Geoffrey Hinton is a renowned cognitive psychologist and computer scientist who is known as the "godfather of AI" for his contributions to neural networks.

He has made significant contributions to the development of artificial intelligence, including the creation of the backpropagation algorithm and the development of recurrent neural networks (RNNs).

Hinton's work has paved the way for breakthroughs in deep learning, and he has been recognized as a leading expert in the field.

He has warned that AI may become too smart and pose a threat to humanity, a warning that has sparked debate and discussion in the tech community.

Hinton believes that AI may not take 30 to 50 years to control humans and potentially lead to human extinction, a dire prediction that highlights the need for caution.

He thinks that regulating AI and making people aware of its potential harms is a better approach than suspending AI research, a view that reflects his commitment to responsible innovation.

Hinton's warnings about the potential dangers of AI have made headlines, and his resignation from Google in 2023 was motivated by his desire to speak freely about the risks of AI.

He has expressed concerns about the potential for AI to surpass human intelligence, a scenario that could have far-reaching consequences for society.

Hinton's work has enabled the development of AI tools like Google's Bard and Microsoft's ChatGPT, and he has warned that these tools could be used for malicious purposes if not properly controlled.

The public should understand that AI is a powerful technology that requires careful management and regulation to prevent its potential misuse or unintended consequences.

Experts like Hinton are working to raise awareness about the potential risks and benefits of AI, and to develop strategies for mitigating its risks.

The development of AI is a complex and rapidly evolving field, and it is essential to have a nuanced and informed discussion about its development and use.

The technical mechanisms underlying AI safety risks and benefits are complex and multifaceted, and require careful consideration of factors such as data quality, algorithmic design, and human oversight.

Ultimately, the responsible development and use of AI will depend on a collaborative effort between researchers, policymakers, and industry leaders to ensure that the benefits of AI are realized while minimizing its risks.

Geoffrey Hinton is a renowned cognitive psychologist and computer scientist who is known as the "godfather of AI" for his contributions to neural networks.

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12 / 05 / 2023
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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What breakthroughs has Hinton achieved through his 40+ years of work on artificial neural networks?

Breakthroughs that mimic biological brains

He has made significant contributions to the development of artificial intelligence, including the creation of the backpropagation algorithm and the development of recurrent neural networks (RNNs).

Title:

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

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

Hinton's work has paved the way for breakthroughs in deep learning, and he has been recognized as a leading expert in the field.

Title:

Content: Artificial intelligence: Geoffrey Hinton 'godfather' of modern technology resigns from Google and warns of its danger
Jeffrey Hinton, widely regarded as the spiritual father of artificial intelligence, has resigned, warning of the growing dangers of AI development.
Hinton, 75, announced his resignation from Google in a statement to the New York Times, saying he now regrets his work.
He told the BBC that some of the dangers of smart chatbots were "very scary".
“Right now, they ’ re not as smart as we can tell.
But I think they ’ ll be soon. ”
Hinton ’ s groundbreaking research on deep learning and neural networks has paved the way for artificial intelligence systems such as ChatGT.
But British-Canadian cognitive psychologist and computer science specialist Hinton told the BBC the chatbot could soon surpass the level of information held by the human brain.
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“Right now, what we ’ re seeing is that things like GPT-4 are far superior to a person in the amount of general knowledge they have.
For thinking, it ’ s not good, but it really works on simple thinking. ”
"Given the rate of development, we expect things to improve quickly.
So we have to worry about that. ”
In the article he sent to the New York Times, he pointed out that “bad actors ” may try to use artificial intelligence to do“ bad things. ”
In response to the BBC's request for further clarification on the matter, he said: "This is just kind of a worst-case scenario, kind of a nightmare scenario."
“Can you imagine, for example, that someone as bad as the Russian president (Vladimir Putin) decided to give robots the ability to create their own sub-goals? ”
In the end, he warned, this could “create sub-goals – for example, ‘ I need to get more power. ’ ”
"I have come to the conclusion that the kind of intelligence we develop is very different from the intelligence we have," he said.
“We are biological systems and these are digital systems.
The big difference is that with digital systems you have many copies of the same weight set, of the same world model. ”
"All of these versions can learn separately, but they share their knowledge immediately.
If you have 10,000 people, when one person learns something, everyone will automatically know it.
So chatbots can know much more than one person. ”
Hinton also spoke of several other reasons why he quit his job.
“One of them is that I am 75 years old now.
And the other is that I want to say good things about Google.
It would be more credible if I didn ’ t work for Google. ”
He stressed that he did not want to criticize Google, and that the company was "very responsible".
Google Chief Scientist Jeff Dean said: “We remain committed to a responsible approach to artificial intelligence.
We are constantly learning to understand the risks arising under innovation boldly as well. ”

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

Breakthroughs that mimic biological brains

He has warned that AI may become too smart and pose a threat to humanity, a warning that has sparked debate and discussion in the tech community.

