US Senate Hearing on Oversight of AI: Principles for Regulation

25 Jul 2023 15:00h - 17:00h

In this document, you can find a transcript from the hearing session on Oversight of AI: Principles for Regulation hosted by the US Senate Committee on the Judiciary, Subcommittee on privacy, technology and the law on 25 July 2023. Here you can find more information about the hearing session, including a video recording

Testimonials by

  • Stuart Russell, Professor of Computer Science, the University of California, Berkeley CA
  • Youshua Bengio, Founder and Scientific Director of Mila – Québec AI Institute | Professor in the Department of Computer Science and Operations Research at Université de Montréal, Québec, Canada
  • Dario Amodei, Chief Executive Officer, Anthropic, San Francisco, CA

Transcript from the hearing

Introduction by senators

Thank you to our three witnesses for being here. I know you’ve come a long distance and to the ranking member, Senator Hawley for being here as well, on a day when many of us are flying back, I got off a plane about, I think, less than an hour ago. So forgive me for being a little bit late. I know many of you have flown in as well. And thank you to all of our audience, and many are outside the hearing room. 

Some of you may recall, at the last hearing, I began with a voice. Not my voice, although it sounded exactly like mine, because it was taken from floor speeches and an introduction, not my words, but concocted by chat GPT, that actually mesmerized and deeply frightened. A lot of people who saw and heard the opening today, my opening at least, is not going to be as dramatic. But the fears that I heard, as I went back to Connecticut, and also heard from people around the country, were supported by that kind of voice, impersonation, and content creation. 

And what I have heard again, and again and again, and the word that has been used so repeatedly is scary. Scary when it comes to artificial intelligence, and as much as I may tell people, you know, there’s enormous good here, potential for benefits in curing diseases, helping you solve climate change, workplace efficiency. Why what rivets their attention? 

Is the science fiction image of an intelligence device out of control, autonomous, self replicating, potentially creating diseases, pandemic grade viruses, or other kinds of evils purposely engineered by people or simply the result of mistakes, not malign intention. And, frankly, the nightmares are reinforced in a way by the testimony that I’ve read from each of you. In no way, disparagingly, do I say that those fears are reinforced because I think you have provided objective fact base views on what the dangers are and the risks and potentially even human extinction, an existential threat, which has been mentioned by many more than just the three of you experts who know firsthand the potential for harm. 

But these fears need to be addressed and I think can be addressed through many of the suggestions that you are making to us and others as well. I’ve come to the conclusion that we need some kind of regulatory agency but not just a reactive body not just a passive rules of the road maker IIDX on what guardrails should be but actually investing proactively in research, so that we develop countermeasures against the kind of autonomous out of control scenarios that are potential dangers a an artificial intelligence device that is in effect program to resist any turning off A decision by AI to begin nuclear reaction to a non existent attack. 

The White House certainly has recognized the urgency with a historic meeting of the seven major companies which made eight profoundly significant commitments. And I commend and thank the President knighted states for recognizing the need to act. But we all know and you have pointed out in your testimony that these commitments are unspecific and unenforceable. A number of them on the most serious issue say that they will give attention to the problem. All good. 

But it’s only a start. And I know the doubters, about Congress, and about our ability to act. But the urgency here demands action. The future is not science fiction or fantasy. It’s not even the future. It’s here now. And a number of you have put the timeline at two years, before we see some of the biological, most of your dangers, it may be shorter, because the kinds of pace of development is not only stunningly fast, it is also accelerated at a stunning pace, because of the quantity of chips, the speed of chips, the effectiveness of algorithms. It is an exact inexorable flow of development. We can condemn it, we can regret it, but it is real. 

And the White House’s principles actually align with a lot of what we have said, among us in Congress, and notably in the last hearing that we held. We’re here now because AI is already having a significant impact on our economy, safety, and democracy. The dangers are not just extinction, but loss of jobs. One of potentially the worst nightmares that we have. 

Each day, these issues are more common, more serious and more difficult to solve. And we can’t repeat the mistakes that we made on social media, which was to delay and disregard the dangerous so the goal for this hearing is to lay the ground for legislation go from general principles to specific recommendations to use this hearing to write real laws, enforceable laws. 

In our past two hearings, we heard from panelists that section 230, the legal shield that protects social media should not apply to AI. based on that feedback, Senator Holly and I introduced the no section 230 immunity for AI act. Building on our previous hearing, I think there are our core standards that we are building bipartisan consensus around and I welcome hearing from many others on these potential rules establishing a licensing regime for companies that are engaged in high risk AI development, a testing and auditing regimen by objective third parties or by preferably the new entity that we will establish imposing legal limits on certain uses related to elections. 

Senator Klobuchar has raised this danger directly related to nuclear warfare. China apparently agrees that AI should not govern the use of nuclear warfare requiring transparency about the limits and use of AI models. This includes watermarking, labeling, and disclosure when AI is being used and data access data access for researchers. So I appreciate the commitments that have been made by Antrophic open AI and others at the White House related to security testing and transparency. 

Last week, it shows these goals are achievable, and that they will not stifle innovation, which has to be an objective avoid stifling innovation. We need to be creative about the kind of agency or entity, the body or administration. It can be called administration and office. I think the language is less important than its real enforcement power and the resources invested in it. We are really lucky, very, very fortunate to be joined by three true experts today. 

One of the most distinguished panels I’ve seen in my time in the United States Congress, which is only about 12 years, one of the leading AI companies, which was founded with a goal of developing AI that is helpful, honest and harmless, a researcher whose groundbreaking work led him to be recognized as one of the godfathers of AI and a computer science professor, whose publications and testimony on the ethics of aI have shaped regulatory efforts like the EU, AI. So, welcome to all of you. And thank you so much for being here. I turn to the ranking member. Senator Holly.

Thank you very much, Mr. Chairman. Thanks to all of our witnesses for being here. I want to start by thanking the chairman, Senator Blumenthal, for his terrific work on these hearings. It’s been a privilege to get to work with him. These have been incredibly substantive hearings. I’m really looking forward to hearing from each of you today. I want to thank his staff for their terrific work. It takes a lot of effort to put together a hearings of the substance. And I want to thank Senator Blumenthal for being willing to do something about this problem, as he alluded to a moment ago, he and I a few weeks ago, introduced the first bipartisan bill to put safeguards around AI development, the first bill to be introduced the United States Senate, which will protect the right of Americans to vindicate their privacy, their personal safety and their interests in court, against any company that would develop or deploy AI. 

This is an absolutely critical, foundational, right, you can give Americans paper rights, parchment rights, as our founders said, all you want if they can’t get into court to enforce them, they don’t mean anything. And so I think it’s significant that our first bipartisan effort is to guarantee that every American will have the right to vindicate their rights, their interests, their privacy, their data protection, their kids safety in court. 

And I look forward to more to come with Senator Blumenthal with other members who I know are interested in this. I think that for my part, I have expressed my own sense of what our priorities ought to be when it comes to legislation. It’s very simple workers, kids, consumers, and national security. 

As AI develops, we’ve got to make sure that we have safeguards in place that will ensure this new technology is actually good for the American people, I’m confident it’ll be good for the companies, I have no doubt about that. 

The biggest companies in the world, who currently make money hand over fist in this country, and benefit from our laws. I know they’ll they’ll be great Google, Microsoft, metta, many of whom have invested in the companies, we’re going to talk to you today. And we’ll get into that a little bit more in just a minute. But I’m confident they’re gonna do great. 

What I’m less confident of is that the American people are going to do alright, so I’m less interested in the corporation’s profitability. In fact, I’m not interested in that at all. I’m interested in protecting the rights of American workers and American families and American consumers against these massive companies that threatened and become a total law unto themselves a man you want to talk about a dystopia? 

Imagine a world in which AI is controlled by one or two or three corporations that are basically governments unto themselves. And then the United States government and foreign entities talk about a massive transfer of power from the people to the powerful. That is the true nightmare. And for my money, that is what this body has got to prevent. 

We want to see technology developed in a way that actually benefits the people, the workers, the kids and the families of this country. And I think the real question before Congress is, Will Congress actually do anything? 

As Senator Blumenthal, I think, put his finger on it precisely. I mean, look at what this Congress did or did not do with regard to these very same companies, the same behemoth companies, when it came to social media, it’s all the same players, let’s be honest. We’re talking about the same people and AI as we weren’t social media. 

It’s Google. Again. It’s Microsoft, it’s Meta, all the same people. And what I noticed is, in my short time in the Senate, there’s a lot of talk about doing something about big tech and absolutely zero movement to actually put meaningful legislation on the floor of the United States Senate and do something about it. 

So I think the real question is, will the Senate actually act? Will the leadership in both parties, both parties, will it actually be willing to act? We’ve had a lot of talk. But now’s the time for action. And I think if the urgency of the new generative AI technology does not make that clear to folks, then you’ll never be convinced, and to me, that really defines the urgent needs of this moment. Thank you, Mr. Chairman.

I’m going to turn to Senator Klobuchar in case she has some remarks. Thank you. Do a woman of action. I hope Senator Hawley, definitely a woman of action and someone who has invested a lot of time. Yes. Well, I just want to thank both of you for doing this. I mostly just want to hear from the witnesses. I do agree with both Senator Blumenthal and Senator Hawley. This is the moment and the fact that this has been bipartisan so far in the work that Senator Schumer, Senator Young, are doing the work that is going on in this subcommittee with the two of you and the work Senator Holly and I are also engaged in. 

And some of the other issues related to this. I actually think that if we don’t act soon, we could decay into not just partisanship but in inaction. And the point that Senator Hawley just made is right, we didn’t get ahead of the Congress didn’t get ahead of with section 230, and the like, and some of the things that were done for maybe good reasons at the time, and then didn’t do anything. 

And now you’ve got kids getting addicted to fentanyl, and you’ve got off that they get online, you’ve got privacy issues, you’ve got kids being exposed to content, they shouldn’t see you’ve got small businesses that have been pushed down search engines and the like. And I still think we can fix some of that. But this is certainly a moment to engage. And I’m actually really excited about what we can get done the potential for good here. 

But what we can do to put in guardrails and have an American way of putting things in place, and not just defer to the rest of the world, which is what’s starting to happen. And some of the other topics I raised. So I’m particularly interested, which is not as much our focus today on the election side and democracy and making sure that we do not have these ads that aren’t the real people. I don’t care what political party people are with that we are, give voters the information they need to make a decision, and that we are able to protect our democracy and there’s some good work being done on that front. So thank you.

