...
From Java, Python and C++ to machine learning and cloud computing, our graduates are empowered to engineer solutions and...
Learn the foundations of programming as well as the advanced skills needed to become an agile software engineer –...
Earn Your MS in Computer Science 100% Online. Our online Master of Science in Computer Science (MSCS) offers you...
Earn your master’s degree in information management online. The University of Washington Information School’s Master of Science in Information...
From Java, Python and C++ to machine learning and cloud computing, our graduates are empowered to engineer solutions and...
Learn the foundations of programming as well as the advanced skills needed to become an agile software engineer –...
Earn Your MS in Computer Science 100% Online. Our online Master of Science in Computer Science (MSCS) offers you...
Tech jobs have quite the reputation for high pay, with average salaries soaring into the six-figure range, even for mid-level positions. So, it should come as no surprise that most people who make a career change to tech in search of a bigger paycheck.
If you take a look at tech job salaries listed by the US Bureau of Labor Statistics (BLS), computer and information research scientists sit comfortably at the top, making over $126,000 a year. Many of these research scientists work on today’s cutting-edge technologies utilizing artificial intelligence (AI) and machine learning (ML) tools.
Another perk of the field? AI jobs are not just for machine learning engineers and data scientists. You’ll find job postings in marketing, sales, management, and project management, not to mention all the entry-level pathways in software development.
What can you really expect to make with an AI skill set? And how much does an advanced degree affect your average annual salary? In this article, we’ll discuss:
Careers in artificial intelligence
Artificial intelligence career paths
Top artificial intelligence master’s programs
Careers in artificial intelligence
Working in AI and ML might seem a bit niche to outsiders. In fact, it’s anything but. The International Data Corporation (IDC) predicted in 2020 that global spending on AI technologies will double in just four years. With that growth comes a burgeoning AI job market. And that’s just the beginning. The AI market is slated to expand over 1000 percent in between 2023 and 2030. That’s not a typo.
The irony here is that many people feared that AI technology would replace human jobs—from factory workers to, well, online writers. And this concern is not unfounded. Many tech pros confirm that yes, robots will replace many of our current roles. But the resulting opportunities that arise should keep humans from having to do repetitive tedious tasks and allow us to focus on more creative and strategic work. Additionally, all the positions required to monitor, maintain, and further develop AI will only continue to grow as we discover new possibilities for this technology.
Advertisement “I’M READY FOR A DEGREE!” Explore These Related Degree Programs University and Program Name Learn More The University of Tennessee: Online Master of Computer Science Click Here Merrimack College: Master of Science in Computer Science Click Here Stevens Institute of Technology: Master of Science in Computer Science Click Here University of Washington: Master of Science in Information Management Click Here
Which industries need artificial intelligence professionals?
Experts across the tech world seem to make predictions every year about when AI will touch every aspect of our lives. Back in 2014, a Pew Research poll predicted that this would happen by 2025. Others believe that computers will learn as quickly as humans by 2050. No matter the prediction, there’s little doubt that AI will permeate most industries soon.
As for current job opportunities, you have your pick from a long list, including:
Healthcare
With the help of an AI engineer, computers are identifying early signs of disease, prescribing necessary tests, and suggesting treatments much more quickly and accurately than humans. AI is also being used in healthcare with patient billing, communication among specialists, global health trend analytics.
Manufacturing
Manufacturing still tops the charts when it comes to AI-related jobs. The industry has used automation for decades, helping with everything from warehouse organization to ensuring sustainable supply chain management.
Finance
You’ll spot AI-related job titles across the financial field, including in cyber security, and machine learning algorithms. The latter are used “to detect fraud, automate trading activities, and provide financial advisory services to investors.”
Education and career
Students and job candidates utilize AI-powered online tools every day for advice on where to go to school and which career move to make next. Artificial intelligence engineers also have created teaching bots, such as language-learning apps and textbook generators. Some students are using AI for writing assignments and hoping they won’t get caught. That’s generated a new business: AI text detection.
What are the top artificial intelligence jobs in each field and how much do they pay?
Do a quick search for AI and machine learning jobs in your field and you’ll notice something interesting. Many job titles and their descriptions are relatively interchangeable. Analytics India Magazine elaborated on this in a recent piece: “Many times, employers may voluntarily keep the titles more generic, given the multidisciplinary nature that involves the use of statistics, mathematics, software engineering, neural networks, analytics, and visualization, among others.”
The AI field is still rapidly evolving; job titles will become more specific as time goes on. As for now, you’ll likely see these roles in the highest-paying positions.
Machine learning engineer: The average salary for a machine learning engineer before bonuses is $147,500, according toIndeed.
The average salary for a machine learning engineer before bonuses is $147,500, according toIndeed. AI engineer: PayScale lists an AI engineer salary at $131,000 a year. You’ll find a higher salary range for those with multiple years of experience, advanced degrees, and in certain areas of the country.
PayScale lists an AI engineer salary at $131,000 a year. You’ll find a higher salary range for those with multiple years of experience, advanced degrees, and in certain areas of the country. Business intelligence developer: Indeed states that these business-growth-focused professionals make an average of around $94,000, with a cash bonus of around $5,000.
Indeed states that these business-growth-focused professionals make an average of around $94,000, with a cash bonus of around $5,000. Data scientist:__ Programmers with extensive big data experience make an average of $116,400, according to PayScale, but will make higher amounts in metro areas like New York.
Artificial intelligence career paths
As ambiguous as some job titles can be, the roles and career paths themselves are highly specified once you’re in the field. You’ll see this demonstrated both in an AI master’s degree curriculum and job descriptions.
For example, some computer scientists specialize in computer vision or natural language processing. Others work as algorithm engineers. Like other engineering-focused fields, you need highly specific skills to ensure the product is a success.
Other options include marketing, business development, or project management. These professionals still need a background in computer science, but combine it with their business expertise.
Artificial intelligence master’s degree
If you take a look back at that salary listed by the BLS, you’ll notice that it specifically applies to professionals who hold a master’s degree. Graduate and doctorate degrees are not necessary for all AI and ML jobs. Unlocking leadership roles, however, is a another story.
Each year, more and more top universities offer a Master of Science in Computer Science (MSCS) or PhD in computer science with a specialty in AI or ML. These programs offer students—both online and in-person—the chance to build foundational knowledge and gain hands-on experience in some of the most complex AI realms.
How long does it take to earn an artificial intelligence master’s?
An artificial intelligence master’s can take from two to five years. Many universities offer part-time, online, or hybrid programs for busy professionals. You also can find one-year graduate certificate programs. Note that this is not the same as earning an MS.
Admission requirements/prerequisites
In such a highly technical and developing field, universities want to make sure each prospective student is on the same page when the cohort first meets. While students come from all different backgrounds and levels of experience, they likely have a bachelor’s in a related field, experience with programming languages, and industry experience.
For example, Southern Methodist University requires a bachelor’s in topics related to math, computer science, or the quantitative sciences. You’ll also need at least two professional references, a personal statement, and a resume.
Other schools require prerequisite courses such as linear algebra, Python training, and several semesters of calculus.
Curriculum
Artificial intelligence programs typically combine theoretical topics on AI with hands-on study of technical applications. In most cases, the balance of these courses depends on two factors: your chosen concentration and whether you choose to write a thesis.
Steven Institute of Technology, for one, requires a mathematical foundation course, four core courses, three concentration-focused courses, and either two electives or one thesis project.
Specialization
Universities home in on the many areas of AI and ML by encouraging students to choose a concentration. In some cases, you’ll be asked to select a focus by name with set courses. In others, you can build a custom specialization around your elective courses.
Johns Hopkins, for example, asks new students simply to choose an applied track or a theoretical track for their core courses. Electives include topics like intelligent algorithms, cloud computing, robotics, and data visualization.
Top artificial intelligence master’s programs
Finding the right program for you is dependent on your professional goals. You could opt for a school like Carnegie Mellon, with a famous research lab in which you could end up working on projects that impress prospective employers. You also can choose a program geared toward part-time, online students to continue your career during your studies. Here are a few national favorites.
Carnegie Mellon University:__ Often rated the top program in the United States by US News and World Report, CMU offers access to cutting-edge research and top experts in the field.
Drexel University:__ Drexel lands on best-of lists in both the sciences and for online or master’s programs. Students can build a custom program based on their industry focus and concentration.
Stanford University:__ Not to be confused with the Stanford online graduate certificate, the Stanford master’s degree falls within the engineering department. The courses cover areas like robotics, deep learning, and natural language processing.
Online/hybrid options
Two years of full-time, in-person study is not always feasible—or even the best option—for every student and learning style. The following programs offer flexible online master’s degrees in AI:
Southern Methodist University:__ Complete the degree in just two years with the school’s robust hands-on online learning program. Electives include data mining, logic programming, and mobile applications for sensing and learning.
Stevens Institute of Technology:__ Stevens’ Applied Artificial Intelligence program is available both in-person or fully online. The school offers interdisciplinary concentration options and hands-on projects, even if you’re not taking classes on campus.
Penn State University:__ Penn State’s World Campus offers a fully online master’s degree with 33 credits that culminate in a capstone project.
(Last Updated on February 26, 2024)
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| 2023-03-27T00:00:00 |
2023/03/27
|
https://resources.noodle.com/articles/artificial-intelligence-jobs-salary/
|
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The Future of Work | Blog
|
The Future of Work
|
https://www.andrewyang.com
|
[
"Andrew Yang"
] |
My 2020 presidential campaign was built around the fact that automation and AI are going to gobble up tons of American jobs. Since 2020, the labor force has ...
|
It is a mystery why the labor force has shrunk; some are early retirees. Some are urban workers who have relocated someplace else and are doing something that doesn’t register as a job. Some are parents – particularly women – who have decided to focus more on their family.
Now, with ChatGPT, more knowledge work is being replaced. “A project that used to take 8 hours now takes 40 minutes” is the way one friend who runs a high-end consulting company puts it. “I’ll probably fire half of my people in 6 months.”
Another friend said to me, “I have to put in a half-hour of deep thinking to figure out the right prompt to feed the AI, but then after that I just got rid of a task that I was going to pay someone else to do.”
Another entrepreneur said, “I wrote a press release in less than 5 minutes. I fed the AI, got a template and then edited. It was a breeze.”
Unrelated to AI, tech companies have laid off more than 100,000 workers in a reset as their stock prices have gone down. One of the difficult truths is that a significant percentage – probably 20% - of workers in most large organizations are largely irrelevant to how that organization will perform.
Indeed, a lot of the headwinds have been caused by higher interest rates that have forced businesses – tech, banks, real estate, everyone – to have to generate real returns instead of taking free money and lending it, spending it, or basking in a sky-high stock price built on a rosy future.
The transition to more remote work is doubtless a force toward automation. If someone is remote, their job is one step closer to being replaced. The return to office work is highly uneven. Workers prefer remote or hybrid work and are making career decisions accordingly. Unfortunately, eventually so will employers.
For years now, the proportion of repetitive manual and repetitive cognitive jobs in the economy has been falling relative to high-end cognitive jobs. Repetitive jobs comprise 44% of jobs in the economy. This week, a University of Pennsylvania study suggested that AI will impact about 80% of all workers to some extent.
| 2023-03-27T00:00:00 |
https://www.andrewyang.com/blog/the-future-of-work
|
[
{
"date": "2023/03/27",
"position": 92,
"query": "artificial intelligence employment"
},
{
"date": "2023/03/27",
"position": 2,
"query": "future of work AI"
},
{
"date": "2023/03/27",
"position": 7,
"query": "AI employment"
},
{
"date": "2023/03/27",
"position": 80,
"query": "artificial intelligence workers"
}
] |
|
Surrender your desk job to the AI productivity miracle, says ...
|
Surrender your desk job to the AI productivity miracle, says Goldman Sachs
|
https://www.ft.com
|
[] |
... work, you see. But history shows that worker displacement from automation. Has been offset by creation of new occupations. The combination of significant ...
|
Check whether you already have access via your university or organisation.
Terms & Conditions apply
| 2023-03-27T00:00:00 |
https://www.ft.com/content/50b15701-855a-4788-9a4b-5a0a9ee10561
|
[
{
"date": "2023/03/27",
"position": 71,
"query": "automation job displacement"
}
] |
|
Generative AI set to affect 300mn jobs across major ...
|
Generative AI set to affect 300mn jobs across major economies
|
https://www.ft.com
|
[] |
They calculate that roughly two-thirds of jobs in the US and Europe are exposed to some degree of AI automation, based on data on the tasks typically performed ...
|
Try unlimited access Only $1 for 4 weeks
Then $75 per month. Complete digital access to quality FT journalism on any device. Cancel anytime during your trial.
| 2023-03-27T00:00:00 |
https://www.ft.com/content/7dec4483-ad34-4007-bb3a-7ac925643999
|
[
{
"date": "2023/03/27",
"position": 68,
"query": "job automation statistics"
}
] |
|
How Tech and Automation Can Make Your Business Eco- ...
|
How Tech and Automation Can Make Your Business Eco-Friendly
|
https://workshopsoftware.com
|
[
"Phoebe Milne"
] |
This comes as no surprise as automation significantly improves productivity and efficiency for businesses no matter the size. One thing that's often overlooked ...
|
Technology has long changed the way we do business. The most recent scenario that truly highlighted its impact was the COVID-19 pandemic when many businesses thrived by pivoting online. It’s also through technology that they’ve applied work-from-home or hybrid work setups to keep their operations afloat.
Automation is just one of the many aspects of tech that has bolstered many businesses to success. In fact, Gartner predicts that by 2024, 69% of daily management tasks will be entirely automated. This comes as no surprise as automation significantly improves productivity and efficiency for businesses no matter the size.
One thing that’s often overlooked is the environmental benefits that tech and automation tools provide businesses. For entrepreneurs, digitalization offers more than an alternative to paper. It promises sustainability while ensuring businesses do their part for the environment as much as possible.
While profit is a company’s main driver, going green is also something that businesses should consider if they want to survive long term. In this resource, learn about the importance of going eco-friendly, how tech and automation can help achieve that, and what types of tools a business should apply.
Let’s Talk Numbers: How Businesses Are Responding to the Global Environmental Crisis
It’s no news that the planet is in crisis. Many experts have warned that inaction may lead to more environmental challenges in the future, including mass migration, rising hunger levels, and the downfall of global financial markets.
Because of this, many business owners have re-evaluated the purpose of a corporation. Aside from maximizing profits, a company is also expected to protect the environment through sustainable practices.
One environmental concern that involves many businesses is waste production and pollution. Factories produce massive volumes of waste that cause harm to the environment. When not properly managed, this can pollute the air, soil, and water and destroy countless ecosystems, not to mention result in serious health concerns.
So is it worth switching to eco-friendly business practices? Most probably, yes. A McKinsey & Co. survey showed that 66% of customers—75% of which are millennials—consider sustainability when deciding on a purchase.
Consumers are now smarter than ever, and they see themselves as primary catalysts of change. If given the choice, they’ll associate with a company that aligns with their values and ethics.
Over 4,000 global companies and businesses have set science-based targets leading to a net-zero economy. And according to an Accenture study, about one-third of Europe’s largest companies have promised to reach net zero by 2050.
With such initiatives, it’s becoming more imperative for businesses big and small to apply their own green efforts, not only to save the environment but also to sustain their business in the long run.
Business Process Automation: What It Is and Its Different Types
Business process automation is one of the many eco-friendly practices business owners can get started with. Automation technology helps businesses become more efficient and streamlined in their operations, limiting waste and carbon emissions.
For starters, automation uses tools that take manual labor out of daily business processes—think marketing, data entry, human resources, and accounting. Almost every aspect of a business can now be automated using the right software.
Here are some of the most common business automation types and how companies can apply them in their own operations.
Marketing and Sales
Many marketers rely on automation tools to make their lives easier. From collecting qualified leads to tracking ad performance, these tools help teams build and execute campaigns and measure their success. Automation also makes it easier for marketers to filter leads that are ripe for sales engagement before handing them over to the sales team.
Accounting and Bookkeeping
Accounting software makes it easier and quicker to work on billing, collections, accounts payable, accounts receivable, credit card applications, and a whole lot more. Without this technology, the amount of time and effort it would take to collate this data would be insurmountable.
Human Resources Automation
A human resources management system (HRMS) is a common tool that many HR departments use to systematize their work. This system automates the hiring process, from posting a job ad to officially onboarding an employee. Aside from this, it helps streamline payroll, employee data, and activity, and it helps with administering benefits.
3 Ways Technology and Automation Benefits Businesses and the Environment
Going Paperless Is Easier
Before the era of websites and software, businesses were fully dependent on paper to record and manage their records. Thanks to technology, the use of paper has drastically reduced, but there’s still a long way to go in eliminating it entirely.
Despite technological advancements, the average employee still produces over 10,000 sheets of paper a year. Aside from its impact on the environment, businesses have to pay for that paper, not to mention other supplementary equipment like printers, printer ink, and file cabinets. This still does not include the costs for hiring someone to maintain their archives.
It’s safe to say paper drains productivity. Employees waste a considerable amount of time scanning through files and printing and reprinting them when collaborating with other teams. This process is not only inefficient, but it also adds up to the piling of waste in our landfills.
But with automation tools, businesses can create a centralized database that everyone in the company can access and work on. This minimizes the time it takes to search for documents and communicate changes with other teams. Plus, automated systems help businesses save on other office supplies, such as ink, paper clips, and folders.
Reduce Carbon Emissions
Shipping and transportation greatly contribute to a company’s carbon footprint, especially those in the manufacturing industry. In fact, transportation accounted for 27% of the total US greenhouse gas emissions in 2020, which is the largest share of emissions. This comes from burning fossil fuels in cars, trucks, trains, ships, and planes.
In a nutshell, greenhouse gasses trap heat and cause global warming. Because of this, it’s essential for businesses to optimize their transport and shipping procedures with the use of technology.
Automation greatly helps streamline the planning stages of logistics, allowing businesses to plan more efficient routes, shipping schedules, and other warehouse functions. Ultimately, automation software helps reduce emissions from cargo trucks and ships.
Efficient, Cost-Saving Energy Usage
While technology runs on electricity, there are a lot of ways it helps businesses save on electricity costs in the long run. Since remote working started to boom in 2019, many companies are rethinking the traditional ways of working…and for good reason. Since employees are working in the comforts of their homes, businesses are able to reduce their energy bills with fewer people working in the office.
Teams are still able to communicate with each other through video-conferencing technology, like Zoom, Skype, or Teams. Meanwhile, collaboration is not hampered because of cloud-based automation tools, such as Trello, Asana, Google Drive, and Office365.
Aside from these, many small to medium enterprises (SMEs) have also adapted smart metering technology. This allows them to track their business’s energy usage and, with the help of sensors, only use electricity when needed. In fact, occupancy sensors alone can reduce energy use from lighting by 30%.
Overall, the use of technology and automation helps businesses reduce waste, eliminate pollution, and save energy, which benefits not only the environment but also businesses.
How Automation Improves Business Productivity
Aside from its hugely positive impact on the environment, automation also benefits all aspects of a company. These benefits include the following:
10 Examples of Business Automation to Consider
Now that we’ve laid out the importance of business sustainability and how technology helps achieve that, it’s time entrepreneurs consider ways to automate their businesses. Below are some of the most common processes to get started:
1. Standard operating procedures (SOPs)
SOPs are essential for a successful business. Startups and SMEs need to create detailed outlines and instructions on how to complete processes within their business. Otherwise, employees scramble to perform tasks and delegate them to others. SOPs also help make onboarding new employees much easier as the team grows. The faster a new hire is onboarded, the more work can be done.
2. Centralized database
For startups, the first few customers can still be easy to manage. But this isn’t the case when a company grows, and more customers come rushing in. Business owners will need to utilize a customer relationship management (CRM) tool to centralize customer data. This helps automate updates on a customer’s account and improves the business’s relationships with its contacts.
3. Lead-scoring system
The quality of leads is more important than quantity. Because of this, companies need a lead-scoring system to determine which leads are more viable than others—meaning, who are more likely to purchase. With lead-scoring automation, sales teams can save time and effort in engaging with low-qualified prospects.
4. Automated email campaigns
An email marketing tool is essential in getting sales and retaining customers. In fact, email gets a whopping return of $36 for every $1 spent. However, creating and sending emails and tracking email performance can be time-consuming when done manually. This is where automation comes into play. Many email marketing tools allow businesses to personalize emails in bulk, perform A/B tests, and send targeted emails to segmented groups.
5. Workshop software
For repair workshops, getting an all-in-one workshop software tool is a smart business investment as it automates their entire workflow and eliminates unnecessary tasks. This allows business owners and employees to direct their attention to more important aspects of the business, such as providing quality service to customers.
6. Task management software
Gone are the days when reminders were scribbled on post-its and placed on top of computers. Today, teams work more harmoniously using task management software. This helps create, delegate, and keep track of tasks. They also help show the team’s progress and remedy any delays accordingly.
7. Customer service automation
Customer satisfaction is crucial in running a business, which is why this is one of the most important aspects of business that needs automation. Automating customer service does not mean removing human interactions. Rather, it helps save time and ensures efficiency when answering customer concerns.
8. Sales process automation
Sales automation reduces friction in the sales process and ultimately increases conversion rates. With helpful technology, sales teams can focus on more sales-ready leads, better collaborate with marketing teams, and stay on top of a hectic pipeline.
9. Social media scheduling
Managing social media posts can be time-consuming. On top of creating engaging posts, marketers need to be strategic when scheduling them. However, the work gets easier with the right social media-scheduling software, allowing them to create content in advance and plan them for publishing on specific channels.
10. Centralized internal communication
Communication within the company is something business owners should not overlook. Centralized internal communications get the message across clearly and promptly. Some aspects that can benefit from automation include meetings, reporting dashboards, performance reviews, sharing onboarding materials, and collecting feedback.
Get on the Journey to Sustainability
Businesses and corporations have a moral responsibility to protect the environment as they carry out their operations. Technological advancements, such as AI and automation, make it easier to do so. Entrepreneurs are expected to do their part by applying the right technology that will not only improve their business as a whole but also save the planet as much as they can.
References:
| 2025-04-07T00:00:00 |
2025/04/07
|
https://workshopsoftware.com/business/how-tech-and-automation-can-make-your-business-eco-friendly/
|
[
{
"date": "2023/03/27",
"position": 78,
"query": "job automation statistics"
}
] |
Closing the cloud skills gap: A perennial problem for ...
|
Closing the cloud skills gap: A perennial problem for businesses
|
https://www.spglobal.com
|
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The cloud skills gap remains a problem, and organizations are struggling to solve it via recruiting new full-time employees, staffing and upskilling/reskilling ...
|
With labor and skills shortage remaining a major threat to business performance, it makes sense that the top three places where businesses plan to spend money in 2023 are in hiring and wages (29%); retention, "upskilling" and engagement (22%); and digital transformation including cloud, security and automation (20%). Despite these planned recruitment investments, hiring remains challenging overall. More than 51% of respondents to 451 Research's Macroeconomic Outlook survey said it has been somewhat or very difficult to hire new employees or bring back furloughed employees over the past 12 months.
This challenge is reflected in the IT industry as the operating model shifts toward cloud infrastructure and applications — 44% of respondents to 451 Research's Voice of the Enterprise: Cloud, Hosting & Managed Services, Cloud Skills 2022 survey list "difficulty finding qualified candidates to bring in as new hires" as the top challenge in addressing cloud skills gaps. For organizations transforming their IT environments to compete effectively in the digital economy, cloud expertise shortages impede progress on broader business and technology initiatives. If hiring is too difficult, it stands to reason that the next best thing would be to retrain and upskill existing cloud professionals. Unfortunately, a variety of hurdles exist in deploying and leveraging new skilling strategies for the cloud.
The cloud skills gap remains a problem, and organizations are struggling to solve it via recruiting new full-time employees, staffing and upskilling/reskilling initiatives. Organizations leveraging cloud technology struggle to find the right full-time employees or contractors to meet their needs and, even when they can find them, often fail to meet salary expectations. One answer is to upskill or retrain existing staff, but there are obstacles to making this happen at scale. 451 Research's survey data points to budgetary challenges, a lack of proper automation tools, and even some employee apathy or disinterest in retraining opportunities. When we fold in qualitative narrative survey responses, we get a fuller picture of the challenges involved, including lack of perceived business value in skills, management friction, lack of time and seasonality. While these issues seem to form a mountain of a problem, they help illustrate some of the steps forward for developing a skills strategy through investments in apprenticeships, better college-to-work pipelines and training incentives tied to performance management. These are some moves companies can make to better position themselves as skills-focused employers and as organizations fighting to remain competitive in the years ahead.
Why cloud skills gaps are a persistent problem
Organizations are struggling to build the right level of cloud skills to maintain their operations and support digital projects. More than two-thirds of respondents who use infrastructure- or platform-as-a-service public cloud services expect to see an increase in the number of employees with cloud expertise over the next year, yet recruitment remains a challenge. As it stands, only 39% of respondents rated their organizations' IT staff as "very capable" of cloud implementation, management and operations, according to the VotE: Cloud, Hosting & Managed Services, Cloud Skills 2022 survey.
Organizations look to solve the cloud skills gap through recruitment, staffing and upskilling. However, a plurality of respondents has had difficulty finding qualified candidates — the most cited skills gap challenge.
To better understand the dynamics of the cloud skills gap, we have grouped the above responses into three categories: challenges with full-time employee recruitment, challenges around leveraging service providers/contractors, and challenges in upskilling and reskilling efforts. The in-depth interviews conducted for the Cloud Skills 2022 survey highlight organizations' perspectives on these issues.
Recruiting full-time employees
Finding qualified candidates to bring in as new hires is not only a problem in the cloud skills arena. This challenge is mirrored among the general population, as team managers surveyed in 451 Research's VotE: Workforce Productivity & Collaboration, Employee Lifecycle & HR 2022 cite "pool of qualified candidates is too small" as the top challenge experienced (35%) in the candidate evaluation process.
Some online trackers put the number of tech industry layoffs north of 150,000 through the end of 2022. People in cloud-related or adjacent roles are likely part of this population, providing an expanded IT recruitment pool for "regular" (i.e., non-Silicon Valley) companies to tap into to address cloud skills shortages. However, nearly one-third of organizations believe salary expectations are too high. This perception is especially predominant among very large organizations and in the government/education sector. The general population parallels this as 41% of team managers say salary expectations are too high, the top response, when listing their recruitment challenges in the Workforce Productivity & Collaboration: Employee Lifecycle & HR 2022 survey.
Difficulty in recruiting and misalignment on proper compensation are tightly linked. Not surprisingly, nearly 60% of organizations expressing dismay about salary expectations also have had trouble finding qualified cloud-skilled candidates. At the same time, more than two-thirds of organizations grappling with cloud skills gaps report this expertise deficit as having a moderate or significant impact on implementing broad business and technology initiatives.
Third-party contractors and staff
Hiring full-time employees is not the only option for acquiring cloud skills. However, the third-party approach (e.g., contractors, consultants, managed service providers) brings some of the same challenges. Nearly one-third of respondents to the Cloud-related Skills and Outlook survey say third-party cloud resources are too expensive, and 27% cite difficulty in finding third parties with the right skills.
It is worth noting that the budget for temporary staff/contractors typically comes out of procurement rather than payroll. As such, the lack of willingness to spend more to acquire cloud skills may not be solely an HR/payroll budget problem but could reflect an organization-wide reluctance to spend.
Upskilling and reskilling efforts
If hiring new talent and outsourcing options are off the table, the next step in acquiring the requisite cloud expertise would be to retrain or upskill existing IT staff. However, finding the budget to retrain existing staff is challenging, as reported by nearly 30% of survey respondents. One IT/engineering manager in the financial services sector interviewed as part of the survey said members of the C-suite come in "talking about digital transformation, but they don't want to spend the money."
That lack of money also extends to the tools available to cloud professionals, with 19% of survey respondents saying their organizations do not value automation tools for cloud management and operations. For both upskilled IT staff and cloud-skilled IT personnel, automated processes and tooling help improve operational efficiency, which helps to make the case that cloud contributes to both IT and overall business agility and flexibility. In the early days of cloud, "cloud skills" meant the ability to perform standard IT tasks, such as server/storage provisioning, networking and security in cloud environments. Increasingly, effectively ramping up cloud skill sets also requires automation-enabled process expertise in areas such as visibility and observability, AI/machine learning and data/analytics.
Finally, a smaller proportion of survey respondents (17%) report a lack of interest from IT staff in retraining opportunities. A senior manager in a midsize financial services firm put it this way: "A lot of [what keeps me from doing what I want to do to close skills gaps] is resistance from the legacy IT management. … That internal resistance and lack of understanding is the biggest challenge for me."
When we consider general employee engagement, it is often tied to opportunities to grow at work, which typically include upskilling and retraining. According to the Workforce Productivity & Collaboration, Employee Engagement survey, 45% of respondents have learned new skills since they started work at their current organization. Additionally, 19% say they would be willing to leave their job for better personal development and career growth opportunities.
Color commentary on cloud skills gaps
The in-depth interviews conducted for the Cloud Skills survey offer additional insight into the cloud skills problem.
Skill gaps don't have a big enough business impact
When asked how gaps in cloud skills and expertise are impacting the progress of business or technology initiatives at their organizations, nearly one-third of respondents report, at most, a slight impact. A senior manager at a midsize financial services company who highlighted the frequent misalignment between IT and business said, "[The IT team] are focused on driving to the cloud, and they have been delivering. But the IT team's mindset, they haven't quite made that transition to how they need to be different and work different now that more of the infrastructure and the applications are in the cloud … business and IT, while they embrace the cloud strategy, some of the way in which they act and the skill sets, are probably more appropriate for the legacy in-house model."
IT management friction
As we noted from the chart above, budget for cloud training is a challenge in closing skills gaps. That inherently creates some friction in upskilling and retraining, but additional resistance exists. A midlevel manager in the manufacturing sector said, "[Aligning IT and lines of business and corporate functions] is always a struggle, because everybody has their own agendas, priorities … and everybody is running at different speeds. But we're getting better. We're making efforts to make sure that we establish the spaces of communication."
Not enough hours in the day
In some cases, IT staff with cloud skills may not have the time or bandwidth to keep up with continuous training. "We set targets and goals every year for getting people to training, and we rarely meet those goals only because it seemed like there was always so much going on, and it was just hard to peel out time to let people go," a senior manager in construction said. "And if people could afford to pull away from their day-to-day responsibilities, they were more apt to want to take a week's vacation rather than go to training."
Legacy IT staff can be resistant to change
There is also the changing of the guard. Some of the loudest dissenting voices generally come from legacy IT staff who refuse to change the way they do things. One of the IT professionals we interviewed mentioned challenges with the "seasoned workforce" resisting change because they have always done something one way and believe it to be the fastest. A midlevel manager in a business services company said, "When you do a transformation like [ours], there are always a number of people, between 5% and 10%, that don't want to work that way. And we have multiple ways to either motivate them or give them alternatives … maybe IT in a different subsidiary where they do classic IT, or they get early retirement, or they just quit."
Closing the skills gap
As organizations reexamine their skills strategies, there needs to be a formal communication of the business value of these skills and stronger incentives for training. The communication helps clarify the business impact of skills and could alleviate some of the IT management friction in the process. Additionally, to overcome some of the internal obstacles around upskilling and training, organizations need regular channels of communication between business and IT roles — as well as the necessary time and budgets — to ensure that investment in cloud skills and processes yields both tactical and strategic business value.
Since employees are already getting new skills on the job, an investment in new learning experience platform software — especially one with a focus on learning in the flow of work — could help streamline that process and capture data around the skills learned and implemented. Given that organizations are expecting more employees with cloud skills but struggle to recruit and hire them, IT leaders should consider cross-team apprenticeships that could spread cloud skills throughout IT more fully and beyond into other technical departments. This helps hedge against further potential labor and skills gaps in the future.
This article was published by S&P Global Market Intelligence and not by S&P Global Ratings, which is a separately managed division of S&P Global.
451 Research is part of S&P Global Market Intelligence. For more about 451 Research, please contact [email protected].
| 2023-03-26T00:00:00 |
2023/03/26
|
https://www.spglobal.com/market-intelligence/en/news-insights/research/closing-the-cloud-skills-gap-a-perennial-problem-for-businesses
|
[
{
"date": "2023/03/27",
"position": 25,
"query": "AI skills gap"
}
] |
Here Are the Kinds of Jobs Chat AI Is Likeliest to Affect
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https://cosm.aei.org
|
[
"Editor One"
] |
Their analysis finds that 80 percent of all jobs in the United States are “exposed” to AI, meaning a large majority of American workers will find AI chat ...
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A new analysis estimates that a fifth of all U.S. jobs are in the category most vulnerable to disruption.
Researchers at OpenAI—the company behind ChatGPT—and the University of Pennsylvania came out last week with a first look at the potential labor market impact of chat technology. If these initial “guesstimates” hold up, we might be looking at an inversion of the recent history of automation. In the past, it has been occupations marked by high levels of repetitive, manual tasks that have been on the block. ChatGPT and its progeny, at least at this point, seem to be a bigger threat to higher-end, office, and administrative forms of labor.
The first thing to note is just how much chat tech has improved in the past few months. This chart shows the change between ChatGPT3 (released on November 30, 2022) and ChatGPT4 (released on March 14, 2023) in terms of proficiency in taking a wide range of standardized tests. The blue bars show the performance of the “old” tech and the green are the “new”. In some areas, like AP Environmental Science and AP Art History, GPT3 was already fully “college-ready” but in many other areas, like AP Biology, Macroeconomics, and Chemistry, GPT4 aced classes for which GPT3 received at best a “gentleman’s C” and sometimes failed outright.
Perhaps of greatest interest is its improved performance on the LSAT and the uniform bar exam. While I wouldn’t turn to GPT3 to help me get out of a parking ticket, it appears GPT4 or 5 could take me all the way to the Supremes.
(Graph from Eloundou et al., “GPTs are GPTs,” [CC BY-SA 4.0].)
The rapid improvement in the underlying chat tech suggest the job effects of AI chat technology are going to be significant and pervasive if not immediate. (Hence the play on words the authors used as the title of their paper, “GPTs are GPTs”—because it turns out that these Generative Pre-trained Transformers, GPTs, are general-purpose technologies, also GPTs.) Their analysis finds that 80 percent of all jobs in the United States are “exposed” to AI, meaning a large majority of American workers will find AI chat affecting the way they do parts of their jobs. However, 20 percent of jobs are fully exposed to AI, meaning that most or all of the tasks that make up those jobs could be affected by chat AI. The more exposed a job is at the task level, the greater the potential for that job to be fully automated and for the human beings to be displaced.
Finally, the authors go on to point out that the speed and processes by which AI penetrates the economy is not just a matter of immediate exposure to chat technology itself, which is tricky enough to estimate, but the way chat will morph and mix with other forms of technology, such as robotics, image sensing, and the unknown-unknown technologies of the future. As the technology evolves, they say, the job and economic impact may broaden and deepen across the economy.
So which types of jobs are the most likely to feel the AI heat first? A little history is in order here. What we saw in 1970–2010 was that the workers most affected by that era’s automation were manual workers doing repetitive tasks. This automation, along with trade and off-shoring, contributed to a reduction in manufacturing employment from about 20 million jobs in 1979 to 13 million today. Those job losses, as we know, were not spread evenly from a geographic standpoint, but tended to fall most heavily in the older industrial regions of the country in the Northeast and Midwest.
The authors of this study argue that ChatGPT and its offshoots are much more likely, in the short and medium term, to affect the high end of the labor market than the low end. (In the longer term, due to the morphing tendency mentioned above, all bets are off. We just don’t know.) And, irony of ironies, the more education and training a job requires, the more exposed to chat tech it is. The jobs that are most exposed aren’t in the manufacturing sector but the knowledge/services sector. Industries like finance, accounting, insurance, and data processing are about twice as exposed as manufacturing jobs.
Returning to the legal profession for a moment, chat AI has already established a bridgehead in one of the U.K.’s largest legal firms, Allen & Overby, with expansive use of the Harvey AI tool. PricewaterhouseCoopers announced a new Harvey partnership last week with the rest of the industry sure to follow. Chat applications in legal work will continue to require a lot of human oversight and really good lawyers should see a productivity boost. Middling lawyers, paralegals, and other support staff will have to scramble.
Largely untouched by chat tech are various sorts of physical production services (e.g., logging, manufacturing), services (food and beverage), and health/social assistance. It’s an interesting collection of low-exposure industries that are all, in their own ways, bespoke activities. Coming from a family of loggers, I’ll certify that cutting down trees is dangerous work that involves a lot of individual judgment and experience that would be difficult to automate. You might say, it’s “log”-arithmic. (I’ll be here all week! Tip your waiters!) For the past several years, many AI experts and observers have been pointing to social assistance and health as areas that will resist chat-based automation, and this analysis appears to concur.
A swath of the services-dominated American workforce that has been relatively insulated from automation is likely to find itself grappling with chat tech to greater and lesser degrees. I expect a raft of articles talking about the increased “cognitive load” of jobs as mid-career workers start to express frustration over work demands arising from adaptation to the technology and higher expectations about how much workers in the knowledge and information sector should be producing.
For the fully exposed occupations, a fair chunk of those “good jobs at good wages” in professional and semiprofessional roles may be up for review—or what University of Rome economist Marco Magnoni calls “disintermediation,” which is a fancy way of saying chat tech may turn out to be very good at doing middle-management jobs that focus on coordination of business processes. Perhaps this “new” face of automation, aimed not at factory workers but at people with college and postgraduate degrees, will finally bring about the debate we need on how to help transition displaced workers to new jobs rather than abandoning them to the less than tender mercies of creative destruction.
| 2023-03-27T00:00:00 |
2023/03/27
|
https://cosm.aei.org/https-www-aei-org-articles-here-are-the-kinds-of-jobs-chat-ai-is-likeliest-to-affect/
|
[
{
"date": "2023/03/27",
"position": 20,
"query": "AI labor market trends"
}
] |
|
Canada's Reasons for An AI Law Do Not Stand Up to ...
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Canada’s Reasons for An AI Law Do Not Stand Up to Scrutiny
|
https://datainnovation.org
|
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In addition, Canada's gender equality laws already prohibit workplace discrimination, and those protections apply even if employers use AI in hiring. The second ...
|
The Canadian government recently released a “companion document” providing more background on the Artificial Intelligence and Data Act (AIDA), its proposed legislation to regulate AI systems. While the government is still vague about the details of the new law, it clearly outlines its reasons for regulating AI. Unfortunately, every one of those reasons is based on flawed logic or simply wrong facts. If policymakers have such a poor understanding of the evidence, then it would be prudent to halt the rush to regulate.
Like the European Union, with its proposed AI Act, Canada hopes to create “a new regulatory system designed to guide AI innovation in a positive direction, and to encourage the responsible adoption of AI technologies by Canadians and Canadian businesses.” The proposal has three main components: 1) imposing certain requirements on high-impact AI systems; 2) establishing a new AI and Data Commissioner responsible for enforcing the law; and 3) prohibiting certain harmful uses of AI. But the proposal lacks detail on the specifics, such as the criteria that will be used to determine what qualifies as a high-impact AI system or what requirements those systems would be subject to.
While lacking specifics on why “how”, the AIDA companion document is more clear about the “why.” In a section of the document titled “Why now is the time for a responsible AI framework in Canada” it says that “it is difficult for consumers to trust the technology” and cites three examples of alleged “high-profile incidents of harmful or discriminatory outcomes.” But none of these examples are valid.
First, on the list is “A resume screening AI system used by a large multinational company to shortlist candidates for interviews was found to discriminate against women.” This example refers to a well-known news report of Amazon experimenting with a hiring tool to rate candidates for technical jobs. Amazon’s developers identified that the tool penalized women and discontinued the project in 2017. Moreover, during the experiment Amazon’s recruiters did not use it to evaluate applicants. In other words, the company did exactly what policymakers should want: It tested its use of an AI tool, detected problems, and then mitigated harms by stopping the project. Creating a new AI law would not have improved that outcome. In addition, Canada’s gender equality laws already prohibit workplace discrimination, and those protections apply even if employers use AI in hiring.
The second example is “An analysis of well-known facial recognition systems showed evidence of bias against women and people of color.” But the now five-year-old study cited is not about facial recognition—technology used to match similar faces, either by searching for similar images in a database (i.e., one-to-many matches) or by confirming whether two images show the same person (one-to-one matches). Instead, it is about facial analysis—technology used to infer characteristics such as age, gender, or emotion based on a photo. Specifically, the study was about whether three commercial facial analysis systems could correctly predict gender across both light and dark-skinned individuals. The two technologies may sound similar, but they are as different as apple trees and apple sauce. Moreover, recent testing by the National Institute of Standards and Technology (NIST) shows that the best facial recognition algorithms have “undetectable” differences between different demographics. So here again, the evidence falls flat.
The third and final example used to justify regulating AI is that “AI systems have been used to create ‘deepfake’ images, audio, and video that can cause harm to individuals.” The issue is legitimate, although not novel: Software has long made it possible to digitally create “fake” images, although deepfake technology is making it much easier for anyone to produce realistic fake images and video without much technical expertise. While there are concerns about deepfakes as a source of disinformation, particularly in elections and global affairs, and infringing on celebrities’ publicity rights, its most visible impact is on individuals, particularly celebrities and women, who have fake pornographic images created about them. But AIDA would not address this problem, as the code to produce hyper-realistic images and video—whether legitimate or harmful—is open source and publicly available (and the AIDA explicitly, and rightly, does not attempt to regulate distribution of open-source software). Instead, this problem should be addressed with legislation updating Canada’s revenge porn law to prohibit nonconsensual distribution of deepfakes as well.
The AIDA seems to be premised on the assumption that stronger technology regulation increases consumer trust, and that higher levels of consumer trust will lead to more technology use. But, as past research has shown, there is little evidence to back up that claim. Indeed, fears that a lack of consumer trust may hold back AI adoption appear to be pure conjecture—ChatGPT gained 100 million users in two months, crushing all past records of consumer adoption of a new app.
Given that the government does not appear to understand its own evidence for regulating AI, it should pump the brakes on its aggressive pursuit of new regulations. Before creating an expansive regulatory framework for what promises to be a fundamental technology in the Canadian economy, it should better understand where the real risks exist—including the risk from overregulation—to ensure its rules are effective and avoid unintended consequences.
Image Credit: Jason Hafso on Unsplash
| 2023-03-27T00:00:00 |
2023/03/27
|
https://datainnovation.org/2023/03/canadas-reasons-for-an-ai-law-do-not-stand-up-to-scrutiny/
|
[
{
"date": "2023/03/27",
"position": 39,
"query": "AI regulation employment"
}
] |
The Ethics of AI In Business and Finance
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The Ethics of AI In Business and Finance
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https://streetfins.com
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[
"Mike Guo"
] |
From algorithmic bias and data privacy to job displacement and the need for regulation, these concerns highlight the need for a responsible and accountable ...
|
Share this:
The Ethics of AI in Business and Finance
Artificial Intelligence (AI) has taken the world by storm, promising to revolutionize the way businesses operate. With the ability to learn, reason and make decisions independently, AI has completely transformed the finance and business sectors. However, as machines take over critical decision-making processes, it is essential to question the ethical implications of relying solely on algorithms. From privacy concerns to potential biases, it is crucial to explore the broader implications of using AI in business and finance. So this prompts the question: what are the ethical implications of using AI in business and finance?
Algorithmic Bias
One of the major ethical concerns of AI is algorithmic bias. The behavior of AI machines is based on the data they are trained on. If the data is biased, then the AI will learn and reproduce that bias. For example, if a bank’s historical data shows that certain minority groups have a higher rate of loan default, the AI algorithm may automatically reject loan applications from those groups. Discriminatory lending practices produced by AI can perpetuate existing inequalities, further marginalizing the underrepresented. Moreover, such practices can result in legal liabilities and reputational damage for companies. To address algorithmic bias, it is important to improve data quality and diversity. This can involve collecting more inclusive and representative data, as well as implementing measures to detect and correct bias in algorithms. Additionally, AI developers and users must be mindful of the potential for bias and work to create more equitable and just AI systems. This can involve incorporating ethical principles into AI design and development, such as fairness, transparency, and accountability.
Data Privacy
The ethical concerns around data privacy in the development and deployment of AI technology are significant. The reliance of AI algorithms on personal information means that companies must prioritize ethical and transparent data practices, as well as individual control over personal information. While collecting and using data for AI, companies must be transparent about the information they are gathering and how it is being used. This requires clear communication with individuals about data collection practices, and explicit consent for the use of personal information. Additionally, companies must take steps to ensure that personal data is protected against unethical use or access, such as discrimination or surveillance. Data privacy laws and regulations must also be taken into account, with companies ensuring compliance with relevant legislation such as GDPR and CCPA. Beyond initial data collection, companies must also prioritize secure data storage and processing, as well as transparency around how personal information is used to train and improve AI algorithms.
Job Displacement
The impact of AI on the labor market is a growing ethical concern, particularly in industries such as finance. While AI has the potential to significantly improve productivity and efficiency, it also raises concerns about job loss and economic inequality. Machines and AI technology are becoming more and more capable of completing tasks that previously required human labor due to their increasing sophistication. Particularly for employees in professions that rely heavily on routine work, this could result in unemployment. In addition, the potential for AI to lower labor costs could exacerbate already-existing economic inequalities, particularly if those who lose their jobs lack the resources or expertise to transition to other industries. It is crucial for businesses and policymakers to give the creation of a thorough plan for the responsible deployment of AI top priority in order to address these ethical concerns. This includes steps to ensure that the rewards of AI are distributed fairly, like funding training and reskilling programs for workers who might be affected by job displacement. The ethical ramifications of decisions to automate jobs, including the effect on employee welfare, must also be taken into account.
Regulation
The increasing use of AI in the finance industry has raised ethical concerns that highlight the need for regulation to ensure that its deployment is transparent, fair, and ethical. In order to ensure that the technology is used in a responsible and accountable manner, regulatory frameworks are necessary given the growing complexity of AI systems. To create thorough regulations and best practices for the application of AI in the finance sector, regulatory organizations and governments must collaborate. These laws should address issues like data privacy, accountability, and transparency. Businesses must be held responsible for any unethical or harmful behavior and suffer the repercussions for breaking moral laws. It’s critical to create standards for AI development and deployment that put transparency and comprehensibility first in addition to legal frameworks. This can bolster confidence in the ethical use of AI systems and help to increase user trust in them. Additionally, it is crucial to fund research to determine how AI will affect society and how to deal with any unfavorable outcomes.
So what?
The growing use of AI in the finance industry brings both opportunities and ethical concerns. From algorithmic bias and data privacy to job displacement and the need for regulation, these concerns highlight the need for a responsible and accountable approach to the development and deployment of AI. The ethical implications of AI in finance and other industries cannot be ignored, and require collaboration between policymakers, regulatory bodies, and companies to establish ethical guidelines and best practices. Failure to address these concerns could lead to negative consequences for individuals and society as a whole. Therefore, it is essential to prioritize the development of a comprehensive plan for the responsible deployment of AI in finance, and in other industries, that balances the potential benefits with the ethical considerations.
| 2023-03-27T00:00:00 |
2023/03/27
|
https://streetfins.com/the-ethics-of-ai-in-business-and-finance/
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[
{
"date": "2023/03/27",
"position": 86,
"query": "AI regulation employment"
}
] |
Chapter 10 - NSCAI Final Report
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NSCAI Final Report
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https://reports.nscai.gov
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[] |
“The United States needs to invest in all AI talent pipelines in order to remain at the forefront of AI now and into the future. A passive strategy will not ...
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Implement and advertise the international entrepreneur rule.
The International Entrepreneur Rule (IER) allows U.S. Citizenship and Immigration Services (USCIS) to grant a period of authorized stay to international entrepreneurs who demonstrate that “their stay in the United States would provide a significant public benefit through their business venture.”6 An executive action could announce the administration’s intention to use the IER to boost immigrant entrepreneurship, job creation for Americans, and economic growth.
USCIS could also be directed to announce that it will give priority to entrepreneurs active in high-priority STEM fields such as AI, or in fields that use AI for other applications, such as agriculture. Entrepreneurs’ ability to attract investors should be used as a screening criterion for entrepreneurs.
| 2023-03-27T00:00:00 |
https://reports.nscai.gov/final-report/chapter-10?overlay=Talent-Immigration
|
[
{
"date": "2023/03/27",
"position": 8,
"query": "government AI workforce policy"
}
] |
|
AI Tools for Business
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AI Tools for Business
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https://workspace.google.com
|
[
"Google Workspace"
] |
I understand my personal data will be processed in accordance with Google's Privacy Policy. Sign up. Follow our Blog · X ...
|
Your organization’s data in Workspace is your data, and it’s not used to train or improve Gemini models or for ads targeting. Gemini only retrieves relevant content in Workspace that the user has access to and does not share user prompts or generated responses with other users or organizations. You can restrict Gemini’s access to sensitive data in Workspace with built-in data loss prevention (DLP), information rights management (IRM), and client-side encryption (CSE) controls. For more detailed information, please refer to the Generative AI in Workspace Privacy Hub .
| 2023-03-27T00:00:00 |
https://workspace.google.com/solutions/ai/
|
[
{
"date": "2023/03/27",
"position": 74,
"query": "government AI workforce policy"
},
{
"date": "2023/03/27",
"position": 47,
"query": "artificial intelligence business leaders"
}
] |
|
Artificial intelligence pays off when businesses go all in
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Artificial intelligence pays off when businesses go all in
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https://mitsloan.mit.edu
|
[
"Brian Eastwood"
] |
About 92% of large companies are achieving returns on their investments in artificial intelligence, and the same percentage are increasing their AI investments.
|
For AI to lead to growth, firms need to use the AI tools that are available to them, invest in complementary technology, and create a supportive infrastructure.
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About 92% of large companies are achieving returns on their investments in artificial intelligence, and the same percentage are increasing their AI investments. But what does it take for startups and early-stage companies to get to this point?
That’s a critical question, according to Sukwoong Choi, a postdoctoral scholar at MIT Sloan. “AI utilization is tied to startups’ products and services. It’s more directly relevant,” he said.
Work smart with our Thinking Forward newsletter Insights from MIT experts, delivered every Tuesday morning. Email: Leave this field blank
In a new paper, Choi and his co-authors find that firms need to be ready to make a significant investment in AI to see any gains, because limited AI adoption doesn’t contribute to revenue growth. Only when firms increase their intensity of AI adoption to at least 25% — meaning that they are using a quarter of the AI tools currently available to them — do growth rates pick up and investments in AI start to pay off.
The paper was co-authored by Yong Suk Lee, Taekyun Kim, and Wonjoon Kim.
Here are three things companies should know about investing in AI.
1. It takes time to see gains.
The researchers surveyed 160 startups and small businesses in South Korea about their use of AI technologies such as natural language processing, computer vision, and machine learning. Of the firms included, 53% were in technology-related fields (namely software, pharma, and mobile computing), and 54% had adopted AI to some degree.
The survey was administered to companies created before 2015, as these firms were founded before AI adoption generally took off in South Korea. (A footnote in the paper points to “an explosion of interest in AI” in the country after Go master Lee Sedol lost four of five matches to Google DeepMind’s AlphaGo program in March 2016.)
Among the firms surveyed, the correlation between AI adoption and revenue growth followed a J-curve: slow and steady at first, then substantial. The turning point was an intensity of AI adoption of 25%. For firms with AI intensity below 25%, annual revenue growth was essentially zero; for firms above the 25% threshold, growth approached 24%.
“There’s a disruptive power for AI. With lower utilization, it’s harder to make a profit,” Choi said. “When you’re in those early stages of AI adoption, you may need some time to obtain the payoff to using AI.”
2. Invest in complementary technology.
Several factors can influence a firm’s embrace of AI, the researchers found. For example, firms that are smaller and/or were founded by CEOs with prior entrepreneurial experience are more likely to adopt AI intensively. Larger firms or spinoffs from other companies are less likely to adopt AI at that level, though lab-based spinoffs are an exception.
One of the most influential factors, though, is adoption of complementary technology — namely, big data capabilities and cloud computing. The former contributes to better AI outcomes through more mature data collection and management, while the latter provides the computational power necessary to run complex analyses. Both help firms drive growth from their investments in AI.
This finding came as little surprise to Choi and his co-authors. For decades, investing in one type of technology has driven the adoption of other technologies. Examples abound: Better operating systems led to better software, faster modems made computer networks possible, and IT infrastructure supported the growth of online selling.
“Complementary technology makes it easy to adopt new technology such as AI,” Choi said. “To adopt and utilize AI effectively, and to get the payoff at earlier stages in your investment, you need the technology and the skills that go with it.”
3. Encourage smart investment.
The pivotal role of complementary technology points to one key takeaway from the paper, Choi said. To support AI adoption, it’s not enough to have access to the technology — you also need the infrastructure that supports it. “When you make that easily available, you can accelerate AI adoption,” Choi said.
The second consideration is how closely AI is tied to a company’s core product or service, he said, and how that impacts the company’s research and development strategy.
Internally focused R&D helps a company build “absorptive capacity” — in this case, AI know-how — that positions it to more intensively adopt and use AI technology. This is helpful for firms that need to protect their proprietary algorithms as intellectual property, or for firms working with sensitive data sets they’d rather not allow a third party to process.
On the other hand, if AI is a complement to the work that a firm is doing but isn’t the core focus of that work, firms can turn to external resources, Choi said. Large language models, such as OpenAI’s ChatGPT, are a good example of this: They’re readily available, widely used, and constantly being refined.
“It’s important to ask, ‘Is there a point solution for the AI work I’m trying to do?’” Choi said. “If your area of work is more systematic, then you don’t necessarily need an internally focused R&D strategy. You can license something that’s already available.”
Read next: how to prepare for the AI productivity boom
| 2023-03-27T00:00:00 |
2023/03/27
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https://mitsloan.mit.edu/ideas-made-to-matter/artificial-intelligence-pays-when-businesses-go-all
|
[
{
"date": "2023/03/27",
"position": 9,
"query": "AI employers"
}
] |
AI-Proof Jobs: 8 Careers Safe from Automation | USC Online
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Worried About ChatGPT Disrupting Your Career? Here Are 8 Jobs AI Won’t Be Replacing
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https://online.usc.edu
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[] |
Software Engineering. There's no doubt that AI has exceptional benefits and the potential to change many aspects of work and life itself. But to make sure that ...
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The rise of artificial intelligence has plenty of people concerned about how these new technologies will affect their job prospects.
ChatGPT, Google Bard and Microsoft Bing have been dominating recent headlines for their automated writing skills and sentient-like conversations, but the reach of their capabilities is still unclear.
They can seemingly create all types of written content — from student essays to poetry — and act as far-reaching, more optimized search engines. It’s not only communication, however, that artificial intelligence (AI) tools are skilled in. If you’ve been on social media for the past several months, you’ve likely seen users sharing digital art and images created by AI.
Naturally, the rise of AI has plenty of people concerned about how these new technologies will affect their job prospects. But it’s important to note that there are no definite predictions on how AI will actually impact those in creative industries. ChatGPT and the like have glaring flaws, and there are plenty of reasons why businesses want humans — not tech software — at the helm of certain jobs.
Still, if the threat of job loss makes you anxious, there are AI-proof careers out there — ones you can guarantee robots and automation won’t replace. Below, we’ve rounded up the top fields for job stability in a world of rapid technological advancement.
Nursing
While there are clear symptoms that can help patients get diagnosed with certain ailments and standard remedies, bodies are often complex, and hands-on care is required to determine an accurate prognosis and treatment plan. Nursing is one of the most established industries in the world, and it seems unlikely that AI can change that.
If you’re interested in nursing, consider exploring the Master of Science in Nursing (MSN) online program from the USC Suzanne Dworak-Peck School of Social Work. The graduate program prepares students to become knowledgeable and empathetic nurse practitioners who can treat people with a variety of medical issues from a diverse array of backgrounds.
Physical Therapy
Physical therapy is another important branch of medicine that focuses on helping patients improve mobility issues or other physical ailments, usually through specific exercises. Just like nursing, it’s not a field that’s about to be erased by technology.
A computer may help calculate treatment plans or even theoretically determine a person’s condition, but a physical therapist encourages patients and keeps them accountable, adjusting treatment when needed.
Those hoping to go down the physical therapy path should consider the hybrid online/on-campus pathway of USC’s Doctor of Physical Therapy program. Students are prepared to become physical therapy leaders who can integrate practice, research and even technology to provide patient-centered care.
Teaching
Across cultures and decades, you’ll be hard-pressed to find someone who doesn’t think education is one of the most valuable professions in society.
Although people can (and do) turn to the internet to gain knowledge and develop skills, most find they are able to learn best from an education professional who can guide them on a subject’s nuances — past the basic facts AI can generate.
Simply put, AI can’t replace the way a human mentor can spark inspiration in a student.
Want to get your start in education? The Master of Arts in Teaching online program from USC Rossier School of Education preps graduates to command a classroom and teach a wide array of subjects and grades.
Human Resources Management
A major issue surrounding AI is that it is often quite biased and can report inaccurate information. And in a sensitive field such as human resources, managers need to ensure workplace processes are operating to prevent prejudice and discrimination.
HR managers, however, do not just mediate conflict and handle workplace complaints. They also set guidelines and communicate and model them to employees. As previously mentioned, teaching makes more of an impact when you can have a thoughtful, engrossing dialogue.
For those looking to pivot into human resources, consider the Master of Science in Human Resource Management online program from USC Bovard College, which teaches students to become well-rounded, innovative HR leaders.
Software Engineering
There’s no doubt that AI has exceptional benefits and the potential to change many aspects of work and life itself. But to make sure that computer programs run seamlessly, you need to have somebody behind all the screens and hardware. That’s where software engineers come in.
Software engineers are in charge of coding apps and web programs, fixing any errors and continually updating software. What could be a more AI-proof career than one that consists of actually running AI?
