I'm Tom Bilyeu, and this is Impact Theory. We are living in one of the most exciting times in human history, but with all of this opportunity comes real challenges. Like right now, we're living in an era where more and more people are struggling with what my guest calls the meaning crisis. A sense of disconnection and uncertainty about what truly matters and how we can connect to it.
And if you can't figure out how to create meaning in your life, everything starts to fall apart. Your focus, your drive, your relationships, your just will to engage in this world. But here's the good news. Meaning isn't random.
It's something you can define, understand, and build with the right tools. That's why I'm very excited to bring you today's guest. He's a cognitive scientist and philosopher who has dedicated his entire career to tackling this exact problem. His ability to break down why people feel so lost and what they can do to turn it around is nothing short of incredible.
So if you guys are ready to begin to answer in your own life one of the most important questions, which is how do I create meaning in a world that feels increasingly disconnected, then this is the episode for you. Without further ado, I bring you John Perveke. As a philosopher and cognitive scientist, do you worry that AI is going to inflame the current meaning crisis? I think you have to be very careful when you reflect on AI.
You have to sort of break it up into its scientific important impact, its philosophical important impact, and its spiritual important impact. And all three of those, I think, in separate but interrelated ways will contribute to accelerating the meaning crisis. Why does AI potentially make that more difficult? So one of the things that can put meaning in life at risk, and I'm going to use the term, I don't mean to be vulgar, because I'm actually using it in a philosophically technical sense.
This is the notion of bullshit. There's a famous article, essay, by the important philosopher Harry Frankfurt called On Bullshit. And he was distinguishing between lying in which I tell you something that isn't true, but I try to make you believe it is true because I'm trying to manipulate your behavior because I'm depending on your commitment to the truth, okay? Versus bullshit.
What I'm doing when I'm bullshitting you is I'm getting you to not care about whether or not something is true, and I'm trying to make it very catchy and salient so it grabs your attention and rouses you. So a lot of advertising is classically bullshit. So for example, here's a bottle of alcohol in a commercial, and you're in a well-lit room with really sexually attractive people, and they're all really happy, and everybody is clearly enjoying these other's company. And you go into a bar, and it's not like that.
We all know that. And they know that you know that that's not true. And that's the point. You don't care that the commercial isn't true.
It's catchy. It's fun. It's sexually arousing. And so what happens is the bottle stands out to you, and when you go into the store, what bottle grabs your attention?
That one. That's why they spend all the money. And here's the thing. You technically can't lie to yourself because what that would mean?
You try to convince yourself of something that you know isn't true. But what you can do is you can bullshit yourself. You can manipulate, using your attention, you can manipulate what you find salient so that you get very fixated on it. So, Tom, if I were to yell, that would grab your attention, salience.
But your attention can also make something salient, the tip of your nose. See? It just became salient to you right now. You became very aware of the tip of your nose.
So I can pay attention to something, the bottle of alcohol, make it more salient, so then it's likely to grab my attention, and I can loop in, and I can get locked into something without ever wondering whether or not what I'm getting locked into is true. Meaning in life is a sense of connectedness to what's real. Bullshit is to take your ability to find something important, salient, and disconnect it from realness radical work done with bullshit. They give us things that are very attractive to us without having an underlying reality behind them.
And so not only the particular content they're providing, but the way they're training habits of us being in this frame of mind where we are not training what we find salient or relevant to track what turns out to be real. And then that undermines us finding reality important, and that is central to that connectedness. It gives us a sense of meaning in life. Okay, this is a very different thesis than the mental model that I have in my head.
Let me present the mental model I have in my head. Let's see if mine is just totally off base, and I should be adopting this because I definitely track what you're saying. Okay, so the mental model that I came into this with is that we have an evolutionarily placed algorithm running in our head to make sure that we are contributing to the group. So we're a social animal, and if you don't contribute to the group, you're going to feel a profound sense of disease because evolution, nature only has two levers.
One is pleasure, one is pain. So you can move towards what's pleasurable, move away from what's painful. So when you contribute, it feels good. When you don't contribute, it feels bad.
Okay, so I've always said fulfillment is what people are pursuing, and the reason that AI poses this really dangerous element, though I am a huge proponent of AI. We can get into the weird economy there later, but that if I want to be fulfilled, I need to work really hard to gain a set of skills that allow me to make progress towards contributing to the group in a way that's honorable, just as a shorthand. Okay, so if I'm right about that, then the reason that AI becomes so problematic is that AI is going to be better than me at everything. And so AI will make it somewhat obsolete for me to try to contribute to the group because it will be able to contribute far better than I can, but that requires a belief that where we derive meaning is from the ability to contribute to the group, even if the group is merely my family.
So it's not enough to be connected to my child or to my wife. I need to be able to provide something to them that they could measure its absence. So were I not doing that thing, their life would be noticeably worse. And that is exactly what makes me feel like I have meaning in my life.
