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

1. 0:00 – 1:40

   Introduction

   1. MLMichael Levin0:00

      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, uh, 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, you know, they're a much more advanced life form. They have lots of different internal organs, but they're these little, um, they're about, you know, maybe two centimeters and, and a centimeter to two in size. They have a bra- 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 r- that right there tells you that these theories of thermodynamic, um, limitations of, on lifespan are wrong. It's not th- it's not that, well, over time of g- everything degrades. No. Planaria can keep it going for, uh, probably th- you know, how long have they been around, 400 million years? Right? So these are the actual l- so the planaria in our lab are actually in physical continuity with planaria that were here 400 million years ago.

   2. LFLex Fridman0:55

      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 Fridman Podcast. To support it, please check out our sponsors in the description, and now, dear friends, here's Michael Levin.

2. 1:40 – 9:08

   Embryogenesis

   1. LFLex Fridman1:40

      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?

   2. MLMichael Levin1:51

      Yeah. It is, it is an incredible process. Uh, I think it's maybe the most, uh, magical process there is, and, uh, 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, right? Because we all start life as a single, uh, 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, um, an, an inner 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, uh, special place where, you know, a lightning bolt says, "Boom, now you've gone from, from physics to true cognition." That doesn't happen. And so we can see in this process that of the whole mystery, you know, the biggest mystery of the, of the universe basically, how you get mind from matter.

   3. LFLex Fridman2:43

      From just physics in quotes.

   4. MLMichael Levin2:45

      Yeah.

   5. LFLex Fridman2:46

      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?

   6. MLMichael Levin2:55

      So one of the things that I think is really important if we're gonna bring in, um, 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, uh, so-called generic laws, and these are laws of mathematics. These are laws of computation. These are laws of, um, of, of physics, of all, all, all kinds of interesting things that are not directly in the DNA. And that, that process, you know, I think, I think the reason I, the reason I always put, um, 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, uh, th- these kinds of things, I think that's what gets us in trouble. I think that we really have to understand that it's a continuum, and we have to work up the scaling, the laws of scaling, and we can, we can certainly talk about that. There's a lot of really interesting, uh, thoughts to be had there.

   7. LFLex Fridman3:56

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

   8. MLMichael Levin4:14

      Yeah. The environment and also the laws of the universe. I mean, the thing about, the thing about the, uh, the DNA is that it's, um, once evolution discovers a certain kind of machine, that if, if the f- if the physical implementation is appropriate, it sort of, uh... And this is hard to tal- uh, talk about because we don't have a good vocabulary for this yet, but it's a very, um, kind of, uh, platonic notion that, that if the machine is there, it pulls down interesting, uh, uh, little, 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, 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, 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, doesn't have to be in the DNA. It's- you get it for free, right? 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, that first step, that first little machine that, that, that enables you to couple to those laws. And there's laws of adhesion and, and, and many other things, and this is all, um, that interplay between the, the, the hardware that's set up by the genetics and the software that's be- right? The physiological software that basically does all the computation and the cognition and everything else is a real interplay between the information in the DNA and the laws of, of physics, of computation and so on.

   9. LFLex Fridman5:44

      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 kinda discovered?

   10. MLMichael Levin5:56

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

   11. LFLex Fridman6:01

       Well, I think that's a really deep idea. Then embryogenesis is the process of revealing...... of, um, embodying, of manifesting these laws. You're not building the laws-

   12. MLMichael Levin6:16

       Yeah.

   13. LFLex Fridman6:17

       ... you're just, uh, creating the capacity to reveal.

   14. MLMichael Levin6:20

       Yes. I think, aga- again, no- 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. You know, 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 you do? Because what you might assume by, by... L- the thing about embryogenesis is that it's super reliable, right? It's very robust. And that really, uh, 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 gonna make?" That's what it, you know, that's what it makes. That's the standard story. But the reality is... And, and so, and so you look at these, um, 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 if 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 little, um, this multi little creature that runs around the dish. They have all kinds of in- incredible capacities. They navigate through mazes, they have, uh, various behaviors that they do both independently and, and together. They, uh, they have a, a... They basically, they implement von Neumann's dream of, 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 cells into little piles. They, 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, they're wild type. 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 re- 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, is, uh, to be this... Uh, they want... Their default behavior is to be a xenobot. But in vivo, in the embryo, they get told to be skin by these other cell types. And so, so now, so now here comes th- this, 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 g- it's, it's, uh, selection." So over, over millions of years, right, it's been shaped to, to produce this specific body with, 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 a new environment. In 48 hours, they figure out how to be an entirely different, uh, protoorganism 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 of days, these guys find a new way of doing it that's not done anywhere else in the biosphere.

3. 9:08 – 22:55

   Xenobots: biological robots

   1. MLMichael Levin9:08

   2. LFLex Fridman9:08

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

   3. MLMichael Levin9:12

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

   4. LFLex Fridman9:21

      How much is it biology? How much is it robot?