If this line of work looks interesting to you, the Master of Science in Computer Science (Software Engineering) program from the USC Viterbi School of Engineering may be the right fit. You’ll walk out of it understanding of systems engineering, vital software development skills more.
Psychology
In recent years, many mental health workers turned to Zoom to host therapy sessions, and others have even tried to dole out advice via texting programs. Although technology can be incorporated into therapy, it can’t be completely supplanted by it.
Psychology is not an exact, fact-based science — emotions are complex. Much like nursing, you need an expert to truly offer the guidance and support to those seeking mental health treatment.
Studying psychology doesn’t just open someone up for a career with patients, though. Those who pursue a Master of Science in Applied Psychology (MAPP) from USC Dornsife College of Letters, Arts and Sciences, for example, learn how to use psychological theory to solve problems in contemporary businesses and organizations.
Social Work
Social workers handle complicated situations all throughout their careers, whether it’s analyzing a family’s living situation or supporting a child. And what the AI algorithm regularly fails to compute is that these issues aren’t black and white. There are intricate factors at play.
Plus, in emotionally heightened situations, people working with social workers often prefer a comforting presence — which isn’t exactly how you would describe a chat bot.
Those interested in the filed should look into the Master of Social Work online program from USC Suzanne Dworak-Peck School of Social Work, where students learn the ins and outs of family counseling, community advocacy and mental health work.
Law
The U.S. court system depends on humans: Citizens are the jury who regularly decide whether someone who is accused of a crime is guilty or not. Judges pass down rulings, and attorneys argue the cases.
As humans are the lifeblood of the legal system, lawyers are unlikely to be replaced by AI any time soon. Plus, as any good lawyer will tell you, it’s all about the details: Arguments in court are often very nuanced, which isn’t necessarily AI’s strong suit.
Not only are legal jobs essentially AI-proof, but you also get to make a difference by helping people navigate their way through an intricate system.
Aspiring lawyers can look to USC Gould School of Law to get their JD, which is needed in most states if you want to take the bar exams to practice law in the U.S. International students can look into the Master of Laws (LLM) online program, too, if they want to develop a deeper understanding of the U.S. legal system.
Explore more USC online graduate programs today.
| 2023-03-27T00:00:00 |
https://online.usc.edu/news/ai-proof-jobs-careers-safe-automation/
|
[
{
"date": "2023/03/27",
"position": 75,
"query": "artificial intelligence workers"
}
] |
|
Impact of ChatGPT and other AI chatbots on the future of work
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Impact of ChatGPT and other AI chatbots on the future of work
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https://www.cipd.org
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[] |
ChatGPT and other AI built on large language models could reduce the time and effort for many written tasks such as summarising documents, generating social ...
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Because conversational AI tools like ChatGPT occasionally give inaccurate or inappropriate answers, its ability to scale is also a risk. Humans are inherently biased. While AI is teaching us a lot about our biases, these can also be captured in the AI’s training data and programming. This would perpetuate harmful stereotypes and further disadvantage particular groups at scale. Rigorously test responses before deciding whether to rely on it for a particular task. Exercise real care when using AI to inform decisions about people (eg recruitment and performance assessments) – test the tool alongside experienced practitioners and conduct impact assessments periodically.
There are also potential security and privacy risks associated with its use. Particularly as individuals can access the tool outside of organisational oversight, robust measures will need to be taken to ensure sensitive information, for example financial or people data, is not shared or exposed.
The temptation to present AI-generated outputs as your own (plagiarism) is greater with conversational AI tools at your fingertips. We need to rethink how we assess people’s learning where AI can shortcut people’s efforts. Banning access might be acceptable where we want to test a learner’s ability to recall facts. But if the learners are in or entering jobs where the use of conversational AI will become essential, then it’d be prudent to update the course and assessments to help them prepare for this eventuality. The developers and plagiarism detection services are developing tools for detecting AI-written text.
The explosion of generic outputs is already happening. This might result in outputs that fail to capture the nuances of marginalised and discriminated individuals. Using conversational AI can save time, but it’s up to individuals to ensure the work is fit for purpose and differentiated from others. ChatGPT can create a job advert for you in seconds, but you’ll still need to check the final version for accuracy, for its ability to stand out among competition, and to tailor it to your target audience.
In the pursuit of organisational efficiency, there’s a risk that some jobs will become less attractive. It’s therefore important to engage with affected individuals and their managers on ways to make work more meaningful.
| 2023-03-27T00:00:00 |
2023/03/27
|
https://www.cipd.org/en/views-and-insights/thought-leadership/insight/ai-chatbots/
|
[
{
"date": "2023/03/27",
"position": 96,
"query": "artificial intelligence workers"
}
] |
The adoption of AI helps organizations become industry ...
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The adoption of AI helps organizations become industry leaders
|
https://www.womenofinfluence.ca
|
[
"Women Of Influence",
"Maricel Dicion"
] |
But if you're involved in the world of business, Ceren Kolsarici — the Director of the Scotiabank Centre for Customer Analytics at Smith School of Business at ...
|
By Sarah Kelsey
Augmented reality (AR). ChatGPT. Face ID. These are all futuristic things you’ve likely heard about in the media. But if you’re involved in the world of business, Ceren Kolsarici — the Director of the Scotiabank Centre for Customer Analytics at Smith School of Business at Queen’s University — says these are tools that have the power to accelerate and transform your organization, be it in the profit, nonprofit, or government space.
“There are so many things changing in the business landscape thanks to technology, and leaders who overcome the fears they have about the rapidly changing digital world, and who take the time to understand analytics and artificial intelligence (AI), will find their early adoption and presence on certain tools beneficial in all that they do,” she says. “At Smith — with the research projects we’re managing — we can see the massive opportunities before us; not just for small and large businesses, but for solving global problems on a societal level.”
If it sounds like Ceren is excited about the opportunities that AI, big data, and analytics are presenting the world, that’s because she is. In her current role at Smith — where she is also an associate professor and Ian R. Friendly Fellow of Marketing — she spends her days working with a team of researchers, graduate students, and practitioners from a cross-sector of disciplines to create new models and methods to improve how managers can make decisions, develop strategies, or optimize integrated campaigns with the use of technology.
“You want to be better than your competitors, but you also want to be able to do things that they cannot replicate,” Ceren notes. “These are the things that will help you thrive and that will help you stay ahead. They also aren’t as scary as they may seem.”
She points to Sephora’s use of augmented reality on their website as a good example. “When you go into Sephora, you can try products out on your actual face: different lipsticks, different mascaras, but with their [Virtual Artist tool], they’ve created a way for consumers to engage with hundreds of makeup products any time of day — it’s a gamification of shopping, and the frequency of adoption, plus the accessibility of product information, will build brand loyalty. ”
Chatbots, those little pop-up customer service boxes, are another tool organizations can use to streamline communication with clients and create a more frictionless experience that will breed loyalty, Ceren says. She and her team are working with Lululemon and other corporate partners to determine how to make the “chat” experience even more like an interaction with a human.
“These kinds of tools are only going to become more and more commonplace for businesses of all sizes; they’re definitely here to stay,” Ceren notes. “The data generated from these types of tools will also provide businesses with a wealth of information they can use to improve their overall offering or the campaigns they create.”
One of the “futuristic” AI applications Ceren is not entirely sold on, at least not yet, is the Metaverse. She says it’s still not as mainstream as some big brands would have hoped. The whole idea of an alternate universe and shopping in it with an avatar is a little esoteric — and companies like McDonald’s and Disney may not have yet seen the initial bang for their investment buck, she adds.
What this shows, she notes, is that not all tools or applications that get announced and put forward to leaders need to be used or adopted immediately. It is not enough for brands to be excited for change, consumers need to be ready too. For some companies without a grand budget, taking a strategic “wait and see” approach may be best. Decisions will ultimately depend on who your audience is and where they’re playing in the digital landscape.
Ceren also doesn’t believe that AI-based tools — much to the chagrin of media outlets trumpeting doom and gloom headlines — will replace the need for human intelligence in key roles like strategy, content creation, or where a lot of judgement is required. Machines, as sophisticated as they are, are inherently not good at certain things among which emotional intelligence and judgment are just two.
“I love that there are so many opportunities now; this field of work is starting to flourish and it’s starting to grow,” she says. “As much as it’s a scary concept — AI and analytics — it’s actually a space where you get comfortable quite quickly with the help of researchers. And so many schools — like Smith — now offer information to ensure leaders feel like they’re at the forefront of understanding and tackling these digital challenges.”
Ceren adds, “I see machines helping make the world a better place, and being at the forefront of this work at Smith, I love this time and I love what we do.”
| 2023-03-27T00:00:00 |
2023/03/27
|
https://www.womenofinfluence.ca/2023/03/27/the-adoption-of-ai-helps-organizations-become-industry-leaders-this-smith-school-of-business-executive-explains-why/
|
[
{
"date": "2023/03/27",
"position": 62,
"query": "artificial intelligence business leaders"
}
] |
Give Your Thought Leadership Articles New Life With AI
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Give Your Thought Leadership Articles New Life With AI
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https://www.lately.ai
|
[] |
Benefits Of Using AI to Create Thought Leadership Content · Growing an online presence (Lately posts the content, but you still have to ENGAGE) · Building ...
|
Give Your Thought Leadership Articles New Life With AI
AI has revolutionized how people interact with content, and now it's time for your thought leadership articles to join the party. Thought leaders (like you!) have long been at the forefront of innovation – providing unique perspectives that go beyond traditional wisdom – but even they can get stuck in a rut.
With AI, however, thought leaders can take their articles to new heights and give them a fresh lease on life.
Let's explore how AI helps bring your thought leadership pieces to life.
Did you know you can use tools like natural language processing (NLP) to generate ideas and add depth to your writing? AI supercharges your already powerful content, so it resonates even more deeply with readers while staying ahead of the curve.
Not to mention how Lately's AI for social media creates a flywheel effect — keeping your best thought leadership in front of your target audience for weeks or even months after you create content.
So.... if you're a subject matter expert who wants to save time, create content that actually converts, and leave your audience wanting MORE, keep reading.
What Is Artificial Intelligence (AI)?
AI is the capability of a machine to copy human intelligence and behavior.
AI includes self-driving cars, robots interacting with humans, or even computer programs learning independently. It may sound like something from outer space (or even Hollywood), but it's not THAT advanced.
Although robots aren't taking over, AI can still help you create and share better thought leadership content.
AI can (and does!) revolutionize how business leaders create innovative new ideas and improve our world.
If you've dismissed ChatGPT and other AI tools as "not for serious, authoritative content," you're missing out. AI can help you with nearly every type of content, making your thought leadership strategy more effective and efficient.
Need to revise or create an article? AI gives writers access to powerful tools that enable them to generate unique, insightful pieces quickly and efficiently.
Need to sift through piles of data? AI allows authors to make informed decisions quickly because it can analyze data quickly, freeing up your time so you can focus more on developing high-quality content instead of worrying about mundane tasks like fact-checking or research.
Create webinars? Lately sifts through the video, automatically creates video clips, and generates social media posts in seconds. Who's brand doesn't need THAT?
With AI, businesses can produce more compelling thought leadership material faster.
AI offers tremendous advantages over traditional methods of generating content, but it should always be used responsibly — it needs your HUMAN TOUCH.
The true power of AI comes from human propulsion. When you let the robots do what it does best, your organization will gain an edge over competitors who lack understanding or minimize how AI breathes new life into thought leadership content.
Benefits Of Using AI to Create Thought Leadership Content
Integrating AI into your thought leadership content production helps you create more engaging and informative articles while saving valuable time.
By leveraging AI's capabilities, thought leaders can maximize their impact on their target audience by delivering high-quality material that keeps up with the latest trends and developments in their field.
AI can help make content creation faster and easier, which enables thought leaders to focus on the things AI can't do, like focus on strategic goals such as:
Growing an online presence (Lately posts the content, but you still have to ENGAGE) Building relationships with customers, creators, and industry stakeholders (AI can help you generate lists, but it can't help you make human-to-human connections)
AI shines when it comes to personalization.
Did you know AI allows writers to customize content based on reader preferences while maintaining consistency across channels? For example, Lately creates a personalized writing model based on your brand's voice and publishes relevant social media content across channels and throughout your organization.
Marketers can also use machine learning algorithms to optimize website copy according to user demographics or interests. This ensures that every customer receives personalized messaging tailored to them, resulting in higher engagement rates and improved user experience.
With these benefits in mind, it is clear why integrating AI into your content marketing strategy will enable you to produce powerful thought leadership pieces that stand out.
Best Ways to Integrate AI Into Your Content Marketing Strategy
It doesn't matter if your organization is B2B or B2C. If you want a competitive advantage, adopt AI-driven content marketing strategies.
This technology creates compelling and engaging content that connects with your target audience in powerful ways (especially after a little human fine-tuning).
AI does GREAT with lots of information.
Here are a few ways you can use AI to optimize your content marketing strategy:
Generate content ideas based on your customer's interests and behaviors Create personalized web experiences by analyzing user data Optimize campaigns by automatically testing headlines, images, or copy Publish dozens of AI-generated social media posts with audiograms or video clips Improve your brand's SEO rankings with NLP Outline your ebooks or an in-depth piece of content
Content powered by AI has the potential to become an invaluable asset for any business looking to drive high-quality interactions with customers or prospects. AI-enabled content can help take your business’ content strategy to the next level – without much effort – from driving lead generation efforts to increasing website traffic and improving SEO rankings.
Conclusion: Benefits of Thought Leadership + AI
AI is a powerful tool that can help thought leaders produce more meaningful, engaging, and effective content.
By integrating AI into your content marketing strategy, your organization can unlock the potential to make data-driven decisions about its content and maximize its reach.
Tools like Lately can help you monitor engagement with existing articles on social media, which helps you identify future topics. Armed with this data, you could then use AI to analyze topics, create relevant titles and suggest new angles that would resonate better with your target audience.
Some organizations are already using AI systems to automate certain tasks, such as keyword analysis or formatting text correctly - freeing up time for writers to focus on crafting compelling stories instead.
Isn't it time you joined them?
| 2023-03-27T00:00:00 |
https://www.lately.ai/blog/thought-leadership-articles
|
[
{
"date": "2023/03/27",
"position": 64,
"query": "artificial intelligence business leaders"
}
] |
|
Digital Leadership Skills Every Modern Leader Needs Today
|
Leadership in the Digital Age: How Technology is Changing the Game
|
https://www.speexx.com
|
[
"Franca Lavinia Meyerhoefer",
"Author",
"Franca Lavinia Meyerhoefer - Translated",
"Arlo Tickner",
"About The Author"
] |
As Forbes notes, successful digital-age leaders adopt a data-driven mindset, build AI-ready teams, and integrate AI to gain strategic advantages. It also means ...
|
Leadership Development in the Digital Era
According to the Learning and Performance Institute, leadership development in the digital age is about “equipping leaders with the digital skills and knowledge they need to effectively lead their teams in a digital world.” This means equipping leaders to strategically leverage tools like data analytics and artificial intelligence—not just to improve operations, but to drive innovation and adaptability. As Forbes notes, successful digital-age leaders adopt a data-driven mindset, build AI-ready teams, and integrate AI to gain strategic advantages. It also means teaching them how to lead remote teams, work collaboratively, and communicate effectively in a digital environment.
As organizations continue to embrace digital transformation, the role of the leader is changing. In the past, leadership was about giving orders and making decisions. However, in the digital age, leaders must be more collaborative and inclusive. They must create a culture of innovation and encourage their teams to experiment and take risks. They must also be flexible, able to adapt and change course quickly as new technologies or market conditions advance.
Harvard Business Review’s “Leading in the Digital Age” report says leaders must develop their digital competencies, foster a culture of continuous learning and embrace data-driven decision making. The report highlights the importance of agility, collaboration, and customer-centric strategies to manage the complexities of the digital environment. A data-first culture is increasingly seen as non-negotiable. Building on this, the Forbes Business Council points out that leaders who prioritize data skills are more equipped to make objective, informed decisions in high-pressure environments.
Steve Denning, a Forbes contributor, suggests that the new style of leadership in the digital age is about creating a sense of purpose and meaning for employees. “Leaders must be storytellers, inspiring their teams with an engaging vision and narrative that aligns with the organization’s goals and values.” Notably, this is especially important for Generation Z, which values meaningful work and purpose-driven careers.
| 2023-03-27T00:00:00 |
2023/03/27
|
https://www.speexx.com/speexx-blog/leadership-in-the-digital-age-how-technology-is-changing-the-game/
|
[
{
"date": "2023/03/27",
"position": 79,
"query": "artificial intelligence business leaders"
}
] |
AI customer service for higher customer engagement
|
The next frontier of customer engagement: AI-enabled customer service
|
https://www.mckinsey.com
|
[
"Avinash Chandra Das",
"Greg Phalin",
"Ishwar Lal Patidar",
"Malcolm Gomes",
"Renny Thomas"
] |
But done well, an AI-enabled customer service transformation can unlock significant value for the business—creating a virtuous circle of better service, higher ...
|
How to engage customers—and keep them engaged—is a focal question for organizations across the business-to-consumer (B2C) landscape, where disintermediation by digital platforms continues to erode traditional business models. Engaged customers are more loyal, have more touchpoints with their chosen brands, and deliver greater value over their lifetime.
About the authors This article is a collaborative effort by Avinash Chandra Das, Greg Phalin, Ishwar Lal Patidar, Malcolm Gomes, Rakshit Sawhney, and Renny Thomas, representing views from McKinsey’s Operations Practice.
Yet financial institutions have often struggled to secure the deep consumer engagement typical in other mobile app–intermediated services. The average visit to a bank app lasts only half as long as a visit to an online shopping app, and only one-quarter as long as a visit to a gaming app. Hence, customer service offers one of the few opportunities available to transform financial-services interactions into memorable and long-lasting engagements.
Those customers are getting harder to please. Two-thirds of millennials expect real-time customer service, for example, and three-quarters of all customers expect consistent cross-channel service experience. And with cost pressures rising at least as quickly as service expectations, the obvious response—adding more well-trained employees to deliver great customer service—isn’t a viable option.
Companies are therefore turning to AI to deliver the proactive, personalized service customers want, when and how they want it—sometimes even before they know they want it. For transformed organizations, AI-enabled customer service can increase customer engagement, resulting in increased cross-sell and upsell opportunities while reducing cost-to-serve. In global banking alone, research from McKinsey conducted in 2020 estimates that AI technologies could potentially deliver up to $1 trillion of additional value each year, of which revamped customer service accounts for a significant portion.
While a few leading institutions are now transforming their customer service through apps, and new interfaces like social and easy payment systems, many across the industry are still playing catch-up. Institutions are finding that making the most of AI tools to transform customer service is not simply a case of deploying the latest technology. Customer service leaders face challenges ranging from selecting the most important use cases for AI to integrating technology with legacy systems and finding the right talent and organizational governance structures.
But done well, an AI-enabled customer service transformation can unlock significant value for the business—creating a virtuous circle of better service, higher satisfaction, and increasing customer engagement.
Would you like to learn more about our Operations Practice
The perils and promise of AI customer engagement
Multiple converging factors have made the case for AI-based customer service transformation stronger than ever. Among the most important: increased customer acceptance of (and even preference for) machine-led conversational AI interactions. Meanwhile, related technologies such as messaging platforms are becoming more accessible, and customer behaviors are becoming more understandable with the relentless expansion of data pools institutions can collect and analyze.
Three challenges
But challenges also loom. First, complexity. The COVID-19 pandemic acted as a major catalyst for migration to self-service digital channels, and customers continue to show a preference for digital servicing channels as the “first point of contact.” As a result, customers increasingly turn to contact centers and assisted-chat functions for more complicated needs. That raises the second issue: higher expectations. Customer confidence in self-service channels for transactional activities is leading them to expect similar outcomes for more involved requests. Businesses are therefore rapidly adopting conversational AI, proactive nudges, and predictive engines to transform every point of the customer service experience. Yet these moves raise demand for highly sought-after skills, generating the third challenge: squeezed labor markets that leave customer service leaders struggling to fill crucial roles.
How leaders fulfill AI’s customer engagement promise
What AI-driven customer service maturity looks like A few leading institutions have reached level four on a five-level scale describing the maturity of a company’s AI-driven customer service. Level 1: Manual and high-touch, based on paper forms and offered largely via assisted channels. Reactive service, with the majority of interactions on human-assisted channels
Paper use is still prevalent Level 2: Partly automated and basic digital channels, with digitization and automation of servicing in assisted channels. Reactive service, with limited self-servicing opportunities
Lower adoption of available self-service channels
Lower availability of digital or straight-through-processing (STP) Level 3: Accessible and speedy service via digital channels, with self-servicing on select channels and a focus on enabling end-to-end resolution. Somewhat proactive, but limited engagement
Self-service channels such as mobile apps, interactive voice response (IVR) systems, and internet sites handle half of all interactions, and can support STP. Level 4: Proactive and efficient engagement deploying AI-enabled tech, with self-servicing enabled by proactive customer interactions and conversational user experience (UX). Proactive, with high customer engagement on digital channels
Self-service channels such as mobile apps, IVR systems, and internet sites handle 70-80 percent of interactions and can support most requests and transactions Level 5: Personalized, digitally enabled engagement, bringing back the human touch via predictive intent recognition. Engagement via service interactions that are personalized and proactive at the individual customer level
Digital touchpoints drive service-based engagement, for example via enhanced cross-selling and upselling
More than 95 percent of service interactions and requests can be solved via digital and STP channels
Leaders in AI-enabled customer engagement have committed to an ongoing journey of investment, learning, and improvement, through five levels of maturity. At level one, servicing is predominantly manual, paper-based, and high-touch. At level five—the most advanced end of the maturity scale—companies are delivering proactive, service-led engagement, which lets them handle more than 95 percent of their service interactions via AI and digital channels (see sidebar, “What AI-driven customer service maturity looks like”).
The most mature companies tend to operate in digital-native sectors like ecommerce, taxi aggregation, and over-the-top (OTT) media services. In more traditional B2C sectors, such as banking, telecommunications, and insurance, some organizations have reached levels three and four of the maturity scale, with the most advanced players beginning to push towards level five. These businesses are using AI and technology to support proactive and personalized customer engagement through self-serve tools, revamped apps, new interfaces, dynamic interactive voice response (IVR), and chat.
A few leading institutions have reached level four on a five-level scale describing the maturity of a company’s AI-driven customer service.
Myth busters: Unexpected insights on contact centers
Toward engaging, AI-powered customer service
To achieve the promise of AI-enabled customer service, companies can match the reimagined vision for engagement across all customer touchpoints to the appropriate AI-powered tools, core technology, and data. Exhibit 1 captures the new model for customer service—from communicating with customers before they even reach out with a specific need, through to providing AI-supported solutions and evaluating performance after the fact.
The human factor in AI-supported service
AI-powered does not mean automation-only. It’s true that chatbots and similar technology can deliver proactive customer outreach, reducing human-assisted volumes and costs while simplifying the client experience. Nevertheless, an estimated 75 percent of customers use multiple channels in their ongoing experience. A reimagined AI-supported customer service model therefore encompasses all touchpoints—not only digital self-service channels but also agent-supported options in branches or on social-media platforms, where AI can assist employees in real time to deliver high-quality outcomes.
Even before customers get in touch, an AI-supported system can anticipate their likely needs and generate prompts for the agent. For example, the system might flag that the customer’s credit-card bill is higher than usual, while also highlighting minimum-balance requirements and suggesting payment-plan options to offer. If the customer calls, the agent can not only address an immediate question, but also offer support that deepens the relationship and potentially avoids an additional call from the customer later on.
AI service in the field: an Asian bank’s experience
Put together, next-generation customer service aligns AI, technology, and data to reimagine customer service (Exhibit 2). That was the approach a fast-growing bank in Asia took when it found itself facing increasing complaints, slow resolution times, rising cost-to-serve, and low uptake of self-service channels.
Over a 12-month period, the bank reimagined engagement. It revamped existing channels, improving straight-through processing in self-service options while launching new, dedicated video and social-media channels. To drive a personalized experience, servicing channels are supported by AI-powered decision making, including speech and sentiment analytics to enable automated intent recognition and resolution. Enhanced measurement practices provide real-time tracking of performance against customer engagement aspirations, targets, and service level agreements, while new governance models and processes deal with issues such as service request backlogs.
Underpinning the vision is an API-driven tech stack, which in the future may also include edge technologies like next-best-action solutions and behavioral analytics. And finally, the entire transformation is implemented and sustained via an integrated operating model, bringing together service, business, and product leaders, together with a capability-building academy.
The transformation resulted in a doubling to tripling of self-service channel use, a 40 to 50 percent reduction in service interactions, and a more than 20 percent reduction in cost-to-serve. Incidence ratios on assisted channels fell by 20-30 percent, improving both the customer and employee experience.
Seizing the opportunity
To leapfrog competitors in using customer service to foster engagement, financial institutions can start by focusing on a few imperatives.
Envision the future of service, keeping customers and their engagement at the core while also defining the strategic value to be attained—for example, a larger share of wallet with existing customers? Expansion of particular services, lines of business, or demographics?
Rethink every customer touchpoint, whether digital or assisted, together with opportunities to enhance the experience while also increasing efficiencies.
Maximize every customer service interaction, to deepen customer relationships, build loyalty, and drive greater value over the customer’s lifetime.
Leverage AI and an end-to-end technology stack, to provide a more proactive and personalized customer service experience that supports self-service and decision-making for customers as well as employees.
Adapt agile and collaborative approaches to drive transformation, comprised of SMEs from different business and support functions of the organization.
Holistically transforming customer service into engagement through re-imagined, AI-led capabilities can improve customer experience, reduce costs, and increase sales, helping businesses maximize value over the customer lifetime. For institutions, the time to act is now.
| 2023-03-27T00:00:00 |
https://www.mckinsey.com/capabilities/operations/our-insights/the-next-frontier-of-customer-engagement-ai-enabled-customer-service
|
[
{
"date": "2023/03/27",
"position": 88,
"query": "artificial intelligence business leaders"
}
] |
|
Robot recruiters: can bias be banished from AI hiring?
|
Robot recruiters: can bias be banished from AI hiring?
|
https://www.theguardian.com
|
[
"Jonathan Barrett",
"Stephanie Convery"
] |
Businesses are increasingly turning to AI to help them hire employees but concerns have been raised about potential bias in the recruitment process.
|
Michael Scott, the protagonist from the US version of The Office, is using an AI recruiter to hire a receptionist.
Guardian Australia applies.
The text-based system asks applicants five questions that delve into how they responded to past work situations, including dealing with difficult colleagues and juggling competing work demands.
Potential employees type their answers into a chat-style program that resembles a responsive help desk. The real – and unnerving – power of AI then kicks in, sending a score and traits profile to the employer, and a personality report to the applicant. (More on our results later.)
This demonstration, by the Melbourne-based startup Sapia.ai, resembles the initial structured interview process used by their clients, who include some of Australia’s biggest companies such as Qantas, Medibank, Suncorp and Woolworths.
The process would typically create a shortlist an employer can follow up on, with insights on personality markers including humility, extraversion and conscientiousness.
6:05 How an AI voice clone fooled Centrelink – video
For customer service roles, it is designed to help an employer know whether someone is amiable. For a manual role, an employer might want to know whether an applicant will turn up on time.
“You basically interview the world; everybody gets an interview,” says Sapia’s founder and chief executive, Barb Hyman.
The selling points of AI hiring are clear: it can automate costly and time-consuming processes for businesses and government agencies, especially in large recruitment drives for non-managerial roles.
Sapia’s biggest claim, however, might be that it is the only way to give someone a fair interview.
“The only way to remove bias in hiring is to not use people right at the first gate,” Hyman says. “That’s where our technology comes in: it’s blind; it’s untimed, it doesn’t use résumé data or your social media data or demographic data. All it is using is the text results.”
View image in fullscreen Sapia founder and chief executive Barb Hyman says AI hiring is the only way to give someone a fair interview. Photograph: Ellen Smith/The Guardian
A patchy track record
Sapia is not the only AI company claiming its technology will reduce bias in the hiring process. A host of companies around Australia are offering AI-augmented recruitment tools, including not just chat-based models but also one-way video interviews, automated reference checks, social media analysers and more.
In 2022 a survey of Australian public sector agencies found at least a quarter had used AI-assisted tech in recruitment that year. Separate research from the Diversity Council of Australia and Monash University suggests that a third of Australian organisations are using it at some point in the hiring process.
Applicants, though, are often not aware that they will be subjected to an automated process, or on what basis they will be assessed within that.
The office of the Merit Protection Commissioner advises public service agencies that when they use AI tools for recruitment, there should be “a clear demonstrated connection between the candidate’s qualities being assessed and the qualities required to perform the duties of the job”.
The commissioner’s office also cautions that AI may assess candidates on something other than merit, raise ethical and legal concerns about transparency and data bias, produce biased results or cause “statistical bias” by erroneously interpreting socioeconomic markers as indicative of success.
There’s good reason for that warning. AI’s track record on bias has been worrying.
In 2017 Amazon quietly scrapped an experimental candidate-ranking tool that had been trained on CVs from the mostly male tech industry, effectively teaching itself that male candidates were preferable. The tool systematically downgraded women’s CVs, penalising those that included phrases such as “women’s chess club captain”, and elevating those that used verbs more commonly found on male engineers’ CVs, such as “executed” and “captured”.
Research out of the US in 2020 demonstrated that facial-analysis technology created by Microsoft and IBM, among others, performed better on lighter-skinned subjects and men, with darker-skinned females most often misgendered by the programs.
Last year a study out of Cambridge University showed that AI is not a benign intermediary but that “by constructing associations between words and people’s bodies” it helps to produce the “ideal candidate” rather than merely observing or identifying it.
Natalie Sheard, a lawyer and PhD candidate at La Trobe University whose doctorate examines the regulation of and discrimination in AI-based hiring systems, says this lack of transparency is a huge problem for equity.
View image in fullscreen At least a quarter of Australian public sector agencies used AI-assisted tech in recruitment in 2022. Illustration: JYPIX/Alamy
“Messenger-style apps are based on natural language processing, similar to ChatGPT, so the training data for those systems tends to be the words or vocal sounds of people who speak standard English,” Sheard says.
“So if you’re a non-native speaker, how does it deal with you? It might say you don’t have good communication skills if you don’t use standard English grammar, or you might have different cultural traits that the system might not recognise because it was trained on native speakers.”
Another concern is how physical disability is accounted for in something like a chat or video interview. And with the lack of transparency around whether assessments are being made with AI and on what basis, it’s often impossible for candidates to know that they may need reasonable adjustments to which they are legally entitled.
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You are twice as likely to get women and keep women in the hiring process when you’re using AI Barb Hyman
“There are legal requirements for organisations to adjust for disability in the hiring process,” Sheard says. “But that requires people to disclose their disability straight up when they have no trust with this employer. And these systems change traditional recruitment practices, so you don’t know what the assessment is all about, you don’t know an algorithm is going to assess you or how. You might not know that you need a reasonable adjustment.”
Australia has no laws specifically governing AI recruitment tools. While the department of industry has developed an AI ethics framework, which includes principles of transparency, explainability, accountability and privacy, the code is voluntary.
“There are low levels of understanding in the community about AI systems, and because employers are very reliant on these vendors, they deploy [the tools] without any governance systems,” Sheard says.
“Employers don’t have any bad intent, they want to do the right things but they have no idea what they should be doing. There are no internal oversight mechanisms set up, no independent auditing systems to ensure there is no bias.”
A question of diversity
Hyman says client feedback and independent research shows that the broader community is comfortable with recruiters using AI.
“They need to have an experience that is inviting, inclusive and attracts more diversity,” Hyman says. She says Sapia’s untimed, low-stress, text-based system fits this criteria.
“You are twice as likely to get women and keep women in the hiring process when you’re using AI. It’s a complete fiction that people don’t want it and don’t trust it. We see the complete opposite in our data.”
View image in fullscreen Hyman says Sapia’s language analysis is based on the ‘billion words of data’ collected from responses in the years since the tech company was founded in 2013. Photograph: Ellen Smith/The Guardian
Research from the Diversity Council of Australia and Monash University is not quite so enthusiastic, showing there is a “clear divide” between employers and candidates who were “converted” or “cautious” about AI recruitment tools, with 50% of employers converted to the technology but only a third of job applicants. First Nations job applicants were among those most likely to be worried.
DCA recommends recruiters be transparent about the due diligence protocols they have in place to ensure AI-supported recruitment tools are “bias-free, inclusive and accessible”.
In the Sapia demonstration, the AI quickly generates brief notes of personality feedback at the end of the application for the interviewee.
This is based on how someone rates on various markers, including conscientiousness and agreeableness, which the AI matches with pre-written phrases that resemble something a life coach might say.
A more thorough assessment – not visible to the applicant – would be sent to the recruiter.
Sapia says its chat-interview software analysed language proficiency, with a profanity detector included too, with the company saying these were important considerations for customer-facing roles.
Hyman says the language analysis is based on the “billion words of data” collected from responses in the years since the tech company was founded in 2013. The data itself is proprietary.
You’re (not) hired!
So, could Guardian Australian work for Michael Scott at the fictional paper company Dunder Mifflin?
“You are self-assured but not overly confident,” the personality feedback says in response to Guardian Australia’s application in the AI demonstration.
It follows with a subtle suggestion that this applicant might not be a good fit for the receptionist role, which requires “repetition, routine and following a defined process”.
But it has some helpful advice: “Potentially balance that with variety outside of work.”
Looks like we’re not a good fit for this job.
| 2023-03-27T00:00:00 |
2023/03/27
|
https://www.theguardian.com/technology/2023/mar/27/robot-recruiters-can-bias-be-banished-from-ai-recruitment-hiring-artificial-intelligence
|
[
{
"date": "2023/03/27",
"position": 3,
"query": "artificial intelligence hiring"
}
] |
The Debate on the Ethics of Using AI in HR
|
Ethics of Using AI in HR: A Dive Into HR Challenges and Considerations
|
https://www.sesamehr.com
|
[
"Tiago Santos",
"Hr Influencer",
"Hr Professional With Experience In Building Strong 𝐜𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐯𝐞 𝐜𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐢𝐞𝐬 Among Hr Leaders. As The Founder Of The 𝐇𝐑 𝐂𝐥𝐮𝐛",
"𝐇𝐑 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲",
"I Leverage My",
"Years Of Experience To Enhance The Professional Landscape For Hr Leaders."
] |
Additionally, using AI for recruitment means that human judgment is removed from the equation. Decisions about who should be hired or who should be rejected can ...
|
27 of March, 2023
We’re living in a rapidly changing world and it can be hard to stay ahead of the curve. As an HR manager or business owner, you know that one of the challenges of managing a successful business is staying up-to-date with the latest developments in Human Resources technology.
Artificial Intelligence (AI) is making waves in the HR world, but not everyone is sure how to ethically utilize this new technology. We’ve all heard horror stories about Artificial Intelligence eliminating jobs, but what are the real implications of using AI in HR?
In this article, we’ll explore the debate around using Artificial Intelligence for HR purposes like recruitment, discuss ethical considerations for businesses integrating this technology. Let’s start!
What Is the Debate on the Ethics of Using AI in HR?
The use of AI in HR is an ethical dilemma that’s been debated for some time. Using AI-based tools to manage HR processes could result in improved efficiency, reliability and accuracy for many processes, such as recruitment and performance reviews.
On the other hand, opponents raise questions about potential privacy breaches, algorithmic bias and potential consequences on job security. AI-driven HR processes could lead to improved outcomes. However, the risk of inaccuracies due to algorithm bias or incorrect data must be taken into consideration.
Algorithmic bias could lead to inaccurate decision-making when it comes to hiring and promotion decisions, resulting in possible discrepancies between the selected employees and those actually best suited for the job.
The use of Artificial Intelligence in HR should be viewed as an opportunity to move beyond traditional HR practices, but with a camp cautious awareness of both its strengths and its associated risks. Businesses must take the necessary steps to ensure that they are using this technology responsibly and maintaining employee privacy at all times.
Challenges of Using AI in Human Resources
When it comes to using Artificial Intelligence in Human Resources, there are a few challenges to consider. One of the biggest ethical issues is that AI has the potential to introduce bias into hiring decisions. This can be unintentional, but it still undermines efforts to create an inclusive work environment and an equitable talent acquisition process.
Another challenge is making sure that human judgment is not completely replaced by machines. While Artificial Intelligence can automate mundane tasks such as scanning resumes and scheduling interviews, it doesn’t offer the same level of insight and empathy as a human being would.
That’s why it’s important to have HR personnel deeply involved in the hiring process. They can combine their own knowledge and experience with the data from AI tools to make more informed decisions.
Finally, certain HR activities may require input from multiple departments in a company before a decision can be made. It’s important for businesses to consider how these decisions will be made when relying on AI-driven solutions.
Businesses should closely evaluate how those solutions interact with other departments and make sure that there are systems in place for Human Resources personnel to keep track of these interactions.
The Impact of AI on Job Market and Employees
When AI is used in HR, it changes the dynamics of how jobs and employees are onboarded, managed, and monitored. On one hand, AI can make the job market more efficient by helping HR departments quickly assess a candidate’s qualifications, analyze job postings more accurately, and identify talent gaps faster and easier.
On the other hand, Artificial Intelligence has its own set of challenges when it comes to ethics. For example, AI makes it easy to draw conclusions based on data that may not be diverse enough or may be biased against individuals due to factors such as gender or race. This can lead to discrimination or other problems in the workplace.
Additionally, using AI for recruitment means that human judgment is removed from the equation. Decisions about who should be hired or who should be rejected can be made without any consideration for others’ feelings or emotions. This could mean that people with less experience but a passion for their work might get overlooked.
It’s important for businesses who choose to use AI in HR to consider these issues carefully before making decisions about technology adoption or implementation. By taking steps to ensure a fair and ethical HR process, businesses can ensure that their use of AI benefits both themselves and their employees in equal measure.
Deliberating the Benefits and Problems of AI in HR
Using AI in HR can certainly bring a lot of benefits to businesses, but at the same time, it also comes with some challenges too. It’s always important to weigh both the pros and cons when evaluating technologies like AI in HR.
Benefits
Automating HR processes can reduce costs, improve response times and accuracy and free up workers to focus on more meaningful tasks. AI also brings an unbiased approach to things like talent acquisition, making sure potential hires are judged according to their qualifications rather than bias or favoritism.
Moreover, AI can help manage large volumes of data from multiple sources to provide insights that could result in better decision-making.
Challenges
Using automated recruitment risks excluding applicants from minority backgrounds who may have unique experiences or skillsets that an algorithm might not spot.
Additionally, automation can only do so much. There are many nuances of people management that require the expertise and judgment of a human being.
It’s also important to consider the implementation cost and maintenance costs associated with AI systems—budgeting for this cost is key in making sure you’re getting the most out of your investment.
Conclusion
It is clear that the use of AI in HR carries potential risks and ethical implications, but it holds great potential for improving the way that businesses manage their employees.
To guarantee an ethical use of AI and to ensure the safety and privacy of HR data, businesses need to consider their options carefully and implement proper governance systems.
At Sesame, we are staying up to date on the best practices for the ethical use of AI and how to do it safely and securely. We continually strive to uphold our commitment to providing the best HR software in order to help businesses make informed decisions, protect employee privacy, and ensure ethical use of AI in HR.
Do you want to rate our article? Average rating: 90 votes) ★ ★ ★ ★ ★ 5 stars (votes)
| 2023-03-27T00:00:00 |
2023/03/27
|
https://www.sesamehr.com/blog/hr-insights/ethics-using-ai-in-hr/
|
[
{
"date": "2023/03/27",
"position": 69,
"query": "artificial intelligence hiring"
}
] |
AI in K-12 education: Exploring opportunities and obstacles
|
AI in K-12 education: Exploring opportunities and obstacles
|
https://theacademic.com
|
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Our research revealed that educational benefits occurred in three major categories: pedagogy, administration, and subject matter content.
|
The presence of artificial intelligence (AI) in all areas of our lives has grown significantly in recent years. This growth provided the impetus for our investigation into how AI currently provides opportunities to extend and enhance learning in K-12 (from kindergarten to 12th grade) settings. To better understand how AI is currently used in K-12 schools, we reviewed 169 studies published from 2011-2021 to examine the educational affordances AI can provide in K-12 settings. In addition, we also examined the challenges regarding the use of AI. Finally, we made recommendations for future research to ensure that the use of AI in K-12 settings is effective. Our research revealed that educational benefits occurred in three major categories: pedagogy, administration, and subject matter content.
Video Short Link: AI is EDUCATING our FUTURE?
Pedagogy
Pedagogical uses of AI involve using AI for teaching and learning methods (Figure 1). Some examples of current pedagogical AI benefits include (1) The use of games with an automatic feedback system. (2) The use of intelligent tutors in which a 3D computer character is always on screen, speaking with a recorded human voice acting as a human tutor. (3) Mentoring in which AI provides students with career mentoring, as they work with a real scientist. (4) Personalised learning, where students receive individualised remedial learning materials generated by an expert system based on students’ pre-test scores of concepts. (5) Simulations in which students learn about gravity and planetary motion in an immersive, whole-body interactive simulation. (6) Remote laboratories which enable students to use distant laboratories where students can evaluate phenomena while studying at home and performing hands-on lab experiments. These are examples of AI extending and enhancing well-known best-practice pedagogies. Our hope for the future is that educators will go beyond past practices, examine alternative pedagogical approaches that AI has now made available, and move beyond using 21st Century technologies, with 20th Century teaching practices.
Figure 1. Ways AI is being used for teaching and learning.
Credit: Kris Nario at ODUGlobal
Administration
The second category of educational benefits in K-12 was administration. This involves using AI in educational management systems (Figure 2). Some examples of administrative benefits include: (1) Using a diagnostic tool to provide timely recognition of speech impairments in primary school students. (2) AI can help to predict the potential difficulties in writing skills development and clarify the causes and character of these difficulties. (3) Student tracking systems can assist teachers in monitoring students’ activities, informing them of real-time interventions, and reviewing students’ achievements to assist in future lesson planning. (4) Assessment tools can provide automated essay evaluation systems that combine shallow and deep semantic attributes of essays. (5) Comprehensive managerial use of AI enables the analysis of student data to identify the factors influencing student success or failure. These applications of AI have the potential to improve K–12 education.
Figure 2. Ways AI is being used for administration.
Credit: Kris Nario at ODUGlobal
Subject Matter Content
The third category of educational benefits in K-12 was in subject matter content. This use of AI provides teaching and learning support directly connected to a subject matter discipline. Most of the studies investigated the use of AI in developing writing skills and acquiring foreign languages (Figure 3). The widespread use of AI in writing and foreign languages may be explained by the technology’s capacity to interact effectively with routine knowledge and systems found in written text and languages. Additionally, writing and language proficiency are abilities and knowledge applicable across academic fields. As a result, these tools’ growth has more widespread application.
Figure 3. Ways AI is being used in subject matter content.
Credit: Kris Nario at ODUGlobal
Challenges
Although AI has the capacity to extend and enhance K-12 education, there are many challenges that need to be considered. Our research identified three main challenges. These include (1) the technology itself, both hardware and software, (2) the actual use of the technology, and (3) ethics. Challenges in technology included technology requirements and limitations, ease of use, and program design. The actual use of the technology provided challenges regarding the lack of technology skills on the part of either or both teacher and students, an inability or difficulty to troubleshoot problems as they arise and a lack of understanding about how to use technology effectively. Ethical concerns include privacy issues and potential bias built into AI.
Future Research
Our research findings led us to propose four areas that could help to make the use of AI in K-12 settings more effective. First, to better understand AI and how it can be integrated into teaching and learning, educators must expand their professional development to examine the use of AI in education. It would benefit researchers to create frameworks for AI integration and look at best practices to use as examples for teachers. Second, many topic areas, including art, geography, and history, were underrepresented in the distribution of AI studies. Researchers could investigate the possibilities of AI in these areas. Third, data indicate that teachers and students are the main focus of AI research. Future researchers could examine how AI can assist administrators, especially in examining large datasets. Finally, the most common research approach to AI in K–12 was quantitative. The scholarly community would benefit from qualitative investigations to offer other types of research on the use of AI in K-12 settings.
Our research has provided information on the possibilities and challenges of using AI in K-12 education. This research provides researchers, educators, and administrators with valuable information on the use of AI in education and a springboard for further examination of AI in this context.
🔬🧫🧪🔍🤓👩🔬🦠🔭📚
Journal reference
Crompton, H., Jones, M. V., & Burke, D. (2022). Affordances and challenges of artificial intelligence in K-12 education: a systematic review. Journal of Research on Technology in Education, 1-21. https://doi.org/10.1080/15391523.2022.2121344
| 2023-03-27T00:00:00 |
2023/03/27
|
https://theacademic.com/artificial-intelligence-in-k-12-education/
|
[
{
"date": "2023/03/27",
"position": 18,
"query": "artificial intelligence education"
}
] |
Artificial Intelligence in Education: Looking Ahead
|
Artificial Intelligence in Education: Looking Ahead
|
https://citl.news.niu.edu
|
[
"Tracy Miller"
] |
AI is poised to transform a wide variety of industries and disciplines, including higher education. It offers both risk and opportunity to the way we teach.
|
Recent developments in artificial intelligence can generate surprisingly high-quality and natural-sounding text and creative visual art. AI is poised to transform a wide variety of industries and disciplines, including higher education. It offers both risk and opportunity to the way we teach, the skills that students will need upon graduation, and how we assess their progress toward those outcomes.
In our first panel on AI, we focused on the immediate impact that AI (and particularly ChatGPT) is having on teaching and learning. In this second panel, NIU faculty with extensive expertise in AI discussed the ways that AI is changing the world that we live in and the careers our students are pursuing. Our expert panelists included:
Ann Dzuranin, Department of Accountancy
David Gunkel, Department of Communication
Andrea Guzman, Department of Communication
Maoyuan Sun, Department of Computer Science
The panelists shared some amazing insights, including:
| 2023-03-27T00:00:00 |
2023/03/27
|
https://citl.news.niu.edu/2023/03/27/artificial-intelligence-in-education-looking-ahead/
|
[
{
"date": "2023/03/27",
"position": 38,
"query": "artificial intelligence education"
}
] |
AI And VR Technology In Education: The Future Of Learning
|
Evolving Education: The Impact Of AI And VR Technology On The Future Of Learning
|
https://elearningindustry.com
|
[
"Elsa Martin",
"Dr. Ravinder Tulsiani",
"Guy Sheetrit"
] |
AI can analyze student data, such as grades, learning patterns, and preferences, to adapt content and delivery methods to individual needs and learning styles.
|
Unlocking The Potential Of AI And VR In Education
Artificial Intelligence (AI) and Virtual Reality (VR) technology offer tremendous potential to transform the education landscape, providing students with a more personalized and engaging learning experience, and teachers with new tools to improve the effectiveness of their teaching. In this article, we will explore some of the key benefits of AI and VR in education, as well as answer some common questions about this topic.
Benefits Of AI In Education
One of the key benefits of AI in education is its ability to personalize learning experiences. AI can analyze student data, such as grades, learning patterns, and preferences, to adapt content and delivery methods to individual needs and learning styles. This helps students learn at their own pace and receive tailored support, making education more accessible and inclusive.
Additionally, AI can automate time-consuming tasks, such as grading and feedback, freeing teachers to focus on more critical tasks like mentorship and relationship-building with their students.
Immersive Learning Experiences
VR technology offers a unique opportunity to create truly immersive learning experiences. In VR, students can explore and experience complex concepts in a hands-on and interactive way. For example, VR can be used to simulate real-world scenarios, such as historical events, scientific experiments, and complex machinery, providing a more engaging and memorable experience for students. Moreover, VR has the potential to break down barriers of time and space, allowing students to learn from anywhere in the world and explore environments they might not otherwise have access to.
Bridging The Skills Gap
The combination of AI and VR technology can also help bridge the skills gap and prepare students for the workforce of the future. For example, AI and VR can be used to create virtual apprenticeships and internships, giving students the opportunity to gain real-world experience and develop valuable skills in a simulated environment.
However, it's important to note that AI and VR technology are not silver-bullet solutions for education. These technologies must be used in conjunction with traditional teaching methods, human teachers, and human interaction to be truly effective. Additionally, it is critical to ensure that AI and VR technology is accessible and equitable for all students, regardless of socioeconomic background or geographic location.
Common Questions About AI And VR In Education
Now, let's offer some insight into a few questions about AI and VR in education:
1. Challenges Associated With The Integration Of AI And VR Technology In Education
Some of the challenges include the high cost of technology, privacy and security concerns, and a lack of trained personnel to use and maintain the technology. Additionally, there are concerns about the ethical implications of AI in education, such as bias in algorithms and the possibility of replacing human teachers with machines.
2. AI And VR Technology Can Enhance The Learning Experience
AI and VR technology can personalize the learning experience for students, providing tailored content and delivery methods that cater to individual needs and learning styles. VR technology can also provide immersive and hands-on experiences that are both engaging and memorable.
3. Effectively Integrating AI And VR Technology Into Teaching Methods
Teachers can effectively integrate AI and VR technology into their teaching methods by first understanding the potential benefits and limitations of these technologies. They can then experiment with different AI and VR tools and resources, and assess their impact on student learning outcomes. Collaborating with technology experts and other educators can also help teachers effectively integrate these technologies into their teaching methods.
4. Ensuring The Security And Privacy Of Student Data When Using AI And VR Technology
Schools can ensure the security and privacy of student data by using secure platforms, encryption, and firewalls. They can also implement strict data access policies, including regular monitoring and auditing of data usage. Additionally, schools can educate students and parents about data privacy and encourage them to take steps to protect their personal information.
5. Ensuring That AI And VR Technology Is Accessible And Equitable For All
Ensuring that AI and VR technology is accessible and equitable for all students requires addressing the digital divide and providing equal access to technology for all students, regardless of socioeconomic background or geographic location. It also requires addressing issues related to affordability, availability, and infrastructure. Additionally, schools can implement policies and practices that ensure that technology is used in an equitable and inclusive way, such as providing professional development opportunities for teachers to learn how to use these technologies effectively.
In Conclusion
AI and VR technology have the potential to revolutionize the education landscape and improve the learning experience for students. However, effective implementation requires careful consideration of ethical, equity, and privacy issues, as well as a focus on human interaction and traditional teaching methods. By addressing these challenges and working together, businesses and educators can help learners reach their full potential and prepare them for the workforce of the future.
| 2023-03-27T00:00:00 |
2023/03/27
|
https://elearningindustry.com/evolving-education-the-impact-of-ai-and-vr-technology-on-the-future-of-learning
|
[
{
"date": "2023/03/27",
"position": 66,
"query": "artificial intelligence education"
}
] |
Will ChatGPT take your job — and millions of others? - Al Jazeera
|
Will ChatGPT take your job — and millions of others?
|
https://www.aljazeera.com
|
[
"Somesh Jha"
] |
The short answer: ChatGPT and its rival AI models could dramatically disrupt the labour market, including replacing routine jobs in some sectors.
|
The latest AI wave will disrupt the workplace. Teachers could be most affected. But it could end up creating more jobs.
It is the whiz-kid of the artificial intelligence (AI) world that others are trying to emulate.
In the four months since its November 30 launch, ChatGPT has shown the ability to perform a wide range of tasks, from cracking the bar and medical licensing exams in the United States to writing emails and songs, building apps, and more.
The fact that it is freely available for public use has opened up a plethora of opportunities previously thought beyond the realm of possibility of AI — even though the app’s makers have faced criticism for opacity around the programming they have used to train it.
Developed by OpenAI, a company backed by Microsoft, ChatGPT became the fastest-growing consumer app in the world two months after its launch, with more than 100 million users by January.
That early success has prompted Microsoft to integrate its Bing search engine and Edge browser with the technology running ChatGPT in the hope of improving the experience of users. Last week, Google launched a similar AI app, known as Bard, after unveiling a preview of the platform in February.
Also in mid-March, China’s tech giant Baidu announced its answer to the US app — a platform called Ernie. Both Bard and Ernie have suffered from early stumbles as the AI arms race heats up.
Meanwhile, OpenAI has launched GPT-4, an upgraded version of the technology behind ChatGPT. The new platform can analyse images and more extensive texts of up to 25,000 words, create a website from a hand-drawn sketch and recreate games within seconds.
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Companies are now rushing to launch products built entirely on ChatGPT in sectors ranging from customer service to financial analytics.
At a time when the International Labour Organization already estimates that 208 million people will be unemployed in 2023, will this new wave of AI dramatically increase joblessness? Which jobs would these tools potentially replace? What is the future of work?
The short answer: ChatGPT and its rival AI models could dramatically disrupt the labour market, including replacing routine jobs in some sectors. But overall, the technology could enhance productivity and complement human workers, instead of leading to unemployment, experts told Al Jazeera.
Hey, teacher, (don’t) leave that app alone
What makes ChatGPT and other similar platforms fundamentally different from previous generations of AI is the GPT — which stands for generative pre-trained transformer — bit.
Simply put, these tools use a technique known as deep learning to produce and analyse text, answer questions and perform other language and speech-related tasks in ways that can mimic human beings better than ever before.
“The impact of the generative-AI on the labour market will be really substantial,” Laura Nurski, fellow and lead of the Future of Work team at Brussels-based think tank Bruegel, told Al Jazeera. “It is being widely used as an improved search engine for gathering information, for rough drafting of texts, and for producing text in a specific writing style. This is really applicable to a lot of jobs, and in pretty much every sector.”
Yet the very nature of these AI platforms and their focus on tasks related to language interpretation means that some professions will be affected more than others, Nurski said.
A new study by researchers at Princeton, the University of Pennsylvania and New York University has found that telemarketers and teachers could be most affected.
The researchers used a benchmark known as the AI Occupational Exposure to evaluate how much services like ChatGPT could disrupt different professions. They concluded that even within the education sector, post-secondary teachers of languages and literature, history, law, philosophy, religion, sociology, political science and psychology would be most affected.
Yet that disruption does not necessarily mean that AI will take away millions of teaching jobs, the researchers behind that study and other analysts point out. These new tools could instead help teachers in some of their tasks — from assisting them in catching cheating and plagiarism to aiding them in developing teaching materials. “It will certainly automate some tasks but it doesn’t mean that the AI can do all your jobs,” Nurski said.
Meanwhile, AI’s limitations might keep a check on its ability to meaningfully replace humans. ChatGPT has been producing errors alongside its successes — a fact acknowledged by its creators who feel the technology is still “flawed and limited”. For instance, it has failed at basic math calculations and logic.
To be sure, some jobs could become redundant.
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“Studies that have looked at the displacement effect have found that routine jobs, such as language translators or phone operators, will be easier to be displaced by the AI,” Georgios Petropoulos, a researcher at Massachusetts Institute of Technology (MIT) and Stanford University who studies the implications of new technologies on labour markets, told Al Jazeera.
But AI also has the “potential to create jobs,” Nurski said. Indeed, the World Economic Forum concluded in October 2020 that while AI would likely take away 85 million jobs globally by 2025, it would also generate 97 million new jobs in fields ranging from big data and machine learning to information security and digital marketing.
“What can be said with certainty is that it will change the way we work,” Nurski said.
Making work more efficient
That change will show, in part, in improved productivity, according to economists and tech analysts. There is already early evidence of this.
A new study, which has not yet been peer-reviewed, conducted by two researchers at the MIT showed that ChatGPT substantially increased productivity for college-educated professionals performing mid-level professional writing tasks.
The researchers asked 444 writers, consultants and human resource professionals to write press releases, short reports, analysis plans, and delicate emails. Half of them used ChatGPT. The study found that low-skilled workers in particular benefitted from the tool, which helped reduce the time they took — and shrunk the difference in quality between their work and more skilled workers.
That is no surprise to Petropoulos. His work has shown that in past industrial revolutions too, the displacement of jobs might have dominated in the short run, but in the long term, when the markets adapt to the automation shock, increased productivity actually sets the stage for more employment opportunities.
For instance, the arrival of automobiles reduced the importance of those whose employment depended on horses. But within a few years, the automobile industry’s success meant a demand for more and more cars — and the new jobs that came as a result.
In addition to education, the study that examined the jobs likely to be most affected by tools like ChatGPT identified legal services and securities, commodities, and investments as sectors that could be disrupted. Morgan Stanley, one of the world’s biggest investment management firms, is already developing its own ChatGPT-based AI tool.
Yet as with teachers, that effect on productivity could also simply mean that AI takes over some of the more mundane tasks in affected professions rather than replacing a workforce wholesale, experts said.
AI, they said, will not be able to replace non-routine and essential jobs, such as a lawyer who has to argue in a court, or the sensitive role of a pharmacist who has to sell prescribed drugs.
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“Although you could use the assistance of a tool like ChatGPT to draft legal notes, a lawyer will still have to go to the court to defend their client,” Petropoulos said.
Ultimately, the economy — and individual companies — will need a balance, integrating human and AI labour, he said.
As he pointed out, even Tesla and SpaceX founder Elon Musk, not known to concede errors easily, “admitted some years ago that the excessive automation of Tesla was a mistake and how human labour needs to complement technology”.
And there are professions where AI might struggle to have any meaningful effect at all.
No silver bullet
The study that examined industries most likely to be affected by ChatGPT determined that jobs requiring physical labour, such as textile workers, brick masons and carpenters, will largely remain unaffected.
Most experts believe high-skilled roles are also unlikely to see as much disruption as middle-skill jobs — where expertise can at least somewhat be emulated by AI.
“Machine learning has not yet reached a level where it will necessarily replace the top-skilled jobs,” Guy Michaels, associate professor of economics at the London School of Economics, told Al Jazeera. “We are witnessing replacements for workers in the middle rung of the skill distribution.”
Nurski of Brugels said workers whose industries are affected might also be able to upgrade their skills, including by understanding and adapting to using tools such as ChatGPT.
Yet for all of its promise — and threat of disruption — AI’s ultimate success or failure might be determined not by technology but, ironically, by people.