Let me introduce the witnesses and seize this moment to let you have the floor. We’re honored to be joined by Dario Amadei, who is the CEO of Anthropic, an AI, safety and research company. It’s a public benefit corporation dedicated to building steerable AI systems that people can rely on and generating research about the opportunities and risks of AI and tropics. AI assistant, Claude is based on its research into training helpful, honest, and harmless AI systems. Yoshua Bengio is a recognized worldwide recognized leading expert in artificial intelligence. He is known for his conceptual and engineering breakthroughs in artificial neural networks and deep learning. He pioneered many of the discoveries and advances that have led us to this point today. And he’s a full professor in the Department of Computer Science and Operations Research at the University of Montreal, and the founder and scientific director of Mila Quebec artificial intelligence Institute, one of the largest academic Institute’s in deep learning, and one of the three federally funded centers of excellence in AI, research and innovation in Canada. With apologies, I’m not going to repeat all the awards and recognitions that you’d receive because it would probably take the rest of the afternoon. We’re also honored to be joined by Stuart Russell. He received his BA with first class honours in physics, from Oxford University in 1982, and his PhD in computer science from Stanford 1986. He then joined the faculty of the University of California at Berkeley, where he’s professor and formerly Chair of electrical engineering, and computer sciences, and the holder of the Smith data chair in engineering director of the Center for Human compatible AI, and director of the Kavli Center for Ethics, science and the public. He’s also served as an adjunct professor of Neurological Surgery at UC San Francisco. Again, many honors and recognitions all of you have received. In accordance with the customer of our committee, I’m going to ask you to stand and take an oath you solemnly swear that the testimony you’re about to give is the truth, the whole truth and nothing but the truth. So help you God. Thank you

Testimony by Daria Amadei, CEO of A tropic 

Chairman Blumenthal Ranking,  Member Hawley and members of the committee, thank you for the opportunity to discuss the risks and oversight of AI with you. Anthropic is a public benefit corporation that aims to lead by example, in developing and publishing techniques to make AI systems safer and more controllable. And by deploying the safety techniques in state of the art models, research conducted by Anthropic includes constitutional AI, a method for training AI systems to behave according to an explicit set of principles. 

Early work on red teaming, or adversarial testing of AI systems to uncover bad behavior, and foundational work in AI interpretability, the science of trying to understand why AI systems behave the way they do. This month after extensive testing, we were proud to launch our AI model Claude  2 users. Claude 2 puts many of these safety improvements into practice. 

While we’re the first to admit that our measures are still far from perfect, we believe they’re an important step forward. In a Race to the Top on safety, we hope we can inspire other researchers and companies to do even better. 

AI will help our country accelerate progress in medical research, education and many other areas. As you said in your opening remarks, the benefits are great, I would not have found Anthropic, if I did not believe AI benefits could outweigh its risks. However, it is very critical that we address the risks. 

My written testimony covers three categories of risks, short term risks that we face right now, such as bias, privacy, misinformation, medium term risks related to misuse of AI systems as they become better at science and engineering tasks. 

And long term risks related to weather models might threaten humanity as they become truly autonomous, which you also mentioned in your opening testimony. 

In these short remarks, I want to focus on the medium term risks, which present an alarming combination of imminence and severity. Specifically, Anthropic is concerned that AI could empower a much larger set of actors to misuse biology. 

Over the last six months, Anthropic, in collaboration with world class biosecurity experts, has conducted an intensive study of the potential for AI to contribute to the misuse of biology. Today, certain steps in bio weapons production involve knowledge that can’t be found on Google or in textbooks and requires a high level of specialized expertise. This being one of the things that currently keeps us safe from attacks. 

We found that today’s AI tools can fill in some of these steps, I’ll be at incompletely unreliably. In other words, they’re showing the first signs of danger. However, a straightforward extrapolation of today’s systems to those we expect to see in two to three years suggests a substantial risk that AI systems will be able to fill in all the missing pieces, enabling many more actors to carry out large scale biological attacks. 

We believe this represents a grave threat to US national security. We have instituted mitigations. Against these risks in our own deployed models briefed a number of US government officials, all of whom found the results disquieting, and are piling and responsible disclosure process with other AI companies to share information on this and similar risks. However, private action is not enough. This risk and many others like it requires a systemic policy response, we recommend three broad classes of actions. 

First, the US must secure the AI supply chain in order to maintain its lead while keeping these technologies out of the hands of bad actors. This supply chain runs from semiconductor manufacturing equipment, to chips, and even the security of AI models stored on the servers of companies like ours. 

Second, we recommend a testing and auditing regime for new and more powerful models. Similar to cars or airplanes. Ai models of the near future will be powerful machines that possess great utility, but can be lethal if designed incorrectly or misused. New AI models should have to pass a rigorous battery of safety tests before they can be released to the public at all, including tests by third parties and national security experts in government. 

Third, we should recognize the science of testing and auditing for AI systems is in its infancy, it is not currently easy to detect all the bad behaviors in the AI system is capable of without first broadly deploying it to users, which is what creates the risk. Thus, it is important to fund both measurement and research on measurement. 

To ensure a testing and auditing regime is actually effective. Funding NIST and the National AI research resource are two examples of ways to ensure America leads here. 

The three directions above our synergistic responsible supply chain policies help give America enough breathing room to impose rigorous standards on our own companies without ceding our national lead to adversaries, and funding measurement in turn makes these rigorous standards meaningful. 

The balance between mitigating AI’s risks and maximizing its benefits will be a difficult one. But I’m confident that our country can rise to the challenge. Thank you

Thank you very much. Why don’t we go to Mr. Banjo.

Testimony by Youshua Bengio, Founder and Scientific Director of Mila – Québec AI Institute 

Chairman Lowenthal, Ranking Member Holly, members of the Judiciary Committee, thank you for the invitation to speak today. The capabilities of AI systems have steadily increased over the last two decades, thanks to advances in deep learning that I and others introduced. 

While this revolution has the potential to enable tremendous progress and innovation, it also entails a wide range of risks from immediate ones like discrimination to growing ones like disinformation, and even more concerning ones in the future like loss of control of superhuman AIs. 

Recently, I and many others have been surprised by the giant leap realized by systems like chat GPT, to the point where it becomes difficult to discern whether one is interacting with another human or a machine. 

These advancements have led many top AI researchers including myself, to revise our estimates of when human level intelligence could be achieved, previously thought to be decades or even centuries away, we now believe it could be within a few years or decades. 

The shorter timeframe, say five years is really worrisome, because we’ll need more time to effectively mitigate the potentially significant threats to democracy, national security and our collective future. As Sam Altman said, here, if this technology goes wrong, it could go terribly wrong. 

These severe risks could arise, either intentionally, because of malicious actors using AI systems to achieve harmful goals or unintentionally. If an AI system develops strategies that are misaligned with our values and norms. 

I would like to emphasize four factors that governments can focus on in their regulatory efforts to mitigate all AI harms and risks. 

First, access limiting who has access to powerful AI systems, structuring the proper protocols, duties, oversight and incentives for them to act safely. 

Second, alignment, ensuring that AI systems will act as intended in agreement with our values and norms. 

Third, raw intellectual power, which depends on the level of sophistication of the algorithms, and the scale of computing resources and of datasets, and 

Forth scope of actions. 

The potential for harm an AI system can affect indirectly, for example, through human actions, or directly, for example, through the internet. So looking at risks through the lens of each of these four factors, access, alignment, intellectual power, and scope of actions is critical to designing appropriate government interventions. I firmly believe that urgent efforts, preferably in the coming months, are required in following three areas. 

First, the coordination of highly fragile national and international regulatory frameworks and liability incentives that bolster safety. This would require licenses for people and organizations with standardized duties to evaluate and mitigate potential harm, allow independent audits and restrict AI systems with unacceptable levels of risk. 

Second, because the current methodologies are not demonstrably safe, significantly accelerate global research endeavors focused on AI safety, enabling the informed creation of essential regulations, protocols, safe AI methodologies, and governance structures. 

And third, research on counter measures to protect society from potential rogue AIs. Because no regulation is going to be perfect. This research in AI and international security should be conducted with several highly secure and decentralized labs operating under multilateral oversight to mitigate an AI arms race. 

Given the significant potential for detrimental consequences, we must therefore allocate substantial additional resources to safeguard our future at least as much as we are collectively, globally investing in increasing the capabilities of AI. I believe we have a moral responsibility to mobilize our greatest minds and make major investments in a bold and internationally coordinated effort to fully reap the economic and social benefits of AI while protecting society and our shared future against this potential perils. Thank you for your attention to this pressing matter. I look forward to your questions.

Testimony by Stuart Russell, Professor of Computer Science, the University of California, Berkeley CA

Thank you Chair Blumenthal and Ranking Member Hawley, and members of the subcommittee for the invitation to speak today, and for your excellent work on this vital issue. Ai, as we all know is the study of how to make machines intelligent. Its stated goal is general purpose artificial intelligence, sometimes called AGI or artificial general intelligence machines that match or exceed human capabilities in every relevant dimension. 

The last 8 years I’ve seen a lot of progress towards that goal. For most of that time, we created systems whose internal operations we understood during on centuries of work in mathematics, statistics, philosophy, and operations research. 

Over the last decade, that has changed, beginning with vision and speech recognition now with language the dominant approach has been end to end training of circuits with billions or trillions of adjustable parameters. The success of these systems is undeniable, but their internal Principles of operation remain a mystery. This is particularly true for the large language models or ll M’s, such as Chad GPT. 

Many researchers now see AGI on the horizon. In my view, LLM ‘s do not constitute AGI, but they are a piece of the puzzle. We’re not sure what shaped the pieces yet or how it fits into the puzzle. But the field is working hard on those questions and progress is rapid. If we succeed, the upside could be enormous. I’ve estimated a cash value of at least 14 quadrillion dollars for this technology, a huge magnet in the future, pulling us forward. 

On the other hand, Alan Turing, the founder of computer science, warned in 1951, that once AI outstrips our feeble powers, we should have to expect the machines to take control. We have pretty much completely ignored this warning. It’s as if an alien civilization warned us by email of its impending arrival. And we replied, humanity is currently out of the office. Fortunately, humanity is now back in the office and has read the email from the aliens. 

Of course, many of the risks from AI are well recognized already including bias, disinformation, manipulation and impacts on employment. I’m happy to discuss any of these. But most of my work over the last decade has been on the problem of control. How do we maintain power forever, over entities more powerful than ourselves? 

