Michael Levin: Biology, Life, Aliens, Evolution, Embryogenesis & Xenobots | Lex Fridman Podcast #325

It turns out that if you train a planarian and then cut their heads off, the tail will regenerate a brand new brain that still remembers the original information. I think planaria hold the answer to pretty much every deep question of life. For one thing, they're similar to our ancestors. So they have true symmetry, they have a true brain. They're not like earthworms. They're much more advanced life form. They have lots of different internal organs, but they're these little. They're about maybe 2 centimeters in the centimeter to 2 in size. They have a head and a tail. And the first thing is planaria are immortal, so they do not age. There's no such thing as an old planarian. So that right there tells you that these theories of thermodynamic limitations of on lifespan are wrong. It's not that. Well, over time everything degrades. No planaria can keep it going for probably, you know, how long have they been around? 400 million years. Right. So these are the actual. So the planaria in our lab are actually in physical continuity with planaria that were here 400 million years ago.

The following is a conversation with Michael Levin, one of the most fascinating and brilliant biologists I've ever talked to. He and his lab at Tufts University works on novel ways to understand and control complex pattern formation in biological systems. Andrej Karpathy, a world class AI researcher, is the person who first introduced me to Michael Levin's work. I bring this up because these two people make me realize that biology has a lot to teach us about AI and AI might have a lot to teach us about biology. This is the Lex Friedman podcast to support it. Please check out our sponsors in the description. And now, dear friends, here's Michael Levin. Embryogenesis is the process of building the human body from a single cell. I think it's one of the most incredible things that exists on earth from a single embryo. So how does this process work?

Yeah, it is an incredible process. I think it's maybe the most magical process there is. And I think one of the most fundamentally interesting things about it is that it shows that each of us takes the journey from so called just physics to mind, because we all start life as a single quiescent unfertilized oocyte. And it's basically a bag of chemicals. And you look at that and you say, okay, this is chemistry and physics. And then nine months and some years later you have an organism with high level cognition and preferences and an inert life and so on. And what embryogenesis tells Us is that that transformation from physics to mind is gradual, it's smooth. There is no special place where, you know, a lightning bolt says boom. Now you've gone from physics to true cognition. That doesn't happen. And so we can see in this process that the whole mystery, you know, the biggest mystery of the, of the universe, basically how you get mind from.

Matter from just physics in quotes.

Yeah.

So where's the magic into the thing? How do we get from information encoded in DNA and make physical reality out of that information?

So one of the things that I think is really important if we're going to bring in DNA into this picture is to think about the fact that what DNA encodes is the hardware of life. DNA contains the instructions for the kind of micro level hardware that every cell gets to play with. So all the proteins, all the signaling factors, the ion channels, all the cool little pieces of hardware that cells have, that's what's in the DNA. The rest of it is in so called generic laws. And these are laws of mathematics, these are laws of computation, these are laws of physics of all kinds of interesting things that are not directly in the DNA. And that process. I think the reason I always put just physics in quotes is because I don't think there is such a thing as just physics. I think that thinking about these things in binary categories like this is physics, this is true cognition, this is as if it's only faking these kinds of things. I think that's what gets us in trouble. I think that we really have to understand, understand that it's a continuum and we have to work up the scaling, the laws of scaling, and we can certainly talk about that. There's a lot of really interesting thoughts to be had there.

So the physics is deeply integrated with the information. So the DNA doesn't exist on its own. The DNA is integrated as in some sense in response to the laws of physics at every scale. The laws of the environment it exists in.

Yeah, the environment and also the laws of the universe. I mean the thing about the DNA is that it's. Once evolution discovers a certain kind of machine, that if the physical implementation is appropriate, it's sort of. And this is hard to talk about because we don't have a good vocabulary for this yet, but it's a very kind of Platonic notion that if the machine is there, it pulls down interesting things that you do not have to evolve from scratch because the laws of physics give it to you for free. So just as a really stupid example, if you're trying to evolve A particular triangle, you can evolve the first angle and you evolve the second angle, but you don't need to evolve the third. You know what it is already. Now why do you know that's a gift for free from geometry in a particular space, you know what that angle has to be. And if you evolve an ion channel, which is. Ion channels are basically transistors, right? They're voltage gated current conductances. If you evolve that ion channel, you immediately get to use things like truth tables, you get logic functions. You don't have to evolve the logic function, you don't have to evolve a truth table, it doesn't have to be in the DNA, you get it for free. And the fact that if you have NAND gates, you can build anything you want, you get that for free. All you have to evolve is that first step, that first little machine that enables you to couple to those laws and there's laws of adhesion and many other things. And this is all that interplay between the hardware that's set up by the genetics and the software. That's right, the physiological software that basically does all the computation and the cognition and everything else is a real interplay between the information and the DNA and the laws of physics, of computation and so on.

So is it fair to say, just like this idea that the laws of mathematics are discovered, they're latent within the fabric of the universe, in that same way the laws of biology are kind of discovered?

Yeah, I think that's absolutely. And it's probably not a popular view, but I think that's right on the money.

Yeah, well, I think that's a really deep idea then. Embryogenesis is the process of revealing, of embodying, of manifesting these laws. You're not building the laws, you're just creating the capacity to reveal.

Yes, I think, again, not the standard view of molecular biology by any means, but I think that's right on the money. I'll give you a simple example. Some of our latest work with these xenobots, right? So what we've done is to take some skin cells off of an early frog embryo and basically ask about their plasticity. If we give you a chance to sort of reboot your multicellularity in a different context, what would do. Because what you might assume the thing about embryogenesis is that it's super reliable, right. It's very robust and that really obscures some of its most interesting features. We get used to it. We get used to the fact that acorns make oak trees and frog eggs make frogs and we say, well what else is it going to make? That's what it makes. That's a standard story. But the reality is, and so you look at these skin cells and you say, well what do they know how to do? Well they know how to be a passive, boring, two dimensional outer layer keeping the bacteria from getting into the embryo. That's what they know how to do. Well, it turns out that you take these skin cells and you remove the rest of the embryo. So you remove all of the rest of the cells and you say, well you're by yourself now, what do you want to do? So what they do is they form this multi little creature that runs around the dish. They have all kinds of incredible capacities. They navigate through mazes, they have various behaviors that they do both independently and together. Basically they implement Von Neumann's dream of self replication. Because if you sprinkle a bunch of loose cells into the dish, what they do is they run around, they collect those cel piles, they sort of mush them together until those little piles become the next generation of xenobots. So you've got this machine that builds copies of itself from loose material in its environment. None of this are things that you would have expected from the frog genome. In fact, the genome is wild type. There's nothing wrong with their genetics. Nothing has been added, no nanomaterials, no genomic editing, nothing. And so what we have done there is engineer by subtraction. What you've done is you removed the other cells that normally basically bully these cells into being skin cells and you find out that what they really want to do is to be this. Their default behavior is to be a xenobot. But in vivo in the embryo, they get told to be skinned by these other cell types. So now here comes this really interesting question that you just posed. When you ask where does the form of the tadpole and the frog come from? The standard answer is, well, it's selection. So over millions of years it's been shaped to produce the that's fit for froggy environments. Where does the shape of the xenobot come from? There's never been any xenobots, there's never been selection to be a good xenobot. These cells find themselves in the new environment in 48 hours. They figure out how to be an entirely different proto organism with new capacities like kinematic self replication. That's not how frogs or tadpoles replicate. We've made it impossible for them to replicate their normal way. Within a couple days, these guys find a new way of Doing it. That's not done anywhere else in the biosphere.

Well, actually, let's step back and define what are xenobots.

So a xenobot is a self assembling little proto organism. It's also a biological robot. Those things are not distinct. It's a member of both classes.

How much is a biology, how much is it robot?

At this point, most of it is biology because what we're doing is we're discovering natural behaviors of the cells and also of the cell collectives. Now, one of the really important parts of this was that we're working together with Josh Baungaert's group at University of Verm. Computer scientists do AI and they've basically been able to use an evolutionary, a simulated evolution approach to ask how can we manipulate these cells, give them signals, not rewire their DNA. So not hardware, but experience signals. So can we remove some cells, can we add some cells, can we poke them in different ways to get them to do other things? So in the future there's going to be. We're now, and this is future unpublished work, but we're doing all sorts of interesting ways to reprogram them to new behaviors. But before you can start to reprogram these things, you have to understand what their innate capacities are.

Okay, so that means engineering, programming, you're engineering them in the future. And in some sense the definition of a robot is something you in part engineer versus evolve. I mean, it's such a fuzzy definition anyway. In some sense, many of the organisms within our body are kinds of robots.

Yes.

And I think robots is a weird line because it's. We tend to see robots as the other. I think there will be a time in the future when there's going to be something akin to the civil rights movements for robots. But we'll talk about that later perhaps. Sure. Anyway, so how do you. Can we just linger on it? How do you build a xenobot? What are we talking about here? From, from whence does it start? And how does it become the glorious xenobot?

Yeah, so just to take one step back, one of the things that a lot of people get stuck on is they say, well, engineering requires new DNA circuits or it requires new nanomaterials. The thing is, we are now moving from old school engineering which used passive materials, things like wood, metal, things like this, that basically the only thing you could depend on is that they were going to keep their shape. That's it. They don't do anything else. You, it's on you as an engineer to make them do everything they're going to do. And then there were active materials and now computational. This is a whole new era. These are agential materials. This is your. You're now collaborating with your substrate because your material has an agenda. These cells have, you know, billions of years of evolution. They have goals, they have preferences. They're not just going to sit where you put them.

That's hilarious, that you have to talk your material and to keep it shaped.

That is exactly right. That is exactly right. That is.

It's like getting a bunch of cats or something and trying to organize the shape out of them.

It's funny we're on the same page here because in a paper, this is, this has currently just been accepted in nature bioengineering. One of the figures I have is building a tower out of Legos versus dogs, right? So think about the difference, right? If you build out of Legos, you have full control over where it's going to go. But if somebody knocks it over, it's game over with the dogs. You cannot just come and stack them. They're not going to stay that way. But the good news is that if you train them, then somebody knocks it over, they'll give right back up. So it's all right. So as an engineer, what you really want to know is what can I depend on this thing to do? Right? That's really, you know, a lot of people have definitions of robots as far as what they're made of or how they got here, you know, design versus evolve, whatever. I don't think any of that is useful. I think, I think as an engineer, what you want to know is how much can I depend on this thing to do when I'm not around to micromanage it? What level of dependency can I. Can I give this thing, how much agency does it have? Which then tells you what techniques do you use? So do you use micromanagement? Like you put everything where it goes, do you train it, do you give it signals, do you try to convince it to do things right? How much you know, how intelligent is your substrate? And so now we're moving into this, into this area where you're working with agential materials. That's a collaboration, that's not old style.

What's the word you're using? Agential.

Agential? Yeah.

What's that mean?

Agency. It comes from the word agency. So basically the material has agency, meaning that it has some level of, obviously not human level, but some level of preferences, goals, memories, ability to remember things, to compute into the future, meaning, anticipate when you're working with cells, they have all of that to various degrees.

Is that empowering or limiting? Having material has a mind of its own, literally, I think it's both.

Right. So it raises difficulties because it means that if you're using the old mindset, which is a linear kind of extrapolation of what's going to happen, you're going to be surprised and shocked all the time. Because biology does not do what we linearly expect materials to do. On the other hand, it's massively liberating. And so in the following way, I've argued that advances in regenerative medicine require us to take advantage of this, because what it means is that you can get the material to do things that you don't know how to micromanage. So just as a simple example, right, if you, if you had a rat and you wanted this rat to do a circus trick, put a ball in the little hoop, you can do it the micromanagement way, which is try to control every neuron and try to play the thing like a puppet, right? And maybe someday that'll be possible. Maybe. Or you can train the rat. And this is why humanity, for thousands of years, before we knew any neuroscience, we had no idea what's between the ears of any animal. We were able to train these animals because once you recognize the level of agency of a certain system, you can use appropriate techniques. If you know the currency of motivation, reward and punishment, you know how smart it is, you know what kinds of things it likes to do, you are searching a much more, much smoother, much nicer problem space than if you try to micromanage the thing. And in regenerative medicine, when you're trying to get, let's say, an arm to grow back, or an eye to repair or say a birth defect or something, do you really want to be controlling tens of thousands of genes at each point to try to micromanage it, or do you want to find the high level modular controls that say, build an arm here, you already know how to build an arm. You did it before, do it again. So that's, I think it's both, it's both difficult and it challenges us to develop new ways of engineering. And it's hugely empowering.

