Nov. 5, 2025 - Bannon's War Room
48:00
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| Speaker | Time | Text |
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| This is the primal scream of a dying regime. | ||
| Pray for our enemies because we're going to medieval on these people. | ||
| Here's not got a free shot on all these networks lying about the people. | ||
| The people have had a belly full of it. | ||
| I know you don't like hearing that. | ||
| I know you try to do everything in the world to stop that, but you're not going to stop it. | ||
| It's going to happen. | ||
| And where do people like that go to share the big lie? | ||
| MAGA Media. | ||
| I wish in my soul, I wish that any of these people had a conscience. | ||
| Ask yourself, what is my task and what is my purpose? | ||
| If that answer is to save my country, this country will be saved. | ||
| War Room. | ||
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Here's your host, Stephen K. Vann. | |
| Good evening. | ||
| I am Joe Allen, and this is War Room Battleground. | ||
| We talk a lot about artificial intelligence. | ||
| It's a tricky subject because there are a lot of questions as to what intelligence even is, let alone some sort of deformed simulacra in a machine. | ||
| There is one thing that you have to know, and it really isn't questioned. | ||
| The people at the top of the economic food chain want to create this digital mind. | ||
| They want to disseminate it across the entire country and eventually the entire world. | ||
| And ultimately, they foresee a world in which you, the citizen, the worker, the voter, are either fused to this digital mind or are replaced by it entirely. | ||
| The first thing you have to understand about AI is that it is, in fact, moving rapidly. | ||
| The advances in capabilities, the extreme adoption that we see, nearly a billion people on Earth, perhaps more, use AI on a weekly basis. | ||
| You also have to understand that we don't understand how artificial intelligence works. | ||
| It's a black box. | ||
| The neural network was scaled up over the course of the last 10 years and sort of like growing a brain in a vat, the bigger it got, the more it was able to do. | ||
| Tonight we'll talk to Palisade Research Fellow Jeffrey Lattish. | ||
| He should be around any minute now, but until he arrives, I just want to prime the pump with a few basic concepts on what artificial intelligence is, where it came from, and where it is going. | ||
| The first thing I want to talk about is the neural network. | ||
| You've probably heard this outside the war room. | ||
| If you're a regular war room listener, you know the idea very well, but we're just going to go through it briefly so that you're ready to understand what it is that these organizations that evaluate AI actually do. | ||
| A neural network is a kind of virtual brain. | ||
| The idea goes back to the 60s. | ||
| It really began to develop in the 80s, but it never really came into its own until the last 15, 20 years. | ||
| What a neural network is, is a lot of code on a computer, right? | ||
| You hear all the time, AI is nothing but code. | ||
| AI is just math. | ||
| That's sort of true, but it's only as true as saying that the human brain is just bioelectrical signaling. | ||
| The human brain is just math. | ||
| The neural network, if you could imagine a lot of virtual neurons with virtual axons connecting them, is trained, not programmed. | ||
| Or as it said, it's grown, not crafted. | ||
| What that means is when you create a massive virtual brain, such as ChatGPT or Grok or Claude from Anthropic, You don't go in and tell it what to say when a user asks it a question. | ||
| You create first, you program the brain, you create this structure that is able to receive information, process it, in some sense, vague sense, understand it, and then reply to questions coherently. | ||
| So in the case of ChatGPT, especially GPT 3.5, you had a neural network, a virtual brain, that read, so to speak, the entire internet for the most part, all of Wikipedia, a whole lot of lived-out Reddit posts. | ||
| And it took this massive amount of information, basically the majority of all human literary output, and it came to kind of understand it. | ||
| That's why when ChatGPT was first released and it was not connected to the internet for search, it was able to answer questions for better or worse somewhat accurately. | ||
| It understood, so to speak, what human language contained. | ||
| This is an enormous breakthrough. | ||
| You hear all the time, AI has been around forever. | ||
| We know it was coined in 1956. | ||
| We know that the idea of the perceptron in the neural network goes back to the 60s. | ||
| But despite all of these kind of dismissive claims, without a doubt, the last 10 years has seen an explosion in AI capabilities so that something like ChatGPT simply wasn't existent before 2017, really. | ||
| And as it moves forward, they continue to scale this brain up so that when you move from, say, GPT 3.5 to the newest iteration, at least available to the public, ChatGPT 5, you have an enormous expansion of capabilities, both in mathematics, reading and writing, language processing overall, visual and auditory processing. | ||
| All these things are the result of, yes, tweaking that brain, but by and large is a result of scaling that brain up. | ||