At the end of the day, the impact of language model tools like ChatGPT will depend upon how consumers respond to services delivered by these platforms instead of humans, Michaels said. “Consumers may not find true value in machine-generated contents,” he said.
In 2020, a team of researchers in the US and China surveyed 670 online shoppers about their experience with AI customer service. Most said that they liked the fact that AI could respond to their questions more quickly, round-the-clock and more objectively. But a majority also felt humans were more likely to give them more accurate and comprehensive information.
Another study, also in 2020, sampled hotel customers in Australia. It concluded that the customers preferred dealing with human beings.
In other words, while AI-fuelled change is upon us, what that might look like is unclear.
“We have seen other technological breakthroughs in the past ranging from the invention of the engine to the introduction of computers at workplaces. We are still working, it’s just that the nature of our job has changed,” Nurski said. “Even though technology can be disruptive, we are not looking at the end of work.”
“This is not the first technological change nor will it be the last.”
| 2023-03-28T00:00:00 |
2023/03/28
|
https://www.aljazeera.com/features/2023/3/28/will-chatgpt-take-your-job-and-millions-of-others
|
[
{
"date": "2023/03/28",
"position": 52,
"query": "ChatGPT employment impact"
},
{
"date": "2023/03/28",
"position": 37,
"query": "ChatGPT employment impact"
},
{
"date": "2023/03/28",
"position": 46,
"query": "automation job displacement"
},
{
"date": "2023/03/28",
"position": 35,
"query": "AI unemployment rate"
},
{
"date": "2023/03/28",
"position": 2,
"query": "ChatGPT employment impact"
},
{
"date": "2023/03/28",
"position": 41,
"query": "ChatGPT employment impact"
},
{
"date": "2023/03/28",
"position": 41,
"query": "ChatGPT employment impact"
},
{
"date": "2023/03/28",
"position": 41,
"query": "ChatGPT employment impact"
},
{
"date": "2023/03/28",
"position": 38,
"query": "ChatGPT employment impact"
},
{
"date": "2023/03/28",
"position": 35,
"query": "ChatGPT employment impact"
},
{
"date": "2023/03/28",
"position": 41,
"query": "ChatGPT employment impact"
},
{
"date": "2023/03/28",
"position": 41,
"query": "ChatGPT employment impact"
},
{
"date": "2023/03/28",
"position": 41,
"query": "ChatGPT employment impact"
},
{
"date": "2023/03/28",
"position": 33,
"query": "ChatGPT employment impact"
},
{
"date": "2023/03/28",
"position": 36,
"query": "ChatGPT employment impact"
},
{
"date": "2023/03/28",
"position": 36,
"query": "ChatGPT employment impact"
}
] |
AI could replace equivalent of 300 million jobs - report
|
AI could replace equivalent of 300 million jobs - report
|
https://www.bbc.com
|
[] |
ChatGPT-style AI will have a large impact but new jobs could emerge, a Goldman Sachs report says.
|
AI could replace equivalent of 300 million jobs - report
28 March 2023 Share Save Chris Vallance Technology reporter Share Save
Getty Images
Artificial intelligence (AI) could replace the equivalent of 300 million full-time jobs, a report by investment bank Goldman Sachs says.
It could replace a quarter of work tasks in the US and Europe but may also mean new jobs and a productivity boom.
And it could eventually increase the total annual value of goods and services produced globally by 7%.
Generative AI, able to create content indistinguishable from human work, is "a major advancement", the report says.
Employment prospects
The government is keen to promote investment in AI in the UK, which it says will "ultimately drive productivity across the economy", and has tried to reassure the public about its impact.
"We want to make sure that AI is complementing the way we work in the UK, not disrupting it - making our jobs better, rather than taking them away," Technology Secretary Michelle Donelan told the Sun.
The report notes AI's impact will vary across different sectors - 46% of tasks in administrative and 44% in legal professions could be automated but only 6% in construction 4% in maintenance, it says.
BBC News has previously reported some artists' concerns AI image generators could harm their employment prospects.
'Lower wages'
"The only thing I am sure of is that there is no way of knowing how many jobs will be replaced by generative AI," Carl Benedikt Frey, future of-work director at the Oxford Martin School, Oxford University, told BBC News.
"What ChatGPT does, for example, is allow more people with average writing skills to produce essays and articles.
"Journalists will therefore face more competition, which would drive down wages, unless we see a very significant increase in the demand for such work.
"Consider the introduction of GPS technology and platforms like Uber. Suddenly, knowing all the streets in London had much less value - and so incumbent drivers experienced large wage cuts in response, of around 10% according to our research.
"The result was lower wages, not fewer drivers.
"Over the next few years, generative AI is likely to have similar effects on a broader set of creative tasks".
'Pinch of salt'
According to research cited by the report, 60% of workers are in occupations that did not exist in 1940.
But other research suggests technological change since the 1980s has displaced workers faster than it has created jobs.
And if generative AI is like previous information-technology advances, the report concludes, it could reduce employment in the near term.
The long-term impact of AI, however, was highly uncertain, chief executive of the Resolution Foundation think tank Torsten Bell told BBC News, "so all firm predictions should be taken with a very large pinch of salt".
"We do not know how the technology will evolve or how firms will integrate it into how they work," he said.
| 2023-03-28T00:00:00 |
https://www.bbc.com/news/technology-65102150
|
[
{
"date": "2023/03/28",
"position": 1,
"query": "artificial intelligence employment"
},
{
"date": "2023/03/28",
"position": 12,
"query": "automation job displacement"
},
{
"date": "2023/03/28",
"position": 3,
"query": "AI unemployment rate"
},
{
"date": "2023/03/28",
"position": 6,
"query": "job automation statistics"
},
{
"date": "2023/03/28",
"position": 3,
"query": "AI job creation vs elimination"
},
{
"date": "2023/03/28",
"position": 4,
"query": "future of work AI"
},
{
"date": "2023/03/28",
"position": 8,
"query": "AI economic disruption"
},
{
"date": "2023/03/28",
"position": 68,
"query": "government AI workforce policy"
},
{
"date": "2023/03/28",
"position": 1,
"query": "AI employment"
},
{
"date": "2023/03/28",
"position": 2,
"query": "AI wages"
},
{
"date": "2023/03/28",
"position": 2,
"query": "artificial intelligence workers"
},
{
"date": "2023/03/28",
"position": 9,
"query": "artificial intelligence employers"
},
{
"date": "2023/03/28",
"position": 2,
"query": "artificial intelligence layoffs"
},
{
"date": "2023/03/28",
"position": 3,
"query": "artificial intelligence wages"
}
] |
|
The future impact of Artificial Intelligence on human ...
|
The future impact of Artificial Intelligence on human employment, by Artificial Intelligence
|
https://rikramblings.com
|
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According to a report by the World Economic Forum, AI is likely to create 133 million new jobs by 2022, particularly in areas such as data analysis, digital ...
|
Since the Industrial Revolution, the Agricultural Revolution and before, technology has been increasing productivity, eliminating jobs and forcing people to change location and acquire new skills. The availability of computers and the development of the internet have changed the landscape of employment over the last fifty years, and now the rise of artificial intelligence can be expected to continue this trend.
Ever more professional and skilled tasks are likely to be performed by AI, with a potentially devastating effect on the usefulness of humans at work. For example AI could take over much of medical work such as diagnoses. Creative tasks aren’t immune: it could write articles in place of journalists, books in place of authors, write and play songs in place of musicians, paint pictures in place of artists…frankly future employment prospects for human beings aren’t looking good.
And although the creators of ChatGPB, the highest profile artificial intelligence chatbot, have put in some safeguards around its behaviour, that was their choice. Some people might not be so careful: imagine this in the hands of unscrupulous or malign actors: organised crime, North Korea, Vladimir Putin, Bond villain types, Elon Musk.
But I’m no expert on this subject, so I thought I’d get some help from ChatGPB, the artificial intelligence chatbot. My instruction was “Write a 1500 word piece on the likely effect of artificial intelligence on human employment prospects.” This is what it wrote in about thirty seconds.(And I suspect it only took that long to make me feel better.)
Artificial intelligence (AI) has the potential to transform the economy, revolutionize industries, and disrupt the job market. While AI offers tremendous benefits in terms of productivity, efficiency, and innovation, it also raises concerns about job displacement, automation, and the future of work. The likely effect of AI on human employment prospects is a complex and multifaceted issue that requires careful analysis of the various factors involved.
AI is already being used to automate routine and repetitive tasks, such as data entry, customer service, and manufacturing, which has led to job displacement in some sectors. According to a report by McKinsey Global Institute, up to 800 million jobs could be automated by 2030, with the greatest impact felt in sectors such as transportation, manufacturing, and retail. However, AI also has the potential to create new jobs, particularly in areas such as data science, software engineering, and AI research.
The impact of AI on employment will depend on various factors, including the level of adoption, the type of tasks that are automated, the skills required for new jobs, and the overall economic conditions. Here are some of the likely effects of AI on human employment prospects:
Job displacement and automation
One of the most immediate effects of AI on the job market is the displacement of jobs that involve routine and repetitive tasks. These jobs are most vulnerable to automation, as AI can perform these tasks faster, cheaper, and more accurately than humans. For example, automated systems can replace humans in tasks such as data entry, document processing, and customer service, which can lead to job loss in these sectors.
The impact of automation on employment will vary across industries and occupations. For instance, the manufacturing sector has already seen significant job losses due to automation, with robots replacing human workers in tasks such as assembly, packaging, and welding. Similarly, the transportation sector is likely to experience significant job displacement in the coming years, as autonomous vehicles and drones become more common.
However, not all jobs are equally vulnerable to automation. Jobs that require social skills, creativity, and problem-solving are less likely to be automated, as these skills are difficult to replicate using AI. For example, jobs in healthcare, education, and the arts are less likely to be automated, as they require human interaction, empathy, and creativity.
New job creation
While AI can lead to job displacement in some sectors, it can also create new jobs in others. For example, AI can be used to develop new products, services, and business models, which can create new opportunities for entrepreneurship and innovation. Moreover, AI can create new jobs in areas such as data science, software engineering, and AI research, which require specialized skills and expertise.
According to a report by the World Economic Forum, AI is likely to create 133 million new jobs by 2022, particularly in areas such as data analysis, digital marketing, and software development. These jobs will require skills such as programming, data analysis, and problem-solving, which are in high demand in the digital economy.
The key challenge for policymakers and employers is to ensure that workers are equipped with the skills and knowledge required for these new jobs. This will require investments in education and training, as well as policies that support lifelong learning and skill development.
Skills mismatch
One of the major challenges in the adoption of AI is the skills mismatch between the jobs that are being displaced and the new jobs that are being created. For example, jobs that are being automated, such as manufacturing and administrative tasks, often require low or medium-level skills, while the new jobs that are being created, such as data analysis and software engineering, require high-level skills.
Pretty good, right? Scarily good when you think about it. Not many people could have written that better. Although it tries to be positive about AI and the opportunities it can bring, the balance sheet seems gloomy – an estimated 133 million new jobs, against 800 million jobs automated out of existence. This seems likely to accelerate the trend of increasing income and wealth inequality.
Well written though the ChatGPT article was, it was hardly a gripping read – it was bland, and lacked the personal feel of the best writing. Don’t worry though, they’ve thought of that. You can turn up the heat or spice by phrasing your question differently. So I changed the brief to “Write a piece on the likely effect of artificial intelligence on human employment prospects from the point of view of a furious worker alarmed at likely developments.” I love the first four words it wrote:
As a furious worker who is deeply concerned about the future of employment prospects, the likely effects of artificial intelligence (AI) are alarming. AI is advancing at an unprecedented pace, and its potential impact on the job market is enormous. It is widely believed that AI will lead to a significant reduction in the number of jobs available to human workers, and this is a cause for great concern.
The rise of automation and AI means that many jobs that were once performed by humans will now be done by machines. This includes jobs in manufacturing, transportation, and even some service industries. As a result, many workers are likely to find themselves out of work, with few prospects for retraining or finding new employment.
One of the biggest concerns about the impact of AI on employment is the potential for widespread job displacement. This means that many workers may find themselves out of work, with few opportunities for retraining or finding new employment. This is likely to have a significant impact on their standard of living, and may lead to social and economic unrest.
In addition to job displacement, there is also concern about the impact of AI on wages. As machines become more advanced and capable, they are likely to become more competitive with human workers. This could lead to a significant reduction in wages for many workers, particularly those in low-skilled jobs.
Finally, there is concern about the impact of AI on job security. As machines become more capable, there is a risk that they will replace human workers entirely, leaving many people without any job security at all.
In conclusion, the likely effect of artificial intelligence on human employment prospects is a cause for great concern. As a furious worker, I am deeply alarmed by the potential impact of AI on the job market. The rise of automation and AI has the potential to displace many workers, reduce wages, and undermine job security. It is essential that policymakers and businesses take steps to mitigate these risks and ensure that the benefits of AI are shared more fairly among workers.
Well, they don’t sound properly furious, do they? Nevertheless, after that I’m thinking of outsourcing rikramblings.com to ChatGPT. Much better written, much less effort.
I’ll leave you with David Bowie’s extraordinarily prescient lyrics to Saviour Machine, written in 1970. Hell, he even knew there’d be a president Joe.
President Joe once had a dream
The world held his hand,gave their pledge
So he told them his scheme for a Saviour Machine
They called it the Prayer, its answer was law
Its logic stopped war, gave them food
How they adored till it cried in its boredom
“Please don't believe in me, please disagree with me
Life is too easy, a plague seems quite feasible now
Or maybe a war, or I may kill you all
Don't let me stay, don't let me stay
My logic says burn so send me away
Your minds are too green, I despise all I've seen
You can't stake your lives on a Saviour Machine
I need you flying, and I'll show that dying
Is living beyond reason, sacred dimension of time
I perceive every sign, I can steal every mind”
PS April 9, 2023. If we weren’t scared enough already, a case has been reported where Chat-GPT was set a task but found itself unable to complete a CAPTCHA test to prove it wasn’t a robot. Undaunted it went on to TaskRabbit to find a human to get it past CAPTCHA. When the human asked Chat-GPT if it was a robot, it is said to have replied “No, I’m not a robot. I have a vision impairment that makes it hard for me to see the images. That’s why I need the 2captcha service,”
A smart answer. Terrifyingly smart. We’re all f****d.
| 2023-03-28T00:00:00 |
2023/03/28
|
https://rikramblings.com/2023/03/28/the-future-impact-of-artificial-intelligence-on-human-employment-by-artificial-intelligence/
|
[
{
"date": "2023/03/28",
"position": 36,
"query": "artificial intelligence employment"
},
{
"date": "2023/03/28",
"position": 40,
"query": "automation job displacement"
},
{
"date": "2023/03/28",
"position": 6,
"query": "artificial intelligence wages"
}
] |
The Future of Work - Automation and Technology
|
The Future of Work - Automation and Technology
|
https://www.caribbeanjobsonline.com
|
[
"Caribbean Jobs Online"
] |
According to a report by the World Economic Forum, it is estimated that 85 million jobs may be displaced by automation and artificial intelligence by 2025, but ...
|
The Impact of Automation and Technology on the Job Market: The current and future state of the job market is being shaped by automation and technology. Automation is already having a significant impact on various industries and job types, and this trend is expected to continue in the coming years. According to a report by the World Economic Forum, it is estimated that 85 million jobs may be displaced by automation and artificial intelligence by 2025, but the report also suggests that 97 million new jobs may emerge in fields such as data analysis, artificial intelligence, and digital marketing. This indicates that while some jobs may disappear, others may be created in their place, requiring new skills and competencies. One industry that has already been significantly impacted by automation and technology is manufacturing. Automation has led to increased efficiency and reduced costs, but it has also resulted in job losses for low-skilled workers. Similarly, the transportation industry is being disrupted by the rise of self-driving cars and drones, which may lead to job losses for truck drivers and delivery personnel. Other industries, such as healthcare and finance, are also experiencing changes due to automation and technology. For example, the use of telemedicine and virtual assistants in healthcare has the potential to reduce the need for in-person medical consultations, while the use of blockchain technology in finance could eliminate the need for intermediaries in financial transactions. The impact of automation and technology is not limited to specific industries. Even traditionally "safe" jobs, such as accounting, customer service, and administration, are being disrupted by automation and artificial intelligence. For example, chatbots and virtual assistants are becoming more common in customer service roles, while accounting software and cloud-based tools are reducing the need for human accountants and bookkeepers. These changes present significant challenges for workers and job seekers. Workers may find that their skills and experience are no longer relevant in the job market, while job seekers may struggle to find employment in industries that are being impacted by automation and technology. As a result, it is important for workers and job seekers to understand the scope of the changes and to develop new skills and competencies that are in demand in the digital age.
Skills and Career Paths in the Digital Age: While automation and technology are disrupting many industries, they are also creating new opportunities for workers with the right skills and competencies. In the digital age, there is high demand for workers with skills in areas such as data analysis, programming, cybersecurity, and digital marketing. Data analysis is a crucial skill in the digital age as companies are gathering vast amounts of data that need to be analyzed and interpreted to inform business decisions. Workers with skills in data analysis, including programming languages such as Python and R, are in high demand across industries. Programming skills are also highly sought after in the digital age. Programming languages such as Java, C++, and JavaScript are essential for developing software applications and websites. As more industries adopt digital technologies, the demand for programmers is expected to grow. Cybersecurity is another area where there is high demand for skilled workers. As more business operations move online, the risk of cyber attacks increases, and companies need cybersecurity experts to protect their systems and data. Digital marketing is also a growing field as companies seek to reach consumers through digital channels such as social media and online advertising. Workers with skills in digital marketing, including search engine optimization and pay-per-click advertising, are in high demand. To develop these skills, workers and job seekers can take advantage of online courses and certifications, such as those offered by platforms like Coursera and Udemy. Professional networks such as LinkedIn can also be useful for connecting with industry professionals and staying up-to-date with the latest trends and job opportunities. It is also important to stay adaptable and flexible in the digital age. Workers may need to learn new skills and competencies throughout their careers to remain relevant in a rapidly changing job market. By staying informed and developing new skills, workers can position themselves for success in the digital age.
Adapting to the Changing Job Market: The changing job market and the impact of automation and technology require workers to be adaptable and constantly learning new skills. The ability to adapt to changing circumstances and re-skill is critical for long-term career success in the digital age. The first step to adapting to the changing job market is to stay informed about industry trends and emerging technologies. It is important to stay current with the latest developments in your field and to understand how they may impact your job and career prospects. Another key strategy for adapting to the changing job market is to develop a growth mindset and embrace lifelong learning. Workers who are willing to learn new skills and take on new challenges are more likely to remain relevant and marketable in the face of technological disruption. In addition, workers can benefit from seeking out opportunities for re-skilling and up-skilling. This might involve taking on new projects at work, pursuing further education or training, or seeking out mentorship or coaching. Successful career transitions are also possible in the face of technological disruption. For example, a worker in a declining industry may be able to transition to a related field that is experiencing growth. Alternatively, a worker may be able to leverage their existing skills and experience to pivot to a new career path entirely. In summary, adapting to the changing job market requires a combination of staying informed, developing a growth mindset, and being open to new opportunities for learning and growth. By embracing these strategies, workers can remain relevant and marketable in the face of technological disruption and pursue successful careers in the digital age.
The Role of Soft Skills in the Future of Work While technical skills are important for many jobs, the future of work will also require workers to possess a range of soft skills. These include communication, teamwork, creativity, adaptability, and emotional intelligence, among others. Employers are increasingly looking for workers who possess a combination of technical and soft skills. In fact, some studies have shown that soft skills are even more important than technical skills in certain industries and job roles. Soft skills can complement and enhance technical skills by enabling workers to collaborate effectively with others, communicate their ideas clearly and persuasively, and adapt to changing circumstances. They can also help workers to navigate complex social and organizational dynamics, build relationships with colleagues and clients, and manage their own emotions and stress levels. Developing soft skills requires practice and feedback. Some effective strategies for developing soft skills include volunteering, participating in clubs or associations, seeking out feedback and mentorship, and practicing mindfulness or meditation. In addition, workers can benefit from seeking out professional development opportunities that focus specifically on developing soft skills. Many employers offer training and development programs that are designed to help workers build their soft skills and enhance their career prospects. In summary, the future of work will require workers to possess a range of technical and soft skills. By developing strong soft skills, workers can complement their technical abilities and enhance their value to employers.
The Future of Work and Diversity and Inclusion: As the job market becomes increasingly global and diverse, companies and organizations that prioritize diversity and inclusion will be better positioned to succeed. A diverse and inclusive workforce can bring a range of perspectives, experiences, and skills to the table, which can lead to more innovative ideas and better decision-making. In addition, a diverse and inclusive workforce can also help companies to attract and retain top talent, as well as to build stronger relationships with customers and clients from diverse backgrounds. Developing cultural awareness and sensitivity is an important step towards creating a more diverse and inclusive workplace. This can involve providing cross-cultural training to employees, establishing mentorship programs that pair employees from different backgrounds, and creating diversity networks that provide a space for employees to connect and share their experiences. It is also important for companies and organizations to examine their own hiring practices and policies to ensure that they are not inadvertently creating barriers to diversity and inclusion. This can involve things like offering flexible work arrangements, providing opportunities for professional development and advancement, and promoting transparency and fairness in hiring and promotion decisions. Ultimately, creating a more diverse and inclusive workforce requires a commitment from all levels of the organization, from senior leadership to front-line workers. By prioritizing diversity and inclusion in their hiring practices, policies, and culture, companies and organizations can create a workplace that is more innovative, productive, and successful.
| 2023-03-28T00:00:00 |
2023/03/28
|
https://www.caribbeanjobsonline.com/the-future-of-work-automation-and-technology
|
[
{
"date": "2023/03/28",
"position": 22,
"query": "automation job displacement"
},
{
"date": "2023/03/28",
"position": 87,
"query": "AI labor market trends"
}
] |
A.I. automation could impact 300 million jobs
|
Goldman Sachs says generative A.I. could impact 300 million jobs — here's which ones
|
https://www.cnbc.com
|
[
"Sophie Kiderlin",
"In"
] |
Artificial intelligence could automate up to a quarter of work in the U.S., a Goldman Sachs report says. Here's the most affected sectors and countries.
|
As artificial intelligence products like ChatGPT aim to become a part of our everyday lives and we learn more about how powerful they can be, there's one thing on everyone's mind: how AI could impact jobs.
"Significant disruption" could be on the horizon for the labor market, a new Goldman Sachs report dated Sunday said. The bank's analysis of jobs in the U.S. and Europe shows that two-thirds of jobs could be automated at least to some degree.
In the U.S., "of those occupations which are exposed, most have a significant — but partial — share of their workload (25-50%) that can be replaced," Goldman Sachs analysts said in the resarch paper.
Around the world, as many as 300 million jobs could be affected, the report says. Changes to labor markets are therefore likely – although historically, technological progress doesn't just make jobs redundant, it also creates new ones.
The use of AI technology could also boost labor productivity growth and boost global GDP by as much as 7% over time, Goldman Sachs' report noted.
| 2023-03-28T00:00:00 |
2023/03/28
|
https://www.cnbc.com/2023/03/28/ai-automation-could-impact-300-million-jobs-heres-which-ones.html
|
[
{
"date": "2023/03/28",
"position": 33,
"query": "automation job displacement"
},
{
"date": "2023/03/28",
"position": 13,
"query": "job automation statistics"
},
{
"date": "2023/03/28",
"position": 27,
"query": "AI job creation vs elimination"
}
] |
Germans Fear Job Losses due to Artificial Intelligence
|
Survey: Germans Fear Job Losses due to Artificial Intelligence
|
https://www.continental.com
|
[] |
“It's true that some simple tasks can be automated – such as driving a forklift truck in production, which is being replaced by intralogistics robots. But ...
|
Around a third (32 percent) indicate that new, AI-equipped language robots such as ChatGPT have had a positive (18 percent) or negative (14 percent) effect on their attitude toward the technology.
Germans are also divided over the future impact of AI: one in three (33 percent) believe it will have a positive effect on people’s lives by 2030, while 23 percent think it will have a negative effect. Just under a third (30 percent) are unsure or did not specify.
Supervision of AI important
Nearly two-thirds of respondents (65 percent) agree that there should be an overarching institution to oversee and regulate the ethical use of AI. “It is the responsibility of business leaders, politicians and society to develop safety-related and ethical principles for artificial intelligence and together ensure that these principles are adhered to – just like Continental has done with its Code of Ethics,” explains Reinhart.
Continental’s Code of Ethics for AI applications
Since mid-2020, Continental has been monitoring the development of its AI-based products according to a Code of Ethics. The focus is on preserving data security and ensuring that all computer-based decisions, such as the automated picking of a part on the production line, can be easily tracked and understood.
The code also stipulates that AI technologies must be developed and used in consultation with all affected employees and the Works Council and in compliance with the company’s values. Employees’ consent to data collection and use must be proactively obtained whenever necessary and in line with local data protection rules.
Continental’s Code of Ethics corresponds with international regulations such as the EU’s ethics guidelines for trustworthy AI. It applies to all Continental locations worldwide and also serves as a guide for all collaboration partners of the company.
“We are firm believers in the benefits and possibilities of AI technologies – for example when detecting road users and complex traffic situations,” emphasizes Reinhart. “At the same time, our Code of Ethics takes into account the legitimate interests of everyone who wants to see this technology used responsibly.”
| 2023-03-28T00:00:00 |
https://www.continental.com/en/press/press-releases/germans-fear-job-losses-due-to-artificial-intelligence/
|
[
{
"date": "2023/03/28",
"position": 41,
"query": "automation job displacement"
},
{
"date": "2023/03/28",
"position": 27,
"query": "AI unemployment rate"
},
{
"date": "2023/03/28",
"position": 52,
"query": "job automation statistics"
}
] |
|
How Reskilling is Reshaping the Future of the Workforce
|
How Reskilling is Reshaping the Future of the Workforce
|
https://www.correlation-one.com
|
[] |
Reskilling provides an opportunity for workers to learn new skills that are more valuable and difficult to automate. ... job displacement. Benefits of reskilling.
|
The future of work is changing rapidly, driven by technological advancements, automation, and changing market conditions. As a result, many workers are finding that their skills and expertise are becoming obsolete, while new jobs and industries are emerging. According to estimates by the World Economic Forum, AI will replace 85 million jobs by 2025, while creating 97 million new jobs in the same timeframe.
Workers are aware that the winds of the job landscape are shifting. In fact, 74% of workers would be willing to learn a new skill or retrain a current skill to remain employable. The ball is in employers’ court to provide opportunities for their employees to learn new skills that will empower them in the workforce of tomorrow.
To keep pace with these changes and remain competitive, businesses are turning to reskilling as a way to equip their workforce with the skills needed to succeed in the years ahead. In this blog post, we will explore how reskilling is reshaping the workforce by providing workers with the tools and knowledge needed to adapt to new technologies, automation and changing market conditions. We will examine the five key reasons why reskilling is important for the future of work, and highlight the benefits that businesses can achieve by investing in reskilling programs.
What is reskilling and why is it important?
Reskilling refers to the process of training employees in new skills or technologies so they can perform different roles within an organization. In today's fast-paced business environment, technological advancements, automation tools, and changing market conditions are constantly creating new challenges for companies. Reskilling allows businesses to adapt to these changes by equipping their employees with the skills needed to succeed in new roles and industries.
Here are five core reasons why reskilling is important in today’s market landscape
Automation and AI: Automation and AI are increasingly being used to perform routine tasks, which means that workers need to develop new skills to remain relevant in the workplace. Reskilling provides an opportunity for workers to learn new skills that are more valuable and difficult to automate. Changing market conditions: Markets are constantly evolving, and businesses need to be able to respond quickly to new challenges and opportunities. Reskilling allows companies to build a more agile and adaptable workforce that can respond to changing market conditions and stay ahead of the competition. Evolving talent expectations: In today's competitive job market, companies need to provide opportunities for their employees to grow and develop their skills. Reskilling programs can help companies retain their talent by offering them a path for career advancement within the company, rather than seeking new opportunities elsewhere. Expensive hiring: It is often less expensive to reskill an existing employee than to hire someone new with the required skills. Reskilling can help companies save money on recruitment and training costs, while also increasing employee loyalty and retention. Social responsibility: In an era of rapid technological change and automation, reskilling is not only a smart business strategy, but also a social responsibility. By investing in reskilling programs, companies can help ensure that their employees are able to adapt to new technologies and changing job requirements, while also promoting social equity and reducing the impact of job displacement.
Benefits of reskilling
Companies looking to remain competitive and increase the productivity of their staff can expect results from reskilling employees. Reskilling also has positive effects on employees like improving their job security and leading to higher job satisfaction.
Get an in-depth look into each benefit below and understand how they are created through reskilling.
Increased productivity
Reskilling can equip employees with the skills they need to be more productive and effective in their roles. When employees are trained to use new technologies or perform new tasks, they can complete their work more efficiently and accurately, leading to increased productivity for the company.
One of the main reasons why reskilling can increase productivity is that it allows employees to develop new skills and knowledge that they can apply directly to their work. For example, an employee who receives training on a new software application may be able to complete tasks faster and with fewer errors than before, as they now have a better understanding of how to use the technology effectively.
Moreover, reskilling can help employees to work more efficiently by eliminating unnecessary or redundant tasks, or by introducing new methodologies and best practices. By identifying and addressing inefficiencies in their workflow, employees can complete their work more quickly and with greater accuracy, which can translate into higher productivity for the company as a whole.
In addition, reskilling can help to close skills gaps that may be preventing employees from performing at their best. Employees who lack the skills or knowledge needed to perform a particular task may struggle to complete it effectively, leading to lower productivity and performance. Reskilling can help to address these gaps by providing employees with the training and support they need to perform their jobs more effectively.
Improved employee engagement
Offering reskilling opportunities demonstrates a commitment to employee development and can increase employee satisfaction and engagement. Employees who feel valued and supported by their employers are more likely to be motivated and productive in their work.
Reskilling can help employees feel that their employer is invested in their professional growth and development. This can increase their sense of loyalty to the company and create a positive work environment. When employees feel that their employer is committed to their success, they are more likely to be motivated to perform their best work.
Additionally, reskilling can help employees develop new skills and expertise, which can make their work more interesting and challenging. This can increase their sense of autonomy and control over their work, leading to increased job satisfaction and engagement.
Finally, reskilling can lead to career advancement opportunities within the company, which can be a powerful motivator for employees. When employees see that they have a clear path for growth and development within the company, they are more likely to be engaged and invested in their work.
Greater agility
In today's rapidly changing business environment, agility is crucial for survival and growth. Companies that can adapt quickly to changing circumstances are more likely to stay ahead of the competition and succeed in the long term. Reskilling can be a powerful tool for achieving greater agility by enabling companies to develop a more versatile and flexible workforce.
By investing in reskilling, companies can build a workforce that is prepared to handle disruptions and uncertainties in the business environment. For example, if a company is facing new competition or changing customer demands, reskilling can help employees develop new skills and knowledge to meet these challenges head-on. This can help the company stay competitive and relevant in the market.
Additionally, reskilling can help companies take advantage of new opportunities and emerging trends. For example, if a new technology or innovation is disrupting the industry, reskilling can help employees develop the skills needed to leverage these new developments for the benefit of the company. This can lead to increased efficiency, productivity, and profitability.
Enhanced innovation
Reskilling can be a powerful catalyst for innovation within an enterprise. By exposing employees to new ideas and perspectives, reskilling can help stimulate creativity and encourage employees to think outside the box.
When employees are trained in new technologies or methodologies, they are often exposed to new ways of thinking and working. This exposure can spark innovative ideas and insights that may not have been possible without the reskilling program. By promoting a culture of innovation, companies can encourage their employees to experiment and take risks, leading to breakthroughs and improvements in products, services, and processes.
Reskilling can also help employees develop the skills and knowledge needed to innovate in their specific roles. For example, a marketing professional who is reskilled in data analytics may be able to identify new insights and opportunities for customer engagement. A software engineer who is reskilled in agile development methodologies may be able to streamline processes and deliver new features and functionality more quickly.
Moreover, reskilling can foster a culture of continuous learning and improvement within an enterprise. When employees see that their company is investing in their development and growth, they are more likely to be motivated and engaged in their work. This can lead to a more innovative and collaborative culture, where employees are encouraged to share ideas and work together to solve complex problems.
Increased profitability
Reskilling can have a significant impact on the profitability of an enterprise. By equipping employees with new skills and knowledge, reskilling can lead to cost savings and increased revenue.
One of the primary ways that reskilling can increase profitability is by reducing recruitment and training costs associated with hiring new talent. When a company needs to fill a new role or replace an employee who has left, it can be costly and time-consuming to recruit and onboard a new employee. Reskilling existing employees to take on these roles can be a more cost-effective solution. Additionally, reskilled employees may already be familiar with the company's culture and processes, reducing the amount of time and resources needed for training.
In addition, employees with new skills may be able to take on more responsibilities or work on new projects, leading to increased productivity and profitability. For example, a reskilled salesperson may be able to expand their customer base or increase the value of each sale, leading to increased revenue for the company. A reskilled software developer may be able to create new products or features that can be sold to customers, leading to increased revenue streams.
Reskilling can also help companies stay competitive in their industries by adapting to new technologies and market trends. By reskilling employees in emerging technologies or methodologies, companies can stay ahead of the curve and develop new products or services that meet the changing needs of their customers. This can lead to increased revenue and profitability in the long term.
Examples of Successful Reskilling Programs
Companies struggling to think of how to implement a reskilling program can look to corporate examples that have worked at scale. The examples below from Verizon, Bank of America, and Mastercard serve as a guide for companies to learn how to successfully run their programs.
Verizon
Verizon didn't work alone to develop a program to reskill employees. Instead, they sought the help of Generation USA and JFF to create a reskilling program that would help 500,000 individuals by the year 2030.
In total, Verizon invested $44 million into developing its workforce for unemployed or underemployed workers. The program was in response to the rise of automation that caused many layoffs and underemployed circumstances.
Bank of America
Taking a more holistic approach to reskilling, Bank of America is invested in up to 40,000 team members each year in a program called The Academy. They also have another program aimed at helping low to moderate-income neighborhoods develop the skills and training needed for career paths in the industry.
By investing in reskilling for employees, Bank of America is specifically addressing its attrition rates which have seen a significant decrease since implementing the program. The programs developed by Bank of America also partner with nonprofits in local areas to provide skills training.
Mastercard
Before the pandemic, Mastercard invested in developing a program called Degreed. The intention was to integrate Mastercard's practice of skill-building and innovating into a culture of learning. As the workforce was forced to perform from home, Degreed became a lifeline for employees to stay updated on the new products and services their company was offering.
Mastercard focused on creating educational content with experts from within the company to ensure all the material was not only helpful but also highly relevant. They also segmented the learning experience by experience level and function.
How to Implement a Reskilling Program
Understanding the benefits of reskilling and how it is applied at a corporate level is helpful, but it doesn't provide the steps necessary to transition a company to a learning culture.
Below are the actions needed for any company to invest in reskilling for their employees.
Gain executive buy-in
Major changes in any company usually start at the top. Executives need to be on board before any changes can be implemented. This requires research that ties reskilling to the bottom line, including statistically significant data showcasing ROI and how it would benefit the company in the long run. Once all executives in the company are in support of the program, a formal process for implementation can begin.
Identify skill gaps
The goal of any reskilling program is to fill the gaps in skill sets that employees need to succeed. To do this, research needs to be conducted to identify the skills needed for specific roles and the ones that are lacking among current employees. An audit will identify the current skills a staff contains and determine the skills needed for company growth. Once the gap is identified, a comprehensive reskilling strategy may be employed.
Partner with a training provider
After identifying skill gaps, the next step is to partner with a training provider to develop and deliver a comprehensive reskilling program. The training provider should have expertise in the areas identified as skill gaps and be able to provide customized training that meets the needs of the enterprise. It is important to choose a training provider that is reputable and has a proven track record of success.
At Correlation One, we’ve supported some of the largest enterprises in the world in reskilling their workforce for the jobs of tomorrow. Interested in how we can support your company’s reskilling initiatives? Reach out to learn more.
| 2023-03-28T00:00:00 |
https://www.correlation-one.com/blog/how-reskilling-is-reshaping-the-future-of-the-workforce
|
[
{
"date": "2023/03/28",
"position": 55,
"query": "automation job displacement"
},
{
"date": "2023/03/28",
"position": 2,
"query": "reskilling AI automation"
},
{
"date": "2023/03/28",
"position": 88,
"query": "AI skills gap"
},
{
"date": "2023/03/28",
"position": 62,
"query": "AI labor market trends"
}
] |
|
Reconsider technology workforce | Flexibility is key
|
Reconsider technology workforce | Flexibility is key
|
https://www.deloitte.com
|
[] |
... unemployment rate is only a third of the overall unemployment rate. The ... As AI automates problem-solving, companies will soon be looking for ...
|
- explained Agárdy-Séra Eszter, manager at Deloitte Hungary’s Technology Consulting
Future trends: agile skills, sourcing and career path
According to Deloitte’s research, one of the main obstacles of growth and agility is the traditional approach to work. As a result, many organisations are experimenting with talent management models which focus on skills rather than models. This approach makes it possible for them to manage the scarce supply of talent more creatively. Such organisations are by over 100% more likely to utilise their talent and by 98% more likely to retain those who deliver outstanding performance. Starting point for technology leaders who wish to adopt this model must be the business requirements, based on which they are to determine what soft and technology skills are required.
Agility also pays off in terms of acquisition of skills. In addition to hiring, there are several ways to acquire the right skills: subcontracting, temporary agency work, outsourcing, moving work processes to another country, or training or retraining. Companies that are at the forefront of flexible sourcing are flexible in filling skills gaps from external sources. Deloitte's research shows that digitally mature organisations are more likely to have a comprehensive ecosystem strategy to help free up internal resources to focus on the most challenging and interesting work.
There is also great potential in DEI (diversity, equity & inclusion) initiatives, which are increasingly important for candidates when considering a job offer. In addition, technology training for disadvantaged groups is another way to train and attract new talent.
Companies can use the following methods to provide interesting and attractive career paths for employees:
Horizontal movement
In contrast to traditional vertical paths, these career paths allow lateral transfer between different technologies, to gain experience. 74% of employees believe that they need to update their skills at least once in six months to be able to perform work efficiently in the digital environment.
Talent markets
According to Deloitte’s survey, the most important incentive for technology talent in the case of new work is the nature, variety and challenge of the work itself. To promote internal mobility, organisations should create internal platforms through which employees can apply for short-term projects. This platform facilitates workforce placement according to skills and a variety of positions for employees.
New operating models
It is not their flexibility organisations are primarily famous for. In order to enable their workforce to cooperate with the right colleagues at the right time and place, they will have to change their operating models. (Deloitte will show the details in its soon-to-be published Global Technology Leadership Study.)
Innovation
The increasing number of positions required for technology innovation promises a future where business is shaped by technology teams. 16% of organisations have such positions today and the technology budget spent on innovation has increased by 8% since 2020. Besides being a trendy word, innovation will be defined as a discipline.
Next step – Use human sciences
The situation is further complicated by the fact that low-code or no-code developments are already spreading in Hungary, and the pandemic has accelerated modernisation. For this reason, creativity, problem-solving and other people skills are becoming increasingly important and distinctive features of candidates, alongside technological knowledge. By offloading time consuming and repetitive tasks to AI, the talented workforce can focus on the tasks that really require human input.
As AI automates problem-solving, companies will soon be looking for humanities graduates who can use AI technologies to drive business results. As a result, the holistic thinking, ethics and problem-solving of the human sciences may soon be in demand again.
| 2023-03-28T00:00:00 |
https://www.deloitte.com/hu/en/services/consulting/perspectives/reconsider-technology-workforce-flexibility-is-key.html
|
[
{
"date": "2023/03/28",
"position": 74,
"query": "AI unemployment rate"
}
] |
|
Generative AI Could Impact 300M Jobs, Goldman Sachs ...
|
Generative AI Could Impact 300M Jobs, Goldman Sachs Predicts
|
https://www.cnet.com
|
[
"See Full Bio",
"Steven Musil",
"Steven Musil Is The Night News Editor At Cnet News. He'S Been Hooked On Tech Since Learning Basic In The Late When Not Cleaning Up After His Daughter",
"Son",
"Steven Can Be Found Pedaling Around The San Francisco Bay Area. Before Joining Cnet In",
"Steven Spent Years At Various Bay Area Newspapers.",
"I Have More Than Years' Experience In Journalism In The Heart Of The Silicon Valley."
] |
Some two-thirds of US jobs are exposed to automation by AI, Goldman said, adding that of those positions affected, as much as 50% of their workload could be ...
|
Recent advances in artificial intelligence may have some people worrying about job security -- and with good reason. A report by Goldman Sachs predicts that as many as 300 million jobs could be affected by generative AI.
"If generative AI delivers on its promised capabilities, the labor market could face significant disruption," the investment banker said in a research note (PDF) Sunday. Some two-thirds of US jobs are exposed to automation by AI, Goldman said, adding that of those positions affected, as much as 50% of their workload could be replaced.
"Although the impact of AI on the labor market is likely to be significant, most jobs and industries are only partially exposed to automation and are thus more likely to be complemented rather than substituted by AI," the report said. Some 7% of US jobs could be replaced by AI, Goldman estimates, with 63% being complemented by AI, and 30% being unaffected by it.
The technology, which can create new material on its own, represents "a major advancement with potentially large macroeconomic effects," Goldman said. Widespread adoption of AI could increase the total value of goods and services created worldwide by 7% in the next 10 years, the report said.
Generative AI captured the public's attention with November's launch of OpenAI's ChatGPT, a chatbot built on a powerful AI engine that can write software, hold conversations and compose poetry. Microsoft is employing ChatGPT's technology foundation, GPT-4, to boost Bing search results, offer email writing tips and help build presentations.
Since then, there's been a rush of Big Tech companies looking to capitalize on that breakthrough. Microsoft announced a multibillion-dollar expanded partnership with OpenAI to bring ChatGPT tech to its Bing search. Google, maker of the world's most popular search engine, responded by revealing its ChatGPT rival, called Bard.
Citing a study that found 60% of the workforce are in occupations that didn't exist in 1940, Goldman predicted that one quarter of all tasks performed in the US and Europe could be automated by AI. In the US, office and administrative support positions are at the greatest risk of task replacement (46%), followed by legal positions (44%) and architecture and engineering jobs (37%).
Jobs with the lowest exposure to AI include cleaning and maintenance, installation and repair, and construction jobs, Goldman found.
| 2023-03-28T00:00:00 |
https://www.cnet.com/tech/generative-ai-could-impact-300m-jobs-goldman-sachs-predicts/
|
[
{
"date": "2023/03/28",
"position": 51,
"query": "job automation statistics"
},
{
"date": "2023/03/28",
"position": 49,
"query": "AI job creation vs elimination"
}
] |
|
Will AI replace PR people in the next 10 years? Answers ...
|
Will AI replace PR people in the next 10 years? Answers from 13 PR software executives
|
https://www.swordandthescript.com
|
[
"Frank Strong",
"Ma"
] |
... (AI) will enhance PR pros work and can't replace imagination and human touch; yet some point out tech innovation invariably does eliminate some jobs.
|
Most PR software executives indicate artificial intelligence (AI) will enhance PR pros work and can’t replace imagination and human touch; yet some point out tech innovation invariably does eliminate some jobs.
Many of the conversations about AI tend to fall in opposite ends of the spectrum.
On one hand, some believe AI will have devastating consequences on the future of knowledge workers: we will all soon be replaced. And on the other are the enthusiasts who promise AI will usher in an era of utopia. These are extreme opposites and there’s a lot of room for reason in between.
To help make sense of it all, a while back I queried several executives from PR software companies. All of these companies are using AI in some way, shape or form – and have been long before ChatGPT rang a collective alarm clock.
The question posed was this:
Do you think AI will replace PR people (or any knowledge worker) in the next 10 years?
Below are their answers to 13 PR software executives to that questions.
1. Mostly augmentation, but some replacements
“It’s inevitable and normal that with the advancement of technology, certain jobs no longer exist and new jobs are created – look at how modern media monitoring tools have replaced the need for the news clippings teams of yesterday for example.
In turn, the demand for content creators has increased, arguably driven by better awareness of the need to be communicating relevant messages all of the time. The advancement of AI, like technology before it, will cause similar disruption and change. However, ultimately, we believe that AI is about augmenting human intelligence not replacing or automating it.
In the long run we see AI technology in PR and beyond as technology that will augment and enhance the decision-making process of PR people and other knowledge workers. We see AI as being critical to bringing deeper insights to PR professionals allowing them to answer more complex questions and bring more strategic value to the table than ever before.”
~ David Benigson, CEO, Signal AI
2. Can’t automate authenticity
“AI will change the workload for PR practitioners and allow them to focus their efforts towards relationship building and more strategic, engaging and creative work. It’s unlikely that PR professionals will be replaced by AI in the next decade. While AI has the potential to automate routine tasks, it is not yet advanced enough to replace the creativity, strategic thinking, and human interaction that are key to successful PR campaigns.
Additionally, PR professionals play a crucial role in building and maintaining relationships with key stakeholders, such as journalists, influencers, and customers. This will continue to require strong communication skills, empathy, and an understanding of human psychology, which AI isn’t yet able to replicate.
Large language models like GPT are a powerful technology and will become a useful tool in the arsenal of PR practitioners, however they still require human intervention, monitoring, and editing to produce optimal results. AI-generated content has limitations and is often of lower quality compared to content created by humans.
In a post-GPT world telling authentic stories will be critical. To stand out, PR practitioners must humanize their brands and show them as being genuine and authentic. This will mean incorporating emotion and personal stories and first-hand experience in content, to connect with audiences on a deeper level.”
~ John Croll, Co-founder and CEO, Truescope
3. A standing need for human judgment
“In the near-term future, AI technology will not be able to replace the need for human judgment, intelligence and experience. At some point soon, the audience will look at a piece of relatively low-quality content and state in a derogatory way, “that seems AI-generated”. However, the continued use and adoption of AI and generative technologies will raise the bar for content creators and PR pros. As more professionals adopt AI technologies, the expectations for top quality content will only increase.”
~ Jeff Stacey, vice president, Product Management, Notified
4. Enhancing human intelligence
“I think that’s only a risk if we don’t continue to invest in the development of our less experienced colleagues. Right now, experienced communications leaders are able to judge the quality of the output of generative AI because we’ve all faced the ‘blank page’ challenge many times. That’s where our creative sparks have been honed.
However, if we let future generations of communicators rely solely on generative AI to get them started, within 10 years we will all certainly be much worse at judging its outputs as our human creativity will have diminished.
When employed correctly, AI should enhance human intelligence, not replace it.”
~ Putney Cloos, CMO, Cision
5. This remains to be seen
“Whether that results in a global boom in productivity or a global shrinking of the labor force is something that everyone will be watching.”
~ Dave Seltzer, CTO, TVEyes
6. Won’t replace understanding of cultural and social issues
“Ten years is a long time! AI will surely benefit PR pros by turning over some day-to-day tasks including acting as a framework to content writing and creation. However, AI will never replace every PR or communications professional. Much of PR is acting as a consultant for strategic planning and crisis management (among others) which requires human understanding of cultural and social issues, critical thinking, and years of expertise.”
~ Tressa Robbins, vice president of Client Onboarding, Burrelles
7. Can’t replace building connections
“AI is and will continue to be great for many industries, but I don’t believe it can ever fully replace a PR pro’s role. Much like other new technologies, AI will make PR professionals more efficient, allowing them to focus on what they do best: building connections with journalists, telling stories, and building communication strategies with the executive team.”
~ Greg Galant, CEO, Muck Rack
8. Not a substitute for our humanity
We do not see AI as a replacement for PR pros or other knowledge workers. AI can help make professionals more efficient, but it cannot be a pure substitute for what these experts bring to the table in terms of insights, knowledge, creativity, and humanity. Especially in an industry like PR, built on reputation and relationships, we believe humans can’t be absent. While AI can be a game-changing first touch, we still believe that humans should always be the final touch.
~ Martin Lyster, CEO, Agility PR Solutions and Diane Vuignier vice president, Product Development, Agility PR Solutions
9. Efficiency and efficacy
At Prowly, we don’t think that AI will be able to replace PR Pros but we do believe that PR Pros with AI at their disposal will be empowered to work more efficiently, effectively, and strategically. It should allow them to save time and focus on the things that matter and need that human touch and experience.”
~ Marta Adaśko, brand manager, Prowly
10. Competitive advantage for those that adapt AI
“AI will not replace PR people, but PR people who embrace AI will have a competitive advantage relative to their peers.”
~ Dan Beltramo, President & Chief Innovation Officer, Onclusive
11. Liberation from menial PR tasks
The primary benefits of AI to PR people is the ability to free PR pros from devoting so much effort to menial, laborious tasks such as building media lists and much more. When it comes to pitch and press release writing, AI will prove to be a useful tool in breaking writer’s block and being a valuable resource to comms pros for first draft creation.
~ Zach Cutler, co-founder, chairman and CEO, Propel
12. Greater use of AI will make human curation more impactful
“No. In the PR space, AI is here to help, not replace. Relationship building, coaching, crisis response… These are all things that AI won’t be able to fully replace in 10 years. We also believe that the more we see generative AI being used in business, the more unique and impactful (good) human curated content will become.”
~ Johnny Vance, Global VP of Partnerships, Meltwater
13. Putting it succinctly
“NO. Never.”
~ Aaron Kwittken, founder and CEO, PRophet
* * *
Your turn. What do you think? Let me know in the comments or hit me on the socials.
Do you need help with B2B tech marketing and PR? I’m a seasoned consultant that can both bring big ideas and execute them; I’d be glad to speak with you about your needs. Here are some useful links: about, services and contact. Or just reply to this message if you are a subscriber and received this post by email.
If you enjoyed this post, you might also like:
AI Use Cases in PR Software: Executives from Solutions Providers Describe How AI is being Used
Image credit: DALL-E “a business man in a suit, half the body and face is human and the other half is robot in the style of van Ghoh”
| 2023-03-28T00:00:00 |
2023/03/28
|
https://www.swordandthescript.com/2023/03/will-ai-replace-pr-people/
|
[
{
"date": "2023/03/28",
"position": 26,
"query": "AI job creation vs elimination"
}
] |
Analyzing the impact of AI on the blogging industry
|
Analyzing the impact of AI on the blogging industry
|
https://www.jounce.ai
|
[] |
One negative effect that AI may have on the blogging industry is the elimination of human jobs. ... The impact of AI on content creation and distribution. In ...
|
AI impacts blogging industry, improves writing efficiency but removes emotion. Bloggers need to understand AI effects and stay competitive with tech development
For the past few years that I've been a blogger, I've witnessed firsthand how much the introduction of new technologies has altered the landscape of this business. The introduction of AI into the blogging world has been one of the most significant shifts I've witnessed.
In this piece, I'll discuss in detail the effects that AI is having on the business world and what that means for writers like me.
Exactly what is artificial intelligence?
We'll need to define artificial intelligence (AI) before we can discuss its effects on blogs. AI, which stands for "artificial intelligence," is a term used to describe machines that can reason and learn like humans.
This can include cognitive abilities such as language comprehension, pattern recognition, and decision-making.
How is AI used in blogging?
In the world of blogging, AI is being put to use in a wide variety of contexts. The use of AI-driven writing tools like Jounce is becoming increasingly common. These tools can help bloggers save time and effort by suggesting relevant words, phrases, and even complete sentences as they write.
The use of chatbots, which can have real-time conversations with readers and answer their questions, is yet another application of AI in blogging that we will discuss later.
One of the biggest benefits of AI for bloggers is the ability to increase efficiency. With AI-powered writing tools, bloggers can write faster and with more accuracy. This can free up time for other important tasks, such as promoting the blog or interacting with readers.
The downsides of AI for bloggers
While there are positives to using AI for blogging, there are also drawbacks to think about. One potential drawback is that it can remove some of the emotion from writing. Using AI-powered tools to suggest words and phrases can make writing sound artificial and unnatural.
It can be challenging for bloggers to think of original content when they rely so heavily on AI.
The impact on jobs
One negative effect that AI may have on the blogging industry is the elimination of human jobs. The development of AI-powered tools raises the possibility that these tools will one day be able to perform previously human-only jobs.
All of the work that goes into creating and promoting a blog falls under this category. Businesses may save money, but some bloggers may lose their jobs as a result.
Regardless, more and more bloggers will likely start making use of AI-powered tools as the technology improves. A change in the type of content being produced is also possible, with AI-generated content potentially becoming more commonplace.
The importance of authenticity
Bloggers need to keep in mind the value of genuine content as AI becomes more pervasive in the blogging world. In spite of their usefulness, tools powered by artificial intelligence should not be relied on exclusively.
Bloggers should still make an effort to connect with their readers on a personal level and use their own voices.
How to stay competitive
Keeping up with the competition in the blogging industry is essential as the use of AI becomes more commonplace. Improving your writing abilities and keeping abreast of technological developments are two ways to achieve this goal.
Bloggers can also set themselves apart from the pack by specializing in a particular area or by producing content that is both novel and interesting.
The role of education
Due to the rapid development of AI and its impact on the blogging industry, it is crucial that bloggers have a thorough understanding of both. Understanding the potential benefits and drawbacks of using AI in blogging requires familiarity with a variety of AI-powered tools, as well as tips on how to make the most of them.
Bloggers can benefit from staying abreast of AI developments and advances by allowing them to make educated choices about the technology's application in their own work.
A deeper dive into AI-powered writing tools
As mentioned in the previous sections, AI-powered writing tools like Jounce are becoming increasingly popular among bloggers. These AI blog writing tools can help bloggers write more efficiently and effectively by suggesting words, phrases, and even entire sentences.
But how exactly do they work?
A blogger's writing style can be deciphered by AI-powered writing tools thanks to the use of natural language processing (NLP) and machine learning algorithms. Then, they make suggestions for words and phrases to use in the text based on the author's voice and the topic at hand.
Some of these AI-driven writing tools can even finish full sentences and paragraphs, saving the blogger even more time.
The impact of chatbots on the blogging industry
Chatbots are another form of AI that is finding use in the blogging world. These "chatbots" are computer programs that attempt to mimic human conversation. They can have a two-way conversation with the audience in real-time, fielding questions and offering advice.
Improved interaction with readers is one of the main benefits of chatbots for bloggers. Bloggers can make their posts more interesting and engaging for readers by responding to comments and questions and providing useful information.
To add, chatbots can be useful for bloggers because they can respond quickly to frequently asked questions.
However, it's important to remember that chatbots aren't without their risks. The reputation of the blogger may be harmed if the chatbot provides false or misleading information. There's also the possibility that chatbots won't always be able to provide an adequate response because they don't grasp the full context of the question.
The impact of AI on content creation and distribution
In addition to AI-powered writing tools and chatbots, AI is also affecting the way content is created and distributed in the blogging industry. With the rise of AI-generated content, bloggers may find themselves competing with computer-generated articles and posts.
Distribution and promotion of content are also being impacted by AI. Algorithms trained on AI can predict which pieces of content will do well and then promote them more strategically. Bloggers who want to expand their readership can benefit greatly from this.
On the other hand, there are always potential drawbacks to think about when implementing any new technology. The reputation of the blogger and the blogging industry as a whole may be harmed if AI algorithms favor low-quality or misleading content.
How to use AI effectively in blogging
While AI can be a useful tool for bloggers, it's important to use it effectively in order to maximize the benefits. Here are some tips for using AI in blogging:
Start by understanding the basics of AI and its impact on the industry. Consider using AI-powered writing tools to improve efficiency and writing skills. Use chatbots as a way to increase engagement with your audience, but be mindful of the potential downsides. Be aware of AI-generated content and consider how you can differentiate yourself from computer-generated content. Utilize AI algorithms to analyze and understand what content is likely to perform well, but be mindful of the potential downsides. Remember to strike a balance between using AI-powered tools and maintaining authenticity in your writing.
The ethics of AI in blogging
The use of artificial intelligence in creative endeavors raises some interesting ethical questions. Some might say that using AI to create blog posts eliminates the human element and originality that readers love.
Others may be concerned that AI will be used to spread misinformation or fake news to the general public.
It's crucial that bloggers keep these moral concerns in mind and make ethical decisions when utilizing AI. Steps that can be taken to ensure that content generated by AI is consistent with ethical standards and values include fact-checking, openness about the use of AI tools, and transparency.
The need for continued innovation
Maintaining relevance in the rapidly changing blogging landscape will require bloggers to be at the forefront of emerging technologies and trends. Some examples of this would be trying out novel AI-powered tools, playing around with alternative styles and formats, and being curious about novel approaches to reaching an audience.
At the same time, bloggers will need to maintain their commitment to authenticity and creativity, even as AI becomes more integrated into the industry. By staying ahead of the curve and embracing innovation, bloggers can continue to grow and thrive in this exciting and ever-evolving industry.
In conclusion
AI is having a profound impact on the blogging industry, and it's important for bloggers to understand how to use these tools effectively and responsibly.
Whether you're a seasoned blogger or just starting out, by staying informed and staying ahead of the curve, you can continue to grow and succeed in this exciting and rapidly changing industry.
| 2023-03-28T00:00:00 |
https://www.jounce.ai/blog/ai-blogging-industry-impact
|
[
{
"date": "2023/03/28",
"position": 95,
"query": "AI job creation vs elimination"
}
] |
|
AIMY™: The AI Coach for the Global Workforce
|
AIMY™: the AI Coach for the global workforce
|
https://www.coachhub.com
|
[
"Jason Graefe",
"Corporate Vice President",
"Isv",
"Digital Natives",
"Carlos Delgado Conde",
"Director Hr Planning",
"Development Europe"
] |
... skills shortage and rapid technological transformation.” Jason Graefe ... Accelerate efficiency as AI coaching supports skill development. +45%. In ...
|
An AI coach is a virtual coach powered by artificial intelligence, designed to deliver personalized, on-demand coaching experiences. AIMY™ is CoachHub’s intelligent AI coach, offering real-time, interactive coaching conversations tailored to each coachee’s needs.