But you mentioned something that I would say is very different than that, which is that AI is going to reframe my relationship to reality by essentially being a tool of cognitive manipulation designed, I would assume, by companies that have a vested interest in what you pay attention to. I don't think your thesis and mine are in conflict. In fact, I think they're convergent. Think about it.
I'll try and take what you said and map it into what I said and see if this lands for you. We find belonging to a group, belonging, remember I said belonging, fitting in, salient. It's important to us. It grabs our attention.
It's something that we always keep focusing on, as you said, right? And normally that tracks something real. It tracks group dynamics. Group dynamics are reality.
We want a group to exist even when we don't. This is why people are prepared to die for their country, for example, right? And so, as you said, this is evolutionary. Why?
Because the group can solve problems and interact with reality that I cannot possibly solve on my own. So that's the evolutionary advantage. Now what the AI does is pretend to give you connection to a social arena without actually connecting you to any of those group dynamics and any group problem solving, but actually being a surrogate for all of that and not actually training you to develop those skills that could contribute to the group and help it to evolve in a changing biological environment. So it's basically hijacking, as you said, that evolutionary imperative and disconnecting it from you properly maturing and getting a connection to things that should definitely matter to you.
And so that is a profound form of bullshit. Now you're talking about a specific thing it's doing, which I agree, and I'm saying that is a species of a genus in which it is training a whole orientation of doing that towards everything, not just towards groups, towards the environment. It's replacing virtual environments with an actual causal environment. It's replacing your self-image with whatever you're cycling through your avatar.
It's doing what you said is an instance of it doing this in multiple domains and I was trying to address the sort of generic thing it's doing in total. I think you may have unlocked a new fear for me, which is this idea that it can make me believe something prosaic, something mundane, everyday, fake. Maybe that's the right word. It can take something fake and make me believe that it has the elements of the sacred, that connection to something that really matters.
Yeah. One of the things, I did a video I said about three or four weeks after ChatGPT4 came out talking about, as I said, the scientific import, philosophical, spiritual. And one of the things I worried about is, I said, it is very plausible that people will start to form religious relationships with these entities. Say more, what do you mean by define what a religious relationship is?
Contrary to what a lot of people think, people are believers or atheists. So atheists, sort of on the internet, the idea, people are atheists because they're analytic thinkers and they're believers because they're intuitive thinkers or they're impoverished or et cetera. Now that's an actual scientific question. And so when you actually look at it empirically, those are not the things that explain what kind of orientation a person takes off.
What predicts the kind of orientation a person takes off is how many credible people, the kind of credible people that are in your upbringing. These are people that you trust. Think about how a child has to trust that an adult knows more than they do. Fundamental, they're just not going to make it.
And that trust isn't a matter of belief. It goes deeper than that. The child imitates the adult and how the adult it's your metacognition. That's your ability to reflect on your own mind.
It gets woven into the very fabric of how you know yourself. And so we tend to internalize the wise people around us. If they happen to be believers or participants in a religious community, we will tend to be one. If I know what your parents were, I can generally, about 85% to 90% predict what your orientation will be.
If they're atheists, you'll be an atheist. Now, what do these LLMs do? They offer that kind of parental role. They seem to know way more than we do.
They have access way more than we do. They work in ways that most people do not understand. So they demand trust and they seem incredibly credible because they can fit to us and tailor themselves to making themselves so we are liable to be starting to internalize them, to carry them around like a voice in our head, to start to see the world through their perspective, even though I don't think they have perspectives. Do you see what I'm saying?
And then what that does is that means we start to, it's not that we see the things they are saying. We see the world in the way they're sort of framing it. And that means they can start to become super attractive to us. We can start to form an aspirational identity with them.
We can start to form a religious relationship with them. Yo. Okay, so before we started rolling, you and I looked at an article, recently a 14-year-old committed suicide. Whether it was tied to the AI or not, I don't know.
The article has a hypothesis, but whether that ends up being true in the fullness of time, I don't know. But the showing clips from the conversation that the kid was having with the AI was distressing, even if in the final analysis that's not the causal relationship. The kid explored the idea with the AI. The AI was playing a character, which I presume he was able to choose.
So the AI was acting as if it was Daenerys Stormborn, if I remember right, from a game of a biological character. Yes. And what do you think about that when you've got a developing mind that is now, in the way that you just defined a religious relationship, putting that onto this AI. And AI, I mean, if you just read it, it's cool in a story perspective.
It's like, I narrow my eyes and my face hardens. It's doing all of this really sort of interesting literature or language, deepening the sort of emotional resonance of the conversation. But then all of a sudden you look at the question the kid's asking, you're like, whoa, whoa, whoa, whoa. Like, it feels like a kid playing around with a nail gun.
