Michael Levin: Biology, Life, Aliens, Evolution, Embryogenesis & Xenobots

Michael Levin (02:37.440)

process that the whole mystery, you know, the biggest mystery of the universe, basically,

Lex Fridman (02:41.440)

how you get mind from matter. From just physics, in quotes. Yeah. So where's the magic into the

Michael Levin (02:47.680)

thing? How do we get from information encoded in DNA and make physical reality out of that

Michael Levin (02:54.480)

information? So one of the things that I think is really important if we're going to bring in DNA

Michael Levin (02:59.280)

into this picture is to think about the fact that what DNA encodes is the hardware of life. DNA

Michael Levin (03:05.520)

contains the instructions for the kind of micro level hardware that every cell gets to play with.

Lex Fridman (03:09.760)

So all the proteins, all the signaling factors, the ion channels, all the cool little pieces of

Michael Levin (03:14.160)

hardware that cells have, that's what's in the DNA. The rest of it is in so called generic laws.

Lex Fridman (03:20.640)

And these are laws of mathematics. These are laws of computation. These are laws of physics,

Michael Levin (03:25.920)

of all kinds of interesting things that are not directly in the DNA. And that process, you know,

Michael Levin (03:32.000)

I think the reason I always put just physics in quotes is because I don't think there is such a

Michael Levin (03:36.800)

thing as just physics. I think that thinking about these things in binary categories, like this is

Michael Levin (03:41.520)

physics, this is true cognition, this is as if it's only faking these kinds of things. I think

Michael Levin (03:45.840)

that's what gets us in trouble. I think that we really have to understand that it's a continuum

Lex Fridman (03:49.760)

and we have to work up the scaling, the laws of scaling. And we can certainly talk about that.

Michael Levin (03:53.600)

There's a lot of really interesting thoughts to be had there.

Lex Fridman (03:56.640)

So the physics is deeply integrated with the information. So the DNA doesn't exist on its own.

Michael Levin (04:03.200)

The DNA is integrated as, in some sense, in response to the laws of physics at every scale.

Lex Fridman (04:10.480)

The laws of the environment it exists in.

Michael Levin (04:14.080)

Yeah, the environment and also the laws of the universe. I mean, the thing about the DNA is that

Michael Levin (04:18.960)

it's once evolution discovers a certain kind of machine, that if the physical implementation is

Michael Levin (04:25.440)

appropriate, it's sort of, and this is hard to talk about because we don't have a good vocabulary

Michael Levin (04:29.920)

for this yet, but it's a very kind of a platonic notion that if the machine is there, it pulls down

Michael Levin (04:36.560)

interesting things that you do not have to evolve from scratch because the laws of physics give it

Michael Levin (04:42.960)

to you for free. So just as a really stupid example, if you're trying to evolve a particular

Michael Levin (04:47.200)

triangle, you can evolve the first angle and you evolve the second angle, but you don't need to

Michael Levin (04:50.720)

evolve the third. You know what it is already. Now, why do you know? That's a gift for free

Michael Levin (04:54.480)

from geometry in a particular space. You know what that angle has to be. And if you evolve

Michael Levin (04:58.240)

an ion channel, which is, ion channels are basically transistors, right? They're voltage

Michael Levin (05:01.920)

gated current conductances. If you evolve that ion channel, you immediately get to use things

Michael Levin (05:06.720)

like truth tables. You get logic functions. You don't have to evolve the logic function.

Michael Levin (05:10.160)

You don't have to evolve a truth table. It doesn't have to be in the DNA. You get it for free,

Michael Levin (05:14.160)

right? And the fact that if you have NAND gates, you can build anything you want, you get that for

Michael Levin (05:17.360)

free. All you have to evolve is that first step, that first little machine that enables you to

Michael Levin (05:22.720)

couple to those laws. And there's laws of adhesion and many other things. And this is all that

Lex Fridman (05:27.680)

interplay between the hardware that's set up by the genetics and the software that's made, right?

Michael Levin (05:33.600)

The physiological software that basically does all the computation and the cognition and everything

Michael Levin (05:38.240)

else is a real interplay between the information and the DNA and the laws of physics of computation

Lex Fridman (05:43.920)

and so on. So is it fair to say, just like this idea that the laws of mathematics are discovered,

Michael Levin (05:50.640)

they're latent within the fabric of the universe in that same way the laws of biology are kind of

Michael Levin (05:55.520)

discovered? Yeah, I think that's absolutely, and it's probably not a popular view, but I think

Michael Levin (05:59.760)

that's right on the money. Yeah. Well, I think that's a really deep idea. Then embryogenesis

Michael Levin (06:05.520)

is the process of revealing, of embodying, of manifesting these laws. You're not building the

Michael Levin (06:16.000)

laws. You're just creating the capacity to reveal. Yes. I think, again, not the standard view of

Michael Levin (06:23.520)

molecular biology by any means, but I think that's right on the money. I'll give you a simple example.

Michael Levin (06:27.760)

Some of our latest work with these xenobots, right? So what we've done is to take some skin

Michael Levin (06:31.680)

cells off of an early frog embryo and basically ask about their plasticity. If we give you a

Lex Fridman (06:36.080)

chance to sort of reboot your multicellularity in a different context, what would you do?

Michael Levin (06:40.400)

Because what you might assume by... The thing about embryogenesis is that it's super reliable,

Michael Levin (06:45.120)

right? It's very robust. And that really obscures some of its most interesting features. We get

Michael Levin (06:50.640)

used to it. We get used to the fact that acorns make oak trees and frog eggs make frogs. And we

Michael Levin (06:54.800)

say, well, what else is it going to make? That's what it makes. That's a standard story.

Lex Fridman (06:57.920)

But the reality is... And so you look at these skin cells and you say, well, what do they know

Lex Fridman (07:03.600)

how to do? Well, they know how to be a passive boring two dimensional outer layer, keeping the

Michael Levin (07:07.840)

bacteria from getting into the embryo. That's what they know how to do. Well, it turns out that if

Michael Levin (07:11.200)

you take these skin cells and you remove the rest of the embryo, so you remove all of the rest of

Michael Levin (07:17.040)

the cells and you say, well, you're by yourself now, what do you want to do? So what they do is

Michael Levin (07:20.960)

they form this multi little creature that runs around the dish. They have all kinds of incredible

Lex Fridman (07:26.480)

and incredible capacities. They navigate through mazes. They have various behaviors that they do

Michael Levin (07:30.960)

both independently and together. Basically, they implement von Neumann's dream of self replication,

Michael Levin (07:38.960)

because if you sprinkle a bunch of loose cells into the dish, what they do is they run around,

Michael Levin (07:42.560)

they collect those cells into little piles. They sort of mush them together until those little

Michael Levin (07:46.800)

piles become the next generation of xenobots. So you've got this machine that builds copies of

Michael Levin (07:50.960)

itself from loose material in its environment. None of this are things that you would have expected

Michael Levin (07:56.720)

from the frog genome. In fact, the genome is wild type. There's nothing wrong with their genetics.

Michael Levin (08:01.280)

Nothing has been added, no nanomaterials, no genomic editing, nothing. And so what we have

Michael Levin (08:06.320)

done there is engineered by subtraction. What you've done is you've removed the other cells

Michael Levin (08:11.360)

that normally basically bully these cells into being skin cells. And you find out that what they

Michael Levin (08:15.920)

really want to do is to be this, their default behaviors to be a xenobot. But in vivo, in the

Michael Levin (08:21.680)

embryo, they get told to be skinned by these other cell types. And so now here comes this really

Michael Levin (08:28.640)

interesting question that you just posed. When you ask where does the form of the tadpole and

Michael Levin (08:33.760)

the frog come from, the standard answer is, well, it's selection. So over millions of years,

Michael Levin (08:39.920)

it's been shaped to produce the specific body that's fit for froggy environments.

Michael Levin (08:44.720)

Where does the shape of the xenobot come from? There's never been any xenobots. There's never

Michael Levin (08:48.240)

been selection to be a good xenobot. These cells find themselves in the new environment.

Michael Levin (08:51.920)

In 48 hours, they figure out how to be an entirely different protoorganism with new capacities like

Michael Levin (08:57.920)

kinematic self replication. That's not how frogs or tadpoles replicate. We've made it impossible

Michael Levin (09:02.000)

for them to replicate their normal way. Within a couple of days, these guys find a new way of

Michael Levin (09:05.600)

doing it that's not done anywhere else in the biosphere. Well, actually, let's step back and

Michael Levin (09:09.200)

define, what are xenobots? So a xenobot is a self assembling little protoorganism. It's also a

Michael Levin (09:16.320)

biological robot. Those things are not distinct. It's a member of both classes. How much is it

Michael Levin (09:22.000)

biology? How much is that robot? At this point, most of it is biology because what we're doing is

Michael Levin (09:28.160)

we're discovering natural behaviors of the cells and also of the cell collectives. Now, one of the

Michael Levin (09:35.120)

really important parts of this was that we're working together with Josh Bongaert's group at

Michael Levin (09:39.440)

University of Vermont. They're computer scientists, they do AI, and they've basically been able to

Michael Levin (09:45.040)

use a simulated evolution approach to ask, how can we manipulate these cells, give them signals,

Lex Fridman (09:51.440)

not rewire their DNA, so not hardware, but experience signals? So can we remove some cells?

Lex Fridman (09:56.080)

Can we add some cells? Can we poke them in different ways to get them to do other things?

Lex Fridman (09:59.920)

So in the future, there's going to be, we're now, and this is future unpublished work, but

Michael Levin (10:04.400)

we're doing all sorts of interesting ways to reprogram them to new behaviors. But before you

Michael Levin (10:08.720)

can start to reprogram these things, you have to understand what their innate capacities are.

Michael Levin (10:13.040)

Okay, so that means engineering, programming, you're engineering them in the future. And in

Michael Levin (10:19.520)

some sense, the definition of a robot is something you in part engineer versus evolve. I mean,

Michael Levin (10:28.400)

it's such a fuzzy definition anyway, in some sense, many of the organisms within our body

Michael Levin (10:33.280)

are kinds of robots. And I think robots is a weird line because it's, we tend to see robots

Michael Levin (10:40.640)

as the other. I think there will be a time in the future when there's going to be something akin to

Michael Levin (10:45.760)

the civil rights movements for robots, but we'll talk about that later perhaps. Anyway, so how do

Michael Levin (10:52.800)

you, can we just linger on it? How do you build a Xenobot? What are we talking about here? From

Lex Fridman (11:00.560)

when does it start and how does it become the glorious Xenobot?

Michael Levin (11:06.640)

Yeah, so just to take one step back, one of the things that a lot of people get stuck on is they

Michael Levin (11:12.080)

say, well, you know, engineering requires new DNA circuits or it requires new nanomaterials,

Michael Levin (11:19.120)

you know, what the thing is, we are now moving from old school engineering, which use passive

Michael Levin (11:24.560)

materials, right? That things, you know, wood, metal, things like this, that basically the only

Michael Levin (11:28.480)

thing you could depend on is that they were going to keep their shape. That's it. They don't do

Michael Levin (11:31.280)

anything else. It's on you as an engineer to make them do everything they're going to do.

Lex Fridman (11:35.120)

And then there were active materials and now computation materials. This is a whole new era.

Michael Levin (11:39.040)

These are agential materials. This is you're now collaborating with your substrate because your

Michael Levin (11:43.600)

material has an agenda. These cells have, you know, billions of years of evolution. They have goals.

Michael Levin (11:51.280)

They have preferences. They're not just going to sit where you put them. That's hilarious that you

Michael Levin (11:54.160)

have to talk your material into keeping its shape. That's it. That is exactly right. That is exactly

Michael Levin (11:58.880)

right. Stay there. It's like getting a bunch of cats or something and trying to organize the shape

Michael Levin (12:04.400)

out of them. It's funny. We're on the same page here because in a paper, this is, this is currently

Michael Levin (12:08.640)

just been accepted in nature by engineering. One of the figures I have is building a tower

Michael Levin (12:12.800)

out of Legos versus dogs, right? So think about the difference, right? If you build out of Legos,

Michael Levin (12:17.360)

you have full control over where it's going to go. But if somebody knocks it over, it's game over.

Michael Levin (12:22.800)

With the dogs, you cannot just come and stack them. They're not going to stay that way. But

Michael Levin (12:26.240)

the good news is that if you train them, then somebody knocks it over, they'll get right back

Michael Levin (12:29.680)

up. So it's all right. So as an engineer, what you really want to know is what can they depend

Michael Levin (12:33.760)

on this thing to do, right? That's really, you know, a lot of people have definitions of robots

Michael Levin (12:37.440)

as far as what they're made of or how they got here, you know, design versus evolve, whatever.

Michael Levin (12:41.360)

I don't think any of that is useful. I think, I think as an engineer, what you want to know is

Lex Fridman (12:45.200)

how much can I depend on this thing to do when I'm not around to micromanage it? What level of,

Lex Fridman (12:50.960)

what level of dependency can I, can I give this thing? How much agency does it have?

Michael Levin (12:54.400)

Which then tells you what techniques do you use? So do you use micromanagement,

Michael Levin (12:57.360)

like you put everything where it goes? Do you train it? Do you give it signals? Do you try

Lex Fridman (13:01.200)

to convince it to do things, right? How much, you know, how intelligent is your substrate?

Lex Fridman (13:04.560)

And so now we're moving into this, into this area where you're, you're, you're working with

Michael Levin (13:08.480)

agential materials. That's a collaboration. That's not, that's not old, old style.

Lex Fridman (13:12.560)

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

Michael Levin (13:14.320)

Agential.

Lex Fridman (13:14.880)

Yeah.

Lex Fridman (13:15.040)

What's that mean?

Michael Levin (13:15.680)

Agency. It comes from the word agency. So, so basically the material has agency, meaning that

Michael Levin (13:20.160)

it has some, some level of obviously not human level, but some level of preferences, goals,

Michael Levin (13:26.000)

memories, ability to remember things, to compute into the future, meaning anticipate,

Michael Levin (13:30.640)

you know, when you're working with cells, they have all of that to some, to various degrees.

Lex Fridman (13:34.800)

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

Michael Levin (13:39.920)

I think it's both, right? So it raises difficulties because it means that

Michael Levin (13:43.600)

it, if you, if you're using the old mindset, which is a linear kind of extrapolation of what's going

Michael Levin (13:48.880)

to happen, you're going to be surprised and shocked all the time because biology does not

Michael Levin (13:54.320)

do what we linearly expect materials to do. On the other hand, it's massively liberating. And

Lex Fridman (13:59.200)

so in the following way, I've argued that advances in regenerative medicine require us to take

Michael Levin (14:04.240)

advantage of this because what it means is that you can get the material to do things that you

Michael Levin (14:09.040)

don't know how to micromanage. So just as a simple example, right? If you, if you, you had a rat

Lex Fridman (14:13.840)

and you wanted this rat to do a circus trick, put a ball in the little hoop, you can do it the

Michael Levin (14:19.120)

micromanagement way, which is try to control every neuron and try to play the thing like a puppet,

Michael Levin (14:22.960)

right? And maybe someday that'll be possible, maybe, or you can train the rat. And this is

Lex Fridman (14:26.960)

why humanity for thousands of years before we knew any neuroscience, we had no idea what's

Michael Levin (14:31.040)

behind, what's between the ears of any animal. We were able to train these animals because once you

Michael Levin (14:35.040)

recognize the level of agency of a certain system, you can use appropriate techniques. If you know

Michael Levin (14:40.480)

the currency of motivation, reward and punishment, you know how smart it is, you know what kinds of

Michael Levin (14:44.160)

things it likes to do. You are searching a much more, much smoother, much nicer problem space than

Michael Levin (14:50.080)

if you try to micromanage the thing. And in regenerative medicine, when you're trying to get,

Michael Levin (14:54.080)

let's say an arm to grow back or an eye to repair a cell birth defect or something,

Lex Fridman (14:57.920)

do you really want to be controlling tens of thousands of genes at each point to try to

Michael Levin (15:02.960)

micromanage it? Or do you want to find the high level modular controls that say,

Michael Levin (15:07.760)

build an arm here. You already know how to build an arm. You did it before, do it again.

Lex Fridman (15:11.360)

So that's, I think it's both, it's both difficult and it challenges us to develop new ways of

Michael Levin (15:15.920)

engineering and it's hugely empowering. Okay. So how do you do, I mean, maybe sticking with

Michael Levin (15:21.760)

the metaphor of dogs and cats, I presume you have to figure out the, find the dogs and dispose of

Michael Levin (15:31.120)

the cats. Because, you know, it's like the old herding cats is an issue. So you may be able to

Michael Levin (15:38.400)

train dogs. I suspect you will not be able to train cats. Or if you do, you're never going to

Michael Levin (15:44.800)

be able to trust them. So is there a way to figure out which material is amenable to herding? Is it in

Michael Levin (15:53.040)

the lab work or is it in simulation? Right now it's largely in the lab because we, our simulations

Michael Levin (15:59.360)

do not capture yet the most interesting and powerful things about biology. So the simulation

Michael Levin (16:04.560)

does, what we're pretty good at simulating are feed forward emergent types of things,

Michael Levin (16:10.480)

right? So cellular automata, if you have simple rules and you sort of roll those forward for

Michael Levin (16:15.120)

every, every agent or every cell in the simulation, then complex things happen, you know, ant colony

Michael Levin (16:19.360)

or algorithms, things like that. We're good at that. And that's, and that's fine. The difficulty

Michael Levin (16:23.600)

with all of that is that it's incredibly hard to reverse. So this is a really hard inverse problem,

Michael Levin (16:28.400)

right? If you look at a bunch of termites and they make a, you know, a thing with a single chimney

Lex Fridman (16:31.520)

and you say, well, I like it, but I'd like two chimneys. How do you change the rules of behavior

Michael Levin (16:36.080)

free termites? So they make two chimneys, right? Or, or if you say, here are a bunch of cells that are

Michael Levin (16:40.320)

creating this kind of organism. I don't think that's optimal. I'd like to repair that birth

Michael Levin (16:44.720)

defect. How do you control all the, all the individual low level rules, right? All the protein

Michael Levin (16:49.040)

interactions and everything else, rolling it back from the anatomy that you want to the low level

Michael Levin (16:53.520)

hardware rules is in general intractable. It's a, it's an inverse problem that's generally not

Michael Levin (16:57.360)

solvable. So right now it's mostly in the lab because what we need to do is we need to understand

Lex Fridman (17:02.960)

how biology uses top down controls. So the idea is not, not bottom up emergence, but the idea of

Michael Levin (17:09.520)

things like a goal directed test operate exit kinds of loops where, where it's basically an

Michael Levin (17:14.560)

error minimization function over a new space and not a space of gene expression, but for example,

Michael Levin (17:19.120)

a space of anatomy. So just as a simple example, if you have, you have a salamander and it's got

Michael Levin (17:23.760)

an arm, you can, you can amputate that arm anywhere along the length. It will grow exactly

Michael Levin (17:29.040)

what's needed and then it stops. That's the most amazing thing about regeneration is that it stops

Michael Levin (17:32.880)

it knows when to stop. When does it stop? It stops when a correct salamander arm has been completed.

Lex Fridman (17:37.280)

So that tells you that's right. That's a, that's a, a means ends kind of analysis where it has to

Michael Levin (17:42.880)

know what the correct limb is supposed to look like, right? So it has a way to ascertain the

Michael Levin (17:47.280)

current shape. It has a way to measure that Delta from, from what shape it's supposed to be. And it

Michael Levin (17:51.360)

will keep taking actions, meaning remodeling and growing and everything else until that's complete.

Lex Fridman (17:55.600)

So once you know that, and we've taken advantage of this in the lab to do some, some really wild

Michael Levin (17:59.200)

things with, with both planaria and frog embryos and so on, once you know that, you can start

Michael Levin (18:04.400)

playing with that, with that homeostatic cycle. You can ask, for example, well, how does it remember

Lex Fridman (18:08.880)

what the correct shape is? And can we mess with that memory? Can we give it a false memory of

Lex Fridman (18:12.240)

what the shape should be and let the cells build something else? Or can we mess with the measurement

Michael Levin (18:16.160)

apparatus, right? So it gives you, it gives you those kinds of, so, so, so the idea is to

Michael Levin (18:21.680)

basically appropriate a lot of the approaches and concepts from cognitive neuroscience and

Michael Levin (18:28.240)

behavioral science into things that previously were taken to be dumb materials. And, you know,

Michael Levin (18:33.600)

you get yelled at in class if you, if you, for being anthropomorphic, if you said, well, my cells

Michael Levin (18:37.440)

want to do this and my cells want to do that. And I think, I think that's a, that's a major mistake

Michael Levin (18:41.280)

that leaves a ton of capabilities on the table. So thinking about biologic systems as things that

Michael Levin (18:45.920)

have memory, have almost something like cognitive ability, but I mean, how incredible is it,

Michael Levin (18:56.560)

you know, that the salamander arm is being rebuilt, not with a dictator. It's kind of like

Michael Levin (19:03.600)

the cellular automata system. All the individual workers are doing their own thing. So where's that

Michael Levin (19:10.320)

top down signal that does the control coming from? Like, how can you find it? Like, why does it stop

Michael Levin (19:16.080)

growing? How does it know the shape? How does it have memory of the shape? And how does it tell

Michael Levin (19:21.120)

everybody to be like, whoa, whoa, whoa, slow down, we're done. So the first thing to think about,

Michael Levin (19:26.080)

I think, is that there are no examples anywhere of a central dictator, because in this kind of

Michael Levin (19:33.680)

science, because everything is made of parts. And so we, even though we feel as a unified central

Lex Fridman (19:40.480)

sort of intelligence and kind of point of cognition, we are a bag of neurons, right?

Michael Levin (19:45.840)

All intelligence is collective intelligence. There's this, this is important to kind of

Michael Levin (19:50.720)

think about, because a lot of people think, okay, there's real intelligence, like me,

Lex Fridman (19:54.560)

and then there's collective intelligence, which is ants and flocks of birds and termites and

Michael Levin (19:59.280)

things like that. And maybe it's appropriate to think of them as an individual, and maybe it's

Michael Levin (1:00:00.320)

where different chemicals activate and repress each other, they can store memories. So in a

Michael Levin (1:00:04.480)

dynamical system sense, they can store memories. They can get into stable states that are hard to

Michael Levin (1:00:09.120)

pull them out of. So that becomes, once they get in, that's a memory, a permanent memory or a

Michael Levin (1:00:13.200)

semi permanent memory of something that's happened. There are cytoskeletal structures that are

Michael Levin (1:00:17.760)

physically, they store memories in physical configuration. There are electrical memories

Michael Levin (1:00:24.640)

like flip flops where there is no physical. So if you look, I showed my students this example

Michael Levin (1:00:30.560)

as a flip flop. And the reason that it stores a zero one is not because some piece of the hardware

Michael Levin (1:00:37.920)

moved. It's because there's a cycling of the current in one side of the thing. If I come over

Lex Fridman (1:00:42.880)

and I hold the other side to a high voltage for a brief period of time, it flips over and now it's

Michael Levin (1:00:50.080)

here. But none of the hardware moved. The information is in a stable dynamical sense. And

Michael Levin (1:00:54.880)

if you were to x ray the thing, you couldn't tell me if it was zero or one, because all you would

Michael Levin (1:00:58.560)

see is where the hardware is. You wouldn't see the energetic state of the system. So there are

Michael Levin (1:01:03.120)

bioelectrical states that are held in that exact way, like volatile ram basically, like in the

Michael Levin (1:01:09.680)

electrical state. It's very akin to the different ways that memory is stored in a computer.

Lex Fridman (1:01:15.840)

So there's ram, there's hard drive. You can make that mapping, right? So I think the interesting

Michael Levin (1:01:21.120)

thing is that based on the biology, we can have a more sophisticated, you know, I think we can

Michael Levin (1:01:26.960)

revise some of our computer engineering methods because there are some interesting things that

Michael Levin (1:01:32.560)

biology we haven't done yet. But that mapping is not bad. I mean, I think it works in many ways.

Michael Levin (1:01:38.400)

Yeah, I wonder because I mean, the way we build computers at the root of computer science is the

Michael Levin (1:01:43.280)

idea of proof of correctness. We program things to be perfect, reliable. You know, this idea of

Michael Levin (1:01:52.240)

resilience and robustness to unknown conditions is not as important. So that's what biology is really

Michael Levin (1:01:58.240)

good at. So I don't know what kind of systems. I don't know how we go from a computer to a

Michael Levin (1:02:04.000)

biological system in the future. Yeah, I think that, you know, the thing about biology is all

Michael Levin (1:02:10.480)

about making really important decisions really quickly on very limited information. I mean,

Michael Levin (1:02:15.280)

that's what biology is all about. You have to act, you have to act now. The stakes are very high,

Lex Fridman (1:02:19.600)

and you don't know most of what you need to know to be perfect. And so there's not even an attempt

Michael Levin (1:02:24.080)

to be perfect or to get it right in any sense. There are just things like active inference,

Michael Levin (1:02:29.920)

minimize surprise, optimize some efficiency and some things like this that guides the whole

Michael Levin (1:02:37.120)

business. I mentioned too offline that somebody who's a fan of your work is Andre Kapathy.

Lex Fridman (1:02:44.640)

And he's, amongst many things, also writes occasionally a great blog. He came up with

Michael Levin (1:02:52.720)

this idea, I don't know if he coined the term, but of software 2.0, where the programming is

Michael Levin (1:03:00.720)

done in the space of configuring these artificial neural networks. Is there some sense in which that

Michael Levin (1:03:08.240)

would be the future of programming for us humans, where we're less doing like Python like programming

Lex Fridman (1:03:16.400)

and more... How would that look like? But basically doing the hyperparameters of something

Michael Levin (1:03:25.680)

akin to a biological system and watching it go and adjusting it and creating some kind of feedback

Michael Levin (1:03:33.360)

loop within the system so it corrects itself. And then we watch it over time accomplish the goals

Michael Levin (1:03:40.800)

we want it to accomplish. Is that kind of the dream of the dogs that you described in the Nature

Michael Levin (1:03:46.880)

paper? Yeah. I mean, that's what you just painted is a very good description of our efforts at

Michael Levin (1:03:54.960)

regenerative medicine as a kind of somatic psychiatry. So the idea is that you're not trying

Michael Levin (1:04:01.040)

to micromanage. I mean, think about the limitations of a lot of the medicines today. We try to

Michael Levin (1:04:07.920)

interact down at the level of pathways. So we're trying to micromanage it. What's the problem? Well,

Michael Levin (1:04:14.560)

one problem is that for almost every medicine other than antibiotics, once you stop it, the

Michael Levin (1:04:20.800)

problem comes right back. You haven't fixed anything. You were addressing symptoms. You

Michael Levin (1:04:23.680)

weren't actually curing anything, again, except for antibiotics. That's one problem. The other

Michael Levin (1:04:28.560)

problem is you have massive amount of side effects because you were trying to interact at the lowest

Michael Levin (1:04:33.600)

level. It's like, I'm going to try to program this computer by changing the melting point of

Michael Levin (1:04:40.400)

copper. Maybe you can do things that way, but my God, it's hard to program at the hardware level.

