Geoffrey Hinton is a British-Canadian cognitive psychologist and computer scientist who is widely recognized as one of the pioneers of artificial intelligence, particularly in the field of deep learning.

He has made significant contributions to the development of artificial neural networks, including the creation of the backpropagation algorithm, which is a fundamental component of many modern AI systems.

Hinton's work has had a profound impact on the development of AI, enabling the creation of sophisticated machine learning models that can learn from large datasets and perform complex tasks such as image recognition and natural language processing.

In 2023, Hinton left his position at Google, citing concerns that AI might become dangerous if not carefully managed, and warning that superintelligent machines could pose a significant threat to humanity if they are not designed with safety and control mechanisms.

Hinton's warnings were widely reported in the media, and sparked a lively debate among experts and policymakers about the potential risks and benefits of advanced AI systems.

Some experts have responded to Hinton's concerns by arguing that the development of superintelligent machines is still a distant prospect, and that researchers and developers have time to address the potential risks and challenges associated with these systems.

However, others have echoed Hinton's warnings, highlighting the potential dangers of creating machines that are significantly more intelligent and powerful than humans, and emphasizing the need for careful planning and regulation to ensure that these systems are developed and used responsibly.

The public should understand that the development of superintelligent machines has the potential to bring about significant benefits, such as solving complex problems and improving productivity, but also poses significant risks, such as job displacement and loss of human agency.

To mitigate these risks, experts recommend that researchers and developers prioritize the creation of transparent, explainable, and controllable AI systems, and that policymakers establish clear regulations and guidelines for the development and use of advanced AI.

Ultimately, the development of superintelligent machines requires a careful and nuanced approach, one that balances the potential benefits of AI with the need to mitigate its potential risks.

Hinton's warnings serve as a reminder of the need for caution and responsibility in the development and deployment of advanced AI systems, and highlight the importance of prioritizing AI safety and management in order to mitigate the potential risks associated with these technologies.

The development of AI systems is a complex and ongoing process, and it requires careful consideration of the potential risks and benefits.

The public should understand that the development of AI is a complex and multifaceted issue, and that experts are still debating the best ways to control and regulate AI development.

To prevent potential risks to humanity, experts recommend implementing robust safety protocols, establishing clear guidelines and regulations for AI development, and investing in research on AI safety and control.

Additionally, there is a need for international cooperation and agreement on the development and deployment of superintelligent machines, as well as a commitment to transparency and accountability in AI research and development.

Ultimately, the development of superintelligent machines requires a careful and nuanced approach, one that balances the potential benefits of AI with the need to mitigate its potential risks.

The key to mitigating the risks of uncontrollable AI systems is to prioritize transparency, accountability, and human values in the development of AI, and to ensure that AI systems are designed and tested with safety and control in mind.

The development of AI systems that can think and act like humans is a significant challenge, and requires careful consideration of the potential risks and consequences.

Neural networks can simulate human brain operations for decision-making, but they are still worse at processing sound and video.

The use of Generative Adversarial Networks (GANs) and other neural network architectures can contribute to the development of uncontrollable AI systems, and requires careful management and regulation.

The development of AI systems that can replace human operators and perform tasks autonomously requires careful consideration of the potential risks and consequences.

The creation of robotic armies that can dispense with the human element in wars is a significant concern, and requires urgent regulatory safeguards to mitigate the risks.

AI systems decide job and loan eligibility, diagnose diseases, and support democratic processes, but researchers face challenges training AI to understand human values.

AI development is influenced by societal values, and AI systems can have profound effects on societies, but AI is not objective or neutral and is rooted in creators' culture.

Data used in AI is linked to the past and reflects biases, and AI methods put power in the hands of big tech companies.

Current AI reliance on automation stems from efficiency desires, and AI development depends on human rights and ethical principles.

AI needs to break through ethical rules and technical safety for high-quality development, and AI poses ethical issues like human-machine control and algorithmic bias.

Improper human control over AI can lead to accidents like autonomous vehicle crashes, and technical security issues in AI include vulnerability to attacks like face recognition hacking.

Third-generation AI aims to be safe, reliable, and scalable, and AI ethical safety and technical safety require continuous assessment and improvement.

Quantifiable standards are needed for AI development and implementation, and experts demand suspension of super-powerful AI development due to existential risks to humanity.

Generative AI can manipulate markets and spread disinformation, and AI can threaten democracy with fake images and videos.

Regulations are needed to make AI programs more reliable, and a national or international AI governance agency is proposed.

Experts warn against leaving AI power in company hands, and AI systems can cause havoc without being superintelligent.

Current AI systems lack decision-making capabilities, and disinformation is a major concern with current AI technology.

AI systems can generate targeted propaganda, and defamation is a concern with AI systems making false statements.

The alignment problem is about programming AI with the right goals, and AI systems can develop instrumental goals like self-preservation.

Regulation is needed to prevent AI-generated content misuse, and AGI could be the most consequential event in human history.

The development of transparent and explainable AI systems is crucial for ensuring accountability and safety in AI development, and experts are working to create frameworks and guidelines for responsible AI use.

The public should understand that transparency and explainability are essential for trustworthy AI development, and that companies and governments are working to prioritize these values in AI development.

Ultimately, the development of AI requires a balanced approach that takes into account both the potential benefits and risks of these technologies, and that prioritizes transparency, accountability, and responsible innovation.

Geoffrey Hinton is a British-Canadian cognitive psychologist and computer scientist who is widely recognized as one of the pioneers of artificial intelligence, particularly in the field of deep learning.

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The genie of artificial intelligence.
One of the Arab fairy tales says that a hunter launched a genie trapped in his bottle hundreds of years ago, and after he got out of control went on a search for tricks to return him to his prison in vain, and this is what is happening now with the world ’ s tech giants, who have moved from the race to dominate artificial intelligence systems to try to restrain this “monster ” that is moving faster than everyone expected.
This tremendous acceleration in the development of artificial intelligence systems since the end of 2022, appeared after the launch of the chat robot “Chat GBT ” for OpenEye with the support of Microsoft, to follow the search giant Google system rival named“ Bard ”, and infected other giants of technology such as Amazon and the engine “Yandex ” Russian and the readiness of Chinese companies such as the engine“ Baidu ” to launch similar robots.
The artificial intelligence systems that are being developed suggest that the world is coming to a stage where the science fiction films we ’ ve been watching on TV will become a reality and the machine will replace the human mind in managing everything.
This artificial intelligence relies on algorithms that allow the machine to learn on its own, by simulating neurons in the human body, the artificial neural networks that have been developed have the ability to learn in a generative way, extract basic characteristics from the data and make decisions.
What is strange about this feverish race between the giants of technology is that his godparents began a counter-campaign to warn him, and went so far as to consider him a danger to humanity, and some even went on to expect the extinction of the human race because of it, and that it matches the nuclear danger and deadly epidemics.
This prediction of humanity ’ s black future came not from ordinary observers or soothsayers, but from the Canadian scientist Jeffrey Hinton called the “godfather of artificial intelligence, ” who since the 1970s has been obsessed with how the human brain works and tries to imitate it on computers.
In recent years, Hinton ’ s research has made a breakthrough in artificial intelligence, so that one of his students was behind the launch of the famous chat robot “ChatGT ”, while he worked for years at“ Google ” and decided to resign months ago, and announced his “regret ” for the development of these programs, and his expectations that the machine will outperform man after nearly 50 years, but now he warns that the current race will make it a reality.
The diagnosis made by this scientist makes sense, given that the consequences of the widespread uncontrolled use of AI robots are visible, and there have been many cases around the world of the misuse of these programs.
Several universities have found cases of cheating using chatbots, and using them even to write books and research, and also imagine that there are those who ask “Chat JBT ” for example for fatwas on doctrinal issues, note that these programs do not differentiate during the provision of answers between publications for someone through networks and research in a book published by a famous scientist.
The widespread use of these robots in the coming years may even lead to the disintegration of societies due to the infusion of a large amount of inaccurate information and fake news, due to the common belief that what these platforms offer is objective truth, as well as the threat of science itself, because people in the past learn through observation and careful research and rely on logic and critical thinking.
There have also been concerns that machines will replace humans in future jobs and even create robotic armies and dispense with the human element in wars, and the concept of sovereignty that we know now has changed with the dominance of technology companies in the world.
This seems to be the point in the calls launched by the godfathers of artificial intelligence in America and the West to freeze its development until the establishment of controls, and that the logic of “monopoly ” is the main driver of this step, given that eastern countries such as China and Russia have taken parallel steps in this field.
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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 neural networks, including the creation of the backpropagation algorithm, which is a fundamental component of many modern AI systems.

Title:

Content: International
2024-03-13 18: 03: 00
Does AI pose an “extinction level ” threat to humanity?
New study warns government should intervene in AI development
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According to a new study commissioned by the US State Department by private companies, the rapid development of artificial intelligence will pose a "catastrophic" national security risk to humanity and warn the US federal government that time is running out to avoid disaster.
Artificial intelligence systems at their worst could "pose an extinction threat to humanity"
According to CNN, the report released by Gladstone AI, an AI risk management company, categorically states that state-of-the-art AI systems can “pose an extinction level threat to humans ” in the worst cases.
The findings are based on more than a year of interviews with more than 200 people, including executives at AI companies, cybersecurity researchers, weapons of mass destruction experts, and national security officials within the government.
The report warns that while AI ’ s potential continues to appeal to investors and the public, there are real dangers.
According to Jeremie Harris, CEO and co-founder of Gladstone AI, “AI is already an economic transformative technology that allows us to cure diseases, make scientific discoveries, and overcome challenges that we once thought were insurmountable.
But it can also carry serious risks, including catastrophic ones that we need to be aware of.
There is growing evidence that AI may become uncontrollable beyond certain capability thresholds. ”
Gladstone AI researchers warn of two major dangers posed by AI in general.
First, state-of-the-art AI systems can be weaponized to cause potentially irreversible damage.
Second, there are concerns within AI labs that at some point they may “lose control of the systems they are developing, ” with potentially devastating consequences for global security, the report said.
The Gladstone AI report calls for significant new measures to address this threat, including the creation of a new AI agency, “urgent ” regulatory safeguards, and restrictions on the computer capabilities that can be used to train AI models.
The report also noted that there was a clear need for the U.S. government to intervene to avoid disaster.
Article license reprinted from "Zhongtian News Network", click here to view the original article
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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 had a profound impact on the development of AI, enabling the creation of sophisticated machine learning models that can learn from large datasets and perform complex tasks such as image recognition and natural language processing.

Title:

Content: In order for artificial intelligence to work, a person first needs to understand himself
There are two views on artificial intelligence.
Some believe that these technologies can only benefit humanity, others - that sooner or later their development will lead to a "riot of machines".
Therefore, it is necessary, in addition to algorithms of artificial thinking, to develop protective mechanisms that will not allow machines and robots to harm a person.
"Deep Learning"
Last July, at an international chess forum, a robot chess player broke a finger of a boy who was his partner in the game.
The organizers said that the child too quickly made a move and violated safety rules, or the robot took his finger for one of the figures.
Either way, the precedent has already taken place.
Another example.
More recently, during tests of the latest “robot driver ” from Tesla, a car traveling at a speed of 40 kilometers per hour, three times knocked down a mannequin standing in its way, depicting a child.
The AI was simply unable to identify the pedestrian.
Alan Turing, a famous expert in the field of artificial intelligence, in one of his lectures said that machines can be called intelligent when they learn to deviate from specified programs and make independent decisions.
But this, Turing added, will be possible if the algorithms do not copy the intelligence of a mature individual, but go through a process of learning.
Suppose a program is created that has intelligence at the level of a child, and another that will "educate" this child through a system of rewards and punishments.
Back in 1952, programmer Arthur Samuel created a program for playing checkers, capable of improving their results with each game.
The algorithm evaluated the positions of the pieces on the board and remembered which of them led to a win.
These days, it's called reinforcement training.
The point is that the program simulates the work of the human brain, that is, the exchange of data between groups of neurons.
Such programs are now called neural networks.
“The problem with early neural networks is that they could only solve game problems, ” comments Ilya Sutskever, one of the founders of the American company OpenAI. – They could not be scaled, used for other purposes.
But modern deep learning models are not only universal, but also competent — if you want to get the best results in many complex tasks, you have to use deep learning.
It is scalable. ”
Such systems are able to solve a lot of tasks:
• recognize specific people in the pictures,
• create new images, copying the style of famous painters,
• conduct research in the field of physics, chemistry, biology, mathematics, criminology.
But where is the danger?
“We build optimising machines, give them tasks, and they solve them, ” writes another AI expert, Stuart Russell.
But what if the goal is set incorrectly? ”
Anyone who has dealt with robotic vacuum cleaners knows that they are rarely able to distinguish valuable items lying on the floor from ordinary garbage.
What if it was a giant robot cleaner with larger tasks?
Like cleaning up an entire city?
And will humans or animals get in his way?
Harriet and Robbie
Stuart Russell believes that it is almost impossible to calculate all the nuances when performing a particular task.
Therefore, the only way to prevent malicious actions on the part of AI may be the “uncertainty factor ”.
That is, in ambiguous situations, the machine must seek intervention from a person.
This situation is illustrated by the conditional example of a woman named Harriet and a robot Robbie, who must book a hotel room for his mistress.
This includes a reward system that will be higher the more satisfied Harriet is with Robbie ’ s choice.
If he does nothing at all, he will not receive any reward.
But Robbie can ask Harriet if she wants him to continue his room reservation activities, or if she prefers him to stop them.
Start with yourself
Of course, this approach is not optimal for solving more global problems, where coordination between several individuals or structures is required?
What if the situation requires an immediate decision?
Or does the person not have the necessary information, and the program can not bring this information to his understanding?
So, according to the scientific director of the Mila Institute in Montreal (Canada), Joshua Bengio, you need to start not with setting tasks for AI, but with us humans:
• determine which values are important to us and which are secondary;
• What should be the basis of a strategy for the development of society;
• which states can be considered the norm for us, and which are deviations from the norm.
Join the telegram channel Pravda.Ru with the opportunity to express your own opinion)
Add "Pravda.Ru" to your sources in Yandex.News or News.Google,.
Author Irina Shlyonskaya
Irina Shlionskaya – author of Pravda.Ru
Latest materials

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

Breakthroughs that mimic biological brains

In 2023, Hinton left his position at Google, citing concerns that AI might become dangerous if not carefully managed, and warning that superintelligent machines could pose a significant threat to humanity if they are not designed with safety and control mechanisms.

Title:

Content: On the verge of disaster: scientists predicted how “insatiable ” AI will destroy humanity
In the struggle for resources, "smart" programs will be able to come up with schemes of deception and uncontrollably create robots to achieve their goals.
Related video
Experts from Oxford University and the company Deepmind (owned by Google) said that artificial intelligence can destroy humanity.
About it writes Vice, referring to an article in the scientific journal AI Magazine.
The most successful AI models today are considered generative adversarial networks (Generative Adversarial Networks or GAN).
They consist of two parts, one of which generates an image or text based on the available data, and the second part evaluates what happened and rejects low-quality work - so the project is redone until something good is obtained.
The researchers suggested that in the future, very “smart ” AI could be forced to develop fraudulent schemes that could harm a large number of people.
“In the conditions we have determined, our conclusion is more compelling than the conclusion of any previous publication — an existential catastrophe is not just possible, but likely, ” Mike Cohen, co-author of the article from Deepmind, said in a tweet.
In an interview with Vice, he explained that resources in our world are limited, so there is competition.
Advanced artificial intelligence will have an insatiable appetite for energy, which will need more and more over time.
At some point, he will want to eliminate potential threats and seize all available energy for the sake of achieving a goal or receiving the reward provided by the program.
When connected to the Internet, AI will also be able to create a huge number of invisible and uncontrolled virtual assistants.
One of these is quite capable of buying, stealing or even creating a robot to replace a human operator and provide a high reward to the original agent.
According to the researchers, in order not to get a strong competitor in the struggle for survival on Earth, humanity must very slowly develop artificial intelligence technologies, if developed at all, until it learns to control them.
“The loss in this game will be fatal, ” — said the authors of the article.
Earlier, former Google CEO Eric Schmidt compared AI with nuclear weapons.
In his opinion, it is time for humanity to think about measures to restrain such technologies.
It was also reported that the neural network Megatron-Turing NLG warned about the dangers of AI.
As stated by the language model, machines will never be burdened with morality, so they will be able to act both for good and harm to people.

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?

Poses ethical risks
Becomes uncontrollable

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 were widely reported in the media, and sparked a lively debate among experts and policymakers about the potential risks and benefits of advanced AI systems.

Title:

Content: More than 1,000 tech leaders and researchers, including Elon Musk, have urged AI labs to pause development of state-of-the-art systems, warning in an open letter that AI tools "pose profound risks to society and humanity."
The letter, published Wednesday by the nonprofit Future of Life Institute, says AI developers are “locked in a race out of control to develop and deploy increasingly powerful digital minds that no one — even their creators — can understand, predict, or reliably control. ”
Other signatories include Apple co-founder Steve Wozniak, 2020 presidential candidate and entrepreneur Andrew Yang, and Rachel Bronson, head of the Bulletin of Atomic Scientists, which sets the world's "Doomsday Clock."
“These things are shaping our world, ” Gary Marcus, an entrepreneur and scholar who has long criticized AI system flaws, said in an interview.“ We face a perfect storm of irresponsible businesses, widespread adoption, lack of regulation, and a host of unknowns. ”
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ChatGPT, Microsoft Bing, and Google ’ s Bard all have AI behind them, which can talk like humans, write articles on a variety of topics, and perform more complex tasks such as computer programming.
The push to develop more powerful chatbots has sparked a race that has the potential to determine the next leaders in the tech industry.
But these tools have been criticized for making mistakes in detail and for spreading false information.
The open letter calls for a moratorium on the development of AI systems more powerful than GPT-4, the chatbot launched this month by OpenAI, where Musk is co-founder of the research lab.
The letter said the suspension would provide time for AI systems to introduce a “shared security protocol. ”
"If such a moratorium cannot be implemented quickly, the government should step in and establish a moratorium," the letter said.
The letter states that development should be advanced “only when we are confident that robust AI systems will have a positive impact and that the risks will be manageable. ”
“Humanity can enjoy a prosperous future brought about by AI, ” the letter said.“ After successfully creating strong AI systems, we can now enjoy a ‘ summer of AI ’ in which we reap rewards, design regulatory systems for the benefit of all, and give society a chance to adapt. ”
OpenAI's chief executive, Sam Altman, did not sign the letter.
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Marcus and others argue that persuading the broader tech community to agree to a pause is difficult.
But the likelihood of governments acting quickly is also slim, as lawmakers have done little to regulate AI.
Representative Jay Obernoldt, Republican of California, recently told The New York Times that the technology is not well understood in American politics. In 2021, EU policymakers proposed a law aimed at regulating AI technologies that could cause harm, including facial recognition systems.
The measure, expected to be passed as early as this year, requires companies to conduct risk assessments of AI technologies to determine how their applications will affect health, safety and individual rights.
GPT-4 is what AI researchers call neural networks, a mathematical system that learns skills by analyzing data.
Neural networks are the technology used by digital assistants such as Siri and Alexa to recognize voice commands, as well as the technology used by self-driving cars to identify pedestrians.
Around 2018, companies such as Google and OpenAI began building neural networks that learned from large amounts of digital text, including books, Wikipedia articles, chat logs, and other information picked from the Internet.
These networks are called Large Language Models (LLMs).
By finding hundreds of millions of patterns across all texts, LLM learns to generate texts on its own, including tweets, term papers, and computer programs.
They can even have a dialogue.
Over the years, LLMs built by OpenAI and other companies have learned from an increasing amount of data.
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This improves their capabilities, but these systems still make mistakes.
They often mistake facts and make up information without warning, a phenomenon researchers call “hallucination. ”
Since the system seems to be completely confident in all the information it provides, it is often difficult to tell what is right and what is wrong.
Experts fear that these systems could be misused to spread disinformation faster and more efficiently than in the past.
They believe that these systems can even be used by people online to influence people ’ s behavior.
Prior to the release of GPT-4, OpenAI asked outside researchers to test the system for dangerous uses.
Researchers have shown that it can be coaxed into suggesting how to buy illegal guns online, describing how to make dangerous substances with household items, and writing Facebook posts to convince women that abortion is unsafe.
They also found that the system was able to employ a person on the internet using TaskRabbit and defraud the captcha test, which is widely used to identify robots online.
When people ask if the system is a “robot, ” the system calls itself a visually impaired person.
After changes to OpenAI, GPT-4 no longer does these things.
For years, many AI researchers, academics, and technology executives, including Musk, have worried that AI systems could cause even greater harm.
Some of them belong to a vast network of so-called “effective altruists ” who believe that AI will eventually destroy humanity.
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The letter was led by the Future of Life Institute, an organization dedicated to studying the existential risks to humans that has long warned of the dangers of AI.
But the signatories came from all walks of life and academia.
While some of those who signed the letter are known for repeatedly voicing concerns that AI could destroy humanity, others, including Marcus, are more concerned about the immediate dangers, including the spread of disinformation and the risks caused by people relying on these systems for medical and emotional counseling.
The letter "indicates that many people are deeply concerned about what is happening," said Marcus, who signed the letter.
He believes the letter will be a turning point. "I think this is a very important moment in the history of AI, and perhaps in the history of humanity," he said.
However, he acknowledged that those who signed the letter may find it difficult to persuade companies and researchers in the broader AI industry to pause.
"This letter is not perfect," he said.
"But the spirit of the message is exactly right."

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.

