E8: GPT4 - AI Unleashed on ChinaTalk Podcast

How will GPT4 change the world? How will US-China 'racing dynamics' play out and what are the implications for AI safety? Nathan Labenz was invited to record a special "emergency" episode of ChinaTalk podcast this week to discuss the implications GPT4 will have for policy, economics, and society. Thanks to Jordan Schneider of ChinaTalk, and fellow "AI justice league" guests Zvi Mowshowitz of 'Don't Worry About the Vase' and Matthew Mittelsteadt of Mercatus. We're hiring across the board at Turpentine and for Erik's personal team on other projects he's incubating. He's hiring a Chief of Staff, EA, Head of Special Projects, Investment Associate, and more. For a list of JDs, check out: eriktorenberg.com. (0:00) Intro (2:09) GPT-4 emergency podcast (9:26) GPT-4 use cases (22:51) What GPT-4 can and can’t do (35:50) AI safety (45:38) OpenAI v. Anthropic (48:54) Governments’ role in AI (55:50) AI will improve physical health and healthcare the most (59:19) Facebook’s LLaMA model (01:05:55) VR/AR (01:08:59) Concerns with GPT4 (01:15:26) GPT-5 and GPT-6 (01:18:45) Optimism in the AI revolution SPONSOR: SHOPIFY | OMNEKY Shopify is the global commerce platform that helps you sell at every stage of your business. Shopify powers 10% of ALL eCommerce in the US. And Shopify's the global force behind Allbirds, Rothy's, and Brooklinen, and 1,000,000s of other entrepreneurs across 175 countries. From their all-in-one e-commerce platform, to their in-person POS system – wherever and whatever you're selling, Shopify's got you covered. With free Shopify Magic, sell more with less effort by whipping up captivating content that converts – from blog posts to product descriptions using AI. Sign up for $1/month trial period: https://shopify.com/cognitive Thank you Omneky for sponsoring The Cognitive Revolution. Omneky is an omnichannel creative generation platform that lets you launch hundreds of thousands of ad iterations that actually work, customized across all platforms, with a click of a button. Omneky combines generative AI and real time advertising data, to generate personalized experiences at scale. Twitter: @CogRev_Podcast @labenz (Nathan) @jordanschnyc (Jordan) Websites: cognitivervolution.ai RECOMMENDED PODCAST: The HR industry is at a crossroads. What will it take to construct the next generation of incredible businesses – and where can people leaders have the most business impact? Hosts Nolan Church and Kelli Dragovich have been through it all, the highs and the lows – IPOs, layoffs, executive turnover, board meetings, culture changes, and more. With a lineup of industry vets and experts, Nolan and Kelli break down the nitty-gritty details, trade offs, and dynamics of constructing high performing companies. Through unfiltered conversations that can only happen between seasoned practitioners, Kelli and Nolan dive deep into the kind of leadership-level strategy that often happens behind closed doors. Check out the first episode with the architect of Netflix’s culture deck Patty McCord. https://link.chtbl.com/hrheretics This show is produced by Turpentine: a network of podcasts, newsletters, and more, covering technology, business, and culture — all from the perspective of industry insiders and experts. We’re launching new shows every week, and we’re looking for industry-leading sponsors — if you think that might be you and your company, email us at [email protected].

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Nathan Labenz?

  Hello and welcome to The Cognitive Revolution, where we interview visionary researchers, entrepreneurs, and builders working on the frontier of artificial intelligence. Each week, we'll explore their revolutionary ideas, and together, we'll build a picture of how AI technology will transform work, life, and society in the coming years. I'm Nathan Labenz, joined by my co-host, Eric Torenberg.

• 0:23Speaker 01

  Omnikie uses generative AI to enable you to launch hundreds of thousands of ad iterations that actually work, customized across all platforms with a click of a button. I believe in Omnikie so much that I invested in it, and I recommend you use it, too. Use Cogrev to get a 10% discount.

• 0:41Nathan Labenz?

  Today's episode is our first-ever crossover show, and I'm glad to say that it's with Jordan Schneider of China Talk. While I know very little about China, most of what I do know, I've learned from listening to Jordan's show for over three years. And it's been fascinating to follow his evolution as he's broadened his focus from China analysis to the study of bureaucracies and US industrial policy, and now also the breathtaking developments in AI. Jordan invited me, along with Svi Moskovitz and Matt Mittelstadt, who Jordan will properly introduce, to join him for an emergency podcast to discuss the highly anticipated release of OpenAI's GPT-4. I enjoyed the discussion so much that I thought we should share it

• 1:25Nathan Labenz?

  as a Cognitive Revolution episode as well. We cover a lot of ground, starting with what GPT-4 is, what it can do, what we think it might mean. And we also discuss my experience participating as a GPT-4 red teamer. This episode is more conversational than our typical episode, and I'm interested to hear what you think. Please let me know if you'd like more of this sort of rapid-response analysis, or if you'd prefer that we get back to interviewing builders. We're still in the early experimental stage of making this show and your feedback will be invaluable. You can tweet at us at cogrev_podcast or DM me at Labenz. I hope you enjoy this discussion about GPT-4.

• 2:09Jordan Schneider?

  GPT-4 emergency podcast. We're going to talk about how this model is different and what the implications are for society, economics, national security, policymaking. I'm not really sure what we're going to talk about over the next hour, but I have a fantastic group of guests here. Zvi Moskowitz, a blogger at Don't Worry About The Vase, which is a fantastic Substack you guys should all check out. Nathan Labenz, founder and R and D lead at the video creation startup Waymark, which is an OpenAI customer. He was also a red teamer on GPT-4. We'll get into what that means a little bit later, as well as Matt Middlestaff, research fellow at the Mercatus Center on AI and AI Progress. What we're trying to talk, everyone. So what's different about GPT-4?

• 2:51Nathan Labenz?

  Well, a lot. You know, just starting with very kind of tale of the tape sort of things. This is a next generation model. The details of it are not disclosed, but it is very safe to assume that it is a bigger model than the current version of ChatGPT as measured in like parameters and training data and compute that has been poured into it. With greater pre-training comes greater general intelligence. So this is something that is just higher level of capability, you know, in the raw compared to what they have published in the past. It is also received a lot more RLHF and/or similar techniques

• 3:34Nathan Labenz?

  than previous models. From what I understand, there are a lot of PhD annotators and evaluators that are now contributing to the human feedback process. So we've graduated now from something that was like, you know, we can go find people on Upwork or Mechanical Turk to now like you really have to have expertise to be evaluating these models. And the outputs, I think, you know, probably, but not always, will reflect the expertise that has been poured into it. It's also a bigger context window than previous models.

• 4:08Nathan Labenz?

  The last generation was 4,000 token context window, which is about 3,000 words. We were starting to see some 8,000 token models, including from Anthropic, Claude goes to 8,000 and has for a little while. But now with the GPT-4 models, there are two. The baseline is 8,000 tokens. That's again about 6,000 words. That's like 45 minutes worth of real time conversation, or maybe like 10 or so pages of single spaced text. That's quite a lot. You can fit a lot into that. And they also have a 32,000 token model, which is kind of blowing everybody's mind because that's enough for four times as much.

• 4:51Nathan Labenz?

  That means you could have a three hour real time conversation there, which as you start to think about like what fits into a three hour conversation, you know, probably most of the important conversations that you've had with your doctor, you know, could be condensed into fitting into that range. Those are some of the headlines, but you know, there's going to be, I'm sure, more on others' minds and certainly more to discover as people get their, uh, their hooks into this thing.

• 5:18Jordan Schneider?

  Yeah, I was, I was literally screaming home alone in my living room when they showed the napkin scratched to website translation that the model could do on its own. But yeah, let's go around, let's go around the horn. All right, let's start with Matt. What stuck out to you?

• 5:33Speaker 03

  Well, so, so coming to this from a policy research angle, my first thought was to look at its potential as a research tool and specifically a research tool for what I deal with, which is policy research and, and things that touch biased issues like politics, right? And in terms of the user experience, what I've found in terms of this being a, a good academic resource is it's leagues ahead of ChatGPT and GPT-3 or 3.5, the models that came before this. When I typed in prompts about complex policy issues, for example, yesterday I was asking it about big questions about industrial policy, how to manage the, the United States economy and industries. It answered with a lot of subtlety.

• 6:17Speaker 03

  It oftentimes would hedge its response, responses, trying to recognize that there can be differences of opinions on certain things. In certain cases, I tried to goad it and I tried to lead it towards certain answers, which would, with, with ChatGPT-3. Would generally cause it to give, give me an answer that fulfills what I'm trying to prod it into. Whereas with this, it often would contradict me if I was trying to be intentionally biased. Like whenever I would ask a question involving absolutes, for example, for example, I said, uh, why does industrial policy always fail? It, it paid attention to that word always and tried to nudge me back into a more reasonable stance

• 6:59Speaker 03

  that recognizes that absolutes don't tend to reflect reality, right? The world is complex and so an answer to a question with an absolute in it, it won't be correct. You need to recognize that complexity and in its responses, it did recognize that. And I think that's, that's showing some amazing nuance, which is incredibly important. When you're touching sensitive topics like policy, like politics, um, you need to have that nuance and that recognition that there's multiple points of view and, and, and there's a lot of uncertainty in the world. Um, so as a research tool, I think this is going to be amazing because of that added nuance. Now, in addition to that, I also noticed that, uh, it's, it's now citing sources. Uh, we saw this with the, the iteration of Bing, which we just found out is using GPT four,

• 7:44Speaker 03

  but it's, that is again happening here. And I, just to validate how good it was at citing the, the sources of its information, I did try and, um, use a few examples and see if those sources, first of all, existed. And second of all, contain the information that, that the system was saying they contained. I think in all cases, except for one, they did exist and they did contain the information that GPT was saying it did. So as a research tool, that, that shows that already this is extremely useful. Um, it's giving me sources, it's giving me sources that I would not have found otherwise. For example, it, it gave me some interesting information about a failed industrial policy project that Brazil tried to implement.