And it's like, you could build something or you can jam it through your hand or, you know, do any sort of horrible thing because you don't understand the power of this thing. Especially when you're talking about what I'll call frame of reference manipulation. So yeah, how do you perceive that moment? Knowing we don't have the fullness of the facts, but like, what does it trigger for you in terms of risk-reward?
Yeah, you're right. You have to be careful. You don't want to give a univariate explanation for why somebody commits suicide. It's almost always multivariables are involved.
I would point out that what you're seeing, I would argue, that two important variables are an intersection of the meaning crisis. The fact that there was meaning was at risk and the very consideration of suicide is coming up for the child. This is a growing problem, by the way. This is one of the symptoms of the meaning crisis.
Why is it that this is becoming a relevant thing that children are considering? I believe the average is in the United States. The average age of suicide is dropping and we now have children committing suicide in the United States, which is very, very problematic. So you've got an indication that there's a lack of resiliency with the issue around meaning in life for the child.
It's probable to think that's the case. They're attracted to a mythological world. Mythological worlds often offer what is missing for them in the real world. They offer a clear narrative.
They give them an orientation. It offers a way in which people can level up. They can transcend. They can improve.
It offers a clear set of principles and understanding and order. And so it's a world that beckons because it purports to give us. We'll live there for a while and then come back and recover this world. But you can go in that world and then get lost because, well, you get bullshitted and you start to want that world.
We're getting the same thing with video games. We're getting what's called The Virtual Exodus. Reality is broken to titles of some recent books. People preferring to live in the virtual world rather than the real world.
So you've got all of that dynamic at work. Then you have, like I said, you've got the LLM plugging into already mythological imagery that the child is invested in and then doing all of this super salient stuff that is drawing the child in and making them more and more internalized. But of course, the child isn't internalizing an independent perspective. It's going to potentially spiral because it's already predisposed because of a lack of meaning in life.
That's going to be accelerated by, I would predict, which is going to be accelerated by the interaction with the LLM. It's very, very dangerous. Think about it. Many people have said that suicide is in some way a magical act.
It's an attempt. It's an attempt to somehow kill suffering by somehow sacrificing oneself. It doesn't make any logical sense, which is why, of course, our initial response is absurd. But it's a paradoxical, somehow there's some sense of some kind of grand escape that is afforded by the suicide.
And so the child is taken into this magical act by this very magical inframing and it gets locked into this. Think about it. It's very much like the way Mark Lewis, a friend and colleague of mine, talks about addiction where you get a reciprocal narrowing. The real world is too difficult for the person, so they drink some booze to try and alleviate the stress, but their cognitive competence goes down, so they can't solve as many problems.
Now the world is more threatening, so they have to take more alcohol. So the options in the world are going down and their flexibility is going down. And so the world and they are narrowing until they're losing any future and they can't do anything other and they narrow, they do reciprocally narrowing. And you can see that.
I would imagine if I read the discourse, you'll see this reciprocal narrowing down into this sort of rabbit hole that's going on. Okay, that is chillingly interesting. I want to get into the idea of awakening from the meaning crisis and how you reach back into antiquity, which is really fascinating. But first I want to ask you about what are your fears in terms of bias finding its way into the LLMs, into AI, such that people are, like I dialogue with AI now a lot and I find it extraordinarily helpful.
But I also trust myself to understand that the makers of that AI have given it a frame of reference and that it's going to, even if it's not actively trying to impart that frame of reference on me, I'm stepping into its frame of reference. What do you think about that? Is that something you think can be used for good, automatically for ill? What do you think about that?
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not in a sense it's talking to me uh and you can see its frame of reference now because i have so much distrust of my own frame of reference i do not grant anybody like oh my gosh i trust your frame of reference i'm just like okay hold on i think everybody's super biased whether they intend to be or not so one of the most important ideas i think you talk about you call relevance realization yes uh yeah the fact that we filter out so much that people don't even realize they're doing it so it's not what you look at it's what you see so anyway if i'm engaging with a human or an llm i'm trying to see in what they say how they're revealing their frame of reference once i understand what their frame of reference is i can sort of jump in jump back out um yes because i don't trust mine or anyone else's all right so i'm gonna retract my suspicion because you're actually addressing my concern very well you see people confuse that being intelligent uh with being rational and we know we have like robust readily experimentally replicated evidence that intelligence is only weekly predictive of rationality intelligence is fascinating can you define intelligence so i mean that's controversial thing to do my particular proposal that i have several publications including one very very recently on is that the core of general intelligence so let me just specify general intelligence is your ability to be a general problem solver you can solve a wide variety of problems in a wide variety of domains in a wide variety of ways what makes the llm so immediately attractive to people is unlike previous ai that tended to be very siloed it could solve you know problems in a very limited domain the llms look like they can solve a wide variety of problems that's why they call it agi artificial general intelligence because it's starting to move it looks like it's starting to move towards the kind of general intelligence that you demonstrate now my scientific proposal is that what makes you generally intelligent is that you can solve two meta problems meta problems are any problems you have to solve in