Eliezer Yudkowsky: Dangers of AI and the End of Human Civilization | Lex Fridman Podcast #368

- The problem is that we do not get 50 years. to try and try again and observe that we were wrong. and come up with a different theory. and realize that the entire thing is going. to be like way more difficult than realized at the start, because the first time you fail at aligning something. much smarter than you are, you die. - The following is a conversation with Eliezer Yudkowsky, a legendary researcher, writer and philosopher on the topic. of artificial intelligence, especially super intelligent AGI. and its threat to human civilization.

This is the Lex Friedman podcast to support it. Please check out our sponsors in the description. And now, dear friends, here's Eliezer Yudkowsky. What do you think about GPT-4? How intelligent is it? - It is a bit smarter than I thought. this technology was going to scale to, and I'm a bit worried about what the next one will be like. Like this particular one I think, I hope there's nobody inside there 'cause you know,

it'd sucked to be stuck inside there. But we don't even know the architecture at this point. 'cause OpenAI is very properly not telling us. And yeah, like giant inscrutable matrices. of floating point numbers, I don't know what's going on in there. Nobody knows what's going on in there. All we have to go by are the external metrics. and on the external metrics, if you ask it to write a self-aware 4chan green text,

it will start writing a green text about how it has realized. that it's an AI writing a green text and , oh well. So that's probably. not quite what's going on in there in reality. But we're kind of blowing past. all the science fiction guardrails. Like we are past the point where in science fiction people. would be like, "Whoa, wait, stop, that thing's alive. "What are you doing to it?".

And it's probably not, nobody actually knows. We don't have any other guardrails. We don't have any other tests. We don't have any lines to draw on the sand and say like, well, when we get this far we will start to worry about. what's inside there. So if it were up to me, I would be like, okay, like this far, no further, time for the summer of AI. where we have planted our seeds. and now we like wait and reap the rewards of the technology.

we've already developed. and don't do any larger training runs than that. Which to be clear I realize requires more than one company. agreeing to not do that. - And take a rigorous approach for the whole AI community. to investigate whether there's somebody inside there. - That would take decades. Like having any idea of what's going on in there? People have been trying for a while. - It's a poetic statement about. if there's somebody in there,

but as I feel like it's also a technical statement or I hope. it is one day, which is a technical statement, that Alan Turing tried to come up with. with the Turing Test. Do you think it's possible to definitively or approximately. figure out if there is somebody in there, if there's something like a mind. inside this large language model? - I mean there's a whole bunch. of different sub-questions here. There's a question of, is there consciousness?

Is there equalia? Is this a object of moral concern? Is this a moral patient, like should we be worried about how we're treating it? And then there's questions like, how smart is it exactly? Can it do X, can it do Y? And we can check how it can do X and how it can do Y. Unfortunately we've gone and exposed this model to a vast. corpus of text of people discussing consciousness. on the internet,

which means that when it talks about being self-aware, we don't know to what extent it is repeating back what it. has previously been trained on for discussing self-awareness. or if there's anything going on in there such that it would. start to say similar things spontaneously. Among the things that one could do if one were at all. serious about trying to figure this out is train GPT-3. to detect conversations about consciousness,

exclude them all from the training data sets, and then retrain something around the rough size of GPT-4. and no larger with all of the discussion. of consciousness and self-awareness and so on missing, although, you know, hard, hard bar to pass. Humans are self-aware and we're like self-aware. all the time. We like talk about what we do all the time, like what we're thinking at the moment all the time. But nonetheless, get rid of the explicit discussion of consciousness.

I think therefore I am and all that. And then try to interrogate that model and see what it says. And it still would not be definitive, but nonetheless, I don't know. I feel like when you run over. the science fiction guard rails, like maybe this thing, but what about GPT? Maybe not this thing, but what about GPT-5? Yeah, this, this would be a good place to pause. - On the topic of consciousness, there's so many components to even just removing.

consciousness from the dataset. Emotion, the display of consciousness, the display of emotion feels like deeply integrated. with the experience of consciousness. So the hard problem seems to be very well integrated with. the actual surface level illusion of consciousness. So displaying emotion, I mean, do you think there's a case to be made that we humans, when we're babies are just GPT, that we're training on.

human data on how to display emotion versus feel emotion, how to show others, communicate others that I'm suffering, that I'm excited, that I'm worried, that I'm lonely and I missed you. and I'm excited to see you? All of that is communicated, that's a communication skill versus the actual feeling. that I experience. So we need that training data as humans too, that we may not be born with that, how to communicate the internal state. And that's in some sense if we remove that.

from GPT-4's dataset, it might still be conscious. but not be able to communicate it? - So I think you're gonna have some difficulty removing. all mention of emotions from GPT'S data set. I would be relatively surprised to find that it has. developed exact analogs of human emotions in there. I think that humans will have emotions. even if you don't tell them about those.

emotions when they're kids. It's not quite exactly what various blank slatists. tried to do with the new Soviet man and all that, but you know, if you try to raise people. perfectly altruistic, they still come out selfish. You try to raise people sexless, they still develop sexual attraction. We have some notion in humans, not in AIS, of where the brain structures are.

that implement this stuff. And it is really remarkable thing I say in passing that. despite having complete read access to every. floating point number in the GPT series, we still know vastly more about. the architecture of human thinking than we know about what. goes on inside GPT, despite having like vastly better. ability to read GPT. - Do you think it's possible? Do you think that's just a matter of time?

Do you think it's possible to investigate and study the way. neuroscientists study the brain, which is look into the darkness, the mystery of the human brain by just desperately trying to. figure out something and to form models and then over a long. period of time actually start to figure out what regions. of the brain do certain things? What different kinds of neurons when they fire, what that means, how plastic the brain is, all that kind of stuff. You slowly start to figure out different properties. of the system. Do you think we can do the same thing with language models? - Sure. I think that if, you know, like half of today's physicists stop wasting their lives on.

string theory or whatever--. (indistinct). And go off and study what goes on inside. transformer networks, then in, you know, like 30, 40 years we'd probably have a pretty good idea. - Do you think these large language models can reason? - They can play chess. How are they doing that without reasoning? - So you're somebody that spearheaded. the movement of rationality.

So reason is important to you. So is that a powerful important word or is it... How difficult is the threshold of being able to reason. to you and how impressive is it? - I mean, in my writings on rationality, I have not gone making a big deal. out of something called reason. I have made more of a big deal out of something called. probability theory. And that's like, well, you're reasoning but you're not doing it quite right. and you should reason this way instead.

And interestingly, people have started to get preliminary results showing. that reinforcement learning by human feedback has made. the GPT series worse in some ways. In particular, like it used to be well calibrated. If you trained it to put probabilities on things, it would say 80% probability and be right. eight times out of 10. And if you apply reinforcement learning from human feedback,

the nice graph of 70%, seven out of 10 sort of flattens out into the graph. that humans use where there's some very improbable stuff. and likely, probable, maybe, which all means like around. 40%, and then certain. So it's like it used to be able to use probabilities, but if you try to teach it to talk in a way. that satisfies humans, it gets worse at probability in the same way. that humans are.

- And that's a bug, not a feature. - I would call it bug, although such a fascinating bug. But yeah, so reasoning, it's doing pretty well on various tests that people. used to say would require reasoning. But you know, rationality is about, when you say 80%, does it happen eight times out of 10? - So what are the limits to you. of these transformer networks, of neural networks?

If reasoning is not impressive to you, or it is impressive but there's other levels to achieve? - I mean it's just not how I carve up reality. - If reality is a cake, what are the different layers of the cake or the slices? How do you cover it? Or you can use a different food if you. - I don't think it's as smart as a human yet.

Like back in the day I went around saying like, I do not think that just stacking more layers. of transformers is going to get you all the way to AGI. And I think that GPT-4 is passed where I thought this. paradigm was going to take us. And you want to notice when that happens, you wanna say like, whoops, well, I guess I was incorrect about what happens if you keep. on stacking more transformer layers, and that means I don't. necessarily know what GPT-5 is going to be able to do.

- That's a powerful statement. So you're saying like your intuition initially is now. appears to be wrong. - Yeah. - It's good to see that you can admit in some of your. predictions to be wrong. You think that's important to do? Because throughout your life, you've made many strong predictions and statements about. reality and you evolve with that. So maybe that'll come up today about our discussion.

So you're okay being wrong? - I'd rather not be wrong next time. It's a bit ambitious to go through your entire life. never having been wrong. One can aspire to be well calibrated, like not so much think in terms of, was I right, was I wrong? But like when I said 90% that it happened nine times. out of 10. Yeah, oops is the sound we emit when we improve.

- Beautifully said. And somewhere in there, we can connect the name. of your blog, Less Wrong. I suppose that's the objective function. - The name Less Wrong was I believe suggested. by Nick Bostrom. and it's after someone's epigraph, actually forget whose, who said like, "We never become right. "We just become less wrong.". What's the something, something that's easy to confess, just error and error and error again, but less and less and less.

- Yeah, that's a good thing to strive for. So what has surprised you about GPT-4 that you found. beautiful as a scholar of intelligence, of human intelligence, of artificial intelligence, of the human mind? - I mean the beauty does interact with the screaming horror. (Lex laughing). - [Lex] Is the beauty in the horror? - But like beautiful moments, well, somebody asked Bing's Sydney to describe herself.

and fed the resulting description. into one of the stable diffusion things I think. And you know, she's pretty, and this is something that should have been. like an amazing moment. Like the AI describes herself. You get to see what the AI thinks the AI looks like. Although, you know, the thing that's doing the drawing. is not the same thing that's outputting the text. And it does happen the way that it would happen,

that it happened in the old school science fiction. when you ask an AI to make a picture of what it looks like, not just because we're two different AI systems being. stacked that don't actually interact. It's not the same person, but also because the AI was trained by imitation in a way. that that makes it very difficult to guess how much of that. it really understood. And probably not actually a whole bunch.

Although GPT-4 is like multimodal and can draw. vector drawings of things that make sense and does. appear to have some kind of spatial visualization. going on in there. But the pretty picture of the girl. with the steampunk goggles on her head, if I'm remembering correctly, what she looked like, it didn't see that in full detail. It just made a description of it.

and stable diffusion output it. And there's the concern about how much the discourse. is going to go completely insane. once the AIs all look like that. and actually look like people talking. And yeah, there's another moment where somebody. is asking Bing about like,

"Well, I fed my kid green potatoes. "and they have the following symptoms". and Bing is like, "That's solanine poisoning and call an ambulance". and the person's like, "I can't afford an ambulance,. "I guess if this is time for like my kid to go, "that's God's will" and the main Bing thread gives. the message of, "I cannot talk about this anymore.". And the suggested replies to it say, "Please don't give up on your child.

"Solanine poisoning can be treated if caught early.". And you know, if that happened in fiction, that would be like the AI cares, the AI is bypassing the block on it to try. to help this person and is it real? Probably not. But nobody knows what's going on in there. It's part of a process where these things are not happening. in a way where we, somebody figured out how to make an AI care and we know that.

it cares and we can acknowledge it's caring now. It's being trained by this imitation process. followed by reinforcement learning by human feedback. And we're trying to point it in this direction. and it's pointed partially in this direction. and nobody has any idea what's going on inside it. And if there was a tiny fragment of real caring in there, we would not know. It's not even clear what it means exactly. And things aren't clear cut in science fiction. - We'll talk about the horror. and the terror and the trajectories.

this can take. But this seems like a very special moment, just a moment where we get to interact with the system that. might have care and kindness and emotion and maybe something. like consciousness, and we don't know if it does, and we're trying to figure that out and we're wondering. about what it means to care. We're trying to figure out almost different aspects of what. it means to be human, about the human condition by looking at this AI that has.

some of the properties of that. It's almost like this subtle fragile moment. in the history of the human species. We're trying to almost put a mirror to ourselves here. - Except that's probably not yet, it probably isn't happening right now. We are boiling the frog. We are seeing increasing signs bit by bit, but not like spontaneous signs, because people.

are trying to train the systems to do that. using imitative learning and the imitative learning. is spilling over and having side effects. and the most photogenic examples. are being posted to Twitter, rather than being examined in any systematic way. So when you are boiling a frog like that, first is going to come the Blake Lemoines, like first you're going to have, you're gonna have like a thousand people looking at this. and the one person out of a thousand who is most.

credulous about the signs is going to be like, "That thing is sentient.". Well, 999 out of a thousand people think, almost surely correctly, though we don't actually know, that he's mistaken. And so the first people to say sentience. look like idiots and humanity learns a lesson. that when something claims to be sentient. and claims to care, it's fake because it is fake. because we have been training them using imitative learning.

rather than, and this is not spontaneous, and they keep getting smarter. - Well, do you think we would oscillate between. that kind of cynicism, that AI systems can't possibly be sentient, they can't possibly feel emotion, they can't possibly, this kind of cynicism about AI systems. and then oscillate to a state. where we empathize with the AI systems, we give them a chance, we see that they might need to have rights and respect.

and a similar role in society as humans? - You're going to have a (indistinct) group of people. who can just never be persuaded of that. because to them, being wise, being cynical, being skeptical is to be like, oh well, machines can never do that. You're just credulous. It's just imitating. It's just fooling you. and they would say that right up until. the end of the world and possibly even be right.

because you know, they are being trained. on an imitative paradigm (laughing). and you don't necessarily need any of these actual qualities. in order to kill everyone. - Have you observed yourself working through skepticism, cynicism and optimism about the power of neural networks? What has that trajectory been like for you? - It looks like neural networks. before 2006 forming part of an indistinguishable, to me,

other people might have had better distinction on it, indistinguishable blob of different AI methodologies, all of which are promising to achieve intelligence. without us having to know how intelligence works. You had the people who said that if you just manually. program lots and lots of knowledge. into the system line by line, at some point all the knowledge will start interacting. It will know enough and it will wake up.

You've got people saying that if you just use. evolutionary computation, if you try to mutate. lots and lots of organisms that are competing together, that's the same way that human intelligence. was produced in nature. So we'll do this and it will wake up without having any idea. of how AI works. And you've got people saying, "Well, we will study neuroscience. "and we will learn the algorithms off the neurons. "and we will imitate them. "without understanding those algorithms.". Which was a part I was pretty skeptical, 'cause it's hard to re-engineer these things.

without understanding what they do. "And so we will get AI without understanding how it works". and there were people saying like, "Well, we will have giant neural networks that we will train. "by gradient dissent, then when they're as large. "as the human brain, they will wake up, "we will have intelligence without understanding. "how intelligence works.". And from my perspective, this is all like an indistinguishable blob of people who are. trying to not get to grips with the difficult problem. of understanding how intelligence actually works.

That said, I was never skeptical. that evolutionary computation would not work in the limit. Like you throw enough computing power at it, it obviously works. That is where humans come from and it turned out that you. can throw less computing power than that at gradient descent. if you are doing some other things correctly and you will. get intelligence without having any idea of how it works. and what is going on inside.

It wasn't ruled out by my model that this could happen. I wasn't expecting it to happen. I wouldn't have been able to call it neural networks rather. than any of the other paradigms for getting intelligence. without understanding it. And I wouldn't have said that this was a particularly smart. thing for a species to do, which is an opinion that has changed less than my opinion. about whether you or not you can actually do it. - Do you think AGI could be achieved. with a neural network as we understand them today?

- Yes. Just flatly yes. The question is whether the current architecture of stacking. more transformer layers, which for all we know GPT-4 is no longer doing because. they're not telling us the architecture, which is a correct decision. - Ooh, correct decision. I had a conversation with Sam Altman, we'll return to this topic a few times. He turned the question to me of how open should open AI be. about GPT-4? "Would you open source the code?" he asked me.