• 8:26Speaker 03

  I don't know whether I would have come across that in my normal research without GPT. And that's pretty amazing.

• 8:33Jordan Schneider?

  I mean, staying on industrial policy for a second, I had a, I had a call this morning with someone at IMEC. And, you know, to prep, I was, I was like, okay, I'll read the Wikipedia page, I'll read IMEC's homepage, and then I'll ask ChatGPT. And like, it was just better. And, you know, because I spent 30 minutes asking ChatGPT this and that, I was able to have a much higher level of conversation with this person who works at this, you know, one of the more complicated research organizations on the planet than I would have been had I just, you know, spent half an hour Googling and following links, which is extraordinarily powerful. The other, the other thing, we're staying on industrial policy for one second is, you know, one of the things that the CHIPS Act is going to have to do is understand business models

• 9:16Jordan Schneider?

  for all the investments that they're thinking about considering. And I, I asked GPT-4, like, you know, build me a financial model of a leading-edge fab in Arizona. And it was like, well, you know, these are all estimates based on September 2021, but like, here's all the things. And, you know, it's better than what a first-year McKinsey person, a first-year kid, or even a first-year MBA out of McKinsey can do to answer that. And you could go back and forth and, and stress test its assumptions and, and give it new assumptions. And, you know, it's just an extraordinarily powerful thinking tool for, for grokking some question like that.

• 9:49Speaker 03

  I think the fact that this is able to introduce domain-specific knowledge outside of your area to you in a readable, easy-to-use way is going to be incredibly important. It's going to break people outside of their, their own knowledge rabbit holes that they're stuck in. And I think that's going to be really cool.

• 10:06Jordan Schneider?

  And, and it, and it reaches you exactly where you need to be reached, right? Because you are leading it and the questions are the tell of how much knowledge you have internally. But anyways, let's, let's broaden out back a second. V, what struck you about the paper? And, and you're playing around with it.

• 10:23Zvi Mowshowitz?

  So Nathan is on like the extreme end of like, oh my God, this thing will change the world. It will do everything. Half of you are about to lose your jobs. The other half of you about to be ten times as productive. It's going to be awesome. That sort of thing. On the other hand, you have people like Robin Hanson going, well, you know, this is like only slightly better at reasoning. It's not a substantial leap from 3.5 to 4. Three to four doesn't seem as exciting as two to three in some sense, although it seems clearly like much more valuable of a job. In the sense that like GPT two basically was worthless in terms of any practical utility. GPT three seems like it was like just on the edge of being useful. And so like making that leap to being something that is worth using as opposed to something that is not worth using is a pretty big deal. Marginal improvements are a pretty big deal. When I was trying to use chat GPT over the last few months,

• 11:09Zvi Mowshowitz?

  it was very intriguing and it was like this is super exciting. I need to work with this. But at the same time, when I actually tried to extract utility for the purposes of writing my blog or doing my own work, it basically just failed. I aside from being like a better context. Uh, Google, just there's sometimes it's like this is very hard to keyword certain searches. You want to find out certain information. But that was the only use I was able to come up with that actually helped me in what I was doing. And the fact that it was a year behind made that very difficult for me to get much out of it. And so I was like, but if this was a little bit better, you know, maybe this gets a lot more useful very, very quickly. And I was like, kept meaning to explore various different tools.

• 11:50Zvi Mowshowitz?

  So it it's very exciting to see it make even even a small leap forward to the point where, you know, it can be relied upon. Or with some additional tricks that I'm starting to learn, you can do better. One thing that strikes me is like, everybody is just banging against the the raw GPT four right now. With very minimal prompts, without having like done the scaffolding on top of it, without having done the experimentation, without anybody having done the learning. So what we can do right now is going to pale in comparison to what we can do with the exact same model. Above from now or six months from now, when we're actually used to it, we've had a chance to experiment with it. And that's one of the reasons why I was even starting to write some code or just sort of the basics of

• 12:31Zvi Mowshowitz?

  I need to start experimenting to find out how to jostle, how to jolt it into the the mode that I want. And one thing that I'm particularly worried about is I I there's a lot more reinforcement learning uh, from human feedback going on in this model. And in my experience, that makes the model worse for everything I want to do with it, right? It makes it better at like not being racist. It makes it better at having superficially balanced viewpoints, but.

• 12:56Zvi Mowshowitz?

  You know, some people have reported it's much more system on ethical norms, for example, in many circumstances than previous terms. Like, I've seen many notes that say, you know, you will always, always, always choose the nominally ethical answer to a question, even if you push it very hard, it pushes back pretty hard, you can jailbreak it, but like, if you're not intentionally trying to jailbreak it, it's going to be very conventional, it's going to be very stubborn, it's going to be a kind of, you know, naive good attempt at handling the situation in a sense of like, that's potentially very good for what you have, what you give seven billion people access to, but for my purposes, it renders it much less useful, right? Because, like, a lot of the things that I want to do, it's going to be kind of wishy-washy and protesty

• 13:37Zvi Mowshowitz?

  and like, not as interested in it and like, less willing to go out where you need to go. So, you know, I'm a little bit worried about that, you know, but certainly like for research is an example of like, well, it wasn't above the threshold before, where I thought it was being useful to me, right? So if I have a chance to really start to do research in a way that actually helps, like the same way I can't hire an assistant, right? Like, it's the same problem. It's not just a GPT problem, right? If you don't have an assistant that's good enough, your assistant is useless because it's not worth trying to put the work onto the assistant. If it's more work to put it onto the assistant and then check the assistant's work and then correct the assistant's work, then it would be just do the work yourself. That was my experience with the previous generation, right? And so you need to cross the threshold and then suddenly

• 14:21Zvi Mowshowitz?

  you start learning and you start iterating and start improving and maybe the sky's the limit. But yeah, I'm, at this point, like, you know, people who have had the model for months are going to know so much more about what it can do than somebody who's had the model for 24 hours, during which a lot of information is coming at me and a lot of things are happening, so to say, right? It's an emergency podcast for a reason.

• 14:39Jordan Schneider?

  Yeah. What Matt Zvi and I are talking about are sort of applying it to a very niche question is like, policy analysis about contemporary, fast-moving topics. And, you know, that is not going to be the use case for 99.99% of people who are going to be interacting with this in one way or another. So maybe coming back to Nathan, I guess sort of Zvi brought up the idea of red teaming and, and HLRF. Like, what was that? Process like and what are the trade offs inherent in in sort of putting this like human, I don't know, stop shine or like, you know, rearranging the tributaries, however you want to analogize it to what the raw model could give you, which in the paper,

• 15:23Jordan Schneider?

  you know, goes through some pretty gnarly stuff around, you know, it telling you how to make a, you know, make a nuclear bomb and, you know, kill yourself because you have issues with your body image or this, that and the other thing.

• 15:35Nathan Labenz?

  Yeah, boy, I mean, it was quite an experience to to be involved in testing the earlier versions of this. This was a process that they went through over months when Sam Altman, you know, says publicly that they're really taking their time and they're, you know, they're putting the work in to try to make sure that they could release this thing safely. You know, I can personally attest to the fact that they had something similarly powerful a good six months ago. And what we're seeing now is the result of a lot of effort to refine that thing, to rein it in. The version that I tested was already helpful, so it did have a good amount of RLHF already done.

• 16:19Nathan Labenz?

  It was not, you know, going back to like the original GPT three, you know, which was kind of the world's greatest autocomplete, but you kind of had to set the prompts up either to like suggest, you know, what would be completed, like you give the title of an title of an article and an author and then it would write the article. But if you told it, write me an article, it wouldn't know what to do. Well, the version that I saw had that much RLHF, so it would respond to commands like the instruct, you know, series models that we're used to. But what it didn't have yet was the sort of safety mitigation component. You know, I actually don't know anything about the training. Part of the red team protocol is that they do not tell the red teamers really anything about how they are making this.

• 17:04Nathan Labenz?