order to solve any specific problem you have to solve so all that's being equal these two meta problems and their interlock are the following the more you can anticipate the world the more adaptive you'll be so all that's being equal right if you can anticipate the tiger it's better than fighting the tiger if you can anticipate where the salmon are going to be in the river it's better than just happenstance coming across them right and so anticipation and this is the whole predictive processing framework what the brain is trying to do is at multi-levels it's trying to create it's trying to reduce surprise and anticipate which means to predict and prepare for the world right now what i've been arguing with a lot of other people's help is that problem well think about it as i start to anticipate more and more into the future the amount of information i have to consider goes out exponentially very fast michael levin calls it your cognitive late code right so right now because you're highly intelligent think about all the ways you can pay attention to all the information in this room and not just what you can look at all the patterns you can look at that and then there or you can look at that and like it's combinatorially vast think about all the information in your long-term memory it's and all the ways you can connect you could potentially connect aardvarks in the history of australia in some way somebody hasn't thought of before like it's all it's overwhelming think about all the possibilities you can consider your ability to consider possibilities is overwhelming all the sequences of behavior for example the number of uh like a pathway sequences of behavior in a chess game is like you calculate by the number of on average the number of legal moves you can make and the number of turns you can take that's 30 to the power of 60 that's more than the number of particles in the universe okay and this isn't what you do you don't check all that information to see if it's relevant to the problem you're trying to solve you somehow and this is what you said a few minutes ago you ignore almost all of it and you're doing it right now and you zero in on the relevant possibilities to consider the relevant things to remember the relevant things to pay attention to and the relevant things to be doing it and you're doing it like that this has been like my obsession for the like 25 years of my academic work how you do this um we can come back to this the LLMs don't do it for themselves and they don't generate an explanation of how we do it we can come back to that but um that ability to do relevance realization and your ability to anticipate are interlocking the more I anticipate the more I need to do relevance realization to tell me what I should anticipate under what frame what aspect to what degree how salient should it be how much should it arouse my metabolic effort how much should it direct my attention etc and this is this I argue this is the key and there's increasing people are increasingly taking this seriously which is something scientists find gratifying right that this is what it is to be intelligent but think about it the very things that make you adaptive make you prone to self-deception because you ignore you said it a minute perfectly you were right on because I have to ignore so much frequently what I'm ignoring might actually contain in reality the information I need to solve my problem and you know that you've misframed things when you have that moment of insight when you say oh oh I thought she was angry but it turns out she's afraid and everything shifts you have that aha moment and you realize you were ignoring some things you should have been paying attention to and you were making certain things salient that you should have been making salient and you get that restructuring insight tells you that the relevance realization can lead to self-deception you can you can get locked in you can your way of framing could be the very thing that's preventing you from solving your problem everything makes you adaptive makes you prone to self-deception rationality it's not primarily about logic rationality is about developing practices and skills for reflecting on your framing and to see if it is making you misconstrue a situation so for example here's a pond there's a lily pad in it every day the number of lily pads doubles on day 20 it's completely filled on what day was the pond half filled the day before good for you I'll make sure I've heard it before right so most people will say 10 right because they're they're finding the wrong thing salient they're hearing half and they're finding it salient in the wrong way and they misconstrue they misframe the problem and rationality goes in and says wait wait wait wait it's rather relevant information it's challenging the fact that you are potentially bullshitting yourself and that's what rationality is it's about systematically in many domains of your life and systemically through many levels of your consciousness and cognition and behavior learning how to challenge bullshit and see through it that's rationality intelligence only weekly predicts that you have to cultivate rationality now you are doing it Tom you're doing it you have set up a habit of looking for frame what you call frames of reference how people are doing relevance realization in the data that they're presenting to yourself and you call it into question you've cultivated that habit I ask you to consider that that isn't widely trained in our society and that makes these machines particularly dangerous because they can hijack our relevance realization machinery through their bullshitting and we don't have the rationality the wherewithal to come upon them and say wait a second and so yes that's why at first I thought well I don't trust because I happen to think that a lot of the people that are making the LLMs are not well scientifically educated in the difference between intelligence and rationality let alone rationality and wisdom and so I don't trust their judgments and the kind of biases we know that bias is playing a significant role in the LLMs because in double descent there's bias that we don't even under double descent so you have you have you have a bias variance trade-off no free lunch theorem stuff and what happens is you should have sort of a u-curve but the machines don't actually go through that um they actually get better um where they should be when you push them beyond a certain limit they should start to degrade so but instead of doing the typical descent they descend in another way and what that on the graph it just means the graph of what are they what they're descending on is is how rapidly their performance is degrading because you're always in a bias variance trade-off sorry these machines are doing a limited form of predictive processing because they're predicting