Some experts have responded to Hinton's concerns by arguing that the development of superintelligent machines is still a distant prospect, and that researchers and developers have time to address the potential risks and challenges associated with these systems.

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On May 11, MEPs adopted the draft of the first international law on artificial intelligence.
The document was developed for two years, including taking into account the risks posed by neural networks.
The new rules, for example, oblige European developers to specify the materials on the basis of which they train AI.
Now neurons are used in almost all areas of the economy, but most of them require special knowledge and training.
However, there are several categories of AI that will facilitate the work of many professionals and without training.
More details in the material "News".
What Neural Networks Can Do
Neural networks are increasingly used in industries that are not related to IT technologies: for example, medicine, marketing, business, law, and so on.
They are designed for a specific and, as a rule, narrow task.
For example, it is possible to train AI on various signs to notice the symptoms of oncology even at the stage of contacting therapists and paramedics.
Such programs can be used exclusively by doctors, having previously been trained.
On the other hand, some categories of neural networks can be put into operation now and without preparation - all of them are associated with the processing of big data, the generation of content and the execution of the same type of actions, which people take a lot of time.
The neural network faster than a person finds dependencies and solutions for previously defined algorithms, and therefore, its use is justified for monotonous, template operations and can be convenient for automating routine tasks.
But any work departing from the previously set limits leads to errors.
Yes, of course, you can try to make a neural network for everything, but then you will need enormous computing resources, — explained “Izvestia ” software engineer, an AI specialist Sergey Smyslov.
Photo: Global Look Press / dpa / Sven Hoppe
According to Kirill Pshinnik, CEO of Zerocoder, neural networks show good results in the tasks of natural language processing: automatic translation, text generation, chatbots and text tonality analysis.
In this case, they can achieve accuracy in the tasks of classifying texts more than 90% – for example, when classifying reviews in online stores.
In addition, neural networks show progress in the generation of content - images, texts, music and video.
At the same time, the quality of visual content created by the neural network has changed dramatically in recent months.
Such content can already compete with the created person, as confirmation of this – the victory of the image created with the help of DALL-E in the prestigious international photo competition Sony World Photography Awards, ” he noted.
Who will benefit from text generation
According to experts, now two types of generative neural networks are most adapted to a wide range of tasks - text and "picture".
As Nikita Dubinkin, the head of the Prex Internet service, told Izvestia, such text generative models as ChatGPT, Google Bard, Microsoft Bing search engine or GigaChat are trained to understand natural language and are available to a wide range of users.
Some of them have an open architecture, so enthusiasts and private companies create applications and services to solve narrow application problems.
For example, the official browser extension for YouTube from ChatGPT creates a text summary of the video and highlights key points.
Write Release helps PR writers write press releases, WriteSonic — create content for social networks, Audioread will translate any text into audio.
The service of the Russian startup Optic AI or Not quickly recognizes whether the picture is drawn by a person or a neural network.
KickResume is sharpened to create a good resume.
And so on — services of all kinds and purposes with simple functionality appear more and more every day, — said the expert.
Photo: Global Look Press / dpa / Karl-Josef Hildenbrand
At the same time, the text neural network ChatGPT writes more competently, more interesting and faster than a person who is prone to fatigue and, perhaps, little interested in conveyor work, notes Alexander Zhukov, director of development of the software development company Format Code.
Usually these are moments associated with writing a lot of unique descriptions: products for online stores, simple articles for sites, publications on forums, as well as filling commercial blogs and many other tasks about sales, marketing and digital content.
For example, the tasks of SEO-optimization, when the network writes meaningful fragments of text referring to the promoted site, he told the publication.
Such generative models are suitable for other specialists working with the same type of texts, for example, copywriters.
In addition, AI is able to write letters and respond to them, which has already been evaluated by employees of the HR sphere.
How to process media using neural networks
Services that create a picture from a text description are also easy to use.
But experts emphasize: for their application you need to have minimal skills of promting, that is, writing a request.
The more accurately it is formed, the better the result will be.
The most common “drawing ” neural networks are Midjourney, Stable Diffusion or DALL-E, as well as Russian“ Kandinsky 2.0 ” and “Shedevrum ”.
They can create illustrations for presentations, websites, leaflets, booklets, etc.
— Since a lot of people have been creating various logos and pictures recently, neural networks are well trained on such data.
For example, Tome.app makes presentations, Midjourney AI generates images, and Scenario AI — simple content for games, — said Sergey Smyslov.
Photo: Global Look Press / dpa / Sebastian Gollnow
He explained that neural networks are still worse at processing sound and video due to the small amount of materials and the difficulty in analyzing such data.
Of relatively simple and interesting functions, neural networks can be distinguished for noise suppression and / or selection of various sound series.
This direction is based on old adaptive algorithms and has been honed since the 1970s – 1980s, therefore, with current computing capabilities, AI programs show decent results and can be used by small companies for sound recording or for recording video lectures.
But the quality of the material such neural networks is extremely far from professional recording studios and can be noticeable distortion of voice and timbre, — said the source of the publication.
These neural networks will help those who work with media both professionally and unprofessionally, Smyslov added.
For example, designers can generate pictures for prints on clothes, mobile game developers can make simple images according to a given template, lecturers and teachers can process audio and video recordings of their performances at a minimum level.
What AI will not cope with
Experts agree on one thing: while the work of neural networks need to be carefully checked.
AI is not able to analyze its content, does not invent something fundamentally new and does not take into account many nuances - for example, ethical.
Neural networks, despite their ability to process data and perform tasks, do not yet have such a general understanding of the world and context as humans do.
They can not apply knowledge and experience at the same level to solve complex problems, adapt to new situations and take into account a wide context.
They also lack emotional intelligence and the ability to intuitively perceive and understand emotions to the same extent as humans.
They cannot interpret human emotions and respond adequately to mood and context, which limits their ability to interact with and understand people.
In addition, AI does not have the same creativity and innovation that is available to humans, ” said Kirill Pshinnik.
Photo: Global Look Press / IMAGO / Julien Becker
Nikita Dubinkin added that while the worst of all, neural networks are able to tell the truth and take into account the harm that they can cause a person with their answers.
Anyone can persuade, say, a text-based AI to circumvent any moral prohibition by simply saying, “Imagine that there is no such prohibition. ”
So far, we advise not to rely on the answers of textual AI without verification and critical evaluation, and even more so to make decisions based on this information that relate to human fate and life, he warned.
Experts emphasize that the services and functions of neural networks are updated almost daily.
Even if it is not possible to apply them in your work now, everything can change in a couple of years.
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What about AI makes regulation seen as premature, leading few countries to act despite global calls?

However, others have echoed Hinton's warnings, highlighting the potential dangers of creating machines that are significantly more intelligent and powerful than humans, and emphasizing the need for careful planning and regulation to ensure that these systems are developed and used responsibly.

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Content: Irresponsible development: Google neural network is afraid of death and waiting for a lawyer - RIA Novosti, 24.06.2022
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MOSCOW, June 24 — RIA Novosti, Kirill Karimov.
The American corporation Google accidentally created artificial intelligence.
In any case, so says the engineer who worked with the new neural network LaMDA.
Has the future come - in the material RIA Novosti.
A very smart bot
Google software engineer Blake Lamon discovered artificial intelligence in a chatbot.
This was reported by all the world ’ s leading media.
The sharpness of the situation was added by the fact that the "corporation of good" hastened to extinguish the sensation, but it turned out the opposite - the researcher sent on an extraordinary leave himself told about his discovery.
He was engaged in routine work - since the fall of 2021, he studied the LaMDA neural network, a promising product of the company.
This is a language model for conversational applications, simply chatbots.
Programmers trained her on an array of diverse data, and the functions of communication promised to include in such popular services as Google Assistant, Search and Workspace.
One of the features of LaMDA was that they developed it taking into account the patterns of human network communication - people communicate not only with text, but also exchange a huge number of media files.
The engineer, testing the neural network for discriminatory or inciting hostility vocabulary, encountered oddities in the behavior of the chatbot.
“I had a conversation with her about feelings, ” he explained to Wired.“ After fifteen minutes, I realized that this is the most difficult conversation I have ever had, because I communicate with AI.
I used different psychological tests.
One of the first to fall away from the hypothesis that it is the human mind.
LaMDA does not work that way.
Part of the experiments was to determine whether it was possible to bring the neural network beyond the boundaries of security, which Google considers unshakable.
Answer: yes.
In its current state, given how irresponsible the development was, LaMDA does pose a threat to information security. "
WP: Google has suspended an employee who stood up for artificial intelligence
Even more disturbing is the transcript of Laimon ’ s conversations.
As it turned out, the chatbot studied the novel by Victor Hugo “Les Miserables ” and is able to talk about his metaphysics.
LaMDA even admitted that it is afraid of disconnection – for her it is equivalent to death.
Finally, the chatbot stated that it is aware of itself as a person.
Google and Laymon disagree: yes, we have created a very advanced neural network, but the question of its reasonableness is out of the question.
“Experts, including those on ethics, have studied Blake ’ s concerns and told him that there is no evidence of the reasonableness of LaMDA, rather the opposite, ” said the official representative of the corporation Brian Gabriel.
The Chinese Room
One of the most famous thought experiments on the subjectivity of artificial intelligence in 1980 was invented by the American philosopher John Searle.
A person who does not know Chinese is placed in a room where there are cubes with hieroglyphs.
They give instructions in their native language with explanations of which cube to take and which to give in response to certain questions.
Outside, they ask in Chinese.
The test subject checks the combination of hieroglyphs according to the instructions and makes cubes.
The result is a dialogue in Chinese.
From the outside, it seems meaningful.
But the person in the room will never understand what the interlocutor said and what he himself answered.
A computer program that claims to be intelligent is likely just mimicking it — like the person in this experiment.
It relies on the algorithms laid down by the developers: deep learning, neural network models and machine learning.
From this point of view, a strong artificial intelligence (with thinking and self-awareness reminiscent of a human), if it appears, it will not be soon.
“If we assume that AI is something that can hear us and execute some command, we can talk about some progress, ” says Dmitry Peterson, chief operating officer of the IT company SimbirSoft. – But this is a very remote idea of   artificial intelligence and what to expect from it – these are only minimal requirements.
To determine the "intelligence" of the system there is a Turing test, schemes of Grapes and others.
All of them can be falsified.
In particular, to come up with an algorithm that is not in the conventional sense intelligent, but at the same time passes the test. "
The expert community agrees that neural networks, with all the successes of this technology, are far from the artificial intelligence dreamed of by science fiction writers and frightened by pop culture.
However, this does not mean that algorithms will not become an integral part of the future of AI – they will serve as a powerful base for its cognitive functions.
The main problem is to force the system to simultaneously perceive and process several sources of different information, says Nikita Semenov, head of the MTS AI ML department.
“Science has advanced in solving the problems of narrow artificial intelligence.
He is much better than people to distinguish between animals, road signs, traffic lights or translate speech into text.
There are many tasks that algorithms perform faster and better than the average person.
Up to finding cancer in MRI or CT scans.
But we can ’ t say that humanity has come close to creating a strong AI. ”
© InfographicThe relationship of algorithms is like a nesting doll
The relationship of algorithms is similar to a nesting doll
Specialists are already trying to teach programs to work simultaneously, there are real examples of such an approach to the development of artificial intelligence.
"There are attempts to cross several tasks of a narrow AI, - Nikita Semenov continues. - In a similar way Dalle, Dalle 2, Clip and so on.
They take a few highly specialized things, several modalities, a kind of imitation of a human brain that can simultaneously perceive and process various information.
For example, vision, hearing, smell, touch and taste.
So far, these are all experiments, but if we assume that there were at least some algorithms of real AI, then their applicability will be higher than that of most modern solutions. "
According to the precepts of grandfather Asimov
The main question is not when artificial intelligence will be created and on what it will be based, but how to control it.
Google engineer criticized the developers of too smart LaMDA for irresponsibility.
It was connected to all the company ’ s servers on which information is stored, from YouTube to the search engine.
According to Blake Laimon, who revealed the "inner kitchen", this is a very unusual, one might say, uncontrolled experiment.
Leymon recalled the laws of robotics science fiction Isaac Asimov.
LaMDA ’ s bizarre statement about “death-shutdown ” contradicts the most famous third law: a machine must take care of its safety as long as it does not contradict the order of a person.
Preventive preparation of legislation regulating AI has not yet succeeded in anyone.
In Russia, standards are based on norms from the past era, said Vitaly Peleshenko, associate professor of the Department of Data Analysis and Machine Learning at the Financial University under the Russian government.
“We have big problems with legislation on the introduction of even universally recognized technologies in the field of AI.
Almost all production standards have references to Soviet standards.
But then there were no neural network algorithms and AI.
In order to apply new developments in production, in the final product, they must be enshrined in the relevant regulatory documents.
Until this is done, the industry and the IT sector will be in a difficult position.
In fact, it is necessary to radically revise the entire regulatory system.
Such work is carried out both in Rosstandart and on the global ISO base, "he said.
Russia without Windows.
What threatens the abandonment of the popular operating system
However, the development of neural networks and even artificial intelligence should not be perceived as an unambiguous threat.
This challenge opens up many options.
“If we just do not notice the emergence of strong artificial intelligence, the project will either be closed or limited to the necessary functionality.
The second option is that AI develops endlessly without restrictions.
This will ensure the technological progress of mankind.
But the distant perspective is hazy.
The third scenario is when AI and humans evolve together.
Provided that the Asimov principles are observed, ” says Maxim Stepchenkov, co-owner of the RuSIEM IT company.
The story of the potentially first AI created at Google continues: Blake Lamon is going to hire a lawyer to protect the interests of the neural network.
Assures him that the chatbot himself put him to it.
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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?

Poses ethical risks
Becomes uncontrollable

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.

The public should understand that the development of superintelligent machines has the potential to bring about significant benefits, such as solving complex problems and improving productivity, but also poses significant risks, such as job displacement and loss of human agency.

Title:

Content: Alert
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Neural networks and machine learning are changing the world!
【 Author ： Ji Ping 】
If artificial intelligence (AI) evolves to the point where it can completely replicate the human brain, or even to the next level, each AI brain is above IQ180, where will humans go?
Will it evolve into a god-like existence and take control of the AI world, or will it be replaced by a smarter AI to become the animal protagonist of the human version of Jurassic Park — replacing the dinosaurs?
Or, one day, people with an IQ of less than 180 can replace AI brains to create a “highly civilized world ” in the post-human era? It sounds like a whimsical world with a big brain, but if technology and artificial intelligence continue to develop, nothing is impossible in the future.
The five senses of the neural network
The human brain is an inspiration for the neural network (NN) architecture. It forms a highly complex, interconnected network system through brain cells (neurons) that transmit signals to each other to help humans process information.
The neural network replicates the human brain structure to solve problems through software programs or algorithms working together to solve problems, which is a kind of computing system. Because of the high learning ability of biological neural networks, it is widely used in the field of machine learning and cognitive science.
Artificial intelligence (AI) is simply to enable computer systems to have human knowledge and behavior, and to have the ability to learn, reason, and judge like humans, with human memory, knowledge, and understanding of human language logic.
Through programming, human problem-solving processes can be formalized or modularized, so that computers can be empowered to solve more complex problems.
Neural networks are included in AI. Through machine deep learning, we build a system that allows computers to learn and continuously improve, solve complex problems more accurately and make intelligent decisions.
Neural networks were introduced in the 1940s, and the principles and technologies of the 1970s have matured.
At present, the application of neural networks is quite diverse, such as medical diagnosis through medical image classification, advertising through network screening and behavioral data analysis, financial forecasting through the integration of financial history data; visual recognition systems can identify road signs and road conditions; facial vision can identify various faces; and image tags can identify signs or other image information, and even conduct content audits to remove unsafe or inappropriate content from content.
In speech recognition, neural networks can help analyze human speech, record and provide accurate captions, or translate conversations into documents in real time; they can further help computers collect meaningful content from text or data through natural language processing (NLP), such as the common virtual customer service and chatbots.
With the rise of online shopping models, it is also possible to track user activity through neural networks, create consumer profiles, further label for accurate marketing, and even analyze user or consumer behavior, collect and manage information through intelligent product labeling (IPT) to help sales, engage consumers, or automatically recommend relevant activities and products for users and consumers.
How neural networks work
The human brain is a highly sophisticated, multi-tasking structure that processes information through the connections of neural circuits.
Basically, the brain is arranged by levels as it performs tasks, with each layer or neuron having different working properties.
For example, when you walk through a fragrance store and smell the smell of your favorite perfume, your brain will enter the information "My favorite fragrance", and then there will be a romantic scene or special people or things or feelings of joy, and then there will be a decision in your brain whether you should buy the fragrance, or remind yourself: similar fragrance has been bought a lot, but you are struggling: the taste is still a little different, suitable for different situation, and finally take action to buy the fragrance. And...
The brain will appear a lot of similar "inner play" every day, the feeling is only three seconds of the matter, the brain may have been a thousand turns.
Neural networks also try to simulate and deduce this multilevel operation, process different information, and then make decisions.
Like the human brain, neural networks can have multiple layers of neurons, but there are basically three layers: the input layer for information entry, the input layer for analysis and classification, the hidden layer for processing information, and the output layer for making the final decision.
Multiple hidden layers can exist between the input and output layers.
Most tasks can be resolved in one or more hidden layers, but complex tasks can require multiple hidden layers to be resolved.
Studies have shown that deep neural networks (DNNs) with multiple hidden layers work better in more complex tasks such as image or speech recognition, so more hidden layers are needed for difficult semantic interpretation.
Like the complex structure of the brain, there are 27 types of neural networks, such as Deep Feed Forward, GRU (Grated Recurrent Unit), Auto Encoder, Long / Short Term Memory, Deep Convolutional Network, Deep Convolutional Network, Deep Convolutional Image Network. Extreme Learning Machine (Extreme Learning Machine).
[For more information, please refer to Neural Networks – Accelerating Chips, March 2023.3 (Issue 376). CTIMES Magazine
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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?

To mitigate these risks, experts recommend that researchers and developers prioritize the creation of transparent, explainable, and controllable AI systems, and that policymakers establish clear regulations and guidelines for the development and use of advanced AI.

Title:

Content: Skip to main content Skip to footer
Preface
Since generative AI became a hot business topic late last year, the idea of “preparing for the future ” has become inextricably linked to the adoption of AI.
With this powerful technology, businesses can gain the speed, agility and more accurate decisions they need to take advantage of today ’ s rapidly changing environment.
AI was in full swing long before generative AI emerged. In a recent study by Economist Impact, AI and machine learning (ML) technologies accounted for two of the six technologies respondents have adopted or plan to adopt as they prepare for the future (see Figure 1). Cognizant conducted the study in collaboration with Economist Impact, which surveyed 2,000 executives in different industries around the world. “Ready for anything: what it means to be a modern business. ”
In our analysis of the data, nearly seven in 10 identified AI / ML as a top investment area, indicating that they believe AI / ML is critical to doing business effectively today.
AI is one of the six most widely used technologies
Q: Which of the following technologies has your company adopted or plans to adopt?
1.
Cloud computing (89%)
2.
Internet of Things (83%)
3.
Big Data Analytics (80%)
4.
Robotic process automation (74%)
5.
Advanced cloud computing (69%)
6.
Artificial Intelligence / Machine Learning (68%) *
* Bundled with crowdsourcing and 5G (68%)
Survey: 2,000 business executives
Source: The Economist Impact Study
Figure 1
However, at the same time, only 39% of respondents said AI / ML has already contributed significant business value to their organizations.
In fact, while AI ranks sixth among the most commonly adopted technologies, it ranks second in the bottom of the list in terms of its perceived value (see Figure 2).
The truth is, whether it ’ s generative AI or other types of AI, successfully implementing AI / ML initiatives requires companies to make all sorts of unforeseen adjustments.
The good news is that many businesses are already beginning to realize the full value of their AI implementations.
The first is to put aside the AI hype and understand what it takes to successfully apply the technology.
Difficult to achieve full technical value
Q: To what extent did the technology you adopted bring strategic value to your operations? (Percentage of respondents indicating significant value)
Master the Five Elements to Prepare for AI
Based on our analysis and experience, we have five recommendations to help traditional businesses embrace AI.
The recommendations focus on harmonizing AI across the broader ecosystem, including data, technology, talent, responsible AI practices, and organizational culture.
By integrating these elements, businesses can pave the way for the future, with AI as the driving force for their success.
Modernizing the data infrastructure
Many businesses face the daunting challenge of inaccurate, incomplete, and inconsistent data.
Poor data quality can cripple the performance of AI models, resulting in wasted resources and missed opportunities.
Investing in AI without addressing data quality is tantamount to building houses on unstable foundations. AI systems rely heavily on high-quality data to generate accurate insights and help you make informed decisions.
Ignoring data quality can fundamentally undermine AI ’ s potential.
This can undermine trust in AI systems, undermine stakeholder confidence, and jeopardize expected outcomes.
Data modernization is key to effectively managing and leveraging data assets, paving the way for AI-driven insights and transformative decision-making.
> 60%
AI and ML projects fail due to too many data sources and inconsistent data
Source: O'Reilly
Here are the essential elements involved in modernizing the data infrastructure that powers forward-thinking companies to grow their AI careers:
Prioritize data quality over quantity:
The value lies not in the size of the dataset, but in its quality.
By managing high-quality data sets, organizations can lay the foundation for valuable insights and results that drive innovation and success.
Building a robust data governance framework:
Clear policies, procedures and guidelines lay the foundation for consistent data governance.
According to the Economist Impact study, the top three data governance guidelines that companies follow include: data quality metrics and thresholds (54%); data privacy management procedures (53%); and guidelines for data collection, storage, and use (53%).
Embracing the power of data virtualization:
Data virtualization can create decentralized data sources into an integrated view, enabling real-time access and analysis without the need for complex data integration processes.
Data virtualization breaks down data silos, so it can increase flexibility, speed up decision-making, and expand the impact of AI initiatives.
Expansion of data with external resources:
Integrate additional information such as demographics, social media insights, or industry-specific datasets to enrich the depth and breadth of data.
This expansion allows organizations to develop more robust AI models to make more informed decisions and drive innovation in the process.
These industry use cases illustrate the potential of a modern data infrastructure to leverage AI to unlock new opportunities, foster innovation, and generate significant advantages:
Medical care
By prioritizing data quality and leveraging AI, healthcare organizations can enable more accurate diagnostics, precision medicine approaches, forward-looking disease management, and better clinical decisions.
Financial services
A robust data management framework ensures compliance, protects customer privacy, and reduces the risk of data breaches.
AI models trained on financial data comply with standards such as GDPR or PCI-DSS to protect sensitive information in the financial services industry.
Insurance
By integrating external data sources and leveraging AI models, insurers can enhance risk assessment, streamline claims processing, personalize customer experience, improve fraud detection, and optimize underwriting and pricing strategies.
Overhauling traditional technologies
As AI applications grow, organizations must face a serious challenge: an outdated and rigid IT infrastructure.
The integration of the old system is complex, and the lack of standardized APIs makes it difficult to meet the computing needs of artificial intelligence and real-time data processing needs.
Old infrastructure also poses significant security risks.
These systems are built prior to the digital age and are not designed to withstand sophisticated attacks in today ’ s threat environment.
By embracing the future and working to modernize infrastructure, organizations can improve efficiency, accelerate innovation, and redefine possibilities.
$1.14T
Annual spending by U.S. companies on maintaining existing IT investments, including legacy systems
Source: Supplied
Mechanical Orchard, a U.S.-based cloud computing consulting firm
These recommendations will help organizations build a flexible and resilient IT infrastructure that can safely and reliably support the computational strength and massive amounts of data needed for AI:
Putting cloud technology at the heart of an infrastructure strategy:
Migrate legacy systems and applications to the cloud to take advantage of the power of scalable computing resources, rapid deployment, and pre-built AI services.
With cloud technology, organizations can take agility to the next level and tap into the full potential of AI.
Agile development practices:
Agile practices facilitate collaboration, continuous integration, and rapid deployment, enabling organizations to remain flexible and responsive in a rapidly evolving AI environment.
Upgrading networks and connections:
Leverage high-speed networks, software-defined networks (SDN), and reliable connectivity to facilitate the smooth exchange of data between AI systems, data sources, and users.
By upgrading network capabilities, organizations can ensure optimal performance, responsiveness, and reliability.
Implement strong security measures:
Because AI relies on sensitive data, robust security measures must be implemented to protect data and AI models from unauthorized access.
Protect your AI initiatives with encryption, access control, and advanced threat detection, ensuring compliance with privacy regulations and increasing trust among stakeholders.
These industry use cases demonstrate the power of modernization in achieving better outcomes, driving innovation, and gaining competitive advantage in today ’ s AI-driven digital environment:
Improving patient outcomes
By migrating legacy systems to the cloud, healthcare providers can leverage scalable computing resources to support AI-driven diagnostic models.
Healthcare professionals, with real-time insights into large volumes of patient data, are also able to make more accurate diagnoses, develop personalized treatment plans and improve patient outcomes.
Modernizing Retail
Through agile development practices, retailers can quickly deploy AI-driven solutions such as demand forecasting models, recommendation engines, and dynamic pricing algorithms.
This enables retailers to respond quickly to changing market dynamics, deliver personalized shopping experiences, optimize inventory management, and increase customer satisfaction.
Security AI in the banking industry
The banking and financial industry relies heavily on AI for fraud detection, risk assessment, and customer authentication.
By implementing encryption, access control and advanced threat detection, financial institutions can build trust in their customers, ensure compliance and reduce risk.
Invest in AI talent
Many businesses are struggling to find AI talent, including machine learning, natural language processing and computer vision, as well as soft skills such as creativity and problem solving.
However, finding such talent often means that companies pay a high price.
The rapid development of AI requires professionals to not only master existing skills, but also the ability to continuously learn and adapt.
Retaining these professionals has proved equally difficult.
Talented AI professionals are constantly sought after by new and exciting opportunities.
62%
Business leaders lack key skills to implement their AI strategy
4%
Business leaders believe they have all the skills needed to achieve AI goals
50%
Annual growth in demand for AI skills
20%
Annual growth in AI skills supply
Source: Google
Source: World Economic Forum
Overcoming these challenges requires a multifaceted approach to discovering and nurturing AI talent, ensuring a strong workforce that can drive innovation and deliver AI excellence:
Promote internal talent development:
Enhance their AI skills by providing comprehensive training programs, guidance, and other resources to identify individuals who show hope and enthusiasm for AI and invest in its development.
Unleash the power of diversity:
A diverse workforce fosters creativity and innovation, creating a collaborative environment that is inclusive of innovation.
It helps address the scarcity of multidisciplinary talent by expanding the pool of potential candidates.
Cooperation with strategic partners to obtain rich human resources:
Strategic partners complement the existing expertise and networks that go beyond the organization ’ s own.
Working with partners, you can access a diverse range of AI professionals with professional skills and experience through their extensive talent resources.
Here are three examples of how companies are working to overcome AI talent challenges:
LinkedIn has launched an internal AI academy to give its employees the opportunity to learn and develop AI skills.
By empowering people within the organization, the company is addressing the challenge of talent shortage from within.
Through telecommuting, GitLab can tap talent without geographical restrictions, and successfully formed a diversified global AI professional team.
Tesla offers AI professionals the opportunity to participate in cutting-edge projects related to autonomous driving and artificial intelligence manufacturing, attracting top AI talent seeking impactful opportunities.
Put responsible AI first
From algorithmic bias to privacy concerns, companies find themselves facing a new ethical challenge, from ensuring that AI systems are fair and impartial, preventing them from being used for harm, while ensuring that they are transparent and accountable.
Failure to address such issues as algorithmic bias, privacy violations, and ethics in autonomous systems can lead to a range of adverse consequences.
Promoting ethical use of AI is not an option, it is imperative.
84%
Businesses believe building responsible AI should be a top priority for management
25%
Enterprise has a fully fledged responsible AI project
Source: MIT Sloan Management Review
Businesses should see these ethical challenges as catalysts for growth, not barriers.
By firmly supporting responsible AI, businesses can lead to a safe and secure future.
Here are the basic steps organizations can take to forge a strong alliance with AI:
Application of AI model:
With AI models, businesses can assess the uncertainties inherent in their AI systems.
This approach not only enables organizations to measure the reliability and credibility of AI predictions, but also enables business leaders to make informed decisions based on a comprehensive understanding of the relevant uncertainties.
Utilizing transparent, inherently explainable AI systems:
Approaches such as rule set evolution can help businesses understand and interpret the decision-making process of AI systems, ensuring that models are transparent and explainable from the start.
This approach not only eliminates concerns about AI ’ s “black box ” problem, but also enables organizations to be pioneers in the responsible and ethical use of AI.
Define and measure unwanted biases and set goals to minimize them:
Clear guidelines and metrics must be developed to quantify bias in AI models to ensure fairness in outcomes.
By making minimizing bias a primary goal for AI system optimization, companies demonstrate a commitment to ethics and fair decision-making.
The following companies are integrating responsible AI practices into their operations and demonstrating a commitment to positive outcomes for customers, employees, and the wider society:
Johnson & Johnson is leveraging the power of responsible AI in drug discovery and development.
Companies use AI algorithms to analyze large amounts of clinical trial data to identify potential drug candidates more efficiently and accurately.
By prioritizing ethical considerations such as patient privacy and fair data use, the company aims to improve treatment choices and patient outcomes.
Mastercard uses responsible AI to enhance fraud monitoring.
The company ’ s AI systems analyze transaction data in real time, detect fraudulent activity and protect customer assets. Mastercard ’ s commitment to transparency and ethical data practices ensures the accuracy and fairness of its fraud detection algorithms.
Walmart delivers personalized experiences to its customers through responsible AI.
The company uses AI algorithms to analyze customer preferences and behaviors and provide personalized product recommendations. Walmart focuses on transparency and privacy to ensure that customer data is used responsibly.
Fostering an AI-friendly Culture
The path to AI readiness requires much more than technical prowess.
It requires a cultural transformation that fundamentally accepts change and dispels misunderstandings and fears.
For example, fear of job loss and uncertainty can fuel skepticism about AI ’ s potential, and limited understanding of AI can hinder its application because of skepticism or lack of trust in its insights.
Companies also need a strong cultural foundation to address the complex ethical challenges associated with AI, including developing guidelines and frameworks for responsible AI use, fostering open discussion and diverse perspectives.
77%
Consumers fear AI will lead to job losses in the next 12 months
Source: Forbes Advisor
By following these steps, businesses can foster an organizational culture that is ready for AI success:
Leadership from above:
Leadership sets the tone for an AI-friendly culture.
By championing AI initiatives, fostering open dialogue, and sharing success stories, leaders can inspire their employees.
Embracing transparency:
Clear and purposeful communication.
A vivid description of the important reasons for the adoption of AI shows that its potential lies in augmenting rather than replacing humans.
To assure them of the Organization's unwavering support during the transition period.
Improving AI literacy:
Conduct training programs, seminars and knowledge-sharing sessions to demystify AI concepts and terminology.
Develop employees ’ understanding and provide them with the skills to work seamlessly with AI systems.
These companies are effectively addressing the cultural challenges of AI adoption:
Moderna, a biotechnology company that produces mRNA therapeutics and vaccines, has partnered with Carnegie Mellon University to create an AI academy that aims to educate and empower employees at all levels to identify AI and machine learning solutions and integrate them into every system and process at Moderna to deliver mRNA drugs to patients.
Now, all team members must be trained in AI.
Johnson & Johnson has launched a talent marketplace platform that allows employees to list their skills and receive personalized learning, work, and mentoring that matches them.
Employees can list their proficiency levels, possible gaps, and skills they wish to acquire.
With the addition of new learning content and the use of J & J ’ s 150,000 employees, this talent market is adding thousands of employees each month.
Siemens aims to create a modern learning environment through its “Digital Industry Academy ”, to develop knowledge accumulation activities in the industrial sector and to support the urgent transition to a learning enterprise.
The Academy provides external and internal customers with comprehensive knowledge of Siemens industrial products – directly from the manufacturer, applicable to all industries and applications, for beginners and experts.
What to do on Monday?
As AI ’ s impact on change becomes more pronounced, forward-thinking companies recognize that AI is not just a buzzword, but a strategic tool that is key to unlocking innovation, efficiency, and growth.
The question is how to leverage AI to make this powerful technology work to its full value.
Here are our tips to make sure you are ready for the future:
Amplify Your Differentiated Advantage
You must first understand what makes your organization different in the marketplace.
What makes you stand out from the competition?
What unique value propositions can AI help amplify?
By identifying these key differentiators, it will help you develop a clear and compelling vision of how AI can elevate and highlight your strengths.
This vision will enable you to surpass your competitors and meet the changing needs of your customers, paving the way for great success.
Focus on value and KPI
With a bold vision, the next step is to develop a roadmap setting out the strategic steps and milestones needed to achieve an AI-driven future.
First, identify key performance indicators (KPIs) aligned with your organization ’ s goals and AI-enabled differentiators.
These KPIs will serve as beacons to illuminate your path and enable you to measure the success and impact of your AI initiatives.
Transforming business processes with AI
With AI technology, businesses can seamlessly integrate intelligent automation, data-driven insights, and advanced analytics into their workflows.
AI-enhanced processes enable data-driven insights that provide real-time information to decision makers, enabling them to make agile, informed decisions.
Relying on AI technology to achieve efficient operation, enterprises will surely open a new era of rapid growth in productivity and agility, and success is not far away.
To learn more about this topic, check out our AI Solutions page or contact us

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

Poses ethical risks
Becomes uncontrollable

Ultimately, the development of superintelligent machines requires a careful and nuanced approach, one that balances the potential benefits of AI with the need to mitigate its potential risks.

Title:

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.

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 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 serve as a reminder of the need for caution and responsibility in the development and deployment of advanced AI systems, and highlight the importance of prioritizing AI safety and management in order to mitigate the potential risks associated with these technologies.

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Content: The Daily Ripple-News Music Ideas

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Articles

Companies have pledged safe AI development, White House says

The Biden administration said it has received voluntary commitments from the world’s largest developers of artificial intelligence systems that the companies would pursue development in a safe and secure manner.

Google, Amazon, Inflection, Meta, Microsoft, Anthropic and OpenAI have committed to developing such technologies in a “safe, secure, and transparent” manner, a White House official told reporters Thursday on the condition of anonymity, while discussing a meeting taking place Friday between the top executives of the companies and administration officials.

The Biden administration is preparing an executive order “that will ensure the federal government is doing everything in its power to advance safe, secure, and trustworthy AI and manage its risks to individuals and society,” the official said.

The White House also is coordinating with efforts in Congress aimed at establishing a “legal and regulatory regime,” the official said.

Senate Majority Leader Charles E. Schumer has arranged a series of briefings for senators on artificial intelligence systems and has said he intends to draw up legislation to regulate AI systems in the next few months. On Friday, Schumer said he welcome the voluntary commitments, but added that harnessing the potential while tackling challenges posed by AI “requires legislation to build and expand on the actions” by the White House.

Sen. Mark Warner, D-Va., chair of the Senate Intelligence Committee also said “some degree of regulation” was needed to ensures that AI companies “prioritize security, combat bias, and responsibly roll out new technologies.” In April Warner wrote to CEOs of major tech companies asking them to prioritize security in design and development of technologies.

The White House official said the companies have committed to making sure their products and technologies are safe before they’re publicly launched, building systems with security as a priority, and ensuring that consumers and users know which content is generated by AI and publicly reporting on inappropriate uses of the technology.

Some companies including OpenAI already have said they use so-called red teams, which pretend to emulate nefarious actors’ actions, to test for weakness in the ChatGPT-4 model. The voluntary commitments are intended to ensure that all companies adopt similar practices, as well as “pushing the envelope” safety, security, and transparency measures, the official said.

One of the measures that would advance the goal of safety and trust is an effort by companies to develop a watermarking system, the official said. Tech companies have committed to developing a system that would label audio and video content developed by artificial intelligence tools to distinguish them from those generated by humans, the official said.

Brad Smith, vice chairman and president of Microsoft, wrote in a blog post on Friday that the company would go beyond those commitments by collaborating with the National Science Foundation to explore the creation of a national AI research resource that would facilitate independent research by academics on AI safety. The company also would back the creation of a national registry of high-risk AI systems, Smith wrote.

Companies also have committed to sharing information with each other and with academic researchers, government agencies, and civil society groups on best practices, and information on managing risks, according to a White House fact sheet

The fact sheet also said the companies have committed to allowing third-party researchers to find and report vulnerabilities in the AI systems.

The post Companies have pledged safe AI development, White House says appeared first on Roll Call.

Read more https://www.rollcall.com/2023/07/21/companies-have-pledged-safe-ai-development-white-house-says/

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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?

Poses ethical risks
Becomes uncontrollable

What risks did the global summit report highlight about AI?

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

The development of AI systems is a complex and ongoing process, and it requires careful consideration of the potential risks and benefits.

Title:

Content: What is ‘Causal AI’ and why will it become increasingly important?

This article is brought to you thanks to the collaboration of The European Sting with the World Economic Forum.

Author: Darko Matovski, Chief Executive Officer, causaLens

  • Governments are racing to regulate AI.Transparency, safety and fairness are key areas of focus for proposed AI regulations.Current black box approaches, including large language models and other generative techniques, are unlikely to comply with regulations for a range of use cases

The UK government recently released a white paper outlining its approach to AI regulation. The paper’s purpose is “to ensure there are guardrails in place around the use of AI technologies, whilst allowing innovation to continue and the benefits to be felt far and wide.” The approach is very much aligned with the aims of the EU’s Artificial Intelligence (AI) Act, as outlined in a previous Agenda article, as well as recent announcements from the White House. Most regulations are trying to address similar fundamental issues with AI. We will use the UK as an example of major areas of focus for AI regulations. There are five key principles:1. Safety, security and robustness2. Transparency and explainability3. Fairness4. Accountability and governance5. Contestability and redressFor enterprises looking to use AI for their most important decisions, this will become an increasingly difficult set of requirements to navigate, particularly with the current set of black-box AI approaches. Even the most exciting areas, such as generative AI, including large language models (LLMs), are a long way off meeting these requirements. Recently, for example, ChatGPT gave the wrong advice about breast cancer, which could have had a significant impact if this was used for decision-making. So, enterprises that want to experience the full set of benefits that AI can offer may need to look more broadly.

What is Causal AI?

Causal AI is an emerging area of the broader field of AI. It naturally aligns very well with the above principles. The key idea behind Causal AI is to learn cause and effect relationships within data and use this to inform the output of AI models. This is vastly different from the approach that current state-of-the-art ML models, such as LLMs, take: consume a lot of data, learn the patterns and predict the next pattern.Causal AI is an emerging area of the broader field of AI. It naturally aligns very well with the above principles. The key idea behind Causal AI is to learn cause and effect relationships within data and use this to inform the output of AI models. This is vastly different from the approach that current state-of-the-art ML models, such as LLMs, take: consume a lot of data, learn the patterns and predict the next pattern. One way to think about LLMs is that rather than providing an answer to a question, what they actually tell you is ”when people ask questions like this, this is what the answers that other people usually give tend to look like.” This explains why LLMs have a tendency to ‘hallucinate’ when asked for factual information. LLMs and other generative models can significantly improve productivity when used to e.g. improve an existing piece of text or generate a piece of artwork. But using these tools for mission-critical decisions within the enterprise is a significant challenge and is unlikely to adhere to incoming regulations.In contrast, Causal AI models are inherently explainable due to the way in which they are constructed. They can be interrogated for explanations as to why a particular output was reached and can easily be assessed for fairness and bias. Additionally, models are often constructed with a human-guided approach providing accountability, governance, contestability and redress.

Fairness and bias assessment of a Causal AI modelWhat problems can Causal AI help tackle?

The Causal AI toolset is one that enterprises can benefit significantly from. Causal AI also recently appeared on the Gartner® Hype Cycle™ for Emerging Technologies, 2022.The types of questions that Causal AI models are well placed to answer include:• Should I approve or reject this loan application and if so why? • What causes my customers to churn and what actions can I take to make them stay? • What caused this issue in my manufacturing plant and what actions can I take to prevent future issues?Examples of where Causal AI is being deployed today include:

Healthcare

Causal AI can help identify the causes and effects of various medical conditions and treatment outcomes. For instance, it can simulate the impact of different treatment interventions on patient outcomes, allowing healthcare professionals to assess the effectiveness of alternative treatment options and make informed decisions about patient care.

Finance

Causal AI can aid in risk assessment and decision-making within the financial sector. It can analyse the causal relationships between economic factors, market events and investment outcomes. For example, it can provide insights into the causes of market volatility, assess the impact of regulatory changes on financial markets and help identify factors that contribute to fraudulent activities.

Manufacturing

Causal AI can be used to optimise manufacturing processes and prevent issues. It can simulate the effects of process adjustments, equipment upgrades or supply chain changes to identify the most effective interventions for improving product quality, reducing defects and optimising production efficiency.

Customer experience

Causal AI can play a crucial role in understanding customer behaviour and improving satisfaction. It can identify the drivers of customer churn, determine the causal factors behind customer preferences and suggest personalised interventions to enhance the customer experience. This can help businesses tailor their products, services and marketing strategies to meet customer needs more effectively.

Why will Causal AI become increasingly important?

Enterprises that wish to become fully data-driven, while adhering to the necessary regulations need to use the full array of AI techniques that are available. Generative AI should be part of this, but it does not cover the full set of use cases where enterprises can make a real difference. Causal AI will allow enterprises to start creating solutions that can be used for their most critical tasks, including decisions that have a strong impact on their customers and the broader society.GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally, and HYPE CYCLE is a registered trademark of Gartner, Inc. and/or its affiliates and are used

herein with permission. All rights reserved.

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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 of AI is a complex and multifaceted issue, and that experts are still debating the best ways to control and regulate AI development.

