#486 – Michael Levin: Hidden Reality of Alien Intelligence & Biological Life

27:29And there are lots of people that study those, right? So things that thermodynamic kind of metabolic things and all kinds of architectures and so on. But I don't think it's about finding a line. I think it's about finding a scaling process. the scaling process. But then there is more rapid scaling and there's slower scaling. So innovation, invention, I think is useful to understand so you can predict how likely it is on other planets, for example, or to be able to describe the likelihood of these kinds of phenomena happening in certain kinds of environments. Again, specifically in answering how many alien civilizations there are.

28:08that's why it's useful but it's also useful on a scientific level to have categories not just because it makes us feel good and fuzzy inside but because it makes conversation possible and productive i think if everything is a spectrum is it becomes um difficult to make concrete statements i think like we even use the terms of biology and physics those are categories Technically, it's all the same thing, really. Fundamentally, it's all the same. There's no difference between biology and physics, but it's a useful category. If you go to the physics department and the biology department, those people are different in some kind of categorical way.

28:49So somehow, I don't know what the chicken or the egg is, but the categories, maybe the categories create themselves because of the way we think about them and use them in language. But it does seem useful. Let me make the opposite argument. They're absolutely useful. they're useful specifically when you want to gloss over certain things the categories are exactly useful when there's a whole bunch of stuff and this is this is what's important about science is like the art of being able to say something without first having to say everything right which would make it impossible so so categories are great when you when you want to say look i i know there's a bunch of stuff hidden here i'm going to ignore all that and we're just gonna like let's get on with this particular thing and all of that is great as long as you don't lose track of the stuff that you glossed over.

29:30And that was what I'm afraid is happening in a lot of different ways. And in terms of, look, I'm very interested in life beyond Earth and all of these kinds of things. Although we should also talk about what I call SUTI, the search for unconventional terrestrial intelligences. I think we got much bigger issues than actually recognizing aliens off Earth. But I'll make this claim. I think the categorical stuff is actually hurting that search because if we try to define categories with the kinds of criteria that we've gotten used to, we are going to be very poorly set up to recognize life in novel embodiments.

30:08I think we have a kind of mind blindness. I think this is really key. To me, the cognitive spectrum is much more interesting than the spectrum of life. I think really what we're talking about is a spectrum of cognition. And I know it's weird as a biologist to say, I don't think life is all that interesting a category. I think the categories of different types of minds, I think is extremely interesting. And to the extent that we think our categories are complete and are cutting nature at its joints, we are going to be very poorly placed to recognize novel systems. So for example, a lot of people will say, well, this is intelligent and this isn't right.

30:44And there's a binary thing and that's useful. And occasionally that's useful for some things. I would like to say, instead of that, let's make us, let's, let's, let's admit that we have a spectrum, but instead of just saying, oh, look, everything's intelligent, right? Because if you do that, you're right. You can't, you can't do anything after that. What I'd like to say instead is, no, no, you have to be very specific as to what kind and how much, in other words, what problem space is it operating in? What kind of mind does it have? What kind of cognitive capacities does it have? You have to actually be much more specific and, and we can even name, right?

31:15That's fine. We can name different types of, I mean, this is doing predictive processing, saying this can't do that, but it can form memories. What kind? Well, habituation and sensitization, but not associative conditioning. Like it's fine to have categories for specific capabilities, but it's, it's, uh, it, it actually, I think it actually makes, makes for much more rigorous discussions because it makes you say, what is it that you're claiming this thing does? And it works in both directions. So, and so some people will say, well, that's a, that's a cell. That can't be intelligent. And I'll say, well, let's be very specific.

31:43Here are some claims about here's some problem solving that it's doing to tell me why that doesn't you know why doesn't that match or in the opposite direction somebody comes to me he says you're right you're right you know the whole the whole solar system and it's just like this amazing like okay what is it doing like tell me tell me what what tools of cognitive and behavioral science are you using to to reach that conclusion right and so i think i think it's actually much more productive to take this operational stance and say tell me what protocols you think you can deploy with this thing that would lead you to use these terms.

32:14To have a bit of a meta conversation about the conversation, I should say that part of the persuadability argument that we two intelligent creatures are doing is me playing devil's advocate every once in a while, and you did the same, which is kind of interesting, taking the opposite view and see what comes out. Because you don't know the result of the argument until you have the argument. And it seems productive to just take the other side of the argument. For sure. It's a very important thinking aid to, first of all, you know, what they call steel manning, right? To try to make the strongest possible case for the other side and to ask yourself, okay, what are all the places that I'm sort of glossing over because I don't know exactly what to say and where all the holes in the argument and what would a really good critique really look like?

33:02Yeah. Sorry to go back there just to linger on the term because it's so interesting, persuadability. Did I understand correctly that you mean that it's kind of synonymous with intelligence? So it's an engineering-centric view of an intelligence system because if it's persuadable, you're more focused on how can I steer the goals of the system, the behaviors of the system, which meaning an intelligence system maybe is a goal-oriented, the goal-driven system with agency. And when you call it persuadable, you're thinking more like, okay, here's an intelligent system that I'm interacting with that I would like to get it to accomplish certain things.

33:47But fundamentally, they're synonymous or correlated, persuadability and intelligence. They're definitely correlated. So let me, I want to preface this with one thing. When I say it's an engineering perspective, I don't mean that the standard tools that we use in engineering and this idea of enforced control and steering is how we should view all of the world. I'm not saying that at all. And I want to be very clear because people do email me and say, this engineering thing, you're going to drain the life and the majesty out of these high end human conversations. my whole my whole point is not that at all it's that of course at the right side of the spectrum it doesn't look like engineering anymore right it looks like it looks like friendship and love and psychoanalysis and all these other tools that we have but here's what i want to do i want to be very specific to my colleagues in regenerative medicine and just imagine if i you know if i if i went to a bioengineering department or a genetics department and i started talking about high level you know cognition and psychoanalysis right they didn't want to hear that so so i i I focus on the engineering approach because I want to say, look, this is not a philosophical problem.

34:56This is not a linguistics problem. We are not trying to define terms in different ways to make anybody feel fuzzy. What I'm telling you is if you want to reach certain capabilities, if you want to reprogram cancer, if you want to regrow new organs, you want to defeat aging, you want to do these specific things, you are leaving too much on the table by making an unwarranted assumption that the low-level tools that we have. So these are the rules of chemistry and the kind of molecular rewiring, that those are going to be sufficient to get to where you want to go. It's an assumption only, and it's an unwarranted assumption.

35:27And actually, we've done experiments now, so not philosophy, but real experiments, that if you take these other tools, you can in fact persuade the system in ways that has never been done before. And we can unpack all of that. But it is absolutely correlated with intelligence. So let me flesh that out a little bit. what I think is scaling in all of these things right because I keep talking about the scaling so what is it that's scaling what I think is scaling is something I call the cognitive light cone and the cognitive light cone is the size of the biggest goal state that you can pursue this doesn't mean how far do your senses reach this doesn't mean how far can you affect it so the James Webb telescope has enormous sensory reach but that doesn't mean that's that's the size of its cognitive Lycone.

36:11The size of the cognitive Lycone is the scale of the biggest goal you can actively pursue. But I do think it's a useful concept to enable us to think about very different types of agents of different composition, different provenance, you know, engineered, evolved, hybrid, whatever, all in the same framework. And by the way, the reason I use Lycone is that it has this idea from physics that you're putting space and time kind of in the same diagram, which I like here. So if you tell me that all your goals revolve around maximizing the amount of sugar in this 10, 20 micron radius of space time, and that you have 20 minutes memory going back and maybe five minutes predictive capacity going forward, that tiny little cognitive light cone, I'm going to say probably a bacterium.

36:56And if you say to me that, well, I'm able to care about several hundred yards sort of scale, I could never care about what happens three weeks from now, two towns over. It's just impossible. I'm saying, you might be a dog. And if you say to me, okay, I care about really what happens, you know, the financial markets on earth, you know, long after I'm dead and this and that. So you're probably a human. And if you say to me, I care in the linear range, I actively, I'm not just saying that I can actively care in the linear range about all the living beings on this planet i'm going to say well you're not a standard human you must be something else because humans i don't know these standard humans today i don't think can do that you you must be some kind of a body sat for some other thing that has these massive cognitive lycones so i think what's scaling from zero and i do think it goes all the way down i think we can talk about um even even particles doing something like this i think what scales is the size of the cognitive lycone and so now this is an interesting here i'll try for a definition of life or whatever for whatever it's worth i spend no time trying to make that stick but if we wanted to uh i think we call things alive to the extent that the cognitive light cone of that thing is bigger than that of its parts so in other words rocks aren't very exciting because the things it knows how to do are the things that its parts already know how to do which is follow gradients and things like that but living things are amazing at aligning their their competent parts so that the collective has a larger cognitive lycone than the parts.

38:29I'll give you a very simple example that comes up in biology and that comes up in our cancer program all the time. Individual cells have little tiny cognitive lycones. What are their goals? Well, they're trying to manage pH, metabolic state, some other things. There are some goals in transcriptional space, some goals in metabolic space, some goals in physiological state space, but they're generally very tiny goals. One thing evolution did was to provide a kind of cognitive glue, which we can also talk about, that ties them together into a multicellular system. And those systems have grandiose goals.

39:05They're making limbs. And if you're a salamander limb and you chop it off, they will regrow that limb with the right number of fingers. Then they'll stop when it's done. The goal has been achieved. No individual cell knows what a finger is or how many fingers you're supposed to have, but the collective absolutely does. And that process of growing that cognitive lycone from a single cell to something much bigger. And of course, the failure mode of that process. So cancer, right? When cells disconnect, they physiologically disconnect from the other cells. Their cognitive lycone shrinks, the boundary between self and world, which is what the cognitive lycone defines, shrinks.

39:37Now they're back to an amoeba. As far as they're concerned, the rest of the body is just an external environment. And they do what amoebas do. They go where life is good. They reproduce as much as they can. So that cognitive lycone, that is the thing that I'm talking about that scales. And so when we're looking for life, I don't think we're looking for specific materials. I don't think we're looking for specific metabolic states. I think we're looking for scales of cognitive lycone. We're looking for alignment of parts towards bigger goals in spaces that the parts could not comprehend. And so cognitive lycone, just to make clear, is about goals that you can actively pursue now.

40:15You said linear, like within reach immediately. No, I didn't. Sorry, I didn't mean that. First of all, the goal necessarily is often removed in time. So in other words, when you're pursuing a goal, it means that you have a separation between current state and target state at minimum, your thermostat, right? Let's just think about that. There's a separation in time because the thing you're trying to make happen so that the temperature goes to a certain level is not true right now. And all your actions are going to be around reducing that error, right? That basic homeostatic loop is all about closing that gap.

40:45when i meant when i said linear range this is what i meant uh if i say to you this this terrible thing happened to uh you know 10 people and and you know you have some some degree of activation about it and then i say no no actually it was 100 you know 10 000 people you're not a thousand times more activated about you're somewhat more activated but it's not a thousand and if i say oh my god it was actually 10 million people you're not a million times more activated you don't have that capacity in the linear range, you sort of, right, if you think about that curve, we sort of, we reach a saturation point.

41:18I have some amazing colleagues in the Buddhist community with whom we've written some papers about this. The radius of compassion is like, can you grow your cognitive system to the point that, yeah, it really isn't just your family group. It really isn't just a hundred people, you know, in your, in your, you know, circle. Can you grow your cognitive light cone to the point where, no, no, we care about the whole, whether it's all of humanity or the whole ecosystem or the whole whatever can you actually care about that the exact same way that we now care about a much smaller um set of people that's what i mean by linear range but you said separated by time like a thermostat but a bacteria i mean if you zoom out far enough a bacteria could be formulated to have a goal state of creating human civilization because if you look at the you know bacteria has a role to play in the whole history of earth and so if you anthropomorphize the cause of bacteria enough i mean it has a concrete role to play in the history of the evolution of human civilization so you do need to when you define a cognitive light cone you're looking at directly short-term behavior well no how do you know what the cognitive light cone of something is because as much as you've said it could be it could be almost anything the key is you have to do experiments and the way you do experiments is you put barrier you have to do interventional experiments you have to put barriers between it and its goal and you have to ask what happens and intelligence is the degree of ingenuity that it has in overcoming barriers between it and its goal now if it were to be that now this is the This is, I think, a totally doable but impractical and very expensive experiment.

43:04But you could imagine setting up a scenario where the bacteria were blocked from becoming more complex. And you can ask if they would try to find ways around it or whether it's actually, nah, their goals are actually metabolic. And as long as those goals are met, they're not going to actually get around your barrier. This business of putting barriers between things and their goals is actually extremely powerful. because we've deployed it in all kinds of, and I'm sure we'll get to this later, but we've deployed it in all kinds of weird systems that you wouldn't think are goal-driven systems. And what it allows us to do is to get beyond just the, what you call anthropomorphizing claims of saying, oh yeah, I think this thing is trying to do this or that.

43:45The question is, well, let's do the experiment. And one other thing I want to say about anthropomorphizing is people say this to me all the time. I don't think that exists. I think that's kind of like, you know, and I'll tell you why. I think it's like heresy or like other terms that aren't really a thing. Because if you unpack it, here's what anthropomorphism means. Humans have a certain magic and you're making a category error by attributing that magic somewhere else. My point is we have the same magic that everything has. We have a couple of interesting things besides the cognitive icon and some other stuff.

44:22and it isn't that you have to keep the humans separate because there's some bright line it's just it's it's that same old uh all all i'm all i'm arguing for is the scientific method really that's really all this is all i'm saying is you can't just make pronouncements such as the humans are this and let's not sort of push that you have to do experiments after you've done your experiments you can say either i've done it and i found look at that that thing actually can predict the future for the next, you know, 12 minutes. Amazing. Or you say, you know what, I've tried all the things in the behaviorist handbook.

44:55They just don't help me with this. It's a very low level of like, that's it. It's a very low level of intelligence. Fine. Right. Done. So that's really all I'm arguing for is an empirical approach. And then things like anthropomorphism go away. It's just a matter of, have you done the experiment and what did you find? And that's actually one of the things you're saying that if you remove the categorization of things you can use the tools of one discipline on everything you can try to try and then see that's the underpinnings of the criticism anthropomorphization because uh what is that that's like psychoanalysis of another human could technically be applied to to robots to ai systems to more primitive biological systems and so on try yeah we've used everything from basic habituation conditioning all the way through anxiolytics, hallucinogens, all kinds of cognitive modification on the range of things that you wouldn't believe.

45:55And by the way, I'm not the first person to come up with this. So there was a guy named Bose well over a hundred years ago, who was studying how anesthesia affected animals and animal cells and drawing specific curves around electrical excitability. And he then went and did it with plants and saw some very similar phenomena and being the genius that he was, he then said, well, how do I don't know when to stop, but there's no, there's no, you know, everybody thinks we should have stopped long before plants because people made fun of them for that. And he's like, yeah, but, but the science doesn't tell us where to stop.

46:26The tool is working. Let's keep going. And he showed interesting phenomena on materials, metals and, and, and other kinds of materials. Right. And so, uh, the interesting thing is that, yeah, there is no, there is no, uh, you know, generic rule that tells you when, when do you need to stop? We make those up. Those are completely made up. You have to just, you have to do the science and find out. Yeah, you will probably get to it. You've been doing recent work on looking at computational systems, even trivial ones like algorithms, sorting algorithms, and analyzing the behavioral kind of way, see if there's minds inside those sorting algorithms.

47:02And of course, let me make a pothead statement question here that But you can start to do things like trying to do psychedelics with a sorting. Yeah. And what does that even look like? Yeah. It looks like a ridiculous question. It'll get you fired from most academic departments. But it may be if you take it seriously, you could try and see if it applies. Yeah. If a thing could be shown to have some kind of cognitive complexity, some kind of mind, why not apply to it the same kind of analysis and the same kind of tools like psychedelics that you would to a human mind, a complex human mind? at least might be a productive question to ask.

47:46Because you've seen spiders on psychedelics, like more primitive biological organisms on psychedelics. Why not try to see what an algorithm does on psychedelics? Well, yeah, because you see, the thing to remember is we don't have a magic sense or really good intuition for what the mapping is between the embodiment of something and the degree of intelligence it has. We think we do because we have an N of one example on Earth and we kind of know what to expect from cells, snakes, primates. But we really don't. We don't have, and we'll get into more of the stuff on the platonic space, but our intuitions around that stuff is so bad that to really think that we know enough not to try things at this point is, I think, really short-sighted.

48:33before we talk about the platonic space let's uh let's lay out some foundations i think one useful one comes from the paper technological approach to mind everywhere an experimentally grounded framework for understanding diverse bodies and minds could you tell me about this framework and maybe can you tell me about figure one from this paper that has a few components one is the tiers of biological cognition that goes from group to whole organism to whole tissue organ, down to neural network, down to cytoskeleton, down to genetic network. And then there's layers of biological systems from ecosystem, down to swarm, down to organism, tissue, and then finally cell.