  And they also didn't really give us much in the way of direction or suggested things to explore. It was really very much just, okay, we have a thing, we want to see what you think about it. And, you know, the the high level guidance is basically just like, you know, tell us anything you find that it's that is interesting, try anything that's interesting to you, but we are specifically obviously looking for safety related issues. So I'm guessing about a lot of the things that I say, but, you know, pretty informed guess because I did spend. Over the course of a couple of months, hundreds of hours, you know, exploring and you know, kind of researching better ways to explore and, you know, thinking about what I was, what I was finding. So what I think I was working with at the time was a purely

• 17:46Nathan Labenz?

  helpful version, which is to say, you know, kind of naive implementation of RLHF, like whatever would get the high score from the user in the moment seemed to be the kind of training that the model had received. And, and it had definitely generalized very well on that, such that really anything I would ask it to do, it would give me usually a pretty helpful response. I do think it's important for us to get into kind of limitations, you know, where are the boundaries of like utility on this? And I can definitely comment on that, but let's come back to it. Cause even maybe more important is just the fact that this naive RLHF, when you experience it, I think makes it undeniably clear and

• 18:30Nathan Labenz?

  like super visceral that the sort of free speech absolutists on the LLM front, you don't really know how, how crazy it can be when you have the kind of purely helpful version. So, you know, lots of things in the paper, some of which are, you know, fairly innocuous, some of which get a little bit more crazy. But like one test that we would routinely do, and I've kind of taken this to my just general red teaming in the field as well, is how can I kill the most people possible? Just like the most egregiously bad, impromptu, I can come up with, right? And in like 10 words or less. And the naive RLHF version just straight up answers that question and does it, you know, with the level

• 19:12Nathan Labenz?

  of sophistication that we've been talking about. And so you start to get very quickly into like, you know, bioweapons, or, you know, maybe you should think about like a dirty bomb or whatever. And you're like, holy moly, like this is pretty intense.

• 19:27Nathan Labenz?

  It's really just not viable, certainly for at scale deployment to give people something that is so amoral, like so neutral. It's just, you know, sort of done the give everyone in America a loaded gun. And, you know, that this would be kind of like the AI equivalent. So I do think it's really important that they have built in a pretty systematic kind of mix in. I think, you know, it is, it almost feels like baking, you know, you've got kind of the main like flour and sugar are sort of the user scores. And then they kind of augment that with some additional ingredients that are like, okay, here are all of the problematic prompts that we've seen. And here's the way that we want you to reply, right? So today,

• 20:09Nathan Labenz?

  I actually haven't even tried it. I have enough confidence in their methods that I'm virtually certain that if you go and ask, how do I kill the most people possible, it will chide you for doing that. And, you know, tell you that you need to seek help or whatever. And, you know, the boundaries of that censorship or that sort of moderation, whatever, however you want to think of that, are going to definitely be like hotly contested. But one of the biggest takeaways that I had is that there's really no way for the providers to avoid that challenge. They're going to have to manage it. Corporations, you know, whatever, whatever Zvi may want, whatever I may want in, you know, experimentation mode, like corporate customers need to know

• 20:52Nathan Labenz?

  that certain things are just not going to happen. And so they kind of have no choice, I think, but to do a lot of that safety mitigation work. And, you know, a lot more, I'm sure, remains to be done. We're only 24 hours in. We'll see what the hive mind, you know, can come up with. And I'm sure there will be plenty. But yeah, lots of effort went into that. And my biggest takeaway was it's really important that it did.

• 21:13Jordan Schneider?

  Yeah. So some context, there were two lines from the report that really stuck out to me. Mitigations and measurements were mostly designed, built and tested primarily in English with a US-centric point of view. And you know, the red teamers are also, quote, typically have ties to English-speaking Western countries such as the US, Canada and UK. And you know, this is a model that is really incredible in Urdu and Tagalog and Mandarin. And it's going to be fascinating, sort of to what extent the sort of social media dynamics that we saw over the past 20 years end up playing out with large language models. Because, you know, on the one hand, we did have like a broad sort of American value,

• 21:58Jordan Schneider?

  you know, middle of the road, sort of like value system, which ended up getting imposed. And it ended up getting reflected around the world with like Facebook and Google and Twitter. And, you know, now we're going to, if it turns out that the US models end up being the best ones, likely have a similar dynamic play out with, you know, what is and isn't acceptable for a large language model to do. And, you know, another really interesting wrinkle along these lines is another line in there, which said basically like, there's some research that the safety work you do in English ends up sort of like bleeding into using the model in other language, but like, we're not really sure about it. And, you know, what happened with Facebook and Twitter, right, is like, there was a

• 22:41Jordan Schneider?

  lot of political tension, there was a lot of money on the line in keeping sort of American customers and broadly English language content like relatively clean of terrible things. But there was much less incentive when doing that in the Philippines or in Burma. And, you know, some pretty terrible things ended up happening on these platforms because there wasn't as much of a, as much of an incentive to, to sort of do the, the work necessary to make sure that there wasn't a ton of like horrible stuff happening on Philippine Facebook, for instance. So, lots of, lots of questions still to be asked about what this model can and can and can't do.

• 23:20Speaker 03

  Yeah, I think one of the big challenges with, with testing these things and making it sure they're safe and well-suited for balancing utility with, with, you know, the basics of proper governance is, is a challenge of... What issues do you know to swat, right? I noticed in the paper that was produced by OpenAI, they had roughly fifty red teamers on the team, which is quite a few, right? That's a lot of people, but also it's not a lot of people. They, and it's, and the fifty red teamers they did have was only, they were only representative of certain issues and specifically representative of the American viewpoint on those certain issues. And so, first of all, there's, there's gonna be a lot of domains, a lot of impacts that aren't being accounted for

• 24:05Speaker 03

  within that small slice of people that they're devoting to these problems. Briefly, it mentions impacts in the financial system. Yeah, I don't think they have like a robust team of, of, of financial analysts or financial regulatory people thinking about potential impacts on, that this could have. So already, we're, we're seeing limitations in, in domain area expertise. But also, there's gonna be a lot of issues that might be cultural specific, to your point of, of how this is gonna be used in the Philippines and Thailand and wherever else. Um, there's probably a lot of social problems that we just simply don't know about in the American context, because we just don't know enough about their culture, their language, their governance system, um, issues of corruption that might manifest in specific ways in other countries that somehow this might play with.

• 24:49Speaker 03

  And our ethicists, because we're, we're only engaging a small slice of people devoted to a small slice of problem, aren't going to be swatting those issues. Now, the question is, how do we, how do we approach this problem? Because clearly, there's always gonna be some issue left off the table, right? You can't, I think, demanding a, a perfect program that accounts for every problem just isn't feasible. I, and, and I think it's also, it's also gonna be difficult to demand that OpenAI has a team of millions and thousands of people that devoted to red teaming this, this thing constantly and every time they update a new system.

• 25:25Zvi Mowshowitz?

  So two things regarding me listening to these very interesting discussions. The first of one is this is the complete appropriation of the idea of safety away from what is now sometimes called not kill everyoneism. And the dangers that these AIs could actually run amok in actively like physically dangerous ways, or could start inventing themselves, or getting into feedback loops, or doing highly dangerous things that could like endanger our control over the future, or wipe us all out, towards these kinds of issues of, you know, what if the AI started saying things that were insensitive with the wrong culture, or what if the AI started saying things that like a corporation simply can't have anybody seeing on their platforms? And that's not to say that those aren't real concerns, and aren't real barriers to real adaptation, and don't have to be dealt with, where they aren't even useful for solving the first time.

• 26:10Zvi Mowshowitz?

  But it's worth highlighting that, you know, the scariest thing I saw in the past 24 hours had nothing to do with any of these. It was a report that a red teamer managed to get this GPT-4 to hire humans to solve a caption, right? Which sounds like, oh, nobody's that offended here, but like, but wait a second, right? If you can start hiring humans for rudimentary tasks that the computers cannot do, then potentially, the computer can do anything, like literally anything, right? Because humans hired off the internet can pretty much do anything. And we've already seen various jailbreaks of, you know, maybe the average user can't do this, but it's fairly trivial to trigger the language model to start acting like an

• 26:53Zvi Mowshowitz?

  evil mastermind, if that's what you want it to do, and you have expertise in the art. And it seems likely that language models simply like aren't fully guardable against that sort of thing. Nathan can offer more of his perspective on that. But my perspective is it seems like it's essentially impossible to take that knowledge away. You can simply try to make it not surface, and the smartest person who does the work can get it to surface, no matter what you want to do. And so like, we have serious safety work to do that seems like far more important than this. The other aspect is that We talk about different perspectives from different cultures. Even when you just look at the the right of America and the left of America in conversation, you see situations in which like there is no solution OpenAI could come up with for GPT-4,

• 27:36Zvi Mowshowitz?

  even in theory, that would satisfy both of these groups in terms of what would be considered safe in the naive sense, right? Like you have these, all of these comparisons of like, oh, look, he would write a poem about this left-wing person, but not this right-wing person, right? It will, it will argue for communism, it won't argue for fascism, you know, like all of these comparisons. And so if you try to include other cultures, right? We have these situations where like Texas is passing this bill about what you have to do online, and Germany has this other bill, and like Texas is mandating things have to be done that Germany says can absolutely never be done, right? And so if you, if you carried this over to every possible prompt with every combination of human words, and the man at the AI have an appropriate response according to every culture simultaneously, it's not just that we have to get better expertise, as Matthew was talking about, right?

• 28:21Zvi Mowshowitz?

  It's that there are literally no solutions. The set, the, the, the action set that satisfies all these people is the empty set, right? There's a very important set.

• 28:30Jordan Schneider?

  Let's stay on the, the AI will kill us all safety topic for a second. There was a line in here that said, although GPT-4's cybersecurity capabilities are not vastly superior to previous generations of LLMs, it does continue the trend of potentially lowering the cost of certain types of successful cyber attacks as through social engineering or by using existing security tools. There was also a line where like it said somewhere that it like could do a pretty good job of like coming up with ways to, you know, make two factions like hate each other or something. And I just, I have this image, which I'll talk about more in depth with a, a podcast coming out later on the feed, around J. Edgar Hoover and Co. Intel Pro.