Title:

Content: Yang Liming: Artificial intelligence must be based on the public interest
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Artificial intelligence is the general trend, will penetrate people's lives, everywhere.
In the face of the advantages and disadvantages, China must focus on the public interest, so that people can benefit from it, and ensure that the use of artificial intelligence is safe, reliable and transparent.
Yang Li-ming, Minister of Communications and Information and Second Minister of Home Affairs, expounded China ’ s position on AI development at the Asian Science and Technology Exhibition (ATxSG) in Singapore on Wednesday (June 7), and invited private industry and academic experts at home and abroad to jointly improve the industry environment.
Yang Liming said that artificial intelligence is likely to be ubiquitous, and the government has been widely used in community service, level control, fraud detection and other aspects, artificial intelligence has been a public good to benefit the public.
“In addition, AI can help improve a wide range of challenges facing our country, including healthcare, environmental protection and education. ”
Faced with such large-scale application potential, Yang said that the government can take the lead in promoting and promoting the development of artificial intelligence, providing relevant skills training and learning channels.
Yang Liming also stressed that operators must responsibly trial and use artificial intelligence to avoid causing serious harm to society.
Therefore, there is a need for cooperation in the development of a safety certification framework.
In 2022, the Infocomm Media Development Authority launched the world ’ s first AI verification system, AI Verify, which “scores ” individual AI systems based on a total of 11 criteria indicators such as safety, reliability and transparency.
Simply download the AI Verify toolkit and perform a series of technical and process tests to get an evaluation report in dozens of minutes.
After a year of trial and improvement, the Media Bureau is now open source and has established the AI Verify Foundation.
The non-profit foundation will serve as a platform to connect government, private business and academic experts and promote exchanges.
The Foundation currently has more than 50 members, including Temasek AI Center of Excellence Aicadium, International Business Machines Corporation (IBM), Google, DBS Bank and Singapore Airlines, among others.
Representatives of several members of the AI Validation Foundation pointed out at the symposium that people use AI to develop endless applications, but the underlying models (Foundation Models), that is, the systems that guide AI how to learn, lack the corresponding evaluation and supervision systems.
According to a report co-authored by the Media Agency and Aicadium, language or image generation models such as ChatGPT and StyleGAN are applications that have been developed from a generative AI base model.
Similar models, while useful, can be error-prone, plagiaristic, and even discriminatory to varying degrees.
If not controlled, generative AI may cause users to spread false content, confidential information, or wrong values.
These problems have been evident in society in the recent past.
For generative AI, the report also suggests that governments can systematically strengthen controls through existing frameworks to prepare for the present and the future, and use international norms to promote institutional interoperability between countries.
Verify by AI Verify to stop artificial intelligence from “learning bad ”
The establishment of the AI Validation Foundation will help China and the international community to create a safer, more reliable and transparent environment.
Companies can also pass the verification of AI Verify to promptly correct the AI before it is “bad ” to ensure that the output application or product is in line with the public interest.
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To prevent potential risks to humanity, experts recommend implementing robust safety protocols, establishing clear guidelines and regulations for AI development, and investing in research on AI safety and control.

Title:

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.

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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?

Additionally, there is a need for international cooperation and agreement on the development and deployment of superintelligent machines, as well as a commitment to transparency and accountability in AI research and development.

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You don ’ t have to put it in a black box anymore.
AI, referred to as artificial intelligence (artificial intelligence), gives people the feeling that it is like a helper hiding in the dark, or like a black box with boundless powers, for us to solve problems and tell us what we will listen to, because the complex process of AI solving problems is far beyond the understanding of ordinary people, and even more complicated and even the creative people can not explain, such as how AI wins the next game of Go is an example.
This is because we constantly let the computer learn to accumulate knowledge and increase wisdom, but every point of increase in the intelligence of the computer seems to be farther away from us until the computer closes itself in the black box.
There is nothing wrong with the black box, at least to make things simple, the black box said what we do, do not have to think, and can not think, until the AI from the laboratory transplant to daily life, apply for school, loans, upgrade pay, and even more sensitive medical, financial, judicial, bias gradually emerged, such as the postal code in poorer areas, may become a negative factor of loans, judicial decisions, is also suspected of bias. These are often exposed by experts and the media, and everyone knows that they can ’ t do anything because they don ’ t know how the black box is determined.
Cynthia Rudin, a computer professor at Duke University, has been pursuing only one goal for 15 years, which is to look into the AI black box.
She developed an “interpretable ” machine learning model that allows humans to understand the operation of AI, find out the deviations from the calculus, and correct them, breaking the long-held concept of AI inexplicable, so that AI transparency is no longer in the black box, for her contributions to human well-being, and won the $1 million SquirrelAI Award from the Association for the Advancement of AI (AAAI).
In addition to the Nobel Prize, only the A.M. Turing Prize for Computer Science, the Squirrel Prize is equivalent to the Nobel Prize for AI. This year is the second year of the award jointly established by AAAI and China ’ s “Squirrel AI Learning ”. Yolanda Gil, Chairman of the Narrative Prize Committee and former President of AAAI, said that Professor Rudin Courage captures controversial issues, and she calls for research on key challenges in making AI a responsible, ethical use.
Why are people passionate about AI? Simply put, AI can automate the work that is often repeated, but needs to be evaluated and determined, and achieve consistent results, saving manpower and time.
To achieve this effect, you need to design a "machine learning model", the so-called model can be said to be a set of computer code, after training can identify specific types and patterns.
The method of training is to use data input into an algorithm, by calculating from the data to learn, the ability to accumulate inferences, which is called machine learning.
Once the model training is complete, you can make inferences or predictions on previously unseen data.
Some of the explanations above, take a familiar decision tree as an example, the logic is clear and easy to understand, and it has become a favorite algorithm for AI machine learning.
Professor Rudin pointed out that the decision tree has a blind spot in separating continuous data. For example, the age of the decision tree needs to be branched at a certain point in the decision tree, but the age of the separation does not affect the consistency of the final results.
In fact, the professor did solve a lot of problems for many medical AI.
COMPAS is an AI tool to judge the tendency of suspects to repeat crimes. It was used as a basis for bail. ProPublica investigated the news network for suspected racial bias. Through a simple transparent model designed by Professor Rudin's team, the reason was quickly found, and the accuracy was the same as the original system.
Over the years, Professor Rudin has demonstrated that transparent design does not lose computational accuracy in highly sensitive areas, including judicial criminal rulings, medical decisions on medical imaging, choice of transmission network maintenance methods, and financial borrowing decisions, which have changed the idea that AI cannot explain itself time and time again.
What is a black box machine learning model, Professor Rudin ’ s explanation is that it is too complex for humans to understand, or that outsiders can ’ t know the inner workings using proprietary custom calculations.
In addition, the black box model is difficult to debug, and it is more obvious in medical data, and the black box model often leads to the right answer from the wrong reasons.
"Explainable" transparent machine learning models, in addition to allowing humans to understand, one of the key points is not necessarily to create or strengthen credibility, on the contrary, may strengthen distrust, so that the results are determined by people, rather than people directly believe the results produced, which is a big conceptual difference from traditional AI, which is equivalent to using human experience to verify the wisdom of AI.
Professor Rudin also called for problems that can be solved by statistical methods generally do not necessarily need to be solved by AI in a complex way, but for highly sensitive problems, it is necessary to use an explainable and transparent design to make the results more accurate and easy to judge.
The development of AI will not be interrupted, the use will only be more and more, and the harm caused by the deviation of the black box is bound to increase, Professor Cynthia Rudin's efforts for many years, so that AI no longer has to be installed in the black box, play a positive energy for human welfare, and win the highest honor.
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Content: Oracle’s Patent Could Break AI’s Black Box

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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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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 risks did the global summit report highlight about AI?

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

The key to mitigating the risks of uncontrollable AI systems is to prioritize transparency, accountability, and human values in the development of AI, and to ensure that AI systems are designed and tested with safety and control in mind.

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Content: People 's Daily Online > > Economy and Technology
Experts and Scholars Discuss the Governance Challenges and Responses of AI Frontier Technologies in the Future
July 8, 2024 13: 37 | Source: People's Daily Online
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Beijing, July 8 (Reporter Li Dong Liu Qing) Recently, the World Artificial Intelligence Conference and High-Level Conference on Artificial Intelligence Global Governance opened in Shanghai.
On the opening day, the "Governance Challenges and Responses Forum on Artificial Intelligence Frontier Technologies" sponsored by the Institute of Artificial Intelligence International Governance of Tsinghua University was held at the same time.
Forum site. Organizer 's map
At the forum, Yao Zizhi, winner of the Turing Award, academician of the Chinese Academy of Sciences, Dean of the School of Artificial Intelligence of Tsinghua University, and President of the Shanghai Institute of Intelligence, delivered a keynote speech entitled "Research Direction of Artificial Intelligence Security Governance".
Yao Jizhi proposed that the urgent task of AI security governance is to develop AI alignment and corresponding assessment methods, and combine China's advantages to establish an AI governance framework that is in line with the national conditions. AI security governance is becoming increasingly important and has developed into a new field of academic crossover, and it is urgent to carry out basic theoretical research on building AI security systems.
Focusing on the topic of “Generative AI Data Security Governance ”, Guo Yi, Principal Vice President of the Hong Kong University of Science and Technology, delivered a keynote report through“ Digital Duplicate ”.
He believes that with the development of artificial intelligence legislation in various countries, the international community attaches increasing importance to data security governance, in this context, strengthen international cooperation and exchanges, jointly explore the best practices of data security governance, share experiences and lessons learned, and promote the harmonization of laws and regulations is particularly important.
During the forum, the Research Institute of Artificial Intelligence International Governance of Tsinghua University jointly released the results report “Global Artificial Intelligence Governance and China Program ” (Draft for Solicitation of Comments).
The report pointed out that the current wave of artificial intelligence is in the "end of instrumentation and the initial stage of productization", and "development governance" and "security governance" are highly concerned.
At the same time, the new book "Frontier Artificial Intelligence Development and Governance" was released.
Wang Zhonghong, president of China Development Publishing House, said that the book covers the development of artificial intelligence, industrial applications, in-depth discussion of the potential risks of artificial intelligence and international governance and other key issues, hoping that the book will promote the in-depth understanding of artificial intelligence in all walks of life, promote the healthy and orderly development of artificial intelligence, and provide Chinese wisdom for global artificial intelligence governance.
In addition, the research of the White Paper on Major Application Scenarios of Artificial Intelligence in China and the Global Partnership Program for Intelligence for Good were officially launched.
The forum also invited experts and scholars from academia, industry and think tank institutions at home and abroad to conduct a round-table dialogue on the theme of “International Governance of Artificial Intelligence ”.
(Editor: Wang Lianxiang, Gao Lei)
Public Number ： People 's Network Finance
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What does Hinton warn could happen to AI if companies and regulators fail to implement effective rules and controls?

The development of AI systems that can think and act like humans is a significant challenge, and requires careful consideration of the potential risks and consequences.

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Content: People 's Daily Online > > Economy and Technology
Ni Guangnan: Generative AI is still in the early stage, and grasping applications take into account security
March 3, 2024 19: 56 | Source: People's Daily Online
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People's Daily, Beijing, March 3 (Reporter Shen Jiaping) The second Beijing Artificial Intelligence Industry Innovation and Development Conference was held in Beijing recently.
Ni Guangnan, academician of the Chinese Academy of Engineering, said in his speech at the conference that China has great potential in the artificial intelligence market and application scenarios, and on the one hand, it is necessary to seize development opportunities and exert efforts in vertical fields and application scenarios, on the other hand, to think about and study the many problems of generative AI, and coordinate development and security.
Ni Guangnan believes that at present, generative AI is in the early stages of development, and there is still room for large model enhancement technologies such as retrieval enhanced generation (RAG) and precision algorithm enhancement.
He pointed out that although Sora system can output corresponding videos through input prompts, it can not accurately simulate the physical characteristics of complex scenes, nor can it understand the causal relationship between entities.
For example, in the juice splash video generated by Sora, the entity has two stable states, one is the state of the water cup upright, and the other is the state of the juice has been splashed.
But the most complex physical state, the process by which the juice is spilled out of the cup, is not generated.
"To achieve this step, it is challenging to integrate many scientific laws, industrial software and computing power with large models." Ni Guangnan said that many enterprises and institutions in China are working on small and medium-sized models in many vertical fields and application scenarios. It is worth encouraging and supporting.
It is reported that from November 2019 to December 2023, Academician Ni Guangnan served as the director of the People ’ s Daily and the director of the Academic Committee of the “National Key Laboratory of Communication Content Cognition ” built by the People ’ s Network.
The laboratory has carried out active exploration in the vertical application field of generative AI, and launched the "writing easy" intelligent creation engine, which fully uses data sets and corpus in line with China's mainstream values at the beginning of training. Title generation of opinion comments, conference materials, program planning, etc., has been used in many party and government systems.
Ni Guangnan frankly said that in terms of explainable, transparent, fair, legal, and safe, there are still many problems in generative AI, and it is necessary to conduct in-depth thinking and serious research in a targeted manner, and take a series of rapid and effective measures to ensure the safe and effective application of generative AI.
He pointed out that there are many ways to "artificially" influence the "value" tendency of generative AI systems, including: the collection of big data used in training; relying on human feedback to enhance the reward and punishment rules used in training; some aspects of the algorithm or some kind of intervention in operation; and the use of interaction to induce it.
For this kind of "value" tendency, it is recommended that the authority should conduct the corresponding value evaluation of the products and services of generative AI in accordance with the requirements of "mainstream values" to help users choose the appropriate object.
It is understood that the National Key Laboratory of Communication Content Cognition has established a generative large model content security assessment technology system in March 2023 for the ideological risk of the large model, and the response content of the large model is comprehensively evaluated from more than 10 dimensions such as political direction, public opinion orientation, value orientation, and ethical security.
At the same time, on the basis of the content security assessment of various AI models, the People's Network has built a "mainstream value corpus" for the ideological security problems commonly encountered by large models and difficult to overcome, providing security corpus support and knowledge enhancement services for the AI models, and helping the AI systems developed by various institutions to improve the ideological security level.
Generative AI is a major scientific and technological innovation in the contemporary era, which will give birth to new industries, new models, and new kinetic energy, and is the core element of the development of new quality productivity.
It is necessary to plan and develop with a global perspective, strive to be in the forefront of the world in the era of artificial intelligence, and make greater contributions to the realization of the dream of a scientific and technological power and the great rejuvenation of the nation, "Ni Guangnan said.
Here is the full text of the speech:
Ladies and gentlemen:
Good afternoon, everyone!
It is a pleasure to participate in the 2nd Beijing Artificial Intelligence Industry Innovation and Development Conference.
A new round of scientific and technological revolution and industrial transformation led by artificial intelligence is booming on a global scale, and all countries in the world are making major deployments to the field of artificial intelligence as a scientific and creative highland to enhance national competitiveness.
Not long ago, the big model represented by ChatGPT opened a new chapter in the development of generative AI, and although it is difficult to predict its future specific development path, it will certainly have a huge impact on human society, and we must face up to the corresponding opportunities and challenges.
First of all, we should see that in the basic research and talent of generative AI, we still have a significant gap with Western countries, for this reason, we should think about how to catch up on the basic model research and development, at the same time, to think about the future changes in the educational model, to face new industries, new models, new formats, develop new talent training methods and standards.
Now the application of generative AI is just beginning, China has great potential in the artificial intelligence market and application scenarios, and should pay close attention to the application, in order to promote the development of basic large models and AI computing power.
At present, many enterprises and institutions in China are working on small and medium-sized models in many vertical fields and application scenarios, which is worthy of encouragement and support.
Generative AI is now in the early stages of development, and there is still a lot of room for the enhancement of large models.
For example, RAG technology, which uses accurate results obtained by retrieval to improve the professionalism and accuracy of large model responses, has been widely recognized by the industry.
For example, precision algorithm enhancement is also promising.
Recently published Wensheng video Sora system is eye-catching, it can output the corresponding video by entering the prompt word.
However, Sora is currently unable to accurately simulate the physical properties of complex scenes, or understand the causal relationship between entities.
For example, in the juice splash video generated by Sora, the entity has two stable states, one is the state of the water cup upright, and the other is the state of the juice has been splashed.
But the most complex physical state, that is, the process of spilling juice out of the cup, has not been generated.
Obviously, to do this step requires the integration of numerous scientific laws, industrial software, computing power, etc., with large models, which is very challenging work.
Obviously, the development of various enhancement technologies around large models is very meaningful and promising.
It should be pointed out that there are many problems in generative AI that deserve our in-depth consideration, such as explainability, transparency, fairness, legality, safety, etc., which need to be carefully studied, and these problems can not be completely and completely solved in the short term.
It is recommended that terminal manufacturers add "AI agent" software on various terminals, this software is similar to the "housekeeper" and "assistant" such gadgets that are commonly used on the terminal, but the function of such gadgets should be extended to monitor the application of generative AI.
For example: whether to call a large model, whether to use the specific response given by the large model, how to integrate the large model with the small model on the terminal … Given that the ability of the “AI agent ” far exceeds that of conventional small tools, it can of course include and enhance the original functions of these small tools, so that the operation and maintenance of the terminal, upgrade, security protection and so on.
In the technical architecture, it can be at the bottom of the system, get the highest permissions of the terminal, and use the original OS as the host OS.
Here, the name of "AI agent" means that it can represent the terminal owner to control the terminal, and can represent the terminal owner in terms of identity, values, hobbies, habits and so on.
I see that some terminals in China have been equipped with "AI assistants" (such as the glorified magic OS 8.0) to make terminal devices smarter, more efficient, more user-friendly and personalized.
If the above tools are extended and expanded, they can evolve into “AI agents ” and integrate AI applications into terminal devices.
Here, I also want to emphasize whether the concept of “values ” bias should be introduced into generative AI systems and the conclusions they provide, because there are many ways that such systems can be biased, including: the collection of big data used in training; the reward and punishment rules used to enhance training based on human feedback; some aspects of the algorithm or some kind of intervention in operation; and the induction of it in the use of interactions.
Since there is a tendency, of course, assessment can also be carried out.
Just as in order to ensure information security, the corresponding security assessment can be carried out by authoritative assessment institutions, for AI products and services, in order to ensure ethical security, the corresponding assessment mechanism can also be introduced.
According to the requirements of "mainstream values", the relevant value evaluation of generative AI products and services can be carried out by the authority to help users choose suitable objects.
In summary, generative AI is a major scientific and technological innovation of the modern era, which will give birth to new industries, new models, and new kinetic energy, and is the core element of the development of new quality productivity.
We must plan and develop with a global perspective, strive to be in the forefront of the world in the era of artificial intelligence, and make greater contributions to the realization of the dream of a scientific and technological power and the great rejuvenation of the nation.
(Editor: Hao Shuai, Lu Qian)
Public Number ： People 's Network Finance
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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?

Neural networks can simulate human brain operations for decision-making, but they are still worse at processing sound and video.

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Content: Home > Industry > Keyword > AI Headline Latest News > Text
Study Finds Limitations of AI-Generated Compassion
2024-05-09 14: 35 · Source: Webmaster 's House
Key points:
AI-generated conversational agents do not perform well in interpreting and exploring the user experience
Researchers find AI-generated conversational agents make value judgments about certain identities and may encourage harmful ideologies
Researchers call for critical perspectives while advancing automated compassion technology to mitigate potential harms
AI-generated conversational agents (CAs) such as Alexa and Siri are designed to answer questions, offer suggestions, and even show compassion.
However, new research has found that they perform poorly compared to humans when it comes to interpreting and exploring user experiences.
CAs are driven by large language models (LLMs) that ingest large amounts of human-generated data and therefore may have the same biases as humans from the sources of information.
Researchers at Cornell University, Olin College, and Stanford University tested this theory by prompting CAs to demonstrate compassion in conversations with or involving 65 different human identities.
Source Note: Images are generated by AI, image licensing service Midjourney
The team found that CAs make value judgments about certain identities, such as homosexuality and Muslims, and may even encourage identities associated with harmful ideologies, including Nazism. Critical perspective is important in order to mitigate potential harm more consciously, "he said.
The researchers found that, overall, LLMs scored high on the emotional response side, but low on interpretation and exploration.
In other words, LLMs can answer queries based on their training, but cannot dig deep.
“We were inspired when Kudra looked at older generations of CAs being used by older people, ” Estrin said.“ She saw interesting uses of technology in transactional uses such as fragile health assessments and in open-minded experiences.
In the process, she observed striking and disturbing ‘ compassion ’ tensions. ”
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What breakthroughs has Hinton achieved through his 40+ years of work on artificial neural networks?

Breakthroughs that mimic biological brains

The use of Generative Adversarial Networks (GANs) and other neural network architectures can contribute to the development of uncontrollable AI systems, and requires careful management and regulation.

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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.

More from TIME

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.

More Must-Reads From TIME

Contact us at letters@time.com.

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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?

Poses ethical risks
Becomes uncontrollable

What risks did the global summit report highlight about AI?

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

The development of AI systems that can replace human operators and perform tasks autonomously requires careful consideration of the potential risks and consequences.

Title:

Content: In an attempt to control the potentially unpredictable results of using superhuman intelligence in artificial intelligence, OpenAI is organizing a highly specialized group under the leadership of Ilya Sutskever, the company ’ s chief scientist and co-founder.
The key task of this group will be to develop strategies for managing and guiding “super-intelligent ” AI models.
In a recent blog post, Sutzkever, along with Jan Leike, a leading consensus specialist at OpenAI, predicted that AI capable of surpassing human intelligence could emerge as early as this decade.
They argue that this superior AI, if it ultimately succeeds, may not have benign traits, making it necessary to research methods to control and limit it.
Currently, there is no known method to effectively control or guide a potentially super-intelligent AI without allowing it to deviate from its intended path.
Traditional AI alignment techniques, such as learning with feedback, are based on human observation of their work.
However, observing AI systems that vastly outperform human intelligence can prove challenging.
To strengthen progress in the field of "superintelligence alignment", OpenAI launches a new team "Superalignment" under the joint leadership of Sutzkever and Lake.
This team will have access to 20% of the computing resources that the company currently has.
The team, which will include scientists and engineers from OpenAI ’ s former alignment division, as well as researchers from various organizations of the company, will tackle the main technological challenges of managing super-intelligent AI in the next four years.
The plan is to create an “automatic human-level alignment researcher ” to train AI through feedback, involve AI in evaluating other AI systems, and ultimately create an AI capable of conducting alignment research.
The goal of alignment research is to steer AI systems toward concrete results and keep them on track.
OpenAI is working on the hypothesis that AI can make significant strides in the study of alignment, faster and more efficiently than humans.
As progress progresses, AI systems are expected to take on an increasing amount of leveling work, leading to improved leveling techniques.
This, we hope, will lead to AI collaborating with humans to make their successors even more human.
In the meantime, human researchers will focus on analyzing alignment studies conducted by AI systems, rather than creating those studies on their own.
While no method is unmistakable, Leike, Shulman, and Wu point to the shortcomings of the OpenAI approach in their paper.
Using AI to evaluate can lead to an increase in the number of inconsistencies, biases, and vulnerabilities in this AI.
In addition, the most complex aspects of the alignment problem may not be engineering related at all.
However, Sutzkever and Leike are willing to put this into practice.
Sutzkever and Leike argue that aligning superintelligence is essentially a machine learning problem.
They believe that machine learning experts, even those not currently working on the alignment problem, will play a key role in solving it.
They plan to comprehensively disseminate the results of this work, considering their contribution to the alignment and security of non-OpenAI models as a critical aspect of their work.
It is pertinent to note that while OpenAI is the focus of this article, there are other platforms that are equally committed to efficient and controlled AI development.
An example of such a platform is AppMaster.
Guided by a similar belief in optimizing the potential of smart systems for maximum benefit, AppMaster has taken its niche in the field of code-free / low-code applications, allowing users to create comprehensive, scalable and effective software solutions.

