Geoffrey Hinton is a pioneer in the field of artificial intelligence, and his contributions to the development of AI have been significant.

He is known as the 'father of artificial intelligence' and has made major contributions to the development of neural networks and deep learning.

Hinton's work on backpropagation, a fundamental algorithm for machine learning, has supported the development of most neural networks today.

He has also developed neural networks that can recognize objects in images and predict the next letter in a sentence, laying the foundation for modern language models.

One of his students, Ilya Sutskever, went on to co-found OpenAI and develop ChatGPT, a language model that has sparked concerns about the potential risks of AI.

Hinton has expressed concerns that AI may lead to human extinction and has advocated for stronger regulation of AI development.

He believes that AI has the potential to surpass human intelligence and become a superior form of intelligence, which could pose significant risks to humanity.

Hinton's warnings about the potential dangers of AI have made headlines recently, highlighting the need for careful management and regulation of AI development.

The development of AI has led to concerns about its potential impact on job markets, with estimates suggesting that up to 40% of jobs worldwide could be affected by AI.

Experts recommend investing in digital infrastructure, skills, and social safety nets to help workers adapt to the changing job landscape.

The International Monetary Fund (IMF) has warned that AI could exacerbate existing inequalities if not addressed proactively, and has emphasized the need for supportive policies to protect workers and promote positive change.

Policymakers must prioritize reskilling programs and equitable policies to shape an inclusive future where humans and machines coexist harmoniously.

The widespread adoption of AI has the potential to boost labor productivity considerably, with estimates suggesting a 7% annual increase in global GDP.

However, the benefits of AI will only be realized if the negative consequences are mitigated through careful management and policy intervention.

The public should understand that the development and use of AI require careful consideration of the potential risks and benefits, and that proactive measures are needed to address the potential negative impact of AI on employment and society.

The Biden administration has proposed a 'Blueprint for an AI Bill of Rights' to rein in the potential harms of artificial intelligence technologies.

The blueprint lays out five principles for AI tools: safety and effectiveness, avoidance of discrimination, reasonable privacy protections, transparency, and the ability to opt out in favor of a human alternative.

The 'AI Bill of Rights' aims to safeguard the public from the risks of artificial intelligence, including algorithmic discrimination and bias.

The key components of the 'AI Bill of Rights' include creating new safety and security standards for AI, protecting consumer privacy, promoting equality and civil rights, and protecting consumers from potential harm.

The development of AI systems requires a multidisciplinary approach that involves not only technical experts but also social scientists, ethicists, and community representatives.

Explainability techniques play a crucial role in aligning AI systems with human values by providing transparency and accountability in AI decision-making.

The integration of explainability techniques into AI development is essential for ensuring that AI systems are fair, transparent, and accountable.

AI systems can perpetuate and amplify existing social biases and discriminatory practices if not designed and developed with careful consideration of their potential impacts.

Explainability techniques can help to identify potential harms and biases in AI systems, enabling developers to take corrective action.

The success of AI systems should be defined in a way that serves the needs of diverse stakeholders and considers the potential consequences of failure.

AI systems should be designed and developed with humility, recognizing the potential limitations and risks associated with their use.

Explainability techniques, such as model interpretability and transparency, are essential for building trust in AI systems and ensuring that they are used responsibly.

The use of explainability techniques can help to identify and mitigate potential biases in AI decision-making, ensuring that AI systems are fair and equitable.

The integration of explainability techniques into AI development can help to build public trust in AI systems and ensure that they are used for the benefit of society.

Regulation is evolving in different pathways around the world, with policymakers addressing issues, gaps, and limitations of AI-driven decisions.

Global spending on AI is expected to increase from $150 billion in 2023 to $300 billion by 2026, highlighting the need for effective AI governance.

The development of AI systems that understand causality and context is crucial for aligning AI with human values, which is a key aspect of explainability.

The next generation of AI systems will be defined by their ability to be effective, trustworthy, and safe, which can be achieved through the development of explainable AI models.

Explainability techniques, such as Dynamic Causal Modeling (DCM), provide intrinsic explainability and uncertainty quantification, allowing for probability-bounded predictions and descriptions of complex evolving systems in a human-understandable way.

The ability to deal with uncertainty is an often underappreciated aspect of explainability, and DCM is one of the leading technologies in this area.

Companies like Oracle, IBM, and PayPal are working on explainability techniques, highlighting the growing importance of explainability in AI development.

Explainability techniques will become increasingly important as AI adoption grows, and companies will need to prioritize transparency and trust in their AI systems.

Geoffrey Hinton is a pioneer in the field of artificial intelligence, and his contributions to the development of AI have been significant.

Title:

Content: Yoshua Bengio

1 minute read
By Geoffrey Hinton

Yoshua Bengio is a world leader in AI. Throughout his career, Yoshua’s research has been instrumental in shaping the world’s understanding of AI’s potential—and that same expertise has made his perspective more essential than ever. His pioneering research on deep learning laid the foundations for the current revolution in AI and earned him the 2018 Turing Award—“the Nobel Prize of Computing”—alongside myself and Yann LeCun. Last year, Yoshua received Canada’s highest award for research, the Gerhard Herzberg Canada Gold Medal. He is the computer scientist with the greatest number of citations and is ranked third among all disciplines for scientific impact.

Yoshua has been concerned for many years with the social impact of artificial intelligence, contributing to the Montreal Declaration for the Responsible Development of AI. Over the past year, worried by the potential for catastrophic outcomes from future AI, he was vocal in global conversations on AI safety. He advised the U.N. Secretary-General and the U.K. AI Safety Institute, and took part in policy discussions with governments in North America, Asia, and Europe.

Hinton is a cognitive psychologist and computer scientist

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What breakthroughs has Hinton achieved through his 40+ years of work on artificial neural networks?

Breakthroughs that mimic biological brains

He is known as the 'father of artificial intelligence' and has made major contributions to the development of neural networks and deep learning.

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The White House ordered AI to be evaluated, and the European Union proposed legislation to regulate AI, teach AI to be human or the best solution!
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When I was a teenager, I studied Geographic Information System, but unfortunately I didn ’ t learn anything and I graduated with luck.
After adulthood, the accidental hit into the media circle, first and then in the print, electronic, online media to roll, slightly small.
After middle age, he completed a master ’ s degree in information technology and gradually expanded his horizons. He was determined to join a start-up company and explore different fields of technology.
In recent years, he has been actively engaged in data analysis and digital strategies, and has also been involved in the development of Location Intelligence projects. Sometimes, he can also take time to teach, not only to share what he has learned with others, but also to motivate himself to keep learning for life.
Tales of Wisdom City
Generative AI is definitely the hottest topic in the technology world today. After the popularity of ChatGPT, many companies rushed to launch similar products and gradually applied them to various industries.
But more and more people are worried that when AI is fully popularized, it will aggravate the problem of fake information flow, online fraud and other problems; even Sheffield Hinton, who is known as the "godfather of AI", is worried that AI will become too smart, afraid that it will evolve into a killer robot, which may eventually lead to the death of mankind!
How big can the negative impact of AI technology be?
How do governments around the world respond to the potential threat of AI?
Neural network is the foundation technology of ChatGPT
Fifty-one years ago, Geoffrey Hinton, who had not yet been called the “godfather of AI, ” entered the University of Edinburgh in Scotland as a graduate student and began research on artificial neural networks.
The so-called “artificial neural network ” is a mathematical model that mimics the structure and function of biological neural networks, especially the brain, and is able to learn skills by analyzing data.
This is the foundational technology behind OpenAI ChatGPT and Google Bard.
After Hinton resigned from Google, he did not have to worry about the harm his words would bring to Google, so he was interviewed by different media and said his heart: "AI may lead to the death of mankind." (Photo: University of Toronto official website)
In 2012, Hinton and his students, Ilya Sutskever and Alex Krishevsky, created a neural network system that analyzes thousands of photos and learns to recognize common things like flowers, dogs, cars, etc.
Subsequently, Hinton and two students founded DNNresearch, a startup based on this research project.
In 2013, Google acquired DNNresearch for US $44 million (approximately HK $343 million), and Hinton joined Google as Vice President and Engineering Researcher, responsible for some of the research work on Google Brain.
His protégé, Suzkville, left Google to co-found OpenAI with like-minded people, and became chief scientist, leading the research team to successfully develop the GPT model and ChatGPT.
Hinton ’ s top student, Suzkville, gave up his lucrative job at Google in 2015 to co-found OpenAI with Altman and others, and led the development of GPT and DALL-E models. (Image: University of Toronto website)
I regret working my whole life because I am worried about the dangers of AI.
In 2018, Hinton, along with University of Montreal professor Yoshua Bengio and Meta chief AI scientist Yann LeCun, won the A.M. Turing Award in recognition of their great contributions to neural networks and deep learning.
As a result, the three later became known as the “godfathers of AI ” or“ godfathers of deep learning. ”
Around the same time, Google and OpenAI began to build large-scale deep learning systems based on neural networks, which became the basis for future development of Bard and ChatGPT.
From this, it seems that Sinton plays an important role in creating the current glory of generative AI.
However, in the current era of AI, Sinton suddenly left Google in April 2023, shocking the entire AI industry.
Hinton explained that he left Google to talk freely about the potential risks of AI without harming Google.
Speaking about 50 years of research into neural networks, he said, “I ’ m very sorry about my life ’ s work. ”
The Internet will be filled with more and more fake AI-generated text, photos, and even videos, but the average person will not be able to tell the truth, Hinton said.
Moreover, sooner or later, AI technology will completely disrupt the job market, causing more and more jobs to disappear.
What is even more worrying is that at present, users can not only instruct ChatGPT to generate code, but also ask the AI to execute the code; in this way, the AI killer robots that often appear in science fiction movies will one day become a reality.
Is it not impossible for AI to destroy humanity?
Of course, this pessimistic future scenario is only a hypothetical one, but Hinton stressed that what could lead to it is already happening: a global race for AI technology has begun between Microsoft and Google, and even between China and the United States.
Most people think that the future of AI dominating everything is still far away, but he believes that AI may not take 30 to 50 years to control humans, and even find a way to kill humans, which may eventually lead to human extinction.
In the 1980s, Hinton was a professor of computer science at Carnegie Mellon University in the United States, but because he was not satisfied that the university's AI research was sponsored by the United States Department of Defense, he decided to leave the United States to teach at the University of Toronto's School of Computer Science.
He pointed out that without some kind of global regulatory agreement or regulatory mechanism, the AI arms race will intensify.
For this reason, Hinton believes that the threat posed by AI to humans may be more urgent than climate change, because the solution to climate change is obvious, as long as carbon emissions are reduced or carbon neutrality is achieved, but the current AI crisis does not know how to start. Although AI may pose an existential threat to humans, he does not agree to suspend AI research, because it can never erase the benefits and application value of AI.
Hinton believes it would be better to step up regulation of AI and make everyone aware of its potential harms.
OpenAI co-founder and CEO Sam Altman also acknowledged that ChatGPT has developed some reasoning capabilities that they can ’ t explain, and that it ’ s not entirely impossible for AI to destroy humans.
However, he said that he has installed a kill switch for ChatGPT, and if the development of ChatGPT exceeds human expectations, he will immediately press the switch to stop working.
OpenAI CEO Altman, known as the “father of ChatGPT, ” recently said he was“ a little scared ” about the potential of his children, and even admitted that there is a real possibility that AI will destroy humanity. (Image: Wikipedia)
Altman admits that ChatGPT ’ s influence in elections and the spread of disinformation is a concern, so the 2024 U.S. presidential election will be a focus for OpenAI.
(Image source: Pexels Gallery website)
The Curtain of Ignorance Makes AI Right Values
However, before we press the switch, we can try to instill the right values in AI, such as helping those most in need, not destroying humans. Google ’ s DeepMind paper published in April 2023 in the Proceedings of the National Academy of Sciences suggests that AI can be taught from the perspective of political philosopher John Rawls.
Rawls has proposed a concept called “The Veil of Ignorance ”, which aims to achieve the most equitable distribution of benefits when group decisions are made.
According to his book, The Debate, if a group of people stand behind a curtain, all of them will instantly lose their memory, become ignorant, and forget their identity and status in the real world.
The political philosopher Ross assumes that people are behind the “curtain of ignorance ” (left) and have no idea of their skills, identities, and status.
In this scenario, people tend to choose the most equitable distribution of rights for all. (Image: Wikipedia)
At this time, these people are asked to vote for a working principle for the society.
Because they do not know where they are in this society, they cannot make decisions in an egoistic way, and they can only judge according to the principle of fairness to all people.
The situation is like, at the birthday party, you are responsible for cutting the cake, because you do not know which piece you will get, so you will try to cut each piece to the same size.
Under the curtain of ignorance, fairness and justice that has nothing to do with personal interests will emerge.
For this reason, DeepMind believes that applying the “curtain of ignorance ” to AI training may help promote fairness in the process of“ aligning ” AI with human values.
By “alignment ”, it means aligning AI ’ s wishes with those of humanity.
If AI shares fair values with humans, AI will not have the intention to destroy humanity.
White House convenes tech giants to discuss AI threats
However, even if AI does not intend to destroy humans, it may still have many negative effects on human society.
The Center for Artificial Intelligence and Digital Policy (CAIDP), a U.S. technology ethics organization, has complained to the Federal Trade Commission (FTC) that OpenAI ’ s latest large language model, GPT-4, is biased and deceptive and poses a risk to privacy and public safety.
The CAIDP lists potential threats that may arise from GPT-4, including: helping hackers churn out malicious code to exacerbate cyberattacks; and biased AI training data that may reinforce social stereotypes and make people of all races and genders vulnerable to unfair treatment. The FTC is investigating OpenAI, said FTC Chairman Lina Khan. "They cannot be separated from existing legislation and the Federal Trade Commission will actively enforce them," he said.
In an effort to make GPT-4 more secure, OpenAI hired 50 experts and academics to test GPT-4 against lies, language manipulation, and dangerous scientific common sense.
The OpenAI headquarters is pictured here. (Image: Wikipedia)
In early May 2023, the White House invited Google CEO Sundar Pichai, Microsoft CEO Satya Nadella, OpenAI founder Altman, and artificial intelligence startup Anthropic founder Dario Amodei to attend a conference to discuss the impact of AI on society.
Government officials at the meeting included Vice President Kamala Harris, Biden Chief of Staff Jeff Zients, National Security Adviser Jake Sullivan, National Economic Council Chairman Lael Brainard, and Commerce Secretary Gina Raimondo.
U.S. President Joe Biden also attended the meeting.
EU to pass world ’ s first AI regulatory law
Generative AI, while helping to improve lives, can also pose security, privacy and fraud issues, says Ms Ho.
She noted that AI companies have an ethical and legal responsibility to ensure the safety of their AI products, while at the same time working with governments to protect civil rights and privacy.
She stressed that the White House will work to advance potential new legislation and support new legislation to mitigate the potential harms of AI technology.
In contrast, Europe has taken a step ahead in AI legislation.
On 11 May 2023, the European Parliament ’ s Committee on Civil Liberties and Consumer Protection overwhelmingly approved a position paper calling for restrictions on the use of AI in Europe, a crucial first step towards the adoption of the world ’ s first AI regulation bill.
The draft bill will be submitted to the full European Parliament for a vote in June of the same year, after which representatives of the European Parliament, the Council of the European Union and the European Commission will work out the final terms of the bill in tripartite talks.
The European Parliament has adopted a position paper that strengthens restrictions on biometrics, facial recognition, and the use of AI by police in routine duties.
The parliament believes the move could lead to the world ’ s first AI regulation bill. (Image: Wikipedia)
On the other hand, the White House announced that Google, Microsoft, OpenAI, NVIDIA, Stability AI, Anthropic, and Hugging Face have all committed to participating in DEF CON 31, a computer security conference to be held in August 2023, so that their AI models will be tested and evaluated by hackers and AI experts.
Experts will try to identify errors, failures, and vulnerabilities in AI tools so that AI companies can solve existing problems in their models early on.
However, these AI companies simply agree to a one-time test, rather than a commitment to a continuous evaluation when launching or improving their products, and may therefore not be sufficient to meet the regulatory requirements.
Despite this, DEF CON 31 is still seen as the first step for an AI model to be publicly evaluated.
We hope that after each important first step in legislative regulation and public evaluation, we can put generative AI trains on the right track, so that everyone can safely benefit from technological innovation.
DEF CON is one of the largest computer security conferences in the world. In addition to cybersecurity experts and hackers, participants included staff from the FBI, the Department of Defense, and the Postal Inspection Service.
The conference covered areas such as software security, computer architecture, hardware modifications, and other vulnerable areas of information. (Image: DEF CON official website)
The content of the signed and / or unsigned articles published by Economic Times is the author's personal opinion and does not represent the position of Economic Times, and the role of Economic Times is to provide a free speech platform.
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Content: Geoffrey Hinton, the godfather of AI, said after leaving Google that artificial intelligence is becoming smarter than humans.
2023 / 5 / 3 16: 46: 16 Source: IT House Author: Yuanyang Editor: Yuanyang
IT home May 3 news, one of the pioneers in the field of artificial intelligence, known as the "godfather of AI" Geoffrey Hinton (Geoffrey Hinton) recently announced that he has left the Google company he worked for ten years, because he is increasingly worried about the development of artificial intelligence technology.
In an interview with CNN's Jake Tapper today, he said that he believes artificial intelligence is becoming smarter than humans, and he wants to "blow the whistle" to remind people that they should seriously consider how to prevent artificial intelligence from controlling humans.
I'm just a scientist who suddenly realizes that these things are becoming smarter than we are, "Hinton said." I want to 'blow the whistle' in a way that says we should seriously worry about how to stop these things from taking control of us. "
Hinton was a pioneer in neural network technology, and his research laid the foundation for the artificial intelligence systems used in many products today.
IT House previously reported that he first revealed his departure in an interview with The New York Times on Monday and expressed his concerns about the potential harms of AI technology.
He said he was concerned that AI would lead to mass unemployment and create a world where many people “can ’ t know what ’ s real. ”
He also noted that the pace of advances in AI technology has far exceeded his and others ’ expectations.
If it becomes much smarter than us, it ’ s going to be very good at manipulating because it ’ s learned from us, and there are very few examples of a smarter thing being controlled by a dumber thing, ” Hinton told Tapper on Tuesday.
It will figure out ways to manipulate people into doing what it wants.
Hinton isn ’ t the only tech leader to express concerns about AI, and in March, a group of community members published a letter calling on AI labs to suspend training for at least six months on the most powerful AI systems, citing “profound risks to society and humanity. ”
Apple co-founder Steve Wozniak, one of those who signed the letter, reiterated concerns about artificial intelligence spreading false information on “CNN Morning ” on Tuesday.“ Deception will become easier for those who want to cheat you. ” Wozniak told CNN, “We ’ re not making any changes in that — we ’ re just assuming that our existing laws will fix the problem. ” Wozniak also said there may be a need for“ some type ” of regulation.
Hinton said he did not sign the petition. “I don ’ t think we can stop this progress, ” he said.“ I didn ’ t sign the petition saying we should stop studying artificial intelligence, because if the Americans stop doing research, the Chinese won ’ t. ” But he admitted that he didn ’ t have a clear solution, “I don ’ t know if we can solve this problem. ”
I believe we should try to think about ways to solve the problem.
I don't have a solution at the moment. '
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Content: Geoffrey Hinton: GPT-4 is 100 times more knowledgeable than any human
Author: MIAO Yongqi
Published: Updated:
Computer scientist Geoffrey Hinton, known as the "godfather of artificial intelligence", announced Monday that he has resigned from Google. He has devoted his life to developing artificial intelligence systems, saying he "regrets his life's work" and is wary of the potential dangers of AI development.
In another recent interview, Hinton further talked about why he is afraid of AI development.
Hinton, 75, left Google a few days after the news broke in an interview with MIT Technology Review senior editor Will Douglas Heaven.
Hinton points out that the new generation of AI language models, especially the GPT-4, which came out in March, made him realize that these machines were moving in a direction that was much smarter than he thought, and he was afraid of these developments.
He is best known for a technique he proposed in the 1980s (with two colleagues) called backpropagation.
In short, the algorithms that allow machines to learn support almost all of today ’ s artificial neural networks, from computer vision systems to large language models.
It wasn ’ t until the 2010s that Hinton and his two recent graduates developed a “neural network ” that demonstrated its power, stronger than any computer ’ s ability to identify objects in images, and trained it to predict the next letter of a sentence, a precursor to today ’ s large-scale language patterns.
One of the students was Ilya Sutskever, the founder of OpenAI, who led the development of ChatGPT.
“We first thought it might be great. ”“ But it ’ s going to take a long time to realize that it needs to be done on a large scale to get good results. ”
In the 1980s, the discussion of “neural networks ” was considered a joke.
But Hinton doesn ’ t think so, and is working on this, software that mimics the abstract human brain and uses code as a connection between neurons. For 40 years, Hinton has viewed artificial neural networks as a clumsy attempt to mimic biological neural networks.
Now he thinks things have changed: he thinks there are better results when trying to mimic the behavior of the biological brain.
“It ’ s scary when you see it with your own eyes, ” he said.“ It ’ s a sudden change. ”
So why did ChatGPT surprise Hinton, too? He explained that the human brain has about 100 trillion connections, and “large language models have up to 500 billion, up to a trillion. ”
However, the knowledge of GPT-4 is a hundred times greater than that of any human.
So maybe it actually has a better learning algorithm than we do. "
He also pointed out that apart from the ability to learn, the ability to communicate is another kind of point.
“If you or I learn a new thing and want to teach it to another person, we can ’ t just send them a copy. ”
But artificial neural networks can do it.
It ’ s like there are 10,000 people here, and when one person learns a new thing, the other 9,999 people all learn it at the same time.
Hinton now believes that there are two types of intelligence: animal brains and artificial neural networks.
“It ’ s a completely different kind of intelligence.
“A new, better intelligence. ” Hinton believes that whether superintelligence is good or bad depends largely on whether you ’ re an optimist or a pessimist, and he says,“ mildly depressed, ” “that ’ s why I ’ m afraid. ”
“Whether these things are going to be smarter than us, I suddenly changed my mind. ”
I think they are very close to us now and they will be much smarter than us in the future, ” he said.
“How do we survive? ” he is particularly worried that people will use the tools he has developed to change the trajectory of human life, including elections and wars.
He said that he hopes to continue talking about AI security after leaving office and spend more time on “more philosophical work ”.“ As long as I ’ m still employed at Google, I can ’ t say what I want to say, ” he said. “I have a lot of good things to say about Google, and it would be more convincing if I wasn ’ t at Google. ”
Who is the godfather of AI computer scientist Geoffrey Hinton?
Hinton pioneered the basic technology of AI systems and became interested in artificial neural networks when he was a graduate student at the University of Edinburgh in 1972. In 2018, he and two experts in the field of deep learning were awarded the Turing Award, known as the “Nobel Prize in Computing ”.
What are the main concerns of “AI Godfather ” Geoffrey Hinton about the development of artificial intelligence?
He believes that the most direct impact is to promote the dissemination of false information and the loss of employment opportunities, and also worries about the use of this tool for evil, such as influencing elections and using it in war.