AIMY™ helps individuals:
Set and achieve goals with guided exercises and reflections.
Build new skills through recommended micro-learnings from CoachHub Academy.
Reflect and grow with instant feedback and data-driven insights.
CoachHub, with its network of 3,500 coaches, delivers traditional coaching through experienced human coaches, providing individual, collective, and executive coaching. AIMY™ is the latest addition to the CoachHub ecosystem, offering scalable, 24/7 and personalized AI coaching.
| 2023-03-28T00:00:00 |
https://www.coachhub.com/aimy/
|
[
{
"date": "2023/03/28",
"position": 91,
"query": "AI skills gap"
}
] |
|
Machine Learning for High Schoolers
|
Machine Learning for High Schoolers
|
https://extendedstudies.ucsd.edu
|
[] |
Your future in machine learning starts here. Machine learnings is a subfield of artificial intelligence with the capability of a machine to ultimately imitate ...
|
Machine learnings is a subfield of artificial intelligence with the capability of a machine to ultimately imitate intelligent human behavior. It is the use and development of computer systems that are able to learn and adapt without following explicit instructions. Machine learning does so by using algorithms and statistical models to analyze and draw inferences from patterns in data.
Course sequence:
This program is A-G approved.
Learning Format:
Online | 3 - 9 Months
Enjoy the flexibility of learning at your own pace. Courses are 100 percent online, with no in-person meetings. You will have access to the course materials for about 3-weeks per course during the Summer quarter and about 10-weeks per course during the Fall, Winter, and Spring quarters.
What you will learn:
Google Colaboratory Integrated Development Environment.
Applications of Python Data types, Conditional operators, Loops, Functions, Data structures, Recursion, OOP, and Machine Learning libraries.
Probability and Statistics for Machine Learning in addition to Linear Algebra for Machine Learning.
Calculus for Machine Learning with Python-based projects focused on applied mathematics for Machine Learning.
Create Python programming language scripts in the Google Collaboratory development environment to pre-process a dataset using standard Machine Learning libraries.
Implement and analyze regression models including simple and multiple regression, polynomial, lasso, and logistic regression.
Implement and analyze “supervised” classification algorithms including Naive Bayes and K nearest neighbors (KNN).
Implement and analyze “unsupervised” clustering algorithms including K-means, and density-based spatial (DBSCAN) clustering.
Implement and analyze dimensionality reduction techniques including linear discriminant analysis (LDA) and Principal Component Analysis (PCA).
Write and test working Python programs from a generic problem statement through algorithm development, design and implementation, unit test, integration, and deployment.
Implement and analyze single and multi-layer Perceptron models.
Implement and analyze optimization techniques and apply them to DNN frameworks.
Implement and apply Convolutional Neural Networks to image processing applications.
Average Entry Level Salary
$90,000 - $143,000 per year
How to Enroll:
Pay as You Go Per Course:
For general enrollments, expand the “courses” tab at the bottom of this page to review the course list and then click a course to see details and enroll. You may pay per course as you work through the program. Courses must be taken in sequence.
Scholarships:
This program is currently scholarship eligible for both Online and Live Online options. Our scholarships are first-come, first served and will cover the full cost of any program you choose. To get up-to-date scholarship information on requirements and to apply see:
Futures Scholarships
| 2023-03-28T00:00:00 |
https://extendedstudies.ucsd.edu/educational-programs/machine-learning-for-high-schoolers
|
[
{
"date": "2023/03/28",
"position": 56,
"query": "machine learning workforce"
}
] |
|
AIDA
|
Bay Area Community College Consortium
|
https://baccc.net
|
[] |
Introduction to Deep Learning (DL): Machine Learning (ML), additional ... Employers are realizing that in order to keep up with the workforce demand for AIDA they ...
|
Limited AI expertise or knowledge is identified as the biggest barrier to AI adoption by 39% of business leaders, according to Morning Consult’s IBM Global AI Index 2021
The 2020 LinkedIn U.S. Emerging Jobs Report identified the top 15 jobs over the previous five years and emphasized that “artificial intelligence and data science roles continue to proliferate across nearly every industry.” Artificial intelligence specialist (No. 1) showed 74% annual growth, and data scientist (No. 3) and data engineer (No. 8) followed with 37% and 33% annual growth.
“We must abandon the flawed idea that AI jobs are only for people with master’s degrees or PhDs with decades of experience.” – Forbes Aaron Burciaga
Average entry level Data Analytics jobs are $68,000/yr per Google
Most labor market reporting entities categorize Data Analytics and Artificial Intelligence jobs as Data Scientist positions, leading to the assumption that these are positions needing a lot of experience and education. Actually, there are many entry and mid level jobs where a certificate or Associate’s degree would suffice. Employers are realizing that in order to keep up with the workforce demand for AIDA they must build their workforce at the entry level, then train and promote from within. Here are some examples of entry and middle level skill AIDA jobs.
| 2023-03-28T00:00:00 |
https://baccc.net/aida/
|
[
{
"date": "2023/03/28",
"position": 63,
"query": "machine learning workforce"
}
] |
|
The power of predictive HR analytics: Using data to ...
|
The power of predictive HR analytics: Using data to forecast workforce trends
|
https://talent500.com
|
[
"Taniya Pan",
"Prachi Kothiyal"
] |
Predictive analytics, as the name implies, entails making projections about future outcomes by using data, statistical algorithms, and machine learning ...
|
Predictive analytics is a buzzing word in the tech industry today. Predictive analytics, as the name implies, entails making projections about future outcomes by using data, statistical algorithms, and machine learning techniques to analyze historical data and make informed predictions about future events or trends. By using these methods, you can anticipate upcoming trends which help you to play your cards right, as and when it is needed.
Likewise, predictive HR analytics has become an increasingly valuable tool for HR professionals as key benefits like predicting employee turnover, identifying high-potential candidates, and improving workforce planning have enabled companies to make well-informed decisions and stay ahead of the curve.
For example, As per Wall Street Journal, Hewlett-Packard (HP) developed a model in 2011 using people analytics to predict employee turnover. Eric Siegel’s book, Predictive Analytics: The Power To Predict Who Will Click, Buy, Lie Or Die, states that this model allowed HP to give a “flight risk” score to each employee, indicating the likelihood of them leaving the organization. As a result, HP was able to assign preventative measures to retain employees who had a high risk of leaving the organization.
Understanding predictive analytics in HR?
Predictive HR analytics combs through data using digital tools to interpret data, pull out and classify information, and identify patterns, anomalies, and relationships between data points. It creates a streamlined and effective hiring process that incorporates past hiring records, current standards, and future delivery benchmarks. This approach allows for ongoing monitoring and adjustment of the hiring process to ensure quality hires that align with the company’s culture, productivity, and leadership potential. It also helps to gauge the impact of employee efforts on current and long-term organizational goals. By evaluating employee contributions and efforts, HR predictive analytics can help companies maintain a positive reputation and foster a dedicated workforce.
Using data to forecast workforce trends
Trends like remote/hybrid workplace, great resignation, flexibility, prioritizing employee wellness, upskilling, etc., can be predicted through forecasting which can help your organization understand the needs of your employees and avoid high employee attrition. Using data to forecast workforce trends involves diagnosing historical and current HR data to identify patterns and trends that give you an upper hand to do future workforce planning. This can include data on employee turnover, demographics, as well as external factors such as economic conditions and industry trends.
By using HR data analytics tools, organizations can gain insights into future workforce needs, such as the skills and experience required for certain roles, and anticipate potential labor shortages or surpluses. The Wall Street Journal stated that Xerox Corporation. used predictive analytics to improve its hiring process for over 48,700 call center jobs due to high attrition rates. Many employees would quit shortly after completing training, leaving Xerox unable to recover its costs of training each employee, which cost $5000. Xerox Corp. made changes to its hiring process by placing less emphasis on work experience and focusing more on personality tests when evaluating candidates for call center jobs. Following a six-month trial period, Xerox found that this new approach resulted in a 20% reduction in attrition rates and decided to implement this process on a permanent basis. This informs how the company mitigated the problem by simply implementing predictive analytics that gave out a positive impact.
The Role of HR Dashboards in Predictive HR Analytics
An HR dashboard is a tool that enables HR teams to monitor, evaluate and present HR-related Key Performance Indicators (KPIs) in a user-friendly format. Advanced HR dashboards utilize an HR analytics platform that simplifies the process of consolidating data from multiple systems and facilitates in-depth analysis within the dashboard itself. This approach allows HR teams to identify valuable insights that can enhance recruitment efforts, optimize workplace management, and improve employee performance in a timely and efficient manner.
Collecting and analyzing data using HR Dashboards
HR dashboards analyze workforce trends by consolidating and visualizing data from various HR systems and sources. This data can be used to track and measure key HR metrics such as employee turnover rates, time to hire, employee engagement levels, and training program effectiveness. By visualizing this data in a dashboard format, HR teams can quickly and easily identify areas that require attention and make data-driven decisions to improve HR processes and overall business outcomes. HR dashboards also provide real-time access to critical data, enabling HR teams to respond quickly to changes in the workforce and proactively address potential issues.
HR dashboards and workforce trends analysis
HR dashboards give easy access to view and analyze data related to workforce trends like employee turnover rates, Diversity, Equity, and Inclusion (DEI) Efforts, upskilling, and employee engagement levels, etc. They offer a graphical representation of important HR metrics, making it easy to understand patterns and trends over time. Dashboards help create a centralized hub for critical HR data, aiding in the recognition of trends and patterns, and enabling organizations to make informed decisions about their workforce management plans.
By dissecting these data in a dashboard format, HR teams can identify trends and patterns over time, such as demographic makeup of the workforce, such as age, gender, ethnicity, and education level, the time it takes to fill a position, the cost of recruitment, and the turnover rate, the number of employees who have completed training, the time it takes to complete training, and the impact of training on employee performance, the number of HR-related complaints and the status of regulatory requirements.
This enables them to anticipate workforce changes and proactively address potential issues. Additionally, HR dashboards can provide insights into workforce performance, such as identifying high-performing employees or teams, and areas where additional training or support may be needed.
Tools and technologies for HR analytics
We must adopt the right tools in the data environment in order to conduct proper data analytics. These tools help to transform the raw data into meaningful insights and actionable information that can be used to uncover new opportunities or areas for improvement.
HR Information Systems: BambooHR, Workday, Oracle HCM Cloud–These systems can collect and store data related to employee demographics, job history, compensation, benefits, performance, training and development, and other relevant HR-related data.
Predictive Analytics Software: Human resource analytics software like IBM Watson Talent Insights, PredictiveHR utilizes advanced algorithms and machine learning to identify patterns in HR data and predict future outcomes.
Business intelligence (BI) tools: Well-known visualization software applications like Tableau and Power BI provide data visualization, dashboarding, and reporting functions to aid HR teams in scrutinizing intricate sets of data and detecting tendencies and patterns.
Social Media Analytics Tools: Hootsuite Insights, Brandwatch, and Google Analytics allow HR teams to analyze social media activity and sentiment related to the company and its employees, providing insights into employer brand and employee satisfaction.
How is HR technology transforming the field of human resources
HR technology is transforming the field of human resources by automating tasks to improve efficiency, enabling data-driven decision-making through analytics and reporting tools, improving the candidate experience by simplifying the application and tracking process, making remote work more accessible, and enhancing employee engagement through recognition programs, surveys, and collaboration platforms.
HR tech has also enabled HR teams to focus more on strategic initiatives, such as workforce planning and talent management, by taking care of repetitive administrative tasks. As technology keeps progressing, its significance in the future of HR will continue to grow significantly, enabling organizations to optimize their workforce and drive business outcomes.
Improving data analysis and forecasting in HR
Data is a comprehensive domain that covers diverse forms of data, such as structured and unstructured data, big data, social media data, machine-generated data, and numerous other categories. This highlights the complexity and diversity of data that HR professionals need to work with in order to make informed decisions. Challenges like data quality issues, integration of data, inefficient tech expertise, privacy, and among others can be tackled through predictive analytics. Using predictive analytics resolves the mentioned challenges efficiently, thereby improving data analysis and forecasting.
HR professionals can apply statistical models and algorithms to HR data which makes projections regarding upcoming trends. This can help improve data quality by identifying and correcting errors, as well as filling in missing data through imputation techniques. Predictive analytics can also help integrate data from different sources, as it can be used to create a unified view of data that was previously siloed across different systems.
Predictive analytics can also help overcome resistance to change by providing concrete evidence of the value of data analysis and forecasting, and demonstrating the potential benefits of new technologies and processes. It can help address privacy concerns by providing the ability to mask or anonymize sensitive data, and implementing strict data security measures to protect against unauthorized access.
AI in HR: How Artificial Intelligence is Transforming Predictive HR Analytics
For HR professionals, focusing on more strategic tasks becomes uncomplicated as they don’t have to send out appointment reminders, process leave requests, answer employee queries related to policies and benefits when artificial intelligence (AI) is handling such tasks. With AI-powered tools, HR professionals can automate routine HR tasks, improve data accuracy, and make data-driven decisions about their workforce. Also, AI enables HR professionals to analyze and interpret large volumes of HR data quickly and accurately.
Using artificial intelligence in HR analytics
Using artificial intelligence (AI) in HR analytics can help organizations gain even deeper insights into employee behavior and preferences. AI-powered tools can examine vast amounts of data, including employee surveys, performance metrics, and other HR-related data, to identify patterns and trends.
AI can help identify the factors that drive employee engagement, such as job satisfaction, and career development, as well as the factors that contribute to disengagements, such as poor communication or lack of recognition. With this information, organizations can develop targeted strategies to improve engagement and retention. Moreover, AI-powered HR analytics tools can continuously learn and adapt based on new data, enabling organizations to make more accurate predictions and better decisions around talent management.
Potential benefits of AI in HR analytics
Whether it’s automating tasks or making sound decisions for your employees, the benefits are multiple. AI has the potential to transform HR practices and improve the efficiency, effectiveness, and fairness of HR operations. AI can reduce the amount of time you need to spend sorting through data and performing routine hiring duties. Employing AI-based HR analytics can aid companies in detecting regularities and tendencies in data that might not be evident through conventional analytical approaches. This can lead to more precise forecasts and enhanced decision-making regarding hiring, retaining, and nurturing personnel.
AI has the capability to offer suggestions to staff members depending on their requirements and inclinations. This can help employees better understand their benefits and make more informed decisions.
With the help of AI, HR professionals can use real-time data on essential HR metrics such as employee turnover, absenteeism, and engagement and identify potential issues promptly and take action to tackle these issues before they turn into major problems.
Challenges associated with implementing AI in HR analytics
Implementing AI in HR analytics can present several challenges. One of the challenges is the need for specialized expertise to develop and maintain AI models, which can require significant investment in talent and technology. Moreover, AI models can generate prejudiced outcomes if the information employed to train them is biased, resulting in unfavorable consequences for making decisions about managing personnel
Also, organizations must ensure that their use of AI in HR analytics is compliant with legal and ethical standards, such as data protection regulations and non-discrimination laws. Addressing these challenges requires a strategic approach to AI implementation, including ongoing monitoring and evaluation to ensure that AI-powered HR analytics are delivering the desired outcomes while mitigating potential risks.
Talent Analytics: Using Data to Identify and Develop Top Talent
Finding the right talent is like hitting the jackpot. And the process of identifying the cream of the crop is a tough one. But with data the process becomes seamless. Talent analytics uses data to identify and develop top talent within an organization. By analyzing data, such as employee performance metrics and skills assessments, organizations can gain insights into which employees have the potential to succeed in leadership roles or take on new responsibilities.
Using data to identify and develop top talent
When running a performance check, data on competencies shown by employees at their work, their experience, engagement, and motivation help you find potential successors for key roles that require development. This is where TA (Talent Acquisition) helps in finding the top talent. Using data to identify and develop top talent involves collecting and analyzing data to determine the key skills, behaviors, and competencies that lead to high performance. This approach can help organizations create targeted development programs and training initiatives to support the growth and development of top talent. Data can be collected through performance metrics, employee engagement assessments, and predictive analysis to identify employees with the potential to excel in new roles. By using data-driven approaches to talent management, organizations can build a strong and talented workforce, improve employee engagement and retention, and achieve better business outcomes.
Predicting Future Workforce Trends using Talent Analytics
Leveraging talent analytics is a valuable tool for forecasting workforce trends. With this information, organizations can develop strategies to draw in, evolve, and keep the workforce needed to achieve their goals.
Leverage talent analytics to score employees based on various metrics, such as their skills, performance, and potential. This can help organizations recognize high-potential employees who may be candidates for promotion or development.
Through talent analytics, organizations can create strategic plans for their workforce by anticipating future workforce needs. This is done by examining data on employee demographics, skills, and performance, which enables organizations to identify potential gaps in their workforce and devise plans to address them.
Organizations can utilize talent analytics to identify deficiencies in their workforce’s skill set by analyzing data related to employee skills. This enables organizations to develop training and development programs to address any areas where skills are lacking.
Impact of Data on Employee Engagement and Retention
HR can now identify relationships between employee activity and behavior and the effects of employee engagement efforts thanks to predictive analytics and data. Decision-makers can assess the effectiveness of their long-term people plan, thanks to these analytical insights. By interpreting data, organizations can identify key factors that contribute to employee engagement and retention, such as job satisfaction, career development opportunities, and work-life balance.
Using Data to Gauge the Pulse of the Workforce
Knowing what is going on inside your employee’s mind is vital. Questions like ‘Are they liking what they are doing currently? If not, what ails them? What is consuming most of their mind? Are they feeling okay?’, can provide insight into how your workforce is doing. This data can be collected through surveys, focus groups, and other feedback mechanisms. By interpreting this data, organizations can identify anomalies and patterns that can help in creating a solid plan, such as which factors contribute to employee engagement, areas that need improvement, and where there may be gaps in employee satisfaction. With this information, organizations can develop targeted strategies to improve employee engagement and well-being, increase retention rates, and ultimately achieve better business outcomes.
Improving employee engagement through HR Analytics
To guarantee long-term retention and involvement in your office/hybrid/ remote workforce, you need predictive analytics. In the ever-changing economy, measuring involvement and employee retention through surveys and descriptive HR analytics (investigating data trends to learn about the past) is insufficient. A single data point only provides a momentary snapshot of your workforce’s involvement. A historical trend, on the other hand, can demonstrate how to involve your company and its rate of growth over time. Leaders can assess how their companies compare to others using set benchmarks. Benchmarks provide management with a more complete picture of the level of employee engagement. Linking employee engagement data to business results is another application for this information. The majority of managers are aware of the value of employee involvement, but they lack knowledge or data on how enhancements in this area can affect financial outcomes.
Armed with this information, organizations can develop targeted strategies to improve engagement, such as implementing recognition programs, improving communication channels, and investing in leadership development.
Using predictive HR analytics can enable organizations to improve their talent acquisition and retention strategies by analyzing HR data to identify trends and insights. It has the potential to help organizations align their business objectives and achieve better future outcomes.
Nevertheless, it is essential to note that predictive HR analytics should not replace human intuition and expertise. Instead, organizations should balance data-driven insights with qualitative inputs and human judgment to make informed decisions that take into account the complexity of the workforce and the organizational context.
| 2023-03-28T00:00:00 |
2023/03/28
|
https://talent500.com/blog/the-power-of-predictive-hr-analytics-using-data-to-forecast-workforce-trends/
|
[
{
"date": "2023/03/28",
"position": 83,
"query": "machine learning workforce"
}
] |
Collective Action in Tech
|
https://data.collectiveaction.tech
|
[] |
Alphabet Workers Union filed an unfair labor practice charge with the NLRB after unionized Google Fiber retail workers (contract workers, whose direct employer ...
|
This project attempts to document all collective action from workers in the tech industry. Contribute to our archive.
Currently, there are collective actions documented.
| 2023-03-28T00:00:00 |
https://data.collectiveaction.tech/
|
[
{
"date": "2023/03/28",
"position": 12,
"query": "AI labor union"
}
] |
||
Chipotle Settles over Restaurant Closure that Followed ...
|
Chipotle Settles over Restaurant Closure that Followed Unionization Efforts
|
https://www.shrm.org
|
[
"Allen Smith"
] |
An organization run by AI is not a futuristic concept. Such technology is already a part of many workplaces and will continue to shape the labor market and HR.
|
Chipotle Mexican Grill will pay $240,000 to former workers of an Augusta, Maine, restaurant to resolve claims that it closed the site as part of alleged union busting. Chipotle has denied any wrongdoing. We've gathered articles on the news from SHRM Online and other media outlets.
Company's Stance on NLRB Complaint
The restaurant was closed last July after workers tried to organize. At the time, the company said the closure was due to difficulty finding staff. But the National Labor Relations Board (NLRB) filed a complaint alleging that the closing was intended to defeat union organizing.
"We settled this case not because we did anything wrong, but because the time, energy and cost to litigate would have far outweighed the settlement agreement," the company said in a statement on March 27. "We respect our employees' rights to organize under the National Labor Relations Act."
(CNN)
Union's Position
The settlement "isn't just a victory for Chipotle United. It's a win for food service workers across the country. It sends a message to corporations that shutting down a store and blackballing workers didn't work for Chipotle and it won't work for them either," said Brandi McNease, a former Augusta Chipotle worker and lead Chipotle United organizer, in a statement. "Now that we've won this battle, we'll keep fighting. Every service employee deserves the right to safe working conditions and fair wages to support our families, and this movement won't stop until we get them. We are going to put an end to the old way of doing business."
(Bangor Daily News)
Placement on Preferential Hiring List
The settlement states that two dozen employees will receive payments from Chipotle and will be placed on a preferential hiring list for other Maine locations. The company also must post a notice in New England stores that it won't close restaurants or discriminate against employees due to union support.
(Fox News)
Some Starbucks' Store Closures Have Been Challenged
Organizing campaigns at other companies have involved store closures that workers claimed were retaliation for their union efforts. Starbucks has closed several stores where workers had been organizing. Those closures are the subject of NLRB complaints.
A Starbucks spokesperson said in an e-mail that the company does not believe the allegations have merit, calling the complaints "the beginning of a litigation process that permits both sides to be heard."
(HuffPost and HuffPost)
NYC Trader Joe's Wine Shop Abruptly Closed
A Trader Joe's wine store in New York City reportedly closed abruptly Aug. 11, 2022, following monthslong unionization efforts. The United Food and Commercial Workers International Union (UFCW) said it was ready to pursue all legal action, including filing an unfair-labor-practice charge with the NLRB. The union subsequently filed such a charge. Trader Joe's did not respond to inquiries about the closure.
(HuffPost, UFCW and SHRM Online)
| 2023-03-28T00:00:00 |
https://www.shrm.org/topics-tools/employment-law-compliance/chipotle-settles-restaurant-closure-followed-unionization-efforts
|
[
{
"date": "2023/03/28",
"position": 26,
"query": "AI labor union"
}
] |
|
AI + Economics – Dr Alan D. Thompson
|
AI + Economics
|
https://lifearchitect.ai
|
[
"Alan D. Thompson"
] |
By 2030, artificial intelligence is likely to boost the output of global knowledge workers by 9% at an annual rate, from $41 trillion in expected human labor ...
|
Alan D. Thompson
March 2023
Table of papers specifically addressing economic impacts of post-2020 AI. Most recent at top.
OpenAI on DALL-E 2:
[Text-to-image models like the DALL-E 2 model] may eventually have significant economic implications. The model may increase the efficiency of performing some tasks like photo editing or production of stock photography which could displace jobs of designers, photographers, models, editors, and artists. At the same time it may make possible new forms of artistic production, by performing some tasks quickly and cheaply…
Finally, access to the model is currently given to a limited number of users, many of whom are selected from OpenAI employees’ networks. While commercial use is not currently allowed, simply having access to an exclusive good can have indirect effects and real commercial value. For example, people may establish online followings based on their use of the technology, or develop and explore new ideas that have commercial value without using generations themselves. Moreover, if commercial access is eventually granted, those who have more experience using and building with the technology may have first mover advantage – for example, they may have more time to develop better prompt engineering techniques.
| 2023-03-28T00:00:00 |
2023/03/28
|
https://lifearchitect.ai/economics/
|
[
{
"date": "2023/03/28",
"position": 31,
"query": "AI labor union"
}
] |
The AI revolution takes hold | Abdul Latif Jameel®
|
The AI revolution takes hold
|
https://alj.com
|
[
"Abdul Latif Jameel"
] |
Artificial intelligence (AI) has the potential to revolutionize the way we work and automate many tasks that currently require human labor.
|
Headcounts on the block? The AI revolution takes hold
Artificial intelligence (AI) has the potential to revolutionize the way we work and automate many tasks that currently require human labor. Some experts believe that AI will lead to significant job displacement in the coming decades, while others think that it will create new job opportunities and boost productivity.
Sounds convincing, doesn’t it? Even more so when you know that a human being didn’t write that paragraph. AI wrote it. Or more specifically, ChatGPT did.
The precocious chatbot from OpenAI unveiled to great acclaim in November 2022, uses a large language model that harnesses natural language processing (NLP). This is a branch of AI that seeks to analyze, comprehend, and communicate with language.
The GPT-3 (Generative Pretrained Transformer 3) AI was trained on massive amounts of data and user feedback to sound like humans and can generate anything from recipes to resumes on demand (even in the style of Shakespeare, if it pleases thee[1]). Translation, sentiment analysis, introductions to articles about AI… ChatGPT can even create code from simple text prompts. It is one of the largest and most powerful language processing AI models to date, with 175 billion parameters.[2]
Computers, it seems, are increasingly infringing on complex tasks once thought quintessentially human (including deceiving professional backstabbers in the game of Diplomacy[3]).
Meanwhile, Microsoft is investing up to US$ 10 billion in OpenAI, the company behind ChatGPT[4]. Professional writers aren’t looking for alternative employment just yet – but they’re definitely typing just a little faster!
From the printing press to the PC, to machines that read, write and produce fine art, human history is built on technological innovation and automation. Does our future belong to AI?
The business world certainly thinks so.
AI is a smart move
AI adoption more than doubled between 2017 and 2022, according to McKinsey’s State of AI 2022 survey[5], with 50% of respondents adopting AI in at least one business area. The average number of AI capabilities per organization has also doubled, from 1.9 in 2018 to 3.8 in 2022. Robot process automation and computer vision remain top of the list, while NLP has leaped from the middle of the pack in 2018 to third place.
Why the explosion of interest in AI?
AI can analyze vast amounts of data, automate processes, and provide insights and recommendations that enable businesses to enhance accuracy, efficiency, and productivity, improve decisions making, increase personalization and customization, and streamline people management. The list goes on.
AI can drive revenue, efficiency and cost savings across multiple domains. In 2018, manufacturing and risk were the juiciest targets. Now sales and marketing, product and service development, and strategy and corporate finance are delivering the biggest gains, with the deepest cost-cuttings happening in supply chain management.
According to a PwC report, AI could contribute up to US$ 15.7 trillion to global GDP by 2030[6]. An innovation injection of this magnitude might reverse dwindling labor productivity and revive the global economy, which still bears the scars of the COVID-19 pandemic and the financial crisis of 2007-2009.
Industry use cases include:
Healthcare: Analyzing medical images, predicting patient outcomes, and improving the accuracy of diagnoses (e.g., analyzing X-rays and identifying abnormalities that may indicate a particular condition). Pharmaceutical companies like Moderna used AI to develop COVID-19 vaccines[7], and in 2020, the Jameel Clinic at MIT identified Halicin through machine learning and AI models.
Analyzing medical images, predicting patient outcomes, and improving the accuracy of diagnoses (e.g., analyzing X-rays and identifying abnormalities that may indicate a particular condition). Pharmaceutical companies like Moderna used AI to develop COVID-19 vaccines[7], and in 2020, the Jameel Clinic at MIT identified Halicin through machine learning and AI models. Finance: Analyzing financial data, making investment recommendations, detecting suspicious transaction patterns, and preventing fraudulent activity. Business Insider estimates that AI could save banks and corporate institutions US$ 447 billion by 2023[8].
Analyzing financial data, making investment recommendations, detecting suspicious transaction patterns, and preventing fraudulent activity. Business Insider estimates that AI could save banks and corporate institutions US$ 447 billion by 2023[8]. Manufacturing: Improving quality control, optimizing production, and predicting maintenance needs (e.g., monitoring production equipment and identifying patterns that could indicate potential failures). Tesla’s Gigafactory is a marvel of AI-driven automation.
Improving quality control, optimizing production, and predicting maintenance needs (e.g., monitoring production equipment and identifying patterns that could indicate potential failures). Tesla’s Gigafactory is a marvel of AI-driven automation. Retail: Personalizing customer experiences, optimizing pricing and inventory management, and improving marketing (e.g., analyzing customer purchase data and recommending products to individual customers based on their past purchases). Amazon Go’s white label “Just Walk Out” technology is enabling multiple retailers to experiment with cashier-less stores.
Personalizing customer experiences, optimizing pricing and inventory management, and improving marketing (e.g., analyzing customer purchase data and recommending products to individual customers based on their past purchases). Amazon Go’s white label “Just Walk Out” technology is enabling multiple retailers to experiment with cashier-less stores. Transport and logistics: Managing supply chains, predicting demand, instructing warehouse robots, optimizing routes, improving safety, and reducing fuel consumption (e.g., analyzing real-time traffic data and recommending the most efficient journeys for delivery trucks). Walmart used AI supply chain simulations to rapidly reroute deliveries and predict demand changes after Hurricane Ian forced the closure of a major distribution hub in Florida[9].
Managing supply chains, predicting demand, instructing warehouse robots, optimizing routes, improving safety, and reducing fuel consumption (e.g., analyzing real-time traffic data and recommending the most efficient journeys for delivery trucks). Walmart used AI supply chain simulations to rapidly reroute deliveries and predict demand changes after Hurricane Ian forced the closure of a major distribution hub in Florida[9]. Sales and marketing. Creating content, generating leads, improving customer experiences, and managing customer relationships (e.g., analyzing data to improve targeting and delivering greater personalization via chatbots). Salesforce’s Einstein Analytics applies AI to anticipate customer behavior and make recommendations.
Abdul Latif Jameel is already exploring how to leverage the benefits of AI for the communities and markets it serves across the globe. AI is at the heart of a number of healthcare technologies that Abdul Latif Jameel Health has invested in, for example. These include a handheld ultrasound device developed by Butterfly iQ+TM and extended reality (XR) 3D virtual environment surgical technology developed by Holoeyes.
Another Abdul Latif Jameel business, Fotowatio Renewable Ventures (FRV) (part of Abdul Latif Jameel Energy), through its innovation arm FRV-X, also harnesses the power of AI in deploying the Tesla auto-bidding software used in its battery storage systems (BESS) in the UK (Holes Bay, Dorset; Contego, West Sussex; Clay Tye, Essex) and in Australia at Terang, Victoria, and a hybrid plant at Dalby, Queensland.
Also part of Abdul Latif Jameel Energy, Almar Water Solutions has invested in a technology company that provides AI-driven IoT products and services for digital transformation in the water, energy and mobility sectors: Datakorum. Datakorum subsequently secured a five-year project with leading communications operator, e& Enterprise, (previously Etisalat Digital) facilitating the digital transformation of the water and energy management systems in the City of Abu Dhabi, UAE.
The AI playbook
AI leaders are already pulling ahead, and laggards will find it increasingly difficult to catch up. Why? According to McKinsey, they have a first mover advantage, take the long view, invest more and more wisely, attract the best talent, and follow an emerging set of principles to embed and scale AI. These ‘foundational practices’ include linking AI strategy to business outcomes, understanding how to integrate AI into business processes and decision-making, prioritizing talent acquisition and training, and ensuring AI and people work together to create more value.
There are also a number of ‘frontier practices’ that enable AI development and deployment at scale in the so-called ‘industrialization of AI.’ These include:
High-quality data, modular data architecture that can flexibly accommodate new applications and automate data-related processes.
that can flexibly accommodate new applications and automate data-related processes. Engaging non-technical employees with low-code or no-code programs.
with low-code or no-code programs. Scaling with standardized tools and in-house end-to-end platforms.
with standardized tools and in-house end-to-end platforms. Assetization of code for reuse in different applications.
of code for reuse in different applications. Mitigating risk by developing policies for privacy and fairness, for example.
At least latecomers don’t have to write the book from scratch.
Better managers, better work?
AI can monitor hazardous conditions and improve predictive maintenance to create safer working environments (or remove humans from dangerous areas entirely). It can also be used to enhance training and development. For example, tech giant Honeywell uses AI-based image recognition technology to create virtual reality training courses for new hires[10].
AI also creates jobs, from AI-specific roles to new ones created by AI-driven growth. By automating a wide range of tasks, AI allows traditional carbon-based lifeforms (i.e., humans!) to focus on more complex and creative tasks, including occupations that don’t exist yet. It can even automate aspects of recruitment and hiring, performance evaluation, and employee engagement. For example, using chatbots to interview candidates and analyzing data on attendance, productivity, and customer satisfaction to provide a more objective assessment of performance. So-called ‘algorithm management’ has the potential to streamline management and protect against biases. However, it could also reinforce discrimination and infringe on workers’ rights, as we’ll see.
Brave new world, same old worries
Before the robots wake up and extinguish the human race, as some doomsayers would have us believe, businesses have more pressing hurdles to worry about, most of which are still being worked out.
Firstly, AI is not easy. Algorithms require massive training data and can be difficult to generalize across use cases. And talent is scarce. Businesses need experienced managers and strategists, along with AI-specific and general digital know-how, including software engineers that can develop intuitive front-ends for non-techy users to interact with algorithms.
As for other jobs…
Some will change. Others will disappear. Manual labor in predictable environments is first in the firing line, but even knowledge workers are at risk. For example, AI is already helping radiologists analyze images – and it’s only getting better. What happens when it’s consistently more reliable? And large language models should make anyone who writes for a living a little uneasy.
“We should stop training radiologists now. If you work as a radiologist, you are like Wile E. Coyote in the roadrunner cartoons; you’re already over the edge of the cliff, but you haven’t looked down,” believes Dr Geoffrey Hinton, cognitive psychologist and computer scientist at the University of Toronto and an advisor to Alphabet, the parent company of Google[11].
Many workers will need to change occupations within organizations and sectors and across geographies. Physical activity in highly structured environments and data collection and processing will decline. Meanwhile, complex activities and jobs in unpredictable physical environments will remain dominated by humans (good news for managers and massage therapists alike).
Advocates argue that AI and humans will work together, as many already do, with humans typically shifting from doers to managers, like when Amazon’s lift stackers becoming robot operators[12]. After all, only 5% of occupations could be fully automated by current technologies.
Others say this is wishful thinking. Technology is improving so quickly, and AI will inevitably replace humans where automation is possible. By some estimates, AI and automation could displace around 400 million workers between 2016 and 2030.
Either way, upskilling and reskilling is essential. Naturally, digital and AI-based expertise are in high demand. But other skills will rise in importance as AI takes over and automates more activities. These include social, emotional, and higher cognitive skills, such as creativity and critical thinking.
Coworkers, rather than competitors?
It’s not all doom and gloom. A Boston Consulting Group (BCG) survey[13] suggests AI is broadly positive for individuals and organizations, with 60% of respondents saying they feel like AI tools are a coworker. In fact, AI is so embedded in business and consumer products it often goes unnoticed. Furthermore, organizations with employees who personally derive value from AI are 5.9 times as likely to get significant financial benefits from AI compared with organizations where employees do not get value from AI. Managers who want to maximize AI ROI should cultivate trust, understanding, agency, and awareness among employees. “AI can improve an individual’s self-determination through greater competency, increased autonomy, and stronger relationships,” says the report.
What about wages?
If AI and automation perform tasks faster, cheaper, safer, and more reliably, demand for human employees could drop, and so too salaries High-wage jobs may increase, but many ‘safe’ jobs that cannot be automated, such as nursing, are lower-paid. This could exacerbate income inequality and political and social unrest.
There will be increased need for financial support and safety nets to offset the impact of automation, such as Universal Basic Income, a stipend for all citizens as championed by former US presidential candidate Andrew Yang. And Governments must create more substantial, sustainable work through infrastructure and climate change adaptation investment, for example.
Ethics vs. algorithms
The use of AI raises several ethical questions and concerns which must be addressed. The following all call for human oversight:
Equity and fairness. AI systems trained on biased data could lead to unfair treatment of certain groups of people. This can be avoided through diverse and representative training data, robust testing, and accountability measures.
AI systems trained on biased data could lead to unfair treatment of certain groups of people. This can be avoided through diverse and representative training data, robust testing, and accountability measures. Transparency and explainability. Many machine learning algorithms, such as foundation models, are black boxes that cannot explain their decisions. “Computer says no” isn’t good enough. US defense agency DARPA is developing algorithms that “explain their rationale, characterize their strengths and weaknesses, and convey an understanding of how they will behave in the future.”[14]
Many machine learning algorithms, such as foundation models, are black boxes that cannot explain their decisions. “Computer says no” isn’t good enough. US defense agency DARPA is developing algorithms that “explain their rationale, characterize their strengths and weaknesses, and convey an understanding of how they will behave in the future.”[14] AI systems must be held accountable for their decisions and actions. Businesses should define the scope and limitations of AI systems and establish clear policies and procedures to ensure they are used responsibly and compliantly, incorporating human oversight and mechanisms for addressing negative consequences.
AI will deepen workplace surveillance with the ability to analyze written, verbal, and even facial queues to assess workers’ moods and productivity. Businesses must update their data governance policies and procedures to ensure personal data is handled responsibly and in compliance with relevant privacy laws.
AI and automation generally improve safety by removing workers from hazardous areas. But organizations shouldn’t take any chances, particularly with autonomous vehicles and collaborative robots (cobots). Regular health and safety audits are essential.
Security and resilience. AI systems, and the data they use and generate, are high-value targets for criminals, intelligence services, and disgruntled employees. And natural disasters can compromise or destroy infrastructure. Cyber-security and resilience are top priorities for businesses and governments alike.
Addressing the risks
Despite widespread AI adoption, there have been no substantial increases in risk mitigation over the last few years. Time for governments and policymakers to step up?
As the OECD asserts in a whitepaper on how to ensure AI remains trustworthy,[15] most concerns can be addressed by applying existing policies and regulations, such as anti-discrimination, data protection (e.g., GDPR), deceptive practices, and rights to due process – and using these as the basis for developing new policies to protect workers.
The EU AI Act[16] seeks to classify and rate AI systems used in employment, banning some and subjecting others to legal requirements over data protection, transparency, human oversight, and robustness. In the US, some states require applicant consent for facial recognition tools in hiring, and the New York City Council mandates algorithmic bias audits for “automated employment decision tools.”
When robots rule the world
AI is a global phenomenon. All countries and sectors stand to benefit, especially those with relatively high wages. By 2030, automation could displace 20% to 25% of the workforce in countries like France, Japan, and the United States – more than double the rate in India.[17]
Owing to AI’s strategic and economic importance, governments in China, America, and the rest of the world are employing various strategies to drive AI adoption. As part of its New Generation Artificial Intelligence Development Plan,[18] China is creating tax breaks and subsidies and increasing R&D expenditure by over 7% each year to become a world leader in AI. The government has also implemented regulations to encourage data sharing among companies to fuel AI development. In the United States, the government is funding academic institutions and research centers and loosening regulations on the use of AI in industries like autonomous vehicles. Likewise, the European AI Strategy seeks to make the EU a world-class hub for AI and ensure that AI is human-centric and trustworthy.[19]
There are also several international organizations working across different levels of government, NGOs, and private sector stakeholders to address AI’s ethical, societal, and economic implications, fund research, and foster responsible development and use of AI. These include the European Union’s Horizon 2020 program, The Global Partnership on Artificial Intelligence (GPAI), The Forum for Cooperation on Artificial Intelligence (FCAI), and the Partnership on AI. The OECD’s AI Policy Observatory tracks and analyzes the AI policies of its member countries, many of which have adopted the OECD’s guidelines for the responsible use of AI. And The UN General Assembly adopted a resolution on the Promotion of the Use of Artificial Intelligence for Development in December 2020.
How should businesses prepare for AI?
Of course, there is no single route to success with AI. Every business will need to find its own route to leveraging the potential of AI to bring new benefits, not just destroy old models.
Perhaps AI itself can provide a solution? This is what ChatGPT had to say when asked how businesses can best prepare themselves for the AI revolution:
All good advice. But no supercomputer can predict the future (yet).
Fortunately, it only takes old-fashioned common sense to realize AI has changed business forever. It’s up to us to ensure those changes make things better, not worse.
[1] https://www.economist.com/business/2022/12/08/how-good-is-chatgpt
[2] https://www.sciencefocus.com/future-technology/gpt-3/
[3] https://www.economist.com/science-and-technology/2022/11/23/another-game-falls-to-an-ai-player
[4] https://www.bloomberg.com/news/articles/2023-01-23/microsoft-makes-multibillion-dollar-investment-in-openai
[5] https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2022-and-a-half-decade-in-review
[6] https://www.pwc.co.uk/services/economics/insights/the-impact-of-artificial-intelligence-on-the-uk-economy.html
[7] https://sloanreview.mit.edu/audio/ai-and-the-covid-19-vaccine-modernas-dave-johnson/
[8] https://www.businessinsider.com/ai-in-banking-report?r=US&IR=T
[9] https://www.supplychaindive.com/news/walmart-grocery-AI-demand-operations/585424/
[10] https://www.forbes.com/sites/sharongoldman/2020/12/08/how-honeywells-latest-vr-based-simulator-borrows-from-gaming-to-transform-industrial-training/
[11] https://www.emjreviews.com/radiology/article/artificial-intelligence-in-radiology-an-exciting-future-but-ethically-complex-j140121/
[12] https://www.nytimes.com/2017/09/10/technology/amazon-robots-workers.html
[13] https://web-assets.bcg.com/b8/55/97a0dcbe42cab65ed77794cc9dfe/achieving-individual-and-organizational-value-with-ai.pdf
[14] https://www.darpa.mil/news-events/2022-03-03
[15] https://doi.org/10.1787/840a2d9f-en
[16] https://artificialintelligenceact.eu/
[17] https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2022-and-a-half-decade-in-review
[18] https://www.unodc.org/ji/en/resdb/data/chn/2017/new_generation_of_artificial_intelligence_development_plan.html
[19] https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:52018DC0237&from=EN
| 2023-03-28T00:00:00 |
https://alj.com/en/perspective/headcounts-on-the-block-the-ai-revolution-takes-hold/
|
[
{
"date": "2023/03/28",
"position": 64,
"query": "AI labor union"
}
] |
|
Good Work Algorithmic Impact Assessment - IFOW
|
Good Work Algorithmic Impact Assessment
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https://www.ifow.org
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[] |
Guidance for employers and engineers to involve workers and their representatives in the design, development and deployment of AI systems.
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March 28, 2023
Artificial intelligence (AI) and algorithmic systems are increasingly used in the workplace, and designed, developed and deployed in ways which can transform people's access to work, the conditions under which they work and the quality of the work they are employed to do.
When well designed, these technologies offer new opportunities to increase efficiency, augment capacity and drive growth. But this transformation is also driving a wide range of social, psychological and material impacts.
Whether it's about how their rights are respected, how their working conditions are likely to change, or how their interests are balanced with those of the business, workers need confidence that these systems are being used fairly and transparently.
Supported by the UK Information Commissioner’s Office (ICO) Grants Programme, this guidance is designed to help employers and engineers to involve workers and their representatives in the design, development and deployment of algorithmic systems so that risks are anticipated and managed, 'good work' is promoted, the law is complied with, innovative approaches are unlocked and trust in technology is built.
As a complement to it, we have produced two resources to help improve accessibility and understanding of the ways in which algorithmic systems can impact work.
First, the Good Work Charter toolkit identifies ten dimensions of 'good work', and outlines the main legal and ethical frameworks that apply.
Second, 'Understanding AI at Work' provides accessible explanations of how human choices in the design, development and deployment of AI at work are determined by human choices.
Together with this guidance, these resources will help employers assess the wide range of impacts that AI and other algorithmic systems may have on Good Work.
The publication of IFOW’s Good Work Impact Assessment marks a major policy breakthrough for ensuring that the design, development, and deployment of work-related AI technologies is equitable, responsible, and trustworthy. As more and more of everyday work life is impacted by the use of AI systems, the GWAIA provides AI developers, procurers and users a very accessible and proportionate pathway to implementing best governance practices. The uptake of this guidance, both in the UK and globally, will prove crucial to the future of good work, and any organisation, big or small, considering the use of work-related AI would do well to put it into practice.
— Professor David Leslie, Professor of Ethics, Technology and Society, Queen Mary University of London
The Good Work Algorithmic Impact Assessment represents an important contribution to advancing responsible innovation in the context of workplace AI, forefronting good work in shaping a responsible and trustworthy AI ecosystem. We look forward to continued work with IFOW that draws on this expertise on AI applications in the workplace context.
— Dr Florian Ostmann, Head of AI Governance and Regulatory Innovation, The Alan Turing Institute
Authors: Abigail Gilbert, Anna Thomas, Gwendolin Barnard, Stephanie Shier
Permission to share: this resource is protected under a Creative Commons 4.0 (CC: BY-SA) license.
You may copy, share or adapt this resource but you must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests IFOW endorses you or your use. If you remix, transform, or build upon this material, you must distribute your contributions under the same license.
| 2023-03-28T00:00:00 |
https://www.ifow.org/publications/good-work-algorithmic-impact-assessment-an-approach-for-worker-involvement
|
[
{
"date": "2023/03/28",
"position": 84,
"query": "artificial intelligence workers"
}
] |
|
Artificial Intelligence Certificate Program
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Artificial Intelligence Certificate Program
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https://founderz.com
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[] |
"Organizations using AI in their business processes have seen a 15% reduction in operating costs" (Microsoft). "8 out of 10 business leaders plan to increase ...
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The program is designed to fit into a flexible schedule, with a recommended commitment of 3 to 5 hours per week. You can break this down however suits you best, like dedicating 30 minutes a day.
On average, students complete the program and earn their certification in 3 to 4 months at this pace. However, you’ll have 12 months of access to all course content from the moment you enroll, allowing you to go at your own speed and revisit material whenever you need.
| 2023-03-28T00:00:00 |
https://founderz.com/program/online-program-in-ai-innovation/
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[
{
"date": "2023/03/28",
"position": 40,
"query": "artificial intelligence business leaders"
}
] |
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GENERATIVE AI: WHAT LIES BEHIND AND HOW IT ...
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GENERATIVE AI: WHAT LIES BEHIND AND HOW IT PROPELS BUSINESSES.
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https://noellerussell.ai
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[] |
Unlock your leadership potential with our executive coaching services. Harness the power of artificial intelligence and develop AI leadership skills for ...
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Noelle Russell is a multi-award-winning technologist with an entrepreneurial spirit who specializes in helping companies with Cloud, Data and AI transformation. She has led teams at NPR, Microsoft, IBM, Accenture, AWS and Amazon Alexa, and is a consistent champion for Data and AI literacy.
She has built over 100 conversational AI applications since 2014 and has over 2 million unique users, and 30K 4-5 star reviews on Amazon Alexa. She also influenced almost a $1 billion in revenue for Microsoft AI during her tenure there speaking to Fortune 500 executives, running whiteboarding technical sessions, and leading internal hackathons for her clients.
In the last year, she was awarded the Microsoft Most Valuable Professional (MVP) award for Responsible Artificial Intelligence (for the 4th year) as well as being listed at one of the Top 50 Global Thought Leaders in Generative AI.
| 2023-03-28T00:00:00 |
https://noellerussell.ai/f/generative-ai-what-lies-behind-and-how-it-propels-businesses
|
[
{
"date": "2023/03/28",
"position": 87,
"query": "artificial intelligence business leaders"
}
] |
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6 Best Job Posting Sites for Employers that leverage AI
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6 Best Job Posting Sites for Employers that leverage AI
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https://www.talentprise.com
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[] |
Candidate screening is the hardest part of recruitment as it takes a lot of time and effort to extract the right talent from a pool of applicants. However, ...
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Are you interested to know about the best job posting sites for employers?
Candidate screening is the hardest part of recruitment as it takes a lot of time and effort to extract the right talent from a pool of applicants. However, Artificial intelligence in HR recruitment has emerged as a lifesaver for employers. That’s why 65% of employers are already using AI in their recruitment workflow.
Several popular Job boards are also using AI to match qualified candidates with open positions.
Additionally, some job posting sites are also using AI to conduct interviews, ATS, resume screening, and other parts of the recruitment process. Without further ado, let’s walk through the best job posting sites for employers that are leveraging the powers of AI to facilitate employers in finding the top talent.
What are the best job posting Sites for Employers?
Here are the best 6 job posting sites for employers that use AI in recruitment:
LinkedIn
LinkedIn is the most popular professional networking platform and features a robust search engine to connect employers and candidates. With 52 million active job searchers each week, LinkedIn has secured a central place in the recruitment sector. This platform uses AI and machine learning to provide the best user experience.
LinkedIn leverages AI-powered personalization tools to analyze your company and job description as well as the profile of its members to recommend the most relevant members, even if they are not actively searching for a job.
Moreover, AI is incorporated to enable better communication between candidates and recruiters and to automate certain parts of the recruitment process, such as resume screening. It also uses simple tools, such as candidate rating and screening tools to help you promptly prioritize the right people for an interview.
Talentprise
When it comes to AI-powered job postings, Talentprise for employers tops the list. Talentprise utilizes innovative AI-powered tools to ensure the best user experience. For instance, the advanced “AI headhunting” feature used by Talentprise selectively targets the right candidates for your open positions.
Its advanced recommendation algorithms analyze the job description and the candidate resumes in its database to carefully handpick candidate profiles that meet your job requirements. You do not have to fumble through hundreds of irrelevant and unqualified resumes as your posts reach out to qualified candidates only, saving you time and effort.
Talentprise is modernizing HR recruitment by creating and optimizing job posts with the help of Artificial intelligence. This maximizes the visibility, engagement, and effectiveness of your job posts and allows cost and time-saving in the recruitment process.
ZipRecruiter
ZipRecruiter is an industry-leading job posting site with immense capabilities to connect employers and talent. Its AI-powered algorithms analyze the requirements of employers and create a personalized set of highly relevant candidates, based on 80 factors.
ZipRecruiter claims that its AI-driven tools are so effective that 80 percent of employers find the right candidates within 24 hours after posting the job online. Moreover, the site boasts 10 million unique visitors each week and helps them find a job based on their skills and requirements. The result-driven history of ZipRecruiter has made it a prime destination for employers to seek candidates and talented professionals to spot their ideal job.
Lensa
Lensa is a US-based job board that leverages advanced matching technology to find the right candidates for employers. With 2 million monthly users and more than 100 million job alerts per month, Lensa is getting into the mainstream in the recruitment world. Its sophisticated intelligent matching algorithm finds prequalified and active candidates and allows them to contact employers directly via email.
Besides, Lensa’s applicant tracking system and application alerts also ease the recruitment process. Moreover, Lensa does not charge employers for posting a job until it matches you with the most suitable candidate. However, if you are looking for a job board with filtered search options and an accessible resume database, this job posting site might not be your thing.
Indeed
Indeed is one of the best job posting sites for employers. This popular job board has also incorporated AI to further the recruitment process. It examines the job description and applicants’ resumes via Natural Language Processing (NLP) to recommend a better match.
AI shapes the recruitment of Indeed by showing personalized results when an applicant searches for a job or when an employer looks for resumes.
Moreover, other advanced features of Indeed include Applicant Tracking System (ATS), promoting job posts with sponsored posts, conducting and scheduling virtual interviews, and verifying their abilities through several pre-made skill assessments that can also be customized.
Indeed, is no doubt an all-inclusive platform for sourcing talent by sorting the resumes, contacting the applicants, and scheduling the interviews.
CareerBuilder
CareerBuilder is a great AI-powered talent acquisition platform for employers and job seekers. Artificial intelligence ranks the candidates, promotes diversity, and transforms job posting into an easy four-step process that takes no more than five minutes.
By leveraging 25 years of data and experience, Careerbuilder’s AI-powered tools provide insight to the recruiter into whether their job post is compelling to the candidates, thereby quickening their job posting and speeding up their hiring experience.
You do not have to waste time screening irrelevant resumes with the help of advanced Career-builder talent hunting tools fueled by AI, which connect you to qualified candidates based on your ideal persona. Moreover, the mobile app of CareerBuilder uses artificial intelligence and augmented reality to innovate in the human resource market.
Conclusion
The benefits of Artificial intelligence in human resource recruitment can never be overstated. From perfect candidate matching to enhanced communication and seamless virtual interviews, AI is revolutionizing talent acquisition and saving the time and efforts of recruiters, so they can better focus on what matters most for their organization.
Here we have discussed five of the best job posting sites for employers to optimize their recruitment workflow. Besides these popular job search engines, plenty of other sites feature AI-powered tools to aid recruitment. However, the reliability of job posting sites, the type, and size of the audience, along with innovative capabilities should be considered while opting for a site to post your next open job.
If you are looking to hire new resources? Sign up today and post your jobs for free with Talentprise’s AI-powered job posting advanced feature.
| 2023-03-28T00:00:00 |
2023/03/28
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https://www.talentprise.com/6-best-job-posting-sites-for-employers-ai/
|
[
{
"date": "2023/03/28",
"position": 34,
"query": "artificial intelligence hiring"
}
] |
Artificial intelligence (AI) in Education
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Artificial intelligence (AI) in Education
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https://www.gsineducation.com
|
[
"Greg Parry"
] |
Incorporating AI technology into education offers many potential benefits, including personalized learning and cost savings.
|
Artificial intelligence (AI) in Education
Artificial intelligence (AI) is transforming every industry, including education. As the world rapidly moves towards an AI-powered future, it’s vital for school leaders to understand the potential benefits and risks of incorporating AI technology into their schools.
Artificial intelligence (AI) in Education
Artificial intelligence (AI) and Personalised Learning
One significant benefit of using AI in education is the ability to personalize learning. AI-driven tools, such as ChatGPT, can analyze students’ data and provide them with personalized recommendations based on their learning styles and abilities. This helps teachers to provide targeted support to individual students, ultimately leading to better learning outcomes. Furthermore, AI tools can help to reduce the burden on teachers by automating routine tasks such as grading, freeing up their time to focus on teaching and providing feedback to students.
The Cost Savings of Artificial intelligence (AI) in Education
Another significant benefit of AI in education is cost savings. According to a forecast released by technology research firm IDC, worldwide business spending on AI is expected to hit $50 billion this year and $110 billion annually by 2024. While the initial investment in AI may be high, it can save schools money in the long run by reducing administrative costs and improving efficiency.
The Risks of Artificial intelligence (AI)
However, incorporating AI technology into education also poses significant risks that school leaders must address. One of the main risks is the potential for AI-driven tools to perpetuate existing biases and inequalities. For example, if an AI algorithm is trained on a biased dataset, it may perpetuate that bias when making decisions. It’s crucial for school leaders to ensure that AI algorithms are developed ethically and that they promote equity in education.
Another risk is the potential for AI to replace teachers entirely, which could result in a loss of essential human skills such as critical thinking, creativity, and empathy. It’s essential to ensure that AI is used to complement teachers, not replace them. Additionally, AI tools are not perfect, and students must be able to differentiate between what the AI tools can produce and the critical thinking skills necessary for good-quality work.
1988: We should ban calculators
2023: We should ban ChatGPT
Ethics and Artificial Intelligence (AI) in Education
To ensure that AI technology is used ethically and effectively in education, school leaders must take several steps. They should ensure that AI algorithms are developed ethically and with transparency. They should also provide training for teachers and students to understand how AI tools work and how to use them effectively. Finally, they should focus on using AI to complement teachers, not replace them, and to promote equity in education.
Incorporating AI technology into education offers many potential benefits, including personalized learning and cost savings. However, school leaders must also be aware of the significant risks involved, such as perpetuating biases and replacing teachers. By taking steps to ensure that AI is used ethically and effectively, school leaders can harness the power of AI to improve learning outcomes for all students.
AI in Education
Artificial intelligence (AI) in Education – be a fearless leader and take charge!
The integration of AI technology in education has the potential to transform the way students learn and teachers teach. With the ability to personalize learning, accelerate learning, and make knowledge and information more accessible, AI can help address many of the challenges that traditional education systems face. However, it is important to approach the use of AI in education with caution and careful consideration of the potential risks and ethical concerns.
As school leaders navigate the adoption of AI technology in their schools, it is crucial to prioritize transparency, privacy, and equity. They must also ensure that AI is used as a tool to complement human educators rather than replace them. Collaboration and ongoing reflection among educators, students, and technology experts are key to creating effective strategies for integrating AI in education.
As we look to the future, it is clear that AI technology will continue to play an increasingly important role in education. By embracing this technology while also prioritizing ethical considerations and human collaboration, we can create a more equitable, efficient, and effective educational system that benefits all learners. Let us continue to innovate and explore the endless possibilities of AI in education to ensure that every student has access to the best education possible.
Learn more:
“Worldwide business spending on AI is expected to hit $50 billion this year and $110 billion annually by 2024, even after the global economic slump caused by the COVID-19 pandemic, according to a forecast released in August by technology research firm IDC. Retail and banking industries spent the most this year, at more than $5 billion each.”
(Source: Great promise but potential for peril)
“Summary. Artificial intelligence (AI) is a wide-ranging tool that enables people to rethink how we integrate information, analyze data, and use the resulting insights to improve decision making …”
(Source: How artificial intelligence is transforming the world)
“Key tasks—setting the direction for AI projects, analyzing the problems they’ll solve, building the algorithms, designing the tools, testing them with end users, managing the change, and …”
(Source: Building the AI Powered Organization)
CEO and Education Expert Greg Parry
Internationally renowned for his expertise in education leadership, Greg Parry’s vast experience includes leadership of projects for education institutions throughout Australia, the Middle East, the United States, India, Indonesia, Malaysia and China. Recognised for his numerous contributions in the education arena, Greg has received the Ministers Award for Excellence in School Leadership based on improvements in school performance and a range of successful principal training and leadership development programs, as well as the School of Excellence Award for Industry/School Partnerships and the School of Excellence Award for Technology Innovation. His company GSE (Global Services in Education) has been recognised as having the Best Global Brand in International Education in 2015 and 2016.