49:20So can you explain this figure and can you explain the TAME so-called framework. So this is the version 1.0, and there's a kind of updated 2.0 that I'm writing at the moment, trying to formalize in a careful way all the things that we've been talking about here, and in particular, this notion of having to do experiments to figure out where any given system is on a continuum. And let's just start with figure two maybe for a second, and then we'll come back to figure one. And first, just to unpack the acronym, I like the idea that it spells out tame because the central focus of this is interactions and how do you how do you interact with a system to have a productive interaction with it and the idea is that cognitive claims are really protocol claims when you tell me that something has some degree of intelligence what you're really saying is this is the set of tools i'm going to deploy and we can all find out how that worked out for you and so um technological because i wanted to be clear with my colleagues that this was not a project in just philosophy this had very specific empirical implications that are going to play out in engineering and regenerative medicine and so on.

50:30Technological approach to mind everywhere, this idea that we don't know yet where different kinds of minds are to be found, and we have to empirically figure that out. And so what you see here in figure two is basically this idea that there is a spectrum, and I'm just showing four waypoints along that spectrum. And as you move to the right of that spectrum, a couple things happen. Persuadability goes up, meaning that the systems become more reprogrammable, more plastic, more able to do different things than whatever they're standardly doing. So you have more ability to get them to do new and interesting things.

51:00The effort needed to exert influence goes down. That is autonomy goes up. And to the extent that you are good at convincing or motivating the system to do things, you don't have to sweat the details as much. Right. And this also has to do with what I call engineering agential materials. So when you engineer wood, metal, plastic, things like that, you are responsible for absolutely everything because the material is not going to do anything other than hopefully hold its shape. If you're engineering active matter or you're engineering computational materials or better yet agential materials like living matter, you can do some very high level prompting and let the system then do very complicated things that you don't need to micromanage.

51:41And we all know that that increases when you're starting to work with intelligent systems like animals and humans and so on. And the other thing that goes down as you get to the right is the amount of mechanism or physics that you need to exert the influence goes down. So if you know how your thermostat is to be set as far as its set point, you really don't need to know much of anything else, right? You just need to know that it is a homeostatic system and that this is how I change the set point. You don't need to know how the cooling and heating plant works in order to get it to do complex things.

52:10By the way, a quick pause just for people who are listening. Let me describe what's in the figure. So there's four different systems going up the scale of persuadability. So the first system is a mechanical clock. Then it's a thermostat. Then it's a dog that gets rewards and punishments, Pavlov's dog. And then finally, a bunch of very smart looking humans communicating with each other and arguing, persuading each other using hashtag reasons. And then there's arrows below that showing persuadability going up as you go up these systems from the mechanical clock to a bunch of Greeks arguing and then going down as the effort needed to exert influence.

52:50And once again, going down as mechanism knowledge needed to exert that influence. Yeah. I'll give you an example about that panel C here with the dog. Isn't it amazing that humans have been training dogs and horses for thousands of years, knowing zero neuroscience. Also amazing is that when I'm talking to you right now, I don't need to worry about manipulating all of the synaptic proteins in your brain to make you understand what I'm saying and hopefully remember it. You're going to do that all on your own. I'm giving you very thin, in terms of information content, very thin prompts. And I'm counting on you as a multi-scale agential material to take care of the chemistry underneath.

53:27So you don't need a wrench to convince me. Correct. I don't need, and I don't need physics to convince you. And I don't need to know how you work. Like I don't need to understand all of the steps. What I do need to have is trust that you are a multi-scale cognitive system that already does that for yourself. And you do like, this is an amazing thing. I don't, people don't think about this enough. I think when you wake up in the morning and you have social goals, research goals, financial goals, whatever, whatever it is that you have in order for you to act on those goals, sodium and calcium and other ions have to cross your muscle membranes.

53:59Those incredibly abstract goal states ultimately have to make the chemistry dance in a very particular way, right? our entire body is is is a transducer of very abstract things and and by the way not just our brains but other you know our organs have um uh anatomical goals and other things that we can talk about because all of this uh plays out in uh in in in regeneration and development and so on but that the scaling right of all of these things the way that the way you regulate yourself is not by oh my god you don't have to sit there and think wow i really have to push some some you know, some sodiums across this membrane, all of that happens automatically.

54:36And that's the, that's the incredible benefit of these multiscale materials. So what I was trying to do in this paper is a couple of things. All of these were, by the way, drawn by Jeremy Gay, who's this amazing graphic artist that works with me. First of all, in panel A, which is the spiral I was trying to point out is that at every level of biological organization, like we all know we're sort of nested dolls of, you know, organs and tissues and cells and molecules and whatever. But what I was going to point out is that this is not just structural. Every one of those layers is competent and is doing problem solving in different spaces and spaces that are very hard for us to imagine.

55:10We humans are, because of our own evolutionary history, we are so obsessed with movement in three-dimensional space that even in AI, you see this all the time. They say, well, this thing doesn't have a robotic body. It's not embodied. Yeah, it's not embodied by moving around in 3D space, but biology has embodiments in all kinds of spaces that are hard for us to imagine, right? So your cells and tissues are moving in high dimensional physiological state spaces and in gene expression state spaces in anatomical state spaces. They're doing that perception decision making action loop that we do in 3D space.

55:44When we think about robots wandering around your kitchen, they're doing those loops in these other spaces. And so the first thing I was trying to point out is that, yeah, every layer of your body has its own ability to solve problems in those spaces. and then on the right what i was saying is that this distinction between you know people say well there are living beings and then there are engineered machines and then they often follow up with all the things machines are never going to be able to do and whatever and so what i was trying to point out here is that it is very difficult to maintain those kind of distinctions because life is incredibly interoperable uh life doesn't really care if if um the thing it's working with was evolved through random trial and error or was engineered with a higher degree of agency because at every level within the cell, within the tissue, within the organism, within the collective, you can replace and substitute engineered systems with the naturally evolved systems.

56:36And that question of, is it biology or is it technology? I don't think it's a useful question anymore. So I was trying to warm people up with this idea that what we're going to do now is talk about minds in general, regardless of their history or their composition. It doesn't matter what you're made of it doesn't matter how you got here let's talk about what you're able to do and what your inner world looks like that was the the goal of that is it useful to as a thought experiment as an experiment of radical empathy to try to put ourselves in the space of the different minds at each stage of the spiral it's like what state space is human civilization as a collective embodied?

57:19What does it operate in? So humans, individual organisms operate in 3D space. That's what we understand. But when there's a bunch of us together, what are we doing together? It's really hard and you have to do experiments, which at larger scales are really difficult. But there is such a thing. There may well be. We have to do experiments. I don't know. There's an example. Somebody will say to me, well, you know, with your kind of panpsychist view, you probably think the weather is is in is agential to say well i can't say that but we don't know but have you ever tried to see if a hurricane has habituation or sensitization maybe we haven't done the experiment it's hard but you could right and maybe maybe whether systems can have certain kinds of memories i have no idea we have to do experiments so i don't know what the entire human society is doing but but i'll just give you a simple example of um the kinds of tools and we're actively trying to build tools now to enable radically different agents to communicate.

58:17So we are doing this using AI and other tools to try and get this kind of communication going across very different spaces. I'll just give you a very kind of dumb example of how that might be. Imagine that you're playing tic-tac-toe against an alien. So you're in a room, you don't see him. So you draw the tic-tac-toe thing on the floor and you know what you're doing. you're trying to uh you're trying to make straight lines with x's and o's and you're having a nice game it's obvious that he understands the process like sometimes you win sometimes you lose like it's obvious in that in that one little little segment of activity you guys are sharing a world what what's happening in the other room next door well let's say the alien doesn't know anything about geometry doesn't understand straight lines what he's doing is he's got a he's got a box and it's full of uh basically billiard balls each one of which has a number on it and all he's looking he's doing is he's looking through the box to find billiard balls whose numbers add up to 15.

59:16He doesn't understand geometry at all. All he understands is arithmetic. You don't think about arithmetic. You think geometry. The reason you guys are playing the same game is that there's this magic square, right? That somebody could construct it that basically is a three by three square where if you pick the numbers right, they add up to 15. He has no idea that there's a geometric interpretation to this. He is solving the problem that he sees, which is totally algebra. You don't know anything about that. But if there is an appropriate interface like this magic square, you guys can share that experience.

59:46You can have an experience. It doesn't mean you start to think like him. It means that you guys are able to interact in a particular way. Okay. So there's a mapping between the two different ways of seeing the world that allows you to communicate with each other. Of seeing a thin slice of the world. Thin slice of the world. How do you find that mapping? So you're saying we're trying to figure out ways of finding that mapping for different kinds of systems what's the process for doing that so so the process the process is twofold one is to get a better understanding of what the system what space is the system navigating what goals does it have what level of ingenuity does it have to reach those goals for example xenobots right we make xenobots this is or anthrobots these are biological systems that have never existed on earth before we have no idea what their cognitive properties are, we're learning, we found some things, but you can't predict that from first principles because they're not at all what their past history would inform you of.

1:00:44Can you actually explain briefly what a xenobot is and what an anthropot is? So one of the things that we've been doing is trying to create novel beings that have never been here before. The reason is that typically when you have a biological system, an animal or a plant, and you say, hey, why does it have certain forms of behavior, certain forms of anatomy certain forms of physiology why does it have those the answer is very is always the same well there's a history of evolutionary selection and there's a long long history going going back of adaptation and there are certain environments and this is what survived and so that's why it has so uh what i what i wanted to do was was break out of that mold and to to basically force us as a community to dig deeper into where these things come from.

1:01:31And that means taking away the crutch where you just say, well, it's evolutionary selection. That's why it looks like that. So in order to do that, we have to make artificial synthetic beings. Now, to be clear, we are starting with living cells. So it's not that they had no evolutionary history. The cells do. They had evolutionary history in frogs or humans or whatever. But the creatures they make and the capabilities that these creatures have were never directly selected for. And in fact, they never existed. So you can't tell the same kind of story. And what I mean is we can take epithelial cells off of an early frog embryo and we don't change the DNA, no synthetic biology circuits, no material scaffolds, no nanomaterials, no weird drugs, none of that.

1:02:10What we're mostly doing is liberating them from the instructive influences of the rest of the cells that they were in in their bodies. And so when you do that, normally these cells are bullied by their neighboring cells into having a very boring life. They become a two-dimensional outer covering for the embryo, and they keep out the bacteria, and that's that. So you might ask, well, what are these cells capable of when you take them away from that influence? So when you do that, they form another little life form we call a xenobot, and it's this self-motile little thing that has cilia covering its surface.

1:02:44The cilia are coordinated, so they row against the water, and then the thing starts to move and has all kinds of amazing properties. It has different gene expression, so it has its own novel transcriptome. it's able to do things like kinematic self-replication meaning make copies of itself from loose cells that you put in its environment it has the ability to respond to sound which normal embryos don't do it has these novel capacities and we did that and we said look here are some amazing features of this novel system let's try to understand where they came from and some people said well maybe it's a frog specific thing you know uh maybe maybe this is just something unique to frog cells.

1:03:21And so he said, okay, what's the furthest you can get from frog embryonic cells? How about human adult cells? And so we took cells from adult human patients who were donating tracheal epithelia for biopsies and things like that. And those cells in, again, no genetic change, nothing like that. They self-organized into something we call anthrobots, again, self-motile little creature, 9 ,000 different gene expressions. So about half the genome is now different and they have interesting abilities. For example, they can heal human neural wounds. So in vitro, if you plate some neurons and you put a big scratch through it so you damage them, anthrobots can sit down and they will try, they will spontaneously, without us having to teach them to do it, they will spontaneously try to knit the neurons across.

1:04:06What is this video that we're going to hear? So this is an anthrobot. So often when I give talks about this, I show people this video and I say, what do you think this is? And people will say, well, it looks like some primitive organism you got from the bottom of a pond somewhere. And I'll say, well, what do you think the genome would look like? And is it, well, the genome would look like some primitive creature, right? If you sequence that thing, you'll get a hundred percent homo sapiens. And that doesn't look like any stage of normal human development. It doesn't act like, uh, like any stage of human development.

1:04:34It has the ability to move around. It has, as I said, over 9 ,000 differential gene expressions. Uh, also interestingly, it is, uh, younger, um, than the cells that it comes from. So it actually has the ability to roll back its age. And we can talk about that and what the implications of that are. But to go back to your original question, what we're doing with these kinds of systems... Try and talk to it. We're trying to talk to it. That's exactly right. And not just to this, we're trying to talk to molecular networks. So we found a couple of years ago, we found that gene regulatory networks, nevermind the cells, but the molecular pathways inside of cells can have several different kinds of learning, including Pavlovian conditioning.

1:05:10And what we're doing now is trying to talk to it. the biomedical applications are obvious instead of hey siri you want hey liver why do i feel like crap today and you want an answer well you know your potassium levels are this and that and i don't feel uh you know i don't feel good for these reasons and you should be able to talk to these things and there should be able to be an interface that allows us to communicate right and and i think ai is going to be a huge component of that interface of allowing us to talk to these systems It's a tool to combat our mind blindness, to help us see diverse other very unconventional minds that are all around us.

1:05:43Can you generalize that? Let's say we meet an alien or an unconventional mind here on Earth. Think of it as a black box. You show up. What's the procedure for trying to get some hooks into a communication protocol with the thing? Yeah, that is exactly the mission of my lab. It is to enable us to develop tools to recognize these things, to learn to communicate with them, to ethically relate to them, and in general to expand our ability to do this in the world around us. I specifically chose these kinds of things because they're not as alien as proper aliens would be. So we have some hope. I mean, we're made of them.

1:06:33We have many things in common. There's some hope of understanding them. You're talking about xenobots and antrobots. Xenobots and antrobots and cells and everything else. But they're alien in a couple of important ways. One is the space they live in is very hard for us to imagine. What space do they live in? Well, your body, your body cells, long before we had a brain that was good for navigating three-dimensional space, was navigating the space of anatomical possibilities. It was going from you start as an egg and you have to become, you know, a snake or a, you know, a giraffe or whatever, or a human, whatever, whatever we're going to be.

1:07:07And I specifically am telling you that this, this, this general idea, when people model that with kind of cellular automata type of ideas, this open loop kind of thing where, well, everything just follows local rules and eventually there's complexity and, and, and here you go. Now, now you've got a, now you've got a giraffe or a human. I'm specifically telling you that that model is totally insufficient to grasp what's actually going on. What's actually going on, and there have been many, many experiments on this, is that the system is navigating a space. It is navigating a space of anatomical possibilities.

1:07:38If you try to block where it's going, it will try to get around you. If you try to challenge it with things it's never seen before, it will try to come up with a solution. If you really defeat its ability to do that, which you can, you know, they're not infinitely intelligent, so you can defeat them. you will either get birth defects or you will get creative problem solving, such as what you're seeing here with xenobots and anthrobots. If you can't be a human, you'll find another way to be in. You can be an anthrobot, for example, or you'll be something else. Just to clarify, what's the difference between cellular automata type of action where you're just responding to your local environment and creating some kind of complex behavior and operating in the space of anatomical possibilities?

1:08:21Sure. So there's a kind of goal, I guess, you're taking, there is some kind of thing. There's a will to X something. The will thing, let's put that aside. Okay, sorry. Well, it's fine too. There I go, anthropomorphic. I just always love to quote Nietzsche. Yeah, yeah, yeah. And I'm not saying that's wrong. I'm just saying I don't have data for that one, but I'll tell you the stuff that I'm quite certain of. There are a couple of different formalisms that we have in control theory. One of those formalisms is open loop complexity. In other words, I've got a bunch of subunits like a cellular automaton.

1:08:55They follow certain rules and you turn the crank, time goes forward, whatever happens, happens. Now, clearly you can get complexity from this. Clearly you can get some very interesting looking things, right? So the game of life, all those kinds of cool things, right? You can get complexity. No, no, no problem. But the idea that that model is going to be sufficient to explain and control things like morphogenesis is a hypothesis. It's okay to make that hypothesis, but we know it's false. despite the fact that that is what we learn, you know, in basic cell biology and developmental biology classes, when the first time you see something like this, inevitably, especially if you're an engineer in those classes, you raise it and you go, hey, how does it know to do that?

1:09:41How does it know, you know, four fingers instead of seven? What they tell you is it doesn't know anything. That's very clear. They all insist, right? When we learn these things, they insist. Nothing here knows anything. There are rules of chemistry. They roll forward and this is what happens. Okay. Now that model is testable. We can ask, does that model explain what happens? Here's where that model falls down. If you have that model and situations change, either, either there's damage or something, something in the environment that that's happened, those kinds of open loop models do not adjust to give you, uh, to give you the same goal by different means.

1:10:19This is William James's definition of intelligence is same goal by different means. and in particular working them backwards let's say you're in regenerative medicine and you say okay but this is the situation now i want it to be different what should the rules be it's not reversible so the thing with those kind of open loop models is they're not reversible you don't know what to do to make the outcome that you want all you know how to do is roll them forward right now in biology we see the following uh if if you have a developmental system and you put barriers between, so I'm going to give you two pieces of evidence that suggest that there is a goal.