Okay, so how do you do, I mean, maybe sticking with the metaphor of dogs and cats, I presume you have to figure out the find the dogs and dispose of the cats, because, you know, it's like the old herding cats is an issue. So you may be able to train dogs, I suspect you will not be able to train cats, or if you do, you're never going to be able to trust them. So is there a way to figure out which material is amenable to herding? Is it in the lab work or is it in simulation?

Right now it's largely in the lab because we are, our simulations do not capture yet the most interesting and powerful things about biology. So the simulation, what we're pretty good at simulating are feedforward emergent types of things, right? So cellular automata, if you have simple rules and you sort of roll those forward for every agent or every cell in the simulation and complex things happen, you know, ant colony algorithms, things like that, we're good at that and that's fine. The difficulty with all of that is that it's incredibly hard to reverse. So this is a really hard inverse problem, right? If you look at a bunch of termites and they make up a thing with a single chimney and you say, well I like it, but I'd like two chimneys, how do you change the rules of behavior for each termite? So they make two chimneys, right? Or if you say here are a bunch of cells that are creating this kind of organism, I don't think that's optimal. I'd like to repair that birth defect. How do you control all the individual low level rules, right? All the protein interactions and everything else. Rolling it back from the anatomy that you want to the low level hardware rules is in general intractable. It's an inverse problem that's generally not solvable. So right now it's mostly in the lab because what we need to do is we need to understand how biology uses top down controls. So the idea is not bottom up emergence, but the idea of things like goal directed test operate, exit kinds of loops where it's basically an error minimization function over a new space. Not a space of gene expression, but for example, a space of anatomy. So just as a simple example, if you have a salamander, it's got an arm, you can amputate that arm, anywhere along the length of it will grow, exactly what's needed and then it stops. That's the most amazing thing about regeneration, is that it stops. It knows when to stop, when does it stop? It stops when a correct salamander arm has been completed. So that tells you that's a means ends kind of analysis where it has to know what the correct limb is supposed to look like. So it has a way to ascertain the current shape, it has a way to measure that delta from what shape it's supposed to be, and then it will keep taking actions, meaning remodeling and growing and everything else until that's complete. So once you know that, and we've taken advantage of this in the lab to do some really wild things with both planaria and frog embryos and so on. Once you know that, you can start playing with that homeostatic cycle. You can ask, for example, well, how does it remember what the correct shape is? And can we mess with that memory? Can we give it a false memory of what the shape should be and let the cells build something else? Or can we mess with the measurement apparatus? Right, so it gives you those kinds of. So the idea is to basically appropriate a lot of the approaches and concepts from cognitive neuroscience and behavioral science into things that previously were taken to be dumb materials. And you get yelled at in class for being anthropomorphic if you said, well, my cells want to do this and my cells want to do that. And I think that's a major mistake. That leaves a ton of capabilities on the table.

So thinking about biological systems as things that have memory have almost something like cognitive ability. But I mean, how incredible is it, you know, that the salamander arm is being rebuilt? Not with a dictator. It's kind of like the cellular automata system. All the individual workers are doing their own thing. So where's that top down signal that does the control coming from? Like, how can you find it? Why does it stop growing? How does it know the shape? How does it have memory of the shape? And how does it tell everybody to be like, whoa, whoa, whoa, slow down, we're done.

So the first thing to think about, I think, is that there are no examples anywhere of a central dictator in this kind of science, because everything is made of parts. And so we, even though we feel as a unified, central sort of intelligence and kind of point of cognition, we are a bag of neurons, right? All intelligence is collective intelligence. This is important to think about because a lot of people think, okay, there's real intelligence, like me, and then there's collective intelligence, which is ants and flocks of birds and termites and things like that. And maybe it's appropriate to think of them as an individual, and maybe it's not. A lot of people are skeptical about that and so on, but you've got to realize that, that there's no such thing as this indivisible diamond of intelligence that's like this one central thing that's not made of parts. We are all made of parts. And so if you believe, which I think is hard to get around, that we in fact have a centralized set of goals and preferences and we plan and we do things and so on. You are already committed to the fact that a collection of cells is able to do this because we are a collection of cells. There's no getting around that. In our case, what we do is we navigate the three dimensional world and we have behavior.

This is blowing my mind right now because we are just a collection of cells.

Oh, yeah, yeah.

So when I'm moving this arm, I feel like I'm the central dictator of that action. But there's a lot of stuff going on. Like every, all, all the cells here are collaborating in some interesting way. They're getting signal from the central nervous system.

Well, even the central nervous system is misleadingly named because it isn't really central. Again, it's just a bunch of cells. It's just a bunch of cells. I mean, all of. Right. There are no singular indivisible intelligences anywhere. We are all. Every example that we've ever seen is a collective of something. It's just that we're used to it. We're used to that. We're used to, okay, this thing is kind of a single thing, but it's really not. You zoom in, you know what you see? You see a bunch of cells running around.

And so is there some unifying? I mean, we're just jumping around. But that something that you look as the bioelectrical signal versus the biochemical, the chemistry, the electricity, maybe the life is in that versus the cells. There's an orchestra playing and the resulting music is the dictator.

That's not bad. That's Dennis Noble's kind of view of things. He has two really good books where he talks about this musical analogy, right? So I think I like it. I like it.

Is it wrong, though?

I don't think it's. No, I don't think it's wrong. I don't think it's wrong. I think the important thing about it is that we have to come to grips with the fact that a true proper cognitive intelligence can still be made of parts. Those things are. And in fact it has to be. And I think it's a real shame, but I see this all the time when you have a collective like this, whether it be a group of robots or a collection of cells or neurons or whatever. As soon as we gain some insight into how it works. Meaning that. Oh, I see. In order to take this action, here's the information that got processed via this chemical mechanism or whatever, immediately people say, oh, well, then that's not real cognition. That's just physics. I think this is fundamentally flawed because if you zoom into anything, what are you going to see? Of course, you're just going to see physics. What else could be underneath? Right. It's not going to be fairy dust. It's going to be physics and chemistry. But that doesn't take away from the magic of the fact that there are certain ways to arrange that physics and chemistry, and in particular the bioelectricity, which I like a lot to give you an emergent collective with goals and preferences and memories and anticipations that do not belong to any of the subunits. So I think what we're getting into here, and we can talk about how this happens during embryogenesis and so on, what we're getting into is the origin of a self with a capital S. So we ourselves, there are many other kinds of selves, and we can tell some really interesting stories about where selves come from and how they become unified.

Yeah. Is this the first, or at least humans tend to think that this is the level of which the self with a capital S is first born. And we really don't want to see human civilization or Earth itself as one living organism. Yeah, that's very uncomfortable to us.

It is. Yeah.

But is. Yeah. Where's the self born?

We have to grow up past that. So what I like to do is. Well, I'll tell you two quick stories about that. I like to roll backwards. So. So as opposed to. So if you start and you say, okay, here's a paramecium, and you see it, you know, it's a single cell organism, you see it doing various things, and people will say, okay, I'm sure there's some chemical story to be told about how it's doing it. So if that's not true cognition. Right. And people will argue about that, that I like to work it backwards, I say let's agree that you and I as we sit here are examples of true cognition. If anything is. If there's anything that's true cognition, we are examples of it. Now let's just roll back slowly, right? So you roll back to the time we were a small child and used to doing whatever, and then just sort of day by day, you roll back and eventually you become more or less that paramecium, and then you sort of even below that. Right. As an unfertilized osa. So no one has, to my knowledge, no one has come up with any convincing discrete step at which my cognitive powers disappear.

Right.

It just doesn't. The biology doesn't offer any specific step. It's incredibly smooth and slow and continuous. And so I think this idea that it just sort of magically shows up at one point and then, and then, you know, humans have true selves that don't exist elsewhere. I think it runs against everything we know about evolution, everything we know about developmental biology. These are all slow continua. And the other really important story I want to tell is where embryos come from. So think about this for a second. Amniote embryo. So this is humans, birds and so on, mammals and birds and so on. Imagine a flat disk of cells. So there's maybe 50,000 cells. And in that. So when you get an egg from a fertilized, let's say you buy a fertilized egg from a farm, right? That egg will, will have about 50,000 cells in a flat disk. It looks like a little tiny little Frisbee. And in that flat disk, what will happen is there'll be one set of cells will become special and it will tell all the other cells, I'm going to be the head, you guys don't be the head. And so it'll amplify, symmetry breaking amplification. You get one embryo, there's some neural tissue and some other stuff forms. Now you say, okay, I had one egg and one embryo, and there you go. What else could it be? Well, the reality is, and I used to, I did all of this as a grad student. If you take a little needle and you make a scratch in that blastoderm, in that disc, such that the cells can't talk to each other for a while it heals up. But for a while they can't talk to each other. What'll happen is that both regions will decide that they can be the embryo and there will be two of them. And then when they heal up, they become conjoint twins. And you can make two, you can make three, you can make lots. The question of how many selves are in there cannot be answered until it's actually played all the way through. It isn't necessarily that there's just one, there can be many. So what you have is you have this medium, this undifferentiated. I'm sure there's a psychological version of this somewhere that I don't know the proper terminology, but you have this ocean of potentiality. You have these thousands of cells and some number of individuals are going to be formed out of it. Usually one, sometimes zero, sometimes several. And they form out of these cells because a region of these cells organizes into a collective that will have goals, goals that individual cells don't have. For example, make a limb, make an eye. How many eyes? Well, exactly two. So individual cells don't know what an eye is. They don't know how many eyes you're supposed to have, but the collective does. The collective has goals and memories and anticipations that the individual cells don't. And the establishment of that boundary with its own ability to pursue certain goals, that's the origin of selfhood.

But is that goal in there somewhere? Were they always destined? Are they discovering that goal? Where the hell did evolution discover this? When you went from the prokaryotes to eukaryotic cells, and then they started making groups. And when you make a certain group, you make a. You make it sound such a tricky thing to try to understand. You make it sound like this. Cells didn't get together and came up with a goal. But the very act of them getting together revealed the goal that was always there. There was always that potential for that goal.

So the first thing to say is that there are way more questions here than certainties. Okay? So everything I'm telling you is cutting edge developing stuff. So it's not as if any of us know the answer to. But here's my opinion on this. I think what evolution. I don't think that evolution produces solutions to specific problems. In other words, specific environments. Like here's a frog that can live well in a froggy environment. I think what evolution produces is problem solving machines that will solve problems in different spaces. So not just three dimensional space. This goes back to what we were talking about before. The brain is evolutionarily a late development. It's a system that is able to pursue goals in three dimensional space by giving commands to muscles. Where did that system come from? That system evolved from a much more ancient, evolutionarily much more ancient system where collections of cells gave instructions for cell behaviors, meaning cells move to divide, to die, to change into different cell types, to navigate morphous space, the space of anatomies, the space of all possible anatomies. And before that, cells were navigating transcriptional space, which is a space of all possible gene expressions. And before that, metabolic space. So what evolution has done, I think, is produced hardware that is very good at navigating different spaces using a bag of tricks, which I'm sure many of them we can steal for autonomous vehicles and robotics and various things. And what happens is that they navigate these spaces without a whole Lot of commitment to what the space is. In fact, they don't know what the space is. We are all brains in a vacuum that so to speak, every cell does not know. Every cell is some other cell's external environment. Right. So where does that border between you and the outside world? You don't really know where that is. Right. Every collection of cell has to figure that out from scratch. And the fact that evolution requires all of these things to figure out what they are, what effectors they have, what sensors they have, where does it make sense to draw a boundary between me and the outside world? The fact that you have to build all that frame from scratch. This autopoiesis is what defines the border of a self. Now biology uses a multi scale competency architecture, meaning that every level has goals. So molecular networks have goals, cells have goals, tissues, organs, colonies, and it's the interplay of all of those that enable biology to solve problems in new ways. For example, in xenobots and various other things, things. This is, you know, it's exactly as you said. In many ways the cells are discovering new ways of being, but at the same time, evolution certainly shapes all this. So evolution is very good at this agential bioengineering. Right. When evolution is discovering a new way of being an animal, you know, an animal or a plant or something, sometimes it's by changing the hardware, you know, protein, changing protein structure and so on. But much of the time it's not by changing the hardware, it's by changing the signals that the cells give to each other. It's doing what we as engineers do, which is try to convince the cells to do various things by using signals, experiences, stimuli. That's what biology does. It has to, because it's not dealing with a blank slate every time. If you're evolution and you're trying to make an organism, you're not dealing with a passive material that is fresh and you have to specify it already wants to do certain things. So the easiest way to do that search to find whatever is going to be adaptive is to find the sign are going to convince cells to do various things. Right.