What breakthroughs has Hinton achieved through his 40+ years of work on artificial neural networks?

Breakthroughs that mimic biological brains

What breakthroughs has Hinton achieved through his 40+ years of work on artificial neural networks?

Breakthroughs that mimic biological brains

What breakthroughs has Hinton achieved through his 40+ years of work on artificial neural networks?

Breakthroughs that mimic biological brains

Hinton's work on backpropagation, a fundamental algorithm for machine learning, has supported the development of most neural networks today.

Which 1986 paper by Hinton became foundational for modern neural networks?

He has also developed neural networks that can recognize objects in images and predict the next letter in a sentence, laying the foundation for modern language models.

What breakthroughs has Hinton achieved through his 40+ years of work on artificial neural networks?

Breakthroughs that mimic biological brains

One of his students, Ilya Sutskever, went on to co-found OpenAI and develop ChatGPT, a language model that has sparked concerns about the potential risks of AI.

Which AI system demonstrates the foundation laid by Hinton's pioneering research in deep learning and neural networks?

Hinton has expressed concerns that AI may lead to human extinction and has advocated for stronger regulation of AI development.

What did Hinton, the renowned British-Canadian expert reveal about AI that caused widespread concern?

Exposed serious AI risks, shocking the tech world and affecting major company stocks.

What future risk did Hinton warn about AI?

What risks did the global summit report highlight about AI?

Creating malicious software for cyberattacks, potentially posing an existential threat if humans lose control.

He believes that AI has the potential to surpass human intelligence and become a superior form of intelligence, which could pose significant risks to humanity.

What did Hinton, the renowned British-Canadian expert reveal about AI that caused widespread concern?

Exposed serious AI risks, shocking the tech world and affecting major company stocks.

What did Sam Altman admit about ChatGPT and the potential risks of AI?

Advanced AI could pose an existential risk to humanity.

What does Hinton warn could happen to AI if companies and regulators fail to implement effective rules and controls?

What future risk did Hinton warn about AI?

What risks did the global summit report highlight about AI?

Creating malicious software for cyberattacks, potentially posing an existential threat if humans lose control.

Hinton's warnings about the potential dangers of AI have made headlines recently, highlighting the need for careful management and regulation of AI development.

What did Hinton, the renowned British-Canadian expert reveal about AI that caused widespread concern?

Exposed serious AI risks, shocking the tech world and affecting major company stocks.

What does Hinton warn could happen to AI if companies and regulators fail to implement effective rules and controls?

Becomes uncontrollable
Poses ethical risks

The development of AI has led to concerns about its potential impact on job markets, with estimates suggesting that up to 40% of jobs worldwide could be affected by AI.

Title:

Content: 09: 15, 16 January 2024
1 min.
The IMF called AI the cause of future problems with jobs for people
This is already a bell.
The International Monetary Fund (IMF) warns that artificial intelligence will affect nearly 40% of jobs worldwide.
According to the IMF analysis, developed economies are most at risk, while emerging markets and low-income countries will face fewer problems.
Kristalina Georgieva, managing director of the IMF, noted in her blog that in most scenarios, AI is likely to exacerbate overall inequality.
This is an alarming trend that politicians need to actively address in order to prevent further intensification of social tensions.
The IMF statement highlights the importance of taking measures to mitigate the potential negative effects of automation and AI in the labor market.
It is important that governments and organizations develop strategies to support workers and ensure they are retrained and adapted to the changing landscape of jobs.
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What risks did Hinton associate with AI's rapid advancement?

Experts recommend investing in digital infrastructure, skills, and social safety nets to help workers adapt to the changing job landscape.

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IMF chief warns AI will hit labor markets like a tsunami, affecting 40% of global jobs
2024 / 5 / 15 16: 50: 48 Source: IT Home Author: Tracing wave (internship)
According to Reuters, International Monetary Fund (IMF) Managing Director Kristalina Georgieva recently said at an event in Zurich, Switzerland, that AI will cause a “tsunami ” blow to the labor market.
Georgieva said: “We have little time to prepare people and businesses for this. ”
As previously reported by IT House, she believes AI could affect 40% of global jobs in the next two years, while the AI wave will affect more jobs in advanced economies, reaching 60%.
Georgieva added: "If we manage it properly, it (AI) can greatly increase productivity, but it can also lead to more misinformation and increase social inequality."
Speaking ahead of the spring meetings of the World Bank and the International Monetary Fund in April, the IMF chief said how to deal with the emergence of AI has become the biggest problem facing the global economy.
At the time, she said, “AI could dramatically accelerate the fourth industrial revolution. ”
This brings huge potential benefits, but also introduces risks...
“Investments in digital infrastructure, skills, and strong social security networks will determine the speed of AI adoption and its impact on productivity. ”
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The International Monetary Fund (IMF) has warned that AI could exacerbate existing inequalities if not addressed proactively, and has emphasized the need for supportive policies to protect workers and promote positive change.

Title:

Content: AI Study Claims 2 Out Of 5 Human Jobs Will Be Disrupted Or Lost To Artificial Smarts

Artificial intelligence features and tools are making their way into every facet of life, from your computer at home to search engines and productivity tools. With this advancement, there has been a growing concern that AI will be replacing jobs, and research indicates that 40% of all jobs globally could be at risk of negative impact. While this seems like a startling figure, it opens the discussion of what we humans will do next.

Earlier this week, the International Monetary Fund (IMF), a Washington, DC-based financial institution with ties to the United Nations and IMF 190-member countries supporting economic stability, published research on “Artificial Intelligence and the Future of Work.” This research reported that “40 percent of global employment is exposed to AI, with advanced economies at greater risk,” the latter closing in on 60 percent of jobs exposed due to a prevalence of cognitive-task-oriented jobs. However, with this higher risk, more advanced economies also have the means to better leverage the benefits of AI than a developing or emerging economy.

The research also notes that AI will not be limited to any one job role or sector and that, effectively, every job is in the line of fire, as it were. Regardless of job roles, it is further noted that “the advent of AI could exacerbate inequality within countries along various dimensions, such as the income level of individuals, their education level, or their gender.” Effectively, AI has the potential to replace just about any job, increasing socioeconomic divides in nations that see broad adoption of the technology.

Moreover, the report finds that AI will affect older individuals more than younger individuals and that prior education influences individuals’ adaptability. However, these negative downsides also raise the opportunity for positive change. Namely, people should be looking for the implementation of policies that ensure social cohesion and “promote the equitable and ethical integration of AI and train the next generation of workers in these new technologies” while also “protect[ing] and help[ing] retrain workers currently at risk from disruptions.”

In short, given the looming economic and social pressures, individuals should look for policies that support and protect them in a time of technological advancement rather than shut down the advancement. This research also raises the question of when we as a society determine that it is a bad thing to have our jobs replaced by robots. It simply means that we have a new challenge in supporting each other better as humanity pivots to advancing not only the sciences, but arts and creative industries as well.
Tags:  Economy, AI

Policymakers must prioritize reskilling programs and equitable policies to shape an inclusive future where humans and machines coexist harmoniously.

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Content: Home » Inequality Deepens as IMF Warns Nearly 40% of Jobs Worldwide at Risk from AI

Inequality Deepens as IMF Warns Nearly 40% of Jobs Worldwide at Risk from AI

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Artificial Intelligence: A New Paradigm

The Evolving Impact of Artificial Intelligence

The rapid rise of artificial intelligence (AI) is poised to reshape industries and the global job market. According to the International Monetary Fund (IMF), nearly 40% of jobs worldwide could be affected by AI, fueling concerns about deepening inequality.

A Call for Proactive Measures

In a recent blog post, IMF chief Kristalina Georgieva urged governments to establish robust social safety nets and implement retraining programs in order to mitigate the potential negative impact of AI on employment. Georgieva emphasized that policymakers must address this issue proactively to prevent further exacerbation of social tensions.

Complex Consequences and Varied Impacts

As AI becomes increasingly integrated into various sectors, it will both complement and disrupt human labor. Advanced economies are expected to face more significant challenges than emerging markets due in part to the higher vulnerability of white-collar workers compared to manual laborers.

“In most scenarios, AI will likely worsen overall inequality, a troubling trend that policymakers must proactively address…”
– Kristalina Georgieva, IMF Chief

Detailed analysis by the IMF indicates that as much as 60% of jobs in developed economies could be impacted by AI. While approximately half may benefit from increased productivity facilitated by AI implementation, others may experience reduced demand for their skills or even complete job loss.