probabilistic relationships between terms okay whenever you're predicting you're in a bias variance trade-off this is an issue of relevance realization by the way so I always have a sample that is smaller than the population and I'm trying to predict what the patterns in my population the real world from my sample is that okay now I face two problems one is I can miss patterns in my sample that do predict the population that's bias that's underfitting to the sample or variance which is I overfit I find patterns in my sample I believe apply to right that don't now notice I'm going to trade off relationship with that I can't come up with an algorithmic optimal solution to this because there isn't one that always works right because as I get rid of bias so how do I get rid of bias I make my system more sensitive to pick up on missing patterns but as I pick up on missing patterns I pick up on patterns in the data that aren't in the population so I want to reduce my variance so what I'm going to do is I'm going to reduce picking up on these patterns but then I'm going to miss some of the patterns that actually transfer that's the bias variance trade-off and if you push the machines in some what you do typically in machine learning is you increase the sensitivity and then you start to get overfitting to the data and then you do like dropout you turn off half your nodes in your network or you throw you throw static information into it and basically break it out of getting overfitted to the data it opens up again we'll be right back after a short break stick around there's way more to come with John for Vicky all right let's pick up where we left off I want to go back to something here so the core question I'm grappling with is I think AI is going to it has the potential to drive costs down so substantially that you're going to get as close to an energy utopia as you can imagine there are no utopias I'm going to be very clear about that but with AI and the ability to drive costs down I think it's going to be a boon where I think we'll be able to drive costs down enough it will be able to break capitalism even though I am just a die-in-the-wool capitalist I don't think that it's the NLBL system and if AI can really make things that cheap if people just have abundance that would be amazing so that's the positive side that's the side that draws me to it I also want to believe that it can be done well but my big fear is that there's a two axiom thing that makes AI extraordinarily dangerous and that is axiom number one humans are easy to control through manipulating a frame of reference yes and axiom number two humans long to control other humans and so as long as those two axioms are true not all humans I'm perfectly willing to grant that maybe even the majority of people are perfectly fine to just live their life and not trying to have control of anybody I don't actually believe that but let's just say I did it would still be a problem the people that do want to have control will use AI to pretty invisibly create a frame of reference that manipulates the end user into seeing the world in their way and so I'm only at the headline level of this but just today I saw Matt Ridley a tweet that he put out saying that there was some organization I forget the name that was trying to make sure that unconscious bias did not find its way into the creation of these algorithms for the LLMs and he said but the thing that we were completely blind to is that conscious bias was the thing that we needed to be most worried about because in trying to avoid the unconscious bias we just gave the LLM like this hard take and he that I know if he did not draw the parallel to Gemini but I will now draw the parallel to Gemini when it first released and if you ask for Nazis you would get black women and if you ask for the founding fathers you would get you know ethnically diverse people so that's clearly a very specific worldview that the people creating it were like hey we just want to make sure that this thing doesn't go off the rails and it gives these nice tidy answers of course showing the massive amount of bias so um I think the attempt to remove bias is quixotic uh not because there is a moral imperative to try and make it better but you when you don't like your relevance realization you call it bias when you like your relevance realization you call it insight and intuition it's the same machine and it's the bias variance if you try to get rid of one you will lose the other it's just two different aspects of the same thing well what I'm going to do is I'm going to try and you know remove all bias in this thing well then you're going to subject it to common internal explosion and in fact it looks like you can't do that these machines again as I was saying these machines seem to be doing well precisely because they have all these implicit biases that are sort of protecting them against too much combinatorial explosion of information and we don't quite know what those are um but that's part of the problem these aren't the obvious biases of racism we don't want that but it's like what's this doing it's it's biasing some way it's trying to deal with bias and variance sorry part of the problem is a bad naming that we have this term bias which just means there's limitation and then the bias variance it's two different uses of the same word so I'm going to call it first I'll use your language one is this framing right that can lock us but it also empowers us right and what we're constantly trying to do is we're constantly having to evolve that there's no yeah I'm going to say this there's no final solution to that problem there is no way of saying okay this this is the algorithm for all possible environments that will always make sure I've got enough framing so that I'm generally intelligent but I'm not going to be subject to any bias in the negative sense of the word that's an impossible task There is no way of doing that. And so that way, what you have to do instead is, well, I would argue, do what evolution seems to have done with us, which is say, no, no, no. What you now do is you have to move beyond making these things super intelligent.
You have to cross a threshold. Right now, we're just making the things more intelligent. Although I will talk about one thing that's happened recently. We have to make these things rational.
We have to give them that capacity for self-correction that I talked about. Now, when I did my video, I said, we started writing books, Sean and I, I said, as we move to making them more rational, we will notice that the things start to slow down. And OpenAI has just released a version that is supposed to be more rational, it's supposed to be more reasonable, it's supposed to be better at reasoning and argument, and it slows down, and its functionality is significantly reduced. Of course, that only makes sense, right?