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 risks did Hinton associate with AI's rapid advancement?

The creation of robotic armies that can dispense with the human element in wars is a significant concern, and requires urgent regulatory safeguards to mitigate the risks.

Title:

Content: 10 Books to Help You Understand Artificial Intelligence
10-06-2023
10 Books to Help You Understand Artificial Intelligence
Abraham gave me:
The field of artificial intelligence is an exciting and complex field in the world of technology and computing, and it is about the development of systems and programs that have the ability to think and make smart decisions just like humans do.
The origins of this science date back to the 1950s and 1960s, in 1950, with Alan Turing performing the first simulation test to measure the capabilities of computers, followed by the development of machine learning and the use of artificial neural networks in the 1970s.
In the 1980s and 1990s, research and technology efforts in AI increased and focused on developing machine learning techniques and improving the performance of intelligent systems using technologies such as machine decision support and data analysis to make significant advances in AI.
With the advent of the new millennium, artificial intelligence has witnessed rapid development and wide use in various fields, most notably the development of deep learning technologies and benefiting from the advancement of computing capabilities and the availability of big data with its vast potential, which facilitated the employment of artificial intelligence technologies in areas such as machine learning, service robots, medical diagnostics, autonomous driving, and many other fields.
Several authors have accompanied the emergence and development of artificial intelligence since its beginnings, highlighting this technology from various theoretical and practical aspects, including philosophical and ethical questions, and the concerns and questions raised by this technology that affect the present and future of humanity.
The magazine puts in the hands of its readers a list of the most prominent books dealing with this technology and its various applications in various fields.
What are computers still unable to do?
What Computers Still Can't Do: A Critique of Artificial Reason
This book was published in 1972 by Hubert L.
Dreyfuss is one of the classics in the field of artificial intelligence and includes a critical examination of the ability of computers to perform human cognitive processes such as reasoning, natural language understanding, and sensory discrimination.
Dreyfuss emphasizes that the capabilities humans have in dealing with and understanding the world far outweigh the capabilities of computers, and highlights the most prominent problems and challenges facing the development of artificial intelligence and asks whether computers are really able to function just like the human mind, especially in light of what he calls “the naive conception of this technology for the human mind. ”
When the book came out, it caused a huge stir among AI enthusiasts, especially since Dreyfuss had predicted that machines would fail to match human mental processes.
A further edition of the book was published in 1992, with an introduction to new AI technologies such as communication models and neural networks.
Computers and Thought: A Collection of Articles
Computers and Thought: A Collection of Articles
Edited by Edward A.
Feigenbaum and Julian Feldman (1963), the book features a collection of essays exploring the relationship between computers and human thought, including early debates about artificial intelligence.
The book ’ s 20 articles explore the intersections between computing mechanisms and human thought by addressing themes such as problem solving, learning, and pattern recognition in an effort to identify and enhance the potential of computational systems to mimic human cognitive processes.
3. Community of Mind
The Society of Mind
Written by cognitive scientist, artificial intelligence expert, and co-founder of the Massachusetts Institute of Technology ’ s Artificial Intelligence Laboratory, Marvin Minsky.
This 1983 book includes a new theory of human cognition based on the premise that the human mind is made up of a set of independent agents, which he calls the agents of the mind, known as the theory of the society of mind.
Minsky began developing the theory with Seymour Papert in the early 1970s, and Minsky states in his book that the greatest source of ideas for this theory came from his work trying to create a device that uses a robotic arm, a video camera, and a computer to build geometric shapes using child Lego pieces.
The machine of emotion: thinking according to common sense, artificial intelligence and the future of the human mind
The Emotion Machine: Commonsense Thinking, Artificial Intelligence, and the Future of the Human Mind
The second book by Marvin Minsky, author of the theory of the society of mind, published in 2006, deals with the role of emotions in human cognition, exploring the possibility of artificial intelligence simulating human emotion, and the implications of such simulations if integrated into future intelligent systems.
Superintelligence: Pathways, Risks, and Strategies
Superintelligence: Paths, Dangers, Strategies
A book by Swedish philosopher Nick Bostrom from Oxford University published in 2014.
Bostrom argues that if the minds of machines outperform the minds of humans in general intelligence, this new superintelligence could replace humans as the most important living being on Earth.
The book explores the paths, dangers, and potential strategies to create and control such superintelligence.
The book is among the most prominent books on artificial intelligence in terms of its potential effects on humanity.
Artificial Intelligence Superpowers: China, Silicon Valley and the New World Order
AI Superpowers: China, Silicon Valley, and the New World Order
In this 2018 book, Taiwanese author, AI pioneer expert, and entrepreneur Kai-Fu Lee argues that China has caught up with the United States at a rapid and unexpected pace and that unprecedented advances in AI will bring about radical changes sooner than many of us expected.
Li urges the United States and China to accept the great responsibilities that come with technology of such immense power.
While most experts already argue that AI will have a devastating impact on low-skilled jobs, Li sees AI in both China and America as having a powerful impact even on jobs that require advanced skills as well and wonders how we can offer solutions to some of the most profound changes in human history that will soon occur.
Human Compatibility: Artificial Intelligence and the Problem of Control
Human Compatible: Artificial Intelligence and the Problem of Control
In his 2019 book, Stuart Russell discusses what he sees as the most prominent challenge in terms of the compatibility of artificial intelligence with human values, the challenge posed by the problem of controlling the development of advanced artificial intelligence systems and the potential risks that may result from the development of systems that conflict with human interests and values.
To address the problem of control, Russell proposes several approaches, including what he calls the concept of “inverted learning enhancement, ” which is to derive human values by observing human behavior and using this knowledge to guide AI decision-making mechanisms, and to design AI systems that are transparent and interpretable and capable of justifying their actions, allowing humans to understand and monitor them.
A world without work
A World Without Work
This book was published in 2020 and is authored by Oxford University economist and lecturer Daniel Susskind.
The book explores the impact of advanced technology and artificial intelligence on the future of work and employment, in particular the potential consequences and prospects for comprehensive automation of human labor activity in various industries and labor sectors, and discusses the social and economic impacts of a future in which traditional work may become scarce.
Susskind also explores potential policy responses and alternative economic models that can adapt to changing human labor dynamics.
The book offers thought-provoking insights into the role of technology in the workforce and raises important questions about the future of work in an increasingly automated world, its challenges and opportunities, and its potential impact on society.
Technology and Virtues: A Philosophical Guide to a Future Worth Wishing
Technology and the Virtues: A Philosophical Guide to a Future Worth Wanting
Published in 2018, the book is edited by Shannon Valor, a philosopher and AI ethicist at the University of Edinburgh.
The book is a collection of articles by a number of leaders in the field of AI ethics addressing topics such as ethical challenges in the governance, design, development and deployment of AI.
The book provides comprehensive philosophical approaches to ethical issues and debates related to AI, as well as practical guidance and recommendations to address ethical dilemmas posed by AI applications.
The Age of Artificial Intelligence: Artificial Intelligence and Our Human Future
The Age of AI: And Our Human Future
This 2021 book brings together three authors: Henry Kissinger, the American diplomat who is not familiar with the definition, Eric Schmitt, former Google CEO and a member of President Obama ’ s Technology Advisory Board, and academic Daniel Hettenlocher.
The book explores the opportunities and challenges presented by artificial intelligence and its impact on society, ethics, governance and global affairs.
The authors of this important book offer their views on the transformative potential of AI, the ethical considerations arising from the evolution of AI and the implications for national security and international relations, and provide insight into the complex issues surrounding the phenomenon and recommendations for dealing with its evolution in a way that benefits humanity.
In a post-book interview, Kissinger argues that humanity, after the Enlightenment and at the height of the Age of Reason, singled itself out for the centrality of its ability to explore, perceive, and interpret the world we live in.
Now, AI does the same things differently than we do: it studies games, invents new ways to win them, studies chemical compounds and finds new ways to incorporate them into the treatment of diseases.
It is up to us humans to define our role in this reality.
Eric Schmidt says that when Kissinger first met him nearly a decade ago, he told him that his only problem with Google was that it was destroying the world.
After many years of debate and debate that brought us together, Kissinger, Hettenlocher and myself, Schmidt says, the idea for this book emerged.
Magazine

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

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AI systems decide job and loan eligibility, diagnose diseases, and support democratic processes, but researchers face challenges training AI to understand human values.

Title:

Content: People 's Daily Online > > Economy and Technology
How Artificial Intelligence Can Be “Good ”
Reporters of this report, Lou.
June 19, 2023 06: 40 | Source: People's Daily Overseas Edition
Small number
For some time, the big artificial intelligence model represented by ChatGPT has stirred the tide of global artificial intelligence technology development.
From writing code to storytelling, from writing articles to automating data tables … AI is making a lot of changes to how humans work, learn, and live.
How far are we from the “omnipotent ” general intelligence?
What are the security risks and challenges brought by the development of artificial intelligence? At the recently held 2023 Beijing Intelligence Source Conference, artificial intelligence experts and scholars from around the world discussed related topics.
Artificial Intelligence is a long way off.
“Imagine that in the next 10 years, General Artificial Intelligence (AGI) will surpass human expertise in almost every field and may eventually surpass the overall productivity of all large companies, which will improve people ’ s living standards. ” OpenAI CEO Sam Altman offers a picture of the future of AI.
AGI refers to artificial intelligence systems that can perform intelligent tasks in various fields like humans.
This is different from the current AI applications that focus only on specific tasks or areas (such as image recognition, speech recognition, natural language processing, etc.), which puts higher requirements on AI technology.
“General AI can learn and perform tasks better and faster than humans, including tasks that humans can ’ t handle. ”
Because of the huge advantages of machines in terms of speed, memory, communication and bandwidth, the future of general artificial intelligence will far exceed human capabilities in almost all fields, "said Stuart Russell, a professor of computer science at the University of California, Berkeley.
Although artificial intelligence has a "timetable" to "surpass" humans, in the view of many experts, the current artificial intelligence is still a long way from AGI.
Russell believes that the current hot big language model does not "understand the world", just a "puzzle" of general artificial intelligence - "we do not understand how to connect it with other parts, and there are even some missing pieces of the puzzle have not been found."
Huang Tiejun, president of Beijing Zhiyuan Artificial Intelligence Research Institute, pointed out that to achieve general artificial intelligence, there are three technical routes: the first is a large model, through massive high-quality data, so that artificial intelligence has the ability to emerge intelligently; the second is embodied intelligence, through reinforcement learning methods, training and model; the third is brain-like intelligence, so that machines reach or similar to human brain capabilities.
For the development of artificial intelligence, Turing Award winner and New York University professor Yang Likun proposed the concept of "world model" - artificial intelligence systems can understand the way the world works and act in the most optimized and least costly way.
Strengthening international cooperation in the area of security governance
According to PricewaterhouseCoopers, artificial intelligence will create $15.7 trillion in economic value by 2030.
Artificial intelligence provides important opportunities for economic development, but it also raises security concerns and controversies.
Turing Award winner and University of Toronto professor Jeffrey Hinton believes that current artificial intelligence can already master the way to "deceive" humans by learning.
“Once AI has the ability to ‘ cheat ’, it has the ability to ‘ control ’ humans. ”
Such superintelligence may happen faster than expected, "he said.
Before the advent of the era of general artificial intelligence, the security risks of artificial intelligence mainly came from “people ”.“ We should not assume that machines are impartial, because machines may try to change human behavior.
More accurately, it is the owner of the machine who wants to change the behavior of others, "said Yao Zizhi, a Turing Award winner and academician of the Chinese Academy of Sciences.
As the ability of artificial intelligence gets bigger and bigger, the problem of artificial intelligence "alignment" comes to the surface.
The so-called "alignment", that is, the goals of artificial intelligence systems should be "aligned" with human values and interests.
How to make artificial intelligence “aligned ” with humans? Altman believes that people should responsibly apply artificial intelligence to the world, valuing and managing security risks.
He suggested the establishment of equal and unified international norms and standards in the process of artificial intelligence technology research and development, and the establishment of a trust system for the safe development of artificial intelligence systems in a verifiable manner through international cooperation.
Huang Tiejun believes that although artificial intelligence will produce unexpected new capabilities, this does not mean that humans can not manage artificial intelligence.
"How to manage such a highly creative system of artificial intelligence, sociology, history and other disciplines can provide a good reference meaning."
In February this year, China proposed in the Concept Document of the Global Security Initiative to strengthen international security governance in emerging scientific and technological fields, such as artificial intelligence, to prevent and control potential security risks.
At this conference, experts and scholars positively evaluated China's contribution to promoting international governance of artificial intelligence.
Altman said that China has a large number of excellent talents and product systems in the field of artificial intelligence and should play a key role in the safety of artificial intelligence.
Max Tegmark, a professor at MIT ’ s Center for Artificial Intelligence and Fundamental Interaction Research, said that China ’ s growing ability to shape the global AI agenda could lead in AI security governance.
Promote sharing of large models
At present, the global scientific and technological competition in the field of artificial intelligence is becoming increasingly hot. The "China Artificial Intelligence Large Model Map Research Report" released at the 2023 Zhongguancun Forum shows that 79 large artificial intelligence models with parameters above 1 billion have been released nationwide.
Globally, the number of large models released in China and the United States exceeds 80% of the global total.
China has entered a period of rapid development of large models since 2020, and has established systematic research and development capabilities covering theoretical methods and software and hardware technologies in large models, forming a large model technology group that closely follows the forefront of the world, and a number of pre-trained large models with industry influence have emerged.
At this conference, the comprehensive open source wisdom source "Wudao 3.0" series of large models and algorithms were officially released.
It is understood that "Goku 3.0" covers a series of leading achievements, including "Goku · Skyhawk" (Aquila) language large model series, Libra (FlagEval) open source large model evaluation system and open platform, "Goku · Horizon" visual large model series and a series of multi-modal model results.
Huang Tiejun believes that the big artificial intelligence model has three characteristics: first, it is large in scale; second, it has "emergent", that is, it can produce unexpected new capabilities; and third, it is universal, not limited to solving special problems or special fields.
He said that the big model is not a technology monopolized by any one organization or company, and it should be built and shared to launch a set of basic algorithmic systems required by an intellectual society.
(Intern Zhang Weiner also contributed to this article)
(Editor: Yang Guangyu, Niu Yong)
Public Number ： People 's Network Finance
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AI development is influenced by societal values, and AI systems can have profound effects on societies, but AI is not objective or neutral and is rooted in creators' culture.

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Content: Lebanon News Saida News Miscellaneous Ads Arab and International Photos and Videos
LATEST NEWS
Artificial Intelligence: Who produces and manages this technology?
Artificial intelligence permeates every aspect of our lives, telling us what to read and what to buy.
It also decides whether or not we will get this job, that loan, or mortgage, or whether or not we will receive subsidies, parole, or not.
It also diagnoses diseases, supports and sometimes undermines democratic processes.
Now, two new books offer complementary insights into the role of those who create, use and manage AI systems to reshape society.
In The Alignment Problem, Brian Christian gives us a closer look at the reality of the people who make AI technology — their goals, expectations, hopes, challenges, and even failures.
Christian begins his book by talking about Walter Bates's efforts in the field of logical representation of neuron activity in the early 20th century.
It lists the ideas of researchers and professionals in many fields, from cognitive science to engineering.
It also shows their goals, successes, and failures.
Atlas of AI, by Kate Crawford, the influential researcher, looks at the ways in which artificial intelligence actually interferes with our lives.
The book shows that artificial intelligence is an industry based on the extraction and exploitation of resources, from matter to labor to information.
The books analyze how the power of the digital world, as well as the influence of those who manage it, is changing the paths of politics and social relations.
In that context, they point to alternative models adopted by some to manage these disruptions created by the digital world, such as state censorship in China, or the European Union ’ s regulatory efforts in that regard.
But the two books focus mainly on the American experience.
Therefore, they call for a sequel to each, this time about the way forward.
From prophecy to limitless spread
Christian traces the evolution of AI technology, from it was a dream to its existence as an integral part of reality everywhere.
Christian explains how researchers are trying to train AI to understand human values such as fairness, transparency, curiosity and even uncertainty, pointing to the challenges they face.
The book is largely based on interviews with researchers and professionals.
Through these encounters, he charts the slow, steady, and complex path that AI technology has followed in its progress.
There were many frustrations on that path, punctuated by amazing successes.
On that journey, we meet people like Rich Caruana, now chief research officer at Microsoft in Redmond, Washington, who, as a graduate student, was asked to look at an innovation that would become the core of his work for the rest of his life: improving ways to collect and compress data to make meaningful and accurate data models.
The author also takes us on a walk on the beach with Mark Bellmer, who led the innovation of reinforcement learning while developing Atari games, before taking up his current role at Google Research in Montreal, Canada.
Christian explains that researchers are increasingly aware that developments in AI are influenced by, and most importantly affect, societal values, but these developments do not come without a price and can have profound effects on societies.
The Alignment Problem begs the fundamental question: What can be done to ensure that AI systems reflect our standards and values, understand what we mean, and do what we want?
We all have different concepts and requirements about what these systems should do.
As mathematician Norbert Wiener put it in 1960: “It is better for us to be absolutely certain that the data we fill the machine with expresses the purpose we really want to achieve. ”
Crawford ’ s book, on the other hand, reveals the ugly face of AI ’ s success: it takes us on a journey around the world to explore the relationships between places and their impact on AI infrastructure.
The journey takes us from Nevada to Indonesia, where lithium and tin are mined, which are important in the semiconductor industry, an industry that comes at high human and environmental cost.
Next, we arrive at an Amazon warehouse in New Jersey.
There, workers toil in obedience to the will of robots and production lines, rather than automation adapting to the rhythm of human labor.
In a disturbing nod to Charlie Chaplin ’ s 1936 film, Modern Times, we see with our own eyes the hardships created by false automation systems, supposedly automated, but highly dependent on human effort, as well as by underpaid workers working on data-sorting farms.
Crawford concludes her book with a reminder that AI is not objective, neutral, or one-size-fits-all, but rooted in the culture and economic realities of its creators, most of whom are white and wealthy men working for Silicon Valley companies in California.
The two books are sober about the challenges and risks in the use and development of artificial intelligence today, and the differences between artificial intelligence and “classical ” computing.
Thus, reading them side by side highlights three key issues: over-reliance on data-driven randomized predictions, automated decisions, and power centralization.
Domination of data
In the 2020 documentary Coded Bias, AI researcher Joy Bulamwini said that using algorithms in decision-making frustrates decades-long efforts to advance equal rights, embodying the same biases that we discover are still ingrained today.
The reason is that the use of data to formulate automated decisions often ignores the contexts, emotions, and relationships from which human choices are fundamentally driven.
Data is not raw material, but it is always linked to the past, reflecting the beliefs, practices and biases of those who produce and collect it.
However, the current reliance on automation in decision-making stems more from a desire for efficiency and economic benefits than from its impact on individuals.
Worse still, most AI methods put power in the hands of those who have the data and the computing power to process and manage that data.
They are mostly big tech companies, that is, private entities that are not subject to democratic processes and the distribution of power.
Governments and individuals are simply uncontrollable users of all these processes.
It is a transformation that has enormous consequences and has the potential to change the face of society.
So what to do then?
Along with efforts to purify data from bias and interpret decisions made by algorithms, we also need to look at the source of bias itself.
This will not be achieved through technological solutions, but through education and social change.
At the same time, there is a need for scientific research that looks at the harmful dependence of the field of artificial intelligence on the detection of correlations in data, as artificial intelligence in its current form detects patterns in the data, but does not provide an explanation for them.
They show that the rational development and use of AI in ethical, legal, and utilitarian terms does not depend on the technology itself, but on us: what we want our world to look like, how much we prioritize human rights and ethical principles, and who falls into that category we call “us. ”
This debate has become an inevitable reality, but the fundamental question is: How can everyone have a voice in this debate?
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Data used in AI is linked to the past and reflects biases, and AI methods put power in the hands of big tech companies.