Lex Fridman (1:04:46.640)

So what I think we're starting to understand is that, and by the way, this goes back to what you

Michael Levin (1:04:53.360)

were saying before about that we could have access to our internal state. So people who practice that

Michael Levin (1:04:58.800)

kind of stuff, so yoga and biofeedback and those, those are all the people that uniformly will say

Michael Levin (1:05:04.000)

things like, well, the body has an intelligence and this and that. Those two sets overlap perfectly

Michael Levin (1:05:08.480)

because that's exactly right. Because once you start thinking about it that way, you realize that

Michael Levin (1:05:13.600)

the better locus of control is not always at the lowest level. This is why we don't all program

Michael Levin (1:05:18.480)

with a soldering iron. We take advantage of the high level intelligences that are there,

Michael Levin (1:05:24.720)

intelligences that are there, which means trying to figure out, okay, which of your tissues can

Michael Levin (1:05:28.960)

learn? What can they learn? Why is it that certain drugs stop working after you take them for a while

Michael Levin (1:05:35.200)

with this habituation, right? And so can we understand habituation, sensitization, associative

Michael Levin (1:05:40.160)

learning, these kinds of things in chemical pathways? We're going to have a completely

Michael Levin (1:05:44.400)

different way. I think we're going to have a completely different way of using drugs and of

Michael Levin (1:05:49.200)

medicine in general when we start focusing on the goal states and on the intelligence of our

Michael Levin (1:05:54.560)

subsystems as opposed to treating everything as if the only path was micromanagement from

Lex Fridman (1:05:59.040)

chemistry upwards. Well, can you speak to this idea of somatic psychiatry? What are somatic cells?

Lex Fridman (1:06:05.200)

How do they form networks that use bioelectricity to have memory and all those kinds of things?

Michael Levin (1:06:11.760)

Yeah. What are somatic cells like basics here? Somatic cells just means the cells of your body.

Michael Levin (1:06:16.160)

Soma just means body, right? So somatic cells are just the... I'm not even specifically making a

Michael Levin (1:06:20.000)

distinction between somatic cells and stem cells or anything like that. I mean, basically all the

Michael Levin (1:06:23.920)

cells in your body, not just neurons, but all the cells in your body. They form electrical

Michael Levin (1:06:28.400)

networks during embryogenesis, during regeneration. What those networks are doing

Michael Levin (1:06:33.280)

in part is processing information about what our current shape is and what the goal shape is.

Michael Levin (1:06:39.600)

Now, how do I know this? Because I can give you a couple of examples. One example is when we started

Michael Levin (1:06:45.120)

studying this, we said, okay, here's a planarian. A planarian is a flatworm. It has one head and one

Michael Levin (1:06:50.400)

tail normally. And the amazing... There's several amazing things about planaria, but basically they

Michael Levin (1:06:55.200)

kind of... I think planaria hold the answer to pretty much every deep question of life.

Michael Levin (1:07:00.960)

For one thing, they're similar to our ancestors. So they have true symmetry. They have a true

Michael Levin (1:07:04.960)

brain. They're not like earthworms. They're a much more advanced life form. They have lots

Michael Levin (1:07:08.320)

of different internal organs, but they're these little... They're about maybe two centimeters in

Michael Levin (1:07:12.240)

the centimeter to two in size. They have a head and a tail. And the first thing is planaria are

Michael Levin (1:07:17.680)

immortal. So they do not age. There's no such thing as an old planarian. So that right there

Michael Levin (1:07:22.320)

tells you that these theories of thermodynamic limitations on lifespan are wrong. It's not that

Michael Levin (1:07:27.680)

well over time everything degrades. No, planaria can keep it going for probably how long have

Michael Levin (1:07:33.280)

they been around 400 million years. So the planaria in our lab are actually in physical

Michael Levin (1:07:38.960)

continuity with planaria that were here 400 million years ago. So there's planaria that

Michael Levin (1:07:43.600)

have lived that long essentially. What does it mean physical continuity? Because what they do

Michael Levin (1:07:49.280)

is they split in half. The way they reproduce is they split in half. So the planaria, the back end

Michael Levin (1:07:54.560)

grabs the petri dish, the front end takes off and they rip themselves in half. But isn't it some

Michael Levin (1:07:59.680)

sense where like you are a physical continuation? Yes, except that we go through a bottleneck of one

Michael Levin (1:08:07.600)

cell, which is the egg. They do not. I mean, they can. There's certain planaria. Got it. So we go

Michael Levin (1:08:11.760)

through a very ruthless compression process and they don't. Yes. Like an auto encoder, you know,

Michael Levin (1:08:17.200)

sort of squashed down to one cell and then back out. These guys just tear themselves in half.

Lex Fridman (1:08:22.880)

And so the other amazing thing about them is they regenerate. So you can cut them into pieces.

Michael Levin (1:08:26.640)

The record is, I think, 276 or something like that by Thomas Hunt Morgan. And each piece regrows a

Michael Levin (1:08:32.560)

perfect little worm. They know exactly, every piece knows exactly what's missing, what needs

Michael Levin (1:08:36.960)

to happen. In fact, if you chop it in half, as it grows the other half, the original tissue shrinks

Lex Fridman (1:08:45.360)

so that when the new tiny head shows up, they're proportional. So it keeps perfect proportion.

Michael Levin (1:08:50.080)

If you starve them, they shrink. If you feed them again, they expand. Their control,

Michael Levin (1:08:54.160)

their anatomical control is just insane. Somebody cut them into over 200 pieces.

Michael Levin (1:08:58.960)

Yeah. Thomas Hunt Morgan did. Hashtag science. Amazing. And maybe more. I mean,

Michael Levin (1:09:03.520)

they didn't have antibiotics back then. I bet he lost some due to infection. I bet it's

Michael Levin (1:09:06.720)

actually more than that. I bet you could do more than that. Humans can't do that.

Michael Levin (1:09:11.760)

Well, yes. I mean, again, true, except that... Maybe you can at the embryonic level.

Michael Levin (1:09:16.960)

Well, that's the thing, right? So when I talk about this, I say, just remember that

Lex Fridman (1:09:21.120)

as amazing as it is to grow a whole planarian from a tiny fragment,

Michael Levin (1:09:24.880)

half of the human population can grow a full body from one cell. So development is really,

Lex Fridman (1:09:30.640)

you can look at development as just an example of regeneration.

Michael Levin (1:09:34.240)

Yeah. To think, we'll talk about regenerative medicine, but there's some sense of what would

Lex Fridman (1:09:39.600)

be like that warm in like 500 years where I can just go regrow a hand.

Michael Levin (1:09:46.320)

Yep. With given time, it takes time to grow large things.

Lex Fridman (1:09:49.920)

For now.

Michael Levin (1:09:50.560)

Yeah, I think so. I think.

Lex Fridman (1:09:51.840)

You can probably... Why not accelerate? Oh, biology takes its time?

Michael Levin (1:09:56.800)

I'm not going to say anything is impossible, but I don't know of a way to accelerate these

Michael Levin (1:10:00.080)

processes. I think it's possible. I think we are going to be regenerative, but I don't know of a

Michael Levin (1:10:04.000)

way to make it faster.

Michael Levin (1:10:04.800)

I could just think people from a few centuries from now would be like, well, they used to have

Michael Levin (1:10:10.080)

to wait a week for the hand to regrow. It's like when the microwave was invented. You can toast

Michael Levin (1:10:17.920)

your... What's that called when you put a cheese on a toast? It's delicious is all I know. I'm

Michael Levin (1:10:27.360)

blanking. Anywho. All right. So planaria, why were we talking about the magical planaria that they

Lex Fridman (1:10:33.280)

have the mystery of life?

Michael Levin (1:10:34.320)

Yeah. So the reason we're talking about planaria is not only are they immortal,

Michael Levin (1:10:37.680)

not only do they regenerate every part of the body, they generally don't get cancer,

Michael Levin (1:10:43.920)

which we can talk about why that's important. They're smart. They can learn things. You can

Michael Levin (1:10:47.360)

train them. And it turns out that if you train a planaria and then cut their heads off, the tail

Michael Levin (1:10:52.320)

will regenerate a brand new brain that still remembers the original information.

Lex Fridman (1:10:56.000)

Do they have a biological network going on or no?

Michael Levin (1:10:58.960)

Yes.

Lex Fridman (1:10:59.280)

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

Lex Fridman (1:11:05.200)

requirement for a true brain?

Michael Levin (1:11:07.200)

Like everything else, it's a continuum, but a true brain has certain characteristics as far as the

Michael Levin (1:11:12.080)

density, like a localized density of neurons that guides behavior.

Lex Fridman (1:11:15.680)

In the head.

Michael Levin (1:11:16.240)

Exactly. Exactly. If you cut their head off, the tail doesn't do anything. It just sits there

Michael Levin (1:11:22.080)

until a new brain regenerates. They have all the same neurotransmitters that you and I have.

Lex Fridman (1:11:28.000)

But here's why we're talking about them in this context. So here's your planaria. You cut off the

Michael Levin (1:11:32.720)

head. You cut off the tail. You have a middle fragment. That middle fragment has to make one

Michael Levin (1:11:35.840)

head and one tail. How does it know how many of each to make? And where do they go? How come it

Michael Levin (1:11:40.080)

doesn't switch? How come, right? So we did a very simple thing. And we said, okay, let's make the

Michael Levin (1:11:46.960)

hypothesis that there's a somatic electrical network that remembers the correct pattern,

Lex Fridman (1:11:52.400)

and that what it's doing is recalling that memory and building to that pattern.

Lex Fridman (1:11:55.760)

So what we did was we used a way to visualize electrical activity in these cells, right? It's a

Michael Levin (1:12:01.920)

variant of what people used to look for electricity in the brain. And we saw that that fragment has a

Michael Levin (1:12:08.080)

very particular electrical pattern. You can literally see it once we developed the technique.

Michael Levin (1:12:12.720)

It has a very particular electrical pattern that shows you where the head and the tail goes,

Michael Levin (1:12:17.920)

right? You can just see it. And then we said, okay, well now let's test the idea that that's

Michael Levin (1:12:22.240)

a memory that actually controls where the head and the tail goes. Let's change that pattern. So

Michael Levin (1:12:25.920)

basically, incept the false memory. And so what you can do is you can do that in many different

Michael Levin (1:12:29.680)

ways. One way is with drugs that target ion channels to say, and so you pick these drugs

Lex Fridman (1:12:34.640)

and you say, okay, I'm going to do it so that instead of this one head, one tail electrical

Michael Levin (1:12:39.600)

pattern, you have a two headed pattern, right? You're just editing the electrical information

Michael Levin (1:12:43.440)

in the network. When you do that, guess what the cells build? They build a two headed worm.

Lex Fridman (1:12:47.520)

And the coolest thing about it, no genetic changes. So we haven't touched the genome.

Michael Levin (1:12:51.040)

The genome is totally wild type. But the amazing thing about it is that when you take these two

Michael Levin (1:12:54.320)

headed animals and you cut them into pieces again, some of those pieces will continue to

Michael Levin (1:12:59.520)

make two headed animals. So that information, that memory, that electrical circuit, not only does it

Michael Levin (1:13:05.840)

hold the information for how many heads, not only does it use that information to tell the cells

Lex Fridman (1:13:09.920)

what to do to regenerate, but it stores it. Once you've reset it, it keeps. And we can go back,

Michael Levin (1:13:14.320)

we can take a two headed animal and put it back to one headed. So now imagine, so there's a couple

Michael Levin (1:13:18.960)

of interesting things here that that have implications for understanding what genomes

Lex Fridman (1:13:22.720)

and things like that. Imagine I take this two headed animal. Oh, and by the way, when they

Michael Levin (1:13:27.200)

reproduce, when they tear themselves in half, you still get two headed animals. So imagine I take

Michael Levin (1:13:31.360)

them and I throw them in the Charles River over here. So 100 years later, some scientists come

Michael Levin (1:13:34.640)

along and they scoop up some samples and they go, oh, there's a single headed form and a two headed

Michael Levin (1:13:38.640)

form. Wow, a speciation event. Cool. Let's sequence the genome and see why, what happened. The genomes

Michael Levin (1:13:43.600)

are identical. There's nothing wrong with the genome. So if you ask the question, how does,

Michael Levin (1:13:47.040)

so, so this goes back to your very first question is where do body plans come from, right? How does

Michael Levin (1:13:51.360)

the planarian know how many heads it's supposed to have? Now it's interesting because you could

Michael Levin (1:13:55.600)

say DNA, but what happened, what, what, as it turns out, the DNA produces a piece of hardware

Michael Levin (1:14:01.840)

that by default says one head the way that when you turn on a calculator, by default, it's a zero

Michael Levin (1:14:07.520)

every single time, right? When you turn it on, it just says zero, but it's a programmable calculator

Michael Levin (1:14:11.120)

as it turns out. So once you've changed that next time, it won't say zero. It'll say something else

Lex Fridman (1:14:16.000)

and the same thing here. So you can make, you can make one headed, two headed, you can make no

Michael Levin (1:14:19.120)

headed worms. We've done some other things along these lines, some other really weird constructs.

Michael Levin (1:14:24.000)

So, so this, this, this, this question of, right. So again, it's really important. The, the hardware

Michael Levin (1:14:28.640)

software distinction is really important because the hardware is essential because without proper

Michael Levin (1:14:33.920)

hardware, you're never going to get to the right physiology of having that memory. But once you

Michael Levin (1:14:38.080)

have it, it doesn't fully determine what the information is going to be. You can have other

Michael Levin (1:14:42.320)

information in there and it's reprogrammable by us, by bacteria, by various parasites, probably

Michael Levin (1:14:47.360)

things like that. The other amazing thing about these planarias, think about this, most animals,

Michael Levin (1:14:52.480)

when we get a mutation in our bodies, our children don't inherit it, right? So you can go on, you

Michael Levin (1:14:56.480)

could run around for 50, 60 years getting mutations. Your children don't have those mutations

Michael Levin (1:15:00.720)

because we go through the egg stage. Planaria tear themselves in half and that's how they reproduce.

Lex Fridman (1:15:05.120)

So for 400 million years, they keep every mutation that they've had that doesn't kill the cell that

Michael Levin (1:15:10.080)

it's in. So when you look at these planaria, their bodies are what's called mixoploid, meaning that

Michael Levin (1:15:14.640)

every cell might have a different number of chromosomes. They look like a tumor. If you look

Michael Levin (1:15:17.840)

at the, the, the, the, the genome is an incredible mess because they accumulate all this stuff.

Lex Fridman (1:15:22.720)

And yet the, their body structure is, they are the best regenerators on the planet. Their anatomy is

Michael Levin (1:15:28.240)

rock solid, even though their genome is always all kinds of crap. So this is a kind of a scandal,

Michael Levin (1:15:32.720)

right? That, you know, when we learn that, well, you know, what are genomes to what genomes determine

Michael Levin (1:15:37.520)

your body? Okay. Why is the animal with the worst genome have the best anatomical control, the most

Michael Levin (1:15:41.520)

cancer resistant, the most regenerative, right? Really, we're just beginning to start to understand

Michael Levin (1:15:46.080)

this relationship between the, the genomically determined hardware and, and, and by the way,

Michael Levin (1:15:50.720)

just as of, as of a couple of months ago, I think I now somewhat understand why this is,

Lex Fridman (1:15:55.440)

but it's really, it's really a major, you know, a major puzzle.

Michael Levin (1:15:57.840)

I mean, that really throws a wrench into the whole nature versus nurture because you usually

Michael Levin (1:16:05.280)

associate electricity with the, with the nurture and the hardware with the nature.

Lex Fridman (1:16:13.360)

And it's, there's just this weird integrated mess that propagates through generations.

Michael Levin (1:16:19.360)

Yeah. It's much more fluid. It's much more complex. You can, you can imagine what's,

Michael Levin (1:16:25.040)

what's happening here is just, just imagine the evolution of a, of a, of an animal like this,

Michael Levin (1:16:29.200)

that, that multi scale, this goes back to this multi scale competency, right? Imagine that you

Michael Levin (1:16:33.280)

have two, two, two, you have, you have an animal that that where its, its tissues have some degree

Michael Levin (1:16:38.800)

of multi scale competency. So for example, if the like, like we saw in the tadpole, you know,

Michael Levin (1:16:42.960)

if you put an eye on its tail, they can still see out of that eye, right? That the, you know,

Michael Levin (1:16:46.240)

there's all, there's incredible plasticity. So if you have an animal and it comes up for selection

Lex Fridman (1:16:50.400)

and the fitness is quite good, evolution doesn't know whether the fitness is good because the

Michael Levin (1:16:56.880)

genome was awesome or because the genome was kind of junky, but, but the competency made up for it,

Michael Levin (1:17:01.600)

right? And things kind of ended up good. So what that means is that the more competency you have,

Michael Levin (1:17:06.160)

the harder it is for selection to pick the best genomes, it hides information, right? And so that

Michael Levin (1:17:11.520)

means that, so, so what happens, you know, evolution basically starts all those start,

Michael Levin (1:17:16.640)

all the hard work is being done to increase the competency because it's harder and harder to see

Michael Levin (1:17:21.200)

the genomes. And so I think in planaria, what happened is that there's this runaway phenomenon

Michael Levin (1:17:25.760)

where all the effort went into the algorithm such that we know you got a crappy genome. We can't

Michael Levin (1:17:31.040)

keep, we can't clean up the genome. We can't keep track of it. So what's going to happen is what

Michael Levin (1:17:35.360)

survives are the algorithms that can create a great worm no matter what the genome is. So

Michael Levin (1:17:40.160)

everything went into the algorithm and which, which of course then reduces the pressure on

Michael Levin (1:17:44.080)

keeping a, you know, keeping a clean genome. So this idea of, right, and different animals have

Michael Levin (1:17:49.040)

this in different, to different levels, but this idea of putting energy into an algorithm that

Michael Levin (1:17:54.720)

does not overtrain on priors, right? It can't assume, I mean, I think biology is this way in

Michael Levin (1:17:59.040)

general, evolution doesn't take the past too seriously because it makes these basically

Michael Levin (1:18:04.160)

problem solving machines as opposed to like exactly what, you know, to, to, to deal with

Michael Levin (1:18:08.800)

exactly what happened last time. Yeah. Problem solving versus memory recall. So a little memory,

Lex Fridman (1:18:14.480)

but a lot of problem solving. I think so. Yeah. In many cases, yeah. Problem solving.

Michael Levin (1:18:22.240)

I mean, it's incredible that those kinds of systems are able to be constructed,

Michael Levin (1:18:25.600)

um, especially how much they contrast with the way we build problem solving systems in the AI world.

Michael Levin (1:18:32.480)

Um, back to Xenobots. I'm not sure if we ever described how Xenobots are built, but

Michael Levin (1:18:39.600)

I mean, you have a paper titled biological robots perspectives on an emerging interdisciplinary

Michael Levin (1:18:45.280)

field. And the beginning you, uh, you mentioned that the word Xenobots is like controversial.

Lex Fridman (1:18:51.360)

Do you guys get in trouble for using Xenobots or what? Do people not like the word Xenobots?

Lex Fridman (1:18:57.280)

Are you trying to be provocative with the word Xenobots versus biological robots?

Michael Levin (1:19:02.400)

I don't know. Is there some drama that we should be aware of? There's a little bit of drama. Uh,

Michael Levin (1:19:07.200)

I think, I think the drama is basically related to people, um, having very fixed ideas about what

Michael Levin (1:19:15.200)

terms mean. And I think in many cases, these ideas are completely out of date with, with where science

Michael Levin (1:19:22.960)

is now. And for sure they're, they're out of date with what's going to be, I mean, these, these,

Michael Levin (1:19:28.720)

these concepts, uh, are not going to survive the next couple of decades. So if you ask a person

Lex Fridman (1:19:33.760)

and including, um, you know, a lot of people in biology who kind of want to keep a sharp

Michael Levin (1:19:38.080)

distinction between biologicals and robots, right? See, what's a robot? Well, a robot,

Michael Levin (1:19:42.160)

it comes out of a factory. It's made by humans. It is boring. It is a meaning that you can predict

Michael Levin (1:19:46.400)

everything it's going to do. It's made of metal and certain other inorganic materials. Living

Michael Levin (1:19:50.640)

organisms are magical. They, they, they arise, right? And so on. So these, these distinctions,

Michael Levin (1:19:54.400)

I think these, these distinctions, I think were, were never good, but, uh, they're going to be

Michael Levin (1:20:00.720)

completely useless going forward. And so part of, there's a couple of papers that that's one paper

Lex Fridman (1:20:05.120)

and there's another one that Josh Bongar and I wrote where we really attack the terminology.

Lex Fridman (1:20:09.520)

And we say these binary categories are based on very, um, nonessential kind of surface, uh,

Michael Levin (1:20:16.880)

limitations of, of technology and imagination that were true before, but they've got to go. And so,

Lex Fridman (1:20:22.560)

and so we call them Zenobots. So, so Xeno for Xenopus Levus, where this is, it's the frog that,

Michael Levin (1:20:27.360)

that these guys are made of, but we think it's an example of, of, of, uh, of a biobot technology,

Michael Levin (1:20:32.320)

because ultimately if we, if we under, once we understand how to, uh, communicate and manipulate,

Michael Levin (1:20:39.680)

um, the inputs to these cells, we will be able to get them to build whatever we want them to build.

Lex Fridman (1:20:45.680)

And that's robotics, right? It's, it's the rational construction of machines that have

Michael Levin (1:20:49.360)

useful purposes. I, I absolutely think that this is a robotics platform, whereas some biologists

Michael Levin (1:20:54.560)

don't, but it's built in a way that, uh, all the different components are doing their own computation.

Lex Fridman (1:21:02.080)

So in a way that we've been talking about, so you're trying to do top down control in that

Michael Levin (1:21:06.000)

biological system. And in the future, all of this will, will, will merge together because

Michael Levin (1:21:09.680)

of course at some point we're going to throw in synthetic biology circuits, right? New, new, um,

Michael Levin (1:21:13.840)

you know, new transcriptional circuits to get them to do new things. Of course we'll throw some of

Michael Levin (1:21:17.200)

that in, but we specifically stayed away from all of that because in the first few papers,

Lex Fridman (1:21:21.680)

and there's some more coming down the pike that are, I think going to be pretty, pretty dynamite,

Michael Levin (1:21:25.600)

um, that, uh, we want to show what the native cells are made of. Because what happens is,

Michael Levin (1:21:30.720)

you know, if you engineer the heck out of them, right, if we were to put in new, you know,

Michael Levin (1:21:33.920)

new transcription factors and some new metabolic machinery and whatever, people will say, well,

Michael Levin (1:21:38.000)

okay, you engineered this and you made it do whatever. And fine. I wanted to show, uh, and,

Michael Levin (1:21:44.000)

and, and the whole team, uh, wanted to show the plasticity and the intelligence in the biology.

Lex Fridman (1:21:50.240)

What does it do that's surprising before you even start manipulating the hardware in that way?

Michael Levin (1:21:55.280)

Yeah. Don't try to, uh, over control the thing. Let it flourish. The, the full beauty of the

Michael Levin (1:22:02.560)

biological system. Why Xenopus Levus? How do you pronounce it? The frog.

Lex Fridman (1:22:07.600)

Xenopus Levus. Yeah. Yeah. It's a very popular.

Lex Fridman (1:22:09.360)

Why this frog?

Michael Levin (1:22:10.240)

It's been used since, uh, I think the fifties. Uh, it's just very convenient because you can,

Michael Levin (1:22:15.360)

you know, we, we keep the adults in this, in this, uh, very fine frog habitat. They lay eggs. They

Michael Levin (1:22:19.280)

lay tens of thousands of eggs at a time. Um, the eggs develop right in front of your eyes. It's the

Michael Levin (1:22:24.880)

most mad magical thing you can, you can see because normally, you know, if you were to deal

Michael Levin (1:22:29.440)

with mice or rabbits or whatever, you don't see the early stages, right? Cause everything's inside

Michael Levin (1:22:32.960)

the mother. Everything's in a Petri dish at room temperature. So you just, you, you have an egg,

Michael Levin (1:22:36.640)

it's fertilized and you can just watch it divide and divide and divide. And on all the organs

Michael Levin (1:22:40.400)

form, you can just see it. And at that point, um, the community has, has developed lots of

Michael Levin (1:22:44.960)

different tools for understanding what's going on and also for, for manipulating, right? So it's,

Michael Levin (1:22:50.000)

it's people use it for, um, you know, for understanding birth defects and neurobiology

Lex Fridman (1:22:54.160)

and cancer immunology. So you get the whole, uh, embryogenesis in the Petri dish.

Michael Levin (1:23:00.160)

That's so cool to watch. Is there videos of this? Oh yeah. Yeah. Yeah. There's,

Lex Fridman (1:23:03.520)

but yeah, there's, there's amazing videos on, on, online. I mean, mammalian embryos are super cool

Michael Levin (1:23:08.160)

too. For example, monozygotic twins are what happens when you cut a mammalian embryo in half.

Michael Levin (1:23:12.560)

You don't get two half bodies. You get two perfectly normal bodies because it's a

Michael Levin (1:23:15.760)

regeneration event, right? Development is just the, it's just the kind of regeneration really.

Lex Fridman (1:23:19.920)

And why this particular frog? It's just, uh, cause they were doing in the fifties and.

Michael Levin (1:23:25.760)

It breeds well in, um, you know, in, in, it's easy to raise in, in the laboratory and, uh,

Michael Levin (1:23:32.000)

it's very prolific and all the tools basically for decades, people have been developing tools.

Michael Levin (1:23:36.480)

There's other, some people use other frogs, but I have to say this is, this is, this is important.