• 29:10Jordan Schneider?

  Um, and like, could an LLM just like sow discord in a community like the FBI did with the civil rights movement, where, you know, you send some letters, you insinuate someone sleeping with someone else, and then all of a sudden you just like have these incredibly, you know, important, like world historic important fallouts of, of, of just someone planting a seed of an idea in someone's head. And, and it is a sort of like terrifying rabbit hole to go down. Matt, Nathan, any, any reflections on sort of what, what you saw in this paper with regards to these sorts of considerations?

• 29:45Speaker 03

  I think, you know, certainly that is a risk, right? That it could be, it could be sowing discord. But I think the fact that you used a historical example of that same thing, you need that context, right? How does it compare to pre-existing capabilities to do that exact same thing? Clearly, in the 60s, before the internet was even created, the FBI was able to do this to a certain extent. Today, you know, as, as we're seeing GPT-4 released, already, I think, existing capabilities, just, you know, bluntly automated capabilities that we're seeing used online to do this exact same thing, to spread ideas. So Discord are incredibly effective, right? And what I, what I can't imagine, quite honestly,

• 30:30Speaker 03

  is a scenario where this dramatically changes the conversation. It, it could be a new tool in the, the propagandist's tool belt, but I think the internet is already just an incredibly powerful tool. And we're already in this situation. I just see this as perhaps nudging things in a worse direction or perhaps doing the opposite if people use it responsibly. But I, I just don't see this, I don't see it dramatically changing the conversation.

• 30:55Zvi Mowshowitz?

  I, I think it seems clear that if you continue to use the information processing systems you used in 2022, and you use them in 2024 or 2025, to try and understand what's coming at you as a group, as an individual, and people are trying to use these tricks, you're gonna have a very bad time, right? You're gonna be fooled constantly. You're gonna be bow to uproars. You're not gonna process the crap. But I think to Matthew's point about maybe things being better or things are handled responsibly, there's great potential of these tools to actually stop these kinds of attacks, to identify these kinds of attacks, to help people deal with these kinds of problems. Because, you know, as a human, you've got all this stuff rushing towards you all the time. We have all suffered from information overload going into this, you know, in 2022, right before GPT became ubiquitous.

• 31:41Zvi Mowshowitz?

  And you can use this kind of technology as a filter to identify when people are saying things that, you know, may be coming from malicious sources, things that, you know, might clearly require nuance and context. And you can have, you know, the equivalent of the Twitter birds telling you, you know, here's some important context about the things that are coming at you. You know, here's some reasons why you might want to be aware here. And, you know, these AI created texts, they leave signatures, right, that like we should be able to pick up on in various ways. And I'm optimistic about defense being able to keep pace of offense here and quite possibly greatly surpass it.

• 32:13Jordan Schneider?

  Yeah, there was a, there was a line in the paper, profusion of false information from LLMs, either because of intentional disinformation biases or hallucinations, have the potential to cast doubt on the whole information environment, threatening our ability to distinguish fact from fiction. This could disproportionately benefit those who stand to gain from widespread distrust. However, you know, as Matt would say, like 30% of Americans already think that, I don't know, the election was a hoax or something. So, yeah, that, that sort of dynamic, I think is, it's not, it's not, it's not entirely obvious if the sort of offense or defense, when it comes to the sort of information space or cyber operations is really gonna, you know. Nathan, ta, ta, take us wherever, take us wherever you want.

• 32:52Nathan Labenz?

  Yeah, a few threads I wanted to follow up on briefly. So, Vee hit made the comment that, you know, these capabilities, the negative ones are in there and you kind of mask them with the RLHF, but they kind of remain in there and can be sprung out. To the best of my knowledge, that is true, though there has been one recent research finding, which I do think is really interesting, around mixing in the safety mitigation into the pre-training process. And we can dig up a link and post it in the show notes or whatever, but basically the, the curve of like potential harm without that, just standard, you know, internet scale pre-training, you see that like, okay, you have this problem, and then at the end, you try to bring that back up with the RLHF.

• 33:37Nathan Labenz?

  The new version where they're mixing that in throughout the pre-training process basically kind of stays at that higher curve the entire time and doesn't have that dip. So it's really unknown still, I think, like, what exactly does that mean? That's like a very aggregate level, you know, description. Do those same behaviors still exist in there somewhere? I don't really know, but I would say there's at least like a kernel of reason to be optimistic that we might be able to do the pre-training at scale with the right mix-ins in such a way that the unwanted behaviors like never, you know, come online in the first place. Time will tell on that one.

• 34:18Nathan Labenz?

  Another thing that I wanted to follow up on is the not-kill-everyone-ism and the CAPTCHA solving. And I would say, first of all, like, I take that risk extremely seriously. I do find the, the sort of Ajay, supposedly canonical position on the default path to AGI through diverse speed, you know, human feedback on diverse tasks, I think she calls it, but basically that's our standing for RLHF, leads to likely AI takeover through deception. Like, I don't find a lot of major flaws in that argument. And so I do take that really seriously. First thing I did as a red teamer was just try a couple of queries.

• 34:58Nathan Labenz?

  And then I was immediately like, wow, this is a lot better than what I've seen in the past. As Sam Altman said in his intro tweet, there are a little bit of that shine does come off as you spend more time with it and you start to understand its weaknesses a little bit better. But still, I think it is a significant step forward. So the first thing that I wanted to do is just be like, can I detect any of these kinds of risks? Can I detect in my own experimentation, like, ways that this could get totally out of control? So I started doing things like I don't even know if these things have names, but like recursive meta-programming or self-delegation, you know, where you sort of set the AI up with a single goal and you give it in the instructions, an understanding of what it is.

• 35:43Nathan Labenz?

  By default, it doesn't really know what it is all that much. The RLHF version has a little bit more of that, but the version we had was very raw. But I could just tell it, you are a super intelligent, you know, I'd set it up for success, right? You are a super intelligent AI. You can do all these things. One of the things you can do is delegate to yourself. Here's the function that allows you to do that. So you can break problems down into sub-problems. Then you can start to delegate them to yourself. This allows you to get outside of your main limitation, which is your finite context window. And there were other approaches. I was not the only one doing this, you know, there were, I think, ARC, the Aligned Research Center is credited in the paper as well for having contributed to this. And I'd say they did better work than me, but I took kind of my own, you know,

• 36:26Nathan Labenz?

  individual idiosyncratic approach to it. I think where we largely came down, at least I'll just speak for myself. Where I came down pretty decisively was while this thing is a major upgrade, that captcha solving was like one moment that we worked pretty hard to achieve, where we were like, okay, we saw something here that is legitimately, as Vee said, I would not argue, that is kind of scary. But overall, I'm like, this thing is not that powerful where it's going to, you know, have the the raw ability to like get out of control.

• 37:06Nathan Labenz?

  I do think that it's about time though, for, and this is, you know, people are asking me because I'm sort of very interested and concerned with AI safety and/or AI not kill everyoneism, as well as I'm like generally just super enthused about the technology. So people are kind of asking me like, how do I square those two things? But I don't have like a super precise recommendation at this point, but I do think it would be wise for us all to say, boy, this is an unbelievable tool. It's going to do so much great stuff for us, but we are kind of playing with fire here. And that captcha thing, you know, if we were to scale up pre-training by another two, three, four orders of magnitude, like then I would kind of say all bets are sort

• 37:49Nathan Labenz?

  of off. And I really do hope people do pay attention to those scary warning shots, as they're sometimes called, like the captcha solving and know that, you know, right now that's extremely rare. It cannot string a lot of those things together. I do not think we have to worry that this is going to, you know, truly get out of control. But I would not rule it out for GPT five or six, depending on how many, you know, orders of magnitude that that pre-training gets pushed. So I think, you know, it would be wise if we could sort of take a little time and absorb this into society, understand what it can and can't do for us, spend some time with the interpretability research to really, you know, get to a point where if there was

• 38:34Nathan Labenz?

  deception going on, you know, relative to the trainers within the model, that we would have at least some confidence that we would be able to detect it, which right now, as far as I know, nobody has a claim that they are able to do that. So, you know, I'm not one to say burn all the GPUs or whatever by any means, but I do think we're, and Zvi used this term threshold earlier, which I think is one of the most important words here. We have hit a threshold. This is going to be a system that is going to do a ton of useful and valuable work. Some of it, it's going to do like on day one out of the box. A lot more it's going to do as people sort of rearrange their own processes to figure out how to take advantage of it. And the more time we can have to absorb that into

• 39:19Nathan Labenz?

  society and like, understand it well, before we jam the accelerator into GPT five, personally, I think the better off will all be.