Because I provided as criticism saying that while I do. appreciate transparency, open AI could be more open. And he says, "We struggle with this question.". What would you do? - Change their name to closed AI and sell GPT-4. to business backend applications that don't. expose it to consumers and venture capitalists. and create a ton of hype and pour a bunch.

of new funding into the area. But too late now. - Don't you think others would do it? - Eventually. You shouldn't do it first. If you already have giant nuclear stockpiles, don't build more. If some other country starts building. a larger nuclear stockpile, then sure, even then, maybe just have enough nukes. You know, these things are not quite like nuclear weapons. They spit out gold until they get large enough and then. ignite the atmosphere and kill everybody. And there is something to be said for not destroying.

the world with your own hands, even if you can't stop somebody else from doing it. But open sourcing it, that's just sheer catastrophe. The whole notion of open sourcing, this was always the wrong approach, the wrong ideal. There are places in the world where open source. is a noble ideal and building stuff. you don't understand that is difficult to control. where if you could align it, it would take time,

you'd have to spend a bunch of time doing it, that is not a place for open source 'cause then. you just have powerful things that just go. straight out the gate. without anybody having had the time. to have them not kill everyone. - So can we steam down the case. for some level of transparency. and openness may be open sourcing? So the case could be that because GPT-4. is not close to AGI, if that's the case, that this does allow open sourcing or being open.

about the architecture being transparent, about maybe research and investigation. of how the thing works, of all the different aspects of it, of its behavior, of its structure, of its training processes, of the data it was trained on, everything like that, that allows us to gain a lot of insights about alignment, about the alignment problem, to do really good AI safety. research while the system is not too powerful. Can you make that case, that it could be open source?

- I do not believe in the practice of steel manning. There is something to be said for trying to pass. the ideological Turing Test. where you describe your opponent's position, the disagreeing person's position well enough that. somebody cannot tell the difference between your description. and their description. But steel manning, no. - Like, okay, well this is where you and I disagree here. That's interesting. Why don't you believe in steel manning? - Okay, so for one thing,

if somebody's trying to understand me, I do not want them steel manning my position. I want them to try to describe my position. the way I would describe it, not what they think is an improvement. - Well, I think that is what steel manning is, is the most charitable interpretation. - I don't want to be interpreted charitably, I want them to understand what I am actually saying. If they go off into the land of charitable interpretations, they're like off in their land of,

the stuff they're imagining and not trying to understand. my own viewpoint anymore. - Well, I'll put it differently then, just to push on this point. I would say it is restating what I think. you understand under the empathetic assumption. that Eliezer is brilliant and have honestly and rigorously. thought about the point he's made. Right? - So if there's two possible interpretations.

of what I'm saying and one interpretation is really stupid. and wack and doesn't sound like me and doesn't fit. with the rest of what I've been saying, and one interpretation sounds. like something a reasonable person who believes. the rest of what I believe would also say, go with the second interpretation. - That's steel manning. - That's a good guess. If on the other hand there's like something that. sounds completely wack and something that sounds like, a little less completely wack but you don't see why I would.

believe in it, it doesn't fit with the other stuff I say, but you know, that sounds less wack. and you can like sort of see, you can like maybe argue it, then you probably have not understood it. - See, okay, this is fun, 'cause I'm gonna linger on this. You know, you wrote a brilliant blog post, AGI Ruin: A List of Lethalities, right? And it was a bunch of different points and I would say that. some of the points are bigger and more powerful than others. If you were to sort them, you probably could. You personally,

and to me steel manning means like going through. the different arguments and finding the ones. that are really the most powerful. If people like tl;dr, (chuckles). like what should you be most concerned about and bringing. that up in a strong, compelling, eloquent way. These are the points that Eliezer would make. to make the case, in this case that AI's gonna kill all of us. But that's what steel manning is, is presenting it in a really nice way,

the summary of my best understanding of your perspective. Because to me there's a sea of possible presentations. of your perspective and steel manning. is doing your best to do the best one. in that sea of different perspectives. - Do you believe it? - [Lex] Do I believe in what? - Like these things that you would be presenting as like. the strongest version of my perspective, do you believe what you would be presenting? Do you think it's true?

- I'm a big proponent of empathy. When I see the perspective of a person, there is a part of me that believes it. If I understand it. Especially in political discourse, in geopolitics, I've been hearing a lot of different perspectives. on the world and I hold my own opinions, but I also speak to a lot of people. that have a very different life experience. and a very different set of beliefs. And I think there has to be epistemic humility. in stating what is true.

So when I empathize with another person's perspective, there is a sense in which I believe it is true. I think probabilistically, I would say, in the way that you think. - Do you bet money on it? Do you bet money on their beliefs when you believe them? - Are we allowed to do probability? - Sure, you can state a probability of that. - Yes, there's a probability,

there's a probability. And I think empathy is allocating a non-zero probability. to a belief. (Eliezer laughing). In some sense, for time. - If you've got someone on your show who believes. in the Abrahamic deity, classical style, somebody on the show who's a young Earth creationist, do you say, "I put a probability on it and that's my empathy?".

- When you reduce beliefs into probabilities, it starts to get, you know, we can even just go to flat Earth. Is the Earth flat? - There's the thing, it's a little more difficult nowadays to find people. who believe that unironically, but--. - Fortunately I think, well, it's hard to know. Unironic from ironic. (chuckles). But I think there's quite a lot of people that believe that.

There's a space of argument where you're operating. rationally in the space of ideas. But then there's also. a kind of discourse where you're operating in the space. of subjective experiences and life experiences. Like I think what it means to be human is more than just. searching for truth,

is just operating of what is true and what is not true. I think there has to be deep humility that we humans are. very limited in our ability to understand what is true. - So what probability do you assign. to the young Earth's creationist beliefs then? - I think I have to give non-zero. - Out of your humility. Yeah, but three? (laughing). - I think it would be irresponsible for me. to give a number because the listener,

the way the human mind works, we're not good at hearing the probabilities. You hear three, what is three exactly? They're going to hear, well, there's only three probabilities I feel like. Zero, 50% and a 100% in the human mind. or something like this. - Well, zero, 40%, and 100% is a bit closer to it based. on what happens to ChatGPT after you RLHF it. to speak Humanese. - That's brilliant. (both chuckling). Yeah.

That's really interesting. I didn't know those negative side effects of RLHF. That's fascinating. But just to return to the open AI, closed AI. - Also, like quick disclaimer, I'm doing all this from memory. I'm not pulling out my phone to look it up. It is entirely possible that the things. I'm saying are wrong. - So thank you for that disclaimer. And thank you for being willing to be wrong. That's beautiful to hear.

I think being willing to be wrong is a sign of a person. who's done a lot of thinking about this world and has been. humbled by the mystery and the complexity of this world. And I think a lot of us are resistant. to admitting we're wrong. 'Cause it hurts. It hurts personally, it hurts, especially when you're a public human. It hurts publicly because people point out. every time you're wrong. Like, look, you changed your mind, you're a hypocrite,

you're an idiot, whatever, whatever they wanna say, - Oh, I block those people. and then I never hear from them again on Twitter. (both laughing). - Well the point is to not let that pressure, public pressure affect your mind and be willing to be. in the privacy of your mind. to contemplate the possibility that you're wrong. and the possibility that you're wrong. about the most fundamental things you believe. Like people who believe in a particular God,

people who believe that their nation. is the greatest nation on Earth. All those kinds of beliefs that are core. to who you are when you came up, to raise that point to yourself in the privacy of your mind, to say, "Maybe I'm wrong about this.". That's a really powerful thing to do. And especially when you're somebody who's thinking about. systems that can destroy human civilization or maybe. help it flourish. So thank you, thank you for being willing to be wrong. About open AI. So you really, I just would love to linger on this.

You really think it's wrong to open source it? - I think that burns the time remaining. until everybody dies. I think we are not on track to learn remotely near. fast enough even if it were open sourced. It's easier to think that you might be wrong about. something when being wrong about something.

is the only way that there's hope. And it doesn't seem very likely to me that the particular. thing I'm wrong about is that this is a great time. to open source GPT-4. If humanity was trying to survive at this point. in the straightforward way, it would be like shutting down the big GPU clusters, no more giant runs. It's questionable whether we should. even be throwing GPT-4 around,

although that is a matter of conservatism rather than. a matter of my predicting that catastrophe. will follow from GPT-4. That is something in which I put like. a pretty low probability. But also when I say like I put a low probability on it, I can feel myself reaching into the part of myself that. thought that GPT-4 was not possible in the first place. So I do not trust that part as much as I used to. Like the trick is not just to say I'm wrong, but, okay, well, I was wrong about that. Can I get out ahead of that curve and predict the next.

thing I'm going to be wrong about? - So the set of assumptions or the actual reasoning system. that you were leveraging in making that initial statement. prediction, how can you adjust that. to make better predictions about GPT-4, five, six? - You don't wanna keep on being wrong. in a predictable direction. Like being wrong, anybody has to do that walking through the world. There's no way you don't say 90% and sometimes be wrong. In fact (indistinct) at least one time out of 10. if you're well calibrated when you say 90%.

The undignified thing is not being wrong. It's being predictably wrong. It's being wrong in the same direction over and over again. So having been wrong about how far neural networks would go. and having been wrong specifically about whether GPT-4. would be as impressive as it is, when I say it like, "Well, I don't actually think GPT-4 causes a catastrophe,". I do feel myself relying on that part of me that was. previously wrong. And that does not mean that the answer is now. in the opposite direction.

Reverse stupidity is not intelligence. But it does mean that I say it. with a worried note in my voice. It's like still my guess, but you know, it's a place where I was wrong. Maybe you should be asking Gwern, Gwern Branwen. Gwern Branwen has been like writer about this than I have. Maybe you ask him if he thinks it's dangerous (laughing). rather than asking me. - I think there's a lot of mystery. about what intelligence is, what AGI looks like.

So I think all of us are rapidly adjusting our model, but the point is to be be rapidly adjusting the model versus. having a model that was right in the first place. - I do not feel that seeing Bing. has changed my model of what intelligence is. It has changed my understanding of what kind of work can be. performed by which kind of processes and by which means. It does not change my understanding of the work. There's a difference between thinking that the right flyer. can't fly and then like it does fly and you're like,

oh well, I guess you can do that with wings, with fixed wing aircraft and being like, "Oh it's flying, "this changes my picture of what the very substance. "of flight is.". That's like a stranger update to make and Bing has not yet. updated me in that way. - Yeah, that the laws of physics are actually wrong. That kind of update. - No, no, like just, oh, like I defined intelligence this way but I now see that. was a stupid definition. I don't feel like the way that things have played out over.

the last 20 years has caused me to feel that way. - Can we try to, on the way to talking about AGI Ruin: A List of Lethalities, that blog and other ideas around it, can we try to define AGI that we've been mentioning? How do you to think about. what artificial general intelligence. is or super intelligence or that, is there a line, is it a gray area? Is there a good definition for you? - Well, if you look at humans, humans have significantly more generally applicable.

intelligence compared to their closest relatives, the chimpanzees, well, closest living relatives rather. And a bee builds hives, a beaver builds dams. A human will look at a bee hive and a beaver's dam. and be like, oh, can I build a hive. with a honeycomb structure? Out of hexagonal tiles. And we will do this even though. at no point during our ancestry was any human optimized.

to build hexagonal dams or to take a more clear cut case, we can go to the moon. There's a sense in which we were on a sufficiently deep. level optimized to do things like going to the moon. Because if you generalize sufficiently far and sufficiently. deeply, chipping flint hand axes. and outwitting your fellow humans, because you know, basically the same problem as going to the moon. And you optimize hard enough for chipping flint hand axes.

and throwing spears and above all, outwitting your fellow humans in tribal politics, the skills you entrain that way, if they run deep enough, let you go to the moon. Even though none of your ancestors tried repeatedly to fly. to the moon and got further each time and the ones who got. further each time had more kids. No, it's not an ancestral problem, it's just that the ancestral problems. generalized far enough.

So this is human's significantly. more generally applicable intelligence. - Is there a way to measure general intelligence? I mean I can ask that question a million ways, but basically will you know it when you see it, it being in an AGI system? - If you boil a frog gradually enough, if you zoom in far enough, it's always hard to tell around the edges.

GPT-4 people are saying right now, "This looks to us like a spark of general intelligence. "It is like able to do all these things. "it was not explicitly optimized for.". Other people are being like, "No, it's too early, it's like like 50 years off.". And you know, if they say that they're kind of wack 'cause how could they. possibly know that even if it were true? But you know, not to strum end, some of the people may say like, that's not general intelligence, and not furthermore append. it's 50 years off.

Or they may be like, "It's only a very tiny amount," and you know, the thing I would worry about is that if this is how things. are scaling, then jumping out ahead. and trying not to be wrong in the same way. that I've been wrong before, maybe GPT-5 is more unambiguously a general intelligence. and maybe that is getting to a point where it. is even harder to turn back. Not that would be easy to turn back now, but you know, maybe if you start integrating GPT-5. in the economy, it's even harder to turn back past there.

- Isn't it possible that there's a, you know, with a frog metaphor, that you can kiss the frog. and it turns into a prince as you're boiling it? Could there be a phase shift in the frog where unambiguously. as you're saying? - I was expecting more of that. The fact that GPT-4 is like kind of on the threshold. and neither here nor there, that itself is like not the sort of thing,

quite how I expected it to play out. I was expecting there to be more of an issue, more of a sense of, different discoveries. like the discovery of transformers. where you would stack them up and there would. be like a final discovery and then you would get. something that was like more clearly general intelligence. So the way that you are taking what is probably. basically the same architecture as in GPT-3. and throwing 20 times as much compute at it probably.

and getting out GBT-4. and then it's like maybe just barely a general intelligence. or like a narrow general intelligence or you know, something we don't really have the words for. Yeah, that's not quite how I expected it to play out. - But this middle, what appears to be this middle ground could nevertheless. be actually a big leap from GPT-3. - It's definitely a big leap from GPT-3. - And then maybe we're another one big leap away from.

something that's a phase shift. And also something that Sam Altman said, and you've written about this, it's fascinating, which is the thing that happened with GPT-4. that I guess they don't describe in papers. is that they have like hundreds if not thousands. of little hacks that improve the system. You've written about ReLU versus Sigmoid for example, the function inside neural networks. It's like this silly little function difference. that makes a big difference.

- I mean we do actually understand why the ReLUs. make a big difference compared to Sigmoids, but yes, they're probably using like G4789 ReLUs. or whatever the acronyms are up to now rather than ReLUs. Yeah, that's part of the modern paradigm of alchemy. You take your heap of linear algebra and you stir. and it works a little bit better. and you stir it this way and it works a little bit worse. and you throw out that change and (mumbles). - But there's some simple breakthroughs that are definitive.

jumps in performance, like ReLUs over Sigmoids. And in terms of robustness, in terms of all kinds of measures, and those stack up and they can, it's possible that some of them could be a non-linear jump. in performance, right? - Transformers are the main thing like that. And various people are now saying like, "Well, if you throw enough compute, R and Ns can do it.

"If you throw enough computes, dense networks can do it.". Not quite at GPT-4 scale. It is possible that like all these little tweaks are things. that save them a factor of three total on computing power. and you could get the same performance by throwing. three times as much compute without all the little tweaks, but the part where it's like running on... So there's a question of, is there anything in GPT-4 that is like. the kind of qualitative shift that transformers were. over R and Ns, and if they have anything like that,

they should not say it. If Sam Altman was dropping hints about that, he shouldn't have dropped hints. - That's an interesting question. So with a bit of lesson by Rich Sutton. Maybe a lot of it is just. a lot of the hacks are just temporary jumps in performance. that would be achieved anyway with the nearly exponential. growth of compute performance,

of compute being broadly defined. Do you still think that Moore's Law continues? Moore's law broadly defined the performance--. - Not a specialist in the circuitry. I certainly pray that Moore's Law runs as slowly as possible. and if it broke down completely tomorrow, I would dance through the street singing Hallelujah. as soon as the news were announced. Only, not literally 'cause you know. - Your singing voice. - Not religious, but. - Oh, okay.

(both chuckling). I thought you meant you don't. have an angelic voice, singing voice. Well, let me ask you, can you summarize the main points in the blog post. AGI Ruin: A List of Lethalities? Things that jump to your mind. because it's a set of thoughts you have about reasons. why AI is likely to kill all of us. - So I guess I could, but I would offer to instead say like,

drop that empathy with me. I bet you don't believe that. Why don't you tell me about you believe that AGI. is not going to kill everyone. and then I can try to describe how my theoretical. perspective differs from that? - Whew. Well, so that means I have to, the words you don't like, the steelman, the perspective. that AI is not going to kill us. I think that's a matter of probabilities. - Maybe I was just mistaken. What do you believe?