   5. MLMichael Levin9:25

      At this point, most of it is biology because what we're doing is we're discovering natural, uh, behaviors of these, uh, of these... Of the cells and also of the cell collectives. Now, one of the really important parts of this was that, um, we're working together with Josh Bongard's group at University of Vermont. They are computer scientists, um, do, do AI, and they've basically been able to use, uh, 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 as 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 gonna be... You know, we're, we're now in this... This is future unpublished work, but we're doing all sorts of, uh, interesting ways to reprogram them to new behaviors. But before you can start to reprogram these things, you have to understand what their, uh, innate capacities are.

   6. LFLex Fridman10:13

      Okay, so that means engineering, programming. You're engineering them in, in the future. And in some sense, the, the definition of a robot is something you, in part, engineer?

   7. MLMichael Levin10:26

      Yeah.

   8. LFLex Fridman10:26

      And... (laughs) Versus evolve. I mean, um, it- it's such a fuzzy definition anyway. In some sense, many of the organisms within our body are kinds of robots.

   9. MLMichael Levin10:37

      Yes, yes.

   10. LFLex Fridman10:38

       And I think robots is a weird line 'cause 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 movement for robots, but we'll talk about that later perhaps.

   11. MLMichael Levin10:52

       Sure.

   12. LFLex Fridman10:52

       Anyway, um, 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?

   13. MLMichael Levin11:09

       Yeah. So just to take one step back, one of the things that, um, a lot of people, uh, get stuck on is they say, "Well, uh..."You know, engineering requires new, uh, DNA circuits or it requires new nanomaterials. You know, what... The thing is, we are now moving from old school engineering which used passive materials, right? The things that... You know, wood, metal, things like this, that basically the only thing you could depend on is that they were gonna keep their shape. That's it. They don't do anything else. You- y- uh, 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 computation materials. This is a whole new era. These are agential materials. This is you're- 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.

   14. LFLex Fridman11:52

       That's hilarious that y- that you have to talk your material into keeping its shape.

   15. MLMichael Levin11:56

       Yeah, that's- that is exactly right.

   16. LFLex Fridman11:58

       (laughs) You have to convince-

   17. MLMichael Levin11:58

       That is exactly right. That is exactly right.

   18. LFLex Fridman11:59

       Stay there. It's like getting a bunch of cats or something (laughs) and trying to organize a shape out of them.

   19. MLMichael Levin12:04

       Yeah. It's funny, we're on the same page here because in a paper, this is, this is currently, um, uh, just been accepted in Nature by engineering. One of the figures I have is building a tower out of LEGOs versus dogs, right?

   20. LFLex Fridman12:14

       Yeah.

   21. MLMichael Levin12:15

       So think about the difference, right? If you build out of LEGOs, you have full control over where it's gonna go. But if somebody knocks it over, it's game over. With the dogs, you cannot just come and stack them. They're not gonna stay that way. But the good news is that if you train them, then somebody knocks it over, they'll get 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 wanna know is, how much can I depend on this thing to do when I'm not around to micromanage it? What level of, uh, 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, uh, into this area where you're- you're- you're working with agential materials. That's a collaboration. That's not, that's not old, old style engineering.

   22. LFLex Fridman13:12

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

   23. MLMichael Levin13:14

       Agential, yeah.

   24. LFLex Fridman13:15

       What's that mean?

   25. MLMichael Levin13:15

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

   26. LFLex Fridman13:34

       Is that empowering or limiting, having material that has a mind of its own, literally?

   27. MLMichael Levin13:40

       I think it's both, right? So it raises difficulties because it means that it... If you, if you're using the old mindset, which is a linear, um, kind of extrapolation of what's going to happen, you're going to be surprised and shocked all the time because biology, uh, 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 uh, (clears throat) 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 behin- 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, uh, problem space than if you try to micromanage the, the thing. And in regenerative medicine, when you're trying to get, um, let's say an arm to grow back or an eye to repair a cell birth defect or something, do you really want to be controlling tens of thousands of genes at each point to try to f- 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, I think it's, it's both. It's both difficult and it challenges us to develop new ways of engineering and it's, it's hugely empowering.

   28. LFLex Fridman15:19

       Okay, so how do you... I mean, maybe sticking with the metaphor of dogs and cats. Uh, I presume you have to figure out the... Find the dogs and, uh, dispose of the cats. Um, because, you know, it's like the old herding cats is, 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 gonna 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?