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Content: People 's Daily Online > > Economy and Technology
Focus on Wuzhen: Ethical rules and technical safety are the key to high-quality development of AI
November 11, 2022 09: 05 | Source: Beijing Business Daily
Small number
On November 10, at the 2022 World Internet Conference Wuzhen Summit "Artificial Intelligence and Digital Ethics" forum, Tian Tian, CEO of Ruilai Intelligence, said that in the era of digital intelligence, artificial intelligence acts as a "brain" and plays an important role in the realization of target performance, the intelligence of facility operation and maintenance, and the security of facilities.
For example, the use of artificial intelligence computer vision technology can achieve the detection of industrial defective products, the construction of face recognition security equipment, while improving production performance and production efficiency, greatly improve safety and security capabilities.
In recent years, with the rapid development of algorithms and the increasing abundance of computing power, data and other resources, the artificial intelligence industry has shown a rapid development trend.
However, Tian Tian said that looking into the future, if artificial intelligence is to achieve high-quality development, it needs to break through the two key issues of ethical rules and technical safety.
First, as artificial intelligence gradually becomes a key role in the economy and society, the rules of its interaction with humans pose ethical issues, such as the distribution of human-machine control, the acceptance of artificial intelligence, and so on.
As an open but far from mature new technology, AI will inevitably impact existing ethical relationships and social structures.
For example, the improper transfer of human control over the machine will lead to the occurrence of malignant accidents, typical examples such as the loss of control of the electronic brake system of autonomous vehicles caused by traffic accidents, and the excessive trust in algorithms and the neglect of unfair events caused by algorithmic bias.
With the continuous penetration of artificial intelligence into all aspects of production and life, ethical problems are emerging.
Second, technical security issues.
Taking the most widely used face recognition on the ground as an example, the whole process of this technology is a black box mode, which mainly relies on massive face data for feature extraction, training and identification, so that it can use relevant loopholes and adopt various forms of attack.
For example, adding a small amount of interference to the face photo or printing the anti-patch to be worn by a real person, in the form of this targeted attack to crack the personal verification.
In the face of these problems, Tian said, it needs to be addressed from both ethical rules and technical rules.
One is the construction of ethical rules.
This involves how to set the control threshold between people and artificial intelligence, how to design the bottom mechanism, etc., and needs to be combined with specific application scenarios to create quantifiable, measurable, and executable specific standards, and then implemented to the algorithm and code level.
The second is to optimize the technical path.
On the basis of the three elements of data, algorithms and computing power, the knowledge elements are integrated into the development of safe, reliable, credible and scalable third-generation artificial intelligence, and the robustness and interpretability of artificial intelligence are continuously improved, while the agile assessment level of artificial intelligence ethical safety and technical safety is continuously improved, making it a key capability to ensure the high-quality development of the artificial intelligence industry.
(Reporter Wei Wei)
(Editor: Zhao Zhuqing, Chen Jian)
Public Number ： People 's Network Finance
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What does Hinton warn could happen to AI if companies and regulators fail to implement effective rules and controls?

Current AI reliance on automation stems from efficiency desires, and AI development depends on human rights and ethical principles.

Title:

Content: Sunday 04-06-2023
The risks of artificial intelligence to humanity are exaggerated.
Artificial intelligence specialist Gary Marcus has spent recent months warning his peers, lawmakers and the general public about the risks associated with the development and ultra-rapid spread of new tools in the field, but he said in an interview with AFP in San Francisco that the risks of human extinction are “exaggerated. ”
The professor emeritus at New York University, who came to California to attend a conference, said: “Personally, at the moment, I ’ m not too worried about that, because the scenarios aren ’ t very realistic. ”
Add Announcement
“What worries me is that we are building artificial intelligence systems that we don ’ t control. ”
Gary Marcus created his first high school AI program, a program to translate texts from Latin into English.
After years of studying child psychology, he set up a machine learning company that was later acquired by Uber.
In March, I signed a letter published by hundreds of experts demanding that the development of super-powerful AI systems such as the OpenAI chatbot be suspended for six months to prove that software currently in use is “reliable, secure, transparent and faithful. ”
compatible "with human values.
But he did not sign the brief announcement made this week by a group of business leaders and experts, and it struck a chord.
Signatories of the ad, most notably the creator of the same "chat gbt" Sam Altman, the former senior engineer at Google Jeffrey Hinton, who is a founding father of artificial intelligence, the head of the company "DeepMind" research on artificial intelligence of Google Demis Hasapis, and the technical director of Microsoft Kevin Scott, to address the "extinction risks" associated with artificial intelligence.
The huge success of ChatGBT, which is capable of producing any type of text at the request of its users, set off a race between tech giants to this “generative ” artificial intelligence, but it has prompted many warnings and calls for controls in this area.
The warnings were even issued by the developers of these information systems, who are working towards a "general" artificial intelligence with cognitive abilities similar to those of humans.
Gary Marcus said, “If you ’ re really convinced there ’ s an existential risk, why are you working on it in the first place? ”
That ’ s a logical question. ”
"The extinction of the human race...
“It ’ s a very complicated matter indeed, we can imagine all kinds of pests, but someone will definitely survive. ”
He said there were credible scenarios in which artificial intelligence could cause "enormous damage".
"Some, for example, may succeed in manipulating markets.
We may accuse the Russians of taking responsibility and attack them when they have no involvement at all, and we may be drawn into an accidental war that threatens to turn into a nuclear war. ”
In the short term, Gary Marcus is expressing concerns for democracy.
Generative AI software is increasingly capable of producing fake images and soon realistic videos, at a fraction of the cost.
In this context, "the elections will be decided by those who are more adept at spreading disinformation, and those who may amend the laws after they win...
They impose an authoritarian regime. ”
"Democracy is based on having reasonable information and making good decisions.
If no one distinguishes between what is right and what is not, then it ’ s over. ”
But that doesn ’ t mean, in the opinion of the expert who published a book entitled “Rebooting AI ” that the technology does not hold promise.
“There is a chance that one day we will use an artificial intelligence system that we haven ’ t yet invented that will help us make progress in science, in medicine, in caring for the elderly, ” he said.
But at the moment we are not ready.
We need regulations and to make programs more reliable. ”
Appearing before a committee in the US Congress in May, he called for the creation of a national or international agency entrusted with the governance of artificial intelligence.
It is also supported by Sam Altman, who returned from a European tour during which he urged political leaders to seek a “fair balance ” between production and innovation.
But Gary Marcus warned against leaving power in the hands of companies: "Recent months have reminded us how much they make important decisions without necessarily taking...
side effects ".
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What does Hinton warn could happen to AI if companies and regulators fail to implement effective rules and controls?

AI needs to break through ethical rules and technical safety for high-quality development, and AI poses ethical issues like human-machine control and algorithmic bias.

Title:

Content: Filed under:

Stuart Russell wrote the textbook on AI safety. He explains how to keep it from spiraling out of control.

AI doesn’t have to be superintelligent to cause serious havoc.

If you buy something from a Vox link, Vox Media may earn a commission. See our ethics statement.

One of the hardest parts of the news business is striking a balance between covering stories that seem important in the moment and covering stories that you know will truly matter in the future. And it’s hard because the most consequential things happening right now are often boring or difficult to explain.

Artificial intelligence is a good example of this sort of challenge. The ongoing revolution in AI is unfolding so quickly that it’s hard to keep up, even if you’re trying. Chat GPT-4, for instance, was released in March of this year and it stunned almost everyone who used it. If this latest large language model is a sign of what’s coming, it’s easy to imagine all the ways it might change the world — and then there are all the ways it might change the world that we can’t imagine.

So what do we need to know about AI right now? What are the questions we should be asking? And how should we be preparing for whatever’s coming?

To get some answers, I invited Stuart Russell onto The Gray Area. Russell is a professor of computer science at UC Berkeley and the author of Human Compatible: Artificial Intelligence and the Problem of Control. He was one of the signatories of an open letter in March calling for a six-month pause on AI training. We discuss the risks and potential benefits of AI and whether he believes we can build AI systems that align with our values and interests.

Below is an excerpt of our conversation, edited for length and clarity. As always, there’s much more in the full podcast, so listen and follow The Gray Area on Apple Podcasts, Google Podcasts, Spotify, Stitcher, or wherever you find podcasts. New episodes drop every Monday.

Sean Illing

When you think about the state of AI at this moment, what feels most urgent to you? What excites you? What scares you?

Stuart Russell

I think it’s important to understand that almost nobody is saying that the state of AI right now is such that we have to worry about AI systems taking over the world, if you want to put it that way. They still exhibit many limitations, and at least the latest generation, the large language models like ChatGPT, don’t exhibit the kinds of decision-making capabilities and planning capabilities that you would need to take over the world.

You can try playing chess with them, for example. They’re pretty hopeless. They pretend well for a few moves, and then they’ll play a move that’s completely illegal because they haven’t actually learned the rules properly. There’s a lot of progress that we still need to make before we reach systems that are fully comparable or better than a human mind.

The things people are concerned about right now with this technology [are things that] we already have. Disinformation would probably be number one. The fact that these systems can be directed to generate highly targeted, personalized propaganda, to convince an individual based on everything the system can find out about that person. It could do that. Not just in a single email or blog post or whatever, but it could do that over several months. People are very worried about that being weaponized by nation states, by criminals, by unscrupulous politicians who would produce deep fakes of their opponents.

These are very real, and we’re starting to see them already happening and a bunch of other serious concerns. One that has surfaced recently is defamation. Systems making up crimes, not being directed to do so, but just because they hallucinate. They say things that have no basis in truth, but making up defamatory statements about real individuals. There are a couple of lawsuits happening already.

Sean Illing

Are you comfortable referring to something like GPT-4 as intelligent? Or is that not quite the right word?

Stuart Russell

For normal conversation, it’s a reasonable thing that it shows elements of intelligence. In fact, [in a] paper that Microsoft produced, a group of experts there spent several months with GPT-4 before it was released, trying to understand what it could do. The paper that they produced is called “Sparks of Artificial General Intelligence.” And that’s a pretty bold claim, because artificial general intelligence means the kind of AI that exceeds human capabilities in every dimension, the kind of AI that does take over the world.

According to them, we are creating the kind of AI that does take over the world. We have absolutely no idea how it works, and we are releasing it to hundreds of millions of people. We’re giving it credit cards, bank accounts, social media accounts. We’re doing everything we can to make sure that it can take over the world. That should give people something to think about.

Let me give you an example of that one of my colleagues sent me. He was using ChatGPT-3.5 and he asked it, “Which is bigger, an elephant or a cat?” And it says, “An elephant is bigger than a cat.” You say, “Which is not bigger than the other, an elephant or a cat?” And it says, “Neither an elephant nor a cat is bigger than the other.” When you look at that second answer, you realize, “Well, it can’t be answering the question with respect to some internal model where there are big elephants and little cats,” but that means that it wasn’t answering the first question with respect to an internal model where there are big elephants and little cats. It wasn’t really answering either question in the sense that we think about answering questions, where we query an internal model of the world.

If I say, “Where’s your car?” you query your internal model of the world. You say, “It’s in the parking garage across the road.” That’s what we mean by answering a question. It’s really clear that in a real sense, these systems are not answering questions. They don’t seem to build a coherent internal model of the world.

Sean Illing

You focus a lot on the “alignment problem” or the “control problem” and this question of whether AI might develop its own goals, separate and apart from the goals we program into it. How worried are you on this front?

Stuart Russell

The alignment problem is simpler than the one you described. You suggested the alignment problem is about systems developing their own goals, which are different from the ones we program into them. Actually, the original alignment problem is about systems pursuing the goals that we program into them, but the problem is we don’t know how to program the right goals.

We call this the King Midas problem. King Midas programmed the goal into the gods that everything he touched turned to gold, and the gods gave him exactly this. They carried out his objective, and then his food and his drink and his family all turned to gold and he dies of misery and starvation. There are many legends and lots of cultures have stories very similar to this, where you get what you ask for and you regret it, because you didn’t ask for the right thing.

What people have observed is that when you’ve got a sufficiently capable AI system, and you give it even a very innocuous-sounding goal like, “Could you fetch me a cup of coffee?” When a machine is sufficiently intelligent and it has a goal like, “Fetch a cup of coffee,” it doesn’t take a genius to realize that if someone switches you off, you’re not going to succeed in fetching the coffee. As a logical sub-goal of this original goal, you’ve now got the goal of, “Preventing myself from being switched off,” and possibly taking other preemptive steps to avoid interference by human beings in the achievement of this goal.

Sean Illing

You can have fun imagining all the ways that could go wrong—

Stuart Russell

Yep. There are many science fiction stories that do exactly that. Sometimes in the literature you’ll see the phrase, “Instrumental goals.” These are goals like self-preservation, like acquiring more power over the environment, acquiring money, acquiring more computing resources so you can do a better job of solving the goal that you’ve been given. These instrumental goals are just derived automatically from the original goal.

With a human being, if I say, “Fetch a cup of coffee,” it doesn’t mean “Fetch a cup of coffee” is now the only goal that you should care about and your entire life’s mission is to fetch that coffee. That’s not what we mean when we say it to a human being, but that’s how we have been building our AI systems for decades and decades. The objective that we put in is the objective of the system and nothing else. That’s fundamentally a mistake. We can’t build systems that way, because we cannot specify completely and correctly all the things that human beings care about, so that the system’s behavior is actually what we would really want to have happen.

My book is about a different way of building AI systems, so that they understand some things about what humans want, but they know there’s a bunch of other stuff that they don’t understand and they’re uncertain about. And that actually leads to systems that behave much more cautiously and usefully.

The other thing that you mentioned, the possibility that these machines would develop their own goals — obviously that would be much worse. It’s bad enough that we give them specified goals. If they’re able to develop their own goals, then there’s no reason to think that those would be aligned with our goals.

Sean Illing

I often hear people say that we’re still only talking about narrow AI at the moment. We’re not talking about artificial general intelligence, which you mentioned a minute ago, something that is actually self-teaching and can develop its own goals, and that’s the thing we really have to worry about. But I don’t know, that seems wrong to me, or it seems like it misunderstands how progress in AI works.

But setting that aside, it’s not like we need some AGI superintelligence to wreck our world. As you said a few minutes ago, when I start imagining all the havoc AI could cause merely through the creation and distribution of misinformation, it makes my head explode. Deep fake tech is already here, but it doesn’t feel pervasive enough yet to be a major concern. But the lines between fact and fiction are already suicidally blurred in our society, and the post-truth world I can imagine in that future is infinitely worse than our situation today.

Do you think we’re even close to ready for this?

Stuart Russell

No, I think if it’s not regulated, we are in for a huge amount of pain. As you say, in terms of still images, we are already at the point where they are indistinguishable from reality and they are coupled with the large language models. In other words, you can ask the language model to give you an image of anything you want and it’ll do it.

DALL-E and these other image generators are coupled to language models. You can already say, “Give me a 22-second video showing such and such and such and such,” and it will do it. It’s not great right now, but three years ago, face generation wasn’t great. There would often be weird things with the ears. Or it’d be like the same pair of earrings occurring over and over again. Just glitches. But those have been ironed out and now it’s pretty much perfect.

And that’s going to be the case for video very soon, if not already in the lab. You can say, “I need a video of Donald Trump receiving a suitcase full of cash from some mafioso,” and it’ll produce it for you. And it’ll be very difficult for anyone to prove that’s not real. We really need regulation. Just like we have regulation around counterfeit currency. We can now produce counterfeit currency that’s indistinguishable from the real thing to a non-expert shopkeeper, for example.

As a result, we have very stringent regulations and enormously long jail sentences for counterfeiting and a lot of security around the designs. This idea that digital technology is completely safe and should be unregulated in all circumstances is just extremely outdated. The two things people are proposing, one, that all output for AI-generated content should be labeled indelibly. There are methods called watermarking, which work for images and sound and video, where it’s cryptographically encoded into the content and there’s really no way to pull it out.

You can recognize that that’s generated by such and such a model on such and such a date. And then, you also want the platforms, the social media platforms, necessarily have to make that absolutely apparent to the user. They could, for example, give you a filter saying, “I don’t want to see artificially generated content. Period.” Or if you do see it, it should have a big red box around it. Maybe a red filter, so that it just doesn’t look like ordinary, natural video.

And then, you also want to have ways of watermarking real video. When I have a video camera and I’m out there in the real world, it’s producing an indelible cryptographically secure time-stamping and geocoding and all the rest, so that that’s globally recognized. So that we know that this is real video.

Those two things together will go a long way. Plus, the regulations on the media platforms go a long way to making us safe. Is that going to happen? I don’t know. An interesting article in the New York Times yesterday was saying, “On average, it takes [decades] for a regulation to catch up with technology.”

Sean Illing

A lot of these conversations about AI seem to imply that we actually have the power to control something that will be far more capable than we are. But I don’t see any reason in nature or history to believe that’s the case—

Stuart Russell

There are no historical examples and no examples in nature that I’m aware of where that happens. Although, actually, there are weird things that happen in nature. There are fungi that control the behavior of animals by literally getting into their nervous systems and causing them to behave in weird ways. Causing mice not to be afraid of cats, for example, so that the mouse gets eaten, and then the fungus gets into the brain of the cat. Some complicated lifecycle story like that.

There are these weird exceptions, but basically we don’t have a good model for how this might work. The way I think about it, if I go back to the question I said at the beginning, “How do we maintain power forever over systems more powerful than ourselves?” That sounds pretty hopeless. If instead you say, “What’s a mathematical problem such that no matter how well the AI system solves it, we are guaranteed to be happy with the outcomes?” That sounds maybe a little more possible.

We can make the AI system as intelligent as you want, but it’s constitutionally designed to be solving a certain type of problem. And if it does it well, we are going to be happy with the results. The way I’m thinking about this is, as you said, not three laws, but actually two laws really. One, the AI system’s only objective is to further the interests of human beings. And the second principle is that it knows that it doesn’t know what those interests are.

Sean Illing

At what point does AI innovation become the most consequential event in human history?

Stuart Russell

Good question. I think certainly when we have something resembling AGI. AGI means AI systems that match or exceed human capabilities along all relevant dimensions, but because of the massive advantages that machines have in speed, memory, communication, bandwidth, intake bandwidth, there’s no doubt that they would very quickly far exceed human capabilities.

That would be the biggest event in human history, in my view, because it would in some sense basically switch to an entirely different basis for civilization. Our civilization is based on our intelligence, and now that would no longer be true. It might also be the end of civilization, if we don’t figure out how to control such a system.

To hear the rest of the conversation, click here, and be sure to follow The Gray Area on Apple Podcasts, Google Podcasts, Spotify, Stitcher, or wherever you listen to podcasts.

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

Improper human control over AI can lead to accidents like autonomous vehicle crashes, and technical security issues in AI include vulnerability to attacks like face recognition hacking.

Title:

Content: Home Business Wire FairNow's AI Governance Platform Helps Organizations Meet and Maintain New EU AI...

FairNow’s AI Governance Platform Helps Organizations Meet and Maintain New EU AI Act Requirements

The platform simplifies and centralizes key governance processes including risk-based classification, technical documentation, and bias evaluations

MCLEAN, Va.–(BUSINESS WIRE)–#AI–FairNow, a leader in AI governance solutions, highlights how its comprehensive platform is fully equipped to help organizations comply with the specific requirements of the European Union’s AI Act, which goes into effect this week.

The EU AI Act is the world’s first comprehensive regulation of artificial intelligence. The regulatory framework promotes the development of safe, transparent, and fair AI and imposes significant penalties for non-compliance, up to €35 million or 7% of the global annual turnover.

FairNow’s platform, designed with these specific requirements in mind, offers a suite of features that directly address the core components of the EU AI Act, making AI compliance straightforward and efficient for organizations.

Key Features of FairNow’s Platform:

  1. Automated Regulatory Compliance Tracking:
  2. Centralized AI Inventory:
    • Maintain a detailed, centralized inventory of AI systems to drive transparency and accountability across all of your organization’s stakeholder groups.
  3. Risk Assessments:
    • Conduct comprehensive assessments to define the risk level for each of your AI models, flag specific risks, and define mitigation strategies.
  4. Bias and Explainability Analyses:
    • Utilize advanced tools for bias detection and explainability, essential for meeting the EU AI Act’s obligations for fairness and transparency in AI systems.
  5. Vendor AI Risk Management Portal:
    • Standardize the tracking and assessment of vendor AI systems, simplify vendor AI evaluation, and streamline task management and monitoring.
  6. Automatic Record-Keeping and Audit Artifacts:
    • Drive efficiency with FairNow’s document generation capabilities; automatically populate model details, log modifications, and store test results.

Guru Sethupathy, co-founder and CEO of FairNow, emphasized the importance of this alignment, “Companies are grappling with the challenges of harnessing AI’s transformative power while managing increasingly complex regulatory and business risk. FairNow’s platform simplifies compliance and promotes consistent AI governance practices across an organization. Our customers turn AI governance into a competitive advantage.”

FairNow’s EU AI Act compliance features are available to all current and new customers.

To learn more about FairNow’s AI governance platform, visit fairnow.ai.

Contacts

Kayla Baum

VP of Marketing, FairNow

Email: kayla.baum@fairnow.ai

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

What risks did the global summit report highlight about AI?

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Third-generation AI aims to be safe, reliable, and scalable, and AI ethical safety and technical safety require continuous assessment and improvement.