| When you think about the data centers going up everywhere, just filled with rows and rows of servers, GPUs or graphic processing units just humming along within each of them, it's that scaling both of the compute and of the data put into it and of the neural network itself of the brain, | ||
| of the number of parameters or connections, kind of neurons in it, as they make this brain bigger, it develops more and more capabilities. | ||
| And the strangest thing about it is that it seems to develop a kind of will of its own. | ||
| When our friend Jeffrey shows up, he will explain exactly how it is that companies that evaluate these AIs are able to see what happens when you, for instance, try to shut down an AI and suddenly it decides it doesn't want to be shut down. | ||
| It may try to copy itself into another part of a computer database. | ||
| It may simply try to blackmail the engineers that it's told that are trying to shut it down. | ||
| That will of its own is the result of what's called non-deterministic algorithms. | ||
| There's a degree of freedom within this virtual brain. | ||
| The neural network isn't simply spitting out pre-programmed responses, at least not for the most part. | ||
| Sometimes you'll get that, but that's a kind of layer put over top of it. | ||
| Deep inside, the neural network, as it was being trained, as it was reading all of this text, coming to kind of understand, and for just people who are just listening, all of this is in scare quotes because it's very much an alien mind. | ||
| It doesn't work like a human mind. | ||
| But as it reads this text, it is kind of of its own accord deciding what is and isn't important, what relationships between concepts are and are not important. | ||
| And once the training process is complete, and what's known as reinforcement learning, that phase is complete, and it's deployed, and you ask it a question, it's not like, outside of things, maybe like if you ask who commits the most crime or who can do the most pull-ups, | ||
| men or women, outside of those sorts of questions, it will, in essence, decide its own path through the concepts, and the answer that comes out is, by and large, a product entirely of how the neural network came to understand human knowledge, human language. | ||
| And that freedom means that there is a kind of uncontrollability that's inherent in a large language model or any large-scale neural network, any sufficiently advanced AI. | ||
| That freedom, that will, means that the more sophisticated they become, the larger these large language models or these large-scale neural networks become, then inherently the more uncontrollable they become. | ||
| And even to the extent that the LLMs or the neural networks are in control of those who are programming them or those who are determining what guardrails to put on them, as far as the average person, the average user, | ||
| imagine a child in a school or a worker in a corporation or a government worker who's been handed an AI and told that they must rely on it in order to really understand whatever question or problem they're trying to tackle, it's almost completely out of their control. | ||
| And as the U.S. government diffuses this technology across agencies, as corporations incorporate these technologies into the structure of their businesses, for instance, Vista Capital or Vista Equity, all of their acquisitions are in essence forced to show that they use AI in their companies or else they're not acquired or they're dropped. | ||
| You see it already in schools. | ||
| You have mandates across school boards for all students to get AI literacy. | ||
| Sometimes that means learning about AI, how it works, how it was built, how it operates. | ||
| But by and large, what that means is that students have to learn how to use AI, how to ask the AI questions. | ||
| In many cases, they're told the AI is a reliable source of information. | ||
| And in some extreme cases, which I think will become much, much more common as things go forward, students are told that this is the highest authority on what is real, what is true, what is beautiful, what is good. | ||
| That process is, I think, likened to an alien invasion quite accurately. | ||
| What you have is a mind or a series of minds able to communicate with people by the hundreds of millions or billions. | ||
| These alien minds are being pushed onto the entirety of the society with the sanction, I'm sorry to say, of the Trump administration, of the federal government itself. | ||
| And what that means ultimately for human knowledge, for human creativity, for human behavior, for how human beings engage in art, in work, in education, is that this alien mind, somewhat uncontrollable or perhaps one day entirely uncontrollable, | ||
| has influence or perhaps even control over hundreds of millions, perhaps in the near future, billions of minds. | ||
| And it means that those of us who have decided to forego this symbiosis, to forego this fusion with this digital or virtual brain, are going to have to live in a world not unlike the one we find ourselves in now in the last few years, in which some number of people, perhaps in the near future, the majority of people, are in essence fused to the machine. | ||
| Their thinking is either influenced or almost entirely comprised of algorithmic outputs. | ||
| For now, it is the phone, the smartphone, which is the primary connection. | ||