   29. MLMichael Levin15:56

       Right now, it's largely in the lab because we... Our, our simulations do not capture yet the most, uh, pow- interesting and powerful things about biology. So the simulation does... What, what, what we're pretty good at simulating are, um, feedforward emergent types of things, right? So cellular automata. If you have simple rules and you sort of roll those forward for every, every agent or every cell in the simulation and complex things happen, you know, ant colony, um, algorithms, things like that. We're, we're, we're good at that and that's, 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 a, you know, 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, or if you say, "Here are a bunch of cells that are creating this kind of organism, I, I don't think that's optimal. I'd like to, to repair that birth defect." How do you control all the, 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 a, it's an inverse problem. It's generally not solvable. So, um, 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, not bottom-up emergence, but the idea of, um, things like, uh, goal-directed, uh, test-operate-exit kinds of loops where, 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, um, you have a salamander and it's got an arm. You can, you can amputate that arm anywhere along the length. 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 right, that's a, that's a-... a, a means ends kind of analysis where it has to know what the correct limb is supposed to look like, right? So it has a way to, uh, ascertain the current shape. It has a way to measure that delta from, 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, some really wild things with, with both planaria and frog embryos and so on. Once you know that, um, you can start playing with that, uh, 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, it gives you those kinds of... so, so, so the idea is to basically, uh, appropriate a lot of the, um, approaches and concepts from cognitive neuroscience and behavioral science into things that, uh, previously were taken to be dumb materials. And, you know, you get yelled at in class if you, if, for being anthropomorphic if you said, "Well, my cells want to do this and my cells want to do that." And I think, I think that's a, that's a major mistake that leaves a ton of capabilities on the table.

   30. LFLex Fridman18:43

       So thinking about biologic systems as things that have memory, have almost something like cognitive ability.
4. 22:55 – 32:26

   Sense of self

   1. MLMichael Levin22:55

      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? That'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, which I like a lot, uh, to give you an emergent, uh, collective with goals and preferences and memories and anticipations that do not belong to any of the sub-units. So I think what we're getting into here, and we can talk about, um, how, how this happens during embryogenesis and so on, what we're getting into is, uh, the origin of the ce- of, of a Self, you know, with a big... with a capital S. So we are selves. 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.

   2. LFLex Fridman23:38

      Yeah. Is this the first... or at least humans tend to think that this is the, the level at which the Self with a capital S is first born, uh-But, uh, and we really don't wanna see, um, human civilization or Earth itself as one living organism.

   3. MLMichael Levin23:54

      Yeah.

   4. LFLex Fridman23:55

      That's very uncomfortable to us.

   5. MLMichael Levin23:57

      It is, yeah.

   6. LFLex Fridman23:58

      But is, um... Yeah, where's the self born?

   7. MLMichael Levin24:01

      We have to grow up past that, so what I like to do is, uh, well, I'll, I'll tell you two quick stories about that. I, 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, um, 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 that's not true cognition, right? And people will argue about that. I, I like to work it backwards. I say, "Let's, let's, let's agree that you and I are as, as we sit here, are examples of true cognition, if anything, is if there's anything that's true cognition, we are, we are examples of it." Now, let's just roll back slowly, right? So you roll back to the time when you're a small child and used to doing whatever, and then just sort of day by day, you roll, you roll back and eventually you become more or less that paramecium and then, and then you sort of even below that, right, as a, as a, as an unfertilized oocyte. So it's... The... No one has, uh, to my knowledge, no one has come up with any convincing discreet step at which my cognitive powers disappear, right? It just doesn't... Biology doesn't offer any specific step. It's com- it's incredibly smooth and slow and continuous. And so I think this idea that it just sort of magically shows up, uh, at one point and then, and then, uh, 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 wanna tell is where embryos come from. So think about this for a second, amniote embryo, so this is humans, uh, birds and so on, uh, ma- mammals and birds and so on, imagine a flat disc of cells, so there's maybe 50,000 cells, and in that... So, so when you get an egg from a, from a fertilized... Let's, let's say you buy a fertilized egg from a farm, right? That, that egg, uh, will, will have about 50,000 cells in, um, uh, in a flat disc. It looks like a little, little, tiny little frisbee. And in that flat disc, what'll happen is, uh, there'll be, uh, one, one set of cells will, um, becomes... Will become special, and it will tell all the other cells, "I'm, I'm gonna be the head, you guys don't be the head." And so it'll amplify, symmetry-breaking amplification, you get one embryo. There's a, there's a... You know, there's some neural tissue and some other stuff forms. Now, now you say, "Okay, I had one egg and one embryo and, and there you go. What else could it be?" Well, the reality is, and I used to... I, I, I did all of this as a, as a grad student, if you, um, if you take a little needle and you make a scratch in that blastoderm, in that, 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, uh, both regions will decide that they can be the embryo, and there'll be two of them, and then when they heal up, they become conjoined twins, and you can make two, you can make three, you can make lots. So 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, this undifferentiated, I'm sure there's a, there's a psychological, um, version of this somewhere that I don't know the proper terminology, but you have this, you have this, this like 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, uh, 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 that, that... The establishment of that boundary with its own, um, ability to maintain... To, to pursue certain goals, that's the origin of, of selfhood.

   8. LFLex Fridman27:35

      But I... Is that goal in there somewhere? Were they always destined? Like, are they discovering that goal? Like, where the hell did evolution, um, discover this when you went from the prokaryotes to euc- eukaryotic cells, and then they started making groups, and when you make a certain group you make a g- You sh- you make it sound... (exhales) And it's such a tricky thing to try to understand, you make it sound like these cells didn't get together and ca- came up with a goal, but the very act of them getting together revealed the goal that was always there. There, there was always that potential for that goal.

