Oversight of AI: Rules for Artificial Intelligence with Sam Altman

The Video:

https://www.youtube.com/watch?v=TO0J2Yw7usM&embedable=true

This hearing is on the oversight of our artificial intelligence the first in a series of hearings intended to write the rules of AI. Our goal is to demystify and hold accountable those new technologies to avoid some of the mistakes of the past. And now for some introductory remarks.

Too often we have seen what happens when technology outpaces regulation: the unbridled exploitation of personal data, the proliferation of disinformation, and the deepening of societal inequalities. We have seen how algorithmic biases can perpetuate discrimination and prejudice, and how the lack of transparency can undermine public trust. This is not the future we want.

If you were listening from home, you might have thought that voice was mine and the words from me. But in fact, that voice was not mine, the words were not mine, and the audio was an AI voice cloning software trained on my floor speeches. The remarks were written by ChatGPT when it was asked how I would open this hearing, and you heard just now the result. I asked ChatGPT, "Why did you pick those themes and that content?" And it answered, and I'm quoting: "Blumenthal has a strong record in advocating for consumer protection and civil rights. He has been vocal about issues such as data privacy and the potential for discrimination in algorithmic decision making. Therefore, the statement emphasizes these aspects."

Mr. Altman, I appreciate ChatGPT's endorsement. In all seriousness, this apparent reasoning is pretty impressive. I am sure that we'll look back in a decade and view ChatGPT and GPT-4 like we do the first cell phone, those big clunky things that we used to carry around, but we recognize that we are on the verge really of a new era. The audio and my playing it may strike you as curious or humorous, but what reverberated in my mind was, what if I had asked it, and what if it had provided an endorsement of Ukraine surrendering or Vladimir Putin's leadership? That would have been really frightening, and the prospect is more than a little scary. To use the word, Mr. Altman, you have used yourself, and I think you have been very constructive in calling attention to the pitfalls as well as the promise, and that's the reason why we wanted you to be here today. And we thank you and our other witnesses for joining us.

For several months now, the public has been fascinated with GPT-Dally and other AI tools. These examples, like the homework done by ChatGPT or the articles and op-eds that it can write, feel like novelties. But the underlying advancements of this era are more than just research experiments. They are no longer fantasies of science fiction; they are real and present. The promises of curing cancer or developing new understandings of physics and biology or modeling climate and weather are all very encouraging and hopeful. But we also know the potential harms, and we've seen them already weaponized: disinformation, housing discrimination, harassment of women, impersonation, fraud, voice cloning, deepfake. These are the potential risks despite the other rewards.

And for me, perhaps the biggest nightmare is the looming new Industrial Revolution: the displacement of millions of workers, the loss of huge numbers of jobs, the need to prepare for this new Industrial Revolution in skill training and relocation that may be required. And already, industry leaders are calling attention to those challenges.

To quote ChatGPT, "This is not necessarily the future that we want. We need to maximize the good over the bad." Congress has a choice now. We have the same choice when we face social media. We failed to seize that moment. The result is predators on the internet, toxic and harmful content.

And Senator Blackburn and I, and others like Senator Durbin on the Judiciary Committee, are trying to deal with it: Kids Online Safety Act. But Congress failed to meet the moment on social media. Now we have the obligation to do it on AI before the threats and the risks become real.

Sensible safeguards are not in opposition to innovation; accountability is not a burden, far from it. They are the foundation of how we can move ahead while protecting public trust. They are how we can lead the world in technology and science but also in promoting our democratic values. Otherwise, in the absence of that trust, I think we may well lose both.

These are sophisticated technologies, but there are basic expectations common in our law. We can start with transparency. AI companies ought to be required to test their systems, disclose known risks, and allow independent researcher access. We can establish scorecards and nutrition labels to encourage competition based on safety and trustworthiness.

Limitations on use: there are places where the risk of AI is so extreme that we ought to impose restrictions or even ban their use, especially when it comes to commercial invasions of privacy, for-profit decisions that affect people's livelihoods, and of course, accountability reliability. When AI companies and their clients cause harm, they should be held liable.

We should not repeat our past mistakes. For example, Section 230 forcing companies to think ahead and be responsible for the ramifications of their business decisions can be the most powerful tool of all.

Garbage in, garbage out. The principle still applies. We ought to beware of the garbage, whether it's going into these platforms or coming out of them. And the ideas that we develop in this hearing, I think, will provide a solid path forward. I look forward to discussing them with you today.

And I will just finish on this note: The AI industry doesn't have to wait for Congress. I hope their ideas and feedback from this discussion and from the industry can lead to voluntary action, such as we've seen lacking in many social media platforms. And the consequences have been huge. So, I'm hoping that we will elevate, rather than have a race to the bottom. And I think these hearings will be an important part of this conversation. This one is only the first.

The ranking member and I have agreed there should be more, and we're going to invite other industry leaders. Some have committed to come, experts, academics, and the public we hope will participate. And with that, I will turn to the ranking member, Senator Hawley.

Thank you very much, Mr. Chairman. Thanks to the witnesses for being here. I appreciate that several of you had long journeys to make in order to be here. I appreciate you making the time. I look forward to your testimony.

I want to thank Senator Blumenthal for convening this hearing, for being a leader on this topic. You know, a year ago, we couldn't have had this hearing because the technology that we're talking about had not burst into public consciousness. That gives us a sense, I think, of just how rapidly this technology that we're talking about today is changing and evolving and transforming our world right before our very eyes.

I was talking with someone just last night, a researcher in the field of psychiatry, who was pointing out to me that the ChatGPT and generative AI, these large language models, it's really like the invention of the internet in scale, at least, and potentially far, far more significant than that. We could be looking at one of the most significant technological innovations in human history.

And I think my question is: What kind of an innovation is it going to be? Is it going to be like the printing press that diffused knowledge and power and learning widely across the landscape, that empowered ordinary, everyday individuals, that led to greater flourishing, that led above all to greater liberty? Or is it going to be more like the atom bomb? Huge technological breakthrough, but the consequences severe, terrible, continue to haunt us to this day.

I don't know the answer to that question. I don't think any of us in the room know the answer to that question because I think the answer has not yet been written. And to a certain extent, it's up to us here and to us as the American people to write the answer.

What kind of technology will this be? How will we use it to better our lives? How will we use it to actually harness the power of technological innovation for the good of the American people, for the liberty of the American people, not for the power of the few?

You know, I was reminded of the psychologist and writer Carl Jung, who said at the beginning of the last century that our ability for technological innovation, our capacity for technological revolution had far outpaced our ethical and moral ability to apply and harness the technology we developed. That was a century ago. I think the story of the 20th century largely bore him out.

And I just wonder, what will we say as we look back at this moment? About these new technologies, about generative AI, about these language models, and about the hosts of other AI capacities that are even right now under development, not just in this country but in China, at the countries of our adversaries and all around the world?

And I think that the question that Jung posed is really the question that faces us: Will we strike that balance between technological innovation and our ethical and moral responsibility to humanity, to liberty, to the freedom of this country?

I hope that today's hearing will take us a step closer to that answer. Thank you, Mr. Chairman. Thanks, Senator Hawley. I'm going to turn to the Chairman of the Judiciary Committee and the ranking member, Senator Graham, if they have opening remarks as well.

Yes, Mr. Chairman, thank you very much, and Senator Hawley as well. Last week, in the full committee Senate Judiciary Committee, we dealt with an issue that had been waiting for attention for almost two decades, and that is what to do with social media when it comes to the abuse of children. We had four bills initially that were considered by this committee, and what may be history in the making, we passed all four bills with unanimous roll calls. I can't remember another time when we've done that on an issue that important. It's an indication, I think, of the important position of this committee in the national debate on issues that affect every single family and affect our future in a profound way.

1989 was a historic watershed year in America because that's when Seinfeld arrived, and we had a sitcom which was supposedly about little or nothing, which turned out to be enduring. I like to watch it, obviously, and I'm always marveling when they show the phones that they used in 1989, and I think about those in comparison to what we carry around in our pockets today. It's a dramatic change. And I guess the question as I look at that is, does this change in phone technology that we've witnessed through the sitcom really exemplify a profound change in America? Still unanswered. But the very basic question we face is whether or not the issue of AI is a quantitative change in technology or a qualitative change. The suggestions that I've heard from experts in the field suggest it's qualitative. Is AI fundamentally different? Is it a game changer? Is it so disruptive that we need to treat it differently than other forms of innovation? That's the starting point.

The second starting point is one that's humbling, and that is the fact that when you look at the record of Congress in dealing with innovation, technology, and rapid change, we were not designed for that. In fact, the Senate was not created for that purpose, but just the opposite. Slow things down, take a harder look at it, don't react to public sentiment, make sure you're doing the right thing. Well, I've heard of the potential, the positive potential of AI, and it is enormous. You can go through lists of the deployment of technology that would say that an idea you can sketch on a website or on a napkin can generate functioning code. Pharmaceutical companies could use the technology to identify new candidates to treat disease. The list goes on and on. And then, of course, the danger, and it's profound as well.

So, I'm glad that this hearing has taken place, and I think it's important for all of us to participate. I'm glad that it's a bipartisan approach. We're going to have to scramble to keep up with the pace of innovation in terms of our government's public response to it, but this is a great start. Thank you, Mr. Chairman.

Thanks, Senator. Thank you very much, Senator Blumenthal and Senator Hawley. I'm going to turn to the witnesses. We're very grateful to you for being here. Sam Altman is the co-founder and CEO of OpenAI, the AI research and deployment company behind ChatGPT and DALL·E. Mr. Altman was President of the early-stage startup accelerator Y Combinator from 2014 to 2019. OpenAI was founded in 2015.

Christina Montgomery is IBM's Vice President, Chief Privacy and Trust Officer, overseeing the company's global privacy program, policies, compliance, and strategy. She also chairs IBM's AI Ethics Board, a multi-disciplinary team responsible for the governance of AI and emerging technologies. Christina has served in various roles at IBM, including Corporate Secretary to the company's Board of Directors. She is a global leader in AI ethics and governance and is a member of the United States Chamber of Commerce AI Commission and the United States National AI Advisory Committee, which was established in 2022 to advise the President and the National AI Initiative Office on a range of topics related to AI.

Gary Marcus is a leading voice in artificial intelligence. He's a scientist, best-selling author, and entrepreneur, founder of Robust.AI and Geometric.AI (acquired by Uber, if I'm not mistaken), and an Emeritus Professor of Psychology and Neuroscience at NYU. Mr. Marcus is well known for his challenges to contemporary AI, anticipating many of the current limitations decades in advance, and for his research in human language development and cognitive neuroscience.

Thank you for being here, and as you may know, our custom on the Judiciary Committee is to swear in our witnesses before they testify. So if you would all please rise and raise your right hand. You solemnly swear that the testimony that you are going to give is the truth, the whole truth, and nothing but the truth, so help you God.

Thank you. Mr. Altman, we're going to begin with you, if that's okay.

Thank you, Chairman Blumenthal, Ranking Member Hawley, members of the Judiciary Committee.

Thank you for the opportunity to speak to you today about large neural networks. It's really an honor to be here, even more so in the moment than I expected. My name is Sam Altman. I'm the Chief Executive Officer of OpenAI.

The big tech company's preferred plan boils down to "trust us," but why should we? The sums of money at stake are mind-boggling. Emissions drift OpenAI's original mission statement proclaimed, "Our goal is to advance AI in the way that most is most likely to benefit Humanity as a whole, unconstrained by a need to generate Financial return." Seven years later, they're largely beholden to Microsoft, embroiled in part in an epic battle of search engines that routinely make things up. And that's forced Alphabet to rush out products and de-emphasize safety. Humanity has taken a back seat.

AI is moving incredibly fast, with lots of potential but also lots of risks. We obviously need government involved, and we need the tech companies involved, both big and small. But we also need independent scientists, not just so that we scientists can have a voice but so that we can participate directly in addressing the problems and evaluating solutions, and not just after products are released, but before.

I'm glad that Sam mentioned that we need tight collaboration between independent scientists and governments in order to hold the company's feet to the fire, allowing independent access to the assist independent Sciences, allowing independent scientists access to these systems before they are widely released as part of a clinical trial-like safety evaluation is a vital first step.

Ultimately, we may need something like CERN, global, international, and neutral but focused on AI safety, rather than high-energy physics. We have unprecedented opportunities here, but we are also facing a perfect storm of corporate irresponsibility, widespread deployment, lack of adequate regulation, and inherent unreliability.

AI is among the most world-changing technologies ever, already changing things more rapidly than almost any technology in history. We acted too slowly with social media; many unfortunate decisions got locked in with lasting consequences. The choices we make now will have lasting effects for decades, maybe even centuries. The very fact that we are here today in bipartisan fashion to discuss these matters gives me some hope. Thank you, Mr. Chairman.

Thanks very much, Professor Marcus. We're going to have seven-minute rounds of questioning, and I will begin. First of all, Professor Marcus, we are here today because we do face that perfect storm. Some of us might characterize it more like a bomb in a china shop, not a bull. And as Senator Hawley indicated, there are precedents here, not only the atomic warfare era but also the Genome Project, the research on genetics, where there was international cooperation as a result. And we want to avoid those past mistakes, as I indicated in my opening statement that we're committed on social media. That is precisely the reason we are here today, Chat GPT makes mistakes, all AI does, and it can be a convincing liar. What people call hallucinations, that might be an innocent problem in the opening of a Judiciary subcommittee hearing where a voice is impersonated, mine in this instance or quotes from research papers that don't exist.