Your sense is that evolution operates both in the software and the hardware and it's just easier and more efficient to operate in the software.

Yes, and I should also say I don't think the distinction is sharp. In other words, I think it's a continuum, but I think it's a meaningful distinction where you can make changes to a particular protein and now the enzymatic function is different and it metabolizes differently and whatever. And that will have implications for fitness. Or you can change the huge amount of information in the genome that isn't structural at all. It's signaling. It's when and how do cells say certain things to each other. And that can have massive changes as far as how it's going to solve problems.

I mean, this idea of multi hierarchical competence architecture, which is incredible to think about. So this hierarchy that evolution builds, I don't know who's responsible for this. I also see the incompetence of bureaucracies of humans when they get together. So how the hell does evolution build this? Where at every level only the best get to stick around. They somehow figure out how to do their job without knowing the bigger picture. And then there's like the bosses that do the bigger things somehow. Or that you can now abstract away the small group of cells as an organ or something, and then that organ does something bigger in the context of the full body or something like this. How is that built? Is there some intuition you can kind of provide of how that's constructed? That hierarchical competence architecture? I love that competence. Just the word competence is pretty cool in this context because everybody's good at their job somehow.

Yep. No, it's really key. And the other nice thing about competency is that. So my central belief in all of this is that engineering is the right perspective on all of this stuff because it gets you away from subjective terms. People talk about sentience and this and that, those things very hard to define. People argue about them philosophically. I think that engineering terms like competency, like pursuit of goals, all of these things are empirically incredibly useful because you know it when you see it and if it helps you build right. If I can pick the right level, I say this thing has. I believe this is X level of competency. I think it's like a thermostat or I think it's like a better thermostat, or I think it's various other kinds of many different kinds of complex systems. If that helps me to control and predict and build such systems, then that's all there is to say. There's no more philosophy to argue about. So I like competency in that way because you can quantify. In fact, you have to, you have to make a claim competent at what? Or if I say, if I tell you it has a goal, the question is what's the goal? And how do you know? And I say, well, because every time I deviated from this particular state, that's what it spends energy to get back to that's the goal. And we can quantify it and we can be objective about it. And we're not used to thinking about this. I give a talk sometimes called why don't robots get cancer? The reason robots don't get cancer is because generally speaking, with a few exceptions, our architectures have been, you've got a bunch of dumb parts and you hope that if you put them together, the overlying machine will have some intelligence and do something rather. But the individual parts don't care. They don't have an agenda. Biology isn't like that. Every level has an agenda. And the final outcome is the result of cooperation and competition both within and across levels. So for example, during embryogenesis, your tissues and organs are competing with each other. And it's actually a really important part of development. There's a reason they compete with each other. They're not all just sort of helping each other. They're also competing for information, for metabolic, for limited metabolic constraints. But to get back to your other point, which is this seems really efficient and good and so on compared to some of our human efforts parts. We also have to keep in mind that what happens here is that each level bends the option space for the level beneath, so that your parts basically they don't see the geometry. So I'm using, and I think I take this seriously, terminology from relativity where the space is literally bent, so the option space is deformed by the higher level, so that the lower levels, all they really have to do is go down their concentration gradient. They don't have to, in fact, they don't. They can't know what the big picture is. But if you bend the space just right, if they do what locally seems right, they end up doing your bidding. They end up doing things that are optimal in the higher space. Conversely, because the components are good at getting their job done, you as the higher level don't need to try to compute all the low level controls. All you're doing is bending the space. You don't know or care how they're going to do it. Give you a super simple example in the tap over we found that, okay, so tadpoles need to become frogs. And to go from a tadpole head to a frog head, you have to rearrange the face. So the eyes have to move forward, the jaws have to come out, the nostrils move, like everything moves. It used to be thought that because all tadpoles look the same and all frogs look the same, if you just remember, if every piece just moves in the right direction, the Right amount, then you get your frog, right? So we decided to test. I had this hypothesis that I thought actually the system is probably more intelligent than that. So what did we do?

Do?

We made what we call Picasso tadpoles. So everything is scrambled. So the eyes are on the back of the head, their jaws are off to the side. Everything is scrambled. Well, guess what they make? They make pretty normal frogs because all the different things move around in novel paths, configurations until they get to the correct froggy sort of frog face configuration. Then they stop. So the thing about that is, now imagine evolution, right? So you make some sort of mutation and it does, like every mutation, it does many things, so something good comes of it. But also it moves your mouth off to the side, right? Now, if there wasn't this multiscale competency, you can see where this is going. If there wasn't this multiscale competency, the organism would be dead. Your fitness is zero because you can't eat. And you would never get to explore the other beneficial consequences of that mutation. You'd have to wait until you find some other way of doing it without moving the mouth. That's really hard. So the fitness landscape would be incredibly rugged. Evolution would take forever. The reason it works, one of the reasons it works so well is because you do that, no worries, the mouth will find its way where it belongs, right? So now you get to explore. So what that means is that all of these mutations that otherwise would be deleterious are now neutral because the competency of the parts make up for all kinds of things. So all the noise of development, all the variability in the environment, all these things, the competency of the parts makes up for it. So that's all fantastic, right? That's all great, right? The only other thing to remember when we compare this to human efforts is this. Every component has its own goals in various spaces, usually with very little regard for the welfare of the other levels. So as a simple example, you, as a complex system, you will go out and you will do jiu jitsu or whatever, you'll have some go. You have to go rock climbing and scrape a bunch of cells off your hands and then you're happy. As a system, you come back and you've accomplished some goals and you're really happy.

Happy.

Those cells are dead. They're gone, right? Did you think about those cells? Not really. Right. You had some bruising al selfish sob. That's it. And so, and so that's the thing to remember is that you know, and we know this from, from history Is that, is that just being a collective isn't enough? Because what the goals of that collective will be relative to the welfare of the individual parts is a massively open question.

Justify the means. I'm telling you, Stalin was onto something thing.

No, that's the danger.

But we can. Exactly. That's the danger of. For us humans, we have to construct ethical systems under which we don't take seriously the full mechanism of biology and apply it to the way the world functions. Which is, which is an interesting line we've drawn. The world that built us is the one we reject in some sense, when we construct human societies. The idea that this country was founded on, that all men are created equal, that's such a fascinating idea. It's like you're fighting against nature and saying, well, there's something bigger here than a hierarchical competency architecture. But there's so many interesting things you said. So from an algorithmic perspective of the act of bending the option space, that's really, that's really profound because if you look at the way AI systems are built today, there's a big system, like I said with robots, and has a goal, and it gets better and better at optimizing that goal, at accomplishing that goal. But if biology built a hierarchical system where everything is doing computation and everything is accomplishing the goal, not only that, it's kind of dumb, you know, with the, with the limited. With a bent, option space is just doing the thing that's the easiest thing for you in some sense. And somehow that allows you to have turtles on top of turtles. Literally dumb systems on top of dumb systems that as a whole create something incredibly smart.

Yeah, I mean, every system has some degree of intelligence in its own problem domain. So cells will have problems they're trying to solve in physiological space and transcriptional space. And then I could give you some cool examples of that. But the collective is trying to solve problems in anatomical space. Right. And forming a creature and growing your blood vessels and so on. And then the whole body is solving yet other problems. They may be in social space and linguist space and three dimensional space, and who knows, the group might be solving problems in, I don't know, some sort of financial space or something. So one of the major differences with most AIs today is a, the kind of flatness of the architecture, but also of the fact that they're constructed from outside their borders and their where. So to a large extent, and of course there are counterexamples now, but to a large extent, our technology had been such that you Create a machine or a robot, it knows what its sensors are, it knows what its effectors are, it knows the boundary between it and the outside world. All of this is given from the outside. Biology constructs this from scratch. Now the best example of this, that originally in robotics was actually Josh Bongard's work in 2006 where he made these robots that did not know their shape, shape to start with. So like a baby, they sort of floundered around. They made some hypotheses. Well, I did this and I moved in this way. Well, maybe I'm a whatever, maybe I have wheels or maybe I have six legs or whatever, right? And they would make a model and then eventually it would crawl around. So that's, I mean that's really good. That's part of the autopoiesis. But we can go a step further and some people are doing this and we're sort of working on some of this too. Is this idea that. Let's even go back further. You don't even know what sensors you have. You don't know where you end and the outside world begins. All you have is certain things like active inference, meaning you're trying to minimize surprise. You have some metabolic constraints. You don't have all the energy you need. You don't have all the time in the world to think about everything you want to think about. So that means that you can't afford to be a micro reductionist. All this data coming in, you have to coarse grain and say, I'm going to take all this stuff and I'm going to call that a cat. I'm going to take all this, I'm going to call that the edge of the table. I don't want to fall off of and I don't want to know anything about the microstates. What I want to know is what is the optimal way to cut up my world. And by the way, this thing over here, that's me. And the reason that's me is because I have more control over this than I have over any of this other stuff. Stuff. And so now you can begin to. Right, so that self construction, that figuring out, making models of the outside world and then turning that inwards and starting to make a model of yourself, which immediately starts to get into issues of agency and control. Because in order to, if you are under metabolic constraints, meaning you don't have the energy, right, that all the energy in the world you have to be efficient, that immediately forces you to start telling stories about coarse grained agents that do things, things you don't have the energy to Laplace's demon calculate every possible state that's going to happen. You have to coarse grain and you have to say that is the kind of creature that does things, either things that I avoid or things that I will go towards. That's a mate or food or whatever it's going to be. And so right at the base of very simple organisms starting to make models of agents doing things. That is the origin of, of models of free will, basically because you see the world around you as having agency and then you turn that on yourself and you say wait, I have agency too. I do things. And then you make decisions about what you're going to do. So all of this one model is to view all of those kinds of things as being driven by that early need to determine what you are and to do so and to then take actions in the most energetically efficient, efficient space possible. Right.

So free will emerges when you try to simplify, tell a nice narrative about your environment.

I think that's very plausible. Yeah.

You think free will is an illusion. So you're kind of implying that it's a useful hack.

Well, I'll say two things. The first thing is I think it's very plausible to say that any organism that self or any agent that's self, whether it's biological or not, any agent that self constructs under energy constraints is going to believe in free will. We'll get to whether it has free will momentarily, but I think what it definitely drives is a view of yourself and the outside world as an agential view. I think that's inescapable.

So that's true for even primitive organisms.

I think so I think that's. Now they don't have. Now obviously you have to scale down. Right. So they don't have the kinds of complex metacognition that we have. So they can do long term planning and thinking about free will and so and so on.

But, but the sense of agency is really useful to accomplish tasks simple or complicated.

That's right. In, in all kinds of spaces, not just in, in obvious three dimensional space. I mean we're very good that. The thing is, humans are very good at detecting agency of, of like medium sized objects moving at medium speeds. In the three dimensional world. Right. We see a bowling ball and we see a mouse and we immediately know what the difference is. Right. And how we're going to.

Mostly things you can eat or get eaten by.

Yeah, yeah, that's our, that's our training set. Right. From the time you're little, your training set is visual data on, on this, this like little chunk of your experience. But imagine if, imagine if from the time that we were born we had innate senses of your blood chemistry. If you could feel your blood chemistry the way you can see, right. You had a high bandwidth connection and you could feel your blood chemistry and you could see, you could sense all the things that your organs were doing. So your pancreas, your liver, all the things if, if we had that, that we would be very good at detecting intelligence in physiological space. We would know the level of intelligence that our various organs were deploying to deal with things that were coming to anticipate the stimuli to, you know, but, but we're just terrible at that. We don't. In fact, in fact people don't even, you know, you talk about intelligence of these other spaces and a lot of people think that's just crazy because, because all we're, all we know is motion.