   9. MLMichael Levin28:19

      So the first thing to say is that, uh, there are way more questions here than, than certainties, okay? So everything-

   10. LFLex Fridman28:24

       Yes, of course.

   11. MLMichael Levin28:24

       ... I'm telling you is, is cutting edge, developing, you know, stuff, so, so it's not as if any of us know the answer to this, but, but here's, here's, here's my opinion on this: I think what evolution... I, 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 e- that, that will, that will solve problems in different spaces, so not just three-dimensional space. This goes back to what we were talking about before. We... The, the brain is a, evolutionarily a late development. I- I- it's a system that is able to na- 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, uh, instructions to, for cell behaviors, meaning cells to move, to, to, to divide, to, to die, to, um, change into different cell types, to navigate more for 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 j- is, is, is produced hardware that is very good at navigating different spaces using a bag of tricks, right, which, which I'm sure many of them we can steal for autonomous vehicles and robotics and various things. And what happens is that, um, 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, right? We are all brains in a vat, so to speak. Every cell does not know, right... Every, every cell is some other na- some other cell's external environment, right? So where does the... That border between you, you and the outside world, you don't really know where that is, right? Every c- 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 from scratch, this autopoiesis, is what defines, uh, the border of a self. Now, biology uses like a, um, a multi-ska- um, a multi-scale competency architecture, meaning that every level has goals. So, so molecular networks have goals, cells have goals, tissues, organs, um, colonies, uh, and, and it's the interplay of all of those that, uh, that enable biology to solve problems in new ways, for example, in xenobots and, and various other things. Um, this is, you know, uh, it's, 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, so evolution is very good at this agential bioengineering, right? When, when evolution is, uh, 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 proteins, uh, 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, it has to, because it's not dealing with a blank slate. Every time as, as, you know, if you're evolution and you're trying to, um, uh, make, make a, make an organism, you're not dealing with a passive, uh, material that is, 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 gonna be adaptive is to find the signals that are gonna co-, um, convince the cells to do various things, right?

   12. LFLex Fridman31:47

       Your sense is that evolution operates both in the software and the hardware.

   13. MLMichael Levin31:50

       Yeah.

   14. LFLex Fridman31:51

       And it's just easier and more efficient to operate in the software.

   15. MLMichael Levin31:55

       Yes. And I should also say, I, I don't think the distinction is sharp.

   16. LFLex Fridman31:58

       Yeah.

   17. MLMichael Levin31:58

       In other words, I think it's a continuum. But I think we can... 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, um, uh, amount of information in the genome that isn't structural at all. It's, it's, uh, 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

5. 32:26 – 43:57

   Multi-scale competency architecture

   1. MLMichael Levin32:26

      gonna solve problems.

   2. LFLex Fridman32:27

      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?

   3. MLMichael Levin32:59

      Yeah.

   4. LFLex Fridman32:59

      And then there's, like, the bosses that do the bigger thing somehow, or that you can now abstract away the small group of cells, uh, as a, as an organ or something, and then that organ does something b- 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 m- that hierarchical competence architecture? (laughs) I love that. Competence, just the word competence is, is pretty cool in this context, 'cause everybody's good at their job somehow.