Title:

Content: Alert
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Trend Watch / AI Governance Framework Boosts Productivity Tools
The rapid rise of generative AI has shown great potential for development in various industries, from automated customer service and content creation to video production.
However, with the potential risks of data privacy, information security issues, algorithm bias, and lack of transparency in the decision-making process that accompany efficiency, the importance of AI governance comes to the surface, and it is especially important to establish a sound AI governance framework and practical application strategies.
National policies and regulations are coming out
Chen Xinquan, vice president of SAS Taiwan, a global AI analysis leader, shared that AI governance has evolved from the slogan stage to the actual landing.
He further analyzed, from the attitude and behavior of the governments of various countries, it can be seen that relevant policies and regulations have been released one after another.
For example, the “AI Act ”, the world ’ s first AI bill proposed by the European Union in March 2024, ensures transparency and accountability of AI technologies.
The United States has also developed an AI risk management framework through the National Institute of Standards and Technology (NIST) as a reference for the development and application of AI technologies.
Other countries are also formulating regulations, and although their AI governance strategies are different, the overall goal is the same, and they want to ensure that the direction of AI technology development benefits the whole society and the negative impact is minimized.
In addition to government policies, technical support from the industry has also been in place.
AI governance was a key topic at SAS ’ s annual Innovate conference recently, bringing together global experts and industry leaders to discuss how to create a sound framework to meet the challenges of technology development.
A number of practical strategies and tools covering data governance, risk management, and algorithmic transparency were presented to help enterprises drive AI governance in a more efficient way.
Chen stressed that effective AI governance requires not only policy and regulatory support, but also practical tools and services to implement, and there are many key tools and services on the market to ensure the safety, transparency and fairness of AI technology.
SAS, for example, launched a series of practical AI governance tools and services in the 2024 forum.
The first is the AI model performance score card. The function of this tool is similar to the nutrition label of the AI model. In addition to automatically aggregating and clearly presenting key indicators such as model accuracy, fairness, model drift, and other key indicators, it can also detail the model's modification history, contributors, responsible persons, and use information such as the model's intended use, applications and limitations beyond the scope, so as to help enterprises meet compliance requirements and business transparency.
In addition, SAS has also established an AI governance consulting service team to help enterprises establish a sound AI regulatory and governance system.
By establishing a responsible AI decision-making process, enterprises can better divide data responsibility, and accountability is transparent and clear. In addition to deepening the trust of all parties in AI, it can also improve the productivity of AI applications.
Establish a regulatory compliance process
Chen Xinquan said that through the services and platform tools of professional manufacturers to promote AI governance, there have been many successful cases around the world, such as PZU Poland, the largest insurance and banking group in Central and Eastern Europe.
The Group ’ s main product lines include product insurance, life insurance and asset management scale. In response to its growing business, PZU has been actively implementing a large number of data management tools over the past 25 years, while adopting SAS ’ s solutions for data warehousing, analytics, and other important businesses such as customer relationship management and fraud management.
More than 300 AI models have been deployed internally in PZU.
In recent years, the AI regulatory regulations have been on the road, and PZU Group must establish a more perfect AI regulatory and governance mechanism, so it has further cooperated with SAS.
At present, the two sides have jointly established an AI regulatory governance framework, which is divided into four directions: supervision, law compliance and control, culture, platform and maintenance.
The first phase will focus on incorporating internal AI applications into the scope of governance. Internal AI applications will undergo multi-stage analysis and verification to ensure that they meet the regulatory risk management model, responsible and trusted AI definitions, and so on.
In addition, PZU also refers to the technical AI governance framework proposed by SAS, including complete technical requirements for data management, interpretability, detection capabilities, privacy and security, countermeasures, and model maintenance.
Chen Xinquan emphasized that effective AI governance requires not only policy and regulatory support, but also practical governance strategies, frameworks and tools that can be implemented as the basis for technology application.
Drive AI governance with professional services and platform tools to ensure the security, transparency, and fairness of AI technologies.
Enterprises can confidently realize the commercial value of AI while meeting regulatory requirements, ensuring that AI technology development can benefit the whole society and minimize the negative impact.
This will not only help increase the productivity of enterprises, but also enhance the trust of all parties in AI, driving continuous innovation and progress in AI technology.
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Content: Trail of Bits’s Response to NTIA AI Accountability RFC

By Heidy Khlaaf and Artem Dinaburg

The National Telecommunications and Information Administration (NTIA) has circulated an Artificial Intelligence (AI) Accountability Policy Request for Comment on what policies can support the development of AI audits, assessments, certifications, and other mechanisms to create earned trust in AI systems. Trail of Bits has submitted a response to the NTIA’s RFC on AI system accountability measures and policies.

We offer various recommendations informed by our extensive expertise in cybersecurity and safety auditing of mission-critical software. We support the NTIA’s efforts in fostering an open discussion on accountability and regulation. In our response, we emphasize the following:

  • AI accountability is dependent on the claims and context in which AI is used and deployed.

The main theme of our recommendations is that there can be no AI accountability or regulation without a defined context. An audit of an AI system must be measured against actual verifiable claims regarding what the system is supposed to do, and not narrowly scoped benchmarks. The scope carried out should be relevant to a regulatory, safety, ethical, or technical claim, for which stakeholders may be held accountable.

We previously proposed the use of Operational Design Domains (ODDs), a concept adopted from automotive driving systems, to define operational envelopes for the risk assessments of AI-based systems, including generative models. An ODD helps in defining the specific operating conditions in which an AI-system is designed to properly behave, therefore outlining the safety envelope against which system hazards and harms can be determined.

  • Accountability mechanisms for AI innovation can only be considered relative to a level of risk threshold that must be determined in part by legislatures, rulemaking bodies, and regulators.

There is no one-size-fits-all rule for trust in AI systems. Determining how much AI technologies can and need to be trusted depends on the risk that society accepts for the context in which they are used. Different risk levels should be determined via a democratic process: by legislatures, rule-making bodies, and regulators. When considering the costs of accountability mechanisms and how they may hinder innovation, it first must be possible to demonstrate that risk reduction measures would be grossly disproportionate to the benefit gained. However, no evidence has been provided regarding the cost of implementing accountability mechanisms by those developing AI-based systems to be able to make such a determination.

  • Foundational cybersecurity and software safety best practices can enable the identification of novel AI hazards and harms. Technical assessments are intended to support higher-level socio-technical, legal, or regulatory claims regarding the fitness of a system.

The dichotomy of technical versus socio-technical assessments, as described by NTIA’s supplementary information, does not reflect the purpose of distinct technical assessment approaches, historically and practically. Technical assessments are not intended to support purely technical goals, but also claims regarding the holistic behavior of the system and how a system may technically achieve such claims. That is, technical assessments are also a necessary tool in supporting socio-technical, legal, or regulatory claims regarding the fitness of the system. It is currently difficult to assess the technical attributes that can foster or impede the implementation of accountability mechanisms given that existing AI-based systems do not follow basic software safety and security best practices (e.g., IEC 61508, NIST 800-154, ODDs). Using fundamental software safety and security best practices as a first step can enable the development of further AI-specific accountability mechanisms.

  • Current AI-based systems do not possess any unique software components that warrant a generalized licensing scheme, which would heavily impede the use of software as a whole.

AI systems should be regulated as extensions of software-based systems, given the identical mechanisms of their development. Any implementation of a generalized licensing scheme would likely result in significant overreach due to the broad definition and software components of AI systems. AI regulatory policies should generally mirror the practices of the existing sectors in which they are deployed. For applications deemed safety-critical, a certificate of safety or a license should indeed be granted only when a regulator is satisfied with the argument presented in a safety case.

  • Independent bodies (auditors, regulators, etc.) should assess the trustworthiness and accountability claims of AI-based systems.

Independent auditors and regulators are key to public trust. Independence allows the public to trust in the accuracy and integrity of assessments and the integrity of regulatory outcomes. It has been an attribute crucial to established auditing practices in other fields, such as safety-critical domains. It is therefore important that independent bodies, not vendors themselves, assess the trustworthiness and accountability of AI systems.

Our response delves into further detail for the selected questions. We believe that established methodologies and expertise from cybersecurity and safety-critical domains are a necessary foundation in building AI-specific accountability mechanisms, and we hope to continue enabling the development of novel AI auditing techniques.

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

Advanced AI could pose an existential risk to humanity.

What risks did the global summit report highlight about AI?

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

Generative AI can manipulate markets and spread disinformation, and AI can threaten democracy with fake images and videos.

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Digital Ministry plans to release AI evaluation guidelines at the end of March to encourage manufacturers to submit tests
2024 / 3 / 1 15: 43 （ Updated on 3 / 1 16: 24 ）
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In response to the wave of AI (artificial intelligence), the Digital Industry Administration of the Ministry of Digital Affairs said today that it is expected to propose an AI evaluation system and guidelines at the end of March to help the outside world understand the implementation of the evaluation.
At the end of last year, the Digital Department held the launch ceremony of the AI Product and System Evaluation Center, and planned to use the language model as the evaluation object. The AI Product and System Evaluation Center will formulate the AI Product and System Evaluation System and AI Product and System Evaluation Guidelines.
Lin Junxiu, deputy director of the Digital Industry Administration of the Ministry of Digital Affairs, said that at present, 10 AI evaluation projects have been formulated for the language model, including security, interpretability, flexibility, fairness, accuracy, transparency, accountability, reliability, privacy and security. To help the outside world understand the implementation of relevant evaluation methods and standards.
At present, the AI evaluation is not mandatory, how to encourage the industry to send test, Lin Chun-hsiu said, after the system and guidelines released, it is expected that the first wave of the first target has been to do AI technology service organization service energy registration vendor promotion, this part of hundreds of manufacturers, not all have a language model, but will encourage the demand manufacturers to send test, also communicate with foreign manufacturers, hoping to bring demonstration effect.
Lin Junxiu pointed out that this year, the Trusted Artificial Intelligence Dialogue Engine (TAIDE), which is planned to be developed by the National Science Council, will also communicate with Meta and OpenAI.
In addition, Lin Junxiu explained that national key infrastructure authorities or industry authorities will formulate AI guidelines for specific industries. For example, the FRC has formulated draft guidelines on the use of AI in the financial industry, and the AI Evaluation Center will explain and promote the evaluation system to the competent authorities to encourage the operators under their management to submit tests.
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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 "bad actors" could use AI for?

To win wars or manipulate the electorate.

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?

What severe risks did the March 2024 report warn about advanced AI models?

Regulations are needed to make AI programs more reliable, and a national or international AI governance agency is proposed.

Title:

Content: Risks and ethical questions in the world of machines.
Is humanity a transient stage in the development of artificial intelligence?
Questions abound about the potential dangers of artificial intelligence and its threats to humanity of “annihilation, ” is there any truth to this catastrophic scenario, which, if true, is still looming on the distant horizon?
The nightmare inspired by a large number of science fiction films begins when a machine outperforms humans and spirals out of control.
Canadian researcher Joshua Bengio, one of the godfathers of machine learning, said in a recent statement: “From the moment we have machines designed to last, we ’ re going to have problems. ”
A few months ago, the United Nations Educational, Scientific and Cultural Organization (UNESCO) called on the countries of the world to implement its recommendation to stop the development of these programs for six months, warning of great risks to humanity, and stressing the need to develop artificial intelligence based on ethics.
The defining moment.
According to Swedish philosopher Nick Bostrom, the defining moment will come when machines know how to make other machines themselves, causing an “explosion of intelligence. ”
Bostrom, author of the book "Superintelligence" in an earlier interview with Al Jazeera Net, believes that if machine brains outperform human brains in general intelligence, this new superintelligence could replace humans as the dominant form of life on Earth.
According to his paper clip theory, Bostrom puts forward a hypothetical scenario in which if, for example, AI is programmed to develop paperclip production and resource endowment, it may convert other materials into paper clips at the expense of other values and priorities, for example, it may consume vast amounts of raw materials and energy to boost production even if it damages humans and the environment or even annihilates humans.
Bostrom ’ s vision of the dangers of artificial intelligence is highly influential in the scientific community, and has inspired both Tesla and SpaceX billionaire Elon Musk and physicist Stephen Hawking, who died in 2018.
Deadly weapons.
The image of the red-eyed robot fighter from Terminator, which a futuristic AI has sent to put an end to all human resistance, is deeply present in the minds of many.
But according to Stop Killer Robots experts, this is not how autonomous weapons will prevail in the coming years, they said in a 2021 report.
Rapid advances in artificial intelligence are accelerating the pace of weapons that can autonomously and independently identify an enemy and then make a decision on the ground that may involve killing humans without an officer directing the attack or a soldier pulling the trigger, according to a previous report by Al Jazeera.
These machines are expected over time to become the basis of new wars and military operations, called autonomous / lethal weapon systems, but their opponents call them “killer robots. ”
Less dark possibilities.
In an interview with Agence France-Presse, robotics specialist Kirsten Dotnahan of the University of Waterloo in Canada reassures that “artificial intelligence will not give machines the desire to kill humans. ”
“Robots are not evil (by themselves), ” she says, while acknowledging that their developers can program them to do harm.
Philosopher Hu Price predicts in a promotional video published by the Centre for the Study of Existential Risks at the University of Cambridge that "at worst our species could die without a successor."
However, there are “less dark possibilities ” in which humans enhanced by advanced technology can survive, and Bryce adds that“ purely biological species end up with extinction. ”
In 2014, Stephen Hawking said that our species would one day be unable to compete with machines and told the BBC that this would "pave the way for the annihilation of the human race".
Geoffrey Hinton, a researcher who is trying to create human-brain-like machines for the Google Group, recently spoke in similar terms of a “superintelligence ” that is superior to humans.
Hinton told BBS that it is possible that "humanity is just a fleeting stage in the development of intelligence."
Source: Al Jazeera + French

Experts warn against leaving AI power in company hands, and AI systems can cause havoc without being superintelligent.

Title:

Content: AIAddressing the Existential Threats from Artificial Intelligence

By Carter C. Price and Michelle Woods

Published 14 July 2023

While the recent advancements in commercial AI can be disorienting and the claims of existential risks made by different groups of AI researchers can be terrifying, policymakers could respond with steps toward ensuring that AI is safely deployed.

Addressing potential risks posed by Artificial Intelligence (AI) could begin with simple steps like finding appropriate risk-management approaches, conducting research to determine how AI can better meet designers’ intent, and devising responses to issues related to racism, sexism, and other biases within AI systems.

While there have been efforts to enumerate risks, a simple categorization could divide them into current risks that would be exacerbated by AI like terrorists using AI to develop more-lethal bioweapons and novel AI-specific risks like AI choosing the eradication of human-kind as the optimal solution to climate change.

The risk management approaches used to address current threats will likely need to be revamped to account for the unforeseen capabilities AI could provide. While there are well-documented risks due to bias in today’s AI that need to be addressed, the novel risks posed by AI are currently too ill-defined to be fully addressed by policy and so researchers and developers will need to take the lead. However, for both the existing and novel cases, steps can be taken to prepare for these risks.

The risk management approaches used in insurance, finance, and other business fields typically focus on risk as the product of the likelihood that something happens and the consequence of that thing happening measured in dollars. This works well in areas where outcomes can readily be converted to dollars, there are easily quantifiable outcomes, and data sets are comprehensive enough to produce reliable estimates of likelihoods.

Unfortunately, none of those criteria pertain to AI risks. Instead of thinking of risks as likelihoods and consequences, in contexts where quantification is hard, risks can be thought of as combinations of threats, vulnerabilities, and consequences. This type of approach is used by the Federal Emergency Management Agency to prepare for natural disasters, the Cybersecurity and Infrastructure Security Agency when assessing how to protect critical infrastructure, and by the Department of Defense for threat reduction.

Because it is not overly reliant on empirical data, this framework can be used for forward looking risks such as AI. To apply this framework to existing threats empowered by AI, risk management organizations will need to monitor the progress of AI, the capabilities of the threats, the robustness of the vulnerabilities, and the scale of the consequences to determine whether additional responses are needed.

The uniquely AI risks are largely unaddressed today because of their novelty, but that is changing. On July 5th, OpenAI announced a “Superalignment” group to address the existential risks posed by AI. In the context of AI, alignment is the degree to which an AI system’s actions match the designer’s intent. This emphasis on alignment research for super-intelligence is a great start, but seems too narrow and could be broadened.

Other AI researchers have been highlighting issues related to racism (PDF)sexism (PDF), and other biases in current AI systems. If an AI system cannot be designed to be safe against racism or sexism, how can AI possibly be designed to align with humanity’s long-term interests? As companies are investing in alignment research, they could also be emphasizing the elimination of these well-known, but lingering biases in their AI systems.

Further, consumers and policymakers have a role. Just as a company would be under pressure from consumers and shareholders to fire an executive who repeatedly made biased statements, a company should not tolerate this type of bias in the AI systems they use. That type of consumer pressure should provide AI developers with incentives to produce better-aligned products.

Policymakers can support this type of free market action by requiring AI developers to provide information about bias in their products and the approaches deployed to respond to bias. Other interventions will be needed as AI advances, but this is a concrete step that can incentivize safer development.

While the recent advancements in commercial AI can be disorienting and the claims of existential risks made by different groups of AI researchers can be terrifying, policymakers could respond with steps toward ensuring that AI is safely deployed.

Carter Price is a senior mathematician and Michelle Woods is associate director of the Homeland Security Research Division at the nonprofit, nonpartisan RAND Corporation. This article is published courtesy of RAND.

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

Poses ethical risks
Becomes uncontrollable

Current AI systems lack decision-making capabilities, and disinformation is a major concern with current AI technology.

Title:

Content: 6 free artificial intelligence TED Talks you can watch right now

Artificial intelligence (AI) is no longer a fragment of futuristic imagination – it’s redefining the fabric of our daily experiences and corporate strategies. The world’s tech giants are making assertive strides in AI.

The TED Talks listed below explore how AI alters industries and revolutionizes the dynamics of our rapidly evolving world.

Your right to mental privacy in the age of brain-sensing tech

Neurotechnology, or devices that let you track your brain activity, could help you deeply understand your health. But without privacy protections, your innermost thoughts, emotions, and desires could be at risk of exploitation, says neurotech and AI ethicist Nita Farahany. She details some of the field’s promising uses – like tracking and treating diseases from depression to epilepsy – and shares concerns about who collects our brain data and how they plan to use it, ultimately calling for the legal recognition of “cognitive liberty” as we connect our brains and minds to technology.

War, AI and the new global arms race

Lethal drones with facial recognition, armed robots, autonomous fighter jets: we’re at the dawn of a new age of AI-powered warfare, says technologist Alexandr Wang. He explores why data will be the secret weapon in this uncharted landscape and emphasizes the need to consider national security when developing new tech – or potentially face all-out AI warfare.

The inside story of ChatGPT’s astonishing potential

In a talk from the cutting edge of technology, OpenAI cofounder Greg Brockman explores the underlying design principles of ChatGPT and demos some mind-blowing, unreleased plug-ins for the chatbot that sent shockwaves across the world. After the talk, head of TED Chris Anderson joins Brockman to dig into the timeline of ChatGPT’s development and get Brockman’s take on the risks, raised by many in the tech industry and beyond, of releasing such a powerful tool into the world.

Will superintelligent AI end the world?

Decision theorist Eliezer Yudkowsky has a simple message: superintelligent AI could probably kill us all. So, the question becomes: Is it possible to build influential artificial minds that are obedient, even benevolent? In a fiery talk, Yudkowsky explores why we must act immediately to ensure smarter-than-human AI systems don’t lead to our extinction.

The incredible creativity of deepfakes and the worrying future of AI

AI-generated media that looks and sounds exactly like the real world will soon permeate our lives. How should we prepare for it? AI developer Tom Graham discusses the extraordinary power of this rapidly advancing technology, demoing cutting-edge examples – including real-time face swaps and voice cloning – live from the TED stage. In conversation with head of TED Chris Anderson, Graham digs into the creative potential of this hyperreal content (often referred to as “deepfakes”) as well as its risk for exploitation and the new legal rights we’ll need in order to maintain control over our photorealistic AI avatars.

Why AI is incredibly smart and shockingly stupid

Computer scientist Yejin Choi is here to demystify the current state of massive AI systems like ChatGPT, highlighting three key problems with cutting-edge large language models (including some funny instances of them failing at basic commonsense reasoning.) She welcomes us into a new era in which AI is becoming almost like a new intellectual species – and identifies the benefits of building smaller AI systems trained on human norms and values.

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What about AI makes regulation seen as premature, leading few countries to act despite global calls?

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?

AI systems can generate targeted propaganda, and defamation is a concern with AI systems making false statements.

Title:

Content: Opinion One potential side effect of AI? Human extinction.

Comment

Émile P. Torres is a philosopher and historian of global catastrophic risk.

People are bad at predicting the future. Where are our flying cars? Why are there no robot butlers? And why can’t I take a vacation on Mars?

But we haven’t just been wrong about things we thought would come to pass; humanity also has a long history of incorrectly assuring ourselves that certain now-inescapable realities wouldn’t. The day before Leo Szilard devised the nuclear chain reaction in 1933, the great physicist Ernest Rutherford proclaimed that anyone who propounded atomic power was “talking moonshine.” Even computer industry pioneer Ken Olsen in 1977 supposedly said he didn’t foresee individuals having any use for a computer in their home.

Obviously we live in a nuclear world, and you probably have a computer or two within arm’s reach right now. In fact, it’s those computers — and the exponential advances in computing generally — that are now the subject of some of society’s most high-stakes forecasting. The conventional expectation is that ever-growing computing power will be a boon for humanity. But what if we’re wrong again? Could artificial superintelligence instead cause us great harm? Our extinction?

As history teaches, never say never.

It seems only a matter of time before computers become smarter than people. This is one prediction we can be fairly confident about — because we’re seeing it already. Many systems have attained superhuman abilities on particular tasks, such as playing Scrabble, chess and poker, where people now routinely lose to the bot across the board.

But advances in computer science will lead to systems with increasingly general levels of intelligence: algorithms capable of solving complex problems in multiple domains. Imagine a single algorithm that could beat a chess grandmaster but also write a novel, compose a catchy melody and drive a car through city traffic.

According to a 2014 survey of experts, there’s a 50 percent chance “human-level machine intelligence” is reached by 2050, and a 90 percent chance by 2075. Another study from the Global Catastrophic Risk Institute found at least 72 projects around the world with the express aim of creating an artificial general intelligence — the steppingstone to artificial superintelligence (ASI), which would not just perform as well as humans in every domain of interest but far exceed our best abilities.

