Title: OpenAI CEO Sam Altman testifies on artificial intelligence before Senate committee
Link: https://youtu.be/P_ACcQxJIsg

Transcript:
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 chat GPT when it was asked how I would open this hearing. And you heard just now the result. I asked chat GPT, 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 chat GPT's endorsement. In all seriousness, this apparent reasoning is pretty impressive. I am sure that we'll look back in a decade and view chat GPT 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's 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, DALI, and other AI tools. These examples, like the homework done by chat GPT or the articles and op-eds that it can write, feel like novelties. But the underlying advancement 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, 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 and impersonation fraud, voice cloning, deep fakes. 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 Chat GPT, this is not necessarily the future that we want. We need to maximize the good over the bad. Congress has a choice now. We had the same choice when we faced social media. We failed to seize that moment. The result is predators on the internet, toxic content, exploiting children, creating dangers for them, 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 technology, 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 restriction or even ban their use, especially when it comes to commercial invasions of privacy for profit and decisions that affect people's livelihoods. And of course, accountability or liability. 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 there are ideas and feedback from this discussion and from the industry and 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 chat GPT and generative AI, these large language models, it's really like the invention of the internet in scale, at least, 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? 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 host of other AI capacities that are even right now underdeveloped, but not just in this country, but in China, 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? And I hope that today's hearing will take us a step closer to that answer. Thank you, Mr. Chairman. Thanks, Senator Hawley. I'm gonna 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 this committee, 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 the 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, unanimous roll calls. I can't remember another time when we've done that in 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 always marvel when they show the phones that he 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 this 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. And the second starting point is one that's humbling, and that is the fact when you look at the record of Congress in dealing with innovation, technology, and rapid change, we're 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, for a website 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 is taking place, and I think it's important for all of us to participate. I'm glad that it's a bipartisan approach. We're gonna have to scramble to keep up with the pace of innovation in terms of our government public response to it, but this is a great start. Thank you, Mr. Chairman. Thanks, thanks, Senator Urban. It is very much a bipartisan approach, very deeply and broadly bipartisan. And in that spirit, I'm gonna turn to my friend, Senator Graham. Thank you. That was not written by AI, for sure. I'm sorry. I'm sorry. I'm sorry. I'm sorry. I'm sorry. Let me introduce now 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 CHAT, GPT, and DALLY. Mr. Altman was President of the early-stage startup accelerator Y Combinator from 1914, I'm sorry, 2014 to 2019, OpenAI was founded in 2015. Christina Montgomery is IBM's vice president and 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 multidisciplinary 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 governments. And Ms. Montgomery also 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 the Robust AI and Geometric AI, acquired by Uber, if I'm not mistaken, and 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. And 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 gonna begin with you, if that's okay. Thank you. 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. OpenAI was founded on the belief that artificial intelligence has the potential to improve nearly every aspect of our lives, but also that it creates serious risks we have to work together to manage. We're here because people love this technology. We think it can be a printing press moment. We have to work together to make it so. OpenAI is an unusual company, and we set it up that way because AI is an unusual technology. We are governed by a nonprofit, and our activities are driven by our mission and our charter, which commit us to working to ensure that the broad distribution of the benefits of AI and to maximizing the safety of AI systems. We are working to build tools that one day can help us make new discoveries and address some of humanity's biggest challenges, like climate change and curing cancer. Our current systems aren't yet capable of doing these things, but it has been immensely gratifying to watch many people around the world get so much value from what these systems can already do today. We love seeing people use our tools to create, to learn, to be more productive. We're very optimistic that they're going to be fantastic jobs in the future and that current jobs can get much better. We also love seeing what developers are doing to improve lives. For example, Be My Eyes used our new multimodal technology in GPT-4 to help visually impaired individuals navigate their environment. We believe that the benefits of the tools we have deployed so far vastly outweigh the risks, but ensuring their safety is vital to our work, and we make significant efforts to ensure that safety is built into our systems at all levels. Before releasing any new system, OpenAI conducts extensive testing, engages external experts for detailed reviews and independent audits, improves the model's behavior, and implements robust safety and monitoring systems. Before we released GPT-4, our latest model, we spent over six months conducting extensive evaluations, external red teaming, and dangerous capability testing. We are proud of the progress that we made. GPT-4 is more likely to respond helpfully and truthfully and refuse harmful requests than any other widely deployed model of similar capability. However, we think that regulatory intervention by governments is a good way to protect our systems and we think that regulatory intervention by governments will be critical to mitigate the risks of increasingly powerful models. For example, the US government might consider a combination of licensing and testing requirements for development and release of AI models above a threshold of capabilities. There are several other areas I mentioned in my written testimony where I believe that companies like ours can partner with governments, including ensuring that the most powerful AI models adhere to a set of safety requirements, facilitating processes to develop and update safety measures, and examining opportunities for global coordination. And as you mentioned, I think it's important that companies have their own responsibility here, no matter what Congress does. This is a remarkable time to be working on artificial intelligence. But as this technology advances, we understand that people are anxious about how it could change the way we live. We are too. But we believe that we can and must work together to identify and manage the potential downsides so that we can all enjoy the tremendous upsides. It is essential that powerful AI is developed with democratic values in mind, and this means that US leadership is critical. I believe that we will be able to mitigate the risks in front of us