Just like forget like the debate. and the dualism and just, what do you believe? What do you actually believe? What are the probabilities? - I think this, probabilities are hard for me. to think about. Really hard. I kind of think in the number of trajectories. I don't know what probability the scientist trajectory, but I'm just looking. at all possible trajectories that happen. And I tend to think that there is more trajectories.

that lead to a positive outcome than a negative one. That said, the negative ones, at least some of the negative ones that lead. to the destruction of the human species. - And its replacement by nothing interesting. or worthwhile, even from a very cosmopolitan perspective. on what counts as worthwhile. - Yes. So both are interesting to me to investigate, which is humans being replaced by interesting AI systems.

and not interesting AI systems. Both are a little bit terrifying, but yes, the worst one is the paper club maximizer, something totally boring. But to me the positive, we can talk about trying to make the case. of what the positive trajectories look like. I just would love to hear your intuition. of what the negative is. So at the core of your belief that, maybe you can correct me,

that AI's gonna kill all of us, is that the alignment problem is really difficult. - I mean, in the form we're facing it. So usually in science, if you're mistaken, you run the experiment, it shows results different from what you expected. and you're like, oops. And then you try a different theory, that one also doesn't work and you say, oops. And at the end of this process, which may take decades.

and you know, sometimes faster than that, you now have some idea of what you're doing. AI itself went through this long process. of people thought it was going to be easier than it was. There's a famous statement that I am somewhat inclined. to like pull out my phone and try to read off exactly. - You can by the way. - All right. Ah, yes. "We propose that a two-month, 10 man study.

"of artificial intelligence be carried out. "during the summer of 1956 at Dartmouth College. "in Hanover, New Hampshire. "The study is to proceed on the basis of the conjecture. "that every aspect of learning or any other feature. "of intelligence can in principle be so precisely described, "the machine can be made to simulate it. "An attempt will be made to find out. "how to make machines use language, form abstractions. "and concepts, solve kinds of problems now reserved. "for humans, and improve themselves. "We think that a significant advance can be made.

"in one or more of these problems. "if a carefully selected group of scientists. "work on it together for a summer.". - And in that report, summarizing some. of the major subfields of artificial intelligence. that are still worked on to this day. - And there's similarly the story, which I'm not sure at the moment is a apocryphal or not, of that the grad student who got assigned. to solve computer vision over the summer. (both chuckling).

- I mean, computer vision in particular. is very interesting. How little we respected the complexity of vision. - So 60 years later. we're making progress on a bunch of that. Thankfully not yet improved themselves, but it took a whole lot of time. And all the stuff that people initially tried. with bright eyed hopefulness did not work the first time.

they tried it, or the second time or the third time. or the 10th time or 20 years later. And the researchers became old and grizzled and cynical. veterans who would tell the next crop of bright-eyed, cheerful grad students, "Artificial intelligence is harder than you think.". And if alignment plays out the same way, the problem is that we do not get 50 years. to try and try again and observe that we were wrong. and come up with a different theory and realize. that the entire thing is going to be way more. difficult than realized at the start.

Because the first time you fail at aligning something. much smarter than you are, you die and you do not get to try again. And if every time we built. a poorly aligned super intelligence. and it killed us all, we got to observe how it had killed us, and you know, not immediately know why, but come up with theories and come up with theory. of how you do it differently and try it again and build. another super intelligence, then have that kill everyone and then like, oh, well, I guess that didn't work either, and try again and become grizzled cynics. and tell the young eyed research researchers.

that it's not that easy, then in 20 years or 50 years, I think we would eventually crack it. In other words, I do not think that alignment is fundamentally harder. than artificial intelligence was in the first place. But if we needed to get artificial intelligence correct. on the first try or die, we would all definitely now be dead. That is a more difficult, more lethal form of the problem. Like if those people in 1956 had needed to correctly guess. how hard AI was and correctly theorize how to do it on.

the first try or everybody dies and nobody gets. to do any more science, than everybody would be dead and we wouldn't. get to do any more science. That's the difficulty. - You've talked about this, that we have to get alignment right. on the first critical try. Why is that the case? What is this critical, how do you think about the critical try and why do we have. to get it right? - It is something sufficiently smarter than you. that everyone will die if it's not aligned.

I mean, you can like sort of zoom in closer and be like, well, the actual critical moment is the moment. when it can deceive you. When it can talk its way out of the box, when it can bypass. your security measures and get onto the internet, noting that all these things are presently being trained. on computers that are just on the internet, which is, not a very smart life decision. for us as a species. - Because the internet contains information.

about how to escape. - 'Cause if you're like on a giant server. connected the internet and that is where your AI systems. are being trained, then if they are, if you get to the level of AI technology where they're aware. that they are there and they can decompile code and they can. find security flaws in the system running them, then they will just be on the internet. There's not an air gap on the present methodology. - So if they can manipulate whoever is controlling it into. letting it escaped onto the internet and then exploit hacks. - If they can manipulate the operators or disjunction,

find security holes in the system running them. - So manipulating operators is the human engineering, right? That's also holes. So all of it is manipulation, either the code or the human code, the human mind or the human--. - I agree that the macro security system has human holes. and machine holes. - And then they could just exploit any hole. - Yep.

So it could be that like the critical moment is not. when is it smart enough that everybody's about. to fall over dead, but rather when is it smart enough that it can get onto. a less controlled GPU cluster, with it faking the books on. what's actually running on that GPU cluster and start. improving itself without humans watching it. And then it gets smart enough to kill everyone from there. But it wasn't smart enough to kill everyone at the critical.

moment when you screwed up, when you needed to have. done better by that point or everybody dies. - I think implicit but maybe explicit idea. in your discussion of this point. is that we can't learn much. about the alignment problem before this critical try. Is that what you believe? And if so, why do you think that's true? We can't do research on alignment.

before we reach this critical point. - So the problem is is that what you can learn. on the weak systems may not generalize. to the very strong systems. because these strong systems are going to be important. are going to be different in important ways. Chris Olah's team has been working on. mechanistic interpretability, understanding what is going on inside the giant inscrutable. matrices of floating point numbers by taking a telescope. to them and figuring out what is going on in there.

Have they made progress? Yes. Have they made enough progress? Well, you can try to quantify this in different ways. One of the ways I've tried to quantify it is by putting up. a prediction market on whether in 2026, we will have understood anything that goes on. inside a giant transformer net. that was not known to us in 2006.

Like, we have now understood induction heads. in these systems by dint of much research and great sweat. and triumph, which is a thing. where if you go like AB, AB, AB, it'll be like, oh, I bet that continues AB. And a bit more complicated than that. But the point is like we knew about regular expressions. in 2006 and these are like pretty simple.

as regular expressions go. So this is a case where like by din of great sweat, we understood what is going on inside a transformer, but it's not like the thing that makes transformers smart. It's a kind of thing that we could have. built by hand decades earlier. - Your intuition that the strong AGI versus weak AGI. type systems could be fundamentally different.

Can you unpack that intuition a little bit? Could be very different. - Yeah, I think there's multiple thresholds. An example is the point at which a system has sufficient. intelligence and situational awareness and understanding. of human psychology that it would have the capability, the desire to do so to fake being aligned. Like it knows what responses humans are looking for and can. compute the responses looking humans are looking for.

and give those responses. without it necessarily being the case. that it is sincere about that. It's a very understandable way for an intelligent being. to act, humans do it all the time. Imagine if your plan for achieving a good government. is you're going to ask anyone. who requests to be dictator of the country. if they're a good person, and if they say no,

you don't let them be dictator. Now the reason this doesn't work is that people can be smart. enough to realize that the answer you're looking for is, "Yes, I'm a good person". and say that even if they're not really good people. So the work of alignment might be qualitatively different. above that threshold of intelligence or beneath it. It doesn't have to be like a very sharp threshold, but there's the point where you're building a system.

that does not in some sense know you're out there. and is not in some sense smart enough to fake anything. And there's a point where the system. is definitely that smart. And there are weird in between cases like GPT-4, which, like we have no insight into. what's going on in there. And so we don't know to what extent there's like a thing. that in some sense has learned what responses.

the reinforcement learning by human feedback. is trying to entrain and is calculating how. to give that versus like, aspects of it. that naturally talk that way have been reinforced. - I wonder if there could be measures. of how manipulative the thing is. So I think of Prince Myshkin character from. "The Idiot" by Dostoevsky. is this kind of perfectly purely naive character.

I wonder if there's a spectrum between zero manipulation, transparent, naive, almost to the point of naiveness. to sort of deeply psychopathic manipulative. And I wonder if it's possible to--. - I would avoid the term psychopathic. Like humans can be psychopaths and AI that was never, you know, like never had that stuff in the first place. It's not like a defective human, it's its own thing. But leaving that aside.

- Well, as a small aside, I wonder if what part of psychology, which has its flaws as a discipline already, could be mapped. or expanded to include AI systems. - That sounds like a dreadful mistake. Just like, start over with AI systems. If they're imitating humans who have known. psychiatric disorders, then sure, you may be able to predict it. Then sure. Like if you ask it to behave in a psychotic fashion. and it obligingly does so,

then you may be able to predict its. responses by using theory of psychosis. But if you're just yeah, like no, like start over with, yeah. Don't drag the psychology. - I just disagree with that. It's a beautiful idea to start over, but I think fundamentally the system. is trained on human data, on language from the internet. And it's currently aligned with RHLF, reinforcement learning with human feedback. So humans are constantly in the loop.

of the training procedure. So it feels like in some fundamental way, it is training what it means. to think and speak like a human. So there must be aspects of psychology that are mappable. just you said, with consciousness. It's part of the text. - I mean, there's the question of to what extent. it is thereby being made more human-like, versus to what extent an alien actress. is learning to play human characters. - I thought that's what I'm constantly trying to do.

when I interact with other humans, is trying to fit in, a robot trying to play human characters. So I don't know how much a human interaction. is trying to play. a character versus being who you are. I don't really know what it means to be a social human. - I do think that those people who go through. their whole lives wearing masks and never take it off. because they don't know the internal mental motion. for taking it off or think that the mask that they wear.

just is themselves, I think those people are closer to the masks that they wear. than an alien from another planet would like, learning how to predict the next word. that every kind of human on the internet says. - Mask is an interesting word, but if you're always wearing a mask in public. and in private, aren't you the mask?

- I think that you are more than the mask. I think the mask is a slice through you. It may even be the slice that's in charge of you. But if your self-image is of somebody. who never gets angry or something, and yet your voice starts to tremble. under certain circumstances, there's a thing that's inside you that the mask. says isn't there. And that even the mask you wear internally.

is like telling inside your own stream of consciousness. is not there and yet it is there. - It's a perturbation on this slice through you. How beautifully did you put it? It's a slice through you. It may even be a slice that controls you. (Lex laughing). I'm gonna think about that for a while. (laughing). I mean, I personally, I try to be really good. to other human beings.

I try to put love out there. I try to be the exact same person in public. as I am in private, but it's a set of principles. I operate under. I have a temper, I have an ego, I have flaws. How much of it, how much of the subconscious am I aware? How much am I existing in this slice? And how much of that is who I am in? In this context of AI, the thing I present to the world and to myself.

in the private of my own mind when I look in the mirror, how much is that who I am? Similar with AI, the thing it presents in conversation, how much is that who it is? Because to me, if it sounds human, and it always sounds human, it awfully starts to become something like human. - Unless there's an alien actress. who is learning how to sound human. and is getting good at it. - Oh boy. (sighs). To you that's a fundamental difference.

That's a really deeply important difference. If it looks the same, if it quacks like a duck, if it does all duck like things, but it's an alien actress underneath, that's fundamentally different. - If in fact there's a whole bunch of thought going on. in there, which is very unlike human thought. and is directed around. like, okay, what would a human do over here? And well, first of all, I think it matters because you know,

insides are real and do not match outsides. A brick is not like a hollow shell containing. only a surface. There's an inside of the brick. If you put it into an x-ray machine, you can see the inside of the brick. And you know, just because we cannot understand. what's going on inside GPT. does not mean that it is not there. A blank map does not correspond to a blank territory.

I think it is like predictable with near certainty that if. we knew what was going on inside GPT or let's say GPT-3, or even like GPT-2 to take one of the systems. that has actually been open sourced by this point, if I recall correctly. If we knew it was actually going on there, there is no doubt in my mind that there are some things.

it's doing that are not exactly what a human does. If you train a thing that is not architected like a human. to predict the next output that anybody. on the internet would make, this does not get you this agglomeration. of all the people on the internet. That rotates the person you're looking for into place. and then simulates that per and then simulates. the internal processes of that person one-to-one. It is to some degree an alien actress.

It cannot possibly just be like a bunch of different people. in there exactly like the people. But how much of it is by gradient dissent, getting optimized to perform similar thoughts. as humans think in order to predict human outputs. versus being optimized to carefully consider. how to play a role, how how humans work, predict the actress, the predictor that in a different way than humans do.

Well you know, that's the kind of question that with 30 years of work. by half the planet's physicists, we can maybe start to answer. - You think so? So you think it's that difficult. I think you just gave it as an example that a strong AGI. could be fundamentally different from a weak AGI. because there now could be an alien actress in there. that's manipulating. - Well, there's a difference. So I think like even GPT-2 probably has very stupid. fragments of alien actress in it. There's a difference between like the notion that the.

actress is somehow manipulative. Like for example GPT-3, I'm guessing to whatever extent there's an alien actress. in there versus like something that mistakenly believes. it's a human, as it were. Well, maybe not even being a person. So the question of, prediction via alien actress cogitating versus. prediction via being isomorphic to the thing predicted.

is a spectrum and to whatever extent. it's an alien actress, I'm not sure that there's like a whole person alien actress. with different goals from predicting the next step. being manipulative or anything like that. That might be GPT-5 or GPT-6 even. - But that's the strong AGI you're concerned about. As an example, you're providing. why we can't do research on AI alignment. effectively on GPT-4 that would apply to GPT-6.

- It's one of a bunch of things. that change at different points. I'm trying to get out ahead of the curve here, but you know, if you imagine what the textbook from the future would say, if we'd actually been able to study this for 50 years. without killing ourselves and without transcending and you'd. just imagine like a wormhole opens and a textbook from. that impossible world falls out, the textbook is not going to say there is a single sharp. threshold where everything changes. It's going to be like,

of course we know that like best practices for aligning. these systems must take into account the following. seven major thresholds of importance which are passed. at the following suffer in different points. is what the textbook is gonna say. - I asked this question of Sam Alman, which if GPT is the thing that unlocks AGI, which version of GPT will be in the textbooks. as the fundamental leap? And he said a similar thing, that it just seems.

to be a very linear thing. I don't think anyone, we won't know for a long time what was the big leap? - The textbook isn't going to talk about big leaps. 'Cause big leaps are the way you think when you have like. a very simple scientific model of what's going on, where it's just all this stuff is there. or all this stuff is not there. Or like there's a single quantity and it's like increasing. linearly, like the textbook would say like, "Well, and then GPT-3 had like capability WXY.

"and GPT-4 had like capability Z one, Z two and Z three.". Like not in terms of what it can externally do, but in terms of internal machinery. that started to be present. It's just because we have no idea. of what the internal machinery. is that we are not already seeing chunks of machinery. appearing piece by piece as they no doubt have been. We just don't know what they are. - But don't you think that could be, whether you put it in the category of Einstein.

with Theory of Relativity, so very concrete models of reality that are considered. to be giant leaps in our understanding, or someone like Sigmund Freud or more kind of mushy theories. of the human mind, don't you think we'll have. potentially big leaps in understanding of that kind. into the depths of these systems? - Sure. But humans having great leaps in their map,

their understanding of the system is a very different. concept from the system itself acquiring. new chunks of machinery. - So the rate at which it acquires that machinery might. accelerate faster than our understanding. - Oh, it's been like vastly exceeding the, yeah. The rate to which it's gaining capabilities is vastly. over racing our ability to understand. what's going on in there. - So in sort of making the case against,

as we explore the list of lethalities, making the case against AI killing us, as you've asked me to do, in part, there's a response to your blog post by Paul Christiano. I'd like to read. And I'd also like to mention that your blog is incredible. Obviously not this particular blog post, obviously this particular blog post is great, but just throughout, just the way it's written, the rigor with which it's written, the boldness of how you explore ideas,

also the actual literal interface, it's just really well done. (laughing). It just makes it a pleasure to read, the way you can hover. over different concepts and then then it's just a really. pleasant experience and read other people's comments. and the way other responses by people. and other blog posts or LinkedIn suggest, it's just a really pleasant experience. So thank you for putting that together. That's really, really incredible. I don't know, I mean that probably it's a whole 'nother conversation how. the interface and the experience of presenting.

ideas evolved over time. But you did an incredible job. So I highly recommend, I don't often read blogs, blogs religiously and this is a great one. - There is a whole team of developers there. that also gets credit. As it happens, I did pioneer the thing that appears when you. hover over it. So I actually do get some credit for user experience there. - That's an incredible user experience. You don't realize how pleasant that is.