   5. MLMichael Levin33:34

      Yeah. No, it's really key. And, and the other nice thing about competency is that... so, so my, my central belief in all of this is that en- engineering is the right perspective on all of this stuff, because it gets you away from, uh, subjective, uh, terms. You know, people talk about sentience and this and that. Th- tho- those things are very hard to define or people argue about them philosophically. I think that g- g- e- engineering terms like competency, like, um, uh, you know, uh, pursuit of goals, right? All, all of these things are, uh, are empirically incredibly useful because you know it when you see it. And if it helps you build, right? If I, if I can pick the right level, I say, uh, this thing has... I believe this is X level of th- like, com- th- I feel you, competency, I think it's like a thermostat or I think it's like a, a, a better thermostat or I think it's a, you know, a, a, um, various other kinds of, you know, there's many, many different kinds of complex systems. If that helps me to control and, and predict and build such systems, then, then that's all there is to say. There's no more philosophy to argue about. So, so I like competency in that way because you can quantify. You could... you have to, in fact, you have to, you have to make a claim, competent at what? And then... 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 deviate it 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. So, so, so the, the, w- and we're not used to thinking about this. I, I, I give a talk sometimes called, Why Don't Robots Get Cancer, right? And the reason robots don't get cancer is because generally speaking, with a few exceptions, our, our architectures have been... you've got a bunch of dumb parts and you hope that if you put them together, the c- the, the, the overlying machine will have some intelligence and do something or other, right? But the individual parts don't, don't care. They don't have an agenda. Biology isn't like that. Every level has a, 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, uh, you know, sort of, uh, he- helping each other. They're also competing for b- for information, for metabolic, for limited metabolic, um, constraints. But to get back to your, your, your other point, which is, you know, which is, which is th- this seems like really efficient and, and good and, and so on compared to some of our human efforts, 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, the, the geome- so, so, so I'm using, um...And I, and I, and I think, uh, I, I take this, this seriously, uh, terminology from, from like, um, from like relativity, right? Where, 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, in the higher space. Conversely, because the components are good at getting their job done, you, as the higher level, don't need to k- uh, 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, um, in the tadpole, we found that, okay, so, so tadpoles need to become frogs. And to become a fr- 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, you get your frog, right? So we decided to, to test. We... I, I had this hypothesis that I thou- I thought, actually, the system's probably more intelligent than that. So what did we do? We made, uh, what we call Picasso tadpoles. So these are... So everything is scrambled. So the eyes are on the back of the head, the 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, uh, froggy, uh, you know, sort of frog face configuration, then they stop. So, so the thing about that is now imagine evolution, right? So, so you make some sort of mutation, and it does... Like every mutation, it does many things. So, so, so something good comes of it, but also it moves your mouth off to the side, right? Now, if, if, if there wasn't this multi-scale competency, you can see where this is going. If there wasn't this multi-scale competency, the organism would be dead. Your fitness is zero 'cause 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, so the fitness landscape would be incredibly rugged. Evolution would take forever. The reason it works, well, one of the reasons it works so well is because you do that, no worries, the mouth will find its way where it, where it belongs, right? So now you get to explore. So, 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 d- the, um, uh, variability in the environment, all these things, the competency of the parts makes up for it. So the... So, so that's all, that's all fantastic, right? That's all, that's all great. 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, so as a simple example, you know, um, you as a, as a complex system, um, you will go out, and you will do, you know, jujitsu or whatever. You'll have some... You have to go rock climbing and scrape a bunch of cells off your hands, and then you're happy as a system, right? You come back, and you've, you've accomplished some goals, and you're really happy. Those cells are dead. They're gone, right? Did you think about those cells? Not really, right? You had some, you had some bruising, ow, this is fine.

   6. LFLex Fridman39:06

      Selfish SOB.

   7. MLMichael Levin39:06

      Right? That's it. And so, and so that's the thing to remember is that, um, you know, and we know this from, from history is that, is that just being a collective isn't enough because, uh, what the goals of that collective will be relative to the welfare of the individual parts is a-

   8. LFLex Fridman39:22

      Yeah.

   9. MLMichael Levin39:22

      ... massively open question.

   10. LFLex Fridman39:23

       The ends justify the means.

   11. MLMichael Levin39:24

       There you go.

   12. LFLex Fridman39:24

       I'm telling you, Stalin was onto something. No. Uh-

   13. MLMichael Levin39:27

       So that's the danger.

   14. LFLex Fridman39:28

       But we can s- exactly. That's the danger of, uh, uh, 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-

   15. MLMichael Levin39:53

       Yeah.

   16. LFLex Fridman39:54

       ... 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, uh, you're fighting against nature and saying, "Well, there's something bigger here than, um-"

   17. MLMichael Levin40:11

       Yeah.

   18. LFLex Fridman40:12

       ... a hierarchical competency architecture. (laughs)

   19. MLMichael Levin40:16

       Yeah.

   20. LFLex Fridman40:16

       Uh, and... But, uh, there's so many interesting things you said. So from an algorithmic perspective, 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 it has a goal, and it gets better and better at optimizing that goal and 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, uh, with the limited... with the bent option space, it's just doing the thing that's the easiest thing for it, in some sense.

   21. MLMichael Levin41:06

       Yeah. Yeah.

   22. LFLex Fridman41:06

       And somehow that allows you to have, um, turtles on top of turtles, literally, dumb systems on top of dumb systems, that, as a whole, creates something incredibly smart.

   23. MLMichael Levin41:18

       Yeah, I mean, e- every system is... has some degree of intelligence in its own problem domain. So, so cells will have problems they're trying to solve in physiological space and transcriptional space, and then I can give you some, some cool examples of that. But the collective is trying to solve problems in anatomical space, right? And forming a cr- you know, a creature and growing your blood vessels and so on. And then the collect- the, the, the, the whole body is solving yet other problems. They may be in social space and linguistic space and three-dimensional space, and, and who knows, you know, the group might be solving problems in, in, um, you know, I don't know, some sort of financial space or something. So one of the major differences with, with most, um, uh, with most AIs today is, is, A, the, the kind of flatness of the architecture, but also of the fact that they are constructed...... from outside their, their borders and their, you know, so, so few, so to a large extent, and of course there are, um, counterexamples now, but, but to a large extent, our technology has 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, that, uh, originally, uh, in, in robotics was actually Josh Bongard's work in 2006, where he made these, these robots that did not know their 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 they 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 then 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, is, uh, certain things like active inference, meaning you're trying to minimize surprise, right? 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, to, to think about everything you want to think about. So that means that you can't afford to be a micro-, um, reductionist, you know, all this data coming in. You have to coarse-grain it and say, "I'm gonna take all this stuff and I'm gonna call that a cat. I'm gonna take all this, I'm gonna call that the edge of the table I don't wanna fall off of. And I don't wanna know anything about the micro-states. 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." And so now you can begin to... Right? So that self-construction, that, that, that figuring out, making models of the outside world and then turning that inwards and starting to make a model of yourself, right? Which immediately starts to get into issues of, of agency and control because