and really capitalize on this technology's potential to grow the US economy and the world. And I look forward to working with you all to meet this moment, and I look forward to answering your questions. Thank you. Thank you, Mr. Altman. Ms. Montgomery. Chairman Blumenthal, ranking member Hawley, and members of the subcommittee, thank you for today's opportunity to present. AI is not new, but it's certainly having a moment. Recent breakthroughs in generative AI and the technology's dramatic surge in the public attention has rightfully raised serious questions at the heart of today's hearing. What are AI's potential impacts on society? What do we do about bias? What about misinformation, misuse, or harmful content generated by AI systems? Senators, these are the right questions, and I applaud you for convening today's hearing to address them head on. While AI may be having its moment, the moment for government to play a role has not passed us by. This period of focused public attention on AI is precisely the time to define and build the right guardrails to protect people and their interests. But at its core, AI is just a tool, and tools can serve different purposes. To that end, IBM urges Congress to adopt a precision regulation approach to AI. This means establishing rules to govern the deployment of AI in specific use cases, not regulating the technology itself. Such an approach would involve four things. First, different rules for different risks. The strongest regulation should be applied to use cases with the greatest risks to people and society. Second, clearly defining risks. There must be clear guidance on AI uses or categories of AI-supported activity that are inherently high-risk. This common definition is key to enabling a clear understanding of what regulatory requirements will apply in different use cases and contexts. Third, be transparent, so AI shouldn't be hidden. Consumers should know when they're interacting with an AI system, and that they have recourse to engage with a real person should they so desire. No person anywhere should be tricked into interacting with an AI system. And finally, showing the impact. For higher-risk use cases, companies should be required to conduct impact assessments that show how their systems perform against tests for bias and other ways that they could potentially impact the public and to attest that they've done so. By following risk-based use case-specific approach at the core of precision regulation, Congress can mitigate the potential risks of AI without hindering innovation. But businesses also play a critical role in ensuring the responsible deployment of AI. Companies active in developing or using AI must have strong internal governance, including, among other things, designating a lead AI ethics official responsible for an organization's trustworthy AI strategy, standing up an ethics board or a similar function as a centralized clearinghouse for resources to help guide implementation of that strategy. IBM has taken both of these steps, and we continue calling on our industry peers to follow suit. Our AI ethics board plays a critical role in overseeing internal AI governance processes. Creating reasonable guardrails to ensure we introduce technology into the world in a responsible and safe manner. It provides centralized governance and accountability while still being flexible enough to support decentralized initiatives across IBM's global operations. We do this because we recognize that society grants our license to operate. And with AI, the stakes are simply too high. We must build, not undermine the public trust. The era of AI cannot be another era of move fast and break things. But we don't have to slam the brakes on innovation either. These systems are within our control today, as are the solutions. What we need at this pivotal moment is clear, reasonable policy and sound guardrails. These guardrails should be matched with meaningful steps by the business community to do their part. Congress and the business community must work together to get this right. The American people deserve no less. Thank you for your time and I look forward to your questions. Thank you, Professor Marcus. Thank you, Senator.
Today's meeting is historic. I'm profoundly grateful to be here. I come as a scientist, someone who's founded AI companies, and as someone who genuinely loves AI, but who is increasingly worried. There are benefits, but we don't yet know whether they will outweigh the risks. Fundamentally, these new systems are going to be destabilizing. They can and will create persuasive lies at a scale humanity has never seen before. Outsiders will use them to affect our elections. Insiders to manipulate our markets and our political systems. Democracy itself is threatened. Chatbots will also clandestinely shape our opinions, potentially exceeding what social media can do. Choices about data sets that AI companies use will have enormous unseen influence. Those who choose the data will make the rules, shaping society in subtle but powerful ways. There are other risks too, many stemming from the inherent unreliability of current systems. A law professor, for example, was accused by a chatbot of sexual harassment, untrue, and it pointed to a Washington Post article that didn't even exist. The more that that happens, the more that anybody can deny anything. As one prominent lawyer told me on Friday, defendants are starting to claim that plaintiffs are making up legitimate evidence. These sorts of allegations undermine the abilities of juries to decide what or who to believe and contribute to the undermining of democracy. Poor medical advice could have serious consequences too. An open source large language model recently seems to have played a role in a person's decision to take their own life. The large language model asked the human, if you wanted to die, why didn't you do it earlier? And then followed up with, were you thinking of me when you overdosed? Without ever referring the patient to the human help that was obviously needed. Another system rushed out and made available to millions of children told a person posing as a 13-year-old how to lie to her parents about a trip with a 31-year-old man. Further threats continue to emerge regularly. A month after GPT-4 was released, OpenAI released chat GPT plugins, which quickly led others to develop something called auto GPT with direct access to the internet, the ability to write source code and increased powers of automation. This may well have drastic and difficult to predict security consequences. What criminals are gonna do here is to create counterfeit people. It's hard to even envision the consequences of that. We have built machines that are like bulls in a China shop, powerful, reckless, and difficult to control. We all more or less agree on the values we would like for our AI systems to honor. We want, for example, for our systems to be transparent, to protect our privacy, to be free of bias, and above all else, to be safe. But current systems are not in line with these values. Current systems are not transparent. They do not adequately protect our privacy, and they continue to perpetuate bias. And even their makers don't entirely understand how they work. But we cannot remotely guarantee that they're safe, and hope here is not enough. The big tech companies' 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 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. And 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 these, independent scientists, 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 consequence. 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 gonna 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 wanna avoid those past mistakes, as I indicated in my opening statement, that were 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, here are 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. 