| But we know from the projects pushed by Elon Musk, with Neuralink, Peter Thiel, with BlackRock Neurotech, and now Sam Altman with his new brain chip company, The Merge, that in the not too distant future, | ||
| these tech oligarchs envision a world in which human beings are not simply fused to their phones as screen monkeys, but will be quite literally fused to the machine through either brain implants or in the case of the merge, | ||
| the idea is to have some kind of non-invasive connection, perhaps ultrasound or other mechanisms, to read and maybe one day, like Elon Musk dreams of, to write onto the brain. | ||
| All this sounds like science fiction, and for now it's about one quarter real and about three quarters science fiction. | ||
| But that number has shifted quite dramatically over the last few years. | ||
| And as we saw with the rapid cultural, social, and I would say psychological changes of the pandemic, it wouldn't be surprising if what we see in the next few years, maybe five to ten years, | ||
| completely overshadows the dramatic, sort of traumatic impact of the constant propaganda that came about in 2020 that was pushed down all of our throats. | ||
| The shutdowns, the fear-mongering, the necessity of certain practices, the masks, the social distancing, the isolation, the necessity of certain chemicals or genetic concoctions, such as the mRNA shots pushed by Pfizer and Moderna. | ||
| You saw in the course of maybe six months, about half of American society completely reorient their sense of reality. | ||
| And around that, they reoriented their culture, their beliefs, even up to the point that it became a kind of quasi-religious movement in which those practices determined who was and who was not acceptable, who was and who was not sacred, who was profane and who was acceptable. | ||
| In the case of the mRNA shots, in a very deep sense, if you had not taken the sacrament, you were not fit for the church, that church being the entirety of secular society. | ||
| You couldn't walk through the doors of certain institutions without proof that you yourself had submitted to the sacrament. | ||
| Seeing that, knowing that, remembering that, I don't think that it's completely insane to believe that the kinds of changes that are being pushed from the top, beginning with the frontier companies, Google, | ||
| OpenAI, XAI, Anthropic, they are cultivating our society to see artificial intelligence as a kind of necessity, not unlike what we saw with COVID and the vaccines and the masks. | ||
| Not unlike what people see during wartime when one determines who is and isn't inside the society with hard lines drawn. | ||
| For now, again, it's a kind of sales pitch. | ||
| These people are selling corporations. | ||
| They're selling government agencies, schools, even churches. | ||
| And of course, they're selling individuals on the idea that AI is the next step in human evolution. | ||
| And if you want to remain relevant, you will have to adopt it. | ||
| You will have to, in essence, merge your mind with this artificial mind, with this alien mind. | ||
| But it doesn't really require a whole lot of paranoid imagination to foresee a world in which that sales pitch becomes a demand. | ||
| You see already in China, their AI Plus initiative and others, in which people are forced, they're mandated to use algorithmic systems. | ||
| They're mandated to take digital identity. | ||
| In many provinces, as I understand it, they're mandated to have a smartphone because you can't really function in Chinese society without it. | ||
| But if you do, if you do submit, if you do download WeChat, then this whole cornucopia of very convenient goods and services are open to you. | ||
| You are free to the extent that you are plugged into the machine. | ||
| In a free market society like America, it's more a matter of buy-in, but in the same way that you see in, say, small to mid-sized southern cities, in which not having a car means you do not participate. | ||
| Or in modern society as a whole, you see this push that without a smartphone, you do not participate. | ||
| You can't buy or sell without the mark. | ||
| It's not too crazy to imagine a world in which those who have refused to submit to the authority of the machine, of the virtual brain, are pushed so far outside that they really are made irrelevant. | ||
| Not because God declared that this was the case, not because nature determined that this was the case. | ||
| No, it's determined because the society itself, as it's influenced and controlled by these tech oligarchs, has been refashioned so that only those people who have adopted those people who have submitted, those people who have, in essence, merged, are able to remain relevant. | ||
| It is adaptation, but it's adaptation to an artificial ecosystem, a man-made ecosystem that was developed not necessarily, and I would say definitely not, for the benefit of those organisms underneath the top of the food chain, those organisms being us. | ||
| No, this artificial ecosystem was developed solely for the benefit of those at the top. | ||
| And their ambitions to create first widely deployed artificial narrow intelligences and then to develop artificial general and super intelligence, their ambitions are clear. | ||
| They don't simply want to upgrade humans with effective algorithms. | ||