1:10:54One piece of evidence is that if you try to block these things from the outcome that they normally have, they will do some amazing things, sometimes very clever things, sometimes not at all the way that they normally do it, right? So this is William James's definition, by different means, by following different trajectories, they will go around various local maxima and minima to get to where they need to go. It is navigation of a space. It is not blind, turn the crank and wherever we end up is where we end up. That is not what we see experimentally. And more importantly, I think what we've shown, and this is something that I'm particularly happy with in our lab, over the last 20 years, we've shown the following.

1:11:33We can actually rewrite the goal states because we found them. We have shown through our work on bioelectric imaging and bioelectric reprogramming, We have actually shown how those goal memories are encoded, at least in some cases. We certainly haven't got them all, but we have some. If you can find where the goal state is encoded, read it out and reset it, and the system will now implement a new goal based on what you just reset, that is the ultimate evidence that your goal-directed model is working. because if there was no goal, that shouldn't be possible. You shouldn't be, right? Once you can find it, read it, interpret it and rewrite it, it means that by any engineering standard, it means that you're dealing with a homeostatic mechanism.

1:12:17How do you find where the goal is encoded? So through lots and lots of hard work. The barrier thing is part of that, creating barriers and observing? The barrier thing tells you that you should be looking for a goal. So step one when you approach a genetic system is create a barrier of different kinds until you see how persistent it is at pursuing the thing it seemed to have been pursuing originally. And then you know, okay, cool. This thing has agency, first of all. And then second of all, you start to build an intuition about exactly which goal it's pursuing. Yes. The first couple of steps are all imagination.

1:12:51You have to ask yourself, what space is this thing even working in? And you really have to stretch your mind because we can't imagine all the spaces that systems work in, right? So step one is what space is it? step two what do i think the goal is and let's not mistake step two you're not done just because you haven't made a hypothesis that doesn't mean you can say well there i see it doing this therefore that's the goal you don't know that you have to actually do experiments now once you've made those hypotheses now you do the experiments you say okay if i want to block it from reaching its goal how do i do that and this by the way is exactly the the approach we took with the sorting algorithms and with everything else you you hypothesize the goal you put a barrier in and then you get to find out what level of ingenuity it has.

1:13:29Maybe what you see is, well, that derailed everything, so probably this thing isn't very smart. Or you see, wow, it can go around and do these things. Or you might say, wow, it's taking a completely different approach using its affordances in novel ways. That's a high level of intelligence. You will find out what the answer is. Another podhead question. Is it possible to look at, speaking of unconventional organisms and going to Richard Dawkins, for example, with memes, is it possible to think of things like ideas? Like how weird can we get? Can we look at ideas as organisms and creating barriers for those ideas and seeing are the ideas themselves?

1:14:06If you take the actual individual ideas and trying to empathize and visualize what kind of space they might be operating in, can they be seen as organisms that have a mind yeah um okay if you want to get really weird we can we can get we can get really weird here uh think about the uh caterpillar butterfly transition okay so you got a caterpillar soft-bodied kind of creature has a particular controller that's suitable for running a soft body you know kind of robot it has a brain for that task and then it has to become this butterfly hard-bodied creature flies around okay during the process of metamorphosis, its brain is basically ripped up and rebuilt from scratch, right?

1:14:51Now, what's been found is that if you train the caterpillar, so you give it a new memory, meaning that if you, if the caterpillar sees this color disc, then it crawls over and eats some leaves. Turns out the butterfly retains that memory. Now, the obvious question is how the hell do you retain memories when the medium is being refactored like that? Let's put that aside. I'm going to get somewhere even weirder than that. There's something else that's even more interesting than that. It's not just that you have to retain the memory. You have to remap that memory onto a completely new context because guess what?

1:15:23The butterfly doesn't move the way the caterpillar moves and it doesn't care about leaves. It wants nectar from flowers. And so if that memory is going to survive, it can't just persist. It has to be remapped into a novel context. Now, here's where things get weird. We can take a couple of different perspectives here. We can take the perspective of the caterpillar facing some sort of crazy singularity and say, my God, I'm going to I'm going to cease to exist. But, you know, I'll sort of be reborn in this new higher dimensional world where I'll fly. OK, so that's one thing we can take the perspective of the butterfly and say that, well, here I am.

1:16:01But, you know, I seem to be saddled with some some tendencies and some memories. And I don't know where the hell they came from. And I don't remember exactly how I got them. And they seem to be a core part of my psychological makeup. up and, you know, they come from somewhere. I don't know where they come from. Right. So you can take that perspective. But there's a third perspective that I think is really interesting and useful. And the third perspective is that of the memory itself. If you take a perspective of the memory, which, which, what, so what is the memory? It is a pattern. It is an informational pattern that was continuously reinforced within one cognitive system.

1:16:34And now here I am, I'm this memory. What do I need to do to persist into the future? Well, now I'm facing the paradox of change. If I try to remain the same, I'm gone. There's no way the butterfly is going to retain me in the original form that I'm in now. What I need to do is change, adapt, and morph. Now, you might say, well, that's kind of crazy. Well, how are you taking the perspective of a pattern within an excitable medium, right? Agents are physical things. You're talking about information, right? so so let me let me tell you another quick science fiction story imagine that uh some creatures come out from the center of the earth they live down in the core they're super dense okay they're incredibly dense because they live down in the core they have gamma ray vision for you know for and so on so they come out to the surface what do they see well all of this stuff that we're seeing here this is like a thin plasma to them they are so dense none of this is is is is solid to them They don't see any of this stuff.

1:17:32So they're walking around, you know, the planet is sort of, you know, covered in like thin gas, you know. And one of them is a scientist and he's taking measurements of the gas. And he says to the others, you know, I've been watching this gas. And there are like little whirlpools in this gas. And they almost look like agents. They almost look like they are doing things. They're moving around. They kind of hold themselves together for a little bit. And they're trying to make stuff happen. And the others say, well, that's crazy. Patterns in the gas can't be agents. We are agents. We're solid. it this is just patterns in an excitable medium and by the way how long do they hold together he says well about a hundred years that's crazy nothing you know no no real agent can can exist to be that dissipate that fast okay we are all metabolic patterns among other things right and so one of the things that and so you see what i'm warming up to do here so so one of the things that we've been trying to uh dissolve and this is like some work that i've done with chris fields and others is this distinction between thoughts and thinkers so uh all agents are patterns within some some excitable medium.

1:18:32We could talk about what, what that is, and they can spawn off others. And now you can have a really interesting spectrum. Here's the, here's the spectrum. Um, you can have fleeting thoughts, which are like waves in, in, in the ocean, when you throw a rock in, you know, they sort of, they sort of go through the excitable medium and then they're gone. They pass through and they're gone. Right. So those are, those are kind of fleeting thoughts. Then you can have patterns that have a degree of persistence. So they might be hurricanes or solitons or persistent thoughts or earworms or depressive thoughts.

1:19:07Those are harder to get rid of. They stick around for a little while. They often do a little bit of niche construction. So they change the actual brain to have to make it easier to have more of those thoughts, right? Like that's a thing. And so they stay around longer. Now, what's further than that? Well, fragments, personality fragments of a dissociative personality disorder. They're more, more stable and they're not just on autopilot. They have goals and they can do things. And then past that is a full-blown human personality. And who the hell knows what's past that? Maybe some sort of transhuman, you know, transpersonal, like, I don't know.

1:19:45Right. But, but this idea, again, I'm back to this notion of a spectrum. It's, there is not a sharp distinction between, between we are real agents and then we have these thoughts. Yeah, patterns can be agents too. But again, you don't know until you do the experiment. So if you want to know whether a soliton or a hurricane or a thought within a cognitive system is its own agent, do the experiment. See what it can do. Can it learn from experience? Does it have memories? Does it have goal states? Does it, you know, what can it do, right? Does it have language? So coming back to then during the original question, Yeah, we can definitely apply this methodology to ideas and concepts and social, you know, whatever.

1:20:26But you've got to do the experiment. That's such a challenging thought experiment of like thinking about memories from the caterpillar to the butterflies and organism. I think at the very basic level, intuitively, we think of organisms as hardware. And software is not possibly being able to be organisms. But what you're saying is that it's all just patterns in an excitable medium. And it doesn't really matter what the pattern is. We need to, and what the excitable medium is. we need to do the testing of what how persistent is it how goal oriented is it and there's certain kind of tests to do that and you can apply that to memories you can apply that to ideas you can apply that to anything really i mean you could probably think about like consciousness you could there's really no um boundary to what you can imagine probably really really wild things could be could be minds yeah stay tuned i mean this is exactly what we're doing we're getting progressively like more and more unconventional i mean so so this so this whole distinction between software and hardware i think i think it's a super important uh concept to think about and and yet the way we've mapped it onto the world i i would i would like to blow that up in in the following way um and and again i want to point out so so i'll tell you what the what the practical consequences are because this is not just, you know, fun stories that we tell each other.

1:22:04These have really important research implications. Think about a Turing machine. So one thing you can say is the machine's the agent, it has passive data, and it operates on the data. And that's it. The story of agency is the story of whatever that machine can and can't do. The data is passive and it moves it around. You can tell the opposite story. You can say, look, the patterns on the data are the agent. The machine is a stigmatic scratch pad in the world of the data doing what data does. The machine is just the consequences, the scratch pad of it working itself out. And both of those stories make sense depending on what you're trying to do.

1:22:36Here's the biomedical side of things. So our program in bioelectrics and aging, okay? One model you could have is the physical organism is the agent and the cellular collective has pattern memories, specifically what I was saying before, goals, anatomical goals. If you want to persist for 100 plus years, your cells better remember what your correct shape is and where the new cells go, right? So there are these pattern memories that exist during embryogenesis, during regeneration, during resistance to aging. We can see them, we can visualize them. One thing you can imagine is, fine, the physical body, the cells are the agent, the electrical pattern memories are just data and what might happen during aging is that the the data might get degraded they might get fuzzy and so what we need to do is reinforce the day reinforce the memories reinforce the pattern memories that's one that's one specific research program and we're doing that but that's not the only research program because the other thing you might imagine is that what if the patterns are the agent in exactly the same sense as we think in our brains, it's the patterns of electrophysiological, you know, computations, whatever else that is the agent, right?

1:23:54And that what they're doing in the brain are the side effects of the patterns working themselves out. And those side effects might be to fire off some muscles and some glands and some other things. From that perspective, maybe what's actually happening is maybe the agents finding it harder and harder to be embodied in the physical world. Why? Because the cells might get less responsive. In other words, the cells are sluggish. The patterns are fine. They're having a harder time making the cells do what they need to do. And that maybe what you need to do is not reinforce the memories. Maybe what you need to do is make the cells more responsive to them.

1:24:28And that is a different research agenda, which we are also doing. We have evidence for that as well, actually, now. And then we've published it recently. And so my point here is when we tell these crazy sci-fi stories, the only worth to them and the only reason I'm talking about them now, and I hadn't been up, you know, a year ago, I wasn't talking about this stuff, is because these are now actionable in terms of specific experimental research agendas that are heading to the clinic, I hope, in some of these biomedical approaches. And so now here, we can go beyond this and we can say, okay, so up until now, we've considered, what are disease states?

1:25:00Well, we know there's organic disease, something is physically broken, we can see the tissue is breaking down, there's damage in the joint, you know, where the liver is doing, we can see these things. But what about disease states that are not physical states, they're physiological states or informational states or cognitive problems? So in other words, in all of these other spaces, you can start to ask, what's a barrier in gene expression space? What's a local minimum that traps you in physiological state space? And what is a stress pattern that keeps itself together, moves around the body, causes damage, tries to keep itself going, right?

1:25:37What level of agency does it have? This suggests an entirely different set of approaches to biomedicine. And, you know, anybody who's, let's say, in the alternative medicine community is probably yelling at the screen and saying, we've been saying this for hundreds of years. And yeah, but, and I'm well aware, these are not, the ideas are not new. What's new is being able to now take this and make them actionable and say, yeah, but we can image this now. I can now actually see the bioelectric patterns and why they go here and not there. And we have the tools that now hopefully will get us to therapeutics.

1:26:12So this is very actionable stuff. And it all leans on not assuming we know minds when we see them because we don't. And we have to do experiments. To return back to the software hardware distinction, you're saying that we can see the software as the organism. and the hardware is just the scratch pad. Or you can see the hardware as the organism and the software as the thing that the hardware generates. And in so doing, we can decrease the amount of importance we assign to something like the human brain, where it could be the activations, it could be the electrical signals that are the organisms, and the brain is the scratch pad.

1:26:54And by saying scratch pad, I don't mean it's not important. When we get to talking about the platonic space, we have to talk about how important the interface actually is. The scratchpad isn't unimportant. The scratchpad is critical. It's just that my only point is that when we have these formalisms of software, of hardware, of other things, the way we map those formalisms onto the world is not obvious. It's not given to us. We get used to certain things, right? But who's the hardware? Who's the software? Who's the agent? And who's the excitable medium is to be determined. So this is a good place to talk about the increasingly radical, weird ideas that you've been writing about.

1:27:34You've mentioned it a few times, the platonic space. So there's this ingressing minds paper where you describe the platonic space. You mentioned there's an asynchronous conference happening, which is a fascinating concept because it's asynchronous. People are just contributing asynchronously. So what happened was this crazy notion, which I'll describe momentarily, I have given a couple of talks on it. I then found a couple of papers in the machine learning community called the Platonic Representation Hypothesis. And I said, that's pretty cool. These guys are climbing up to the same point where I'm getting at it from biology and philosophy and whatever.

1:28:13They're getting there from computer science and machine learning. We'll take a couple hours. I'll give a talk. They'll give a talk. We'll talk about it. I thought there were going to be three talks at this thing. once i started reaching out to people for this everybody sort of said you know i know somebody who's really into this stuff but they never talk about it because there's no audience for this so i reached out to them and then they said yeah oh yeah i know this this mathematician or i know this uh you know uh economist whatever who has these ideas and there's nowhere we can ever talk about them so i got this whole list and it became completely obvious that we can't do this in a normal, you know, it's, we're now booked up through, through December.

1:28:49So every week in our, in our center, somebody gives a talk, we, we kind of discuss it. It all goes on this thing. I'll give you a link to it. And then there's a, there's a huge running discussion after that. And then in the end, we're all going to get together for an actual real-time discussion section and talk about it. But there's going to be probably 15 or so talks about this from, from all kinds of disciplines. it's blown up in a way that i didn't realize how much undercurrent of these ideas had already existed that were ready like now now is the time and i think i've this is like i've been thinking about these things for i don't know 30 plus years i never talked about them before because they weren't actionable before there wasn't a way to actually make empirical progress with this now you know this is something that pythagoras and plato and probably many people before them talked about.

1:29:36But now we're to the point where we can actually do experiments and they're making a difference in our research program. You can just look at our Platonic Space Conference. There's a bunch of different fascinating talks. Yours first on the patterns of forms and behavior beyond emergence, then radical Platonism and radical empiricism from Joe Dietz, and patterns and explanatory gaps in psychotherapy. Does God play dice from Alexei Tolchinsky and so on. So let's talk about it. What is it? And it's fascinating that the origins of some of these ideas are connected to ML people thinking about representation space.

1:30:23Yeah. The first thing I want to say is that while I'm currently calling it the platonic space, I'm in no way trying to stick close to the things that Plato actually thought about in fact to whatever extent we even know what that is I think I depart from that in quite in some ways and I'm gonna have to change the name at some point the reason I'm using the name now is because I wanted to be clear about a particular connection to mathematics which which a lot of mathematicians would call themselves Platonists because what they think they're doing is discovering not not inventing as a human construction, but discovering a structured, ordered space of truths.

1:31:00Let's put it this way. In biology, as in physics, there's something very curious that happens, that if you keep asking why, then something interesting goes on. Well, I'll give you two examples. First of all, imagine cicadas. So the cicadas come out at 13 years and 17 years. Okay. And so if you're a biologist and you say, so why is that? And then you get this explanation for, well, it's because they're trying to be off cycle from their predators. Because if it was 12 years, then every two year, every three year, every four year, every six year predator would, would, would eat you when you come out.

1:31:35Right. So, and you say, okay, okay, cool. That makes sense. What's special about 13 and 17? Oh, they're prime. Uh-huh. And why are they prime? Well, now you're in the math department. You're no longer in the biology department. You're no longer in the physics department. You've now, you're now in the math department to understand why the distribution of primes is what it is. Another example, and I'm not a physicist, but what I see is every time you talk to a physicist and you say, hey, why do the, you know, leptons do this or that or the fermions are doing whatever? Eventually, the answer is, oh, because there's this mathematical, you know, this SU8 group or whatever the heck it is.

1:32:09And it has certain symmetries in these certain structures. Yeah, great. Once again, you're in the math department. So something interesting happens is that there are facts that you come across. Many of them are very surprising. You don't get to design them. you get more out than you put in, in a certain way, because you make very minimal assumptions, and then certain facts are thrust upon you. For example, the value of Feigenbaum's constant, the value of natural logarithm e, these things you sort of discover, right? And the salient fact is this, if those facts were different, then biology and physics would be different, right?