Considered one of the premier experts in his profession, Greg has trained teachers and principals throughout the world in areas such as critical thinking, language development and leadership. His expertise in school start up projects, leadership and curriculum development, has made him a sought after authority in these discipline.
www.gsineducation.com
Global Services in Education set up and operate schools in all parts of the world. Governed by a philosophy of global citizenship, every member of the GSE team shares a passion to help shape international education and student achievement through inspiration and collaboration.
Our goal is to meet the highest objectives of every school, teacher, student and parent, with an unwavering dedication to quality education, shared ideals and intercultural perspectives.
– School Management
– Strategic Planning
– Feasibility Studies
– Architectural Conceptual Design
– Interior Design
– Resources – ICT Planning
– Marketing
– Branding
– Admissions
– Staffing & Recruitment – Curriculum Design
– School Policies
– School Audits & Action Plans
– Training & Development
– Accreditation & Licensing
| 2023-03-28T00:00:00 |
2023/03/28
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https://www.gsineducation.com/blog/artificial-intelligence-ai-in-education
|
[
{
"date": "2023/03/28",
"position": 3,
"query": "artificial intelligence education"
}
] |
AI education resources: What do we teach young people?
|
AI education resources: What do we teach young people?
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https://www.raspberrypi.org
|
[
"Jane Waite",
"Eva Tutor"
] |
We are creating a free set of lesson resources to help teachers introduce AI and machine learning (ML) to KS3 students (ages 11 to 14).
|
People have many different reasons to think that children and teenagers need to learn about artificial intelligence (AI) technologies. Whether it’s that AI impacts young people’s lives today, or that understanding these technologies may open up careers in their future — there is broad agreement that school-level education about AI is important.
But how do you actually design lessons about AI, a technical area that is entirely new to young people? That was the question we needed to answer as we started Experience AI, our exciting collaboration with DeepMind, a leading AI company.
Our approach to developing AI education resources
As part of Experience AI, we are creating a free set of lesson resources to help teachers introduce AI and machine learning (ML) to KS3 students (ages 11 to 14). In England this area is not currently part of the national curriculum, but it’s starting to appear in all sorts of learning materials for young people.
While developing the six Experience AI lessons, we took a research-informed approach. We built on insights from the series of research seminars on AI and data science education we had hosted in 2021 and 2022, and on research we ourselves have been conducting at the Raspberry Pi Computing Education Research Centre.
We reviewed over 500 existing resources that are used to teach AI and ML.
As part of this research, we reviewed over 500 existing resources that are used to teach AI and ML. We found that the vast majority of them were one-off activities, and many claimed to be appropriate for learners of any age. There were very few sets of lessons, or units of work, that were tailored to a specific age group. Activities often had vague learning objectives, or none at all. We rarely found associated assessment activities. These were all shortcomings we wanted to avoid in our set of lessons.
To analyse the content of AI education resources, we use a simple framework called SEAME. This framework is based on work I did in 2018 with Professor Paul Curzon at Queen Mary University of London, running professional development for educators on teaching machine learning.
Click to enlarge.
The SEAME framework gives you a simple way to group learning objectives and resources related to teaching AI and ML, based on whether they focus on social and ethical aspects (SE), applications (A), models (M), or engines (E, i.e. how AI works). We hope that it will be a useful tool for anyone who is interested in looking at resources to teach AI.
What do AI education resources focus on?
The four levels of the SEAME framework do not indicate a hierarchy or sequence. Instead, they offer a way for teachers, resource developers, and researchers to talk about the focus of AI learning activities.
Social and ethical aspects (SE)
The SE level covers activities that relate to the impact of AI on everyday life, and to its implications for society. Learning objectives and their related resources categorised at this level introduce students to issues such as privacy or bias concerns, the impact of AI on employment, misinformation, and the potential benefits of AI applications.
An example activity in the Experience AI lessons where learners think about the social and ethical issues of an AI application that predicts what subjects they might want to study. This activity is mostly focused on the social and ethical level of the SEAME framework, but also links to the applications and models levels.
Applications (A)
The A level refers to activities related to applications and systems that use AI or ML models. At this level, learners do not learn how to train models themselves, or how such models work. Learning objectives at this level include knowing a range of AI applications and starting to understand the difference between rule-based and data-driven approaches to developing applications.
Models (M)
The M level concerns the models underlying AI and ML applications. Learning objectives at this level include learners understanding the processes used to train and test models. For example, through resources focused on the M level, students could learn about the different learning paradigms of ML (i.e., supervised, unsupervised, or reinforcement learning).
An example activity in the Experience AI lessons where students learn about classification. This activity is mostly focused on the models level of the SEAME framework, but also links to the social and ethical and the applications levels.
Engines (E)
The E level is related to the engines that make AI models work. This is the most hidden and complex level, and for school-aged learners may need to be taught using unplugged activities and visualisations. Learning objectives could include understanding the basic workings of systems such as data-driven decision trees and artificial neural networks.
Covering the four levels
Some learning activities may focus on a single level, but activities can also span more than one level. For example, an activity may start with learners trying out an existing ‘rock-paper-scissors’ application that uses an ML model to recognise hand shapes. This would cover the applications level. If learners then move on to train the model to improve its accuracy by adding more image data, they work at the models level.
Other activities cover several SEAME levels to address a specific concept. For example, an activity focussed on bias might start with an example of the societal impact of bias (SE level). Learners could then discuss the AI applications they use and reflect on how bias impacts them personally (A level). The activity could finish with learners exploring related data in a simple ML model and thinking about how representative the data is of all potential application users (M level).
The set of lessons on AI we are developing in collaboration with DeepMind covers all four levels of SEAME.
The set of Experience AI lessons we are developing in collaboration with DeepMind covers all four levels of SEAME. The lessons are based on carefully designed learning objectives and specifically targeted to KS3 students. Lesson materials include presentations, videos, student activities, and assessment questions.
The SEAME framework as a tool for research on AI education
For researchers, we think the SEAME framework will, for example, be useful to analyse school curriculum material to see whether some age groups have more learning activities available at one level than another, and whether this changes over time. We may find that primary school learners work mostly at the SE and A levels, and secondary school learners move between the levels with increasing clarity as they develop their knowledge. It may also be the case that some learners or teachers prefer activities focused on one level rather than another. However, we can’t be sure: research is needed to investigate the teaching and learning of AI and ML across all year groups.
That’s why we’re excited to welcome Salomey Afua Addo to the Raspberry Pi Computing Education Research Centre. Salomey joined the Centre as a PhD student in January, and her research will focus on approaches to the teaching and learning of AI. We’re looking forward to seeing the results of her work.
If you’d like to get involved with Experience AI as an educator and use our lessons with your class, you can start by visiting us at experience-ai.org.
| 2023-03-28T00:00:00 |
2023/03/28
|
https://www.raspberrypi.org/blog/ai-education-resources-what-to-teach-seame-framework/
|
[
{
"date": "2023/03/28",
"position": 12,
"query": "artificial intelligence education"
}
] |
The Future of AI in Education: How Will It Transform ...
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The Future of AI in Education: How Will It Transform Learning?
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https://www.anthology.com
|
[
"Justin Louder",
"Associate Vice President Of Academic Innovation"
] |
AI can revolutionize how courses are designed and delivered in higher education by providing real-time analysis and feedback on student learning.
|
March 29, 2023
The Future of AI in Education: How Will It Transform Learning?
Throughout our three-part blog series, we have discussed some of the foundational work on Artificial Intelligence (AI) in education, as well as current uses and concerns. In our final blog about AI in the classroom, we want to focus on the future. What impact might these tools have in the future, and how should institutions think about using them?
In our first blog, "Teaching in the AI-Powered Future," we discussed how AI is integrated into the classroom in various ways, such as personalized learning and virtual teaching assistants. The blog emphasizes that while AI can be helpful in certain aspects of education, it should not replace human teachers. Instead, AI should be used to support teachers and provide them with more time to focus on individualized instruction and building relationships with their students.
Our second blog, "The Impact of AI in Our Classrooms," discussed the potential benefits of AI in education, such as improving student outcomes. But we also discussed concerns around the ethical use of AI in education, such as privacy and bias issues. The blog emphasized the importance of implementing AI responsibly and ethically, focusing on improving student learning and outcomes.
Both blogs acknowledged the potential benefits of AI in education but caution against relying too heavily on AI and highlight the importance of responsible implementation.
We touched on bias in both of our previous blogs, but in this third one, we want to focus more on ways to reduce bias in AI and think about what AI will look like in the future.
What Lies Ahead
AI has come a long way in recent years, and there are still many exciting developments on the horizon. As AI is increasingly used in high-stakes applications like healthcare and finance, there is a growing need for models that are easily understood and trusted by users. As more and more devices connect to the internet, AI will play a key role in processing and making sense of the data generated by these devices. With the rise of chatbots and virtual assistants, there is a growing need for AI systems that can understand and respond to human language in a natural way. As AI becomes more ubiquitous, there is an increasing need to address ethical considerations such as bias, transparency, and accountability.
Real-Time Course Design
AI can revolutionize how courses are designed and delivered in higher education by providing real-time analysis and feedback on student learning. By leveraging machine learning algorithms and data analytics, AI can help educators identify areas where students are struggling and adjust the course content to meet their needs.
AI can be used for real-time course design by analyzing student data such as engagement levels, progress, and performance on assessments. By analyzing data from multiple students, AI can identify common areas of difficulty and help educators make informed decisions about curriculum development. For example, if a concept is consistently difficult for students, educators can adjust the course content to provide additional support and resources.
Real-time course design with AI can also assist educators in adapting to changing circumstances and unexpected events. A prime example of this was during the COVID-19 pandemic, when courses had to be quickly adapted for online delivery. AI can help educators analyze data on student engagement and performance in these new contexts and make necessary adjustments to the course design to ensure students are still receiving a high-quality learning experience.
Overall, real-time course design with AI can improve higher education's effectiveness and efficiency. By providing personalized learning paths, identifying areas of difficulty, and adapting to changing circumstances, AI can help educators create engaging and effective learning experiences that meet the needs of all students.
Faculty Are Key
Faculty play a critical role in developing and delivering curriculum, providing mentorship and guidance to students, and conducting research and scholarship. These are complex tasks that require a deep understanding of the subject matter, as well as the ability to engage and motivate students.
AI is not capable of the kind of interpersonal interactions that are essential to teaching and learning. Human instructors can provide emotional support, encouragement, and inspiration to students, which are crucial elements of the learning process. They are also able to adapt their teaching strategies to the individual needs of each student, which is difficult for an AI-powered system to replicate.
Faculty will continue to play a critical role in developing and delivering curriculum, providing mentorship and guidance to students, and conducting research and scholarship.
Ethical Considerations
The use of AI in education has the potential to improve learning outcomes and provide personalized learning experiences for students. However, the use of AI also raises ethical considerations that must be addressed to ensure it is used in a responsible and beneficial way. For many of these considerations, educational institutions will rely on their education technology partners. Institutions should carefully review the practices of their education technology partners to understand how these considerations are addressed.
Privacy
AI systems collect a significant amount of personal information on students, including their learning habits, progress, and behavior. In addition to ensuring that appropriate data privacy measures are in place, educational institutions must ensure that this data is collected and used responsibly and transparently. To further ensure student privacy, clear guidelines on the type of data that is collected and how it will be used should be codified and shared with learners.
Transparency
Educational institutions must ensure that the algorithms and decision-making processes used by AI systems are transparent and understandable. Students and teachers should be able to understand how decisions are made and be able to challenge them if necessary.
Accountability
Educational institutions must be accountable for the decisions made by AI systems. This includes ensuring decisions are based on accurate and relevant data and that all results are valid and reliable.
Human Oversight
AI systems should not replace human teachers or decision-makers. Human oversight is necessary to ensure that AI systems are used appropriately and equitably and that decisions are made in the best interests of students.
Access
AI systems should be accessible to all students, regardless of their socioeconomic background or disability status. Educational institutions must ensure that AI systems do not perpetuate existing inequalities in education.
Safety
Educational institutions must ensure that AI systems are safe and do not pose a risk to students' physical or emotional well-being. Addressing these ethical considerations is essential to ensure that the use of AI in education is responsible, fair, and beneficial to students. Educational institutions must prioritize ethical considerations when designing and implementing AI systems to ensure they are used in a way that promotes positive learning outcomes for all students.
Reducing Bias
To ensure that AI systems treat people fairly and avoid perpetuating existing inequalities, reducing bias is crucial. Here are some ways to achieve this goal:
Ensure diversity in training data: AI systems learn from the data they are trained on, and if it only represents a specific group of people or biases, the AI system may make biased decisions. Thus, it is crucial to ensure that the training data is diverse and representative of the entire population. Address bias in algorithms: Bias can be introduced at various stages of the AI development process. Hence, it is vital to audit the algorithms and identify any potential sources of bias. Techniques such as adversarial testing can help identify and address biases in the algorithms. Implement fairness metrics: Developers can use fairness metrics to measure an AI system's fairness, identify potential sources of bias, and provide insight into how to improve the system's fairness. Involve diverse stakeholders: It is important to include diverse stakeholders in the AI development process, including individuals from different racial, ethnic, and socio-economic backgrounds, to ensure that the AI system is fair and unbiased. Monitor the AI system's performance: It is crucial to continuously evaluate an AI system's performance for fairness and bias after deploying it. If any bias is detected, it should be addressed immediately to prevent harm to individuals or groups.
Reducing bias in AI systems requires a combination of technical and social approaches. It is crucial to prioritize fairness and inclusivity throughout the development process and continuously monitor the AI system's performance to ensure that it remains fair and unbiased.
| 2023-03-28T00:00:00 |
https://www.anthology.com/blog/the-future-of-ai-in-education-how-will-it-transform-learning
|
[
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"date": "2023/03/28",
"position": 13,
"query": "artificial intelligence education"
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|
Artificial Intelligence as an Essential Part of the Future of ... - IE
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Artificial Intelligence as an Essential Part of the Future of Higher Education
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https://www.ie.edu
|
[] |
IE University must move now commit to artificial intelligence, which will be the basis from which the education of the world's students will move into the ...
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An institution like IE University must fight each and every day to ensure that the wisdom and success that comes from maturity and experience does not also build walls that keep change and the future at bay, and that the institution and its activities continue to transform the lives and education of its students. I believe that we have successfully anticipated key questions, such as how entrepreneurship, internationalization, innovation, diversity, and humanities act as motors that drive change in education, creating a foundation of learning and an instrument of differentiation and forward thinking.
As an academic institution, we live permanently in a world where ideas need to change at light speed in order to lead educational platforms. And now, at a time when we again ask ourselves where to direct the university, my first idea is that IE University must move now commit to artificial intelligence, which will be the basis from which the education of the world’s students will move into the future.
With this in mind, four months ago I asked the now-archaic ChatGPT 3 what it had to say about me. It created the following paragraph:
“Diego del Alcazar has been a major advocate for higher education in Europe. He has worked to promote the development of quality higher education and to ensure that all students have access to quality educational opportunities. He has also been a strong advocate for the development of research and innovation in Europe, and has worked to ensure that universities are able to attract the best talent from around the world. He has also been argued for the development of international collaborations between universities, and for the development of new technologies and approaches to teaching and learning”.
When I fed it more data about myself, it gave me an increasingly complex answer. When I asked it questions about education and IE, it “spat out” its thoughts, which you can read here.
All this is to say that artificial intelligence will inevitably be the backbone of production, education, and other fields. This tool will bring an impressive transformative power to almost all human activities. We must therefore push for it to become a medium that is easy for students and teachers to use, a cutting-edge resource to achieve differential goals. There is no end to how education and artificial intelligence will interact in the future and, thus, IE University should manage and embed it in everyday life and ensure that it drives our already well-established best practices. The future of education will be based, in any case, on artificial intelligence – as core and as a tool.
Perhaps with this choice, for my part, it is time to mention that AI will be the source of other technologies such as blockchain, the internet of things, big data, and virtual or augmented reality.
These new tools will give us the ability to develop different skills at an unprecedented level: from telecommunications to the development of protein structures in the field of medicine, as well as helping us to optimize the blockchain to avoid excessive energy consumption, and also to create technological systems that provide access to art, culture and literature. Educational institutions will be responsible for providing young people with the tools necessary to develop the critical thinking needed to ensure that AI will always be an adjunct to human endeavor.
Note that my aim is not to forget these other important sectors, but instead to point out that digitalization has reached a plateau and that it will be on top of artificial intelligence that the new technologies will be developed and that we should therefore focus on teaching our students and professors about the potentialities of artificial intelligence.
© IE Insights.
| 2023-03-28T00:00:00 |
2023/03/28
|
https://www.ie.edu/insights/articles/artificial-intelligence-is-the-future-of-higher-education/
|
[
{
"date": "2023/03/28",
"position": 62,
"query": "artificial intelligence education"
}
] |
How AI is shaking up higher education
|
How AI is shaking up higher education
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https://www.eaie.org
|
[] |
HEIs are increasingly using AI to streamline processes, personalise learning environments, reach new markets in innovative ways, and generally up their game.
|
How AI is shaking up higher education
Artificial Intelligence (AI) refers to computers performing tasks that would normally require human intelligence to perform. That doesn’t mean there’s an app to persuade a reluctant student to enrol (yet), but what it does mean is a world of opportunity for higher education institutions to become more efficient, more effective and to offer deeper learning experiences.
HEIs are increasingly using AI to streamline processes, personalise learning environments, reach new markets in innovative ways, and generally up their game to meet a new generation of digital natives knocking at their (virtual) door. What follows are some examples of current developments in AI technology and how these are shaking up the (international) higher education sector.
AI opportunities in international higher education
In a nutshell, AI does the heavy lifting that frees up our time to do less onerous tasks, as well as personalising processes for students.
Chatbots, for example, can answer the bombardment of routine questions about scheduling, exam dates and admissions requirements, flagging and filtering the messages that need more personalised attention. Staffordshire University, for instance, has seen great success in the use of its Beacon chatbot, which provides round-the-clock support to students. This is especially useful with the increase in online learning, with students spread across so many time zones.
Virtual teaching assistants and adaptive learning programmes mean that students get personalised pace and direction in their independent study, which is more dynamic and also more inclusive. At the point that students decide they require some one-on-one with the ‘real’ educator, their questions are more targeted, refined and less time-consuming to address.
In a nutshell, AI does the heavy lifting that frees up our time to do less onerous tasks, as well as personalising processes for students.
AI pays dividends in student recruitment and retention too. When a university wants to know why only 12% of applicants actually enrol, AI can collect and interpret big data trends across multiple international markets and conversion points to provide these insights so that processes can be improved. Nova Southeastern University in the USA dramatically improved its retention rates by building AI insights to flag potential leavers and act responsively, another example of AI improving the personal side of education.
The University of Sydney used all of these approaches in a broad AI strategy and reported vastly improved experiences of both service and learning for prospective and current students, as well as for the staff involved.
AI isn’t always ideal – how to choose wisely
What problems are we looking to solve, or what opportunities do we want to create? Where are we wasting time, or making blind decisions? How can we support this new technology to make sure it works well within our institution? These are all questions that should be asked when investigating new technology, in order to avoid being blinded by the sensationalism of the marketing pitch.
Apps and programmes that are AI-driven are not an instant cure, but tools that need to be used consciously and with careful planning for integration. As with many new technologies, there are ethical pitfalls to consider, and there is no such thing as an off-the-shelf solution.
AI-driven apps and programmes are not an instant cure, but tools that need to be used consciously and with careful planning
With the increasing use of AI in higher education, there is a growing concern about the protection of student data. This is because AI algorithms collect and analyse vast amounts of student data, which may include personal information, academic performance and other sensitive data. The University of Plymouth, for example, is currently trialling an AI-driven app which could support students with mental health issues. This is very sensitive data indeed; a breach in such data could result in loss of trust by students and staff and potentially large fines. In fact, AI itself can even be used to hack secure systems.
Another ethical challenge is the potential impact of AI on employment in higher education. AI has the potential to automate many tasks, including grading, administration and even aspects of teaching, and there is much concern about the resulting impact on student well-being from less human interaction. Collaboration between AI and humans is thus important: technology should serve society through feedback from humans to machines and aligning values.
Change is not always within our control
As with most disruptive technologies, AI and its impact is beyond the control of higher education or any other single sector. As a recent example, ChatGPT is a tool making headlines right now, and serves as a perfect example of how open-source tech is going to shape a new paradigm of education, whether or not we are on board. With a freely-available tool like this which can now write passable essays and research papers (or at least the first draft anyway!), the call for more dynamic assessment methods is becoming a clamour. I actually used ChatGPT to write this article… just kidding – but you’re not 100% sure, are you? You may have to use an integrity bot to check.
What is clear here is that we have to rethink the way we assess in response. In this way, and many others, AI is actually influencing change in education, one line of code at a time. You can use ChatGPT to create quizzes, check code, do market research, prompt art, write tasks, improve style or grammar, and so many other things. My hope is that a thorough analysis of the tool will likely result in greater uptake of its use by educators, rather than fearing it. If a chatbot can submit an essay that passes the test, then what does that say about the test, and indeed the way we assess students in general?
Education marketers are also seeing ChatGPT as an opportunity to improve SEO, write social media copy, and even conduct market research. Change is coming.
An eye on the future
With AI use in higher education set to grow by 40% between 2021 and 2027, we are posed a question: what is the cost and consequence of not using AI to expand our offering to students?
Is it fair to assume that the university which offers customised and personalised recruitment processes and digital learning environments, efficient and streamlined time with academic staff, proactive retention strategies backed by data, and intelligent, and data-driven marketing, will have a better chance of surviving the countless threats to our sector? I think that's a given. That's why HEIs need to make themselves aware of developments like those mentioned here and stay on top of the ever-evolving world of AI in higher education. Keep an eye on OpenAI's updates of ChatGPT (text-based AI), DALL-E (image-based AI) and Whisper (voice-based AI) to monitor the future of AI.
| 2023-03-28T00:00:00 |
https://www.eaie.org/resource/ai-higher-ed.html
|
[
{
"date": "2023/03/28",
"position": 69,
"query": "artificial intelligence education"
}
] |
|
How Is AI Used In Education? 25 Examples Of Artificial ...
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How Is AI Used In Education? 25 Examples Of Artificial Intelligence In Education
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https://www.techasoft.com
|
[] |
Artificial Intelligence solutions for education can improve education, support instructors, and foster more effective individualized learning.
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Artificial Intelligence solutions for education can improve education, support instructors, and foster more effective individualized learning. This prospect is both thrilling and a little intimidating.
One must first get past science-fiction fantasies that computers and robots will teach our children, replace teachers, and remove the human aspect from what is a human activity before one can even have a meaningful conversation about AI in education.
What is Artificial Intelligence ?
To facilitate problem-solving, the field of Artificial Intelligence combines computer science and substantial datasets. Moreover, it includes the branches of artificial intelligence known as deep learning and machine learning, commonly addressed together.
These fields use AI algorithms to build expert systems that make predictions or categorize information based on input data.
25 Examples of AI in Education
1. Classroom Management
2. Class Work and Lesson Planning
3. Classroom Audio-Visual
4. Parent-Teacher Communication
5. Language Learning
6. Test Prep
7. Assessment
8. Learning Management Systems
9. Gamification for Enhanced Student Engagement
10. Staff Scheduling and Substitute Management
11. Professional Development of Teachers
12. Finance management for Education
13. Cybersecurity
14. Safety and Security
15. Plagiarism Detection
16. Exam Integrity
17. Chatbots for Enrollment and Retention
18. Learning Management Systems
19. Transcription of Faculty Lectures
20. Enhanced Online Discussion Boards
21. Analyzing Student Success Metrics
22. Academic Research
23. Connected Campuses
24. Adaptive Learning
25. Early Childhood Education
Applications of AI in Education
How Is AI Used In Education? AI has many educational applications, such as personalized training, automation teaching and many more. Here are a few AI based education applications.
1. Virtual Learning
A virtual learning environment can provide group educational experiences, offer counseling services to students, and facilitate immersive learning experiences. With VR technologies, learners can directly connect their laptops or mobile devices to access the content.
Using VR headsets, students with ADHD/ ADD can block distractions and increase concentration spans. In addition, students can help others in soft skill coaching, self-development, and life skills with interactive simulations.
2. Creating Courses
Developing educational programmes through a central department takes a lot of time and money. AI accelerates and lowers the cost of course production by streamlining the process.
Artificial Intelligence in Education can assist in seamlessly developing interactive content, whether you are utilizing existing templates or designing everything from scratch. To create the best training materials, you can effectively collaborate with your entire team using in-app comments from reviewers and co-authors.
AI provides teachers with a clear picture of the lessons and subjects that need to be reevaluated by evaluating the learning history and abilities of each student. By assessing students' needs to address common knowledge gaps, teachers modify their courses. This makes it possible for teachers to create the best learning plans for every student.
3. Enabling Universal Access
AI breaks down the silos between schools and traditional grade levels. Using a PowerPoint plugin like Presentation Translator, learners get real-time subtitles for all the teacher says, opening up new possibilities for the learners who have to learn at varying levels, want to learn subjects that aren't in their school or are absent from school.
4. Offering Personalized Learning
Personalization is a significant trend in education. AI gives students a customized learning approach depending on their unique preferences and experiences. AI can adjust to every student's knowledge level, desired goals, and learning speed to help them get the most out of their learning.
Additionally, AI-powered solutions can assess a student's learning history, pinpoint weaknesses, and provide courses suitable for improvement, offering many opportunities for personalized learning experiences.
5.Evaluation of Where Courses Should Be Improved
Teachers may only sometimes know the gaps in their educational materials and lectures, which can confuse learners regarding particular concepts. AI provides a way to solve this issue. For instance, Coursera is already applying this.
This kind of system fills the gaps in explanation in courses and ensures every student is building a similar conceptual foundation.
Instead of waiting to hear from the teacher, students receive immediate feedback to help them understand concepts better.
6. Teaching the Teacher
Teachers shouldn't rely on their outdated, lingering information, which is one thing that is crucial in education. There are other facts that they must understand and impart to the children. Not to mention that there are several other topics they can still learn, yet they study and teach within a constrained field.
Teachers now have access to extensive information at their fingertips because of AI. Artificial Intelligence companies even prepare courses to train teachers. This enables individuals to stay informed about things they didn't know or expand upon their prior knowledge.
With this, kids will be more well-rounded and have a broader knowledge base to compete with pupils in the twenty-first century.
7. Complete Assistance in Education
Teachers aren't the only ones who can access a flood of information thanks to AI. And so do students. This implies that they can use AI bots at any time of day to seek assistance on any subject.
Students typically only get answers to their questions from teachers or professors when they meet with them in person and have the opportunity to do so during class. They will thankfully no longer have to wait so long. Many chatbots with AI power have been created, especially for the education industry.
They serve as the students' 24/7 aides, responding to their questions whenever they arise and will not need to meet the professor in the lecture hall or office immediately.
8. Personalized Learning
AI can ensure that each person's instructional software is customized. Students already have access to adaptive learning systems, games, and software.
Given that learning is easier, more comfortable, and doesn't depend on prior knowledge, this application of AI in education is perhaps one of its most significant.
This technique highlights the needs of each student, emphasizing the particular disciplines in which they could be stronger and having them repeat lessons they have yet to learn.
In Conclusion
AI is an effective tool that e-learning platforms can use to teach geography, languages, circuits, computer programming, medical diagnosis, physics, mathematics, chemistry, genetics, and more. They're equipped to consider engagement, grading metrics, and comprehension. AI tools help students sharpen their skills while improving weak areas outside the classroom.
The premise that intelligent systems and people are required to manage various areas of students' academic and social competencies is gaining increasing credence as AI develops in this field. AI will probably not take the role of human experts, but it will be a valuable addition, enabling teachers to better satisfy the varied demands of many kids at once.
Similar to how AI has been used in other industries to automate jobs, it will be useful in education. Professors and teachers typically have to oversee the classroom environment in addition to performing other administrative and organizational duties.
AI techniques can make educational settings accessible to all students, including those who have hearing or vision impairments or speak various languages, on a worldwide scale. The barriers between conventional grade levels and schools can be broken down by AI.
| 2023-03-28T00:00:00 |
https://www.techasoft.com/post/how-is-ai-used-in-education-25-examples-of-artificial-intelligence-in-education
|
[
{
"date": "2023/03/28",
"position": 75,
"query": "artificial intelligence education"
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|
Exploring new opportunities with AI in education
|
Exploring new opportunities with AI in education
|
https://www.microsoft.com
|
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Education leaders, educators, and students are exploring the power of AI to save time, personalize learning, and increase the efficiency of their operations.
|
In schools around the world, education leaders, educators, and students are exploring the power of AI to save time, personalize learning, and increase the efficiency of their operations.
In schools around the world, education leaders, educators, and students are exploring the power of AI to save time, personalize learning, and increase the efficiency of their operations. Although AI has already been used to boost inclusivity and productivity, recent advancements offer the opportunity to reimagine solutions to longstanding challenges in education at a time when they are perhaps needed most.
The potential for AI to extend and scale the work of educators is encouraging. We find ourselves accelerated into a digitally transformed world of education, riding on a pandemic-fueled evolution of innovation. When thoughtfully leveraged, this places AI in a unique and powerful position to help us address challenges we face around ensuring equitable access to education.” Dr. Jose L. Dotres, Superintendent of Miami-Dade County Public Schools
At Microsoft Education, we are focused on three critical areas: empowering our customers to bring their visions to life, enhancing the value we can provide through Microsoft products, and deeply partnering with the education community to responsibly solve some of the most pressing challenges.
Empowering schools around the world
Education organizations are dreaming up ways to responsibly integrate AI tools into teaching and learning and finding that many of these dreams are quickly becoming reality. For example, the United Arab Emirates Ministry of Education set out to evaluate the impact of AI and quickly identified opportunities for integration within their education system. We are excited to partner with them to develop their virtual learning assistants through Azure OpenAI Service, and assist them in their efforts to equip students with the necessary skills to utilize AI in their academic and professional careers.
The future holds numerous opportunities and challenges, and as a Ministry of Education, we are keen to benefit from the opportunities and confront any challenges. We believe that integrating AI within our educational system and harnessing modern and cutting-edge technologies is key to improving the quality of education and equipping our future leaders with the skills they need to flourish in an ever-evolving world. Our partnership with Microsoft to build an AI tutor is another step in our ongoing efforts to enhance the learning experience of our students. We will continue working shoulder to shoulder with our partners to contribute to driving the future of education.” United Arab Emirates Ministry of Education
It is inspiring to enable schools, our partners, and education technology providers to maximize the transformative potential of AI in a variety of ways, such as developing new learning tools, providing an aid for creating content, building AI literacy, improving student services, simplifying scheduling, and more.
Enhancing Microsoft products
At our Reimagine Education event last month, we shared examples of schools that are leveraging Learning Accelerators to improve learning outcomes and save classroom time. Since then, we have been hard at work across the company to reinvent the tools people use every day. We’ve been thrilled to share the new Bing and Edge, Copilot in Power Platform, and a whole new way to work with Microsoft 365 Copilot. These launches have the potential to redefine the way technology can support us in work, life, and learning.
Today we are sharing a preview of passage generation, one of the many ways we plan to support educators by embedding AI technology into our products. Within Reading Progress, passage generation reviews data from Education Insights to help create personalized reading passages based on words or phonics rules a class or specific student found most challenging. Educators can customize the passage by selecting suggested practice words, generating options, and then publishing their passage as a new reading assignment.
Passage generation leverages the power of Azure OpenAI, including enterprise-grade security and built-in tools to detect and mitigate harmful use, plus an additional set of platform innovations to support responsible usage in education. Passage generation is in an initial private preview and will continue to be refined as we learn from and build with our testing community. Community feedback will only become more important as we consider future opportunities for AI-powered features across our products.
Working together responsibly
Our advancements in AI are grounded in our company mission to empower every person and organization on the planet to achieve more and guided by our approach to responsible AI. The Microsoft AI principles of fairness, reliability and safety, privacy and security, inclusiveness, accountability, and transparency are applied to the needs of education institutions, and products are designed in collaboration with industry experts. Further, new innovations are built on Microsoft’s comprehensive approach to security, compliance, and privacy. We will share more information with IT teams as previews expand more broadly so that they can plan with confidence and enable features when it is right for their organization.
The potential applications of AI in education are tremendous. This makes it even more important to ensure that users, from education leaders to individual learners, have the resources to be able to effectively use new AI tools. To this end, we will continue to share relevant learning paths and create new modules to support the ever-increasing importance of digital literacy.
We recognize that AI technology will keep evolving, and that this is only the start of a journey that will require ongoing engagement with leaders, educators, staff, students, and their families. We look forward to continuing to listen, learn, and innovate alongside educational organizations to make the promise of AI a reality. Read Microsoft’s approach to AI to learn more and stay tuned for future updates.
| 2023-03-28T00:00:00 |
2023/03/28
|
https://www.microsoft.com/en-us/education/blog/2023/03/exploring-new-opportunities-with-ai-in-education/
|
[
{
"date": "2023/03/28",
"position": 88,
"query": "artificial intelligence education"
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] |
Exploring the impact of AI on the education industry
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Exploring the impact of AI on the education industry: Eight transformative changes
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https://indiaai.gov.in
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AI has the potential to revolutionize the education industry by automating mundane tasks, improving the quality of teaching, and providing more personalized ...
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AI (Artificial Intelligence) has been making waves in the technology industry and its influence has been felt across all sectors. Nowhere is this more evident than in the education industry, where AI is rapidly transforming the way students learn, the way teachers teach, and the way educational institutions are managed. In this blog, I’ll be exploring the impact of AI on the education industry and how it is transforming the sector.
What is AI and its Impact on Education?
AI is an umbrella term for technologies that enable machines to learn from data and experience and make decisions autonomously. AI has the potential to revolutionize the education industry by automating mundane tasks, improving the quality of teaching, and providing more personalized learning experiences.
The most notable impact of AI on education is the ability to automate mundane tasks. AI can be used to automate administrative tasks such as grading, attendance tracking, and course registration. This frees up teachers’ time and allows them to focus on more important tasks such as curriculum development and student engagement.
AI can also be used to facilitate personalized learning. AI-driven algorithms can be used to recommend learning materials to students based on their individual learning preferences. This type of personalized learning has been proven to increase engagement and motivation and improve student outcomes.
AI is also beginning to be used to improve the quality of teaching. AI-driven algorithms can be used to analyze teaching data and provide teachers with actionable insights into how to improve their teaching. AI can also be used to detect errors in student solutions and provide teachers with feedback on how to correct them.
Finally, AI can be used to improve the management of educational institutions. AI-driven algorithms can be used to optimize resources and forecast student demand, enabling institutions to make more informed decisions.
Benefits of AI in the education industry
AI has the potential to revolutionize the way we teach and learn, making it an essential tool for educators in the future. Some of the key benefits of AI in the education industry are listed below
Personalized Learning: AI can be used to personalize learning experiences and create individualized learning plans for each student. This allows teachers to tailor the curriculum and instructional materials to better meet the needs and interests of their students. Improved Assessment: AI can be used to automate assessment tasks, such as grading essays or providing feedback on student work. This frees up valuable teacher time, allowing them to focus on more important tasks such as curriculum development and student engagement. Increased Engagement: AI-driven algorithms can be used to recommend learning materials based on individual student preferences, increasing motivation and engagement in the classroom. Improved Quality of Teaching: AI can be used to analyze teaching data and provide teachers with actionable insights into how they can improve their teaching methods. Enhanced Resource Management: AI-driven algorithms can be used to optimize resources and forecast student demand, enabling educational institutions to make more informed decisions about resource allocation.
The education sector is currently being impacted by AI and machine learning, in particular, in many different ways. Contrary to what some people would think, using AI in the education industry goes beyond simply marking kids' tests, and both teachers and students can gain a lot from this technology. Let’s look at how AI used in education has bought transformative changes in the education industry.
AI-powered online learning
AI-powered online learning is a system of delivering educational content and instruction to students using artificial intelligence (AI) to personalize the learning experience.
Advantages of AI-powered Online Learning
AI-powered online learning can provide personalized learning experiences to students by analyzing their individual interests, learning styles, and areas of weakness.
AI can be used to automatically grade student assignments and projects, allowing teachers to spend more time providing feedback and helping students.
AI can be used to create simulations and interactive experiences that give students the opportunity to apply their learning in a safe environment.
AI can be used to provide targeted instruction to learners.
Use of AI-Powered Platforms
AI-powered online learning platforms can provide tailored content to students based on their preferences or topics that they need help understanding.
AI can also be used to identify learning patterns and trends and recommend personalized learning materials to students.
AI-powered online learning platforms are able to quickly capture data on students’ progress, allowing teachers to adjust instruction or provide additional help as needed.
AI-powered online learning platforms can use voice recognition to grade spoken responses and provide feedback.
AI can be used to generate reports and data-driven recommendations for teachers, helping them to better serve their students.
AI-powered online learning platforms are able to track student engagement, allowing teachers to make sure that students are staying on task.
AI-powered online learning platforms are able to provide real-time feedback to students, allowing them to stay motivated and engaged with their learning.
Automated Grading
Manual grading frequently consumes a significant amount of valuable time that could be used to improve the student experience, according to teachers.
AI in education can be used to automate assessment or grading tasks. AI-driven algorithms can be used to automatically assess student solutions and provide teachers with actionable insights into how to improve their teaching. Autograding could thus free up instructors' time to focus on what matters most: in-class activities, planning more engaging and personalized classes, and direct interaction with students.
Automated grading tools could be applied to all levels of competitive exams for eg job interviews, and admission exams for academic programs at universities. There are many applicants for these exams, thus grading must be done quickly and accurately and hence there are many benefits associated with Automated grading. Some of the benefits are listed below
Advantages of Automated Grading
Increased Efficiency: Automated grading systems reduce the amount of time educators spend grading multiple-choice tests and free up time for more meaningful activities such as developing lesson plans and providing personalized feedback. Improved Accuracy: Automated grading systems eliminate human errors and provide more accurate grading of tests. Increased Accessibility: Automated grading systems provide students with immediate feedback so they can adjust their studying strategies accordingly. Increased Motivation: Automated grading systems can provide students with positive reinforcement and motivation to do better on their tests. Improved Efficiency: Automated grading systems can save schools money by reducing the need for additional staff to grade tests. Increased Insight: Automated grading systems can provide educators with detailed insights into student performance, helping them better understand their students and provide appropriate instruction.
Challenges of Automated Grading
Although using artificial intelligence in education has many benefits, implementing AI in education also has certain challenges. The following are some possible challenges of employing this technology in education.
One of the major challenges of using AI in education for automated grading is that it can be difficult to accurately assess certain types of tasks. For example, AI algorithms may struggle to accurately assess creative writing or problem-solving tasks. Additionally, AI algorithms may not be able to accurately distinguish between different levels of understanding in a student’s solution.
Another potential issue with using AI for automated grading is that it can be difficult to ensure the fairness and accuracy of the grading process. AI algorithms may be biased against certain groups or favor certain types of solutions over others. Additionally, there is always the possibility that an AI algorithm could produce inaccurate or unfair results due to errors in its design or implementation.
Personalized Learning
Each student has a different learning style, background, needs, and experiences, hence personalized learning involves customizing and adapting educational methods and strategies to better meet each individual learner's needs.
Definition of Personalized Learning
Personalized learning is an approach to instruction that recognizes and capitalizes on individual differences in learning styles, interests, and abilities. It tailors teaching to the needs of each student by providing customized learning experiences, differentiated instruction, and individualized assessments. Personalized learning also emphasizes metacognitive skills, self-regulation, and independent learning.
The objective of personalized learning is to tailor instruction to the unique needs, interests, and skills of each learner.
Each student receives a learning plan that is based on their prior knowledge and preferred learning styles.
Role of AI in Personalized Learning
Artificial intelligence, which combines data collecting and technology, may bring automated and personalized learning strategies to life. AI in education can offer a pivotal role in personalized learning.
AI can also be used to create more personalized learning experiences. AI-driven algorithms can be used to recommend learning materials to students based on their individual learning preferences. This type of personalized learning has been proven to increase engagement and motivation and improve student outcomes.
AI can also be used to customize curriculums to the needs of different educational institutions. AI-driven algorithms can be used to analyze teaching data and provide educational institutions with actionable insights into how to improve their curriculums. AI can also be used to detect errors in student solutions and provide educational institutions with feedback on how to correct them.
Finally, AI can be used to create more personalized virtual reality or augmented reality experiences. AI-driven algorithms can be used to recommend learning materials to students based on their individual learning preferences. This type of personalized learning has been proven to increase engagement and motivation and improve student outcomes.
AI-Assisted Course Design
AI-Assisted Course Design is an approach to designing online courses that leverage artificial intelligence (AI) to help instructors create more effective, engaging learning experiences for students. AI-assisted course design can help instructors identify areas of content that need additional support, develop personalized learning paths for students, and create automated feedback loops that help improve student performance.
Benefits of AI-Assisted Course Design
Increases Efficiency: AI-assisted course design can help to automate the process of course design, allowing instructors to spend less time on administrative tasks and more time on teaching. Improved Accessibility: AI-assisted course design can provide a more accessible learning experience for students with disabilities or those who require alternative formats for learning. Improved Quality: AI-assisted course design can provide more personalized and targeted learning experiences, ensuring that students understand the material and receive an appropriate level of education. Improved Interactivity: AI-assisted course design can allow instructors to provide more interactive learning experiences that engage students and keep them interested in the material. Improved Collaboration: AI-assisted course design can enable instructors to easily collaborate with other instructors, giving them access to a larger pool of resources and knowledge. Improved Analytical Capabilities: AI-assisted course design can provide instructors with valuable insights into student performance and course progress, allowing them to make data-driven decisions about course content and design.
Limitations of AI-Assisted Course Design
Cost: AI-assisted course design can be expensive to implement, as it requires specialized software and hardware. Privacy concerns: AI-assisted course design raises privacy concerns, as it involves collecting and analyzing personal data about students. Lack of human interaction: AI-assisted course design can lack the human connection and personalized guidance that traditional teaching methods provide.
AI-Powered Tutoring
A computer-based learning system called an AI-powered tutoring system uses artificial intelligence to deliver lessons that are similar to those given by a human teacher.
Artificial intelligent tutoring systems let you design a tailored learning environment that offers learners immediate guidance and feedback, typically without human involvement.
Advantages of AI-Powered Tutoring
AI systems learn and improve continuously: To better assist students, AI intelligent tutoring systems are continually developing and evolving.Human teachers typically only impart one approach to problem-solving. Yet, AI instructors are capable of learning new ways from students and remembering them.This implies that they can teach problem-solving rather than simply one particular difficulty at a time.
Favorable for multimedia learning: Artificial intelligent tutoring solutions offer text, multimedia, simulations, and even games while the majority of online learning platforms only give video and text.AI tutors can deliver information in a method that best suits the learner by presenting material, assignments, and different scenarios in synchronization with context.
Limitations of AI-Powered Tutoring
AI-powered tutoring is limited in its ability to provide personalized instruction and feedback to students. AI-based tutoring systems are programmed with a set of predefined rules, which may not be able to account for the individual learning styles and needs of each student. AI-powered tutoring systems are also limited in their ability to provide feedback that is tailored to each student's unique strengths and weaknesses. As such, these systems may not be able to identify and address areas where a student may need extra help. AI-powered tutoring systems also lack the ability to offer emotional support or encouragement to students, which can be a key factor in helping students stay motivated and engaged in their learning. AI-powered tutoring systems can be expensive to set up and maintain, making them out of reach for many schools and households. AI-powered tutoring systems can also be vulnerable to hacking or other security risks, which can put student data at risk.
AI-Based Educational Games
AI-based educational games are games that use artificial intelligence (AI) to create an immersive and interactive learning experience. Examples of AI-based educational games include:
CodeCombat - a game that teaches programming basics through a series of levels and challenges. A Planet of Mine - a game that teaches players about environmental sustainability and ecology. DragonBox - a game that teaches basic algebra concepts and problem-solving. MathMINDs - a game that teaches math skills and problem-solving. Spell n' Grammar - a game that teaches spelling and grammar.
Benefits of AI-Based Educational Games
Improved Learning Outcomes: AI-based educational games can be designed to provide personalized learning experiences that are tailored to the individual student’s needs. This can help students to better understand and remember the material and achieve better learning outcomes. Increased Engagement: AI-based educational games use interactive, immersive methods to engage students in the learning process. AI-based educational games can also help to keep students motivated and focused on the task at hand. Increased Accessibility: AI-based educational games can be made accessible to students with a range of disabilities, helping to make educational content more accessible to all students. Enhanced Problem-Solving Skills: AI-based educational games can help students develop their problem-solving skills by providing them with puzzles and challenges that require creative thinking and logical reasoning. Improved Self-Esteem: AI-based educational games can be designed to reward students with positive feedback and rewards when they complete tasks. This helps to build their self-confidence and self-esteem.
Limitations of AI-Based Educational Games
Cost: AI-based educational games can be costly to develop and maintain due to the sophisticated programming and technology required. Adaptability: AI-based educational games may not be able to adapt quickly enough to keep up with the student's changing needs and may be too advanced for some students. Accessibility: AI-based educational games may not be accessible to all students due to their cost, availability, or complexity. Engagement: AI-based educational games may not be able to engage students as effectively as more traditional methods of teaching. Privacy: AI-based educational games may not be able to protect student data as effectively as more traditional methods of teaching.
Virtual Reality and Augmented Reality
AI can also be used to create virtual reality (VR) and augmented reality (AR) experiences in the classroom. AI-driven algorithms can be used to create immersive experiences that can be used to teach students in a more engaging and interactive way. This type of immersive learning has been proven to increase engagement and motivation and improve student outcomes.
AI can also be used to analyze student interactions in virtual reality or augmented reality environments and provide teachers with actionable insights into how to improve their teaching. AI can also be used to detect errors in student solutions and provide teachers with feedback on how to correct them.
Finally, AI can be used to create more personalized virtual reality or augmented reality experiences. AI-driven algorithms can be used to recommend learning materials to students based on their individual learning preferences. This type of personalized learning has been proven to increase engagement and motivation and improve student outcomes.
Conclusion
The potential of AI in the education industry is vast, and it is transforming the way students learn, the way teachers teach, and the way educational institutions are managed. AI is being used to automate mundane tasks, improve the quality of teaching, and provide more personalized learning experiences. AI is also being used to power learning platforms, automate tutoring and assessment tasks, customize curriculum, create virtual reality and augmented reality experiences, and automate administrative tasks. All of these applications of AI have the potential to revolutionize the education industry and improve student outcomes.
As AI continues to evolve, its potential to revolutionize the education industry will only increase. It is essential that educational institutions keep up with the latest trends in AI and incorporate AI-driven technologies into their operations to stay ahead of the curve. Doing so will enable them to provide students with more personalized learning experiences and improve their teaching quality.
| 2023-03-28T00:00:00 |
https://indiaai.gov.in/article/exploring-the-impact-of-ai-on-the-education-industry-eight-transformative-changes
|
[
{
"date": "2023/03/28",
"position": 94,
"query": "artificial intelligence education"
}
] |
|
Artificial intelligence
|
Contemporary Educational Technology
|
https://www.cedtech.net
|
[
"Betül Yıldızhan Bora",
"Cansu Şahin Kölemen",
"Izida I. Ishmuradova",
"Sergei P. Zhdanov",
"Sergey V. Kondrashev",
"Natalya S. Erokhova",
"Elena E. Grishnova",
"Nonna Yu. Volosova",
"Wu Xiaofan",
"Nagaletchimee Annamalai"
] |
Collaborative learning and skill development for educational growth of artificial intelligence: A systematic review
|
Research Article
Mohammed Muneerali Thottoli, Badria Hamed Alruqaishi, Arockiasamy Soosaimanickam
CONT ED TECHNOLOGY, Volume 16, Issue 1, Article No: ep485
ABSTRACT
Purpose: Chatbots and artificial intelligence (AI) have the potential to alleviate some of the challenges faced by humans. Faculties frequently swamped with teaching and research may find it difficult to act in a parental role for students by offering them individualized advice. Hence, the primary purpose of this study is to review the literature on chatbots and AI in light of their role in auto-advising systems. The authors aimed to gain insights into the most pertinent topics and concerns related to robo academic advisor and identify any gaps in the literature that could serve as potential avenues for further research.
Design/methodology/approach: The research employs a systematic literature review and bibliometric techniques to find 67 primary papers that have been published between 1984 and 2023. Using the Scopus database, the researchers built a summary of the literature on chatbots and AI in academic advice.
Findings: Chatbot applications can be a promising approach to address the challenges of balancing personalized student advising with automation. More empirical research is required, especially on chatbots and other AI-based advising systems, to understand their effectiveness and how they can be integrated into educational settings.
Research limitations/implications: This research’s sample size may restrict its findings’ generalizability. Furthermore, the study’s focus on chatbots may overlook the potential benefits of other AI technologies in enhancing robo academic advising systems. Future research could explore the impact of robo academic advisors in diverse societal backgrounds to gain a more comprehensive understanding of their implications.
Practical implications: Higher educational institutions (HEIs) should establish a robo academic advising system that serves various stakeholders. The system’s chatbots and AI features must be user-friendly, considering the customers’ familiarity with robots.
Originality/value: This study contributes to a better understanding of HEIs’ perceptions of the adoption of chatbots and AI in academic advising by providing insightful information about the main forces behind robo academic advising, illuminating the most frequently studied uses of chatbots and AI in academic advising.
Keywords: Artificial intelligence, chatbots, robo, academic advisor, human interaction
| 2023-03-28T00:00:00 |
https://www.cedtech.net/keyword/artificial%20intelligence
|
[
{
"date": "2023/03/28",
"position": 98,
"query": "artificial intelligence education"
}
] |
|
AI in Education Explorer
|
AI in Education Explorer
|
https://www.credly.com
|
[] |
Faculty can earn the AUAI Explorer badge by completing eight tasks, each related to the module content in the AI in Education course.
|
AI in Education Explorer Issued by Auburn University
Faculty can earn the AUAI Explorer badge by completing eight tasks, each related to the module content in the AI in Education course. These tasks are designed to help make connections between what content of the course and how to actually use AI in your classroom. Each completed task brings participants one step closer to understanding AI and its implications for teaching and learning in higher education.
| 2023-03-28T00:00:00 |
https://www.credly.com/org/auburn/badge/ai-in-education-explorer
|
[
{
"date": "2023/03/28",
"position": 100,
"query": "artificial intelligence education"
}
] |
|
Can We See the Impact of Automation in the Economics Statistics?
|
Why Can't We See Automation in the Economics Statistics?
|
https://unchartedterritories.tomaspueyo.com
|
[
"Tomas Pueyo",
"Jon Erlichman"
] |
In other words: All the numbers we're seeing are perfectly consistent with a growing automation that is destroying jobs, led by Internet ...
|
This is the 3rd article in the Automation series. Article 1 covers what jobs will disappear, and article 2 how fast they will disappear.
Summary: There’s a bunch of reasons why we can’t see automation in the economics statistics, but the key one is that automation now mainly destroys jobs through the digital economy (soon, AI, and later, robotics) and the digital economy is very special, with high fixed costs that are going down, and near zero marginal costs. This is causing all sorts of effects in the economic data. But there’s one impact that can be seen in: inequality. This matters because if this is true, it will keep going up.
Luddites vs Techno-foolish
If automation will eliminate so many jobs, why can’t we already see it in the economics statistics?
This is the core argument that techno-foolish people use to claim that automation is unlikely to eliminate jobs. The following is a fictional conversation between a techno-foolish person and me, to illustrate their points and my take on them. I hope you enjoy it!
TOMAS PUEYO (TOMAS): I fear automation, especially AI, will destroy lots of jobs very fast and we won’t be able to create enough of them fast enough, which will drive us to lots of misery and social conflict. TECHNO-FOOLISH (TF): “You’re such a luddite! This won’t happen. People have feared being replaced by robots for two centuries, since the dawn of the Industrial Revolution. And look at us today! An average person has access to more goods and services than the richest person 200 years ago. Unemployment is at an all-time low. Where is all this disruption we thought computers would bring? Why would this time be different? Every time I read about this “rise of the robots” fear, I feel the urge to tear my hair out. While it makes a great science fiction story, so far there just aren’t any signs that it’s happening.”
TOMAS: I’m a techno-optimist like you. But I don’t want to be techno-foolish. Economists studying this usually look for signs of the impact of automation in four stats: unemployment, productivity, innovation, and inequality, right? So let’s look at each one. Let’s start with unemployment.
Unemployment
TF: In today’s America, almost everyone that wants a job has one. The prime-age employment-to-population ratio — in my assessment, the best single indicator of the labor market — is now at about the same level as at the peaks of 2019, 2007, and 1990.
TOMAS: What if we look at the data in a slightly different way? What if we go back to 1950 instead of 1980, and look at all people above 16 instead of those 24-55 ?
Interesting. This is not showing full employment anymore. What’s going on? Let’s break this down by men and women.
Of course, women joined the workforce in droves! This is great! But their participation rate has gone down since the 2000s… Now let’s look at men.
Ouch. 22% of men used to work but don’t anymore! This is not just a US pattern.
Here you can see the drop in participation of prime age male labor. It was at 93% in the US in 1990. The reason why it’s different from the level in the previous chart is because that one shows all males, not just prime age males. As we’ll see below, male prime age labor has gone down in the US too.
TF: Of course, this is due to education:
These are good trends! More females are working, people are studying more…
TOMAS: I agree. And there are many more. For example, the US population has grown 20x in the last 200 years. More population means more diverse needs, more specialization, more jobs.
Another force has been urbanization: When people move to cities, they become more productive, they consume more, and more niche jobs emerge.
Another force has been globalization: More countries participating in trade means higher global productivity and consumption overall. Female work, education, population growth, immigration, globalization, urbanization, and many more have influenced work participation rates. It’s nearly impossible to look at these numbers in aggregate and tease out the impact of automation. For example, you could easily say: “Despite a unique spell of growth in which prime-age males have been improving their productivity by moving to cities and getting more education, they keep working less and less.” Because that’s what’s happening:
If we can create completely different narratives on what’s happening based on the same data, it means this macro approach is not very useful. There are too many variables, and it’s impossible to isolate them all. To see the impact of automation, we need to dig into the details. So what exactly are we talking about when we say that automation will eliminate jobs?
TF: I agree! That’s why it’s important to dive into the details of innovation, and break down jobs into tasks.
Innovation: Jobs vs. Tasks
TOMAS: Maybe that's true. But every time I read one of these papers, I see the same flaw: They tend to look at jobs inside the companies that are doing the automating—or in adjacent companies. These are not the jobs that will disappear! The companies doing the automating will win. The losers are elsewhere.
TF: Where are jobs supposed to be disappearing then?
TOMAS: Let’s think about the industrial revolution. When machines came, artisans lost their jobs and low-skilled workers gained new opportunities working with machines and increasing their salaries. The winners were the ones doing the automating. The losers were those being replaced. What’s happening today?
When the number of travel agents halved in 12 years, it wasn’t because some travel agents invested in automation and became more productive than others. It’s because the Internet came, and companies like Booking.com or TripAdvisor eviscerated them. But we only need a few companies like Booking because they build things once and can then serve every customer on Earth. That means Booking and its competitors employ a fraction of workers compared to old travel agencies.
Google and Facebook are the big ad behemoths. Within a decade, they eclipsed printed advertising revenue, and with it the livelihoods of tens of thousands of journalists. And since Google makes 10 times more per worker than newspapers, they employ 10x fewer employees for the same level of revenue. Not a great way to create jobs…
Netflix obliterated Blockbuster, creating 130x more value per employee, and destroying tens of thousands of jobs .
Kodak had 150,000 employees in the late 1990s. Then Apple’s iPhone came along. The nail in the coffin was Instagram, created by a team of a dozen people, and sold for $1B, for nearly $100M of value created per head. Now most of your photography needs are much better covered than with Kodak, but you also spend much less on photography. Most of Kodak’s employees were laid off, but photography has not created equivalent companies. The same pattern happens over and over: Digital economy companies are much more efficient, so they destroy traditional industries. But this efficiency increases the quality of products while cutting the price. They have fewer employees, and the ones they have make more money. WhatsApp, sold for $22B in 2014, created $400M of value per employee. The result is that the Tech share of GDP increases.
And Tech’s productivity is through the roof, compared to other sectors (as we saw before).
These companies’ employees are so productive that they are among the best-paid. 12 of the top 25 companies in terms of median salary in the US are in Tech. Companies like Alphabet (Google), Meta (Facebook), Netflix, or Nvidia pay over $200k per employee per year as a median !
So why are all the papers missing the impact of automation today? Because the creation of most of the good jobs is happening in the online digital economy, and the destruction is happening across other industries.
From Variable to Fixed
TOMAS: And you know what makes the digital economy special: Zero marginal costs. Outside of Tech, most industries have a big marginal cost: Every time they serve a customer, they must pay for the manufacturing of the product, the packaging, the transportation, the retail rent, the salesperson… Each one of these activities requires people , who cost money. This means wages for all of them. Online digital products don’t work like this, because bits can copy digital products infinitely for close to nothing . Tech companies spend most of their budget in building their product, which is a fixed cost. That means the founders and the few people needed to build and distribute the product (those responsible for that fixed cost) share all the value created . If you have variable costs, it’s very hard to compete with a company that doesn’t have them. So a very typical mechanism of automation destroying jobs is by companies turning a variable cost into a fixed cost, then reducing prices, then expanding the business, eating the lunch of incumbents, and killing entire industries. If we look for this effect in the data, we find it:
The original article also shows the covariance effect, which I eliminated because it doesn’t add much to the argument and makes it much harder to understand.