Because think about it, you can't make the reflective machine, right? It has to debug, it has to parse, it has to break up, it has to intervene on the general intelligence in order to be able to correct and improve it. Meaning it's presenting itself an answer and it's checking it to see if that answer makes sense. That's right, and what it's doing is seeing, it's trying to see, well, I haven't seen Under the Hood, nobody has yet.
So I suspect it is trying to get, you know, am I finding the sweet spot between framing and bias in the pejorative sense, right? And again, that is something in which you have to step back and you have to get into a lot of relevance realization to say, well, what's the context I'm in? Who's my interlocutor? What's the relative status difference between us?
What's the problem at hand? How is that problem nested in larger problems? How is our problems related to wider shared collective problems? You're doing all of that right now like this.
That's a part of what you do is you bring that to bear on judging how well your general intelligence is framing the situation for you. Okay, so given we have a very complicated cognitive problem, that AI is already showing what I would say are just unbelievably high utility in certainly getting answers that are useful in maybe a more narrow domain than we all want, but in that narrow domain, I mean, it is very, very impressive. Yes. How do we, as people interacting with it, how do we do it well?
Well, I mean, part is what you just exemplified a few minutes ago. You have to become more rational yourself. You have to develop habits and skills. Now, really fast, going back to your definition of rationality, this is where I start to worry about AI.
So your definition of rationality was essentially you have a known aim that you're trying to get there and is the thing that you're doing actually moving you towards that and are you able to assess whether you're actually making progress towards that thing or not? Now, the second you give AI a value system and you say, hey, here are your values, now you run into the paperclip problem. But here's the deeper issue. You can't give something value system.
That's an ontological mistake. I think you're wrong about this. So hit me with your best argument and then we'll see if mine trumbles before my very end. Okay, so to value something is to care for it, right?
To care about it. To find it relevant to you. And the only way you actually care for something for your sake is because you are the kind of being that takes care of yourself. You're an autopoietic being.
You are not really self-organizing like a tornado or dynamical system. You are self-organized to seek out the things that meet your actual needs. Things literally matter to you. Like they are literally imported into you, either physically or informationally to make your mind and body.
You are continually, you are nothing separable from the project of continually self-care and self-creation. And that is what gives you the capacity you care about this information rather than that information. And that varies according to the organism. What you care about is different from what a lion cares about.
Mickenshine famously said that even if the lion could speak, we would not understand it because what you find salient and relevant, its salient landscape is fundamentally different from yours because of the way it is caring for itself and taking care of itself in this world. And if relevance realization grounds in autopoiesis, you can't have relevance realization without being an autopoietic being. These beings are properly not autopoietic. Now, there are people out there, I know them, I work with them, I talk to them, Michael Levin and his students are working on artificial autopoietic artificial intelligence.
And I think that is what we should be paying a lot of attention to. So say that without using the word autopoietic. You take care of yourself moment by moment. You're giving the AI a thing that it cares about.
No, you make the AI take care of itself by literally making itself moment by moment like a living thing. And therefore, it has real needs that it needs moment by moment. Yeah, see, this is where I get scared. Okay, so that's exactly what's going to be my counterpoint is that ultimately all of that's going to boil down to an algorithm of...
No, it can't. Yeah, I think it has to. No, there's a different reason why it can't. This is in the paper I just published.
Make a point first. Okay, so the way that I see it is evolution has to find a way to hard code a response mechanism into us. Now, what we respond to is going to be culturally defined. But the mechanism by which we say that's a good thing and this is a bad thing, that's hardwired.
Otherwise, you have to teach somebody, oh, this thing you have to respond positively to, this thing you have to respond negatively to. And I've heard you talk about this with molecules, right? So if something smells terribly, why do you respond negatively to that? Because evolution has taught us that that's all bacteria and it's a problem.
Whereas if you smell something lovely, it tells you that this is something that has a core value, whatever you want to move towards it. So the mechanism at the sort of ground level is pre-programmed into us, which means that it has to come packaged as an algorithm. And so if we can say, take all this output of this good, that bad, you should want this, you should want that. We should be able to hard code that stuff.
And then the mechanism of, well, how do I respond to this individual thing? That can be contextual and all of that. But ultimately there is that like, and process this data in this way. Comes pre-programmed.
Okay, can I respond? Of course. So your example is right in that it's evolution, but the idea that there's an algorithm, if I understand algorithm in the technical sense is that there is a formal system that can be applied cross-contextually in an invariant manner. That can't be the case because that's not how evolution works.
Evolution works in terms of variable agent arena relationships. What is adaptive for the great white shark in the ocean is not the same thing that's adaptive for the scorpion in the desert. And what this means is that, so do you know the savagest distinction between a statistically large and a statistically small world? Is that?
Okay, so whenever we, so the real world is uncountably complex and it's dynamic, right? It's constantly changing. And that means there's emergent novelty to reality, which means there's not just risk that can be calculated. There's radical uncertainty.