But Chat GPT and Bard are willing to answer questions about life or death matters. For example, drug interactions. And those kinds of mistakes can be deeply damaging. I'm interested in how we can have reliable information about the accuracy and trustworthiness of these models and how we can create competition and consumer disclosures that reward greater accuracy.

The National Institutes of Standards and Technology actually already has an AI accuracy test, the face recognition vendor test. It doesn't solve for all the issues with facial recognition, but the scorecard does provide useful information about the capabilities and flaws of these systems. So there's work on models to assure accuracy and integrity. My question, let me begin with you, Mr. Altman, is, should we consider independent testing labs to provide scorecards and nutrition labels or the equivalent of nutrition labels, packaging that indicates to people whether or not  the content can be trusted, what the ingredients are, and what the garbage going in may be because it could result  in garbage going out.

Yeah, I think that's a great idea. I think that companies should put their own sort of, you know, hear the results of our test of our model before we release it. Here's where it has weaknesses, here's where it has strengths. But also, independent audits for that are very important. These models are getting more accurate over time. You know, this is, this is, as we have, I think, said as loudly as anyone, this technology is in its early stages. It definitely still makes mistakes. We find that people, that users, are pretty sophisticated and understand where the mistakes are, that they need or likely to be, that they need to be responsible for verifying what the models say, that they go off and check it.

Um, I worry that as the models get better and better, uh, the users can have sort of less and less of their own discriminating thought process around it, but I think users are more capable than we could often give them credit for in conversations like this. I think a lot of disclosures, which if you've used ChatGPT, you'll see about the inaccuracies of the model, are also important, and I'm excited for a world where companies publish with the model's information about how they behave, where the inaccuracies are, and independent agencies or companies provide that as well. I think it's a great idea. I alluded in my opening remarks to the jobs issue, the economic effects on employment.

I think you have said, in fact, and I'm going to quote, "development of superhuman machine intelligence is probably the greatest threat to the continued existence of humanity," end quote. You may have had in mind the effect on jobs, which is really my biggest nightmare in the long term. Let me ask you what your biggest nightmare is and whether you share that concern. Like with all technological revolutions, I expect there to be significant impact on jobs, but exactly what that impact looks like is very difficult to predict.

If we went back to the other side of a previous technological revolution, talking about the jobs that exist on the other side, you know, you can go back and read books of this. It's what people said at the time. It's difficult. I believe that there will be far greater jobs on the other side of this, and the jobs of today will get better.

I think it's important. First of all, I think it's important to understand and think about GPT-4 as a tool, not a creature, which is easy to get confused. And it's a tool that people have a great deal of control over and how they use it. And second, GPT-4 and things other systems like it are good at doing tasks, not jobs. And so you see already people that are using GPT-4 to do their job much more efficiently by helping them with tasks. Now GPT-4 will, I think, entirely automate away some jobs, and it will create new ones that we believe will be much better.

This happens again. My understanding of the history of technology is one long technological revolution, not a bunch of different ones put together. But this has been continually happening.

We, as our quality of life raises and as machines and tools that we create can help us live better lives, the bar raises for what we do and our human ability and what we spend our time going after goes after more ambitious, more satisfying projects. So, there will be an impact on jobs. We try to be very clear about that, and I think it will require partnership between the industry and government, but mostly action by government to figure out how we want to mitigate that. But I'm very optimistic about how great the jobs of the future will be.

Thank you. Let me ask Ms. Montgomery and Professor Marcus for your reactions to those questions as well. Is Montgomery on the jobs point? Yeah, I mean, well, it's a hugely important question, and it's one that we've been talking about for a really long time at IBM. You know, we do believe that AI, and we've said it for a long time, is going to change every job. New jobs will be created, many more jobs will be transformed, and some jobs will transition away. I'm a personal example of a job that didn't exist when I joined IBM, and I have a team of AI governance professionals who are in new roles that we created. You know, as early as three years ago, I mean, they're new and they're growing.

So, I think the most important thing that we could be doing and can and should be doing now is to prepare the workforce of today and the workforce of tomorrow for partnering with AI technologies and using them. And we've been very involved for years now in doing that, in focusing on skills-based hiring, in educating for the skills of the future. Our SkillsBuild platform has seven million learners and over a thousand courses worldwide focused on skills. And we've pledged to train 30 million individuals by 2030 in the skills that are needed for society today. Thank you.

Professor Marcus, can you go back to the first question as well? Absolutely. On the subject of nutrition labels, I think we absolutely need to do that. I think that there are some technical challenges in that building proper nutrition labels goes hand in hand with transparency. The biggest scientific challenge in understanding these models is how they generalize, what do they memorize, and what new things do they do? The more that there's in the dataset, for example, the thing that you want to test accuracy on, the less you can get a proper read on that. So, it's important, first of all, that scientists be part of that process, and second, that we have much greater transparency about what actually goes into these systems. If we don't know what's in them, then we don't know exactly how well they're doing when they give something new, and we don't know how good a benchmark that will be for something that's entirely novel. So, I could go into that more, but I want to flag that.

Second is on jobs. Past performance history is not a guarantee of the future. It has always been the case in the past that we have had more jobs than new jobs. New professions come in as new technologies come in. I think this one's going to be different, and the real question is over what timescale is it going to be? 10 years? Is it going to be 100 years? And I don't think anybody knows the answer to that question. I think in the long run, so-called artificial general intelligence really will replace a large fraction of human jobs. We're not that close to artificial general intelligence, despite all of the media hype and so forth. I would say that what we have right now is just a small sampling of the AI that we will build in 20 years.

People will laugh at this, as I think it was Senator Hawley made the... but maybe Senator Durbin made the example about this. It was Senator Durbin made the example about cell phones. When we look back at the AI of today 20 years ago, we'll be like, "Wow, that stuff was really unreliable. It couldn't really do planning, which is an important technical aspect. Its reasoning abilities were limited." But when we get to AGI or artificial general intelligence, let's say it's 50 years, that really is going to have, I think, profound effects on labor, and there's no way around that. And last, I don't know if I'm allowed to do this, but I will note that Sam's worst fear, I do not think, is employment, and he never told us what his worst fear actually is, and I think it's germane to find out. Thank you.

I'm going to ask Mr. Altman if he cares to respond. Look, we have tried to be very clear about the magnitude of the risks here. I think jobs and employment and what we're all going to do with our time really matters. I agree that when we get to very powerful systems, the landscape will change. I think I'm just more optimistic that we are incredibly creative and we find new things to do with better tools, and that will keep happening. Um, my worst fears are that we cause significant, we, the field, the technology, the industry, cause significant harm to the world. Uh, I think that could happen in a lot of different ways. It's why we started the company. It's a big part of why I'm here today and why we've been here in the past.

And we've been able to spend some time with you. I think if this technology goes wrong, it can go quite wrong, and we want to be vocal about that. We want to work with the government to prevent that from happening. But we try to be very clear-eyed about what the downside case is and the work that we have to do to mitigate that. Thank you. And our hope is that the rest of the industry will follow the example that you and IBM, Ms. Montgomery, have set by coming today and meeting with us, as you have done privately, in helping to guide what we're going to do so that we can target the harms and avoid unintended consequences to the good.

Thanks, Senator Hawley. Thank you again, Mr. Chairman. Thanks to the witnesses for being here. Mr. Altman, I think you grew up in St. Louis, if I'm not mistaken. It's great to see it. It's a great place, Missouri, and here it is. Thank you. I want to, I want that noted, especially underlining the record. Missouri is a great place. That is the takeaway from today's hearing. Maybe we just stop there, Mr. Chairman. Um, let me ask you, Mr. Altman. I think I'll start with you, and I'll just preface by saying my questions here are an attempt to get my head around and to ask all of you to help us to get our heads around what these generative AI, particularly the large language models, what they can do.

So, I'm trying to understand their capacities and then their significance. So, I'm looking at a paper here entitled "Large Language Models Trained on Media Diets Can Predict Public Opinion." This was just posted about a month ago. The authors are two Andreasen, Celebrini, and Roy, and their conclusion, this work was done at MIT and then also at Google, the conclusion is that large language models can indeed predict public opinion. And they go through and model why this is the case, and they conclude ultimately that an AI system can predict human survey responses by adapting a pre-trained language model to sub-population-specific media diets. In other words, you can feed the model a particular set of media inputs, and it can, with remarkable accuracy (the paper goes into this), predict then what people's opinions will be.

I want to think about this in the context of elections. If these large language models can even now, based on the information we put into them, quite accurately predict public opinion, you know, ahead of time, I mean, predict it before you even ask the public these questions, what will happen when entities, whether it's corporate entities or whether it's governmental entities or whether it's campaigns or whether it's foreign actors, take this survey information, these predictions about public opinion, and then fine-tune strategies to elicit certain responses, certain behavioral responses?

I mean, we already know, this committee has heard testimony, I think three years ago now, about the effect of something as prosaic it now seems as Google search, the effect that this has on voters in an election, particularly undecided voters in the final days of an election who may try to get information from Google search, and what an enormous effect the ranking of the Google search, the articles that it returns, has, a far more directive, if you like.

So, Mr. Altman, maybe you can help me understand here what some of the significance of this is. Should we be concerned about models that can, large language models that can predict survey opinion and then can help organizations, entities fine-tune strategies to elicit behaviors from voters? Should we be worried about this for our elections?

Yeah, uh, thank you, Senator Hawley, for the question. It's one of my areas of greatest concern—the ability of these models to manipulate, persuade, and provide one-on-one interactive disinformation. I think that's a broader version of what you're talking about. But given that we're going to face an election next year, and these models are getting better, I think this is a significant area of concern. I think there's a lot of policies that companies can voluntarily adopt, and I'm happy to talk about what we do there. I do think some regulation would be quite wise on this topic. Someone mentioned earlier it's something we really agree with—people need to know if they're talking to an AI, if the content they're looking at might be generated or might not. I think it's a great thing to do, to make that clear. I think we also will need rules and guidelines about what's expected in terms of disclosure from a company providing a model that could have these sorts of abilities that you talk about. So, I'm nervous about it. I think people are able to adapt quite quickly when Photoshop came onto the scene a long time ago. You know, for a while, people were really quite fooled by photoshopped images, and then pretty quickly developed an understanding that images might be photoshopped. This will be like that, but on steroids, and the interactivity, the ability to really model and predict humans well, as you talked about, I think is going to require a combination of companies doing the right thing, regulation, and public education. Do you, Mr. Professor Marcus, want to address this?"

"Yeah, I'd like to add two things. One is in the appendix to my remarks, I have two papers to make you even more concerned. One is in The Wall Street Journal just a couple of days ago called 'Help! My political beliefs were altered by a chatbot.' And I think the scenario you raised was that we might basically observe people and use surveys to figure out what they're saying.

But as Sam just acknowledged, the risk is actually worse—that the systems will directly, maybe not even intentionally, manipulate people. And that was the thrust of The Wall Street Journal article, and it links to an article that I've also linked to called 'Interacting'—and it's not yet published, not yet peer-reviewed—'Interacting with opinionated language models changes users' views.' And this comes back ultimately to data. One of the things that I'm most concerned about with GPT-4 is that we don't know what it's trained on. I guess Sam knows, but the rest of us do not. And what it is trained on has consequences for essentially the biases of the system.

We could talk about that in technical terms, but how these systems might lead people about depends very heavily on what data is trained on them. And so, we need transparency about that, and we probably need scientists in there doing analysis in order to understand what the political influences, for example, of these systems might be. And it's not just about politics; it can be about health, it could be about anything. These systems absorb a lot of data, and then what they say reflects that data, and they're going to do it differently depending on what's in that data.

So, it makes a difference if they're trained on The Wall Street Journal as opposed to The New York Times or Reddit. I mean, actually, they're largely trained on all of this stuff, but we don't really understand the composition of that. And so, we have this issue of potential manipulation, and it's even more complex than that because it's subtle manipulation. People may not be aware of what's going.

That was the point of both The Wall Street Journal article and the other article that I called your attention to. Let me ask you about AI systems trained on personal data, the kind of data that, for instance, the social media companies, the major platforms (Google, Meta, etc.) collect on all of us routinely.

We've had many a chat about this in this committee over many a year now, but the massive amounts of personal data that the companies have on each one of us. An AI system that is trained on that individual data, that knows each of us better than ourselves and also knows the billions of data points about human behavior, human language, interaction generally. Wouldn't we be able, wouldn't we... Can't we foresee an AI system that is extraordinarily good at determining what will grab human attention and what will keep an individual's attention?

And so, for the war for attention, the war for clicks that is currently going on on all of these platforms, is how they make their money. I'm just imagining an AI system, these AI models, supercharging that war for attention, such that we now have technology that will allow individual targeting of a kind we have never even imagined before. Where the AI will know exactly what Sam Altman finds attention-grabbing, will know exactly what Josh Hawley finds attention-grabbing, will be able to elicit, to grab our attention and then elicit responses from us in a way that we have never before been able to imagine. Should we be concerned about that for its corporate applications, for the monetary applications, for the manipulation that could come from that, Mr. Altman?