We do have access to that information. So it's actually possible that, so evolution could, if we wanted to construct an organism that's able to perceive the flow of blood through your body the way you see an old friend and say, yo, what's up? How's the wife and the kids? In that same way you would see, you would feel like a connection to the liver. Yeah, yeah, I think, you know, maybe other people's liver. No, just your own. Because you don't have access to other people's liver.

Not yet. But you could imagine some really interesting connection, right?

Like sexual selection. Like, oh, that girl's got a nice liver. Well, that's like the, the way her blood flows, the, the dynamics of the blood is very interesting. It's novel. I've never seen one of those.

But you know, that's, that's exactly what we're, we're trying to half ass. When we, when we judge judgment of beauty by facial symmetry and so on, that's, that's a half assed assessment of exactly that. Of exactly that. Because if your cells could not cooperate enough to keep your, your organism symmetrical.

Yeah.

You know, you can make some inferences about what else is wrong. Right. Like that's a, that's a very, you know, that's a very basic, basic.

Interesting. Yeah. So that in some deep sense, actually that is what we're doing. We're trying to infer how the health. We use the word healthy, but basically how functional is this biological system. I'm looking at so I can hook up with that one and make offspring.

Yeah. Yeah. Well, what kind of hardware might their genomics give me that might be useful in the future?

I wonder why evolution didn't give us higher resolution signals signal. Like why the whole peacock thing with the feathers, it doesn't seem it's a very low bandwidth signal for sexual selection.

I'm gonna, and I'm not an expert on, on this stuff, but on peacocks? Well, no, you know, but, but I'll take a stab at, at the reason. I think that it's because it's an arms race. You see, you don't want everybody to know everything about you. So I think that as much as, as much as. And in fact there's another interesting part of this arms race which is, is if you think about this, the most adaptive evolvable system is one that has the most level of top down control. If it's really easy to say to a bunch of cells make another finger versus okay, here's 10,000 gene expression changes that you need to do to make it to change your finger. The system with good top down control that has memory. And we need to get back to that by the way. That's a question I neglected to answer about where the memory is and so on. A system that uses all of that is really highly evolvable and that's fantastic. But guess what? It's also highly subject to hijacking by parasites, by cheaters of various kinds, by conspecifics. We found that. And then that goes back to the story of the pattern memory in these planaria. There's a bacterium that lives on these planaria. That bacterium has an input into how many heads the worm is going to have because it hijacks that control system and it's able to make a chemical that basically interfaces with the system that calculates how many heads you're supposed to have. And they can have two and they can make them have two heads. And so you can imagine that if you are two. So you want to be understandable for your own parts to understand each other, but you don't want to be too understandable because you'll be too easily controllable. And so I think that, that my guess is that opposing pressure keeps this from being a super high bandwidth kind of thing where we can just look at somebody and know everything about them.

So it's a kind of biological game of Texas Holden. Yeah, you're showing some cards and you're hiding other cards and that's part of it. And there's bluffing and there's, and all that and then there's probably whole species that would do way too much bluffing. That's probably where peacocks fall.

I.

There's a, there's a book that I don't remember if I read or if I, if I wrote, if I read summaries of the book, but it's about the evolution of beauty and birds. Where's that from? Is that a book or does Richard Dawkins talk about it? But basically there's some species start to like over select for beauty. Not over select, they just some reason select for beauty. There is a case to be made, actually. Now I'm starting to remember. I think Darwin himself made a case that you can select based on beauty alone. So yeah, that beauty. There's a point where B doesn't represent some underlying biological truth. Truth you start to select for, for beauty itself. And I think the, the deep question is there is some, is there some evolutionary value to beauty? But it's an interesting kind of thought that this, can we deviate completely from the deep biological truth to actually appreciate some kind of the, the summarization in itself? Let me get back to memory because this is a really interesting idea. How do a collection of cells remember anything? How do biological systems remember anything? How is that akin to the kind of memory we think of humans as having within our big cognitive engine?

Yeah. One of the ways to start thinking about bioelectricity is to ask ourselves, where did neurons and all these cool tricks that the brain uses to run these amazing problem solving abilities on and basically an electronic electrical network. Right. Where did that come from? That didn't just evolve, you know, up here out of nowhere. It must have evolved from something. And what it evolved from was a much more ancient ability of cells to form networks, to solve other kinds of problems, for example, to navigate morphospace, to control the body shape. And so all of the components of, of neurons, so, so ion channels, neurotransmitter machinery, electrical synapses, all this stuff is way older than brains, way older than neurons, in fact older than multicellular. And so it was already even bacterial biofilms. There's some beautiful work from UCSD on brain like dynamics and bacterial biofilms. So evolution figured out very early on that electrical networks are amazing at having memories, at integrating information across distance at different kinds of optimization tasks, image recognition and so on, long before there were brains.

Can you actually step back? We'll return to it. What is bioelectricity? What is biochemistry? What are electrical networks? I think a lot of the biology community focuses on the chemicals as the Signaling mechanisms that make the whole thing work. You have, I think, to a large degree, uniquely. Maybe you can correct me on that. Have focused on the bioelectricity, which is using electricity for signaling. There's also probably mechanical.

Sure, sure.

Knocking on the door. So what, what. What's the difference? And what's an electrical network?

Yeah. So I want to make sure and. And kind of give credit where credit is due. So. So as far back as 1903 and probably late 1800s already, people were thinking about the importance of electrical phenomena in. In life. So I. I'm for sure not the first person to stress the importance of electricity. There were waves of research in the 30s, in the 40s, and then again in the kind of 70s, 80s and 90s of sort of the pioneers of bioelectricity who did some amazing work on all this. I think what we've done that's new is to step away from this idea that. And I'll describe what the bioelectricity is, is step away from the idea that, well, here's another piece of physics that you need to keep track of to understand physiology and development and to really start looking at this as saying, no, this is a privileged computational layer that gives you access to the actual cognition of the tissue of basal cognition. So merging that developmental biophysics with ideas in cognition of computation and so on, I think that's what we've done. That's new. But people have been talking about bioelectricity for a really long time, so I'll define that. So what happens is that if you have a single cell, cell has a membrane. In that membrane are proteins called ion channels. And those proteins allow charged molecules, potassium, sodium, chloride, to go in and out under certain circumstances. And when there's an imbalance of those ions, there becomes a voltage gradient across that membrane. And so all cells, all living cells, try to hold a particular kind of voltage difference across that membrane. And they spend a lot of energy to do so. When you now. So that's a single cell. Cell. When you have multiple cells, the cells sitting next to each other, they can communicate their voltage state to each other via a number of different ways. But one of them is this thing called a gap junction, which is basically like a little submarine hatch that just kind of docks. Right. And the ions from one side can flow to the other side and vice versa.

Isn't it incredible that this evolved? Isn't that wild? Because that didn't exist.

Correct. This had to be evolved and it.

Had to be invented.

That's right.

So somebody invented Electricity in the ocean. When did this get invented?

Yeah, so, I mean, it is incredible. The guy who discovered gap junctions, Werner Lowenstein, I visited him. He was really old human being.

He discovered them because who really discovered them lived probably 4 billion years ago.

Good point.

So you give credit where credit is due.

I'm just saying he rediscovered gap junctions. But when I visited him in woods hole, maybe 20 years ago now, now he told me that he was writing and unfortunately he passed away and I think this book never got written. He was writing a book on gap junctions in consciousness. And I think it would have been an incredible book because gap junctions are magic. I'll explain why in a minute. What happens is that just imagine the thing about both these ion channels and these gap junctions is that many of them are themselves voltage sensitive. So that's a voltage sensitive current conductance, that's a transistor. And as soon as you've invented one, immediately you now get access to, from, from this platonic space of, of mathematical truths, you get access to all of the cool things that transistors do. So now when you have a network of cells, not only do they do they talk to each other, but they can send messages to each other and the differences of voltage can propagate. Now, to neuroscientists, this is old hat because you see this in the brain, right? There's action potentials, the, you know, the electricity. They have these awesome movies where you can take a transparent animal, like a zebrafish, you can literally look down and you can see all the firings as the fish is making decisions about what to eat and things like this. It's amazing. Well, your whole body is doing that all the time, just much slower. So there are very few things that neurons do that other cells that all the cells in your body don't do. They all do very similar things just on a much slower timescale. And whereas your brain is thinking about how to solve problems in three dimensional space space, the cells in an embryo are thinking about how to solve problems in anatomical space. They're trying to have memories like, hey, how many fingers are we supposed to have? Well, how many do we have now? What do we do to get from here to there? That's the kind of problems they're thinking about. And the reason that gap junctions are magic is imagine right from the earliest time, here are two cells, this cell. How can they communicate? Well, the simple version is, is this cell could send a chemical signal. It floats over and it hits a receptor on this cell, right? Because it comes from Outside, this cell can very easily tell that that came from outside. Whatever information is coming, that's not my information, that information is coming from the outside. So I can trust it, I can ignore it, I can do various things with it, whatever, but I know it comes from the outside. Now imagine instead that you have two cells with a gap junction between them. Something happens, let's say the cell gets poked, there's a calcium spike. Spike. The calcium spike or whatever small molecule signal propagates through the gap junction to this cell. There's no ownership metadata on that signal. This cell does not know now that it's didn't that it came from outside because it looks exactly like its own memories would have looked like of being. Of being. Of whatever had happened. Right. So gap junctions to some extent wipe ownership information on data. Which means that if I can't, if you and I are sharing memories and we can't quite tell who the memories belong to, that's the beginning of a mind melt. That's the beginning of a scale up of cognition from here's me and here's you to no, now there's just us.

So they enforce a collective intelligence. Gap junction.

That's right. It helps. It's the beginning. It's not the whole story by any.

Means, but it's the start where's state stored of the system. So there are some. Is it in part in the gap junctions themselves? Is it in the cells?

There are many, many layers to this, as always in biology. So there are chemical networks, so for example, gene regulatory networks, or basically any kind of chemical pathway where different chemicals activate and repress each other. They can store memories. So in a dynamical system sense, they can store memories. They can get into stable states that are hard to pull them out of. That becomes once they get in, that's a memory, a permanent memory or a semi permanent memory of something that's happened. There are cytoskeletal structures that are physically. They store memories in physical configuration. There are electrical memories like flip flops, where there is no physical. Right. So if you look, I show my students this example is a flip flop. And the reason that it stores a 0 or 1 is not because some piece of the hardware moved. It's because there's a cycling of the current in one side of the thing. If I come over and I hold the other side to a high voltage for a brief period of time, time, it flips over and now it's here. But the hard. None of the hardware moved. The information is in a stable dynamical sense. And if you were to X ray the thing, you couldn't tell me if it was 0 or 1 because all you would see is where the hardware is. You wouldn't see the energetic state of the system. So there are also. So there are bioelectrical states that are held in that exact way, like volatile ram, basically like in the electrical state.

It's very akin to the different ways the memory stored in a computer. So there's ram, there's hard drives, you.

Can make that mapping. Right. So I think the interesting thing is that based on the biology we can have a more sophisticated, you know, I think we can revise some of our computer engineering methods because there are some interesting things that biology does we haven't done yet. But you can. But that map. But that mapping is not bad. I mean, I think it works in many ways.

Yeah, I wonder because I mean the way we build computers at the root of computer science is the idea of proof of correctness. We program things to be perfect, reliable. You know, this idea of resilience and robustness to unknown conditions is not as important. So that's what biology is really good at. So I don't know what kind of systems, I don't know how we go from a computer to a biological system in the future.

Yeah, I think that, you know, the thing about biology, like is all about, about making really important decisions really quickly on very limited information. I mean, that's what biology is all about. You have to act, you have to act now. The stakes are very high and you don't know most of what you need to know to be perfect. And so there's not even an attempt to be perfect or to get it right in any sense. There are just things like active inference, minimize surprise, optimize some efficiency and some things like this that guides the whole business.