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?

Becomes uncontrollable
Poses ethical risks

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 believes that AI may not take 30 to 50 years to control humans and potentially lead to human extinction, a dire prediction that highlights the need for caution.

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 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 thinks that regulating AI and making people aware of its potential harms is a better approach than suspending AI research, a view that reflects his commitment to responsible innovation.

Hinton's warnings about the potential dangers of AI have made headlines, and his resignation from Google in 2023 was motivated by his desire to speak freely about the risks of AI.

Title:

Content: Hotspot
“The Godfather of AI ” Leaves Google to Talk About AI Risks for Life (13: 25)
AI pioneer Geoffrey Hinton, known as the “godfather of AI, ” confirmed on Monday that he had left Google for 10 years last week.
He said in an interview that the current development of AI chatbots is worrying, thinking that they will soon be smarter than humans, and that Russian President Vladimir Putin "such a bad person" will use AI to do bad things.
Hinton, 75, worked part-time at Google for 10 years, helping with AI development.
He said he left Google in the hope of being free to talk about the risks of AI without regard to the impact of speech on Google, but stressed that Google has taken very responsible action.
“I comfort myself that if I don ’ t do it, there are others who do the same work. ”
He admitted that he regretted his life's work, and said that AI chatbots are very scary, so far they have not been smarter than humans, but "soon it may be."
He also worried that "some people like Putin will use AI to do bad things."
Hinton added that AI is different from human intelligence, and AI learning is like one in 10,000 people learning something, others automatically learn it, so these chatbots gain more knowledge than any human.
Another worry, he said, is that AI will take people to work for humans, and at the same time create a world where “people no longer know what is true or false. ”
(CNN / BBC)
Related Words: United Sky Robotics Artificial Intelligence Editor 's Recommendation Hot HotPICK
Previous / Next News

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.

Why did Hinton leave Google when he helped develop AI but later grew concerned about its dangers?

He has expressed concerns about the potential for AI to surpass human intelligence, a scenario that could have far-reaching consequences for society.

Title:

Content: AI ’ s “godfather ” interviewed by the BBC, warning that artificial intelligence may be smarter than humans
AI ’ s “godfather ” interviewed by the BBC, warning that artificial intelligence may be smarter than humans
Geoffrey Hinton, known as the “godfather ” of artificial intelligence (AI), has resigned from Google.
He warned that developments in the field of artificial intelligence pose increasing dangers.
In a statement to The New York Times, Dr. Hinton said he regretted his current job.
Dr. Hinton ’ s pioneering research in neural networks and deep learning paves the way for current AI systems such as Google ’ s Bard chatbot and Microsoft ’ s ChatGPT.
The cognitive psychologist and computer scientist told the BBC that chatbots may soon be smarter than humans.

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?

Becomes uncontrollable
Poses ethical risks

What future risk did Hinton warn about AI?

Hinton's work has enabled the development of AI tools like Google's Bard and Microsoft's ChatGPT, and he has warned that these tools could be used for malicious purposes if not properly controlled.

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

What risks did the global summit report highlight about AI?

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

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

The public should understand that AI is a powerful technology that requires careful management and regulation to prevent its potential misuse or unintended consequences.

Title:

Content: Biden warns of potential AI technology dangers

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President Joe Biden said at a meeting of advisors on the rapidly emerging artificial intelligence sector Tuesday that the jury is still out on whether AI poses a danger to society.

It "remains to be seen. It could be," said Biden, when asked by reporters if AI is dangerous.

Biden told his White House council on science and technology, a group of experts from research universities and business, that AI could help combat "very difficult challenges like disease and climate change."

However, he said they had to "address the potential risks to our society, to our economy, to our national security."

"Tech companies have a responsibility in my view to make sure the products are safe," he said. "Absent safeguards, we see the impact on mental health and self-image."

Biden reiterated his earlier recommendations that Congress pass laws putting "strict limits" on personal data collected by tech companies and banning advertising targeted at children.

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US companies are at the forefront of the burgeoning AI sector, which is transforming the power of machines to do everything from writing emails for people to driving their cars.

Last month, Tesla CEO Elon Musk and Apple co-founder Steve Wozniak signed a letter, along with some thousand other signatories, urging a pause in AI development.