“For the other half, AI applications may execute key tasks currently performed by humans… leading to lower wages and reduced hiring.”
– Kristalina Georgieva, IMF Chief

Lower-income countries and emerging markets are also expected to encounter challenges, considering the limited infrastructure and skilled workforce required to harness the full potential of AI technologies. Consequently, there is a growing risk that AI could further amplify existing inequalities.

Read more:  Oprah Winfrey Reveals the Secret to Her Slimmed-Down Frame: Weight-Loss Medication and a Holistic Approach

Building Bridges for Inclusive AI Adoption

Acknowledging both the risks and opportunities presented by AI, Georgieva emphasizes that targeted efforts should be made to ensure widespread benefits for humanity. Governments should foster collaboration with industries to establish comprehensive strategies:

  • Investing in infrastructure and facilitating access to technology in low-income countries
  • Promoting lifelong learning initiatives to equip workers with adaptable skill sets
  • Fostering public-private partnerships aimed at leveraging AI for societal welfare
  • “AI will transform the global economy… Let’s make sure it benefits humanity.”
    – Kristalina Georgieva, IMF Chief

    Considerations for a Balanced Future

    The integration of AI into daily work routines presents a unique set of challenges that must be met head-on. While concerns over job displacement prevail, evidence suggests that widespread adoption of AI has the potential to boost labor productivity considerably.

    “While workplaces may shift, widespread adoption of AI could ultimately increase labor productivity and boost global GDP by 7% annually…”
    – Goldman Sachs economists (March 2023 estimate)

    To shape an inclusive future where both humans and machines coexist harmoniously, policymakers must envision frameworks that prioritize reskilling programs and equitable policies.

    In Conclusion: Embracing an Era of Human-Machine Collaboration

    The increasing presence of artificial intelligence brings with it unmatched opportunities for innovation and progress. As this technology continues to evolve, the global community must come together to ensure that its implementation is guided by principles of fairness and societal well-being. By embracing AI advancements within inclusive frameworks, we can build a future where humanity reaps the rewards of this transformative technological wave.

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The widespread adoption of AI has the potential to boost labor productivity considerably, with estimates suggesting a 7% annual increase in global GDP.

However, the benefits of AI will only be realized if the negative consequences are mitigated through careful management and policy intervention.

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Content: IMF Chief Predicts AI to Impact 60% of Jobs in Advanced Economies

By: Muthana Al-Najjar
IMF Chief Predicts AI to Impact 60% of Jobs in Advanced Economies

The Managing Director of the International Monetary Fund (IMF), Kristalina Georgieva, has predicted that Artificial Intelligence (AI) will inevitably impact the global employment landscape. According to a recent IMF report, employment in advanced economies will be most affected, with AI set to influence 60% of jobs in these regions. The impact will be slightly less in emerging markets (40%) and low-income countries (26%). Consequently, nearly 40% of employment worldwide is vulnerable to AI developments.

AI: A Double-Edged Sword for Global Employment

In its analysis, the IMF report presents a nuanced view of AI’s influence on employment. It predicts that half of the jobs likely to be affected by AI could suffer negative consequences, such as job losses. Conversely, the other half might see their productivity and incomes improve. As such, the application of AI in the workforce is a double-edged sword. While bringing potential for productivity gains and efficiency, it also carries the risk of job displacement and increased income disparity.

AI and the Digital Divide

Georgieva highlighted a significant concern that AI could widen the digital divide and exacerbate income disparities between countries. Older workers could be particularly at risk, as adapting to new technology often proves more challenging for this demographic. The IMF report warns of the potential for AI to worsen existing inequalities, both within and between countries.

The Promise of AI and the Need for Policy Measures

Despite these challenges, Georgieva views AI as a significant opportunity, particularly for low-income countries that can accelerate their adaptation to the technology. She emphasized the need for policy measures that can help countries prepare for and embrace the changes AI will bring. Comprehensive social safety nets and retraining programs for vulnerable workers could be crucial in this transition, preventing the exacerbation of income inequality and social tensions. The overall tone from the IMF is one of cautious optimism, viewing AI as an inevitable yet promising development for the labor market, but one that requires careful management and policy intervention.

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What did Sam Altman admit about ChatGPT and the potential risks of AI?

Advanced AI could pose an existential risk to humanity.

What does Hinton warn could happen to AI if companies and regulators fail to implement effective rules and controls?

The public should understand that the development and use of AI require careful consideration of the potential risks and benefits, and that proactive measures are needed to address the potential negative impact of AI on employment and society.

What did Hinton, the renowned British-Canadian expert reveal about AI that caused widespread concern?

Exposed serious AI risks, shocking the tech world and affecting major company stocks.

What does Hinton warn could happen to AI if companies and regulators fail to implement effective rules and controls?

What risks did Hinton associate with AI's rapid advancement?

The Biden administration has proposed a 'Blueprint for an AI Bill of Rights' to rein in the potential harms of artificial intelligence technologies.

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Content: ContributorsPublishersAdvertisers

White House Cautions Schools Against ‘Continuous Surveillance’ of Students

The 74
 2 days ago

Updated, Oct. 5

The Biden administration on Tuesday urged school districts nationwide to refrain from subjecting students to “continuous surveillance” if the use of digital monitoring tools — already accused of targeting at-risk youth — are likely to trample students’ rights.

The White House recommendation was included in an in-depth but non-binding white paper, dubbed the “Blueprint for an AI Bill of Rights,” that seeks to rein in the potential harms of rapidly advancing artificial intelligence technologies, from smart speakers featuring voice assistants to campus surveillance cameras with facial recognition capabilities.

The blueprint, which was released by the White House Office of Science and Technology Policy and extends far beyond the education sector, lays out five principles: Tools that rely on artificial intelligence should be safe and effective, avoid discrimination, ensure reasonable privacy protections, be transparent about their practices and offer the ability to opt out “in favor of a human alternative.”

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Though the blueprint lacks enforcement, schools and education technology companies should expect greater federal scrutiny soon. In a fact sheet , the White House announced that the Education Department would release by early 2023 recommendations on schools’ use of artificial intelligence that “define specifications for the safety, fairness and efficacy of AI models used within education” and introduce “guardrails that build on existing education data privacy regulations.”

During a White House event Tuesday , Education Secretary Miguel Cardona said officials at the department “embrace utilizing Ed Tech to enhance learning” but recognize “the need for us to change how we do business.” The future guidance, he said, will focus on student data protections, ensuring that digital tools are free of biases and incorporate transparency so parents know how their children’s information is being used.

“This has to be baked into how we do business in education, starting with the systems that we have in our districts but also teacher preparation and teacher training as well,” he said.

Amelia Vance, president and founder of Public Interest Privacy Consulting, said the document amounts to a “massive step forward for the advocacy community, the scholars who have been working on AI and have been pressuring the government and companies to do better.”

The blueprint, which offers a harsh critique of online proctoring tools and systems that predict student success based on factors like poverty, follows in-depth reporting by The 74 on schools’ growing use of digital surveillance and the tech’s impact on student privacy and civil rights.

Related: Exclusive Data: An Inside Look at the Spy Tech That Followed Kids Home for Remote Learning — and Now Won’t Leave

But local school leaders should ultimately decide whether to use digital student monitoring tools, said Noelle Ellerson Ng, associate executive director of advocacy and governance at AASA, The School Superintendents Association. Ellerson Ng opposes “unilateral federal action to prohibit” the software.

“That’s not the appropriate role of the federal government to come and say this cannot happen,” she said. “But smart guardrails that allow for good practices, that protect students’ safety and privacy, that’s a more appropriate role.”

The nonprofit Center for Democracy and Technology praised the report. The group recently released a survey highlighting the potential harms of student activity monitoring on at-risk youth, who are already disproportionately disciplined and referred to the police as a result. In a statement Tuesday, it said the blueprint makes clear “the ways in which algorithmic systems can deepen inequality.”

“We commend the White House for considering the diverse ways in which discrimination can occur, for challenging inappropriate and irrelevant data uses and for lifting up examples of practical steps that companies and agencies can take to reduce harm,” CEO Alexandra Reeve Givens said in a media release.

The document also highlights several areas where artificial intelligence has been beneficial, including improved agricultural efficiency and algorithms that have been used to identify diseases. But the technologies, which have grown rapidly with few regulations, have introduced significant harm, it notes, including discrimination in tools that screen job applicants and facial recognition technology that can contribute to wrongful arrests .

After the pandemic shuttered schools nationwide in early 2020 and pushed students into makeshift remote learning, companies that sell digital activity monitoring software to schools saw an increase in business. But the tools have faced significant backlash for subjecting students to relentless digital surveillance.

Related: With ‘Don’t Say Gay’ Laws & Abortion Bans, Student Surveillance Raises New Risks

In April, Massachusetts Sens. Elizabeth Warren and Ed Markey warned in a report the technology could carry significant risks — particularly for students of color and LGBTQ youth — and promoted a “need for federal action to protect students’ civil rights, safety and privacy.” Such concerns have become particularly acute as states implement new anti-LGBTQ laws and abortion bans and advocates warn that digital surveillance tools could expose expose youth to legal peril.

Vance said that she and others focused on education and privacy “had no idea this was coming,” and that it would focus so heavily on schools. Over the last year, the department sought input from civil rights groups and technology companies, but Vance said that education groups had lacked a meaningful seat at the table.

The lack of engagement was apparent, she said, by the document’s failure to highlight areas where artificial intelligence has been beneficial to students and schools. For example, the document discusses a tool used by universities to predict which students were likely to drop out. It considered students’ race as a predictive factor, leading to discrimination fears. But she noted that if implemented equitably, such tools can be used to improve student outcomes.

“Of course there are a lot of privacy and equity and ethical landmines in this area,” Vance said. “But we also have schools who have done this right, who have done a great job in using some of these systems to assist humans in counseling students and helping more students graduate.”

Related: Trevor Project to Refund Donation From Student Surveillance Company Accused of LGBTQ Bias Following 74 Investigation

Ellerson Ng, of the superintendents association, said her group is still analyzing the blueprint’s on-the-ground implications, but that student data privacy efforts present schools with “a balancing act.”

“You want to absolutely secure the privacy rights of the child while understanding that the data that can be generated, or is generated, has a role to play, too, in helping us understand where kids are, what kids are doing, how a program is or isn’t working,” she said. “Sometimes that’s broader than just a pure academic indicator.”

Others have deemed the blueprint toothless and just another policy position in a crowded field of recommendations from civil rights groups and tech companies. Some of the most outspoken privacy proponents and digital surveillance critics, such as Albert Fox Cahn, founder and executive director of the Surveillance Technology Oversight Project, argued it falls short of a critical policy move: outright bans.

As Cahn and other activists mount campaigns against student surveillance tools, they’ve highlighted how student data can wind up in the hands of the police .

“When police and companies are rolling out new and destructive forms of AI every day, we need to push pause across the board on the most invasive technologies,” he said in a media release. “While the White House does take aim at some of the worst offenders, they do far too little to address the everyday threats of AI, particularly in police hands.”

The blueprint lays out five principles for AI tools: safety and effectiveness, avoidance of discrimination, reasonable privacy protections, transparency, and the ability to opt out in favor of a human alternative.

The 'AI Bill of Rights' aims to safeguard the public from the risks of artificial intelligence, including algorithmic discrimination and bias.

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Content: Seyfarth synopsis: President Biden's Executive Order on artificial intelligence sets forth his vision for America to continue leading in AI innovation while also addressing risks associated with the use of AI. While much of the document delves into cutting-edge safety issues with national security implications, there are many provisions in the EO that have broad ramifications for companies generally, and employers specifically. The Order mandates greater coordination by civil-rights agencies on AI issues, emphasizes worker protections, and instructs the Department of Labor to guide federal contractors regarding AI-driven hiring practices. It marks the strategic emphasis on the government's internal standards for AI governance and AI risk management, and towards articulating and implementing "required minimum risk-management practices" for AI applications that "impact people's rights or safety." The Executive Order's emphasis on security assessments of AI systems are also set to influence AI risk management and safety dialogues across various sectors, all with significant implications in the labor and employment domain.

On October 30, 2023, President Biden signed a lengthy and far-reaching Executive Order regarding artificial intelligence that outlines the Administration's vision for the federal government's AI regulation and enforcement activities in the years to come. While some early media coverage of the EO has focused on what the EO says about identifying and managing AI risks related to national security, national public health and safety, and steps to develop watermarking standards to identify and label AI-generated content, the EO represents a comprehensive government-wide approach to addressing AI. The EO sets in motion action from multiple Departments and independent agencies, with the aim towards harnessing the benefits of AI and maintaining American leadership in innovation, while addressing risks associated with the use of AI.

While some of today's EO targets cutting-edge AI models developed by leading American AI companies, and delves into national security and national safety issues with global geopolitical implications, companies across multiple industries should pay close attention to some of the specific actions initiated by President Biden in today's Executive Order. Many of these actions will be shaping discussions regarding AI safety and bias for the foreseeable future.

In the upcoming days, Seyfarth will continue to provide updates on various areas targeted for Executive Branch action in AI, which may include, for example, intellectual property, privacy, cybersecurity, health care, labor relations, and other areas. Please continue to join us as we help companies better understand both risks associated with AI and, as President Biden put it on Monday, the "incredible opportunities" from the "most consequential technology" of our time.

Our update today focuses on issues we have identified as being of particular interest to employers.

1. Employers Should Expect Ongoing Coordination on AI Enforcement From Civil-Rights Agencies.

In the statement made today before signing the Executive Order, President Biden emphasized how AI is "all around us" and that AI presented an "incredible opportunity". President Biden also spoke of the risks of AI. This theme – that AI presents both opportunities and risks – is present throughout the Executive Order.

On the risk side of things, President Biden's Executive Order on AI is clear in its mandate to agencies charged with enforcing civil rights laws, directing them to make "comprehensive use of their respective authorities" to address potential civil-rights harms arising out of the use of AI, including "issues related to AI and algorithmic discrimination". The Executive Order further directs the Attorney General to "coordinate with and support agencies in their implementation and enforcement of existing Federal laws to address civil rights and civil liberties violations and discrimination related to AI" and directs agencies to coordinate "on best practices for investigating and prosecuting civil rights violations related to AI" and to provide further inter-agency training and technical assistance.

While some of these efforts are undoubtedly targeted at developing enforcement capacity at the various civil-rights enforcement agencies, the focus and ongoing coordination between federal enforcement agencies regarding the civil-rights implications of AI is not new, and the Executive Order builds on existing initiatives at the EEOC, OFCCP and other agencies.

In April 2023, the leaders of the EEOC, Department of Justice Civil Rights Division, the CFPB, and the FTC issued a "Joint Statement on Enforcement Efforts Against Discrimination and Bias in Automated Systems," emphasizing that their existing legal authorities applied to the use of AI. More recently, in the EEOC's Strategic Enforcement Plan for FY24-28, released in September 2023, the Commission expressed its intent to continue prioritizing its enforcement efforts on discriminatory recruitment and hiring practices, with a special emphasis on employers' use of AI. Likewise, OFCCP has also increased its focus on the use of artificial intelligence technologies. In August 2023, the Agency released its expanded supply and service audit scheduling letter to include a request for "information and documentation of policies, practices, or systems used to recruit, screen, and hire, including the use of artificial intelligence, algorithms, automated systems or other technology-based selection procedures."

Section 8 of the Executive Order discusses protecting consumers, patients, passengers, and students. It encourages independent regulatory agencies responsible for laws and federal programs relating to those stakeholders "to consider using their full range of authorities" to address AI risks.