I mentioned too offline that that somebody who's a fan of your work is Andre Karpathy. And he's amongst many things, also writes occasionally a great blog. He came up with this idea, I don't know if he coined the term, but of software 2.0, where the programming is done in the space of configuring these artificial neural networks. Is there some sense in which that would be the future of programming for us humans where we're less doing like Python, like programming and more how would that look like. But basically doing the hyperparameters of something akin to a biological system and watching it go and keeping adjusting it and creating some kind of feedback loop within the system so it corrects itself and then we watch it over time accomplish the goals we want it to accomplish. Is that kind of the, the dream of the, the dogs that you describe in the nature paper?

Yeah, yeah. I mean that, that's what you just painted is, is a very good description of our efforts at regenerative medicine as a kind of somatic psychiatry. So the idea is that you're not, you, you know, you're not trying to micromanage. I mean, think, think about the limitations of, of, of a, of the medicines today. We try to interact down at the level of pathways, right? So we're trying to micromanage it. What's the problem? Well, one problem is that for almost every medicine other than antibiotics, once you stop it, the problem comes right back. You haven't fixed anything. You were addressing symptoms, you weren't actually curing anything again, except for antibiotics. That's one problem. The other problem is you have massive amount of side effects because you were trying to interact at the lowest level. It's like, I'm going to try to program this computer by changing the melting point of copper. Maybe you can do things that way, but my God, it's hard to program at the hardware level. So what I think we're starting to understand is that, and by the way, this goes back to what you were saying before about that we could have access to our internal state. So people who practice that kind of stuff, so yoga and biofeedback and those, those are all the people that uniformly will say things like, well, the body has an intelligence and this, like those two sets overlap perfectly. Because, because that's exactly right. Because once you, once you start thinking about it that way, you realize that the better locus of control is not always at the lowest level. This is why we don't all program with a soldering iron, right? We take advantage of the high level intelligences that are there, which means trying to figure out, okay, which of your tissues can learn, what can they learn? Why is it that certain drugs stop working after you take them for a while? With its habituation, right? And so can we understand habituation, sensitization, associative learning these kinds of things in chemical pathways, we're going to have a completely different way. I think we're going to have a completely different way of using drugs and of medicine in general. When we start focusing on the goal states and on the intelligence of our subsystems as opposed to treating everything as if the only path, path was micromanagement from chemistry upwards.

Well, can you speak to this idea of somatic psychiatry? What Are somatic cells. How do they form networks that use bioelectricity to have memory and all those kinds of things?

Yeah.

What are somatic cells like basics here.

Somatic cells just means the cells of your body. Soma just means body. Right. So. So somatic cells are just the. I'm not even specifically making a distinction between somatic cells and stem cells or anything like that. I mean, basically all the cells in your body, not just neurons, but all the cells in your body. Body, they form electrical networks during embryogenesis, during regeneration. What those networks are doing in part is processing information about what our current shape is and what the goal shape is. Now, how do I know this? Because I can give you a couple of examples. One example is when we started studying this, we said, okay, here's a planarian. A planarian is a flatworm. It has one head and one tail normally. And the amazing. There's several amazing things about planaria, but basically they kind of of. I think planaria hold the answer to pretty much every deep question of life. For one thing, they're similar to our ancestors. So they have true symmetry, they have a true brain. They're not like earthworms. They're much more advanced life form. They have lots of different internal organs, but they're these little. They're about maybe 2 centimeters in the centimeter to 2 in size. They have a head and a tail. And the first thing is, planaria are immortal, so they do not age. There's no such thing as an old planarian. So that right there tells you that these theories of thermodynamic limitations of on lifespan are wrong. It's not. It's not that. Well, over time, everything degrades. No, planaria can keep it going for probably, you know, how long if they've been around 400 million years. Right. So these are the actual. So the planaria in our lab are actually in physical continuity with planaria that were here 400 million years ago.

So there's planaria that have lived that long, essentially. What does it mean, physical continuity?

Because. Because what they do is they split in half. The way they reproduce is they split in half. Half. So, so the planarian, the back. The back end grabs the petri dish, the front end takes off and they. They rip themselves in half.

But is. Isn't, in some sense, we're like. You are a physical continuation.

Yes, except that. Except that we go through a bottleneck of one cell, which is the egg. They do not. I mean, they can. There are certain planaria.

Got it. So we go Through a very ruthless compression process. And they don't.

Yes, like an autoencoder, you know, sort of squash down to one cell and then back out. These guys just tear themselves in half and then each. And then. And so the other amazing thing about them is they regenerate so you can cut them into pieces. The record is, I think, 276 or something like that by Thomas Hunt Morgan. And each piece regrows a perfect little worm. They know exactly. Every piece knows exactly what's missing, what needs to happen. In fact, if you chop it in half, as it grows, the other half, the original tissue, shrinks so that when the new tiny head shows up, they're proportional, so it keeps perfect proportion. If you starve them, they shrink. If you feed them again, they expand their control. Their anatomical control is just insane.

Somebody cut them into over 200 pieces.

Yeah. Thomas Hunt Morgan did.

Hashtag science.

Yep. Amazing. Yeah. And maybe more. I mean, they didn't have antibiotics back then. I bet he lost some due to infection. I bet it's actually more than that. I bet you could do more than that.

Humans can't do that.

Well, yes. I mean, again, yes, true.

Except maybe you can at the embryonic level.

Well, that's the thing. Right. So when I talk about this, I said, just remember that as amazing as it is to grow a whole planarian from a tiny fragment, half of the human population can grow a full body from one cell. Right. So development is really. You can look at development as just an example of regeneration.

Yeah. To think we'll talk about regenerative medicine. But there's some sense what would be like that warm in like 500 years where I can just go regrow a hand.

Yep. With given time. It takes time to grow large things, but for now. Yeah, I think so.

I think you can probably. Why not accelerate? Oh, biology takes its time.

I'm not going to say anything is impossible, but I don't know of a way to accelerate these processes. I think it's possible. I think we are going to be regenerative, but I don't know of a way to make it fast.

I could just think people from a few centuries from now would be like. Well, they have to. They used to have to wait. Wait a week for the hand to regrow. It's like when the microwave was invented. You can. You can toast your. What's that called when you put a cheese on a toast? It's delicious is all. I know. I'm blanking. Anywho. All right, so, planaria. Why were we talking about the magical planaria that they have the mystery of life.

Yeah. So. So the reason we're talking about planaria is not only are they immortal, okay. Not only do they regenerate every part of the body, they generally don't get cancer. Right. So which we can talk about why that's important. They're smart, they can learn things. So you can train them. And it turns out that if you train a planarian and then cut their heads off, the tail will regenerate a brand new brain that still remembers the original information.

Do they have a biological network going on or no?

Yes.

So their somatic cells are forming a network. And that's what you mean by a true brain. What's the requirement for a true brain?

Like everything else, it's a continu. But. But a true brain has certain characteristics as far as the density, like a localized density of neurons that guides behavior in the head. Exactly.

Connected to the head.

Exactly. If you cut their head off, the tail doesn't have that, doesn't do anything. It just sits there until the new brain is. Is. Is. You know, until a new brain regenerates. They have all the same neurotransmitters that you and I have. But here's why, here's why we're talking about them in, in this, in this context. So here's your planaria. You cut off the head, you cut off the tail. You have a middle fragment. That middle fragment has to make one head and one. How does it know how many of each to make and where do they go? How come it doesn't switch? How come? Right. So we did a very simple thing and we said, okay, let's make the hypothesis that there's a somatic electrical network that remembers the correct pattern and that what it's doing is recalling that memory and building to that pattern. So what we did was we used a way to visualize electrical activity in these cells. Right. It's a variant of what people use to look for electricity in the brain. Brain. And we saw that that fragment has a very particular electrical pattern. You can literally see it. Once we develop the technique. It has a very particular electrical pattern that shows you where the head and the tail goes. Right. You can just see it. And then we said, okay, well, now let's test the idea that that's a memory that actually controls where the head and the tail goes. Let's change that pattern. So basically incept a false memory. And so what you can do is you can do that in many different ways. One way is with drugs that target ion charts, channels to say. And so you pick these drugs and you say, okay, I'm going to do it. So that instead of this one head, one tail electrical pattern, you have a two headed pattern. You're just editing the electrical information in the network. When you do that, guess what? The cells build, they build a two headed worm. And the coolest thing about it now, no genetic changes. So we haven't touched the genome. The genome is totally wild type. But the amazing thing about it is that when you take these two headed animals and you cut them into pieces again, some of those pieces will continue to make two headed animals, animals. So that information, that memory, that electrical circuit, not only does it hold the information for how many heads, not only does it use that information to tell the cells what to do to regenerate, but it stores it. Once you've reset it, it keeps. And we can go back, we can take a two headed animal and put it back to one headed. So now imagine. So there's a couple of interesting things here that have implications for understanding of genomes and things like that. Imagine I take this two headed animal. Oh, and by the way, when they reproduce, when they tear themselves in half, you still get two headed animals. So imagine I take them and I throw them in the Charles river over here. So 100 years later, some scientists come along and they scoop up some samples and they go, oh, here's a single headed form and a two headed form. Wow, a speciation event. Cool. Let's sequence the genome and see what happened. The genomes are identical, there's nothing wrong with the genome. So if you ask the question how does. So this goes back to your very first question is where do body plants come from? How does the planarian know how many heads it's supposed to have? Now it's interesting because you could say DNA. But as it turns out, the DNA produces a piece of hardware that by default says one head. The way that when you turn on a calculator, by default it says zero every single time. Right. When you turn it on, it just says zero. But it's a programmable calculator as it turns out. So once you've changed that next time it won't say zero, it'll say something else. And the same thing here. So you can make one headed, two headed, you can make no headed worms. We've done some other things along these lines, some other really weird concepts, constructs. So this question of. Right. So again it's really important, the hardware software distinction is really important because the hardware is essential because without proper hardware, you're never going to get to the right physiology of having that memory. But once you have it, it doesn't fully determine what the information is going to be. You can have other information in there, and it's reprogrammable by us, by bacteria, by various parasites, probably, things like that. The other amazing thing about these planaria is think about this. Most animals, when we get a mutation in our bodies, our children don't inherit it, right? So you could go on, you could run around for 50, 60 years getting mutations. Your children don't have those mutations because we go through the egg stage. Planaria tear themselves in half, and that's how they reproduce. So for 400 million years, they keep every mutation that they've had that doesn't kill the cell that it's in. So when you look at these planaria, their bodies are what's called mixuploid, meaning that every cell might have a different number of chromosomes. They look like a tumor. If you look at the genome is an incredible mess because they accumulate all this stuff, and yet their body structure is. They are the best regenerators on the planet. Their anatomy is rock solid, even though their genome is all kinds of crap. So this is kind of a scandal, right, that when we learn that what are genomes? Genomes determine your body. Okay, why is the animal with the worst genome have the best anatomical control, the most cancer resistant, the most regenerative? Right. Really, we're just beginning to start to understand this relationship between the genomically determined hardware and. By the way, just as of a couple of months ago, I think I now somewhat understand why this is. But it's really a major, you know.

A major puzzle that really throws a wrench into the whole nature versus nurture, because you usually associate electricity with the nurture and the hardware with the nature, and there's just this weird integrated mess that propagates through generations.

Yeah, it's much more fluid, it's much more complex. You can imagine what's happening here is just imagine the evolution of an animal like this, that multiscale. This goes back to this multiscale competency, right. Imagine that you have, you have an animal that. Where its tissues have some degree of multiscale competency. For example, like we saw in the tadpole, if you put an eye on its tail, they can still see out of that eye. There's incredible plasticity. So if you have an animal and it comes up for selection and the fitness is quite good. Evolution doesn't know whether the fitness is good because the genome was awesome or because the genome was kind of junky, but the competency made up for it. Right. And things kind of ended up good. So what that means is that the more competency you have, the harder it is for selection to pick the best genomes. It hides information. Right. And so that means that. So what happens? Evolution basically starts. All the hard work is being done to increase the competency because it's harder and harder to see the genomes. And so I think in planaria, what happened is that there's this runaway phenomenon where all the effort went into the algorithm, such that we know you got a crappy genome, we can't clean up the genome, we can't keep track of it. So what's going to happen is what survives are the algorithms that can create a great worm no matter what the genome is. So everything went into the algorithm, which of course then reduces the pressure on keeping a clean genome. So this idea of. And different animals have this to different levels, but this idea of putting energy into an algorithm that. That does not over train on priors. Right. It can't assume. I mean, I think biology is this way. In general, evolution doesn't take the past too seriously because it makes these basically problem solving machines as opposed to exactly what to deal with. Exactly what happened last time.