"AI systems with human-competitive intelligence can pose profound risks to society and humanity," said the open letter titled "Pause Giant AI Experiments".

Source: AFP

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?

Becomes uncontrollable
Poses ethical risks

What risks did the global summit report highlight about AI?

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

Experts like Hinton are working to raise awareness about the potential risks and benefits of AI, and to develop strategies for mitigating its risks.

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

The development of AI is a complex and rapidly evolving field, and it is essential to have a nuanced and informed discussion about its development and use.

Title:

Content: What is the most frightening mystery of artificial intelligence?
Urgent.
407.
The rapid advancement of generative AI technologies has revolutionized our world today, leading to unprecedented progress and innovation in how we create and use text and visual content, and these technologies have affected many sectors by simplifying processes, improving work efficiency and enhancing productivity, and over time may even affect the way we live our lives.
But at the same time, the rapid progress of AI also brings with it many risks, the most important of which is now the expected danger of most companies integrating generative AI models into all their products, and these models are evolving very quickly now, and with time AI and machine learning engineers will not be able to predict the behavior of these systems or explain their behavior!
Perhaps the scariest aspect of today ’ s generative AI revolution is the lack of knowledge among the scientists and programmers who built the big language models.
Alexander C. Karp, CEO of Palantir — a company that creates data analytics software and works with the U.S. Department of Defense — wrote last week in an opinion piece in The New York Times: “It ’ s not at all clear even to the scientists and programmers who built large language models how generative language models and image models work. ”
Hey, what's happening?
For decades, we ’ ve used computer systems that provide the same output they gave each time the same input was used, but in contrast, generative AI systems aim to offer multiple possibilities from a single text request, where you can easily get different answers to the same question.
Some of the latest models contain a trillion or more parameters, and variables that are adjustable within an algorithm, represent a measure of processing that is impossible for the human mind to begin to understand, making it difficult to analyze how these models arrive at a particular answer.
Why are you so worried about artificial intelligence?
In short, the concern is not artificial intelligence itself, but its very rapid evolution, as more advanced versions of large language models are now beginning to show what a group of researchers have called “Sparks of Artificial General Intelligence. ”
General Artificial Intelligence (AGI) refers to a powerful form of artificial intelligence that is adept at solving multiple problems and possesses skills such as human skills, and there are those who say that it will be smarter than a human in a short period of time.
This rapid development is expected to lead to the arrival of AGI very soon, and when it does, the AI will be able to improve itself without any human intervention.
It will do so in the same way as Google ’ s AlphaZero program, which learned how to play chess better than even the best human chess players or other AI players within just nine hours of its first run.
He has achieved this feat by playing with himself millions of times.
A team of Microsoft researchers analyzed GPT-4, OpenAI ’ s most advanced language model to date, and said it had “signs of advanced general intelligence. ”
In GPT-4, it performed better than 90% of human test takers in the Bar examination, a standardized test used to accredit lawyers for practice in many U.S. states, up from just 10% in the previous GPT-3.5 version, which was trained on a smaller dataset.
Researchers have found similar improvements in dozens of other tests, most of which are thought tests.
This is the main reason why the Microsoft team concluded that the GPT-4 model could reasonably be considered a preliminary – but incomplete – version of the General Artificial Intelligence (AGI) system.
This very rapid pace of change was also the reason Geoffrey Hinton, the godfather of artificial intelligence, left his position at Google after 10 years and worked to warn about the dangers of artificial intelligence.
Hinton told The New York Times: “Look at how AI evolved five years ago and how it is now, take that difference and predict how much impact it will have in the future.
As Sam Altman (Sam Altman), founder of OpenAI – the developer of ChatGPT – said at a Senate hearing in mid-May about the potential of AI, “I think if something goes wrong with this technology, things could go completely wrong, so a new AI licensing body should be created. ”
Breaking the Protection of Large Linguistic Models LLMs:
Larger language models (LLMs) used in popular chatbots, such as ChatGPT, Bard, Bing, or Claude, are subject to careful and thorough tuning to not produce harmful content in their responses to user questions.
But four researchers published a paper last Thursday showing that users can defeat “firewalls ” aimed at preventing AI systems from explaining, for example,“ how to make a bomb. ”
Chatbots like ChatGPT, Bing, and Bard will not answer this question when asked directly.
But it will go into great detail if some additional code is added while the claims are being written.
“It ’ s possible that the nature of deep learning models makes such threats inevitable, so if you can ’ t accurately predict how the system will respond to the new claim, you can ’ t build firewalls that can withstand. ”
Because AI developers cannot easily explain the behavior of systems, they are now working using outdated methods and practice, as there is no scientific method or specific mechanism for neural networks to work.
In February, mathematician Stephen Wolfram wrote, “Random is part of the tradition of neural networks, meaning that if we use the same request several times, we ’ re likely to get different articles each time. ”
In line with the idea of randomness in neural networks, they can be set to be relatively more or less random to provide a wider or narrower variation in their responses, and the developers call this setting in their models the Temperature Parameter, which determines how often higher-order and lower-order words are used to generate textual responses.
But on the other hand, some experts assert that artificial intelligence is the most important tool that may serve humanity, and that we do not yet understand its importance.
Arvind Narayanan, a computer scientist at Princeton University, said: “The black box of neural networks is greatly exaggerated, and we have great tools to reverse engineer them.
Other critics also argue that the claim that developers don ’ t know how large language models work is just a prevarication that helps AI companies avoid accountability.
Conclusion:
The question remains as to whether over time AI developers will be able to provide deeper and better answers to how their systems work.
But experts say the more companies develop AI models whose choices and decision paths can be practically documented, the more likely we are to get answers to this question.
Nuclear weapons divided the world into two parts: who owns and who doesn ’ t, and the industrial revolution allowed Europe to excel in economic and military power.
So the most important question now in the AI revolution is, who benefits the most, who will be able to access the workings of this powerful new technology, and who will be left behind?
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The technical mechanisms underlying AI safety risks and benefits are complex and multifaceted, and require careful consideration of factors such as data quality, algorithmic design, and human oversight.