1:32:45So they matter, they impact instructively, functionally, they impact the physical world, If the distribution of primes was something else, well, then the cicadas would have been coming out at different times. But the reverse isn't true. What I mean is there is nothing you can do in the physical world to change E, as far as I know, to change E or to change Feigenbaum's constant. You could have swapped out all the constants at the Big Bang. You can change all the different things. You are not going to change those things. so so this i think plato and pythagoras understood very clearly that there is a set of truths which impact the physical world but they themselves are not defined by and determined by what happens in the physical world you can't change them by things you do in the physical world right and so i'll make a couple claims about that one claim is i think we call physics those things that are constrained by those patterns when you say hey why is this the way it is ah it's because this is how symmetry symmetries or you know topology or whatever biology are the things that are enabled by those they're free lunches their biology exploits these kinds of truths and uh and really it enables biology and evolution to do amazing things without having to pay for it i think there's a lot of free free lunches going on here and so i show you a xenobot or an anthropot and uh i say hey look here are some amazing things they're doing that tissue has never done before in their history you say first of all where did that come from and when did we pay the computational cost for it because we know when we paid the computational cost to design a frog or a human it was for the eons that the genome was bashing against the environment getting selected right so you paid the computational cost that there's never been any anthrobots there's never been any xenobots when do we pay the computational cost for designing kinematic self-replication and you know all these things that they're able to do so there's two things people say one is well it's sort of you got it at the same time that they were being selected to be good humans and good frogs.

1:34:42Now, the problem with that is it kind of undermines the point of evolution. The point of evolutionary theory was to have a very tight specificity between how you are now and the history of selection that got you here, right? The history of environments that got you to this point. If you say, yeah, okay, so this is what your environmental history was. And by the way, you got something completely different. You got these other skills that you didn't know about. That's really strange, right? And so then what people say is, well, it's emergent. And they say, what's that? What does that mean? And they say, well, besides the fact that you got surprised, right?

1:35:13Emergence is often just means I didn't see it coming. You know, there was something happened. I didn't know that was going to happen. So, uh, what does it mean that it's emergent? And people say, well, and there are many emergent things like this. For example, the fact that gene regulatory networks can do associative learning. Like that's amazing. And you don't need evolution for that. Even random genetic regulatory networks can do associative learning. I say, why, why, why, why does that happen? And he said, well, it's just a fact that holds in the world. Just a fact that holds. So now you have an option.

1:35:41You can go one of two ways. You can either say, okay, look, I like my sparse ontology. I don't want to think about weird platonic spaces. I'm a physicalist. I want the physical world, nothing more. So what we're going to do is when we come across these crazy things that are very specific, like, you know, anthropobots have four specific behaviors that they switch around. Why four? Why not 12? Why not one? Like four. Why four? When we come across these things, just like when we come across the value of E or Feigenbaum's number or whatever, what we're going to do is we're going to write it down in our big book of emergence.

1:36:09And that's it. We're just gonna have to live with it. This is what happens. We're just, you know, there's some cool surprises. You know, when we come across them, we're going to write them down. Great. It's a random grab bag of stuff. And when we come across them, we'll write them down. That's one. The upside is you get to be a physicalist, then you get to keep your sparse ontology. The downside is I find it incredibly pessimistic and mysterious because you're basically than just willing to make a catalog of these amazing patterns. Why not instead, and this is why I started with this platonic terminology, why not do what the mathematicians already do?

1:36:47A huge number of them say, we are going to make the same optimistic assumption that science makes, that there's an underlying structure to that latent space. There's not a random grab bag of stuff. There's a space to it, which where these patterns come from. And by studying them systematically we can get from one to another we can map out the space we can we can find out the relationships between them we can get an idea of what's in that space and we're not going to assume that it's just random we're going to assume there's some kind of structure to it and you'll see all kinds of people i mean you know well-known mathematicians that talk about this stuff you know penrose and lots of other people who will say that yeah there's another space physically and it has it has spatial structure it has components to it and so on we can traverse that space in various ways.

1:37:28And then there's the physical space. So I find that much more appealing because it suggests a research program, which we are now undergoing in our lab. The research program is everything that we make, cells, embryos, robots, biobots, language models, simple machines, all of it, they are interfaces. All physical things are interfaces to these patterns. You build an interface, some of those patterns are going to come through that interface, depending on what you build some patterns versus others are going to come through the research program is mapping out that relationship between the physical pointers that we make and the patterns that come through it right understanding what is the structure of that space what exists in that space and what do i need to make physically to make certain patterns come through now when i say patterns now we have to ask what kinds of things live in that space well the mathematicians will tell what we already know we have a whole list of objects you know the amplituhedrons and the you know, all this crazy stuff that lives in that space.

1:38:25Yeah, I think that's one layer of stuff that lives in that space. But I think those patterns are the lower agency kinds of things that are basically studied by mathematicians. What also lives in that space are much more active, more complex, higher agency patterns that we recognize as kinds of minds, that behavioral scientists would look at that pattern and say, well, I know what that is. That's the competency for delayed gratification or problem solving of certain kinds or whatever. And so what I end up with right now is a model in which that latent space contains things that come through physical objects, so simple patterns, right?

1:39:03So facts about triangles and Fibonacci, you know, patterns and fractals and things like that. But also, if you make more complex interfaces, such as biologicals, but importantly, not just biologicals, but let's say cells and embryos and tissues, what you will then pull down is much more complex patterns that we say ah that's a that's a that's a mind that's a human mind or that's a you know snake mind or whatever so i think the mind brain relationship is exactly the kind of thing that the math physics relationship is that in some very interesting way there are truths of mathematics that become embodied and they kind of haunt physical objects, right?

1:39:44In a very specific functional way. And in the exact same way, there are other patterns that are much more complex, higher agency patterns that basically inform living things that we see as obvious embodied minds. Okay. Given how weird and complicated this we're describing is, we'll talk about it more, but you got to ELI 5, the basics to a person that's never seen this. So again, you mentioned things like pointers. So the physical object themselves or the brain is a pointer to that platonic space. What is in that platonic space? What is the platonic space? What is the embodiment? What is the pointer?

1:40:29Yeah. Okay. Let's, let's try it. Let's try it this way. There are certain facts of mathematics. So the distribution of prime numbers, right? That if you map them out, they make these nice spirals. And there's an image that I often show, which is a very particular kind of fractal. And that fractal is the Halley map, which is, it's pretty awesome that it actually looks very organic. It looks very biological. So if you look at that thing, that image, which has very specific complex structure, it's a map of a very compact mathematical object. That formula is like, you know, Z cubed plus seven, it's something like that.

1:41:06That's it. So now, so now you look at that structure and you say, where does that actually come from? It's definitely not packed into the Z cubed plus seven. It's not, there's not enough bits in that to give you all of that. There's no fact of physics that determines this. There's no evolutionary history. It's not like we selected this based on some, you know, from, from a larger set over time, where does this come from? Or, or the fact that I think about, think about the way that biology exploits these things. Imagine a world in which the highest fitness belonged to a certain kind of triangle, right?

1:41:36So evolution cranks a bunch of generations and it gets the first angle right, and cranks a bunch more generations, gets the second angle right. Now there's something amazing that happens. It doesn't need to look for the third angle because you already know. If you know two, you get this magical free gift from geometry that says, well, I already know what the third one should be. You don't have to go look for it. Or as evolution, if you invent a voltage-gated ion channel, which is basically a transistor, and you can make a logic gate, then all the truth tables and the fact that NAND is special and all these other things, you don't have to have all those things.

1:42:05You get those for free. You inherit those. Where do all those things live? These mathematical truths that you come across that you don't have any choice about. Once you've committed to certain axioms, there's a whole bunch of other stuff that is now just, it is what it is. And so what I'm saying is, and this is what what what pythagoras was was saying i think that there is a whole space of these kinds of uh truths now he was focused on on mathematical ones but but he was embodying them in music and in geometry and things like that there are the space of patterns and uh and they make a difference in the physical world to machines to sound to things like that what i'm extending it and what i'm saying is yeah and so far we've only been looking at the low agency inhabitants of that world there are other patterns that we would recognize as kinds of minds and that you don't see them in this space until there's an interface, until there's a way for them to come through the physical world.

1:43:02That interface, the same way that you have to make a triangular object before you can actually see the rule of, you know, what you're going to gain right out of the rules of geometry and whatever, or you have to actually do the computation on the fractal before you actually see that pattern. If you want to see some of those minds, you have to build an interface, right? At least if you're interact with them in the physical world the way we normally do science as darwin said mathematicians have their own new sense and like a different sense than the rest of us and so that's right you you know mathematicians can can perhaps interact with these uh with these patterns directly in that space but for the rest of us we have to make interfaces and when we make interfaces which might be cells or robots or you know embryos or whatever what we are pulling down our minds that are fundamentally not produced by physics so i don't believe that i don't know if we're going to get into the whole consciousness thing but i don't believe that we create consciousness whether we make babies or whether we make robots nobody's creating consciousness what you create is an interface a physical interface through which specific patterns which we call kinds of minds are going to ingress right and and and consciousness is what it looks like from that direction looking out into the world it's it's what we call the view from the perspective of the platonic patterns.

1:44:17Just to clarify, what you're saying is a pretty radical idea here. So if there's a mapping from mathematics to physics, okay, that's understandable, intuitive, as you've described. But what you're suggesting is there's a mapping from some kind of abstract mind object to an embodied brain that we think of as a mind yeah as us fellow humans what is that what exactly because you said interface you've also said pointer so the brain and i think you said somewhere a thin interface a thin client yeah the brain brain brain is a thin client yeah Thin client. Okay. So you're, a brain is a thin client to this other world.

1:45:11Can you just lay out very clearly how radical the idea is? Sure. Because you're kind of dancing around. I think you could also point to Donald Hoffman and kind of who speaks of an interface to a world. So we've only interact with the quote unquote real world through an interface. What is the connection here? Yeah. Okay. A couple of things. First of all, when you said it makes sense for physics, I want to show that it's not as simple as it sounds. Because what it means is that even in Newton's boring sort of classical universe, long before quantum anything, Newton's world, physicalism was already dead in Newton's world.

1:45:57I mean, think about what that means. This is nuts. Because already he knew perfectly well, I mean, Pythagoras and Plato knew that even in a totally classical deterministic world, already you have the ingression of information that determines what happens and what's possible and what's not possible in that world from a space that is itself not physical. In other words, it's something like the natural logarithm E, right? Nothing in Newton's world is set to the value of E. There is nothing you could do to set the value of E in that world. And yet that fact that it was that and not something else governed all sorts of properties of things that happen.

1:46:34That classical world was already haunted by patterns from outside that world. This should be like, this is wild. This is not saying that, okay, everything was cool. physicalism was great up until, you know, maybe we got quantum interfaces or we got, you know, consciousness or whatever, but originally it was fine. No, this is saying that it was, that worldview was already impossible, really, since from a very long time ago, we already knew that there are non-physical properties that matter in the physical world. This is a chicken or the egg question you're saying newton's laws are creating the physical world that is it that is a very deep follow-on question that that i will we'll come back to in a minute what what i all i was saying about newton is that in the law you don't need quantum anything you don't need to think about consciousness you already long before you get to any of that as as pythagoras i think knew already we have the idea that this physical world is being strongly impacted by truths that do not live in the physical world which truths are we referring to are we talking about newton's law like mathematical equations mathematical mathematical facts so for example the actual value of e or oh like very primitive mathematical facts yeah yeah i mean some of them are you know i mean if you ask don hoffman there's this like amplituhedron thing that that is a set of mathematical objects that determines all the scattering amplitudes of the particles and whatever.

1:48:04They don't have to be simple. I mean, the old ones were simple. Now they're like crazy. I can't, I can't imagine this amplitude heat and thing that maybe they can, but, um, but, but all of these are mathematical structures that explain and determine facts about the physical world, right? If you ask physicists, Hey, why, you know, this many of this type of particle, because this mathematical thing has the symmetries. That's why. So Newton is discovering these things. They're not, he's not inventing. This is very controversial, right? And there are, of course, physicists and mathematicians who disagree with what I'm saying, for sure.

1:48:36But what I'm leaning on is simply this. I don't know of anything you can do in the physical world. You're around at the Big Bang, you get to set all the constants. Set physics however you want. Can you change E? Can you change Feigenbaum's constant? I don't think you can. Is that an obvious statement? I don't even know what it means to change the parameters at the start of the Big Bang. So physicists do this. They'll say, okay, you know, if we made the ratio between the gravitation and the electromagnetic force different, would we have matter? How many dimensions would we have? Would there be inflation?

1:49:13Would there be this or that? Right? You can imagine playing with it. There are however many unitless constants of physics. These are the kind of like knobs on the universe that you could have, could in theory be different. and then you'd have different physical properties. You're saying that's not going to change the axiomatic systems that mathematics has? What I'm not saying is that every alien everywhere is going to have the exact same math that we have. That's not what I'm claiming, although maybe, but that's not what I'm claiming. What I'm saying is you get more out than you put in. Once you've made a choice, and maybe some alien somewhere made a different choice of how they're going to do their math, but once you've made your choice, then you get saddled with a whole bunch of new truths that you discover that you can't do anything about.

1:49:56They are given to you from somewhere. You can say they're random or you can say, no, there's a space of these facts that they're pulled from. There's a latent space of options that they come from. So when you get, so when your E is exactly 2.718 and so on, there is nothing you can do in physics to change it. And you're saying that space is immutable. It's... I'm not saying it's immutable. So I think Plato may or may not have thought that these forms are eternal and unchanging. That's one place we differ. I actually think that space has some action to it. maybe even some computation to it but we're we're just pointers okay that's well so so let's okay so so i'll so i'll circle i'll circle back around to that to that whole thing so so the only thing i was trying to do is blow up the idea that we're cool with how it works in physics no problem there like i don't like i think that's a much bigger deal than than people normally think it is i think already there you have this weird haunting of of the physical world by patterns that are not coming from the physical world.

1:50:52The reason I emphasize this is because now when I amplify this into biology, I don't think it sort of jumps as a new thing. I think it's just a much more, I think what we call biology is our systems that exploit the hell out of it. I think physics is constrained by it, but we call biology those things that make use of those kinds of things and run with it. And so again, I just think it's a scaling. I don't think it's a brand new thing that happens. I think it's a scaling, right? So what I'm saying is, we already know from physics that there are non-physical patterns. And these are generally patterns of form, which is why I call them low agency, because they're like fractals that stand still.

1:51:32And they're like prime number distributions. Although there's a mathematician that's talking in our symposium that's telling me that actually, I'm too chauvinistic, even there, actually, even those things have more more oomph than even I gave them credit for, which I which I love. So, So what I'm saying is those kind of static patterns are things that we typically see in physics, but they're not the full extent of what lives in that space. That space is also home to some patterns that are very high agency. And if we give them a body, if we build a body that they can inhabit, then we get to see different behavioral competencies that the behavior scientists say, oh, I know what that looks like.

1:52:08That's this kind of behavioral, you know, this kind of mind or that kind of mind. In a certain sense, I mean, yes, what I'm saying is extremely radical, but it is a very old idea. It's an old idea of a dualistic worldview, right? Where the mind was not in the physical body and that it in some way interacted with the physical brain. so i just want to be clear i'm not claiming that this is fundamentally a new idea this has been around for forever however it's mostly been discredited and uh it's a very unpopular view nowadays there are very few people in the for example cognitive science community or or anywhere else in science that like this kind of view primarily and already descartes was getting getting crap for this when he first trotted out is this interaction problem right so the idea was okay well if you have this non-physical mind and then you have this brain that presumably obeys conservation of mass energy and things like that how are you supposed to enter you know how are you supposed to interact with it there are many other problems there so uh what i'm trying to point out is that first of all physics already had this problem you didn't have to wait till you had biology and and cognitive science to to ask about it and what i think is happening in the way the way we need to think about this is coming back to the to my my point that i think the mind brain relationship is basically of the same kind as the math physics relationship.

1:53:30The same way that non-physical facts of physics haunt physical objects is basically how I think different kinds of patterns that we call kinds of minds are manifesting through interfaces like brains. How do we prove or disprove the existence of that world? Because it's a pretty radical one. because this physical world can poke. It's there. It feels like all the incredible things like consciousness and cognition and all the goal-oriented behavior and agency all seems to come from this 3D entity. And so we can test it, we can poke it, we can hit it with a stick. Yeah, sort of. Makes noises. Sort of.

1:54:15I mean, so Descartes got some stuff wrong, I think. But one thing that he did get right, the fact that actually you don't know what you can poke and what you can't poke. The only thing you actually know are the contents of your mind. And everything else might be, and in fact, what we know from Anil Seth and Don Hoffman and various other people, it's definitely a construct. You might be on drugs and you might wake up tomorrow and say, my God, I had the craziest dream of being Lex Reed. Amazing. It's a nightmare. Yeah. Who knows? It's a ride. Right? But you see, it's not clear at all that the physical poking is your primary reality.