Yes, we should be concerned about that. To be clear, OpenAI does not have an ad-based business model. So, we're not trying to build up these profiles of our users. We're not trying to get them to use it more. Actually, we'd love it if they use it less because we don't have enough GPUs. But I think other companies are already and certainly will in the future use AI models to create very good ad predictions of what a user will like. I think it's already happening in many ways.

Mr. Marcus, what do you think? You want to add?

Yes, and perhaps Ms. Montgomery will want to as well. But I don't... Hypert... Yes. And perhaps Ms. Montgomery will want to as well. I don't, but hyper-targeting of advertising is definitely going to come. I agree that that's not been OpenAI's business model. Of course, now they're working for Microsoft, and I don't know what's in Microsoft's thoughts. But we will definitely see it. Maybe it will be with open-source language models. I don't know, but the technology there is, let's say, partway there to being able to do that, and we'll certainly get there.

So, we're an Enterprise technology company, not consumer-focused. So, the space isn't one that we necessarily operate in terms of... But these issues are hugely important issues, and it's why we've been out ahead in developing the technology that will help to ensure that you can do things like produce a fact sheet that has the ingredients of what your data is trained on, data sheets, model cards, all those types of things, and calling for, as I've mentioned today, transparency so you know what the algorithm was trained on, and then you also know and can manage and monitor continuously over the life cycle of an AI model the behavior and the performance of that model.

Senator Durbin: Thank you. I think what's happening today in this hearing room is historic. I can't recall when we've had people representing large corporations or private sector entities come before us and plead with  us to regulate them.

In fact, many people in the Senate have based their careers on the opposite, that the economy will thrive if the government gets the hell out of the way. And what I'm hearing instead today is that "stop me before I innovate again" message, and I'm just curious as to how we're going to achieve this. As I mentioned Section 230 in my opening remarks, we learned something there. We decided that in Section 230, we were basically going to absolve the industry from liability for a period of time as it came into being. Well, Mr. Alderman, on the podcast earlier this year, you agreed with host Cara Swisher that Section 230 doesn't apply to generative AI, and that developers like OpenAI should not be entitled to full immunity for harms caused by their products. So, what have we learned from 230 that applies to your situation with AI?

Thank you for the question, Senator. I don't know yet exactly what the right answer here is. I'd love to collaborate with you to figure it out. I do think for a very new technology, we need a new framework. Certainly, companies like ours bear a lot of responsibility for the tools that we put out in the world, but tool users do as well, and how we want and also people that will build on top of it between them and the end consumer and how we want to come up with a liability framework. There is a super important question, and we'd love to work together.

The point I want to make is this: when it came to online platforms, the inclination of the government was to get out of the way. This is a new industry, don't over-regulate it. In fact, give them some breathing space and see what happens. I'm not sure I'm happy with the outcome as I look at online platforms.

Me either, and the harms that they have created, problems that we've seen demonstrated in this committee: child exploitation, cyberbullying, online drug sales, and more. I don't want to repeat that mistake again, and what I hear is the opposite suggestion from the private sector, and that is, come in the front end of this thing and establish some liability standards. Precision regulation. For a major company like IBM to come before this committee and say to the government, please regulate us. Can you explain the difference in thinking from the past and now?

Yeah, absolutely. So, for us, this comes back to the issue of trust and trust in the technology. Trust is our license to operate, as I mentioned in my remarks. And so, we firmly believe and have been calling for precision regulation of artificial intelligence for years now. This is not a new position. We think that technology needs to be deployed in a risk-responsible and clear way. We've taken principles around that trust and transparency, we call them our principles that were articulated years ago and built them into practices. That's why we're here advocating for a precision regulatory approach. So, we think that AI should be regulated at the point of risk, essentially, and that's the point at which technology meets society. Let's take a look at what that might appear to be.

Members of Congress are pretty smart. A lot of people maybe not as smart as we think we are many times, and government certainly has the capacity to do amazing things. But when you talk about our ability to respond to the current challenge and perceive the challenge of the future challenges, which you all have described in terms which are hard to forget, as you said, Mr. Altman, things can go quite wrong. She said, Mr. Marcus, democracy is threatened. I mean, the magnitude of the challenge you're giving us is substantial.

I'm not sure that we respond quickly and with enough expertise to deal with it, Professor Marcus. You made a reference to CERN, the international arbiter of nuclear research. I suppose I don't think that's a fair characterization, but it's a characterization. I'll start with: what is it? What agency of this government do you think exists that could respond to the challenge that you've laid down today?

We have many agencies that can respond in some ways. For example, the FTC, um, we have CC, and there are many agencies that can. But my view is that we probably need a cabinet-level organization within the United States in order to address this. And my reasoning for that is that the number of risks is large, the amount of information to keep up on is so much. I think we need a lot of technical expertise. I think we need a lot of coordination of these efforts.

So there is one model here where we stick to only existing law and try to shape all of what we need to do, and each agency does their own thing. But I think that AI is going to be such a large part of our future and is so complicated and moving so fast. This does not fully solve your problem about a dynamic world, but it's a step in that direction to have an agency whose full-time job is to do this.

I personally have suggested, in fact, that we should want to do this in a global way. I wrote an article in The Economist. I have a link in here, an invited essay for The Economist, suggesting we might want an international Agency for AI. That's what I wanted to go to next.

And that is the fact that, aside from the CERN and nuclear examples because the government was involved in that from day one, at least in the United States, but now we're dealing with innovation which doesn't necessarily have a boundary. We may create a great U.S. agency, and I hope that we do, that may have jurisdiction over U.S. corporations and U.S. activity, but doesn't have a thing to do with what's going to bombard us from outside the United States.

How do you give this international authority the authority to regulate in a fair way for all entities involved in AI?

I think that's probably over my pay grade. I would like to see it happen, and I think it may be inevitable that we push there. I mean, I think the politics behind it are obviously complicated. I'm really heartened by the degree to which this room is bipartisan and supporting the same things, and that makes me feel like it might be possible.

I would like to see the United States take leadership in such an organization. It has to involve the whole world and not just the US to work properly. I think even from the perspective of the companies, it would be a good thing. So the companies themselves do not want a situation where you take these models which are expensive to train, and you have to have 190 some of them, you know, one for every country. That wouldn't be a good way of operating. When you think about the energy costs alone just for training these systems, it would not be a good model if every country has its own policies and each jurisdiction, every company has to train another model. And maybe, you know, different states are different, so Missouri and California have different rules. And so then that requires even more training of these expensive models with huge climate impact.

Um, and I mean, just, it would be very difficult for the companies to operate if there was no global coordination. And so I think that we might get the companies on board if there's bipartisan support here. And I think there's support around the world. That is entirely possible that we could develop such a thing. But obviously, there are many, you know, nuances here of diplomacy that are over my pay grade. I would love to learn from you all to try to help make that happen.

Mr. Altman, can I weigh in just briefly? Briefly, please. Uh, I want to echo support for what Mr. Marcus said. I think the U.S. should lead here and do things first. But to be effective, we do need something global, as you mentioned. This can happen everywhere. There is precedent. I know it sounds naive to call for something like this, and it sounds really hard. There is precedent. We've done it before with the IAEA. We've talked about doing it for other technologies.

Given what it takes to make these models, the chip supply chain, the sort of limited number of competitive GPUs, the power the US has over these companies, I think there are paths to the US setting some international standards that other countries would need to collaborate with and be part of that are actually workable, even though it sounds on its face like an impractical idea. And I think it would be great for the world. Thank you, Mr. Chairman.

Thank you, thanks Senator Durbin. In fact, I think we're going to hear more about what Europe is doing. The European Parliament is already acting on an AI act, particularly on social media. Europe is ahead of us. We need to be in the lead. I think your point is very well taken. Let me turn to Senator Graham. Senator Blackburn, thank you, Mr. Chairman, and thank you all for being here with us today.

I put into my chat GPT account, "Should Congress regulate AI chat GPT?" and it gave me four pros, four cons, and says ultimately the decision rests with Congress and deserves careful consideration. So on that, it was very balanced. I recently visited with the Nashville Technology Council. I represent Tennessee, and of course, you had people there from healthcare, financial services, logistics, educational entities, and they're concerned about what they see happening with AI and the utilization for their companies.

Miss Montgomery, you know, some similar to you, they've got healthcare people looking at disease analytics, they're looking at predictive diagnosis, how this can better the outcomes for patients. The logistics industry is looking at ways to save time and money and yield efficiencies. You've got financial services that are saying, "How does this work with quantum? How does it work with blockchain? How can we use this?"

But I think, as we have talked with them, Mr. Chairman, one of the things that continues to come up is that yes, Professor Marcus, as you were saying, the EU and different entities are ahead of us in this, but we have never established a federally preemptive, given preemption for online privacy, for data security, and put some of those foundational elements in place, which is something that we need to do as we look at this. And it will require that the Commerce Committee and Judiciary Committee decide how we move forward so that people own their virtual "you."

Mr. Altman, I was glad to see last week that your OpenAI models are not going to be trained using consumer data. I think that is important. And if we have a second round, I've got a host of questions for you on data security and privacy. But I think it's important to let people control their virtual "you," their information in these settings. And I want to come to you on music and content creation because we've got a lot of songwriters and artists, and I think we have the best creative community on the face of the Earth. They're in Tennessee, and they should be able to decide if their copyrighted songs and images are going to be used to train these models.

And I'm concerned about OpenAI's Jukebox. It offers some re-renditions in the style of Garth Brooks, which suggests that OpenAI is trained on Garth Brooks songs. I went in this weekend and I said, "Write me a song that sounds like Garth Brooks," and it gave me a different version of "Simple Man." So it's interesting that it would do that. But you're training it on these copyrighted songs, these MIDI files, these sound technologies. So as you do this, who owns the rights to that AI-generated material? And using your technology, could I remake a song, insert content from my favorite artist, and then own the creative rights to that song?

Thank you, Senator. This is an area of great interest to us. I would say, first of all, we think that creators deserve control over how their creations are used and what happens beyond the point of them releasing it into the world. Second, I think that we need to figure out new ways with this new technology that creators can win, succeed, have a vibrant life. And I'm optimistic that this will present it.

Then let me ask you this: How do you compensate the artist?

That's exactly what I was going to say. Okay. We'd like to, we're working with artists now, visual artists, musicians, to figure out what people want. There's a lot of different opinions, unfortunately. And at some point, we'll have to, let me ask you this, do you favor something like SoundExchange?

I'm not familiar with SoundExchange. I'm sorry.

Okay. You've got your team behind you. Get back to me on that. That would be a third-party entity. Okay. So let's discuss that. Let me move on. Can you commit, as you've done with consumer data, not to train ChatGPT, OpenAI Jukebox, or other AI models on artists' and songwriters' copyrighted works or use their voices and their likenesses without first receiving their consent?

So, first of all, Jukebox is not a product we offer. That was a research release. But it's not, you know, unlike ChatGPT, we've lived through Napster.

Yes, but that was something that really cost a lot of artists a lot of money.

Oh, I understand. Yeah, for sure. The digital distribution era. I don't know the numbers on Jukebox off the top of my head. As a research release, I can follow up with your office, but it's not something that gets much attention or usage. It was put out to show that something's possible.

Well, Senator Durbin just said, you know, and I think it's a fair warning to you all. If we're not involved in this from the get-go, and you all already are a long way down the path on this, but if we don't step in, then this gets away from you. So are you working with the Copyright Office? Are you considering protections for content generators and creators in generative AI?

Yes, we are absolutely engaged on that. Again, to reiterate my earlier point, we think that content creators, content owners need to benefit from this technology. Exactly what the economic model is, we're still talking to artists and content owners about what they want. I think there's a lot of ways this can happen, but very clearly, no matter what the law is, the right thing to do is to make sure people get significant upside benefit from this new technology. And we believe that it's really going to deliver that. But that content owners, likenesses, people totally deserve control over how that's used and to benefit from it.

Okay. So on privacy, how do you plan to account for the collection of voice and other user-specific data, things that are copyrighted user-specific data through your AI applications? Because if I can go in and say, "Write me a song that sounds like Garth Brooks," and it takes part of an existing song, there has to be compensation to that artist for that utilization and that use. If it was radio play, it would be there. If it was streaming, it would be there. So if you're going to do that, what is your policy for making certain you're accounting for that and you're protecting that individual's right to privacy and their right to secure that data and that created work?

A few thoughts about this. Number one, we think that people should be able to say, "I don't want my personal data trained on." That's, I think, that's right. That gets to a national privacy law, which many of us here on the dais are working towards, getting something that we can use. Yeah, I think a strong privacy, my time's expired. I'll yield back. Thank you, Mr. Chair. Thank you, Senator.

Senator Blackburn, I love Nashville and I love Tennessee. I love your music. But I will say, I use Chat GPT and just asked, "What are the top creative song artists of all time?" And two of the top three were from Minnesota. That would be Prince, I'm sure they moved Prince, and Bob Dylan. Okay, all right, so let us continue. One thing AI won't change, and you're seeing it here. All right, so on a more serious note, though, my staff and I, in my role as chair of the Rules Committee, and leading a lot of the election bill, we just introduced a bill that Representative Yvette Clark from New York introduced over the house. Senator Booker and Bennett and I did on political advertisements. But that is just, of course, the tip of the iceberg.