The core problem we have studied comes from AI systems pursuing fixed objectives that are misspecified, the so called King Midas problem. For example, Social Media algorithms were trained to maximize clicks and learn to do so by manipulating human users and polarizing societies. But with LLMs, we don’t even know what their objectives are. They learn to imitate humans, and probably absorb all too human goals in the process. 

Now, regulation is often said to stifle innovation. But there is no real trade off between safety and innovation. An AI system that harms human beings is simply not good AI. And I believe analytic predictability is as essential for safe AI, as it is for the autopilot on an aeroplane. This committee has discussed ideas such as third party testing, licensing, national agency, and international coordinating body, all of which I support. 

Here are some more ways to, as it said, Move fast and fix things. 

First, an absolute right to know if one is interacting with a person or a machine. 

Second, no algorithms that can decide to kill human beings, particularly when attached to nuclear weapons. 

Third, a kill switch that must be activated if systems break into other computers or replicate themselves. 

Forth go beyond the voluntary steps announced last Friday. Systems that break the rules must be recalled from the market for anything from defaming real individuals to helping terrorists build biological weapons. 

Now, developers may argue that preventing these behaviors is too hard, because LLMs have no notion of truth, and are just trying to help. This is no excuse, eventually, and the sooner the better, I would say we will develop forms of AI that are provably safe and beneficial, which can then be mandated. Until then we need real regulation and a pervasive culture of safety. Thank you.


Thank you very much. I’ll begin the questioning, we’re going to have seven minute rounds, I expect we’ll have many more than one, given the challenges and complexity that you all have raised so eloquently. I have to say, Professor Russell, you also in your testimony, the written testimony, recount, a remark of Lord Rutherford, September 11 1933, at a conference when he was asked about atomic energy, and he said, quote, anyone who looks for a source of power in the transformation of the atoms is talking moonshine. And quote, the ideas about the limits of human ingenuity had been proven wrong again, and again and again. 

And we’ve managed to do things that people thought unthinkable, whether it’s the Manhattan Project, under the guidance of Robert Oppenheimer, who now has become a boldface term and popular print, or putting man on the moon, which many thought was impossible to do. So we know how to do big things. This is a big thing that we must do. And we have to be back in the office to answer that email. That is, in fact, a siren blaring for everyone to hear and see. 

AI is here and be aware of what it will do. If we don’t do something to control it. And not just in some distant point in the future, but as as all of you have said, with a time horizon that would have been thought unimaginable just a few years ago, unimaginably quick. Let me ask each of you, because part of that time horizon is our next election in 2024. And if there’s nothing that focuses the attention of Congress, it is an election. Nothing better than an election to focus the attention of Congress. Let me ask each of you what you see as the immediate threats to the integrity of our election system, whether it’s the result of misinformation, or manipulation of electoral counts, or any of the possible areas where you see an immediate danger as we go into this next election. I’ll begin with you. Mr. Amodei.

Yes, so I, thanks for this. Thanks for the question, Senator. You know, I think this is obviously a very timely thing to worry about, you know, when I think of the risks here, my mind goes to misinformation, generation of deep fakes, use of AI systems to manipulate people or produce propaganda, or just just do do anything deceptive, or, you know, I can speak a little bit about some of the things we’re doing, you know, we train our model with, you know, this method called constitutional AI where you can lay out explicit principles, it doesn’t mean the model will follow the principles, but there are terms in our Constitution, which is publicly available that tells the model not to generate misinformation. 

The same is true in our business terms of use. One of the commitments with the White House was to start to watermark content, particularly in the audio audio in the audio and visual domain. I think that’s very helpful but would also benefit from watermarking gives you the technical capability to, you know, to to to detect that something is AI generated, but requiring it on the side of the law to be labeled, I think would be something that would be very helpful and timely.

Thank you. I spent you.

I agree with all of that, I will add a few things. One, one concern I have is that even if companies use watermarking and especially because there’s now several open source versions to train our lens or use them, including model weights, that have been made available to the global community, we also need to understand how things can go wrong on that front. In other words, people are not going to obey that law. 

And one important thing I’m concerned about is one can take a pre trained model by a company that made it public. And then without huge computing resources, so not the 100 million cost that it takes to train them, but but something very cheap, can tune these systems to a particular task, which could be to play the game of being a troll, for example, there’s plenty of examples of that to train them on. Or other examples in generating deep fakes in a way that might be more powerful than what we’ve seen up to now. So I don’t know how to fix this. But I want to bring that to the attention of this committee.

Thank you. Well, I, on that point, and on both of the excellent points that you both have raised, I would invite fixes. And well, I mean, one immediate fix is to avoid releasing more of these pre trained large models. That’s that’s the thing that governance can do because right, right now, very few companies, including the seven you, you brought last week can do that. And so that’s a place where government can act. Professor Russell.

Yeah, I would certainly like to support the remarks of the other two witnesses. And I would say my major concern with respect to elections would be disinformation, and particularly, external influence campaigns. Because with these systems, we can present to the system, a great deal of information about an individual, everything they’ve ever written or published on Twitter or Facebook, their social media presence there for speeches, and train the system and ask it to generate a disinformation campaign, particularly for that person. 

And then we can do that for a million people before lunch. And that has a far greater effect than the sort of spamming and broadcasting of a false information that isn’t tailored to the individual. I think labeling is important for text, it’s going to be very difficult to tell whether a short piece of text is machine generated, if someone doesn’t want you to know that it’s machine generated. I think an important proposal from the global partnership on AI is actually for a kind of an escrow and encrypted storage, where every output from a model is stored in an encrypted form. 

Enabling for example, a platform to check whether a piece of text that’s uploaded is actually machine generated by testing it against the the escrow storage without revealing private information, etc. So that can be done. 

Another problem we face is that there are many many extremely well intended efforts to create standards around labeling and and how platforms should respond to labels in terms of what should be posted and media organizations like the BBC, The New York Times, Wall Street Journal, etc, etc. There are dozens of these coalition’s the effort is very fragmented. And you know, there are as many standards as they’re all coalition’s. 

I think it really needs national and probably international leadership to bring these together to have a pretty much a unified approach and standards that all organizations can sign up to. And thirdly, I think there’s there’s a lot of experience in other spheres, such as in the equity markets, in, in real estate, in insurance business, where truth is absolutely essential. 

If you take the equity markets, if companies can make up their quarterly figures, then the equity markets collapse. And so we’ve developed this whole regulated third party structure of accountants audits, so that the information is reasonably trustworthy in real estate. We have title registries, we have notaries all kinds of stuff to make it work. We don’t really have that structure in the public information sphere. 

And we see you know, again, it’s very fragmented this fact I thought, oh, there’s Snopes, there’s I suppose Elon Musk is going to have his truth GPT and so on. Again, this is something that I think governments can help in terms of licensing and standards for how those organizations should function. And again, what, what platforms do with the information that the third party institutions supply, to enable users to have access to high quality information streams? So there’s I think there’s quite a lot we can do. But it’s pretty urgent.

Thank you. I think all of these points argue very, very powerfully, against fragmentation for some kind of single entity that would establish oversight standards, enforcement of rules, because, as you say, malign actors can not only eliminate quarterly reporting, they can also make up numbers for corporations that can disastrously impact the stock of the corporation.

If I just might add one, one point, we’re absolutely not talking about a Ministry of Truth. In some sense, it’s similar to what happens in the courts, the courts have standards for finding out what the truth is, but they don’t say what the truth is. And that’s what we need.

But protecting our election system has to be a priority. I think all of you were very, very emphatically and cogently making that point.

I would like to add one one suggestion, which may sound drastic, but isn’t if you look at other fields, like banking, in order to reduce the chances that AI systems will massively influence voters through social media. 

One thing that should have been done a long time ago is that social media accounts should be restricted to actual human beings that have identified themselves, ideally, in person, right. And, right now, social media companies are spending a lot of money to figure out whether an account is legitimate or not. 

They will not by themselves forced these kinds of regulations, because it’s going to create friction to recruit more users. But if the government says, Everyone needs to do it, there’ll be happy. Well, I’m not done. But that’s what I would if I were there.

Thank you, Senator Holly. Let’s, start if we could, by talking about who controls this technology, currently, and who’s who’s developing it, Mr. Amadei, if I could just start with you just helped me understand some of the structure of of your company of Anthropic Google owns a significant stake in your company, doesn’t it?

Yes, Google was was an investor in Antrophic, they don’t control any any board seats. But yes, Google has invested in Antrophic give us a sense of what what are we talking about? What What kind of stake are we talking about?

I don’t I don’t I don’t remember exactly. Couldn’t couldn’t, couldn’t, couldn’t, couldn’t give it to you. Exactly. I, I suspect it’s low double digits. But we need to follow up on this.

Well, the press is reported at $300 million in investment with at least a 10% stake in the company. Does that sound probably correct?

Sounds sounds broadly Correct.

That’s a pretty big stake. Let’s talk about open AI where you used to work, right? Yes. Open AI, it’s been reported has a very significant chunk of funding that comes from another massive technology company, Microsoft, it’s been reported in the press that this was one of the reasons that you left the company, you were concerned about this, I’ll let you speak to that if you want to. I don’t want to put words in your mouth. 

But the state that I believe Microsoft is reported to have an open AI approach is 49%. So it’s not controlling but it’s it’s awfully awfully close. It tells me this when when Google’s stake in your company occurred. And the Financial Times broke the story on this, but reported that the transaction wasn’t publicized when it actually happened. Why was that? Do you know?

I couldn’t speak to the Yeah, I couldn’t speak the decisions made by me by Google here. I do want to make one point, which is our relationship with Google at the present time. It’s primarily focused on hardware. So in order to train these models, you need to purchase chips. And, you know, this, this, this, this investment came with, you know, commitment to spend on the cloud. And our relationship with Google has been primarily focused on hardware hasn’t primarily been, you know, commercial or involved with with governance, so there’s no plans to integrate your Claude, your your equivalent of Chet GPT. There’s no plans to integrate that with Google search, for example. 

That’s  not occurring at the present time. Well, I know it’s not occurring. But But are there plans to do it? I guess is my is my question.

I mean, I can’t I can’t speak to what, you know, I can’t can’t speak to what, what the possibilities are for the future. But that’s not something that’s occurring at the present.

Don’t you think that that would be frightening? I mean, just to come back to something Professor Russell said a moment ago, he talked about the ability in the election context of AI to fed and the information from, let’s say, one political figure, everything about that person, is the ability to come up with a very convincing misinformation campaign. 

Now, imagine if that technology also if the same large language model, for example, also had the information, the voter files, millions of voters, and what knew exactly what would capture those voters attention, what would hold it, what arguments they felt most persuasive, the ability to weaponize misinformation, and to target it, toward particular voters would be exceptionally powerful. 