1:54:53That's not clear to me at all. I don't know. That's an obvious thing that a lot of people can show is true to take a step to the cart. I think, therefore, I am. That's the only thing you know for sure, and everything else could be an illusion or a dream. That's already a leap. I think from a basic caveman science perspective, the repeatable experiment is the one that most of intelligence comes from here. The reality is exactly as it is. To take a step towards the Donald Hoffman worldview takes a lot of guts and imagination and stripping away of the ego and all these kinds of processes. I think you can get there more easily by synthetic bioengineering in the following sense.

1:55:41Do you feel a lack of x-ray perception? Do you feel blind in the x-ray spectrum or in the ultraviolet? I mean, you don't. You have absolutely no clue that stuff is there. And all of your reality as you see it is shaped by your evolutionary history. It's shaped by the cognitive structure that you have, right? there are tons of stuff going on around us right now that we of which we are completely oblivious there's equally all kinds of other stuff which we construct and this is just this is just modern cognitive science that says that a lot of what we think is going on is is is a total fabrication constructed by us so i i think this is not a i don't think this is a philosophy i mean descartes got there through from a philosophical point that's not what i'm that's not the leap i'm asking us to make i'm saying that depending on your embodiment depending on your interface and this is increasingly going to be more relevant as we make first augmented humans that have sensory substitution.

1:56:39You're going to be walking around, your friend's going to be like, oh man, I have this primary perception of the solar weather and the stock market because I got those implants. And what do you see? Well, I see the, you know, the traffic of the internet through the, you know, trans-Pacific channel. We're all going to be living in somewhat different worlds. That's the first thing. The second thing is we're going to become better attuned to other beings, whether they be cells tissues you know what's what's it like to be a cell living in a 20 000 dimensional transcriptional space okay to novel beings that have never been here before that have all kinds of crazy spaces that they live in and that might be ais it might be cyborgs it might be hybrids it might be all sorts of things so this idea that we have a consensus reality here that's independent of some very specifically chosen aspects of our brain and our interaction.

1:57:28We're going to have to give that up no matter what to relate to these other beings. I think the tension is, and absolutely, and this idea that you're talking about of sort of almost, I think you've termed it cognitive prosthetics, which is different ways of perceiving and interacting with the world. But I guess the question is, is our human experience, the direct human experience, is that just a slice of the real world or is it a pointer to a different world that that's what i'm trying to uh figure out because the claim you're making is a really fascinating one compelling one there's a pretty strong one which is there's another world into which our brain is an interface to which means you could theoretically map that world systematically yeah which which is exactly what we're trying to do.

1:58:18I mean, but it's not clear that that world exists. Yeah. Yeah. Okay. I mean, so, so that's the beautiful part about this. And this is why I'm talking about this now, where I wasn't, you know, about a year ago, up until a year ago, I was never talking about this because I think this is now actionable. So there's this diagram that's called a map of mathematics. And they basically try to show how all the different pieces of math link together. And So there's two features to this. One is that, what is it a map of? Well, it's a map of various truths. It's a map of facts that are thrust on you. You don't have a choice.

1:58:55Once you've picked some axioms, you just, you know, here's some surprising facts that are just going to be given to you. but the other key thing about this is that it has a metric it's not just a random heap of facts they are all connected to each other in a particular way they literally make a space and so when i say it's a space of patterns what i mean is it is not just a random bag of patterns such that when you have one pattern you are no closer to finding any other pattern i'm saying that there's a some kind of a metric to it so that when you find one others are closer to it and then you can get there.

1:59:28So that's the claim. And obviously, not everybody buys this and so on. This is one idea. Now, how do we know that this exists? Well, I'll say a couple of things. If that didn't exist, what is that a map of? If there is no space, if you don't want to call it a space, that's okay. But you can't get away from the fact that as a matter of research, there are patterns that relate to each other in a particular way what what what what's you know the final step of calling it a space is minimal the bigger the bigger issue is what the hell is it a map of then if it's not a space so that's that's the first thing now that that's that's how it plays out i think in math and physics now in biology here's here's how we're going to know if this makes any sense what we are doing now is trying to map out that space by saying look we We know that the frog genome maps to one thing, and that's a frog.

2:00:23Turns out that exact same genome, if you just take the slightest step with the exact same genome, but you just take some cells out of that environment, they can also make xenobots with very specific different transcriptomes, very specific behaviors, very specific shapes. It's not just, oh, well, you know, they do whatever. And then they have very specific behaviors, just like the frog had very specific properties. we can start to map out what all those are right make that late basically try to try to um draw the latent space of from which those things are pulled and one of two things is going to happen in the future and so this is you know come back in 20 years and we'll and we'll see how this worked out one thing that could happen is that we're gonna see oh yeah just like the map of mathematics we we we made that we made a map of the space and we know now that if i want a system that acts like this and this, here's the kind of body I need to make for it because those are the patterns that exist.

2:01:16The anthropots have four different behaviors, not seven and not one. And so that's what I can pull from. These are the options I have. Is it possible that there's varying degrees of grandeur to the space that you're thinking about mapping? Meaning it could be just like with the space of mathematics, might it strictly be just the space of biology versus a space of like minds, which feels like it could encompass a lot more than the just biology. Yeah, except that I don't see how it would be separate because I'm not just talking about an anatomical shape and transcriptional profile. I'm also talking about behavioral competencies.

2:02:02So when we make something and we find out that, okay, it does habituation sensitization, It does not do Pavlovian conditioning and it does do delayed ratification and it doesn't have language. That is a very specific cognitive profile. That's a region of that space. And there's another region that looks different because I don't make a sharp distinction between biology and cognition. If you want to explain behaviors, they are drawn from some distribution as well. So I think in 20 years or however long it's going to take, one of two things will happen. either we and other people who are working on this are going to actually produce a map of that space and say here's why you've gotten systems that work like this and like this and like this but you've never seen any that work like that right or we're going to find out that i'm wrong and that basically it's not worth calling it a space because it is so random and so jumbled up that there is we've been able to make zero progress in linking the, uh, the embodiments that we make to the patterns that come through.

2:03:04Yeah. Just, just to be clear, I mean, from your, uh, blog post on this from the paper, I mean, we're talking about a space that includes a lot of stuff. Yeah. Yeah. Includes human. What is it? Meditating Steve. Hello. My name is Steve AI system. So all the the space of computational systems, objects, biological systems, concepts. It includes everything. Well, it includes specific patterns that we have given names to. Some of those patterns we've named mathematical objects. Some of those patterns we've named anatomical outcomes. Some of those patterns we've made psychological types. So every entry in an encyclopedia, old school Britannica.

2:03:46is a pointer to this space. there is a set of things that i feel very strongly about because the research is telling us that's what's going on and then there's a bunch of other stuff that i see as hypotheses for next steps that guide experiment so what i'm about to tell you i i don't you know these are things i don't actually know these are just uh guesses that that you know you need to make some guesses to make progress i i don't think that there are specific or i don't know but it doesn't mean that there are going to be specific latonic patterns for this is the titanic and this is the sister of the titanic and this is some other kind of boat this is not what i'm saying what i'm saying is in some way that we absolutely need to work out when we make minimal interfaces we get more than we then we put in we get behaviors we get shapes we get mathematical truths and we get all kinds of patterns that we did not have to create we didn't micromanage them we didn't know they were coming we didn't have to put any effort into making them They come from some distribution that seems to exist that we don't have to create.

2:04:54And exactly whether that space is sparse or dense, I don't know. So, for example, if there is, you know, some kind of, you know, a platonic form for the movie The Godfather, if it's surrounded by a bunch of crappy versions and then crappier versions still, I have no idea, right? I don't know if the space is sparse or not. You know, I don't know if it's finite or infinite. These are all things I don't know. What I do know is that it seems like physics and for sure biology and cognition are the benefits of ingressions that are free lunches in some sense. We did not make them. Calling them emergent does nothing for a research program.

2:05:31Okay, that just means you got surprised. I think it's much better If you say If you make the optimistic assumption That they come from a structured space That we have a prayer in hell of actually exploring And in some decades If I'm wrong And it says you know what We tried It looks like it really is random Too bad Fine Is there a difference between Like can we one day prove the existence of this world And is there a difference Between it being a really effective model for connecting things, explaining things, versus like an actual place where the information about these distributions that we're sampling actually exists, that we can hit with a stick.

2:06:17Yeah, you can try to make that distinction, but I think modern cognitive neuroscience will tell you that whatever you think this is, at most, it is a very effective model for predicting the future experiences you're going to have. So all of this that we think about as physical reality is just as a nice, convenient model. I mean, that's not me. That's predictive processing and active infrared. Like that's modern neuroscience telling you this, that this isn't anything that I'm particularly coming up with. All I'm saying is the distinction, the distinction you're trying to make, which is like an old school, like realist, you know, kind of view that is it metaphorical or is it real?

2:06:57all we have in science are metaphors i think and the only question is how good are your metaphors and i think as agents act living in a world all we have are models of what we are and what the outside world is that's it and the question is how good is it a model and and and my claim about this is in some small number of decades either this will either give rise to a very enabling uh mapping of the space for AI, for bioengineering, for biology, whatever, or we're going to find out that it really sucks because it really is a random grab bag of stuff. And we tried the optimistic research program, it failed, and we're just gonna have to live with surprise.

2:07:38I mean, I doubt that's going to happen, but it's a possible outcome. But do you think there is some place where the information is stored about these distributions that are being sampled to the thin interfaces, like actual place place is weird because it isn't the same as our physical space time okay i don't think it's that so calling it a place is a little a little weird like uh physics general relativity describes a space time could other physics theories be able to describe this other space where information is stored that we can apply maybe different but uh in the same spirit laws about information.

2:08:18I definitely think they're going to be systematic laws. I don't think they're going to look anything like physics. You can call it physics if you want, but I think it's going to be so different that that probably just, you know, cracks the word. And whether information is going to survive that, I'm not sure. But I definitely think that it's going to be, there are going to be laws. But I think they're going to look a lot more like aspects of psychology and cognitive science than they're going to look like physics. That's my guess. So what does it look like to prove that world exists? What it looks like is a successful research program that explains how you pull particular patterns when you need them and why some patterns come and others don't and show that they come from an ordered space.

2:09:05Across a large number of organisms? Well, it's not just organisms. I mean, I think it's going to end up, and I mean, you can talk to the machine learning people about how they got to this point again, because this is not not just me there's a bunch of there are a bunch of different disciplines that are converging on this now simultaneously um you're gonna you're gonna find again just like in mathematics where from from from different directions everybody sort of is looking at different things oh my god this is one underlying structure that seems to like inform all of this uh so in in physics in mathematics in uh computer science machine learning possibly in economics uh certainly in biology possibly in cognitive science.

2:09:43We're going to find these structures. It was already obvious in Pythagoras this time that there are these patterns. The only remaining question is, are they part of an ordered structured space? And are we up to the task of mapping out the relationship between what we build and the patterns that come through it? So from the machine learning perspective, is it then the case that even something as simple as LLMs are sneaking up onto this world, that the representations that they form are sneaking up to it? I've given this talk to some audiences, and especially in the organicist community, people like the first part where it's like, okay, now there's an idea for what the magic quote unquote is that's uh that's special about the living things and and so on now now if we could just stop there we would have dumb machines that just do what the algorithm says and we have these magical living interfaces that can be the recipient for these ingressions cool right we can cut up the world in this way uh unfortunately or or fortunately um i think that's not the case And I think that even simple, minimal computational models are, to some extent, beneficiaries of these free lunches.

2:11:05I think that the theories we have, and this goes back to the thin client interface kind of idea, the theories we have of both of physics and computation, so theory of algorithms, you know, Turing machines, all that good stuff. Those are all good theories of the front end interface. and they're not complete theories of the whole thing. They capture the front end, which is why they get surprised, which is why these things are surprising when they happen. I think that when we see embryos of different species, we are pulling from well-trodden, familiar regions of that space, and we know what to expect.

2:11:43Frog, you know, snake, whatever. When we make cyborgs and hybrids and biobots, we are pulling from new regions of that space that look a little weird, and they're unexpected, but, you know, we can still kind of get our mind around them. When we start making AIs, like proper AIs, we are now fishing in a region of that space that may never have had bodies before. It may have never been embodied before. And what we get from that is going to be extremely surprising. And the final thing just to mention on that is that because of this, because of the inputs from this platonic space, some of the really interesting things that artificial constructs can do are not because of the algorithm.

2:12:29They're in spite of the algorithm. They are filling up the spaces in between. There's what the algorithm is forcing you to do, and then there's the other cool stuff it's doing, which is nowhere in the algorithm. And if that's true, and we think it's true even of very minimal systems, then this whole business of language models and AIs in general, watching the language part may be a total red herring because the language is what we force them to do the question is what what what else are they doing that we are not we are not good at noticing and this is you know this this this is something that we are i think um as a as a kind of uh existential uh step for humanity is to is to become better at this because we are not good at recognizing these things now you gotta tell me more about this behavior that is observable that is unrelated to the explicitly stated goal of a particular algorithm.

2:13:23So you looked at a simple algorithm of sorting. Can you explain what was done? Sure. First, just the goal of the study. There are two things that people generally assume. One is that we have a pretty good intuition about what kind of systems are going to have competencies so from observing biologicals we're not terribly surprised when biology does interesting things everybody always says well it's biology you know of course it does all this cool stuff and yeah but but we have these machines and the whole point of having machines and dumb and algorithms and so on is they do exactly what you tell them to do right and people feel pretty strongly that that's a binary distinction and that that's what that's we can carve up the world in that way so i i wanted to do two things i wanted to first of all explore that and hopefully break the assumption that we're good at seeing this because i think we're not and i think it's extremely important that we understand very soon that we need to get much better at at knowing when to when to expect these things and the other thing i wanted to do was to find out uh you know most mostly people assume that you need a lot of complexity for this so when somebody says well the capabilities of my mind are not properly encompassed by the rules of biochemistry.

2:14:40Everybody's like, yeah, that makes sense where, you know, you're very complex and okay, you're, you know, your mind does things that, that you can't, you could, you didn't see that coming from the rules of biochemistry, right? Like we know that. So mostly people think that has to do with complexity. And, and what I would like to find out is as, as part of understanding what kind of interfaces give rise to what kind of aggressions, is it really about complexity? how much complexity do you actually need is there some threshold after which this happens is it really specific materials is it biologicals is it something about evolution like what is it about these kinds of things that allows this this this surprise right allows this idea that we are more than the sum of our parts and so and and i had a strong intuition that none of those things are actually required that this is this kind of magic so to speak seeps into pretty much everything and uh and so to to look at that i wanted also to uh have an example that had significant shock value because the thing with biology is there's always more mechanism to be discovered right like there's infinite depth of what the materials are doing what the you know somebody will always say well there's a mechanism but i just haven't found it yet so i wanted an example that was simple transparent so you could see all the stuff there was nowhere to hide i wanted it to be deterministic because I don't want it to be something around unpredictability or stochasticity.

2:15:56And I want it to be something familiar to people, minimal. And I wanted to use it as a model system for honing our abilities to take a new system and looking at it with fresh eyes. And that's because the sorting algorithms have been studied for over 60 years. We all think we know what they do and what their properties are. The algorithm itself is just a few lines of code. You know, you can see exactly what's there. It's deterministic. and that's that's so that that's why that's why right i wanted i wanted the most shock value out of a system like that if we were to find anything and to use it as an example of taking something minimal and and seeing what can be gotten out of it so i'll describe two interesting things about it and then we have lots of other work coming uh in the next in the next year about even simpler systems i mean it's actually crazy um so the so the very first thing is this the standard sorting So let's take bubble sort, right?

2:16:48And all these sorting algorithms, you know, what you're starting out with is an array of jumbled up digits. Okay, so integers. It's an array of mixed up integers. And what the algorithm is designed to do is to eventually arrange them all into order. And what it does generally is compare some pieces of that array and based on which one is larger than which, it swaps them around. And you can imagine that if you just keep doing that and you just keep comparing and swapping, then eventually you can get all the digits in the same order. So the first thing I decided to do, and this is the work of my student Haining Zhang and then Adam Goldstein on this paper.

2:17:24This goes back to our original discussion about putting a barrier between it and its goals. And the first thing I said, okay, how do we put a barrier in? Well, how about this? The traditional algorithm assumes that the hardware is working correctly. So if you have a seven and then a five and you tell them to swap, the lines of swap the swap the five and the seven. And then you go on, you never check, did it swap? Because you assume that it's reliable hardware. Okay. So what we decided to do was to break one of the digits so that it doesn't move. When you tell it to move, it doesn't move. We don't change the algorithm.

2:18:01That's really key. We do not put anything new in the algorithm that says, what do you do if the damn thing didn't move? Okay. Just run it exactly the same way. What happens? Turns out something very interesting happens it still works it's still so it still sorts it uh but it it eventually sorts sorts it by moving all the stuff around the broken number okay that makes sense but here's something interesting suppose we suppose we plot at any given moment we plot the degree of sortedness of the string as a function of time if you run the normal algorithm and it gets it it's guaranteed to get where it's going.