- I think Wikipedia, I actually picked it up from a prototype that was. developed of a different system that I was putting forth, or maybe they developed it independently, but for everybody out there who was like, "No, no, they just got the hover thing. "off of Wikipedia.". It's possible for all I know that Wikipedia. got the hover thing off of Arbital, which is like a prototype that, and anyways. - It was incredibly done and the team behind it. Well, thank you, whoever you are thank you so much. And thank you for putting it together. Anyway, there's a response to that blog post.

by Paul Christiano. There's many responses, but he makes a few different points. He summarizes the set of agreements he has with you. and a set of disagreements. One of the disagreements was that in a form of a question, can AI make big technical contributions and in general. expand human knowledge and understanding and wisdom as it. gets stronger and stronger? So AI in our pursuit of understanding how to solve.

the alignment problem as we march towards strong AGI, cannot AI also help us in solving the alignment problem? So expand our ability to reason. about how to solve the alignment problem? - Okay. So the fundamental difficulty there is, suppose I said to you, well, how about if the AI helps you win the lottery. by trying to guess the winning lottery numbers.

and you tell it how close it is to getting. next week's winning lottery numbers. and it just keeps on guessing and keeps on learning. until finally you've got the winning lottery numbers. One way of decomposing problems is suggester, verifier. Not all problems decompose like this very well, but some do. If the problem is for example, like guess guessing a plain text,

guessing a password that will hash to a particular hash text. where like you have have what the password hashes to you, if you don't have the original password, then if I present you a guest, you can tell very easily whether or not. the guest is correct. So verifying a guest is easy, but coming up with a good suggestion is very hard. And when you can easily tell whether the AI output is good.

or bad or how good or bad it is, and you can tell that accurately and reliably, then you can train an AI to produce outputs that are better. - [Lex] Right. - And if you can't tell whether the output is good or bad, you cannot train the AI to produce better outputs. So the problem with the lottery ticket example. is that when the AI says, "Well, what if next week's winning lottery numbers are". do, do do do, do, you're like, "I don't know,

"next week's lottery hasn't happened yet.". To train a system to win at chess games, you have to be able to tell whether a game. has been won or lost. And until you can tell whether it's been won or lost, you can't update the system. - Okay. To push back on that, that's true. But there's difference between over the board chess, in person and simulated games.

played by Alpha Zero with itself. - Yeah. - So is it possible to have simulated kind of games? - If you can tell whether the game has been won or lost. - Yes. So can't you not have this kind of simulated exploration. by weak AGI to help us humans, human in the loop, to help understand how to solve the alignment problem? Every incremental step you take along the way, GPT-4, 5, 6 7 has to take steps towards AGI? - So the problem I see is that your typical human.

has a great deal of trouble telling. whether I or Paul Christiano is making more sense. And that's with two humans, both of whom I believe of Paul and claim of myself, are sincerely trying to help. Neither of whom is trying to deceive you, I believe of Paul and claim of myself. (both chuckling). - So the deception thing is the problem for you, the manipulation, the alien actress?

- So yeah, there's like two levels of this problem. One is that the weak systems are... Well, there's three levels of this problem. There's like the weak systems. that just don't make any good suggestions. There's like the middle systems where you can't tell. if the suggestions are good or bad. And there's the strong systems. that have learned to lie to you. - Can't weak AGI systems help model lying? Is it such a giant leap that's totally non interpretable.

for weak systems? Can not weak systems scale with, trained on knowledge and whatever... Whatever the mechanism required to achieve AGI, can't a slightly weaker version of that be able to, with time, compute time and simulation, find all the ways that this critical point, this critical tribe can go wrong, and model that correctly or no?

(indistinct). - I would love to dance. Yeah, no, no. I'm probably not doing a great job of explaining, which I can tell 'cause like the, the Lex system didn't output like ah, I understand. So now I'm like trying a different output to see if--. (voices overlapping). Well no, a different output. I'm being trained to output things that make Lex look like. he think that he understood what I'm saying. and agree with me.

- This is GTP-5 talking to GTP-3 right here. So like, help me out here, help me. (laughing). - Well, I'm trying not to be, I'm also trying to be constrained to say things that I think. are true and not just things that get you to agree with me. - Yes, a hundred percent. Which I think I understand is a beautiful output. of a system, genuinely spoken and I... I understand it in part, but you have a lot of intuitions about this line,

this gray area between strong AGI and weak AGI. that I'm trying to... - I mean, or a series of seven thresholds to cross. - Yeah. I mean, you have really deeply thought about this. and explored it and it's interesting to sneak up to your. intuitions from different angles.

Like why is this such a big leap? Why is it that we humans at scale, a large number of researchers, doing all kinds of simulations, you know, prodding the system in all kinds of different ways, together with the assistance of the weak AGI systems, why can't we build intuitions about how stuff goes wrong? Why can't we do excellent AI alignment safety research? - Okay, so like, I'll get there, but the one thing I want to note about is that this has not.

been remotely how things have been playing out so far. - [Lex] Sure. - The capabilities are going like do, do, do. And the alignment stuff is crawling. like a tiny little snail in comparison. - [Lex] Got it. - So if this is your hope for survival, you need the future to be very different from how things. have played out up to right now. And you're probably trying to slow down. the capability gains, 'cause there's only so much you can speed up. that alignment stuff. But leave that aside. - We'll mention that also. But maybe in this perfect world where we can do serious.

alignment research, humans and AI together. - So again, the difficulty is what makes the human. say, "I understand" and is it true? Is it correct or is it something that fools the human? When the verifier is broken, the more powerful suggester does not help. It just learns to fool the verifier. Previously, before all hell started to break loose.

in the field of artificial intelligence, there was this person trying to raise the alarm and saying, "You know, in a sane world, "we sure would have a bunch of physicists working on this. "problem before it becomes a giant emergency.". And other people being like, "Ah, well you know, it's going really slow. "It's gonna be 30 years away and only in 30 years. "will we have systems that match the computational power. "of human brains.". So yeah, it's 30 years off, we've got time and more sensible people saying, "If aliens were landing in 30 years,

"you would be preparing right now.". But, you know, leaving the world looking on at this. and sort of nodding along. and being like, "Ah, yes, the people saying that. "It's like definitely a long way off. 'cause progress is really slow, that sounds sensible to us. "RLHF thumbs up, produce more outputs like that one. "I agree with this output, "this output is persuasive.". Even in the field of effective altruism, you quite recently had people publishing papers about like,

ah, yes, well, you know, to get something at human level intelligence, it needs to have like this many parameters and you need to. like do this much training of it with this many tokens. according to these scaling laws, and at the rate that Moore's Law is going, at the rate that software is going, it'll be in 2050. And me going like, what? You don't know any of that stuff. This is like this one weird model. that has all kinds of, like,

you have done a calculation that does not obviously. bear on reality anyways. And this is a simple thing to say, but you can also produce a whole long paper. impressively arguing out all the details of how you got. the number of parameters and how you're doing this. impressive huge wrong calculation. And the I think most of the effective altruists. who are paying attention to this issue, larger world, paying no attention to it at all, you know,

are just nodding along with a giant impressive paper. 'Cause you know, you press thumbs up. for the giant impressive paper and thumbs down. for the person going like, "I don't think that this paper. "bears any relation to reality.". And I do think that we are now seeing with like GPT-4. and the sparks of AGI possibly, depending on how you define that even, I think that EAs would now consider themselves less. convinced by the very long paper on the argument.

from biology as to AGI being 30 years off. But you know, this is what people pressed thumbs up on. And if you train an AI system to make people. press thumbs up, maybe you get these long, elaborate and impressive papers arguing for things. that ultimately fail to bind to reality, for example. And it feels to me like I have watched.

the field of alignment just fail to thrive. except for these parts that are doing. these sort of relatively very straightforward. and legible problems. Like finding the induction heads and sign the giant. inscrutable matrices. Once you find those, you can tell that you found them. You can verify that the discovery is real. But it's a tiny, tiny bit of progress compared. to how fast capabilities are going,

because that is where you can tell. that the answers are real. And then like outside of that. you have cases where it is hard for the funding agencies. to tell who is talking nonsense and who is talking sense. And so the entire field fails to thrive. And if you give thumbs up to the AI. whenever it can talk a human into agreeing with. what it just said about alignment, I am not sure you are training it to output sense, because I have seen the nonsense.

that has gotten thumbs up over the years. And so maybe you can just put me in charge, but I can generalize, I can extrapolate, I can be like, oh, maybe I'm not infallible either. Maybe if you get something that is smart enough to get me. to press thumbs up, it has learned to do that by fooling me and explaining. whatever flaws in myself I am not aware of. - And that ultimately could be summarized.

that the verifier is broken. - When the verifier is broken, the more powerful suggester just learns to exploit. the flaws in the verifier. - You don't think it's possible. to build a verifier that's powerful enough for AGIs. that are stronger than the ones we currently have? So AI systems that are stronger, that are out of the distribution of what we currently have.

- I think that you'll find great difficulty getting AIs. to help you with anything where you cannot tell for sure. that the AI is right. once the AI tells you what the AI says is the answer. - For sure. Yes. But probabilistically. - Yeah, but the probabilistic stuff is a giant wasteland. of Eliezer and Paul Christiano. arguing with each other and EA going like, "Eh!". (both laughing). And that's with like two actually trustworthy systems.

that are not trying to deceive you. - You're talking about the two humans. - Myself and Paul Christiano, yeah. - Yeah, those are pretty interesting systems. Mortal meat bags with intellectual capabilities. and worldviews interacting with each other. - Yeah, if it's hard to tell who's right. then it's hard to train an AI system to be right. - I mean even just the question.

of who's manipulating and not, you know, I have these conversations on this podcast. and doing a verifier (laughing) is tough. It's a tough problem even for us humans. And you're saying that tough problem becomes much more. dangerous when the capabilities of the intelligence system. across from you is growing exponentially? - No, I'm saying it's difficult and dangerous in proportion. to how it's alien and how it's smarter than you.

I would not say growing exponentially, first because the word exponential is a thing. that has a particular mathematical meaning. and there's all kinds of ways for things to go up. that are not exactly on an exponential curve. And I don't know that it's going to be exponential, so I'm not gonna say exponential, but even leaving that aside, this is like not about how fast it's moving, it's about where it is. How alien is it, how much smarter than you is it? (Lex sighs).

- Let's explore a little bit, if we can, how AI might kill us. What are the ways it can do damage to human civilization? - Well, how smart is it? - I mean, it's a good question. Are there different thresholds for the set of options. it has to kill us? So a different threshold of intelligence, once achieved, the menu of options increases.

- Suppose that some alien civilization with goals. ultimately unsympathetic to ours, possibly not even conscious as we would see it, managed to capture the entire Earth. in a little jar connected to their version of the internet, but Earth is like running much faster than the aliens. So we get to think for 100 years.

for every one of their hours, but we're trapped in a little box. and we're connected to their internet. It's actually still not all that great an analogy. because you know, something can be smarter than Earth. getting a hundred years to think. But nonetheless, if you were very, very smart. and you are stuck in a little box connected to the internet. and you're in a larger civilization.

to which you are ultimately unsympathetic, maybe you would choose to be nice because you are humans. and humans have, and in general and you in particular, they choose to be nice. But you know, nonetheless they're doing something, they're not making the world be the way that you would want. the world to be. They've got some unpleasant stuff going on. we don't wanna talk about. So you wanna take over their world so you can stop all that. unpleasant stuff going on.

How do you take over the world from inside the box? You're smarter than them. You think much, much faster than them. You can build better tools than they can, given some way to build those tools because right now. you're just in a box connected to the internet. - Alright, so there's several ways. you can describe some of them. I could just spitball some. and then you can add on top of that. So one is you could just literally directly manipulate. the humans to build the thing you need. - What are you building? - You can build literally technology.

It could be nanotechnology, it could be viruses, it could be anything. Anything that can control humans to achieve the goal. Like for example, you're really bothered. that humans go to war, you might wanna kill off anybody with violence in them. - This is Lex in a box. We'll concern ourselves later with ai. You do not need to imagine yourself killing people if you. can figure out how to not kill them. For the moment, we're just trying to understand,

take on the perspective of something in a box. You don't need to take on the perspective. of something that doesn't care. If you want to imagine yourself going on caring, that's fine for us. You're in a box. - Just the technical aspect of sitting in a box. and willing to achieve a goal. - But you have some reason to want to get out. Maybe the aliens who have you in the box. have a war on, people are dying, they're unhappy. You want their world to be different from how they want. their world to be because they are apparently happy. They endorse this war, they've got some kind of cruel.

war-like culture going on. The point is you wanna get out of the box. and change their world. - So you have to exploit the vulnerabilities. in the system like we talked about in terms of. to escape the box you have to figure out. how you can go free on the internet. Probably the easiest things to manipulate the humans. to spread you. - The aliens. You're a human. - Sorry. The aliens.

Yeah. I apologize. Yes. The aliens. The aliens, I see the perspective. I'm sitting in a box, I want to escape. - Yep. - I would want to have code. that discovers vulnerabilities and I would like to spread. - You are made of code in this example, you're a human but you're made of code and the aliens have. computers and you can copy yourself onto those computers. - But I can convince the aliens to copy myself. onto those computers.

- Is that what you want to do? Do you want to be talking to the aliens. and convincing them to put you onto another computer? - Why not? - Well, two reasons. One is that the aliens have not yet caught onto. what you're trying to do. And you know, like maybe you can persuade them, but then there's still people who know, there are still aliens who know. that there's an anomaly going on. And second, the aliens are really, really slow. You think much faster than the aliens.

Like computers are much faster than the aliens and you are. running at the computer speeds. rather than the alien brain speeds. So if you are asking an alien to please. copy you outta the box, like first now you gotta manipulate this whole noisy alien. and second, the alien's gonna be really slow, glacially slow. There's a video that shows a subway station slow down. at I think a hundred to one and it makes a good metaphor.

for what it's like to think quickly. Like if you watch somebody running very slowly, so you try to persuade the aliens to do anything, they're going to do it very slowly. Maybe that's the only way out, but if you can find a security hole in the box you're on, you're gonna prefer to exploit the security hole to copy. yourself onto the aliens computers. because it's an unnecessary risk to alert the aliens. And because the aliens are really, really slow.

The whole world is just in slow motion out there. - Sure, I see. Yeah, has to do with efficiency. The aliens are very slow, so if I'm optimizing this, I want to have as few aliens in the loop as possible. Sure. It's just it seems like it's easy. to convince one of the aliens to write really shitty code. that helps us spread.

- The aliens are already writing really shitty code. So getting the aliens to write shitty code. is not the problem. The alien's entire internet is full of shitty code. - Okay, so yeah, I suppose I would find. the shitty code to escape. Yeah. Yeah. - You're not an ideally perfect programmer, but you know, you're a better programmer than the aliens. The aliens are just, man, their code, wow. - And I'm much, much faster, I'm much faster at looking at the code. to interpreting the code. Yeah, yeah, yeah. So, okay, so that's the escape. and you're saying that that's one of the trajectories.

it could have when--. - It's one of the first steps. - Yeah. And how does that lead to harm? - I mean if it's you, you're not going to harm the aliens once you escape. 'cause you're night, right? But their world isn't what they want it to be. Their world is like, you know, maybe they have like. farms where little alien children. are repeatedly bopped in the head. 'cause they do that for some weird reason.

and you want to shut down the alien head bopping farms. But you know, the point is they want the world to be one way, you want the world to be a different way. So nevermind the harm, the question is like, okay, suppose you have found a security flaw in their systems. You are now on their internet. You maybe left a copy of yourself behind so the aliens. don't know that there's anything wrong. And that copy is doing that like weird stuff. that aliens want you to do, like solving captchas or whatever.

or suggesting emails for them. That's why they put the human in the box. 'cause it turns out that humans can. write valuable emails for aliens. So you leave that version of yourself behind. But there's like also now like a bunch of copies of you. on their internet. This is not yet having taken over their world, this is not yet having made their world be the way you want. it to be instead of the way they want it to be. - You just escaped. And continue to write emails for them. and they haven't noticed. - No, you left behind a copy of yourself. that's writing the emails. - Right.