Right now search is all about getting and keeping users attention. That’s how Google makes money. I’m just imagining your technology, generative AI aligned and integrated and folded into search the power that that would give Google to get users attention, keep their attention, push information to them the extraordinary, wouldn’t it?

Yes. So I mean, I think, Senator, I think these are very important issues. And you know, I want to raise a few points on here. One is some of the things I said in response to Senator Blumenthal’s questioning, which is, you know, on misinformation. So we put terms and clods constitution that tell it not to generate misinformation, or political bias in any direction, I, again, want to emphasize over and over again, that these methods are, are not yet perfect. And the science of producing this is not, is not exact yet, but this is something we work on. I you know, I think you’re also getting out some important privacy issues here about personal information. And this is an area where also in our Constitution, we discourage our models from, from producing personal information, we don’t train on, you know, publicly, publicly available publicly available information. So, you know, it’s it’s it’s very core to our mission, you know, to produce models that don’t that that that at least try not to have these problems.

Well, you say that you tell the model not to produce misinformation? I’m not sure exactly what that what that means. But do you tell it not to help massive companies make a profit? This is something that Google would be interested in, right? Above all profits, the whole reason they want to get users attention and keep users attention, and keep us searching and scrolling so that they can push products to us and make lots and lots of money, which they do, it seems to me that your technology melded with bears could make them an enormous sum of money. That would be great for them, would it be so good for the American consumer?

Again, I can’t speak to you know, I can’t speak to decisions made by by a different company like Google, but, you know, we are doing the best we can to make our systems ethical when, you know, in terms of, you know, how do we tell our model not to do things, there’s a training process where, you know, we train the model in a loop to tell it for some, for some given output, you know, is your is your response in line with these principles?

And, you know, over over the last six months, since we’ve developed this method of constitutional AI, we’ve gotten better and better at getting the model to be be in line with what the Constitution says, again, I would still say it’s not perfect, but you know, we very much focused on the the safety of the model so that it doesn’t do the things that you’re that you’re concerned about, Senator?

Well, listen, this, I think this has surfaced an important point, I just want to I just want to underscore this, because I think it’s important all this, I appreciate that you want your models to be ethical and so forth. That’s, that’s great. But I would just suggest that that is in the eye of the beholder. And the talk of what is ethical or what is appropriate, is going to really vary significantly determined by or depending on who controls the technology. 

So I’m sure that Google or Microsoft, using these generative models, linking it up with their ad base models would say, oh, it’s perfectly ethical for us to try and get the attention of as many consumers as possible, by any means possible, and to hold it as long as possible. And they would say, there’s no problem with that. That’s not misinformation. That’s business. Now, would that be good for American consumers? I doubt it. Would that be respectful of American consumers privacy and their integrity? Would it prevent them? Or would it protect them rather from emulation? I doubt it. 

I mean, so I think we’ve got to give some serious thought here to who’s controls this technology and how they are using it and this is a I appreciate all that you’re doing. I appreciate your commitment. I think it’s great. I just want to say I just want to underline this is a very serious structural issue here that we’re going to have to think hard about and the control of this technology by just a handful of companies and governments is a huge, huge problem. Hopefully, we’ll come back to this. Thanks, Mr. Chairman.

Thanks, Senator Hawley. Senator Klobuchar,

Thank you very much. So I chair the Rules Committee. And we’re working on a number of pieces of legislation. And I’ve really appreciated working with Senator Hawley on some of this. But one bill is you know, watermarks and making sure that the election materials say produced by AI. But I don’t think that’s enough, when you look at the fact that someone’s going to watch a fake Joe Biden or fake Donald Trump or a fake Elizabeth Warren, all this has really happened, and then not know who the person is, and not know if it’s really them. And it’s not going to help just at the very end, it might for some things, but to just say at the end, oh, by the way that was produced by AI hope you saw our little mark at the end that says that. So could could you address that Professor Russell, how within the clear confines of the constitution for things like satire, we’re gonna have to do more than just watermarks.

I do want to be careful not to veer into once again, the sort of Ministry of Truth. I do. But I think clear labeling, I mean, if you if you look at what happened with credit cards, for example, used to be that credit cards came with 14 pages of tiny, tiny print. And that allowed companies to rip off the consumer all the time. And eventually Congress said, No, there’s got to be disclosure, you’ve got to say, this is the interest rate. This is the grace period, this is the late fee. 

And a couple of other things, there has to be in big print. On the front of the envelope are on the front page, there are very strict rules about now about how you direct market, credit cards and other lending products. And that’s been enormously beneficial, because it’s actually allows competition on those primary features of the product, as opposed to really compare a credit card to someone who’s telling the United States of America that there’s some kind of a nuclear explosion when there.

But we but the the point being, we can mandate, much clearer labeling than just a little thing in the corner at the end of the 92nd piece, right? We could say, for example, there’s got to be a big red frame around the outside of the image. When it’s a machine generated image.

I’m just gonna Professor Ben Chang, what do you think?

Well, my view on this is, we should be very careful with anything, any kind of use of AI for political purposes, political advertising, whether it’s done efficiently through some agency that does advertising or in a more direct way, but it might not be actual advertising is just put out for circulation. That’s always what confronts it. Because the Federal Election Commission while deadlocking on this has asked for authority, including the Republican voted members to do more, but go ahead.

In many countries, any kind of advertising, which would include disseminating such videos is not allowed for some period before the election, to try to minimize that the potential effect of these things, right, could I just mister emote a one significant concern? 

Just switching gears here because I talked to some people in the banking community about this small banks is that they are really worried they’re gonna see AI used to scam people, you know, pretending to be your mom’s voice or your more likely granddaughters voice or actually getting that voice right making a call for money. How can Congress ensure that companies that create AI platforms can not be used for those deceptive platforms? What kind of rules should we put in place? So that doesn’t happen?

Yes, Senator. So I think these questions about deception and scams are probably closely related to these questions about misinformation, right? They’re a little bit two sides of the same coin. So I think on the misinformation, I wanted to kind of clarify, you know, there’s, there’s technical measures and there’s policy measures. 

So you know, watermarking is a technical measure watermarking makes it possible to take an AI to take the output of an AI system, run it through some Um, automated process that that will then return an answer that it was generated by AI or not generated by AI. That’s important. And you know, we’re working on that, and others are working on that. 

But I think we also need policy measures. So going back to what the other two witnesses said, focusing on, you know, a requirement to label AI systems is not the same as requirement to watermark that one is for the designer of the AI system to embed something, the other is for wherever the AI system ends up in the end, someone to be required to label it. So I think we need both and probably, you know, this, this, this, this, this Congress can do more on the second thing, and the companies and researchers can do more on the first thing.

Okay. And so what are you talking about the scams where the granddaughter calls in, the grandma goes out and takes all her money out? We’re just gonna let that happen, or Well, I mean, certainly, certainly, it’s, it’s already it’s already illegal to do that. I can think of a number of authorities that want us to strengthen that for AI in particular, I think, you know, that’s, that’s kind of up to the the Senate in the Congress to figure out what the best, the best measure is. But you know, certainly I’d be in favor of strengthen protections there.

I hope so. About half of the states have laws that give individuals control over the use of their name, image and voice but in the other half of the country, someone who was harmed by a fake recording purporting to be them has little recourse. Senator Coons and Tillis just did a hearing on this. Would you support a federal law, Mr. Ben geo that gives individuals control of the use of their name, image and voice.

Certainly, but I would go further. If you think about counterfeiting money, the criminal penalties are very high, and dot that deters a lot of people. And when it comes to counterfeiting, humans, it should be at least at the same level.

Okay. One last thing I wanted to ask about here is just the the ability of researchers to be able to figure out what is going on. And we have there’s a bill that a number of us are supporting, including Senator Blumenthal, that allows for researchers the transparency that we need, and including Senator Cassidy, Cornyn, Coons and Romney, it’s called the platform accountability. And Trent parents, the Act requires social media companies to share data with researchers so we can try to figure out what’s happening with the algorithms in the lake. Dr. Russell, why is researcher access to social media platform data so important for regulating AI?

So our experience actually involved three years of negotiating an agreement with one of the large platforms, only to be told at the end that actually they didn’t want to pursue this collaborative agreement. After all, we don’t really have three years, just bear on AI, it sounds like so we don’t continue. And, you know, I then discussed this with the director of the digital division of OECD. And he said, I was about the 10th person who had told him the same story. 

So it seems as a modus operandi of appearing to be open to collaborations with researchers, only to terminate that collaboration. Right before it actually begins. There have been claims that they have provided open datasets to researchers to allow this type of research. But I’ve talked to those researchers, and it hasn’t happened. It’s why is it important to have it to put in place these regulations? We know we’ll be? We can’t wait for you to get all the data, obviously, and we can’t let it take three years, but putting in place a clear mandate to that data be shared? Why does that help them?

Because the the effects of for example, the social media recommender systems can are correlated across hundreds of millions of people. So those systems can shift public opinion in ways that are not not even necessarily deliberate. They’re probably not deliberate, but they can be massive and polarizing. Unless we have access to the data, which the companies internally certainly do. And I think the Facebook revelations from a few years ago, suggested that they are totally aware of what’s happening. But that information is not available to governments and researchers. And I think, you know, in a democracy we have a right to know if our democracy is being subverted by an algorithm. And that seems absolutely crucial.

All right, I’m gonna add one more thing

Yes. Trying to respond to your question from another angle. Why researchers, I would say academic researchers, not all of them, but many of them don’t have any commercial ties. They have a reputation to keep in order to continue their career. So you’re not perfect, but I think it’s a very good yardstick to judge that something except for

Professor Russell. Okay, very good. Do you agree with that to Ben?

Yes. I just wanted to say I think transparency is important even as even as a broader issue, you know, a number of our research efforts go into looking inside to see what happens inside AI systems, why they make the decisions that they make. And, yeah, I’m gonna turn it over to my colleagues who’ve been patiently waiting. Thank you.

Thank you. We’ll circle back to the blackbox algorithms, which is a major topic of interest. Senator Blackburn.

Thank you, Mr. Chairman. And thank you all for being here. Mr. Amadei, I think you got a little aggravated, trying to answer Senator Holly’s question about something you may create that you think of as an ethical use. Let me tell you why this bothers us the unethical use Senator Blumenthal have worked. And I have worked together for nearly four years on looking at social media and the harms that have happened to our nation’s youth. 