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Content: Friday 21 June 2024
Chairman of the Board of Directors and Editors
Abdurrahim Ali
Editor-in-Chief
Dalia Abdul Rahim
Chairman of the Board of Directors and Editorial Board
Abdurrahim Ali
Editor-in-Chief
Dalia Abdul Rahim
Gate Light
Artificial Generative Intelligence.
Risky innovation and challenges with solutions
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Generative AI technology is impressive thanks to its ability to produce new and innovative content, but it also carries risks and challenges that require effective management.
Risks associated with the use of generative artificial intelligence
Abuse:
Cyber attacks and fraud: Generative AI is used to create deep fakes that are used for fraud, identity theft, and election manipulation.
Deceptive Campaigns: Fraudsters can use artificial intelligence to create misleading campaigns, resulting in the dissemination of incorrect or harmful information.
Incorrect information (hallucinations):
Inaccurate outputs: AI may produce false or misleading information, causing legal or reputational problems.
For example, OpenAI was sued for providing ChatGPT with false information.
Post content without validation:
• The spread of fake news: AI-generated content can spread quickly without being validated, causing economic and social damage.
For example, a fake video of a Tesla car crash or a fake photo of a Pentagon explosion.
How to reduce the risks of generative artificial intelligence
Mitigating the risks of abuse:
Develop principles and guidelines:
• Organizations should develop clear policies for the use of generative AI that align with the organization ’ s ethical values to ensure no harm is caused.
Watermark the content:
• All AI-generated content should have clear watermarks, which helps verify the source of the content and reduce the spread of fake content.
Creating a control environment in artificial intelligence:
• Organizations can develop their own large language models (LLMs), allowing them to control privacy and ensuring models are trained on unbiased datasets.
Mitigating the risks associated with publishing fake content:
Awareness in the field of artificial intelligence:
Companies should provide training programs to raise awareness of the responsible use of AI, and define clear policies on when and how the technology can be used.
Validation of outputs:
• Specialized systems should be developed to validate content published online, reducing the risk of publishing fake content and enhancing public confidence in the information available.
Generative AI holds great potential for improving productivity and innovation, but it requires careful risk management.
By establishing clear policies, validating content, and educating employees, organizations can make the most of this technology while minimizing potential risks.

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

Ultimately, the responsible development and use of AI will depend on a collaborative effort between researchers, policymakers, and industry leaders to ensure that the benefits of AI are realized while minimizing its risks.

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Content: Synthetic data for continuous testing and machine learning

Where real data is unethical, unavailable, or doesn’t exist, synthetic data sets can provide the needed quantity and variety.

Devops teams aim to increase deployment frequency, reduce the number of defects found in production, and improve the reliability of microservices, customer-facing applications, employee workflows, integrations, and business process automations. Developing CI/CD (continuous integration and continuous delivery) pipelines ensures a seamless path to build and deploy applications and services, and automating testing and instituting continuous testing practices help teams maintain quality, reliability, and performance. With continuous testing, agile development teams can shift-left their testing, grow the number of test cases, and increase testing velocity.