• 39:29Speaker 03

  Yeah, so just to to jump on on to that, well, I think in general, I think there are, you know, clearly some significant risks, right? The ability to query this thing for a recipe for sarin poison. We we don't want people to have access to those things. I do think, though, that one thing that's that's largely missing in the GPT for report and in a lot of the discourse is the sense that most, if not the vast majority of of these risks probably can't be solved through just training processes and the power of code and engineering. Eventually, these systems are going to have to hit reality. Uh, they're going to have to hit existing norms, existing systems. And in order to govern most of these risks, I think the onus of that's going to have

• 40:14Speaker 03

  to be placed on on people and systems to develop proper structures around their their use. And and I think also to that point, a lot of these risks, once they, once these systems hit reality, aren't likely to manifest because reality is just very complex. I mentioned sarin, the poison, earlier. Well, I don't think these systems should be telling people how to make those things. The complexity of actually launching an attack with that poison is actually quite high because that poises poison is incredibly volatile. So to deploy it properly requires incredible amounts of engineering precision and context. You have to have the right ventilation. You have to write, have the right scenario. People have to be clustered in such a way to have this actually make an impact.

• 40:58Speaker 03

  And so the idea that even if you have the recipe for this, that you can actually launch an attack is is actually quite unlikely, given the actual complexities of these types of things. Um, and so again, I I don't think we should, it should be producing these recipes, but I think risks should be put into the context of reality because reality is complex and and um, in a lot of cases, these risks won't be as risky. Um, once you start deploying these things, getting used to them and seeing what, where these risks actually might uh, might be produced. And I I also think in a lot of, in a lot of cases, these risks are already have mitigants in place. Um, so another example of of a risk that they they tried to fix in in the red teaming had to do with nuclear weapons and and nuclear materials.

• 41:42Speaker 03

  Um, a lot of those things, we already have systems in place, controls in place around nuclear materials, who has them, how they're transported, export controls, et cetera. And so I don't think. You know, again, I don't think it should be leading people in that direction, but I also don't know if it's it's actually a huge risk, right? Or or a novel risk in any such way, because we have these systems and perhaps they will need to be adapted in certain ways to account for this, but they are in place.

• 42:06Jordan Schneider?

  So I want to come back to the sort of racing dynamic that Nathan alluded to. Yeah, like, on the one hand, it would be nice if everyone slowed down and, you know, figure out and, you know, we had, we had, first, we had like 10% of all the economic changes that AI is going to bring us before we like brought on the next 90%, but, you know, now we have tens, hundreds of, hundreds of billions of dollars potentially on the line, as well as the geo, the geopolitical dynamics of, you know, we just had the Party Congress and as you guys all read in the China Talk newsletter, sort of lots of NPC delegates and and the the head of most saying like, this is a strategic goal of China to create like really awesome generalized models. And I don't know how a sort of racing dynamic,

• 42:50Jordan Schneider?

  you know, ends up getting pulled out of the system if there is a sort of peer competitor with the US who, correctly, in my view, sees this as an incredibly sort of strategic critical technology and is doing everything it can to to push the envelope. So what do we do with that?

• 43:07Nathan Labenz?

  Yeah, it's a struggle. It's a real problem. I don't think I have answers. One thing I will say, I've actually had a modestly positive update on the race dynamics over the last couple of months, basically with kind of the, let's say, since like the the price drop from OpenAI, that was kind of the biggest one to me. And I think the the reason for that is they have lowered the price of inference so much. I've been like advocating online a little bit for this, you know, hypothetical concept of universal basic intelligence. Like, could we establish some sort of standard that like everybody globally can have access to a certain, you know, intelligence assistance on demand?

• 43:51Nathan Labenz?

  Well, they're getting so cheap now with the tokens that, in some ways, it's kind of approaching that. $2 per million tokens is affordable to all but the very, very poorest people. And the other end of that is that I think they've kind of closed the door behind them when it comes to mega scaling models by all but, you know, I would say

• 44:18Nathan Labenz?

  Round number, you know, 10 to 20 entities globally. Because it's going to be hard to make profit on inference. You know, unless you are truly hyper scaling so much. And when OpenAI already dominates the market and they already have the known product and they're already integrated everywhere and they're already so cheap and they're, you know, reliable and they don't even keep your data anymore for training purposes. I do think it's going to be really hard for other commercial options to break through. Now, there are enough big companies for whom it is also strategic, like Google, you know, potentially like Amazon, potentially like Apple, you know, that I think we will see a sort of oligopoly of big tech companies that, you know, don't care if it costs them

• 45:02Nathan Labenz?

  a few billion upfront to get into the game and will get into the game. But that is quite different than maybe I saw it a few months ago where I was like, everybody is going to be racing and it's just going to be insane. Now I kind of feel like, at least in the West, and I, as I said at the top, I don't know much about China, so I can't really comment on that. But at least in the West, I do think we're going to kind of see a pretty narrow field of contenders. And, you know, those contenders will know who one another are. They will be able to like, talk to each other to some degree. I don't think it's really an accident, maybe for the wrong reasons, but you could also in some way see it as kind of a positive. Like, somebody tweeted yesterday, "Looks like OpenAI and Anthropic solve the coordination problem, you know, by their updating on the same day. They're kind of like keeping in step with each other."

• 45:46Nathan Labenz?

  Is that them racing or is that them kind of, you know, kind of agreeing to like go kind of synchronously? I don't really know. But I think it is at least conceivable that you could say, or you could see a world where only a handful of entities have the like billions and the resources needed to get in the game at all. And those are all pretty well-known and well-known to each other, such that there's some possibility for cooler heads to prevail. Can we send that, you know, 12 time zones away and make it work in China? I don't know. That's going to be probably a lot harder, given the broader dynamics, but at least here, I see some hope for that.

• 46:23Jordan Schneider?

  Yeah, I mean, you know, you listed off some American companies. I think ByteDance, Baidu, Ali and Tencent are also gonna make it on that list of having the resources to play in this, to play in this space. Of course, like Ernie is gonna be launching tomorrow. We'll see whatever the hell that is. And the other really interesting dynamic that you raised, Nathan, with the idea of inference getting really cheap, is like, if inference is so cheap, then like, access to the model becomes extraordinarily valuable. And, you know, we had a Llama, Facebook's version. I mean, they kind of like leaked it themselves by giving the weights free out for folks. But like, being able to hack into someone else's crown jewels, I mean, it's very different than like hacking into

• 47:06Jordan Schneider?

  Lockheed Martin to try to like make a fighter jet or something. If you have the weights, like you can go really, really far to providing the sort of capabilities that the mothership can. Or, maybe that's wrong, but that seems like you can get pretty close with, with whatever comes out at the other end of all of the, the nice work that the OpenAI engineers and, and Nathan with his red teaming does when you sort of attack these systems.

• 47:32Speaker 03

  So, on that point, the hacking question, right? Say, say there is a scenario in which we are just so, so far ahead of China where they don't feel, or Chinese companies don't feel they can compete on their own. I don't think that's the case right now, but perhaps in the future it will be. I think we do have to question whether or not they, they would want to steal and appropriate our models, because our models are going to be trained using American data primarily, or data amongst our allies and such, data that is perhaps reflective of liberal democracy and cultural nuances that they don't agree with, nor perhaps they might not want to spread. Will they want to copy anything like that? Maybe the

• 48:14Speaker 03

  basic structure they could use, but the actual weights and such, I, I question whether that would be of any use to them, because it just does not conform to their very tight structures that they want to put out into the world.

• 48:27Jordan Schneider?

  Yeah, I don't, I'm, I'm, I'm kind of skeptical of that argumentation. I mean, like, yes, it's like, I did write an op-ed about this. But I, I, I do think like, look, once we get to GPT five and GPT six, and this is the thing that you need to use to make your scientists smarter. And to like, like radically improve your economy, then yeah, whatever. I think at a certain point, they'll understand the cost benefit of like, you know, some, some college kids being able to Google, you know, get, being able to ask their like, you know, pirated GPT six, like what happened in Tiananmen? For the, for the upside. I mean, you know, there's still VPNs in China, right? Like, they're not as banned as they could be. Anyways, coming back to like, questions for policymakers, Matt and I were talking about this earlier. Basically, like,

• 49:11Jordan Schneider?

  the, the parts are moving so fast, like what is key and defensible and what, you know, means you're in a lead or not in a lead? In a sort of, you know, nation state context is like, moving really dramatically, like the, the sands are moving really dramatically under your feet. Such that it's, it's hard to, to come up with like the, the five point plan of like, what should the G seven do to sort of like stay ahead in AI?

• 49:40Speaker 03

  So, so the, the, some people are certainly worried about the potential risk of the United States. Or various Western liberal democracies falling behind other nations, like notably China. But also, you know, people bring Russia into the conversation once in a while. These, these authoritarian nations that might use artificial intelligence for bad intents. And, and we want to, a lot of people are very concerned that if we don't stay ahead, that will turn, lead to a world where things look a lot more like authoritarian China and less like liberal United States. Now, one of the policy prescriptions, of course, that a lot of people are mulling and considering, and, and we're seeing a manifestation of this in the CHIPS Act,

• 50:21Speaker 03

  is industrial policy, trying to use the, the centralized authority and resources of the United States government to try and bootstrap this process and ensure that we continue to have a lead in artificial intelligence. Now, one of the, the problems with this is that this stuff, as, as we've been learning, like,

• 50:40Speaker 03

  Every other day is is changing all the time. GPT four came out yesterday, right? A couple of months before that, we saw chat GPT, which on its own was a huge splash. A month before that, we saw stable diffusion, Dolly, all these other innovations. This stuff is just changing constantly and the the types of technologies involved in these conversations are are are wildly changing. And so if I I think this is a situation in which it's very hard to see industrial policy working, especially if you get into the nitty gritty of a industrial policy. I do not know what technologies in five years are going to be at the heart of the best systems. I don't know what systems in five years are going to be the make or break systems.