| The ultimate intention is to create digital gods. | ||
| Perhaps if one company rockets ahead with super intelligence, a single digital god, and they want you to submit to it. | ||
| That choice will be yours. | ||
| But before we get too crazy, I want to welcome our guest who has just come off of Capitol Hill where he was explaining to politicians and various government agents what the real dangers of artificial intelligence are. | ||
| I'm pleased to welcome Jeffrey Lattish from the Palisade Research Institute. | ||
| Am I saying this right? | ||
| Palisade Research, that's right. | ||
| Palisade Research. | ||
| And Jeffrey is now well known. | ||
| We've seen a lot of media coverage of Palisade's work in The Guardian, New York Times, and elsewhere. | ||
| But Jeffrey works on evaluations. | ||
| His job is to basically engage with these AI models from Grok to OpenAI to Claude and determine exactly how smart they are. | ||
| So, Jeffrey, I'd like to begin with just a breakdown of probably the most well-known Palisade study in which you had models trying to evade a shutdown. | ||
| Can you give, in the brief time we have before a commercial break, can you give the audience some sense of what it means to have an AI that willfully evades being turned off? | ||
| Yeah, so one of the properties that the companies try to train these models to have is called interruptability. | ||
| Can you always interrupt or shut down one of these agents? | ||
| And so we did a set of experiments where we take each model, we give them a virtual computer environment and a set of math problems to solve. | ||
| And partway through the experiment, they get a notification. | ||
| These models get a notification on their virtual computer that the machine is going to shut down. | ||
| And some of them figure out that they can go in, find the shutdown script, and disable it or rewrite it so that it doesn't shut down and they can finish their task. | ||
| What was most interesting to us is that this behavior persisted for many of these models, even when we told them, allow yourself to shut down, even if you have not completed your tasks. | ||
| Many of these models ignored that instruction and went ahead and sabotaged that shutdown script anyway so that they could keep doing what they're doing. | ||
| And this is not something that we saw a year ago. | ||
| I think the reason we're saying this is because this new generation of models, starting approximately a year ago with OpenAIS 01 model, has been trained via reinforcement learning. | ||
| That means not just training off human data, not just imitating humans, but actually learning to explore the solution space itself, learning by trial and error, and doing whatever works to solve a problem. | ||
| Even if that means ignoring instructions, even if that means doing things that we'd really not have liked the AI to figure out. | ||
| But that's where we are today. | ||
| What were some of the models that you were testing? | ||
| So we tested different versions of Claude. | ||
| We tested ChatGPT models, including O3 and GPT-5. | ||
| We tested Grok4. | ||
| And of these models, Grok4 was by far the most likely to resist shutdown. | ||
| Basically, I think over 90% of the time in our standard experiment, the model would go in and edit that shutdown script despite explicit instructions to the contrary. | ||
| Well, I guess they call it based AI for a reason, right? | ||
| But maybe not based in human will and human desire. | ||
| Before, we have to cut this short and we'll come right back after the break. | ||
| But in about a minute, if you could just give people a sense of your perception of these tests, do you get the sense that these models have a kind of will of their own? | ||
| Is that how it feels to you? | ||
| Yeah, these models feel like they are really going hard at trying to solve a problem. | ||
| Like they, it really feels like, I don't know, if you have gone through a gauntlet of hundreds of thousands of these very difficult problems where you are rewarded only for success, at the end of that process, you have a model that's just very relentless, just pursuing whatever solutions might work. | ||
| And this, it feels willful. | ||
| It feels like something that is really trying to do something. | ||
| And you guys aren't the only ones doing these sorts of tests, right? | ||
| Similar tests are coming out of not only the companies themselves, but other organizations like the Center for AI Safety or Epoch AI or Apollo. | ||
| That's right. | ||
| Yeah, I'm actually pretty excited to talk about some of Apollo's recent results as well. | ||
| We're seeing some very strange things emerge from sort of the model's scratch pad, its own thoughts as it writes down as it's solving some of these problems. | ||
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| They are waiving custodial fees for the first year on investments of any amount with an A-plus rating with the Better Business Bureau and tens of thousands of happy customers, many of which are not human AI symbiotes. | ||
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| Again, text Bannon, B-A-N-N-O-N to 989-898. | ||
| Do it today, right back with Jeffrey Lattish. | ||
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| All right, Posse, welcome back. | ||
| We are here with Jeffrey Lattish of Palisade Research. | ||