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. I worry that as the models get better and better, 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 often give them credit for in conversations like this. I think a lot of disclosures, which if you've used ChatGBT, you'll see about the inaccuracies of the model, are also important. And I'm excited for a world where companies publish, with the models, 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 gonna 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 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 that 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 in how they use it. And second, GPT-4 and 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 reaction to those questions as well. Ms. 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. We do believe that AI, and we've said it for a long time, is gonna 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 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 skills build platform has seven million learners and over 1,000 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? May I 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's some technical challenges and 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 data set, 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 we 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, that new jobs, new professions come in as new technologies come in. I think this one's gonna be different, and the real question is over what time scale? Is it gonna be 10 years? Is it gonna 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. 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. It's reasoning was ability, reasoning abilities were limited. But when we get to AGI, artificial general intelligence, maybe let's say it's 50 years, that really is gonna have, I think, profound effects on labor. And there's just 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 gonna ask Mr. Altman if he cares to respond. Yeah, 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 gonna 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. My worst fears are that we cause significant, we the field, the technology, the industry, cause significant harm to the world. 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. Thank you. Senator Hawley. Thank you again, Mr. Chairman. Thanks to the witnesses for being here. Mr. Altman, I think you grew up in St. Louis. I did. If I'm not mistaken, it's great to see a fellow- Missouri's a great place. Missourian here. It is, thank you. I want that noted, especially underlining the record. Missouri is a great place. That is the takeaway from today's hearing. Maybe we should stop there, Mr. Chairman. Let me ask you, Mr. Altman, I think I'll start with you. And I'll just preface this 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 this generative AI, particularly the large language models, what it can do. So I'm trying to understand its capacities and then its significance. So I'm looking at a paper here entitled, Large Language Models Trained on Media Diets Can Predict Public Opinion. This is just posted about a month ago. The authors are Chu, Andreas, Anselaberry, and Roy. And their conclusion, this work was done at MIT and then also at Google. Their 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 subpopulation specific media diets. So 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 wanna 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, ahead of time, I mean, predict, it's 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 come to enormous effect on an undecided voter. This, of course, is orders of magnitude, far more powerful, far more significant, 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, thank you, Senator Hawley, for the question. It's one of my areas of greatest concern, the more general ability of these models to manipulate, to persuade, to provide sort of one-on-one, interactive disinformation. I think that's like a broader version of what you're talking about, but given that we're gonna 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 content that they're looking at might be generated or might not. I think it's a great thing to do, is to make that clear. I think we also will need rules, 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, for a while, people were really quite fooled by Photoshop 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, 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. Professor Marcus, do you 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 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 gonna 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 on. 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, et cetera, collect on all of us routinely. And we've had many a chat about this in this committee over many a year now. But the massive amounts of data, 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, 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, that's 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 heretofore not even 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, open AI does not, we're not off, we don't 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'd 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 that's already happening in many ways. Mr. Marcus, anything you wanna add? Hyper, yes, and perhaps Ms. Montgomery will want to as well, I don't know. But hyper-targeting of advertising is definitely going to come. I agree that that's not been open AI'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 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, 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. 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, a Data och It was long before that everything was made easy,
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 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 that we were basically going to absolve the industry from liability for a period of time as it came into being. Well, Mr. Altman on the podcast earlier this year, you agreed with host Kara 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 get out of the way. This is a new industry, don't overregulate 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 and the harms that they've created. Problems that we've seen demonstrated in this committee, child exploitation, cyber bullying, online drug sales and more. I don't want to repeat that mistake again. What I hear is the opposite suggestion from the private sector. That is, come in and affront 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 we've 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 responsible and clear way, that people, we've taken principles around that, trust and transparency, we call them, are principles that were articulated years ago and build them into practices. That's why we're here advocating for 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 lot of people, maybe not as smart as we think we are many times. And government certainly has a capacity to do amazing things. But when you talk about our ability to respond to the current challenge and perceived 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. As you 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 know if 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, the FCC, 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 that's 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 the point I wanted to go to next, and that is the fact that I'll get inside from the CERN and nuclear examples because 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 US agency, and I hope that we do, that may have jurisdiction over US corporations and US 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 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. 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, 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 for each jurisdiction, every company has to train another model. Maybe different states are different. Missouri and California have different rules. So then that requires even more training of these expensive models with huge climate impact. It would be very difficult for the companies to operate if there was no global coordination. 