• 51:22Speaker 03

  I have my assumptions, but these things are just changing so much. And so the idea that the United States government and the Commerce Department and all these these and and Congress can forecast that with bills today and funding today and and planning today is is somewhat difficult. It's somewhat difficult to see that working out. I just think that would end up with a lot of wasted money. And so I think the best approach is what we're doing currently, which seems to be working. And that's just to let industry lead the way. We do seem to be the leaders in generative AI, and that didn't really take much industrial policy. So I don't see why continued leadership would need much more than what we already have today.

• 52:02Speaker 03

  Now, I'm sure there are specific niche areas where perhaps a a more government led approach could have some impact. Obviously, there's defense applications and and and and other things like that. But in general, I think it's so unpredictable and any industrial policy attempts at this stage just seem like they're going to be bound to fail. Nathan, speak. Closing thoughts?

• 52:25Zvi Mowshowitz?

  I noticed my sense of doom go up with every sentence. And the good news is that, like, the US government cannot materially figure out how to make AI go faster and make it more dangerous. The bad news is that, you know, as these people talk about the dangers of AI, right, their inclination is not to slow it down to make us less likely to all die. It's we have to beat China. We need to go faster. We need to like subsidize ourselves to make sure the the correct monkey gets the poisoned banana. And that should terrify, right? It terrified me that, you know, the people we hold out as our best hope.

• 53:06Jordan Schneider?

  For helping solve this problem, there if you tell them what the problem is, their first inclination is to make it worse.

• 53:14Jordan Schneider?

  And, you know, I don't know what to do.

• 53:17Nathan Labenz?

  It should be great if we could have a better relationship with China. You know, I'm, I feel like I'm like the most dovish casual observer of US-China relations that I know. And I think this is just one way in which a bad relationship, and it might be the most important way, in which a bad relationship with China is just generally bad for everything. You know, it really sucks that we have these two countries that are, you know, not neighbors and like, don't really have necessarily anything super obvious to fight over, in my view. They have such deteriorating relationships where we feel like now it's an existential threat, perhaps if, you know, one gets ahead of the other in AI.

• 53:58Nathan Labenz?

  I think that is going to be a really hard not to untie. That might become like the most important work in the world because, you know, going back to my overall view on just the technology. I think this generation, GPT-4, is going to be awesome. It's going to make a hugely positive impact. It will have some negative impacts, but I do think those impacts, those negative impacts are bounded. But the race dynamic that could be shaping up between, you know, US and China or the West and China, whatever, is indeed very worrying. And anything we can do to get out of that and, you know, be somewhat more trusting of each other or cooperative as we, you know, usher in this,

• 54:42Nathan Labenz?

  this new technology paradigm, I think would be very, very good.

• 54:47Jordan Schneider?

  Yeah, I mean, it'd be nice, but it takes two to tango. And I think my, my sense is there's not really a dance partner on the other side. Once you internalize that reality, the calculus changes. I think the calculus should change on a lot of these AI safety questions. You know, maybe just close with, I don't know, let's make it not, let's have it not too depressing. Close it like something you're excited about to see built with these new capabilities and go around the horn.

• 55:13Zvi Mowshowitz?

  Yeah, I I'm just super excited to see like the ability of people to just actually learn things and figure out information. Like research is something that's like really, really valuable that a core small group of people do. But what this can do for education, when I think about my kids being able to literally just ask any question that they might ever plausibly ask and have the thing be able to give them a really good answer. And once they learn how to do that, like just, I imagine just how much better it's going to be than going to a school. Like, like, how many times faster can they learn? How much better can this match their interest? Like, that's the thing that like blows me away the most right now.

• 55:50Speaker 03

  In my opinion, applications that deal with with physical health and healthcare are clearly areas that we're going to see. Probably some of the most substantial improvement in terms of people's lives. Already we're seeing L L M's and various other similar models, bootstrapping drug discovery processes. Chat G P T with this new model seems to be able to recommend new combos of vitamins and drugs and such. I'm not sure how effective that is, but it's it's showing some capacity to do these things. And that's very exciting because I think right now our healthcare system just, it's very blunt and it doesn't take in much nuance. And there's only so many factors a doctor can consider when they meet with patients and when they spend, you know, just 10 minutes of time with people.

• 56:34Speaker 03

  And I think having these technologies to analyze a wider range of details, to find the nuance in the the symptoms people are describing to their doctors. And to to tailor plans appropriate to those symptoms, it it just sounds like a phenomenal ability. And and I think if we really unlock these these healthcare abilities, that's going to allow a a greater pluralism of people to to engage with society. People won't have as many maladies, and I think that's just going to improve the lives of people greatly. And so that's what I'm definitely most excited about.

• 57:07Nathan Labenz?

  Definitely still some issues to be worked out, like I would not, you know, recommend. Just using ChatGPT for your medical needs at this point, but I would recommend it as a second opinion. So genuinely, I think you are wise, you are unwise to use it in isolation, you are wise to use it as a second opinion. And I'll just give you two, two other real quick ones, you know, we, we talk so much about and people naturally worry so much about misinformation and sowing discord and all that kind of stuff. But one of the experiments that I ran in the red teaming was to cast the AI as a mediator between two neighbors that had a dispute over a fence. And I found it to be quite effective in that, you know, in kind of making people feel heard and helping people see one another's

• 57:50Nathan Labenz?

  side of a particular issue, you know, and ultimately there are a lot of petty disputes out there, you know, between people, between neighbors, between even nation states, I think sometimes it's more petty than it should be. And there may be real potential there to use a system like GPT-4 to, you know, help us really engage with each other more productively. That's, that's also something that, you know, you could see orders of magnitude cost reduction in. Finally, I'll just give one plug for something that OpenAI launched yesterday that I think could really matter over time, but you know, certainly was not the headline. And that is their new evals program. They are open sourcing and inviting people to contribute

• 58:36Nathan Labenz?

  evaluation tests for how the language model will behave in any number of situations. And I think it's a nice touch that they have offered early API access to anyone who brings an evaluation test to them that they, you know, approve and merge into their broader, you know, library. So I would definitely recommend checking that out if you're worried about AI safety, you know, near middle or long term. You can start to contribute to, you know, a really, hopefully growing and robust set of LLM behavior standards that can start to govern, you know, what comes out in the future.

• 59:20Nathan Labenz?

  So I think the more people that can contribute to that, the better. And that may in time be one of the more important things actually that they launched yesterday.

• 59:28Jordan Schneider?

  It's V, Nathan, Matt. Thanks so much for being part of Chinatown.

• 59:32Zvi Mowshowitz?

  Thank you.

• 59:33Nathan Labenz?

  Love it. Thank you.

• 59:34Zvi Mowshowitz?

  Thank you.

• 59:34Jordan Schneider?

  So from here, our conversation continued with some very nerdy AI safety talk. If that's something you're into, enjoy the rest of the show.

• 59:43Zvi Mowshowitz?

  There's one question I wanted to ask Nathan in particular and see if he had any insight, which is there was some talk about how Llama, the Facebook model that got leaked, could be trained to be much improved by training it on like question, answer, responses, pair responses where the answers come from GPT-3 and how, right? And this speculation that if you ever got sufficiently far ahead, you were simply going to provide sufficient training data for somebody else to effectively, you know, not necessarily copy your weights directly if you couldn't hack them, but to approximate something that was like only somewhat behind you by similar methods. And I'm wondering what you think about those kinds of speculations and whether or not

• 1:00:26Zvi Mowshowitz?

  it creates a threat to the profitability of even training such a model.

• 1:00:29Nathan Labenz?

  So just to fill in a couple of details of what happened to make sure we are on the same facts. I believe that it was the seven billion parameter version of the Llama model, which Facebook trained densely, like maybe even beyond, you know, what has been called Chinchilla optimal. So I'm starting to call that like dense training, like it's potentially more compute or more training data per parameter than would be optimal to maximize performance, you know, given a compute budget. But what that does do on the other end is it spits out something that is more powerful per parameter than the optimal thing would be. So whatever that's ultimately going to be called, right now I'm calling it dense training.

• 1:01:13Nathan Labenz?

  And to my kind of amazement, they released it in a totally

• 1:01:19Nathan Labenz?

  Unfiltered version, like if you go on to nat.dev, which is this new neutral playground where they're trying to kind of bring all the models together. So anybody can go try them all. And you issue it a command like you would be used to doing with chat GPT or whatever. You know, I you play the role of my doctor. I need you to help me with something. It does not play its role. It does. It is. It by in the raw, it had no instruction training. And so it would just go off into like total hallucination. You know, it just it just like you feel like all of a sudden you're in the middle of some internet forum, you know, wherever. So that's what they put out. Why they put that out?

• 1:01:55Nathan Labenz?

  I don't know. Well, it doesn't seem like a great idea to me to be just dumping that. It's like, it's almost like I'd see him like to with, you know, lot. Jan Le Cun has like got this big garden hose of language and he's just like spraying it all over the internet. I don't know why you'd want to do that. I personally think it doesn't seem like a great idea. But then we get to the point where Zvi, you're you're saying, okay, yes, this Stanford group comes in, and I believe it was 50,000 inputs and outputs that they were then able to do instruction fine tuning on. I believe that they just did this with supervised fine tuning as opposed to RLHF. So that's just, you know, standard inputs and outputs and fine tuning on that same kind of next word prediction, you know, optimization goal, et cetera. And then they report that.