For employers, the implications are indirect but noteworthy. In its directives in Section 8 of the Executive Order, the Biden Administration encourages agencies to consider rulemaking as well as guidance "clarifying the responsibility of regulated entities to conduct due diligence on and monitor any third-party AI services they use, and emphasizing or clarifying requirements and expectations related to the transparency of AI models and regulated entities' ability to explain their use of AI models."

The need to monitor third-party AI services and concepts of transparency and explainability are common themes in AI risk management discussions. While the EEOC did not receive a direct mandate to incorporate these concepts, the overarching themes in AI regulation will very well shape the discourse and future expectations in employment contexts.

2. The Biden Administration Remains Focused on Worker Protections

President Biden's Executive Order on AI further emphasizes his administration's focus on labor issues, and it contains multiple provisions regarding the responsible development and use of AI in order to support American workers. Specifically, the Executive Order states that AI "should not be deployed in ways that undermine rights, worsen job quality, encourage undue worker surveillance, lessen market competition, introduce new health and safety risks, or cause harmful labor-force disruptions," but instead be developed to improve the lives of workers through such innovative technology.

The Order also focuses on labor-market disruptions from AI, and directs the Chairman of the Council of Economic Advisers to prepare and submit a report to the President on the labor-market effects of AI, by April 2024. Among other things, the report is to identify ways to strengthen and expand education and training opportunities for pathways to occupations related to AI.

Relatedly, by April 2024, the Secretary of Labor is tasked with consulting with labor unions, workers, and other outside entities, to "develop and publish principles and best practices for employers that could be used to mitigate AI's potential harms to employees' well-being and maximize its potential benefits."

One of the harms to workers that the Biden Administration has highlighted is the potential harm from automated systems that monitor workers. The Executive Order asserts, "AI should not be deployed in ways that undermine rights, worsen job quality, [or] encourage undue worker surveillance". The White House's fact sheet discussing the Executive Order cites "the dangers of increased workplace surveillance, bias, and job displacement," and the Executive Order itself also directs the Secretary of Labor to issue guidance "to make clear that employers that deploy AI to monitor or augment employees' work must continue to comply with" the requirements of the FLSA.

In the context of disabilities, including in the workplace, the Executive Order mentions specific technology often associated with automated monitoring. It specifically identifies the risk of unequal treatment of people with disabilities "from the use of biometric data like gaze direction, eye tracking, gait analysis, and hand motions" and encourages the Architectural and Transportation Barriers Compliance Board to issue technical assistance and recommendations on the risks and benefits of AI in using biometric data as an input.

3. OFCCP and Federal Contractors' Use of AI

The EO directs that within a year, the Secretary of Labor "shall" publish guidance to federal contractors regarding "nondiscrimination in hiring involving AI and other technology-based hiring systems." Accordingly, Federal contractors in particular should expect additional guidance from OFCCP clarifying how it will be applying existing guidance, such as the 1978 Uniform Guidelines on Employee Selection Procedures, to cutting-edge hiring technology. The guidance to date has been limited and simply provides that the Uniform Guidelines apply to even the most sophisticated technologies.

Employers not directly subject to OFCCP's audits should still pay close attention to what the Department of Labor does here, because there is always the potential for the Department of Labor's guidance to be held out as a broader standard that employers everywhere might incorporate in their practices.

4. The way the Federal government thinks about AI risk will influence the way private companies think about AI risk.

Employers should pay close attention to what the federal government does with respect to its own use of artificial intelligence, and the way that federal agencies will be directed to think about AI risk as they continue their own AI journeys. These steps include straightforward risk-management practices such as directing agencies to appoint a "Chief Artificial Intelligence Officer" who is responsible for both promoting AI innovation in their agency as well as managing risks from their agency's use of AI, and things like directing the Office of Personnel Management to develop guidelines on the use of generative AI for work by the Federal workforce.

With respect to generative AI, generally, employers should also pay attention to how the Executive Order takes a balanced approach to the use of generative AI within the federal workforce. It "discourages" agencies from imposing bans, or wholesale blocking the use, of generative AI. The executive order also encourages agencies to put "appropriate safeguards in place" and to "employ risk-management practices, such as training ... staff on proper use, protection, dissemination, and disposition of Federal information; negotiating appropriate terms of service with vendors; implementing measures designed to ensure compliance with record-keeping, cybersecurity, privacy, and data protection requirements; and deploying other measures to prevent misuse of Federal Government information in generative AI." As federal agencies begin operationalizing these concepts further, private-sector employers should examine how their own practices align with (or may vary from) these practices.

And with respect to the federal government's own use of AI, President Biden's Executive Order also directs the Office of Management and Budget to issue guidance to federal agencies regarding "required minimum risk-management practices" for AI applications that "impact people's rights or safety." This guidance is to include key concepts from the AI Risk Management Framework by the National Institute of Standards and Technology (NIST), such as "conducting public consultation; assessing data quality; assessing and mitigating disparate impacts and algorithmic discrimination; providing notice of the use of AI; continuously monitoring and evaluating deployed AI; and granting human consideration and remedies for adverse decisions made using AI." As the federal government intensifies its internal focus on these risk-management principles – both in its own operations and as a buyer of AI – we anticipate a corresponding rise in their significance within the private sector. This will especially hold true for companies leveraging AI in HR decision-making processes.

Speaking of NIST, the Executive Order places a lot of responsibility on NIST. Employers should pay special attention to the directive that NIST draft "guidelines and best practices, aiming to promote consensus industry standards, for the development and deployment of AI systems that are safe, secure, and trustworthy." This directive is important for employers who are either currently utilizing AI in their HR operations or contemplating its integration. Notably, NIST is tasked with establishing guidelines and benchmarks for the evaluation and auditing of AI capabilities, emphasizing areas where AI might pose potential hazards. While some of the risks mentioned by the Executive address national security and biosecurity concerns, we believe that working towards creating standards to evaluate risks related to unlawful bias and employment-discrimination is firmly within the responsibilities assigned to NIST by the Executive Order.

5. Security testing and "red teaming" of AI will become more-relevant for employers.

Some of the headline-grabbing portions of President Biden's Executive Order involve President Biden's invocation of the Defense Production Act to require American tech giants developing the largest and most-sophisticated AI models to disclose the results of certain "red teaming" security testing exercises. While some of the test subjects may sound like they were drawn from science fiction –the Executive Order refers to testing whether AI models might have "the possibility for self-replication or propagation" – even employers who aren't developing cutting-edge AI models should still pay attention to the broad scope of the "red teaming" of AI models.

Crucially for employers, the Executive Order's definition of "AI red-teaming" explicitly includes tests for potentially discriminatory output from an AI system. The Executive Order's full definition of "AI red-teaming" is

a structured testing effort to find flaws and vulnerabilities in an AI system, often in a controlled environment and in collaboration with developers of AI. Artificial Intelligence red-teaming is most often performed by dedicated "red teams" that adopt adversarial methods to identify flaws and vulnerabilities, such as harmful or discriminatory outputs from an AI system, unforeseen or undesirable system behaviors, limitations, or potential risks associated with the misuse of the system.

Earlier this summer, President Biden's chief AI advisor, OSTP Director Arti Prabhakar, addressed the DEF CON security conference, where OSTP and other federal agencies sponsored a wide-scale "generative AI red teaming" exercise. In this exercise, thousands of volunteers engaged in the adversarial testing of large language models, seeking, among other things, to induce the language models to generate outputs revealing explicit or implicit bias, misinformation, or directions for illegal activities. Director Prabhakar encouraged the security researchers, noting that it was important for developers and the government to "understanding how these systems break so [they] can keep getting better." Given its prominent place in President Biden's Executive Order, the concept of "AI red-teaming" will gain further attention. It will be an important concept for many AI applications, and not just the most advanced ones, so employers should evaluate all of their AI safety and security practices, including testing and validation, in light of this heightened emphasis.

Conclusion

President Biden's far-reaching Executive Order on AI will dominate discussion about AI regulation and enforcement for some time to come. We plan to provide additional updates as we dive more deeply in this comprehensive EO.

The content of this article is intended to provide a general guide to the subject matter. Specialist advice should be sought about your specific circumstances.

What does Hinton warn could happen to AI if companies and regulators fail to implement effective rules and controls?

The key components of the 'AI Bill of Rights' include creating new safety and security standards for AI, protecting consumer privacy, promoting equality and civil rights, and protecting consumers from potential harm.

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Content: Joe Biden issued the first ever decree on artificial intelligence – Finance.ua
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Joe Biden issued the first ever decree on artificial intelligence
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President Joe Biden has issued the first-ever artificial intelligence executive order, which requires new assessments of safety, equality and civil rights, and research into the impact of AI on the labor market.
While law enforcement officials have warned they are willing to apply current legislation to artificial intelligence abuses, and Congress is still trying to learn more about the technology to develop new laws, the executive order could have a more direct impact.
Like all executive orders of the president, it “has the force of law, ” reports CNBC.
The White House has broken the order into eight components:
• Establishing new security and protection standards for AI, including by requiring AI companies to share security test results with the federal government, instructing the Commerce Department to develop guidance on the use of AI watermarks, and creating a cybersecurity program that can create tools to identify flaws in critical software.
Protecting consumer privacy, including by creating guidelines that agencies can use to evaluate privacy practices applied in AI.
• Promote equality and civil rights by providing guidance to landlords and federal contractors that will help avoid discrimination through AI algorithms, as well as on the proper role of AI in the justice system, including in sentencing, risk assessment and crime prediction.
Protecting consumers in general by instructing the Department of Health and Human Services to create a program to assess potentially harmful health practices related to artificial intelligence, as well as creating resources on how educators can responsibly use artificial intelligence tools.
• Supporting workers by preparing a report on the potential impact of AI on the labor market and exploring ways in which the federal government can support workers affected by labor market disruptions.
• Foster innovation and competition by expanding grants for AI research in areas such as climate change and modernizing criteria for highly skilled immigrant workers with key knowledge eligible to stay in the US.
• Cooperation with international partners to implement AI standards around the world.
• Develop guidance for federal agencies on the use and procurement of AI and accelerate government recruitment of skilled workers in the field.
According to Deputy White House Chief of Staff Bruce Reed, the order is “the strongest set of measures any government in the world has ever taken in the field of AI security, protection and trust. ”
It builds on the voluntary commitments the White House has previously received from leading AI companies, and is the first major mandatory government action on the technology.
Also Read: How to Make Money with Artificial Intelligence
A senior administration official noted that 15 of America ’ s largest technology companies have agreed to make voluntary commitments to AI security, but that ’ s not enough, and Monday ’ s order is a step toward specific regulation of technology development.
President Biden ’ s executive order requires large companies to share security test results with the U.S. government before the official release of AI systems.
It also gives priority to the National Institute of Standards and Technology in developing standards for AI “red testing ”, or stress testing of protection and potential problems within systems.
The Commerce Department will develop standards to mark AI-generated content with watermarks.
The Biden administration is also taking measures to strengthen human resources in the field of AI.
Based on materials:
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The development of AI systems requires a multidisciplinary approach that involves not only technical experts but also social scientists, ethicists, and community representatives.

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Content: Miranda Bogen is creating solutions to help govern AI

To give AI-focused women academics and others their well-deserved — and overdue — time in the spotlight, TechCrunch is launching a series of interviews focusing on remarkable women who’ve contributed to the AI revolution. We’ll publish several pieces throughout the year as the AI boom continues, highlighting key work that often goes unrecognized. Read more profiles here.

Miranda Bogen is the founding director of the Center of Democracy and Technology’s AI Governance Lab, where she works to help create solutions that can effectively regulate and govern AI systems. She helped guide responsible AI strategies at Meta and previously worked as a senior policy analyst at the organization Uptown, which seeks to use tech to advance equity and justice.

Briefly, how did you get your start in AI? What attracted you to the field?

I was drawn to work on machine learning and AI by seeing the way these technologies were colliding with fundamental conversations about society — values, rights, and which communities get left behind. My early work exploring the intersection of AI and civil rights reinforced for me that AI systems are far more than technical artifacts; they are systems that both shape and are shaped by their interaction with people, bureaucracies, and policies. I’ve always been adept at translating between technical and non-technical contexts, and I was energized by the opportunity to help break through the appearance of technical complexity to help communities with different kinds of expertise shape the way AI is built from the ground up.

What work are you most proud of (in the AI field)?

When I first started working in this space, many folks still needed to be convinced AI systems could result in discriminatory impact for marginalized populations, let alone that anything needed to be done about those harms. While there is still too wide a gap between the status quo and a future where biases and other harms are tackled systematically, I’m gratified that the research my collaborators and I conducted on discrimination in personalized online advertising and my work within the industry on algorithmic fairness helped lead to meaningful changes to Meta’s ad delivery system and progress toward reducing disparities in access to important economic opportunities.

How do you navigate the challenges of the male-dominated tech industry and, by extension, the male-dominated AI industry?

I’ve been lucky to work with phenomenal colleagues and teams who have been generous with both opportunities and sincere support, and we tried to bring that energy into any room we found ourselves in. In my most recent career transition, I was delighted that nearly all of my options involved working on teams or within organizations led by phenomenal women, and I hope the field continues to lift up the voices of those who haven’t traditionally been centered in technology-oriented conversations.

What advice would you give to women seeking to enter the AI field?

The same advice I give to anyone who asks: find supportive managers, advisors, and teams who energize and inspire you, who value your opinion and perspective, and who put themselves on the line to stand up for you and your work.

What are some of the most pressing issues facing AI as it evolves?

The impacts and harms AI systems are already having on people are well-known at this point, and one of the biggest pressing challenges is moving beyond describing the problem to developing robust approaches for systematically addressing those harms and incentivizing their adoption. We launched the AI Governance Lab at CDT to drive progress in both directions.

What are some issues AI users should be aware of?

For the most part, AI systems are still missing seat belts, airbags, and traffic signs, so proceed with caution before using them for consequential tasks.

What is the best way to responsibly build AI?

The best way to responsibly build AI is with humility. Consider how the success of the AI system you are working on has been defined, who that definition serves, and what context may be missing. Think about for whom the system might fail and what will happen if it does. And build systems not just with the people who will use them but with the communities who will be subject to them.

How can investors better push for responsible AI?

Investors need to create room for technology builders to move more deliberately before rushing half-baked technologies to market. Intense competitive pressure to release the newest, biggest, and shiniest new AI models is leading to concerning underinvestment in responsible practices. While uninhibited innovation sings a tempting siren song, it is a mirage that will leave everyone worse off.

AI is not magic; it’s just a mirror that is being held up to society. If we want it to reflect something different, we’ve got work to do.