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. It is entirely possible that we could develop such a thing, but obviously there are many 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. 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. There are – given what it takes to make these models, the chip supply chain, the sort of limited number of competitive GPUs, the power the U.S. has over these companies, I think there are paths to the U.S. 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. Thanks, Senator Durbin. And in fact, I think we're going to hear more about what Europe is doing. The European Parliament already is acting on an AI act. 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 ChatGPT account, should Congress regulate AI, ChatGPT, and it gave me four pros, four cons, and says, ultimately, the decision rests with Congress and deserves careful consideration. So on that, you know, it was very balanced. I recently visited with the Nashville Technology Council. I represent Tennessee. And of course, you had people there from health care, financial services, logistics, educational entities, and they're concerned about what they see happening with AI, with the utilizations for their companies. Ms. Montgomery, you know, similar to you, they've got healthcare people are looking at disease analytics. They're looking at predictive diagnosis, how this can better the outcomes for patients, logistics industry, 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, yes, Professor Marcus, as you were saying, the EU, different entities are ahead of us in this, but we have never established a federally 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 Commerce Committee, Judiciary Committee decide how we move forward so that people own their virtual you. And Mr. Altman, I was glad to see last week that your open AI models are not going to be trained using consumer data. I think that 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 there 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 sort of 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... Then let me ask you this. How do you compensate the artist? That's exactly what I was going to say. 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 that has worked in the area of radio? 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. 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... Unlike ChatGPT or Dolly... Yeah, but we've lived through Napster. Yes. That was something that really cost a lot of artists a lot of money. Oh, I understand. Yeah, for sure. In the digital distribution era. I don't know the numbers on Jukebox on the top of my head as a research release. I can follow up with your office, but Jukebox is not something that gets much attention or usage. It was put out to show that something's possible. Well, Senator Durbin just said, 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 a 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 then, 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 a 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? So 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 toward getting something that we can use. Yeah, I think strong privacy... My time's expired. Let me yield back. Thank you, Mr. Chairman. Thanks, Senator Blackburn. Senator Klobuchar. Thank you very much, Mr. Chairman. And Senator Blackburn, I love Nashville, love Tennessee, love your music, but I will say I use chat GPT and just ask what are the top creative song artists of all time, and two of the top three were from Minnesota. That would be Prince and Bob Dylan. Okay. All right. 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, and 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's 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 in on what my job, 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 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's 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. 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. There's a lot that we can and do do there. There's things that the model refuses to generate. We have policies. We also importantly 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 think what happened in the past with Russian interference and the like, it's just going to be a tip of the iceberg when 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, and I have serious concerns about that. But news content. So 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 US newspapers that roughly existed two decades are going to be gone by 2025, unless you start compensating for everything from movies, books, 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, that report on your high school fall scores and 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 broadcasts. But do you understand that this could be exponentially worse in terms of local news content if they're not compensated? 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. One last thing. May I add something there? Yeah, but let me just ask you a question. You can combine them quick. 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 of 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 his, the question. 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 has a study. I'm 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 sort of 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 generated. Thank you. And thank you on 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've 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. And 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 laws that exist today. Mr. Altman, 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 even the right framework. Okay, so under the law it exists today, this tool you create, if I'm harmed by it, can I sue you? That is beyond my area of legal expertise. Have you ever been sued? Not for that, no. Have you ever been sued at all, your company? Yeah, open AI gets 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 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 anything like 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 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. Altman, that these tools you're creating should be licensed? Yeah, we've been calling for this. That's the simplest way. You get a license, and do you agree with me that 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. You agree with that, Ms. 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's working. Regulators. Wait a minute, nope, nope, nope. 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 the issues. Okay, wait a minute, 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 regulate. That's 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're not a platform company, and we've, again, 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 think we have existing. Is this 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 the domains 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 it 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 an agency. It's a major tool to be used by a lot of people, right? It's a major new technology. We think it'll be. Yeah, if you make a ladder and the ladder doesn't work, you can sue the people that made the ladder, but there's 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 that we need to empower an agency that issues in 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? Clearly that should be part of what an agency can do. Now, and you also agree that China's 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 global effect. You know, because other people doing this, I got 15. Military application. How can AI change the warfare? And you got one minute. I got one minute? All right, this is, that's a tough question for one minute. This is very far out of my area of expertise, but I- I'll give you one example, a drone. Can a drone, you can