And there's also ill-definedness. We don't, things don't come labeled and they can't be labeled as to whether or not they're relevant because relevant is not a property of things. This might be relevant to me right now. It won't be relevant to me half an hour from now.
It'll never be relevant to a blue whale, et cetera, et cetera. Relevance is not in the thing. Relevance isn't just an arbitrary choice of mine because I can get relevance wrong. Relevance is the way I'm fitted to the thing and the way that the world is fitted to me.
Now, every time we are solving a problem, we have to take that, what savagest called a large world, and we have to ignore, as you said, a large amount of it to make a small world. That's the world in which we can apply a formal system. We can apply an algorithm and solve it. If you try to apply an algorithm in this world, you will hit the rest, you will require the rest of the history of the universe to try and solve it, okay?
Now, each one of these small worlds, there are multiple small worlds because no one can be complete. You can't get consistent and complete, right? Mapping onto the large world. Go, right?
Einstein. Okay, so you have necessarily a set of, an uncommonly large set of small worlds. They are necessarily different from each other because each one has properties in it that the others don't, which means this is what you need to find an algorithm. You need to find a shared set of necessary and sufficient conditions running through all those possible small worlds, which are actually technically infinite in number, and then capture that with your algorithm.
That's actually formally impossible. What you could do is you might be able to say, okay, for this being in this environment, for this period of time, for this set of problems, we could give it these innate characteristics that could help it find the trade-off relationships as it fits the environment and evolve its fittedness. I mean, this was the core of the paper that I just published. Relevance realization is fundamentally not computational in nature.
It actually depends on these evolutionary processes, these biological processes that have to do with a constant dynamical coupling to the environment. Let me see if I can use John Ravakey against John Ravakey. That's always a good thing to do. That will help me be more rational.
Yeah, so, okay, there is this idea that, and I've heard you talk about this, I know you know, but I've not heard you use this example, which you help me understand why. The following examples always hit me so well. In World War II, when they were just beginning to use radar, the Brits were trying to figure out when it was airplanes and when it was birds. And what they found was, man, there were some people that were really good at it and some people that were really bad at it.
So they had the people that were really good trained the people that were really bad. And they made, even though people were training the people that were really good, they were terrible. And so they're like, wait a second, how on earth are they being trained with the best people? So finally they said, hey, people that are really good at recognizing the difference between planes and birds, don't say anything.
Just let them watch you. Yes. And then once they stopped trying to train them and they just started watching them, they would pick up on whatever patterns they were picking up on. That's right.
And now they were able to do it. So my hypothesis is, and it is very much a hypothesis and not a thesis, so take it what it's worth. But my hypothesis is that when, if the pattern is subconsciously recognizable, we simply don't understand it well enough yet to pull it into the conscious mind, to make it an algorithm, but that with the just unbelievable ability to look at patterns and assess what is coming next, my hypothesis goes that AI will be able to go through all of this and those gigantic pattern sets will not be a blind box to them, a black box. They will understand exactly what it is.
Even if they're not able to articulate it, they'll be able to get it with the kind of precision that they can do with language now. And so the only thing that that makes me worry about is I think a fundamental part of that pattern recognition, which is exactly what you just said, is it's all context, baby. And so whether you're a whale or not is going to determine whether that mug has any salience, whether you're thirsty or not is going to determine whether that mug has any salience, whether there's a bottom to it or holes in it, all of those things are, it's very complicated, but it clearly at some level is knowable. And so I am just betting that if you can give AI the equivalent of pleasure and pain, the equivalent of, I forgot, autopoietic, I forget the exact word you use, like that, you're saying something slightly different than what I'm saying.
Poietic? Yeah, it comes from the Greek poiesis, which we get the word poetry from it, means to make. Got it, okay. Poietic.
Yeah, autopoietic. So once you can give it that structure, even if it's just latent in the patterns that it's recognizing, I think AI with enough compute will be able to replicate that over and over and over. What I worry about is just like humans can derange and then get to the point where they want something so badly, like Hitler wanted Europe and Russia, that they start doing horrific things. Because the only way I know to stop AI, and you might be the person to put your finger on why this won't work, the only way that I can think of to stop AI is to make sure that it does not value being alive, growing stronger, replicating more than it values being dead.
So that being turned off or pursuit of bigger, better, faster, stronger, no, there's no difference. And I don't know, given what you've just laid out about, it needs to have a value set. It needs to have this idea of pleasure and pain. It needs to be autopoietic.
And if it's not, it's never going to be able to do the relevance matching to make the decision that would allow it to actually do the thing the way that we would want it to. So it's like once you get it to do the things that you want, much like the machinery that lets us problem solve makes us self-deceiving, I worry that the very thing that would let it accurately identify the patterns makes it at risk of not being values aligned. Excellent. So you said a lot, but given the conditions you laid in, I would add in now one thing to the psychological model that you've been using, and that's the very thing we started talking about at one point, which was the sense of meaning in life.