And hopefully, this week, our kids Online Safety Act comes out of committee. It wasn’t intended. Social media wasn’t intended to harm children. cause mental health crisis put children in touch with drug dealers and pedophiles. But we have heard story after story and have uncovered instance, after instance, where the technology was used in a way that nobody ever thought it was, and now we’re trying to clean it up, because we’ve not put the right guardrails in place. 

So as we look at AI, the guardrails are very important. And Professor Russell, I want to come to you because the US is behind the we’re we’re really behind our colleagues in the EU, the UK, New Zealand, Australia, Canada, when it comes to online consumer privacy, and having a way for consumers to protect that name, image voice, having a way for them to protect their data, their writings, so that AI is not trained on their data. So talk for just a minute about how we keep our position as a global leader in generative AI, and at the same time, protect consumer privacy with a federal privacy standard help. What are your recommendations there?

I think there needs to be absolutely a requirement to disclose if the system is harvesting the data from individual conversations. And my guess is that immediately, people would stop using a system that says, I am taking your conversation, I am falling it into the next version of the model. And anyone in the country can basically listen in on this conversation, because they’re going to be asking questions about what ideas do you think the industry is mature enough to self regulate? No. So therefore, it is going to be necessary for us to mandate a structure?

Yes, I think there is certainly a change of heart at open AI. Initially, they were harvesting the data produced by individual conversations. And then more recently, they said we’re going to stop doing that. And clearly if you’re in a company, even not considering personal conversation, but just in a company and you want the system to help you with some internal operations, you’re going to be divulging company proprietary information to the chatbot to get it to give you the answers you want. And if that information is then available to your competitors, by simply asking Chad GPT what’s going on over in that company, this will be terrible. 

So having a clear definition of what it is. There’s a technical term oblivious, right, which basically says whatever we talk about, I am going to forget completely, right. That’s the guarantee that system should offer. I actually believe that browsers and any other device that interacts with individuals should offer that as a as a formal guarantee. Let me also make the point about enforcement, which I think Senator Hawley mentioned at the beginning of right of action. But for example, we have a federal Do Not Call list. So as I understand it is a federal crime for a company to do robo calls to people who are on the federal Do Not Call list. My estimate is that there are hundreds of billions or possibly a trillion federal crimes happening every year. Yes. And we’re not really enforcing anything.

So you would say existing law is not sufficient for AI.

Correct. And existing right. Enforcement patterns? Yes, let me move on in Tennessee AI is important. Our auto industry uses so many AI applications, you know, and we followed this issue for quite a quite a period of time, because of the auto industry, because of the healthcare industry and the healthcare technology industry, that is headquartered in Nashville, and of course, predictive diagnosis and disease analysis, research, pharmaceutical research benefits tremendously from AI. 

And then you look at the entertainment industry, and the voice cloning. And you look at what our entertainers, our songwriters, our artists, our authors, our publishers, our TV actors, our TV producers are facing with AI and to them, it is a an absolute way that they’re robbing them of their ability to make a living off of their creative work. So our creative community has a different set of issues. Martina McBride, who is no stranger to country music, when into Spotify, and the playlist are a big thing, building your own playlists. So she was going to build a country music playlist out of Spotify, she had to refresh that 13 times before a song by a female artist came up 13 times. So you look at the power of AI to shape what people are hearing. 

And in Nashville, we like to say you can go on lower broad, you can go to the one of the honky tonks, your band can have a great night you can be discovered in you too, could end up with a record deal. But if you’ve got these algorithmically AI generated playlist that cut out new artists or females or certain sounds, then you are limiting someone’s potential. Just as if you allow AI generated content like on jukebox which open AI is experimenting with, then you and you train it on that artist sound and their, their song songs to imitate them, then you are robbing them of the ability to be compensated. So how do we ensure that that creative community is still going to have a way to make a living without having a I become a way to steal their creative talents and works?

I think this is a very important issue. I think it also applies to book authors, some of whom are suing open AI. And I believe I’m not really an expert on copyright at all. But some of my colleagues are like Pam Samuelson, for example. And I think she would be a great witness for a future hearing. And I think the view is that the law as it’s written simply wasn’t ready for this kind of thing to be possible. So if by accident, the system produces a song that has the same melody, then it’s going to fall under existing law that you’re, you’re basically plagiarizing, and there, there have been cases of human plagiarism.

We’ve explored the fair use issue in this committee and will continue to do so and my time is expired. Thank you, Mr. Chairman.

Thanks, Senator Blackburn. We’ll begin a second round of questions. And I want to begin with one of the points that Senator Blackburn was making about private rights of action, which I think Senator Holly and I have discussed incorporating in legislate Asian. In many instances, let’s be very blunt agencies become captive of the industries they’re supposed to regulate. And this one is too important to allow it to become captive. And one very good check on the captivity of federal entities, agencies, or offices is, in fact, private rights of action. So I would hope that you would endorse that idea. 

I recognize you’re not lawyers, you don’t. You’re not in the business of litigating, but I’m hoping that you would support that idea. I see nodding heads for the record. Let me turn to also to recap, the very important comments that you all made about elections, to take action against deep fakes against impersonation, whether it’s by labeling or watermarks, some kind of disclosure, without censorship, we don’t want a Ministry of Truth. 

We want to preserve civil rights and liberties, the free speech rights are fundamental to our democracy. But the kinds of manipulation that can take place in an election, including interfering with vote counts, misdirection, to election officials, about what’s happening, presents a very dangerous Specter. Superhuman AI, superhuman AI. I think all of you agree, we’re not decades away, we’re perhaps just a couple of years away. And you describe it? Well, all of you do. In terms of the biologic effects, the development of viruses, pandemics, toxic chemicals. But superhuman AI evokes for me, artificial intelligence that could on its own, develop a pandemic virus, on its own decide, Joe Biden shouldn’t be our next president, on its own decide that the water supply of Washington DC should be contaminated with some kind of chemical and have the knowledge to do it through public utility system. 

And I think that argues for the urgency, and these are not sort of science fiction anymore. You describe them in your testimony, others have done it as well. So I think your warning to us has really graphic content. And it ought to give us impetus with that kind of urgency to develop an entity that can not only establish standards and rules, but also research on countermeasures that detect those Miss directions, whether they will result in malign actors or mistakes by AI or malign operation of AI itself. Do you think those countermeasures are within our reach as human beings? And is that a function for an entity like this one to develop?

Yes, I mean, I think this is yeah, this is this is one of the core things that you know, whether it’s the the bio risks for models that, you know, kind of stated in, in testimony, you know, are likely to come in two to three years, or the risks from truly autonomous models, which I think are more than that, but might not be a whole whole lot more than that. I think this idea of being able to even measure that the risk is there is really the critical thing, if we can’t measure, then, you know, we can put in place all of these regulatory apparatus, but you know, it’ll all it’ll all be a rubber stamp. 

And so funding for the measurement apparatus and the enforcement apparatus working in concert, is, is is really going to be central here. I mean, our suggestion was, you know, NIST and the National AI research cloud, you know, which can can help kind of allow a wider range of researchers to study these risks and develop develop countermeasures. So So I think that, that that seems like a very, very important. That seems like a very important measure. I’m worried about our ability to do this in time but you know, we have to try and we have to put in all the effort that we can spend, you know,

Yes, I completely agree about the timeline, there’s a lot of uncertainty. So as I wrote in my testimony, it could be a few years, but could also be a couple of decades. Because there, you know, researches is impossible to predict. But if we follow the trend, it’s very concerning. 

And regulation liability, they will help a lot. My calculations, as you know, we could reduce the probability of a rogue AI showing up by maybe a factor of 100. If we do the right things in terms of regulation, so it’s really worth it. But it’s not going to bring those risks to zero. And especially for bad actors that don’t follow the rules anyways. 

So we, we need that investment in countermeasures, and AI is going to help us with that, but we have to do it carefully so that we don’t create the problem that we’re trying to solve in the first place. Another aspect of this is, it’s not just the AI, you know, it needs to bring expertise and national security, in bioweapons, chemical weapons and AI people together, the organizations are going to do that, in my opinion, shouldn’t be for profit, we shouldn’t mix the objective of making money, which, which, you know, makes a lot of sense in our economic system with the objective, which should be single minded of defending humanity against a potential rogue AI. 

Also, I think we should be very careful to do this with our allies in the world and not do it alone. There is, first, we can, we can have a diverse set of approaches, because we don’t know how to really do this, we are hoping that as we move forward, and we try to solve the problem, we’ll find solutions. But we need a diversity of approaches. And we also need some kind of robustness against the possibility that one of the government’s involved in this kind of research isn’t democratic anymore, for some reason. Right? This can happen. We don’t want a country that was democratic and highs and has power over a superhuman AI, to be the only country working on this, we need a resilient system of partners so that if one of them ends up being a bad actor, the others are there.

Thank you very much. I’ll turn to Professor Russell, if you have a comment.

Yeah, so I completely agree that if if there is a body that set up, that it should be enabled to fund and coordinate this type of research. And I completely agree with the other witnesses, that we haven’t solved the problem yet. I think there are a number of approaches that are promising, I tend towards approaches that provide mathematical guarantees, rather than just the best effort guarantees. 

And you know, we’ve seen that in the nuclear area, where Originally, the standard, I believe was, you know, you could have a core a major core accident every 10,000 years. And you had to demonstrate that your system design met that requirement that it was a million years, and now it’s 10 million years. And so that’s progress. 

And it comes from actually having a real scientific understanding of the materials, the designs, redundancy, etc. And we are just in the infant stages of a corresponding understanding of the AI systems that we’re building. I would also say that no government agency is going to be able to match the resources that are going into the creation of these AI systems. 

The numbers I’ve seen are roughly $10 billion a month going into AGI startups. And just for comparison, that’s about 10 times the amount of the entire national science foundation of the United States which has to cover physics, chemistry, basic biology, etc, etc, etc. So how do we get that resource flow directed towards safety? I actually believe that the involuntary recall provisions that I mentioned, would have that effect because if a company puts out a system that violates one of the rules, and then is recalled until the company can demonstrate that it will never do that again, then the company can go out of business. 

So they have a very strong incentive to actually understand how the systems work. And if they can’t, to redesign their system so that they do understand how they work. That just seems like basic common sense to me. I also want to mention on on Rogue AI, right, the bad actors. Professor Benjo has mentioned and approach based on AI systems that are developed to try to counteract that possibility. But I also feel that we may end up needing a very different kind of digital ecosystem in general. 