plug into a drone the coordinates and it can fly out and 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. Thanks, Senator Graham. Senator Coon. Thank you, Senator Blumenthal, Senator Hawley, for convening this hearing, for working closely together to come up with this compelling panel of witnesses and beginning a series of hearings on this transformational 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 open AI 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, like why we put these systems out into the world. There's the obvious answer about there's benefits and people are 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 in our institutions and you all time to come to grips with this technology, to understand it, to find its limitations, its 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. And 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 again, 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 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- Could focus briefly on whether or not a constitutional model that gives values would be worth it. I was just about to get there. All right, sorry about that. Yeah, I think giving the models values upfront is an extremely important set. You know, RLHF is another way of doing that same thing, but somehow or other you are with synthetic data or human generated data. You're saying, 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, you know, if you want value system over here or value system over there. We think that's very important. There's multiple technical approaches, but we need to give policymakers and the world as a whole the tools to say, here's 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 of democratic values and the institutions that we have. The Chinese are insisting that AI, as being developed in China, reinforce the core values of the Chinese Communist Party and the Chinese system. And 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. And the EU is moving ahead with an AI act which categorizes AI products based on 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? Yeah, I mean, the conception of the EU AI Act is very consistent with this 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. So 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. And on the point, just come back to the question 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, right? So 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 statutory 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 also 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, I would suggest that the IPCC, a UN body, helped at least 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 just 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, really, because of my great concern about all of these risks. I think certainly the UN, UNESCO has its guidelines, should be involved and at the table and maybe things work under them and maybe they don't, but they should have a strong voice and help to develop this. The OACD 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 there. 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'll be a lot of interest. I look forward to following up with you about that topic. I know Mr. Chairman, I'm a local. 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. Apothesis 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. Thank you. Ms. Montgomery. I think 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, and how it performs, and making sure that there's continuous governance over these models, that we are the leading edge in terms of that regulation. Technology governance, organizational governance, rules and clarification that are needed that this Congress. I mean, this is your chance, folks, to tell us how to get this right. Please use it. 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 eight. So if you look at the EU AI Act, it has certain uses of AI that it says are just simply too dangerous and will be outlawed in the EU. Okay, so we ought to first pass a 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 uses of AI. 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 is your shot, man. Talk in plain English and tell me what, if any, rules we ought to implement. And at least 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're gonna introduce something to 100 million people, somebody has to have their eyeballs on it. There you go, okay. That's a good one. I'm not sure I agree with it, but that's a good one. What else? You didn't ask for three that you would agree with. 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. And 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 ethical fashion and behaving in honest fashion. And 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 fund models to be more trustworthy. Because I'm gonna 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 you, 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 stated 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 open AI. Really? Yeah. That's interesting. You need a lawyer. I need a what? You need a lawyer or an agent. I'm doing this because I love it. Thank you, Mr. Chairman. Thanks, Senator Kennedy. Senator Hirono. Thank you, Mr. Chairman. Listening to all of you 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 my staff, for example, asked 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, a song that they would sing, somebody else's song, but 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, what do I need my staff for? So, don't worry, that's not where we're going. Nervous laughter behind you. Their jobs are safe. But there's so much that can be done, and one of the things that you mentioned, Mr. 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's adult content, not that we think adult content is inherently harmful, but there's 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 obvious harmful kinds of information, but in the election context, for example, I saw a picture of former President Trump being arrested by NYPD, and that went viral. I don't know, is that considered harmful? I've seen all kinds of statements attributed to any one of us that could be put out there that may not rise to your level of harmful content, but there you have it. So, two of you said that we should have a licensing scheme. I can't envision or imagine right now what kind of a licensing scheme we would be able to create to pretty much regulate the vastness of this game-changing tool. So, are you thinking of an FTC kind of a system, an FCC kind of a system? What do the two of you even envision as a potential licensing scheme that would provide the kind of guardrails that we need to protect literally our country from harmful content? To touch on the first part of what you said, there are things besides should this content be generated or not that I think are also important. So, that image that you mentioned was generated, I think it'd be a great policy to say generated images need to be made clear in all contexts that they were generated. And then we still have the image out there, but we're at least requiring people to say this was a generated image. Okay, well, you don't need an entire licensing scheme in order to make that a reality? Where I think the licensing scheme comes in is not for what these models are capable of today, because as you pointed out, you don't need a new licensing agency to do that. But as we head, and this may take a long time, I'm not sure, as we head towards artificial general intelligence and the impact that will have and the power of that technology, I think we need to treat that as seriously as we treat other very powerful technologies. And that's where I personally think we need such a scheme. I agree, and that is why, by the time we're talking about AGI, we're talking about major harms that can occur through the use of AGI. So, Professor Marcus, I mean, what kind of a regulatory scheme would you envision? And we can't just come up with something that is gonna take care of the issues that will arise.