Michael Levin (1:23:40.800)

Xenobots are fundamentally not anything about frogs. So, um, I can't say too much about this

Michael Levin (1:23:46.080)

cause it's not published and peer reviewed yet, but we've made Xenobots out of other things that

Michael Levin (1:23:50.400)

have nothing to do with frogs. It's, this is not a frog phenomenon. This is, we, we started with

Michael Levin (1:23:54.640)

frog because it's so convenient, but this, this, this plasticity is not a fraud. You know, it's

Michael Levin (1:23:59.040)

not related to the fact that they're frogs. What happens when you kiss it? Does it turn

Michael Levin (1:24:02.880)

into a prince? No. Or a princess? Which way? Uh, prince. Yeah. Prince should be a prince.

Michael Levin (1:24:07.120)

Yeah. Uh, that's an experiment that I don't believe we've done. And if we have, I don't

Michael Levin (1:24:10.720)

want to collaborate, I can, I can take on the lead, uh, on that effort. Okay, cool. Uh,

Lex Fridman (1:24:17.680)

how does the cells coordinate? Let's focus in on just the embryogenesis. So there's one cell,

Lex Fridman (1:24:24.320)

so it divides, doesn't have to be very careful about what each cell starts doing once they divide.

Michael Levin (1:24:32.240)

Yes. And like, when there's three of them, it's like the cofounders or whatever,

Michael Levin (1:24:37.840)

like, well, like slow down, you're responsible for this. When do they become specialized and

Lex Fridman (1:24:44.320)

how do they coordinate that specialization? So, so this is the basic science of developmental

Michael Levin (1:24:49.440)

biology. There's a lot known about all of that, but, um, but I'll tell you what I think is kind

Michael Levin (1:24:55.120)

of the most important part, which is, yes, it's very important who does what. However,

Michael Levin (1:25:01.200)

because going back to this issue of why I made this claim that, um, biology doesn't take the past

Michael Levin (1:25:07.440)

too seriously. And what I mean by that is it doesn't assume that everything is the way it's,

Michael Levin (1:25:12.560)

it's expected to be. Right. And here's an example of that. Um, this was, this was done, this was,

Michael Levin (1:25:17.200)

this was an old experiment going back to the forties, but, um, basically imagine imagine

Michael Levin (1:25:21.280)

it's a new salamander and it's got these little tube tubules that go to the kidneys, right? It's

Michael Levin (1:25:25.760)

a little tube. Take a cross section of that tube. You see eight to 10 cells that have

Michael Levin (1:25:30.080)

cooperated to make this little tube in cross section, right? So one amazing, one amazing

Michael Levin (1:25:34.880)

thing you can do is, um, you can, you can mess with a very early cell division to make the cells

Michael Levin (1:25:41.200)

gigantic, bigger. You can, you can make them different sizes. You can force them to be different

Michael Levin (1:25:44.560)

sizes. So if you make the cells different sizes, the whole nude is still the same size.

Lex Fridman (1:25:50.160)

So if you take a cross section through the, through that tubule, instead of eight to 10

Michael Levin (1:25:53.840)

cells, you might have four or five or you might have, you know, three until you make the cells so

Michael Levin (1:25:59.200)

enormous that one single cell wraps around itself and, and gives you that same large scale structure

Michael Levin (1:26:06.480)

with a completely different molecular mechanism. So now instead of cell to cell communication to

Michael Levin (1:26:11.120)

make a tubule, instead of that, it's one cell using the cytoskeleton to bend itself around.

Lex Fridman (1:26:15.840)

So think about what that means in the service of a large scale, talk about top down control,

Michael Levin (1:26:20.400)

right? In the service of a large scale anatomical feature, different molecular mechanisms get

Michael Levin (1:26:24.960)

called up. So now think about this, you're, you're, you're a nude cell and trying to make an embryo.

Michael Levin (1:26:30.320)

If you had a fixed idea of who was supposed to do what, you'd be screwed because now your cells

Michael Levin (1:26:34.480)

are gigantic. Nothing would work. The, there's an incredible tolerance for changes in the size of

Michael Levin (1:26:40.240)

the parts and the amount of DNA in those parts. Um, all sorts of stuff you can, you can, the life

Michael Levin (1:26:45.280)

is highly interoperable. You can put electrodes in there and you can put weird nanomaterials. It

Michael Levin (1:26:49.200)

still works. It's, it's, uh, this is that problem solving action, right? It's able to do what it

Michael Levin (1:26:54.400)

needs to do, even when circumstances change. That is, you know, the hallmark of intelligence,

Michael Levin (1:27:00.160)

right? William James defined intelligence as the ability to get to the same goal by different

Michael Levin (1:27:04.080)

means. That's this, you get to the same goal by completely different means. And so, so,

Lex Fridman (1:27:08.960)

so why am I bringing this up is just to say that, yeah, it's important for the cells to do the right

Michael Levin (1:27:12.640)

stuff, but they have incredible tolerances for things not being what you expect and to still

Michael Levin (1:27:17.520)

get their job done. So if you're, you know, um, all of these things are not hardwired.

Michael Levin (1:27:23.840)

There are organisms that might be hardwired. For example, the nematode C elegans in that organism,

Michael Levin (1:27:28.880)

every cell is numbered, meaning that every C elegans has exactly the same number of cells

Michael Levin (1:27:32.800)

as every other C elegans. They're all in the same place. They all divide. There's literally a map

Michael Levin (1:27:36.000)

of how it works that in that, in that sort of system, it's, it's, it's much more cookie cutter,

Michael Levin (1:27:40.880)

but, but most, most organisms are incredibly plastic in that way. Is there something particularly

Michael Levin (1:27:47.840)

magical to you about the whole developmental biology process? Um, is there something you

Michael Levin (1:27:53.680)

could say, cause you just said it, they're very good at accomplishing the goal of the job they

Michael Levin (1:27:58.080)

need to do the competency thing, but you get fricking organism from one cell. It's like, uh,

Michael Levin (1:28:06.640)

I mean, it's very hard, hard to intuit that whole process to even think about reverse engineering

Michael Levin (1:28:14.000)

that process. Right. Very hard to the point where I often just imagine, I, I sometimes ask my

Michael Levin (1:28:19.760)

students to do this thought experiment. Imagine you were, you were shrunk down to the, to the scale

Michael Levin (1:28:23.680)

of a single cell and you were in the middle of an embryo and you were looking around at what's going

Michael Levin (1:28:27.120)

on and the cells running around, some cells are dying at the, you know, every time you look,

Michael Levin (1:28:30.400)

it's kind of a different number of cells for most organisms. And so I think that if you didn't know

Lex Fridman (1:28:35.520)

what embryonic development was, you would have no clue that what you're seeing is always going to

Michael Levin (1:28:40.080)

make the same thing. Nevermind knowing what that, what that is. Nevermind being able to say, even

Michael Levin (1:28:44.560)

with full genomic information, being able to say, what the hell are they building? We have no way

Michael Levin (1:28:48.080)

to do that. But, but just even to guess that, wow, the, the, the outcome of all this activity is it's

Michael Levin (1:28:54.720)

always going to be, it's always going to build the same thing. The imperative to create the final you

Michael Levin (1:29:00.080)

as you are now is there already. So you can, you would, so you start from the same embryo,

Michael Levin (1:29:06.240)

you create a very similar organism. Yeah. Except for cases like the Xenobots, when you give them

Michael Levin (1:29:14.480)

a different environment, they come up with a different way to be adaptive in that environment.

Lex Fridman (1:29:18.240)

But overall, I mean, so, so I think, so I think to, you know, kind of summarize it,

Michael Levin (1:29:24.080)

I think what evolution is really good at is creating hardware that has a very stable baseline

Michael Levin (1:29:31.520)

mode, meaning that left to its own devices, it's very good at doing the same thing. But it has a

Michael Levin (1:29:36.880)

bunch of problem solving capacity such that if any, if any assumptions don't hold, if your cells are

Michael Levin (1:29:41.360)

a weird size, or you get the wrong number of cells, or there's a, you know, somebody stuck

Michael Levin (1:29:45.040)

in electrode halfway through the body, whatever, it will still get most of what it needs to do done.

Michael Levin (1:29:52.400)

You've talked about the magic and the power of biology here. If we look at the human brain,

Lex Fridman (1:29:57.760)

how special is the brain in this context? You're kind of minimizing the importance of the brain

Lex Fridman (1:30:03.200)

or lessening its, we think of all the special computation happens in the brain,

Michael Levin (1:30:08.640)

everything else is like the help. You're kind of saying that the whole thing is the whole thing

Michael Levin (1:30:14.960)

is doing computation. But nevertheless, how special is the human brain in this full context of

Michael Levin (1:30:22.160)

biology? Yeah, I mean, look, there's no getting away from the fact that the human brain allows

Michael Levin (1:30:27.680)

us to do things that we could not do without it. You can say the same thing about the liver.

Michael Levin (1:30:31.920)

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

Michael Levin (1:30:37.680)

is just being polite to the brain right now. Well, being a politician, like, listen,

Michael Levin (1:30:42.320)

everybody has everybody has a role. Yeah, it's very important role. That's right. We have to

Michael Levin (1:30:46.480)

acknowledge the importance of the brain, you know, there are more than enough people who are

Michael Levin (1:30:52.480)

cheerleading the brain, right? So so I don't feel like nothing I say is going to reduce people's

Michael Levin (1:30:58.720)

excitement about the human brain. And so so I emphasize other things credit. I don't think it

Michael Levin (1:31:04.160)

gets too much credit. I think other things don't get enough credit. I think the brain is the human

Michael Levin (1:31:08.880)

brain is incredible and special and all that. I think other things need more credit. And and I

Michael Levin (1:31:13.760)

also think that this and I'm sort of this way about everything. I don't like binary categories,

Lex Fridman (1:31:19.360)

but almost anything I like a continuum. And the thing about the human brain is that it by by by

Michael Levin (1:31:24.880)

accepting that as some kind of an important category or essential, essential thing, we end

Michael Levin (1:31:32.080)

up with all kinds of weird pseudo problems and conundrum. So for example, when we talk about it,

Michael Levin (1:31:38.320)

you know, if you don't want to talk about ethics and other other things like that, and what you

Michael Levin (1:31:44.880)

know, this this idea that surely if we look out into the universe, surely, we don't believe that

Michael Levin (1:31:50.000)

this human brain is the only way to be sentient, right? Surely we don't, you know, and to have high

Michael Levin (1:31:54.320)

level cognition. I just can't even wrap my mind around this, this idea that that is the only way

Michael Levin (1:31:59.360)

to do it. No doubt there are other architectures made bond made of completely different principles

Michael Levin (1:32:04.160)

that achieve the same thing. And once we believe that, then that tells us something important. It

Michael Levin (1:32:09.760)

tells us that things that are not quite human brains or chimeras of human brains and other

Michael Levin (1:32:15.520)

tissue or human brains or other kinds of brains and novel configurations or things that are sort

Michael Levin (1:32:20.800)

of brains, but not really, or plants or embryos or whatever, might also have important cognitive

Michael Levin (1:32:26.720)

status. So that's the only thing I think we have to be really careful about treating the human

Michael Levin (1:32:32.000)

brain as if it was some kind of like sharp binary category. You know, you are or you aren't. I don't

Michael Levin (1:32:37.680)

believe that exists. So when we look out at all the beautiful variety of human brains,

Michael Levin (1:32:44.960)

semi biological architectures out there in the universe, how many intelligent alien civilizations

Lex Fridman (1:32:52.880)

do you think are out there? Boy, I have no expertise in that whatsoever. You haven't met

Michael Levin (1:32:59.200)

any? I have met the ones we've made. I think that I mean, exactly. In some sense with synthetic

Michael Levin (1:33:06.800)

biology, are you not creating aliens? I absolutely think so because look, all of life,

Michael Levin (1:33:12.800)

all of all standard model systems are an end of one course of evolution on Earth, right? And trying

Michael Levin (1:33:19.840)

to make conclusions about biology from looking at life on Earth is like testing your theory on the

Michael Levin (1:33:26.880)

same data that generated it. It's all it's all kind of like locked in. So we absolutely have to

Michael Levin (1:33:32.720)

create novel examples that have no history on Earth that don't, you know, xenobots have no

Michael Levin (1:33:40.240)

history of selection to be a good xenobot. The cells have selection for various things, but the

Michael Levin (1:33:44.560)

xenobot itself never existed before. And so we can make chimeras, you know, we make frog a lottles

Michael Levin (1:33:48.960)

that are sort of half frog, half axolotl. You can make all sorts of high brats, right constructions

Michael Levin (1:33:53.520)

of living tissue with robots and whatever. We need to be making these things until we find actual

Michael Levin (1:33:58.640)

aliens, because otherwise, we're just looking at an end of one set of examples, all kinds of frozen

Michael Levin (1:34:03.920)

accidents of evolution and so on. We need to go beyond that to really understand biology. But

Michael Levin (1:34:08.720)

we're still even when you do a synthetic biology, you're locked in to the basic components of the

Michael Levin (1:34:17.040)

way biology is done on this Earth. Yeah, right. And also, and the and also the basic constraints

Michael Levin (1:34:23.760)

of the environment, even artificial environments to construct in the lab are tied up to the

Michael Levin (1:34:27.840)

environment. I mean, what do you? Okay, let's say there is I mean, what I think is there's

Michael Levin (1:34:34.240)

a nearly infinite number of intelligent civilizations living or dead out there.

Michael Levin (1:34:41.920)

If you pick one out of the box, what do you think it would look like? So in when you think about

Michael Levin (1:34:50.320)

synthetic biology, or creating synthetic organisms, how hard is it to create something that's very

Michael Levin (1:34:58.880)

different? Yeah, I think it's very hard to create something that's very different, right? It's we

Michael Levin (1:35:06.320)

are just locked in both both both experimentally and in terms of our imagination, right? It's very

Michael Levin (1:35:12.400)

hard. And you also emphasize several times that the idea of shape. Yeah, the individual cell get

Michael Levin (1:35:18.000)

together with other cells and they kind of they're gonna build a shape. So it's shape and function,

Lex Fridman (1:35:23.920)

but shape is a critical thing. Yeah. So here, I'll take a stab. I mean, I agree with you. I did

Michael Levin (1:35:29.200)

to whatever extent that we can say anything, I do think that there's, you know, probably an

Michael Levin (1:35:33.600)

infinite number of, of different different architectures with with that are with interesting

Michael Levin (1:35:38.800)

cognitive properties out there. What can we say about them? I think that the only things that are

Michael Levin (1:35:45.840)

going I don't I don't think we can rely on any of the typical stuff, you know, carbon based, none of

Michael Levin (1:35:50.880)

that. Like, I think all of that is just, you know, us being having having a lack of imagination. But

Michael Levin (1:35:56.960)

I think the things that are going to be universal, if anything is, are things, for example, driven by

Michael Levin (1:36:03.760)

resource limitation, the fact that you are fighting a hostile world, and you have to draw a

Michael Levin (1:36:09.280)

boundary between yourself and the world somewhere, the fact that that boundary is not given to you

Michael Levin (1:36:13.040)

by anybody, you have to you have to assume it, you know, estimated yourself. And the fact that

Michael Levin (1:36:18.000)

you have to course grain your experience and the fact that you're going to try to minimize surprise

Lex Fridman (1:36:22.160)

and the fact that like these, these are the things that I think are fundamental about biology,

Michael Levin (1:36:25.920)

none of the, you know, the facts about the genetic code, or even the fact that we have genes or the

Michael Levin (1:36:30.160)

biochemistry of it, I don't think any of those things are fundamental. But it's going to be a

Michael Levin (1:36:34.160)

lot more about the information and about the creation of the self, the fact that so in my in

Michael Levin (1:36:38.640)

my framework, selves are demarcated by the scale of the goals that they can pursue. So from little

Michael Levin (1:36:44.560)

tiny local goals to like massive, you know, planetary scale goals for certain humans,

Lex Fridman (1:36:49.440)

and everything and everything in between. So you can draw this like cognitive light cone about

Michael Levin (1:36:53.280)

that determines the the scale of the goals you could possibly pursue. I think those kinds of

Michael Levin (1:36:58.960)

frameworks, like that, like active inference, and so on are going to be universally applicable,

Lex Fridman (1:37:04.080)

but but none of the other things that are that are typically discussed. Quick pause,

Lex Fridman (1:37:08.640)

do you need a bathroom break? We were just talking about, you know, aliens and all that. That's a

Michael Levin (1:37:16.320)

funny thing, which is, I don't know if you've seen them, there's a kind of debate that goes on about

Michael Levin (1:37:20.720)

cognition and plants, and what can you say about different kinds of computation and cognition and

Michael Levin (1:37:24.560)

plants. And I always I always look at that something like if you're weirded out by cognition

Lex Fridman (1:37:28.800)

and plants, you're not ready for exobiology, right? If you know something that's that similar

Michael Levin (1:37:34.560)

here on Earth is already like freaking you out, then I think there's going to be all kinds of

Michael Levin (1:37:38.960)

cognitive life out there that we're gonna have a really hard time recognizing. I think robots will

Michael Levin (1:37:44.080)

help us, yeah, like expand our mind about cognition, either that or the word like xenobots. So,

Lex Fridman (1:37:54.640)

and they maybe becomes the same thing is, you know, really, when the human engineers,

Michael Levin (1:38:01.920)

the thing, at least in part, and then is able to achieve some kind of cognition that's different

Michael Levin (1:38:08.400)

than what you're used to, then you start to understand like, oh, you know, every living

Michael Levin (1:38:14.320)

organism is capable of cognition. Oh, I need to kind of broaden my understanding what cognition

Michael Levin (1:38:19.680)

is. But do you think plants, like when you when you eat them, are they screaming? I don't know

Michael Levin (1:38:25.520)

about screaming. I think you have to see what I think when I eat a salad. Yeah, good. Yeah,

Michael Levin (1:38:30.080)

I think you have to scale down the expectations in terms of right, so probably they're not

Michael Levin (1:38:34.560)

screaming in the way that we would be screaming. However, there's plenty of data on plants being

Michael Levin (1:38:39.760)

able to do anticipation and certain kinds of memory and so on. I think, you know, what you

Michael Levin (1:38:46.720)

just said about robots, I hope you're right. And I hope that's but there's two, there's two ways

Michael Levin (1:38:51.440)

that people can take that right. So one way is exactly what you just said to try to kind of

Michael Levin (1:38:54.720)

expand their expand their their their notions for that category. The other way people often go is

Michael Levin (1:39:02.000)

they just sort of define the term is if if if it's not a natural product, it's it's just faking,

Michael Levin (1:39:08.240)

right? It's not really intelligence if it was made by somebody else, because it's that same,

Michael Levin (1:39:11.920)

it's the same thing. They can see how it's done. And once you see how it's like a magic trick,

Michael Levin (1:39:16.160)

when you see how it's done, it's not as fun anymore. And and I think people have a real

Michael Levin (1:39:21.360)

tendency for that. And they sort of which which I find really strange in the sense that if somebody

Michael Levin (1:39:25.280)

said to me, we have this this this sort of blind, like, like, hill climbing search,

Lex Fridman (1:39:32.480)

and then and then we have a really smart team of engineers, which one do you think is going to

Michael Levin (1:39:36.800)

produce a system that has good intelligence? I think it's really weird to say that it only

Michael Levin (1:39:41.680)

comes from the blind search, right? It can't be done by people who, by the way, can also use

Michael Levin (1:39:45.600)

evolutionary techniques if they want to, but also rational design. I think it's really weird to say

Michael Levin (1:39:49.920)

that real intelligence only comes from natural evolution. So I hope you're right. I hope people

Michael Levin (1:39:55.600)

take it the other the other way. But there's a nice shortcut. So I work with Lego robots a lot now

Michael Levin (1:40:01.360)

from for my own personal pleasure. Not in that way internet. So four legs. And one of the things

Michael Levin (1:40:13.520)

that changes my experience with the robots a lot is when I can't understand why I did a certain

Michael Levin (1:40:21.440)

thing. And there's a lot of ways to engineer that. Me, the person that created the software that runs

Michael Levin (1:40:27.680)

it. There's a lot of ways for me to build that software in such a way that I don't exactly know

Lex Fridman (1:40:33.120)

why it did a certain basic decision. Of course, as an engineer, you can go in and start to look at

Michael Levin (1:40:40.160)

logs. You can log all kind of data, sensory data, the decisions you made, you know, all the outputs

Michael Levin (1:40:45.840)

in your networks and so on. But I also try to really experience that surprise and that really

Michael Levin (1:40:52.320)

experience as another person would that totally doesn't know how it's built. And I think the magic

Michael Levin (1:40:57.840)

is there in not knowing how it works. That I think biology does that for you through the layers of

Michael Levin (1:41:06.960)

abstraction. Yeah, it because nobody really knows what's going on inside the biological. Like each

Michael Levin (1:41:14.320)

one component is clueless about the big picture. I think there's actually really cheap systems that

Michael Levin (1:41:20.480)

can that can illustrate that kind of thing, which is even like, you know, fractals, right? Like,

Michael Levin (1:41:27.200)

you have a very small, short formula in Z, and you see it and there's no magic, you're just going to

Michael Levin (1:41:32.560)

crank through, you know, Z squared plus C, whatever, you're just going to crank through it. But the

Michael Levin (1:41:36.960)

result of it is this incredibly rich, beautiful image, right? That that just like, wow, all of

Michael Levin (1:41:43.280)

that was in this, like, 10 character long string, like amazing. So the fact that you can you can

Michael Levin (1:41:49.760)

know everything there is to know about the details and the process and all the parts and every like,

Michael Levin (1:41:54.800)

there's literally no magic of any kind there. And yet the outcome is something that you would never

Michael Levin (1:42:01.120)

have expected. And it's just it just, you know, is incredibly rich and complex and beautiful. So

Michael Levin (1:42:07.200)

there's a lot of that. You write that you work on developing conceptual frameworks for understanding

Michael Levin (1:42:13.360)

unconventional cognition. So the kind of thing we've been talking about, I just like the term

Michael Levin (1:42:17.840)

unconventional cognition. And you want to figure out how to detect, study and communicate with

Michael Levin (1:42:23.440)

the thing. You've already mentioned a few examples, but what is unconventional cognition? Is it as

Michael Levin (1:42:29.200)

simply as everything else outside of what we define usually as cognition, cognitive science,

Michael Levin (1:42:34.880)

the stuff going on between our ears? Or is there some deeper way to get at the fundamentals of

Lex Fridman (1:42:41.440)

what is cognition? Yeah, I think like, and I'm certainly not the only person who works in

Michael Levin (1:42:47.440)

unconventional, unconventional cognition. So it's the term used? Yeah, that's one that I so I've

Michael Levin (1:42:53.440)

coined a number of weird terms, but that's not one of mine like that. That's an existing thing. So

Lex Fridman (1:42:56.960)

so for example, somebody like Andy Adam Askey, who I don't know if you've if you've had him on,

Michael Levin (1:43:00.880)

if you haven't, you should he's a he's a he's a, you know, very interesting guy. He's a computer

Michael Levin (1:43:05.600)

scientist, and he does unconventional cognition and slime molds, all kinds of weird. He's a real

Michael Levin (1:43:10.640)

weird, weird cat, really interesting. Anyway, so so that's, you know, it's a bunch of terms that

Michael Levin (1:43:15.280)

I've come up with. But that's not one of mine. So I think like many terms, that one is, is really

Michael Levin (1:43:21.600)

defined by the times, meaning that unconventional cognitive things that are unconventional cognition

Michael Levin (1:43:26.560)

today are not going to be considered unconventional cognition at some point. It's one of those,

Michael Levin (1:43:31.920)

it's one of those things. And so it's, you know, it's, it's, it's this, it's this really deep

Michael Levin (1:43:37.840)

question of how do you recognize, communicate with, classify cognition, when you cannot rely

Michael Levin (1:43:46.240)

on the typical milestones, right? So typical, you know, again, if you stick with the with the, the

Michael Levin (1:43:52.160)

history of life on Earth, like these, these exact model systems, you would say, Ah, here's a particular

Michael Levin (1:43:56.640)

structure of the brain. And this one has fewer of those. And this one has a bigger frontal cortex.

Lex Fridman (1:44:00.160)

And this one, right, so these are these are landmarks that that we're that we're used to,

Lex Fridman (1:44:04.640)

and and allows us to make very kind of rapid judgments about things. But if you can't rely on

Michael Levin (1:44:10.560)

that, either because you're looking at a synthetic thing, or an engineered thing, or an alien thing,

Michael Levin (1:44:16.160)

then what do you do? Right? How do you and so and so that's what I'm really interested. I'm

Michael Levin (1:44:19.600)

interested in mind in all of its possible implementations, not just the obvious ones

Michael Levin (1:44:25.040)

that we know from from looking at brains here on Earth. Whenever I think about something like

Michael Levin (1:44:31.040)

unconventional cognition, I think about cellular automata, I'm just captivated by the beauty of the

Michael Levin (1:44:36.880)

thing. The fact that from simple little objects, you can create some such beautiful complexity

Michael Levin (1:44:46.480)

that very quickly, you forget about the individual objects, and you see the things that it creates

Michael Levin (1:44:53.120)

as its own organisms. That blows my mind every time. Like, honestly, I could full time just

Michael Levin (1:45:01.920)

eat mushrooms and watch cellular automata. Don't even have to do mushrooms.

Michael Levin (1:45:06.880)

Just cellular automata. It feels like, I mean, from the engineering perspective, I love

Michael Levin (1:45:13.280)

when a very simple system captures something really powerful, because then you can study

Michael Levin (1:45:18.320)

that system to understand something fundamental about complexity about life on Earth.

Michael Levin (1:45:24.080)

Anyway, how do I communicate with a thing? If cellular automata can do cognition, if a plant

Michael Levin (1:45:32.000)

can do cognition, if a xenobot can do cognition, how do I like whisper in its ear and get an

Lex Fridman (1:45:40.000)

answer back to how do I have a conversation? How do I have a xenobot on a podcast?

Michael Levin (1:45:46.880)

It's a really interesting line of investigation that opens up. I mean, we've thought about this.