Yeah. Problem solving versus memory recall. So a little memory, but a lot of problem solving?

I think so, yeah. In many cases, problem solving.

I mean, it's incredible that those kinds of systems are able to be constructed, especially how much they contrast with the way we build problem solving systems in the AI world. Back to xenobots. I'm not sure if we ever described how xenobots are built, but you have a paper titled Biological Robots Perspectives on an Emerging Interdisciplinary Field. In the beginning, you, you mentioned that the word xenobots is like controversial. Do you guys get in trouble for using xenobots or what? Do people not like the word xenobots? Are you trying to be provocative with the word xenobots versus biological robots? I don't know this.

Yeah.

Is there some drama that we should be aware of?

There's a little bit of drama, I think. I think the drama is basically related to people having very fixed ideas about what terms mean. And I think in many cases these ideas are completely out of date with where science is now. And for sure they're out of date with what's going to be. I mean, these concepts are not going to survive the next couple of decades. So if you ask a person and Including a lot of people in biology who kind of want to keep a sharp distinction between biologicals and robots. Right? See, what's a robot? What? Well, a robot, it comes out of a factory, it's made by humans. It is boring. It is meaning that you can predict everything it's going to do. It's made of metal and certain other inorganic materials. Living organisms are magical, they arise. Right. And so on. So there's these distinctions, I think these distinctions I think were never good, but they're going to be completely useless going forward. And so part of there's a couple of papers, that's one paper and there's another one that Josh Baumgart and I wrote where we really attacked, attack the terminology and we say these binary categories are based on very non essential kind of surface limitations of technology and imagination that were true before, but they've got to go. And so we call them xenobots. So xeno for Xenopus laevis, it's the frog that these guys are made of. But we think it's an example of a biobot technology because ultimately once we understand how to, to communicate and manipulate the inputs to these cells, we will be able to get them to build whatever we want them to build. And that's robotics. Right. It's the rational construction of machines that have useful purposes. I absolutely think that this is a robotics platform, whereas some biologists don't.

But it's built in a way that all the different components are doing their own computation. So in the way that we've been talking about. So you're trying to do top down control in that biological system and in.

The future, future all of this will merge together because of course at some point we're going to throw in synthetic biology circuits, right? New transcriptional circuits to get them to do new things. Of course we'll throw some of that in. But we specifically stayed away from all of that because in the first few papers, and there's some more coming down the pike that are, I think going to be pretty dynamite, that we want to show what the native cells are made of. Because what happens is if you engineer the heck out of them, if we were to put in new transcription factors and some new metabolic people will say, okay, you engineered this and you made it do whatever. And fine, I wanted to show, and the whole team wanted to show the plasticity and the intelligence in the biology, what does it do that's surprising before you even start manipulating the hardware in that way.

Yeah, don't try to over control the thing, let it flourish, the full beauty of the biological system. Why Xenopus level this Lavis. How do you pronounce it? The frog?

Cenopus lavis. Yeah, yeah, It's a very popular.

Why this frog, it's been used since.

I think the 50s. It's just very convenient because you can, you, you know, we, we keep the adults in this, in this very fine frog habitat, they lay eggs. They lay tens of thousands of eggs at a time. The eggs develop right in front of your eyes. It's the most magical thing you can, you can see because normally, you know, if you were to deal with mice or rabbits or whatever, you don't see the early stages, right, because everything's inside them. Other here, everything's in a petri dish at room temperature. So you just, you, you have an egg, it's fertilized and you can just watch it divide and divide and divide and all the organs form. You can just see it. And at that point, the community has, has developed lots of different tools for understanding what's going on and also for, for manipulating it. Right. So it's, it's, people use it for, you know, for understanding birth defects and neurobiology and cancer immunology.

So you get the whole embryogenesis in the petri dish.

Yep.

That's so cool to watch. Is there videos of this?

Oh, yeah, yeah, yeah, there's, yeah, there's amazing videos on, online. I mean, mammalian embryos are super cool too. For example, monozygotic twins are. What happens when you cut a mammalian embryo in half. You don't get two half bodies, you get two perfectly normal bodies. Because it's a regeneration event. Right. Development is just the kind of regeneration, really.

And why this particular frog? It's just because they were doing in.

The 50s and it breeds well in, you know, it's easy to raise in the laboratory and it's very prolific. And all the tools, basically, for decades people have been developing tools. There's other, some people use other frogs. But I have to say this is important. Xenobots are fundamentally not anything about frogs. So I can't say too much about this because it's not published and peer reviewed yet. But we've made xenobots out of other things that have nothing to do with frogs. This is not, not a frog phenomenon. This is, we, we started with frog because it's so convenient, but this, this, this plasticity is not a frog. You know, it's not related to the fact that they're frogs.

What happens when you kiss it? Does it turn to a Prince. No. Or princess. Which way? Prince.

Yeah.

Prince.

Prince, yeah. That's an experiment that I don't believe we've done.

And if we have, I don't collaborate, I can, I can take on the lead on that effort. Okay, cool. How does the cells coordinate? Let's focus in on just the embryogenesis. So there's one cell, so it divides. Doesn't have to be very careful about what each cell starts doing once they divide.

Yes.

And like when there's three of them, it's like the co founders or whatever. Like what? Like slow down, you're responsible for this. When do they become specialized and how do they coordinate that specialization?

So, so this is the, the basic science of developmental biology. There's a lot known about all of that. But, but what I'll tell you what I think is the kind of, the most important part, which is, yes, it's very important who does what. However, because going back to this issue of I made this claim that biology doesn't take the past too seriously. And what I mean by that is it doesn't assume that everything is the way it's expected to be. Right. And here's an example of that. This was done, this was an old experiment going back to the 40s, but basically imagine it's a newt salamander and it's got these little tubules that go to the kidneys. This little tube, take a cross section of that tube. You see eight to 10 cells that have cooperated to make this little tube in cross section. One amazing thing you can do is you can mess with the very early cell division to make the cells gigantic, bigger. You can make them different sizes, you can force them to be different sizes. So if you make the cells different sizes, the whole nude is still the same size. So if you take a cross section through that tubule, instead of eight to ten cells, you might have four or five, or you might have three. Until you make the cell so enormous that one single cell wraps around itself and gives you that same large scale structure with a completely different molecular mechanism. So now instead of cell to cell communication to make a tubule, instead of that, it's one cell using the cytoskeleton to bend itself around. So think about what that means in the service of a large scale. Talk about top down control, right? In the service of a large scale anatomical feature, different molecular mechanisms get called up. So now think about this. You're a new cell and trying to make an embryo. If you had a fixed idea of who was supposed to do what, you'd be screwed because now your cells are gigantic. Nothing would work. There's an incredible tolerance for changes in the size of the parts, in the amount of DNA in those parts, all sorts of stuff. The life is highly interoperable. You can put electrodes in there, you can put weird nanomaterials. It still works. This is that problem solving action. It's able to do what it needs to do even when circumstances change. That is the hallmark of intelligence. William James defined intelligence as the ability to get to the same goal by different means. That's this. You get to the same goal by completely different means. And so why am I bringing this up is just to say that, yeah, it's important for the cells to do the right stuff, but they have incredible tolerances for things not being what you expect and to still get their job done. So if you're, you know, all of these things are not hardwired. There are organisms that might be hardwired. For example, the nematode C. Elegans. In that organism, every cell is numbered, meaning that every C. Elegans has exactly the same number of cells as every other C. Elegans. They're all in the same place, they all divide. There's literally a map of how it works in that sort of system. It's much more cookie cutter. But most organisms are incredibly plastic in that way.

Is there something particularly magical to you about the whole developmental biology process? Is there something you could say because you just said it, they're very good at accomplishing the goal, the job they need to do the competency thing. But you get freaking organism from one cell. It's like it's very hard to intuit that whole process, to even think about reverse engineering that process.

Right? Very hard. To the point where I often just imagine, I sometimes ask my students to do this thought experiment. Imagine you were shrunk down to the scale of a single cell and you were in the middle of an embryo and you were looking around at what's going on on, and the cells running around. Some cells are dying every time you look. It's kind of a different number of cells for most organisms and so on. I think that if you didn't know what embryonic development was, you would have no clue that what you're seeing is always going to make the same thing. Never mind knowing what that is, never mind being able to say, even with full genomic information, being able to say what the hell are they building? We have no way to do that. But just even to guess that, wow, the outcome of all this activity is. It's always going to be, it's always going to build the same thing.

The imperative to create the final you as you are now is there already. So you can, you would. So if you start from the same embryo, you would create a very similar organism.

Yeah. Except for cases like the xenobots. When you give them a different environment, they come up with a different way to be adaptive in that environment. But overall, I mean, so, so I think. So I think to, you know, kind of, kind of summarize it, I think what evolution is really good at is creating hardware that has a very stable baseline mode, meaning that left to its own devices, it's very good at doing the same thing. But it has a bunch of problem solving capacity such that if any, if any assumptions don't hold, if your cells are a weird size or you get the wrong number of cells, or there's a, you know, somebody stuck an electrode halfway through the body, whatever, whatever, it will still get most of what it needs to do done.

You've talked about the magic and the power of biology here. If we look at the human brain, how special is the brain in this context? You're kind of minimizing the importance of the brain or lessening its. We think of all the special computation happens in the brain. Everything else is like the help. You're kind of saying that the whole thing is. The whole thing is doing computation. But nevertheless, how special is the human brain in this full context of biology?

Yeah. I mean, look, there's no getting away from the fact that the human brain allows us to do things that we could not do without it.

You can say the same thing about the liver.

Yeah. This is true. And so, and so, you know, I. My goal is not. No, you're right.

My goal is not being polite to the brain right now. Well, being a politician, like, listen, everybody.

Has a, Everybody has a role. Yeah.

And it's very important role.

That's right.

We have to acknowledge the importance of the brain.

You know, there are more than enough people who are cheerleading the, the brain. Right. So, so I, I don't feel like nothing I say is going to reduce people's excitement about the human brain. And so, so I, I emphasize other.

Things too much credit.

I don't think it gets too much credit. I think other things don't get enough credit. Credit. I think the brain is. The human brain is incredible and special and all that. I think other things need more credit. And I also think that this. And I'm sort of this way about everything I Don't like binary categories about almost anything. I like a continuum. And the thing about the human brain is that by accepting that as some kind of an important category or essential thing, we end up with all kinds of weird pseudo problems and conundrums. So for example, when we talk about it, you know, if you do want to talk about ethics and other, other things like that and what, you know, this, this idea that surely if we look out into the universe, surely we don't believe that this human brain is the only way to be sentient, right? Surely we don't. You know, and to have high level cognition, I just, I can't even wrap my mind around this, this idea that that is the only way to do it. No doubt there are other architectures made bond, made of completely different principles, principles that achieve the same thing. And once we believe that, then that tells us something important. It tells us that things that are not quite human brains or chimeras of human brains and other tissue, or human brains or other kinds of brains in novel configurations or things that are sort of brains but not really, or plants or embryos or whatever might also have important cognitive status. So that's the only thing I think we have to be really careful about treating the human brain as if it was some kind of, of like sharp binary category, you know, you are or you aren't. I, I don't believe that exists.

So when we look out at all the beautiful variety of semi biological architectures out there in the universe, how many, how many intelligent alien civilizations do you think are out there?

Boy, I have, you know, no expertise in that whatsoever.

You haven't, you haven't met any?

I, I have met the ones we've made.

I think that, I mean exactly in some sense with synthetic biology. Are you not creating aliens?