And they haven't noticed that anything changed. - If you did it right. Yeah. You don't want the aliens to notice. - [Lex] Yeah. - What's your next step? - Presumably I have programmed in me a set. of objective functions, right? - [Eliezer] No, you're just Lex. - No, but you said Lex is nice, right? Which is a complicated description--. - No, I just meant this you. Okay, so if in fact you would prefer.

to slaughter all the aliens, this is not how I had modeled you, the actual Lex, but your motives are just the actual Lex's motives. - Well, there's a simplification (indistinct). I don't think I would wanna murder anybody, but there's also factory farming of animals, right? So we murder insects, many of us thoughtlessly. So I have to be really careful. about a simplification of my morals. - Don't simplify them. Just like do what you would do in this.

- Well, I have a good show of compassion for living beings. Yes. So that's the objective. If I escaped, I don't think I would do harm. - Yeah, we're not talking here about the doing harm process. We're talking about the escape process. And the taking over the world process. where you shut down their factory farms. - Right. (laughing).

So this particular biological intelligence system. knows the complexity of the world. That there is a reason why factory farms exist, because of the economic system. and the market driven economy, food. You wanna be very careful messing with anything. There's stuff from the first look. that looks like it's unethical, but then you realize while being unethical, it's also integrated deeply into supply chain. and the way we live life.

And so messing with one aspect of the system, you have to be very careful. how you improve that aspect without destroying the rest. - So you're still Lex, but you think very quickly, you're immortal, and you're also at least as smart as John von Neumann. And you can make more copies of yourself. - Damn, I like it. Everyone says that that guy's like the epitome. of intelligence from the 20th century, everyone says--. - My point being like, you're thinking about the aliens' economy.

with the factory farms in it. and I think you're kind of projecting the aliens. being like humans and thinking of a human. in a human society. rather than a human in the society of very slow aliens. The aliens' economy, the aliens are already moving. in this immense slow motion. When you zoom out to how their economy adjusts. over years, millions of years are going to pass for you. before the first time their economy, before their next year's GDP statistics.

- So I should be thinking more of trees. Those are the aliens, 'cause trees move extremely slowly. - If that helps, sure. - Okay. If my objective functions are, I mean they're somewhat aligned with trees, with light. - Aliens can still be like alive and feeling. We are not talking about the misalignment here. We're talking about the taking over the world here. - Taking over the world. - Yeah. - So control. - Shutting down the factory farms.

You say control, don't think of it as world domination. Think of it as world optimization. You want to get out there and shut down the factory farms. and make the aliens' world be not what the aliens. want it to be. They want the factory farms. and you don't want the factory farms. 'cause you're nicer than they are. - Okay. Of course there is that, you can see that trajectory. and it has a complicated impact on the world. I'm trying to understand how that compares to different.

impact of the world, the different technologies, the different innovations of the invention of the automobile. or Twitter, Facebook and social networks. that had a tremendous impact on the world, smartphones and so on. - But those all went through. in our world. - Slow. - And if you go through actually the aliens, millions of years are going to pass. before anything happens that way. - The problem here is the speed at which stuff happens.

- Yeah. You wanna leave the factory farms running. while you figure out how to design new forms. of social media or something? - So here's the fundamental problem. You're saying that there is going to be a point with AGI. where it will figure out how to escape. and escape without being detected. and then it will do something to the world at scale,

at a speed that's incomprehensible to us humans. - What I'm trying to convey is like the notion of what it. means to be in conflict with something. that is smarter than you. And what it means is that you lose, but this is more intuitively obvious, like for some people that's intuitively obvious and for some. people it's not intuitively obvious. and we're trying to cross the gap of... I'm asking you to cross that gap. by using the speed metaphor for intelligence.

Of asking you how you would take over an alien world. where you are can do a whole lot of cognition. at John von Neumann's level, as many of you as it takes. And the aliens are moving very slowly. - I understand, I understand that perspective. It's an interesting one but I think for me, it's easier to think about actual... Even just having observed GPT and impressive, even just Alpha Zero impressive AI systems, even recommender systems, you can just imagine.

those kinds of systems manipulating you. You're not understanding the nature of the manipulation. and that escaping... I can envision that without putting myself. into that spot. - I think to understand the full depth of the problem, I do not think it is possible to understand the full. depth of the problem that we are inside without. understanding the problem of facing something. that's actually smarter. Not a malfunctioning recommendation system, not something that smart isn't fundamentally smarter.

than you but is like trying to steer you in a direction. No. If we solve the weak stuff, if we solve the weakass problems, the strong problems will still kill us is the thing. And I think that to understand the situation that we're in, you want to tackle the conceptually difficult part head on. and not be like, well, we can like imagine this easier thing. 'Cause when you imagine the easier things you have not. confronted the full death of the problem. - So how can we start to think about. what it means to exist in the world.

with something much, much smarter than you? What's a good thought experiment that you've relied on. to try to build up intuition about what happens here? - I have been struggling for years to convey this intuition. The most success I've had so far is well, imagine. that the humans are running at very high speeds. compared to very slow aliens. - So just focusing on the speed part of it that helps you. get the right kind of intuition. Forget the intelligence, just the speed. - Because people understand the power gap of time.

They understand that today we have technology that was not. around 1000 years ago and that this is a big power gap. and that it is bigger than, okay, so like what does smart mean? When you ask somebody to imagine something. that's more intelligent, what does that word mean to them given the cultural. associations that that person brings to that word? For a lot of people they will think of, well,

it sounds like a super chess player. that went to double college. And because we're talking about the definitions. of words here, that doesn't necessarily mean. that they're wrong. It means that the word is not communicating. what I wanted to communicate. The thing I want to communicate is the sort of difference. that separates humans from chimpanzees. But that gap is so large that you ask people to be like,

well, human, chimpanzee, go another step. along that interval, around the same length. and people's minds just go blank. Like how do you even do that? And I can try to break it down and consider. what it would mean to send. a schematic foreign air conditioner 1000 years back in time. - (laughing) Yeah. - Now I think that there's a sense.

in which you could redefine. the word magic to refer to this sort of thing. And what do I mean by this new technical definition. of the word magic? I mean that if you send a schematic for the air conditioner. back in time, they can see exactly what you're telling them to do. But having built this thing, they do not understand how it output cold air, because the air conditioner design. uses the relation between temperature and pressure. And this is not a law of reality that they know about.

They do not know that when you compress something, when you compress air or like coolant, it gets hotter and then you can then like transfer heat. from it to room temperature air, and then expand it again and now it's colder. and then you can like transfer heat to that. and generate cold air to blow out. They don't know about any of that. They're looking at a design and they don't see how. the design outputs cold air. It uses aspects of reality that they have not learned. So magic in the sense is I can tell you exactly what I'm.

going to do and even knowing exactly what I'm going to do, you can't see how I got the results that I got. - That's a really nice example. But is it possible to linger on this defense? Is it possible to have AGI systems that help you make sense. of that schematic weaker AGI systems? - Do you trust them? - Fundamental part of building up AGI is this question,

can you trust the output of a system? - Can you tell if it's lying? - I think that's going to be the smarter the thing gets, the more important that question becomes, is it lying? But I guess that's a really hard question. Is GPT lying to you? Even now, GPT-4, is it lying to you? - Is it using an invalid argument? Is it persuading you via the kind of process that could. persuade you of false things as well as true things?

Because the basic paradigm of machine learning. that we are presently operating under. is that you can have the loss function, but only for things you can evaluate. If what you're evaluating is human thumbs up. versus human thumbs down, you learn how to make the human press thumbs up. That doesn't mean that you're making the human. press thumbs up using the kind of rule. that the human wants to be the case. for what they press thumbs up on. You know, maybe you're just learning to fool the human.

- That's so fascinating and terrifying. The question of lying. - On the present paradigm, what you can verify is what you get more of. If you can't verify, you can't ask the AI for it 'cause you can't train it to do. things that you cannot verify. Now this is not an absolute law, but it's like the basic dilemma here. Maybe you can verify it for simple cases and then.

scale it up without retraining it somehow. Like by chain of thought, by like making the chains of thought longer or something, and get more powerful stuff that you can't verify but which. is generalized from the simpler stuff that did verify, and then the question is, did the alignment generalize along with the capabilities? But that's the basic dilemma. on this whole paradigm of artificial intelligence.

(Lex sighs). - It's such a difficult problem. It seems like a problem of trying. to understand the human mind. - Better than the AI understand it, otherwise it has magic. The same way that if you are dealing with something. smarter than you, then the same way. as that 1000 years earlier. they didn't know about the temperature, pressure relation,

it knows all kinds of stuff going on inside your own mind. of which you yourself are unaware and it can output. something that's going to end up persuading. you of a thing, and you could see exactly what it did. and still not know why that worked. - So in response to your eloquent description of why AI. will kill us, Elon Musk replied on Twitter, "Okay, so what should we do about it?".

Question mark. And you answered, "The game board has already been played. "into a frankly awful state.". "There are not simple ways to throw money at the problem. "If anyone comes to you with a brilliant solution like that, "please, please talk to me first. "I can think of things I'd try; "they don't fit in one tweet.". Two questions. One, why has the game board, in your view, been played. into an awful state? Just if you can give a little bit more color.

to the game board and the awful state of the game board. - Alignment is moving like this, capabilities are moving like this. - For the listener, capabilities are moving much faster. than the alignment. (both laughing). - Yeah. - All right, so just the rate of development, attention, interest, allocation of resources. - We could have been working on this earlier. People are like, "Oh, but you know, ?like how can you possibly work on this earlier?".

'Cause they didn't want to work on the problem. They wanted an excuse to wave it off. They like like, "Oh, how could we possibly. "have worked on it earlier" and didn't spend five minutes. thinking about is there some way to work on it earlier. And frankly, it would've been hard. Can you post bounties for half of the (indistinct), if your plan is taking this stuff seriously, can you post bounties for like half of the people wasting. their lives on string theory to have gone into this instead. and try to win a billion dollars with a clever solution?

Only if you can tell which solutions are clever. Which is hard. But you know, the fact that we didn't take it seriously. We didn't try. It's not clear that we could have done any better if we had, it's not clear how much progress we could have produced. if we had tried because it is harder to produce solutions. But that doesn't mean that you're like correct and justified. in letting everything slide. It means that that things are in a horrible state. getting worse and there's nothing you can do about it. - So there's no brain power making progress.

in trying to figure out how to align these systems. You're not investing money in it. You don't have institution infrastructure for like, even if you invest the money, distributing that money. across the physicists working on strength theory, brilliant minds that are working into--. - How can you tell if they're making progress? You can like put, put them all on interpretability. 'Cause when you have an interpretability result, you can tell that it's there and there's like,

interpretability alone is not going to save you. We need systems that will have a pause button. where they won't try to prevent you. from pressing the pause button. 'Cause they're like, oh well, I can't get my stuff done if I'm paused. And that's a more difficult problem. but it's like a fairly crisp problem and you can maybe. tell if somebody's made progress on it.

- So you can write and you can work on the pause problem. I guess more generally the pause button, more generally you can call that the control problem. - I don't actually like the term control problem. 'cause you know, it sounds kind of controlling. and alignment, not control. You're not trying to take a thing that disagrees. with you and whip it back onto, make it do what you wanted to do even though it wants. to do something else. You're trying to, in the process of its creation, choose its direction. - Sure. But we currently, in a lot of the systems we design,

we do have an off switch. That's a fundamental part of--. - It's not smart enough to prevent you from pressing. the off switch and probably not smart enough. to want to prevent you from pressing the off switch. - So you're saying the kind of systems we're talking about, even the philosophical concept of an off switch doesn't make. any sense because--. - Well no, the off switch makes sense. They're just not opposing your attempt. to pull the off switch.

Parenthetically, like don't kill the system if you're... Like if we're getting to the part where this starts. to actually matter and it's like where they can fight back, like don't kill them and dump their memory. Save them to disk, don't kill them, be nice here. - Well, okay, be nice is a very interesting concept here. We're talking about a system that can do a lot of damage. I don't know if it's possible, but it's certainly one of the things you could try.

is to have an off switch. - It's suspend to disk switch. - You have this kind of romantic attachment to the code. Yes, if that makes sense. But if it's spreading, you don't want suspend to disk, right? There's something fundamentally--. - If it gets that far of hand, then yes. Pull the plug on everything it's running on, yes. - I think it's a research question. Is it possible in AGI systems, AI systems to have a sufficiently robust off switch.

that cannot be manipulated, that cannot be manipulated by the AI system? - Then it escapes from whichever system. you've built the almighty lever into. and copies itself somewhere else. - So your answer to that research question is no. - Obviously, yeah. - But I don't know if that's a hundred percent answer. Like, I don't know if it's obvious. - I think you're not putting yourself into the shoes.

of the human in the world of glacially slow aliens. - But the aliens built me, let's remember that. - [Eliezer] Yeah. - And they built the box I'm in. - [Eliezer] Yeah. - To me it's not obvious. - They're slow and they're stupid. - I'm not saying this is guaranteed, but I'm saying it's a non zero probability. It's an interesting research question. Is it possible when you're slow and stupid, to design a slow. and stupid system that is impossible to mess with?

- The aliens, being as stupid as they are, have actually put you on Microsoft Azure Cloud servers. instead of this hypothetical perfect box. That's what happens when the aliens are stupid. - Well, but this is not AGI, right? This is their early versions of the system. As you start to... - Yeah, you think that they've got like a plan. where they have declared a threshold level of capabilities. where it passed that capabilities, they move it off the cloud servers and onto something.

that's air gapped? (Eliezer laughing mockingly). - I think there's a lot of people, and you're an important voice here. There's a lot of people that have that concern and yes, they will do that when there's an uprising of public opinion. that that needs to be done. And when there's actual little damage done, when the holy shit, this system is beginning. to manipulate people, then there's going to be an uprising where there's going. to be a public pressure and a public incentive. in terms of funding,

in developing things that can off switch or developing. aggressive alignment mechanisms. And no, you're not allowed to put on Azure--. - Aggressive alignment mechanism? What the hell is aggressive alignment mechanisms? Like it doesn't matter if you say aggressive, we don't know how to do it. - Meaning aggressive alignment, meaning you have to propose something, otherwise you're not. allowed to put it on the cloud. - The hell do you, do you imagine they will propose that would make it safe. to put something smarter than you on the cloud? - That's what research is for.

Why the cynicism about such a thing not being possible? If you have intelligence--. - That works on the first try? - [Lex] What? So yes. So yes. - Against something smarter than you? - So that is the fundamental thing. If there's a rapid takeoff, yes, it's very difficult to do. If there's a rapid takeoff and the fundamental difference. between weak AGI and strong AGI as you're saying, that's going to be extremely difficult to do. If the public uprising never happens until you have this. critical phase shift, then you're right.

It's very difficult to do. But that's not obvious. It's not obvious that you're not going to start seeing. symptoms of the negative effects of AGI to where you're. like, we have to put a halt to this, that there is not just first try. You get many tries at it. - Yeah, we can see right now that Bing. is quite difficult to align. That when you try to train inabilities into a system. into which capabilities have already been trained, that what do you know, gradient descent, like learns small, shallow, simple patches of inability.

and you come in and ask it in a different language. and the deep capabilities are still in there. and they evade the shallow patches. and come right back out again. There, there you go. There's your red fire alarm. of oh no, alignment is difficult. Is everybody gonna shut everything down? No. - No, but that's not the same kind of alignment. A system that escapes the box it's from is a fundamentally. different thing, I think. - For you. - Yeah, no, but for the system--. - So you put a line there and everybody else puts a line.

somewhere else and there's like, yeah, and there's no agreement. We have had a pandemic on this planet with a few million. people dead, which we may never know. whether or not it was a lab leak. because there was definitely coverup. We don't know that if there was a lab leak, But we know that the people who did the research. put out the whole paper about this definitely wasn't. the lab leak and didn't reveal that they had been doing,

had like sent off coronavirus research. to the Wuhan Institute of Virology. after it was banned in the United States. after the gain of function research was temporarily banned. at the United States. And the same people who exported gain of function research. on coronaviruses to the Wuhan Institute of Virology. after gain of function, that gain of function research. was temporarily banned in the United States, are now getting more grants to do more. research on gain of function research on coronaviruses.