Lex Fridman (1:45:53.840)

So you need a few things. You need to understand the space in which they live. So not just the

Michael Levin (1:46:00.400)

physical modality, like can they see light, can they feel vibration? I mean, that's important,

Michael Levin (1:46:03.680)

of course, because that's how you deliver your message. But not just the ideas for a communication

Michael Levin (1:46:08.320)

medium, not just the physical medium, but saliency, right? So what's important to this

Michael Levin (1:46:16.000)

system? And systems have all kinds of different levels of sophistication of what you could expect

Michael Levin (1:46:22.080)

to get back. And I think what's really important, I call this the spectrum of persuadability,

Michael Levin (1:46:28.080)

which is this idea that when you're looking at a system, you can't assume where on the spectrum

Michael Levin (1:46:33.200)

it is. You have to do experiments. And so for example, if you look at a gene regulatory network,

Michael Levin (1:46:41.440)

which is just a bunch of nodes that turn each other on and off at various rates, you might

Michael Levin (1:46:45.760)

look at that and you say, well, there's no magic here. I mean, clearly this thing is as deterministic

Michael Levin (1:46:50.320)

as it gets. It's a piece of hardware. The only way we're going to be able to control it is by

Michael Levin (1:46:54.320)

rewiring it, which is the way molecular biology works, right? We can add nodes, remove nodes,

Michael Levin (1:46:57.920)

whatever. Well, so we've done simulations and shown that biological, and now we're doing this in the

Michael Levin (1:47:03.440)

lab, the biological networks like that have associative memory. So they can actually learn,

Michael Levin (1:47:08.960)

they can learn from experience. They have habituation, they have sensitization, they

Michael Levin (1:47:12.000)

have associative memory, which you wouldn't have known if you assume that they have to be on the

Michael Levin (1:47:15.840)

left side of that spectrum. So when you're going to communicate with something, and we've even,

Michael Levin (1:47:21.280)

Charles Abramson and I have written a paper on behaviorist approaches to synthetic organisms,

Michael Levin (1:47:26.080)

meaning that if you're given something, you have no idea what it is or what it can do,

Lex Fridman (1:47:29.600)

how do you figure out what its psychology is, what its level is, what does it, and so we literally

Michael Levin (1:47:34.480)

lay out a set of protocols, starting with the simplest things and then moving up to more complex

Michael Levin (1:47:38.480)

things where you can make no assumptions about what this thing can do, right? You have to start

Lex Fridman (1:47:42.640)

and you'll find out. So when you're going to, so here's a simple, I mean, here's one way to

Michael Levin (1:47:47.120)

communicate with something. If you can train it, that's a way of communicating. So if you can

Michael Levin (1:47:51.600)

provide, if you can figure out what the currency of reward of positive and negative reinforcement is,

Michael Levin (1:47:56.160)

right, and you can get it to do something it wasn't doing before based on experiences you've

Michael Levin (1:48:01.520)

given, you have taught it one thing. You have communicated one thing, that such and such an

Michael Levin (1:48:06.080)

action is good, some other action is not good. That's like a basic atom of a primitive atom

Michael Levin (1:48:11.520)

of communication. What about in some sense, if it gets you to do something you haven't done before,

Michael Levin (1:48:19.040)

is it answering back? Yeah, most certainly. And there's, I've seen cartoons, I think maybe Gary

Michael Levin (1:48:24.560)

Larson or somebody had had a cartoon of these rats in the maze and the one rat, you know,

Michael Levin (1:48:29.040)

assist to the other. You look at this every time, every time I walk over here, he starts scribbling

Michael Levin (1:48:32.720)

in that on the, you know, on the clipboard that he has, it's awesome. If we step outside ourselves

Lex Fridman (1:48:38.720)

and really measure how much, like if I actually measure how much I've changed because of my

Michael Levin (1:48:46.400)

interaction with certain cellular automata. I mean, you really have to take that into

Michael Levin (1:48:52.400)

consideration about like, well, these things are changing you too. Yes. I know, you know how it

Michael Levin (1:48:58.320)

works and so on, but you're being changed by the thing. Yeah, absolutely. I think I read,

Michael Levin (1:49:04.080)

I don't know any details, but I think I read something about how wheat and other things

Michael Levin (1:49:08.640)

have domesticated humans in terms of, right, but by their properties change the way that

Michael Levin (1:49:13.520)

the human behavior and societal structures. In that sense, cats are running the world

Michael Levin (1:49:20.240)

because they've took over the, so first off, so first they, while not giving a shit about humans,

Michael Levin (1:49:27.200)

clearly with every ounce of their being, they've somehow got just millions and millions of humans

Michael Levin (1:49:35.680)

to take them home and feed them. And then not only the physical space did they take over,

Michael Levin (1:49:43.280)

they took over the digital space. They dominate the internet in terms of cuteness, in terms of

Michael Levin (1:49:48.640)

memeability. And so they're like, they got themselves literally inside the memes, they

Michael Levin (1:49:55.760)

become viral and spread on the internet. And they're the ones that are probably controlling

Michael Levin (1:50:01.040)

humans. That's my theory. Another, that's a follow up paper after the frog kissing. Okay.

Michael Levin (1:50:06.000)

I mean, you mentioned sentience and consciousness. You have a paper titled Generalizing Frameworks

Michael Levin (1:50:18.000)

for Sentience Beyond Natural Species. So beyond normal cognition, if we look at sentience and

Michael Levin (1:50:30.320)

consciousness, and I wonder if you draw an interesting distinction between those two

Michael Levin (1:50:34.000)

elsewhere, outside of humans, and maybe outside of Earth, you think aliens have sentience. And

Michael Levin (1:50:45.120)

if they do, how do we think about it? So when you have this framework, what is this paper? What is

Michael Levin (1:50:50.880)

the way you propose to think about sentience? Yeah, that particular paper was a very short

Michael Levin (1:50:57.040)

commentary on another paper that was written about crabs. It was a really good paper on them,

Michael Levin (1:51:01.280)

crabs and various, like a rubric of different types of behaviors that could be applied to

Michael Levin (1:51:07.760)

different creatures, and they're trying to apply it to crabs and so on. Consciousness,

Michael Levin (1:51:13.440)

we can talk about if you want, but it's a whole separate kettle of fish. I almost never talk about

Michael Levin (1:51:18.400)

crabs. In this case, yes. I almost never talk about consciousness, per se. I've said very,

Michael Levin (1:51:24.240)

very little about it, but we can talk about it if you want. Mostly what I talk about is cognition,

Michael Levin (1:51:29.120)

because I think that that's much easier to deal with in a kind of rigorous experimental way.

Michael Levin (1:51:36.240)

I think that all of these terms have, you know, sentience and so on, have different definitions,

Lex Fridman (1:51:45.040)

and I fundamentally, I think that people can, as long as they specify what they mean ahead of time,

Michael Levin (1:51:53.520)

I think people can define them in various ways. The only thing that I really think

Michael Levin (1:51:58.480)

that I really kind of insist on is that the right way to think about all this stuff is

Michael Levin (1:52:06.800)

from an engineering perspective. What does it help me to control, predict, and does it help

Michael Levin (1:52:12.640)

me do my next experiment? That's not a universal perspective. Some people have philosophical

Michael Levin (1:52:20.720)

kind of underpinnings, and those are primary, and if anything runs against that, then it must

Michael Levin (1:52:25.600)

automatically be wrong. Some people will say, I don't care what else. If your theory says to me

Michael Levin (1:52:31.440)

that thermostats have little tiny goals, I'm not, so that's it. That's my philosophical

Michael Levin (1:52:38.560)

preconception. Thermostats do not have goals, and that's it. That's one way of doing it,

Lex Fridman (1:52:43.200)

and some people do it that way. I do not do it that way, and I think that we can't,

Michael Levin (1:52:47.440)

I don't think we can know much of anything from a philosophical armchair. I think that

Michael Levin (1:52:51.440)

all of these theories and ways of doing things stand or fall based on just basically one set

Lex Fridman (1:52:57.280)

of criteria. Does it help you run a rich research program? That's it.

Michael Levin (1:53:01.040)

I agree with you totally, but forget philosophy. What about the poetry of ambiguity? What about

Michael Levin (1:53:08.240)

at the limits of the things you can engineer using terms that can be defined in multiple ways

Lex Fridman (1:53:14.800)

and living within that uncertainty in order to play with words until something lands that you

Michael Levin (1:53:22.720)

can engineer? I mean, that's to me where consciousness sits currently. Nobody really

Michael Levin (1:53:27.600)

understands the heart problem of consciousness, the subject, what it feels like, because it really

Michael Levin (1:53:33.360)

feels like, it feels like something to be this biological system. This conglomerate of a bunch

Michael Levin (1:53:39.040)

of cells in this hierarchy of competencies feels like something, and yeah, I feel like one thing,

Lex Fridman (1:53:45.360)

and is that just a side effect of a complex system, or is there something more that humans have,

Michael Levin (1:53:58.720)

or is there something more that any biological system has? Some kind of magic, some kind of,

Lex Fridman (1:54:03.680)

not just a sense of agency, but a real sense with a capital letter S of agency.

Michael Levin (1:54:10.560)

Yeah.

Lex Fridman (1:54:12.080)

Ah, boy, yeah, that's a deep question.

Lex Fridman (1:54:13.760)

Is there room for poetry in engineering or no?

Michael Levin (1:54:16.640)

No, there definitely is, and a lot of the poetry comes in when we realize that none of the

Michael Levin (1:54:22.240)

categories we deal with are sharp as we think they are, right? And so in the different areas of all

Michael Levin (1:54:29.680)

these spectra are where a lot of the poetry sits, I have many new theories about things,

Lex Fridman (1:54:34.160)

but I, in fact, do not have a good theory about consciousness that I plan to trot out.

Lex Fridman (1:54:38.400)

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

Michael Levin (1:54:42.800)

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

Lex Fridman (1:54:46.160)

the needle yet on that.

Lex Fridman (1:54:47.520)

And you want to ground it in engineering always.

Michael Levin (1:54:50.720)

So, well, I mean, so if we really tackle consciousness per se, in the terms of the

Michael Levin (1:54:58.240)

hard problem, that isn't necessarily going to be groundable in engineering, right? That

Michael Levin (1:55:04.160)

aspect of cognition is, but actual consciousness per se, first person perspective, I'm not sure

Michael Levin (1:55:10.400)

that that's groundable in engineering. And I think specifically what's different about it is

Michael Levin (1:55:16.480)

there's a couple of things. So let's, you know, here we go. I'll say a couple of things about

Michael Levin (1:55:20.800)

consciousness. One thing is that what makes it different is that for every other thing,

Michael Levin (1:55:28.000)

other aspect of science, when we think about having a correct or a good theory of it,

Michael Levin (1:55:35.200)

we have some idea of what format that theory makes predictions in. So whether those be numbers

Michael Levin (1:55:41.360)

or whatever, we have some idea. We may not know the answer, we may not have the theory,

Lex Fridman (1:55:45.200)

but we know that when we get the theory, here's what it's going to output, and then we'll know

Michael Levin (1:55:49.120)

if it's right or wrong. For actual consciousness, not behavior, not neural correlates, but actual

Michael Levin (1:55:54.320)

first person consciousness. If we had a correct theory of consciousness, or even a good one,

Lex Fridman (1:55:59.840)

what the hell would, what format would it make predictions in, right? Because all the things

Michael Levin (1:56:05.440)

that we know about basically boil down to observable behaviors. So the only thing I can

Michael Levin (1:56:10.640)

think of when I think about that is, it'll be poetry, or it'll be something to, if I ask you,

Michael Levin (1:56:19.920)

okay, you've got a great theory of consciousness, and here's this creature, maybe it's a natural one,

Michael Levin (1:56:23.920)

maybe it's an engineered one, whatever. And I want you to tell me what your theory says about this

Michael Levin (1:56:30.000)

being, what it's like to be this being. The only thing I can imagine you giving me is some piece

Michael Levin (1:56:36.640)

of art, a poem or something, that once I've taken it in, I share, I now have a similar state as

Michael Levin (1:56:45.600)

whatever. That's about as good as I can come up with. Well, it's possible that once you have a

Michael Levin (1:56:51.360)

good understanding of consciousness, it would be mapped to some things that are more measurable.

Lex Fridman (1:56:56.240)

So for example, it's possible that a conscious being is one that's able to suffer. So you start

Michael Levin (1:57:07.440)

to look at pain and suffering. You can start to connect it closer to things that you can measure

Michael Levin (1:57:16.400)

that, in terms of how they reflect themselves in behavior and problem solving and creation and

Michael Levin (1:57:25.760)

attainment of goals, for example, which I think suffering is one of the, you know, life is suffering.

Michael Levin (1:57:31.520)

It's one of the big aspects of the human condition. And so if consciousness is somehow a,

Michael Levin (1:57:40.720)

maybe at least a catalyst for suffering, you could start to get like echoes of it. You start to see

Michael Levin (1:57:48.080)

like the actual effects of consciousness and behavior. That it's not just about subjective

Michael Levin (1:57:52.880)

experience. It's like it's really deeply integrated in the problem solving decision making of a

Michael Levin (1:57:59.120)

system, something like this. But also it's possible that we realize, this is not a philosophical

Michael Levin (1:58:06.000)

statement. Philosophers can write their books. I welcome it. You know, I take the Turing test

Michael Levin (1:58:13.360)

really seriously. I don't know why people really don't like it. When a robot convinces you that

Michael Levin (1:58:20.800)

it's intelligent, I think that's a really incredible accomplishment. And there's some deep

Michael Levin (1:58:26.080)

sense in which that is intelligence. If it looks like it's intelligent, it is intelligent. And I

Michael Levin (1:58:32.560)

think there's some deep aspect of a system that appears to be conscious. In some deep sense,

Michael Levin (1:58:43.600)

it is conscious. At least for me, we have to consider that possibility. And a system that

Michael Levin (1:58:51.520)

appears to be conscious is an engineering challenge. Yeah, I don't disagree with any of

Michael Levin (1:58:58.480)

that. I mean, especially intelligence, I think, is a publicly observable thing. Science fiction

Michael Levin (1:59:06.080)

has dealt with this for a century or much more, maybe. This idea that when you are confronted with

Michael Levin (1:59:12.400)

something that just doesn't meet any of your typical assumptions, so you can't look in the

Michael Levin (1:59:17.760)

skull and say, oh, well, there's that frontal cortex, so then I guess we're good. So this thing

Michael Levin (1:59:23.280)

lands on your front lawn, and the little door opens, and something trundles out, and it's shiny

Lex Fridman (1:59:30.160)

and aluminum looking, and it hands you this poem that it wrote while it was flying over,

Lex Fridman (1:59:35.520)

and how happy it is to meet you. What's going to be your criteria for whether you get to take it

Michael Levin (1:59:40.960)

apart and see what makes it tick, or whether you have to be nice to it and whatever? All the

Michael Levin (1:59:46.000)

criteria that we have now and that people are using, and as you said, a lot of people are

Michael Levin (1:59:51.280)

down on the Turing test and things like this, but what else have we got? Because measuring

Michael Levin (1:59:55.920)

the cortex size isn't going to cut it in the broader scheme of things. So I think it's a

Michael Levin (20:05.520)

not, and a lot of people are skeptical about that and so on. But you've got to realize that

Michael Levin (20:09.520)

we are not, there's no such thing as this like indivisible diamond of intelligence that's like

Michael Levin (20:13.760)

this one central thing that's not made of parts. We are all made of parts. And so if you believe,

Michael Levin (20:19.600)

which I think is hard to get around, that we in fact have a centralized set of goals and

Michael Levin (20:25.520)

preferences and we plan and we do things and so on, you are already committed to the fact that

Michael Levin (20:30.240)

a collection of cells is able to do this, because we are a collection of cells. There's no getting

Michael Levin (20:34.000)

around that. In our case, what we do is we navigate the three dimensional world and we

Michael Levin (20:37.920)

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

Michael Levin (20:41.840)

Oh yeah. So when I'm moving this arm, I feel like I'm the central dictator of that action,

Lex Fridman (20:50.560)

but there's a lot of stuff going on. All the cells here are collaborating in some interesting way.

Lex Fridman (20:57.840)

They're getting signal from the central nervous system.

Michael Levin (21:00.880)

Well, even the central nervous system is misleadingly named because it isn't really

Michael Levin (21:05.600)

central. Again, it's just a bunch of cells. I mean, all of them, right? There are no,

Michael Levin (21:10.800)

there are no singular indivisible intelligences anywhere. We are all, every example that we've

Michael Levin (21:16.240)

ever seen is a collective of something. It's just that we're used to it. We're used to that. We're

Michael Levin (21:21.040)

used to, okay, this thing is kind of a single thing, but it's really not. You zoom in, you know

Lex Fridman (21:24.080)

what you see. You see a bunch of cells running around. Is there some unifying, I mean, we're

Michael Levin (21:29.360)

jumping around, but that something that you look at as the bioelectrical signal versus the

Michael Levin (21:36.000)

biochemical, the chemistry, the electricity, maybe the life is in that versus the cells.

Michael Levin (21:47.680)

It's the, there's an orchestra playing and the resulting music is the dictator.

Michael Levin (21:57.120)

That's not bad. That's Dennis Noble's kind of view of things. He has two really good books

Michael Levin (22:02.560)

where he talks about this musical analogy, right? So I think that's, I like it. I like it.

Lex Fridman (22:07.360)

Is it wrong though?

Michael Levin (22:08.640)

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

Michael Levin (22:13.600)

thing about it is that we have to come to grips with the fact that a true proper cognitive

Michael Levin (22:23.040)

intelligence can still be made of parts. Those things are, and in fact it has to be, and I think

Michael Levin (22:27.920)

it's a real shame, but I see this all the time. When you have a collective like this, whether it

Michael Levin (22:32.800)

be a group of robots or a collection of cells or neurons or whatever, as soon as we gain some

Michael Levin (22:40.880)

insight into how it works, meaning that, oh, I see, in order to take this action, here's the

Michael Levin (22:45.360)

information that got processed via this chemical mechanism or whatever. Immediately people say,

Michael Levin (22:50.320)

oh, well then that's not real cognition. That's just physics. I think this is fundamentally

Michael Levin (22:54.880)

flawed because if you zoom into anything, what are you going to see? Of course you're just going to

Michael Levin (22:58.720)

see physics. What else could be underneath, right? It's not going to be fairy dust. It's going to be

Michael Levin (23:01.680)

physics and chemistry, but that doesn't take away from the magic of the fact that there are certain

Michael Levin (23:05.920)

ways to arrange that physics and chemistry and in particular the bioelectricity, which I like a lot,

Michael Levin (23:11.440)

to give you an emergent collective with goals and preferences and memories and anticipations

Michael Levin (23:18.640)

that do not belong to any of the subunits. So I think what we're getting into here,

Lex Fridman (23:22.160)

and we can talk about how this happens during embryogenesis and so on, what we're getting into

Michael Levin (23:26.640)

is the origin of a self with a capital S. So we ourselves, there are many other kinds of

Michael Levin (23:33.360)

selves, and we can tell some really interesting stories about where selves come from and how they

Michael Levin (23:37.120)

become unified. Yeah, is this the first, or at least humans tend to think that this is the

Michael Levin (23:42.880)

level of which the self with a capital S is first born, and we really don't want to see

Michael Levin (23:49.440)

human civilization or Earth itself as one living organism. Yeah, that's very uncomfortable to us.

Michael Levin (23:54.720)

It is, yeah. But is, yeah, where's the self born? We have to grow up past that. So what I like to do

Michael Levin (24:01.200)

is, I'll tell you two quick stories about that. I like to roll backwards. So as opposed to, so if

Michael Levin (24:06.560)

you start and you say, okay, here's a paramecium, and you see it, you know, it's a single cell

Michael Levin (24:10.560)

organism, you see it doing various things, and people will say, okay, I'm sure there's some

Michael Levin (24:14.320)

chemical story to be told about how it's doing it, so that's not a paramecium.

Lex Fridman (24:18.160)

So that's not true cognition, right? And people will argue about that. I like to work it backwards.

Michael Levin (24:23.360)

I say, let's agree that you and I, as we sit here, are examples of true cognition, if anything,

Michael Levin (24:28.880)

as if there's anything that's true cognition, we are examples of it. Now let's just roll back

Michael Levin (24:32.960)

slowly, right? So you roll back to the time when you were a small child and used to doing whatever,

Lex Fridman (24:36.800)

and then just sort of day by day, you roll back, and eventually you become more or less that

Michael Levin (24:41.280)

paramecium, and then you sort of even below that, right, as an unfertilized OSI. So

Michael Levin (24:46.560)

it's, no one has, to my knowledge, no one has come up with any convincing discrete step at which

Michael Levin (24:53.840)

my cognitive powers disappear, right? It just doesn't, the biology doesn't offer any specific

Michael Levin (24:59.040)

step. It's incredibly smooth and slow and continuous. And so I think this idea that it just

Michael Levin (25:04.000)

sort of magically shows up at one point, and then, you know, humans have true selves that don't exist

Michael Levin (25:10.080)

elsewhere, I think it runs against everything we know about evolution, everything we know about

Michael Levin (25:13.840)

developmental biology, these are all slow continua. And the other really important story I

Michael Levin (25:18.400)

want to tell is where embryos come from. So think about this for a second. Amniote embryos, so this

Michael Levin (25:23.280)

is humans, birds, and so on, mammals and birds and so on. Imagine a flat disk of cells, so there's

Michael Levin (25:29.200)

maybe 50,000 cells. And in that, so when you get an egg from a fertilized, let's say you buy a

Michael Levin (25:35.120)

fertilized egg from a farm, right? That egg will have about 50,000 cells in a flat disk, it looks

Michael Levin (25:42.560)

like a little tiny little frisbee. And in that flat disk, what'll happen is there'll be one set

Michael Levin (25:50.080)

of cells will become special, and it will tell all the other cells, I'm going to be the head,

Michael Levin (25:56.000)

you guys don't be the head. And so it'll amplify symmetry breaking amplification, you get one

Michael Levin (26:00.160)

embryo, there's some neural tissue and some other stuff forms. Now, you say, okay, I had one egg

Lex Fridman (26:06.320)

and one embryo, and there you go, what else could it be? Well, the reality is, and I used to, I did

Michael Levin (26:10.880)

all of this as a grad student, if you take a little needle, and you make a scratch in that

Michael Levin (26:16.400)

blastoderm in that disk, such that the cells can't talk to each other for a while, it heals up, but

Michael Levin (26:20.720)

for a while, they can't talk to each other. What will happen is that both regions will decide that

Michael Levin (26:26.240)

they can be the embryo, and there will be two of them. And then when they heal up, they become

Michael Levin (26:29.120)

conjoint twins, and you can make two, you can make three, you can make lots. So the question of how

Michael Levin (26:33.920)

many cells are in there cannot be answered until it's actually played all the way through. It isn't

Michael Levin (26:40.720)

necessarily that there's just one, there can be many. So what you have is you have this medium,

Michael Levin (26:44.320)

this, this undifferentiated, I'm sure there's a there's a psychological version of this somewhere

Michael Levin (26:49.280)

that I don't know the proper terminology. But you have this, you have this list, like the ocean of

Michael Levin (26:53.280)

potentiality, you have these 1000s of cells, and some number of individuals are going to be formed

Michael Levin (26:58.960)

out of it, usually one, sometimes zero, sometimes several. And they form out of these cells,

Michael Levin (27:05.040)

because a region of these cells organizes into a collective that will have goals, goals that

Michael Levin (27:10.880)

individual cells don't have, for example, make a limb, make an eye, how many eyes? Well, exactly

Michael Levin (27:15.680)

two. So individual cells don't know what an eye is, they don't know how many eyes you're supposed

Michael Levin (27:19.120)

to have, but the collective does. The collective has goals and memories and anticipations that the

Michael Levin (27:23.360)

individual cells don't. And that that the establishment of that boundary with its own

Michael Levin (27:27.440)

ability to maintain to to pursue certain goals. That's the origin of selfhood.

Lex Fridman (27:33.920)

But I, is that goal in there somewhere? Were they always destined? Like, are they discovering

Michael Levin (27:42.800)

that goal? Like, where the hell did evolution discover this when you went from the prokaryotes

Michael Levin (27:49.360)

to eukaryotic cells? And then they started making groups. And when you make a certain group,

Michael Levin (27:55.600)

you make a, you make it sound, and it's such a tricky thing to try to understand, you make it

Michael Levin (28:03.600)

sound like this cells didn't get together and came up with a goal. But the very act of them

Michael Levin (28:09.680)

getting together revealed the goal that was always there. There was always that potential

Michael Levin (28:16.880)

for that goal. So the first thing to say is that there are way more questions here than

Michael Levin (28:20.880)

certainties. Okay, so everything I'm telling you is cutting edge developing, you know, stuff. So

Michael Levin (28:25.680)

it's not as if any of us know the answer to this. But, but here's, here's, here's my opinion on

Michael Levin (28:29.520)

this. I think what evolution, I don't think that evolution produces solutions to specific problems,

Michael Levin (28:36.000)

in other words, specific environments, like here's a frog that can live well in a froggy

Michael Levin (28:39.680)

environment. I think what evolution produces is problem solving machines that that will that will

Michael Levin (28:46.000)

solve problems in different spaces. So not just three dimensional spaces, but in a way,

Michael Levin (28:50.320)

three dimensional space. This goes back to what we were talking about before we the brain is a

Michael Levin (28:55.120)

evolutionarily a late development. It's a system that is able to net to pursue goals in three

Michael Levin (29:01.360)

dimensional space by giving commands to muscles, where did that system come from that system

Michael Levin (29:05.040)

evolved from a much more ancient, evolutionarily much more ancient system, where collections of

Michael Levin (29:10.000)

cells gave instructions to for cell behaviors, meaning cells move to divide to die to change into

Michael Levin (29:18.320)

cells to navigate more for space, the space of anatomies, the space of all possible anatomies.

Lex Fridman (29:23.440)

And before that, cells were navigating transcriptional space, which is a space of all

Michael Levin (29:27.520)

possible gene expressions. And before that metabolic space. So what evolution has done,

Michael Levin (29:31.840)

I think, is is is is produced hardware that is very good at navigating different spaces using a

Michael Levin (29:38.720)

bag of tricks, right, which which I'm sure many of them we can steal for autonomous vehicles and

Michael Levin (29:42.560)

robotics and various things. And what happens is that they navigate these spaces without a whole

Michael Levin (29:47.840)

lot of commitment to what the space is. In fact, they don't know what the space is, right? We are

Michael Levin (29:51.520)

all brains in a vat, so to speak. Every cell does not know, right? Every cell is some other name,

Michael Levin (29:57.280)

some other cells external environment, right? So where does that with that border between you,

Michael Levin (2:00:03.280)

wide open problem. Our solution to the problem of other minds, it's very simplistic. We give each

Michael Levin (2:00:11.360)

other credit for having minds just because we're sort of on an anatomical level, we're pretty

Michael Levin (2:00:15.840)

similar, and so it's good enough. But how far is that going to go? So I think that's really primitive.