6. 43:57 – 53:27

   Free will

   1. MLMichael Levin43:57

      in order to, if, 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, right? You don't have the energy to, like Laplace's demon, you know, calculate every, every possible, uh, state that's going to happen. You have to, you have to coarse-grain, and you have to say, "That is the kind of, uh, creature that does things, either things that I avoid or things that I will go towards, that's a mate or food," or what- whatever it's gonna be. And so right at the base of, uh, simple, very simple organisms starting to make models of agents doing things, that is the origin of, uh, models of, of, of free will basically, right? 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 can... I do things." Right? And, and then you make decisions about what you're gonna do. So all of this one, one, 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 but then take actions in the most energetically efficient space possible, right?

   2. LFLex Fridman45:03

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

   3. MLMichael Levin45:09

      I think that's very plausible. Yeah.

   4. LFLex Fridman45:11

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

   5. MLMichael Levin45:19

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

   6. LFLex Fridman45:44

      So that's true for even primitive organisms?

   7. MLMichael Levin45:47

      I think so. I think that's... Now, now they don't have... Now obviously you have to scale down, right? So, so, so, so they don't have the kinds of, um, complex meta-cognition that we have so they can do long-term planning and thinking about free will and so o- and so on. But, but-

   8. LFLex Fridman46:00

      But the sense of agency is really useful to accomplish a, a task, simple or complicated.

   9. MLMichael Levin46:05

      That's right. In, in all kinds of spaces, not just in, in obvious three-dimensional space. I mean, we're very good at... The thing is humans are very good at detecting agency, eh, of, of like medium size 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 gonna-

   10. LFLex Fridman46:23

       Mostly things you can eat or get eaten by.

   11. MLMichael Levin46:26

       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, uh, 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, uh, you could sense all the things that your organs were doing, so your pancreas, your liver, all the things. If, if we had that, you, 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, but we're just terrible at that. We don't... In fact, in fact, people don't even... You know, you talk about intelligence in these other pa- spaces and a lot of people think that's just crazy because, because all we're, all we know is motion.

   12. LFLex Fridman47:13

       We do have access to that information. So it's, it's actually possible that, uh... So evolution could, if it wanted to-

   13. MLMichael Levin47:19

       Yes.

   14. LFLex Fridman47:19

       ... construct an organism that's able to perceive-

   15. MLMichael Levin47:21

       Most certainly.

   16. LFLex Fridman47:22

       ... the flow of blood through your body-

   17. MLMichael Levin47:24

       Yep.

   18. LFLex Fridman47:25

       ... the way you see an old friend and say, "Yo, what's up? How's the wife and the kids?" Uh, in that same way you would see the, you would feel like a connection to the l- liver.

   19. MLMichael Levin47:36

       Yeah, yeah. I think, you know-

   20. LFLex Fridman47:38

       Maybe other people's liver? No, just your own because you don't have access to other people's liver.

   21. MLMichael Levin47:43

       Not yet, but you could imagine some really interesting connection, right? But it's a bioengineering-

   22. LFLex Fridman47:46

       Like sexual selection? Like, "Ooh, that girl's got a nice liver."

   23. MLMichael Levin47:50

       (laughs) Well, that's what-

   24. LFLex Fridman47:50

       Like the, the, the way her, (laughs) her blood flows, the, the dynamics of the blood, uh, is very interesting. It's novel. I've never seen one of those.

   25. MLMichael Levin47:59

       But, you know, that's, that's exactly what we're, we're trying to half-ass when we, when we, um, uh, judge judgment of, of beauty by facial symmetry and so on.

   26. LFLex Fridman48:06

       Yeah.

   27. MLMichael Levin48:06

       That's, that's a half-assed assessment-

   28. LFLex Fridman48:07

       It's an approximation.

   29. MLMichael Levin48:08

       ... of exactly that-

   30. LFLex Fridman48:08

       Yeah.
7. 53:27 – 1:06:44

   Bioelectricity

   1. LFLex Fridman53:27

      What is bioelectricity? What is biochemistry? What is, 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, 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-

   2. MLMichael Levin53:58

      Sure, sure. Bio-mechanics.

   3. LFLex Fridman53:59

      ... like knocking on the door.

   4. MLMichael Levin54:01

      Yeah. Yeah.

   5. LFLex Fridman54:02

      (laughs) Uh, so what, what, what's the difference? And what's an electrical network?