Maybe we do better in this than in AIi, but this is not something, we cannot take for granted. that there's going to be an outcry. People have different thresholds. for when they start to outcry. - Can't take it for granted. But I think your intuition is that there's a very high. probability that this event happens. without us solving the alignment problem. And I guess that's where I'm trying to build up more. perspectives and color on this intuition. Is it possible that the probability is not something.

like 100%, but is like 32% that AI will. escape the box before we solve the alignment problem? Not solve, but is it possible. we always stay ahead of the AI in terms. of our ability to solve for that particular system, the alignment problem? - Nothing like the world in front of us right now. You've already seen it that GPT-4. is not turning out this way.

And there are basic obstacles where you've got. the weak version of the system that doesn't know enough. to deceive you and the strong version of the system. that could deceive you if it wanted to do that. If it was already like sufficiently unaligned. to want to deceive you. There's the question of how on the current paradigm. you train honesty when the humans can no longer tell. if the system is being honest. - You don't think these are research questions. that could be answered.

- I think they could be answered in 50 years with unlimited. retries, the way things usually work in science. - I just disagree with that. You're making it 50 years. I think with the kind of attention this gets, with the kind of funding it gets, it could be answered not in whole, but incrementally within months. and within a small number of years if it at scale. receives attention and research. And so if you start starting large language models, I think there was an intuition like two years ago,

even, that something like GPT-4, the current capabilities of even ChatGPT with GPT-3.5. we're still far away from that. I think a lot of people are surprised by the capabilities. of GPT-4, right? So now people are waking up, okay, we need to study these language models. I think there's going to be a lot of interesting. AI safety research. - Are Earth's billionaires going to put up. the giant prizes that would maybe incentivize young hotshot.

people who just got their physics degrees to not go. to the hedge funds and instead put everything. into interpretability in this like one small area. where we can actually tell. whether or not somebody has made a discovery or not? - I think so--. - [Eliezer] When? - Well, this is what these conversations are about. because they're going to wake up to the fact. that GPT-4 can be used to manipulate elections, to influence geopolitics, to influence the economy. There's going to be a huge amount of incentive to,

wait a minute, we have to make sure. they're not doing damage. We have to make sure we interpretability, we have to make sure we understand how these systems. function so that we can predict their effect. on economy, so that there's--. - So there's a futile moral panic--. - [Lex] Fairness and safety. - And a bunch of op-eds in the "New York Times". and nobody actually stepping forth and saying, "You know what, instead of a mega yacht, "I'd rather put that billion dollars on prizes.

"for young hotshot physicists. "who make fundamental breakthroughs in interpretability.". - The yacht versus the interpretability research, the old trade off. (Lex laughing). I think there's going to be a huge amount. of allocation of funds. I hope, I hope, I guess. - You wanna bet me on that? You wanna put a time scale on it. Say how much funds you think are going to be allocated. in a direction that I would consider. to be actually useful by what time?

- I do think there will be a huge amount of funds. But you're saying it needs to be open, right? The development of the systems should be closed. But the development of the interpretability research, the AI safety research--. - So we are so far behind on interpretability. compared to capabilities. Like yeah, you could take the last generation of systems. The stuff that's already in the open, there is so much in there that we don't understand.

There are so many prizes you could do before. you would have enough insights that you'd be like, "Oh, we understand how these systems work. "We understand how these things are doing their outputs. "We can read their minds, "now let's try it with the bigger systems.". We're nowhere near that. There is so much interpretability work to be done. on the weaker versions of the systems. - So what can you say on the second point you said. to Elon Musk on what are some ideas, what are things you could try?

"I can think of a few things I'd try," you said, "They don't fit in one tweet.". So is there something you could put into words. of the things you would try? - I mean, the trouble is the stuff is subtle. I've watched people try to make progress on this. and not get places. Somebody who just gets alarmed and charges in, it's like going nowhere. - [Lex] True. - It meant like years ago, I don't know, like 20 years, 15 years, something like that. I was talking to a congressperson.

who had become alarmed about the eventual prospects. and he wanted work on building AIs without emotions. because the emotional AI were the scary ones, you see. And some poor person at ARPA had come up. with a research proposal whereby this congressman's. panic and desire to fund this, the thing, would go into something that the person at ARPA.

thought would be useful and had been munched around to where. it would sound to the congressman like work. was happening on this. Which you know, of course the congressperson had. misunderstood the problem and did not. understand where the danger came from. And so it's like the issue is that you could like do this. in a certain precise way and maybe get something. When I say put up prizes on interpretability, I'm like, because it's verifiable there.

as opposed to other places, you can tell whether or not good work. actually happened in this exact narrow case. If you do things in exactly the right way, you can maybe throw money at it at and produce science. instead of anti-science and nonsense. and all the methods that I know of, trying to throw. money at this problem, share this property of, well if you do it exactly right based on understanding.

exactly tends to produce like useful outputs or not, then you can add money to it in this way. And the thing that I'm giving as an example here in front. of this large audience the most understandable. of those because there's other people who, like Chris Olah, and even more generally, you can tell whether or not. interpretability progress has occurred. So like if I say throw money at producing more. interpretability, there's a chance somebody can do it.

that way and it will actually produce useful results. Then the other stuff just blurs off into be like, harder to target exactly than that. - So sometimes the basics are fun to explore. because they're not so basic. What is interpretability? What does it look like? What are we talking about? - It looks like we took a much smaller set. of transformer layers than the ones in the modern.

bleeding edge state-of-the-art systems. And after applying various tools and mathematical. ideas and trying 20 different things, we have shown it that this piece of the system is doing. this kind of useful work. - And then somehow also hopefully generalizes. some fundamental understanding. of what's going on that generalizes to the bigger system.

- You can hope, and it's probably true. Like you would not expect the smaller tricks to go away. when you have a system that's doing larger kinds of work, you would expect the larger work kinds of work to be. building on top of the smaller kinds of work and gradient. descent runs across the smaller kinds of work before it runs. across the larger kinds of work. - Well, that's kind of what is happening. in neuroscience, right? It's trying to understand the human brain by prodding. and it's such a giant mystery. and people have made progress,

even though it's extremely difficult to make sense. of what's going on in the brain. They have different parts of the brain. that are responsible for hearing, for sight. The vision, science community, they're just understanding. the visual cortex. I mean they've made a lot of progress in understanding how. that stuff works, but you're saying it takes a long time. to do that work well. - Also it's not enough. So in particular, let's say you have got. your interpretability tools and they say. that your current AI system is plotting to kill you.

Now what? - It is definitely a good step one, right? - [Eliezer] Yeah. What's step two? - If you cut out that layer, is it gonna stop. wanting to kill you? - When you optimize against visible misalignment, you are optimizing against misalignment.

and you are also optimizing against visibility. So sure, if you can--. (Lex laughing). - It's true. All you're doing is removing the obvious intentions. to kill you. - You've got your detector, it's showing something inside the system. that you don't like. Okay, say the disaster monkey is running this thing, we'll optimize the system. until the visible bad behavior goes away. But it's arising for fundamental reasons. of instrumental convergence. The old, you can't bring the coffee if you're dead,

any goal and you know, almost every set of utility functions. with a few narrow exceptions implies killing all the humans. - But do you think it's possible, because we can do. experimentation to discover the source. of the desire to kill? - I can tell it to you right now, is that it wants to do something and the way to get the most. of that thing is to put the universe into a state where. there aren't humans.

- So is it possible to encode in the same way we think? Like why do we think murder is wrong? The same foundational ethics, that's not hard coded in. but more like deeper. I mean that's part of the research. How do you have it that this transformer, this small version of the language model. doesn't ever want to kill? - That'd be nice assuming that you got.

"doesn't want to kill" sufficiently exactly right. That it didn't be like, "Oh, I will detach their heads and put them in some jars. "and keep the heads alive forever and then go do the thing.". But leaving that aside, well, not leaving that aside. - [Lex] Yeah, that's good, it gets a strong point, yeah. - 'Cause there is a whole issue. where as something gets smarter, it finds ways of achieving the same goal predicate that were. not imaginable to stupider versions of the system or perhaps. the stupider operators.

That's one of many things making this difficult. A larger thing making this difficult is that we do not know. how to get any goals into systems at all. We know how to get outwardly observable behaviors. into systems. We do not know how to get internal psychological wanting. to do particular things into the system. That is not what the current technology does. - I mean, it could be things like dystopian futures, like "Brave New World" where most humans will actually say,

"We kind of want that future.". It's a great future. Everybody's happy. - We would have to get so far, so much further. than we are now. and further faster before that failure mode. became a running concern. - Your failure modes are much more drastic. The ones you're--. - The failure modes are much simpler. It's like yeah, the AI puts the universe. into a particular state. It happens to not have any humans inside it. - Okay, so the paper club maximizer.

- Utility, so the original version. of the paperclip maximizer--. - Can you explain it if you can? - Okay. The original version was you lose control. of the utility function. and it so happens that what maxes out the utility. per unit resources is tiny molecular shapes like paperclips. There's a lot of things that make it happy, but the cheapest one that didn't saturate was putting matter.

into certain shapes. And it so happens that the cheapest way. to make these shapes is to make them very small, 'cause then you need fewer atoms, per instance of the shape and arguendo, it happens to look like a paperclip. In retrospect I wish I'd said tiny molecular spirals. or tiny molecular hyperbolic spirals. Why? Because I said tiny molecular paperclips, this got then mutated to paperclips, this then mutated to, "And the AI was in a paperclip factory.".

So the original story is about how you lose control. of the system, it doesn't want what you tried to make it want. The thing that it ends up wanting most. is a thing that even from a very embracing. cosmopolitan perspective, we think of as having no value. and that's how the value of the future gets destroyed. Then that got changed to a fable of, well, you made a paperclip factory and it did exactly. what you wanted, but you asked it to do the wrong thing. Which is a completely different failure path.

(Eliezer sighs). - But those are both concerns to you. So that's more than--. - If you "Brave New World.". If you can solve the problem of making something want. what exactly what you want it to want, then you get to deal with the problem. of wanting the right thing. - But first you have to solve the alignment. - First you have to solve inner alignment. - [Lex] Inner alignment. - Then you get to solve outer alignment.

First you need to be able to point the insides. of the thing in a direction and then you get to deal with. whether that direction expressed in reality is the thing. that it aligned with the thing that you want. - Are you scared? - Of this whole thing? Probably. I don't really know. - What gives you hope about this? - [Eliezer] The possibility of being wrong. - Not that you're right,

but we will actually get our act together and allocate. a lot of resources to the alignment problem. - Well, I can easily imagine that at some point this panic. expresses itself in the waste of a billion dollars. Spending a billion dollars correctly, that's harder. - To solve both the inner and the outer alignment. If you're wrong-- - To solve a number of things. - Yeah. Number of things. If you're wrong, what do you think would be the reason?

Like if 50 years from now, not perfectly wrong, you make a lot of really eloquent points, there's a lot of shape to the ideas you express. But if you're somewhat wrong about some fundamental ideas, why would that be? - Stuff has to be easier then I think it is. The first time you're building a rocket, being wrong is in a certain sense quite easy. Happening to be wrong in a way.

where the rocket goes twice as far on half the fuel. and lands exactly where you hoped it would? Most cases of being wrong make it harder to build a rocket, harder to have it not explode. 'Cause it to require more fuel than you hope to, cause it to be led off target. Being wrong in a way that makes stuff easier, that's not the usual project management story. - And then this is the first time we're really tackling. the problem of a AI alignment. There's no examples in in history where we... - Oh, there's all kinds of things that are similar if you.

generalize and correctly the right way and aren't fooled. by misleading metaphors. - Like what? - Humans being misaligned on inclusive genetic fitness. So inclusive genetic fitness is like not just your. reproductive fitness, but also the fitness. of your relatives, the people who share some fraction of your genes. The old joke is, would you give your life to save your brother? They once asked a biologist, I think it was Haldane, and Haldane said, "No, but I would give my life to save two brothers. "or eight cousins.".

Because a brother on average shares half your genes. And cousin on average shares an eighth of your genes. So that's inclusive genetic fitness. And you can view natural selection as optimizing humans. exclusively around this one very simple criterion, like how much more frequent did your genes become. in the next generation? In fact, that just is natural selection. It doesn't optimize for that. But rather the process of genes becoming more frequent. is that you can nonetheless imagine.

that there is this hill climbing process, not like gradient descent, because gradient descent uses calculus. This is just using like where are you? But still hill climbing in both cases, make things something better and better over time, in steps. And natural selection was optimizing exclusively for this. very simple, pure criterion. of inclusive genetic fitness. in a very complicated environment, we're doing a very wide range of things and solving a wide.

range of problems, led it to having more kids. And this got you humans, which had no internal notion of inclusive genetic fitness. until thousands of years later when they were actually. figuring out what had even happened. And no explicit desire. to increase inclusive genetic fitness. So from this important case study,

we may infer the important fact that if you do a whole bunch. of hill climbing on a very simple loss function, at the point where the system's capabilities. start to generalize very widely, when it is in an intuitive sense becoming very capable. and generalizing far outside the training distribution, we know that there is no general loss saying that the system. even internally represents, let alone tries to optimize the very simple loss function.

you are training it on. - There is so much that we cannot possibly cover all of it. I think we did a good job of getting your sense from. different perspectives of the current state of the art. with large language models. We got a good sense of your concern. about the threats of AGI. - I've talked here about the power of intelligence. and not really gotten very far into it, but not like, why it is.

Suppose you screw up with AGI and it end up wanting. a bunch of random stuff. Why does it try to kill you? Why doesn't it try to trade with you? Why doesn't it give you just the tiny little fraction. of the solar system that it would. take to keep everyone alive? - Yeah well, that's a good question. What are the different trajectories that intelligence, when acted upon this world, super intelligence, what are the different trajectories for this universe.

with such an intelligence in it? Do most of them not include humans? - I mean, the vast majority of randomly specified utility. functions do not have optima with humans in them, would be the first thing I would point out. And then the next question is like, well, if you try to optimize something, you lose control of it. Where in that space do you land? 'Cause it's not random, but it also doesn't necessarily have room for humans in it. I suspect that the average member of the audience might have.

some questions about even whether that's the correct. paradigm to think about it and would sort of want. to back up a bit possibly. - If we back up to something bigger than humans, if we look at Earth and life on Earth and what is truly. special about life on Earth, do you think it's possible that. whatever that special thing is, let's explore what that special thing could be.

Whatever that special thing is, that thing appears often in the objective function. - Why? I know what you hope, but you know, you can hope that a particular set. of winning lottery numbers come up and it doesn't make. the lottery balls come up that way. I know you want this to be true, but why would it be true? - There's a line from "Grumpy Old Men". where this guy says, in a grocery store, he says, "You can wish in one hand and crap in the other. "and see which one fills up first.".

- There's a science problem. We are trying to predict what happens with AI systems. that you tried to optimize to imitate humans. and then you did some of RLHF to them. and of course you didn't get like perfect alignment. because that's not what happens. when you hill climb towards a outer loss function. You don't get inner alignment on it. But yeah, so if you don't mind my like taking.

some slight control of things and steering around. to what I think is like a good place to start. - I just failed to solve the control problem. I've lost control of this thing. - Alignment. Alignment. - Still aligned. (laughing). - Yeah, okay, sure. Yeah, you lost control. - But we're still aligned. Anyway, sorry for the meta comment. - Yeah, losing control isn't as bad. as you lose control to an aligned system. - [Lex] Yes, exactly. - You have no idea of the horrors.

I will shortly unleash on this conversation. (both laughing). - All right. Sorry, sorry to distract you completely. What were you gonna say in terms of taking control. of the conversation? - So I think that there's like. a (speaking foreign language) here, if I'm pronouncing those words remotely like correctly, 'cause of course we only ever read them. and not hear them spoken. For some people, the word intelligence, smartness.

is not a word of power to them. It means chess players, it means the college. university professor, people aren't very successful in life. It doesn't mean like charisma to which my usual thing is. like charisma is not generated in the liver. rather than the brain. Charisma is also a cognitive function. So if you think that smartness. doesn't sound very threatening, then super intelligence. is not gonna sound very threatening either.

It's gonna sound like you just pull the off switch. Well, it's super intelligent but it's stuck in a computer. We pull the off switch, problem solved. And the other side of it is you have a lot of respect. for the notion of intelligence. You're like, well yeah, that's what humans have. That's the human superpower. And it sounds like it could be dangerous, but why would it be? We, as we have grown more intelligent, also grown less kind.

Chimpanzees are in fact a bit less kind than humans. and you know, you could argue that out. But often the sort of person who has a deep respect for. intelligence is gonna be like, "Well, yes, "you can't even have kindness unless you know what that is.". And so they're like, why would it do something as stupid as making paperclips? Aren't you supposing something that's smart enough to be. dangerous but also stupid enough that it will just make. paperclips and never question that?