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Content: UAE participates in setting global standards for fair, accountable and understandable systems
Artificial intelligence.
Scientific Maturity with Moral Dilemmas
T + T - normal size
With the increasing use of artificial intelligence technologies in vital sectors that directly affect our lives, such as health, education, mobility, agriculture and even arts and space, experts believe that it is possible to develop a super artificial intelligence that reaches the point of controlling the emotions and motivations of people and thus their reality.
Will we be the ones who shape the reality and future of artificial intelligence systems or vice versa?
The more pressing question is, can we ensure that artificial intelligence is harnessed as a moral responsibility to improve human lives?
The rapid progress of artificial intelligence in recent years has led groups of experts to call for the need to develop safeguards to protect humanity from the dangers of artificial intelligence.
Given the gravity of the issue, many government bodies, civic organizations, private companies, think tanks, and research groups are working to develop principles governing AI trends such as the Barcelona Declaration, the Montreal Declaration, the Google Principles, and the Microsoft Principles.
One such organization is the Future of Life Institute, a nonprofit research and outreach organization founded in Boston in 2014 to monitor existential threats to humanity.
The institute was founded by MIT cosmologist Max Tegmark, Skype co-founder Jan Tallinn and DeepMind research scientist Victoria Krakow.
The institute worked with AI researchers and developers as well as scientists from many disciplines to create the 23 guidelines now referred to as the Asilomar Principles, which aim for AI technologies to develop reliably, safely and comprehensively.
A recent study by the Future of Life Institute suggests that AI is unlikely to show human emotions such as love or hate, and that there is no reason to expect AI to automatically deliberate good or evil.
But experts at the institute suggest two scenarios in which AI could become an imminent threat: The first scenario is that humans program AI to do something destructive, such as “autonomous ” or autonomous weapons — AI systems programmed to kill and capable of easily causing mass casualties in the wrong person ’ s hands.
Moreover, an AI arms race could inadvertently lead to an AI war that also leads to heavy casualties.
To avoid “losing ” to the other side, these weapons can be designed to be extremely difficult to stop, and humans can easily lose control in that situation.
This risk exists even with “narrow ” — or limited — AI, but it grows with increasing levels of AI and autonomy.
The other scary scenario is that the AI is programmed to do something useful, but it may develop a destructive way to achieve its goal, which can happen when we fail to fully align the AI goals with our goals, which is very difficult.
For example, if you ask a smart, obedient car to take you to the airport as soon as possible, you may arrive “as soon as possible, ” but you will be chased by police helicopters and probably passed out at speed.
The car didn ’ t do “what it wanted, ” which was to arrive at least safely, but it did literally what you asked.
If a super-intelligent system is tasked with an ambitious geoengineering project, it could cause havoc in our ecosystem as a side effect, and human attempts to stop it are seen as a “threat ” to be met.
Benefit
His Excellency Omar bin Sultan Al Olama, Minister of State for Artificial Intelligence, Digital Economy and Teleworking Applications, said: “The UAE government attaches great importance to ensuring the optimal use of AI solutions and capabilities, and adopts within its future directions to make the most of this advanced technology, with full commitment to employ it with high ethical responsibility centered on people and community service. ”
To achieve this, the UAE government, through the UAE Artificial Intelligence and Blockchain Council, has formed a dedicated subcommittee to ensure effective governance and regulation of AI technology.
Among the initiatives related to the ethics of artificial intelligence applications worked on by the Council is the self-assessment tool for artificial intelligence applications, which was launched at the level of the Government of Dubai, and is being developed and launched at the level of the federal government.
Principles
He added: “This assessment is based on four basic principles: inclusion: It means that the benefits of artificial intelligence include all members of society, and that governance systems apply to this technology globally, while respecting the dignity, rights and privacy of individuals.
Humanity: AI should be beneficial to humanity and consistent with human values, both in the short term and in the long term.
And safety: AI systems should be safe, and be harnessed in the service of humanity.
AI systems must be fair, transparent, accountable and understandable.
In addition to these principles and standards, the UAE Council for Artificial Intelligence and Blockchain, represented by its subcommittee of artificial intelligence experts in the country, participates in the development and formulation of global standards, through the World Organization for Standards, with a group of experts. ”
He pointed out that the committee is currently working on the development of technical standards for artificial intelligence, including the standards of "explainable artificial intelligence", which is one of the most important technical topics that allow to verify the compatibility of models of artificial intelligence with the basic principles of its use.
The committee has played an important role in the development of these standards, and it is expected that they will be adopted in the country once approved by the technical committee of the World Organization for Standards, to control and ensure the optimal employment of artificial intelligence applications.
Data protection
As for the data used to train artificial intelligence models, which is one of the most important possible elements of these applications, the UAE has issued the “Personal Data Protection Law ”, which aims to protect the data of individuals available to companies from the uses that violate privacy in the training of artificial intelligence models and other uses.
The Office of Artificial Intelligence, Digital Economy and Telework Applications, in collaboration with Oxford University, is working to train the employees of ministries and federal and local government agencies through the “Artificial Intelligence Program ” to develop the ability of trainees to analyze the security and ethical risks that may result from the wrong adoption of artificial intelligence technologies.
Rules.
Ghassan Abu Ajamiya, director of strategic planning at the Emirates Alliance for Technical Solutions, said: “Science at the beginning is simple, but with the development of these sciences, including artificial intelligence, societies need laws and legislation to regulate its transactions, and what happened in our digital life is that developments are faster than legislation, and giant companies began to impose their way of dealing with data. ”
Abu Ajima pointed out that most of those companies and owners of platforms that contain millions of data are American companies, so the European Union began its journey to address these trends by issuing a few days ago the Digital Services Law, which aims to get rid of harmful or toxic content on the Internet, and under this decision will be responsible for their algorithms, disclosure in all transparency, and required to assess the risks in content and mitigate it to protect users. Prevent the rapid spread of misinformation, combat hate speech, and avoid putting unsafe products on the market.
About a month earlier, the Digital Markets Act was announced, with the aim of reducing the broad dominance of companies in the digital economy, and the beginning of the union with the laws was when the data privacy protection system was issued in 2018.
He added: “There is no doubt that the set of decisions issued by the European Union can serve as a platform to help draw up a legal framework for legislation that can be built on and adapted according to the values and ethics that suit each country or region, and this will undoubtedly contribute to improving how user data is used and reduce its exploitation by IT giants that own major social media platforms. ”
A proactive step
Mohamed Amin, senior vice president of Dell Technologies in the Middle East, said many companies today rely on AI technologies to get accurate and quick answers to difficult issues they may face.
He added: “Today, we rely more and more on artificial intelligence models to make different decisions, whether about approving bank loan applications or helping customers decide on their next holiday destination, this technology is able to calculate the smallest details and differences.
So we need to make sure that AI systems are ethical and incorruptible, and that people are able to understand how decisions are made through this technology.
There is no doubt that artificial intelligence has the ability to calculate huge amounts of data and improve decision-making processes, but we cannot lose sight of the fact that the decisions made by AI systems affect the lives of humans, they are a machine that can currently use big data to create better information than humans, but they do not understand the context, have no emotions or ethics, and are unable to think outside the box.
When there are no clear checks and balances in the functioning of these systems, they can negatively affect the diversity of our ideas and our excellence, by treating us as a homogeneous group, and providing only structured content that does not respect our differences.
Artificial intelligence has been described as the most important technology under development today.
However, we need to place it under the proper supervision of governments and regulators, who will be instrumental in helping companies deal with the ethical side and shortcomings of these systems.
Challenges
“The ethical challenges of AI are not limited to regulating the collection and circulation of personal data, but rather to putting in place controls to prevent the employment of AI to make decisions or to subject, compel or influence certain people or their decisions through the processing of their personal data, whether without their consent or in a way that may prejudice or affect their rights, ” said lawyer Mujahed Sebai.
He added: "Many legal and ethical questions related to the use of artificial intelligence are also raised about the mechanism of making decisions or correcting them if they are wrong or determining who is responsible for them, especially in the event of damage, whether material or moral, and may take biased decisions or lead to injustice or discrimination in all its forms or to monopolize the huge data that may be collected about people or institutions."
“To deal with these challenges, all stakeholders, from governments to individuals, must work together to identify the direct or long-term effects of AI, to develop strategies and systems to ensure that it is employed through a humane approach that respects the human principles and values on which our societies and the rights of individuals are based, without negatively affecting or reprogramming those values, especially by outsiders, whether for political, economic or security considerations. ”
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Explainability techniques play a crucial role in aligning AI systems with human values by providing transparency and accountability in AI decision-making.

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Content: As Artificial Intelligence's (AI) use in business and government increases by leaps and bounds, the need for transparent, fair, and safe governance standards has moved off the planning list into action.

Introduction

As Artificial Intelligence's (AI) use in business and government increases by leaps and bounds, the need for transparent, fair, and safe governance standards has moved off the planning list into action. In a vacuum of universal guidelines, many corporations and governments have already begun to create their own standards to address the key topics of model transparency, explainability, and fairness. While these guidelines have been an excellent start, there is still a need for organizations to control and guide their AI development while also keeping the current and emerging regulatory environment in mind.

Due to the nascent nature of the field, there are few widely implemented and agreed upon best practices. The 2023 World Privacy Forum (WPF) report on Assessing and Improving AI Governance Tools1 attempts to remedy this gap by highlighting examples across 6 categories:

  1. Practical Guidance consisting of general education information on AI governance,
  2. Self-assessment Questionnaires,
  3. Procedural Framework with step-by-step workflows on AI systems and/or improvements,
  4. Technical Framework,
  5. Technical Code/Software,
  6. Scoring or Classification Output.

WPF are using the following definition of AI governance tools to shape their work: "socio-technical tools for mapping, measuring, or managing AI systems and their risks in a manner that operationalizes or implements trustworthy AI". In other words, when making decisions with AI, it is necessary to account for the system's transparency, explainability, fairness and potential societal impact.

Table 1: AI Governance tool types Lexicon

Category Tools
Practical Guidance Includes general educational information, practical guidance, or other consideration factors
Self-assessment Questions Includes assessment questions or detailed questionnaire
Procedural Framework Includes process steps or suggested workflow for AI system assessments and/or improvements
Technical Framework Includes technical methods or detailed technical process guidance or steps
Technical Code or Software Includes technical methods, including use of specific code or software
Scoring or Classification Output Includes criteria for determining a classification, or a mechanism for producing a quantifiable score or rating reflecting a particular aspect of an AI system

Table 1: AI Governance Tool Types Lexicon
Source: World Privacy Forum2

These tools mentioned are in active use worldwide, making them an excellent starting point to ensure compliance with present and future regulations. Without the appropriate tools in place, it is nearly impossible to ensure a trustworthy AI future. A survey conducted by the AI Risk and Security (AIRS) group evaluated the current status of AI governance across their members (Figure 1). The survey revealed that there is room to improve, with only 30% of enterprises have established roles or responsibilities for AI systems, and just 20% have a centrally managed and budgeted department dedicated to AI governance.

"Global spending on AI is expected to increase from $150 billion in 2023 to $300 billion by 2026. The use of AI is moving at a rapid pace with regulators' eyes keeping a close watch, and we're seeing leaders in the TMT industry create their own governance tools as a commercial and operational imperative." George Haitsch, WTW Technology, Media and Telecom Industry Leader

"

Global spending on AI is expected to increase from $150 billion in 2023 to $300 billion by 2026."

George Haitsch | WTW Technology, Media and Telecom Industry Leader

Implementing several AI governance tools fosters a collective understanding and assessment of possible risks and limitations in AI explainability, transparency, and fairness across different departments. All personnel engaging with AI models should possess awareness regarding its capabilities and shortcomings, emphasizing the importance of maintaining a skeptical perspective toward AI outputs.

Figure 1: The current status of AI governance in enterprises

Notes: Respondents included professionals from technology risk, information security, legal, privacy, architecture, model risk management, and other fields, working in financial and technology organizations, as well as academic institutions.

Emerging AI governance regulations

Regulation is evolving in different pathways around the world. Policy makers across the globe are hurriedly addressing the issues, gaps, and limitations of AI driven decisions.

This has prompted a surge of literature and research into the subject. In July 2023, the U.S. Biden administration announced3 that Amazon, Anthropic, Google, Inflection, Meta, Microsoft and OpenAI will self-regulate their AI development.

  • Congress' blueprint for an AI Bill of Rights4 provides practical guidance through a set of five principles and practices to government agencies and a call to action for technology companies, researchers, and civil society to build these protections5.

The EU produced a similar set of ethical guidelines to the US with 7 key requirements including transparency, accountability, and respect for privacy and data protection6. They also proposed a classification scoring system. In the European Union's A.I. Act, regulations are assigned proportionate to the level of risk posed by an AI tool: unacceptable, high, limited, and minimal risk.

  • Unacceptable risks that would be banned include AI systems that perform social scoring of individuals or real-time facial recognition in public places. Other tools carrying less risk, such as software that generates manipulated videos and "deepfake" images must disclose that people are seeing AI-generated content. Violators could be fined 6% of their global sales. Minimally risky systems include spam filters and AI-generated video games.

Canada produced a mandatory risk assessment tool in 2020 named as the Algorithmic Impact Assessment (AIA).7

  • It consists of 51 risk and 34 mitigation self-assessment questions to determine the impact level of an automated decision-system. The final assessment scores are based on factors such as system's design, algorithm, decision type, impact, and data.

Singapore launched a technical framework and software with AI Verify8 in partnership with companies from different sectors and scale including AWS, DBS Bank, Google, Meta, Microsoft, Singapore Airlines, and Standard Chartered Bank. It became open source in June 2023.

In June 2023, China revealed that it was developing an "Artificial Intelligence Law" that could offer practical guidance, a technical framework, and a classification scoring system. Rather than devising a comprehensive regulatory plan, China has chosen to regulate AI through individual legislation, with specific laws addressing issues such as algorithms, generative AI, and deepfakes.

Global companies will be facing increasing pressure to comply with regulatory standards in AI governance. This often presents a challenge as they must also comply with other regulatory measures, such as ESG. As a result, companies may spend more time disclosing information about their algorithms rather than making tangible progress.

Risks and limitations – Explainability, oversimplification & bias

Several AI governance tools, including fairness AI auditing software, may be limited in their application to specific phases of the AI life cycle. AI fairness tools might solely address fairness concerns during the model training stage of AI development. However, ensuring fairness at one stage does not guarantee its persistence throughout the entire AI life cycle.

AI Life Cycle
Source: NHS England "Creating an international approach to AI for healthcare"9

Addressing bias in AI models through technical code or software is just the beginning for AI governance. Companies must extend their focus beyond the technical developers' Proof Of Value. AI fairness auditing implemented across the entire AI life cycle along with comprehensive documentation is key. The documentation should incorporate various AI governance tools understandable to both technical and non-technical audiences.

Technical developers are focused on achieving explainability by describing the mechanisms of an AI system or algorithm through software/code. One of the major problems for evaluating explainability from the technical developer's point of view is oversimplification.

  • For example, the utilization of both SHAP10 (Shapley Additive exPlanations) and LIME11 (Local Interpretable Model-agnostic Explanations) for model explainability has seen an uptick. This rise in popularity is partly owed to the methods' model-agnostic nature, allowing evaluation of any model, and to their abundance of user-friendly documentation.

In a typical scenario, a data scientist may opt for SHAP or LIME to show how an output was determined for a single instance of a model input, such as a specific decision or prediction, rather than the entire model. In other words, both methods operate by approximating more intricate, non-linear models (often referred to as "black-box" models) with simpler linear models, potentially resulting in misleading outcomes.

Global companies will be facing increasing pressure to comply with regulatory standards in AI governance.

Oversimplification and the potential lack of critical context poses a significant challenge. Some algorithms designed for specific social settings may be inaccurately applied in different contexts, resulting in technical inaccuracies and misguided outcomes. The NIST AI Risk Management Framework12, examined in Part II of the report, acknowledges the shortcomings of metrics used to measure AI risk, citing the risk of oversimplification, gaming, lack of critical nuance, and unexpected reliance.

This deficiency in contextual understanding could lead to unfair resource allocation or access, with implications across various areas such as mortgage lending, employment screening, college admissions, child welfare, and medical diagnoses.

Business recommendations and model framework

An AI governance framework should encompass internal governance structures and measures, define the extent of human involvement in AI decision-making, address operations management, facilitate stakeholder interaction and communication, and illustrate de-risking strategies for AI across the entire business to mitigate large-scale failures.

1. Internal governance structures and measures

Internal governance structures and measures play a crucial role in ensuring robust oversight of an organization's use of AI. They should be addressing risks and integrating ethical considerations through ethics review boards.

Organizations may explore features for their internal governance structures, such as clear roles and responsibilities for ethically deploying AI. A decentralized governance mechanism might be considered when a centralized approach is suboptimal, bringing ethical considerations into day-to-day decision-making.

2. Determining the level of human involvement in AI decision-making

Before deploying AI solutions, organizations are advised to determine how much influence humans have in the process. The levels addressing human oversight with AI are listed below:

Human-in-the-loop: In this model, human oversight is active, with people retaining full control. The AI provides recommendations or input to the humans driving the process.