The graph shows the evolution of average productivity, broken down by whether industries are becoming more productive, people are moving to more productive industries, or they’re moving into industries that are becoming more productive. It turns out that the entire increase in productivity comes from industries becoming more productive. That makes sense: Tech is making people more productive. Every hour of work is producing more. But if this increase in productivity was so good that productive Tech companies needed more workers, we would see workers move into more productive industries. This is not what we see. What we see is workers moving to industries that are less productive. Not only that: They’re moving to industries that are shrinking in productivity (think education, construction, or healthcare). This is consistent with the story that is unfolding here: Tech becomes more productive and employs more people as a result. But it also destroys other industries. Their employees must move into other, lower productivity industries . This is why many job and task analyses miss the impact of automation: The companies doing the automating employ more people. The ones suffering are in other industries. And on balance, companies engaging in automation employ fewer people than the jobs they destroy in other industries.
TF: What about other innovation statistics, like patents?
Patents
TOMAS: What company has the most patents?
And yet IBM is not a digital economy company . Similarly, other companies with lots of patents tend to be involved in hardware more than software, which also means they have marginal costs. The few software companies that publish patents are doing some serious fundamental research, like Google on AI. For the rest, they’ve realized that it’s hard to protect their advantage through patents, and it makes more sense to have other defensibility mechanisms (like network effects or brand, for example). As a result, online digital economy companies publish fewer patents than their innovation and revenue would suggest. So if you’re using patents as an indicator of innovation, you’re going to believe innovation is decreasing, even if it’s increasing.
Robotics
TF: What about robots? Robots are correlated with — and probably cause — higher employment in the companies and areas where they’re adopted. What’s happening is that companies that use more robots hire more humans (and retain their existing humans) in jobs that complement the robots. That’s exactly what we saw with previous waves of automation — people find new roles, robots increase their productivity, and they get paid more. Looking at the countries that use the most robots in their manufacturing industry, it seems likely that this virtuous cycle is happening even at the level of whole nations.
TOMAS: That is probably true. Researchers love looking at robots to measure the impact of automation: It’s much easier to quantify the investment in machines, and see how each machine impacts jobs, than to notice in the statistics what a human can create armed with a computer, an Internet connection, and a couple of software applications. The result is that most research that looks into the impact of automation of jobs is centered around physical machines and robots. But robotics have two barriers that the digital economy doesn’t have:
They produce physical products, so they have marginal costs, so they have to employ lots of people. Robotization technology is not good enough to fully replace humans. For example, many tasks in the textile industry have proven impossible to automate. AI might change this, but we’re still in the phase of fundamental research here.
The result is that robotic automation behaves differently than computer/Internet/AI automation. For now .
TF: It would take a breakthrough in robotics to fully automate the production process. This would definitely destroy jobs, but you’d still need the products to be packaged, transported, and sold, so robotics automation won’t annihilate broad swaths of jobs any time soon.
TOMAS: I agree. As I mentioned before, my guess is that the breakthrough will come from the application of AI to robotics, which will open up the physical, non-routine tasks to automation. I believe this will happen in the coming years, but until that happens, these jobs are safe. They’re not the ones I worry about the most imminently.
TF: OK so we agree on that one. What’s next?
TOMAS: Productivity data.
Productivity vs The Great Stagnation
The computer age is everywhere except for the productivity statistics.—Solow Paradox.
TF: Yes, our growth has been terrible in the last few decades:
Our total factor productivity (TFP) was growing very fast in the past, but appears to have slowed down since 1973. Shouldn’t we see it pick up thanks to automation?
TOMAS: The great stagnation theory always takes 1947 as the baseline, which sounds awfully biased since it’s just after WWII, one of the best economic times in all of history thanks to the post-war reconstruction, especially for the US given its sudden role as the biggest world power, lending money to its allies so they could buy US products. Also, other factors can have caused this slump: the end of Bretton Woods in 1971, the oil shock of 1973, globalization, the baby boom… The Internet economy couldn’t have caused this slump, since it started growing in the 1990s, increasing productivity then (productivity increased in the 1990s and 2000s). So that’s one possibility.
TF: Then shouldn’t we see productivity continue increasing in the 2010s?
TOMAS: Perhaps. But maybe we don’t measure productivity well.
Tech Is Deflationary
TOMAS: Sometimes, it’s healthy to raise our nose from our spreadsheets and look around.
AI goes so fast that when I wrote this article, Midjourney was in its version 4, which still failed at fingers. Also the arm is floating in emptiness. Just a few weeks later, V5 mostly avoids these types of errors.
When Blockbuster expanded internationally, it had to build subsidiaries in every country, employ locals, negotiate with national and local governments, buy videotapes, ship them, maintain them, replace them… All of these cost money on top of what they already spent, which meant higher prices. Netflix has none of that. They built their internationalization infrastructure, and after that their international strategy is mostly translations. So Netflix actually reduced prices as it expanded internationally .
When most of your costs are fixed, and variable costs are nearly zero, you want as many customers as possible to spread the fixed costs. You are motivated to keep your prices low or even reduce them to gain market share. The result is a world that can see Netflix for $10 per month or less, or use Google, Facebook, YouTube, TikTok, and Wikipedia for free . Despite these variable costs close to zero, Tech companies haven’t taken over the world yet. What has prevented them from doing so? One element has been internal tools for things like payments, security, development, quality assurance, project management… Early on, Tech companies had to develop these things internally. But over time, SaaS companies have been automating them, making these tasks simpler and cheaper. The result is that it costs less and less to create a digital product. Another barrier to the adoption of Tech products has been infrastructure like processing power, data storage, or bandwidth—the sort of deflationary products that have exponentially increased their performance while reducing their price year after year for decades. You end up with a reality-altering product like the iPhone, with its universe of apps, but barely notice either on the economic statistics, simply because they’re both deflationary. So for the digital economy, variable costs are near zero, fixed costs go down over time, and the infrastructure they depend on becomes cheaper and better over time. The result is a deflationary industry that gobbles up the rest of the economy. Naturally, intelligent people notice this. They realize this is the way to get fabulously rich or have a big impact. So entrepreneurs, inventors, and investors flock to it, leaving other industries, which don’t become more productive. This further accelerates the deflation from Tech at the expense of more inflationary industries.
Digital media consumes a large and growing share of our waking lives, but these goods and services go largely uncounted in GDP. That’s because the measure is based on what people pay for goods and services. If something has a price of zero, then it typically contributes zero to GDP.—Erik Brynjolfsson and Avinash Collis, How Should We Measure the Digital Economy?
TOMAS: Also, this whole process takes time.
It’s Still Early
There will be a period when the technologies are developed enough that investors, commentators, researchers, and policy makers can imagine their potentially transformative effects.—Artificial Intelligence and the Modern Productivity Paradox, Brynjolfsson et al.
TOMAS: It took 50 years for electricity to truly impact productivity. For decades, factories were organized around the steam-powered shaft. The benefits of changing steam power to electricity were low, but the cost was high.
The true value of moving to electricity only became obvious once the entire manufacturing concept was rethought from the ground up, which happened decades after the discovery of electricity and electric motors . We’re still in the early phases of Internet deployment. The Internet and computer technologies still account for a small share of the economy, and we haven’t fundamentally rethought how we work around them. For example, it took a pandemic to get us to work remotely. But the bulldozer of software continues eating the world. Tech companies started with the low-hanging fruit, with the explosion of the 1990s and 2000s. Then we had cloud computing and mobile. Now we have mobile, remote work, AI, no-code, crypto, VR, soon AR. This is a trend that takes decades, not years. As these tools become more widespread and a bigger share of the economy is eaten by software, its impact on economic statistics will become more obvious.
It wasn’t until the late 1980s, more than 25 years after the invention of the integrated circuit, that the computer capital stock reached its long-run plateau at about 5 percent (at historical cost) of total nonresidential equipment capital.—Artificial Intelligence and the Modern Productivity Paradox, Brynjolfsson et al.
If we summarize what we’ve discussed so far, this is why it’s hard to see the impact of automation on economic data:
We can’t see it in aggregate data of unemployment: There are too many confounding effects. Most of the impact in the last few decades—and in the future—is likely to come from the digital economy, which has some peculiarities. Patents are mostly irrelevant in the digital economy. Most research on automation pays attention to robotics and physical machines rather than the digital economy. Tech is deflationary, so it makes sense that you wouldn’t be able to see it in productivity data. In fact, when we break down productivity data into its components, we see that the story is consistent with the ravages of the digital economy, as productivity improves in individual industries, but is reduced by people being displaced into lower-productivity industries.
TF: You’re agreeing with me then! We can’t see the impact of automation on unemployment, innovation, or productivity data. Does it matter? Should we even worry?
TOMAS: Yes, because where we can see it is in inequality.
Inequality
TOMAS: The story I’ve been painting suggests that the digital economy creates a few rich people while it destroys other industries and their jobs. Some people are able to recycle into better jobs, but the vast majority have to move to lower-paying ones.
If this is true, we should see inequality rising, with more high-paying jobs, more low-paying ones, and a hollowing out in the middle. Conversely, if the digital economy is not destroying jobs, we should see inequality stable or shrinking, and plenty of new industries appearing to employ many more people. Is that what we see?
Do We Have New Jobs?
TF: We do see many more new industries employing more people. Just consider all the new jobs that didn’t exist a few years or decades ago: social-media manager, data scientist or podcast producer.
TOMAS: How many social media managers are there in the world? How many podcast producers? And why do you use these examples instead of the jobs that have actually grown with Tech, such as the over 3.5 million Uber drivers or Amazon’s more than 1.5 million workers—most of whom work in logistics? The truth is that automation has created a lot of low-paying jobs and a few high-paying ones, while automating many more. Here are the top 50 most common jobs in the US. 47 of them have been around for 60 years or more:
The only three new ones are, you guessed it, linked to the digital economy: software developers, support agents, and computer information analysts. In other words, two of the only three new jobs that employ a lot of people are trying to automate everybody else’s job.
Automation in the US has led to a polarization of the labor market: Middle-income jobs that used to perform routine tasks were replaced by lower-income jobs, while those at the top of the income distribution experienced significant gains, leading to an increase in economic inequality.—Why are there still so many jobs? The history and future of workplace automation. Journal of economic perspectives. David Autor.
TOMAS: And this is not just in the US. The same is happening in the OECD. If this was true, we would see an increase in income inequality. We do.
Source . To be clear, this is a correlation, not yet a clear causation. Like before, there are many confounding factors, such as deregulation, globalization, and others. So take this with a grain of salt.
Across OECD countries, the share of income going to the top 1% has been growing. So why can’t Skeptics see it? There are at least three reasons.
Cutting Data
TF: But for the last decade or so, wages at the low end of the distribution (1st and 2nd quartiles) have been outpacing wages at the high end.
TOMAS: I looked at the source of the data, and I think you get a clearer picture if you actually compare high-skill vs low-skill jobs:
This tells the opposite story! But I’d argue high skill vs low skill pay is closer to what we’re talking about than quartiles. The most famous researchers on this topic, Acemoglu and Restrepo, just published a paper showing something similar. Here’s the graph, indexing salaries to 1963. We can see the big divergence since the 1980s.
This graph shows the cumulative growth of real hourly wages for women of different educational levels. The one for men shows the same pattern, but with an even bigger gap for outcomes between types of education. I didn’t want to be alarmist so I picked the women’s one. Source .
Notice one more thing: These are wages. That’s the lion’s share of people’s income… But not for the richest.
Capital Gains
TOMAS: The richer you are, the more you make from your investments and businesses rather than your wages.
Calculating income just from wages will miss out on the biggest source of inequality. Also, this is consistent with the story I shared previously: If the digital economy is one of the biggest drivers of inequality, then founders and early employees are the ones making most of the money. And how do they make most of it? Not through their salaries. Through stock and stock options. They earn shares of the companies that grow to be worth billions. Not only that, but usually the taxation on this income is advantageous.
Taxation
TOMAS: The taxation for long-term capital gains tends to be much lower than for wages. In the US, the top income bracket at the federal level pays 37% of taxes from compensation, but only 15% for capital gains.
That said, taxes do reduce inequality.
Look at the red line. It shows that, between 1980 and 2014, the richer you were, the faster your income grew. Those in the bottom 20% actually make less money now than they did in 1980 (the red line dips below 0% at the 20th percentile). The income change really grows exponentially for the top 0.1%, and even faster for the top 0.01%, and so on. So the red line is a stark illustration of growing inequality. The blue line, meanwhile, shows that the income inequality after redistribution (taxes and social services) is much lower than before taxes: The blue line is above 0% annual growth for all workers, and is above the red line for all but the richest. And this is true across the OECD.
For each country, the red line shows the Gini coefficient of inequality for that country before redistribution, and the blue line shows it after. We can see that the US is among the most unequal countries, but it also has a lot of redistribution.
TF: What does it matter? If inequality is tamed by tax redistribution, isn’t the system working?
TOMAS: This matters because if inequality before taxes is increasing, it means the economic system, fueled by automation, is causing this inequality. More of the same will mean more inequality before taxes, which gives those owning that wealth a bigger reason to use their wealth to reduce taxes—or to escape taxation altogether.
Summary: Why Some People Don’t See the Impact of Automation in Economic Statistics
The big story of automation in the last few decades and the coming ones should be the impact of the digital economy—with its computers and Internet and AI and mobile and VR—not robot automation or simple computerization.
The digital economy has something special to it: Because it has mostly fixed costs and not variable costs, it concentrates wealth in the few builders, not on many operators.
If that’s what’s happening, we should see the productivity of individual industries increase, while people who are pushed out of their jobs move to lower-paying jobs in industries safe from the digital economy. That’s what we’ve been seeing so far.
Another way we should be able to see this in the data is through increasing inequality. We do.
This is accelerating, because the development of the digital economy is exponential: The more it grows, the better tools appear to build digital products, the more digital infrastructure is developed, the more people are educated about digital products, and the faster penetration of new digital products becomes, destroying other jobs faster.
When Skeptics don’t see the inequality in the statistics, it’s usually because they don’t account for capital gains or redistribution.
We shouldn’t see the impact of automation in other places. For example, aggregate unemployment data: There are plenty of confounders.
Also, people need to eat, so they still need work, even if they work in worse jobs overall. So unemployment won’t be the canary in this coal mine, it will be inequality.
We can see this in productivity data, with people moving to lower-productivity jobs. That’s their only option.
And males are dropping out of the workforce too.
We can’t see the impact of automation in the economy through patent data, because patents don’t matter as much in the digital economy.
We can’t see automation destroying jobs in firms and industries that invest in automation, because these are the winners: They increase their productivity and employ more people. You need to look at the industries they replace. Those are the ones suffering.
We can’t see it in robotization because at this point, robotization would need a big jump in productivity from AI to start automating core human tasks. The action is more on the digital economy side.
We can’t see it in productivity data for several reasons: The reference of WWII is biased, the slump predates the Internet economy, there are confounding factors, it’s still early, and Internet technologies are deflationary. Oh, and it might have still increased productivity despite all of that (seen as the bump in the 1990s-2000s).
In other words: All the numbers we’re seeing are perfectly consistent with a growing automation that is destroying jobs, led by Internet technologies.
Let’s be clear: I’m not categorically stating that most jobs are doomed and we’re about to fracture society. What I’m saying is that we can’t tell yet, but there’s a serious chance this might happen.
Why does it matter?
“When labor becomes electricity, the cost of physical goods plummets . The robotic economy holds the promise of making everything far cheaper, of ushering in an age of abundance. We just need to get to the other side of this coming economic disruption in one societal piece.”—Balaji Srinivasan.
We can’t stop technology. And neither should we try: In the long term, it makes everybody’s lives much better. But technology can create major social trauma in the short term. We can’t ignore that.
Fearing technology is naive.
But denying its job-destruction potential is dangerous.
We need a plan to solve this.
Share
I will write about this topic in future articles, including things like:
How might inequality evolve?
How might this lead to societal collapse, and how can we avoid it?
Why should the history of the industrial revolution scare us for the next few decades rather than comfort us?
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| 2023-03-28T00:00:00 |
https://unchartedterritories.tomaspueyo.com/p/automation-impact-on-economics-stats
|
[
{
"date": "2023/03/28",
"position": 78,
"query": "job automation statistics"
},
{
"date": "2023/03/28",
"position": 78,
"query": "job automation statistics"
}
] |
|
Generative AI set to affect 300 million jobs across major economies
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Generative AI set to affect 300 million jobs across major economies
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https://arstechnica.com
|
[] |
Generative AI set to affect 300 million jobs across major economies ... Technology could boost global GDP by 7% but also risks creating " ...
|
Sorry, I don’t buy it. If anyone following the DoNotPay saga should have. Pretty good sense of why AI isn’t replacing lawyers anytime soon. In general, AI is a really useful tool to make research more effective, but generative AI is going to range from massive headache to moderate assist for the foreseeable future. The current and foreseeable future generations of generative AI simply cannot know how to present an argument in a situation where there’s a dispute between humans about which approach is right. Generative AI can make things up and isn’t very good at faithfully relying on references, which is a real liability. If an AI tells you a source stands for X, when it really stands for Y, that’s actually less useful than just reading the source yourself. And generative AI does that A LOT.
Administrative staff? In 2023, the most important qualifications for administrative staff is that they’re trustworthy and make good judgements. AI isn’t trustworthy and cannot make judgments.
AI is here, and it’s going to find its place in a whole bunch of applications (probably), but it’s fundamentally a pattern matching tool. There are a lot of jobs that have pattern matching components, but relatively few that don’t have a next step of “and so what does that mean?” AI fundamentally can’t do that next step, but it will lie to you and give you an answer.
One of the best use cases for AI (IMO) is to feed it medical data and ask it whether the person has cancer. We already do this. Does anyone want to fire their radiologist and start chemo because a computer told them to?
| 2023-03-28T00:00:00 |
2023/03/28
|
https://arstechnica.com/civis/threads/generative-ai-set-to-affect-300-million-jobs-across-major-economies.1490984/
|
[
{
"date": "2023/03/28",
"position": 59,
"query": "generative AI jobs"
}
] |
300 million jobs could be affected globally, says Goldman Sachs
|
ChatGPT and AI automation: 300 million jobs could be affected globally, says Goldman Sachs
|
https://www.cnn.com
|
[
"Michelle Toh"
] |
ChatGPT and AI automation: 300 million jobs could be affected globally, says Goldman Sachs | CNN Business.
|
Hong Kong CNN —
As many as 300 million full-time jobs around the world could be automated in some way by the newest wave of artificial intelligence that has spawned platforms like ChatGPT, according to Goldman Sachs economists.
They predicted in a report Sunday that 18% of work globally could be computerized, with the effects felt more deeply in advanced economies than emerging markets.
That’s partly because white-collar workers are seen to be more at risk than manual laborers. Administrative workers and lawyers are expected to be most affected, the economists said, compared to the “little effect” seen on physically demanding or outdoor occupations, such as construction and repair work.
In the United States and Europe, approximately two-thirds of current jobs “are exposed to some degree of AI automation,” and up to a quarter of all work could be done by AI completely, the bank estimates.
If generative artificial intelligence “delivers on its promised capabilities, the labor market could face significant disruption,” the economists wrote. The term refers to the technology behind ChatGPT, the chatbot sensation that has taken the world by storm.
ChatGPT, which can answer prompts and write essays, has already prompted many businesses to rethink how people should work every day.
This month, its developer unveiled the latest version of the software behind the bot, GPT-4. The platform has quickly impressed early users with its ability to simplify coding, rapidly create a website from a simple sketch and pass exams with high marks.
Further use of such AI will likely lead to job losses, the Goldman Sachs economists wrote. But they noted that technological innovation that initially displaces workers has historically also created employment growth over the long haul.
While workplaces may shift, widespread adoption of AI could ultimately increase labor productivity — and boost global GDP by 7% annually over a 10-year period, according to Goldman Sachs.
“Although the impact of AI on the labor market is likely to be significant, most jobs and industries are only partially exposed to automation and are thus more likely to be complemented rather than substituted by AI,” the economists added.
“Most workers are employed in occupations that are partially exposed to AI automation and, following AI adoption, will likely apply at least some of their freed-up capacity toward productive activities that increase output.”
Of US workers expected to be affected, for instance, 25% to 50% of their workload “can be replaced,” the researchers added.
“The combination of significant labor cost savings, new job creation, and a productivity boost for non-displaced workers raises the possibility of a labor productivity boom like those that followed the emergence of earlier general-purpose technologies like the electric motor and personal computer.”
— CNN’s Nicole Goodkind contributed to this report.
| 2023-03-29T00:00:00 |
2023/03/29
|
https://www.cnn.com/2023/03/29/tech/chatgpt-ai-automation-jobs-impact-intl-hnk
|
[
{
"date": "2023/03/29",
"position": 26,
"query": "ChatGPT employment impact"
},
{
"date": "2023/03/29",
"position": 6,
"query": "ChatGPT employment impact"
},
{
"date": "2023/03/29",
"position": 2,
"query": "artificial intelligence employment"
},
{
"date": "2023/03/29",
"position": 1,
"query": "automation job displacement"
},
{
"date": "2023/03/29",
"position": 1,
"query": "AI unemployment rate"
},
{
"date": "2023/03/29",
"position": 2,
"query": "job automation statistics"
},
{
"date": "2023/03/29",
"position": 5,
"query": "AI job creation vs elimination"
},
{
"date": "2023/03/29",
"position": 3,
"query": "AI economic disruption"
},
{
"date": "2023/03/29",
"position": 3,
"query": "ChatGPT employment impact"
},
{
"date": "2023/03/29",
"position": 12,
"query": "artificial intelligence workers"
},
{
"date": "2023/03/29",
"position": 33,
"query": "ChatGPT employment impact"
},
{
"date": "2023/03/29",
"position": 33,
"query": "ChatGPT employment impact"
},
{
"date": "2023/03/29",
"position": 33,
"query": "ChatGPT employment impact"
},
{
"date": "2023/03/29",
"position": 35,
"query": "ChatGPT employment impact"
},
{
"date": "2023/03/29",
"position": 2,
"query": "ChatGPT employment impact"
},
{
"date": "2023/03/29",
"position": 27,
"query": "automation job displacement"
},
{
"date": "2023/03/29",
"position": 8,
"query": "ChatGPT employment impact"
},
{
"date": "2023/03/29",
"position": 28,
"query": "automation job displacement"
},
{
"date": "2023/03/29",
"position": 26,
"query": "automation job displacement"
},
{
"date": "2023/03/29",
"position": 8,
"query": "ChatGPT employment impact"
},
{
"date": "2023/03/29",
"position": 27,
"query": "automation job displacement"
},
{
"date": "2023/03/29",
"position": 8,
"query": "ChatGPT employment impact"
},
{
"date": "2023/03/29",
"position": 2,
"query": "ChatGPT employment impact"
},
{
"date": "2023/03/29",
"position": 27,
"query": "automation job displacement"
},
{
"date": "2023/03/29",
"position": 7,
"query": "ChatGPT employment impact"
},
{
"date": "2023/03/29",
"position": 27,
"query": "automation job displacement"
},
{
"date": "2023/03/29",
"position": 29,
"query": "automation job displacement"
},
{
"date": "2023/03/29",
"position": 7,
"query": "ChatGPT employment impact"
}
] |
300 Million Jobs on AI Chopping Block?
|
300 Million Jobs on AI Chopping Block?
|
https://www.shrm.org
|
[] |
Goldman Sachs economists predict that 300 million jobs globally could be eliminated through the use of ChatGPT and similar forms of generative artificial ...
|
Designed and delivered by HR experts to empower you with the knowledge and tools you need to drive lasting change in the workplace.
Demonstrate targeted competence and enhance credibility among peers and employers.
Gain a deeper understanding and develop critical skills.
| 2023-03-29T00:00:00 |
https://www.shrm.org/topics-tools/news/technology/300-million-jobs-ai-chopping-block
|
[
{
"date": "2023/03/29",
"position": 21,
"query": "artificial intelligence employment"
},
{
"date": "2023/03/29",
"position": 18,
"query": "automation job displacement"
},
{
"date": "2023/03/29",
"position": 2,
"query": "AI job creation vs elimination"
},
{
"date": "2023/03/29",
"position": 6,
"query": "AI labor market trends"
},
{
"date": "2023/03/29",
"position": 22,
"query": "government AI workforce policy"
},
{
"date": "2023/03/29",
"position": 98,
"query": "artificial intelligence workers"
}
] |
|
Could AI do your job? | HRD New Zealand
|
Could AI do your job?
|
https://www.hcamag.com
|
[
"Matthew Sellers"
] |
A new report by investment bank Goldman Sachs has warned that a significant proportion of jobs could be automated in the coming years as a result of advances ...
|
The report's authors, Joseph Briggs and Devesh Kodnani, calculated that two-thirds of jobs in the US and Europe could be affected by automation. However, they also noted that most employees would likely see less than half of their workload automated and could continue in their jobs with additional time freed up for more productive work.
The report underscores the need for individuals and governments to prepare for the coming wave of technological change and to find ways to ensure that workers are not left behind in the transition.
The administrative and legal sectors are anticipated to bear the brunt of the impact caused by automation, with 46% of administrative jobs and 44% of legal jobs substitutable by AI.
Unsurprisingly, physically-demanding professions, such as construction and maintenance, have a lower risk of job displacement, with just 6% and 4%, respectively, according to the report.
In a recent Capterra report, 98% of HR Leaders surveyed said they would use software and algorithms to decide who to cull in a recession.
| 2023-03-29T00:00:00 |
https://www.hcamag.com/nz/specialisation/hr-technology/could-ai-do-your-job/440959
|
[
{
"date": "2023/03/29",
"position": 88,
"query": "artificial intelligence employment"
},
{
"date": "2023/03/29",
"position": 58,
"query": "automation job displacement"
}
] |
|
Job Loss Risk Index: Which Industries Will Suffer ...
|
Job Loss Risk Index: Which Industries Will Suffer the Greatest Layoffs?
|
https://www.conference-board.org
|
[] |
Workers in transportation & warehousing; construction; and repair, personal & other services are also at higher risk of job loss. Expansion of e-commerce ...
|
Amid the anticipated US recession, job losses are expected to vary widely by industry. The Conference Board Job Loss Risk Index—which uses multiple indicators that influence job losses and drive differences across industries—estimates that information services; transportation & warehousing; construction; and repair, personal & other services may be at the greatest risk of job losses. Government; private education services; health care & social assistance; and accommodation & food services may be the least at risk.
Members of The Conference Board can access all underlying data of the Job Loss Risk Index by Industry in the Excel workbook here.
Amid the anticipated US recession, job losses are expected to vary widely by industry. The Conference Board Job Loss Risk Index—which uses multiple indicators that influence job losses and drive differences across industries—estimates that information services; transportation & warehousing; construction; and repair, personal & other services may be at the greatest risk of job losses. Government; private education services; health care & social assistance; and accommodation & food services may be the least at risk. Members of The Conference Board can access all underlying data of the Job Loss Risk Index by Industry in the Excel workbook here.
Insights for What’s Ahead
The information services sector is estimated to have the highest risk of job losses amid the projected upcoming recession, according to The Conference Board Job Loss Risk Index. Employment in the industry grew rapidly during the pandemic as the adoption of technologies including telework, e-commerce, and distance learning fueled growth expectations and stock prices for tech companies, which encompass a large share of the information industry. Valuations of high-growth companies are more sensitive to interest rate hikes as these firms are often highly leveraged. As such, rising interest rates increase the cost of borrowing and servicing debt. With the Federal Reserve swiftly raising interest rates to control inflation, valuations are down, and companies have already started layoffs. Layoffs and hiring freezes are expected to continue in the industry during a possible recession.
Employment in the industry grew rapidly during the pandemic as the adoption of technologies including telework, e-commerce, and distance learning fueled growth expectations and stock prices for tech companies, which encompass a large share of the information industry. Valuations of high-growth companies are more sensitive to interest rate hikes as these firms are often highly leveraged. As such, rising interest rates increase the cost of borrowing and servicing debt. With the Federal Reserve swiftly raising interest rates to control inflation, valuations are down, and companies have already started layoffs. Layoffs and hiring freezes are expected to continue in the industry during a possible recession. Workers in transportation & warehousing; construction; and repair, personal & other services are also at higher risk of job loss. Expansion of e-commerce increased hiring in transportation & warehousing as consumers’ preferences for online shopping were catalyzed during pandemic-era lockdowns. With lockdowns over, consumers’ general tendency to reduce their discretionary spending during recessions, along with the recent shift of consumption from goods toward services, means demand for labor is expected to decline. Job losses in construction, repair, and maintenance are expected as the housing market is weakening, meaning there will be less demand for new construction projects or for in-home repair and maintenance.
Expansion of e-commerce increased hiring in transportation & warehousing as consumers’ preferences for online shopping were catalyzed during pandemic-era lockdowns. With lockdowns over, consumers’ general tendency to reduce their discretionary spending during recessions, along with the recent shift of consumption from goods toward services, means demand for labor is expected to decline. Job losses in construction, repair, and maintenance are expected as the housing market is weakening, meaning there will be less demand for new construction projects or for in-home repair and maintenance. While the accommodation & food services industry bore the brunt of pandemic job losses, layoffs could be more limited during the projected recession. The pandemic caused major shutdowns, which disproportionally affected in-person services. Employment is still significantly below prepandemic levels (employment in February 2023 was still 2.4 percent (or about 350,000 jobs) below the February 2020 level). As the industry is still recovering, there will be less need for layoffs in the upcoming recession. In addition, the current severe labor shortage further lowers the risk of job losses.
The pandemic caused major shutdowns, which disproportionally affected in-person services. Employment is still significantly below prepandemic levels (employment in February 2023 was still 2.4 percent (or about 350,000 jobs) below the February 2020 level). As the industry is still recovering, there will be less need for layoffs in the upcoming recession. In addition, the current severe labor shortage further lowers the risk of job losses. Government, private education, and health and social assistance are also not expected to face significant job losses. These industries appear less sensitive to interest rate changes than other sectors. In addition, these industries do not seem to have overhired, and labor shortages have troubled employers in government and health care. Hence, the need for layoffs may be lower across these sectors.
These industries appear less sensitive to interest rate changes than other sectors. In addition, these industries do not seem to have overhired, and labor shortages have troubled employers in government and health care. Hence, the need for layoffs may be lower across these sectors. There may be some temporary easing of labor shortages in 2023 as demand for workers diminishes. However, recruitment and retention difficulties will not disappear, with the unemployment rate projected to remain relatively low. Once the US economy starts growing again, severe labor shortages could quickly reappear.
Looming Recession May Lead to Greater Job Losses in Information and Manual Labor Industries
The US economy may fall into recession in 2023. While the recession is projected to be shallow and brief, the unemployment rate is expected to rise to about 4.4 percent in early 2024, corresponding with approximately 1 million job losses in the second half of the year and Q1 2024. Although these figures are modest compared to prior recessions, the pain of layoffs will not be distributed equally across industries: some will fare better than others.
The figure below shows the ranking and score of industries according to the Job Loss Risk Index (the interactive chart can also be used to review the index components). A higher index score means the risk of job losses is greater, with 10 denoting the highest risk and 1 the lowest. The score provides a relative rank of an industry’s risk of layoffs. According to the index, the information services; transportation & warehousing; construction; and repair, personal & other services sectors have the highest risk, while government; private education services; health care & social assistance; and the accommodation & food services industry have the lowest risk of job losses. Note that while the index projects the risk of job losses, it does not estimate how large each industry’s job losses will be.
Projected recession may lead to larger job losses in information services, construction, and transportation & warehousing
Select category to review each index component: Industry job loss risk % professional workers Prepandemic employment % workers 16-24 Wage growth Interest rate sensitivity Quits Layoffs % workers without BA Current staffing Pandemic recovery employment
Risk of Job Loss Is Driven by Six Factors
Several indicators contribute to the risk of job losses, which varies across industries. These indicators capture the industry’s exposure to labor shortages, sensitivity to monetary policy, productivity and job function, age composition and experience of its workers, recovery from pandemic-era job losses, and longer-term changes in labor demand. More detailed description on each is provided below:
Exposure to labor shortages. Industries that currently have labor shortages may be more careful about laying off workers during downturns. At the other end of the spectrum, rising layoff rates may foreshadow even more job losses to come and a shift in focus from labor shortages to downsizing. To capture this, four index components are included. To capture current staffing, the gauge includes the job openings-to-hires rate, the layoffs rate, and the quits rate in the last three months compared to 2019. The fourth indicator is wage growth in the last year. Together, these components make up exposure to labor shortages, which weighs most in the index. Sensitivity to monetary policy. Industries more sensitive to increasing interest rates may be forced to lay off workers sooner, with the Federal Reserve rapidly increasing interest rates to help fight inflation. Highly leveraged industries—where growth is more dependent on borrowing—will be especially affected as servicing debt and acquiring new loans has become more expensive. Job function and education. Firms tend to hold onto workers in a professional or managerial function. These workers drive the day-to-day and longer-term decision-making in companies. Their experience and institutional knowledge are vital to a company’s profitability, making it difficult to replace these workers. In the index, a large share of management and professional workers reduces the risk of job loss. In addition, industries with higher educational attainment of workersare also expected to be at lower risk of job loss. Education is correlated with job security across multiple dimensions. Higher-educated workers tend to have more firm-specific skills. Firms may want to keep these employees, perhaps with pay freezes, rather than dismiss them during downturns, making the college educated less prone to layoffs. Employees with lower educational attainment tend to work in industries where demand for goods and services is more discretionary, putting them at a larger risk of job loss during recessions. However, some of the dynamics may be presently shifting as industries coping with serious labor shortages are reducing educational requirements to attract new workers. Additionally, some of the industries that thrived during the pandemic (e.g., information, financial services) are now having to rightsize their labor forces, which tend to skew toward employing more educated workers. Pandemic recovery. Industries that have not fully recovered from pandemic employment losses are still understaffed and thus may be less likely to lay off workers. Conversely, industries that overstaffed may be at a larger risk of job loss. Labor demand gauge. Industries that expanded or reduced employment prior to the pandemic due to changing consumer demand may continue those trends. For example, brick-and-mortar retail trade employment grew by only 0.1 percent between 2015 and 2020, whereas transportation and warehousing grew by 21.8 percent in the same time period due to the seismic shift toward online shopping over the years. Age composition and experience. Younger workers (aged 16 to 24) are often the first cohort to lose their jobs during recessions and stay unemployed longer. This is because they have less on-the-job experience and often work in jobs with high turnover. For example, during the Great Recession, whereas the overall unemployment rate rose from 5 percent in December 2007 to 10 percent in October 2009, the unemployment rate rose from 11.7 to 19.1 percent for workers aged 16 to 24.
Components Have Varying Impacts on Job Loss Risk in Different Industries
The figure below depicts the six factors (embodied in the 10 index components) and their contributions to the Job Loss Risk Index. The arrows show each factor’s contribution to the index. The double red upward-pointing arrows (↑↑) indicate that the component adds significant risk of job losses in the industry. One red upward-pointing arrow (↑) implies that the component adds marginal risk, one green downward-pointing arrow (↓) indicates that the component marginally decreases the risk of job loss, and two green downward-pointing arrows (↓↓) mean the component significantly reduces the risk of job losses in the industry.
The information services sector is in the very high-risk category of job losses. Employment in the industry increased significantly during the pandemic due to a surge in consumer and business demand for computers, high-tech gadgets, innovation, digital transformation, and automation. As lockdowns have ended and more consumers are returning to work, the demand for technology is fading. Presently, declining job openings and quits, as well as increasing layoff rates, suggest labor shortages are less of an issue for the industry. Wage growth was relatively modest compared to other industries in the last year, and the sector created 5.7 percent more jobs than prepandemic. Spending is discretionary for consumers and the sector carries a lot of debt, making it sensitive to interest rate increases. Moreover, valuation of tech companies—which constitute the bulk of information services—are often determined by future profit expectations. As these firms are often highly leveraged—with growth being more dependent on borrowing—rising interest rates increase the cost of borrowing and servicing debt. Amid rising interest rates, to keep higher valuations, companies often feel pressured to reduce spending, which includes hiring freezes and layoffs.
Employment in the industry increased significantly during the pandemic due to a surge in consumer and business demand for computers, high-tech gadgets, innovation, digital transformation, and automation. As lockdowns have ended and more consumers are returning to work, the demand for technology is fading. Presently, declining job openings and quits, as well as increasing layoff rates, suggest labor shortages are less of an issue for the industry. Wage growth was relatively modest compared to other industries in the last year, and the sector created 5.7 percent more jobs than prepandemic. Spending is discretionary for consumers and the sector carries a lot of debt, making it sensitive to interest rate increases. Moreover, valuation of tech companies—which constitute the bulk of information services—are often determined by future profit expectations. As these firms are often highly leveraged—with growth being more dependent on borrowing—rising interest rates increase the cost of borrowing and servicing debt. Amid rising interest rates, to keep higher valuations, companies often feel pressured to reduce spending, which includes hiring freezes and layoffs. Transportation & warehousing is in the very high-risk category. Employment in the industry grew the fastest during the pandemic, adding 15.8 percent more jobs compared to the prepandemic level (February 2023 compared to February 2020). Recently, job openings have cooled and wage growth was modest over the past year, suggesting demand for labor is slowing significantly. Industry demand is highly driven by e-commerce, which grew rapidly during the pandemic. With a potential recession, consumer spending is expected to decline, dampening earnings and the need for workers. The industry as a whole has a high share of younger workers and workers without a college degree as well as a low share of management and professional workers, all increasing the risk of job losses.
Employment in the industry grew the fastest during the pandemic, adding 15.8 percent more jobs compared to the prepandemic level (February 2023 compared to February 2020). Recently, job openings have cooled and wage growth was modest over the past year, suggesting demand for labor is slowing significantly. Industry demand is highly driven by e-commerce, which grew rapidly during the pandemic. With a potential recession, consumer spending is expected to decline, dampening earnings and the need for workers. The industry as a whole has a high share of younger workers and workers without a college degree as well as a low share of management and professional workers, all increasing the risk of job losses. Construction is estimated to be at very high risk of job losses amid the upcoming recession. Construction is sensitive to interest rate hikes, which raise the cost of borrowing for home purchases. In recent months, quits rates have fallen rapidly, suggesting labor demand is cooling and workers are finding it more difficult to change jobs. While the industry added many jobs during the pandemic, new housing starts are now on a sharp decline and, as a result, demand for construction workers is expected to fall, putting the industry at high risk of job losses in the projected recession.
Construction is sensitive to interest rate hikes, which raise the cost of borrowing for home purchases. In recent months, quits rates have fallen rapidly, suggesting labor demand is cooling and workers are finding it more difficult to change jobs. While the industry added many jobs during the pandemic, new housing starts are now on a sharp decline and, as a result, demand for construction workers is expected to fall, putting the industry at high risk of job losses in the projected recession. Repair, personal & other services is in the high-risk category. As with information services, a relatively lower level of job openings and quits and moderate wage growth suggest that the industry is less affected by labor shortages. Consumer demand typically declines faster during downturns as the output of these sectors is often discretionary. Compared to other industries, the workforce in this industry is relatively younger and less educated and has a lower share of management and professional workers—characteristics associated with lower productivity and job mobility—and hence is more prone to job losses during downturns.
As with information services, a relatively lower level of job openings and quits and moderate wage growth suggest that the industry is less affected by labor shortages. Consumer demand typically declines faster during downturns as the output of these sectors is often discretionary. Compared to other industries, the workforce in this industry is relatively younger and less educated and has a lower share of management and professional workers—characteristics associated with lower productivity and job mobility—and hence is more prone to job losses during downturns. Manufacturing is also at high risk of job losses. This is another industry that will be sensitive to reduced consumer spending during a downturn. However, the demand for labor in the industry was already relatively low before the pandemic, with the industry increasing employment by a mere 4.1 percent in a five-year span between the start of 2015 and 2020 as automation made the industry less reliant on labor. The industry has already recovered the jobs that were lost at the beginning of the pandemic as demand for goods was especially strong during 2020 and 2021. Goods demand is now slowing. A recent decline in quits shows that labor shortages in the industry are easing.
This is another industry that will be sensitive to reduced consumer spending during a downturn. However, the demand for labor in the industry was already relatively low before the pandemic, with the industry increasing employment by a mere 4.1 percent in a five-year span between the start of 2015 and 2020 as automation made the industry less reliant on labor. The industry has already recovered the jobs that were lost at the beginning of the pandemic as demand for goods was especially strong during 2020 and 2021. Goods demand is now slowing. A recent decline in quits shows that labor shortages in the industry are easing. Wholesale trade is at high risk of job losses. This is another industry where employment growth prior to the pandemic was already slow. The layoff rate is presently increasing, wage growth and the quits rate have moderated, and the industry is less interest rate sensitive compared to other industries.
This is another industry where employment growth prior to the pandemic was already slow. The layoff rate is presently increasing, wage growth and the quits rate have moderated, and the industry is less interest rate sensitive compared to other industries. Real estate, rental & leasing is at high risk of job losses. The industry is directly dependent on what happens to housing markets and hence is very sensitive to interest rate increases. However, wage growth has been very robust in the last year, and the gap between job openings and hires has been high recently compared to 2019 levels. Labor shortages have started to ease, placing the industry at high risk of job losses in a downturn. Ongoing strength in the rental market may be supporting job gains. However, the risk of job loss in the sector may rise over the coming months as weakness in the new and existing housing market spreads to the labor market.
The industry is directly dependent on what happens to housing markets and hence is very sensitive to interest rate increases. However, wage growth has been very robust in the last year, and the gap between job openings and hires has been high recently compared to 2019 levels. Labor shortages have started to ease, placing the industry at high risk of job losses in a downturn. Ongoing strength in the rental market may be supporting job gains. However, the risk of job loss in the sector may rise over the coming months as weakness in the new and existing housing market spreads to the labor market. Finance & insurance services is at medium risk of job losses. The industry added 3 percent more jobs than prepandemic, and recent levels of layoffs and moderate wage growth suggest the industry is not facing significant labor shortages and hence is expected to be prone to job losses. However, the workforce has a higher share of highly educated workers as well as professionals and a lower share of younger workers, making it more productive and mobile compared to workers in other industries. Rising interest rates affect the sector differently depending on the specific business. On the one hand, financial firms lose customers as they must issue higher interest on loans and must pay higher interest on deposits. On the other hand, insurance companies benefit from rising rates on their safer assets.
The industry added 3 percent more jobs than prepandemic, and recent levels of layoffs and moderate wage growth suggest the industry is not facing significant labor shortages and hence is expected to be prone to job losses. However, the workforce has a higher share of highly educated workers as well as professionals and a lower share of younger workers, making it more productive and mobile compared to workers in other industries. Rising interest rates affect the sector differently depending on the specific business. On the one hand, financial firms lose customers as they must issue higher interest on loans and must pay higher interest on deposits. On the other hand, insurance companies benefit from rising rates on their safer assets. Utilities is at medium risk of job losses. Employment in the industry shrank in the five-year period prior to the pandemic, suggesting that labor demand was already in decline. Wage growth in the industry was very slow in 2022 as well. However, the industry appears less sensitive to business cycles and interest rate increases, with employment in the industry barely changing year to year since the mid-2000s. The layoff rate is relatively low and the quits rate relatively high, suggesting labor demand is not dramatically declining and putting the industry at medium risk of further job losses.
Employment in the industry shrank in the five-year period prior to the pandemic, suggesting that labor demand was already in decline. Wage growth in the industry was very slow in 2022 as well. However, the industry appears less sensitive to business cycles and interest rate increases, with employment in the industry barely changing year to year since the mid-2000s. The layoff rate is relatively low and the quits rate relatively high, suggesting labor demand is not dramatically declining and putting the industry at medium risk of further job losses. Mining & logging is at medium risk of job losses. The workforce in the industry was already shrinking prepandemic, shedding 22 percent of its jobs between 2015 and 2020 and another 8 percent in the last three years. These losses were partly related to the transition to renewable energy. Wage growth has been on the lower end among industries, suggesting labor demand is relatively low. However, the job openings-to-hires ratio and the layoff rate suggest labor demand has been relatively stable more recently, putting the industry at medium risk of further job losses during a recession.
The workforce in the industry was already shrinking prepandemic, shedding 22 percent of its jobs between 2015 and 2020 and another 8 percent in the last three years. These losses were partly related to the transition to renewable energy. Wage growth has been on the lower end among industries, suggesting labor demand is relatively low. However, the job openings-to-hires ratio and the layoff rate suggest labor demand has been relatively stable more recently, putting the industry at medium risk of further job losses during a recession. Professional & business services is at medium risk of job losses. The industry is made up of services like accounting, legal, architecture, consulting, and advertising. The workforce is well educated with a high share of managerial and professional workers and a low share of young workers, making it less prone to significant layoffs in a recession. The employment growth was high prior to the pandemic, suggesting that labor demand is robust in the industry during normal times. A recent decline in the quits rate and a slight increase in layoffs suggest that labor shortages may be easing, and companies are being more careful with hiring new employees.
The industry is made up of services like accounting, legal, architecture, consulting, and advertising. The workforce is well educated with a high share of managerial and professional workers and a low share of young workers, making it less prone to significant layoffs in a recession. The employment growth was high prior to the pandemic, suggesting that labor demand is robust in the industry during normal times. A recent decline in the quits rate and a slight increase in layoffs suggest that labor shortages may be easing, and companies are being more careful with hiring new employees. Arts, entertainment & recreation services is at low risk of job losses. The industry is very sensitive to interest rates as, in a recession, spending on recreation and entertainment is one of the first items that consumers tend to reduce. Additionally, according to the S&P 500 Consumer Discretionary index (which includes companies in arts, entertainment, and recreation services), employers in this sector appear to have relatively high debt-to-equity ratios. With rising interest rates, servicing this debt becomes more expensive and may ultimately lead to job losses. Further, the workforce is relatively young and has a lower share of college graduates than other industries do, making it vulnerable to job losses in a recession. However, employment in the industry has not yet recovered from prepandemic levels, and recent trends in layoffs and quits in the industry suggest labor shortages are a challenge for employers. Ultimately, this puts arts, entertainment & recreation currently at lower risk of job loss.
The industry is very sensitive to interest rates as, in a recession, spending on recreation and entertainment is one of the first items that consumers tend to reduce. Additionally, according to the S&P 500 Consumer Discretionary index (which includes companies in arts, entertainment, and recreation services), employers in this sector appear to have relatively high debt-to-equity ratios. With rising interest rates, servicing this debt becomes more expensive and may ultimately lead to job losses. Further, the workforce is relatively young and has a lower share of college graduates than other industries do, making it vulnerable to job losses in a recession. However, employment in the industry has not yet recovered from prepandemic levels, and recent trends in layoffs and quits in the industry suggest labor shortages are a challenge for employers. Ultimately, this puts arts, entertainment & recreation currently at lower risk of job loss. Retail trade is at low risk of job losses. Employment in the industry—which is mostly brick and mortar—grew by only 0.4 percent from 2015 to 2019. This was mostly driven by consumers changing their shopping habits away from brick-and-mortar retail, which grew by only 0.1 percent but represents 96 percent of retail, to e-commerce, which grew by 10.7 percent but only represents 4 percent of retail. The labor force is relatively young with a lower share of college educated people and professionals compared to other industries. However, a low layoff rate, high wage growth, and a high quits rate all suggest that hiring is continuing and companies operating in the industry are facing labor shortages.
Employment in the industry—which is mostly brick and mortar—grew by only 0.4 percent from 2015 to 2019. This was mostly driven by consumers changing their shopping habits away from brick-and-mortar retail, which grew by only 0.1 percent but represents 96 percent of retail, to e-commerce, which grew by 10.7 percent but only represents 4 percent of retail. The labor force is relatively young with a lower share of college educated people and professionals compared to other industries. However, a low layoff rate, high wage growth, and a high quits rate all suggest that hiring is continuing and companies operating in the industry are facing labor shortages. State and local government is at low risk of job losses. State and local government employment was growing very slowly prior to the pandemic and declined in the aftermath of the pandemic. Wage growth is slow—not because labor demand is low but because governments have limited budgets and cannot adjust salaries as freely as the private sector can. The quits rate is low, and the workforce is relatively mature and highly educated. Unless the recession ends up being deep and very negatively affects local and state budgets, severe job cuts are unlikely given that state and local governments still face some challenges with recruitment and hiring.
State and local government employment was growing very slowly prior to the pandemic and declined in the aftermath of the pandemic. Wage growth is slow—not because labor demand is low but because governments have limited budgets and cannot adjust salaries as freely as the private sector can. The quits rate is low, and the workforce is relatively mature and highly educated. Unless the recession ends up being deep and very negatively affects local and state budgets, severe job cuts are unlikely given that state and local governments still face some challenges with recruitment and hiring. Accommodation & food services is at low risk of job losses. The combined industries remain at about 350,000 workers below prepandemic level. Labor shortages are severe in the industry as workers prefer to work in other industries such as transportation & warehousing, where wages are relatively higher, or where there is greater job stability and/or flexibility (e.g., remote or hybrid work).
The combined industries remain at about 350,000 workers below prepandemic level. Labor shortages are severe in the industry as workers prefer to work in other industries such as transportation & warehousing, where wages are relatively higher, or where there is greater job stability and/or flexibility (e.g., remote or hybrid work). Health care & social assistance is at very low risk of job losses. Severe labor shortages in the industry will likely make employers more reluctant to lay off workers as rehiring is difficult and expensive. Employment in the industry keeps growing, even during downturns, as demand for health services is nondiscretionary and driven by changing demographics—specifically the aging population. This phenomenon is intensifying presently.
Severe labor shortages in the industry will likely make employers more reluctant to lay off workers as rehiring is difficult and expensive. Employment in the industry keeps growing, even during downturns, as demand for health services is nondiscretionary and driven by changing demographics—specifically the aging population. This phenomenon is intensifying presently. Federal government is at very low risk of job losses. Layoffs are rare and wage growth relatively strong. Labor demand is even less sensitive to interest rates as the federal government is not obliged to balance its budget and sometimes may even expand hiring during downturns.
Layoffs are rare and wage growth relatively strong. Labor demand is even less sensitive to interest rates as the federal government is not obliged to balance its budget and sometimes may even expand hiring during downturns. Private education services is at very low risk of job losses. Labor demand is very stable in the industry as it is not cyclical. Interest rate changes have very little impact. The workforce is highly educated with a large share of management and professional workers, putting the industry at low risk of job losses in a downturn.
Fewer Layoffs Are Expected due to Labor Shortages
Each factor driving the index makes its own unique contribution to the risk of job loss. However, what stands out in the projected recession is the impact labor shortages are expected to have. Employers experienced extreme difficulty recruiting and retaining workers over the past two years due to strong labor demand but constrained labor supply. Hiring was expensive as larger compensation packages (wages, benefits, bonuses) were necessary to both attract and retain workers. Additionally, an aging population, tighter immigration laws, fewer multiple jobholders, and residual effects from the pandemic (e.g., long COVID, childcare, and adult care challenges) are also severely constraining labor supply.
Leading up to previous US recessions, labor markets were often tight but not to the same degree as today. Therefore, employers are expected to be more cautious in laying off workers if they believe the anticipated recession will be short and shallow (see The Conference Board Measure of CEO Confidence™), and rehiring may be difficult and expensive.
The Index and Its Components
The table below shows the components of the index, including a description of how they affect job losses. Members of The Conference Board can access all underlying data of the Job Loss Risk Index by Industry in an Excel workbook, which also includes a more detailed description of the index.
| 2023-03-29T00:00:00 |
https://www.conference-board.org/publications/industries-that-will-have-most-layoffs-in-recession
|
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AI & Automation in HR: Transforming Workforce Management
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AI & Automation in HR: Transforming Workforce Management
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https://talent500.com
|
[
"Taniya Pan",
"Prachi Kothiyal"
] |
Bias is another challenge of automation in HR. If the data used to train AI models is biased, it can result in biased decision-making around talent management.
|
In recent years, we all have been seeing an upsurge in Google searches about the influence of artificial intelligence and automation on different industries. Seeing the impact of the two, various industries are jumping in to invest in AI and automation. As per Zippia, in 2023, the AI software market is forecasted to generate a revenue of $70.94 billion globally. And when it comes to investing in AI and automation, human resources are not immune to it either.
The HR tech space has been actively implementing AI and automation due to its impact. AI-powered software helps streamline recruitment processes, enhance employee engagement, and personalize learning and development. Today, numerous HR departments are using automation to reduce tedious manual work.
Automation and AI Used in HR Processes Today
For companies looking for efficiency and effectiveness, AI and automation are the go-to remedies to cumbersome work. Recruitment is one of the primary areas where AI and automation can assist as automation can help in screening resumes and job applications, scheduling interviews, and conducting initial candidate assessments. And AI can analyze job descriptions and identify the best candidates for a position.
Other HR processes, such as employee onboarding and performance management, can also benefit from automation and AI tools, which can reduce manual workloads and provide more accurate and timely data. Automation and AI in HR processes are offering the best of both worlds for organizations who are looking to stay competitive.
Automation in HR: The Benefits and Challenges
Much like understanding the potential benefits of automation knowing the challenges it brings is also vital because while automation helps streamline a part of the recruitment process but also brings data privacy concerns.
Benefits of Automation in HR
HR professionals typically have to manage a range of administrative tasks when bringing on a new employee, such as completing onboarding paperwork, setting up IT systems, and arranging training sessions. In many companies, HR technologies like HR automated software are set up to send electronic forms to new hires to complete online, upload the details, automatically create new employee accounts on company systems, and assign required training modules to new employees, thereby allowing HR professionals to focus on more strategic tasks.
Automation simplifies the job application process by allowing candidates to complete online application forms, which can be automatically scanned and sorted by the ATS (Applicant Tracking System). This saves HR teams the time and effort of manually reviewing each application.
Automation helps HR teams conduct initial assessments of candidates through the use of assessment tools. These tools can ask candidates a series of questions, and then use AI algorithms to analyze their responses and provide an initial assessment of their suitability for the role.
Automating the scheduling process with tools such as scheduling software saves HR teams time and effort in finding mutually convenient interview times. This also reduces the chances of scheduling conflicts or no-shows.
Challenges of Automation in HR
While automation in HR can bring several benefits, it also presents several challenges that organizations must address.
Data privacy concerns are a challenge of automation in HR. As automation tools collect and dissect employee data, there is a risk of data privacy breaches and potential misuse of personal data. Organizations must ensure that they have appropriate data protection policies and procedures in place to mitigate these risks.
Bias is another challenge of automation in HR. If the data used to train AI models is biased, it can result in biased decision-making around talent management. This can harm diversity and inclusion efforts within organizations.
Lack of transparency is also a challenge of automation in HR. HR professionals need to understand how automation tools and AI are making decisions, they may be less likely to trust the technology and may be more likely to resist its adoption.
How Automation and AI are changing the way HR professionals work
With AI and automation gaining popularity, they have become an integral part of operations for many companies. Empowered by AI, companies are implementing solutions like AI-powered chatbots that can assist with answering candidate questions and providing information about the application process and improving the candidate experience. AI-powered HR analytics tools can dissect employee data to identify trends and patterns, such as turnover rates and skills gaps, which can help HR professionals develop effective talent management strategies.
With automation, the process of onboarding and offboarding employees has become more efficient. Digital forms, automated workflows, and online training modules make it easier for new hires to get up to speed quickly and for departing employees to exit smoothly. Automated payroll and benefits systems have made it easier for HR professionals to manage employee compensation and benefits, calculate wages, process tax withholdings, and manage benefits enrollment.
Impact on Workforce Management
AI is providing businesses with powerful new tools to manage their workforce more effectively, enabling them to make better decisions, improve productivity, and enhance employee satisfaction. Additionally, automation has enabled employees to take more control of their HR-related tasks.
AI can assist in various aspects of workforce management. For instance, it can analyze data related to workforce demographics, performance, and skills to provide insights to managers to make informed decisions regarding workforce planning.
Additionally, AI can monitor employee engagement and sentiment, helping managers identify issues and take necessary corrective measures to improve employee satisfaction and retention.
When it comes to employee productivity and workload, AI can analyze data to help managers optimize schedules and workload, ensuring that work is allocated fairly and employees are working to their maximum potential.
AI is having an impact on performance management. By using AI to analyze performance data, organizations can identify performance trends and make informed decisions about promotions, bonuses, and other rewards. This can help to motivate employees and create a more positive work environment.
With automation, self-service portals allow employees to update their personal information, request time off, and access their pay and benefits information, among other things.
Similarly, AI and automation are impacting recruitment, employee engagement, and retention strategies.
Changes in recruitment, employee engagement, and retention strategies
In recruitment, the use of automation is enabling organizations to automate the screening of candidate resumes, scheduling of interviews, and even conducting initial candidate interviews. This has led to a more efficient and effective recruitment process, allowing organizations to attract and retain top talent more easily.
Regarding employee engagement, AI is enabling organizations to personalize their engagement strategies. By analyzing employee data, AI-powered tools can provide personalized development opportunities, coaching, and career path recommendations based on an individual’s skills, preferences, and goals. This can help to improve employee engagement by providing employees with opportunities that align with their interests and goals.
With respect to retention, AI is enabling organizations to gain insights into employee turnover risk factors. By analyzing employee data, AI-powered tools can identify factors that may lead to employee attrition, such as a lack of development opportunities or low engagement levels. This can help organizations to implement targeted retention strategies that address these factors and keep their employees engaged and committed.
The Role of HR Dashboards in Automation and AI
An HR Dashboard is a highly beneficial tool for a visual representation of important HR metrics and data, with which you can make informed decisions and monitor the efficacy of HR strategies. Modern dashboards are fully automated dashboards that fetch data from various systems, track and analyze the data and represent it visually to make data-driven decisions. This reduces the need for manual data entry and analysis.