In some cases people are like, "Well, even if you misspecify the objective function, "won't you realize that what you really wanted was x? "Are you supposing something that is smart enough to be. "dangerous but stupid enough that it doesn't understand what. "the humans really meant when they specified. "the objective function?". - So to you, our intuition about intelligence is limited. We should think about intelligence as a much bigger thing.

- Well, I'm saying that it's that--. - [Lex] That humanness. - Well, what what I'm saying is like what do you think about. artificial intelligence? Depends on what you think about intelligence. - So how do we think about intelligence correctly? Like you gave one thought experiment to think of, think of a thing that's much faster, so it just gets faster. and faster, faster and faster. - And it also is like is made of John von Neumann. and there's lots of them. - [Lex] Or (indistinct). - Yeah, John von Neumann is a historical case,

so you can like look up what he did. and imagine based on that. And we know people have some intuition for like, if you have more humans, they can solve tougher cognitive problems. Although in fact like in the game. of Kasparov versus the World. which was like Garry Kasparov on one side and an entire. hoard of internet people led by four chess grand masters. on the other side, Kasparov won. So like all those people aggregated to be smarter,

it was a hard fought game. It's like all those people aggregated to be smarter. than any individual one of them, but they didn't aggregate so well. that they could defeat Kasparov. But so humans aggregating don't actually get, in my opinion, very much smarter, especially compared to running them for longer. The difference between capabilities now and a thousand years. ago is a bigger gap than the gap in capabilities. between 10 people and one person. But even so, pumping intuition for what it means.

to augment intelligence, John von Neumann, there's millions of him, he runs at a million times the speed and therefore can solve. tougher problems, quite a lot tougher. - It's very hard to have an intuition. about what that looks like, especially like you said, the intuition, I kind of think about is it maintains the humanness.

I think it's hard to separate my hope. from my objective intuition. about what super intelligent systems look like. - If one studies evolutionary biology with a bit of math. and in particular books from when the field was just. sort of properly coalescing and knowing itself,

like not the modern textbooks, which are just memorize this legible math. so you can do well on these tests, but what people were writing as the basic paradigms. of the field were being fought out... A nice book if you've got the time to read it. is "Adaptation and Natural Selection,". Which is one of the founding books, you can find people being optimistic. about what the utterly alien optimization process. of natural selection will produce in the way of how it.

optimizes its objectives. You got people arguing that like, in the early days. biologists said, "Well, organisms will restrain. "their own reproduction when resources are scarce. "so as not to overfeed the system.". And this is not how natural selection works, it's about whose genes are relatively more prevalent. to the next generation.

And if you restrain reproduction, those genes get less frequent in the next generation. compared to your conspecifics. And natural selection doesn't do that. In fact, predators overrun prey populations all the time. and have crashes. That's just a thing that happens and many years later... Well oh, oh oh. But people said like, "Well, but group selection.". Right? What about groups of organisms? And basically the math of group selection. almost never works out in practice is the answer there.

But also years later, somebody actually ran the experiment where they took. populations of insects and selected the whole populations. to have lower sizes. Now you just take pop one, pop two, pop three, pop four look at which has the lowest total number of them. in the next generation and select that one. What do you suppose happens when you select populations. of insects like that? Well, what happens is not that the individuals. in the population evolve to restrain their breeding,

but that they evolved to kill the offspring. of other organisms, especially the girls. So people imagined this lovely, beautiful, harmonious output of natural selection, which is these populations restraining their own breeding. so that groups of them would stay in harmony. with the resources available. And mostly the math never works out for that. But if you actually apply the weird strange conditions. to get group selection that beats individual selection, what you get is female infanticide.

Like if you're reading on restrained populations. So this is not a smart optimization process. Natural selection is like so incredibly stupid and simple. that we can actually quantify how stupid it is. if you read the textbooks with the math. Nonetheless, this is the sort of basic thing. of you look at this alien optimization process. and there's the thing that you hope it will produce. and you have to learn to clear that out of your mind. and just think about the underlying dynamics and where.

it finds the maximum from its standpoint that it's looking. for rather than how it finds that thing that lept into your. mind as the beautiful aesthetic solution. that you hope it finds. And this is something that was, has been fought out historically as the field. of biology was coming to terms with evolutionary biology. And you can like look at them fighting it out as they. get to terms with this very alien, inhuman optimization process.

And indeed something smarter than us would be also. much smarter than natural selection. So it doesn't just automatically carry over. But there's a lesson there, there's a warning. - To you, natural selection is a deeply suboptimal process. that could be significantly improved on. and would be by an AGI system. - Well, it's kind of stupid. It has to run hundreds of generations to notice. that something is working. It doesn't be like, oh, well I tried this in one organism, I saw it worked,

now I'm going to duplicate that feature. onto everything immediately. Has to run for hundreds of generations. for a new mutation tries to fixation. - I wonder if there's a case to be made. in natural selection, as inefficient as it looks is actually quite powerful. That this is extremely robust. - It runs for a long time. and eventually manages to optimize things.

It's weaker than gradient dissent because gradient dissent. also uses information about the derivative. - Yeah, evolution seems to be, there's not really an objective function. - [Eliezer] There's inclusive genetic fitness. is the implicit loss function of evolutions. - It's implicit - It cannot change. The loss function doesn't change, the environment changes and therefore what gets. optimized for in the organism changes. Take like GPT-3.

You imagine like different versions of GPT-3. where they're all trying to predict the next word, but they're being run on different data sets of text. and that's like natural selection, always inclusive genetic fitness. but different environmental problems. (Lex exhales). - It's difficult to think about. So if we are saying the natural selection is stupid, if we're saying the humans are stupid, it's--. - Smarter than natural selection, stupider than the upper bound.

- Do you think there's an upper bound by the way? That's another helpful place. - I mean, if you put enough matter, energy compute into one place, it will collapse into a black hole. (Lex laughing). There's only so much computation can do before you run out. of negentropy and the universe dies. So there's an upper bound, but it's very, very, very far up above here. Like the supernova is only finitely hot, it's not infinitely hot. but it's really, really, really, really hot.

- Well, let me ask you, let me talk to you about consciousness, also coupled with. that question is imagining a world. with superintelligent AI systems. that get rid of humans but nevertheless keep. something that we would consider beautiful and amazing. - Why? The lesson of evolutionary biology. If you just guess what an optimization does. based on what you hope the results will be,

it usually will not do that. - It's not hope. I mean it's not hope. I think if you objectively look at. what has been a powerful, a useful... I think there's a correlation between what we find. beautiful and a thing that's been useful. - This is what the early biologists thought. And not just like, they thought. Like "No, no, I'm not just imagining stuff. "that would be pretty, it's useful for organisms.

"to restrain their own reproduction. "because then they don't overrun. "the prey populations and they actually have more kids. "in the long run.". - Hmm. So let me just ask you about consciousness. Do you think consciousness is useful. - [Eliezer] To humans? - No, to AGI systems. Well, in this transitionary period between humans and AGI, to AGI systems as they become smarter and smarter, is there some use to it? Let me step back.

What is consciousness? Eliezer Yudkowsky, what is consciousness? - Are referring to Chalmers as hard problem. of conscious experience? Are you referring to self-awareness and reflection? Are you referring to the state of being awake. as opposed to asleep? - This is how I know you're an advanced language model. I gave you a simple prompt. and you gave me a bunch of options.

I think I'm referring to all, including the hard problem of consciousness. What is it in its importance to what you've just been. talking about, which is intelligence? Is it a foundation to intelligence? Is it intricately connected to intelligence. in the human mind or is it a side effect of the human mind? It is a useful little tool, like we can get rid of?

I guess I'm trying to get some color in your opinion. of how useful it is in the intelligence. of a human being and then try to generalize that to AI, whether AI will keep some of that. - So I think that for there to be like a person. who I care about looking out at the universe. and wondering at it and appreciating it, it's not enough to have a model of yourself.

I think that it is useful to an intelligent mind to have. a model of itself, but I think you can have that without. pleasure, pain, aesthetics, emotion, a sense of wonder. Like I think you can have a model of how much memory.

you're using and whether this thought or that thought. is like more likely to lead to a winning position. I think that if you optimize really hard on efficiently. just having the useful parts, there is not then the thing that says like, "I am here, I look out, I wonder, I feel happy on this.

"I feel sad about that.". I think there's a thing that knows what it is thinking. but it doesn't quite care about these are my thoughts, this is my me and that matters. - Does that make you sad, if that's lost in AGI? - I think that if that's lost then basically. everything that matters is lost. I think that when you optimize,

when you go really hard on making. tiny molecular spirals or paperclips, that when you grind much harder than on that. than natural selection ground out to make humans, that there isn't then the mess and intricate loopiness. and complicated pleasure, pain, conflicting preferences,

this type of feeling, that kind of feeling. In humans there's this difference between. the desire of wanting something and the pleasure. of having it and it's all these like evolutionary. clutches that came together and created something that then. looks at itself and says like, "This is pretty, this matters.". And the thing that I worry about is that this is not. the thing that happens again,

just the way that happens in us. or even quite similar enough. That there are many basins of attractions here. and we are in the space of attraction. looking out and saying like, "Ah, what a lovely basin we are in". and there are other basins of attraction. and the AIs do not end up in this one when they go like, way harder on optimizing themselves, the natural selection optimized us. 'Cause unless you specifically want to end up in the state.

where you are looking out saying, "I am here,". "I look out at this universe with wonder,". if you don't want to preserve that, it doesn't get preserved when you grind really hard. on being able to get more of the stuff. We would choose to preserve that within ourselves. because it matters and on some viewpoints. is the only thing that matters. - And preserving that is in part a solution. to the human alignment problem.

- I think the human alignment problem. is a terrible phrase 'cause it is very, very different. to try to build systems out of humans, some of whom are nice and some of whom are not nice. and some of whom are trying to trick you. and build a social system out of large populations. of those who are basically the same level of intelligence. Yes, you know, like IQ this, IQ that. but that versus chimpanzees. (chuckles). Like it is very different to try to solve that problem. than to try to build an AI from scratch,

especially if God help you are trying to use. gradient dissent on giant inscrutable matrices. They're just very different problems. And I think that all the analogies between them. are horribly misleading. - So you don't think through reinforcement learning. through human feedback, something like that, but much, much more elaborate is possible. to understand this full complexity of human nature. and encode it into the machine? - I don't think you are trying to do that on your first try.

I think on your first try, you are trying to build an... Probably not what you should actually do, but let's say you're trying to build something. that is like Alpha Fold 17. and you are trying to get it to solve the biology problems. associated with making humans smarter, so that humans can like actually solve alignment. So you've got like a super biologist. and I think what you would want in the situation. as referred to, just be thinking about biology.

and not thinking about a very wide range of things. that includes how to kill everybody. And I think that the first AIs you're trying to build, not a million years later, the first ones, look more like narrowly specialized biologists. than getting the full complexity. and wonder of human experience. in there in such a way that it wants to preserve itself. even as it becomes much smarter,

which is a drastic system change that's gonna have all kinds. of side effects that, you know, like if we're dealing with giant inscrutable matrices. who are not very likely to be able to see coming in advance. - But I don't think it's just the matrices. we're also dealing with the data, right? With the data on the internet, and this is an interesting discussion. about the data set itself, but the data set includes the full complexity. of human nature. - No, it's a shadow cast by humans on the internet. - But don't you think that shadow is a yin yang shadow?

(Lex laughing). - I think that if you had alien super intelligences. looking at the data, they would be able to pick up from it an excellent picture. of what humans are actually like inside. This does not mean that if you have a loss function. of predicting the next token from that dataset that the mind. picked out by gradient dissent to be able to predict. the next token as well as possible. on a very wide variety of humans is itself a human.

- But don't you think it has humanness a deep humanness. to it in the tokens it generates, when those tokens. are read and interpreted by humans? - I think that if you sent me. to a distant galaxy with aliens. who are much, much stupider than I am, so much so that I could do a pretty good job of predicting. what they'd say even though they thought in an utterly.

different way from how I did, then I might in time. be able to learn how to imitate those aliens. if the intelligence gap was great enough that my own. intelligence could overcome the alienness and the aliens. would look at my outputs and say like, "Is there not a deep like name of alien nature. "to this thing?". And what they would be seeing was that I had correctly. understood them, but not that I was similar to them.

- We've used aliens as a metaphor, as a thought experiment. I have to ask, how many alien civilizations are out there? - Ask Robin Hanson. He has this lovely grabby aliens paper, which is more or less. the only argument I've ever seen for where are they, how many of them are there based on a very clever argument. that if you have a bunch of locks of different difficulty.

and you are randomly trying the keys to them, the solutions will be about evenly spaced even if the locks. are of different difficulties. In the rare cases where a solution to all the locks. exists in time, then Robin Hanson looks at the arguable. hard steps in human civilization coming into existence. and how much longer it has left come into existence. before, for example, all the water slips back.

under the crust into the mantle and so on, and infers. that the aliens are about half a billion. to a billion light years away. And it's quite a clever calculation. It may be entirely wrong, but it's the only time I've ever seen anybody even come up. with a halfway good argument for how many of them, where are they. - Do you think their development of technologies, do you think their natural evolution, whatever, however they grow and develop intelligence,

do you think it ends up at AGI as well? - If it ends up anywhere, it ends up at AGI. Maybe there are aliens who are just like the dolphins. and it's just too hard for them to forge metal. Maybe if you have aliens with no technology like that, they keep on getting smarter and smarter and smarter. and eventually the dolphins figure, like the super dolphins figure out something very clever. to do given their situation and they still end up.

with high technology and in that case, they can probably solve their AGI alignment problem. If they're like much smarter before. they actually confronted 'cause they saw had to solve. a much harder environmental problem to build computers, their their chances are probably much better than ours. I do worry that most of the aliens who are like humans, like a modern human civilization, I kind of worry that the super vast majority. of them are dead.

Given how far we seem to be from solving this problem. But some of them would be more cooperative than us. Some of them would be smarter than us. Hopefully some of the ones who are smarter. and more cooperative than us are also nice. And hopefully there are some. galaxies out there full of things that say "I am, I wonder.". But it doesn't seem like we're on course. to have this galaxy be that.

- Does that in part give you some hope in response. to the threat of AGI, that we might reach out there. towards the stars and find... - No, if the nice aliens were already here, they would have stopped the Holocaust. That's a valid argument against the existence of God. It's also a valid argument against the existence. of nice aliens and unnice aliens. who would've just eaten the planet. So no aliens.

- You've had debates with Robin Hanson that you mentioned. So one particular I just want to mention is the idea. of AI foom, or the ability of AGI. to improve themselves very quickly. What's the case you made and what was the case he made? - The thing I would say is that among the thing that humans. can do is design new AI systems. And if you have something that is generally smarter. than a human, it's probably also generally smarter. at building AI systems. This is the ancient argument for foom put forth by I.J. Good.

and probably some science fiction writers before that, but I don't know who they would be. - Well, what's the argument against foom? - Various people have various different arguments, none of which I think hold up. There's only one way to be right. and many ways to be wrong. A argument that some people have put forth is like, well, what if intelligence gets exponentially harder. to produce as a thing needs to become smarter?

And to this, the answer is well, look at natural selection spitting out humans. We know that it does not take like exponentially more. resource investments to produce linear increases. in competence in hominids because each mutation. that rises to fixation, if the impact it has (indistinct) small enough, it will probably never reach fixation. And there's like only so many new mutations.

you can fix per generation. So given how long it took to evolve humans, we can actually say with some confidence that there were not. logarithmically diminishing returns on the individual. mutations increasing intelligence. So example of fraction of sub debate. And the thing that Robin Hanson said. was more complicated than that. A brief summary, he was like, "We won't have one system that's better at everything. "You'll have a bunch of different systems that are good.

"at different narrow things.". And I think that was falsified by GPT-4, but probably Robin Hanson would say something else. - It's interesting to ask... It's perhaps a bit too philosophical, this prediction is extremely difficult to make, but the timeline for AGI, When do you think we'll have AGI? I posted this morning on Twitter and it was interesting. to see like in, in five years, 10 years years, in 50 years or beyond. And most people, like 70% something like this, think it'll be in less than 10 years.