rise in the future, especially with AGI. So what kind of a scheme would you contemplate? Well, first, if I can rewind just a moment, I think you really put your finger on the central scientific issue in terms of the challenges in building artificial intelligence. We don't know how to build a system that understands harm in the full breadth of its meaning. So what we do right now is we gather examples, and we say, is this like the examples that we have labeled before? But that's not broad enough. And so I thought your questioning beautifully outlined the challenge that AI itself has to face in order to really deal with this. We want AI itself to understand harm, and that may require new technology. So I think that's very important. On the second part of your question, the model that I tend to gravitate towards, but I am not an expert here, is the FDA, at least as part of it, in terms of you have to make a safety case and say why the benefits outweigh the harms in order to get that license. Probably we need elements of multiple agencies. I'm not an expert there. But I think that the safety case part of it is incredibly important. You have to be able to have external reviewers that are scientifically qualified look at this and say, have you addressed enough? So I'll just give one specific example. AutoGPT frightens me. That's not something that OpenAI made, but something that OpenAI did make called ChatGPT plugins led a few weeks later to some building open source software called AutoGPT. And what AutoGPT does is it allows systems to access source code, access the internet, and so forth. And there are a lot of potential, let's say, cybersecurity risks there. There should be an external agency that says, well, we need to be reassured if you're going to release this product that there aren't going to be cybersecurity problems. Or there are ways of addressing it. So Professor, I am running out of time. I just wanted to mention, Ms. Montgomery, your model is a use model similar to what the EU has come up with. But the vastness of AI and the complexities involved, I think, would require more than looking at the use of it. I think that based on what I'm hearing today, don't you think that we're probably going to need to do a heck of a lot more than to focus on what use AI is being used for? For example, you can ask AI to come up with a funny joke or something. But you can use the same. You can ask the same AI tool to generate something that is like an election fraud kind of a situation. So I don't know how you will make a determination based on where you're going with the use model how to distinguish those kinds of uses of this tool. So I think that if we're going to go toward a licensing kind of a scheme, we're going to need to put a lot of thought into how we're going to come up with an appropriate scheme that is going to provide the kind of future reference that we need to put in place. So 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 between this committee and 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 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. Chair, is that 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, right, when we're text messaging or even to email, autocorrect features, including but not limited to spelling and 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 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. I'll tell you why I raise this. Social media companies, for example, have not adequately invested in content moderation, tools, and resources for their non-English language. And I share this not just out of concern for non-US-based users, but so 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, Mr. Altman and Ms. Montgomery, how are open AI and IBM ensuring language and cultural inclusivity that they're in their large language models? And is even an area of focus in the development of your products? So 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, in terms of their deployment. So having diverse people that are actually training those tools, considering the downstream effects as well. We're also very cautious, very aware of the fact that we can't just be articulating and calling for these types of things without having the tools and the technology to test for bias and to apply governance across the lifecycle of AI. So we were one of the first teams and companies to put toolkits on the market, deploy them, contribute them to open source that will do things like help to address, be the technical aspects in which we help to address issues like bias. Can you speak just for a second specifically to language inclusivity? Yeah, I mean, language. So we don't have a consumer platform, but we are very actively involved with ensuring that the technology we help to deploy in the large language models that we use in helping our clients to deploy technology is focused on and available in many languages. Thank you. Mr. Alp? We think this is really important. One example is that we worked with the government of Iceland, which is a language with fewer speakers than many of the languages that are well-represented on the internet to ensure that their language was included in our model. And we've had many similar conversations, and I look forward to many similar partnerships with lower resource languages to get them into our models. GPT-4 is, unlike previous models of ours, which were good at English and not very good at other languages, now pretty good at a large number of languages. You can go pretty far down the list, ranked by number of speakers, and still get good performance. But for these very small languages, we're excited about custom partnerships to include that language into our model run. And the part of the question you asked about values and making sure that cultures are included, we're equally focused on that, excited to work with people who have particular data sets and to work to collect a representative set of values from around the world to draw these wide bounds of what the system can do. I also appreciate what you said about the benefits of these systems and wanting to make sure we get those to as wide of a group as possible. I think these systems will have lots of positive impact on a lot of people, but in particular, historically underrepresented groups in technology, people who have not had as much access to technology around the world, this technology seems like it can be a big lift up. And I know my question was specific to language inclusivity, but I'm glad there's agreement on the broader commitment to diversity and inclusion. And I'll just give a couple more reasons why I think it's so critical. The largest actors in this space can afford the massive amount of data, the computing power, and they have the financial resources necessary to develop complex AI systems. But in this space, we haven't seen from a workforce standpoint the racial and gender diversity reflective of the United States of America. And we risk, if we're not thoughtful about it, contributing to the development of tools and approaches that only exacerbate the bias and inequities that exist in our society. So 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. 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 Ms. Montgomery, in your testimony, as you noted, AI is not you. 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 the like. 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. Thank you, Mr. Chair. Thanks, Senator Padilla. Senator Booker is next. But I think he's going to defer to Senator Ossoff. Because Senator Ossoff is a very big deal. I don't know if you know that. 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 this 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 Ossoff. 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. And 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 define a threshold of compute. And it'll have to go. It'll have to change. It could go up or down. It could go 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 you all to decide. That's now in this licensing regime. But models that are less capable, we don't want to stop our open source community. We don't want to stop individual researchers. We don't stop new startups. Can proceed with a different framework. Thank you. As concisely as you can, please state which capabilities you'd propose we 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 a thoughtful list of things. Well, perhaps 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 the technology. And we're going to have to think about a lot of very complicated constitutional questions that arise from it. With massive data sets, the integrity and accuracy with which such technology can predict future human behavior is 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. OK, 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 off of 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's many, many people that are privacy experts that could weigh on what a law needs. I'd still like you to weigh in. 