2:18:38That's the, you know, it's got a, it's got a sort and it will always reach the end. But when it encounters one of the broken digits, what happens is the actual sordidness goes down in order to then recoup and get better order later. What it's able to do is to go against the thing that it's trying to do to go around in order to meet its goal later on. now if i didn't if if i showed this to a behavior scientist and i didn't tell them what this what what system was doing is they will say well we know what this is this is delayed gratification this is the ability of a system to go against its gradient and get what it needs to do now imagine two magnets imagine you take two magnets you put a piece of wood between them and they're like this what the magnet is not going to do is to go around the barrier and get to its goal the two they're not smart enough to go against their gradient they're just going to keep doing this some animals are smart enough, right?

2:19:33They'll go around and the sorting algorithm is smart enough to do that. But the trick is there are no steps in the algorithm for doing that. You could stare at our algorithm all day long. You would not see that this thing can do delayed gratification. It isn't there. Now, there's two ways to look at this. On the one hand, you could say, so the reductionist physics approach, you could say, did it, did it follow all the steps in the algorithm? Yeah, it did. Well then, uh, well, there's nothing to see here. There's no magic. This is, you know, it does what it does. It didn't, it didn't disobey the algorithm.

2:20:04Right. I'm not claiming that this is a miracle. I'm not saying it disobeys the algorithm. I'm saying it's not failing to sort. I'm saying it's not doing some sort of, you know, crazy quantum thing. Not saying any of that. What I'm saying is other people might call it emergent. What it has is our properties that are not complexity, not unpredictability, not perverse instantiation, as in sometimes in a life. what it has are unexpected competencies recognizable by behavioral scientists, meaning different types of cognition, primitive, well, we want it primitive, so there you go, it's simple, that you didn't have to code into the algorithm.

2:20:41That's very important. You get more than you start with, than you put in. You didn't have to do that. You get these surprising behavioral competencies, not just complexity. That's the first thing. The second thing, which is also crazy, but it requires a little bit of a little bit of explanation the second thing that we said is okay what if instead of in the typical sorting algorithm you have a single controller top down i'm i'm sort of god like looking down at the numbers and i'm swapping them according to the algorithm what if and this goes back to actually the title of the paper talks about agential data self-sorting algorithms this is back to like what's who's the pattern and who's the agent right you said what if we give the numbers a little bit of agency here's what we're going to we're not going to have any kind of top down sort.

2:21:22Every single number knows the algorithm and he's just going to do whatever the algorithm says. So if I'm a five, I'm just going to execute the algorithm and the algorithm will try to make sure that to my right is the six and to my left is a four. That's it. That's it. So every digit is, so it's like a distributed as, you know, it's like an ant colony. There is no central planner. Everybody just does their own algorithm. Okay. We're just going to do that once you've done that. And by the way, one of the values of doing that is that you can simulate biological processes because in biology, you know, if I have like a frog face and I scramble it with all the different organs, every tissue is going to rearrange itself so that ultimately you have, you know, nose, eyes, head, you know, you're going to have an order, right?

2:22:01So you can do that. But okay, fine. But you can do something else cool. Once you've done that, you can do something cool that you can't do with a standard algorithm. You can make a chimeric algorithm. What I mean is not all the cells have to follow the same algorithm. Some of them might follow bubble sort. Some of them might follow selection sort. It's like in biology, what we do when we make chimeras, we make frogolotls. So frogolotls have some frog cells, they have some axolotl cells. What is that going to look like? Does anybody know what a frogolotl is going to look like? It's actually really interesting that despite all the genetics and the developmental biology, you have the genomes, you have the frog genome, you have the axolotl genome, nobody can tell you what a frogolotl is going to look like, even though you have, this is the back to your question about physics and chemistry.

2:22:42Like, yeah, you can know everything there is to know about how, you know, how the physics and the, and the genetics work, but the decision-making, right. Is like baby, baby axolotls have legs. Tadpals don't have legs. Is a frogolotl going to have legs, right? Can you predict that from, from understanding the physics of transcription and all of that? Anyway, so, so we made some, uh, so, so you see, this is like an intersection of biology, physics, cognition. So we made chimeric algorithms and we We said, okay, half the digits randomly. We assign them randomly. So half the digits are randomly doing bubble sort.

2:23:13Half the digits are randomly doing, I don't know, selection sort or something. But once you choose bubble sort, that digit is sticking with bubble sort. It's sticking. We haven't done the thing where they can swap between. No, they're sticking to it, right? You label them and they're sticking to it. The first thing we learned is that distributed sorting still works. It's amazing. You don't need a central planner. When every number is doing its whole thing, still gets sorted. That's cool. The second thing we found is that when you make a chimeric algorithm, where actually the algorithms are not even matching, that works too.

2:23:45The thing still gets sorted. That's cool. But the most amazing thing is when we looked at something that had nothing to do with sorting, and that is we asked the following question. We defined, Adam Goldstein actually named this property, and I think it's well named. We defined the algotype of a single cell. It's not the genotype. It's not the phenotype. It's the algotype. The algotype is simply this. What algorithm are you following? Which one are you? Are you a selection sort or a bubble sort? Right, that's it. There's two algotypes. And we simply ask the following question. During that process of sorting, what are the odds that whatever algotype you are, the guys next to you are your same type?

2:24:22It's not the same as asking how the numbers are sorted because it's got nothing to do with the numbers. It's actually, it's just whatever type you are. It's more about clustering than sorting. Clustering. Well, that's exactly what we call it. We call it clustering. And at first, so now think of what happens. And you can see this on that graph. It's the red. You start off, the clustering is at 50 % because as I told you, we assign the algotypes randomly. So the odds that the guy next to you is the same as you is half, 50%. There's only two algotypes. In the end, it is also 50 % because the thing that dominates is actually the sorting algorithm.

2:24:54And the sorting algorithm doesn't care what type you are. You've got to get the numbers in order. So by the time you're done, you're back to random algotypes because you have to get the numbers sorted. but in between in between you get some amount of increased very significant because look at look at the control is in the middle the pink is in the middle uh in in between you get significant amounts of clustering meaning that certain algotypes like to hang out with their buddies for as long as they can now now here's here's the one more thing and then i'll kind of give the philosophical significance of this so we saw this and i said that's nuts because the algorithm doesn't have any provisions for asking what algotype am I?

2:25:34What algotype is my, is my neighbor? If we're not the same, I'm going to move to be next to, like, if you wanted to implement this, you would have to write a whole bunch of extra steps. There would have to be a whole bunch of observations that you would have to take of your neighbor to see how he's acting. Then you would infer what algotype he is. Then you would go stand next to the one that seems to have the same algotype as you. You would have to take a bunch of measurements to say, wait, is that guy doing bubble sort? Is he doing selections? All right. Like if you want to implement this it's a whole bunch of algorithmic steps none of that exists in our algorithm you don't have any way of knowing what algotype you are or what anybody else is okay we didn't have to pay for that at all so notice notice a couple of interesting things the first interesting thing is that this was not at all obvious from the uh from the algorithm itself algorithm doesn't say anything about algotypes second thing is we paid computationally for all the steps needed to have the numbers sorted right because we know you know you pay you pay for a certain computation cost the clustering was free we didn't pay for that at all there were no extra steps so this gets back to your other question of how do we know there's a platonic space and this is kind of like one of the craziest things that we're doing i actually suspect we can get free compute out of it i suspect that one of the things that we can do here is use these ingressions in a useful way that don't require you to pay cost to pay physical cost right like we know every every bit has a has an energy cost that you have to get the clustering was free nothing extra was done yeah just uh the this plot for people who are just listening on the x-axis is the percentage of completion of the sorting process, and the y-axis is the sortedness of the list of numbers, and then also in the red line is basically the degree to which they're clustered.

2:27:15And you're saying that there's this unexpected competence of clustering. And I should comment that I'm sure there's a theoretical computer scientist listening to this saying, I can model exactly what is happening here and prove that the clustering increases and decreases. So taking the specific instantiation of the thing you've experimented with and prove certain properties of this. But the point is that there's a more general pattern here of probably other that you haven't discovered, unexpected competencies that emerge from this, that you could get free computation out of this thing. So this goes back to the very first thing you said about physicists thinking that physics is enough.

2:28:02You're 100 % correct that somebody could look at this and say, well, I see exactly why this is happening. We can track through the algorithm. Yeah, you can. There's no miracle going on here. right? The hardware isn't doing some crazy thing that it wasn't supposed to do. The point is that despite following the algorithm to do one thing, it is also at the same time doing other things that are neither prescribed nor forbidden by the algorithm. It's the space between the chance and necessity, which is how a lot of people see these things. It's that free space. We don't really have a good vocabulary for it where the interesting things happen.

2:28:36And to whatever extent it's doing other things that are useful, that stuff is computationally without extra cost. Now, there's one other cool thing about this. And this is the beginning of a lot of thinking that I've done about when this relates to AI and stuff like that, intrinsic motivations. The sorting of the digits is what we forced it to do. The clustering is an intrinsic motivation. We didn't ask for it. We didn't expect it to happen. We didn't explicitly forbid it, but we didn't know. This is a great definition of the intrinsic motivation of a system. So when people say, oh, that's a machine, it only does what you programmed it to do.

2:29:16I, as a human, have intrinsic motivation. I'm creative and I have intrinsic motivation. Machines don't do that. Even this minimal thing has a minimal kind of intrinsic motivation, which is something that is not forbidden by the algorithm but isn't prescribed by the algorithm either. And I think that's an important third thing besides chance and necessity. something something else that's that's fun about this is uh when you think about intrinsic motivations think about a child uh if you make him sit in math class all day you're never going to know what the other intrinsic motivations are that he might be doing right my who knows what else he might be interested in so we so i wanted to ask this question i want to say if we let off the pressure on the sorting what would happen now that's hard because because if you mess with the algorithm.

2:30:05Now it's no longer the same algorithm. So you don't want to do that. So we did something that I think was kind of clever. We allowed repeat digits. So if you allow repeat digits in your array, you can still have all the fives can still be after all the fours and after all the sixes, but you can keep them as clustered as you want. So this thing at the end where they have to get declustered in order for the sorting to happen, we thought maybe we could let off the pressure a little bit. If you do that, all you do is allow some extra repeat digits, the clustering gets bigger it will cluster as much as you let it the clustering is what it wants to do the sorting is what we're forcing it to do and my only point is if if the if the bubble sword which has been gone over and gone over how many times has these kinds of things that we didn't see coming what about the ais the language model everything else not because not because they talk not because they say that they're you know have an inner perspective or any of that but just from the fact that this thing is even the most minimal system surprises with what happens.

2:31:06And I frankly, when I see this, tell me if this doesn't sound like all of our existential story. For the brief time that we're here, the universe is going to grind us into dust eventually. But until then, we get to do some cool stuff that is intrinsically motivating to us, that is neither forbidden by the laws of physics, nor determined by the laws of physics. But eventually, it kind of comes to an end. So I think that aspect of it, right, that there are spaces, even in algorithms, there are spaces in which you can do other new things, not just random stuff, not just complex stuff, but things that are easily recognizable to a behavior scientist.

2:31:47You see, that's the point here. And I think that kind of intrinsic motivation is what's telling us that this idea that we can carve up the world, we can say, okay, look, biology is complex. Cognition, who knows what's responsible for that? But at least we can take a chunk of the world aside and we can cut it off and we can say, these are the dumb machines. These are just algorithms. algorithms whereas we know the rules of biochemistry don't explain everything we want to know about how psychology is going to go but at least the rules of algorithms tell us exactly what the machines are going to do right we have we have some hope that we've we've carved off a little part of the world and everything is nice and simple and it is exactly what we said it was going to be i think that failed i think it was a good try i think we have good theories of interfaces but even even the simplest algorithms have have these kinds of things going on and and so that's That's why I think something like this is significant.

2:32:41Do you think that there is going to be, in all kinds of systems of varying complexity, things that the system wants to do and things that it's forced to do? So are there these unexpected competencies to be discovered in basically all algorithms and all systems? That's my suspicion. And I think that is extremely important for us as humans to have a research program to learn to recognize and predict and recognize. We make things, never mind something as simple as this. We make, you know, social structures, financial structures, Internet of Things, robotics, AI. So we make all this stuff. And we think that the thing we make it do is the main show.

2:33:23And I think it is very important for us to learn to recognize the kind of stuff that sneaks in into the spaces. It's a very counterintuitive notion. By the way, I like the word emergent. I hear your criticism, and it's a really strong one, that emergent is like you toss your hands up, but I don't know the process. But it's just a beautiful word because it is, I guess it's a synonym for surprising. And I mean, this is very surprising, but just because it's surprising doesn't mean there's not a mechanism that explains it. But mechanism and explanation are both not all they're cracked up to be in the sense that, you know, anything you and I do, we could come up with the most beautiful theory.

2:34:10We paint a painting. Anything we do, somebody could say, well, I was watching the biochemistry and the Schrodinger equation playing out, and it totally described everything that was happening. You didn't break even a single law of biochemistry. Nothing to see here. Nothing to see. Right? Like, okay, you know, consistent with the low-level rules, you can do the same thing here. You can look at the machine code and say, yeah, this thing is just executing machine code. You can go further and say, oh, it's quantum foam. It's just doing the thing that quantum foam does. You're saying that's what physicists miss.

2:34:44And I'm not saying they're unaware of that. I mean, they're generally a pretty sophisticated bunch. I just think they've picked a level and they're going to discover what is to be seen at that level, which is a lot. And my point is the stuff that the behavior scientists are interested in shows up at a much lower level than you think. How often do you think there's a misalignment of this kind between the thing that a system is forced to do and what it wants to do? And it's particularly I'm thinking about various levels of complexity of AI systems. So right now we've looked at like five other systems.

2:35:21that's a small n okay but but just looking at that i would find it very uh surprising if bubble sort was able to do this and then there was some sort of valley of death where nothing showed up and then blah blah living things like i can't imagine that i i'm gonna say that if something and we and we actually have a system that's even simpler than this which is one d cellular automata that's doing some weird stuff if if these things are to be found in this kind a simple system i i mean they just have to be showing up in in these other more complex ais and things like that the only thing what what we don't know but we're going to find out is to what extent there is interaction between these so i call these things side quests you know it's like they're like like like in a game you know whether it's the main thing you're supposed to do and as long as as long as you still do it the thing about this is you have to sort yeah you have to sort there's no miracle you're going to sort but but but as long as you can do other stuff while you're sorting, it's not forbidden.

2:36:18And what we don't know is to what extent are the two things linked? So if you do have a system that's very good at language, are the others, the side quests that it's capable of, do they have anything to do with language whatsoever? We don't know the answer to that. The answer might be no, in which case all of the stuff that we've been saying about language models because of what they're saying, all of that could be a total red herring and not really important and the really exciting stuff is what we never looked for. Or in complex systems, maybe those things become linked. In biology, they're linked.

2:36:50In biology, evolution makes sure that the things you're capable of have a lot to do with what you've actually been selected for. In these things, I don't know. And so we might find out that they actually do give the language some sort of leg up, or we might find that the language is just, you know, that's not the interesting part. Also, it is an interesting question of this intrinsic motivation of clustering. Is this a property of the particular sorting algorithms? Is this a property of all sorting algorithms? Is this a property of all algorithms operating on lists on numbers? How big is this?

2:37:29So for example, with LLMs, is it a property of any algorithm that's trying to model language, or is it very specific to transformers and that's all to be discovered? We're doing all that. We're doing all that. We're testing the stuff in other algorithms. We're looking for, we're developing suites of code to look for other properties. We, you know, to some extent, it's very hard because we don't know what to look for, but we do have a behaviorist handbook which tells you all kinds of things to look for. The delay gratification, the, you know, problem solving, like we have all that. I'll tell you an end of one of an interesting biological intrinsic motivation because people, So in like the alignment community and stuff, there's a lot of discussion about what are the intrinsic motivations going to be of AIs?

2:38:11What are their goals going to be? Right. What are they going to want to do? Just as an NF1 observation, anthrobots, the very first thing we checked for. So this is not experiment number 972 out of a thousand things. This is the very first thing we checked for. We put them on a plate of neurons with a big wound through them, a big scratch. First thing they did was heal the wound. Okay. So it's an NF1. but I like the fact that the first intrinsic motivation that we noticed out of that system was benevolent and healing. Like I thought that was pretty cool. And we don't know, maybe the, you know, maybe the next 20 things we find are going to be some sort of, you know, damaging effects.

2:38:44I can't tell you that. But the first thing that we saw was kind of a positive one. And I don't know, that makes me feel better. What was the thing you mentioned with the anthrobots that they can reverse aging? There's a procedure called an epigenetic clock, where what you can do is look at a particular epigenetic states of cells and compare to a curve that was built from humans of known age, you can guess what the age is. So we can take now, and this is Steve Havrath's work and many other people, that when you take a set of cells, you can guess what their biological age is. So we make the anthrobat from cells that we get from human tracheal epithelium we collaborated with with steve's group the clock foundation we sent them a bunch of cells and we saw that if you if you check the the anthrobots themselves they are roughly 20 percent younger than the cells they come from and so that's amazing and i i can i can give you a theory of why that happens although we're still investigating and then I can tell you the implications for longevity and things like that.