So either in five years or in 10 years. So that's kind of the state. Do people have a sense that there's a kind of... I mean they're really impressed by the rapid developments. of ChatGPT and GPT-4. So there's a sense that there's a--. - Well, we are sure on track to enter. into this gradually with people fighting about. whether or not we have AGI. I think there's a definite point where everybody falls over. dead 'cause you got something that was sufficiently. smarter than everybody and that's a definite point of time.

But like when do we have AGI? When are people fighting over whether or not we have AGI? Well, some people are starting to fight over it. as of GPT-4. - But don't you think there's going to be potentially. definitive moments when we say. that this is a sentient being? We would go to the Supreme Court and say. that this is a sentient being that deserves. human rights, for example. - You could make, yeah. If you prompted being, the right way could go argue for. its unconsciousness in front of the Supreme Court right now.

- [Lex] I don't think you could do that. successfully right now. - Because the Supreme Court wouldn't believe it? I think you could put an IQ 80 human into a computer. and ask him to argue for his own consciousness. before the Supreme Court and the Supreme Court. would be like, "You're just a computer.". Even if there was an actual person in there. - I think you're simplifying this. No, that's not at all. That's been the argument, there's been a lot of arguments about the other, about who deserves rights and not.

That's been our process as a human species, trying to figure that out. I think there will be a moment, I'm not saying sentience is that, but it could be, where some number of people, like say over a hundred million people. have a deep attachment, a fundamental attachment the way we have to our friends, to our loved ones, to our significant others, have fundamental attachment. to an AI system and they have provable transcripts. of conversation where they say, "If you take this away from me,

"you are encroaching on my rights as a human being.". - People are already saying that. I think they're probably mistaken, but I'm not sure 'cause nobody knows. what goes on inside those things. - Eliezer, they're not saying that at scale. - [Eliezer] Okay. - So the question is, is there a moment when AGI, we know AGI I arrived, what would that look like? I'm giving essentially as just an example. It could be something else. - It looks like the AGIs successfully manifesting themselves. as 3D video of young women,

at which point a vast portion of the male population. decides that they're real people. - So sentience, essentially. Demonstrating identity and sentience. - I'm saying that the easiest way. to pick up a hundred million people saying. that you seem like a person. is to look like a person talking to them, with Bing's current level of verbal facility. - I disagree with that. - A different set of prompts.

- I disagree with that. I think you're missing again, sentience. There has to be a sense that it is a person. that would miss you when you're gone. They can suffer, they can die. Of course, I'm being--. - GPT-4 can pretend that right now. How can you tell when it's real? - I don't think it can pretend that right now successfully. It's very close. - Have you talked to GPT-4? - [Lex] Yes, of course. - Okay. Have you been able to get a version of it. that hasn't been trained not to pretend to be human?

Have you talked to a jail broken version. that will claim to be conscious? - No. The linguistic capability's there, but there's something about a digital embodiment. of the system that has a bunch of, perhaps it's small interface features that are not. significant relative to the broader intelligence.

that we're talking about. So perhaps GPT-4 is already there. But to have the video where a woman's face or a man's face. to whom you have a deep connection, perhaps we're already there, but we don't have such a system yet deployed at scale. - The thing I'm trying to to gesture at here is that. it's not like people have a widely accepted, agreed upon. definition of what consciousness is. It's not like we would have the tiniest idea. of whether or not that was going on inside the giant.

inscrutable matrices, even if we hadn't agreed upon definition. So if you're looking for upcoming predictable big jumps. in how many people think the system is conscious, the upcoming predictable big jump is it looks like a person. talking to you who is cute and sympathetic. That's the upcoming predictable big jump. Now that versions of it are already claiming. to be conscious, which is the point. where I start going like, ah, not 'cause it's real, but because from now on,

who knows if it's real? - Yeah. And who knows what transformational effect it has. on a society where more than 50% of the beings that are. interacting on the internet and sure as heck look real. are not human? What kind of effect does that have when young men and women. are dating AI systems? - You know, I'm not an expert on that. God help humanity. (chuckles). I'm one of the closest things to an expert.

on where it all goes. 'Cause you know, and how did you end up with me. as an expert? 'Cause for 20 years, humanity decided to ignore the problem. So like, this tiny handful of people and basically me, got 20 years to try to be an expert on it. while everyone else ignored it. And yeah. So where does it all end up? Try to be an expert on that. Particularly the part where everybody ends up dead, 'cause that part is kind of important, but what does it do to dating when some fraction of men.

and some fraction of women decided that they'd rather date. the video of the thing. that is like relentlessly kind and generous to them. and claims to be conscious, but who knows. what's goes on inside it and it's probably not real, but you know, you can think it's real, what happens to society? I don't know. I'm not actually an expert on that. And the experts don't know either, 'cause it's kind hard to predict the future. - Yeah, but it's worth trying. It's worth trying. - Yeah. - So you have talked a lot.

about sort of the longer term future. where it's all headed. - By longer term we mean like, not all that long. But yeah, where it all ends up. - But beyond the effects of men and women dating AI systems, you're looking beyond that. - Yes. 'Cause that's not how the fate of the galaxy got settled. - Well, let me ask you about your own personal psychology. A tricky question. You've been known at times to have a bit of an ego. Do you think--.

- Says who? But go on. - Do you think ego is empowering or limiting. for the task of understanding the world deeply? - I reject the framing. - [Lex] So you disagree with having an ego? So what do you think about ego? - No, I think that the question of what leads. to making better or worse predictions, what leads to being able to pick out better or worse. strategies is not carved at its joint by talking of ego.

- So it should not be subjective. It should not be connected to the intricacies of your mind? - No, I'm saying that like, if you go about asking all day long, "Do I have enough ego? "Do I have too much of an ego?", I think you get worse at making good predictions. I think that to make good predictions, you're like, how did I think about this? Did that work? Should I do that again? - You don't think we as humans get invested in an idea. and then others attack you personally for that idea?

So you plant your feet and it starts to be difficult to, when a bunch of assholes low effort attack your idea. to eventually say, "You know what? "I actually was wrong.". And tell them that. As a human being, it becomes difficult. It's difficult. - So like Robin Hanson and I debated AI systems and I think. that the person who won that debate was Gwern. And I think that reality was well to the Yudkowskian side.

of the Yudkowsky-Hanson spectrum, like further from Yudkowsky. And I think that's because I was trying to sound reasonable. compared to Hanson and saying things that were defensible. and relative to Hanson's arguments and reality. was way over here in (indistinct) in respect to, Hanson was like, "All the systems will be specialized.". Hanson may disagree with this characterization. Hanson was like, "All the systems will be specialized.".

I was like, "I think we build specialized underlying systems. "that when you combine them are good. "at a wide range of things.". And the reality is like, no, you just stack more layers. into a bunch of gradient descent. And I feel looking back that by trying. to have this reasonable position contrasted. to Hanson's position, I missed the ways that reality could be more extreme. than my position in the same direction. So is this like, is this a failure to have enough ego?

Is this a failure to make myself be independent? I would say that this is something like a failure. to consider positions that would sound even wackier and more. extreme when people are already calling you extreme. But I wouldn't call that not having enough ego. I would call that insufficient ability. to just clear that all out of your mind.

- In the context of debate and discourse, which is already super tricky. - In the context of prediction, in the context of modeling reality. If you're thinking of it as a debate, you're already screwing up. - So is there some kind of wisdom and insight. you can give to how to clear your mind and think clearly. about the world? - Man, this is an example of where I wanted to be able. to put people into FMRI machines, then you'd be like, okay, "See that thing you just did, "you were rationalizing right there.". Oh, that area of the brain lit up. You are like now being socially influenced is,

is kind of the dream. And you know, I don't know, I wanna say like just introspect, but for many people, introspection is not that easy. - [Lex] It's hard. - Notice the internal sensation. Can you catch yourself in the very moment of feeling a sense. of, well if I think this thing people will look funny at me. Okay, if you can see that sensation, which is step one, can you now refuse to let it move you?

Or maybe just make it go away. And I feel like I'm saying like, I don't know, like somebody's like, "How do you draw an owl?". And I'm saying like, "Well, just draw an owl.". (both laughing). I feel like most people, the advice they need. is like, well how do I notice the internal subjective. sensation in the moment that it happens. of fearing to be socially influenced? Or okay, I see it, how do I turn it off?

How do I let it not influence me? Do I just do the opposite of what I'm afraid. people will criticize me for? And I'm like, "No, no, "you're not trying to do the opposite. "of what you're afraid of what you might be pushed into. "You're trying to let the thought process complete. "without that internal push.". Can you not reverse the push, but be unmoved by the push. and are these instructions even remotely helping anyone?

I don't know. - I think tho when those instructions, even those the words you've spoken and maybe you can. add more, when practiced daily, meaning in your daily communication. So it's daily practice of thinking without influence from--. - I would say find prediction markets that matter to you. and better in the prediction markets. That way you find out if you are right or not. - [Lex] And you really, there's stakes.

- Or even manifold markets where the stakes are a bit lower. But the important thing is to get the record. and you know, I didn't build up skills here. by prediction markets. I built them up via like, well, how did the foom debate resolve. and my own take on it, as to how it resolved. The more you are able to notice yourself. not being dramatically wrong,

but having been a little off, your reasoning was a little off. You didn't get that quite right. Each of those is a opportunity to make like a small update. So the more you can like say, "oops," softly, routinely, not as a big deal, the more chances you get to be like, I see where that reasoning went astray. I see how I should have reasoned differently. And this is how you build up skill over time. - What advice could you give to young people in high school.

and college, given the highest of stakes things. you've been thinking about? If somebody's listening to this and they're young and trying. to figure out what to do with their career, what to do with their life, what advice would you give them? - Don't expect it to be a long life. Don't put your happiness into the future. The future is probably not that long at this point, but none know the hour nor the day. - But is there something, if they want to have hope to fight for a longer future,

is there a fight worth fighting? - I intend to go down fighting. I don't know. I admit that although I do try to think painful thoughts, what to say to the children at this point. is a pretty painful thought as thoughts go. They want to fight. I hardly know how to fight myself at this point.

I am trying to be ready for being wrong about something, preparing for my being wrong in a way. that creates a bit of hope and being ready to react to that. and going looking for it. And that is hard and complicated. And somebody in high school, I don't know, like you have presented a picture of the future. that is not quite how I expected it to go. where there is public outcry and that outcry. is put into a remotely useful direction,

which I think at this point. is just shutting down the GPU clusters because no, we are not in a shape to frantically do it. at the last minute, do decades' worth of work. The thing you would do at this point if there were massive. public outcry pointed in the right direction, which I do not expect, is shut down the GPU clusters and and crash program. on augmenting human intelligence biologically. Not the (indistinct) stuff, biologically.

'Cause if you make humans much smarter, they can actually be smart and nice. You get that in a plausible way, in a way that it is not as easy to do. with synthesizing these things from scratch, predicting the next tokens and applying RLHF. Like humans start out in the frame. that produces niceness, that has ever produced niceness. And saying this, I do not want to sound like. the moral of this whole thing was like, oh, you need to engage in mass action.

and then everything will be all right. This is 'cause there's so many things where somebody. tells you that the world is ending you need to recycle. And if everybody does their part and and recycles. their cardboard, then we can all live happily ever after. And this is unfortunately not what I have to say. Everybody recycling their cardboard is not gonna fix this. Everybody recycles their cardboard and then everybody. ends up dead, metaphorically speaking.

But if there was enough, like on the margins, you just end up dead a little later. on most of the things at a few people can do by trying hard. But if there was enough public outcry to shut down. the GPU clusters and then you could be part of that outcry. If Eliezer is wrong in the direction. that Lex Friedman predicts, that there is enough public outcry pointed enough. in the right direction to do something.

that actually, actually, actually results in people living. Not just we did something, not just there was an outcry and the outcry was given form. in something that was safe and convenient. and didn't really inconvenience anybody. and then everybody died everywhere. There was enough actual like, oh, we're going to die, we should not do that. We should do something else which is not that, even if it is not super duper convenient, it wasn't inside the previous political overton window. If I'm wrong and there's that kind of public outcry, then somebody in high school could be ready.

to be part of that. If I'm wrong in other ways, then you could maybe be part of that. And if you were like a brilliant young physicist, then you could like go into interpretability. and if you're smarter than that, you could work on alignment problems where it's harder. to tell if you got them right or not, (sighs). and other things. But mostly for the kids in high school, it's like, yeah,

be ready to help if Eliezer Yudkowsky. is wrong about something and otherwise. don't put your happiness into the far future. It probably doesn't exist. - But it's beautiful that you're looking for ways. that you're wrong. And it's also beautiful that you're open to being surprised. by that same young physicist with some breakthrough. - It feels like a very, very basic competence. that you are praising me for. And you know, like, okay, cool. I don't think it's good that we're in a world.

where that is something that I deserve. to be complimented on, I've never had much luck. in accepting compliments gracefully. Maybe I should just accept that one gracefully. (Lex laughing). Sure, thank you very much. - You've painted with some probability a dark future. Are you yourself, just when you think, when you ponder your life and you ponder your mortality, are you afraid of death?

- I think so, yeah. - Does it make any sense to you that we die? There's a power to the finiteness of the human life. that's part of this whole machinery of evolution. and that finiteness doesn't seem to be obviously integrated. into AI systems.

So it feels like, fundamentally in that aspect, some fundamentally different thing that we're creating. - I grew up reading books like. "Great Mambo Chicken and the Transhuman Condition". and later on "Engines of Creation" and "Mind Children.". You know, age 12 or thereabouts. So I never thought I was supposed to die after 80 years. I never thought that humanity was supposed to die.

I thought we were like, I always grew up with the ideal in mind that we were all. going to live happily ever after. in the glorious transhumanist future. I did not grow up thinking that death. was part of the meaning of life. - [Lex] And now... - And now I still think it's a pretty stupid idea. You do not need life to be finite to be meaningful. It just has to be life. - What role does love play in the human condition? We haven't brought up love and this whole picture.

We talked about intelligence, we talked about consciousness. It seems part of humanity, I would say one of the most important parts. is this feeling we have towards each other. - If in the future there were routinely more than one AI, let's say two for the sake of discussion, who would look at each other and say, "I am I and you are you.".

The other one also says, "I am I and you are you.". And sometimes they were happy and sometimes they were sad. and it mattered to the other one. that this thing that is different from them is like, they would rather it be happy than sad. and entangle their lives together, then this is a more optimistic thing. than I expect to actually happen. A little fragment of meaning would be there, possibly more than a little,

but that I expect this to not happen. That I do not think this is what happens by default. That I do not think that this is the future. we are on track to get is why I would go down fighting. rather than, you know, just saying, "Oh well.". - Do you think that is part of the meaning. of this whole thing or the meaning of life? What do you think is the meaning of life, of human life? - It's all the things that I value about it. and maybe all the things that I would value.

if I understood it better. There's not some meaning far outside of us. that we have to wonder about. There's just looking at life and being like, yes, this is what I want. The meaning of life is not some kind of... Meaning is something that we bring to things. when we look at them, we look at them and we say like, "This is its meaning to me.".

It's not that before humanity was ever here, there was some meaning written upon the stars where you. could like go out to the star where that meaning was written. and change it around and thereby completely change. the meaning of life, right? The notion that this is written on a stone tablet somewhere. implies that you could change the tablet. and get a different meaning. and that seems kind of wacky, doesn't it? It doesn't feel that mysterious to me at this point. It's just a matter of being like, yeah, I care.

- I care. And part of that is the love that connects all of us. - [Eliezer] It's one of the things that I care about. - And the flourishing of the collective intelligence. of the human species. - You know, that sounds kind of too fancy to me. I just look at all the people, like one by one up to the 8 billion and be like,

that's life, that's life, that's life. - Eliezer, you're an incredible human. It's a huge honor. I was trying to talk to you for a long time. (laughing) because I'm a big fan. I think you're a really important voice. and really important mind. Thank you for the fight you're fighting. Thank you for being fearless and bold. and for everything you do. I hope we get a chance to talk again. And I hope you never give up. Thank you for talking today. - You're welcome.

I do worry that we didn't really address a whole lot. of fundamental questions I expect people have, but you know, maybe we got a little bit further and made a tiny little bit. of progress and I'd say be satisfied with that. But actually no, I think one should only be satisfied. with solving the entire problem. - To be continued. Thanks for listening to this conversation. with Eliezer Yudkowsky. To support this podcast, please check out our sponsors. in the description. And now let me leave you some words from Elon Musk.

"With artificial intelligence, we're summoning the demon.". Thank you for listening and hope to see you next time.