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. 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 would be practically implemented. I mean, as I understand it, your tool and certainly similar tools, one of the inputs will be scraping, for lack of a better word, data off of the open web 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 there, 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. So that was not what I was referring to. I think that 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 a 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 to. How are you thinking about how kids use your product? We, well, you have to be 18 or up, or have your parents' permission at 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 too. In particular, given how much these systems are being used in education, we want to be aware that that's happening. I think what, and Senator Blumenthal has done extensive work investigating this, what we've 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 that you 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 will 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. Well, it's up to the chairman. OK. 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. And I think that's a different shape than ad-supported social media. Second, these systems do have the capability to influence in obvious and in 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. I will allow that comment to stand without objection. Without objection, OK. Thank you. Mr. Chairman and ranking member, it's been. Senator Booker, you are now recognized. Thank you very much. Thank you. It's nice that we finally got down to the bald guys down here at the end. I just want to thank you both. This has been one of the best hearings I've had this Congress. And just a testimony to you two, 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. Sam, you said very broadly, technology has been moving like this. And 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. 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. I really appreciate Senator Welch and I, who've 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, is what kind of regulation? You all have spoken that to a lot of my colleagues. And I want to say, Ms. Montgomery, and I have to give full disclosure, I'm the child of two IBM parents. But 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, NHTSA, 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 can imagine the need for something like that, correct? Oh, absolutely. And so 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. Mr. Marcus, there's no way to put this genie in the bottle. Globally, 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 no way to stop this moving forward. So with that understanding, just building on what Ms. 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 to figure out what we need to build, and also what it is that we need to have transparency around. Understood. Understood. Sam, just going to you for the little bit of time I have left. Real quick, first of all, you're a bit of a unicorn when I sat down with you first. Could you explain why nonprofit, in other words, you're not looking at this as, and you've even capped the VC people. Just really quickly, I want folks to understand that. We started as a nonprofit 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's shifted a lot. 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 interest at heart, and a belief that this technology could, if it goes the way we want, if we can do some of those things 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 on the record. The second part of my question as well, I found it fascinating. But are you ever going to, for 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. We have API developers pay us, and we have chat GPT customers pay us. OK, can I jump to the, can I just jump real quickly? 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 OpenAI backed by Microsoft.
Microsoft, Anthropic is backed by Google, Google has its own in-house product, we know Bar, so I'm really worried about that. And I'm wondering if Sam, you can give me a quick acknowledgement, are you worried about the corporate concentration in this space and what effect it might have in 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. What's happening now in the open source community is amazing, but there will be a relatively small number of providers that can make models at the true edge. I think there is benefits and danger to that, because we're talking about all of 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 I think there needs to be enough and there will, 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 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 record about the Wall Street Journal about how these systems can subtly shape our beliefs, and that has 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 that is somehow set by society as a whole, by governments as a whole. And so creating that data set, that alignment data set, it could be an AI constitution, whatever it is, that has got to come very broadly from society. Thank you very much, Mr. Chairman. My time has expired, and I guess the best for last. Thank you, Senator Booker. Senator Welk. First of all, I want to thank you, Senator Blumenthal, and you, Senator Hawley. This has been a tremendous hearing. Senators are noted for their short attention spans, but I sat through this entire hearing and enjoyed every minute of it. You do have one of our longer attention spans in the United States Senate, 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, it's 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's no rules of the road. Number three, as 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 relate to fundamental privacy rights, bias rights, intellectual property, 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. There's 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 in the Senate side, 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 have said you think we do need an independent commission. You know, and Congress established an independent commission when railroads were running rampant over the interest of farmers. And Wall Street had no rules of the road, and we had the SEC. 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. J.D. Vance actually mentioned that when we were considering his and Senator Brown's bill about railroads and that event in East Palestine, regulation for the 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 A, 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 got 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, I, 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. I think we want America to lead. We want, we want. So 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 like, 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, you 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 putting a regulatory burden that 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. Okay. Mr. Marcus or Professor Marcus. The other obvious peril is regulatory capture. If we make it as 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. Ms. Montgomery. One of the things I would add to the list is 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 is. You know, a regulatory agency would do a lot of the things that Senator Graham was talking about. You know, 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 Wells. 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. 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, 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, the US 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. And it isn't just the models or the generative AI that will run models around, 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, new agency. There's some real hard decision-making, as Ms. 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, you know, 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, what specific steps do you take to protect privacy? 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. Ms. 