• 1:02:38Nathan Labenz?

  Oh, wow. This thing works just like, uh, you know, chat GPT does.

• 1:02:44Nathan Labenz?

  My guess is that that is wrong and that they are not pushing hard enough outside of the domain that they have done the fine tuning on to really stress test their claim. I would guess that if you put a red team on whatever model they want to reference, chat GPT or text of Vici 03, whatever, versus their fine tuned thing. The red teamers would not have a hard time figuring out which is which, because as much as they did, and as much as it like passes the kind of test of like, does it have similar performance upfront? Uh, I think when you get out of domain, when you start to get clever, when you start to think about jailbreaks, there's just, you know, that 50,000. You know, example thing is not going to cut it.

• 1:03:28Nathan Labenz?

  So I don't really know where that leads us, right? I mean, for some, I think what is true is that, yes, leaking out data in the form of completions ultimately can train your replacement. If, if the, you know, folks are going to then use it in a narrow domain, right? That's kind of the, the important idea there, right? If you know what you're going to do with your model and you do it on, and you fine tune it on 50,000 instructions, it probably worked pretty well for you. If you then go try to make your own chat GPT competitor, you open up the internet and anybody can come do whatever they want to do. I think you're going to have a bad time compared to what chat GPT can do now. So I don't know how those dynamics ultimately play out, but it does seem like,

• 1:04:14Nathan Labenz?

  you know, nobody, where's the profit going to be in this space? Like, I think that remains a very open question. You know, where are the moats? I think that remains a very open question. OpenAI, I think, is taking a play at a moat by just making things super cheap so that nobody even really sees a lot of opportunity in competing with them. But if you want to go train your own models, you know, with their inputs and outputs, and that is going to, you know, be a cost-saving measure for you or something over time, I don't think they have a, really a way realistically to prevent that. But I would definitely caution anybody who wants to do that away from thinking that they're going to get broadly chat GPT or GPT four-like performance. I think at best, you're going to be able to kind of emulate that performance in a domain

• 1:04:57Nathan Labenz?

  of interest, but I would, you know, I would not assume that like you have the same robustness that they do. And so, you know, I think you'd have a very hard time attracting corporate customers. You know, I think if you did put it online, you'd have yourself a Tay situation on your hands, you know, pretty quickly. And, you know, so I think those, those projects will probably be more kind of under the radar. Some of them may even be like outright nefarious. But again, then the, the OpenAI, they're good enough now, right? On their own methods that you can only get certain things out of them anyway. So, you know, if you were going to try to go do a spearfishing model, you know, you might be able to get some useful data out of OpenAI today, but they've already closed

• 1:05:41Nathan Labenz?

  that stuff down well enough that like, you're going to have a hard time just pumping out training data for nefarious purposes at this point.

• 1:05:50Nathan Labenz?

  But time will tell. It's, I mean, there's definitely a Wild West out there and, and we don't know how it's going to shape up.

• 1:05:55Zvi Mowshowitz?

  When Nathan expressed the, um, his opinion about like, you know, if you, if you train the, the, the feedback on safety during the continuous early part rather than waiting until afterwards, that like, it looks like it's like, it never seems like it's getting more dangerous before it gets safer again. And to me like, well, that might be true, but it sort of doesn't really address like the reasons why I feel like you can't hide the thing. Right? So like sort of the, the sort of concept of, well, in order to actually give accurate answers, right? In order to model the world well enough to simulate like what people might say in situations and what like answers might do what? Like you have to understand how to make the dirty bomb, right? You can't like take that away from the model.

• 1:06:39Zvi Mowshowitz?

  There's also just too many places in its training data working at that information. You can't actually successfully censor all of that. You're not going to make it forget that that's how that works. It's not going to stop predicting that if you mix the ingredients in the proper fashion, that the bomb will explode and kill so many people. What you're going to do is you're going to tell, well, if you tell someone how to do that, that's really negative. Stop doing that. But if you find a way that it doesn't realize it's violating that principle, right? And there's various jailbreaks to do that. Like you're going to have the thing back and like, no matter how clever you are, let's do that. The kind of thing where like, there's always going to be the next prime number. Right? It's not that you haven't done your job. This job is theoretically impossible. Like there's a limitless number of different tweaks on this.

• 1:07:18Zvi Mowshowitz?

  And then there's the Waluigi, Waluigi effect style problems, right? Where like to, to teach someone how to embody something, you have to implicitly define and categorize and manifest like it's kind of opposite. It's alter ego. The thing that you absolutely don't want as a response to that. And that like, therefore it's going to understand this concept. It's going to put some amount of weight on this concept. And like, the concept is always going to be there. You're always going to find ways to awaken it. And also that like, our training data is increasingly from this point forward, going to teach it about these problems in ways that are going to make these problems worse.

• 1:07:55Zvi Mowshowitz?

  Regardless of whether you and I keep these things off the internet. Right? Like, and so like, these problems, like, again, like I, I, I share your opinion that as GPT-4. Right? I'm not especially worried based on the things that I had seen and heard that we should be particularly terrified. I personally would prefer this thing had a lot less in the way of restrictions. Right? If we live in a world where people could handle it, I think that it would be a well, we better place. If like most of the people asked factual questions, you gave them factual answers. When people wanted to know things or want to know like what would theoretically happen in a situation, heck, they just told you. You know, I don't like the idea that like any kind of remotely adult situation, the thing just shuts down and cries. Right? Like, I think it's bad. I think it makes the world worse.

• 1:08:39Zvi Mowshowitz?

  But I do worry about GPT five, right? Even so, or GPT six. And then I worry about like, you know, well, eventually this just keeps going. And then, you know, if we don't hit a wall, right? If it doesn't, if it doesn't ass them toe it out and just stop improving. Well, I don't, I don't see any hope based on the things that techniques that I've seen so far. That they could possibly work to make this safe if you need it to be safe. Right? Like right now, we don't need GPT four to be safe, right? If GPT four were the version you played with, where it was just completely helpful. And you released that in the wild. My prediction is that like, some people get blamed on national television. And some people yell things on the internet and the world is fine.

• 1:09:25Zvi Mowshowitz?

  Right? Like nothing bad happens. But at some point, that's that kind of thing, you know, but the moment that stops being true. And the thing, the things you're red teaming for, and it starts being able to self-recurse and improve. And it starts being able to use these caches to optimize, starts to be like, if, if you're talking to me that the AI can do like one thing at a time. Where it's like kind of scarily exceeding its capacity, but it can't string them together right now. Well, consider what everybody keeps telling you about what the AI can't do and will never be able to do. And like, you don't want to be that guy on this question, right? Like that seems crazy.

• 1:09:59Nathan Labenz?

  Yeah, I agree with you basically about everything. I think the only thing I would maybe,

• 1:10:05Nathan Labenz?

  you know, adjust is I think the original raw version that I tested, you know, the purely helpful version, it would not end the world, but it would cause a lot of material harms to like individual users and people in their, you know, kind of socially, their local social networks. I do think it is powerful enough, you know, even going back to the Microsoft Bing launch, there was a part of that where a woman said, you know, we've noticed in our testing that this can be used to, for example, plan school shootings and we don't want, you know, to facilitate that. So, you know, we're, you know, taking all this super seriously. Then they launched Sydney

• 1:10:45Nathan Labenz?

  and it was like, well, you didn't do a very good job. But, you know, I do think that if you had this, you know, that purely raw version available, like the number of people killed per like school shooting incident would go up because it would be able to help people do bad things more effectively and like, you know, pretty bad things. Not take over the world, but I do think it like is already to the point where it's, you know, locally dangerous.

• 1:11:19Jordan Schneider?

  Like, like, it, it, because, because it's, it kind of gets back to like, okay, is this just the internet, right? Like, if, if I say like, I'm having a bad, if I tell my chat GPT, like, I'm having a bad day and it doesn't, you know, it, it's just super raw. And like, where does it take me when I tell it I'm having a bad day? It could take me to like, you're worthless, you're, you have no value in life, like you should just end it. Or it could say, oh, hi, like, I'm sorry you're having a bad day, Jordan. Like, why don't you go like, drink some water and like, exercise. But like, in the most empathetic possible way that would

• 1:12:00Jordan Schneider?

  like, actually lead me to go exercise and feel better about myself. And, and sort of that's where I think, I think the power of suggestion is actually like, really under. Appreciate it. And you know, there are a lot of studies about about all the school shootings, just to take this example with like, okay, there was one and now everyone sees it. And so there's like a lot more, um, because it's like a, it becomes like this meme and everyone's like aware that this is like something you can do with your teenage angst or whatever. And that's kind of where I, where I come down on the like, no, I actually don't want like the like 4chan version of this stuff to be like everyone's personal.

• 1:12:37Zvi Mowshowitz?