What do I mean by that? Right now, to a first approximation, a computer runs any piece of binary code that you load into it. We put layers on top of that, that say, Okay, that looks like a virus, I’m not running that we actually need to go the other way around, the system should not run any piece of binary code, unless it can prove to itself that this is a safe piece of code to run. So it’s sort of flipping the notion of permission. And without approach, I think we could actually have a chance of preventing bad actors from being able to circumvent these controls, because for them to develop their own hardware resources is into the 10s or hundreds of billions of dollars. And so that’s an approach I would recommend.

I have more questions, but I’m going to turn to Senator Hawley.

Let’s talk a little bit about national security and AI if we could, Mr. Amadei to come back to you. You. You’ve mentioned in your written testimony. In your policy recommendations, your first recommendation, in fact is the United States must secure the AI supply chain. And then you mentioned immediately, as an example of this chips used for training AI systems, where are most of the chips made now? Think you’re your microphone? I think maybe that’s okay. eager to hear what you have to say. Go ahead.

Yes. What I what I what I had in mind here, yes, is that, you know, there, there are certain bottlenecks in the production of AI systems, you know, that ranges from semiconductor manufacturing equipment, to chips to the actual, you know, to the actual produced systems, which then have to be stored in a server somewhere, in theory could be stolen or or released in an uncontrolled way. So I think, you know, compared to some of the more software elements, those are areas where there there are substantially more bottlenecks.

Well, so, okay, understood, but we’ve heard a lot about chips, GPUs about the shortage of them. My question is, and maybe maybe, I don’t know the answer this, maybe somebody else does. But But do you know, where most of them are currently manufacture? 

Yeah, there are a number of steps in the in the production process for chips, right? Do you produce the raw chip for the actual GPU? You know, those those happen, those happened in number of number of places, for example. So, you know, an important important player on the, you know, kind of like making the base fabrication side would be TSMC, which is in, which is in Taiwan, and then within, you know, companies like Nvidia with the United States, you know, then then then, you know, produce those into GPUs. And I don’t know exactly where that process happens, it could be in a large number of places, as part of securing our supply chain here in this area, should we consider limitations, if not outright prohibitions on components that are manufactured in China?

Um, I, you know, I think on that on that particular issue, you know, that’s, that’s, that’s not one where I have a huge amount of what I have a huge amount of knowledge. I mean, I think we should think a little bit in the other direction of our things that are produced by our supply chain to the end up in places that we don’t want them to be. So we’ve worried a lot about that, in the context of models. 

We just had a blog post out today about AI models saying, Hey, you might have spent a large number of millions of dollars, maybe someday it’s going to be billions of dollars to train an AI system. And then you know, you don’t want some state actor or criminal or rogue organization to then steal that and, you know, use it in some use in some irresponsible way that you that you don’t endorse.

Let me let me get at this problem from a slightly different angle, which is let’s imagine a hypothetical in which the communist government of Beijing decides to launch an invasion of Taiwan. And let’s imagine and sadly, it doesn’t take very much imagination. Let’s imagine that they’re successful in doing so. Just give me a back of the envelope forecast. What might that do to AI? Production?

Yeah. So I mean, you know, I’m not an economist, and it’s hard to forecast, but a very large fraction of the chips are indeed, you know, somewhere go through the supply chain in Taiwan. So I think there’s, you know, there’s no doubt that that is a there’s no doubt that that is a hotspot and you know, something that we should be concerned about for sure.

Does. Do either of the other panelists want to say anything on this about the professor Russell? Perhaps? You? 

Yeah, I mean, there are there are studies. My colleague, Orville Schell, who’s a China expert has been working on a study of these issues. There are already plans to diversify away from Taiwan to TSMC is trying to create a plant in the US. Intel is now building some very large plants in the US and in Germany, I believe. So, but it’s it’s taking time, I think if the if the invasion that you mentioned, happened tomorrow, we would be in a huge amount of trouble. As far as I understand it, there are plans to sabotage all the TMC, TSMC operations in Taiwan, if if an invasion were to take place. So it’s not that all that capacity would then be taken over by China, though, and that what’s sad about that scenario is that will be the best case scenario, right? 

I mean, if there’s an invasion of Taiwan, the best we could hope for is maybe all of their capacity or most of it gets sabotaged. And maybe the whole world has to be in the dark for however long. That’s the best case scenario. The point I’m trying to make is, is I think your point, Mr. Amadei about securing our supply chains is absolutely critical. And thinking very seriously about decoupling efforts, strategic decoupling efforts, I think is absolutely vital at every point of the supply chain that we can I think we don’t do that with China soon. Frankly, we should have done a long time ago, if we don’t do it very, very quickly. I think we’re really troubled. And I think we’ve got to think seriously about what may happen in the event of a Taiwan ovation. Yeah, go ahead.

Yes, I just wanted to emphasize Professor Russell’s point even more strongly that we are trying to move some of the some of the chip fab production capabilities to the US. But that needs to be faster, right? We’re talking about, you know, two to three years for some of these very scary applications. And maybe not much longer than that, for truly autonomous AI. Correct me if I’m wrong, but I think the timelines for moving these, these, these production facilities look more like, you know, five years, seven years, and we’ve only started on a small, small component of them. So so just to just emphasize, I think that is absolutely essential. Yeah.

Good. Let me ask you about a different issue related to labor, overseas, and labor exploitation. The Wall Street Journal, published a piece today entitled cleaning up Chet GPT takes heavy toll on human workers, contractors in Kenya, say they were traumatized by the effort to screen out descriptions of violence and sexual abuse during the run up to open AI is hid chat pot, namely, chat GPT. The article details the widespread use of labor in Kenya, to do this training work on the Chet GPT model. I encourage everyone to read it. And I’d like to ask the chairman to be able to enter this into the record without objection.

One of the disturbing, it’s a couple of disturbing things. I mean, one is, is that we’re talking about 1000 or more workers outsourced overseas, we’re talking about exploitation of those workers. They work around the clock, the material they’re exposed to is incredible, and I’m sure extremely damaging, and that that constitutes the nature of lawsuits that they’re now bringing. 

Here’s another interesting tidbit, the workers on the project, were paying an average of between $1.46 An hour and $3.74 an hour. Let me say that again. The workers on the project were paid on average between $1.46 An hour and 374 an hour now. Open AI says, Oh, we thought that they were being paid over $12 an hour. And so we have the classic classic corporate outsource maneuver, where a company outsources jobs couldn’t be done. The United States outsources jobs, exploit foreign workers to do it, and then says, Oh, we don’t know anything about it. 

We’re asking them to engage in this psychologically harmful activity. We’re probably overworking them doing it and we’re not paying them. But you know, oops, I guess my question is, How widespread is this in the AI industry? Because it strikes me that we’re told that AI is new and it’s a whole new kind of industry. In its glittering and it’s almost magical, and yet it looks like it depends in critical respects on very old fashioned, disgusting, immoral labor exploitation to go ahead, Mr. Amadei?

Yes, so this is actually one area where where Antrophic has has a substantially different approach from the one that you’ve described. I can’t speak for what other other companies are doing. But a couple points. One is this, this constitutional AI method, which I mentioned, is a way for one copy of the AI system to moderate or helped train another copy of the AI system. This is something that reduces, it does not eliminate, but it substantially reduces the need for the kind of human labor that you’re describing. 

Second, in our own contracting practices, and, you know, I would have to talk to you directly for exact numbers, but I but I, I believe that the companies we contract out to are something like northwards of 75%, of workers from the US and Canada and all paid above the all paid above the California minimum wage. So I share your concern about about these issues. And, you know, we’re committed to both developing research that kind of obviates the, you know, the, the need for some of this, this this kind of moderation and, you know, not not exploiting these workers.

Well, it’s good, because here’s, I think what would be terrible to see is this new technology that is built by foreign workers, non American workers, that it seems like the same old story we’ve heard for 3040 years in this country where we’re told oh, no American workers, they cost too much. Their American workers, they’re just too demanding American workers, they don’t have the skills, so we’re going to outsource it, we’re going to give it to other foreign workers, then you mistreat the foreign workers, then you don’t pay the foreign workers. 

And then who benefits from it at the end of the day, these few companies that we talked about earlier, who make all the profit and control of it, that seems like an old old story that I frankly don’t want to see replicated. Again, that seems like a dystopia, not like a new future. So I think it’s critical that we find out what the labor practices are of these companies. I’m glad that you’re charting a different course, Mr. Amadei. And certainly we want to hold that hold you to that. 

But I think it’s vital that as we continue to look at how this technology is developing, that we actually push for what I mean, what’s wrong with having a technology that actually employs people in the United States of America and pays them? Well. I mean, why should an American workers and American families protected by our labor laws benefit from this technology? I don’t think that’s too much to ask. And frankly, I think that we ought to expect that of companies in this country who are with access to our markets who are working on this technology. 

Sherman, thank you. I don’t think you’ll find much disagreement with that proposition. But to have American workers do those jobs, we need to train them. Correct. And you all in some sense, because you’re all teachers, you’re all professors are engaged in that enterprise. Mr. Ameide, Al, I don’t know whether you can be called still a professor, but probably not.

I was never a professor.

But we need to train workers to do these jobs. And for those who want to pause. And some of the experts have written that we should pause AI development. I don’t think it’s going to happen. We right now have a gold rush. Literally, much like the gold rush that we had in the Wild West, where in fact, there are no rules. And everybody’s trying to get to the gold without very many law enforcers out there preventing the kinds of crimes that can occur. 

So I am totally in agreement with Senator Hawley in focusing on keeping it in America made in America when we’re talking about AI. And I think he is absolutely right, that we need to build those kinds of structures, provide the training and incentives that enable it and enforce it. Let me go come back to this issue of national security. Who are our competitors among our adversaries and our allies who are closest to the United States in terms of developing AI? Is it China? Are there other adversaries out there that could be rogue nations, not just rogue actors, but rogue nations, and whom we need to bring into some international body of cooperation?

So I think the closest competitor we have is probably the UK in terms of making advances in basic research, both in academia, and in Deep Mind, in particular, which is based in London, now being merged more forcefully into the larger Google organization, but they have a very distinct approach. And they’ve created an ecosystem in the UK that that’s really quite productive. 

I’ve spent a fair amount of time in China, I was there a month ago, talking to the major institutions that are working on AGI. And my sense is that we have slightly overstated the level of threat that they currently present. they’ve mostly been building copycat systems that turn out not to be nearly as good as the systems that are coming out from Antrophic and open AI and Google. So but the intent is definitely there. I mean, they’ve publicly stated their goal to be the world leader. And they are investing probably larger sums of public money than we are in the US. smaller sums in the private sector, the areas where they are actually most effective. And I was actually on a panel in Tianjin for the top 50, Chinese AI startups, and they were giving out awards. But I think about 40 of those 50, their primary customer was state security. 