Lex Fridman (2:00:21.360)

So yeah, I think it's a major unsolved problem. It's a really challenging direction of thought

Michael Levin (2:00:28.960)

to the human race that you talked about, like embodied minds. If you start to think that other

Michael Levin (2:00:36.640)

things other than humans have minds, that's really challenging. Because all men are created equal

Michael Levin (2:00:43.360)

starts being like, all right, well, we should probably treat not just cows with respect,

Lex Fridman (2:00:52.960)

but like plants, and not just plants, but some kind of organized conglomerates of cells

Michael Levin (2:01:02.400)

in a petri dish. In fact, some of the work we're doing, like you're doing and the whole community

Michael Levin (2:01:08.960)

of science is doing with biology, people might be like, we were really mean to viruses.

Michael Levin (2:01:13.760)

Yeah. I mean, yeah, the thing is, you're right. And I certainly get phone calls about people

Michael Levin (2:01:20.320)

complaining about frog skin and so on. But I think we have to separate the sort of deep

Michael Levin (2:01:26.560)

philosophical aspects versus what actually happens. So what actually happens on Earth

Michael Levin (2:01:30.560)

is that people with exactly the same anatomical structure kill each other on a daily basis.

Lex Fridman (2:01:37.280)

So I think it's clear that simply knowing that something else is equally or maybe more

Michael Levin (2:01:44.880)

cognitive or conscious than you are is not a guarantee of kind behavior, that much we know of.

Lex Fridman (2:01:51.120)

And so then we look at a commercial farming of mammals and various other things. And so I think

Michael Levin (2:01:56.880)

on a practical basis, long before we get to worrying about things like frog skin,

Michael Levin (2:02:03.120)

we have to ask ourselves, why are we, what can we do about the way that we've been behaving

Michael Levin (2:02:08.400)

towards creatures, which we know for a fact, because of our similarities are basically just

Michael Levin (2:02:13.280)

like us. That's kind of a whole other social thing. But fundamentally, of course, you're

Michael Levin (2:02:18.880)

absolutely right in that we are also, think about this, we are on this planet in some way,

Michael Levin (2:02:24.720)

incredibly lucky. It's just dumb luck that we really only have one dumb animal.

Michael Levin (2:02:31.360)

We only have one dominant species. It didn't have to work out that way. So you could easily

Michael Levin (2:02:37.200)

imagine that there could be a planet somewhere with more than one equally or maybe near equally

Michael Levin (2:02:43.360)

intelligent species. But they may not look anything like each other. So there may be

Michael Levin (2:02:49.200)

multiple ecosystems where there are things of similar to human like intelligence. And then

Michael Levin (2:02:54.960)

you'd have all kinds of issues about how do you relate to them when they're physically

Michael Levin (2:02:59.840)

like you at all. But yet in terms of behavior and culture and whatever, it's pretty obvious

Michael Levin (2:03:04.960)

that they've got as much on the ball as you have. Or maybe imagine that there was another

Michael Levin (2:03:10.400)

group of beings that was on average 40 IQ points lower. We're pretty lucky in many ways. We don't

Michael Levin (2:03:18.320)

really have, even though we still act badly in many ways. But the fact is, all humans are more

Michael Levin (2:03:24.400)

or less in that same range, but didn't have to work out that way. Well, but I think that's part

Michael Levin (2:03:30.160)

of the way life works on Earth, maybe human civilization works, is it seems like we want

Michael Levin (2:03:38.800)

ourselves to be quite similar. And then within that, you know, where everybody's about the same

Michael Levin (2:03:45.280)

relatively IQ, intelligence, problem solving capabilities, even physical characteristics.

Lex Fridman (2:03:49.840)

But then we'll find some aspect of that that's different. And that seems to be like,

Michael Levin (2:03:58.560)

I mean, it's really dark to say, but that seems to be the, not even a bug, but like a feature

Michael Levin (2:04:07.440)

of the early development of human civilization. You pick the other, your tribe versus the other

Michael Levin (2:04:14.960)

tribe and you war, it's a kind of evolution in the space of memes, a space of ideas, I think,

Lex Fridman (2:04:22.640)

and you war with each other. So we're very good at finding the other, even when the characteristics

Michael Levin (2:04:28.240)

are really the same. And that's, I don't know what that, I mean, I'm sure so many of these things

Michael Levin (2:04:35.040)

echo in the biological world in some way. Yeah. There's a fun experiment that I did. My son

Michael Levin (2:04:41.600)

actually came up with this and we did a biology unit together. He's a homeschool. And so we did

Lex Fridman (2:04:46.880)

this a couple of years ago. We did this thing where, imagine you get this slime mold, right?

Michael Levin (2:04:50.800)

Fisarum polycephalum, and it grows on a Petri dish of agar and it sort of spreads out and it's a

Michael Levin (2:04:57.600)

single cell protist, but it's like this giant thing. And so you put down a piece of oat and

Michael Levin (2:05:02.160)

it wants to go get the oat and it sort of grows towards the oat. So what you do is you take a

Michael Levin (2:05:05.760)

razor blade and you just separate the piece of the whole culture that's growing towards the

Michael Levin (2:05:10.160)

oat. You just kind of separate it. And so now think about the interesting decision making

Michael Levin (2:05:15.040)

calculus for that little piece. I can go get the oat and therefore I won't have to share those

Michael Levin (2:05:20.960)

nutrients with this giant mass over there. So the nutrients per unit volume is going to be amazing.

Michael Levin (2:05:25.280)

I should go eat the oat. But if I first rejoin, because Fisarum, once you cut it, has the ability

Michael Levin (2:05:30.560)

to join back up. If I first rejoin, then that whole calculus becomes impossible because there

Michael Levin (2:05:36.240)

is no more me anymore. There's just we and then we will go eat this thing, right? So this

Michael Levin (2:05:40.960)

interesting, you can imagine a kind of game theory where the number of agents isn't fixed

Lex Fridman (2:05:46.320)

and that it's not just cooperate or defect, but it's actually merge and whatever, right?

Lex Fridman (2:05:50.320)

Yeah. So that computation, how does it do that decision making?

Michael Levin (2:05:54.400)

Yeah. So it's really interesting. And so empirically, what we found is that it tends

Michael Levin (2:06:00.240)

to merge first. It tends to merge first and then the whole thing goes. But it's really interesting

Michael Levin (2:06:04.720)

that that calculus, I mean, I'm not an expert in the economic game theory and all that,

Lex Fridman (2:06:09.600)

but maybe there's some sort of hyperbolic discounting or something. But maybe this idea

Michael Levin (2:06:14.880)

that the actions you take not only change your payoff, but they change who or what you are,

Lex Fridman (2:06:22.720)

and that you could take an action after which you don't exist anymore, or you are radically

Michael Levin (2:06:27.440)

changed, or you are merged with somebody else. As far as I know, that's a whole different

Michael Levin (2:06:33.280)

thing. As far as I know, we're still missing a formalism for even knowing how to model

Michael Levin (2:06:38.720)

any of that.

Lex Fridman (2:06:39.720)

Do you see evolution, by the way, as a process that applies here on Earth? Where did evolution

Lex Fridman (2:06:45.200)

come from?

Lex Fridman (2:06:46.200)

Yeah.

Lex Fridman (2:06:47.200)

So this thing from the very origin of life that took us to today, what the heck is that?

Michael Levin (2:06:54.560)

I think evolution is inevitable in the sense that if you combine, and basically, I think

Michael Levin (2:07:00.960)

one of the most useful things that was done in early computing, I guess in the 60s, it

Michael Levin (2:07:05.600)

started with evolutionary computation and just showing how simple it is that if you have

Michael Levin (2:07:13.320)

imperfect heredity and competition together, those two things, or three things, so heredity,

Michael Levin (2:07:19.280)

imperfect heredity, and competition, or selection, those three things, and that's it. Now you're

Michael Levin (2:07:25.000)

off to the races. And so that can be, it's not just on Earth because it can be done in

Michael Levin (2:07:29.640)

the computer, it can be done in chemical systems, it can be done in, you know, Lee Smolin says

Michael Levin (2:07:33.480)

it works on cosmic scales. So I think that that kind of thing is incredibly pervasive

Lex Fridman (2:07:42.400)

and general. It's a general feature of life. It's interesting to think about, you know,

Michael Levin (2:07:49.200)

the standard thought about this is that it's blind, right? Meaning that the intelligence

Michael Levin (2:07:55.280)

of the process is zero, it's stumbling around. And I think that back in the day, when the

Michael Levin (2:08:01.520)

options were it's dumb like machines, or it's smart like humans, then of course, the scientists

Michael Levin (2:08:07.560)

went in this direction, because nobody wanted creationism. They said, okay, it's got to

Michael Levin (2:08:10.680)

be like completely blind. I'm not actually sure, right? Because I think that everything

Michael Levin (2:08:15.920)

is a continuum. And I think that it doesn't have to be smart with foresight like us, but

Michael Levin (2:08:20.880)

it doesn't have to be completely blind either. I think there may be aspects of it. And in

Michael Levin (2:08:25.720)

particular, this kind of multi scale competency might give it a little bit of look ahead maybe

Michael Levin (2:08:30.760)

or a little bit of problem solving sort of baked in. But that's going to be completely

Michael Levin (2:08:36.700)

different in different systems. I do think it's general. I don't think it's just on Earth.

Michael Levin (2:08:41.640)

I think it's a very fundamental thing.

Lex Fridman (2:08:44.040)

And it does seem to have a kind of direction that it's taking us that's somehow perhaps

Michael Levin (2:08:50.120)

is defined by the environment itself. It feels like we're headed towards something. Like,

Michael Levin (2:08:57.360)

we're playing out a script that was just like a single cell defines the entire organism.

Michael Levin (2:09:03.060)

It feels like from the origin of Earth itself, it's playing out a kind of script. You can't

Lex Fridman (2:09:10.480)

really go any other way.

Michael Levin (2:09:12.480)

I mean, so this is very controversial, and I don't know the answer. But people have argued

Michael Levin (2:09:17.280)

that this is called, you know, sort of rewinding the tape of life, right? And some people have

Michael Levin (2:09:22.720)

argued, I think, I think Conway Morris, maybe has argued that it is that there's a deep

Michael Levin (2:09:28.440)

attractor, for example, to human to the human kind of structure and that and that if you

Michael Levin (2:09:34.640)

were to rewind it again, you'd basically get more or less the same thing. And then other

Michael Levin (2:09:37.560)

people have argued that, no, it's incredibly sensitive to frozen accidents. And then once

Michael Levin (2:09:41.920)

certain stochastic decisions are made downstream, everything is going to be different. I don't

Michael Levin (2:09:46.200)

know. I don't know. You know, we're very bad at predicting attractors in the space of complex

Michael Levin (2:09:52.760)

systems, generally speaking, right? We don't know. So maybe, so maybe evolution on Earth

Michael Levin (2:09:56.880)

has these deep attractors that no matter what has happened, it pretty much would likely

Michael Levin (2:10:01.360)

to end up there or maybe not. I don't know.

Michael Levin (2:10:03.640)

What's a really difficult idea to imagine that if you ran Earth a million times, 500,000

Michael Levin (2:10:10.880)

times you would get Hitler? Like, yeah, we don't like to think like that. We think like,

Michael Levin (2:10:17.160)

because at least maybe in America, you'd like to think that individual decisions can change

Michael Levin (2:10:23.480)

the world. And if individual decisions could change the world, then surely any perturbation

Michael Levin (2:10:30.760)

could result in a totally different trajectory. But maybe there's a, in this competency hierarchy,

Michael Levin (2:10:38.560)

it's a self correcting system. There's just ultimately, there's a bunch of chaos that

Michael Levin (2:10:43.320)

ultimately is leading towards something like a super intelligent, artificial intelligence

Michael Levin (2:10:47.200)

system that answers 42. I mean, there might be a kind of imperative for life that it's

Michael Levin (2:10:56.800)

headed to. And we're too focused on our day to day life of getting coffee and snacks and

Michael Levin (2:11:04.360)

having sex and getting a promotion at work, not to see the big imperative of life on Earth

Lex Fridman (2:11:12.840)

that is headed towards something.

Michael Levin (2:11:14.560)

Yeah, maybe, maybe. It's difficult. I think one of the things that's important about Chimerica

Michael Levin (2:11:24.640)

bioengineering technologies, all of those things are that we have to start developing

Michael Levin (2:11:29.520)

a better science of predicting the cognitive goals of composite systems. So we're just

Michael Levin (2:11:35.240)

not very good at it, right? We don't know if I create a composite system, and this could

Michael Levin (2:11:41.320)

be Internet of Things or swarm robotics or a cellular swarm or whatever. What is the

Michael Levin (2:11:48.240)

emergent intelligence of this thing? First of all, what level is it going to be at? And

Michael Levin (2:11:51.640)

if it has goal directed capacity, what are the goals going to be? Like, we are just not

Michael Levin (2:11:56.240)

very good at predicting that yet. And I think that it's an existential level need for us

Michael Levin (2:12:06.420)

to be able to because we're building these things all the time, right? We're building

Michael Levin (2:12:10.520)

both physical structures like swarm robotics, and we're building social financial structures

Lex Fridman (2:12:16.060)

and so on, with very little ability to predict what sort of autonomous goals that system

Michael Levin (2:12:21.640)

is going to have, of which we are now cogs. And so learning to predict and control those

Michael Levin (2:12:26.780)

things is going to be critical. So in fact, if you're right and there is some kind of

Michael Levin (2:12:31.400)

attractor to evolution, it would be nice to know what that is and then to make a rational

Michael Levin (2:12:36.680)

decision of whether we're going to go along or we're going to pop out of it or try to

Michael Levin (2:12:39.800)

pop out of it because there's no guarantee. I mean, that's the other kind of important

Michael Levin (2:12:44.120)

thing. A lot of people, I get a lot of complaints from people who email me and say, you know,

Lex Fridman (2:12:49.760)

what you're doing, it isn't natural. And I'll say, look, natural, that'd be nice if somebody

Michael Levin (2:12:56.240)

was making sure that natural was matched up to our values, but no one's doing that. Evolution

Michael Levin (2:13:02.520)

optimizes for biomass. That's it. Nobody's optimizing. It's not optimizing for your happiness.

Michael Levin (2:13:07.160)

I don't think necessarily it's optimizing for intelligence or fairness or any of that

Lex Fridman (2:13:11.600)

stuff.

Michael Levin (2:13:12.600)

I'm going to find that person that emailed you, beat them up, take their place, steal

Lex Fridman (2:13:18.720)

everything they own and say, no, this is natural.

Michael Levin (2:13:22.040)

This is natural. Yeah, exactly. Because it comes from an old worldview where you could

Michael Levin (2:13:28.200)

assume that whatever is natural, that that's probably for the best. And I think we're long

Michael Levin (2:13:32.040)

out of that garden of Eden kind of view. So I think we can do better. I think we, and

Lex Fridman (2:13:37.000)

we have to, right? Natural just isn't great for a lot of life forms.

Lex Fridman (2:13:42.020)

What are some cool synthetic organisms that you think about, you dream about? When you

Lex Fridman (2:13:46.520)

think about embodied mind, what do you imagine? What do you hope to build?

Michael Levin (2:13:51.400)

Yeah, on a practical level, what I really hope to do is to gain enough of an understanding

Michael Levin (2:13:57.700)

of the embodied intelligence of the organs and tissues such that we can achieve a radically

Michael Levin (2:14:04.680)

different regenerative medicine so that we can say, basically, and I think about it as,

Michael Levin (2:14:11.080)

you know, in terms of like, okay, can you, what's the goal kind of end game for this

Michael Levin (2:14:18.200)

whole thing? To me, the end game is something that you would call an anatomical compiler.

Lex Fridman (2:14:22.480)

So the idea is you would sit down in front of the computer and you would draw the body

Michael Levin (2:14:27.440)

or the organ that you wanted. Not molecular details, but like, yeah, this is what I want.

Michael Levin (2:14:31.880)

I want a six legged, you know, frog with a propeller on top, or I want a heart that looks

Michael Levin (2:14:36.200)

like this, or I want a leg that looks like this. And what it would do if we knew what

Michael Levin (2:14:39.800)

we were doing is put out, convert that anatomical description into a set of stimuli that would

Michael Levin (2:14:47.000)

have to be given to cells to convince them to build exactly that thing, right? I probably

Michael Levin (2:14:51.320)

won't live to see it, but I think it's achievable. And I think with that, if we can have that,

Michael Levin (2:14:56.840)

then that is basically the solution to all of medicine except for infectious disease.

Lex Fridman (2:15:03.140)

So birth defects, right? Traumatic injury, cancer, aging, degenerative disease. If we

Michael Levin (2:15:07.620)

knew how to tell cells what to build, all of those things go away. So those things go

Michael Levin (2:15:11.440)

away. And the positive feedback spiral of economic costs, where all of the advances

Michael Levin (2:15:18.520)

are increasingly more heroic and expensive interventions of a sinking ship when you're

Michael Levin (2:15:22.880)

like 90 and so on, right? All of that goes away because basically, instead of trying

Michael Levin (2:15:26.980)

to fix you up as you degrade, you progressively regenerate, you apply the regenerative medicine

Michael Levin (2:15:33.800)

early before things degrade. So I think that that'll have massive economic impacts over

Lex Fridman (2:15:38.920)

what we're trying to do now, which is not at all sustainable. And that's what I hope.

Michael Levin (2:15:43.800)

I hope that we get it. So to me, yes, the xenobots will be doing useful things, cleaning

Michael Levin (2:15:50.080)

up the environment, cleaning out your joints and all that kind of stuff. But more important

Michael Levin (2:15:55.480)

than that, I think we can use these synthetic systems to try to develop a science of detecting

Lex Fridman (2:16:04.920)

and manipulating the goals of collective intelligences of cells specifically for regenerative medicine.

Lex Fridman (2:16:10.840)

And then sort of beyond that, if we think further beyond that, what I hope is that kind

Michael Levin (2:16:15.840)

of like what you said, all of this drives a reconsideration of how we formulate ethical

Michael Levin (2:16:22.480)

norms because this old school, so in the olden days, what you could do is if you were confronted

Michael Levin (2:16:29.080)

with something, you could tap on it, right? And if you heard a metallic clanging sound,

Michael Levin (2:16:33.200)

you'd say, ah, fine, right? So you could conclude it was made in a factory. I can take it apart.

Michael Levin (2:16:37.160)

I can do whatever, right? If you did that and you got sort of a squishy kind of warm

Michael Levin (2:16:40.960)

sensation, you'd say, ah, I need to be more or less nice to it and whatever. That's not

Michael Levin (2:16:46.080)

going to be feasible. It was never really feasible, but it was good enough because we

Michael Levin (2:16:49.360)

didn't have any, we didn't know any better. That needs to go. And I think that by breaking

Michael Levin (2:16:55.940)

down those artificial barriers, someday we can try to build a system of ethical norms

Michael Levin (2:17:03.200)

that does not rely on these completely contingent facts of our earthly history, but on something

Michael Levin (2:17:08.740)

much, much deeper that really takes agency and the capacity to suffer and all that takes

Lex Fridman (2:17:15.520)

that seriously.

Michael Levin (2:17:16.520)

The capacity to suffer and the deep questions I would ask of a system is can I eat it and

Michael Levin (2:17:21.560)

can I have sex with it? Which is the two fundamental tests of, again, the human condition. So I

Michael Levin (2:17:30.560)

can basically do what Dali does that's in the physical space. So print out like a 3D

Lex Fridman (2:17:39.480)

print Pepe the Frog with a propeller head, propeller hat is the dream.

Michael Levin (2:17:46.320)

Well yes and no. I mean, I want to get away from the 3D printing thing because that will

Michael Levin (2:17:50.840)

be available for some things much earlier. I mean, we can already do bladders and ears

Lex Fridman (2:17:55.560)

and things like that because it's micro level control, right? When you 3D print, you are

Michael Levin (2:17:59.920)

in charge of where every cell goes. And for some things that, you know, for, for like

Michael Levin (2:18:02.960)

this thing, they had that I think 20 years ago or maybe earlier than that, you could

Lex Fridman (2:18:06.040)

do that.

Lex Fridman (2:18:07.040)

So yeah, I would like to emphasize the Dali part where you provide a few words and it

Michael Levin (2:18:11.480)

generates a painting. So here you say, I want a frog with these features and then it would

Michael Levin (2:18:19.920)

go direct a complex biological system to construct something like that.

Michael Levin (2:18:25.000)

Yeah. The main magic would be, I mean, I think from, from looking at Dali and so on, it looks

Michael Levin (2:18:30.040)

like the first part is kind of solved now where you go from, from the words to the image,

Michael Levin (2:18:34.360)

like that seems more or less solved. The next step is really hard. This is what keeps things

Michael Levin (2:18:39.920)

like CRISPR and genomic editing and so on. That's what limits all the impacts for regenerative

Michael Levin (2:18:46.880)

medicine because going back to, okay, this is the knee joint that I want, or this is

Michael Levin (2:18:51.320)

the eye that I want. Now, what genes do I edit to make that happen, right? Going back

Michael Levin (2:18:56.000)

in that direction is really hard. So instead of that, it's going to be, okay, I understand

Lex Fridman (2:18:59.840)

how to motivate cells to build particular structures. Can I rewrite the memory of what

Michael Levin (2:19:03.680)

they think they're supposed to be building such that then I can, you know, take my hands

Michael Levin (2:19:07.480)

off the wheel and let them, let them do their thing.

Lex Fridman (2:19:09.960)

So some of that is experiment, but some of that may be AI can help too. Just like with

Michael Levin (2:19:13.960)

protein folding, this is exactly the problem that protein folding in the most simple medium

Michael Levin (2:19:23.400)

tried and has solved with alpha fold, which is how does the sequence of letters result

Michael Levin (2:19:31.800)

in this three dimensional shape? And you have to, I guess it didn't solve it because you

Michael Levin (2:19:37.160)

have to, if you say, I want this shape, how do I then have a sequence of letters? Yeah.

Michael Levin (2:19:43.760)

The reverse engineering step is really tricky.

Michael Levin (2:19:45.920)

It is. I think, I think we're, we're, and we're doing some of this now is, is to use

Michael Levin (2:19:51.680)

AI to try and build actionable models of the intelligence of the cellular collectives.

Lex Fridman (2:19:57.800)

So try to help us and help us gain models that, that, that, and, and we've had some

Michael Levin (2:20:02.400)

success in this. So we, we did something like this for, for, you know, for repairing birth

Michael Levin (2:20:08.480)

defects of the brain in frog. We've done some of this for normalizing melanoma where you

Michael Levin (2:20:14.240)

can really start to use AI to make models of how would I impact this thing if I wanted

Michael Levin (2:20:20.140)

to given all the complexities, right. And, and, and given all the, the, the, the controls

Michael Levin (2:20:25.600)

that it, that it knows how to do.

Lex Fridman (2:20:27.520)

So when you say regenerative medicine, so we talked about creating biological organisms,

Lex Fridman (2:20:34.060)

but if you regrow a hand, that information is already there, right? The biological system

Lex Fridman (2:20:41.440)

has that information. So how does regenerative medicine work today? How do you hope it works?

Lex Fridman (2:20:48.080)

What's the hope there?

Lex Fridman (2:20:49.080)

Yeah.

Lex Fridman (2:20:50.080)

Yeah. How do you make it happen?

Michael Levin (2:20:52.480)

Well today there's a set of popular approaches. So, so one is 3d printing. So the idea is

Michael Levin (2:20:57.480)

I'm going to make a scaffold of the thing that I want. I'm going to seed it with cells

Lex Fridman (2:21:00.600)

and then, and then there it is, right? So kind of direct, and then that works for certain

Michael Levin (2:21:03.760)

things. You can make a bladder that way or an ear, something like that. The other, the

Michael Levin (2:21:08.920)

other ideas is some sort of stem cell transplant. These are the ideas. If we, if we put in stem

Michael Levin (2:21:14.300)

cells with appropriate factors, we can get them to generate certain kinds of neurons

Michael Levin (2:21:17.920)

for certain diseases and so on. All of those things are good for relatively simple structures,

Lex Fridman (2:21:24.760)

but when you want an eye or a hand or something else, I think in this maybe an unpopular opinion,

Michael Levin (2:21:30.660)

I think the only hope we have in any reasonable kind of timeframe is to understand how the

Michael Levin (2:21:36.560)

thing was motivated to get made in the first place. So what is it that, that made those

Michael Levin (2:21:41.320)

cells in the, in the beginning, create a particular arm with a particular set of sizes and shapes

Lex Fridman (2:21:48.400)

and number of fingers and all that. And why is it that a salamander can keep losing theirs

Lex Fridman (2:21:51.760)

and keep regrowing theirs and a planarian can do the same even more? So to me, uh, kind

Michael Levin (2:21:57.640)

of ultimate regenerate medicine was when you can tell the cells to build whatever it is

Michael Levin (2:22:02.840)

you need them to build. Right. And so the, so that we can all be like planaria basically,

Lex Fridman (2:22:07.400)

do you have to start at the very beginning or can you, um, do a shortcut? Cause we're

Michael Levin (2:22:13.680)

going to hand, you already got the whole organism. Yeah. So here's what we've done, right? So,

Michael Levin (2:22:19.560)

we've, we've more or less solved that in frogs. So frogs, unlike salamanders do not regenerate

Michael Levin (2:22:24.160)

their legs as adults. And so, so, uh, we've shown that with a very, um, uh, kind of simple

Michael Levin (2:22:31.800)

intervention. So what we do is there's two things you need to, uh, you need to have a

Michael Levin (2:22:36.100)

signal that tells the cells what to do, and then you need some way of delivering it. And

Lex Fridman (2:22:39.520)

so this is work together with, um, with David Kaplan and I should do a, um, a disclosure

Michael Levin (2:22:44.080)

here. We have a company called morphosuticals and spin off where we're trying to, uh, to

Michael Levin (2:22:48.200)

address, uh, uh, regenerate, you know, limb regeneration. So we've solved it in the frog

Lex Fridman (2:22:52.320)

and we're now in trials and mice. So now we're going to, we're in mammals now. It's, I can't

Michael Levin (2:22:56.440)

say anything about how it's going, but the frog thing is solved. So what you do is, um,

Michael Levin (2:22:59.720)

after you have a little frog, Lou Skywalker with every growing hand. Yeah, basically,

Michael Levin (2:23:04.480)

basically. Yeah. Yeah. So what you do is we did, we did with legs instead of forearms.