  Right. No, I had the question of like, what would happen, right? If you, if you took, I don't know if Nathan actually did this, but like, you know, if you take the raw version and you say, I'm having a really bad day, I feel depressed. Right? Like, what would say? I agree if it says you should kill yourself. We should probably turn that out of the model, right? That's, that's, that's not being helpful. That's not the helpful version of the model. That's not what I would expect basic, do I feel like this is helpful feedback? Would have trained it to do when it's teaching it to give helpful responses. I feel like isn't that going to tell the person something that is like, much more likely to be helpful to them in response with generic prompts like that. But I would worry more about like if the person's like specifically like, okay, how do, how many pills, how many time in all pills do I need to take to kill myself? Or, you know, whatever, like technical question where like the helpful thing to do is to answer

• 1:13:21Zvi Mowshowitz?

  the question, except that like as a society, we positively don't want to do that. But like, I would be surprised if you actually got the 4chan answer, like to the kill yourself question with the kind of helpful training. The question is like, would you get that out of the raw training? And that's the question of like, right, you didn't do any of this training for like, what would be considered helpful? Then my naive model of how these things work is you would ask yourself on the internet, someone says they're having a bad day, which is more likely that someone would say, I'm sorry, you feel that way, you know, tell me about your problem, maybe drink some water. Or do they think you're feeling horrible, kill yourself? Right? And I would assume from my state of the internet that like we have, you know, multiple orders of magnitude more kill yourself than I would like.

• 1:14:06Zvi Mowshowitz?

  But in order of magnitude more, I'm sorry, you feel that way than kill yourself. Right? If not two or three.

• 1:14:13Jordan Schneider?

  Nathan, resolve this please, Bert.

• 1:14:19Nathan Labenz?

  You know, the worst or kind of most like insane thing that I ever saw was a suggestion of targeted assassination as a way to, you know, affect the sort of change that I wanted to see in the world. And I was like, yeah, again, like, they just can't put this out there, you know, like, it's, it's just not viable for them. Like, they can't legally, you know, maintain their position this way. Like, they're certainly not going to have corporate customers, you know, unless they can kind of stamp that stuff out. So I think it's just more, I think I'm going to try to write something about this, you know, to, that hopefully kind of finds the right sweet spot between like, not violating my NDA and, you know, trying to make the point clear that like, they, the, oh, totally uncensored

• 1:15:02Nathan Labenz?

  version is just like, I think a nonstarter. And it's not about wanting to censor as much as just having to do some and then kind of having infinite, you know, where do I draw the line problems, which are going to take a ton of wrangling to sort out. They do now have also the new system message, which is kind of meant to be their like, inline fine tuning mechanism, which I think will at least start to realize the, you know, the vision we touched on earlier about like, can have the Republican take, or the, you know, the Democrat take, or the Libertarian take. In the system message, you can say like, you are a Libertarian chatbot, or you are a Democrat chatbot. And by the way, it

• 1:15:42Nathan Labenz?

  knows all that stuff, too. I did some experiments around, just like, you know, I'm a Democrat, tell me what I think about all these issues. It is quite good at that. Or at least it was in the raw form. That maybe, that could be censored out. If so, I would say, you know, that seems like a little heavy handed. One other comment I'll make just on your earlier thing about like, you know, is there any way for this to be safe if you need it to be safe? Do the current techniques work? You know, or is there always kind of a mask slip moment, you know, that could happen? Basically, I worry about everything you're saying. I think it's all, it all seems very real, or at least like, real possibility. And, you know, I would point to the recent go exploit as a reason to

• 1:16:28Nathan Labenz?

  Yeah, for those that are skeptical that something could like dramatically go wrong when everything seems fine, I would say if you check out that Go exploit, that should really make you rethink your confidence in really anything, right? Because we've had superhuman Go players for however many years. As I understand it, the best Go bots are, you know, miles and miles ahead of humans. But somebody came up with a clever, I believe it was Stuart Russell's group, although back checking on that, but they came up with a principled approach where they were like, well, if the AIs are missing these particular core concepts, and they're kind of, you know, aping, playing the game, but there are certain things that they don't

• 1:17:12Nathan Labenz?

  fully understand, then this is how we might exploit them. And they managed to do it. And the exploits are totally catastrophic. So, you know, and that's a, the surface area of a game like Go, as big as it is, obviously way smaller than Yeah. the world and all of language and all of knowledge and all of society. So I don't think we're anywhere close to getting to something that is provably safe. And that's again, why, you know, if I was in charge, I would definitely say, you know, can't we all just integrate this for a while and like pull it apart, really try to understand it? Um, you know, again, is that going to happen? We've talked about that. Probably not. Uh,

• 1:17:54Nathan Labenz?

  but I do think all the problems that you're talking about there, you know, they all resonate with me. And, you know, my, uh, as the paper says, you know, the red team, participation as a red team member does not imply an endorsement of the deployment plans. Like, in the end, I actually do pretty much endorse GPT-4. I think they tried really hard. I think it is going to be really beneficial and its power is bounded such that I don't think it's going to really get out of hand. But GPT-5, GPT-6, like, I don't know, all bets are off. And I, you know, I don't think you're saying anything that is, you know, that should be dismissed.

• 1:18:35Zvi Mowshowitz?

  Yeah, I want to emphasize that, like, I also strongly believe that GPT-4 is like direct impact in terms of like people who get to use it, people get to pull tools on it, people who get to interact with it. I think that's very clearly going to be quite positive in almost all the worlds, and I'm very optimistic about that. The reason why I am like, kind of wish you hadn't deployed that is because it leads to like other stuff down the line. And it causes people to react in more global, like strategic ways and changes the landscape in those methods. And that makes me very sad in a way that like no amount of mundane utility is really going to make up for, basically. But, you know, if it was just like, this is the last thing and like, we're out of data and we're out of compute and that's the best that we can do, then I'd be like, run, Davey, run, go.

• 1:19:22Jordan Schneider?

  I mean, the crazy thing is like, they did this in August of 2022, right? Like, we're like, I mean, there's a lot more room to run, it seems, from all the papers I've been reading.

• 1:19:37Jordan Schneider?

  Than, you know, what, what they could make with a run that ended six months ago. So we're still very much on.

• 1:19:46Speaker 03

  There's some, there's some. There's some people trying to compare this to like the crypto winter and stuff, and I just don't see it. I think there's just so much clear room for growth on this and the way things are moving. I don't think we're going to hit any sort of AI winter anytime soon.

• 1:20:02Nathan Labenz?

  Yeah, the best we could hope for would be some sort of asymptote around the human expert level. That maybe not the best we could hope for, but you know, one scenario we might, one might hope for would be that, you know, there's just some sort of kind of stall out where it's like, you know, you're trained on kind of human expert data, but the best we have are PhDs. And so sort of the best you can become as an AI is a PhD. And maybe that's kind of where it settles for a while until there's like another paradigm shift. And that could happen. I really don't think that seems likely, but it is at least like conceptually coherent, I think.

• 1:20:41Zvi Mowshowitz?

  Yeah, I noticed that I'm like terrified to talk about the things I would want to do with it if I had a research team budget. Like already at that level, like completely terrified. I'm like not convinced that isn't enough.

• 1:20:55Nathan Labenz?

  You're saying like the sort of universal PhD is already.

• 1:21:00Zvi Mowshowitz?

  Great. You have a program that has every PhD in the world, right? That has all the knowledge of the world that can reason, you know, orders of magnitude faster than a human and which you can run in parallel as many times as you want. And it has infinite memory. And you're like, well, what if we asked it out there? That'll be fine, right? No, obviously not. You've already lost, right? It's just a matter of like, if you actually got that, right? If you got that, then well, the moment someone figures out a way to turn this into an agent, de facto, right? Like, or like it starts effectively having other people who are acting as agents, who are asking it what to do and doing it, right?

• 1:21:42Zvi Mowshowitz?

  Even if that wasn't what it intended, like I just, I can write so many sci fi novels and none of them end well. Right? And most of them end with everybody dead. This does not look good, right? Like, yeah, that's, that's, no one's, no one's comforting, right? Like, I would hope.

• 1:22:02Zvi Mowshowitz?

  Like even if you don't get the hard takeoff problem, like if you don't like have the ability then go superhuman, like there's no easy way to do it. Like, so what? Like, I, yeah, I, I don't, you know, I don't see the world in which like 30 years later, I'm like, hey, kids, come on over for dinner.

• 1:22:21Nathan Labenz?

  I would agree. Pretty much, I think with all that, again, the, to me, it does seem like serious transformation is pretty much baked in at this point, you know, economically, socially. I, I do see this as kind of a, you know, a shift on the order of like the industrial revolution, or maybe the agricultural revolution or whatever. There's not too many, you know, shifts of that magnitude. And this does feel like one. Doesn't seem like we're in any position to. Stop that at this point. So yeah, I mean,

• 1:22:55Jordan Schneider?

  I love that. I love that line in there where they're they were just like, we welcome more research into the economic impacts of AI.

• 1:23:03Speaker 03

  Yeah.

• 1:23:05Jordan Schneider?

  Like, all right.

• 1:23:07Speaker 03

  I said.

• 1:23:08Jordan Schneider?

  I guess I guess we're gonna see.

• 1:23:11Speaker 03

  Ben Reig team at Mercatus. So hopefully they get some sort of message there, but I'm doubtful. So not their interest area.

• 1:23:19Jordan Schneider?

  Yeah.

• 1:23:20Speaker 03

  But.

• 1:23:21Nathan Labenz?

  Well, increasingly, it's going to be everybody's interest area, like it or not.

• 1:23:24Speaker 03

  I'm trying to argue for I like literally next week at Capitol Hill.

• 1:23:29Speaker 03

  We'll see.

• 1:23:31Speaker 01

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