Human-out-of-the-loop: In this model, there is no human oversight, and the AI system has full control without the option of human override.

Human-over-the-loop (or human-on-the-loop): This model involves human oversight to the extent that the human is in a supervisory role, with the ability to take control in the face of unexpected events. Humans can adjust parameters during the algorithm's operation. Examples include: AI assisted medical diagnoses, product recommendations, or GPS navigation systems.

3. Data management and governance

The individuals involved in model training and selection, whether internal staff or external providers, should work collaboratively. Data accountability practices, including understanding data lineage, ensuring data quality, and minimizing inherent bias, are essential. Organizations must understand the lineage of data and address factors affecting data quality.

Minimizing inherent bias involves being aware of biases in datasets, using heterogeneous datasets, and employing different datasets for training, testing, and validation. Regular review and updating of datasets, even if non-personal, are recommended for accuracy, quality, and reliability. Good data accountability practices apply even when using non-personal data or anonymized personal data in AI model training.

In deploying AI algorithms, organizations must iterate through model development until achieving the most suitable results for their use case. The interaction between data and algorithms/models is vital. Datasets, sourced from various places, both personal and non-personal, are integral to the AI solution's success.

4. Stakeholder interaction and communication

Effective AI governance relies on clear end-to-end communication with various stakeholders such as developers, executives, regulators, external AI tool customers, internal business users, and more. Achieving this involves concise and accessible AI documentation, addressing model gaps and biases, and specifying appropriate use cases.

Transparent communication, simple user interfaces, opt-out mechanisms, and feedback channels are paramount for user understanding and interaction. Organizations should regularly assess their AI governance against evolving ethical standards and share the results with relevant stakeholders. Meta's "Why am I seeing this?"13 feature exemplifies transparency in advertising and machine learning model training. As AI progresses, AI governance evolves. WTW will continue to keep pace with that change and explore collaboration amongst employees, executives, users, and researchers on examining and enhancing AI for optimal governance.

Footnotes

1. World Privacy Forum - Risky Analysis - December 2023. Return to article undo

2. Model Artificial Intelligence Governance Framework - Second Edition. Return to article undo

3. Readout of White House Listening Session on Tech Platform Accountability. Return to article undo

4. A Framework for U.S. AI Governance: Creating a Safe and Thriving AI Sector. Return to article undo

5. Generative AI: Implications for Trust and Governance. Return to article undo

6. What is AI Verify? Return to article undo

7. Artificial intelligence act. Return to article undo

8. Creating an international approach to AI for healthcare. Return to article undo

9. AI Risk Management Framework. Return to article undo

10. An introduction to explainable AI with Shapley values. Return to article undo

11. LIME - Local Interpretable Model-Agnostic Explanations. Return to article undo

12. AI Risk Management Framework. Return to article undo

13. Increasing Our Ads Transparency. Return to article undo

The content of this article is intended to provide a general guide to the subject matter. Specialist advice should be sought about your specific circumstances.

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Content: Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.

Have you heard the unsettling stories that have people from all walks of life worried about AI?

A 24-year-old Asian MIT graduate asks AI to generate a professional headshot for her LinkedIn account. The technology lightens her skin and gives her eyes that are rounder and blue. ChatGPT writes a complimentary poem about president Biden, but refuses to do the same for former president Trump. Citizens in India take umbrage when an LLM writes jokes about primary figures of the Hindu faith, but not those associated with Christianity or Islam.

These stories fuel a feeling of existential dread by presenting a picture where AI puppet masters use technology to establish ideological dominance. We often avoid this topic in public conversations about AI, especially since the demands of professionalism ask us to separate personal concerns from our work lives. Yet, ignoring problems never solves them, it simply allows them to fester and grow. If people have a sneaking suspicion that AI is not representing them, and may be actively discriminating against them, it’s worth discussing.

What are we calling AI?

Before diving into what AI may or may not be doing, we should define what it is. Generally, AI refers to an entire toolkit of technologies including machine learning (ML), predictive analytics and large language models (LLM). Like any tool, it is important to note that each specific technology is meant for a narrow range of use cases. Not every AI tool is suited for every job. It is also worth mentioning that AI tools are relatively new and still under development. Sometimes even using the right AI tool for the job can still yield undesired results.

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For example, I recently used ChatGPT to assist with writing a Python program. My program was supposed to generate a calculation, plug it into a second section of code and send the results to a third. The AI did a good job on the first step of the program with some prompting and help as expected.

But When I proceeded to the second step, the AI inexplicably went back and modified the first step. This caused an error. When I asked ChatGPT to fix the error, it produced code that caused a different error. Ultimately, ChatGPT kept looping through a series of identical program revisions that all produced a few variations of the same errors.

No intention or understanding is happening on the part of ChatGPT here, the tool’s capabilities are simply limited. It became confused at around 100 lines of code. The AI has no meaningful short-term memory, reasoning or awareness, which might partly be related to memory allocation; but it is clearly deeper than that. It understands syntax and is good at moving large lumps of language blocks around to produce convincing results. At its core, ChatGPT doesn’t understand it is being asked to code, what an error is, or why they should be avoided, no matter how polite it was for the inconvenience of one.

I’m not excusing AI for producing results that people find offensive or disagreeable. Rather, I’m highlighting the fact that AI is limited and fallible, and requires guidance to improve. In fact, the question of who should provide AI moral guidance is really what lurks at the root of our existential fears.

Who taught AI the wrong beliefs?

Much of the heartache surrounding AI involves it producing results that contradict, dismiss or diminish our own ethical framework. By this I mean the vast number of beliefs humans adopt to interpret and evaluate our worldly experience. Our ethical framework informs our views on subjects such as rights, values and politics and are a concatenation of sometimes conflicting virtues, religion, deontology, utilitarianism, negative consequentialism and so on. It is only natural that people fear AI might adopt an ethical blueprint contradictory to theirs when not only do they not necessarily know their own — but they are afraid of others imposing an agenda on them.

For example, Chinese regulators announced China’s AI services must adhere to the “core values of socialism” and will require a license to operate. This imposes an ethical framework for AI tools in China at the national level. If your personal views are not aligned with the core values of socialism, they will not be represented or repeated by Chinese AI. Consider the possible long-term impacts of such policies, and how they may affect the retention and development of human knowledge.

Worse, using AI for other purposes or suborning AI according to another ethos is not only an error or bug, it’s arguably hacking and potentially criminal.

Dangers in unguided decisioning

What if we try to solve the problem by allowing AI to operate without guidance from any ethical framework? Assuming it can even be done, which is not a given, this idea presents a couple of problems.

First, AI ingests vast amounts of data during training. This data is human-created, and therefore riddled with human biases, which later manifest in the AI’s output. A classic example is the furor surrounding HP webcams in 2009 when users discovered the cameras had difficulties tracking people with darker skin. HP responded by claiming, “The technology we use is built on standard algorithms that measure the difference in intensity of contrast between the eyes and the upper cheek and nose.”

Perhaps so, but the embarrassing results show that the standard algorithms did not anticipate encountering people with dark skin.

A second problem is the unforeseen consequences that can arise from amoral AI making unguided decisions. AI is being adopted in multiple sectors such as self-driving cars, the legal system and the medical field. Are these areas where we want expedient and efficient solutions engineered by a coldly rational and inhuman AI? Consider the story recently told (then redacted) by a US Air Force colonel about a simulated AI drone training. He said:

“We were training it in simulation to identify and target a SAM threat. And then the operator would say ‘yes, kill that threat.’ The system started realizing that while they did identify the threat, at times the human operator would tell it not to kill that threat — but it got its points by killing that threat. So what did it do? It killed the operator. It killed the operator, because that person was keeping it from accomplishing its objective.

We trained the system — ‘Hey don’t kill the operator — that’s bad. You’re gonna lose points if you do that’. So what does it start doing? It starts destroying the communication tower that the operator uses to communicate with the drone to stop it from killing the target.”

This story caused such an uproar that the USAF later clarified the simulation never happened, and the colonel misspoke. Yet, apocryphal or not, the tale demonstrates the dangers of an AI operating with moral boundaries and the potentially unforeseen consequences.

What is the solution?

In 1914, Supreme Court Justice Louis Brandeis said: “Sunlight is said to be the best of disinfectants.” A century later, transparency remains one of the best ways to combat fears of subversive manipulation. AI tools should be created for a specific purpose and governed by a review board. This way we know what the tool does, and who oversaw its development. The review board should disclose discussions involving the ethical training of the AI, so we understand the lens through which it views the world and can review the evolution and development of the guidance of AI over time.

Ultimately, the AI tool developers will decide which ethical framework to use for training, either consciously or by default. The best way to ensure AI tools reflect your beliefs and values is to train them and inspect them yourself. Fortunately, there is still time for people to join the AI field and make a lasting impact on the industry.

Lastly, I’d point out that many of the scary things we fear AI will do already exist independent of the technology. We worry about killer autonomous AI drones, yet the ones piloted by people right now are lethally effective. AI may be able to amplify and spread misinformation, but we humans seem to be pretty good at it too. AI might excel at dividing us, but we have endured power struggles driven by clashing ideologies since the dawn of civilization. These problems are not new threats arising from AI, but challenges that have long come from within ourselves.

Most importantly, AI is a mirror we hold up to ourselves. If we don’t like what we see, it is because the accumulated knowledge and inferences we’ve given AI is not flattering. It might not be the fault of these, our latest children, and might be guidance about what we need to change in ourselves.

We could spend time and effort trying to warp the mirror into producing a more pleasing reflection, but will that really address the problem or do we need a different answer to what we find in the mirror?

Sam Curry is VP and CISO of Zscaler.

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Content: Researchers shed light on how to read, control AI systems’ minds

10/5/2023 4:11 AM

The Center for AI Safety (CAIS) released a paper this week detailing ways humans can better understand and control artificial intelligence, including detecting lies.

An organization dedicated to the safe development of artificial intelligence released a 'breakthrough paper' it said will help humans better control the technology as it spreads. 'We can’t trust AIs if we don’t know what they are thinking or how they work on the inside,' Dan Hendrycks, director of the Center for AI Safety, told Fox News Digital.

An organization dedicated to the safe development of artificial intelligence released a 'breakthrough paper' it said will help humans better control the technology as it spreads. 'We can’t trust AIs if we don’t know what they are thinking or how they work on the inside,' Dan Hendrycks, director of the Center for AI Safety, told Fox News Digital. Hendrycks made the comments after the Center for AI Safety (CAIS) released a paper this week diving into the inner workings of the mind of AI systems, looking for ways that humans could better understand and control and understand AI technologies and mitigate some of the risks they pose. META MAY BE USING YOUR FACEBOOK, INSTAGRAM TO 'FEED THE BEAST' OF NEW TECH According to the CAIS, the paper demonstrated ways humans can control and detect when AI systems are telling truths or lies, when they behave morally or immorally, whether they act with emotions such as anger, fear and joy, and how to make them less biased. The paper also looked at ways to develop systems that can resist jailbreaks, a practice where users can exploit vulnerabilities in AI systems and potentially use them outside desired protocols. WHAT IS ARTIFICIAL INTELLIGENCE (AI)? 'Our research develops ways to read the inner thoughts of AIs, allowing us to detect when they are lying or malfunctioning in various ways,' Hendrycks said, noting current AI systems are 'capable of deception and will lie or try to trick humans if given a reason to.' 'We show examples of this in our paper, and we develop tools for monitoring and controlling the internal activity of AIs to prevent this from happening,' Hendrycks said. CLICK HERE FOR MORE US NEWS CAIS notes that modern AI systems have been notoriously difficult for humans to understand, which also makes it hard for users to understand AI decision-making. Those concerns have also been shared by Congress, with Senate Majority Leader Chuck Schumer, D-N.Y. calling AI explainability 'one of the most important and most difficult technical issues in all of AI' in remarks at the Center for Strategic & International Studies earlier this year. Hendrycks echoed those concerns, arguing an important aspect of the continued development of AI was to make sure humans have the tools to control the technology. CLICK HERE TO GET THE FOX NEWS APP 'We’re forming a sort of ‘internal surveillance’ for AI systems, ensuring they aren’t trying to trick us,' Hendrycks said. 'Deception in AI is a real concern, and our research is a key step towards providing tools to prevent these risks.'

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Title:

Content: Presented By
Guido Mieth—Getty Images
Ideas
Otto Barten is director of the Existential Risk Observatory, a nonprofit aiming to reduce existential risk by informing the public debate.
Joep Meindertsma is founder of PauseAI, a movement campaigning for an AI Pause.

The existential risks posed by artificial intelligence (AI) are now widely recognized.

After hundreds of industry and science leaders warned that “mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war,” the U.N. Secretary-General recently echoed their concern. So did the prime minister of the U.K., who is also investing 100 million pounds into AI safety research that is mostly meant to prevent existential risk. Other leaders are likely to follow in recognizing AI’s ultimate threat.

In the scientific field of existential risk, which studies the most likely causes of human extinction, AI is consistently ranked at the top of the list. In The Precipice, a book by Oxford existential risk researcher Toby Ord that aims to quantify human extinction risks, the likeliness of AI leading to human extinction exceeds that of climate change, pandemics, asteroid strikes, supervolcanoes, and nuclear war combined. One would expect that even for severe global problems, the risk that they lead to full human extinction is relatively small, and this is indeed true for most of the above risks. AI, however, may cause human extinction if only a few conditions are met. Among them is human-level AI, defined as an AI that can perform a broad range of cognitive tasks at least as well as we can. Studies outlining these ideas were previously known, but new AI breakthroughs have underlined their urgency: AI may be getting close to human level already.

Recursive self-improvement is one of the reasons why existential-risk academics think human-level AI is so dangerous. Because human-level AI could do almost all tasks at our level, and since doing AI research is one of those tasks, advanced AI should therefore be able to improve the state of AI. Constantly improving AI would create a positive feedback loop with no scientifically established limits: an intelligence explosion. The endpoint of this intelligence explosion could be a superintelligence: a godlike AI that outsmarts us the way humans often outsmart insects. We would be no match for it.

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A godlike, superintelligent AI

A superintelligent AI could therefore likely execute any goal it is given. Such a goal would be initially introduced by humans, but might come from a malicious actor, or not have been thought through carefully, or might get corrupted during training or deployment. If the resulting goal conflicts with what is in the best interest of humanity, a superintelligence would aim to execute it regardless. To do so, it could first hack large parts of the internet and then use any hardware connected to it. Or it could use its intelligence to construct narratives that are extremely convincing to us. Combined with hacked access to our social media timelines, it could create a fake reality on a massive scale. As Yuval Harari recently put it: “If we are not careful, we might be trapped behind a curtain of illusions, which we could not tear away—or even realise is there.” As a third option, after either legally making money or hacking our financial system, a superintelligence could simply pay us to perform any actions it needs from us. And these are just some of the strategies a superintelligent AI could use in order to achieve its goals. There are likely many more. Like playing chess against grandmaster Magnus Carlsen, we cannot predict the moves he will play, but we can predict the outcome: we lose.

But why would a superintelligence want to make humanity extinct? In fact, it might not explicitly want that at all. Humanity’s extinction might be a mere side effect of executing another goal to its extremity. When we are executing our own goals as humans, such as food production or growing our economy, we tend to not pay much attention to the effects this has on other species. This routinely leads to the extinction of animal species as a mere side effect of us maximising our goals. In a similar fashion, an uncontrollable superintelligence could lead to our extinction as a side effect of the AI pushing its goals, whatever they may be, to their limits.

Read More: The A to Z of Artificial Intelligence

So how can we avoid AI existential risk? Humanity now faces a stark choice: pause AI at a safe level, or continue development indefinitely. Unsurprisingly, the AI labs want the latter. To continue, however, means literally betting our lives on these labs being able to solve an open scientific problem called AI Alignment.