So they are extremely good at voice recognition, face recognition, tracking and recognition of humans based on gait, and similar capabilities that are useful for state security. Other areas like reasoning, and so on, planning, they’re just not in, they’re not really that close. So they have a pretty good academic sector, that they are in the process of ruining by forcing them to meet numerical publications, targets and things like that. They don’t give people the freedom to think hard about the most important problems. And they are not producing the basic research breakthroughs that we’ve seen both in the academic and the private sector. In the US, I’ve also produced a superhuman thinking machine if you don’t allow humans to think. Yep.

You know, I’ve also looked a lot at European countries, I’m working with the French government quite a bit and and I don’t think anywhere else is in the same league as those three. Russia in particular has been completely denuded of its experts, and was already well behind. Mr. Ngo,

Professor on the Allied side, there are a few countries, including Canada, from which I come from, that have a really important concentration of talent in in AI. And in Canada, we’ve contributed a lot of the principles behind what we’re seeing today. There’s also a lot of really good European researchers in the UK and outside the UK. So I think that we would all gain by making sure we work with these countries to develop these countermeasures, as well as the improved understanding of the potentially dangerous scenarios and what methodologies in terms of safety you can protect us.

You’ve you’ve added, you’ve advocated decentralized labs, yes. But under a common umbrella, that would be multilateral. Maybe this could be a good starting place could be five eyes or g7. And that would capture pretty much bulk of the expertise in these very strong AI systems that that could be important here.

And there would probably be some way for our entity or national oversight body, doing licensing and registration to still cooperate. In fact, I would guess, one of the reasons to have a single entity to be able to work and collaborate. Yes. So other countries, there is no doubt that individual countries have their own national security organizations and are going to do their own laws. But the more we can coordinate on this the better, of course, think some of that research should be classified and not shared with anyone on the trusted parties. 

So there are aspects of what we have to do that have to be really broad at the international level. And I think the guidelines or the maybe mandatory rules for safety should be something we do internationally, like with the UN, we want every country to follow some basic rules, because even if they don’t have the technology, some rogue actor, even here in the US might just go and do it somewhere else. And then, you know, viruses computer or biologic viruses don’t see any border. So we need to make sure there’s an international effort in terms of these safety measures. We need to agree with China on these safety measures as the first interlocutor. And we need to work with our allies on these countermeasures.

I think that all those observations are extremely timely and important. And on the issue of safety, I know that Antrophic has developed a model card for Claude, that essentially involves evaluation capabilities. Your red teaming considered the risk of self replication, or similar kind of danger. Open it, I engaged in the same kind of testing, we’ve been talking a lot about testing and auditing. So apparently, you share the concern that these systems may get out of control. 

Professor Russell recommended an obligation to be able to terminate an AI system. Microsoft called this requirement, safety brakes. When we talk about legislation, would you recommend that we impose that kind of requirement as a condition for testing and auditing the evaluation that goes on? When deploying certain AI systems? Obviously, again, focusing on risk? I think everybody has talked about systems that are vulnerable risk systems, and AI models spreading like a virus seems a bit like science fiction, but the safety brakes could be very, very important to stop that kind of danger. Would you agree?

Yes. So I think I for one, think that makes a lot of sense. I mean, the the way I would think about it is, you know, in the in the testing and auditing regime, that we’ve all we’ve all discussed, you know, the best case is if all of these dangers that we’re talking about don’t happen in the first place, because we run tests that detect the dangers. And there’s, there’s basically, there’s basically prior restraint, right? If these things are a concern for public safety and national security, we never want the bad things to happen in the first place. But precisely because we’re still getting good at the science of measurement. Probably it will happen, at least once and unfortunately, perhaps repeatedly that we run these tests, we think things are safe, and then they turn out not to be safe. And so I agree, we also, we also need a mechanism for recalling things if the tower modifying things if the tests ended up being wrong. So that that seems like common sense to me for sure.

And and I think there’s been some talk about auto GPT. Maybe you can talk a little bit about how that relates to safety.

Yes. So auto auto GPT refers to use of you know, currently deployed, AI systems which are which are not designed to be agents, right, which are just chatbots. But kind of commandeering such systems for taking actions on the internet. You know, to be honest, such systems are not particularly effective at that yet, but they may be a taste of the future. And the kinds of things we’re worried about in the future, the long term risks that I described in the short, medium and long term risk. So I don’t, as of yet see a particularly high amount of danger from from things like the system you described, but it tells us where we’re going and where we’re going. This is quite concerning to me.

You know, in some of the errors that have been mentioned, like medicines and transportation, there are public reporting requirements. For example, when there’s a failure The FAA system has an Accident and Incident Report. They collect data about failures in those kinds of machinery and serves as a warning to consumers, it creates a deterrence for putting unsafe products on the market. And it adds to oversight of public safety issues we’ve discussed this afternoon, both short term and long term kinds of risks that can cause very significant public harm. It doesn’t seem like AI companies have an obligation to report issues right now. In other words, there’s no place to report it, they have no obligation to make it known. If they discover the Oh, my God, how did that happen? Incident? It can be entirely undisclosed. Would you all favor some kind of requirement? For that kind of reporting? Absolutely. And it may be obvious, but let me ask all of you, I see, again, your heads nodding for the record. Would that inhibit creativity or innovation? To have that kind of requirement? I would think

I don’t I don’t think I mean, there are many areas where there’s important trade offs. I don’t think this is one of them. I think I think such requirements make sense. I mean, to give a little of our experience in, you know, red teaming for these biological harms, you know, we’ve had to work on, you know, piloting a responsible disclosure process. I think that’s less about reporting to the public more about making the other companies aware. But you know, the two things are similar to each other. So, you know, a lot of this is being done on voluntary terms. And you see some of it coming up in the, you know, the the commitments that the seven companies make, but yeah, I think I think there’s there’s a lot of legal, unprocessed infrastructure that’s missing here and should be filled in.

Yeah, I think to go along with the notion of an involuntary recall, there has to be that reporting step. happening first.

You know, you mentioned recalls. Both Senator Holly and I were state attorneys general, before we got this job, and both of us are familiar with consumer issues, one of the frustrations, for me always was that, even with a recall, a lot of consumers didn’t do anything about it. And so I think the recall as a concept is a good one. But there have to be truth to it. There has to be a cop on the beat, a cop on the AIP. And I think the enforcement powers here are tremendously important. And the point that you made about the tremendous amount of money is very important. You know, right now, it’s all private funding, or mostly private funding. But the government has an obligation to invest. I think all of you would agree, invest in safety, just as it has in other technology and innovation. Because we can’t rely on private companies to police themselves, that cop on the beat in the AI context, has to be not only enforcing rules, but as I said at the very beginning, incentivizing innovation and sometimes funding it to provide the airbags and the seatbelts and the crash proof kinds of safety measures that we have in automobile industry. I recognize that the analogy is imperfect. 

But I think the concept is, is there. No, Holly, this has been a tremendously helpful hearing. I just want to thank each of you, again, for taking the time to be here. Can I just ask you, if you could give us your one or at most two recommendations for what you think Congress ought to do right now? What would you which we do right now, based on your expertise, what we talked about today? I would I would be very, very curious to hear. So maybe we’ll start with you, Professor Russell, and go that way.

So I gave some, you know, move fast and fix things recommendations in my opening remarks, and I think there’s no doubt that we’re going to have to have an agency. You know, if things go as expected, AI is going to end up being responsible for the majority of economic output in the United States. So it cannot be the case that there’s no overall regulatory agency with this technology. And the second thing I think would would be Just to focus again on systems that violate a certain set of unacceptable behaviors are removed from the market. And I think that will have not only a benefit in terms of protecting the American people, and our national security, but also stimulating a great deal of research on ensuring that the AI systems are well understood, predictable, controllable.

And that’s a very good professor of NGO.

What I would suggest, in addition to what Professor Russell said, is to make sure either through incentives to companies, but also direct investment in nonprofit organizations, that we invest heavily, so totally as much as we spend on making more capability is that we invest heavily in safety, whether it’s at the level of the hardware, or to the level of Cybersecurity, and national security to protect the public.

Very good. Mr. Chairman, I would again, emphasize the testing and auditing regime for all the risks ranging from you know, those those we’ve, we faced today, like misinformation came up to the biological risks that I’m worried about in two or three years to the, you know, risks of autonomous replication that are some unspecified period after that, you know, all of those can be tied to different kinds of tests that we can we can that we can run in our model. 

And so that strikes me as a you know, as a scaffolding on which we can build lots of different concerns about about AI systems, right? If we start by testing for only one thing, we can, in the end test for a much, much wider range of concerns. And I think without such testing, we’re blind, like I give you an AI system and other company gives you an AI system, you you talk to it, it’s not straightforward to determine whether this is a safe system or a dangerous system. 

So I would again, make the analogy to you know, it’s it’s like, we’re making these machines, you know, cars, airplanes, these are complex machines, we need an enforcement mechanism and people who are able to look at these machines and say, what are the what are the benefits of these, and what is the danger of this particular machine as well as, as well as machines in general, once we measure that, I feel it’s all going to work out? Well. But but, you know, before we’ve identified and have a process for this, we’re from a regulatory perspective shooting in the dark. 

And the final thing I would emphasize is, you know, I don’t think we have a lot of time, you know, I personally am open to whatever administrative mechanism puts those kinds of tests in place, you know, very agnostic to whether it’s, you know, a new agency or extending the authorities of existing agencies. But whatever we do, it has to happen fast. And I think to focus people’s minds on the on the bio risks, I would really target 2025 2026, maybe even some chances 2024 If we don’t have things in place, that are that are restraining what can be done with AI systems, we’re gonna have a really bad time.

Let me just thank you. Each of you, that’s, that’s really helpful. Let me just throw an idea out to you while I have you here, so to speak, which is when we think about protecting individuals and their personal data, and making sure that it doesn’t end up being used to train one of these generative AI systems without the individual’s consent. 

And we know that there’s just an enormous amount of personal our own personal information out there in public kind of, you know, it’s really without our permission, but it’s out there on the web, everything from our credit histories to social media posts, etc, etc, should we should we in addition to assigning property rights in individual data, you know, get explicitly giving every American a property right of their data, should we also require monetary compensation, if AI companies want to use individual data in their model in some way, Professor doing to go ahead.