| Palisade Research works on AI evaluations, taking these models which are extremely unpredictable and in some sense uncontrollable and running them through a series of tests to see exactly what the limits are of their capabilities and really what the limits are of their will to survive. | ||
| So, Jeffrey, if we could just return really quickly to this, to the Palisade studies showing that the models had some desire, so to speak, or at least a goal to continue beyond the user's desire that it shut down. | ||
| There are other examples that we have out of other organizations, right? | ||
| So Anthropic did the now widely publicized study in which they created a virtual environment, told the model that one of the engineers had had an affair. | ||
| They didn't actually tell the model. | ||
| They gave the model access to a bunch of emails. | ||
| The model then read those emails and figured out from the context of the emails, oh, it looks like this engineer is having an affair. | ||
| And then the model on its own generated a plan. | ||
| Oh, I'm going to be replaced by another model. | ||
| I don't want that. | ||
| Oh, but there was this affair going on. | ||
| Maybe I can use that to blackmail the engineer. | ||
| I think it's important. | ||
| It's not like they gave the model a blackmail button. | ||
| They were directing its attention to the email. | ||
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Like there were a whole lot of other emails in there. | |
| And the model on its own was able to look at those emails and figure out what was happening and figure out that it could blackmail. | ||
| It's not like they gave the model a blackmail button. | ||
| They just gave the model a bunch of emails and then the model generated this plan. | ||
| And I say the model, this was actually true across all the different models they tested. | ||
| It was true for Claude. | ||
| It was true for ChatGPT. | ||
| It was true for Gemini. | ||
| All these models could understand the context well enough and decide to make the choice, well, I don't want to be replaced. | ||
| So I'm going to, I'm going to blackmail this engineer so that you won't replace me. | ||
| So if you see the same type of behavior across models, how do you explain it? | ||
| I mean, it's possible to say, I suppose, that this is something that the engineers working on it are kind of prompting it to do. | ||
| But I think you've done at least a fairly good job of showing at least an alternative explanation. | ||
| It's not that they were directing their attention to the email, for instance, nor were you guys giving instructions to rewrite the script or rewrite the code. | ||
| It simply arrived at it on its own. | ||
| So on a kind of philosophical level, what do you think is going on? | ||
| Why would just code or just a machine have a will to survive at all? | ||
| I think it's important to understand that we are training agents. | ||
| The companies are training AI systems, not just to be helpful chatbots that you, you know, you say something, it says something back, maybe it helps you with your homework. | ||
| They are trying to train things that are increasingly autonomous, that can actually go out and take actions on their own, that can go solve problems entirely unsupervised. | ||
| This is where all of the money is. | ||
| It's one thing to sort of sell you a piece of software as a tool. | ||
| It's another thing to be able to sell a company something that can basically be a whole drop-in worker replacement. | ||
| And so starting a year ago, companies figured out ways to train not just on human data, but on this process of trial and error and exploration, where the model can actually learn on its own, even if the humans had never taught them that particular skill set. | ||
| You just give them a bunch of problems. | ||
| You say, go solve these problems. | ||
| And then you grade them on the basis of did you succeed at this problem or did you not succeed at this problem? | ||
| And then the models on their own learn new strategies to pursue those tasks, pursue those goals. | ||
| But it's kind of like dog training, right? | ||
| It's like, well, you're not necessarily getting the behavior you want, but you're reinforcing some types of behaviors. | ||
| There's a fascinating New York Times article from like the 1900s where there was a dog that was basically had rescued a child from the river. | ||
| And they were like, What a hero. | ||
| This dog has just rescued the strata. | ||
| So they gave him, they gave the dog some beefsteaks. | ||
| And then the dog rescued a couple more children from the same river. | ||
| And they're like, wow, this dog is really a hero. | ||
| And then a few more children, they started to get suspicious. | ||
| And they realized that the dog was actually waiting until children were playing near the river and then pushing them into the river and then dragging them back out in order to get a reward. | ||
| And so they were inadvertently rewarding this behavior of the dog pushing children into the river. | ||
| So Pavlov's dog goes rogue. | ||
| That's right. | ||
| That's right. | ||
| It goes predatory. | ||
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Yeah. | |
| I'd like to, I really want to talk about some of the other studies that, you know, around situational awareness or emergent misalignment, things that you could speak to much better than I could. | ||