You know this from your discussions with Senator Hawley and others about the images. And my own view, Senator Graham, of Section 230 is that we just can't let people make stuff up and then not have any consequence. But I'm going to focus on what one of my jobs will be on the Rules Committee, and that is election misinformation. And we just asked Chat GPT to do a tweet about a polling location in Bloomington, Minnesota, and it said there are long lines at this polling location at Atonement Lutheran Church. Where should we go now? Albeit it's not an election right now, but the answer the tweet that was drafted was a completely fake thing. Go to 1234 Elm Street. And so you can imagine what I'm concerned about here, with an election upon us, with primary elections upon us, that we're going to have all kinds of misinformation. And I just want to know what you're planning on doing about it. I know we're going to have to do something soon, not just for the images of the candidates, but also for misinformation about the actual polling places and election rules. Thank you, Senator. We talked about this a little bit earlier. We are quite concerned about the impact this can have on elections.

I think this is an area where hopefully the entire industry and the government can work together quickly. There are many approaches, and I'll talk about some of the things we do. But before that, I think it's tempting to use the frame of social media. But this is not social media. This is different. And so the response that we need is different. You know, this is a tool that a user is using to help generate content more efficiently than before. They can change it. They can test the accuracy of it. If they don't like it, they can get another version. But it still then spreads through social media or other ways. Like Chat GPT is a single-player experience where you're just using this.

And so I think as we think about what to do, that's important to understand. That there's a lot that we can and do do. There are things that the model refuses to generate. We have policies. Importantly, we have monitoring. So at scale, we can detect someone generating a lot of those tweets, even if generating one tweet is okay. Yeah, and of course, there's going to be other platforms. And if they're all spouting out fake election information, I just think what happened in the past with Russian interference and like, it's just going to be the tip of the iceberg with some of those fake ads. So that's number one. Number two is the impact on intellectual property. And Senator Blackburn was getting at some of this with song rights. I have serious concerns about that. But news content, Senator Kennedy and I have a bill that was really quite straightforward, that would simply allow the news organizations an exemption to be able to negotiate with basically Google and Facebook. Microsoft was supportive of the bill, but basically negotiate with them to get better rates and be able to not have some leverage.

And other countries are doing this, Australia and the like. And so my question is, when we already have a study by Northwestern predicting that one-third of the U.S. newspapers, or roughly, that existed two decades ago, are going to be gone by 2025 unless you start compensating for everything from books, movies, yes, but also news content, we're going to lose any realistic content producers. And so I'd like your response to that. And of course, there is an exemption for copyright in Section 230, but I think asking little newspapers to go out and sue all the time just can't be the answer. They're not going to be able to keep up. Yeah, like it is my hope that tools like what we're creating can help news organizations do better. I think having a vibrant national media is critically important. And let's call it round one of the internet has not been great for that, right? We're talking here about local news, report on your high school sports, a scandal in your city council, those kinds of things. For sure, they're the ones that are actually getting the worst, the little radio stations and broadcast.

But do you understand that this could be exponentially worse in terms of local news content if they're not compensated well? Because what they need is to be compensated for their content and not have it stolen. Yeah, again, our model, the current version of GPT-4 ended training in 2021. It's not a good way to find recent news. And I don't think it's a service that can do a great job of linking out, although maybe with our plugins, it's possible. If there are things that we can do to help local news, we would certainly like to. Again, I think it's critically important. May I add something there? Yeah, but let me just ask you a question. You can combine them quickly. More transparency on the platforms.

Senator Coons and Senator Cassidy and I have the Platform Accountability Transparency Act to give researchers access to this information, the algorithms and the like, on social media data. Would that be helpful? And then why don't you just say yes or no and then go at it? The transparency is absolutely critical here to understand the political ramifications, the bias ramifications, and so forth. We need transparency about the data. We need to know more about how the models work. We need to have scientists have access to them. I was just going to amplify your earlier point about local news. A lot of news is going to be generated by these systems.

They're not reliable. NewsGuard already is a study, sorry, it's not in my appendix, but I will get it to your office, showing that something like 50 websites are already generated by bots. We're going to see much, much more of that. And it's going to make it even more competitive for the local news organizations. And so the quality of the overall news market is going to decline as we have more generated content by systems that aren't actually reliable in the content they're generating. Thank you. And thank you at a very timely basis to make the argument why we have to mark up this bill again in June. I appreciate it. Thank you, Senator Graham.

Thank you, Mr. Chairman and Senator Hawley for having this. I'm trying to find out how it is different than social media and learn from the mistakes we made with social media. The idea of not suing social media companies is to allow the internet to flourish. Because if I slander you, you can sue me. If you're a billboard company and you put up the slander, can you sue the billboard company? We said no. Basically, Section 230 is being used by social media companies to avoid liability for activity that other people generate. When they refuse to comply with their terms of use, a mother calls up the company and says, "This app is being used to bully my child to death. You promised in the terms of use you would prevent bullying." She calls three times. She gets no response. The child kills herself and they can't sue.

Do you all agree we don't want to do that again? Yes. If I may speak for one second, there's a fundamental distinction between reproducing content and generating content. Yeah, but you would like liability where people are harmed. Absolutely. Yes. In fact, IBM has been publicly advocating to condition liability on a reasonable care standard. So let me just make sure I understand the law as it exists today, Mr. Almond. Thank you for coming. Your company is not claiming that Section 230 applies to the tool you have created. Yeah, we're claiming we need to work together to find a totally new approach.

I don't think Section 230 is that even the right framework. Okay, so under the law as it exists today, this tool you create, if I'm harmed by it, can I sue you? That is beyond my area of legal... Have you ever been sued? Not for that. No. Have you ever been sued at all, your company? Yeah, I get sued. Yeah, we've gotten sued before. Okay, and what for? I mean, they've mostly been pretty frivolous things, like I think happens to any company, but like the examples my colleagues have given from artificial intelligence that could literally ruin our lives, can we go to the company that created that tool and sue them? Is that your understanding? Yeah, I think there needs to be clear responsibility by the companies. But you're not claiming any kind of legal protection, like Section 230 applies to your industry. Is that correct? No, I don't think we're saying that.

Mr. Marcus, when it comes to consumers, there seems to be like three time-tested ways to protect consumers against any product: statutory schemes, which are non-existent here, legal systems, which may be appear here, but not social media, and agencies. Go back to Senator Hawley, the atom bomb has put a cloud over humanity, but nuclear power could be one of the solutions to climate change. So what I'm trying to do is make sure that you just can't go build a nuclear power plant. "Hey, Bob, what would you like to do today?

Let's go build a nuclear power plant." You have a Nuclear Regulatory Commission that governs how you build a plant and is licensed. Do you agree, Mr. Almond, that these tools you're creating should be licensed? Yeah, we've been calling for this. We think any... That's the simplest way. You get a license. And do you agree with me the simplest way and the most effective way is to have an agency that is more nimble and smarter than Congress, which should be easy to create, overlooking what you do?

Yes, we'd be enthusiastic about that. Do you agree with that, Mr. Marcus? Absolutely! Do you agree with that, Senator Montgomery? I would have some nuances. I think we need to build on what we have in place already. Today, we don't have an agency that regulates the technology, so should we have one? But a lot of the issues... I don't think so. A lot of these... Wait a minute, so IBM says we don't need an agency. Interesting. Should we have a license required for these tools? So, what we believe is that we need to ask a simple question: Should you get a license to produce one of these tools? I think it comes back to some of them, potentially, yes. So, what I said at the onset is that we need to clearly define risks. Do you claim Section 230 applies in this area at all? We are not a platform company, and we've long advocated for a reasonable care standard in Section 230. I just don't understand how you could say that you don't need an agency to deal with the most transformative technology, maybe ever. Well, I have existed. This is a transformative technology that can disrupt life as we know it, good and bad. I think it's a transformative technology, certainly, and the conversations that we're having here today have been really bringing to light the fact that this domain and the issues... This one with you has been very enlightening to me, Mr. Allman. Why are you so willing to have an agency, Senator? We've been clear about what we think the upsides are, and I think you can see from users how much they enjoy and how much value they're getting out of it.

But we've also been clear about what the downsides are, and so that's why we think we need a system. It's a major tool to be used by a lot. It's a major new technology. Yeah, if you make a ladder and the ladder doesn't work, you'll see the people who made the ladder. But there are some standards out there to make a ladder. So, that's why we're agreeing with you. Yeah, that's right. I think you're on the right track. So here's what my two cents' worth for the committee is: We need to empower an agency that issues a license and can take it away. Wouldn't that be some incentive to do it right if you could actually be taken out of business? It's clear that should be part of what an agency can do now. And you also agree that China is doing AI research, is that right? Correct. This world organization that doesn't exist, maybe it will. But if you don't do something about the China part of it, you'll never quite get this right. Do you agree? Well, that's why I think it doesn't necessarily have to be a world organization, but there has to be some sort of... And there's a lot of options here. There has to be some sort of standard, some sort of set of controls that do have a global effect.

You know, because you know other people doing this. I got 15 military applications. How can AI change warfare? And you got one minute. I got one minute. Yeah, alright. This is a tough question for one minute. Um, this is very far out of my area of expertise. Uh, but I'll give you one example. A drone. A drone, you program, you can plug into a drone the coordinates and it can fly out. It goes over this target and it drops a missile on this car moving down the road. And somebody's watching it. Could AI create a situation where a drone can select the target itself? I think we shouldn't allow that. Well, can it be done? Sure. Thanks, Senator. Thank you, Senator Blumenthal, Senator Hawley, for convening this hearing, for working closely together to come up with this compelling panel of witnesses, and for beginning a series of hearings on this transformative technology.

We recognize the immense promise and substantial risks associated with generative AI technologies. We know these models can make us more efficient, help us learn new skills, open whole new vistas of creativity. But we also know that generative AI can authoritatively deliver wildly incorrect information. It can hallucinate, as is often described. It can impersonate loved ones. It can encourage self-destructive behaviors, and it can shape public opinion and the outcome of elections. Congress, thus far, has demonstrably failed to responsibly enact meaningful regulation of social media companies, with serious harms that have resulted that we don't fully understand. Senator Klobuchar referenced in her questioning a bipartisan bill that would open up social media platforms' underlying algorithms. We have struggled to even do that, to understand the underlying technology, and then to move towards responsible regulation.

We cannot afford to be as late to responsibly regulating generative AI as we have been to social media, because the consequences, both positive and negative, will exceed those of social media by orders of magnitude. So let me ask a few questions designed to get at both how we assess the risk, what's the role of international regulation, and how does this impact AI? Mr. Altman, I appreciate your testimony about the ways in which OpenAI assesses the safety of your models through a process of iterative deployment.

The fundamental question embedded in that process, though, is how you decide whether or not a model is safe enough to deploy and safe enough to have been built and then let go into the wild. I understand one way to prevent generative AI models from providing harmful content is to have humans identify that content and then train the algorithm to avoid it. There's another approach that's called constitutional AI that gives the model a set of values or principles to guide its decision-making. Would it be more effective to give models these kinds of rules instead of trying to require or compel training the model on all the different potentials for harmful content?

Thank you, Senator. It's a great question. I'd like to frame it by talking about why we deploy at all, why we put these systems out into the world. There's the obvious answer about the benefits and people using it for all sorts of wonderful things and getting great value, and that makes us happy. But a big part of why we do it is that we believe that iterative deployment and giving people, our institutions, and you all time to come to grips with this technology, to understand it, to find its limitations, it benefits the regulations we need around it, what it takes to make it safe.

That's really important. Going off to build a super powerful AI system in secret and then dropping it on the world all at once, I think would not go well. So, a big part of our strategy is, while these systems are still relatively weak and deeply imperfect, to find ways to get people to have experience with them, to have contact with reality, and to figure out what we need to do to make it safer and better. That is the only way that I've seen, in the history of new technology and products of this magnitude, to get to a very good outcome. And so, that interaction with the world is very important. Now, of course, before we put something out, it needs to meet a bar of safety.

And we spent well over six months with GPT-4, after we finished training it, going through all of these different things and deciding also what the standards were going to be before we put something out there, trying to find the harms that we knew about, put it, and how to address those. One of the things that's been gratifying to us is even some of our biggest critics have looked at GPT-4 and said, "Wow, OpenAI made huge progress on good."

On the point of giving models values, I think it's extremely important. There are multiple technical approaches, but we need to give policymakers and the world as a whole the tools to say, "Here are the values, here's what I want you to reflect," or, "Here are the wide bounds of everything that society will allow," and then within there, you pick, as the user, if you want a value system over here or a value system over there. We think that's very important.

There are multiple technical approaches, but we need to give policymakers and the world as a whole the tools to say, "Here are the values, and implement them." Thank you, Ms. Montgomery. You serve on an AI ethics board of a long-established company that has a lot of experience with AI. I'm really concerned that generative AI technologies can undermine the faith in democratic values and the institutions that we have. The Chinese are insisting that AI being developed in China reinforces the core values of the Chinese Communist Party and the Chinese system.