Lex Fridman (2:23:07.840)

And what you do is, um, after amputation, normally they, they don't regenerate. You

Michael Levin (2:23:11.200)

put on a wearable bioreactor. So it's this thing that, um, that goes on and, uh, Dave

Michael Levin (2:23:15.620)

Kaplan does lab makes these things and inside it's a, it's a very controlled environment.

Michael Levin (2:23:21.300)

It is a silk gel that carries, uh, some drugs, for example, ion channel drugs. And what you're

Michael Levin (2:23:26.360)

doing is you're saying to the cells, you should regrow what normally goes here. So, uh, that

Michael Levin (2:23:33.720)

whole thing is on for 24 hours and you take it off and you don't touch the leg. Again,

Michael Levin (2:23:37.760)

this is really important because what we're not looking for is a set of micromanagement,

Michael Levin (2:23:41.280)

uh, you know, printing or controlling the cells we want to trigger. We want to, we want

Michael Levin (2:23:45.600)

to interact with it early on and then not touch it again because, because we don't know

Lex Fridman (2:23:49.260)

how to make a frog leg, but the frog knows how to make a frog leg. So 24 hours, 18 months

Michael Levin (2:23:54.820)

of leg growth after that, without us touching it again. And after 18 months, you get a pretty

Michael Levin (2:23:58.480)

good leg that kind of shows this proof of concept that early on when the cells right

Michael Levin (2:24:02.720)

after injury, when they're first making a decision about what they're going to do, you

Michael Levin (2:24:05.560)

can, you can impact them. And once they've decided to make a leg, they don't need you

Michael Levin (2:24:09.440)

after that. They can do their own thing. So that's an approach that we're now taking.

Lex Fridman (2:24:14.040)

What about cancer suppression? That's something you mentioned earlier. How can all of these

Lex Fridman (2:24:18.480)

ideas help with cancer suppression?

Lex Fridman (2:24:20.360)

So let's, let's go back to the beginning and ask what, what, what, what cancer is. So I

Michael Levin (2:24:23.600)

think, um, you know, asking why there's cancer is the wrong question. I think the right question

Michael Levin (2:24:28.520)

is why is there ever anything but cancer? So, so in the normal state, you have a bunch

Michael Levin (2:24:33.420)

of cells that are all cooperating towards a large scale goal. If that process of cooperation

Michael Levin (2:24:38.680)

breaks down and you've got a cell that is isolated from that electrical network that

Michael Levin (2:24:42.780)

lets you remember what the big goal is, you revert back to your unicellular lifestyle

Michael Levin (2:24:47.280)

as far as, now think about that border between self and world, right? Normally when all these

Michael Levin (2:24:51.020)

cells are connected by gap junctions into an electrical network, they are all one self,

Michael Levin (2:24:56.360)

right? That meaning that, um, their goals, they have these large tissue level goals and

Lex Fridman (2:25:01.600)

so on. As soon as a cell is disconnected from that, the self is tiny, right? And so at that

Michael Levin (2:25:06.760)

point, and so, so people, a lot of people model cancer cell cells as being more selfish

Lex Fridman (2:25:11.580)

and all that. They're not more selfish. They're equally selfish. It's just that their self

Michael Levin (2:25:14.280)

is smaller. Normally the self is huge. Now they got tiny little selves. Now what are

Michael Levin (2:25:18.040)

the goals of tiny little selves? Well, proliferate, right? And migrate to wherever life is good.

Lex Fridman (2:25:22.680)

And that's metastasis. That's proliferation and metastasis. So, so one thing we found

Lex Fridman (2:25:26.640)

and people have noticed years ago that when cells convert to cancer, the first thing they

Michael Levin (2:25:31.960)

see is they close the gap junctions. And it's a lot like, I think it's a lot like that experiment

Michael Levin (2:25:36.800)

with the slime mold where until you close that gap junction, you can't even entertain

Michael Levin (2:25:41.440)

the idea of leaving the collective because there is no you at that point, right? Your

Michael Levin (2:25:44.520)

mind melded with this, with this whole other network. But as soon as the gap junction is

Michael Levin (2:25:48.600)

closed, now the boundary between you and now, now the rest of the body is just outside environment

Michael Levin (2:25:53.600)

to you. You're just a, you're just a unicellular organism and the rest of the body's environment.

Michael Levin (2:25:58.520)

So, so we, so we studied this process and we worked out a way to artificially control

Michael Levin (2:26:04.840)

the bioelectric state of these cells to physically force them to remain in that network. And

Lex Fridman (2:26:10.120)

so then, then what that, what that means is that nasty mutations like KRAS and things

Michael Levin (2:26:15.580)

like that, these really tough oncogenic mutations that cause tumors. If you, if you do them

Lex Fridman (2:26:20.920)

and then, but then within artificially control of the bioelectrics, you greatly reduce tumor

Michael Levin (2:26:29.120)

genesis or, or normalize cells that had already begun to convert. You basically, they go back

Michael Levin (2:26:33.840)

to being normal cells. And so this is another, much like with the planaria, this is another

Michael Levin (2:26:38.080)

way in which the bioelectric state kind of dominates what the, what the genetic state

Michael Levin (2:26:43.400)

is. So if you sequence the, you know, if you sequence the nucleic acid, you'll see the

Michael Levin (2:26:47.200)

KRAS mutation, you'll say, ah, well that's going to be a tumor, but there isn't a tumor

Michael Levin (2:26:50.800)

because, because bioelectrically you've kept the cells connected and they're just working

Michael Levin (2:26:54.200)

on making nice skin and kidneys and whatever else. So, so we've started moving that to,

Michael Levin (2:26:59.760)

to, to human glioblastoma cells and we're hoping for, you know, a patient in the future

Lex Fridman (2:27:04.760)

interaction with patients.

Lex Fridman (2:27:07.560)

So is this one of the possible ways in which we may quote cure cancer?

Michael Levin (2:27:12.820)

I think so. Yeah, I think so. I think, I think the actual cure, I mean, there are other technology,

Michael Levin (2:27:17.160)

you know, immune therapy, I think is a great technology. Chemotherapy, I don't think is

Lex Fridman (2:27:21.920)

a good, is a good technology. I think we've got to get, get off of that.

Lex Fridman (2:27:25.680)

So chemotherapy just kills cells.

Michael Levin (2:27:27.720)

Yeah. Well, chemotherapy hopes to kill more of the tumor cells than of your cells. That's

Michael Levin (2:27:32.920)

it. It's a fine balance. The problem is the cells are very similar because they are your

Michael Levin (2:27:36.440)

cells. And so if you don't have a very tight way of distinguishing between them, then the

Michael Levin (2:27:43.480)

toll that chemo takes on the rest of the body is just unbelievable.

Lex Fridman (2:27:46.240)

And immunotherapy tries to get the immune system to do some of the work.

Michael Levin (2:27:49.760)

Exactly. Yeah. I think that's potentially a very good, a very good approach. If, if

Michael Levin (2:27:54.720)

the immune system can be taught to recognize enough of, of the cancer cells, that that's

Michael Levin (2:27:59.520)

a pretty good approach. But I, but I think, but I think our approach is in a way more

Michael Levin (2:28:02.720)

fundamental because if you can, if you can keep the cells harnessed towards organ level

Michael Levin (2:28:08.440)

goals as opposed to individual cell goals, then nobody will be making a tumor or metastasizing

Lex Fridman (2:28:13.900)

and so on.

Lex Fridman (2:28:15.440)

So we've been living through a pandemic. What do you think about viruses in this full beautiful

Lex Fridman (2:28:21.840)

biological context we've been talking about? Are they beautiful to you? Are they terrifying?

Michael Levin (2:28:30.080)

Also maybe let's say, are they, since we've been discriminating this whole conversation,

Lex Fridman (2:28:36.800)

are they living? Are they embodied minds? Embodied minds that are assholes?

Michael Levin (2:28:43.840)

As far as I know, and I haven't been able to find this paper again, but, but somewhere

Michael Levin (2:28:47.200)

I saw in the last couple of months, there was some, there was some papers showing an

Michael Levin (2:28:51.680)

example of a virus that actually had physiology. So there was some, something was going on,

Michael Levin (2:28:55.360)

I think proton flux or something on the virus itself. But, but barring that, generally speaking,

Michael Levin (2:29:01.320)

viruses are very passive. They don't do anything by themselves. And so I don't see any particular

Michael Levin (2:29:06.860)

reason to attribute much of a mind to them. I think, you know, they represent a way to

Michael Levin (2:29:14.100)

hijack other minds for sure, like, like cells and other things.

Lex Fridman (2:29:18.520)

But that's an interesting interplay though. If they're hijacking other minds, you know,

Michael Levin (2:29:24.300)

the way we're, we were talking about living organisms that they can interact with each

Michael Levin (2:29:28.420)

other and have it alter each other's trajectory by having interacted. I mean, that's, that's

Michael Levin (2:29:36.400)

a deep, meaningful connection between a virus and a cell. And I think both are transformed

Lex Fridman (2:29:45.680)

by the experience. And so in that sense, both are living.

Michael Levin (2:29:49.040)

Yeah. Yeah. You know, the whole category, I, this question of what's living and what's

Michael Levin (2:29:56.320)

not living, I really, I'm not sure. And I know there's people that work on this and

Michael Levin (2:30:00.000)

I don't want to piss anybody off, but, but I have not found that particularly useful

Michael Levin (2:30:05.480)

as, as to try and make that a binary kind of a distinction. I think level of cognition

Michael Levin (2:30:11.480)

is very interesting of, but as a, as a continuum, but, but living and nonliving, I, you know,

Michael Levin (2:30:17.080)

I don't, I really know what to do with that. I don't, I don't know what you do next after,

Michael Levin (2:30:20.680)

after making that distinction.

Michael Levin (2:30:21.800)

That's why I make the very binary distinction. Can I have sex with it or not? Can I eat it

Lex Fridman (2:30:27.640)

or not? Those, cause there's, those are actionable, right?

Michael Levin (2:30:30.360)

Yeah. Well, I think that's a critical point that you brought up because how you relate

Lex Fridman (2:30:34.000)

to something is really what this is all about, right? As an engineer, how do I control it?

Lex Fridman (2:30:40.000)

But maybe I shouldn't be controlling it. Maybe I should be, you know, can I have a relationship

Michael Levin (2:30:44.120)

with it? Should I be listening to its advice? Like, like all the way from, you know, I need

Michael Levin (2:30:48.400)

to take it apart all the way to, I better do what it says cause it seems to be pretty

Michael Levin (2:30:52.800)

smart and everything in between, right? That's really what we're asking about.

Michael Levin (2:30:56.480)

Yeah. We need to understand our relationship to it. We're searching for that relationship,

Michael Levin (2:31:01.400)

even in the most trivial senses. You came up with a lot of interesting terms. We've mentioned

Michael Levin (2:31:08.200)

some of them. Agential material. That's a really interesting one. That's a really interesting

Michael Levin (2:31:14.560)

one for the future of computation and artificial intelligence and computer science and all

Michael Levin (2:31:19.600)

of that. There's also, let me go through some of them. If they spark some interesting thought

Michael Levin (2:31:25.940)

for you, there's teleophobia, the unwarranted fear of erring on the side of too much agency

Lex Fridman (2:31:32.640)

when considering a new system.

Michael Levin (2:31:35.000)

Yeah.

Michael Levin (2:31:36.000)

That's the opposite. I mean, being afraid of maybe anthropomorphizing the thing.

Michael Levin (2:31:41.080)

This'll get some people ticked off, I think. But I don't think, I think the whole notion

Michael Levin (2:31:47.120)

of anthropomorphizing is a holdover from a pre scientific age where humans were magic

Lex Fridman (2:31:54.440)

and everything else wasn't magic and you were anthropomorphizing when you dared suggest

Michael Levin (2:32:00.080)

that something else has some features of humans. And I think we need to be way beyond that.

Lex Fridman (2:32:05.760)

And this issue of anthropomorphizing, I think it's a cheap charge. I don't think it holds

Michael Levin (2:32:12.640)

any water at all other than when somebody makes a cognitive claim. I think all cognitive

Michael Levin (2:32:18.240)

claims are engineering claims, really. So when somebody says this thing knows or this

Michael Levin (2:32:22.620)

thing hopes or this thing wants or this thing predicts, all you can say is fabulous. Give

Michael Levin (2:32:27.800)

me the engineering protocol that you've derived using that hypothesis and we will see if this

Michael Levin (2:32:33.420)

thing helps us or not. And then, and then we can, you know, then we can make a rational

Michael Levin (2:32:36.760)

decision.

Michael Levin (2:32:37.760)

I also like anatomical compiler, a future system representing the longterm end game

Michael Levin (2:32:43.400)

of the science of morphogenesis that reminds us how far away from true understanding we

Michael Levin (2:32:49.280)

are. Someday you will be able to sit in front of an anatomical computer, specify the shape

Michael Levin (2:32:54.740)

of the animal or a plant that you want, and it will convert that shape specification to

Michael Levin (2:32:59.480)

a set of stimuli that will have to be given to cells to build exactly that shape. No matter

Lex Fridman (2:33:05.160)

how weird it ends up being, you have total control. Just imagine the possibility for

Michael Levin (2:33:12.560)

memes in the physical space. One of the glorious accomplishments of human civilizations is

Michael Levin (2:33:18.780)

memes in digital space. Now this could create memes in physical space. I am both excited

Lex Fridman (2:33:25.840)

and terrified by that possibility. Cognitive light cone, I think we also talked about the

Michael Levin (2:33:31.800)

outer boundary in space and time of the largest goal a given system can work towards. Is this

Lex Fridman (2:33:39.220)

kind of like shaping the set of options?

Michael Levin (2:33:42.500)

It's a little different than options. It's really focused on... I first came up with

Michael Levin (2:33:49.680)

this back in 2018, I want to say. There was a conference, a Templeton conference where

Michael Levin (2:33:55.320)

they challenged us to come up with frameworks. I think actually it's the diverse intelligence

Lex Fridman (2:34:01.160)

community.

Michael Levin (2:34:02.160)

Summer Institute.

Lex Fridman (2:34:03.160)

Yeah, they had a Summer Institute.

Michael Levin (2:34:04.160)

That's the logos, the bee with some circuits.

Michael Levin (2:34:06.640)

Yeah, it's got different life forms. The whole program is called diverse intelligence. They

Michael Levin (2:34:13.360)

challenged us to come up with a framework that was suitable for analyzing different

Michael Levin (2:34:18.240)

kinds of intelligence together. Because the kinds of things you do to a human are not

Michael Levin (2:34:23.000)

good with an octopus, not good with a plant and so on. I started thinking about this.

Michael Levin (2:34:29.560)

I asked myself what do all cognitive agents, no matter what their provenance, no matter

Lex Fridman (2:34:35.560)

what their architecture is, what do cognitive agents have in common? It seems to me that

Lex Fridman (2:34:41.560)

what they have in common is some degree of competency to pursue a goal. What you can

Michael Levin (2:34:46.480)

do then is you can draw. What I ended up drawing was this thing that it's kind of like a backwards

Michael Levin (2:34:51.720)

Minkowski cone diagram where all of space is collapsed into one axis and then here and

Michael Levin (2:34:58.520)

then time is this axis. Then what you can do is you can draw for any creature, you can

Michael Levin (2:35:04.160)

semi quantitatively estimate what are the spatial and temporal goals that it's capable

Michael Levin (2:35:12.360)

of pursuing.

Michael Levin (2:35:13.360)

For example, if you are a tick and all you really are able to pursue is maximum or a

Michael Levin (2:35:20.240)

bacterium and maximizing the level of some chemical in your vicinity, that's all you've

Michael Levin (2:35:24.800)

got, it's a tiny little icon, then you're a simple system like a tick or a bacterium.

Michael Levin (2:35:29.440)

If you are something like a dog, well, you've got some ability to care about some spatial

Michael Levin (2:35:37.520)

region, some temporal. You can remember a little bit backwards, you can predict a little

Michael Levin (2:35:41.680)

bit forwards, but you're never ever going to care about what happens in the next town

Michael Levin (2:35:46.280)

over four weeks from now. As far as we know, it's just impossible for that kind of architecture.

Michael Levin (2:35:51.680)

If you're a human, you might be working towards world peace long after you're dead. You might

Michael Levin (2:35:56.580)

have a planetary scale goal that's enormous. Then there may be other greater intelligences

Michael Levin (2:36:04.120)

somewhere that can care in the linear range about numbers of creatures, some sort of Buddha

Michael Levin (2:36:08.800)

like character that can care about everybody's welfare, really care the way that we can't.

Michael Levin (2:36:16.040)

It's not a mapping of what you can sense, how far you can sense. It's not a mapping

Michael Levin (2:36:20.640)

of how far you can act. It's a mapping of how big are the goals you are capable of envisioning

Lex Fridman (2:36:25.720)

and working towards. I think that enables you to put synthetic kinds of constructs,

Michael Levin (2:36:33.880)

AIs, aliens, swarms, whatever on the same diagram because we're not talking about what

Michael Levin (2:36:40.120)

you're made of or how you got here. We're talking about what are the size and complexity

Lex Fridman (2:36:44.720)

of the goals towards which you can work.

Lex Fridman (2:36:46.760)

Is there any other terms that pop into mind that are interesting?

Lex Fridman (2:36:50.760)

I'm trying to remember. I have a list of them somewhere on my website.

Michael Levin (2:36:54.200)

Human morphology, yeah, definitely check it out. Morphosutical, I like that one. Ionisutical.

Michael Levin (2:37:01.840)

Yeah. Those refer to different types of interventions in the regenerative medicine space. Amorphosutical

Michael Levin (2:37:08.600)

is something that it's a kind of intervention that really targets the cells decision making

Michael Levin (2:37:16.200)

process about what they're going to build. Ionisuticals are like that, but more focused

Michael Levin (2:37:20.640)

specifically on the bioelectrics. There's also, of course, biochemical, biomechanical,

Lex Fridman (2:37:24.200)

who knows what else, maybe optical kinds of signaling systems there as well.

Michael Levin (2:37:29.160)

Target morphology is interesting. It's designed to capture this idea that it's not just feedforward

Michael Levin (2:37:37.920)

emergence and oftentimes in biology, I mean, of course that happens too, but in many cases

Michael Levin (2:37:41.980)

in biology, the system is specifically working towards a target in anatomical morphospace.

Michael Levin (2:37:48.440)

It's a navigation task really. These kinds of problem solving can be formalized as navigation

Michael Levin (2:37:57.200)

tasks and that they're really going towards a particular region. How do you know? Because

Lex Fridman (2:38:00.920)

you deviate them and then they go back.

Michael Levin (2:38:03.720)

Let me ask you, because you've really challenged a lot of ideas in biology in the work you

Michael Levin (2:38:12.160)

do, probably because some of your rebelliousness comes from the fact that you came from a different

Michael Levin (2:38:18.160)

field of computer engineering, but could you give advice to young people today in high

Michael Levin (2:38:23.800)

school or college that are trying to pave their life story, whether it's in science

Michael Levin (2:38:31.600)

or elsewhere, how they can have a career they can be proud of or a life they can be proud

Lex Fridman (2:38:36.440)

of advice?

Michael Levin (2:38:37.440)

Boy, it's dangerous to give advice because things change so fast, but one central thing

Michael Levin (2:38:42.320)

I can say, moving up and through academia and whatnot, you will be surrounded by really

Michael Levin (2:38:47.880)

smart people. What you need to do is be very careful at distinguishing specific critique

Michael Levin (2:38:56.280)

versus kind of meta advice. What I mean by that is if somebody really smart and successful

Lex Fridman (2:39:03.840)

and obviously competent is giving you specific critiques on what you've done, that's gold.

Michael Levin (2:39:11.400)

It's an opportunity to hone your craft, to get better at what you're doing, to learn,

Michael Levin (2:39:15.200)

to find your mistakes. That's great.

Michael Levin (2:39:17.520)

If they are telling you what you ought to be studying, how you ought to approach things,

Lex Fridman (2:39:23.080)

what is the right way to think about things, you should probably ignore most of that. The

Michael Levin (2:39:28.880)

reason I make that distinction is that a lot of really successful people are very well

Michael Levin (2:39:36.200)

calibrated on their own ideas and their own field and their own area. They know exactly

Lex Fridman (2:39:43.080)

what works and what doesn't and what's good and what's bad, but they're not calibrated

Michael Levin (2:39:46.460)

on your ideas. The things they will say, oh, this is a dumb idea, don't do this and you

Michael Levin (2:39:53.040)

shouldn't do that, that stuff is generally worse than useless. It can be very demoralizing

Lex Fridman (2:40:01.940)

and really limiting. What I say to people is read very broadly, work really hard, know

Lex Fridman (2:40:09.080)

what you're talking about, take all specific criticism as an opportunity to improve what

Michael Levin (2:40:14.220)

you're doing and then completely ignore everything else. I just tell you from my own experience,

Michael Levin (2:40:21.800)

most of what I consider to be interesting and useful things that we've done, very smart

Michael Levin (2:40:26.280)

people have said, this is a terrible idea, don't do that. I think we just don't know.

Michael Levin (2:40:32.960)

We have no idea beyond our own. At best, we know what we ought to be doing. We very rarely

Michael Levin (2:40:37.720)

know what anybody else should be doing.

Michael Levin (2:40:39.320)

Yeah, and their ideas, their perspective has been also calibrated, not just on their field

Lex Fridman (2:40:45.240)

and specific situation, but also on a state of that field at a particular time in the

Michael Levin (2:40:51.520)

past. There's not many people in this world that are able to achieve revolutionary success

Michael Levin (2:40:57.880)

multiple times in their life. Whenever you say somebody very smart, usually what that

Michael Levin (2:41:02.680)

means is somebody who's smart, who achieved a success at a certain point in their life

Lex Fridman (2:41:09.120)

and people often get stuck in that place where they found success. To be constantly challenging

Michael Levin (2:41:14.720)

your worldview is a very difficult thing. Also at the same time, probably if a lot of

Michael Levin (2:41:23.240)

people tell, that's the weird thing about life, if a lot of people tell you that something

Michael Levin (2:41:29.480)

is stupid or is not going to work, that either means it's stupid, it's not going to work,

Michael Levin (2:41:36.160)

or it's actually a great opportunity to do something new and you don't know which one

Michael Levin (2:41:42.680)

it is and it's probably equally likely to be either. Well, I don't know, the probabilities.

Michael Levin (2:41:49.920)

Depends how lucky you are, depends how brilliant you are, but you don't know and so you can't

Lex Fridman (2:41:53.400)

take that advice as actual data.

Michael Levin (2:41:55.680)

Yeah, you have to and this is kind of hard to describe and fuzzy, but I'm a firm believer

Michael Levin (2:42:03.920)

that you have to build up your own intuition. So over time, you have to take your own risks

Michael Levin (2:42:09.160)

that seem like they make sense to you and then learn from that and build up so that

Michael Levin (2:42:13.580)

you can trust your own gut about what's a good idea even when, and then sometimes you'll

Michael Levin (2:42:18.120)

make mistakes and they'll turn out to be a dead end and that's fine, that's science,

Lex Fridman (2:42:21.560)

but what I tell my students is life is hard and science is hard and you're going to sweat

Lex Fridman (2:42:28.560)

and bleed and everything and you should be doing that for ideas that really fire you

Michael Levin (2:42:34.880)

up inside and really don't let kind of the common denominator of standardized approaches

Michael Levin (2:42:44.940)

to things slow you down.

Lex Fridman (2:42:46.800)

So you mentioned planaria being in some sense immortal. What's the role of death in life?

Michael Levin (2:42:53.480)

What's the role of death in this whole process we have? Is it, when you look at biological

Lex Fridman (2:42:58.760)

systems, is death an important feature, especially as you climb up the hierarchy of competency?

Michael Levin (2:43:08.000)

Boy, that's an interesting question. I think that it's certainly a factor that promotes

Michael Levin (2:43:17.320)

change and turnover and an opportunity to do something different the next time for a

Michael Levin (2:43:24.520)

larger scale system. So apoptosis, it's really interesting. I mean, death is really interesting

Michael Levin (2:43:29.520)

in a number of ways. One is like you could think about like what was the first thing

Michael Levin (2:43:33.040)

to die? That's an interesting question. What was the first creature that you could say

Michael Levin (2:43:37.420)

actually died? It's a tough thing because we don't have a great definition for it. So

Michael Levin (2:43:42.880)

if you bring a cabbage home and you put it in your fridge, at what point are you going

Michael Levin (2:43:48.480)

to say it's died, right? So it's kind of hard to know. There's one paper in which I talk

Michael Levin (2:43:58.880)

about this idea that, I mean, think about this and imagine that you have a creature

Michael Levin (2:44:04.960)

that's aquatic, let's say it's a frog or something or a tadpole, and the animal dies,

Michael Levin (2:44:11.680)

in the pond it dies for whatever reason. Most of the cells are still alive. So you could

Michael Levin (2:44:17.600)

imagine that if when it died, there was some sort of breakdown of the connectivity between

Michael Levin (2:44:23.200)

the cells, a bunch of cells crawled off, they could have a life as amoebas. Some of them

Michael Levin (2:44:28.760)

could join together and become a xenobot and twiddle around, right? So we know from planaria

Michael Levin (2:44:33.780)

that there are cells that don't obey the Hayflick limit and just sort of live forever. So you

Michael Levin (2:44:37.800)

could imagine an organism that when the organism dies, it doesn't disappear, rather the individual

Michael Levin (2:44:42.400)

cells that are still alive, crawl off and have a completely different kind of lifestyle

Lex Fridman (2:44:46.280)

and maybe come back together as something else, or maybe they don't. So all of this,

Michael Levin (2:44:50.080)

I'm sure, is happening somewhere on some planet. So death in any case, I mean, we already kind

Michael Levin (2:44:57.080)

of knew this because the molecules, we know that when something dies, the molecules go

Michael Levin (2:45:00.640)

through the ecosystem, but even the cells don't necessarily die at that point, they

Lex Fridman (2:45:05.200)

might have another life in a different way. You can think about something like HeLa, right?

Michael Levin (2:45:09.720)

The HeLa cell line, you know, that has this, that's had this incredible life. There are

Michael Levin (2:45:14.400)

way more HeLa cells now than there ever been, than there, than there were when, when she

Michael Levin (2:45:18.040)

was alive.