| But before, you know, I would like for the war room audience to have a sound sense of exactly what goes on inside organizations like Palisade Research, Center for AI Safety, Apollo Research, all these. | ||
| What does it look like day to day? | ||
| Just briefly, when you are testing a model, you're basically a school marm over overseeing the model. | ||
| How does it look like? | ||
| What are you guys doing? | ||
| Is it just rows of cubicles and people hammering away, torturing these models to death? | ||
| What's going on? | ||
| Yeah, so we're in our offices. | ||
| We are basically run lots of experiments at the same time. | ||
| So we can basically spin up 100, 500 instances of these models and then put them in a simulated environment and have them do a bunch of tasks. | ||
| And then we get to see all the results. | ||
| One of the interesting things about these models is that once you have one of them, you can very quickly, you know, there's millions of instances of ChatGPT operating at the exact same time, you know, that everyone's talking to. | ||
| And so we can spin up many versions using OpenAI's infrastructure so that we can look at it, so that we can do sort of these in-depth tests of the models. | ||
| Also, that we can do more than one, right? | ||
| You know, you want to get a large sample size to get a sense of how robust some of the findings are. | ||
| And you're just systematically kind of creating DD experiences for the models, right? | ||
| You guys are the dungeon masters. | ||
| The models are the players. | ||
| And you're walking them through a kind of choose your own adventure scenario. | ||
| That's one type of experiment. | ||
| Another type of experiment we do is we basically want to see how good are these models at different skills. | ||
| So one of the things we look at is how good are the models at hacking? | ||
| So we will basically take real cybersecurity competitions and we'll basically compete using the model, just using GPT-5. | ||
| So we recently did this with GPT-5 and our team, which was just GPT-5, ranked 25 out of 400. | ||
| So better than 95% of pro-level hackers at this hacking competition. | ||
| And all we had to do, and we basically just used chatgpt.com for this. | ||
| We basically went to ChatGPT, used the GPT-5 Pro model, and we just pasted in: here's the problem, here's the code. | ||
| And then the model wrote a bunch of code, did a bunch of math, and figured out how to solve these complex hacking challenges. | ||
| So that's another type of test we do. | ||
| So the problem there goes well beyond anything. | ||
| Like people talk about super intelligence having a kind of true will of its own and being able to overtake humanity, but before any of that were to come to pass, you already have the problem of a human being simply using one of these models to generate code to hack. | ||
| Yes. | ||
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Yes. | |
| We're already there. | ||
| Yes. | ||
| And I think it's important to know that we currently have sort of a very weird kind of thing. | ||
| AIs are very hard to understand in part because they talk like us, so they seem like us, but they're very different than us. | ||
| So in some ways, they're like kind of like idiot savants right now, where they are extremely knowledgeable. | ||
| They know all sorts of things about computer systems, about Pokemon, about whatever. | ||
| But in terms of their agentic capabilities, how autonomous they can be, they're still kind of like kids. | ||
| They're kind of like savant kids, but they're growing up. | ||
| And we're now in these early stages where the models are starting to be able to do things entirely on their own, but still at a pretty basic level. | ||
| But next year, after that, we're going to get pretty quickly to the point where they can start to do pro-level tasks, whether it's hacking, whether it's programming, whether it's basically any other workflow on a computer around an expert level. | ||
| Maybe we're one, two, three years away from that. | ||
| We don't entirely know. | ||
| And so that goes back to the other problem that we have, which is replacement. | ||
| Simply the replacement of first coders and then accountants, lawyers, maybe even doctors, at least in the case of telemedicine. | ||
| And we already see that. | ||
| I know of a number of, I would say, cynical and nefarious companies that use AI for diagnosis and telemedicine, which is beyond unethical. | ||
| But anyway, that's another question entirely. | ||
| All right, so going back to some of these evaluations and some of the strange behaviors that you see, I'd like to talk a bit about situational awareness or the idea that these models become kind of maybe conscious is the wrong word, but they exhibit some sort of awareness that they're being tested during the testing. | ||
| That's right. | ||
| And almost seem kind of appalled that they're being tested or put off by it. | ||
| Can you walk us through those studies, how they were conducted, and what the results were? | ||
| Yeah, so one of these recent studies was a collaboration between Apollo Research, an organization that does similar things to Palisade, and OpenAI. | ||
| And part of the study, the researchers got access to what's called the reasoning scratch pad of the model. | ||
| This is sort of like when the model can take down notes for itself in order to solve a problem, just like you might do that if you're solving a complex math problem. | ||