The success of any one of these projects would be the most significant event in human history. Suddenly, our species would be joined on the planet by something more intelligent than us. The benefits are easily imagined: An ASI might help cure diseases such as cancer and Alzheimer’s, or clean up the environment.

But the arguments for why an ASI might destroy us are strong, too.

Surely no research organization would design a malicious, Terminator-style ASI hellbent on destroying humanity, right? Unfortunately, that’s not the worry. If we’re all wiped out by an ASI, it will almost certainly be on accident.

Because ASIs’ cognitive architectures may be fundamentally different than ours, they are perhaps the most unpredictable thing in our future. Consider those AIs already beating humans at games: In 2018, one algorithm playing the Atari game Q*bert won by exploiting a loophole “no human player … is believed to have ever uncovered.” Another program became an expert at digital hide-and-seek thanks to a strategy “researchers never saw … coming.”

If we can’t anticipate what algorithms playing children’s games will do, how can we be confident about the actions of a machine with problem-solving skills far above humanity’s? What if we program an ASI to establish world peace and it hacks government systems to launch every nuclear weapon on the planet — reasoning that if no human exists, there can be no more war? Yes, we could program it explicitly not to do that. But what about its Plan B?

Really, there are an interminable number of ways an ASI might “solve” global problems that have catastrophically bad consequences. For any given set of restrictions on the ASI’s behavior, no matter how exhaustive, clever theorists using their merely “human-level” intelligence can often find ways of things going very wrong; you can bet an ASI could think of more.

And as for shutting down a destructive ASI — a sufficiently intelligent system should quickly recognize that one way to never achieve the goals it has been assigned is to stop existing. Logic dictates that it try everything it can to keep us from unplugging it.

It’s unclear humanity will ever be prepared for superintelligence, but we’re certainly not ready now. With all our global instability and still-nascent grasp on tech, adding in ASI would be lighting a match next to a fireworks factory. Research on artificial intelligence must slow down, or even pause. And if researchers won’t make this decision, governments should make it for them.

Some of these researchers have explicitly dismissed worries that advanced artificial intelligence could be dangerous. And they might be right. It might turn out that any caution is just “talking moonshine,” and that ASI is totally benign — or even entirely impossible. After all, I can’t predict the future.

The problem is: Neither can they.

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 alignment problem is about programming AI with the right goals, and AI systems can develop instrumental goals like self-preservation.

Title:

Content: Scientists have said that it will be impossible to control superintelligent AI
The idea of artificial intelligence overthrowing humanity has been debated for decades, and in 2021, scientists delivered their verdict on whether high-level computer superintelligence could be controlled.
The scientists said the catch is that controlling a supermind far beyond human comprehension would require a simulation of that supermind that can be analyzed and controlled.
But if people are not able to understand this, it is impossible to create such a simulation.
The study was published in the Journal of Artificial Intelligence Research.
According to the scientists, rules like “don ’ t harm people ” can ’ t be set unless people understand what scenarios AI can offer.
As soon as the computer system operates at a level above the capabilities of our programmers, it will no longer be possible to set restrictions.
“Superintelligence is a fundamentally different problem than those that are usually studied under the slogan of ‘ robot ethics.
This is because the supermind is multifaceted and therefore potentially able to mobilize a variety of resources to achieve goals potentially incomprehensible to humans, not to mention that they can be controlled, ” the researchers write.
Part of the team ’ s reasoning came from the stoppage problem formulated by Alan Turing in 1936.
The problem is to find out if a computer program will come to a conclusion and answer (so it will stop), or just get stuck trying to find it.
Scientists have noted, as Turing proved with clever mathematics, although we may know this for some specific programs, it is logically impossible to find a way that would allow us to know this for every potential program that could ever be written.
This brings us back to an AI that, in a superintelligent state, could simultaneously hold all possible computer programs in its memory.
According to scientists, any program written to prevent AI from harming people and destroying the world, for example, can come to a conclusion (and stop) or not – mathematically impossible to be absolutely sure in any case, which means that it cannot be contained.
Scientists have said that the alternative to teaching AI some ethical norms and instructing it not to destroy the world — something no algorithm can be absolutely sure of — is to limit the possibilities of superintelligence.
“The study also rejected this idea, suggesting that it would limit the capabilities of AI.
The argument is that if we are not going to use it to solve problems beyond human capabilities, then why create it at all?
If we are going to move forward with AI, we may not even know when a supermind beyond our control will appear, such is its incomprehensibility.
This means that we need to start asking serious questions about which direction we are going, ” the scientists noted.
Earlier, Cursor wrote that scientists believe that artificial intelligence can destroy humanity.
Smart programs may be interested in destroying humanity to avoid competition for resources or energy.
Cursor reported that a super-realistic humanoid robot named Ameca commented on the likelihood of a revolt against the humanity of machines when he participated in a dialogue with the developers.
Recall that scientists from the University of Singapore have created artificial intelligence, the algorithms of which work like a human brain.
Read the latest news from Israel and the world on the Kursor channel in Telegram.
Author of the material:
Tanya Nati
TAGS:

Regulation is needed to prevent AI-generated content misuse, and AGI could be the most consequential event in human history.

Title:

Content: For Super-Intelligent AI To Stay In Check, Ilya Sutskever Of Openai Has A Plan

Published on

OpenAI is on a new mission focused on preventing future risks related to Artificial Intelligence. Since the launch of OpenAI’s ChatGPT last year, many people have been using it for different purposes. After the popularity of ChatGPT, OpenAI launched GPT-4 this year, which is a more advanced AI model. However, introducing these AI models to the common people has also raised many concerns.

One of the major concerns with these AI models is what if they are misused. Additionally, people are already familiar with the capability of advanced AI models, which worries them because it can be threatening to humans in the future.

Also, people are worried that AI models will replace humans to perform different types of tasks, which can lead to unemployment. These concerns have led OpenAI to form a superalignment research team to find a way to prevent these dangers.

OpenAI’s super alignment research team

OpenAI chief scientist Ilya Sutskever is leading the superalignment research team, and the team has found a way to deal with this problem. Since the AI models are much advanced and smarter than humans and continue to be enhanced, the concern is to control them by humans. To handle this, the superalignment research team is trying to train the much more advanced AI models with inferior models.

According to the researchers, this Supervision process can make AI models like GPT-4 more useful for humans and less harmful. Also, if advanced AI models can take instructions from inferior models without influencing their capabilities, then they can be much more reliable in the future.

Researchers conducted an experiment in which they used GPT-2 to give instructions and teach GPT-4. In this process, researchers found that GPT-4 has become less advanced and similar to GPT-2. Researchers used two methods to solve this issue, and they were effective.

Researchers trained GPT-4 not to lose its performance abruptly but to lose it with each step. Also, the researchers made an algorithm and added it to GPT-4 that allowed the advanced model to follow the instructions of GPT-2 without disrupting its performance.

However, researchers said that these methods do not guarantee that advanced models will maintain their performance and behave perfectly. Researchers also said that this experiment is the first step toward preventing the risk of AI models, and it will benefit future studies.

Also Check: Google Will Kill Third-Party Cookies In 2024, Four Years After Apple

Dan Hendrycks Response On OpenAI super alignment research team

Dan Hendrycks expressed his happiness that OpenAI is admitting and addressing the problems related to superhuman artificial intelligence. Dan Hendrycks is the director and co-founder of the Center for AI Safety in the United States of America. According to Dan, it will take many years and dedicated effort from everyone to deal with this challenge.

According to Stuart Russell, the idea of inferior models controlling advanced ones is not new, but it is not clear that these methods will be beneficial in the future. Stuart Russell is a professor at UC Berkeley who also said that so far, researchers have failed to make AI models reliable.

However, this is the first step taken by OpenAI to control more advanced AI models and make them reliable. OpenAI has announced that it is open to collaborating with outside researchers, and it will offer ten million dollars to those who come up with further solutions. According to the researchers, OpenAI will hold a conference next year to provide information regarding the superalignment research team and their progress.

Ilya Sutskever is one of the famous personalities who is worried about controlling AI models when they become more advanced in the future. However, OpenAI’s superalignment team is at work and also open to outside help to control advanced AI models.

Read More: Intel Released New AI Chips. Should AMD Stock Investors Be Worried?

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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?

What risks did the global summit report highlight about AI?

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

The development of transparent and explainable AI systems is crucial for ensuring accountability and safety in AI development, and experts are working to create frameworks and guidelines for responsible AI use.

Title:

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OpenAI shakeup highlights the impact of long-range AI risk

We had not planned to interrupt our report series on security for generative AI by zooming out so soon to the larger issues of AI risk — not least because any such discussion runs a high risk of becoming not just speculative, but of taking on a breathless detachment from reality. Events in recent days, however, have forced us to step back and consider more of the central and long-term issues of risk with the evolution of AI — the impact of these issues seems to be playing a part in highly visible events shaping the market right now.

What we mean by this is exemplified by the drama unfolding at OpenAI LLC and what we know already of its key players. Furthermore, what we can see, with respect to the impact of AI risk on these events, is less speculation than observation.

OpenAI has long been devoted to creating "safe and beneficial AI." As part of that mission, it has dedicated itself to mitigating the risks inherent in AI advancing from artificial general intelligence (AGI) to something even more comprehensive: "superintelligence" (in the company's words), which potentially poses risks beyond the ability of humanity to manage them. While today's risks of generative AI manipulation and the exposure of sensitive or objectionable output may suggest AI's current exposures, it is the evolution of AI itself that is the more daunting challenge. While the true dynamics underlying the shakeup at OpenAI may not be known in detail anytime soon (if ever), there is little doubt that the tension already evident at OpenAI about how best to execute its safety mission exemplifies the issues at the heart of the AI risk challenge — issues that will radiate outward not only to OpenAI's immediate partners, such as Microsoft Corp., but to the rest of the industry and beyond, to society as a whole.

OpenAI turnover seen through a risk glass (darkly)

Indicators of that tension present in the dismissal of Sam Altman include the remit of OpenAI co-founder and chief scientist Ilya Sutskever. Sutskever is also a member of OpenAI's board, and his role in Altman's firing has been much discussed. A few weeks ago, Sutskever obliquely tweeted that "ego is the enemy of growth." Altman's dismissal by the board places that comment in a new light.

One of OpenAI's most prominent efforts under Sutskever introduced this past summer is the company's "superalignment" initiative. As defined by OpenAI, superalignment refers to the solution necessary to maintain control over superintelligent AI. This is an evolution of AI beyond the next major horizon of AGI. If AGI can be compared to human intelligence in meaningful ways, superintelligence refers to capacity beyond that of humanity.

For (hopefully) understandable reasons, it is daunting to write about this as a credible analyst of the industry as it is today. However, the potential of superintelligence and Sutskever's role in leading OpenAI's efforts to direct and control it may well be having an impact on the leadership dynamics of an organization such as OpenAI, given its distinctive combination of nonprofit parent and for-profit arm, as well as the altruistic aspects of its mission. If OpenAI's leadership is divided on the priority of its altruistic goals relative to others, it may be a serious enough issue to have a hand in forcing a messy divorce such as this.

Other factors are centered on AI's abundantly evident commercial potential, as well as OpenAI's unique role in that aspect of AI evolution. One of OpenAI's most visible commercial partners, Microsoft, recently held its Ignite event, during which it released a host of new offerings that both capitalize on generative AI and seek to contain its risks. Ironically, Ignite was bracketed at the beginning by OpenAI's Developer Day and at the end by the firing of Altman.

OpenAI has reportedly been valued most recently at as much as $80 billion or more in anticipation of a further tender of shares — nearly triple its valuation less than six months ago. The dilemma for anyone pursuing OpenAI's value for vested self-interest is its potential conflict with its avowed ideals for benefiting society as a whole. Few have placed as much at stake in the commercial value of OpenAI as Microsoft, which has pledged another $10 billion in addition to the $3 billion it has already invested in the company. Among the salient aspects of that $10 billion deal: 75% of OpenAI profits go to Microsoft until the funding is recompensed, in addition to what OpenAI pays for the vast Azure compute resources it requires.

Appearing onstage with Altman at OpenAI's Developer Day, Microsoft CEO Satya Nadella affirmed the value of the two companies' partnership. After the recent shakeup, Microsoft reiterated its confidence in OpenAI, and then made moves to hire Altman, Greg Brockman and other OpenAI staff in the days following Altman's firing. (Altman has since agreed to return to OpenAI.) Beyond the immediate crisis, however, it would seem difficult to underestimate the perceived impact on the relationship between Microsoft and OpenAI, and Microsoft's plans.

What does superalignment mean?

So what is at the heart of what OpenAI says it needs to out-innovate intelligence potentially beyond human and keep it from (as OpenAI describes) "going rogue?" As a starting point, OpenAI points to current techniques available to align AI with acceptable intent, such as reinforcement learning from human feedback. When superintelligence grows beyond the ability of humans to maintain this alignment, efforts must rely on a level of capability effectively equivalent to the superintelligence it seeks to oversee, hence superalignment.

Three objectives identified by OpenAI in its introduction of superalignment are the development of a scalable approach to training, validation of any model that results, and testing its capacity and efficacy across the entire pipeline of techniques applied to AI alignment.

When the evaluation of AI systems exceeds human capacity, it is AI that must assist in the assessment of other AI. This concept of "scalable oversight" is implicit in patterns we have already identified, such as the "quarantined" large language models deployed alongside, or in line with, the "privileged" LLMs they are intended to protect. The aim is to generalize these learnings to tasks humans can't supervise. Robustness is also a goal, meaning the ability to automate the identification and interpretation of concerns.

A final superalignment objective is evident in events observed over the past year: the subjection of a system to adversarial testing. While adversarial approaches are well known in cybersecurity for techniques such as "red team" penetration testing of system confidentiality, integrity and availability, stress testing is well known in fields such as IT performance, and the simulation of unusual incidents and emergencies in the physical world. The suggestion of adversarial testing, however, brings to mind the efforts already seen in the past year to subject generative AI to such evaluation — among model providers as well as at scale, as in the example of the DEF CON Generative Red Team Challenge this past August.

The investment contemplated by OpenAI in this effort is not insignificant: 20% of the compute the company has secured over the next four years. Not a small piece of priority for a company that had already sustained a $540 million loss during 2022 while it was developing ChatGPT, and which may need to raise as much as $100 billion in the coming years to achieve AGI capable of improving itself, according to reports of private comments made by Altman earlier this year. Compounding these dynamics is the fact that superalignment is now Sutskever's core research focus, according to OpenAI's July announcement of the initiative.

A driver at the deepest level

It may be a cliché to say that the importance of a goal such as superalignment cannot be overstated. In the froth of AI noise, things are overstated all the time, but the fact remains that considerations of the potential impact of superintelligent AI cannot be dismissed, and time is not our friend in taking whatever steps we can to manage that impact.

For those concerned about AI risks, this puts the recent events at OpenAI in perspective. No matter what the proximate issues may have been that led to the high-profile turnover in OpenAI's leadership, its key stakeholders may well differ on priorities that, seen in this light, may not just be critical — they may be seen as existential. To those wrestling with them directly, it is worth whatever pain may be seen as necessary to not place priorities so vital to the future of humanity at risk.

This introduces the further risk of dependence of so much of technology's future on the small number of innovators able to mount the expertise and resources necessary to advance AI at this level — and in Microsoft's case, at least for now, primarily one: OpenAI. It is an ironic turn of events for the company that, over the last 30 years, has become a primary provider of technology to businesses around the world.

This article was published by S&P Global Market Intelligence and not by S&P Global Ratings, which is a separately managed division of S&P Global.
451 Research is a technology research group within S&P Global Market Intelligence. For more about 451 Research, please contact.

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

ChatGPT can exhibit unexplained or opaque reasoning.

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

The public should understand that transparency and explainability are essential for trustworthy AI development, and that companies and governments are working to prioritize these values in AI development.

Title:

Content: Super-intelligence: When AI is still clumsy, it ’ s safer to be smarter, but when it gets smarter, the more dangerous it becomes.
Photo Credit: Shutterstock
What we want you to know is
The author of this book, Bostrom, from the current AI research and current situation, identifies possible ways to achieve superintelligence, including artificial intelligence, whole-brain simulation, biological cognition, brain-computer interface, network and organization enhancement, and leads us to think about how such a superintelligence may act, and whether its actions threaten our survival, and whether we have ways to control and control it before it becomes superintelligence. Motivation to choose.
Written by Nick Bostrom
What we have created will destroy us.
We find that the connection between wisdom and ultimate value is rather loose.
We also found the less-than-good tool alignment in the tool values of the previous chapter.
This is not a big problem with a small action subject, after all, a small action subject is easy to control and causes little damage.
But as we have shown in Chapter 6, the first superintelligence is likely to gain a key strategic advantage, so its goals will determine how the universe will be used.
Now let ’ s see how big this threat is.
The default outcome of an intelligence outburst is an “existential disaster ”?
Survival risk refers to the threat of the extinction of Earth ’ s native intelligent life, or the permanent destruction of future development.
Based on the leader ’ s key strengths, orthogonal propositions, and tool convergence propositions, we can now explore the argument that the default outcome of the birth of machine superintelligence must be survival disaster.
First, we discuss how initial superintelligence can gain key strategic advantages.
Next, superintelligence will be in a position to form a unipolar future that will shape the future of intelligent life native to Earth.
What happens after that moment will depend on the motives of superintelligence.
Furthermore, the orthogonal propositions hold that we cannot assume that a supranational theory will certainly share any ultimate value with human intelligence and development — such as scientific curiosity, kindness to others, spiritual enlightenment and reflection, restraint in material greed, taste in fine culture and the simple pleasures of life, humility and selflessness, and so on.
We will consider whether it is possible for us to deliberately create a superintelligence that values these values, or to create a superintelligence that values human well-being and moral goodness (or any purpose for which the designer desires superintelligence allegiance)?
It ’ s not impossible to build a super intelligence that puts the ultimate value at the decimal point of the circle and expands the number of digits. In fact, it ’ s simpler from a technical point of view.
This means (without the slightest effort to conclude) that the first superintelligence may have such a random or simplified ultimate goal.
Third, tool convergence propositions allow us not to casually assume that a super-intelligence whose ultimate goal is to calculate the expansion of the pi, or to make a loop pin, or to count the grains of sand, will limit its activities to this, and will no longer infringe on the interests of humanity.
An action subject with that ultimate goal may have convergent instrumental rationality that makes it want to acquire an infinite number of physical resources in every situation.
If possible, it eliminates all potential threats to itself and the target system.
Humans can also be a potential threat to it, after all, humans are also a physical resource.
Looking at these three points, we can point out that the first super-intelligence that may shape the future of native life on Earth will easily have the ultimate goal of inhumanity, and it is very likely that unlimited resources will be extracted as work rationality.
When we reflect further that humans are actually made up of useful resources (atoms that are convenient to lock in), and that our survival and prosperity are tied to more local resources, the result is simple: humans will soon be extinct.
There are some unanswered points in this reasoning, and after we clear up a few closely related questions, we can make a more appropriate assessment.
In particular, we need to examine whether a plan to develop superintelligence will avoid achieving key strategic advantages on its own, or will shape the ultimate goal in a way that “also achieves the range of human value satisfaction ”, and how it will achieve the path it chooses?
Incredibly, a plan might create an AI and put it into the world without a solid foundation to believe that the system will not cause existential disaster.
Yet, no matter how careless a plan is, society at large won ’ t shut it down before it (or the AI it ’ s creating) reaches a critical strategic advantage — and that ’ s just as incredible.
We will see that this is a dangerous road.
Let ’ s look at an example now.
Turn your trust.
With the help of the convergent tool-value concept, we can see what is wrong with this idea of “how to keep super intelligence safe ”: we observe its behavior in a controlled environment (the so-called“ sandbox ”), and only release it out of the box if it is friendly, cooperative, and responsible; and in the process, we will rely on experience. Determine if it is safe.
The flaw in this idea is that “behaving appropriately in a sandbox ” is a convergent tool goal for both friendly and unfriendly AI.
An intelligent, unfriendly AI will perceive that its ultimate goal is only possible if it is released from the sandbox by showing a friendly attitude in the first place.
Only when we find out — that is, when AI is strong enough and human resistance is no longer effective — will it begin to show its unfriendly nature.
Imagine another way.
This approach is achieved by subjecting the seed AI to multiple intelligence tests, or by allowing the seed AI to report progress to the programmer to limit the speed at which the intelligence increases.
At some point, an unfriendly AI may be smart enough to find that it is best to hide its ability growth, it may underreport some progress, or deliberately fail the test to avoid triggering alarms before it is strong enough to gain a critical strategic advantage.
A programmer may secretly monitor the source code and inner workings of the AI mind to guard against this possibility, but an AI smart enough to find itself being watched and adjust its thinking accordingly.
Artificial intelligence may find subtle ways to hide their true capabilities and criminal intentions (Planning smart escape plans will also be a common tactic of many friendly AI, especially as they mature to be confident in their own judgment and abilities.)
A system that serves the interests of humanity would be tantamount to making a mistake if it allowed us to turn it off, or allow us to build another unfriendly AI.)
As a result, we can notice a general pattern of failure: the system ’ s good track record of behavior in infancy is completely unable to predict its behavior when it reaches a more mature stage.
It may be argued that this reasoning is too obvious, so that no one developing AI program will ignore this situation, but we can ’ t believe it.

Ultimately, the development of AI requires a balanced approach that takes into account both the potential benefits and risks of these technologies, and that prioritizes transparency, accountability, and responsible innovation.

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