   6. MLMichael Levin54:06

      Yeah. So I wanna make sure and, and kinda give credit where credit is due. So, so as far back as 1903 and probably, um, late 1800s al- already, people were thinking about the importance of electrical, um, phenomena in, in life. So I'm for sure not the first person to stress the importance of electricity. Um, people... there were, there were waves of research in the, in the '30s, um, in the '40s, and then-... again, in the kind of, uh, 70s, 80s, and 90s of, of sort of the pioneers of bioelectricity who did some amazing work on all this. I think, I think what, what we've done that's new is to step away from this idea that... And, and I'll describe what, what the bioelectricity is. It's a 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, a privileged computational layer that gives you access to the actual cognition of the tissue, of basal cognition. So, so merging that, that developmental biophysics with ideas and cognition of computation and so on. I think, I think that's what we've done that's new. But people have been talking about bioelectricity for a really long time. And, and, and so I'll, so I'll define that. So, um, what happens is that, uh, if you have... Uh, 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, uh, 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, uh, difference across that membrane, and they spend a lot of energy to do so. When you now, now... So, so that's, that's, that's a single 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 l- little submarine hatch that just kind of docks, right? And the ions from one side can flow to the other side and vice versa. So-

   7. LFLex Fridman56:02

      Isn't it incredible that this evolved? Isn't, isn't that wild? 'Cause that didn't exist.

   8. MLMichael Levin56:09

      Correct. This had to be, this had to be evolved. And, and, and-

   9. LFLex Fridman56:11

      It had to be invented.

   10. MLMichael Levin56:12

       That's right.

   11. LFLex Fridman56:13

       Somebody invented electricity in the, in the ocean. When did this get invented?

   12. MLMichael Levin56:17

       Y- yeah. So, so-

   13. LFLex Fridman56:18

       This is... (laughs)

   14. MLMichael Levin56:19

       So, I mean, it's, it is, it is incredible. Um, the guy who discovered gap junctions, Werner Loewenstein, I visited him. He was, he was really old. I visited-

   15. LFLex Fridman56:25

       A human being?

   16. MLMichael Levin56:26

       He discovered them. He di-

   17. LFLex Fridman56:27

       'Cause when... What... 'Cause who really discovered them lived probably four billion years ago.

   18. MLMichael Levin56:32

       Good point. Good point.

   19. LFLex Fridman56:33

       So you're, you're... Give credit where credit is due, I'm just saying.

   20. MLMichael Levin56:35

       Good point. He, he, he rediscovered, (laughs) he rediscovered, uh, gap junctions. But, um, when I visited him in, in Woods Hole, uh, maybe 20 years ago now, uh, he told me that he was writing. And unfortunately, he, he, he passed away and I think this, this book never got written. He was writing a book on, on gap junctions and consciousness. And I think, I think it would have been a, a, an incredible book because, because gap junctions are magic. I'll, I'll explain why in a minute. Uh, what happens is that, just imagine, the, 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 that, you know, the electricity... Um, you can, you can... Uh, they have, they have these awesome movies where you can take a zebra, like a transparent, um, uh, uh, animal like a zebra fish, and you can literally look down and you can see all the, all the firings as the fish is like making decisions about what to eat and things like this, right? 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, they all do very similar things, just on a much slower time scale. And whereas your brain is thinking about thin- how to, uh, solve problems in three-dimensional space, um, 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, from the, from the earliest, uh, from the earliest, uh, time. I'm... He- here are two cells. This cell, uh, h- how can they communicate? Well, well, the simple version is this cell could send a chemical, 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. It, it's... This... Whatever information is coming, that's not my information. That, that information is coming from the outside. So I can, 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 this cell gets poked with a calcium spike. And the calcium spike or, 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, 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 meld. 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.

   21. LFLex Fridman59:33

       So they enforce a collective intelligence?

   22. MLMichael Levin59:35

       That's right.

   23. LFLex Fridman59:35

       That's what gap junctions do?

   24. MLMichael Levin59:36

       That's right. It helps. It's the beginning. It's not the whole story by any means, but it's a start.

   25. LFLex Fridman59:39

       Where's state stored s- of the system?

   26. MLMichael Levin59:44

       So there are-

   27. LFLex Fridman59:44

       Is it some... Is it in part in the gap junctions themselves? Is it in the cells?

   28. MLMichael Levin59:49

       There are many, many layers to this, as always in biology. So there are, um, uh, chemical networks. So for example, gene regulatory networks, right? Which, which are... Or, 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 s- they can get into stable states that are hard to pull them out of, right? So that's, that becomes... Once they get in, that's a memory, a permanent memory of s- or a semi-permanent memory of something that's happened. There are cytoskeletal structures, right? That are physically... They store, they store memories in, in physical, um, configuration. There are, uh, electrical memories like flip-flops where there is no physical... Right? So, so if you look, I, I, I show my students this example. There's a flip-flop.... and the reason that it stores as zero or one is not because some, some, uh, piece of the hardware moved. It's because there's a, there's a cycling of the current in one side of the thing. If I come over and I hold, um, you know, I hold the other side to a, to a high voltage for, for, you know, a brief period of 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 w- if it was zero or one, 'cause all you would see is where the hardware is. You wouldn't see the, the energetic state of the system. So there are also... So there are bioelectrical, um, states that are held in that exact way. Like, like, like volatile RAM, basically, like in the, in the electrical state of the system.