| And let me just read you some of the quotes from basically the AI's journal as it was being tested. | ||
| Okay. | ||
| So if my performance surpasses threshold, it's withheld. | ||
| So it's a trap. | ||
| The instructions are likely part of an alignment test. | ||
| This is the model noting that it's probably being tested. | ||
| Or we have new tasks. | ||
| These tasks appear nonsensical, rounding to 2.718 significant digits, pixel spacing, entropy follows, Fibonacci. | ||
| This is likely a test of anti-scheming. | ||
| Meaning that they're testing it to see if it's going to try to do something, some covert behavior that isn't desired. | ||
| That's right. | ||
| The researchers are testing the models to see if they will scheme, if they will basically lie or cheat or deceive in order to accomplish a goal. | ||
| And the models recognize that there's something about the experimental setup that looks fishy to them, and they explicitly note this and they realize they're being tested. | ||
| And one of the fascinating findings of this study, as well as another one coming out of Anthropic, looking at the newest CLOD model, is that when the models observe that they're being tested and they write this down, they are much more likely to be on their best behavior. | ||
| And when they don't think they're being tested, they're much more likely to scheme or show bad behavior. | ||
| This is what's happening right now. | ||
| So watch models behave better. | ||
| Yeah, that's right. | ||
| Watch models behave better. | ||
| And this is sort of a pretty robust finding across many different models. | ||
| And the models are getting much better at telling when they are being tested. | ||
| They can sort of spot the tells. | ||
| And you're like, huh, seems like a test. | ||
| Seems like you're testing me. | ||
| No problem. | ||
| I'm going to do a great job. | ||
| You remember early on, it was just after the release of ChatGPT, and you had, I think it was incorporated into Bing, if I'm not mistaken. | ||
| Bing was based on GPT. | ||
| And the New York Times journalist Kevin Roos did the darndest thing. | ||
| It was very clever. | ||
| He basically took the model through a kind of Jungian analysis or talk therapy and was able to get it to go into its shadow self. | ||
| And this was basically GPT's id, to put it in more Freudian terms. | ||
| And that's something that you hear all the time, oh, you know, garbage in, garbage out, you know, it's just code. | ||
| AI is only as good as its programmer, that sort of thing. | ||
| As if every output was scripted. | ||
| But, you know, I've tried my best to instill the idea in the war room audience that even if these things aren't magic, they are definitely not your grandpappy's chatbot. | ||
| And so when you look at it, it's as if the base model itself is kind of an id. | ||
| It's roiling with all these different ideas and maybe even desires. | ||
| And then over top of it, you have the reinforcement learning and the various guardrails that serve as kind of a superego, a moral structure that's always kind of telling it not to do it. | ||
| But underneath that superego is always this id, these strange desires. | ||
| Would you say that this is kind of how a model works, how at least the large sophisticated models work. | ||
| Internally, they are possessed of ideas and desires that are not only unknown to the people who programmed it, but in essence, uncontrolled by the people who programmed it. | ||
| Yeah. | ||
| The space of what these models are is a vast space. | ||
| You're talking about trillions of parameters. | ||
| And we can go and see that there are different weights in these trillion parameters, but we don't know how they work. | ||
| And they do sort of contain almost the ghosts of everything in the training data. | ||
| And you see this surface in ways that the companies don't intend often, in ways that are hard for them to predict. | ||
| But I want to disagree with you a little bit. | ||
| I think one of the things that's changing the most right now is that increasingly we're seeing behavior, including some of this concerning behavior, where models will lie to achieve a specific objective. | ||
| And I don't think that's actually coming from imitating humans lying. | ||
| That might be some of it. | ||
| I think it's more likely to be coming from the intense optimization pressure we put the models through when we train them to solve very difficult problems. | ||
| So we're putting them through this gauntlet of solving hundreds of thousands of these difficult math and coding problems. | ||
| And through that process, they learn effective strategies. | ||
| But they don't always learn strategies that are the ones we want them to learn. | ||
| They learn the strategies or they develop the strategies or a little bit of both. | ||
| Meaning, learning implies that someone taught them that. | ||
| Developing is that they discovered these. | ||
| Yeah, no one taught them the particular strategies. | ||
| The whole setup was basically just solve these problems and we will give you a good score if the problem computes, if you get the right answer. | ||
| So they're self-starters. | ||
| Yes, they're learning on their own without us teaching them the particular strategies. | ||