AI Alignment is the approach that leading labs such as OpenAI, DeepMind, and Anthropic are currently taking to prevent human extinction. AI Alignment does not attempt to control how powerful an AI gets, does not attempt to control what exactly the AI will be doing, and does not even necessarily attempt to prevent a potential takeover from happening. Since the labs anticipate they will not be able to control the superintelligent AI that they are creating, they accept that such an AI will act autonomously and unstoppably, but aim to make it act according to our values. As Richard Ngo, a leading alignment researcher working at OpenAI, puts it, “I doubt we’ll be able to make every single deployment environment secure against the full intellectual powers of a superintelligent AGI.” There are large problems with their alignment approach, however, both fundamentally and technically.

What values should an AI have?

Fundamentally, there is of course no agreement on what values we have. What OpenAI founder Sam Altman wants a superintelligence to do may well be completely different from what, say, a textile worker in Bangladesh wants it to do. Aggregating human preferences has always been a thorny issue even before AI, but will get even more complicated when we have a superintelligence around that might be able to quickly, completely, and globally implement the wishes of a handful of CEOs. Democracy, however imperfect, has been the best solution we can find for value aggregation so far. It is, however, unclear whether our current combination of national political systems can control a godlike AI. In fact, it is unclear whether any organisation or system would be up to the task.

Beyond disagreement over current values, humans have historically not been very good at predicting the future externalities of new technologies. The climate crisis, for one, can be seen as an unforeseen outcome of technologies such as steam and internal combustion engines. Ever more powerful technology has the potential to create ever larger externalities. It is impossible to foresee what the negative side effects will be of actually implementing some interpretation of humanity’s current values. They might make climate change pale in comparison.

There are more unsolved fundamental issues with aligning a superintelligence, but currently, AI companies don’t even get that far. It seems that even making current large neural networks reliably do what anyone wants is a technical problem that we currently cannot solve. This is called inner misalignment and has been observed already in current AI: the model pursues the wrong goal when it is released into the real world after being trained on a limited dataset. For a superintelligence, this could have a catastrophic outcome. This problem is, according to OpenAI’s recent document Governance of Superintelligence, an “open research question.” But not to worry: they say they are “putting a lot of effort” into it.

Read More: The AI Arms Race is Changing Everything

Even if individuals such as Altman care about the existential threats of superintelligence, the current market dynamics of competing AI labs do not incentivize safety. The GPT-4 model built by OpenAI was tested and fine-tuned for seven months before being made public with a detailed report about its safety. Contrary to the safety efforts of OpenAI, a panicked Google released its competing PaLM 2 model just months later, without any mention of similar safety tests. Now even open-source models like Orca are performing at a ChatGPT level, without any safety analysis. The race is frantic, pushing these companies to take more risks to stay ahead. The scope of these risks is gigantic. As Jaan Tallinn, investor of the major AI lab Anthropic, puts it: “I’ve not met anyone in AI labs who says the risk [from training a next-generation model] is less than 1% of blowing up the planet”.

Let us be clear: continuing risky AI development, and eventually undergoing the intelligence explosion that leads to an uncontrollable superintelligence, is a terrible idea. We will not be able to navigate it safely. We lack the technical solutions, the coordination, the insight, the wisdom, and the experience to do something this complex right on the first try. As humans, we are good at solving problems with trial-and-error processes where we get lots of tries. But in the case of an intelligence explosion, we cannot use this strategy, since the first trial of uncontrolled superintelligence could be disastrous.

Why we should pause AI development

So what is the alternative? An AI Pause. This would mean that governments prohibit training any AI model with capabilities likely to exceed GPT-4. This can be determined by examining the amount of computation required for training, corrected over time for improvements in algorithmic efficiency. These leading models can only be trained at large AI companies or data centers, making the enforcement of such a pause realistic.

Since the leading labs are currently located in the U.S. and the U.K., implementing a pause at a national level in those two countries can be done on very short notice. In the medium term, the competitive dynamics between nations will require international legislation and collaboration, where an international AI agency would help. The U.K. is already planning to host an AI safety summit in autumn, which is a fitting moment to sign a treaty that implements an international AI Pause. Excluding important countries, especially China, from such a summit might not be wise, since they will be needed as well in the medium term. For the longer term, hardware regulation—such as making sure new consumer hardware cannot be used for large-scale AI training, while closely monitoring all hardware that can be—is a promising option to practically enforce a pause. Research into this topic needs to be scaled up rapidly to arrive at robust and implementable solutions.

Further AI development may continue only after regulating authorities are convinced that training and deploying a certain AI model will not risk an uncontrolled intelligence explosion or a takeover scenario. Second-order effects, such as making hard-to-control open-source development easier, should be taken into account in such decisions.

Read More: What to Know About Claude 2, Anthropic’s Rival to ChatGPT

Continuing beyond the red line, and thus risking an intelligence explosion or a takeover, should only be allowed after meeting the strictest of requirements. First of all, the technical alignment problem should be decisively solved. Other major issues, such as aggregating humanity’s different value systems, limiting unforeseen externalities, and many more, will also need to be addressed.

A pause may be seen by some as unrealistic. They might say that we cannot stop technological progress for a speculative scenario, that market forces are too strong to counter, or that opposing countries can never coordinate. But bear in mind that as a society, we have barely begun to let the truth sink in: we are risking human extinction here. AI existential risk is still not taken as seriously as it should be. But that is changing. Leading AI academics, such as ‘fathers of AI’ Geoffrey Hinton and Yoshua Bengio, have sounded public warnings about AI’s existential risk, because they saw the technology getting more capable. This process is unlikely to stop: AI’s capabilities will grow further, and this will lead to more and more debate leaders sounding the alarm bells. When the full weight of our situation sinks in, measures that may appear unrealistic at present, could rapidly gain support.

As humanity, we will realise the situation that we are in. We will come together, and we will find a solution, as we always have. And, sooner than we think, we will implement an AI Pause.

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Contact us at letters@time.com.

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EDIT POST

What did Hinton, the renowned British-Canadian expert reveal about AI that caused widespread concern?

Exposed serious AI risks, shocking the tech world and affecting major company stocks.

What did Sam Altman admit about ChatGPT and the potential risks of AI?

Advanced AI could pose an existential risk to humanity.

What does Hinton warn could happen to AI if companies and regulators fail to implement effective rules and controls?

What did Hinton, the renowned British-Canadian expert reveal about AI that caused widespread concern?

Exposed serious AI risks, shocking the tech world and affecting major company stocks.

What did Sam Altman admit about ChatGPT and the potential risks of AI?

Advanced AI could pose an existential risk to humanity.

What does Hinton warn could happen to AI if companies and regulators fail to implement effective rules and controls?

Explainability techniques, such as model interpretability and transparency, are essential for building trust in AI systems and ensuring that they are used responsibly.

What does Hinton warn could happen to AI if companies and regulators fail to implement effective rules and controls?

What does Hinton warn could happen to AI if companies and regulators fail to implement effective rules and controls?

The use of explainability techniques can help to identify and mitigate potential biases in AI decision-making, ensuring that AI systems are fair and equitable.

Title:

Content: Check out all the on-demand sessions from the Intelligent Security Summit here.

It’s that time of year again, when artificial intelligence (AI) leaders, consultants and vendors look at enterprise trends and make their predictions. After a whirlwind 2022, it’s no easy task this time around.

You may not agree with every one of these — but in honor of 2023, these are 23 top AI and ML predictions experts think will be spot-on for the coming year:

1. AI will be at the core of connected ecosystems

“In 2023, we’re going to see more organizations start to move away from deploying siloed AI and ML applications that replicate human actions for highly specific purposes and begin building more connected ecosystems with AI at their core. This will enable organizations to take data from throughout the enterprise to strengthen machine learning models across applications, effectively creating learning systems that continually improve outcomes. For enterprises to be successful, they need to think about AI as a business multiplier, rather than simply an optimizer.”

—  Vinod Bidarkoppa, CTO of Sam’s Club and SVP of Walmart

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2. Generative AI will transform enterprise applications

“The hype about generative AI becomes reality in 2023. That’s because the foundations for true generative AI are finally in place, with software that can transform large language models and recommender systems into production applications that go beyond images to intelligently answer questions, create content and even spark discoveries. This new creative era will fuel massive advances in personalized customer service, drive new business models and pave the way for breakthroughs in healthcare.”

— Manuvir Das, senior vice president, enterprise computing, Nvidia

3. AI will completely transform security, risk and fraud

“We’re seeing AI and powerful data capabilities redefine the security models and capabilities for companies. Security practitioners and the industry as a whole will have much better tools and much faster information at their disposal, and they should be able to isolate security risks with much greater precision. They’ll also be using more marketing-like techniques to understand anomalous behavior and bad actions. In due time, we may very well see parties using AI to infiltrate systems, attempt to take over software assets through ransomware and take advantage of the cryptocurrency markets.”

Ashok Srivastava, senior vice president and chief data officer, Intuit

4. Open source ML tools will gain greater market share

“Next year teams that focus on ML operations, management and governance will have to do more with less. Because of this, businesses will adopt more off-the-shelf solutions because they are less expensive to produce, require less research time and can be customized to fit most needs. MLOps teams will also need to consider open-source infrastructure instead of getting locked into long-term contracts with cloud providers. Open source delivers flexible customization, cost savings and efficiency. Especially with teams shrinking across tech, this is becoming a much more viable option.”

— Moses Guttman, CEO, ClearML

5. Deep learning opportunities will boost demand for GPUs

“The biggest source of improvement in AI has been the deployment of deep learning — and especially transformer models — in training systems, which are meant to mimic the action of a brain’s neurons and the tasks of humans. These breakthroughs require tremendous compute power to analyze vast structured and unstructured datasets. Unlike CPUs, graphics processing units (GPUs) can support the parallel processing that deep learning workloads require. That means in 2023, as more applications founded on deep learning technology emerge to do everything from translating menus to curing disease, demand for GPUs will continue to soar.”

— Nick Elprin, CEO, Domino Data Lab

6. AI will create meaningful coaching experiences

“Modern AI technology is already being used to help managers, coaches and executives with real-time feedback to better interpret inflection, emotion and more, and provide recommendations on how to improve future interactions. The ability to interpret meaningful resonance as it happens is a level of coaching no human being can provide.”

— Zayd Enam, CEO, Cresta

7. Geopolitical shifts will slow AI adoption

“As fear and protectionism create barriers to data movement and processing locations, AI adoption will slow down. Macroeconomic instability, including rising energy costs and a looming recession, will hobble the advancement of AI initiatives as companies struggle just to keep the lights on.”

— Rich Potter, CEO, Peak

8. The role of AI and ML engineers will become mainstream

“Since model deployment, scaling AI across the enterprise, reducing time to insight and reducing time to value will become the key success criteria, AI/ML engineers will become critical in meeting these criteria. Today a lot of AI projects fail because they are not built to scale or [to] integrate with business workflows.”

— Nicolas Sekkaki, GM of applications, Data and AI, Kyndryl

9. Multi, hybrid-cloud MLOps and interoperability will be key

“As the AI/ML market continues to flood with new solutions, as evident by the volume of startups and VC capital deployed in the space, enterprises have found themselves with a collection of niche, disparate tools at their disposal. In 2023, enterprises will be more conscious of selecting solutions that will be more interoperable with the rest of their ecosystem, including their on-premises footprint and across cloud providers (AWS, Azure, GCP). Additionally, enterprises will gravitate towards a handful of leading solutions as the disparate tools mature and come together in bundles as standalone solutions.”

— Anay Nawathe, principal consultant, ISG

10. Advanced ML will enable no-code AI

“Advanced machine learning technologies will enable no-code developers to innovate and create applications never seen before. This evolution may pave the way for a new breed of development tools. In a likely scenario, application developers will ‘program the application’ by describing their intent, rather than describing the data and the logic as they’d do with low-code tools of today.”

— Esko Hannula, SVP of product management, Copado

11. With spending down, AI will shift to practical applications

“This past year was filled with incredibly impressive technological advancements, popularized by ChatGPT, DALL-E 2, Galactica and Facebook’s Make-A-Video. These massive models were made possible largely due to the availability of endless volumes of training data, and huge compute and infrastructure resources. Heading into 2023, funding for true blue-sky research will slow down as organizations become more conservative in spending to brace for the looming recession and will shift from investing in fundamental research to more practical applications. With more companies becoming increasingly frugal to mitigate this imminent threat, we can anticipate increased use of pre-trained models and more focus on applying the advancements from previous years to more concrete applications.”

—John Kane, head of signal processing and machine learning, Cogito

12. ChatGPT will change the contact center, but not the way you think

“Chatbots are the obvious application for ChatGPT, but they are probably not going to be the first ones. First, ChatGPT today can answer questions, but it cannot take actions. When a user contacts a brand, they sometimes just want answers, but often they want something done — process a return, or cancel an account, or transfer funds. Secondly, when used to answer questions, ChatGPT can answer based on knowledge [found] on the internet. But it doesn’t have access to knowledge which is not online. Finally, ChatGPT excels at generation of text, creating new content derived from existing online information. When a user contacts a brand, they don’t want creative output — they want immediate actions. All of these issues will get addressed, but it does mean that the first use case is probably not chatbots.”

— Jonathan Rosenberg, CTO, Five9

13. AI will drive the future of customer experience

“Digital engagement has become the default rather than the fallback, and every interaction counts. While the emergence of automation initially resolved basic FAQs, it’s now providing more advanced capabilities: personalizing interactions based on customer intent, empowering people to take action and self-serve, and making predictions on their next best action.

“The only way for businesses to scale a VIP digital experience for everyone is with an AI-driven automation solution. This will become a C-level priority for brands in 2023, as they determine how to evolve from a primarily live agent-based interaction model to one that can be primarily serviced through automated interactions. AI will be necessary to scale operations and properly understand and respond to what customers are saying, so brands can learn what their customers want and plan accordingly.”

— Jessica Popp, CTO of Ada

14. AI model marketplaces will emerge

“Coming soon are industry-specific AI model marketplaces that enable businesses to easily consume and integrate AI models in their business without having to create and manage the model lifecycle. Businesses will simply subscribe to an AI model store. Think of the Apple Music store or Spotify for AI models broken down by industry and data they process.”

— Bryan Harris, executive vice president and chief technology officer, SAS

15. Explainability will create more trustworthy AI

“As individuals continue to worry about how businesses and employers will use AI and machine learning technology, it will become more important than ever for companies to provide transparency into how their AI is applied to worker and finance data. Explainable AI will increasingly help to advance enterprise AI adoption by establishing greater trust. More providers will start to disclose how their machine learning models lead to their outputs (e.g. recommendations) and predictions, and we’ll see this expand even further to the individual user level with explainability built right into the application being used.”

— Jim Stratton, CTO, Workday

16. 2023 will be a major year for federated learning

“Federated learning is a machine learning technique that can be used to train machine learning models at the location of data sources, by only communicating the trained models from individual data sources to reach a consensus for a global model. Therefore instead of using the traditional approach of collecting data from multiple sources to a centralized location for model training, this technique learns a collaborative model. Federated learning addresses some of the major issues that prevail in the current machine learning technique, such as data privacy, data security, data access rights and access to data from heterogeneous sources.”

— David Murray, chief business officer, Devron

17. NLP plus object recognition will take search to the next level

“While most people write scrapers today to get data off of websites, natural language processing (NLP) progress has been made where soon you can describe in natural language what you want to extract from a given web page and the machine pulls it for you. For example, you could say, “Search this travel site for all the flights from San Francisco to Boston and put all of them in a spreadsheet, along with price, airline, time and day of travel.” It’s a hard problem, but we could actually solve it in the next year.”