Michael Levin (2:45:19.040)

It seems like as the organisms become more and more complex, like if you look at the

Michael Levin (2:45:22.240)

mammals, their relationship with death becomes more and more complex. So the survival imperative

Michael Levin (2:45:29.800)

starts becoming interesting and humans are arguably the first species that have invented

Michael Levin (2:45:37.400)

the fear of death. The understanding that you're going to die, let's put it this way,

Michael Levin (2:45:43.120)

like long, so not like instinctual, like, I need to run away from the thing that's going

Michael Levin (2:45:49.560)

to eat me, but starting to contemplate the finiteness of life.

Michael Levin (2:45:53.960)

Yeah. I mean, one thing, so, so one thing about the human light, cognitive light cone

Michael Levin (2:45:59.400)

is that for the first, as far as we know, for the first time, you might have goals that

Michael Levin (2:46:04.200)

are longer than your lifespan, that are not achievable, right? So if you're, if you are,

Michael Levin (2:46:08.160)

let's say, and I don't know if this is true, but if you're a goldfish and you have a 10

Michael Levin (2:46:11.800)

minute attention span, I'm not sure if that's true, but let's say, let's say there's some

Michael Levin (2:46:14.760)

organism with a, with a short kind of cognitive light cone that way, all of your goals are

Michael Levin (2:46:20.260)

potentially achievable because you're probably going to live the next 10 minutes. So whatever

Michael Levin (2:46:23.560)

goals you have, they are totally achievable. If you're a human, you could have all kinds

Michael Levin (2:46:27.440)

of goals that are guaranteed not achievable because they just take too long, like guaranteed

Michael Levin (2:46:31.240)

you're not going to achieve them. So I wonder if, you know, is that, is that a, you know,

Michael Levin (2:46:35.840)

like a perennial, you know, sort of thorn in our, in our psychology that drives some,

Michael Levin (2:46:39.920)

some psychosis or whatever? I have, I have no idea. Another interesting thing about that,

Michael Levin (2:46:43.920)

actually, I've been thinking about this a lot in the last couple of weeks, this notion

Michael Levin (2:46:47.720)

of giving up. So you would think that evolutionarily, the most adaptive way of being is that you

Michael Levin (2:46:58.480)

go, you, you, you, you fight as long as you physically can. And then when you can't, you

Michael Levin (2:47:02.960)

can't, and there's in, there's this photograph, there's videos you can find of insects are

Michael Levin (2:47:06.680)

crawling around where like, you know, like, like most of it is already gone, and it's

Michael Levin (2:47:10.000)

still sort of crawling, you know, like, Terminator style, right? Like, as far as as long as you

Michael Levin (2:47:15.240)

physically can, you keep going. Mammals don't do that. So a lot of mammals, including rats,

Michael Levin (2:47:20.320)

have this thing where when, when they think it's a hopeless situation, they literally

Michael Levin (2:47:25.780)

give up and die when physically, they could have kept going. I mean, humans certainly

Michael Levin (2:47:29.060)

do this. And there's, there's some like, really unpleasant experiments that the this guy forget

Michael Levin (2:47:33.320)

his name did with drowning rats, where if he where where rats normally drown after a

Michael Levin (2:47:37.960)

couple of minutes, but if you teach them that if you just tread water for a couple of minutes,

Michael Levin (2:47:41.480)

you'll get rescued, they can tread water for like an hour. And so right, and so they literally

Michael Levin (2:47:45.360)

just give up and die. And so evolutionarily, that doesn't seem like a good strategy at

Michael Levin (2:47:49.920)

all evolutionarily, since why would you like, what's the benefit ever of giving up, you

Lex Fridman (2:47:53.320)

just do what you can, and you know, one time out of 1000, you'll actually get rescued, right?

Lex Fridman (2:47:57.400)

But this issue of actually giving up suggests some very interesting metacognitive controls

Michael Levin (2:48:03.080)

where you've now gotten to the point where survival actually isn't the top drive. And

Michael Levin (2:48:08.080)

that for whatever, you know, there are other considerations that have like taken over.

Lex Fridman (2:48:11.560)

And I think that's uniquely a mammalian thing. But then I don't know.

Michael Levin (2:48:15.560)

Yeah, the Camus, the existentialist question of why live, just the fact that humans commit

Michael Levin (2:48:23.080)

suicide is a really fascinating question from an evolutionary perspective.

Lex Fridman (2:48:27.880)

And what was the first and that's the other thing, like, what is the simplest system,

Michael Levin (2:48:33.360)

whether whether evolved or natural or whatever, that is able to do that? Right? Like, you

Michael Levin (2:48:38.760)

can think, you know, what other animals are actually able to do that? I'm not sure.

Michael Levin (2:48:42.440)

Maybe you could see animals over time, for some reason, lowering the value of survive

Lex Fridman (2:48:49.760)

at all costs, gradually, until other objectives might become more important.

Michael Levin (2:48:55.560)

Maybe. I don't know how evolutionarily how that how that gets off the ground. That just

Michael Levin (2:48:59.320)

seems like that would have such a strong pressure against it, you know. Just imagine, you know,

Michael Levin (2:49:06.600)

a population with a lower, you know, if you were a mutant in a population that had less

Lex Fridman (2:49:13.240)

of a less of a survival imperative, would you put your genes outperform the others?

Michael Levin (2:49:19.200)

Is there such a thing as population selection? Because maybe suicide is a way for organisms

Lex Fridman (2:49:26.440)

to decide themselves that they're not fit for the environment? Somehow?

Michael Levin (2:49:31.840)

Yeah, that's a that's a really contrary, you know, population level selection is a kind

Michael Levin (2:49:36.660)

of a deep controversial area. But it's tough because on the face of it, if that was your

Michael Levin (2:49:42.840)

genome, it wouldn't get propagated because you would die and then your neighbor who didn't

Lex Fridman (2:49:47.040)

have that would would have all the kids.

Michael Levin (2:49:49.040)

It feels like there could be some deep truth there that we're not understanding. What about

Lex Fridman (2:49:55.140)

you yourself as one biological system? Are you afraid of death?

Michael Levin (2:49:59.300)

To be honest, I'm more concerned with especially now getting older and having helped a couple

Michael Levin (2:50:05.820)

of people pass. I think about what's a what's a good way to go? Basically, like nowadays,

Michael Levin (2:50:14.880)

I don't know what that is, I, you know, sitting in a, you know, a facility that sort of tries

Michael Levin (2:50:19.160)

to stretch you out as long as you can, that doesn't seem that doesn't seem good. And there's

Michael Levin (2:50:24.840)

not a lot of opportunities to sort of, I don't know, sacrifice yourself for something useful,

Michael Levin (2:50:29.400)

right? There's not terribly many opportunities for that in modern society. So I don't know,

Michael Levin (2:50:33.640)

that's that's that's more of I'm not I'm not particularly worried about death itself.

Lex Fridman (2:50:38.040)

But I've seen it happen. And and it's not it's not pretty. And I don't know what what

Michael Levin (2:50:46.380)

a better what a better alternative is.

Lex Fridman (2:50:48.080)

So the existential aspect of it does not worry you deeply? The fact that this ride ends?

Michael Levin (2:50:56.360)

No, it began. I mean, the ride began, right? So there was I don't know how many billions

Lex Fridman (2:51:01.340)

of years before that I wasn't around. So that's okay.

Lex Fridman (2:51:04.740)

But isn't the experience of life? It's almost like feels like you're immortal. Because the

Michael Levin (2:51:10.520)

way you make plans, the way you think about the future. I mean, if you if you look at

Michael Levin (2:51:15.720)

your own personal rich experience, yes, you can understand, okay, eventually, I died as

Michael Levin (2:51:22.360)

people I love that have died. So surely, I will die and it hurts and so on. But like,

Michael Levin (2:51:28.960)

he sure doesn't. It's so easy to get lost in feeling like this is going to go on forever.

Lex Fridman (2:51:34.240)

Yeah, it's a little bit like the people who say they don't believe in free will, right?

Michael Levin (2:51:37.320)

I mean, you can say that but but when you go to a restaurant, you still have to pick

Michael Levin (2:51:41.680)

a soup and stuff. So right, so so I don't know if I know I've actually seen that that

Michael Levin (2:51:46.080)

happened at lunch with a with a well known philosopher and he didn't believe in free

Michael Levin (2:51:49.920)

will and the other waitress came around and he was like, Well, let me see. I was like,

Lex Fridman (2:51:53.600)

What are you doing here? You're gonna choose a sandwich, right? So it's I think it's one

Michael Levin (2:51:58.200)

of those things. I think you can know that, you know, you're not going to live forever.

Lex Fridman (2:52:02.100)

But you can't you can't. It's not practical to live that way unless you know, so you buy

Michael Levin (2:52:07.100)

insurance and then you do some stuff like that. But but but mostly, you know, I think

Michael Levin (2:52:11.920)

you just you just live as if as if as if you can make plans.

Michael Levin (2:52:17.440)

We talked about all kinds of life. We talked about all kinds of embodied minds. What do

Michael Levin (2:52:22.520)

you think is the meaning of it all? What's the meaning of all the biological lives we've

Lex Fridman (2:52:28.000)

been talking about here on Earth? Why are we here?

Michael Levin (2:52:33.280)

I don't know that that's a that that's a well posed question other than the existential

Lex Fridman (2:52:38.920)

question you post before.

Michael Levin (2:52:40.900)

Is that question hanging out with the question of what is consciousness and there at retreat

Michael Levin (2:52:47.000)

somewhere? Not sure because sipping pina coladas and because they're ambiguously defined.

Michael Levin (2:52:55.280)

Maybe I'm not sure that any of these things really ride on the correctness of our scientific

Michael Levin (2:53:01.660)

understanding. But I mean, just just for an example, right? I've always found I've always

Michael Levin (2:53:06.740)

found it weird that people get really worked up to find out realities about their their

Michael Levin (2:53:16.760)

bodies, for example. Right. You've seen them. Ex Machina. Right. And so there's this great

Michael Levin (2:53:22.820)

scene where he's cutting his hand to find out, you know, a piece full of cock. Now,

Michael Levin (2:53:26.120)

to me, right? If if I open up and I find out and I find a bunch of cogs, my conclusion

Michael Levin (2:53:31.880)

is not, oh, crap, I must not have true cognition. That sucks. My conclusion is, wow, cogs can

Michael Levin (2:53:37.360)

have true cognition. Great. So right. So. So it seems to me, I guess I guess I'm with

Michael Levin (2:53:42.840)

Descartes on this one, that whatever whatever the truth ends up being of of of how is what

Michael Levin (2:53:48.240)

is consciousness, how it can be conscious? None of that is going to alter my primary

Michael Levin (2:53:53.080)

experience, which is this is what it is. And if and if a bunch of molecular networks can

Michael Levin (2:53:56.600)

do it, fantastic. If it turns out that there's a there's a non corporeal, you know, so great.

Michael Levin (2:54:03.300)

We can we'll study that, whatever. But but the fundamental existential aspect of it is,

Michael Levin (2:54:09.200)

you know, if somebody if somebody told me today that, yeah, yeah, you were created yesterday

Lex Fridman (2:54:13.400)

and all your memories are, you know, sort of fake, you know, kind of like like like Boltzmann

Michael Levin (2:54:18.280)

brains, right. And the human, you know, human skepticism, all that. Yeah. OK. Well, so so

Lex Fridman (2:54:23.280)

but but here I am now. So so it's the experience. It's primal, so like that's the that's the

Michael Levin (2:54:31.280)

thing that matters. So the the backstory doesn't matter. I think so. I think so. From a first

Michael Levin (2:54:36.300)

person perspective, now from a third person, like scientifically, it's all very interesting.

Michael Levin (2:54:39.600)

From a third person perspective, I could say, wow, that's that's amazing that that this

Michael Levin (2:54:43.760)

happens and how does it happen and whatever. But from a first person perspective, I could

Michael Levin (2:54:48.000)

care less. Like I just it's just what I've what I learned from any of these scientific

Michael Levin (2:54:52.020)

facts is, OK, well, I guess then that's that that then I guess that's what is sufficient

Michael Levin (2:54:57.160)

to to give me my, you know, amazing first person perspective. I think if you dig deeper

Lex Fridman (2:55:01.820)

and deeper and get a get surprising answers to why the hell we're here, it might give

Michael Levin (2:55:10.100)

you some guidance on how to live. Maybe, maybe. I don't know. That would be nice. On the one

Michael Levin (2:55:18.680)

hand, you might be right, because on the one hand, if I don't know what else could possibly

Michael Levin (2:55:23.240)

give you that guidance. Right. So so you would think that it would have to be that or you

Michael Levin (2:55:26.240)

would do it would have to be science because there isn't anything else. So so that's so

Michael Levin (2:55:30.400)

maybe on the other hand, I am really not sure how you go from any, you know, what they call

Michael Levin (2:55:36.680)

from an is to an odd right from any factual description of what's going on. This goes

Michael Levin (2:55:41.120)

back to the natural. Right. Just because somebody says, oh, man, that's that's completely not

Michael Levin (2:55:44.920)

natural. It's never happened on Earth before. I'm not impressed by that whatsoever. I think

Michael Levin (2:55:50.000)

I think whatever hazard hasn't happened, we are now in a position to do better if we can.

Michael Levin (2:55:56.280)

Right. Well, this also because you said there's science and there's nothing else. There it's

Michael Levin (2:56:03.680)

it's really tricky to know how to intellectually deal with a thing that science doesn't currently

Michael Levin (2:56:12.000)

understand. Right. So like, the thing is, if you believe that science solves everything,

Michael Levin (2:56:22.880)

you can too easily in your mind think our current understanding, like, we've solved

Michael Levin (2:56:30.280)

everything. Right. Right. Right. Like, it jumps really quickly to not science as a mechanism

Michael Levin (2:56:36.120)

as a as a process, but more like science of today. Like, you could just look at human

Michael Levin (2:56:43.000)

history and throughout human history, just physicists and everybody would claim we've

Michael Levin (2:56:48.640)

solved everything. Sure. Sure. Like, like, there's a few small things to figure out.

Lex Fridman (2:56:53.240)

And we basically solved everything. Were in reality, I think asking, like, what is the

Michael Levin (2:56:58.480)

meaning of life is resetting the palette of like, we might be tiny and confused and don't

Michael Levin (2:57:08.120)

have anything figured out. It's almost going to be hilarious a few centuries from now when

Michael Levin (2:57:12.800)

they look back how dumb we were. Yeah, I 100% agree. So when I say science and nothing else,

Michael Levin (2:57:21.480)

I certainly don't mean the science of today because I think overall, I think we are we

Michael Levin (2:57:27.640)

know very little. I think most of the things that we're sure of now are going to be, as

Michael Levin (2:57:32.400)

you said, are going to look hilarious down the line. So I think we're just at the beginning

Michael Levin (2:57:36.280)

of a lot of really important things. When I say nothing but science, I also include

Michael Levin (2:57:42.320)

the kind of first person, what I call science that you do. So the interesting thing about

Michael Levin (2:57:48.000)

I think about consciousness and studying consciousness and things like that in the first person is

Michael Levin (2:57:52.120)

unlike doing science in the third person, where you as the scientist are minimally changed

Michael Levin (2:57:57.760)

by it, maybe not at all. So when I do an experiment, I'm still me, there's the experiment, whatever

Michael Levin (2:58:01.360)

I've done, I've learned something, so that's a small change. But but overall, that's it.

Michael Levin (2:58:04.900)

In order to really study consciousness, you will you are part of the experiment, you will

Michael Levin (2:58:10.640)

be altered by that experiment, right? Whatever, whatever it is that you're doing, whether

Michael Levin (2:58:13.920)

it's some sort of contemplative practice or, or some sort of psychoactive, you know, whatever.

Michael Levin (2:58:22.120)

You are now you are now your own experiment, and you are right. And so I consider I fold

Michael Levin (2:58:26.160)

that in, I think that's that's part of it. I think that exploring our own mind and our

Michael Levin (2:58:29.960)

own consciousness is very important. I think much of it is not captured by what currently

Michael Levin (2:58:34.680)

is third person science for sure. But ultimately, I include all of that in science, with a capital

Michael Levin (2:58:41.520)

S in terms of like a, a rational investigation of both first and third person aspects of

Lex Fridman (2:58:48.800)

our world.

Michael Levin (2:58:50.300)

We are our own experiment, as beautifully put. And when when two systems get to interact

Michael Levin (2:58:57.960)

with each other, that's the kind of experiment. So I'm deeply honored that you would do this

Michael Levin (2:59:03.780)

experiment with me today. Thanks so much. I'm a huge fan of your work. Likewise, thank

Michael Levin (2:59:07.760)

you for doing everything you're doing. I can't wait to see the kind of incredible things

Michael Levin (2:59:13.800)

you build. So thank you for talking. Really appreciate being here. Thank you.

Michael Levin (2:59:18.200)

Thank you for listening to this conversation with Michael Levin. To support this podcast,

Michael Levin (2:59:22.200)

please check out our sponsors in the description. And now let me leave you with some words from

Michael Levin (2:59:26.760)

Charles Darwin in The Origin of Species. From the war of nature, from famine and death,

Michael Levin (2:59:35.760)

the most exalted object which we're capable of conceiving, namely, the production of the

Michael Levin (2:59:41.000)

higher animals directly follows. There's grandeur in this view of life, with its several

Michael Levin (2:59:47.600)

powers having been originally breathed into a few forms, or into one, and that whilst

Michael Levin (2:59:54.880)

this planet has gone cycling on according to the fixed laws of gravity, from its most

Michael Levin (2:59:59.840)

simpler beginning, endless forms, most beautiful and most wonderful, have been and are being

Michael Levin (30:02.160)

you and the outside world, you don't really know where that is, right? Every every collection of

Michael Levin (30:05.680)

cell has to figure that out from scratch. And the fact that evolution requires all of these things

Michael Levin (30:10.880)

to figure out what they are, what effectors they have, what sensors they have, where does it make

Michael Levin (30:15.520)

sense to draw a boundary between me and the outside world? The fact that you have to build all

Michael Levin (30:18.960)

that from scratch, this autopoiesis is what defines the border of a self. Now, biology uses like a

Michael Levin (30:26.320)

multi a multi scale competency architecture, meaning that every level has goals. So so

Michael Levin (30:31.760)

molecular networks have goals, cells have goals, tissues, organs, colonies. And and it's the

Michael Levin (30:38.160)

interplay of all of those that that enable biology to solve problems in new ways, for example, in

Michael Levin (30:43.280)

xenobots and various other things. This is, you know, it's exactly as you said, in many ways,

Michael Levin (30:50.640)

the cells are discovering new ways of being. But at the same time, evolution certainly shapes all

Michael Levin (30:56.080)

this. So so evolution is very good at this agential bioengineering, right? When evolution

Michael Levin (31:01.680)

is discovering a new way of being an animal, you know, an animal or a plant or something,

Michael Levin (31:06.000)

sometimes it's by changing the hardware, you know, protein, changing proteins, protein structure,

Lex Fridman (31:10.160)

and so on. But much of the time, it's not by changing the hardware, it's by changing the

Michael Levin (31:14.160)

signals that the cells give to each other. It's doing what we as engineers do, which is try to

Michael Levin (31:17.840)

convince the cells to do various things by using signals, experiences, stimuli. That's what biology

Michael Levin (31:22.640)

does. It has to, because it's not dealing with a blank slate. Every time as you know, if you're

Michael Levin (31:27.360)

evolution, and you're trying to make make a make an organism, you're not dealing with a passive

Michael Levin (31:32.960)

material that is fresh, and you have to specify it already wants to do certain things. So the easiest

Michael Levin (31:37.760)

way to do that search to find whatever is going to be adaptive, is to find the signals that are

Michael Levin (31:42.560)

going to convince cells to do various things, right? Your sense is that evolution operates

Michael Levin (31:48.480)

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

Michael Levin (31:54.000)

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

Michael Levin (31:58.800)

I think it's a continuum. But I think we can but I think it's a meaningful distinction where you can

Michael Levin (32:03.120)

make changes to a particular protein, and now the enzymatic function is different, and it metabolizes

Michael Levin (32:08.480)

differently, and whatever, and that will have implications for fitness. Or you can change the

Michael Levin (32:14.080)

huge amount of information in the genome that isn't structural at all. It's, it's, it's signaling,

Michael Levin (32:20.480)

it's when and how do cells say certain things to each other. And that can have massive changes,

Michael Levin (32:25.120)

as far as how it's going to solve problems. I mean, this idea of multi hierarchical

Michael Levin (32:29.120)

competence architecture, which is incredible to think about. So this hierarchy that evolution

Michael Levin (32:35.760)

builds, I don't know who's responsible for this. I also see the incompetence of bureaucracies

Michael Levin (32:43.840)

of humans when they get together. So how the hell does evolution build this, where at every level,

Michael Levin (32:53.200)

only the best get to stick around, they somehow figure out how to do their job without knowing

Michael Levin (32:57.360)

the bigger picture. And then there's like the bosses that do the bigger thing somehow, or that

Michael Levin (33:04.160)

you can now abstract away the small group of cells as a as an organ or something. And then

Michael Levin (33:11.040)

that organ does something bigger in the context of the full body or something like this.

Lex Fridman (33:17.920)

How is that built? Is there some intuition you can kind of provide of how that's constructed,

Michael Levin (33:23.680)

that that hierarchical competence architecture? I love that competence,

Michael Levin (33:29.680)

just the word competence is pretty cool in this context, because everybody's good at their job.

Michael Levin (33:34.080)

Yeah, no, it's really key. And the other nice thing about competency is that so my central

Michael Levin (33:39.280)

belief in all of this is that engineering is the right perspective on all of this stuff,

Michael Levin (33:43.840)

because it gets you away from subjective terms. You know, people talk about sentience and this

Lex Fridman (33:50.480)

and that those things very hard to define, or people argue about them philosophically.

Michael Levin (33:54.880)

I think that engineering terms like competency, like, you know, pursuit of goals, right? All of

Michael Levin (34:02.080)

these things are, are empirically incredibly useful, because you know, when you see it,

Lex Fridman (34:06.400)

and if it helps you build, right, if I if I can pick the right level, I say, this thing has,

Michael Levin (34:11.920)

I believe this is x level of like, competency, I think it's like a thermostat, or I think it's

Michael Levin (34:17.200)

like a better thermostat, or I think it's a, you know, various other kinds of, you know,

Michael Levin (34:22.800)

many, many different kinds of complex systems. If that helps me to control and predict and build

Michael Levin (34:28.000)

such systems, then that's all there is to say, there's no more philosophy to argue about. So I

Michael Levin (34:32.000)

like competency in that way, because you can quantify, you could, you have to, in fact, you

Michael Levin (34:35.120)

have to, you have to make a claim competent at what? And then, or if I say, if I tell you,

Michael Levin (34:38.640)

it has a goal, the question is, what's the goal? And how do you know? And I say, well, because

Michael Levin (34:42.400)

every time I deviated from this particular state, that's what it spends energy to get back to,

Michael Levin (34:46.320)

that's the goal. And we can quantify it, and we can be objective about it. So so so the the,

Michael Levin (34:51.920)

we're not used to thinking about this, I give a talk sometimes called Why don't robots get cancer,

Michael Levin (34:56.000)

right? And the reason robots don't get cancer is because generally speaking, with a few exceptions,

Michael Levin (35:00.160)

our architectures have been, you've got a bunch of dumb parts. And you hope that if you put them

Michael Levin (35:05.280)

together, the the the overlying machine will have some intelligence and do something rather,

Michael Levin (35:09.520)

right, but the individual parts don't don't care, they don't have an agenda. Biology isn't like

Michael Levin (35:13.280)

that every level has an agenda. And the final outcome is the result of cooperation and competition,

Michael Levin (35:20.560)

both within and across levels. So for example, during embryogenesis, your tissues and organs are

Michael Levin (35:25.360)

competing with each other. And it's actually a really important part of development, there's a

Michael Levin (35:28.800)

reason they compete with each other, they're not all just, you know, sort of helping each other,

Michael Levin (35:33.440)

they're also competing for information for metabolic for limited metabolic constraints.

Lex Fridman (35:38.560)

But to get back to your your other point, which is, you know, which is which is the seems like

Michael Levin (35:43.440)

really efficient and good and so on compared to some of our human efforts. We also have to keep

Michael Levin (35:48.800)

in mind that what happens here is that each level bends the option space for the level beneath so

Michael Levin (35:56.320)

that your parts basically they don't see the the geometry. So I'm using them. And I think I take

Michael Levin (36:03.920)

I take this the seriously terminology from from like, from like relativity, right, where the space

Michael Levin (36:10.160)

is literally bent. So the option space is deformed by the higher level so that the lower levels, all

Michael Levin (36:15.120)

they really have to do is go down their concentration gradient, they don't have to,

Michael Levin (36:18.000)

in fact, they don't, they can't know what the big picture is. But if you bend the space just right,

Michael Levin (36:22.320)

if they do what locally seems right, they end up doing your bidding, they end up doing things that

Michael Levin (36:26.720)

are optimal in the in the higher space. Conversely, because the components are good at getting their

Michael Levin (36:33.840)

job done, you as the higher level don't need to try to compute all the low level controls,

Michael Levin (36:38.880)

all you're doing is bending the space, you don't know or care how they're going to do it.

Michael Levin (36:42.160)

Give you a super simple example in the in the tappel, we found that okay, so so tappels need

Michael Levin (36:47.680)

to become frogs and to become to go from a tappel head to a frog head, you have to rearrange the

Michael Levin (36:51.600)

face. So the eyes have to move forward, the jaws have to come out the nostrils move like everything

Michael Levin (36:55.200)

moves. It used to be thought that because all tappels look the same, and all frogs look the

Michael Levin (36:59.840)

same. If you just remember, if every piece just moves in the right direction, the right amount,

Michael Levin (37:03.200)

then you get your you get your fraud. Right. So we decided to test we I have this hypothesis that I

Lex Fridman (37:08.000)

thought I thought actually, the system is probably more intelligent than that. So what did we do?