I'm concerned about how we promote AI that reinforces and strengthens open markets, open societies, and democracy. In your testimony, you're advocating for AI regulation tailored to the specific way the technology is being used, not the underlying technology itself. The EU is moving ahead with an AI Act, which categorizes AI products based on the level of risk. You all, in different ways, have said that you view elections and the shaping of election outcomes, and disinformation that can influence elections, as one of the highest risk cases, one that's entirely predictable. We have attempted, so far unsuccessfully, to regulate social media after the demonstrably harmful impacts of social media on our last several elections. What advice do you have for us about what kind of approach we should follow and whether or not the EU direction is the right one to pursue? I mean, the conception of the EU AI Act is very consistent with the concept of precision regulation, where you're regulating the use of the technology in context. So, absolutely, that approach makes a ton of sense. It's what I advocated for at the onset - different rules for different risks.

In the case of elections, absolutely, any algorithm being used in that context should be required to have disclosure around the data being used, the performance of the model, anything along those lines is really important. Guardrails need to be in place. On the point of whether we need an independent agency, I mean, I think we don't want to slow down regulation to address real risks right now. We have existing regulatory authorities in place who have been clear that they have the ability to regulate in their respective domains. A lot of the issues we're talking about today span multiple domains - elections and the like. So, if I could, I'll just assert that those existing regulatory bodies and authorities are under-resourced and lack many of the statutes or regulatory powers that they need. We have failed to deliver on data privacy, even though industry has been asking us to regulate data privacy. If I might, Mr. Marcus, I'm interested in what international bodies are best positioned to convene multilateral discussions to promote responsible standards.

We've talked about a model being CERN and nuclear energy. I'm concerned about proliferation and non-proliferation. We've also talked about the IPCC, a UN body that helped provide a scientific baseline of what's happening in climate change so that even though we may disagree about strategies globally, we've come to a common understanding of what's happening and what should be the direction of intervention. I'd be interested, Mr. Marcus, if you could give us your thoughts on who's the right body internationally to convene a conversation and one that could also reflect our values. I'm still feeling my way on that issue. I think global politics is not my specialty. I'm an AI researcher, but I have moved towards policy in recent months because of my great concern about all of these risks. I think, certainly, the UN and UNESCO, with its guidelines, should be involved and at the table. Maybe things work under them, and maybe they don't, but they should have a strong voice and help to develop this. The OECD has also been thinking greatly about this. A number of organizations have internationally.

I don't feel like I personally am qualified to say exactly what the right model is. Well, thank you. I think we need to pursue this both at the national level and the international level. I'm the chair of the IP subcommittee of the Judiciary Committee. In June and July, we will be having hearings on the impact of AI on patents and copyrights. You can already tell from the questions of others, there will be a lot of interest. I look forward to following up with you about that topic.

I know, Mr. Chairman, I look forward to helping as much as possible.

Thank you very much. Thanks, Senator Coons. Senator Kennedy, thank you all for being here. Permit me to share with you three hypotheses that I would like you to assume for the moment to be true. Hypothesis number one: Many members of Congress do not understand artificial intelligence. Hypothesis number two: That absence of understanding may not prevent Congress from plunging in with enthusiasm and trying to regulate this technology in a way that could hurt this technology. Hypothesis number three: That I would like you to assume, there is likely a berserk wing of the artificial intelligence community that intentionally or unintentionally could use artificial intelligence to kill all of us and hurt us the entire time that we are dying. Assume all of those to be true. Please tell me in plain English two or three reforms/regulations, if any, that you would implement if you were queen or king for a day.

Ms. Montgomery, it comes back again to transparency and explainability in AI. We absolutely need to know and have companies attest. What do you mean by transparency? So, disclosure of the data that's used to train AI, disclosure of the model and how it performs, and making sure that there's continuous governance over these models. That we are the leading edge into governance foundation, technology governance, organizational governance, rules, and clarification that are needed. This is your chance, folks, to tell us how to get this right. Please use it. Right, I mean, I think again the rules should be focused on the use of AI in certain contexts. So, if you look at, for example, the EU AIS, it has certain uses of AI that it says are just simply too dangerous and will be outlawed. In these, okay, so we ought to first pass the law that says you can use AI for these uses but not others. Is that what you're saying? We need to define the highest risk usages. Is there anything else? And then, of course, requiring things like impact assessments and transparency, requiring companies to show their work, protecting data that's used to train AI in the first place as well.

Professor Marcus, if you could be specific, this was your shot, man, talking plain English, and tell me what, if any, rules we ought to implement. And please don't just use concepts. I'm looking for specificity.

Number one, a safety review, like we use with the FDA, prior to widespread deployment. If you can introduce something to 100 million people, somebody has to have their eyeballs on it. There you go, okay, that's a good one.

Number two, a nimble monitoring agency to follow what's going on, not just pre-review but also post as things are out there in the world, with authority to call things back which we've discussed today.

Number three would be funding geared towards things like AI Constitution, AI that can reason about what it's doing. I would not leave things entirely to current technology, which I think is poor at behaving in an ethical fashion and behaving in an honest fashion. So, I would have funding to try to basically focus on AI Safety Research

That term has a lot of complications in my field. There's both safety, let's say short-term and long-term, and I think we need to look at both rather than just funding models to be bigger, which is the popular thing to do. We need to find out to be more trustworthy because I want to hear from Mr. Alton. Mr. Altman, here's your shot. Thank you, Senator. Number one, I would form a new agency that licenses any effort above a certain scale of capabilities and can take that license away and ensure compliance with safety standards.

Number two, I would create a set of safety standards focused on what you said in your third hypothesis as the dangerous capability evaluations. One example that we've used in the past is looking to see if a model can self-replicate and self-exfiltrate into the wild. We can give your office a long other list of the things that we think are important there, but specific tests that a model has to pass before it can be deployed into the world. And then third, I would require independent audits, so not just from the company or the agency, but experts who can say the model is or isn't in compliance with these safety thresholds and these percentages of performance on question X or Y. Can you send me that information? We will do that. Would you be qualified to, if we promulgated those rules, to administer those rules? I love my current job. Cool. Are there people out there that would be qualified? We'd be happy to send you recommendations for people out there. Yes, okay. You make a lot of money, do you? I make no. I paid enough for health insurance. I have no equity in OpenAI, really? Yeah, that's interesting. You need a lawyer, and you know what, you need a lawyer or an agent. I'm doing this because I love it. Thank you, Mr. Chairman.

Thanks, Senator. Kennedy, Senator Corona, thank you, Mr. Chairman. Listening to all of your testifying, thank you very much for being here. Clearly, AI truly is a game-changing tool, and we need to get the regulation of this tool right. Because, for example, AI, it might have been GPT-4, it might have been, I don't know, one of the other entities, to create a song that my favorite band BTS, a favorite song that they would sing. Somebody else's song, but, you know, neither of the artists were involved in creating what sounded like a really genuine song. So you can do a lot. We also asked, can there be a speech created talking about the Supreme Court decision in Dobbs and the chaos that it created using my voice, my kind of voice, and it created a speech that was really good. It almost made me think about, you know, what do I need my staff for?

So don't worry, that's not very nervous laughter behind you. Okay, their jobs are safe, but there's so much that can be done. And one of the things that you mentioned, Mr. May Altman, that intrigued me was you said GPT-4 can refuse harmful requests. So you must have put some thought into how your system, if I can call it that, can refuse harmful requests. What do you consider a harmful request? You can just keep it short. Yeah, I'll give a few examples. One would be about violent content. Another would be about content that's encouraging self-harm. Another is adult content. Not that we think adult content is inherently harmful, but there are things that could be associated with that that we cannot reliably enough differentiate. So we refuse all of it.

So, those are some of the more blatant harmful forms of information. However, in the election context, for instance, I came across an image of former President Trump being arrested by the NY PD, and it quickly went viral. I'm unsure if that qualifies as harmful. I've encountered various statements attributed to any one of us that could be disseminated but may not meet your threshold for harmful content. Nonetheless, there it is.

Two of you mentioned the need for a licensing scheme. At present, I cannot envision or imagine the kind of licensing scheme we could establish to effectively regulate the vastness of this domain and its ever-evolving tools. Are you considering something along the lines of an FTC or FCC system? What do the two of you envision as a potential licensing scheme that would offer the necessary safeguards to protect our country from harmful content?

Regarding the first part of your statement, there are other factors beyond determining whether content should be generated that I find significant. For example, in the case of the mentioned image, if it was generated, it would be wise to mandate clear labeling in all contexts to indicate its generative nature. By doing so, the image remains accessible, but we would at least require individuals to acknowledge its generative origin.

Where I believe the licensing scheme becomes relevant is not for what these models can currently achieve, as you correctly pointed out, we don't need a new licensing agency for that. However, as we progress towards artificial general intelligence (AGI) and consider the resulting impact and power of that technology, it is crucial to treat it with the same level of seriousness as other highly potent technologies. That's why I personally believe we need a scheme of that nature. I concur that when discussing AGI, we are essentially addressing potential significant harms that can arise from its usage.

Professor Marcus, how would you envision a regulatory scheme? We cannot simply devise something to address future issues, particularly with AGI. So, what kind of scheme would you contemplate?

Well, first, if I may backtrack for a moment, I believe you've highlighted the central scientific issue concerning the challenges in constructing artificial intelligence. We lack the knowledge to build a system that comprehends harm in its complete breadth of meaning. Currently, we rely on gathering examples and comparing them to previously labeled ones, but this approach is insufficiently comprehensive. Hence, your thought-provoking question effectively highlights the challenge that AI itself must confront to genuinely address this matter. We aspire for AI itself to grasp the concept of harm, which may necessitate novel technologies. Thus, I consider this aspect to be highly important.

Regarding the second part of your inquiry, the model that I tend to favor (though I am not an expert in this area) is the FDA, at least in part. It could involve presenting a safety case, wherein the benefits outweigh the harms, to obtain a license. Perhaps multiple agencies would need to contribute elements to this scheme. I cannot provide expert insight in that regard, but I strongly emphasize the significance of the safety case. It is essential to have external reviewers who possess scientific qualifications examine the situation and determine if sufficient measures have been taken.

Allow me to provide a specific example that concerns me: Auto GPT.

This is not a creation of OpenAI, but rather a derivative of OpenAI's GPT-3 model called ChatGPT plugins. A few weeks later, this led to the development of an open-source software called Auto GPT. Auto GPT enables systems to access source code, the internet, and other resources, thereby introducing potential cybersecurity risks. An external agency should be responsible for providing reassurance that if this product is released, there will be no cybersecurity issues or that effective measures are in place to address them.

So, Professor, I am running out. Your model is similar to what the EU has come up with, but the vastness of AI and the complexities involved, I think would require more than just looking at its use. Based on what I'm hearing today, don't you think we're probably going to need to do a heck of a lot more than focusing on its use? For example, you can ask AI to come up with a funny joke, but you can also use the same tool to generate something related to election fraud. I don't know how you would make a determination based on the use model alone and how to distinguish between those kinds of uses. So, if we're going to move towards a licensing scheme, we'll need to carefully consider how to develop an appropriate scheme that provides the necessary future reference. I thank all of you for coming in and providing further food for thought. Thank you, Mr. Chairman.

Thanks very much, Senator Hirono. Senator Padilla?

Thank you, Mr. Chairman. I appreciate the flexibility as I've been back and forth to maintain this committee and the Homeland Security committee, where there's a hearing going on right now on the use of AI in government. So, it's AI day on the hill, or at least in the Senate, apparently. Now, for folks watching at home, if you never thought about AI until the recent emergence of generative AI tools, the developments in this space may feel like they've just happened all of a sudden. But the fact of the matter is, Mr. Chairman, they haven't. AI is not new, not for government, not for business, not for the public. In fact, the public uses AI all the time. And just for folks to be able to relate, I want to offer the example of anybody with a smartphone. Many features on your device leverage AI, including suggested replies when we're text messaging or even email autocorrect features, including but not limited to spelling in their email and text applications. So, I'm frankly excited to explore how we can facilitate positive AI innovation that benefits society while addressing some of the already known harms and biases that stem from the development and the use of the tools today.

Now, with language models becoming increasingly ubiquitous, I want to make sure that there's a focus on ensuring equitable treatment of diverse demographic groups. My understanding is that most research into evaluating and mitigating fairness harms has been concentrated on the English language, while non-English languages have received comparatively little attention or investment. And we've seen this problem before. Social media companies, for example, have not adequately invested in content moderation tools and resources for their non-English users. And I share this concern not just for non-US-based users but also because many US-based users prefer a language other than English in their communication.

So, I'm deeply concerned about repeating social media's failure in AI tools and applications. Question for Mr. Altman and Miss Montgomery: How are OpenAI and IBM ensuring language and cultural inclusivity in their large language models? And is it an area of focus in the development of your products?

Bias and equity in technology is a focus of ours and always has been. I think diversity in terms of the development of the tools and their deployment, having diverse people who are actually training those tools and considering the downstream effects as well. We're also very cautious and aware of the fact that we can't just articulate and call for these types of things without having the tools and the technology to test for bias and apply governance across the lifecycle of AI. We were one of the first teams and companies to put toolkits on the market, deploy them, and contribute them to open source. These toolkits help address technical aspects related to bias.

Can you speak specifically to language inclusivity?