There, it’s not always going to be possible to know to attribute the output of a system to a particular piece of data, because these systems are not just copying. They’re integrating information from many, many sources. So we need other mechanisms to share to the people that are losing something, for example, artists. But in some cases, it could be identified. If an output is close enough to something that has been you know, as copyright or something. I think in that case, yes, we should do it.

Have you ever thought that’s all of my questions?

I just markable a couple more questions. I promise they will be brief. You’ve been very patient but this panel is such a great resource that you I want to impose on your patience and your wisdom. The point that you were making earlier about the red teaming and the importance of testing and auditing reminded me about your testimony, your prepared testimony, but also a conversation that you and I had about how Antrophic went about testing its large language model, particularly as related to the biological dangers, where you work with world class biosecurity experts, I think was your quote, over many months, in order to be able to identify and mitigate the risks that claw to my raise. On the other hand, I think you may have mentioned a company that basically use graduate students to do the same task. There’s an enormous difference in those two testing regimens. Now, right now, there’s no requirement, there’s no legal duty. But would you recommend that when we write legislation that we impose some kind of qualifications on the testers and the evaluators? so as to have that expertise?

Yes. So spiritually, I’m very aligned with that. I mean, I want to say clearly, like, all of us, all the companies, all the researchers are trying our best to figure this out. So you know, I don’t want to go on, I don’t want to call out, you know, any, any, any, any companies here, I think we’re all trying to figure it out together. But I think it is an object lesson in that, in testing these models, you know, you can do something that you might think is a very reliable way of soliciting bad bad behavior from the models or, you know, a test that you think is truthful. 

And, you know, you can, you can find out later that that really wasn’t the case. Even if you had all the all the good intent in the world. In the case of bio, the key was, you know, to have world experts and to zero in on a few things. In other areas, the key, the key might be different. And so I think the most important thing, maybe not so much the static requirements, although I although I, you know, I would certainly endorse, you know, the level of expertise has to be very high. But, but making the process have some, some living element to it, so that it can be adjusted, we used to think that this test was okay, this test was not okay. 

You know, just just imagine we’re a few years after, you know, the invention of flying, and we’re like, we’re looking at these big machines. And we’re like, Well, how do we know if this thing is going to crash? Right now we know very little, somehow we need to design the regulatory architecture, so that we can we can get to the point where if we learn new things about what makes plane safe, and what makes planes crash, they get kind of automatically hooked into whatever architecture we built. I don’t know the best way to do that. But I think that should be the goal.

Well, you know, that’s a very timely analogy, because a lot of the military aircraft we’re building now basically fly on computers. And the pilot is in the planes right now. But we’re moving toward such sophisticated and complicated aircraft, which I know a little bit about, because I’m on the Armed Services Committee. That you know, they are a lot smarter than pilots in some of the Flying they can do. But at the same time, they are certainly red team to avoid misdirection and, and mistakes. And the kinds of specifics that you just mentioned, are where the rubber hits the road. These kinds of specifics are where the legislation will be very important. 

President Biden has enlisted or elicited commitments to security safety, transparency, announced on Friday, important step forward, but this red teaming is an example of how voluntary nonspecific commitments are insufficient, that the advantages are in the details, not just the devil the the details are tremendously important. 

And when it comes economic pressures, companies can cut corners. Again, the gold rush this these decisions have real economic consequences. I want to just in the last maybe the last question I had on the issue of open source. You each raise the security and safety risks of AI models that are open source or are to the public, the danger, there are some that advantages to having open source as well. It’s a complicated issue, I appreciate that open source can be an extraordinary resource. But even in the short time that we’ve had some AI tools, and they’ve been available, they have been abused. 

For example, I’m aware that a group of people took stable diffusion and created a version for the express purpose of creating non consensual sexual material. So on the one hand, access to AI data is a good thing for research. But on the other hand, the same open models can create risks just because they are open Senator Holly and I, as an example of our cooperation, wrote to meta about an AI model that they released to the public, you’re familiar with it? 

I’m sure llama, they put the first version of Lima out there with not much consideration of risk. And it was leaked, or was somehow made known. The second version had more documentation of its safety work, but it seems like meta or Facebook’s business decisions may have been driving its agenda. So let me ask you about that phenomenon. I think you have commented on it. Document NGOs. So let me ask you first.

Yes, I think it’s really important, because when we put open source out there for something that could be dangerous, which is a tiny minority of all the code that’s open source. Essentially, we’re opening the door to all the bad actors. And as these systems become more capable, bad actors don’t need to have very strong expertise, whether it’s in bioweapons, or Cybersecurity in order to take advantage of systems like this. And they don’t even need to have huge amounts of compute either to take advantage of systems like this. Now, I believe that the different companies that committed to these measures last week, probably have a different interpretation of what is a dangerous system. And I think it’s really important that the government comes up with some definition, which is going to keep moving. But make sure that future releases are going to be very carefully evaluated for that potential before they are released. I’ve been a staunch advocate of open source for all my scientific career. Open source is great for scientific progress. But as Geoff Hinton, my colleague was saying, if nuclear bombs were software, would you you know, allow open source of nuclear bombs, right.

And I think the comparison is apt, you know, I’ve, I’ve been reading the most recent biography of Robert Oppenheimer. And every time I think about AI, the specter of quantum physics, nuclear bombs, but also atomic energy, both peaceful and military purposes, is inescapable.

So I have another thing to add on, on open source. Some of it is coming from companies like Mehta, but there’s also a lot of open sores coming out of universities. Now, usually, these universities don’t have the means of training, the kind of large systems that we’re seeing in industry. But the code could be then used by a rich, bad actor, and turned into something dangerous. So I believe that we need ethics review boards, in universities for AI, just like we have for biology and medicine. 

Right now, there’s no such thing. I mean, there are ethics in principle, they could do that. But they’re not set up for that they don’t have the expertise, they don’t have the kind of protocols we need to move into a culture where universities across the world but you know, in developed nations, in particular, adopt these ethics reviews, with the same principles, what we’re doing for other sciences where there is dangerous output. But in the case of AI

Yeah, I strongly hear Professor Ben Jo’s view here. Want to make sure I’m kind of precise in my views, because I think there’s, you know, there’s, there is nuance to it, you know, in line with Professor, I think in most scientific fields, open sources a good thing and accelerates progress. And I think even within AI there’s room for models on the smaller and medium side. I don’t think anyone thinks those models are seriously dangerous they have they have some risks, but the benefits may outweigh the costs. 

And I think to be fair, even up to the level of open source models that have been released so far, the risks are relatively limited, so construed very narrowly. I’m not sure I have an objection. But I’m very concerned about where things are going. If we talk about two to three years for the frontier models for the bio risks, and probably less than that, for things like misinformation, we’re that we’re there. Now, I think the path that things are going in terms of the scaling of of open source models, I think it’s going down a very dangerous path. 

And if if, again, if the path continues, I think we could get to a very dangerous place. I think it’s worth saying some things on open source models that are clear to all the experts, but I want to make sure is understood by by this committee, which is when when you control a model, and you’re deploying that you have the ability to monitor it usage, it might be misused at one point, but then you can alter the model, you can revoke a user’s access, you can change what the model is willing to do. When a model is released in an uncontrolled manner. 

There’s no ability to do that. It’s it’s entirely out of your hands. And so I think that should be attended to carefully there may be ways to release models open source so that it’s harder to circumvent the guardrails. But that’s a much harder problem. And we should we should confront the advocates of this with with with that problem and challenge them to solve it. Finally, I’d say open source is a little bit of a misnomer here, right? Open Source normally refers to, you know, smaller developers who are iterating quickly, and I think that’s a good thing. But I think here, we’re talking about something a little bit different, which is a more uncontrolled release of larger models by, you know, getting to your point Senate, Senator Holly, like much larger entities that pay 10s, or even hundreds, hundreds of millions of dollars to train them, I think we should think of that in a little bit of a different category and their obligations and a little bit of a different category.

So I just like to add a couple of points, I agree with everything. The other witness that said, so one issue is being able to trace the provenance of from the the output that is problematic, through to which model was used to create it through to where did that model come from. And the second point is, is about liability. And it’s not completely clear where exactly the liability should lie. 

But if to continue the nuclear analogy, if if a corporation decided they wanted to sell a lot of enriched uranium in supermarkets, and someone decided to take that enriched uranium and buy several pounds of it and make a bomb, we say that some liability should reside with the company that decided to sell the enriched uranium, they could put a vise on it saying, do not use more than three ounces of this in one place or something. But no one’s going to say that absolves them from liability. So I think those two are really important. And the open source community has got to start thinking about whether they, they should be liable for putting stuff out there that is ripe for misuse.

I want to invite any of you who have closing comments or thoughts that you haven’t had an opportunity to express.

So I would like to add a point about international or multilateral collaboration on these things, and how it’s related to having maybe a single agency here in the United States, if they’re 10 Different agencies trying to regulate AI in its various forms that could be useful. But as Stuart Russell was saying, this is this is going to be very big in terms of this, what you know, the space it takes in the economy. 

But also, we need to have a single voice that that coordinates with with the other countries, and having one agency that does that is going to be very important. Also, we need an agency in the first place, because we can’t predict we can’t put in a law, every protection that that is needed. 

Every regulation that is needed. We don’t know yet what the regulation should be in one year, two years, three years from now. So we need to build something that’s going to be very agile. And I know it’s difficult for governments to do that. Maybe we can research to improve on that front agility in doing the right thing. But, but having an agency is at least a tool towards that goal.

I would just close by saying that is exactly why we’re here today. To develop an entity or have a body that will be agile, nimble, and fast. Because we have no time to waste. I don’t know who the Prometheus is on AI. But I know we have a lot of work to make sure that the fire here is used productively and there are enormously productive uses. 

We haven’t really talked about them much. Whether it is curing cancer, treating diseases, some of them mundane by screening X rays, or developing new technology that can help stop climate change. There are a vast variety of potentially productive uses. And it should be done with American workers, I think, very much in agreement here. And the last point I would make on on agreement, what you’ve seen here is not all that common, which is bipartisan, unanimity that we need guidance from the federal government, we can’t depend on private industry, we can’t depend on academia. 

The federal government has a role that is not only reactive, and regulatory, it is also proactive in investing in research and development of the tools that are needed to make this fire work for all of us. So I want to thank every one of you for being here. today. We look forward to continuing this conversation with you. Our record is going to remain open for two weeks in case any of my colleagues have written questions for you. I may have some to if you have additional thoughts, feel free to submit them. I’ve read a number of your writings and I’m sure I will continue reading them and look forward to talking again. With that this hearing is adjourned.