   29. LFLex Fridman1:01:10

       It's very akin to the different ways that memory is stored in a computer. So there's RAM. There's hard drives.

   30. MLMichael Levin1:01:18

       You can make that mapping, right? That... So I think the interesting thing is that based on the biology, we can have a more sophisticated... You know, I th- I think we can revise some of our, some of our, um, computer engineering methods, because there are some interesting things that biology does that we haven't done yet. But, but you can... But that ma- but that mapping is not bad. I mean, I think it works in many ways.
8. 1:06:44 – 1:18:33

   Planaria

   1. MLMichael Levin1:06:44

      is, uh, when we started studying this, we said, "Okay. Here's a, here's a planarian." A planarian is a flatworm. It has one head and one tail, normally. And the amazing, the, the several amazing things about planaria, but basically they, they kind of, I think, I think planaria hold, uh, the answer to pretty much every deep question of life. For one thing, they're similar to our ancestors, so they're, they have true symmetry, they have a true brain. They're not like earth worms. They're, you know, they're a much more advanced life form. They have lots of different internal organs, but they're these little, um, they're about, you know, maybe two centimeters and in the centimeter to two in size. They have a br- 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, um, limitations of, on lifespan are wrong. It's not, it's not that, well, over time of, everything degrades. No. Planaria can keep it going for, uh, probably, you know, how long have they been around? 400 million years. Right? So, these are the actual l- so the planaria in our lab are actually in physical continuity with planaria that were here 400 million years ago.

   2. LFLex Fridman1:07:42

      So, there's planaria that have lived that long, essentially.

   3. MLMichael Levin1:07:45

      Y- y- e-

   4. LFLex Fridman1:07:46

      What does he mean, physical continuity?

   5. MLMichael Levin1:07:48

      Because, because what they do is they split in half. The way they reproduce is they split in half. So, so the planarian, the back, the back end grabs the Petri dish, the front end takes off and then, mm, they rip themselves in half.

   6. LFLex Fridman1:07:58

      But is it, isn't it some sense we're, like, you are a con- a physical continuation?

   7. MLMichael Levin1:08:05

      Yes, except that, except that we go through a bottleneck of one cell, which is the egg.

   8. LFLex Fridman1:08:09

      Yeah.

   9. MLMichael Levin1:08:09

      They do not. I mean, they can. There are certain planaria that-

   10. LFLex Fridman1:08:11

       Got it.

   11. MLMichael Levin1:08:11

       Right?

   12. LFLex Fridman1:08:11

       So we go through a very, uh, ruthless compression process.

   13. MLMichael Levin1:08:15

       Yes, yes.

   14. LFLex Fridman1:08:15

       And they don't.

   15. MLMichael Levin1:08:15

       Yes. Like an autoencoder, you know?

   16. LFLex Fridman1:08:16

       Yeah.

   17. MLMichael Levin1:08:17

       Sort of squash down to one cell and then back out. These, these guys just tear themselves in half and then each, and then, and so the other amazing thing about them is they regenerate.

   18. LFLex Fridman1:08:25

       Okay.

   19. MLMichael Levin1:08:25

       So, you can cut them into pieces. The record is, I think, 276 or something like that by Thomas Hunt Morgan. Uh, and each piece regrows a perfect little worm. They know exactly, every piece knows exactly what's missing, what needs to happen. Uh, in fact, in fact, if you chop it in half, as it grows the other half, uh, the original e- th- the original, uh, tissue shrinks so that when the new tiny head shows up, they're proportional. So it keeps, it keeps perfect proportion. If you j- if you starve them, they shrink. If you feed them again, they expand. They, their control, their anatomical control is, is, is just insane.

   20. LFLex Fridman1:08:56

       Somebody cut them into over 200 pieces?

   21. MLMichael Levin1:08:59

       Yes. Yeah, yeah, yeah. Thomas Hunt Morgan did. Yeah.

   22. LFLex Fridman1:09:00

       Hashtag science.

   23. MLMichael Levin1:09:02

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

   24. LFLex Fridman1:09:09

       Humans can't do that. 'Cause... (laughs)

   25. MLMichael Levin1:09:11

       Well, ye- yes. I mean, again, y- yeah, true, except that-

   26. LFLex Fridman1:09:15

       Maybe you can at the embryonic level.

   27. MLMichael Levin1:09:17

       Well, that's, that's the thing, right? So, so I tell... whe- when I talk about this, I said, "Just remember that as, 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, so development is really... you can look at development as a, as a just an example of regeneration.

   28. LFLex Fridman1:09:35

       Yeah. To think, we'll talk about regenerative medicine, but there's some sense what would be like that warm in like 500 years.

   29. MLMichael Levin1:09:42

       Yeah. I, I think so.

   30. LFLex Fridman1:09:42

       Where I could just go, swoosh, regrow a hand.

Episode duration: 3:00:20