Yes, I mean, language... So we don't have a consumer platform, but we are actively involved in ensuring that the technology we hope to deploy in the large language models we use to help our clients is focused on and available in many languages.

Thank you. At OpenAI, we think this is really important. One example is that we worked with the government of Iceland, which has fewer speakers than many of the languages well-represented on the internet, to ensure that their language was included in our model. We've had many similar conversations, and I look forward to many similar partnerships with lower resource languages to include them in our models. GPT-4 is unlike previous models of ours, which were good at English but not very good at other languages. It's now pretty good at a large number of languages. You can go pretty far down the list ranked by the number of speakers and still get good performance. But for these very small languages, we're excited about custom partnerships to include them in our model. As for the values and cultures, we're equally focused on that. We're excited to work with people who have particular datasets and to collect a representative set of values from around the world to ensure the system can be used within a wide range of bounds. I also appreciate what you said about wanting to make sure these systems benefit as wide a group as possible.

This technology can be a big lift up for historically underrepresented groups in technology and people who have not had as much access to technology around the world. There's a lot of follow-up work to do there.

In my time remaining, I do want to ask one more question: this committee and the public are right to pay attention to the emergence of generative AI. Now, this technology has a different opportunity and risk profile than other AI tools, and these applications have felt very tangible for the public due to the nature of the user interface and the outputs that they produce. But I don't think we should lose sight of the broader AI ecosystem as we consider AI's broader impact on society, as well as the design of appropriate safeguards.

So, Miss Montgomery, in your testimony, as you noted, AI is not... Can you highlight some of the different applications that the public and policymakers should also keep in mind as we consider possible regulations?

Yeah, I mean, I think the generative AI systems that are available today are creating new issues that need to be studied – new issues around the potential to generate content that could be extremely misleading, deceptive, and alike. So, those issues absolutely need to be studied, but we shouldn't also ignore the fact that AI is a tool. It's been around for a long time; it has capabilities beyond just generative capabilities. And again, that's why I think going back to this approach where we're regulating AI where it's touching people and society is a really important way to address it.

Thank you, Mr. Chair. Thanks, Senator. Senator Booker is next, but I think he's going to defer to Senator Ossoff.

It's a very big deal. I don't know if you... I have a meeting at noon, and I'm grateful to you, Senator Booker, for yielding your time. You are, as always, brilliant and handsome. And thank you to the panelists for joining us. Thank you to the subcommittee leadership for opening us up to all committee members.

If we're going to contemplate a regulatory framework, we're going to have to define what it is that we're regulating. So, Mr. Alban, any such law will have to include a section that defines the scope of regulated activities – technologies, tools, products. Just take a stab at it.

Yeah, thanks for asking, Senator. Also, I think it's super important. I think there are very different levels here, and I think it's important that any new approach, any new law, does not stop the innovation from happening with smaller companies, open-source models, researchers that are doing work at a smaller scale. That's a wonderful part of this ecosystem and of America. We don't want to slow that down. There still may need to be some rules there, but I think we could draw a line at systems that need to be licensed in a very intense way. The easiest way to do it – I'm not sure if it's the best, but the easiest would be to talk about the amount of compute that goes into such a model. So, we could divide, you know, we could define a threshold of compute that'll have to go... It'll have to change; it could go up or down. It could down as we discover more efficient algorithms. That says, above this amount of compute, you are in this regime. What I would prefer, it's hard to do, but I think more accurate, is to define some capability thresholds and say a model that can do things X, Y, and Z – up to all to decide – that's now in this licensing regime. But models that are less capable, you know, we don't want to stop our open-source community; we don't want to stop individual researchers; we don't want to stop new startups. They can proceed, you know, with a different framework.

Thank you, as concisely as you can, please state which capabilities you'd propose we'd consider for the purposes of this definition.

I would love, rather than to do that off the cuff, to follow up with your office with, like, well, perhaps openings to opine, understanding that you're just responding and you're not making law.

All right, in the spirit of just opining... I think a model that can persuade, manipulate, influence a person's behavior or a person's beliefs, that would be a good threshold. I think a model that could help create novel biological agents would be a great threshold, things like that. I want to talk about the predictive capabilities of a technology, and we have to think about a lot of very complicated constitutional questions that arise from it with massive datasets. The integrity and accuracy with which such technology can predict future human behaviors potentially pretty significant at the individual level, correct?

I think we don't know the answer to that for sure, but let's say it can, at least, have some impact there.

Okay, so we may be confronted by situations where, for example, a law enforcement agency deploying such technology seeks some kind of judicial consent to execute a search or to take some other police action on the basis of a modeled prediction about some individual's behavior. But that's very different from the kind of evidentiary predicate that normally police would take to a judge in order to get a warrant. Talk me through how you're thinking about that issue.

Yeah, I think it's very important that we continue to understand that these are tools that humans use to make human judgments and that we don't take away human judgment. I don't think that people should be prosecuted based on the output of an AI system, for example.

We have no national privacy law. Europe has rolled one out to mixed reviews. Do you think we need one?

I think it'd be good.

And what would be the qualities or purposes of such a law that you think would make the most sense based on your experience?

Again, this is very far out of my area of expertise. I think there are many, many privacy experts that could weigh in on what a law needs.

I'd still like... I'd still like you to weigh in.

Um, I mean, I think a minimum is that users should be able to, sort of, opt-out from having their data used by companies like ours or the social media companies. It should be easy to delete your data. I think those are... It should, but the thing that I think is important from my perspective, running an AI company, is that if you don't want your data used for training these systems, you have the right to do that. So let's think about how that will be practically implemented.

I mean, as I understand it, your tool and certainly similar tools, one of the inputs will be, sort of, scraping, for lack of a better word, data off the open web, right? As a low-cost way of gathering information, and there's a vast amount of information out there about all of us. How would such a restriction on the access or use or analysis of such data be practically implemented?

So, I was speaking about something a little bit different, which is the data that someone generates – the questions they ask our system, things that they input, they're training on that data that's on the public web, that's accessible, even if we don't train on that, the models can certainly link out to it. That was not what I was referring to. I think that, you know, there's ways to have your data or there should be more ways to have your data taken down from the public web. But certainly, models with web browsing capabilities will be able to search the web and link out to it.

When you think about implementing a safety or regulatory regime to constrain such software and to mitigate some risk, is your view that the federal government would make laws such that certain capabilities or functionalities themselves are forbidden in potential? In other words, one cannot deploy or execute code capable of X, yes? Or is it the act itself, X, only when actually executed?

Well, I think both. I'm a believer in defense in depth. I think that there should be limits on what a deployed model is capable of and then what it actually does too.

How are you thinking about how kids use your product?

Well, you have to be, I mean, you have to be 18 or up or have your parents' permission if you're 13 and up to use a product. But we understand that people get around those safeguards all the time, and so what we try to do is just design a safe product. And there are decisions that we make that we would allow if we knew only adults were using it, that we just don't allow in the product because we know children will use it some way or other. In particular, given how much these systems are being used in education, we want to be aware that that's happening.

I think what Senator Blumenthal and Senator Hawley, others on the subcommittee and I have seen repeatedly is that companies whose revenues depend upon volume of use, screen time, intensity of use, design these systems in order to maximize the engagement of all users, including children, with perverse results in many cases. And what I would humbly advise you is to get way ahead of this issue, the safety for children of your product, or I think you're going to find that Senator Blumenthal, Senator Hawley, others on the subcommittee, and I are going to look very harshly on the deployment of technology that harms children.

We couldn't agree more. I think we're out of time, but I'm happy to talk about that if I can respond.

Go ahead. What's up, Mr. Chairman? Okay. Um, first of all, I think we try to design systems that do not maximize for engagement. In fact, we're so short on GPUs, the less people use our products, the better. But we're not an advertising-based model. We're not trying to get people to use it more and more. Um, and I think that's a different shape than ad-supported social media.

Secondly, these systems do have the capability to influence in obvious and very nuanced ways, and I think that's particularly important for the safety of children, but that will impact all of us.

One of the things that we'll do ourselves, regulation or not, but I think a regulatory approach would be good for, also, is requirements about how the values of these systems are set and how these systems respond to questions that can cause influence. So we'd love to partner with you, couldn't agree more on the importance. Thank you, Mr. Chairman.

For the record, I just want to say that the senator from Georgia is also very handsome and brilliant too, but I will allow that comment to stand without objection. Without objection. Okay. Um, I'm now recognized. Thank you very much.

Thank you. It's nice that we finally got down to the ball guys down here at the end. Um, I just want to thank you both. This has been one of the best hearings I've had this Congress and, uh, just a testimony to you to seeing the challenges and the opportunities that AI presents. So I appreciate you both.

I want to just jump in, I think very broadly, and then I'll get a little more narrow. Uh, Sam, you said very broadly, technology has been moving like this, and we are a lot of people have been talking about regulation. And so I use the example of the automobile, what an extraordinary piece of technology. I mean, New York City did not know what to do with horse manure. They were having crises, forming commissions, and the automobile comes along, ends that problem. But at the same time, we have tens of thousands of people dying on highways every day, we have emissions crises and the like. There are multiple federal agencies, multiple federal agencies that were created or are specifically focused on regulating cars.

Um, and and so this idea that this equally transforming technology is coming, and for Congress to do nothing, which is not what anybody here is calling for, little or nothing, is obviously unacceptable. Uh, I really appreciate Senator Welsh and I have been going back and forth during this hearing, and him and Bennett have a bill talking about trying to regulate in this space. Not doing so for social media has been, I think, very destructive and allowed a lot of things to go on that are really causing a lot of harm. And so the question is, what kind of regulation? You all have spoken back to a lot of my colleagues.

Um, and and I want to say, Miss Montgomery, and I have to give full disclosure, I'm the child of two IBM parents. Um, uh, but I, you know, you talked about defining the highest-risk uses. We don't know all of them. We really don't. We can't see where this is going. Regulating at the point of risk, and you sort of called not for an agency, and I think when somebody else asks you to specify because you don't want to slow things down, we should build on what we have in place, but you can envision that we can try to work on two different ways that ultimately a specific like we have in cars EPA Nitza, the federal motor car carrier safety administration, all of these things, you can imagine something specific that is as Mr. Marcus points out, a nimble agency that could do monitoring. Other things you could imagine the need for something like that, correct? Oh, absolutely, yeah.

And so, uh, just for the record, then, in addition to trying to regulate with what we have now, you would encourage Congress and my colleague Senator Welsh to move forward in trying to figure out the right tailored agency to deal with what we know and perhaps things that might come up in the future. I would encourage Congress to make sure it understands the technology, has the skills and resources in place to impose regulatory requirements on the uses of the technology, and to understand emerging risks as well. So yes, yeah.

Mr. Marcus, there's no way to put this genie in the bottle globally. This is it's exploding. I appreciate your thoughts, and I shared some with my staff about your ideas of what the international context is, but there's there's no way to stop this moving forward. So with that understanding, just building on what Miss Montgomery said, what kind of encouragement do you have, as specifically as possible, to forming an agency, to using current rules and regulations? Can you just put some clarity on what you've already stated?

Let me just insert there are more genies yet to come from more bottles. Some genies are already out, but we don't have machines that can really, for example, self-improve themselves. We don't really have machines that have self-awareness, and we might not ever want to go there. So there are other genies to be concerned about.

Onto the main part of your question. I think that we need to have some international meetings very quickly with people who have expertise in how you grow agencies, in the history of growing agencies. We need to do that in the federal level, we need to do that in the international level. I'll just emphasize one thing I haven't as much as I would like to, which is that I think science has to be a really important part of it, and I'll give an example. We've talked about misinformation. We don't really have the tools right now to detect and label misinformation with nutrition labels that we would like to. We have to build new technologies for that. We don't really have tools yet to detect a wide uptick in cybercrime. Probably we probably need new tools there. We need science to probably help us figure out what we need to build and also what it is that we need to have transparency around and understood, understood.

Sam, just going to you for the little bit of time I have left, real quick. Um, first of all, you're a bit of a unicorn. When I sat down with you first, could you explain why non-profit? In other words, you're not looking at, and you've even capped the VC, people just really quickly, I want folks to understand that we started as a non-profit, really focused on how this technology was going to be built. At the time, it was very outside the Overton window that something like AGI was even possible. That shifted a lot. Um, we didn't know at the time how important scale was going to be, but we did know that we wanted to build this with Humanity's best interests at heart and a belief that this technology could, if it goes the way we want, if we can do some of those things for Professor Marcus mentioned, really deeply transform the world, and we wanted to be as much of a force for getting to a positive. I'm going to interrupt you. I think that's all good. I hope more of that gets out on the record.

The second part of my question as well. Um, I found it fascinating, uh, but are you ever gonna form a revenue model for return on your investors? Are you ever going to do ads or something like that? I wouldn't say never. I don't think, like, I think there may be people that we want to offer services to, and there's no other model that works. But I really like having a subscription-based model. Uh, we have API developers pay us, and we have one of my biggest concerns about this space is what I've already seen in the space of web 2 web 3 is this massive corporate concentration. It is really terrifying to see how few companies now control and affect the lives of so many of us, and these companies are getting bigger and more powerful.