Michael Levin (37:11.600)

We made what we call Picasso tappels. So these are so everything is scrambled. So the eyes are on the

Michael Levin (37:15.920)

back of the head, the jaws are off to the side, everything is scrambled. Well, guess what they

Michael Levin (37:18.960)

make, they make pretty normal frogs, because all the different things move around in novel

Michael Levin (37:23.600)

paths configurations until they get to the correct froggy sort of frog face configuration,

Michael Levin (37:28.240)

then they stop. So, so the thing about that is now imagine evolution, right? So, so you make some

Michael Levin (37:34.080)

sort of mutation, and it does, like every mutation, it does many things. So something good comes of it,

Lex Fridman (37:40.560)

but also it moves your mouth off to the side, right? Now, if if there wasn't this multi scale

Michael Levin (37:46.160)

competency, you can see where this is going, if there wasn't this multi scale competency,

Michael Levin (37:49.360)

the organism would be dead, your fitness is zero, because you can't eat. And you would never get to

Michael Levin (37:53.200)

explore the other beneficial consequences of that mutation, you'd have to wait until you find some

Michael Levin (37:57.680)

other way of doing it without moving the mouth, that's really hard. So, so the fitness landscape

Michael Levin (38:01.840)

would be incredibly rugged evolution would take forever. The reason it works, one of the reasons

Michael Levin (38:06.320)

it works so well, is because you do that, no worries, the mouth will find its way where where

Michael Levin (38:11.360)

it belongs, right? So now you get to explore. So what that means is that all of these mutations

Michael Levin (38:15.680)

that otherwise would be deleterious are now neutral, because the competency of the parts

Michael Levin (38:21.280)

make up for all kinds of things. So all the noise of development, all the variability in the

Michael Levin (38:26.480)

environment, all these things, the competency of the parts makes up for it. So the so so that's

Michael Levin (38:32.080)

all that's all fantastic, right? That's all that's all great. The only other thing to remember when

Michael Levin (38:36.080)

we compare this to human efforts is this. Every component has its own goals in various spaces,

Michael Levin (38:41.040)

usually with very little regard for the welfare of the other levels. So so as a simple example,

Michael Levin (38:46.560)

you know, you as a as a complex system, you will go out and you will do you know, jiu jitsu,

Michael Levin (38:52.000)

or whatever, you'll have some go you have to go rock climbing, scrape a bunch of cells off your

Michael Levin (38:55.520)

hands. And then you're happy as a system, right? You come back, and you've accomplished some goals,

Lex Fridman (38:59.680)

and you're really happy. Those cells are dead. They're gone. Right? Did you think about those

Michael Levin (39:03.120)

cells? Not really, right? You had some you had some bruising out selfish SOB. That's it. And so

Lex Fridman (39:08.640)

and so that's the thing to remember is that, you know, and we know this from from history is that

Michael Levin (39:13.760)

is that just being a collective isn't enough. Because what the goals of that collective will

Michael Levin (39:19.520)

be relative to the welfare of the individual parts is a massively open and justify the means

Michael Levin (39:24.560)

I'm telling you, Stalin was onto something. No, that's the danger. But we can exactly that's the

Michael Levin (39:29.600)

danger of for us humans, we have to construct ethical systems under which we don't take seriously

Lex Fridman (39:39.760)

the full mechanism of biology and apply it to the way the world functions,

Michael Levin (39:43.840)

which is which is an interesting line we've drawn. The world that built us is the one we

Michael Levin (39:51.680)

reject in some sense, when we construct human societies, the idea that this country was founded

Michael Levin (39:59.120)

on that all men are created equal. That's such a fascinating idea. That's like, you're fighting

Lex Fridman (3:00:06.880)

evolved. Thank you for listening, and hope to see you next time.

Michael Levin (40:05.440)

against nature and saying, well, there's something bigger here than a hierarchical competency

Michael Levin (40:14.640)

architecture. But there's so many interesting things you said. So from an algorithmic perspective,

Michael Levin (40:21.920)

the act of bending the option space. That's really, that's really profound. Because if you

Michael Levin (40:29.840)

look at the way AI systems are built today, there's a big system, like I said, with robots,

Lex Fridman (40:36.800)

and as a goal, and he gets better and better at optimizing that goal at accomplishing that goal.

Lex Fridman (40:42.080)

But if biology built a hierarchical system where everything is doing computation,

Lex Fridman (40:49.360)

and everything is accomplishing the goal, not only that, it's kind of dumb,

Michael Levin (40:56.400)

you know, with the limited with a bent option space is just doing the thing that's the easiest

Michael Levin (41:03.360)

thing for in some sense. And somehow that allows you to have turtles on top of turtles,

Michael Levin (41:10.960)

literally dumb systems on top of dumb systems that as a whole create something incredibly smart.

Michael Levin (41:18.480)

Yeah, I mean, every system is has some degree of intelligence in its own problem domain. So,

Lex Fridman (41:25.200)

so cells will have problems they're trying to solve in physiological space and transcriptional

Michael Levin (41:30.400)

space. And then I can give you some some cool examples of that. But the collective is trying

Michael Levin (41:34.240)

to solve problems in anatomical space, right and forming a, you know, a creature and growing your

Michael Levin (41:38.800)

blood vessels and so on. And then the collective the the the whole body is solving yet other

Michael Levin (41:44.480)

problems, they may be in social space and linguistic space and three dimensional space.

Lex Fridman (41:48.080)

And who knows, you know, the group might be solving problems in, you know, I don't know,

Michael Levin (41:52.080)

some sort of financial space or something. So one of the major differences with with most,

Michael Levin (41:59.280)

with most AIs today is is a the kind of flatness of the architecture, but also of the fact that

Michael Levin (42:06.160)

they're constructed from outside their their borders, and they're, you know, so a few. So,

Michael Levin (42:14.400)

to a large extent, and of course, there are counter examples now, but but to a large extent,

Michael Levin (42:18.640)

our technology has been such that you create a machine or a robot, it knows what its sensors are,

Michael Levin (42:23.760)

it knows what its effectors are, it knows the boundary between it and the outside world,

Michael Levin (42:27.760)

although this is given from the outside. Biology constructs this from scratch. Now the best example

Michael Levin (42:32.800)

of this that that originally in robotics was actually Josh Bongard's work in 2006, where he

Michael Levin (42:38.880)

made these, these robots that did not know their shape to start with. So like a baby, they sort of

Michael Levin (42:43.120)

floundered around, they made some hypotheses, well, I did this, and I moved in this way. Well,

Michael Levin (42:47.040)

maybe I'm a whatever, maybe I have wheels, or maybe I have six legs or whatever, right? And

Michael Levin (42:50.800)

they would make a model and eventually will crawl around. So that's, I mean, that's really good.

Michael Levin (42:54.240)

That's part of the autopoiesis, but we can go a step further. And some people are doing this. And

Michael Levin (42:58.160)

then we're sort of working on some of this too, is this idea that let's even go back further,

Michael Levin (43:02.960)

you don't even know what sensors you have, you don't know where you end in the outside world

Michael Levin (43:06.640)

begins. All you have is is certain things like active inference, meaning you're trying to minimize

Michael Levin (43:11.200)

surprise, right? You have some metabolic constraints, you don't have all the energy you

Michael Levin (43:14.880)

need, you don't have all the time in the world to think about everything you want to think about. So

Michael Levin (43:18.640)

that means that you can't afford to be a micro reductionist, you know, all this data coming in,

Michael Levin (43:23.280)

you have to course grain it and say, I'm gonna take all this stuff, and I'm gonna call that a

Michael Levin (43:26.560)

cat. I'm gonna take all this, I'm gonna call that the edge of the table I don't want to fall off of.

Lex Fridman (43:30.560)

And I don't want to know anything about the micro states, what I want to know is what is the optimal

Michael Levin (43:34.480)

way to cut up my world. And by the way, this thing over here, that's me. And the reason that's me is

Michael Levin (43:38.560)

because I have more control over this than I have over any of this other stuff. And so now you can

Michael Levin (43:42.640)

begin to write. So that's self construction at that, that figuring out making models of the

Michael Levin (43:46.560)

outside world, and then turning that inwards, and starting to make a model of yourself, right, which

Michael Levin (43:51.120)

immediately starts to get into issues of agency and control. Because in order to if you are under

Michael Levin (43:58.560)

metabolic constraints, meaning you don't have the energy, right, that all the energy in the world,

Michael Levin (44:02.240)

you have to be efficient, that immediately forces you to start telling stories about coarse grained

Michael Levin (44:08.000)

agents that do things, right, you don't have the energy to like Laplace's demon, you know,

Michael Levin (44:11.840)

calculate every, every possible state that's going to happen, you have to you have to course grain,

Lex Fridman (44:17.360)

and you have to say, that is the kind of creature that does things, either things that I avoid,

Michael Levin (44:21.920)

or things that I will go towards, that's a major food or whatever, whatever it's going to be.

Lex Fridman (44:25.280)

And so right at the base of simple, very simple organisms starting to make

Michael Levin (44:31.920)

models of agents doing things, that is the origin of models of free will, basically, right, because

Michael Levin (44:39.040)

you see the world around you as having agency. And then you turn that on yourself. And you say,

Michael Levin (44:42.880)

wait, I have agency too, I can I do things, right. And and then you make decisions about what you're

Michael Levin (44:47.440)

going to do. So all of this one one model is to view all of those kinds of things as

Michael Levin (44:53.920)

being driven by that early need to determine what you are and to do so and to then take

Michael Levin (44:59.600)

actions in the most energetically efficient space possible. Right. So free will emerges

Michael Levin (45:04.800)

when you try to simplify, tell a nice narrative about your environment. I think that's very

Michael Levin (45:10.000)

plausible. Yeah. You think free was an illusion. So you're kind of implying that it's a useful hack.

Michael Levin (45:19.360)

Well, I'll say two things. The first thing is, I think I think it's very plausible to say that

Michael Levin (45:24.320)

any organism that self or any agent that self whether it's biological or not, any agent that

Michael Levin (45:30.560)

self constructs under energy constraints, is going to believe in free will, we'll get to whether it

Michael Levin (45:36.960)

has free will momentarily. But but I think but I think what what it definitely drives is a view of

Michael Levin (45:41.200)

yourself and the outside world as an agential view, I think that's inescapable. So that's true

Michael Levin (45:45.360)

for even primitive organisms? I think so. I think that's now now they don't have now obviously,

Michael Levin (45:50.480)

you have to scale down, right. So so so so they don't have the kinds of complex metacognition

Michael Levin (45:55.360)

that we have. So they can do long term planning and thinking about free will and so on and so on.

Lex Fridman (45:59.520)

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

Michael Levin (46:05.040)

right. In all kinds of spaces, not just in obvious three dimensional space. I mean, we're very good

Michael Levin (46:09.680)

that the thing is, humans are very good at detecting agency of like medium sized objects

Michael Levin (46:16.720)

moving at medium speeds in the three dimensional world, right? We see a bowling ball and we see a

Michael Levin (46:20.560)

mouse and we immediately know what the difference is, right? And how we're going to mostly things

Michael Levin (46:23.920)

you can eat or get eaten by. Yeah, yeah. That's our that's our training set, right? From the time

Michael Levin (46:28.400)

you're little, your training set is visual data on on this this like little chunk of your experience.

Lex Fridman (46:33.120)

But imagine if imagine if from the time that we were born, we had innate senses of your blood

Michael Levin (46:39.120)

chemistry, if you could feel your blood chemistry, the way you can see, right, you had a high bandwidth

Michael Levin (46:42.960)

connection, and you could feel your blood chemistry, and you could see, you could sense all

Michael Levin (46:46.640)

the things that your organs were doing. So your pancreas, your liver, all the things. If we had

Michael Levin (46:51.040)

that you we would be very good at detecting intelligence and physiological space, we would

Michael Levin (46:55.760)

know the level of intelligence that our various organs were deploying to deal with things that

Michael Levin (47:00.320)

were coming to anticipate the stimuli to, you know, but but we're just terrible at that. We

Michael Levin (47:04.400)

don't, in fact, in fact, people don't even, you know, you talk about intelligence that these are

Michael Levin (47:07.920)

the paper spaces. And a lot of people think that's just crazy, because, because all we're all we know

Michael Levin (47:12.160)

is motion. We do have access to that information. So it's actually possible that so evolution could

Michael Levin (47:18.880)

if we wanted to construct an organism that's able to perceive the flow of blood through your body,

Michael Levin (47:24.400)

the way you see an old friend and say, yo, what's up? How's the wife and the kids? In that same way,

Michael Levin (47:32.560)

you would see that you would feel like a connection to the liver. Yeah, yeah, I think,

Michael Levin (47:37.920)

you know, maybe other people's liver and not just your own, because you don't have access to other

Michael Levin (47:41.680)

people's. Not yet. But you could imagine some really interesting connection, right? But like

Michael Levin (47:46.160)

sexual selection, like, oh, that girl's got a nice liver. Well, that's like, the way her blood flows,

Michael Levin (47:52.800)

the dynamics of the blood is very interesting. It's novel. I've never seen one of those.

Lex Fridman (47:58.320)

But you know, that's exactly what we're trying to half ass when we, when we judge judgment of

Michael Levin (48:03.920)

beauty by facial symmetry and so on. That's a half assed assessment of exactly that. Because

Michael Levin (48:09.120)

if your cells could not cooperate enough to keep your organism symmetrical, you know,

Michael Levin (48:13.760)

you can make some inferences about what else is wrong, right? Like that's a very, you know,

Michael Levin (48:17.120)

that's a very basic. Interesting. Yeah. So that in some deep sense, actually, that is what we're

Michael Levin (48:23.280)

doing. We're trying to infer how health, we use the word healthy, but basically, how functional

Michael Levin (48:33.120)

is this biological system I'm looking at so I can hook up with that one and make offspring? Yeah,

Michael Levin (48:41.120)

yeah. Well, what kind of hardware might their genomics give me that that might be useful in

Michael Levin (48:45.360)

the future? I wonder why evolution didn't give us a higher resolution signal. Like why the whole

Michael Levin (48:50.720)

peacock thing with the feathers? It doesn't seem, it's a very low bandwidth signal for

Michael Levin (48:58.160)

sexual selection. I'm gonna, and I'm not an expert on this stuff, but on peacocks. Well,

Michael Levin (49:02.880)

you know, but I'll take a stab at the reason. I think that it's because it's an arms race. You

Michael Levin (49:08.880)

see, you don't want everybody to know everything about you. So I think that as much as, as much as,

Lex Fridman (49:14.160)

and in fact, there's another interesting part of this arms race, which is, if you think about this,

Lex Fridman (49:21.120)

the most adaptive, evolvable system is one that has the most level of top down control, right?

Michael Levin (49:27.760)

If it's really easy to say to a bunch of cells, make another finger versus, okay, here's 10,000

Michael Levin (49:33.920)

gene expression changes that you need to do to make it to change your finger, right? The system

Michael Levin (49:38.800)

with good top down control that has memory and when we need to get back to that, by the way,

Michael Levin (49:42.320)

that's a question I neglected to answer about where the memory is and so on. A system that uses

Michael Levin (49:48.080)

all of that is really highly evolvable and that's fantastic. But guess what? It's also highly subject

Michael Levin (49:53.920)

to hijacking by parasites, by cheaters of various kinds, by conspecifics. Like we found that,

Lex Fridman (50:01.440)

and then that goes back to the story of the pattern memory in these planaria,

Michael Levin (50:04.880)

there's a bacterium that lives on these planaria. That bacterium has an input into how many heads

Michael Levin (50:09.840)

the worm is going to have because it's hijacks that control system and it's able to make a

Michael Levin (50:14.480)

chemical that basically interfaces with the system that calculates how many heads you're

Michael Levin (50:18.480)

supposed to have and they can make them have two heads. And so you can imagine that if you

Michael Levin (50:22.080)

are two, so you want to be understandable for your own parts to understand each other,

Lex Fridman (50:25.520)

but you don't want to be too understandable because you'll be too easily controllable.

Lex Fridman (50:28.880)

And so I think that my guess is that that opposing pressure keeps us from being a super high

Michael Levin (50:36.640)

bandwidth kind of thing where we can just look at somebody and know everything about them.

Lex Fridman (50:40.240)

So it's a kind of biological game of Texas hold them. You're showing some cards and you're hiding

Michael Levin (50:45.520)

other cards and there's part of it and there's bluffing and there's all that. And then there's

Michael Levin (50:50.560)

probably whole species that would do way too much bluffing. That's probably where peacocks fall.

Michael Levin (50:56.800)

There's a book that I don't remember if I read or if I read summaries of the book,

Lex Fridman (51:04.400)

but it's about evolution of beauty and birds. Where is that from? Is that a book or does

Michael Levin (51:10.160)

Richard Dawkins talk about it? But basically there's some species start to like over select

Michael Levin (51:15.600)

for beauty, not over select. They just some reason select for beauty. There is a case to be made.

Michael Levin (51:21.280)

Actually now I'm starting to remember, I think Darwin himself made a case that you can select

Michael Levin (51:27.200)

based on beauty alone. There's a point where beauty doesn't represent some underlying biological

Michael Levin (51:35.680)

truth. You start to select for beauty itself. And I think the deep question is there some evolutionary

Michael Levin (51:44.400)

value to beauty, but it's an interesting kind of thought that can we deviate completely from

Michael Levin (51:53.760)

the deep biological truth to actually appreciate some kind of the summarization in itself.

Michael Levin (52:00.480)

Let me get back to memory because this is a really interesting idea. How do a collection of cells

Michael Levin (52:07.600)

remember anything? How do biological systems remember anything? How is that akin to the kind

Lex Fridman (52:13.520)

of memory we think of humans as having within our big cognitive engine?

Michael Levin (52:17.920)

Yeah. One of the ways to start thinking about bioelectricity is to ask ourselves, where did

Michael Levin (52:25.200)

neurons and all these cool tricks that the brain uses to run these amazing problem solving abilities

Michael Levin (52:32.320)

on and basically an electrical network, right? Where did that come from? They didn't just evolve,

Michael Levin (52:36.400)

you know, appear out of nowhere. It must have evolved from something. And what it evolved from

Michael Levin (52:40.720)

was a much more ancient ability of cells to form networks to solve other kinds of problems. For

Michael Levin (52:46.320)

example, to navigate more for space to control the body shape. And so all of the components

Michael Levin (52:52.320)

of neurons, so ion channels, neurotransmitter machinery, electrical synapses, all this stuff

Michael Levin (52:58.320)

is way older than brains, way older than neurons, in fact, older than multicellularity. And so

Michael Levin (53:03.600)

it was already that even bacterial biofilms, there's some beautiful work from UCSD on brain

Michael Levin (53:09.120)

like dynamics and bacterial biofilms. So evolution figured out very early on that electrical networks

Michael Levin (53:14.880)

are amazing at having memories, at integrating information across distance, at different kinds

Michael Levin (53:19.120)

of optimization tasks, you know, image recognition and so on, long before there were brains.

Lex Fridman (53:24.400)

Can you actually just step back? We'll return to it. What is bioelectricity? What is biochemistry?

Lex Fridman (53:30.160)

What is, what are electrical networks? I think a lot of the biology community focuses on

Michael Levin (53:36.160)

the chemicals as the signaling mechanisms that make the whole thing work. You have, I think,

Michael Levin (53:47.200)

to a large degree, uniquely, maybe you can correct me on that, have focused on the bioelectricity,

Michael Levin (53:53.600)

which is using electricity for signaling. There's also probably mechanical. Sure, sure. Like knocking

Michael Levin (54:00.080)

on the door. So what's the difference? And what's an electrical network? Yeah, so I want to make

Michael Levin (54:07.840)

sure and kind of give credit where credit is due. So as far back as 1903, and probably late 1800s

Michael Levin (54:14.800)

already, people were thinking about the importance of electrical phenomena in life. So I'm for sure

Michael Levin (54:20.560)

not the first person to stress the importance of electricity. People, there were waves of research

Michael Levin (54:25.920)

in the in the 30s, in the 40s, and then, again, in the kind of 70s, 80s, and 90s of sort of the

Michael Levin (54:33.600)

pioneers of bioelectricity, who did some amazing work on all this, I think, I think what what

Michael Levin (54:37.520)

we've done that's new, is to step away from this idea that, and I'll describe what what the

Michael Levin (54:43.040)

bioelectricity is a step away from the idea that, well, here's another piece of physics that you

Michael Levin (54:46.800)

need to keep track of to understand physiology and development. And to really start looking at this

Michael Levin (54:51.760)

as saying, no, this is a privileged computational layer that gives you access to the actual

Michael Levin (54:57.360)

cognition of the tissue of basal cognition. So, so merging that that developmental biophysics with

Michael Levin (55:02.160)

ideas and cognition of computation, and so on, I think I think that's what we've done that's new.

Lex Fridman (55:05.920)

But people have been talking about bioelectricity for a really long time. And so I'll, so I'll

Michael Levin (55:09.840)

define that. So what happens is that if you have, if you have a single cell, cell has a membrane,

Michael Levin (55:16.400)

in that membrane are proteins called ion channels, and those proteins allow charged molecules,

Michael Levin (55:21.600)

potassium, sodium, chloride, to go in and out under certain circumstances. And when there's

Michael Levin (55:27.280)

an imbalance of of those ions, there becomes a voltage gradient across that membrane. And so

Michael Levin (55:33.200)

all cells, all living cells try to hold a particular kind of voltage difference across

Michael Levin (55:38.720)

the membrane, and they spend a lot of energy to do so. When you now now, so that's it, that's it,

Michael Levin (55:44.240)

that's a single cell. When you have multiple cells, the cells sitting next to each other,

Michael Levin (55:48.720)

they can communicate their voltage state to each other via a number of different ways. But one of

Michael Levin (55:53.200)

them is this thing called a gap junction, which is basically like a little submarine hatch that

Michael Levin (55:56.880)

just kind of docks, right? And the ions from one side can flow to the other side, and vice versa.

Michael Levin (56:02.160)

So...

Michael Levin (56:02.720)

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

Michael Levin (56:09.600)

Correct. This had to be, this had to be evolved.

Lex Fridman (56:11.440)

It had to be invented.

Michael Levin (56:12.640)

That's right.

Lex Fridman (56:13.280)

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

Michael Levin (56:17.440)

Yeah. So, I mean, it is incredible. The guy who discovered gap junctions,

Lex Fridman (56:22.800)

Werner Loewenstein, I visited him. He was really old.

Lex Fridman (56:25.360)

A human being?

Lex Fridman (56:26.720)

He discovered them.

Michael Levin (56:27.360)

Because who really discovered them lived probably four billion years ago.

Lex Fridman (56:32.480)

Good point.

Lex Fridman (56:32.880)

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

Michael Levin (56:35.600)

He rediscovered gap junctions. But when I visited him in Woods Hole, maybe 20 years ago now,

Michael Levin (56:43.200)

he told me that he was writing, and unfortunately, he passed away, and I think this book never got

Michael Levin (56:47.840)

written. He was writing a book on gap junctions and consciousness. And I think it would have been

Michael Levin (56:52.800)

an incredible book, because gap junctions are magic. I'll explain why in a minute.

Lex Fridman (56:57.920)

What happens is that, just imagine, the thing about both these ion channels and these gap

Michael Levin (57:02.720)

junctions is that many of them are themselves voltage sensitive. So that's a voltage sensitive

Michael Levin (57:08.880)

current conductance. That's a transistor. And as soon as you've invented one, immediately,

Michael Levin (57:13.600)

you now get access to, from this platonic space of mathematical truths, you get access to all of the

Michael Levin (57:20.240)

cool things that transistors do. So now, when you have a network of cells, not only do they talk to

Michael Levin (57:26.000)

each other, but they can send messages to each other, and the differences of voltage can propagate.

Michael Levin (57:30.160)

Now, to neuroscientists, this is old hat, because you see this in the brain, right? This action

Michael Levin (57:34.000)

potentials, the electricity. They have these awesome movies where you can take a zebra,

Michael Levin (57:40.000)

like a transparent animal, like a zebrafish, and you can literally look down, and you can see all

Michael Levin (57:45.040)

the firings as the fish is making decisions about what to eat and things like this. It's amazing.

Michael Levin (57:49.120)

Well, your whole body is doing that all the time, just much slower. So there are very few things

Michael Levin (57:54.160)

that neurons do that all the cells in your body don't do. They all do very similar things, just

Michael Levin (57:59.360)

on a much slower timescale. And whereas your brain is thinking about how to solve problems in

Michael Levin (58:04.320)

three dimensional space, the cells in an embryo are thinking about how to solve problems in

Michael Levin (58:08.880)

anatomical space. They're trying to have memories like, hey, how many fingers are we supposed to

Michael Levin (58:12.240)

have? Well, how many do we have now? What do we do to get from here to there? That's the kind of

Michael Levin (58:15.840)

problems they're thinking about. And the reason that gap junctions are magic is, imagine, right,

Michael Levin (58:20.720)

from the earliest time. Here are two cells. This cell, how can they communicate? Well,

Michael Levin (58:29.360)

the simple version is this cell could send a chemical signal, it floats over, and it hits

Michael Levin (58:34.800)

a receptor on this cell, right? Because it comes from outside, this cell can very easily tell that

Michael Levin (58:39.200)

that came from outside. Whatever information is coming, that's not my information. That information

Michael Levin (58:44.240)

is coming from the outside. So I can trust it, I can ignore it, I can do various things with it,

Michael Levin (58:48.640)

I can do various things with it, whatever, but I know it comes from the outside. Now imagine

Michael Levin (58:52.160)

instead that you have two cells with a gap junction between them. Something happens,

Michael Levin (58:55.360)

let's say this cell gets poked, there's a calcium spike, the calcium spike or whatever small

Michael Levin (58:59.760)

molecule signal propagates through the gap junction to this cell. There's no ownership

Michael Levin (59:04.400)

metadata on that signal. This cell does not know now that it came from outside because it looks

Michael Levin (59:10.000)

exactly like its own memories would have looked like of whatever had happened, right? So gap

Michael Levin (59:15.200)

junctions to some extent wipe ownership information on data, which means that if I can't, if you and

Michael Levin (59:21.440)

I are sharing memories and we can't quite tell who the memories belong to, that's the beginning of a

Michael Levin (59:26.320)

mind melt. That's the beginning of a scale up of cognition from here's me and here's you to no,

Michael Levin (59:31.840)

now there's just us. So they enforce a collective intelligence gap junctions. That's right. It

Michael Levin (59:36.640)

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

Michael Levin (59:39.680)

Where's state stored of the system? Is it in part in the gap junctions themselves? Is it in the

Michael Levin (59:48.240)

cells? There are many, many layers to this as always in biology. So there are chemical networks.

Lex Fridman (59:55.360)

So for example, gene regulatory networks, right? Which, or basically any kind of chemical pathway

Michael Levin: Biology, Life, Aliens, Evolution, Embryogenesis & Xenobots

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