I absolutely think so. Because, because look, all of life, all of all standard model systems are an n of one course of evolution on Earth, right? And trying to make conclusions about biology from looking at life on Earth is like testing your theory on the same data that generated it. It's all kind of like locked in. So we absolutely have to create novel examples that have no history on Earth that don't, you know, xenobots have no history of selection. To be a good xenobot, the cells have selection for various things, but the xenobot itself never existed before. And so we can make chimeras, you know, we make frog a lottles that are sort of half frog, half axolotl. You can make all sorts of hybrids, right? Constructions of living Tissue with robots and whatever we need to be making these things until we find actual aliens. Because otherwise we're just looking at an N of one set of examples. All kinds of frozen accidents of evolution and so on. We need to go beyond that to really understand biology.

But we're still. Even when you do a synthetic biology, you're locked in to the basic components of the way biology is done on this earth.

Yeah, right.

And the, and also the basic, basic constraints to the environment, even artificial environments that construct in the lab are tied up to the environment. I mean, what do you. Okay, let's say there is. I mean, what I think is there's a nearly infinite number of intelligent civilizations, living or dead, out there. If you pick one out of the box, what do you think it would look like? So when you think about synthetic biology or creating synthetic organisms, how hard is it to create something that's very different?

Yeah, I think it's very hard to create something that's very different. Right. We are just locked in both experimentally and in terms of our imagination. Right. It's very hard.

And you also emphasize several times the idea of shape. Shape? Yeah, the individual cell get together with other cells and they kind of, they're going to build a shape. So it's shape and function, but shape is a critical thing.

Yeah. So here I'll take a stab. I mean, I agree with you to whatever extent that we can say anything. I do think that there's probably an infinite number of different architectures that are with interest in cognitive properties out there. What can we say about them? I think that the only things that are going. I don't think we can rely on any of the typical stuff, you know, carbon based. None of that. Like, I think all of that is just us having a lack of imagination. But I think the things that are going to be universal, if anything is, are things, for example, driven by resource limitation. The fact that you are fighting a hostile world and you have to draw a boundary between yourself and the world somewhere. The fact that that boundary is not given to you by anybody, you have to assume it, you know, estimate it yourself. And the fact that you have to coarse grain your experience and the fact that you're going to try to minimize surprise and the fact that like these, these are the things that I think are fundamental about biology. None of the, you know, the facts about the genetic code or even the fact that we have genes or the biochemistry of it, I don't think any of those things are fundamental. But it's going to be a lot more about the information and about the creation of the self. The fact that. So in my, in my framework, selves are demarcated by the scale of the goals that they can pursue. So from little tiny local goals to massive planetary scale goals for certain humans and everything in between. So you can draw this cognitive light cone that determines the scale of the goals you could possibly pursue. I think those kinds of frameworks like that, like active inference and so on are going to be universally applicable, but none of the other things that are typically discussed. Discussed.

Quick pause. Do you need a bathroom break?

We were just talking about, you know, aliens and all that. That's a funny thing. Which is. Yeah. I don't know if you've seen them. There's a kind of debate that goes on about cognition in plants and what can you say about different kinds of computation and cognition in plants. And I always, I always look at that stuff like if, if you're weirded out by cognition in plants, you're not ready for exobiology. Right. If, if, if you know something that's that similar here on Earth is already like freaking you out out, then I think there's going to be all kinds of cognitive life out there that we're going to have a really hard time recognizing.

I think robots will help us.

Yeah.

Like expand our mind about cognition. Either that or the word like xenobots. So. And they maybe becomes the same thing is, you know, really when the human engineers the, the thing, at least in part, and then is able to achieve some kind of cognition that's different than what you're used to, then you start to understand like, oh, you know, every living organism is capable of cognition. Oh, I need to kind of broaden my understanding what cognition is. But do you think plants, like, when you, when you eat them, are they screaming?

I don't know about screaming. I think you have to.

That's what I think when I eat a salad. Yeah, Good.

Yeah. I think you have to scale down the expectations in terms of. Right. So probably they're not screaming in the way that we would be screaming. However, there's plenty of data on plants being able to do anticipation and certain kinds of memory and so on. I think what you just said about robots. I hope you're right and I hope that's. But there's two ways that people can take that. Right. So one way is exactly what you just said, to try to kind of expand their, their, their, their notions for that category. The other way people often go is they just sort of define the term if if, if it's not a natural product, it's. It's just faking. Right. It's not really intelligence if it was made by somebody else because it's that same, it's the same thing. They can see how it's done. And once you see how it's, it's like a magic trick. When you see how it's done, it's not as fun anymore. And, and, and I think people have a real tendency for that and they sort of. Which, which I find really strange in the sense that if somebody said to me, me, we have this sort of blind hill climbing search and then we have a really smart team of engineers, which one do you think is going to produce a system that has good intelligence? I think it's really weird to say that it only comes from the blind search. Right. It can't be done by people who by the way can also use evolutionary techniques if they want to, but also rational design. I think it's really weird to say that real intelligence only comes from, from natural evolution. So I hope you're right. I hope people take it in the other, the other way.

But there's a nice shortcut. So I work with Lego robots a lot now for my own personal pleasure. Not in that way. Internet. So four legs. And one of the things that changes my experience of the robots a lot is when I can't understand why I did a certain thing. And there's a lot of ways to engineer that. Me, the person that created the software that runs it. There's a lot of ways for me to build that software in such a way that I don't exactly know why it did a certain basic decision. Of course as an engineer you can go in and start to look at logs. You can log all kind of data, sensory data, the decisions you made, all the outputs in neural networks and so on, on. But I also try to really experience that surprise and that really experience as another person would that totally doesn't know how it's built. And I think the magic is there. And not knowing how it works that I think biology does that for you through the layers of abstraction.

Yeah it.

Because nobody really knows what's going on inside the biological like each one component, it is clueless about the big picture.

I think there's actually really cheap systems that can illustrate that kind of thing, which is even like fractals. Right. Like you have a very small short formula in Z and you see it and there's no magic. You're just going to crank through Z squared plus C Whatever, you're just going to crank through it. But the result of it is this incredibly rich, beautiful image that just like, wow, all of that was in this like 10 character long string, like, amazing. So the fact that you can know everything there is to know about the details and the process and all the parts and everything, there's literally no magic of any kind there. And yet the outcome is something that you would never have expected. And it just is incredibly rich and complex and beautiful. So there's a lot of that.

You write that you work on developing conceptual frameworks for understanding unconventional cognition. So the kind of thing we've been talking about. Talking about. I just like the term unconventional cognition. And you want to figure out how to detect, study and communicate with the thing. You've already mentioned a few examples. But what is unconventional cognition? Is it as simply as everything outside, outside of what we define usually as cognition, cognitive science, the stuff going on between our ears? Or is there some deeper way to get at the fundamentals of what is cognition?

Yeah, I think like, and I'm certainly not the only person who works in unconventional unconventional cognition.

So it's the term used.

Yeah, that's one that. So I've coined a number of weird terms, but that's not one of mine. Like, that's an existing thing. So for example, somebody like Andy Adamatsky, who I don't know if you've had him on, if you haven't, you should. He's a very interesting guy. He's a computer scientist and he does unconventional cognition and slime molds and all kinds of weird, weird. He's a real weird cat. Really interesting. Anyway, there's a bunch of terms that I've come up with, but that's not one of mine. So I think, like many terms, that one is really defined by the times, meaning that things that are unconventional cognition today are not going to be considered unconventional cognition at some point. It's one of those things. And so it's this really deep question of how do you recognize, communicate with, classify cognition when you cannot rely on the typical milestones. Right. So typical again, if you stick with the history of life on earth, like these exact model systems, you would say, ah, here's a particular structure of the brain and this one has fewer of those and this one has a bigger frontal cortex and this one one. So these are landmarks that we're used to and it allows us to make very kind of rapid judgments about things. But if you can't rely on that either because you're looking at a synthetic Thing or an engineered thing or an alien thing, then what do you do? Right? How do you. And so that's what I'm really interested in. I'm interested in, mind, in all of its possible implementations, not just the obvious ones that we know from looking at brains here on Earth.

Whenever I think about something like unconventional cognition, I think about cellular automata. I'm just captivated by the beauty of the thing. The fact that from simple little objects you can create some such beautiful complexity that very quickly you forget about the individual objects and you see the things that it creates as its own organism. That blows my mind every time. Like, honestly, I could full time just eat mushrooms and watch cellular automata. I don't even have to do mushrooms. Just, just cellular automata. It feels like, I mean, from the engineering perspective, I love when a very simple system captures something really powerful because then you can study that system to understand something fundamental about complexity, about life on a Earth. Anyway, how do I communicate with a thing? For cellular automata can, can do cognition if a plant can do cognition, if a xenobot can do cognition, how do I like whisper in its ear and, and, and get an answer back too? How do I have a conversation?

Yeah, well, how do I have a.

Xenobot on a podcast?

It's a really, really interesting line of investigation that, that, that, that, that opens up. I mean, I mean, we've, we've thought about this. So you need a few things. You need to understand the space in which they live. So not just the physical modality, like can they see light, can they feel vibration? I mean, that's important, of course, because that's how you deliver your message. But not just the ideas for a communication medium, not just the physical medium, but saliency, right? So what's important to this system? And systems have all kinds of different levels of sophistication of what you could expect to get back. And I think what's really important, I call this the spectrum of persuadability, which is this idea that when you're looking at a system, you can't assume where on the spectrum it is. You have to do experiments. So for example, if you look at a gene regulatory network, which is just a bunch of nodes that turn each other on and off at various rates, you might look at that and you say, wow, there's no magic here. I mean, clearly this thing is as deterministic as it gets. It's a piece of hardware. The only way we're going to be able to control it is by rewiring It. Which is the way molecular biology works, right? We can add nodes, remove nodes or whatever. Well, so we've done simulations and shown that biological. And now we're doing this in the lab. The biological networks like that have associative memory, so they can actually learn. They can learn from experience. They have habituation, they have sensitization, they have associative memory, which you wouldn't have known if you assume. Assume that they have to be on the left side of that spectrum. So when you're going to communicate with something, and we've even. Charles Abrams and I have written a paper on behaviorist approaches to synthetic organisms, meaning that if you're given something, you have no idea what it is or what it can do, how do you figure out what its psychology is, what its level is, what does it. And so we literally lay out a set of protocols, starting with the simplest things and then moving up to more complex things where you can make no assumptions about what this thing is, can do, right? Just. You have to start and you'll find out. So here's a simple. I mean, here's one way to communicate with something. If you can train it, that's a way of communicating. So if you can provide. If you can figure out what the currency of reward of positive and negative reinforcement is, and you can get it to do something it wasn't doing before based on experiences you've given it, you have taught it one thing. You have communicated one thing that such and such an action is good. Some other action is not good. That's like a basic atom of. A primitive atomic atom of communication.

What about, in some sense, if it gets you to do something you haven't done before, is it answering back?

Yeah, most. Most certainly. And there's, There's. I've. I've seen cartoons. I think maybe Gary Larson or somebody had had a cartoon of these, of these rats in the maze. And the one rat, you know, assist to the other. Hey, look at this. Every time. Every time I walk over here, he starts scribbling in that on the, you know, on the clipboard that he has. It's awesome.

If we step outside ourselves and really measure how much, like if I, If I actually measure how much I've changed because of my interaction with certain cellular automata. I mean, you really have to take that into consideration about, like, well, these things are changing you too. Yes, I know. You know, how it works and so on, but you're being changed by the thing.

Absolutely. I think, I think I read. I don't know Any details? But I think I read something about, about how wheat and other things have domesticated humans in terms of. Right. But by their properties change the way that the human behavior and societal structure.

So in that sense, cats are running the world because they've took over the. So first of all, so first they, while not giving a shit about humans, clearly with every ounce of their being, they've somehow got, got just millions and millions of humans to take them home and feed them. And then not only the physical space did they take over, they took over the digital space. They dominate the Internet in terms of cuteness, in terms of memeability. And so they got themselves literally inside the memes that become viral and spread on the Internet. And they're the ones that are probably controlling humans. That's my theory. Another, that's a follow up paper after the frog kissing. Okay. I mean, you mentioned sentience and consciousness. You have a paper titled Generalizing Frameworks for Sentience Beyond Natural Species. So beyond beyond normal Cognition. If we look at sentience and consciousness, and I wonder if you draw an interesting distinction between those two elsewhere, outside of humans and maybe outside of Earth, you think aliens have sentience, and if they do, how do we think about it? So when you have this framework, what is this paper? What is, what is the way you propose to think about essentials?