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 data sets as well. So we apply additional level of filtering. Professor Marcus, you made reference to self-awareness, self-learning. Already we're talking about the potential for jail breaks. How soon do you think that new kind of generative AI will be usable, will 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 of you, 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? Ms. Montgomery. The space around misinformation, I think, is a hugely important one. And coming back to the points of transparency, 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 kind of 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 risks. 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. Not only manipulating people, but also generative AI can manipulate the manipulators. It can. I mean, there's 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 my coming? 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 that industry should wait for Congress. 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, I think, a large consensus. The UNESCO guidelines and so forth show 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 enforced. So, for example, we don't have transparency yet. We all know we want it, but we're not doing enough to enforce it. Mr. Altman? I certainly agree that those are important points. I would add that, and 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, and I'm very glad we have done it, as these systems do 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. Having talked to you privately, 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 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. 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, Ms. Montgomery, has announced that it's potentially laying off 7,800 people, a third of your non-consumer facing workforce, because of AI. 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? This is quite a list. I noticed that an eclectic group of about 1,000 technology and AI leaders, everybody from Andrew Yang to Elon Musk, recently called for a six-month moratorium on any further AI development. Do you join those calls? Are they right to do that? Should we pause for six months? Your characterization is not quite correct. I actually signed that letter. About 27,000 people signed it. It did not call for a ban on all AI research, nor on all AI, but only on a very specific thing, which would be systems like GPT-5. Every other piece of research that's ever been done, it was actually supportive or neutral about. It specifically called for more AI, specifically called for more research on trustworthy and safe AI. So, you think that we should take a moratorium, a six-month moratorium or more, on anything beyond CHAT-GPT-4? I took the letter, what is the famous phrase? Spiritually, not literally, what was the famous phrase? I'm asking for your opinion now, though. My opinion is that the moratorium that we should focus on is actually deployment until we have good safety cases. I 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? 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. Altman? Do you agree with that? Would you pause any further development for six months or longer? So first of all, after we finished training GPT-4, we waited more than six months to deploy it. We are not currently training what will be GPT-5. 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 sure 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 GPT-4 deployment, we want to build on those, but we think that's the right direction, 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 yet? You're comfortable with all of the potential ramifications from the current existing technology? 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's limits we put. We have to pull things back sometimes, add new ones. I mean, 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, Ms. Montgomery? I think we need to use the time to prioritize ethics and responsible technology, as opposed to pausing 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 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 a hundred 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 can do that. We know how to do that. If we 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. 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 to 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. Altman? I mean, please forgive my ignorance. Can't people sue us? Well, you're not protected by Section 230, but there's not currently, 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 a lot of other laws where if technology harms you, there's standards that we could be sued under, unless I'm really misunderstanding how things work. If the question is, are clearer laws about the specifics of this technology and consumer protection is 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. The plan that you propose, I think, is a hypothetical, 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 have- Wait, you think it'd be slower than Congress?
Yes, I do in some ways. Really? Well, litigation can take a decade or more. Oh, but the threat of litigation is a powerful tool. I mean, how would IBM like to be sued for a hundred billion dollars? I'm in no way asking to take litigation off the table among the tools. But I think, for example, if I can continue, 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. Well, we could fix that. Well, the question is how? Oh, easy. It would be easy for us to say that Section 230 doesn't apply to generative AI. Ms. Montgomery, I'll give you the last word. I think it's an important start. You suggested, Ms. Montgomery, a duty of care, which I think fits the idea of a private right of action. No, that's exactly right. Also, AI is not a shield. 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 really massive scale. You could deploy things at the scale of a million people or 10 million people, but not a 100 million people or a billion people. 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. There will be no pause. 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 forgive me for sounding skeptical. Nobody's pausing. This thing is racing. I would agree. 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. I'm a futurist. I love exciting about the future, and I guess there's a famous question. If you couldn't control for your race, your gender, where you 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. 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 a lot of technologies of the last 25 years is what really concerns me. One of the things, especially with companies that are designed to want to keep my attention on screens, and I'm not just talking about new media. 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. 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 absent 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? 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 is I anticipated the hallucinations. I wrote an essay which I have in the appendix, what to expect when you're expecting GPT-4. 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. Then along came Sydney and the initial press reports were quite favorable. Then there was the famous article by Kevin Roos in which it recommended he'd get a divorce. I had seen Tay and I had seen Galactica from Meta, and those had been pulled after they had problems. 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. 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. 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. That doesn't even get into the risks that someone might repurpose them deliberately for all kinds of bad purposes. 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 last question or so. Don't you have concerns about, I graduated from Stanford. 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'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 said, 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 and certain zip codes. But you seem to be saying, and I heard this with Web3, that this is going to be 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? So, this point that I made about use of the model and building on top of it, this is really 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 to whose values we're going to align these models. 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 are, 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, not how it's going to be, but 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. And so, I'll say, Chairman, as I close, that I have, 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 every one 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.

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Pub: 18 May 2023 19:11 UTC
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