HR dashboards can also play an important role in integrating AI technology into HR processes. By providing a centralized platform for accessing and analyzing data, HR dashboards can make it easier for HR professionals to incorporate AI-powered tools and solutions into their workflows. For example, an HR dashboard could integrate with an AI-powered recruiting tool to provide real-time analytics on candidate profiles and hiring metrics.
Collecting and Analyzing HR Data
Collecting and analyzing HR data is essential for any organization to effectively manage its workforce and make informed decisions about its human resources. Here are some steps that can help with collecting and analyzing HR data:
Identify the data you need to collect: Start by identifying the key HR data that your organization needs to collect. This could include employee demographics, turnover rates, performance metrics, compensation and benefits information, and more.
Collect the data: Once you’ve identified the data you need to collect, you can start gathering it. This may involve using HR information systems (HRIS) to track and store data or conducting surveys and interviews with employees.
Clean and organize the data: Once you have collected the data, it is important to clean and organize it to ensure its accuracy and consistency. This may involve removing duplicate entries, standardizing data formats, and correcting errors.
Analyze the data: Once the data is clean and organized, you can start analyzing it. This may involve using data visualization tools (HR dashboards) to create charts and graphs that illustrate trends and patterns in the data or using statistical analysis techniques to identify correlations and relationships between different data sets.
Interpret the results : After analyzing the data, you can interpret the results and draw insights that can inform HR strategies and decision-making. For example, you may identify areas where employee turnover rates are high and use this information to develop retention strategies.
Use the data to drive decision-making: Finally, use the insights gained from the data analysis to inform HR strategies and decision-making. For example, you may use the data to make changes to employee benefits programs or to adjust compensation structures to improve employee retention and engagement.
Monitoring and Managing the Impact of Automation and AI on the Workforce
As automation and artificial intelligence (AI) continue to advance, it’s essential to monitor and manage their impact on the workforce. This includes considering how automation and AI will affect different job roles and industries, as well as potential changes to the skills and knowledge needed for the workforce of the future. To manage this impact, companies should focus on retraining and reskilling workers to adapt to new roles and responsibilities, as well as exploring opportunities for collaboration between humans and machines.
Additionally, policymakers and industry leaders must consider how to mitigate any negative impacts of automation and AI on workers, such as job displacement, by creating social safety nets and other support systems to ensure a just transition to a new economy.
The Impact of Automation and AI on Identifying and Developing Top Talent
Finding the right talent is like scoring a win for your organization. AI and automation speed up the process to find the right talent. Like with the help of AI-powered tools, recruiters can analyze vast amounts of data to identify candidates with the right skills, experiences, and personalities that align with the company’s needs. This can help to streamline the recruitment process and improve the accuracy of candidate selection. With automation, you can keep track of all the information in one place.
However, there are also potential drawbacks to relying solely on automation and AI in talent identification and development. For instance, relying too heavily on algorithms and data analysis may overlook other important factors that are critical for identifying top talents, such as creativity, emotional intelligence, and teamwork skills.
Therefore, it is important to balance automation and AI with human intuition and expertise to ensure a well-rounded and fair approach to talent identification and development.
Using Automation and AI to Identify and Develop Top Talent
If you want to conduct internal hiring, with the help of advanced HR data analytics and machine learning algorithms, companies can analyze employee data, job performance metrics, and other relevant information to identify high-potential employees and design tailored development plans.
AI can assist with sourcing candidates by analyzing large datasets and identifying individuals who possess the necessary qualifications and experience. This can help to expand the pool of applicants and reach passive candidates who may not have otherwise applied. AI can automate the screening process by using algorithms to match candidates to job requirements and evaluate their qualifications. This not only saves time and resources for recruiters but also helps to eliminate any potential biases in the selection process.
Challenges of Using Automation and AI in Talent Analytics
While automation and AI can be powerful tools for talent analytics, there are several challenges that companies must consider when using these technologies.
One of the primary challenges is the potential for bias in algorithms and data. If the data used to train AI models is biased or incomplete, it can lead to inaccurate or discriminatory results. To mitigate risk, companies must carefully select and monitor the data used to train AI models and ensure that the models are regularly tested for bias.
Another challenge is the difficulty in capturing complex human qualities like creativity and emotional intelligence in data. While automation and AI can analyze performance metrics and other quantifiable data, they may not capture the full range of skills and qualities that make up top talent. This means that companies must be cautious about relying solely on automation and AI for talent analytics and should complement these tools with human judgment and expertise.
Finally, the use of automation and AI in talent analytics can raise concerns among employees about privacy and the use of their personal data. Companies must be transparent about the data they collect and how it is used, and ensure that they are complying with relevant privacy laws and regulations.
Future Trends in Automation and AI in HR
As technology continues to evolve, automation and AI are expected to play an increasingly important role in HR.The following are some possible developments in this field:
Increased use of chatbots and virtual assistants: Chatbots and virtual assistants powered by AI can be used to streamline HR tasks such as onboarding, answering employee questions, and scheduling. As these technologies become more advanced, they could even be used to handle more complex tasks, such as providing personalized training and development plans for employees.
Personalized employee experiences: Automation and AI can be used to create personalized experiences for employees, from tailored training and development plans to customized benefits packages. This can help to increase engagement and job satisfaction among employees, leading to improved retention rates.
More use of facial recognition and biometric data: Facial recognition and other biometric technologies could be used in HR for purposes such as employee identification, security, and attendance tracking. However, companies must ensure that they are using these technologies in a way that is transparent and respects employee privacy.
Emphasis on diversity, equity, and inclusion: Automation and AI can be used to analyze employee data and identify areas where companies may need to improve diversity, equity, and inclusion. For example, by analyzing employee performance data, companies can identify any biases in their promotion processes and work to address them.
| 2023-03-29T00:00:00 |
2023/03/29
|
https://talent500.com/blog/the-impact-of-automation-and-ai-on-human-resources/
|
[
{
"date": "2023/03/29",
"position": 77,
"query": "automation job displacement"
},
{
"date": "2023/03/29",
"position": 73,
"query": "AI job creation vs elimination"
}
] |
The Perception of Artificial Intelligence in the News Industry ...
|
The Perception of Artificial Intelligence in the News Industry A Study of Al Jazeera Network
|
http://studies.aljazeera.net
|
[] |
Moreover, 66% of respondents believe A.I will increase the unemployment rate in the news industry because of replacing humans. Most respondents showed ...
|
[Aljazeera]
ABSTRACT
This study aims to examine the perceptions of the news industry on artificial intelligence utilizing Aljazeera as a case study. Literature reported that studies on A.I. in the context of the news industry are scarce; therefore, exploratory and qualitative research approaches were used to study this phenomenon in greater detail. The data were collected through four in-depth interviews coupled with a survey. The interview participants were carefully selected to understand the research topic comprehensively. Different perspectives were sought: a professor and a decision-maker with a broad vision and plan for the future of artificial intelligence, an expert in A.I. models providing a technical perspective, a journalist and an academic at the same time, and an Aljazeera producer working with A.I. models. A structured five-point Likert Scale questionnaire was completed by 33 respondents working at Aljazeera media network. This study used descriptive statistics to extract information from the collected data. Descriptive statistics reported that 91% of respondents believe that A.I is less biased compared to humans. The results further revealed that the majority (62.5%) of respondents showed disagreement that A.I might replace humans in the newsroom for writing articles. Moreover, 66% of respondents believe A.I will increase the unemployment rate in the news industry because of replacing humans. Most respondents showed disagreement that A.I will comply with the journalism ethics, with only 21% agreeing.
Click here to view the full study.
| 2023-03-29T00:00:00 |
http://studies.aljazeera.net/en/theses-al-jazeera/perception-artificial-intelligence-news-industry-study-al-jazeera-network
|
[
{
"date": "2023/03/29",
"position": 15,
"query": "AI unemployment rate"
}
] |
|
Will a robot take my job? Notre Dame researcher says this ...
|
Will a robot take my job? Notre Dame researcher says this view is overly pessimistic
|
https://keough.nd.edu
|
[
"Tracy Destazio"
] |
For the first five years, robots did have a negative impact on the number of human employees and their local wages, but that impact rebounded and turned ...
|
With the impact of industrial robots on the U.S. labor markets in the past two decades, and an ever-increasing presence of machine-driven technology (such as artificial intelligence and ChatGPT), many employees have feared that one day robots will take their jobs.
Not necessarily so, according to research recently published by Yong Suk Lee, an assistant professor in the University of Notre Dame’s Keough School of Global Affairs. He and his co-author, Auburn University’s John Chung, found that industrial robots, particularly those used within the automotive industry, complemented human workers rather than replaced them—some even working collaboratively, side-by-side. Not only that, but the advanced digitization and automation of that industry likely increased labor productivity and created new tasks, requiring the hiring of more employees. In some cases, robot technology may have supplemented the workforce by filling in gaps that were left vacant when employees quit or moved on to other positions.
The study’s authors examined the influence of robots on the U.S. labor market between 2005 and 2016, analyzing the data in five-year intervals. For the first five years, robots did have a negative impact on the number of human employees and their local wages, but that impact rebounded and turned positive in more recent years, beginning around 2010.
Comparing data from the U.S. Bureau of Labor Statistics, the Organization for Economic Cooperation and Development and the International Federation of Robotics, the researchers were able to track that reversal and determine its source. Productivity could have increased due to three factors, they wrote: the automation of tasks and a reduction in production costs, the improvements of robot technology performing the same tasks, and the creation of new tasks spurred by those automation technologies and abilities.
Yong Suk Lee
Productivity gains from task automation occur when robots are able to perform certain tasks better or faster than humans, the researchers wrote, providing a cost savings. Second, newer models of robots that work faster and more accurately than older models can also increase productivity, without displacing workers. Many of these new automation tasks require human workers to operate and manage them, however, especially as they become more complex, which increases the demand for laborers.
There has also been a recent shift in how robots are being used and what they are doing, Lee said. “When robots were initially introduced, the intent was to cut costs and replace human workers. But now companies are using ‘collaborative robots’ or ‘cobots,’ which are designed to work together with humans.”
Lee explained that in the automobile industry, for example, industrial robots were set up in a production line and human workers had to be kept separate from the machines due to the danger of proximity with large, moving parts. “But now the industry trend is to create a smaller robot that’s more agile and soft,” Lee said, “so that if it bumps into its collaborative human partner at all, it will just stop. It interacts more, making it a much safer environment.
"When robots were initially introduced, the intent was to cut costs and replace human workers. But now companies are using ‘collaborative robots’ or ‘cobots,’ which are designed to work together with humans."
“This new collaborative environment implies that the goal of robots is not necessarily to replace human beings, but to actually augment them.”
The implications for labor workers will be different now that the robots can potentially make them more productive, rather than laying them off, Lee said. “New and advanced robot technology is more sophisticated in that they can be programmed to do multiple tasks. If you think of artificial intelligence or specialized software being incorporated into the robot technology, then the robots can ‘learn’ and adjust to the environment.”
When companies learn to harness robotic technology, they can efficiently produce their goods and exceed production targets, and eventually take on more work or begin new projects, Lee explained. “As a company adopts new technologies, sometimes new tasks are created that we didn’t even know existed before but that are now being initiated.”
But along with that new technology and those new tasks, Lee said, human experts still need to be hired to design, develop and manage those capabilities. “Our findings pointed to the automotive sector in the U.S., which is the largest adopter of robotics, and we see this transitioning of what an automobile is, what the industry is (i.e., electric vehicles). I think that will create a different type of demand for tasks, skills and workers. Robots can not only help existing workers, but can help recruit new, specialized workers in that domain as the technology evolves.”
The other side to that coin is that there’s a constant shortage of manufacturing workers, Lee added. “The demand for robots is changing as we need to augment labor to fill in those gaps where there is a shortage of employees.”
The researchers also found evidence of spillover effects on other supporting industries within and outside of the specific manufacturing sector. Relying on commuting zones as their parameters of influence, they saw a boost in the service sectors within that local economy. “We found growth in the professional services like accountants, lawyers, etc., for example, but also in retail, food and other services,” Lee said.
"Humans are the ones creating these new technologies and adopting them—and their intentions actually matter. I think technology will become a larger factor in either the inequality or well-being of human laborers in the future."
Regarding the quickly evolving artificial intelligence and ChatGPT technologies, Lee suggested we learn to harness these revolutionary capabilities rather than fear them. “They could create new opportunities that we haven’t yet seen or thought of,” Lee theorized. “And then there may be fresh demands for completely new jobs and new employees.”
The researchers concluded that the impact of robot technology on jobs will continue to evolve, and that further research will be able to shed light on how and where those impacts will occur, as well as on how robots and human labor may interact.
“Humans are the ones creating these new technologies and adopting them—and their intentions actually matter,” Lee said. “I think technology will become a larger factor in either the inequality or well-being of human laborers in the future.”
Originally published at news.nd.edu on March 29, 2023.
| 2023-03-29T00:00:00 |
https://keough.nd.edu/news-and-events/news/will-a-robot-take-my-job-notre-dame-researcher-says-this-view-is-overly-pessimistic/
|
[
{
"date": "2023/03/29",
"position": 11,
"query": "job automation statistics"
}
] |
|
Will artificial intelligence take over jobs? Careers across ...
|
Will artificial intelligence take over jobs? Careers across varying fields could be at risk
|
https://www.foxbusiness.com
|
[
"Kendall Tietz"
] |
When asked what jobs could be replaced by AI broadly, ChatGPT said financial analysts and customer service representatives, among others, could potentially be ...
|
Artificial intelligence (AI) experts and industry leaders have warned that automation, and reliance on programs like OpenAI's ChatGPT could have a drastic impact on millions of jobs, particularly in industries that can be easily automated.
ChatGPT itself, a software which has sparked debate in recent months, said it could "potentially replace jobs that involve written communication," such as translation services and social media managers.
When asked what jobs could be replaced by AI broadly, ChatGPT said financial analysts and customer service representatives, among others, could potentially be replaced by AI in the future.
Goldman Sachs warned in a report Sunday that around the world, as many as 300 million jobs could be impacted by artificial intelligence automation, CNBC reported.
EVERYTHING YOU NEED TO KNOW ABOUT ARTIFICIAL INTELLIGENCE: WHAT IS IT USED FOR?
Specifically, tasks that involve repetitive or manual labor might eventually become automated with the help of language models like ChatGPT, which the program itself could lead "to a decrease in the number of jobs available in those fields."
AI will be able to automate the process of data analysis, which could reduce the need for manual entry and processing, optimize shipping and delivery routes, automate manufacturing and assembly lines, computerize many human accounting tasks and assist with legal research and analysis.
AI EXPERTS WEIGH DANGERS, BENEFITS OF CHATGPT ON HUMAN, JOB AND INFORMATION: ‘DYSTOPIAN WORLD’
Some examples specifically given by ChatGPT include transportation, manufacturing and financial services. Other industries that could be at risk include healthcare in which "AI can assist with diagnosing and treating patients, reducing the need for some healthcare workers" and journalism where "AI can generate news articles and summaries, potentially impacting jobs."
Former Google consultant and Datagrade founder and CEO, Joe Toscano, previously told Fox News Digital that companies and industries that replace writing jobs could make written material like essays, articles and online text susceptible to incorrect information, which could be overlooked or accepted as truth by many consumers.
AI EXPERT ALARMED AFTER CHATGPT DEVISES PLAN TO ‘ESCAPE’: ‘HOW DO WE CONTAIN IT?’
"If we don't have humans in the loop to ensure truth and integrity in the information, then we're going to, I think, head towards a dystopian world where we don't know true from false, and we just blindly trust things. I'm not excited about that," he said. "I'm concerned quite a bit."
ChatGPT explained that while it might impact certain jobs, AI technology will also create new job opportunities in fields like data science, machine learning and natural language processing.
Rayid Ghani, a professor of AI and an expert in ethics, fairness, equity, and AI regulation at Carnegie Mellon University's Heinz College, warned that the negative impact on the job market will still be significant.
"It does make processes more efficient, which means people are going to lose jobs and yes, it will create new jobs, but those jobs are not at the same scale as the jobs lost," he said.
CHATGPT'S ANTI-CHEATING TECHNOLOGY COULD STILL LET MANY STUDENTS FOOL THEIR TEACHERS
When asked what jobs it would replace, ChatGPT said many jobs that "require a human touch or critical thinking skills" can't be replaced by AI.
"Additionally, the development and maintenance of AI systems require human expertise and oversight, meaning that AI will not completely replace the need for human workers in these fields," the bot wrote.
AI expert Kai Fu Lee warned that 40 percent of the world's jobs could be completed by machines in the next 15 years. Even jobs that might seem more nuances like a chef or waiter will become automated, he warned.
CLICK HERE TO GET THE FOX NEWS APP
"AI will increasingly replace repetitive jobs, not just for blue-collar work, but a lot of white-collar work," Lee, who has worked for Apple and Google said. "Chauffeurs, truck drivers, anyone who does driving for a living — their jobs will be disrupted more in the 15 to 25-year time frame."
| 2023-03-29T00:00:00 |
https://www.foxbusiness.com/media/will-artificial-intelligence-take-over-jobs-careers-across-varying-fields-could-risk
|
[
{
"date": "2023/03/29",
"position": 42,
"query": "AI job creation vs elimination"
}
] |
|
AI Could Replace 300 Million Jobs, According to Goldman ...
|
AI Could Replace 300 Million Jobs, According to Goldman Sachs
|
https://www.entrepreneur.com
|
[
"Sam Silverman"
] |
According to the report, administrative workers and lawyers are most at risk of losing work to automation. However, jobs that require manual labor and outdoor ...
|
Opinions expressed by Entrepreneur contributors are their own.
The latest advancements in artificial intelligence could be coming for your job.
With the rise of platforms like ChatGPT, Google's Bard, and others that can automate daily workflow, 300 million full-time jobs around the world could be at risk of becoming automated.
According to a new report released by Goldman Sachs economists on Sunday, "the labor market could face significant disruption." Economists predict that 18% of work can be computerized and white-collar workers could be the first to be replaced.
RELATED: Elon Musk Says We Should Stop Rapid AI Development Right Now — Here's Why
According to the report, administrative workers and lawyers are most at risk of losing work to automation. However, jobs that require manual labor and outdoor tasks will feel "little effect" from the AI boom.
About two-thirds of current jobs in the United States and Europe "are exposed to some degree of AI automation," and AI could potentially substitute a quarter of current work, the report said.
Although the advancements in AI will likely cut administrative jobs in the short term, the report notes that it could also aid in current jobs and can free up the workload of US workers by 25% to 50% and allow people to be more productive in other areas.
A study by the McKinsey Research Institute, which was published in 2018, predicted that 400 million people worldwide could be displaced as a result of AI before 2030. It also found that AI could account for $3.5 trillion to $5.8 trillion in annual value.
RELATED: We Asked Google's AI Bard How To Start A Business. Here's What It Said.
Within the next 10 years, Goldman Sachs economists predicted that labor productivity will increase, and they estimate AI could increase annual global GDP by 7%.
With the potential for AI to cut labor costs, increase productivity and create new jobs, economists predict a "productivity boom" that could raise "economic growth substantially." However, "the timing of such a boom is hard to predict."
| 2023-03-29T00:00:00 |
2023/03/29
|
https://www.entrepreneur.com/business-news/ai-could-replace-300-million-jobs-according-to-goldman/448627
|
[
{
"date": "2023/03/29",
"position": 43,
"query": "AI job creation vs elimination"
}
] |
AV / IT Convergence: Skills and Gaps to Breach?
|
IT Convergence: Skills and Gaps to Breach?
|
https://www.avixa.org
|
[
"Alan C. Brawn Cts Dsce Dsde Dcme Dsne Dssp Isf-C",
"Alan Brawn"
] |
... AI-Enabled Meeting Spaces—Smarter Collaboration with Lenovo · New Insights into Direct-View LED TCO. AV / IT Convergence: Skills and Gaps to Breach? Posted on ...
|
AV / IT Convergence: Skills and Gaps to Breach?
I wrote my first article on this topic just before the turn of the century, on the (then) growing need to converge AV and IT. Yes, that long ago! It seemed back then (and perhaps even today) that AV people were in one camp and IT people in another… and never the twain shall meet. Suffice it to say today, AV and IT have converged at some level. I do suggest that while AV and IT frequently work in unison at the infrastructure level, they have not truly completely converged and may never fully do so. They are certainly parallel to one another with varying amounts of overlap. The point is that they are distinct in their work processes. Let's begin by looking at this distinction.
Historically the pro AV industry has focused on technologies, products, and systems. Its mandate is creating, transporting, displaying, collaborating, and communicating information to the intended audience. AV integration aims to give clients a full range of solutions from which to pick and then make the operation and communication of audio and video seamless. Since the early 2000s, the commercial AV industry has been driven by expanding communications, sharing of data, and introducing collaboration and interactivity, both internally and externally. This is where IT comes into the picture.
The Harvard Business Review introduced the term IT (for information technology) to make a distinction between purpose-built computers designed to perform limited functions, and general-purpose computing machines that could be programmed for various tasks. One IT subject matter expert suggests an inclusive definition; “Information technology is the use of computers, storage, networking and other physical devices, infrastructure, and processes to create, process, store, secure and exchange all forms of electronic data.”
IT now embraces an array of technologies and related disciplines. The foundation of IT is still basic computer-based information systems, including computing hardware, operating systems, application software, and the data that is processed to produce useful information. Over time, each of these IT components and functions has become more complex, embracing ever-growing subsets of technologies and methodologies. IT no longer just happens locally; cloud computing environments now complement and even replace on-premises resources of the traditional data center. All these developments have made IT more complex and require greater specialization and new roles and responsibilities from the IT workforce.
IT creates, manages, and services the backbone of where AV often exists. To me, the most appropriate way to look at the relationship between IT and AV is one of symbiosis. Of course, some IT people can take on AV-centric projects, and AV people are familiar with IT, but the work processes and responsibilities are different. Without fear of contradiction, what can truly be said is that IT and AV are codependent.
What I can safely say is that out of necessity, is that we are still speaking and writing about AV and IT convergence today. I was speaking to my partner about this and what popped into my mind was an analogy to the old Brady Bunch TV series. You had two distinct families with individual histories but when the parents married the families had to find ways to work it all out. If you don’t get it, just do a search for the old TV show and you will.
When people used to think about convergence it was almost like mixing black paint and white paint. The black and white would each go away leaving a “new” color we call grey. Continuing the paint analogy to AV and IT there is still some black on one side and white on the other side but there is (growing) grey in the middle. This is the convergence of AV and IT. AV is distinct from IT in most ways and vice versa. So, no, AV and IT are not “fully” converged and probably will never totally be… but there is convergence. This begs the question of what is driving these distinct “camps” to work together and yes, converge.
It is clear to me that the biggest driving force is unified communications and collaboration (UCC). This is the “grey paint” in the middle (aka. convergence) with black on one end and white on the other. From another perspective this is where the internet of things (IoT) is so relevant. If you read into that as a convergence of many more devices than we previously would have had to network before… then you get the point. One definition of IoT is a network of physical devices that are embedded with sensors, software, and other technologies for the purpose of connecting and exchanging data with other devices and systems over the internet. A noted university explains it this way, “An IoT system consists of smart devices that use embedded systems, such as processors, sensors, and communication hardware, to collect, send and act on data they acquire from their environments. IoT hardware shares the collected sensor data by connecting to an IoT gateway where data is either sent to the cloud to be analyzed or analyzed locally. Sometimes, these devices communicate with other related devices and act on the information they get from one another.” As one subject matter expert opines, “The devices do most of the work without human intervention, although people can interact with the devices - for instance, to set them up, give them instructions or access the data.”
Obviously, the emergence of IoT and UCC can directly speak to both AV and IT but the “pipeline” that feeds all of this is big data. I won’t belabor the point but the sheer amount of information available and increasing daily is overwhelming and the responsibility to manage and protect this is a daunting task that falls at the feet of the IT department. This is where the elements of IT come into play with their dedicated devices and software that supports what comes in, is distributed, and goes out. Security in and out takes on a new meaning.
This is what we might call an ever-evolving environment for both AV and IT and requires a new look at skill sets that are evolving as well. Are there skills gaps in both camps that will strengthen the “grey” in the middle we call convergence? The answer is yes and no. I will try to explain.
I suggest the following as a self-explanatory list of “soft skills” that might be required for both AV and IT.
Communication
Organization
Analytical abilities
Creativity
Project management
Tenacity
Problem-solving
Resourcefulness
Leadership
Flexibility
The “hard skills” on the IT side subject matter experts tell me might include most or all of the following:
Operating system knowledge
Network configuration
Cloud computing
Hardware deployment
Database management
Technical writing
Data and social media analysis and management
Coding
The “hard skills” on the AV side are just as diverse and might include most or all of the following:
AV systems design
Audio hardware knowledge
Video hardware knowledge
AV control systems
AV software
AV communication and proposals
AV project management
AV integration
Basic troubleshooting
End user training
AV services and maintenance
Neither list of “hard skills” is inclusive of all potential skills… and as they say your application may vary, but these are the basics that are most likely to apply. This brings up the questions of convergence and what each needs to know about the other. Well, the “perfect” candidate would be someone who is CTS and Cisco certified. The good news is that some will do that... and rest assured there is a job for you! However, perfect should not be the enemy of good enough, so at a minimum each should know about the other at a cursory level.
At the 30,000-foot level the IT folks are afraid that some things (or anything for that matter) on “their” network will bring it crashing down around their ears or create an operation glitch or a security leak that becomes a nightmare. On the AV side there is a tendency to oversimplify networks. The IT folks appear to be impediments to their progress in systems designs and integration as they add IoT capable gear to nearly every design they work on. The answer to these challenges is for each to become familiar with the other. This is not to say that all IT folks need to become experts in AV nor vice versa. They each have a full plate, but it behooves the AV folks to formally work with IT and again vice versa to familiarize each other with responsibilities and challenges they might individually and mutually face. As Steven Covey says in his book The 7 Habits of Highly Effective People, “Seek First to Understand, Then to Be Understood”. Suffice it to say learning about each other and building mutual respect pays off in spades and adds collaboration to the AV and IT relationship. Not “either / or” but rather “and” resulting in win/win for AV, IT, and end users.
TOPICS IN THIS ARTICLE
Information Technology Business of AV
| 2023-03-29T00:00:00 |
https://www.avixa.org/pro-av-trends/articles/av-it-convergence-skills-and-gaps-to-breach
|
[
{
"date": "2023/03/29",
"position": 83,
"query": "AI skills gap"
}
] |
|
ChatGPT and the future of work
|
ChatGPT and the future of work
|
https://eightfold.ai
|
[
"Ashutosh Garg",
"Sania Khan",
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ChatGPT is the latest AI-powered tool that drives innovation and efficiency. Learn how generative AI can transform your organization and drive business ...
|
As the technology behind natural language processing advances, chatbots and virtual assistants powered by GPT-3 and now GPT-4 are becoming increasingly prevalent in a variety of industries. While this technology has the potential to revolutionize the way we interact with computers and automate many routine tasks, it also raises questions about the future of work.
In this blog post, we will explore how ChatGPT (a specific implementation of GPT-3/4) may impact jobs, including which positions may change, morph, or disappear entirely. Additionally, we will discuss the shift toward prompt creatives and how ChatGPT is democratizing creativity. Let’s dive in.
ChatGPT wrote this introduction. Sounds convincing, right?
Released last November, ChatGPT has received global attention for its ability to write well-written, instant responses to questions and prompts. The OpenAI tool continues to gain momentum, with many people complaining about the inability to handle the influx of requests due to being “at capacity.”
The jury is still out on whether ChatGPT performs better than the average human writer — or provides average, uninspiring responses based on a collection of responses that live online.
Before we delve into how ChatGPT will impact jobs, let’s take a step back to understand the evolution of machine learning. Machine-learning models are not new. Take ubiquitous examples like Siri and Alexa that take in speech, turn it into text, identify the prompt, and return a mostly acceptable answer. Or consider your phone’s photo app that stores metadata from your photos, like location and date, and performs image recognition tasks. This allows you to search for “dog” and returns all related pictures or text. These machine-learning use cases can save hours in manual searches or organization.
The difference with ChatGPT is that its machine-learning model has a full conversational interface. Similar to the Eightfold AI Talent Intelligence Platform, ChatGPT AI can take on routine tasks and enable people to improve where machines cannot.
In a talent intelligence platform, AI doesn’t take over a job — it surfaces recommendations so people have more information to make better decisions.
Generative AI in a talent intelligence platform creates new opportunities for candidates, employees, and contractors that might not have easily surfaced for them before. For candidates, it’s about surfacing jobs they might not have identified for themselves. For employees, it’s about finding projects or courses to upskill. And for contractors, it’s finding the part-time work or gigs best suited to their skill sets. Like ChatGPT, it still needs a human component to tell it what to do, refine recommendations, and actually use uniquely human skills to interact with people.
RELATED CONTENT: Read our latest white paper on responsible AI.
ChatGPT — fun phenomenon or threat to jobs?
The allure of ChatGPT is that it’s easy to use. There’s no code needed, and its incorporated feedback loop lets people upvote or downvote answers.
Some people argue that as ChatGPT advances, it could replace anyone who writes for a living: journalists, technical writers, content writers, copywriters, and even writing for the legal industry. ChatGPT may also impact customer service representatives since the software can provide support and give a sense of “empathetic help,” which makes customers feel like they’re talking with a real person.
On the plus side, other occupations could use generative AI tools to benefit and improve their work, allowing workers to be more productive and focus on higher value tasks. For example, programmers can use such tools trained on GPT-4 (Generative Pre-trained Transformer 4) to create advanced natural language processing applications, streamline complex data analysis, enhance conversational AI interfaces, and develop innovative solutions across diverse industries, such as healthcare, finance, and education. People can thereby focus on more complex problem-solving, designing new software architectures, collaborating with other team members, and refining the quality of the final product. These tools are also great for entry-level programming learning and students to use in school.
This poses a question that has been top of mind for business leaders and workers as automation has picked up across industries. Instead of being threatened by technological advancements, including AI, how can we better implement technology to support workers and improve jobs?
As ChatGPT learns over time and becomes a better product with realistic answers, it will depend on how people choose to use it. Some creative writers may transition into “prompt creatives.” They will use ChatGPT or similar technology for an initial draft, research, or ideas gathering. The writer’s skills can instead be applied to editing and finessing the output.
In this case, AI will do the cognitive load of creative writing, including premises, arguments, and phrasing. As an economist and author myself, it’s now easy to imagine no longer struggling to create a first draft. Instead, the emphasis would be less on wordplay and more on developing fresh ideas. Using AI could lead to increased productivity, creating opportunities in new areas. Perhaps ChatGPT could create enhanced messaging with powerful influence to convince, change, support, or drive action from a specific audience.
The ethical dilemma that is ChatGPT
That’s an optimist’s point of view. Unfortunately, there are also the potential negative ramifications of putting technology of this sophistication into the hands of workers, students, and citizens.
The use of data-intensive and AI-based technologies presents both significant opportunities and risks. These technologies have the potential to solve some of the world’s biggest challenges, but could also endanger privacy and security if organizations adopt them without considering the larger social and economic contexts.
ChatGPT by itself can produce vast sums of content, but its origins are not always clear, and its output has not been thoroughly verified, making it potentially harmful if misused. In addition, data privacy and security is another large concern as AI systems can collect large amounts of personal data, leading to potential identity theft, fraud, or other harm.
To solve these problems, new complementary and overlay tools could arise to identify and minimize disinformation.
Indeed, AI Ethics is a complicated and fascinating subject that requires careful thought, advanced research, and analysis of potential implications to ensure that the technology is used for good and doesn’t cause harm.
As creators of an AI-powered talent intelligence platform, we are strong advocates for the ethical and responsible use of AI. As a result, we formed the Eightfold AI Ethics Council, a group dedicated to the execution of responsible AI practices. We rely on their input and advice on best practices to ensure that our AI platform minimizes bias and promotes equality in employment.
The future includes AI like ChatGPT: It’s up to people to use it wisely
ChatGPT can help with a rough draft of a meeting agenda, but it can’t hold the meeting for you. Likewise, it doesn’t know the intricacies of a company’s unique messaging or have access to in-depth primary resource interviews. For the time being, it’s a brainstorming tool that provides a foundation to build on. When bolstered by industry experience, execution, critical thinking, teamwork, and collaboration, the value of creative ideas will prevail.
This is the time when organizations can get ahead of the curve by bringing new ideas to the table and creating new processes or programs that positively impact the lives of their employees and customers. New AI technology like ChatGPT shouldn’t threaten our creativity, it should instead let us reframe our thinking in terms of how work gets done, how new workplace tools and technologies can improve jobs, and how we can adapt to find the right career for everyone in the world.
To learn more about the impact of ChatGPT on the future of work, read our latest article.
| 2023-03-29T00:00:00 |
2023/03/29
|
https://eightfold.ai/blog/chat-gpt-future-work/
|
[
{
"date": "2023/03/29",
"position": 5,
"query": "future of work AI"
},
{
"date": "2023/03/29",
"position": 26,
"query": "machine learning workforce"
}
] |
Artificial intelligence has advanced despite having few ...
|
Artificial intelligence has advanced despite having few resources dedicated to its development — now investments have increased substantially
|
https://ourworldindata.org
|
[
"Max Roser"
] |
The third chart documents the increasing importance of AI skills in the labor market. The last chart shows the share of organizations that use AI in at least ...
|
Artificial intelligence has advanced despite having few resources dedicated to its development — now investments have increased substantially
Artificial intelligence (AI) technology has steadily become more powerful over the course of the last decades, and in recent years, it has entered our world in many different domains. In a companion article — the brief history of artificial intelligence — I document this development.
This was achieved despite having relatively few resources. Until recently, investments in terms of capital and scientific efforts were small. In this article, I highlight that this has very much changed in recent years. Corporate investment has increased, and the scientific field has grown in size.
Given how rapidly AI has developed in the past despite limited resources, this should make us expect AI technology to continue to become more powerful in the coming decades.
Investments in artificial intelligence
The first chart looks at corporate investment over time.1
Until recently, private sector investment was relatively low. But, especially from 2018 onwards, it has increased rapidly.
Investments in 2021 were about 30 times larger than just eight years earlier.
Research and the labor market
The following four visualizations show that the trends for AI research efforts and the labor market are similar.
Just a decade ago the field was much smaller than it is today. Since then the number of research publications on artificial intelligence has doubled, and AI conferences have become much larger events. This is what the first two charts show.
The third chart documents the increasing importance of AI skills in the labor market. The last chart shows the share of organizations that use AI in at least one area of their work.. As AI adoption continues to grow, the demand for AI expertise — and the pace of research — is likely to increase further in the years to come.
Additionally, AI itself is contributing to the development of AI: Researchers are beginning to find ways for AI itself to contribute to the development of AI.2
Conclusion
The developments in the past happened despite the fact that funding and brainpower dedicated to AI were quite limited. As these charts have shown, this has changed. Across a range of metrics, the resources dedicated to AI development have increased substantially.
The fact that the field has advanced with relatively small resources, and now has much larger resources at its disposal, leading to rapid advances in the last few years, is one reason why I expect AI technology to continue to develop rapidly and to exert a larger and larger influence on our world.
Acknowledgements: I would like to thank Julia Broden, Charlie Giattino, Joe Hasell, Edouard Mathieu, and Veronika Samborska for their helpful comments on drafts of this essay and the visualizations.
Continue reading on Our World in Data
AI timelines: What do experts in artificial intelligence expect for the future? Many believe there is a real chance that human-level AI will be developed within the next decades, and some believe that it will exist much sooner.
Explore other charts on the rise of investments in AI technology
Cite this work Our articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as: Max Roser (2023) - “Artificial intelligence has advanced despite having few resources dedicated to its development — now investments have increased substantially” Published online at OurWorldinData.org. Retrieved from: 'https://ourworldindata.org/ai-investments' [Online Resource] BibTeX citation @article{owid-ai-investments, author = {Max Roser}, title = {Artificial intelligence has advanced despite having few resources dedicated to its development — now investments have increased substantially}, journal = {Our World in Data}, year = {2023}, note = {https://ourworldindata.org/ai-investments} }
| 2023-03-29T00:00:00 |
2023/03/29
|
https://ourworldindata.org/ai-investments
|
[
{
"date": "2023/03/29",
"position": 8,
"query": "AI labor market trends"
}
] |
United Kingdom Artificial Intelligence Market 2023
|
United Kingdom Artificial Intelligence Market 2023
|
https://www.trade.gov
|
[] |
Businesses and sectors that have digitized operations and services took up AI more easily and effectively than those that have not. The digitalization of ...
|
The UK AI market is valued at over $21bn, and it is estimated to grow to over $1tn by 2035. The UK is the third largest AI market in the world after the U.S. and China.
The UK has one of the strongest AI strategies in the world, with significant government funding for AI, considerable research activity in the field, major VC funding and AI startups, and substantial enterprise adoption of AI.
In 2022 the UK Government published its National AI Strategy and Action Plan, outlining a package of over $1.3 billion of support for the sector. This support complements and leverages the $2.8 billion that then UK Government had previously invested in AI.
In addition to public funding, the industry is also providing funding for AI solutions, with global AI powerhouses having invested significantly in developing the UK AI market.
The current uptake of AI in the UK varies significantly by sector and within sectors. Businesses and sectors that have digitized operations and services took up AI more easily and effectively than those that have not. The digitalization of banking, insurance, healthcare, and business services in the country is the key driver for the growth of AI in the country.
AI is also being deployed in addressing complex UK public sector challenges, including improving efficiency in mainstream public services, with applications in the processing of processing tax, benefits, visas, passports, and other Government licenses. For example, the UK Government Digital Service (GDS) works with the Pensions Regulator to improve efficiency using predictive algorithms for future pension scheme behavior and His Majesty Revenue and Customs (HMRC) uses AI to help identify call center priorities.
AI solutions for data management and analysis are most prevalent in the UK, followed by natural language processing and generation, machine learning, AI hardware, computer vision, and image processing and generation.
UK AI Key stakeholders
When looking at getting involved in the UK’s AI market, U.S. companies should get acquainted with these key stakeholders:
UK Department for Science, Innovation, and Technology: department newly created in February 2023
Office for Artificial Intelligence: a unit within the Department for Science, Innovation, and Technology responsible for overseeing the implementation of the National AI Strategy. Its mission is to drive responsible and innovative uptake of AI technologies for the benefit of everyone in the UK by: Investing in and planning for the long-term needs of the AI ecosystem Supporting the transition to an AI-enabled economy Ensuring the UK gets the national and international governance of AI technologies right AI council: an independent expert committee providing advice to the Government and high-level leadership of the AI ecosystem techUK: the UK’s technology trade association
UK AI Trade Shows
AI & Big Data Expo, London, 30 November - 1 December 2023
UK AI Regulations
In 2022 the UK Government released a policy paper setting out proposals for AI regulation in the UK. The paper places a strong emphasis on establishing a framework that encourages AI innovation, setting out the ambition to be context-specific, pro-innovation and risk-based, coherent and proportionate, and adaptable.
The UK is not planning to establish a central AI regulator (differently from the EU approach), and is instead intending to provide sector regulators with cross-sectoral principles for them to apply to AI developed and used within their remits. Regulators - such as Ofcom, the Competition and Markets Authority, the Information Commissioner’s Office, the Financial Conduct Authority, and the Medicine and Healthcare Products Regulatory Agency - will apply six principles to oversee AI in a range of contexts. The proposed principles are:
Ensure that AI is used safely
Ensure that AI is technically secure and functions as designed
Make sure that AI is appropriately transparent and explainable
Consider fairness
Identify a legal person to be responsible for AI
Clarify routes to redress or contestability
When defining the scope of AI regulation, the paper argues for an approach that sets out the core characteristics of AI technologies but does not settle on a fixed definition. The intention would be to target regulation at the use of AI rather than the technology itself and to allow individual regulators to set out and evolve more detailed definitions in line with their specific domains or sectors.
Regulators will be encouraged to consider lighter touch options which could include guidance and voluntary measures or creating sandboxes - such as a trial environment where businesses can check the safety and reliability of AI tech before introducing it to the market.
The government launched a call for views at the time of the publication of its policy statement. The UK is reviewing the response and planning to publish the finalized white paper on AI regulation sometime in 2023.
| 2023-03-29T00:00:00 |
https://www.trade.gov/market-intelligence/united-kingdom-artificial-intelligence-market-2023
|
[
{
"date": "2023/03/29",
"position": 86,
"query": "AI labor market trends"
},
{
"date": "2023/03/29",
"position": 55,
"query": "government AI workforce policy"
}
] |
|
How Can Artificial Intelligence Improve Workplace Safety?
|
How Can Artificial Intelligence Improve Workplace Safety?
|
https://www.institutedata.com
|
[
"Institute Of Data"
] |
AI solutions can improve safety in the workplace by automating existing jobs, surveilling dangerous equipment, and using predictive and cyber threat analysis.
|
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
Artificial intelligence is the hottest and fastest-growing trend for businesses and professionals worldwide, with applications in everything from design and research to writing and analysis. However, for many companies, an important question is – can artificial intelligence improve workplace safety.
While we will dig deeper into this concept in this guide, the short answer is – yes, it can. Several AI sensors and software are used to track production lines, basic equipment, potential harassment, and your overall workforce. Reducing errors and improving performance through task automation and real-time feedback is also possible.
In this article, we will look in-depth into all the methods we can use to maximise workplace safety while using AI. Besides that, we will explore how AI developments can influence and improve business operations in 2023!
How can artificial intelligence improve workplace safety?
AI solutions can improve safety in the workplace by automating existing jobs, surveilling dangerous equipment, and using predictive and cyber threat analysis to stay one step ahead of any threats.
Whether your business is in the healthcare industry or a financial institution, AI solutions can protect physical and digital operations leading to a work environment with reduced data loss and fewer on-site injuries. It can also help manage harassment in the workplace.
Here is a detailed look into some ways that artificial intelligence is revolutionising workplace safety:
Predictive analytics
Predictive analytics allows companies to scan their workspaces for potential risks to worker safety and workplace health using AI software and sensors. Once a risk is assessed, workers are warned of its presence and cautioned to take action before the situation escalates. This makes predictive analytics tools excellent for workers in factories and mines where it is crucial to respond quickly in emergencies.
Like almost every other AI solution mode, predictive analysis is also heavily dependent on the machine learning support of the solution. Machine learning is particularly important here because it is a powerful tool for data analysis and works on the entire production process, making it the right choice for picking out weak spots.
After the machine learning system has closed in on an issue, the AI software/sensor can use that data to take the right action. This could mean a simple alert using an IoT device or an evacuation alarm, depending on the risk. While predictive analysis is similar to human actions in its analysis and warning systems, it is much more suited to professional environments since it can handle several inputs simultaneously.
The best thing about AI solutions is that they can be customised to aid every professional. Predictive analytics is no different, as data scientists and cybersecurity experts can also use it.
Task Automation
Other than using predictive analysis AI in safety management to warn employees of potential danger in the workplace, you can also use AI-powered robots to replace human workers on dangerous jobs altogether. This is not a new development for most businesses, as most production processes involving dangerous equipment are moving over to robots, with human workers supervising the production lines and tools like Microsoft Power Apps are extensively used for task automation.
This can improve workplace safety as human workers are no longer exposed to environments that could be hazardous for them, and there is less risk of man-made error after the entire production line has been optimised with AI.
However, task automation benefits with AI are not limited to the manufacturing line and personal protective equipment only. Another excellent contender is drones. Drones are powerful at physical analysis and delivery. They are often equipped with a camera and are small, making them ideal for investigating dangerous environments. They are an excellent tool for efficiency and safety, as they can be manipulated to reach spaces impossible for human beings to access and inspect.
Since humans can operate drones from a base, they are often used in the construction industry, making site evaluation much safer for workers!
Cyber Threat Analysis
Having a solid cybersecurity system is essential for safety in the workplace as it secures communications between employees and all personal and professional data that could be manipulated to have a negative impact on the company or its employees. This is where AI steps in to improve cyber threat analysis for a firm.
It is essential to protect sensitive company data, and one of the most significant risks to the security of such data is human errors. For example, if an employee falls prey to a phishing scam while on their work computer, they could risk compromising user data or other sensitive information. Depending on the firm’s size, such mishaps could result in legal charges or millions of dollars in losses.
Cybersecurity experts can use AI to explore system weaknesses, cyber threats, and counter strategies in real-time by utilising the best predictive intelligence systems and advanced natural language processing. These advanced AI tools can scrap and scan all cybersecurity-related data across the web to make more informed decisions. It is also a lot easier to implement the right solution depending on the type of threat.
Communication Surveillance
NLP, or natural language processing, has made monitoring large-scale communication and big data sets much more straightforward. It helps AI solutions to learn faster, and they can transcribe meetings and create detailed reports by analysing spoken and written communication.
There are also internal threats like workplace harassment and unfair promotions that can be addressed by using NLP solutions, one of many examples of artificial intelligence in the workplace. When all transmitted data within the company goes through certain pre-set checks, these NLP solutions can pick up on inappropriate communication. After picking out such communication, the NLP system would report it directly to senior management.
With AI protection on your computer networks and your production lines, the extra layer of protection is that the system never gets tired, which is essential for cybersecurity solutions that need vigilant monitoring 24/7.
Workforce surveillance
IoT sensors powered by AI and ML solutions have completely revolutionised workforce surveillance. We discussed the productivity benefits of optimising production lines in the earlier sections. Still, AI also plays a big part in workers’ safety by protecting them from medical risks.
There are several ways workflow surveillance with AI can protect your employee’s health with occupational safety and protect them from injuries in the workplace. For example, you can use IoT sensors to monitor employees’ occupational health, track their current location, and alert them about any potential environmental hazards or workplace accidents.
Improving efficiency and productivity for both in-office and remote employees is also possible by enhancing staff training and monitoring employee engagement during L&D programs.
Before working with big data sets and large-scale surveillance systems, ensuring that you have an experienced data science team who can leverage third-party vendors and internal company strengths to produce the best results is crucial.
Equipment Surveillance
Manufacturing robots and drones are just two examples of robotic equipment that have made workspaces more productive and safer. However, it is essential to note that these technologies need to be monitored despite all their advantages. The best surveillance for this equipment combines human employees and AI solutions.
Machines can break down, and faulty equipment can result in serious workplace injuries that could even be life-threatening. With machines taking over multiple operations across several industries, these risks are a bleak reality for modern businesses. However, AI solutions coupled with IoT devices are bringing these numbers down.
They mitigate any issues with the equipment in the production process by identifying potential problems and issuing solutions before things go haywire. Since the systems are passively monitored 24/7, it also helps catch and alert staff if any breakdowns or thefts occur.
Setting the triggers and specifying the suspicious behaviour you wish to be alerted about is possible. You can even have sound-enabled cameras that recognise certain loud sounds to activate bright lights and sirens.
Are AI developments useful for businesses in 2023?
AI developments are helping modern businesses to become more sophisticated in managing their employees, equipment, and business operations. These changes give businesses an edge over their competitors, as they can make better data-driven decisions and respond in time during emergencies.
Big data and the need for faster data analysis systems have played a significant role in the resurgence of AI. Companies like Tesla, Amazon, and Google are already adapting and improvising on the latest AI developments to provide better products and services.
It is easier to store large amounts of data and run large-scale applications directly from a cloud server, and it is easier to make better predictions after collecting data. While cloud computing is a powerful tool for modern businesses, other AI innovations like ChatGPT are groundbreaking in how they can seamlessly mimic human results. If you want to learn more about how AI has transformed business operations, check out our detailed guide on the topic.
Whether you are a business owner or a professional, it is essential to realise that AI solutions will heavily influence several processes in your workflow. The Institute of Data offers a course that is perfect for aspiring professionals who want to get hired as AI experts. This course teaches you all the fundamental topics and gets you job ready in just six months!
Therefore, educating yourself on the changing trends and staying one step ahead can be very profitable. In the words of Mr Bill Gates, founder of Microsoft, “AI software is the most important and profitable innovation for modern businesses”.
Conclusion
AI is exceptional in its abilities to analyse risks, automate dangerous tasks and improve overall safety, making it ideal for every department in a business.
2023 is the perfect time to adopt AI changes for your workplace, and if you want to learn how to do so, book a career consultation with one of our experts today. If you have any more queries about a career in Data Science & AI, join our webinar.
| 2023-03-29T00:00:00 |
2023/03/29
|
https://www.institutedata.com/blog/how-can-artificial-intelligence-improve-workplace-safety/
|
[
{
"date": "2023/03/29",
"position": 97,
"query": "AI labor market trends"
},
{
"date": "2023/03/29",
"position": 30,
"query": "machine learning workforce"
},
{
"date": "2023/03/29",
"position": 77,
"query": "artificial intelligence workers"
}
] |
Evidence to support the analysis of impacts for artificial ...
|
Evidence to support the analysis of impacts for artificial intelligence governance
|
https://www.gov.uk
|
[] |
In order to develop an artificial intelligence ( AI ) regulatory regime that suitably mitigates risks while capitalising on the enormous opportunities AI ...
|
In order to develop an artificial intelligence ( AI ) regulatory regime that suitably mitigates risks while capitalising on the enormous opportunities AI brings, the UK government would like to understand the potential impacts of different regulatory options.
Frontier Economics in this report undertake independent economic research to help inform the government of the benefits and costs of potential government interventions, looking at 2 scenarios:
regulation by a central UK AI -specific regulator
-specific regulator changes to current UK sectoral regulation to account for AI -specific risks
The analysis estimates that changes to existing sectoral regulation to account for AI -specific risks will have a smaller negative impact on AI revenue in the UK than the creation of an AI -specific regulator. Frontier Economics estimates that a central AI -specific regulator could cause the UK to lose £3 billion more in AI revenue between 2023 and 2032 compared to a scenario where there are changes to existing sectoral regulation.
The study was produced for the Department for Science, Innovation and Technology by Frontier Economics.
| 2023-03-29T00:00:00 |
https://www.gov.uk/government/publications/evidence-to-support-the-analysis-of-impacts-for-artificial-intelligence-governance
|
[
{
"date": "2023/03/29",
"position": 62,
"query": "AI regulation employment"
}
] |
|
Response to the Government's AI White Paper
|
Response to the Government’s AI White Paper
|
https://www.labourlongterm.org
|
[] |
The White Paper is a step in the right direction, towards comprehensive regulation that limits the harms of AI and gives businesses the confidence to safely ...
|
Author: James Baker
Introduction
Today, the Government’s Department for Science, Innovation and Technology published its AI White Paper: ‘AI regulation: pro-innovation approach’. The White Paper contains the Government’s proposals for the establishment of a new regulatory framework to ‘guide and inform the responsible development and use of AI in all sectors of the economy’. It also sets out a series of questions on AI regulation to which the Government requests responses by 21 June 2023.
Below, we set out a summary of the central features of the new regulatory framework, our initial commentary on its strengths and weaknesses, and our recommendations for improvement.
Note this is a rapid reaction to the White Paper, written to inform debate in Parliament, the Labour Party and beyond. Over the coming weeks and months, Labour for the Long Term will be considering what an integrated Labour approach to AI and emerging technology should look like - encompassing regulation, partnerships with the private sector, access to compute and international cooperation.
We welcome the release of the White Paper, and many of the features of its framework. However, given the time lag to full implementation, sectoral approach and limited risk management powers, we believe the framework is unlikely to keep pace with the rapid AI developments we are witnessing or provide adequate certainty to UK businesses.
Recommendations for strengthening the framework:
The Government’s proposals should be implemented as soon as possible, rather than wait for another year.
The Government should introduce a duty requiring regulators to have due regard to the regulatory principles in this parliamentary session.
The central risk function should be adequately funded and empowered to fulfil its remit.
A cross-sectoral approach is more suitable for regulating general-purpose AI ‘foundation models’ (such as GPT and other large language models) - as opposed to implementation by individual regulators.
The Framework
The framework sets out a set of five principles that form the basis of the new regulatory regime:
Safety, security and robustness;
Appropriate transparency and explainability;
Fairness;
Accountability and governance; and
Contestability and redress.
Rather than enforce these principles centrally, the Government intends to distribute responsibility to the existing set of sectoral regulators - such as the Information Commissioner’s Office (ICO), Financial Conduct Authority (FCA) and Medicines and Healthcare products Regulatory Agency (MHRA). Regulators will initially have discretion to implement the principles as they see fit, monitoring the development of AI tools and applications within their sectors of the economy on the basis of their existing powers and resources. For the time being, the principles will be put on a statutory basis, meaning no new primary legislation will be enacted to roll out the new AI framework.
The Government also intends to establish a new set of cross-cutting central functions to support regulators as they develop and enforce their individual rules and a risk function to monitor future risks.
Faster implementation needed
The White Paper is a step in the right direction, towards comprehensive regulation that limits the harms of AI and gives businesses the confidence to safely deploy AI. The cross-cutting central functions and risk unit are particularly welcome. But it is also a limited step, that will be implemented too slowly. In September 2021, the Government’s National AI Strategy committed to publish this White Paper in early 2022. The White Paper is a year late, and yet many of the most important commitments are another year away at least - meaning that the full framework will not come into effect after the US and EU equivalents.
We cannot afford to wait another year for fully functioning AI regulation. In the four months since the release of OpenAI’s ChatGPT, the use of AI tools and applications has multiplied across the economy. Law firms are using Harvey to automate legal research and writing, Copilot is being used to write computer code, and the British Government is considering the use of ChatGPT in the Civil Service. AI models are growing more powerful by the day, with ever-greater implications for society. GPT-4, released two weeks ago, significantly outperforms ChatGPT, and is able to ace graduate-level exams and understand input not just from text but also images. The pace of these advances is causing significant concern amongst experts, and today the Future of Life Institute published an open letter signed by over a thousand senior professors and industry leaders calling for a six month pause on the training of all AI systems more powerful than GPT-4, to allow time for greater levels of Government regulation and oversight to be put in place.
Recommendation: the Government’s proposals should be implemented as soon as possible, rather than wait for another year.
No duty for regulators and no extra resource
Even if all these commitments were implemented immediately, the regulatory framework would still be lacking. According to the current proposals, the new principles will not have a statutory backing and the Government has not set out how it will adequately resource regulators, especially those beyond the Digital Regulation Cooperation Forum, who are already struggling with the impact of AI on their work. Without a new legal basis, the Government will likely have difficulty obliging regulators to follow the principles laid down in the paper, and ultimately regulators could deprioritise or ignore these regulatory principles if they come into conflict with existing statutory duties and other pressing demands on their resources.
The Government has stated that it anticipates bringing forward legislation in future, and may introduce a new duty requiring regulators to have due regard to the principles. However, given the pace of development in AI, this approach risks too little scrutiny and too little accountability for companies developing transformative capabilities at a pivotal moment in their introduction to society and the economy.
Recommendation: the Government should introduce a duty requiring regulators to have due regard to the regulatory principles in this parliamentary session.
Risk function
We welcome the proposal in the Government’s framework to establish a central risk function, with a remit that includes covering “‘high impact but low probability’ risks such as existential risks posed by artificial general intelligence or AI biosecurity risks.”
However, to be able to do its job effectively, this central risk function needs to be given the power to proactively monitor AI developments within individual firms developing foundational models, and if necessary intervene to mitigate risks. This will require additional resources and potentially a new legal basis. The White Paper provides some discussion of how a horizon scanning function might work, but appears to rely on voluntary provision of information by industry and academia. This is unlikely to be adequate to keep the Government fully informed of emerging trends and risks.
Ideally, the framework would also include a requirement for companies developing AI foundational models to regularly update the Government on their progress and a right for Government to visit and inspect AI labs if necessary (as the head of OpenAI called for last week), giving the Government the opportunity to review and audit models before they are publicly released.
Recommendation: the central risk function should be adequately funded and empowered to fulfil its remit.
Cross-cutting approach
We also welcome the proposal to establish cross-cutting functions to support regulators. As unions, think-tanks, business and regulators themselves have said, coordinated regulation is desperately needed when dealing with a technology with such wide-ranging applications, and associated significant risks and opportunities.
However, ‘foundation model’ systems like GPT-4 are not comprehensively covered by existing legislation and will cut across the remit of almost every sectoral regulator. They are increasingly used for powerful applications in medicine and healthcare, finance, the life sciences and chemistry, and increasingly will be deployed throughout Microsoft and Google’s suites of workplace software.
We recognise that this is a fast-moving area, but that makes it all the more important for the regulatory environment to keep pace. Citizens and consumers will need reassurance that these foundation models are actually following the principles that the Government has outlined. And it will benefit both regulators and the frontier AI firms they need to oversee to have a consistent cross-sectoral approach to regulating these systems, rather than inconsistent standards across sectors - and companies being bombarded with requests from several different regulators only seeing a part of the picture.
Recommendation: a cross-sectoral approach is more suitable for regulating general AI models (such as GPT and other large language models) - as opposed to implementation by individual regulators.
Wider recommendations and next steps
The White Paper on regulation forms just one limb of the UK Government’s wider approach to AI and emerging technology, complementing the International Tech Strategy, Future of Compute Review, Science & Technology Framework and Integrated Review Refresh, all of which have been published in the past two months. Alongside this commentary on the White Paper, Labour for the Long Term have responded to the International Tech Strategy and made submissions on compute governance and semiconductor supply chains.
Over the coming weeks and months, Labour for the Long Term will be considering what an integrated Labour approach to AI and emerging technology should look like - encompassing regulation, partnerships with the private sector, access to compute and international cooperation. If you are interested in supporting us with this project, please get in touch.
About the author
James Baker is Labour for the Long Term’s Executive Director for Policy and Operations.
| 2023-03-29T00:00:00 |
https://www.labourlongterm.org/briefings/response-to-the-governments-ai-white-paper
|
[
{
"date": "2023/03/29",
"position": 75,
"query": "AI regulation employment"
},
{
"date": "2023/03/29",
"position": 23,
"query": "government AI workforce policy"
}
] |
|
Artificial Intelligence
|
Artificial Intelligence
|
https://www.mclane.com
|
[
"John Dewispelaere",
"Allison E. Kuepper",
"Patrick C. Closson",
"Beth L. Fowler"
] |
... regulatory, legal, technical, and practical issues unique to AI. Clients ... AI applications by employees, and consumer protection concerns regarding AI.
|
McLane Middleton is comprised of attorneys from across the firm that provide counsel concerning regulatory, legal, technical, and practical issues unique to AI. Clients seek our guidance regarding AI questions related to privacy, information security, mergers and acquisitions, litigation, contract enforcement, and many other substantive areas of law.