Yeah, that particular paper was a very short commentary on another paper that was written about crabs. It was a really good paper on them. Crabs and various, like a rubric of different types of behaviors that could be applied to different creatures and they're trying to apply it to crabs and so on. Consciousness, we can talk about a few, but it's a whole separate kettle of fish. I almost never talk about consciousness in this case. Yes, I almost never talk about consciousness per se. I've said very little about it, but we can talk about it if you want. Mostly what I talk about is cognition because I think that that's much easier to deal with in a kind of rigorous experimental way. I think that all of these terms have sentience and so on, have different definitions. And fundamentally I think that people can, as long as they specify what they mean ahead of time. I think people can define them in various ways. The only thing that I really kind of insist on is that the right way to think about all this stuff from an engineering perspective, what does it help me to control, predict, and does it help me do my next experiment? So. So that's not a universal perspective. So some people have philosophical kind of underpinnings and those are primary. And if anything runs against that, then it must automatically be wrong. So some people will say, I don't care what else. If your theory says to me that thermostats have little tiny goals, I'm out. So that's it. That's my philosophical preconception that thermostats do not have goals and that. That's it. So that's one way of doing it. And some people do it that way. I do not do it that way. And I think that we can't. We can't. I don't think we can know much of anything from a philosophical armchair. I think that all of these theories and ways of doing things stand or fall based on just basically one set of criteria. Does it help you run a rich research program? That's it.

I agree with you totally. But so forget philosophy. What about the poetry of ambiguity? What about at the limits of the things you can engineer using terms that are. That can be defined in multiple ways, and living within that uncertainty in order to play with words until something lands that you can engineer? I mean, that's, to me, where consciousness sits currently. Nobody really understands the. The. The hard problem of consciousness, the subject, what it feels like, because it really feels like. It feels like something. To be this biological logical system, this conglomerate of a bunch of cells in this hierarchy of competencies feels like something. And yeah, I feel like one thing. And is that just. Is that just a side effect of a complex system? Or is there something more that humans have? Or is there something more that any biological system has? Some kind of magic? Magic. Some kind of. Not just a sense of agency, but a real sense with a capital letter S of agency?

Yeah. Ah, boy. Yeah. That's a deep question.

Is there room for poetry and engineering or no?

No, there definitely is. And a lot of the poetry comes in when we realize that none of the categories we deal with are sharp as we think they are. Right. And so, and so in the, you know, in the different areas of all these spectra are where a lot of the poetry sits. I have many new theories about. I, in fact, do not have a good theory about consciousness that I plan to trot out, so.

And you almost don't see it as useful for your current work to think about consciousness?

I think it will come. I have some thoughts about it, but I don't feel like they're going to move the needle yet on that.

And you want to ground in engineering always so.

Well, I mean, I don't. So if we really tackle consciousness per se, in terms of the Hard problem that isn't necessarily going to be groundable in engineering. Right. That aspect of cognition is. But actual consciousness per se, first person perspective, I'm not sure that that's groundable in engineering. And I think specifically what's different about it is there's a couple things. So let's. Here we go. I'll say a couple things about consciousness. One thing is that what makes it different is that for every other aspect of science, when we think about having a correct or a good theory of it, we have some idea of what format that theory makes predictions in. So whether those be numbers or whatever, we have some idea. We may not know the answer, we may not have the theory, but we know that when we get the theory, here's what it's going to output, and then we'll know if it's right or wrong for actual consciousness. Not behavior, not neural correlates, but actual first person consciousness. If we had a correct theory of consciousness or even a good one, what the hell would, what format would it make predictions in? Right? Because all the things that we know about basically boil down to observable behaviors. So the only thing I can think of when I think about that is it'll be poetry or it'll be something to. If I ask you, okay, you've got a great theory of consciousness and here's this creature, maybe it's an asteroid, natural one, maybe it's an engineer one, whatever. And I want you to tell me what your theory says about this being's. What it's like to be this being. The only thing I can imagine you giving me is some piece of art, a poem or something that once I've taken it in, I share. I now have a similar state as whatever. That's about as good as I can come up with.

Well, it's possible that once you have a good understanding of consciousness, it would be mapped to some things that are more measurable. So, for example, it's possible that a conscious being is one that's able to suffer. So you start to look at pain and suffering, you can start to connect it closer to things that you can measure that in terms of how they reflect themselves in behavior and problem solving and creation and attainment of goals, for example, which I think suffering is one of the, you know, life is suffering. It's one of the, one of the big aspects of the, the human condition. And so if consciousness is somehow a, maybe at least a catalyst for suffering, you could start to get like echoes of it. And you start, you, you start to see like the actual effects of consciousness on behavior, that it's not just about subjective experience. It's like it's really deeply integrated in the problem solving, decision making of a system. Something like this. But also it's possible that we realize this is not a philosophical statement. Philosophers can write their books. I welcome it. I take the Turing test really seriously. I don't know why people really don't like it when a robot convinces you that it's intelligent. I think that's a really incredible accomplishment. And there's some deep sense in which that is intelligence. If it looks like it's intelligent, it is intelligent. And I think, think there's some deep aspect of a system that appears to be conscious. In some deep sense, it is conscious. At least for me, we have to consider that possibility. And a system that appears to be conscious is an engineering challenge.

Yeah, I don't disagree with any of that. I mean, especially intelligence, I think is a publicly observable thing. I, I, and, and I mean, you know, science fiction has dealt with this for a century or, or much more. Maybe this idea that when you are confronted with something that just doesn't meet any of your typical assumptions, so you can't look in the skull and say, oh well, there's that frontal cortex. So then I guess we're good, right? If it's, if it's, you know, so, so this thing lands on your front lawn and this, you know, with the, the little door opens and something trundles out and it's sort of like kind of shiny and aluminum looking and it hands you this poem that it wrote while it was on flying over and how happy it is to meet you. What's going to be your criteria for whether you get to take it apart and see what makes it tick or whether you have to be nice to it and whatever all the criteria that we have now that people are using. And as you said, a lot of people are down on the Turing test and things like this, but what else have we got? That because measuring a cortex size isn't going to cut it right in the broader scheme of things. So I think it's a wide open problem. Our solution to the problem of other minds. It's very simplistic. We give each other credit for having minds just because we sort of, on an anatomical level we're pretty similar. And then, so that's good enough, but how far is that going to go? So I think that's really primitive. So yeah, I think it's a major uncertainty solve problem.

It's a really challenging direction of thought to the human race that you talked about, like embodied minds. If you start to think that other things other than humans have minds, that's really challenging.

Yeah.

Because all men are created equal. Starts. Starts being like, all right, well, we should probably treat. Eat not just cows with respect, but like plants. And not just plants, but some kind of organized conglomerates of cells in a petri dish. In fact, some of the work we're doing, like you're doing and the whole community of science is doing with biology, people might be like, we were really mean to viruses.

Yeah. I mean. Yeah. The thing is, you're right. And I certainly get phone calls about people complaining about frog skin and so on. But I think we have to separate the sort of deep philosophical aspects versus what actually happens. So what actually happens on Earth is that people with exactly the same anatomical structure kill each other on a daily basis. Right. So I think it's clear that simply knowing that something else is equally or maybe more cognitive or conscious than you are is not a guarantee of. Of kind behavior. That much we know of. And so then we look at commercial farming of mammals and various other things. And so I think on a practical basis, long before we get to worrying about things like frog skin, we have to ask ourselves, why are we. What can we do about the way that we've been behaving towards creatures which we know for a fact, because of our similarities, are basically just like us? That's kind of a whole other social thing. But fundamentally, of course, you're absolutely right in that we are also. Think about this. We are on this planet in some way incredibly lucky. It's just dumb luck that we really only have one dominant species. It didn't have to work out that way. So you could easily imagine that there could be a planet somewhere with more than one equally or maybe near equally intelligent species. But they may not look anything like each other. Right. So there may be multiple ecosystems where there are things of. Similar to human, like intelligence. And then you'd have all kinds of issues about how do you relate to them when they're physically not like you at all, but yet in terms of behavior and culture and whatever, it's pretty obvious that they've got as much on the ball as you have. Or maybe imagine that there was another. Another group of beings that was like, on average, you know, 40 IQ points lower. Right. Like. Like, we're just. We're pretty lucky in many ways. We, you know, we don't really have. Even though we sort of, you know, we still act badly in many ways. But, but, but the fact is, you know, all humans are more or less in this, like in the same, that same range. But it didn't have to work out that way.

Well, but I think that's part of the way life works on Earth. Maybe human civilization works. Works is it seems like we want ourselves to be quite similar. And then within that, you know what, everybody's about the same relatively iq, intelligence, problem solving capabilities, even physical characteristics. But then we'll find some aspect of that that's different. And that seems to be like. I mean it's really dark to say but. But it seems to be not even a bug, but like a feature of the early development of human civilization. You pick the other, your tribe versus the other tribe and you war. It's a kind of evol. Evolution in the space of. Of memes, a space of ideas, I think, and you war with each other. So we're very good at finding the other even when the characteristics are really the same.

Same.

I don't know what that. I mean, I'm sure so many of these things echo in the biological world in some way.

Yeah. There's a fun experiment that I did. My son actually came up with this. We did a biology unit together. We used to homeschool and so we did this a couple years ago. We did this thing where imagines you get this slime mold, right? Fisarium polycephalum, and it grows on a petri dish of agar and it's of spreads out and it's a single cell protist, but it's like this giant thing. And so you put down a piece of oat and it wants to go get the oat and it sort of grows towards the oat. So what you do is you take a razor blade and you just separate the piece of the whole culture that's growing towards the oat. You just kind of separate it. And so now think about the interesting decision making calculus for that little piece. I can go get the oat and therefore I won't have to share those nutrients with this giant mass over there. So the nutrients per unit volume is going to be amazing. So I should go eat the oat, but if I first rejoin, because physaram, once you cut it has the ability to join back up. If I first rejoin, then that whole calculus becomes impossible because there is no more me anymore. There's just we. And then we will go eat this thing, right? So this interesting. You can imagine a kind of game theory where the number of agents isn't fixed and that it's not just cooperator defect, but it's actually merge and whatever. Right, yeah.

So that computation, how does it do that, this decision making.

Yeah, right. So it's really interesting and so empirically what we found is that it tends to merge first. It tends to merge first and then the whole thing goes. But it's really interesting that calculus, like, do we even have. I mean, I'm not an expert in the economic game theory and all that, but maybe there's a calculator needs some sort of hyperbolic discounting or something. But maybe this idea that the actions you take not only change your payoff, but they change who or what you are and that you may not you. You could take an action after which you don't exist anymore, or you are radically changed or you are merged with somebody else. Like that's, you know, as far as I know, that's a whole. You know, we're still missing a formalism for even knowing how to. How to model any of that.

Do you see? Evolution, by the way, is a process that applies here on Earth, or is it some. Where did evolution come from?

Yeah, yeah.

So this thing that. From the very origin of life that took us to today. What the heck is that?

I think evolution is inevitable in the sense that if you combine and basically, I think one of the most useful things that was done in early computing, I guess in the 60s it started, was evolutionary computation and just showing how simple it is. That if you have imperfect heredity and competition together, other. Those two things or three things, right? So heredity, imperfect heredity and competition or selection, those three things, and that's it. Now you're off through the races, right? And so that can be. It's not just on Earth because it can be done in the computer, it can be done in chemical systems, it can be done in. You know, Lee Smolin says it works on cosmic scales. So I think that that kind of thing is incredibly pervasive and general. It's a general feature of life. It's interesting to think about. The standard thought about this is that it's blind, right? Meaning that the intelligence of the process is zero. It's stumbling around. And I think that back in the day when the options were it's dumb like machines or it's smart like humans, then of course, the scientists went in this direction because nobody wanted creationism. And they said, okay, it's got to be like completely. I'm not actually sure, right. Because I think that everything is a continuum and I think that it doesn't have to be smart with foresight like us, but it doesn't have to be completely blind either. I think there may be aspects of it, and in particular this kind of multiscale competency might give it a little bit of look ahead maybe, or a little bit of problem solving sort of baked in, but that's going to be completely different in different systems. I do think it's general. I don't think it's just on Earth. I think it's.