2:39:53My theory for why it happens, I call this age evidencing. And I think that what's happening here, like with a lot of biology, is that cells have to update their priors based on experience. And so I think that they come from an old body, they have a lot of priors about how many years they've been around and all that, but their new environment screams, I'm an embryo. Basically, there's no other cells around, you're being bent into a pretzel they actually express some embryonic genes they say you're an embryo and i think it doesn't it's not enough new evidence to roll them like all the way back but it's enough to update them to about 28 back yeah so it's similar to like uh when older adult gives birth to a a child so you're you're saying you can just fake it till you make it with uh with age like the environment convinces the cell that is young well first of all yes yes yes and that's that's that's my hypothesis and we have a whole bunch of research uh being done on this there was a study where they went into a um an old age home and they redid the decor like 60s style when all these folks were really young and they they found all kinds of improvements in blood chemistry and stuff like that because they say it was sort of mentally taking them back to when you you know, when they were the way they were at that time.

2:41:17I think this is a basal version of that, that basically if you're finding yourself in an embryonic environment, what's more plausible that you're young or what? You know, like I think this is the basic feature of biology is to update priors based on experience. Do you think that's actually actionable for longevity? Like you can convince cells that they're younger and thereby extend the lifespan? this is what we're trying to do yeah could it be as simple as that why that's not well i'm not claiming it's simple that is in no way simple but because because again you have to all all of this all of the regenerative medicine stuff that we do balances on one key thing which is learning to communicate to the system we have to if you're going to convince that system you know so so when we make a gut tissue into an eye you have to convince those cells that they're priors about we are we are gut precursors those priors are wrong and you should adopt this new worldview that you're going to be you know you're going to be an eye so being convincing and figuring out what what kind of messages are convincing to two cells and how to speak the language and how to make them take on new uh new beliefs literally is is at the root of all of these future advances in birth defects and regenerative medicine and cancer and that's that's what's going on here So I'm not saying it's simple, but I can see the path.

2:42:41Going back to the platonic space, I have to ask if our brains are indeed thin client interfaces to that space, what does that mean for our mind? Can we upload the mind? Can we copy it? Can we ship it over to other planets? It's like how, what does that mean for exactly where the mind is stored? Yeah. A couple of things. So we are now beyond anything that I can say with any certainty. This is total, total conjecture. Okay. So because we don't know yet. The whole point of this is we actually don't really understand very well the relationship between the interface and the thing. And the thing you're currently working on is to map this space.

2:43:32And we are beginning to map it, but you know, this is a massive effort. So a couple of conjectures here. One is that I strongly suspect that the majority of what we think of as the mind is the pattern in that space. And one of the interesting predictions from that model, which is not a prediction of modern neuroscience, is that there should be cases where there is very minimal brain and yet normal IQ function. This has been seen clinically. Karina Kaufman and I reviewed this in a paper recently, a bunch of cases of humans where there's very little brain tissue and they have normal or sometimes above normal intelligence.

2:44:19now things are not simple because that obviously doesn't happen all the time right most of the time that doesn't happen so so what's going on we don't understand but it is a very curious thing that is not a prediction of i'm not saying i'm not saying it can't you know you can take modern neuroscience and sort of bend it into a pretzel to accommodate it you can say well there are these you know kind of redundancies and things like this right so you can accommodate it but it doesn't predict this so uh there are these incredibly curious cases now do i think you can copy it no i don't think you can because what you're going to be copying is the is the interface the front end of the brain or the the whatever you the act the action is actually the pattern in the platonic space you're going to be able to copy that i doubt it but what you could do is produce another interface through which that particular pattern is going to come through I think that's probably possible.

2:45:13I can't say anything at this point about what that would take, but my guess is that that's possible. Is your guess, your gut, is that that process, if possible, is different than copying? Like, it looks more like creating a new thing versus copying. For the interface. So if you could, so here's my prediction for Star Trek Transporter. For whatever reason right now, your brain and body are very attuned and attractive to a particular pattern, which is your set of psychological propensities. if we could if we could rebuild that exact same thing somewhere else i don't see any reason why that same pattern wouldn't come through it the same way it comes through this one that's that would be a guess you know so so i think what you what you will be copying is the physical interface and hoping to maintain whatever it is about that interface that was appropriate for that pattern we don't really know what that is at this point when we've been talking about mind in this particular case is the most important to me because I'm a human.

2:46:19Does self come along with that?

2:46:25The feeling like this mind belongs to me? Yeah. Does that come along with all minds? The subjective, not the subjective experience. The subjective experience is important too, consciousness, but like the ownership. I suspect so. And I think so because of the way we come into being. So, so one of the things that, um, I should be working on is, uh, this paper called booting up the agent. And it talks about the very earliest steps of becoming a being in this world. Kind of like you can do this for a computer, right? And before you switch the power on, it belongs to the domain of physics, right? It obeys the laws of physics.

2:47:08You switch the power on some number of what nanoseconds microseconds i don't know later you have a thing that oh look it's taking instructions off the stack and doing them right so so now you're now it's executing an algorithm how did you get from from physics to executing an algorithm like what what what what was happening during the boot up exactly before it starts to run code or whatever right and so we can ask that same question uh in biology what are the earliest steps of uh of becoming a being yeah That's a fascinating question. Through embryogenesis, at which point are you booting on? Yeah, yeah, yeah, yeah, exactly.

2:47:44Do you have a hope of an answer to that? Well, I think so. I think so in two ways. The first thing is just physically what happens. So I think that your first task as a being, and again, I don't think this is a binary thing. I sort of cranks on up and up. Your first task as a being coming into this world is to tell a very compelling story to your parts. As a biological, you are made of agential parts. Those parts need to be aligned, literally, into a goal they have no comprehension of. If you're going to move through anatomical space by means of a bunch of cells which only know physiological and metabolic spaces and things like that, you are going to have to develop a model and give them, uh, bend their action space.

2:48:38You're gonna have to deform their option space with signals, with, uh, behavior shaping cues, with rewards and punishments, whatever you got your job as a, as an agent is ownership of your parts is alignment of your parts. I think that fundamentally is going to give rise to this, this, this ability. Now, now that also means having a boundary saying, okay, this is the stuff I control. This is me. this other stuff over here is outside world i have to figure out you don't know where that is by the way you have to figure it out and in embryogenesis it's really cool you can uh as a as a as a grad student i used to do this experiment with duck embryos which is a flat blasted disc you can take a needle and put some scratches into it and every every island you make for a while until they heal up thinks it's the only embryo there's nothing else around so it becomes an embryo and eventually you get twins and triplets and quadruplets and things like that but each one of them at the border, you know, they're joined.

2:49:28Well, where do I end and where does, where does he begin? You have to, you know, you have to know what your, what your borders are. So, um, that action, that action of aligning your parts and coming to be this, this, this, uh, I mean, I'm even going to say this emergence, we just don't have a good vocabulary for it. This, this, this emergence of a model that aligns all the parts is really critical to keep that thing going. There's something else that's really interesting. And, uh, I was thinking about this in the context of, this question of these beautiful kind of ideas. There's this amazing thing that we found, and this is largely the work of Federico Pagosi in my group.

2:50:06So a couple of years ago, we saw that networks of chemicals can learn. They have five or six different kinds of learning that they can do. And so what I asked them to do was to calculate the causal emergence of those networks while they're learning. And what I mean by that is this. If you're a rat and you learn to press a lever and get a reward, there's no individual cell that had both experiences. The cells at your paw had touched the lever, the cells in your gut got the delicious reward. No individual cell has that both experiences. Who owns that associative memory? Well, the rat. So that means you have to be integrated, right?

2:50:48If you're going to learn associative memories from different parts, You have to be an integrated agent that can do that. And so we can measure that now with metrics of causal emergence like phi and things like that. So we know that in order to learn, you have to have significant phi. But I wanted to ask the opposite question. What does learning do for your phi level? Does it do anything for your degree of being an agent that is more than the sum of its parts? So we train the networks. And sure enough, some of them, not all of them, but some of them, as you train them, their phi goes up. Okay. And so basically what we were able to find is that there is this positive feedback loop between every time you learn something, you become more of an integrated agent.

2:51:34And every time you do that, it becomes easier to learn. And so it's this. It's a virtuous cycle. It's a virtuous cycle. It's an asymmetry that points upwards for agency and intelligence. And now back to our platonic space stuff, where does that come from? It doesn't come from evolution. You don't need to have any evolution for this. Evolution will optimize the crap out of it for sure, but you don't need evolution to have this. Does it come from physics? It comes from the rules of information, the causal information theory and the behavior of networks, the mathematical objects. This is not anything that was given to you by physics or by a history of selection.

2:52:10It's a free gift from math. And those two free gifts from math lock together into a spiral that I think causes simultaneously a rise in intelligence and a rise in collective agency. And I think that's just, you know, that's been, you know, just amazing to think about. Well, that free gift from, I think, is extremely useful in biology. When you have small entities forming networks, hierarchy that builds more and more complex organisms. That's obvious. I mean, this speaks to embryogenesis, which I think is one of the coolest things in the universe. And in fact, you acknowledge its coolness in Ingressing Mind's paper, writing, quote, Most of the big questions of philosophy are raised by the process of embryogenesis, right in front of our eyes.

2:53:02A single cell multiplies and self-assembles into a complex organism, with order on every scale of organization and adaptive behavior. Each of us takes the same journey across the Cartesian cut, starting off as a quiescent human oocyte, a little blob thought to be well described by chemistry and physics. Gradually, it undergoes metamorphosis and eventually becomes a mature human with hopes, dreams, and a self-reflective metacognition that can enable it to describe itself as not a machine. It's more than its brain, body, and underlying molecular mechanisms, and so on. What in all of our discussion can we say as the clear intuition how it's possible to take a leap from a single cell to a fully functioning organism full of dreams and hopes and friends and love and all that kind of stuff in everything we've been talking about which has been a little bit technical like how what how do we understand because that's one of the most magical things the universe is able to create perhaps the most magical from simple physics and chemistry create this us two talking about ourselves i i think we have to keep in mind that physics and chemistry are not real things they are lenses that we put on the world that that they they are uh perspectives where we say we are for the time being uh for the duration of this chemistry class or career or whatever, we are going to put aside all the other levels and we're going to focus on this one level.

2:54:49And that what is fundamentally going on during that process is an amazing positive feedback loop of collective intelligence for the interface. It's the physical interface is scaling. It's the cognitive light cone that it can support. So it's going from a molecular network. The molecular network can already do things like Pavlovian conditioning. You don't start with zero. when you have a simple molecular network you are already hosting some patterns from the platonic space that look like pavlovian conditioning you've already got that and starting out that's that's just a molecular network then you become a cell and then you're many cells and now you're navigating anatomical morphous space and you're hosting all kinds of other patterns and eventually you and and i think again i think there's and this is like what you know all the stuff that we're trying to work out now.

2:55:38There's a consistent feedback between the ingressions you get and the ability to have new ones, which again, I think it's this like positive feedback cycle where the more of these free gifts you pull down, they allow you physically to develop to a ways where, oh, look, now we're suitable for more and higher ones. And this continuously goes and goes and goes until you're able to pull down a full human set of behavioral capacities. What is the mechanism of such radical scaling of the cognitive cone? Is it just this kind of the same thing that you were talking about with the network of chemicals being able to learn?

2:56:14I'll give you two mechanisms that we found. But again, just to be clear, these are mechanisms of the physical interface. What we haven't gotten is a mature theory of how they map onto the space. That's just like just beginning. But I'll tell you what the physical side of things look like. the first one has to do with um stress uh propagation so imagine that um you got a bunch of cells and there's a cell down here that needs to be up there okay all of these cells are exactly where they need to go so they're happy their stress is low this cell this now now let's imagine stress is basically a uh uh it's a it's a it's a physical implementation of the error function it's basically the amount of stress is basically the delta between where you are now and where you need to be not necessarily in physical position this could be an anatomical space and physiological space and in transcriptional space whatever right it's just there's just the delta from your set point so so you're stressed out but these guys are happy they're not moving you can't get past them now imagine if what you could do is you could leak your stress whatever your stress molecule is and the cool thing is that evolution has actually conserved these highly so these are all and we're studying all of these things they're um they're actually highly conserved if you start leaking your stress molecules then all of this stuff around here is starting to get stressed out when things get stressed starting to stress out their temperature in the not not physical temperature but in the sense of like simulated annealing or something right there their ability to their plasticity goes up because because they're feeling stress they need to relieve that stress and because all the stress molecules are the same they don't know it's not their stress they are equally irritated by them as if it was their own stress so they become a little more plastic they become ready to kind of uh you know adopt different fates you get up to where you're going and then everybody's stress can drop.

2:57:59So notice what can happen by a very simple mechanism. Just be leaky for your own stress. My problems become your problems. Not because you're altruistic, not because you actually care about my problems. There's no mechanism for you to actually care about my problems. But just that simple mechanism means that faraway regions are now responsive to the needs of other regions, such that complex rearrangements and things like that can happen. it's a it's it's alignment of everybody to the same goal through this very dumb simple stress sharing thing via leaky stress leaky stress right so there's another one there's another one which i call memory anonymization so imagine um here are two cells and imagine something happens to this cell and uh it sends a signal over to this cell traditionally you send a signal over this cell receives it it's very clear that it came from outside so this cell can do many things.

2:58:50It could ignore it. It could take on the information. It could just ignore it. It could reinterpret. It could do whatever. But it's very clear that came from outside. Now imagine the kind of thing that we study, which is called gap junctions. These are electrical synapses that could directly link the internal miliars of two cells. If something happens to this cell, let's say it gets poked and there's a calcium spike or something, that propagates through the gap junction here, this cell now has the same information, but this cell has no idea. Wait a minute. Was that, is that my memory or is that his memory?

2:59:22Because it's the same, right? It's the same, it's the same components. And so what you're able to do now is to have a mind melt. You can have a mind melt between the two cells where nobody's quite sure whose memory it is. And when you share memories like this, it's harder to say that I'm separate from you. If we share the same memories, we're kind of, and I don't mean every single memories, right? So they still have some identity. But to a large extent, they have a little bit of a mind melt. And there's many complexities you can lean on top of it. But what it means is that if you have a large group of cells, they now have joint memories of what happened to us, as opposed to you know what happened to you and I know what happened to me.

3:00:01And that enables a higher cognitive light cone because you have greater computational capacity. You have a greater area of concern of things you want to manage. I don't just want to manage my tiny little memory states because I'm getting your memories Now I know I got to manage this whole thing. So both of these things end up scaling the size of things you care about. And that is a major ladder for cognition is scale the degree of, you know, the size of concern that you have. It'd be fascinating to be able to engineer that scaling, probably applicable to AI systems. How do you rapidly scale the cognitive cone?

3:00:37Yeah, we have some collaborators in a company called Softmax that we're working with to do some of that stuff. in biology that that's our cancer therapeutic which is that what you see what you see in cancer literally is cells electrically disconnect from their neighbors when they were part of a giant memory that was working on making a nice organ well now they can't remember any of that now they're just amoebas and the rest of the body is just external environment and what we found is if you then physically reconnect them for to to the to the network you don't have to fix the dna you don't have to kill the cells with chemo you can just reconnect them and they go back to because they're now part of this larger collective they go back to what they were working on and so so yeah i think we can intervene at that at that scale let me ask you more explicitly on the search the city the search for unconventional terrestrial intelligence what do you hope to do there how do you actually find uh try to find unconventional intelligence all around us first So do you think on Earth there is all kinds of incredible intelligence we haven't yet discovered?

3:01:46I mean, guaranteed. We've already seen in our own bodies. And I don't just mean that we are hosts to a bunch of microbiome or any of that. I mean that your cells, and we have all kinds of work on this, every day they traverse these alien spaces, 20 ,000 dimensional spaces and other spaces. They solve problems. I think they suffer when they fail to meet their goals They have stress reduction when they meet their goals These things are inside of us, they are all around us I think that we have an incredible degree of mind blindness To all of the very alien kinds of minds around us And I think that looking for aliens off the earth is awesome and whatever But if we can't recognize the ones that are inside our own bodies what chance do we have to really you know to really recognize the ones that are out there do you think that could be a measure like IQ for for mind what would it be not mindedness but intelligence that's broadly applicable to the unconventional minds that's generalizable to unconventional minds where we could even quantify like, holy shit, this discovery is incredible because it has this IQ.

3:03:05Yeah, yes and no. The yes part is that what, as we have shown, you can take existing IQ metrics, I mean, literally existing kinds of ways that people used to measure intelligence of animals and humans or whatever, and you can apply them to very weird things. If you have the imagination to make the interface, you can do it. And we've done it and we've shown creative problem solving and all this kind of stuff. So yes. However, we have to be humble about these things and recognize that all of those IQ metrics that we've come up with so far were derived from an N of one example of the evolutionary lineage here on Earth.