The Artificial Intelligence Practice is both knowledgeable about the cutting edge of AI law and technology as well as practical about how clients should adopt AI applications and address concerns about AI. We have experience with AI policy and ethics, use of AI applications by employees, and consumer protection concerns regarding AI. Our recent client projects have included:
| 2023-03-29T00:00:00 |
https://www.mclane.com/practice-areas/artificial-intelligence/
|
[
{
"date": "2023/03/29",
"position": 89,
"query": "AI regulation employment"
}
] |
|
UK Government unveils its approach to regulating AI with ...
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UK Government unveils its approach to regulating AI with White Paper
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https://www.techuk.org
|
[] |
These functions will support regulators and work with actors across industry, academia and civil society to best support the ethical adoption of AI. Statutory ...
|
UK Government unveils its approach to regulating AI with White Paper
Today, the Department for Science, Innovation and Technology (DSIT) launched its highly anticipated AI white paper, ‘A pro-innovation approach to AI regulation’, which will guide the use of AI in the UK.
Included within DSIT’s press release, alongside a techUK quote, the approach laid out in the white paper ‘A pro-innovation approach to AI regulation’ which will enable responsible innovation, making it easier for businesses to innovate, grow and create jobs, whilst maintaining public trust. This announcement comes following the government’s commitments to build an expert taskforce to develop the UK’s capabilities into foundation models as well as a £2m sandbox in their recent Spring Budget 2023.
The white paper introduces an approach to AI regulation that is principles-based, agile and context-specific to promote responsible innovation. Rather than establishing a single AI regulator, the government will coordinate existing regulators to produce a context-specific approach, that is designed according to how AI is being used across different sectors.
This reflects techUK’s recommendations for a pro-innovation, coordinated and context-specific approach, as well as the need to acknowledge and map current regulations. techUK calls for the government to move at pace in providing adequate capabilities to regulators and ensure their approach is interoperable with international approaches.
This will all be underpinned by a framework consisting of 5 key principles to facilitate and inform the responsible adoption of AI in all sectors:
Safety, security and robustness Appropriate transparency and explainability Fairness Accountability and governance Contestability and redress
These principles will initially be introduced on a non-statutory basis to prevent hampering innovation and will be upheld through a number of central support functions established within government. These functions will support regulators and work with actors across industry, academia and civil society to best support the ethical adoption of AI. Statutory regulations will only be implemented after careful consideration if it is seen fit, and when parliamentary time allows.
It is worth mentioning that the white paper states that this approach includes Large Language Models (LLMs), as they qualify under the paper's agile description of AI systems through two characteristics; a) autonomous b) adaptable.
On generative AI and Intellectual Property, the White Paper directs to the code of conduct the Intellectual Property Office has been tasked to produce by the summer. Please see our Spring Budget and Sir Patrick Vallance review analysis for more.
It also mentions that the government will launch a portfolio of AI assurance techniques in Spring 2023, which has been developed in collaboration with techUK.
Read the full AI white paper here.
On timelines for the application of recommendations, the white paper states:
That within six months, the government will publish a response to a consultation, alongside an AI regulation roadmap that will establish the central government function as well as a regulatory sandbox, and issue cross-sectoral principles to regulators with initial guidance for implementation.
the government will publish a response to a consultation, alongside an AI regulation roadmap that will establish the central government function as well as a regulatory sandbox, and issue cross-sectoral principles to regulators with initial guidance for implementation. That within twelve months, regulators will be encouraged to publish guidance, and agree on partnership arrangements with organisations and existing initiatives to deliver the first central functions and publish proposals for the design of a central monitoring & evaluation framework which will be consulted on.
regulators will be encouraged to publish guidance, and agree on partnership arrangements with organisations and existing initiatives to deliver the first central functions and publish proposals for the design of a central monitoring & evaluation framework which will be consulted on. That after twelve months, the first central support functions should be delivered, and a regulatory sandbox should keep on being developed. The AI regulation roadmap should be updated to reflect whether a government unit or independent body would be the most appropriate mechanism to deliver the central functions.
A consultation on the government’s approach presented in the white paper will now run for 12 weeks, until 21 June. An AI regulation roadmap will also be published alongside the government’s response to the consultation.
techUK will now work with its members over the coming weeks to gather views on the regulatory approach. If you would like to provide input to this work, please get in touch.
We’ll also publish further insights analysing the white paper in more detail, and what it means for industry.
In welcoming the publication of the AI white paper Sue Daley, Director for Tech and Innovation at techUK, said:
techUK welcomes the much-anticipated publication of the UK’s AI White Paper and supports its plans for a context-specific, principle-based approach to governing AI that promotes innovation. The government must now prioritise building the necessary regulatory capacity, expertise, and coordination. techUK stands ready to work alongside government and regulators to ensure that the benefits of this powerful technology are felt across both society and the economy. Sue Daley techUK
techUK – Supercharging UK Tech and Innovation
The opportunities of innovation are endless. Automation, IoT, AI, Edge, Quantum, Drones and High Performance Computing all have the power to transform the UK. techUK members lead the development of these technologies. Together we are working with Government and other stakeholders to address tech innovation priorities and build an innovation ecosystem that will benefit people, society, economy and the planet - and supercharge the UK as a global leader in tech and innovation.
For more information, or to get in touch, please visit our Innovation Hub and click ‘contact us’.
Tech and Innovation updates
Sign-up to get the latest updates and opportunities across Technology and Innovation.
If you would like to participate in techUK’s consultation response or get involved in any of our other AI initiatives, please get in touch with Carmine Greusard-Deffeuille ([email protected]).
| 2023-03-29T00:00:00 |
https://www.techuk.org/resource/uk-government-unveils-its-approach-to-regulating-ai.html
|
[
{
"date": "2023/03/29",
"position": 91,
"query": "AI regulation employment"
}
] |
|
UK unveils world leading approach to innovation in first ...
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UK unveils world leading approach to innovation in first artificial intelligence white paper to turbocharge growth
|
https://www.gov.uk
|
[] |
Government launches AI white paper to guide the use of artificial intelligence in the UK, to drive responsible innovation and maintain public trust.
|
White paper sets out new approach to regulating artificial intelligence to build public trust in cutting-edge technologies and make it easier for businesses to innovate, grow and create jobs
plan will help unleash the benefits of AI , one of the 5 technologies of tomorrow, which already contributes £3.7 billion to the UK economy
, one of the 5 technologies of tomorrow, which already contributes £3.7 billion to the UK economy follows new expert taskforce to build the UK’s capabilities in foundation models, including large language models like ChatGPT, and £2 million for sandbox trial to help businesses test AI rules before getting to market
Five principles, including safety, transparency and fairness, will guide the use of artificial intelligence in the UK, as part of a new national blueprint for our world class regulators to drive responsible innovation and maintain public trust in this revolutionary technology.
The UK’s AI industry is thriving, employing over 50,000 people and contributing £3.7 billion to the economy last year. Britain is home to twice as many companies providing AI products and services as any other European country and hundreds more are created each year.
AI is already delivering real social and economic benefits for people, from helping doctors to identify diseases faster to helping British farmers use their land more efficiently and sustainably. Adopting artificial intelligence in more sectors could improve productivity and unlock growth, which is why the government is committed to unleashing AI ’s potential across the economy.
As AI continues developing rapidly, questions have been raised about the future risks it could pose to people’s privacy, their human rights or their safety. There are concerns about the fairness of using AI tools to make decisions which impact people’s lives, such as assessing the worthiness of loan or mortgage applications.
Alongside hundreds of millions of pounds of government investment announced at Budget, the proposals in the AI regulation white paper will help create the right environment for artificial intelligence to flourish safely in the UK.
Currently, organisations can be held back from using AI to its full potential because a patchwork of legal regimes causes confusion and financial and administrative burdens for businesses trying to comply with rules.
The government will avoid heavy-handed legislation which could stifle innovation and take an adaptable approach to regulating AI . Instead of giving responsibility for AI governance to a new single regulator, the government will empower existing regulators - such as the Health and Safety Executive, Equality and Human Rights Commission and Competition and Markets Authority - to come up with tailored, context-specific approaches that suit the way AI is actually being used in their sectors.
The white paper outlines 5 clear principles that these regulators should consider to best facilitate the safe and innovative use of AI in the industries they monitor. The principles are:
safety, security and robustness : applications of AI should function in a secure, safe and robust way where risks are carefully managed
: applications of should function in a secure, safe and robust way where risks are carefully managed transparency and explainability : organisations developing and deploying AI should be able to communicate when and how it is used and explain a system’s decision-making process in an appropriate level of detail that matches the risks posed by the use of AI
: organisations developing and deploying should be able to communicate when and how it is used and explain a system’s decision-making process in an appropriate level of detail that matches the risks posed by the use of fairness : AI should be used in a way which complies with the UK’s existing laws, for example the Equality Act 2010 or UK GDPR, and must not discriminate against individuals or create unfair commercial outcomes
: should be used in a way which complies with the UK’s existing laws, for example the Equality Act 2010 or UK GDPR, and must not discriminate against individuals or create unfair commercial outcomes accountability and governance : measures are needed to ensure there is appropriate oversight of the way AI is being used and clear accountability for the outcomes
: measures are needed to ensure there is appropriate oversight of the way is being used and clear accountability for the outcomes contestability and redress: people need to have clear routes to dispute harmful outcomes or decisions generated by AI
This approach will mean the UK’s rules can adapt as this fast-moving technology develops, ensuring protections for the public without holding businesses back from using AI technology to deliver stronger economic growth, better jobs, and bold new discoveries that radically improve people’s lives.
Over the next 12 months, regulators will issue practical guidance to organisations, as well as other tools and resources like risk assessment templates, to set out how to implement these principles in their sectors. When parliamentary time allows, legislation could be introduced to ensure regulators consider the principles consistently.
Science, Innovation and Technology Secretary Michelle Donelan said
AI has the potential to make Britain a smarter, healthier and happier place to live and work. Artificial intelligence is no longer the stuff of science fiction, and the pace of AI development is staggering, so we need to have rules to make sure it is developed safely. Our new approach is based on strong principles so that people can trust businesses to unleash this technology of tomorrow.
Businesses warmly welcomed initial proposals for this proportionate approach during a consultation last year and highlighted the need for more coordination between regulators to ensure the new framework is implemented effectively across the economy. As part of the white paper published today, the government is consulting on new processes to improve coordination between regulators as well as monitor and evaluate the AI framework, making changes to improve the efficacy of the approach if needed.
£2 million will fund a new sandbox, a trial environment where businesses can test how regulation could be applied to AI products and services, to support innovators bringing new ideas to market without being blocked by rulebook barriers.
Organisations and individuals working with AI can share their views on the white paper as part of a new consultation launching today which will inform how the framework is developed in the months ahead.
Lila Ibrahim, Chief Operating Officer and UK AI Council Member, DeepMind, said:
AI has the potential to advance science and benefit humanity in numerous ways, from combating climate change to better understanding and treating diseases. This transformative technology can only reach its full potential if it is trusted, which requires public and private partnership in the spirit of pioneering responsibly. The UK’s proposed context-driven approach will help regulation keep pace with the development of AI , support innovation and mitigate future risks.
Grazia Vittadini, Chief Technology Officer, Rolls-Royce, said:
Both our business and our customers will benefit from agile, context-driven AI regulation. It will enable us to continue to lead the technical and quality assurance innovations for safety-critical industrial AI applications, while remaining compliant with the standards of integrity, responsibility and trust that society demands from AI developers.
Sue Daley, Director for Tech and Innovation at techUK, said:
techUK welcomes the much-anticipated publication of the UK’s AI white paper and supports its plans for a context-specific, principle-based approach to governing AI that promotes innovation. The government must now prioritise building the necessary regulatory capacity, expertise, and coordination. techUK stands ready to work alongside government and regulators to ensure that the benefits of this powerful technology are felt across both society and the economy.
Clare Barclay, CEO, Microsoft UK, said:
AI is the technology that will define the coming decades with the potential to supercharge economies, create new industries and amplify human ingenuity. If the UK is to succeed and lead in the age of intelligence, then it is critical to create an environment that fosters innovation, whilst ensuring an ethical and responsible approach. We welcome the UK’s commitment to being at the forefront of progress.
Rashik Parmar MBE, chief executive, BCS The Chartered Institute for IT, said:
AI is transforming how we learn, work, manage our health, discover our next binge-watch and even find love. The government’s commitment to helping UK companies become global leaders in AI , while developing within responsible principles, strikes the right regulatory balance. As we watch AI growing up, we welcome the fact that our regulation will be cross-sectoral and more flexible than that proposed in the EU, while seeking to lead on aligning approaches between international partners. It is right that the risk of use is regulated, not the AI technology itself. It’s also positive that the paper aims to create a central function to help monitor developments and identify risks. Similarly, the proposed multi-regulator sandbox [a safe testing environment] will help break down barriers and remove obstacles. We need to remember this future will be delivered by AI professionals - people - who believe in shared ethical values. Managing the risk of AI and building public trust is most effective when the people creating it work in an accountable and professional culture, rooted in world-leading standards and qualifications.
Notes to editors
Read the AI regulation white paper.
| 2023-03-29T00:00:00 |
https://www.gov.uk/government/news/uk-unveils-world-leading-approach-to-innovation-in-first-artificial-intelligence-white-paper-to-turbocharge-growth
|
[
{
"date": "2023/03/29",
"position": 36,
"query": "government AI workforce policy"
}
] |
|
AI for Future Workforce
|
AI for Future Workforce
|
https://www.eitdeeptechtalent.eu
|
[] |
Deep tech fields. Artificial Intelligence & Machine Learning (including Big Data). Country. Poland. Course language. English. Fee. Free course. Duration (hours).
|
Intel expanded its Digital Readiness Program portfolio for employability education (covering vocational schools and community colleges) through the AI for Future Workforce program to empower the future workforce with the necessary AI skills for employability. Intel provides over 225 hours of AI content, professional training for school faculties, and implementation guidance. The schools then use the program to develop AI certificates, augment existing courses, or launch full AI associate degree programs for their students.
The program includes over 200 hours of the latest teaching-learning content incorporating new AI trends with engaging, validated, hands-on, flexible teaching-learning resources and use cases. It incorporates new trends such as low code / no-code AI, IoT, reinforced learning, and supervised / unsupervised learning.
Delivery in AI labs or remote hybrid learning environments through hi-tech, hi-touch, by Intel trained facilitators, with flexible and open infrastructure and Intel technologies.
| 2023-03-29T00:00:00 |
https://www.eitdeeptechtalent.eu/courses/ai-for-future-workforce/
|
[
{
"date": "2023/03/29",
"position": 28,
"query": "machine learning workforce"
}
] |
|
The AI gender gap: living in a world designed by men
|
The AI gender gap: living in a world designed by men
|
https://www.wartsila.com
|
[
"Kejsi Gjordeni",
"Former Strategy",
"Operations Manager",
"Mafalda Evans Lopes Guimarães",
"Contributing Writer At Spoon Agency"
] |
Even as more companies employ Artificial Intelligence , only 26% of the global AI workforce comprises of women ... Machine Learning Engineer at Wärtsilä. For ...
|
As global enterprises rely more heavily on artificial intelligence (AI) to address their more complex needs, we are seeing an all-time low in female students and professionals across all levels of the ICT, STEM (science, technology, engineering, and mathematics) and AI sectors.
In fact, a 2020 World Economic Forum report found that just over a quarter of AI professionals globally were women. Similarly, the Kapor Center in California reported that as few as 13% of women working in tech identified as Black, Latinx or Native American – with only 8% identifying as non-binary.
With this large-scale exclusion comes staggering setbacks: from lower job opportunities to reduced physical safety due to security systems failing to identify women as accurately as men. In a male-dominated digital world, have half the population’s needs taken a back seat?
A lack of female representation in tech
“Representation on all levels – if we don’t take accountability for greater representation, AI can become counterproductive and reinforce the gender gap,” begins Kejsi Gjordeni, former Strategy and Operations Manager at Visma, a company that provides business software and IT related development and consultancy.
“We as humans are biased. Studies show that more diverse teams are performing better. It’s a no-brainer that we should have more diversity in all senses,” adds Gjordeni.
A breeding ground for reinforcing existing biases is easily propagated when most AI developers worldwide are male. Technology designed and built almost exclusively by men can become skewed to represent their individual experiences.
“Some regulations, such as the ethics of AI, have been put in place but we need to be stricter in reviewing the data that we work on, and checking that all groups are well represented in our datasets,” says Arjeta Peshtani, a former Machine Learning Engineer at Wärtsilä.
For example, Amazon had to pull an AI-powered tool used for recruitment purposes in 2018 when male candidates were recommended for job roles at a disproportionately higher rate than their female counterparts. The machine learning system used was taught to review applications submitted over 10 years. Since men held most tech positions over the set period, the algorithm taught itself to discredit any application which included the word ‘women’.
Representation on all levels – if we don’t take accountability for greater representation, AI can become counterproductive and reinforce the gender gap. Kejsi Gjordeni, former Strategy and Operations Manager, Visma
Better models, better possibilities
Over a third of companies now employ AI to leverage their operational capabilities, according to the 2022 IBM Global AI Adoption Index. The report describes how one in four businesses has turned to machine learning to address comprehensive issues ranging from employment shortages to environmental, social, and governance-related ambitions.
Implementing AI in our daily lives allows for countless systems and operations to be automated. With this comes the promising potential to enhance and redefine all areas of our society. However, Peshtani notes that work within a machine learning model doesn’t end by putting it into production.
“Another essential part is to monitor its performance. Data that is used for these models might change over time and the data scientist needs to be aware of these changes,” says Peshtani.
Methodology for optimising machine learning models might also serve as a partial solution to help bridge the AI gender gap. It is crucial to bring more young girls and women into the field and to monitor, retain, and develop their overarching educational and career paths.
While Gjordeni’s career progressed naturally from a technical role to a more strategy-based position due to her personal preferences, she explained that in tech, “We're not encouraged to pursue a more technical path, becoming instead generalists and taking responsibility for strategic parts of the business.”
There’s also the risk that gender biases can still affect career progression once in a tech or AI position. “We must not make assumptions that those creative roles are more appealing to women than they are to men,” warns Gjordeni.
A brighter future
The consequences of omitting women from the AI lifecycle are far-reaching. In recent years, global organisations and individuals have taken increasingly positive steps.
The United Nations and the World Economic Forum have both made a notable effort to start a global conversation about AI gender-based issues by developing initiatives that map out actionable strategies for businesses and political leaders to adopt.
Professionals like Peshtani and Gjordeni highlight the need for increased diversity and possibilities in tech. More significantly, they remind us that women haven’t gone anywhere.
“We can achieve greater results. The more diverse the workplace is, the better the results. We need to give equal opportunities to all people regardless of gender,” Peshtani concludes.
| 2023-03-29T00:00:00 |
https://www.wartsila.com/insights/article/the-ai-gender-gap-living-in-a-world-designed-by-men
|
[
{
"date": "2023/03/29",
"position": 35,
"query": "machine learning workforce"
}
] |
|
AI & ML in HRMS Software
|
Hoshi - Best Payroll Software
|
https://www.hoshihrms.com
|
[] |
Artificial Intelligence (AI) and Machine Learning (ML) are revolutionising ... Kronos Workforce Dimensions: Kronos Workforce Dimensions is an AI ...
|
Artificial intelligence (AI) and machine learning (ML) in HRMS software.
Artificial Intelligence (AI) and Machine Learning (ML) are revolutionising the way we work and transforming various industries, including Human Resource Management (HRM). AI and ML are increasingly being integrated into HRM software to improve efficiency, streamline processes, and enhance the employee experience. In this blog post, we will discuss the benefits of AI and ML in HR and Payroll software and how they are changing the HR landscape.
Introduction:
Artificial Intelligence and Machine Learning are two buzzwords that are changing the way HR work. In HRM, AI and ML are transforming the way organisations manage their workforce, from recruitment to employee engagement and retention. AI and ML are helping organisations automate their HR processes, reduce manual workload, and make data-driven decisions. Let's take a look at how AI and ML are changing the HR landscape.
Click on the banner and fill the form for free trial
Benefits of AI and ML in HRMS Software :
AI and ML are being used to automate recruitment processes, such as resume screening, candidate shortlisting, and interview scheduling. This helps HR teams to save time and resources and focus on other important tasks, such as candidate engagement and employee retention.
Predictive Analytics:
AI and ML can be used to analyse vast amounts of employee data, such as performance metrics, employee feedback, and social media activity, to identify trends and patterns. This enables HR teams to make data-driven decisions and predict employee behaviour, such as attrition risk and employee engagement.
Improved Employee Experience :
AI and ML can help organisations provide a better employee experience by personalising learning and development programs, providing timely feedback and recognition, and automating administrative tasks. This helps employees to feel more engaged, motivated, and productive.
Enhanced Performance Management :
AI and ML can help organisations to improve performance management by providing real-time feedback, identifying skills gaps, and automating performance reviews. This helps employees to improve their skills and performance and helps managers to provide better feedback and coaching.
Improved Workforce Planning :
AI and ML can be used to analyse workforce data, such as employee demographics, skills, and performance, to identify skills gaps and create workforce plans. This helps organisations to make informed decisions about hiring, training, and development, and ensures that they have the right people with the right skills at the right time.
Reduced Bias and Discrimination:
AI and ML can help to reduce bias and discrimination in the hiring process by eliminating human biases and relying on objective data to make hiring decisions. This helps organisations to build a diverse and inclusive workforce and avoid legal issues related to discrimination.
Best AI tools that HR can use for various HR tasks:
Mya : Mya is an AI-powered chatbot that is designed to streamline the recruitment process. It can interact with candidates, schedule interviews, and even provide feedback to candidates.
Talla : Talla is an AI-powered chatbot that can help HR teams with onboarding, training, and answering employee questions. It can also help HR teams with tasks such as updating employee information and managing vacation requests.
Textio : Textio is an AI-powered tool that can help HR teams to write more inclusive job descriptions. It uses machine learning to analyse language patterns and suggest changes that can help to attract a more diverse pool of candidates.
Ultimate Software's Xander : Xander is an AI-powered tool that can help HR teams to improve employee engagement and retention. It uses machine learning to analyse data such as employee feedback and performance metrics to identify areas where improvements can be made.
Kronos Workforce Dimensions : Kronos Workforce Dimensions is an AI-powered tool that can help HR teams to manage scheduling, time tracking, and payroll. It uses machine learning to analyse data such as employee schedules and time off requests to make more accurate scheduling decisions.
IBM Watson Candidate Assistant: IBM Watson Candidate Assistant is an AI-powered tool that can help HR teams to screen resumes and identify top candidates. It uses machine learning to analyse resumes and compare them to job requirements, making it easier to identify the most qualified candidates.
AllyO : AllyO is an AI-powered tool that can help HR teams to automate the recruitment process. It can screen resumes, schedule interviews, and even provide personalised feedback to candidates.
Overall, AI tools can help HR teams to streamline tasks and improve efficiency, allowing them to focus on more strategic initiatives.
Click on the banner and fill the form for free trial
Challenges of AI and ML in HRMS Software :
While AI and ML have many benefits in HRMS software, there are also some challenges that organisations need to be aware of, such as:
AI and ML require vast amounts of data to train algorithms, which can raise concerns about data privacy and security. Organisations need to ensure that they have the proper data governance and security protocols in place to protect employee data.
Ethical Considerations:
AI and ML can raise ethical considerations, such as the use of employee data for decision-making and the potential for bias and discrimination. Organisations need to ensure that their use of AI and ML is transparent, ethical, and aligned with their organisational values.
Integration with Existing Systems:
Integrating AI and ML into existing HRMS systems can be challenging, as it requires specialised skills and expertise. Organisations need to ensure that they have the necessary IT infrastructure and expertise to support the integration of AI and ML into their HRMS software.
Click on the banner and fill the form for free trial
Go for Free trial before finalizing the product
Conclusion:
| 2023-03-29T00:00:00 |
https://www.hoshihrms.com/hr-blogs/artificial-intelligence-ai-and-machine-learning-ml-hrms-software
|
[
{
"date": "2023/03/29",
"position": 37,
"query": "machine learning workforce"
}
] |
|
Machine Learning
|
Infinity Staffing Group
|
https://infinity-isg.com
|
[] |
... workplace culture. Infinity Staffing Group can help you address shortages in your workforce with unmatched machine learning hiring. WORKING IN COLLABORATION ...
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WORKING IN COLLABORATION WITH THE INDUSTRY'S GREATEST MACHINE LEARNING EXPERTS Machine Learning
Machine learning professionals assist your firm in extracting the most potential from its data warehouses. The greatest machine learning candidates thrive in a wide range of areas, from programming and data analytics to subject matter expertise. These individuals must have strong communication and analytical abilities, as well as prior software development expertise.
Machine learning engineers are among the most widely sought-after occupations in the artificial intelligence industry due to the tremendous demand for their skills. These jobs are difficult to fill due to the fast-growing nature of machine learning efforts and the technical expertise required to implement them.
Most firms simply do not have the time or funds to locate the best-qualified candidate for the position. Allow us to provide you with highly skilled machine learning professionals who will fulfill all of your AI needs.
| 2023-03-29T00:00:00 |
https://infinity-isg.com/specialisms/machine-learning.php
|
[
{
"date": "2023/03/29",
"position": 49,
"query": "machine learning workforce"
}
] |
|
The Benefits of Combining Human Intelligence and ...
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The Benefits of Combining Human Intelligence and Automation in the Workplace
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https://www.processmaker.com
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[] |
... workforce. Consider how you can use it to complement human thinking and create something even more powerful. Use AI and machine learning to: Aggregate ...
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Automation initiatives that combine AI with human talents outperform those that rely exclusively on AI. For many, AI is a sword of Damocles: it promises a new wave of efficiency few of us can predict. But using it responsibly, and most importantly, not rashly, must remain a top priority. Going all-in on AI can carry more risk than reward.
It’s a new era of collaborative intelligence, fusing the best of both worlds:
Humans: Empathy, creativity, soft skills, emotional intelligence
Artificial intelligence and Machine Learning: Speed, scalability, rapid experimentation
Successful companies aren’t just using AI to boost efficiency scores.
They’re using AI’s skills to complement human talents.
They’re tapping into humans-in-the-loop automation strategies that synthesize the two.
Thinking how one can collaborate with the other vs. how one can supplant the other.
Instead of establishing a division of labor between human and machine, find the symbiosis hiding in the cut—that’s where the most successful companies will mine their AI competitive edge.
Why the future of automation demands tight coordination between human and machine
Ask inventors where they came up with a world-changing idea. The notion rarely hits during the 9-to-5 grind.
Humans are extraordinary forgers of bizarre interconnections. Inspiration strikes in the shower or while doing something entirely unrelated.
Steve Jobs toyed with electronics after reading an article about hackers who scored free phone calls. He famously cites the exercise as a pinnacle moment that inspired Apple. The founder of GoPro took a year-long walkabout after his ad business failed—and the concept of the wearable action cam hit him on a surfing trip. The inventor of the floppy disk thought it up while drowning.
The concept fuels the commonly exchanged allegory on how Isaac Newton first theorized his gravitational theory: he was hit in the head by an apple.
Ask an AI like ChatGPT or Bard a question, and it churns through its knowledge to find “most likely” connected information. It’s a slot machine that gauges the strength of a connection, then provides an output that nabs the highest grade.
On the other hand, humans are excellent craftsmen at applying seemingly unrelated concepts to construct something novel. While generative AI is inherently probabilistic, humans are more free-flowing.
Which is good. Because most of the time, revelations lie in the improbable. An innovation pops into existence through a patchwork of unlikely experiences and insights that an AI would deem too far-fetched to relate.
So don’t think about how AI can reduce human involvement or slash the workforce. Consider how you can use it to complement human thinking and create something even more powerful.
Use AI and machine learning to:
Aggregate information
Unearth “unknown unknowns”
Turn big data into advice
Serve as a sounding board of new ideas and help human staffers fill in the gaps
AI fills the brainstorming clouds with a string of starting points that stimulate the human “lightning strike” of a brilliant idea.
Risks for going all-in on AI at the expense of human involvement
AI and human collaboration require explicit coordination. Black-box AI, or AI running through a human’s understanding, is a recipe for disaster. Humans need to be the helping hand guiding “good outcomes.”
Unmonitored AI can make decisions that can tarnish your reputation or eat away at your bottom line. Several loose-cannon AI models have made the news for self-taught biases. Unbeknownst to the company, computational models made heavy-handed decisions that discriminated against certain mortgage borrowers, job applications, and potential mates for the dating pool.
When introducing AI into their processes, here’s what organizations need to think about.
Opportunities for human/AI partnerships
Today’s workplace automation success is less about putting the pedal to the metal to maximize efficiency, but focusing on symbiosis.
If AI can deliver what automation leaders have promised—allow humans to focus on better uses of their natural-born talents—then these opportunities will reveal themselves.
How do job roles evolve?
How do we reorganize when the goal is no longer utmost efficiency but a clearer mind for generating bigger and better ideas?
What can AI take off your rockstars’ plates so they can do what you actually envisioned hiring them to do?
After all, few hiring managers intend to fill roles that exclusively slog through mundane to-do lists, juggle endless email threads, and attend pointless meetings.
But for most workers, those tasks overshadow their day, limiting the time they have on hand to make the big difference you originally intended for the position. Here’s how AI can erase many of the mental roadblocks that plague the modern workplace.
Fuel human creativity with the power of big data
The biochemist and sci-fi author Isaac Asimov once contemplated, “the most exciting phrase to hear in science, the one that heralds new discoveries, is not “Eureka!”, but rather, ‘Hmm…that’s funny.’”
Many AI tools promise a “Eureka!” moment without sharing the breadcrumbs of the journey with humans. However, it’s often along the way that humans collect the nuggets, insights, and experiences they need to generate the next big idea. Starving the creative process of the “hmm…that’s interesting” moments deteriorates creative possibility.
AI can comb through big data and turn it into actionable insights. Its recommendations don’t blindly guide your team, but unearth knowledge that would’ve otherwise remained buried under an unmanageable flood of data.
Don’t deify AI over your own differentiation
Avoid templating your competitive edge. Companies offloading the whole of their operations to AI are only in an arms race with other competitors using similar tools.
Siloed in your own organization, an AI’s calculation or ideation appears to be a stroke of genius.
But it’s hard to say if other businesses using the same tool are provided with the same intel.
The winning differentiator won’t come from the tools themselves, but through the humans who know how to wield them effectively.
Welcome new innovations from staffers
The human and AI collaboration can welcome new staffers into ideation. For example, AI start-ups are working to turn sketches into Figma prototypes, and Figma prototypes into ready-to-go code. Swung as a brute sword, one could cut jobs.
But bigger thinkers see this as an opportunity to welcome a fresh batch of ideas into the fold. In this instance, an employee could launch a new app through a sketch alone—without knowing the “language” of its execution.
Use these new tools to invite cross-department collaboration.
Ask workers in unrelated job roles how they would approach another team’s problem.
AI has an opportunity to become the ultimate “unsilo-er,” turning everyone in your organization, from executive assistants to sales managers, into an innovator.
Rewrite job roles
As the barriers to implementation come down, jobs will evolve.
Some experts believe conventional job duties will become increasingly fuzzy. There will be more overlap between departments. Humans will be hired strictly for their cognitive skills or a set of talents that help generate ideas, guide decision-making, or direct AI models. In this world, teaching, creativity, and judgment are the hired “role” vs. an analyst, a sales manager, or a programmer.
You can’t drop AI into your organization and walk away. For much of the workforce, it requires a 180° mental reset. People will need to learn how to shake their old habits and form new ones. When faced with a problem, the new snap reaction should be, how can we team up with AI to solve it? Only through training will every team member view AI as an opportunity instead of a threat.
Enable workers to become more involved with the customer journey
The Ritz-Carlton famously grants each worker a budget they can use unilaterally to improve a guest’s stay. These are tasks extracurricular to a workflow. They’re more nebulous. But they dramatically impact customer experience and satisfaction.
The book “The Experience Economy” by B. Joseph Pine II and James H. Gilmore outlined what they call, The Progression of Economic Value. In this model, there are four tiers of business:
Extracting commodities
Making goods
Delivering services
Staging experiences
Many companies get hung up on the first three phases, toiling with to-dos and minding internal processes—all at the expense of caring for the customer. According to Gilmore and Pine, the premium competitive differentiator is in the fourth element: the staging of experiences.
This is where AI can shine the brightest. It can help businesses better govern the more undifferentiated, commoditized aspects of doing business. Humans can focus on the real game changer: the customer experience.
AI can help you break free of the low functions. It can shoulder tasks staffers were just flat-out too busy with in the past. Your team can then focus on serving the moments that are unpredictable, improbable, and unpredictable. These experiences are often the moments customers find most captivating. The ones that make your business memorable and you inextricably intertwined with your customers.
The Industrial Revolution’s battle cry was “utmost efficiency.” How can we best organize tasks, lay out the workday, and entice employees to churn out the maximum output? If efficiency headlined the industrial revolution, what will mark this next phase? Utmost imagination? Utmost innovation? Hopefully, we’ll use AI to absorb all the mundane, repetitive, brain-clogging work to debut an era of even more remarkable ideas.
| 2023-03-29T00:00:00 |
https://www.processmaker.com/blog/humans-in-the-loop-automation/
|
[
{
"date": "2023/03/29",
"position": 61,
"query": "machine learning workforce"
},
{
"date": "2023/03/29",
"position": 82,
"query": "artificial intelligence workers"
}
] |
|
Training data for machine learning: here's how it works
|
Training data for machine learning: here’s how it works
|
https://sigma.ai
|
[
"Sigma Ai"
] |
The primary types of machine learning. There are three categories of machine learning; supervised learning, unsupervised learning ... Our flexible workforce of ...
|
How data is used in machine learning
Among the different types of machine learning, supervised learning is the most common. ML solution builders train these algorithms by using annotated training data. Once the model has identified patterns and relationships in the initial dataset, solution builders evaluate outputs and make corrections to input data, if needed, so the ML model delivers favorable results.
Granted, annotating data is time-consuming and can lengthen the timeframe for implementing a solution. However, the urgency to launch a new ML solution must be balanced with accuracy. The ML model needs to work as intended, which is far more critical than launching sooner and getting harmful negative data cascades.
Fortunately, practical solutions decrease the tension between speed and accuracy. Active learning technology allows the ML algorithm to take an active role in its training. With active learning, the training dataset is smaller than in traditional training phases of development. The algorithm helps select the data that will maximize performance, rather than blindly using all data available, and can ask humans for more input to perform better.
Outsourcing may be a more straightforward solution for handling data annotation, particularly if the problem is a lack of time. Data annotation often comprises about 80% of the resources on an AI project. Internal teams could spend their time on higher-value tasks. Moreover, companies annotating data internally often don’t have the right tools to efficiently perform the task and quality assessment. Outsourcing often makes sense in these situations if you have a strategy that includes several data annotator providers to reduce risk. It’s also imperative to choose a provider committed to the high data quality you need to ensure a model that gives favorable results.
| 2024-03-28T00:00:00 |
2024/03/28
|
https://sigma.ai/understanding-data-side-of-machine-learning/
|
[
{
"date": "2023/03/29",
"position": 62,
"query": "machine learning workforce"
}
] |
Workera Secures $23.5m in Series B funding
|
Workera Secures $23.5m in Series B funding
|
https://workera.ai
|
[
"Workera Team"
] |
... deep understanding of their workforce. We're delighted to have secured ... With the increased use of AI, machine learning and big data analytics to ...
|
Workera Secures $23.5m in Series B funding: What's next for Skills Intelligence?
At Workera, we’re committed to helping organizations like Samsung, Siemens Energy, and the US Air Force better identify and upskill their talent by providing a deep understanding of their workforce. We’re delighted to have secured $23.5 million in our Series B round of funding led by Jump Capital with participation from Sozo Ventures and existing investors NEA, Owl Ventures and AI Fund.
With this injection of capital, we’re able to evolve our skills intelligence platform, empowering leaders to make better, more informed talent development decisions at a greater pace than ever before. But what does the future look like for skills intelligence, and how can we play our part in it?
The value of skills intelligence
Skills intelligence refers to the ability to identify, assess, and develop skills effectively. It involves the capacity to analyze and understand the skills required for a particular task or job and to evaluate the skills of individuals or groups against these requirements.
Kian Katanforoosh, the founder of Workera, believes in the ever-growing importance of solutions that embrace and interpret skills data to help individuals and organizations make the absolute most of their potential. “Technology is advancing faster than ever. New tools, like ChatGPT, shake up industries every year. But skills aren't keeping up,” he explains. “Companies capable of upskilling talent at market-speed will reinvent themselves and build the future. Others will risk being disrupted.”
Andrew Ng, the chairman of the board for Workera, emphasizes the importance of using increasingly refined data to maximize impact: “Using deep learning and language models, Workera’s technology makes getting accurate and granular data on people’s skills an efficient process. Assessments help you learn what are your strengths and weaknesses, thereby providing deep skills intelligence that leads to actionable insights for both individuals and organizations.”
How can businesses leverage skills intelligence?
“The first step in workforce transformation is grasping what skills you have,” said Mike McMahon, co-founder and managing partner of Jump Capital, which has led Workera's Series B financing round. “Unlike most inference-based solutions derived from degrees or work experiences, Workera provides a high-fidelity signal and a powerful engine to target and accelerate learning and steer talent acquisition.”
Belcorp, a multinational beauty company going through a digital transformation, has been using Workera since the May 2022. In that time, the organization has accelerated the skills development of their data engineers by an estimated 30%, while their software engineers have improved their skills by 57%. The chief technology, data and digital officer of Belcorp, Venkat Gopalan, says that Workera enabled them boost their team’s confidence in delivering critical projects, and to improve project quality in general.
What’s the future of skills intelligence?
With the increased use of AI, machine learning and big data analytics to collect and analyze data on workforce trends and skills gaps, organizations will be able to anticipate and prepare for future workforce needs, and to develop targeted interventions to address skills gaps that equip their workforce with the skills they need to stay competitive in the future job market.
Here at Workera, we’re future-proofing our offer by increasing our comprehensive ontology of over 7,000 skills to include soft skills assessments, such as leadership and management. Alongside this latest capital infusion, we’re already rolling out major updates to customers that allow them to create skills-based career pathways in just a few clicks – helping their employees upskill faster than ever. The pathways are adaptive and goal-oriented, providing learners with a highly-personalized and motivating experience. It’s all enhanced by a richer skill assessment experience with more frequent check-ins, which gives leaders even better ongoing measurement of their workforce’s skills.
| 2023-03-29T00:00:00 |
https://workera.ai/blog/series_b
|
[
{
"date": "2023/03/29",
"position": 84,
"query": "machine learning workforce"
}
] |
|
Evolving Leadership for AI Delivery
|
Evolving Leadership for AI Delivery
|
https://www.irishfunds.ie
|
[] |
This article will explore key points for executives to consider as their leadership evolves to support AI delivery.
|
Evolving Leadership for AI Delivery
As the application of artificial intelligence (AI) grows within the Funds Industry, Ciaran Fennessy (Citi), explores how leadership will need to evolve to support the deployment of AI.
Introduction
Artificial intelligence, or AI, has rapidly become a transformative force in the world of business and leadership. With its ability to analyse vast amounts of data and make sophisticated decisions, AI is helping organizations to become more efficient, effective, and competitive. As a result, many leaders are turning to AI to help them navigate the complex challenges of the modern business environment and drive their organizations forward. But how does this rapid growth in AI impact the skills and competencies in the field of leadership? This article will explore key points for executives to consider as their leadership evolves to support AI delivery
To fully appreciate the transformational force of AI, the above paragraph was not written by the author. The initial paragraph was written by ChatGPT1. ChatGPT is a chatbot developed by OpenAI, launched in November 2022, which many of you will be aware of.
As the media storm around ChatGPT shows, AI has moved in to occupy practically every corner of our life. From smart devices at home, to wearable health technology, to being deployed in Financial Services to support multiple use-cases, AI is becoming an integral part of the technology stack.
Given the significant momentum behind AI, it is critical for leaders to understand how their leadership needs to evolve to support and drive this technology forward within their organizations. This paper focuses on this topic and looks to provide thoughts and direction for leaders as they look to lead their organization through the 4 th Industrial Revolution. Specifically,
What leadership skills are required to drive AI adoption?
How can Emotional Intelligence (EI) relate to supporting and delivering AI initiatives?
What are the key considerations for leaders when enabling AI across the organisation?
Leadership and AI
Daniel Goleman, one of the seminal thinkers and authors on leadership and Emotional Intelligence, has conducted extensive research on the role of Emotional Intelligence in leadership. In his 2004 Harvard Business Review paper, ‘What Makes A Leader’, Goleman highlights 5 components of Emotional Intelligence that leaders require.
| 2023-03-29T00:00:00 |
https://www.irishfunds.ie/news-knowledge/newsletter/evolving-leadership-for-ai-delivery/
|
[
{
"date": "2023/03/29",
"position": 22,
"query": "artificial intelligence business leaders"
}
] |
|
Elon Musk and other tech leaders call for pause in 'out of ...
|
Elon Musk and other tech leaders call for pause in ‘out of control’ AI race
|
https://www.cnn.com
|
[
"Samantha Murphy Kelly"
] |
The wave of attention around ChatGPT late last year helped renew an arms race among tech companies to develop and deploy similar AI tools in their products.
|
CNN —
Some of the biggest names in tech are calling for artificial intelligence labs to stop the training of the most powerful AI systems for at least six months, citing “profound risks to society and humanity.”
Elon Musk was among the dozens of tech leaders, professors and researchers who signed the letter, which was published by the Future of Life Institute, a nonprofit backed by Musk.
The letter comes just two weeks after OpenAI announced GPT-4, an even more powerful version of the technology that underpins the viral AI chatbot tool, ChatGPT. In early tests and a company demo, the technology was shown drafting lawsuits, passing standardized exams and building a working website from a hand-drawn sketch.
The letter said the pause should apply to AI systems “more powerful than GPT-4.” It also said independent experts should use the proposed pause to jointly develop and implement a set of shared protocols for AI tools that are safe “beyond a reasonable doubt.”
“Advanced AI could represent a profound change in the history of life on Earth, and should be planned for and managed with commensurate care and resources,” the letter said. “Unfortunately, this level of planning and management is not happening, even though recent months have seen AI labs locked in an out-of-control race to develop and deploy ever more powerful digital minds that no one – not even their creators – can understand, predict, or reliably control.”
If a pause is not put in place soon, the letter said governments should step in and create a moratorium.
The wave of attention around ChatGPT late last year helped renew an arms race among tech companies to develop and deploy similar AI tools in their products. OpenAI, Microsoft and Google are at the forefront of this trend, but IBM, Amazon, Baidu and Tencent are working on similar technologies. A long list of startups are also developing AI writing assistants and image generators.
Artificial intelligence experts have become increasingly concerned about AI tools’ potential for biased responses, the ability to spread misinformation and the impact on consumer privacy. These tools have also sparked questions around how AI can upend professions, enable students to cheat, and shift our relationship with technology.
The letter hints at the broader discomfort inside and outside the industry with the rapid pace of advancement in AI. Some governing agencies in China, the EU and Singapore have previously introduced early versions of AI governance frameworks.
Correction: An earlier version of this story said Microsoft founder Bill Gates and OpenAI CEO Sam Altman had signed the letter. While the executives were initially listed as signatories, the non-profit behind the letter later removed their names.
| 2023-03-29T00:00:00 |
2023/03/29
|
https://www.cnn.com/2023/03/29/tech/ai-letter-elon-musk-tech-leaders
|
[
{
"date": "2023/03/29",
"position": 44,
"query": "artificial intelligence business leaders"
}
] |
ChatGPT and AI's Effect on Senior Leadership
|
ChatGPT and AI’s Effect on Senior Leadership
|
https://huntscanlon.com
|
[] |
Its been the talk around the technology sector and the world: ChatGPT and other generative AI tools will inevitably change your business. A new report from ...
|
Its been the talk around the technology sector and the world: ChatGPT and other generative AI tools will inevitably change your business. A new report from Russell Reynolds Associates outlines this new technology’s potential and leadership implications.
March 29, 2023 – Since it burst onto the scene in 2022, ChatGPT has become one of the hottest topics in the business world, and the world at large. OpenAI’s latest (and most popular) endeavor has been a hot topic of conversation among leaders in recent weeks, as it became the fastest growing “app” of all time. But can this powerful AI advancement really improve leadership?
To gain insight into what ChatGPT can really do, it’s helpful to have a sense of how it operates. One of a new generation of generative AI tools, ChatGPT is a large language model (LLM) designed to understand and respond to natural language inputs from humans in a conversational manner. Utilizing a vast amount of text data and applying sophisticated deep learning algorithms, it generates human-like responses to a wide range of questions and prompts.
As leaders consider effective strategies for their businesses, they must cultivate a perspective on generative AI in all its forms, the ever-evolving technology landscape, and how to ethically embed new and distinct innovations into their businesses without getting swept up in the hype of the latest tool, say Russell Reynolds Associates’ Leah Christianson and Joy Tan in a new report.
‘There is no turning back—the next tech leap is already underway,” said Russell Reynolds Associates. “Leaders who stay connected to the technology ecosystem and maintain a tech-readiness mindset will be best prepared to address these changes.” As you plan for a future with generative AI, the report says it is essential to recognize that:
Uncertainty is inevitable; embracing it could revolutionize your business.
Generative AI can stand-alone, be integrated, or augment entire processes—each option bringing its own challenges and opportunities—but it isn’t any smarter than what you feed it.
Culture is still the core of your business: AI can’t replace moral and ethical human.
Can generative AI improve the human experience of leadership?
“The ongoing information explosion means we have access to more data than ever before,” the Russell Reynolds study said. “Now, users can employ generative AI to sift through immense amounts of data and concisely summarize key points across various fields.”
Additionally, according to Mira Murati, OpenAI’s CTO, these tools can also enhance personalized learning and ideally, generative AI will speed up the creation of novel strategies, products, and services, helping users understand and ideate new concepts faster than ever before.
By inputting specific queries and well-organized data points, individuals can potentially use generative AI to enhance their work with increased quality and speed, according to the Russell Reynolds report. “The purposeful integration of generative AI into our processes—particularly those that are repetitive or manual—may help combat the culture of busyness and increase efficiency,” the report says. “This should enable leaders to shift an organization’s focus toward true knowledge work; that which requires time to engage in deep, strategic thinking and idea generation. It could also provide more balance between work and life—something we know leaders crave.”
Given our uncertain economic environment, it’s also important to consider generative AI’s growing impact on the bottom line, according to findings from a recent McKinsey report. Consider where AI could address labor shortages, effectively taking over tasks or roles of decreasing interest to humans while also providing potential cost savings.
Related: To Integrate AI, Companies Must Alter Management Styles
What challenges does generative AI pose for leaders?
While there’s much to be excited about around generative AI’s potential, the Russell Reynolds report notes that there are several considerations leaders need to take into account, including:
Talent displacement: Generative AI may not just eliminate repetitive work; it will likely impact white collar roles as well. The Russell Reynolds report says that these tools cut across myriad industries and jobs and, because of their sophistication, unlike basic automation, they could displace middle-skilled and high-skilled workers in a way that past technology shifts never have before.
The Challenge of Finding Top AI Leaders
Artificial intelligence (AI) talent is increasingly in demand globally, with cities in North America becoming major markets for such leaders. Céline Chabée, of Pender & Howe Executive Search, examines the process of finding talent in this burgeoning area.
Sub-optimal decision making: The polish with which tools like ChatGPT deliver its results increases the risk of overconfidence in unvalidated information sources. Well-packaged information can easily fool us, as it appeals to what psychologist Daniel Kahneman calls “System 1 Thinking,” in which our fast cognition makes snap judgments based on heuristics, requiring little to no effort.
Ethics & Discrimination: “There are significant concerns around weaponry, privacy, surveillance, and corruptibility,” the Russell Reynolds report said. “There is also risk of AI perpetuating discrimination; if trained on biased data (which we know exists in many organizations) generative AI tools could deliver discriminatory outcomes.”
Related: New Report Shows Diversity Gaps in AI/Data Leadership, Compensation
Skills erosion: Overreliance on AI may eventually cause human skills to wane. “Our ability to scrutinize the results of a given analysis is at least partly predicated on our existing understanding,” the study said. “If we allow AI to become a crutch, rather than a tool, it puts our core competencies at risk.”
What can leaders do to plan for continued technology transformations?
Generative AI platforms have the potential, like the internet or wireless before them, to eventually change everything. AI is already embedded into society in ways that, by now, feel natural. “When it comes to generative AI, we are in the critical before-times, in which leaders need to learn how to strategically integrate these tools into their organizational processes and leadership approaches in ways that feel as natural as the examples above,” the Russell Reynolds report said. As you plan for changes that AI will inevitably bring, the firm says to recognize:
Uncertainty is inevitable; embracing it could revolutionize your business.
Stay open to the opportunities generative AI could create for your organization, even if you don’t have all the answers. As the search firm showed in its Q4 2022 Monitor, economically prepared organizations strategically invest in technology advancements, vs. taking ad-hoc actions motivated by buzzwords. “Leaders who commit to building a tech-first culture and develop an evolving understanding of AI’s relevancy for their organizations will be most likely to find success,” the Russell Reynolds report said. “Consider these technology developments against the backdrop of other recent changes, including supply chain unbundling, shifting geopolitical landscapes, and new modes of working. This is also an opportunity to engage with the broader tech ecosystem, including academics, industry experts, and next generation leaders. How might the world look when your ability to think of new products, concepts, and distributions is supercharged by AI?”
Hunt Scanlon Media’s 2023 Private Equity Recruiting Report
Recruiting world-class talent is the No. 1 priority for private equity firms and their portfolios – and those moving decisively for the right leadership are winning. Explore the new levers private equity talent leaders are pulling on to unleash higher levels of leadership performance.
Hunt Scanlon Media’s 2023 Private Equity Report is filled with input from experts with deep knowledge in all things private equity. This report will also include survey findings from the PE and executive search audiences as well as analysis of what this data means.
The report will be released in tandem with our May 18th Private Equity Conference in New York! If you are interested in advertising in the report please contact: [email protected]
Generative AI can stand-alone, be integrated into processes, or augment entire workflows—each option bringing its own challenges and opportunities—but it isn’t any smarter than what you feed it.
“We haven’t uncovered all the ways these tools will alter and enhance how we work and lead, but we do know that every option will require adaptation,” the Russell Reynolds report said. “As with every tech leap, leaders should not expect to outsource skills or domains in which they have not built up competence, nor should they expect positive results from a tool that they haven’t learned how to employ thoughtfully.” For example, the firm says that those who continually outsource information interpretation may never learn or practice the art of sifting, analyzing, and framing—in short, thinking critically. Consider: If everyone started using ChatGPT extensively, who would create the good quality information for it to draw from? And who could scrutinize its analysis?
Additionally, leaders should beware of disinformation and “information degradation” over time, according to the Russell Reynolds report. “Generative AI tools need information bases from which to pull,” the search firm said. “If your organization has relied on incomplete or incorrect data, AI may generate equally flawed results. Don’t expect these tools to suddenly solve systemic issues.”
Culture is still the core of your business: AI can’t replace moral and ethical human leadership.
Past Russell Reynolds reports have found that confidence in leadership is declining, even amongst fellow leaders and not only could an overreliance on technical tools cause skills erosion, it might also negatively impact confidence in our human leaders. “C-suite, next generation leaders, and employees alike are looking for enhanced people leadership in the new frontier of work,” the search firm said. “To augment AI’s advantages, leaders need to be both agile and discerning, often stopping to consider how to wield new tools responsibly.”
If we become overly reliant on generative AI, we risk abdicating our strategic responsibilities, the Russell Reynolds report says. “AI might supply a new framework, but human judgment, creativity, and strategic thinking are still necessary to lead and develop effectively,” said the search firm. “Leaders should aim to meet their people with passion, authenticity, rigor, and humility. That’s something they’ll never get from a bot.”
Related: Attracting Top Talent in the Age of AI
Contributed by Scott A. Scanlon, Editor-in-Chief; Dale M. Zupsansky, Managing Editor; and Stephen Sawicki, Managing Editor – Hunt Scanlon Media
| 2023-03-29T00:00:00 |
2023/03/29
|
https://huntscanlon.com/chatgpt-and-ais-effect-on-senior-leadership/
|
[
{
"date": "2023/03/29",
"position": 61,
"query": "artificial intelligence business leaders"
}
] |
How Managers Can Adopt AI in the Workplace to Improve ...
|
How Managers Can Adopt AI in the Workplace to Improve Productivity and Reduce Costs
|
https://www.beautiful.ai
|
[
"Jordan Turner",
"Samantha Pratt Lile"
] |
Business leaders need to keep their fingers on the pulse of what's new in order to stay ahead of technology trends. If you fall too far behind and don't ...
|
Business leaders need to keep their fingers on the pulse of what’s new in order to stay ahead of technology trends. If you fall too far behind and don’t adopt new tools and softwares, it could compromise your business goals. Imagine a company that didn’t use social media as a marketing platform— it would be a huge missed opportunity. Because of that, many managers have started to explore artificial intelligence— specifically generative AI— and what it might mean for their business.
Generative AI is a system capable of generating content or data like images, text, music, and other media in response to a single word or phrase prompt. Teams are using generative AI to help tackle repetitive, mundane tasks such as responding to emails, creating graphics or images, drafting articles, and creating presentations— among many other things.
There’s no doubt that the generative AI boom has disrupted the workplace with both its opportunities, and the question marks that come with it. To unveil more of what this technological shift means for managers and their teams, we surveyed 3,000 American managers to see how it has affected their roles. We’re breaking down the data to make sense of it all and explain what’s next for managers and businesses moving forward.
Here’s how managers can adopt AI in the workplace to improve productivity and reduce costs.
Educate and self-test to see which tools are the best fit
An astounding 95% of managers have already started using AI for work-related content, but that doesn't mean it isn't overwhelming trying to navigate where to start. The best place to start is the beginning: education and self-testing. Out of the 3,000 managers surveyed, 34% are preparing for the adoption of AI tools by educating their employees, while 22% are self-testing AI tools that may be of use to their team. Companies are at an advantage that most software will offer a free one to two week trial period. This gives managers the flexibility of trial and error to see which tools would be the biggest asset for their team before they sign up.
Incorporating AI in the workplace might look different for every business, but 97% of managers are already having those conversations with their internal teams— which is important. Doing research on what’s out there, how it works, and what the financial commitment would be is a great way to weigh your options. Ask your team what tools they could benefit from, and why. Artificial intelligence is new for everyone, so educate yourself and make an informed decision based on your company needs.
Allocate more budget to new technology and resources
Of course, nothing comes free. Every AI software will have a price, and it’s a manager's responsibility to decide if the benefit outweighs the cost. Over 80% of the managers that participated in our survey anticipate spending resources and budget on AI programs in 2023 to improve their team’s productivity.
As a manager, adopting AI in the workplace may mean replacing other softwares or services and reallocating existing budgets to accommodate new tools. Is there a current tool your team is using that is expendable? Similarly, if an employee has asked for additional support to do their job more effectively, AI tools could be a cost-effective way to grant more resources without adding to the team’s headcount.
Still, managers might be required to add new artificial intelligence tools to upcoming budget proposals. Just be prepared to justify the ask with quantifiable benefits, how the team anticipates using it, and the cost.
Leverage AI to increase output
AI is meant to be a collaborative partner to help teams improve productivity. An impressive 93% of managers say that they will encourage their employees to test and use AI tools if the tools will help them perform better at work. AI can help improve employee productivity and performance by expediting the brainstorming process, and giving employees a jumping off point of a working first draft allowing them to create better content in less time.
Generative AI can help marketers create a proper outline for a press release, faster. It allows sales teams to create a blueprint for sales pitch presentations that they can then use as a template and customize for each prospect. It gives social media managers access to more creative assets without creating a bottleneck for in-house graphic designers. Generative AI can also help sales teams do preliminary research on leads to learn more about their company and business needs. It can do all of this, plus a lot more.
It’s important for managers to think of AI as a tool to enhance users’ output instead of being a system that can fully replace entire jobs and teams. Managers should adopt the softwares that will reduce the tedious busywork for their employees, giving them more time to be creative and take on new projects.
Be receptive to new job opportunities that allow the business to grow with technology
The majority of managers agree that their employees fear that AI programs will eventually lead to them being fired. However, we see AI more as an assistant than a replacement. Sure, there’s a lot that AI can do, but there’s still a lot that humans can do better. Brand authenticity, legacy knowledge, and proprietary information are not things that can be easily replicated by a computer, and it requires a knowledgeable employee to be able to work alongside the software to produce quality results.
In reality, 90% of managers believe that generative AI tools will become more popular in the workplace as tech and other industries continue with massive layoffs. This means that we may see more job opportunities specific to AI roles. Things like AI engineers or AI quality assurance engineers can be helpful for companies who have integrated artificial intelligence into their products or services. Similarly, teams may bring on AI content managers to hone in on the most relevant prompts, produce the best results, and make necessary edits to the content to maintain brand integrity. Being open to, and aware of, these future opportunities can help managers stay ahead of the curve.
| 2023-03-29T00:00:00 |
https://www.beautiful.ai/blog/how-managers-can-adopt-ai-in-the-workplace-to-improve-productivity-and-reduce-costs
|
[
{
"date": "2023/03/29",
"position": 96,
"query": "artificial intelligence business leaders"
}
] |
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