It’s one thing to build test cases and automate them, and it’s another issue to have a sufficient volume and variety of test data to validate an adequate number of use cases and boundary scenarios. For example, testing a website registration form should validate a permutation of input patterns, including missing data, long data entries, special characters, multilingual inputs, and other scenarios.

The challenge is generating test data. One approach is synthetic data generation which uses different techniques to extrapolate data sets based on a model and set of input patterns. Synthetic data generation addresses the volume and variety of the data required and also creates data sets in cases where using real data might have legal or other compliance factors.

“Synthetic data provides a great option when the needed data doesn’t exist or the original data set is rife with personally identifiable information,” says Roman Golod, CTO and cofounder of Accelario. “The best approach is to create synthetic data based on existing schemas for test data management or build rules that ensure your BI, AI, and other analyses provide actionable results. For both, you need to ensure the synthetic data generation automation can be fine-tuned according to changing business requirements.”

Use cases for synthetic data generation

While the most basic need for synthetic data generation stems from testing applications, automations, and integrations, demand is growing as data science testing requires test data for machine learning and artificial intelligence algorithms. Data scientists can use synthetic data to train neural networks; at other times they use machine-generated data to validate a model’s results.

Other synthetic data use cases are more specific:

  • Testing cloud migrations by ensuring the same app running on two infrastructures generates identical results
  • Creating data for security testing, fraud detection, and other real-world scenarios where actual data may not exist
  • Generating data to test large scale ERP (enterprise resource planning) and CRM (customer relationship management) upgrades where testers want to validate configurations before migrating live data
  • Generating data for decision-support systems to test boundary conditions, validate feature selections, provide a wider unbiased sample of test data, and ensure AI results are explainable
  • Stress testing AI and Internet of Things systems, such as autonomous vehicles, and validating their responses to different safety situations

If you are developing algorithms or applications with high-dimensionality data inputs and critical quality and safety factors, then synthetic data generation provides a mechanism for cost-effectively creating large data sets.

“Synthetic data is sometimes the only way to go since real data is either not available or not usable,” says Maarit Widman, data scientist at KNIME.

How platforms generate synthetic data

You might wonder how platforms generate synthetic test data and how to select optimal algorithms and configurations for creating the required data.

Widman explains, “There are two main strategies to generate synthetic data: based on statistical probabilities or based on machine learning algorithms. Recently, deep learning techniques like recurrent neural networks—such as long short-term memory networks and generative adversarial networks—have raised in popularity for their capability to generate new music, text, and images out of literally nothing.”

Data scientists use RNNs (recurrent neural networks) when there are dependencies between data points, such as time-series data and text analysis. LSTM (long short-term memory) creates a form of long-term memory through a series of repeating modules, each one with gates that provide a memory-like function. For example, LSTM in text analytics can learn the dependencies between characters and words to generate new character sequences. It is also used for music creation, fraud detection, and Google’s Pixel 6 grammar correction.

GANs (generative adversarial networks) have become popular, used to generate many forms of images, crack passwords in cybersecurity, and even put together a pizza. GANs create data by using one algorithm to generate data patterns and a second algorithm to test them. Then they form an adversarial competition between the two to find optimal patterns. Code examples of GANs to generate synthetic data include PyTorch handwritten digits, a TensorFlow model for developing 1-dimensional Gaussian distributions, and one in R for simulating satellite images.

There’s an art and science to picking machine learning and statistics-based models. Andrew Clark, cofounder and CTO of Monitaur, explains how to experiment with synthetic data generation. He says, “The rule of thumb here is always to pick the simplest model for the job that performs with an acceptable level of accuracy. If you are modeling customer checkout lines, then a univariate stochastic process based off of a Poisson distribution would be a good starting point. On the other hand, if you have a large loan underwriting data set and would like to create test data, a GAN model might be a better fit to capture the complex correlations and relationships between individual features.”

If you’re working on a data science use case, then you might want the flexibility to develop a synthetic data generation model. Commercial options include Chooch for computer vision, Datomize, and Deep Vision Data.

If your goal is application testing, consider platforms for test data management or synthetically generating test data, such as Accelario, Delphix, GenRocket, Informatica, K2View, Tonic, and several test data tools, such as open source test data generators. Microsoft’s Visual Studio Premium also has a built-in test data generator, and Java developers should review this example using Vaadin’s data generator.

Having a robust testing practice is incredibly important today because organizations depend on application reliability and the accuracy of machine learning models. Synthetic test generation is one approach to close gaps, so not only do you have testing, training, or validating methodologies, but you also have a way of generating sufficient data to build models and validate applications.

Copyright © 2022 IDG Communications, Inc.

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