3:03:43And so we are probably missing a lot of them. So I would say we have plenty to start. We have so much to start with. We could keep tens of thousands of people busy just testing things now, but we have to be aware that we're probably missing a lot of important ones. What do you think has more interesting, intelligent, unconventional minds? inside our body, the human body, or like we were talking off mic, the Amazon jungle, like nature, natural systems outside of like the sophisticated biological biological biological systems. we're aware of. Yeah. We don't know because it's really hard to do experiments on larger systems.

3:04:24It's a lot easier to go down than it is to go up. But my suspicion is, you know, like the Buddhists say, innumerable sentient beings. I think by the time you get to that degree of infinity, it kind of doesn't matter to compare. I suspect there's just massive numbers of them. Yeah, I think it really matters which kind of systems are amenable to our current methods of scientific inquiry. I mean, I've spent quite a lot of hours just staring at ants when I was in the Amazon. And it's such a mysterious, wonderful collective intelligence. I don't know how amenable it is to research. I've seen some folks try.

3:05:01You could simulate, you can. But I feel like we're missing a lot. I'm sure we are. But one of my favorite things about that kind of work, have you seen that there's at least three or four papers showing that ant colonies fall for the same visual illusions that we fall for? Not the ants, the colonies. The colonies together. So if you lay out food in particular patterns, they'll do things like complete lines that aren't there and like all the same shit that we fall for, they fall for. So, you know, I don't think it's hopeless, but I do think that we need a lot of work to develop tools. Do you think all the tooling that we develop and the mapping that we've been discussing will help us do the SETI part, finding aliens out there?

3:05:42I think it's essential. I think it's essential. We are so parochial in what we expect to find in terms of life that we are going to be just completely missing a lot of stuff. If we can't even agree on, never mind definitions of life, but what's actually important. I led a paper recently where I asked 65 or so modern working scientists for a definition of life. and we had so many different definitions across so many different dimensions. We had to use AI to make amorphous space out of it. And there was zero consensus about what actually is important. And if we're not good at recognizing it here, I just don't see how we're gonna be good at recognizing it somewhere else.

3:06:32So given how miraculous life is here on earth, so it's clear to me that we have so much more work to do. That said... Would that be exciting to you if we find life on other planets in the solar system? Like, what would you do with that information? Or is that just another life form that we don't understand? I would be very excited about it because it would give us some more unconventional embodiments to think about, right? A data point that's pretty far away from our existing data points, at least in the solar system. So that'd be cool. I'd be very excited about it. but I must admit that my, my level of my, my set point for surprise has been pushed so high at this point that it would have to, you know, it would have to be something really weird to, to make me shocked.

3:07:23I mean, I, the, the things that we see every day is just, uh, yeah. I think you've mentioned in a few places that, uh, uh, like you wrote that the ingressing minds paper is not the weirdest thing you plan to write. Yeah. Um, how weird are you going to get? Can you hit maybe a better question is like, in which direction of weirdness do you think you will go in your, in your life? In which direction of the weird Overton window are you going to expand? Yeah. Well, the kind of a background to this is simply that I've, I've, I've had a lot of weird ideas for many, many decades. And my general policy is not to talk about stuff until it becomes actionable.

3:08:15And the amazing thing, I mean, I'm really kind of shocked, is that in my lifetime, the empirical work, like, I really didn't think we would get this far. and the knob, I have this like mental, mental knob of, of what percentage of the weird things I think do I actually say in public, right? And, and every few years when the, when the, um, empirical work moves forward, I sort of turn that knob a little, right. As we keep going. So I have no idea if we'll continue to be that fortunate or how long I can keep doing this or however, like, I don't know, but, um, just to give you, um, just to give you a direction for it, it's going to be in the direction of what kinds of things do we need to take seriously as other beings with which to relate to.

3:09:02So I've already pushed it, you know, so like we knew brainy things and then we said, well, it's not just brains. And then we said, well, it's not just, so, you know, it's not just in physical space and it's not just biologicals and it's not just complexity. There's a couple of other steps to take that I'm pretty sure are there, but we're going to have to do the actual work to make it actionable before we really talk about it. So that direction. I think it's fair to say you're one of the more unconventional humans, scientists out there. So the interesting question is, what's your process of idea generation?

3:09:43What's your process of discovery from you've done a lot of really incredibly interesting like you said actionable but interesting out there ideas that you've actually engineered with xenobots and anthropots these kinds of things like what when you go home tonight go to the lab what's the process empty sheet of paper when you're thinking through it Well, the mental part is a lot of it, uh, much like funny enough, much like making Xenobots, you know, we, we make Xenobots by releasing constraints, right? We, we don't do anything to them. We just release them from the constraints they already have.

3:10:28And then we see. So a lot of it is releasing the constraints that mentally have been placed on us. And, and part of it is my, my education has been a little weird because I was a computer scientist first and, and only later biology. And so by the time I heard all the biology things that we typically just take on board, I was already a little skeptical and thinking a little differently. But a lot of it comes from releasing constraints. And I very specifically think about, OK, this is what we know. What would things look like if we were wrong? Or what would it look like if I was wrong? What are we missing?

3:11:01What is our worldview specifically not able to see, right? Whatever model I have. or another way i often think is uh i'll take two things that are considered to be very different things and i'll say let's just imagine those as two points on a continuum what what does that look like what does the middle of that continuum look like what's the what's the symmetry there what's the what's the parameter that i can you know what's the knob i can turn from to get from here to there so those kinds of i look for symmetries a lot i'm like okay this thing is like that way in what way what's the what's the fewest number of things i would have to move to make this map onto that right so so these so those are you know those are kind of mental mental tools the physical process um for me is basically uh i mean obviously i'm fortunate to have a lot of discussions with very smart people and so so in my in my group there's something i've hired some amazing people so we of course have a lot of discussions and some stuff comes out of that my process is uh i do pretty much pretty much every morning um or i i'm outside for sunrise and I walk around in nature.

3:12:01There's just not really anything better than as inspiration, right, than nature. I do photography and I find that it's a good meditative tool because it keeps your hands and brain just busy enough. Like you don't have to think too much, but you know, you're sort of twiddling and looking and doing some stuff. And it keeps your brain off of the linear, like logical, like careful train of thought enough to release it so that you can ideate a little more while your hands are busy. So it's not even the thing you're photographing. It's the mechanical process of doing the photography. And mentally, right?

3:12:34So because I'm not walking around thinking, okay, let's see. So for this experiment, I got to get this piece of equipment and this, like that goes away. And it's like, okay, what's the lighting and what am I looking at? And during that time, when you're not thinking about that other stuff, then I say, well, yeah, I got to get a notebook. And I'm like, look, this is what we need to do. So that kind of stuff. And the actual idea of writing down stuff as a notebook, as a computer, are you super organized thinking or is it just like random words here and there with drawings? And also, what is the space of thoughts you have in your head?

3:13:14Is this sort of amorphous things that aren't very clear? Are you visualizing stuff? Is there something you can articulate there? I tend to leave myself a lot of voicemails because as I'm walking around, I'm like, oh, man, this idea. And so I'll just call my office and leave myself a voicemail for later to transcribe. I don't have a good enough memory to remember any of these things. And so what I keep is a mind map. So I have an enormous mind map. One piece of it hangs in my lab so that people can see like these are the ideas. This is how they link together. Here's everybody's project. I'm working on this.

3:13:51How the hell does this attach to everybody else so they can track it? The thing that hangs in the lab is about nine feet wide. It's a silk sheet and it's out of date within a couple of weeks of my printing it because new stuff keeps moving around. um and then and then there's more that isn't you know isn't for anybody else's uh view but um yeah i try i try to be very organized because otherwise otherwise i forget so so everything is in the mind map things are in manuscripts i have something like at the right now probably 163 62 um open manuscripts that are in process of being written at various stages and and when things come up i stick them in the right manuscript in the right place so that when i'm finally ready to finalize then then i'll put words around it whatever but there's like outlines of everything so i i try to be organized because i can't i don't have to you know so there's a wide front of uh manuscripts of work that's being done and it's continuously like pushing towards completion but you're not clear where what's going to be finished when and how that's i mean that's yes but that's just the that's just the theoretical philosophical stuff the the empirical work that we're doing with in the lab i mean those are we know exactly you know it's more focused like we know this is this is you know anthropoid aging this is limb regeneration this is the new cancer paper this is whatever yeah those things are very linear where do you think ideas come from when you're taking a walk that eventually materialized in a voicemail where's that what is that from you is that you know a lot of really some of the most interesting people feel like they're channeling from somewhere else.

3:15:27I mean, I hate to bring up the platonic space again, but I mean, if you talk to any creative, that's basically what they'll tell you, right? And certainly that's been my experience. So I feel like it's a, the way it feels to me is a collaboration. So the collaboration is, I need to bust my ass and be prepped in one, A, to work hard, to be able to recognize the idea when it comes, and B, to actually have an outlet for it so that when it does come, we have a lab and we have people who can who can help me do it and then we can actually get it out right so that's that's that's my part is you know be up at 4 30 a.m doing your thing and be ready for it but the other side of the collaboration is that yeah when you do that like amazing ideas come and you know to say that it's me i don't think would be would be right uh i you know i think it's it's definitely coming from from other places what advice would you give to scientists PhD students, grad students, young scientists that are trying to explore the space of ideas, given the very unconventional, non-standard, unique set of ideas you've explored in your life and career?

3:16:34Let's see. Well, the first and most important thing I've learned is not to take too much advice. And so I don't like to give too much advice, but I do have one technique that I found very useful. And this isn't for everybody, but there's a specific demographic. A lot of unconventional people reach out to me and I try to respond and help them and so on. This is a technique that I think is useful for some people. How do I describe it? It's the act of bifurcating your mind and you need to have two different regions. One region is the practical region of impact. In other words, how do I get my idea in out into the world so that other people recognize it?

3:17:20What should I say? What are people hearing? What are they able to hear? How do I pivot it? What parts do I not talk about? Which journal am I going to publish? Is it time now? Do I wait two years for this? Like all the practical stuff that is all about how it looks from the outside, right? All the stuff that I can't say this, or I should say this differently, or this is going to freak people out or this is, you know, this community wants to hear this so I can pivot it this way. Like all that practical stuff. It's got to be there. Otherwise, you're not going to be in a position to follow up any of your ideas.

3:17:49You're not going to have a career. You can't, you're not going to have resources to do anything, but it's very important that that can't be the only thing. You need another part of your mind that ignores all that shit completely because this other part of your mind has to be pure. It has to be, I don't care what anybody else thinks about this. I don't care whether this is publishable, describable. I don't care if anybody gets it. I don't care if anybody thinks it's stupid. This is, this is what I, what I think and why, and, and give it space to, to sort of grow. Right. And if you keep the, if you try to mush them, if you try to mush them together, I found that impossible because, because the practical stuff poisons the other stuff.

3:18:22If you're, if you're too much, if you're too much on the creative end, you can be an amazing thinker. It just, nothing ever materializes. But if you're very practical, it tends to poison the other stuff because the more you think about how to present things so that other people get it, it constrains and it bends how you start to think. And, you know, what I tell my students and others is there's two kinds of advice. There's very practical, specific things. Like somebody says, well, you forgot this control or this isn't the right method or you shouldn't be. That stuff is gold and you should take that very seriously and you should use it to improve your craft.

3:19:00Right. And that's like super important. But then there's the meta advice where people like that's not a good way to think about it don't work on this this isn't that that stuff is is garbage and and even very successful people often give very constraining terrible advice like one of my one of my reviewers in a paper years ago said i love this freudian slip he said he's going to give me constrictive criticism right and that's exactly what he gave me was constrictive criticism i was like that's awesome uh that's a great typo it was very true. I mean, that second, the bifurcation of the mind is beautifully put.

3:19:32I do think some of the most interesting people I've met are sometimes fall short on the normie side, on the practical, how do I, having the emotional intelligence of how do I communicate this with people that have a very different worldview, that are more conservative and more conventional and more kind of fit into the norm. You have to be able to have the skill to fit in. And then you have to, again, beautifully put, be able to shut that off when you go on your own and think. And having two skills is very important. I think a lot of radical thinkers think that they're sacrificing something by learning the skill of fitting in.

3:20:15But I think if you want to have impact, if you want ideas to resonate and actually lead to, first of all, be able to build great teams that help bring your ideas to life. And second of all, for your ideas to have impact and to scale and to resonate with a large number of people, you have to have that skill. And those are very different. Those are very different. Let me ask a ridiculous question. You already spoke about it, but what to you is one of the most beautiful ideas that you've encountered in your various explorations? maybe maybe not just beautiful but one that makes you happy to be a scientist to be able to be a curious humans exploring ideas i mean i must say that you know i i sometimes think about um these these ingressions from this from this space as a kind of steganography you know so so steganography is is when you hide data and messages within the the bits of another pattern that don't matter right and the rule of steganography is you can't mess up the main thing you know it's a picture of a cat or whatever you got to keep the cat but if there's bits that don't matter you can kind of stick stuff so i feel like i feel like all these aggressions are a kind of universal steganography that there's this like these patterns seep into everything everywhere they can and they're kind of they're kind of shy meaning that they're very subtle not invisible if you work hard you can catch them but but they're not invisible but but but they're hard to see and the fact that the fact that i think they also affect quote-unquote machines as much as they certainly affect living living organisms i think is incredibly incredibly beautiful and i personally am happy to be part of that same spectrum and the fact that that that magic is sort of uh applicable to everything i a lot of people find that extremely disturbing and that's that's some of the some of the hate mail i get is like yeah we were with you you know on the majesty of life thing until you got to the fact that machines get it too.

3:22:19And now, now like, like terrible, right. You're kind of devaluing the, the, the majesty of life. And I don't, I don't know. I, I, the, the idea that we're now catching these patterns and we're able to do meaningful research on the, on the interfaces and all that is just to me, absolutely beautiful. And that, that it's all one spectrum. I think to me is, is amazing. I'm, I'm, I'm, I'm enriched by it. I agree with you. I think it's incredibly beautiful. I lied. There's a more ridiculous question uh so it seems like we are progressing towards possibly creating a super intelligence system um and agi and asi uh if i had one gave it to you put you in the room what would be the first question you ask it maybe the first set of questions like there's so many topics that you've worked on and interested in is there like a first question that you really just if you can get an answer solid answer well the first thing i would ask is uh how much should i even be talking to uh for sure because it's not clear to me at all that getting somebody to tell you an answer in the long run is optimal it's the difference between when you're a kid learning math and having an older sibling that'll just tell you the answers right like sometimes it's just like come just give me the answer let's move on with this you know cancer protocol and whatever like great but in the long run the process of discovering it yourself how much of that are we willing to give up and by getting a final answer how much have we missed of stuff we might have found along the way now i don't know what the the thing is i you know i i don't think it's correct to say don't do that at all you know take take the time and all the blind alleys and like that may not be optimal either, but we don't know what the optimal is.

3:24:12We don't know how much we should be stumbling around versus having somebody tell us the answer. That's actually a brilliant question to ask AGI then. I mean, if it's really an AGI, I'm like, tell me what the balance is. Like how much should I be talking to you versus stumbling around in the lab and making all my, you know, all my own mistakes. It was at 70, 30, you know, 10, 90. I don't know. So that would be, that would be the first. And then the AGI will say you shouldn't be talking to me. It may well be. It may say, what the hell did you make me for in the first place? You guys are screwed.

3:24:41Like, that's possible. Yeah. You know, the second question I would ask you is, what's the answer I should be asking you that I probably am not smart enough to ask you? That's the other thing I would say. This is really complicated. That's a really, really strong question. but again there the answer might be you wouldn't understand the question it proposes most likely so i think for me i would probably assuming you can get a lot of questions i'll probably go for questions where i would understand the answer like it would uncover some small mystery that i'm super curious about because if you ask big questions like you did, which is really strong questions, I just feel like I wouldn't understand the answer.

3:25:29If you ask it, what question should I be asking you? It would probably say something like, what is the shape of the universe? And you're like, why is that important? You would be very confused by the question it proposes. It would just be nice for me to know, straight up, first question, how many living, intelligent alien civilizations are in the observable universe yeah that would just be nice yeah to know if is it zero or is it a lot i just want to know that and then unfortunately it might answer it might it might be a give me a a mike 11 answer that's what i was about to say is that my guess is it's going to be exactly the problem you said which is it's going to say oh my god i mean right in this room you got you know like oh man yeah yeah everything you need to know about alien civilizations is right here in this room in fact it's inside your own body just for starters agi thank you all right michael dear one of my favorite scientists one of my favorite humans thank you for everything you do in this world thank you so much truly truly fascinating work and keep going for all of us.

3:26:45Thank you so much. It's great to see you. It's always a good discussion. Thank you so much. I appreciate it. Thank you. Thanks for listening to this conversation with Michael Levin. To support this podcast, please check out our sponsors in the description where you can also find links to contact me, ask questions, get feedback, and so on. And now, let me leave you with some words from Albert Einstein. The most beautiful thing we can experience is the mysterious. It is the source of all true art and science. Thank you for listening. I hope to see you next time.