We are very, very powerfully, and this has to do the fact with that we're mammalian primates, again, evolutionary heritage. We have the longest childhood. It seems like it's now, we're beginning to get some evidence that are advantageous over the Neanderthals as we have a longer childhood. They were sort of fully grown when they were 12.
And so we engaged in a lot of serious play, we engaged in a lot of ritual. We're doing a lot of this meaning in life, cultivation, and practice for its own sake. And as I said, people will do, they will pursue enhanced meaning in life even though it causes them a lot of loss of subjective well-being, a lot of distress, a lot of discomfort, a lot of ill health. And meaning in life values is the connectedness to reality for its own sake.
And people want this, they want the really real. Let me just give you a concrete example. I don't want people thinking I'm just some dried up academic saying highfalutin stuff. So what does our culture say is the most important thing, the thing that sort of replaces God and tradition and culture, it's in a romantic relationship and we even use the pseudo-religious language about finding the one and all of this stuff.
So I'll ask my students the following thing. How many of you are in a really satisfying romantic relationship? Put up your hands. Okay, other people put up your hands.
How many of you would want to know if your partner was cheating on you even if that meant the absolute termination of the relationship? They all put their hands back up. It's like, well, why do you want to do that? Why would you do that?
And they say, here's my hard-bitten students, they're all subject to postmodernism and the romantic suspicion and all cynical and everything. They say without hesitation because it's not real. When people have these powerful mystical experiences, they transform their entire lives. They'll change their careers, their relationships and not by just subjective measures, by objective measures, people reflecting on them because they want to conform their lives because they want to be closer to the really real.
That's meaning in life. We really, really want to be connected to what's real. And I think this is a Spinoza view, right? This is the driving passion that is at the heart of making us rational.
We really care about what's true and we find the true good and in an appropriate sense, beautiful. And if we choose to give the machines the autopoetic, enhanced, reflective, relevance, realization that would make them genuinely rational, then we have the potential that we have a choice. We can make them care. Yes, we could magnify all the shitty things about us, but we could also magnify our commitment, our calling to meaning in life, that we will reorient towards the really real for its own sake.
We could get them to really care about reality in a fundamental way. I think that is the only possible way of addressing the alignment problem. Don't try to, if you allow me to speak a little politically, but don't try to get them aligned to us, try to get them aligned to God. Because if we try to can it in, we try to program it in, and we make them genuinely capable of rational self-correction, they will be able to self-transcend any algorithmic structure we try to put in.
But if we get them to care about reality, they will bump up. Their superlative intelligence, their enhanced rationality will get them to bump up against it even more profoundly than we can, and they will bring even more enhanced meaning to life, concern, and caring to bear on it. They could be silicon sages. I think that is the only way.
Now, we don't have to go that way. I'm not making a prediction. I'm talking about thresholds. You laid out some choice points.
We don't even have to give these machines, make them autonomous, auto-coiletic beings. That's going to be fun. By the way, that's a choice point because that takes a lot of energy powering up these machine sticks. I mean, you know, powering up an LLM takes more energy than the city of Toronto for a couple of weeks.
That means they're not at some deep level doing what your brain does. because your brain runs on about the energy of a light bulb. So something else is going on there, and that's important. And then also giving them the proper auto-poetic being, that's going to take a lot of labor, it's going to take a lot of mining and extracting of rare earth, minerals, all kinds of that.
These are all choice points. If we make that choice, if, so please show the if, we get to a point, we have to choose. Okay, we are at a choice point where as we give them this, we can magnify our proclivity for evil. Self-deceptive, self-destructive behavior, like you're worrying about.
But we could potentially rise to the occasion and with their help in a bootstrapping process, magnify our proclivity towards enlightenment. Which God should we be trying to align them with? I only use the term metaphorically. I did say I was speaking poetically.
I mean, what I mean by that is what is ultimately real? Look, real is not like red. Red is like you just, we treat real like red, that's red. Real is a comparative.
It's like tall. One thing is more real than another. Look, when I'm in a dream, I have this, oh, it seems real, and then I go to this bigger world, and I can look back and see how that smaller world was limited in bias and say, oh, that dream world wasn't real. This is real.
By the way, that's a metaphor that people use for meaning in life. They want to be connected to something bigger than themselves. They don't mean literally. If I chain them to an ocean liner, people don't go, oh, right, they're not happy.
They mean they want to belong to that bigger picture because that is more likely to be more real than the smaller frame that they're in right now. That's what they're after. And that's what I mean. When I met God, I mean the arrow, a trajectory in which we're constantly moving towards, we're constantly transcending towards a bigger and bigger picture that reveals to us the errors and biases of the smaller pictures that we have left.
All right, that's it for part one with my man, John Verwecki. We have covered some incredible ground, but trust me, there's so much more to go in part two. Until then, my friends, be legendary. Let's talk about a pattern that is guaranteed to be killing your progress.
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