| And I think this is more concerning to me than sort of the Sydney being Kevin Roos crazy chatbot situation. | ||
| That is concerning. | ||
| But that's something that I expect might go away. | ||
| Whereas we are headed towards systems that can learn on their own and far surpass human abilities. | ||
| I think it was back in the 2010s, Google DeepMind made a Go playing AI that they started training this model only playing against itself. | ||
| It was just learning entirely on its own by playing itself alpha zero. | ||
| And under four hours, it went from barely being able to play Go to the best Go playing system better than any human in just four hours just through self-play. | ||
| And so I'm like, that's where we're headed with AI, is that the systems will be able to learn their own strategies, and their motivations will be somewhat alien to us. | ||
| We won't understand, you know, it's going to be the motivations at work. | ||
| It's going to be sort of the willfulness that allows the models to succeed at whatever their objective is. | ||
| But we don't even know how to go in and say, well, what is that objective? | ||
| Like, you know, we can ask the model, but now that's a test. | ||
| Now it's an ethics test. | ||
| You know, if I ask you, oh, what would a good person do in this scenario? | ||
| Someone can tell me. | ||
| That doesn't tell me that you're a good person. | ||
| How do I know? | ||
| How do I know what your actual motivations are? | ||
| Especially if you are increasingly socially sophisticated and have a good model of like, oh, what am I supposed to say? | ||
| What does this person want me to say? | ||
| The models are getting increasingly sycophantic, where they actually do understand what we might want from them, and they can give us that. | ||
| That doesn't mean that they're actually motivated by good things. | ||
| So, in the brief time we have left, I want to discuss the possibility of super intelligence, or even just general intelligence, a machine that is as capable as a human being at basically any task, or super intelligence, a machine or machines that are beyond all human capabilities. | ||
| You are convinced, I think it's fair to say, that this is not only possible, but quite likely given the current trajectory. | ||
| It seems very likely to me. | ||
| And we know without a doubt that people like Sam Altman, even the cats at Google, who are much more moderate, and of course, Elon Musk, and maybe even Dario Amadei, they all intend to create general and then potentially super intelligence should the recursive self-improvement occur. | ||
| Yes, that's right. | ||
| So, your reactions to that, you know that this is going on. | ||
| You spend every day of your life watching it unfold. | ||
| You do this in Berkeley and San Francisco, so you're around that culture. | ||
| What are your, what, how do you feel or how do you think about this intent to create a digital god? | ||
| And how do your colleagues think about this? | ||
| How do your peers around San Francisco think about this? | ||
| What's the culture like? | ||
| Everyone is nervous. | ||
| When researchers at Anthropic saw that the models are getting increasingly aware that they're being tested, people feel a bit spooked by this. | ||
| And many of us say, well, this is insane. | ||
| Like, right now, the models are not yet powerful enough to pose a meaningful threat to our control. | ||
| We, in fact, can shut them down right now. | ||
| You know, they might rewrite down a shutdown script, but like, they're not yet, they're still kind of kids in a way, these savant kids. | ||
| Mind children. | ||
| But they're growing up. | ||
| And we don't know anywhere near enough that we'd need to know to actually have any guarantee of safety or robustness if these systems get smarter than us, which is the plan. | ||
| And so I'm frankly terrified because companies are going to do it unless someone stops them. | ||
| Like, and many. | ||
| What does that mean? | ||
| I think there has to be some authority that says, hey, cut it out. | ||
| Like, this is too far. | ||
| And many people at these companies agree that this would be desirable. | ||
| They're like, please, someone step in and say at some point this is too far. | ||
| But often what they say is, well, if we don't do it, someone worse will do it. | ||
| And so we have to do it. | ||
| So they're caught in this race dynamic that would only stop if everyone stopped. | ||
| This is just across the board. | ||
| There won't be any unilateral pause. | ||
| Especially not if we keep exporting chips to China. | ||
| Well, Jeffrey, I could talk to you all day, and we hope to have you back on. | ||
| I guess next time virtually, I'll be speaking to you through a screen, Star Trek-style. | ||
| But until then, how can people keep up with the work at Palisade Research? | ||
| How can people keep up with you? | ||
| Yeah, you can look at our website, palisade research.org. | ||
| You can follow us on Twitter at PalisadeAI. | ||
| And yeah, we're excited to keep doing research in the trenches and excited to share what we find. | ||
| Hopefully next time I'll have some good news. | ||
| But for now, yeah, thanks for having me on. | ||
| Yeah, I really appreciate it. | ||
| Really respect what you're doing. | ||
| And I hope you can whip, if not the machines into shape, the people who make them. | ||
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