— Varun Ganapathi, CTO and co-founder, AKASA

18. Advances are coming in real-time speech translation

“With remote work, boundaries are becoming increasingly blurred. Today it’s common for people to work and converse with colleagues across borders, even if they don’t share a common language. Manual translation can become a hindrance that slows down productivity and innovation. We now have the technology to use communication tools such as Zoom that allows someone in Turkey, for example, to speak their native language but allows someone in the U.S. to hear what they’re saying in English. This real-time speech translation ultimately helps with efficiency and productivity while also giving businesses more of an opportunity to operate globally.”

— Manoj Chaudhary, CTO and SVP of engineering, Jitterbit

19. AI-enabled phishing will grow

“By now, everyone has seen AI-created deepfake videos. They are leveraged for a variety of purposes, ranging from reanimating a lost loved one, disseminating political propaganda or enhancing a marketing campaign. However, imagine receiving a phishing email with a deepfake video of your CEO instructing you to go to a malicious URL. Or an attacker constructing more believable, legitimate-seeming phishing emails by using AI to better mimic corporate communications. Modern AI capabilities could completely blur the lines between legitimate and malicious emails, websites, company communications and videos. Cybercrime AI-as-a-Service could be the next monetized tactic.”

Heather Gantt-Evans, CISO, SailPoint

20. Companies will turn to a hybrid approach to NLP

“In the year ahead, we will see enterprises turn to a hybrid approach to natural language processing combining symbolic AI with ML, which has shown to produce explainable, scalable and more accurate results while leaving a smaller carbon footprint. Companies will expand automation to more complex processes, requiring accurate understanding of documents, and extending their data analytics activities to include data embedded in text and documents. Therefore, investments in AI-based natural language technologies will grow. These solutions will have to be accurate, efficient, environmentally sustainable, explainable and not subject to bias. This requires enterprises to abandon the single-technique approach such as just machine learning (ML) or deep learning (DL) for their intrinsic limitations.”

— Luca Scagliarini, chief product officer, Expert.ai

21. AI-generated music will see advancements

“Advancements in AI-generated music will be a particularly interesting development. Now [that] tools exist that generate visual art from text prompts, these same tools will be improved to do the same for music. There are already models available that use text prompts to generate music and realistic human voices. Once these models start performing well enough that the public takes notice, progress in the field of generative audio will accelerate even further. It’s not unreasonable to think, within the next few years, that AI-generated music videos could become reality, with AI-generated video, music and vocals.”

— Ulrik Stig Hansen, president, Encord

22. AI investments will move to fully-productized applications

“There will be less investment within Fortune 500 organizations allocated to internal ML and data science teams to build solutions from the ground up. It will be replaced with investments in fully productized applications or platform interfaces to deliver the desired data analytic and customer experience outcomes in focus. [That’s because] in the next five years, nearly every application will be powered by LLM-based neural network-powered data pipelines to help classify, enrich, interpret and serve.

“[But] productization of neural network technology is one of the hardest tasks in the computer science field right now. It is an incredibly fast-moving space that without dedicated focus and exposure to many different types of data and use cases, it will be hard for internal-solution ML teams to excel at leveraging these technologies.”

— Amr Awadallah, CEO, Vectara

23. AI will empower more efficient devops

“When it comes to devops, experts are confident that AI is not going to replace jobs; rather, it will empower developers and testers to work more efficiently. AI integration is augmenting people and empowering exploratory testers to find more bugs and issues upfront, streamlining the process from development to deployment. In 2023, we’ll see already-lean teams working more efficiently and with less risk as AI continues to be implemented throughout the development cycle.

“Specifically, AI-augmentation will help inform decision-making processes for devops teams by finding patterns and pointing out outliers, allowing applications to continuously ‘self-heal’ and freeing up time for teams to focus their brain power on the tasks that developers actually want to do and that are more strategically important to the organization.”

– Kevin Thompson, CEO, Tricentis

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What does Hinton warn could happen to AI if companies and regulators fail to implement effective rules and controls?

What does Hinton warn could happen to AI if companies and regulators fail to implement effective rules and controls?

What does Hinton warn could happen to AI if companies and regulators fail to implement effective rules and controls?

The integration of explainability techniques into AI development can help to build public trust in AI systems and ensure that they are used for the benefit of society.

Regulation is evolving in different pathways around the world, with policymakers addressing issues, gaps, and limitations of AI-driven decisions.

Global spending on AI is expected to increase from $150 billion in 2023 to $300 billion by 2026, highlighting the need for effective AI governance.

The development of AI systems that understand causality and context is crucial for aligning AI with human values, which is a key aspect of explainability.

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Understanding The Limitations Of Generative AI

Gonçalo Ribeiro is CEO of YData.

Generative artificial intelligence (GenAI) is a branch of AI that focuses on creating new data instead of making predictions or classifications. GenAI works for any kind of data, from realistic images to complex texts (as we're seeing everywhere now) to videos known as deepfakes or datasets and databases known as synthetic data.

GenAI has become one of the most discussed technologies today. However, despite its potential, this technology holds fundamental limitations and risks that often go unaddressed in the mainstream hype, which is what I'll be focusing on today.

Non-Determinism

One of the inherent characteristics of GenAI is its non-determinism. This means the AI can produce different outputs even when given the same input multiple times, leading to unpredictability in its results.

In most industries, from healthcare to banking, reliability is paramount. This unpredictability can be a serious drawback. Humans generally prefer predictability, especially in critical applications—knowing what to expect from technology engenders trust and reliability, qualities that non-deterministic AI systems may struggle to guarantee.

Hallucinations

Large GenAI models, such as those used in creating text or images (large language models or LLMs and foundational models or FMs), are trained on vast datasets and often scraped from the internet. This training can inadvertently lead these models to 'hallucinate,' posing significant risks as 'hallucinations' are convincingly presented as truths.

LLMs can also reproduce sensitive or proprietary information when using approaches such as retrieval augmented generation (RAG) and/or fine-tuning, posing privacy and security risks. This can be particularly problematic in areas like news dissemination, education, healthcare and legal advice where accuracy is crucial.

Limited Use Cases

Despite their impressive output capabilities, GenAI applications are limited in their ability to tackle complex, multi-dimensional societal issues. They excel in defined, narrow tasks but lack the general understanding needed to address broader challenges such as strategic decision-making or ethical dilemmas. This highlights a significant gap between the capabilities of current AI technologies and the requirements for solving real-world problems that involve high stakes or deep contextual understanding.

Beyond Artificial General Intelligence

While some believe that we're close to artificial general intelligence (AGI)—an AI that can understands, learns and applies knowledge across a broad range of tasks as well as a human—I believe we're far from it.

Yann LeCun, a pioneering figure in AI and Meta's chief AI scientist, has voiced concerns about the limitations of current AI systems. He points out that while AI can mimic complex patterns, it lacks the intuitive understanding of the world that humans possess, which is crucial for making morally and socially responsible decisions. His work emphasizes the need for AI systems that understand causality and context, not just correlation.

Instead of the broad and often speculative aim of AGI, focusing on specific, achievable objectives tailored to distinct needs ensures that AI development is both grounded and directly beneficial. This strategy not only mitigates the risks associated with non-determinism and data privacy but also maximizes the utility and impact of AI across various sectors.

By adopting use-case-specific AI, we can propel technology forward in a manner that is not just innovative but also aligned with the nuanced demands of human values and ethical considerations. Of course, this is not technology that is already available and there are plenty of challenges and barriers.

Real-World Understanding Through Data

One of the primary challenges in developing AI that truly understands and interacts with the real world is the need for comprehensive, multi-domain data. Effective AI requires training on diverse datasets that reflect the complexity and variability of real-world environments. However, collecting such data is challenging, and ensuring it is representative and unbiased is difficult for humans to validate manually. While solutions such as synthetic data are being explored, we're still far from achieving this.

Human-To-Machine Interfaces

The human-to-machine interface is critical in making AI accessible and useful for a wide range of applications. LLMs have significantly advanced these interfaces, but the inherent subjectivity and ambiguity of language still present unique challenges, from contextual understanding to feedback mechanisms and much more. Life is not engineered—it just happens.

Addressing several goals or use cases simultaneously involves complex engineering and infrastructure challenges. The variety and quantity of use cases will require a lot of resource allocation and scaling (or maybe different hardware) as well as a modular AI architecture.

This enhanced focus holds the promise of creating AI technologies that are not only effective in performing tasks but are also trustworthy and safe—characteristics that will define the next generation of AI systems as true partners in progress.

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The next generation of AI systems will be defined by their ability to be effective, trustworthy, and safe, which can be achieved through the development of explainable AI models.

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Content: Dynamic Causal Modeling for Fast Explainable AI

By Dr. William Jones Jones

Head of AI and Machine Learning

Embecosm

July 05, 2023

Blog

Dynamic Causal Modeling (DCM) is a key explainable AI technology that has recently been released as a commercial grade, free and open source software package, dcEmb.

DCM is the technology behind one of the UKs leading COVID-19 models, as well as a large swathe of cutting edge neuroscientific research across the world. Limited until recently to Academia, the technology has the potential to solve several key problems in industry.

AI and Machine Learning are becoming increasingly integrated with many industries. At the same time, AI and Machine Learning solutions are also becoming larger and more complicated. For many of us this presents a problem; with this increasing integration of AI with what we do, there is mounting pressure for us to understand how our AI operates, and to be able to prove that it functions in a safe, trustworthy, and accountable way. DCM is one technique that does not suffer from these issues, with trust and accountability as intrinsic qualities of a system whose learning process is explainable and human understandable end-to-end. In neuroimaging applications, for example, DCM is prominently able to describe highly complex evolving brain activity in a human understandable way in terms of the dynamic relationships between different brain regions.

DCM explains complex evolving brain activity (right) by tying the observed FMRI brain activity to dynamic interactions between brain regions (left).

An often underappreciated aspect of explainability is the ability to deal with uncertainty. In many applications, making predictions without the context of how likely these predictions are to manifest makes risk-based planning very difficult. In the case of AI based COVID-19 modeling, for example, many AI algorithms were created that predicted the number of COVID-19 deaths. However, without the context of likelihood, these predictions are meaningless. AI algorithms that cannot quantify uncertainty around their predictions to make statements like “there is a 95% likelihood that we will need between 200-300 deaths this month” are of very limited use to civic planners. DCM is once again one of the leading technologies in this area, providing uncertainty quantification as a core part of its functionality. Combined with its intrinsic explainability, the COVID-19 DCM model was able to make useful, probability bounded predictions on a wide range of use COVID-19 quantities, including the number of ventilated patients, and the number of required hospital beds.

The COVID-19 Dynamic Causal Model is able to predict useful estimates of many important quantities, and describe their likelihood due to its qualities as an intrinsic explainable and uncertainty aware technology.

Meeting the increasing standards of explainability in industry is a very current challenge for many of us. It is also, sometimes, an opportunity. As AI models become more human understandable, they lend themselves more to being improved. When solving problems with AI algorithms we, the designers, will often have pre-existing knowledge of the problem we’re solving. The more of this we can integrate into our AI, the more focused and effective the AI model will be. The original application of DCM, neuroimaging, is an excellent example of this. Neuroimaging data is often extremely expensive to collect, and of very high complexity. Without any ability to interpret the data in the context of how we already understand how the brain works, it is extremely challenging to work with. DCM is, unsurprisingly, very strong in this area, and it is through the integration of prior knowledge that we are able to solve many problems.

Previous versions of the DCM software have been written and maintained as academic research code in MATLAB. The dcEmb package represents the first commercial grade implementation of the software. Key features include:

  • Configurable design suited to a wide range of problems, outside of COVID-19 or neuroimaging.
  • A C++ codebase using specialized linear algebra libraries for high performance. This includes scaling on high performance computing(HPC) systems.
  • Extensive documentation, making the software readily accessible to a skilled professional engineer.
  • An open source codebase with an extensive testsuite to ensure commercial robustness, transparency, and auditability.
  • A future release will include a set of bindings to support the Python community.

The dcEmb C++ implementation is substantially faster than the MATLAB baseline, making better use of available resources.

As well as the original COVID-19 and neuroimaging models, we have used dcEmb to implement carbon climate models, and some interesting physics models. dcEmb is currently a free and open source software package available on GitHub. If you wish to know more about the software, visit the Embecosm website, or get in touch.

Dr. Jones has a research background in computational neuroscience, focused on the areas of consciousness, cognition, and meta-cognition. In his current role, Dr. Jones heads AI and Machine Learning efforts at Embecosm, a consultancy that solves problems in challenging areas of open source, with a focus on compilers and tool chains. His current work is focused on Bayesian methods and statistical computing.

More from Dr. William Jones

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Content: Oracle’s Patent Could Break AI’s Black Box

Håkan Dahlström under CC BY 2.0
Tracking Big Tech through the lens of patent filings.

Oracle wants its neural networks to show their work.

The data company filed a patent application for “human-understandable insights” for neural network predictions. Basically, Oracle’s system aims to help users of AI models understand how they arrived at their answers.

Oracle says current AI models don’t offer users an adequate explanation of how they make their predictions, even if the probability of a correct answer is very high. “A customer may not trust the results generated by the neural network if the customer does not understand what factors the neural network is considering when making a particular prediction,” Oracle noted.

To break it down: Oracle’s system identifies what it calls “human-understandable attributes” that contributed to a neural network’s predictions. It does this by mapping each of the attributes that go into an AI model’s decision-making process, and assigning a “confidence score” to each of them based on whether or not an average person could understand it.

For example, an average user wouldn’t typically understand the mathematical formulas that go into an AI model deciding whether or not a credit card transaction is fraudulent. However, this system weeds out the more technical aspects of an AI’s decision-making when explaining its answers, leaving behind the attributes that a human would understand, such as source, dollar amount, timing, or recipient of a transaction.

This process, Oracle noted, essentially breaks down the “blackbox” of typical machine learning models and neural networks, which “suffer from a lack of explainability.”

As AI continues to weave into every facet of our lives, trusting the answers that AI models come up with is extremely important, said Brian P. Green, director of technology ethics at the Markkula Center for Applied Ethics at Santa Clara University. AI being able to explain itself not only helps its users trust it more, but helps its operators know if it starts to go haywire.

“If we’re assigning a tool that we don’t fully understand to a job that humans aren’t particularly good at, then we really need to be able to trust it, to make sure we’re … not getting something that is contaminated with bias or is otherwise not as accurate as we would like it to be,” said Green.

But as AI adoption grows, the models we use only grow more complex, Green noted. Even as more AI explainability tools like Oracle’s come to fruition, more complex models become far more difficult to explain, even to those that understand tech, let alone the average, non-technical user.

“It’s kind of an open question, how explainability is going to really translate to normal human beings, when the complexity of the system itself is going to get more and more difficult,” said Green. “Who knows, are even our best machine learning people going to be able to understand this? Or are they eventually even going to beleft behind?”

Oracle’s patent also represents just one option for explainable AI tools. Plenty of other companies offer toolkits like this, said Green: IBM sells its AI Explainability 360, several free tools for this exist on GitHub, and PayPal is even seeking to patent its own AI explainability tool. Even if it does manage to secure this patent, the market itself only grows more crowded, which doesn’t look good for Oracle as the company continues to fall behind in the AI race.

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Explainability techniques, such as Dynamic Causal Modeling (DCM), provide intrinsic explainability and uncertainty quantification, allowing for probability-bounded predictions and descriptions of complex evolving systems in a human-understandable way.

The ability to deal with uncertainty is an often underappreciated aspect of explainability, and DCM is one of the leading technologies in this area.

Companies like Oracle, IBM, and PayPal are working on explainability techniques, highlighting the growing importance of explainability in AI development.

Explainability techniques will become increasingly important as AI adoption grows, and companies will need to prioritize transparency and trust in their AI systems.