And I see, you know, OpenAI backed by Microsoft, uh, anthropic is backed by Google. Google has its own in-house products, you know, bar. So I'm really worried about that, and I'm wondering if Sam, you can give me a quick acknowledgment. Are you worried about the corporate concentration in this space and what effect it might have, uh, um, uh, and the associated risks perhaps with market concentration in AI? And then Mr. Marcus, can you answer that as well? I think there will be many people that develop models. Uh, what's happening on the open-source community is amazing, but there will be a relatively small number of providers that can make models at the church. Um, I think there are benefits and danger to that. Like, as we were talking about all the dangers with AI, the fewer of us that you really have to keep a careful eye on on the absolute bleeding edge of capabilities, there's benefits there. But then I think there needs to be enough in there. Because there's so much value that consumers have choice, that we have different ideas.

Mr. Marcus, real quick, there is a real risk of a kind of technical technocracy combined with oligarchy, where a small number of companies influence people's beliefs through the nature of these systems. Again, I put something in the Wall Street Journal about how these systems can subtly shape our beliefs and have an enormous influence on how we live our lives. And having a small number of players do that with data that we don't even know about, that scares me. Sam, I'm sorry, one more thing I wanted to add. One thing that I think is very important is that what these systems get aligned to, whose values, what those bounds are, that is somehow set by society as a whole, by governments as a whole. And so creating that dataset, the alignment dataset, it could be, you know, an AI constitution, whatever it is, that has to come very broadly from society.

Thank you very much, Mr. Jeremiah Thomas expired, and I guess the best for last. Thank you, Senator Booker. Senator, well, uh, first of all, I want to thank you, Senator Blumenthal, and you, Senator Hawley. This has been a tremendous hearing. Uh, senators are noted for their short attention spans, but I've sat through this entire hearing and enjoyed every minute of it. You have one of our longer attention spans in the United States to your great credit. Well, we've had good witnesses, and it's an incredibly important issue. And here's just—I don't—all the questions I have have been asked, really, but here's a kind of a takeaway and what I think is the major question that we're going to have to answer as a Congress. Number one, you're here because AI is this extraordinary new technology that everyone says can be transformative as much as the printing press.

Number two is really unknown. What's going to happen, but there's a big fear you've expressed to all of you about what bad actors can do and will do if there are no rules of the road. Number three is a member who served in the House and now in the Senate. I've come to the conclusion that it's impossible for Congress to keep up with the speed of technology, and there have been concerns expressed about social media and now about AI that relates to fundamental privacy rights, bias rights, intellectual property, and the spread of disinformation, which in many ways, for me, is the biggest threat because that goes to the core of our capacity for self-governing.

There's the economic transformation, which can be profound through safety concerns. And I've come to the conclusion that we absolutely have to have an agency. What its scope of engagement is, it has to be defined by us. But I believe that unless we have an agency that is going to address these questions from social media and AI, we really don't have much of a defense against the bad stuff, and the bad stuff will come. So last year, I introduced in the House side, and Senator Bennett did incentives. It was the End of the Year Digital Commission Act, and we're going to be reintroducing that this year. And the two things that I want to ask, one you've somewhat answered because I think two of the three of you said you think we do need an independent commission.

You know, Congress established an independent commission when railroads were running rampant over the interests of farmers, when Wall Street had no rules of the road, and we had the SEC, and I think we're at that point now. But what the commission does would have to be defined and circumscribed. But also, there's always a question about the use of regulatory authority and the recognition that it can be used for good. JD Vance actually mentioned that when we were considering his and Senator Brown's Bill about railroads in that event in East Palestine, regulation for public health. But there's also a legitimate concern about regulation getting in the way of things being too cumbersome and being a negative influence. So, two of the three of you have said you think we do need an agency. What are some of the perils of an agency that we would have to be mindful of in order to make certain that its goals of protecting many of those interests I just mentioned (privacy, bias, intellectual property, disinformation) would be the winners and not the losers? And I'll start with you, Mr. Altman. Thank you, Senator.

One, I think America has got to continue to lead. This happened in America. I'm very proud that it happened in America, by the way. I think that's right, and that's why I'd be much more confident if we had our agency as opposed to getting involved in international discussions. Ultimately, you want the rules of the road, but I think if we lead and get rules of the road that work for us, that is probably a more effective way to proceed.

I personally believe there's a way to do both, and I think it is important to have the global view on this because this technology will impact Americans and all of us wherever it's developed. But I think we want America to lead, we want to get to the perils issue, though, because I know, well, that's one. I mean, that is a peril, which is you slow down American industry in such a way that China or somebody else makes faster progress. A second, and I think this can happen with the regulatory pressure, should be on us, it should be on Google, it should be on the other small set of people in the lead the most. We don't want to slow down smaller startups. We don't want to slow down open source efforts. We still need them to comply with things.

They can still cause great harm with a smaller model but leaving the room and the space for new ideas and new companies and independent researchers to do their work and not put in a regulatory burden to say a company like us could handle but a smaller one couldn't. I think that's another peril, and it's clearly a way that regulation has gone. The other obvious peril is regulatory capture. If we make it appear as if we are doing something, but it's more like greenwashing and nothing really happens, we just keep out the little players because we put so much burden that only the big players can do it.

So, there are also those kinds of perils. I fully agree with everything that Mr. Altman said, and I would add that to the list. Okay, thanks. Does the risk of not holding companies accountable for the harms that they're causing today, right? So, we talk about misinformation in electoral systems. So, no agency or no agency, we need to hold companies responsible today and accountable for AI that they're deploying that disseminates misinformation on things like elections and where the risk, you know, a regulatory agency would do a lot of the things that Senator Graham was talking about. You don't build a nuclear reactor without getting a license.

You don't build an AI system without getting a license that gets tested independently. I think it's a great analogy. We need both pre-deployment and post-deployment. Okay, thank you all very much. I yield back, Mr. Chairman. Thanks. Thanks, Senator Welsh. Let me ask a few more questions. You've all been very, very patient, and the turnout today, which is beyond our subcommittee, I think reflects both your value in what you're contributing as well as the interest in this topic. There are a number of subjects that we haven't covered at all, but one was just alluded to by Professor Marcus, which is the monopolization danger, the dominance of markets that excludes new competition and thereby inhibits or prevents innovation and invention, which we have seen in social media as well as some of the old industries (airlines, automobiles, and others) where consolidation has narrowed competition.

And so, I think we need to focus on kind of an old area of antitrust which dates more than a century. It's still inadequate to deal with the challenges we have right now in our economy, and certainly, we need to be mindful of the way that rules can enable the big guys to get bigger and exclude innovation and competition and responsible good guys such as are represented in this industry right now. We haven't dealt with national security. There are huge implications for national security. I will tell you as a member of the Armed Services Committee, classified briefings on this issue have abounded, and the threats that are posed by some of our adversaries, China has been mentioned here, but the sources of threats to this nation in this space are very real and urgent.

We're not going to deal with them today, but we do need to deal with them, and we will, hopefully, in this committee. And then on the issue of a new agency, you know, I've been doing this stuff for a while. I was Attorney General of Connecticut for 20 years. I was a federal prosecutor, U.S. attorney. Most of my career has been in enforcement, and I will tell you something. You can create 10 new agencies, but if you don't give them the resources, and I'm talking not just about dollars, I'm talking about scientific expertise, you guys will run circles around them.

And it isn't just the models or the generative AI that will run circles around them, but it is the scientists in your company. For every success story in government regulation you can think of, five failures, that's true of the FDA, it's true of the IAEA, it's true of the SEC, it's true of the whole alphabet list of government agencies. And I hope our experience here will be different, but the Pandora's Box requires more than just the words or the concepts licensing a new agency. There's some real hard decision making as Montgomery has alluded to about how to frame the rules to fit the risks. First, do no harm, make it effective, make it enforceable, make it real. I think we need to grapple with the hard questions here that, you know, frankly, this initial hearing, I think, has raised very successfully but not answered. And I thank our colleagues who have participated and made these very creative suggestions. I'm very interested in enforcement. I literally, 15 years ago, I think advocated abolishing Section 230.

What's old is new again. You know, now people are talking about abolishing Section 230. Back then, it was considered completely unrealistic. But enforcement really does matter. I want to ask Mr. Altman because of the privacy issue, and you've suggested that you have an interest in protecting the privacy of the data that may come to you or be available. How do you take specific steps to protect privacy? Well, one is that we don't train on any data submitted to our API. So if you're a business customer of ours and submit data, we don't train on it at all. We do retain it for 30 days solely for the purpose of trust and safety enforcement. But that's different than training on it. If you use ChatGPT, you can opt out of us training on your data. You can also delete your conversation history or your whole account. Miss Montgomery, I know you don't deal directly with consumers, but do you take steps to protect privacy as well? Absolutely, and we even filter our large language models for content that includes personal information that may have been pulled from public datasets as well. So we apply an additional level of filtering.

Um, Professor Marcus, you made reference to self-awareness and self-learning. Already, we're talking about the potential for jailbreaks. How soon do you think that new kind of generative AI will be usable? Will it be practical? New AI that is self-aware and so forth?

Yes, I mean, I have no idea on that one. I think we don't really understand what self-awareness is, and so it's hard to put a date on it. In terms of self-improvement, there's some modest self-improvement in current systems, but one could imagine a lot more, and that could happen in two years, it could happen in 20 years. The basic paradigms that haven't been invented yet, some of them we might want to discourage, but it's a bit hard to put timelines on them. And just going back to enforcement for one second, one thing that is absolutely paramount, I think, is far greater transparency about what the models are and what the data are.

That doesn't necessarily mean everybody in the general public has to know exactly what's in one of these systems, but I think it means that there needs to be some enforcement arm that can look at these systems, can look at the data, can perform tests, and so forth. Let me ask you all, I think there has been a reference to elections and banning outputs involving elections. Are there other areas where you think, what are the other high-risk or highest-risk areas where you would either ban or establish especially strict rules?

Miss Montgomery, the space around misinformation, I think, is hugely important. One and coming back to the points of transparency, you know, knowing what content was generated by AI is going to be a really critical area that we need to address. Any others?

I think medical misinformation is something to really worry about. We have systems that hallucinate things; they're going to hallucinate medical advice. Some of the advice they'll give is good, some of it's bad. We need really tight regulation around that.

Same with psychiatric advice, people using these things as ersatz therapists. I think we need to be very concerned about that. I think we need to be concerned about internet access for these tools. When they can start making requests both of people and internet things, it's probably okay if they just do search. But as they do more intrusive things on the internet, like do we want them to be able to order equipment or order chemistry and so forth? So as we empower these systems more by giving them internet access, I think we need to be concerned about that. And then we've hardly talked at all about long-term risk. Sam alluded to it briefly. I don't think that's where we are right now, but as we start to approach machines that have a larger footprint on the world, beyond just having a conversation, we need to worry about that and think about how we're going to regulate that and monitor it and so forth. In a sense, we've been talking about bad guys or certain bad actors manipulating AI to do harm, manipulating people and manipulating people, but also generative AI can manipulate the manipulators. It can, I mean, there are many layers of manipulation that are possible, and I think we don't yet really understand the consequences.

Dan Dennett just sent me a manuscript last night that will be in the Atlantic in a few days on what he calls counterfeit people. It's a wonderful metaphor. These systems are almost like counterfeit people, and we don't really, honestly, understand what the consequence of that is. They're not perfectly human-like yet, but they're good enough to fool a lot of the people a lot of the time, and that introduces lots of problems.

For example, cybercrime and how people might try to manipulate markets and so forth, so it's a serious concern. In my opening, I suggested three principles: transparency, accountability, and limits on use. Would you agree that those are a good starting point? Is Montgomery 100? And as you also mentioned, industry shouldn't wait for Congress. That's what we're doing here at IBM. There's no reason to absolutely wait for Congress. Yep, Professor Marcus. I think those three would be a great start. I mean, there are things like the White House Bill of Rights, for example, that show a large consensus. The UNESCO guidelines and so forth, a large consensus around what it is we need. And the real question is definitely now, how are we going to put some teeth in it, try to make these things actually enforce? So, for example, we don't have transparency yet. We all know we want it, but we're not doing enough to enforce it. Thanks for all that.

I certainly agree that those are important points. I would add that Professor Marcus touched on this. I would add that as we spend most of the time today on current risks, and I think that's appropriate, I'm very glad we have done it. As these systems become more capable, and I'm not sure how far away that is, but maybe not super far, I think it's important that we also spend time talking about how we're going to confront those challenges. I mean, talk to you privately. I know how much I care. I agree that you care deeply and intensely, but also that the prospect of increased danger or risk resulting from even more complex and capable AI mechanisms certainly may be closer than a lot of people appreciate. Let me just add for the record that I'm sitting next to Sam, closer than I've ever sat to him except once before in my life, and that his sincerity in talking about those fears is very apparent, um, physically, in a way that just doesn't communicate on the television screen, but communicates from here. Thank you, Senator Hawley.

Thank you again, Mr. Chairman, for a great hearing. Thanks to the witnesses. So, I've been keeping a little list here of the potential downsides or harms, risks of generative AI, even in its current form. Let's just run through it: loss of jobs, and this isn't speculative. I think your company, Miss Montgomery, has announced that it's potentially laying off 7,800 people, a third of your non-consumer-facing workforce, because of AI. So, loss of jobs, invasion of privacy, personal privacy on a scale we've never before seen, manipulation of personal behavior, manipulation of personal opinions, and potentially the degradation of free elections in America. Did I miss anything? I mean, this is quite a list.

I noticed that an eclectic group of about a thousand technology and AI leaders, everybody from Andrew Yang to Elon Musk, recently called for a six-month moratorium on any further AI development. Were they right? Do you join those calls? Are you right to do that? Should we pause for six months? Characterization is not quite correct. Um, I actually signed that letter. About 27,000 people signed it. Um, it did not call for a ban on all AI research, but only on a very specific thing, which would be systems like GPT-5. Um, every other piece of research that's ever been done, it was actually supportive or neutral about, and specifically called for more AI, specifically called for more research on trustworthy and safe AI. So you think, just so you think, that we should take a moratorium, a six-month moratorium or more on anything.

Beyond Chat GPT4, I took the letter. What is the famous phrase? Uh, spiritually, not literally. What was the famous phrase? Um, well, I'm asking for your opinion now. Though, so my opinion is that the moratorium that we should focus on is actually deployment until we have good safety cases. I'd don't know that we need to pause that particular project, but I do think its emphasis on focusing more on AI safety, on trustworthy, reliable AI is exactly right. Deployment means not making it available to the public. Yeah, so my concern is about things that are deployed at a scale of, let's say, 100 million people without any external review. I think that we should think very carefully about doing that.

What about you, Mr. Almond?

Do you agree with that? Would you pause any further development for six months or longer? Uh, so first of all, after we finish training GPT4, we waited more than six months to deploy it. Um, we are not currently training what will be GPT5. We don't have plans to do it in the next six months, but I think the frame of the letter is wrong. What matters is audits, red teaming, safety standards that a model needs to pass before training. If we pause for six months, then I'm not what we sure share what we do then. Do we pause for another six? Do we kind of come up with some rules? Then the standards that we have developed and that we've used for GPT4 deployment, uh, we want to build on those, but we think that's the right direction. Uh, not a calendar clock pause. There may be times, I expect there will be times when we find something that we don't understand and we really do need to take a pause, but we don't see that yet. Never mind all the benefits. You don't see what? Yeah, you're comfortable with all of the potential ramifications from the current existing technique. I'm sorry, we don't see the reasons to not train a new one for deploying.

As I mentioned, I think there's all sorts of risky behavior and there are limits. We put, we have to pull things back sometimes, add new ones. I meant we don't see something that would stop us from training the next model where we'd be so worried that we'd create something dangerous, even in that process, let alone the deployment. What about you, Miss Montgomery? I think we need to use the time to prioritize ethics and responsible technology as opposed to posing development. Well, wouldn't a pause in development help the development of protocols for safety standards and ethics? I'm not sure how practical it is to pause, but we absolutely should be prioritizing safety protocols. Okay, the point about practicality leads me to this. I'm interested in this talk about an agency, and you know, maybe that would work, although having seen how agencies work in this government, they usually get captured by the interests that they're supposed to regulate. They usually get controlled by the people who they're supposed to be watching. I mean, that's just been our history for 100 years. Maybe this agency would be different. I have a little different idea. Why don't we just let people sue you? Why don't we just make you liable in court? We know how to do that. We can pass a statute. We can create a federal right of action that will allow private individuals who are harmed by this technology to get into court and to bring evidence into court, and it can be anybody. I mean, you want to talk about crowdsourcing. We'll just open the courthouse doors. We'll define a broad right of action, private right of action, private citizens, be class actions. We'll just open it up. We'll allow people to go into court. We'll allow them present evidence.

They say that they were harmed by they were given medical misinformation, they were given election misinformation, whatever. Why not do that, Mr. Almond? I mean, please forgive my ignorance. Can't people sue us? Well, you're not protected by Section 230, but there's not currently a, I don't think, a federal right of action, private right of action that says that if you are harmed by generative AI technology, we will guarantee you the ability to get into court. Oh, well, I think there's, like, a lot of other laws where, if, you know, technology harms you, uh, there's standards that we could be sued under, unless I'm really misunderstanding how things work. Uh, if the question is, are more, are clearer laws about the specifics of this technology and consumer protection a good thing, I would say definitely yes. The laws that we have today were designed long before we had artificial intelligence, and I do not think they give us enough coverage. Uh, the plan that you propose, I think, is a hypothetical. It would certainly make a lot of lawyers wealthy, but I think it would be too slow to affect a lot of the things that we care about, and there are gaps in the law. For example, we don't really... wait, you think it'd be slower than Congress? Yes, I do, in some ways. Really? Well, you know, litigation can take a decade or more. Oh, I think the threat litigation is a powerful tool. I mean, how would I become, like, to be 100 million dollars in? No way. I'm not asking to take litigation off the table among the tools, but I think, for example, if I can continue... um, we, there are areas like copyright where we don't really have laws.

We don't really have a way of thinking about wholesale misinformation as opposed to individual pieces of it, where say a foreign actor might make billions of pieces of misinformation or a local actor. We have some laws around market manipulation we could apply, but we get in a lot of situations where we don't really know which laws apply. There would be loopholes. This system is really not thought through. In fact, we don't even know that 230 does or does not apply here, as far as I know. I think that that's something a lot of people speculated about this afternoon, but it's not solid. We could fix that. Well, the question is how? Oh, easy. You just... it would be easy for us to say that Section 230 doesn't apply to generative AI, and this one important, Miss Montgomery, a duty of care, which I think fits the idea of a private right of action. Now, that's exactly right. And also, AI is not a shield, right? So, if a company discriminates in granting credit, for example, or in the hiring process, by virtue of the fact that they relied too significantly on an AI tool, they're responsible for that today, regardless of whether they used a tool or a human to make that decision.

I'm going to turn to Senator Booker for some final questions, but I just want to make a quick point here on the issue of the moratorium. I think we need to be careful. The world won't wait. The rest of the global scientific community isn't going to pause. We have adversaries that are moving ahead, and sticking our head in the sand is not the answer. Safeguards and protections, yes, but a flat stop sign, sticking our head in the sand, I would be very, very worried. Without militating for any sort of pause, I would just again emphasize there is a difference between research, which surely we need to do to keep pace with our foreign rivals, and deployment at a really massive scale. You know, you could deploy things at a scale of a million people or 10 million people, but not 100 million people or a billion people. And if there are risks, you might find them out sooner and be able to close the barn doors before the horses leave, rather than after. Senator Booker, yeah, I just, there will be no pause. I mean, there's no enforcement body to force a pause. It's just not going to happen. It's nice to call for it for any just reasons or whatsoever, but I'm just, forgive me for sounding skeptical. Nobody's pausing this thing. It's crazy.

I would agree, and I don't think it's a realistic thing in the world. The reason I personally signed the letter was to call attention to how serious the problems were and to emphasize spending more of our efforts on trustworthy and safe AI rather than just making a bigger version of something we already know to be unreliable. Yeah, so, I'm a futurist. I love the excitement about the future, and I guess there's a famous question: if you couldn't control for your race, your gender, where you would land on the planet Earth, or what time in humanity would you want to be born?

Everyone would say right now. It's still the best time to be alive because of technology innovation and everything. And I'm excited about what the future holds, but the destructiveness that I've also seen as a person that's seen the transformative technologies of, uh, of a lot of the technologies of the last 25 years is what really concerns me. And one of the things, especially with, um, companies that are designed to want to keep my attention on screens, and I'm not just talking about new media, I, 24-hour cable news is a great example of people that want to keep your eyes on screens. I have a lot of concerns about the corporate intention. And, Sam, this is again why I find your story so fascinating to me and your values that I believe in from our conversations so compelling to me. But perhaps in that, I really want to just explore what happens when these companies that are already controlling so much of our lives.

A lot has been written about the Fang companies. What happens when they are the ones that are dominating this technology, as they did before? So, Professor Marcus, does that have any concern? The role that corporate power, corporate concentration has in this realm? That a few companies might control this whole area? I radically changed the shape of my own life in the last few months, and it was because of what happened with Microsoft releasing Sydney.

And it didn't go the way I thought it would, in one way it did, which, as I anticipated, the hallucinations. I wrote an essay, which I have in the appendix "What to Expect When You're Expecting GPT-4," and I said that it would still be a good tool for misinformation, that it would still have trouble with physical reasoning, psychological reasoning, that it would hallucinate. And then along came Sydney, and the initial press reports were quite favorable. And then there was the famous article by Kevin Roos in which he recommended he get a divorce.

And I had seen Tay and I had seen Galactica from Meta, and those had been pulled after they had problems. And Sydney clearly had problems. What I would have done, had I run Microsoft, which clearly I do not, would have been to temporarily withdraw it from the market. And they didn't, and that was a wake-up call to me and a reminder that even if you have a company like OpenAI that is a non-profit and Sam's values, I think, have become clear today, other people can buy those companies and do what they like with them. And you know, maybe we have a stable set of actors now, but the amount of power that these systems have to shape our views and our lives is really, really significant. And that doesn't even get into the risks that someone might repurpose them deliberately for all kinds of bad purposes. So, in the middle of February, I stopped writing much about technical issues in AI, which is most of what I've written about for the last decade, and said, "I need to work on policy. This is frightening."

Sam, I want to give you an opportunity. It's my sort of last question or so. Do you, don't you have concerns about, I mean, you, I graduated from Stanford, the I know so many of the players in the valley, from VC people, folks, angel folks, to a lot of founders of companies that we all know. Do you have some concern about a few players with extraordinary resources and power, power to influence Washington? I mean, I see us, I love, I'm a big believer in the free market, but the reason why I walk into a bodega and a Twinkie is cheaper than an apple or a Happy Meal costs less than a bucket of salad is because of the way the government tips the scales to pick winners and losers. So the free market is not what it should be when you have large corporate power that can even influence the game here. Do you have some concerns about that in this next era of technological innovation?

Yeah, I mean, again, that's so much of why we started OpenAI. We have huge concerns about that. I think it's important to democratize the inputs to these systems, the values that we're going to align to. And I think it's also important to give people wide use of these tools. When we started the API strategy, which is a big part of how we make our systems available for anyone to use, there was a huge amount of skepticism over that. And it does come with challenges, that's for sure. But we think putting this in the hands of a lot of people and not in the hands of a few companies is really quite important.

And we are seeing the resultant innovation boom from that. But it is absolutely true that the number of companies that can train the true frontier models is going to be small, just because of the resources required. And so, I think there needs to be incredible scrutiny on us and our competitors. I think there is a rich and exciting industry happening of incredibly good research and new startups that are not just using our models but creating their own. And I think it's important to make sure that whatever regulatory stuff happens, whatever new agencies may or may not happen, we preserve that fire because that's critical. I'm a big believer in the democratizing potential of technology, but I've seen the promise of that fail time and time again where people say, "Oh, this is going to have a big democratizing force." My team works on a lot of issues about the reinforcing of bias through algorithms, the failure to advertise certain opportunities in certain zip codes. But you seem to be saying, and I heard this with Web3, that this is going to be deified, decentralized finance, all these things are going to happen. But this seems to me not even to offer that promise because the people who are designing these, it takes so much power, energy, resources. Are you saying that my dreams of technology further democratizing opportunity and more are possible within a technology that is ultimately, I think, going to be very centralized to a few players who already control so much?

This point that I made about use of the model and building on top of it as a new platform, right? It is definitely important to talk about who's going to create the models. I want to do that. I also think it's really important to decide whose values we're going to align these models with. But in terms of using the models, the people that build on top of the OpenAI API do incredible things. And it's, you know, people frequently comment like, "I can't believe you get this much technology for this little money." And so, what people and the companies people are building, putting AI everywhere, using our API, which does let us put safeguards in place, I think that's quite exciting. And I think that is how it is being democratized right now.

There is a whole new Cambrian explosion of new businesses, new products, new services happening by lots of different companies on top of these models. So, I'll say, Chairman, as I close, that I have seen most industries resist even reasonable regulation, from seatbelt laws to, we've been talking a lot recently about rail safety. The only way we're going to see the democratization of values, I think, and while there are noble companies out there, is if we create rules of the road that enforce certain safety measures, like we've seen with other technology. Thank you. Thanks, Senator Booker. And I couldn't agree more that, in terms of consumer protection, which I've been doing for a while, participation by the industry is tremendously important, and not just rhetorically, but in real terms. Because we have a lot of industries that come before us and say, "Oh, we're all in favor of rules, but not those rules. Those rules we don't like." And it's every rule, in fact, that they don't like. And I sense that there is a willingness to participate here that is genuine and authentic. I thought about asking Chat GPT to do a new version of "Don't Stop Thinking About Tomorrow" because that's what we need to be doing here.

And as Senator Hawley has pointed out, Congress doesn't always move at the pace of technology. And that may be a reason why we need a new agency. But we also need to recognize the rest of the world is going to be moving as well. And you've been enormously helpful in focusing us and illuminating some of these questions and performed a great service by being here today. So thank you to everyone, all of our witnesses. And I'm going to close the hearing, leave the record open for one week in case anyone wants to submit anything. I encourage any of you who have either manuscripts that are going to be published or observations from your companies to submit them to us. And we look forward to our next hearing. This one is closed.

The lead image for this article was generated by HackerNoon's AI Image Generator via the prompt "A government hearing with robots in the audience"