# Ep111 "Might we be surrounded with undetected minds?" (with Michael Levin)

An episode of [Inner Cosmos with David Eagleman](https://invisiblenetwork.ai/podcast/1677842672), 7 Jul 2025, 52 min.

Audio: https://podtrac.com/pts/redirect.mp3/traffic.omny.fm/d/clips/e73c998e-6e60-432f-8610-ae210140c5b1/534dc725-2971-42a1-b1bf-afaa0107416e/d7b6bac4-68f5-4c82-86c3-b313005d97bb/audio.mp3?utm_source=Podcast&in_playlist=bee00526-7528-4581-b6b6-afaa01083a3d

What is intelligence? If we look hard, can we find it in unexpected places: not just in brains but in all kinds of structures? How should we recognize it? And what does any of this have to do with a bipedal dog born without front legs, or making small new organisms out of single cells, or how Wikipedia might be like an axolotl, or why we are so blind to the vast variety of minds that might surround us? Join Eagleman with guest Michael Levin, professor at Tufts, about how we might discover intelligence all around us in ways we don't typically intuit.

## Transcript

[0:05] Speaker 1: What is intelligence And if we look hard, might we
[0:09] Speaker 1: find it in very weird places, not just in brains,
[0:13] Speaker 1: but in all kinds of structures in our universe? And
[0:17] Speaker 1: how would we even recognize it? And what does any
[0:20] Speaker 1: of this have to do with a dog born without
[0:23] Speaker 1: front legs learning how to walk bipedally, or making new
[0:28] Speaker 1: little organisms out of single cells, or how Wikipedia might
[0:33] Speaker 1: be like an axilautel and why we are so blind
[0:37] Speaker 1: to the vast variety of minds that might surround us.
[0:44] Speaker 1: Welcome to Intercosmos with me, David Eagleman. I'm a neuroscientist
[0:48] Speaker 1: and an author at Stanford, and in these episodes we
[0:51] Speaker 1: sail deeply into our three pound universe to understand some
[0:55] Speaker 1: of the most surprising aspects of the world around us.
[1:15] Speaker 1: Today's episode is about intelligence, not in the way that
[1:18] Speaker 1: I've talked about in earlier episodes, about how the structure
[1:22] Speaker 1: of the human brain gives rise to intelligence and how
[1:25] Speaker 1: we can measure whether AI's intelligence. Today's episode is way
[1:29] Speaker 1: beyond that. Today I'm going to talk with one of
[1:32] Speaker 1: my most brilliant and creative colleagues, Michael Levin, about how
[1:37] Speaker 1: we might find intelligence all around us in ways that
[1:41] Speaker 1: we don't typically into it. So let's start at the beginning.
[1:45] Speaker 1: What is intelligence. It's a word that we usually reserve
[1:49] Speaker 1: for something abstract and cerebral, something associated with problem solving
[1:54] Speaker 1: and planning and passing IQ tests. We tend to picture
[1:58] Speaker 1: intelligence as a property of brains, and especially big human brains.
[2:04] Speaker 1: We're generally willing to grant some intelligence to dolphins and
[2:08] Speaker 1: chimps and clever birds like ravens, but it's hard to
[2:12] Speaker 1: know how to think about so many other things happening
[2:15] Speaker 1: in the world. For example, my skin cells heal a wound.
[2:21] Speaker 1: Is that intelligence or is that just biochemical cascades? A
[2:25] Speaker 1: plant grows towards sunlight intelligent? A worm gets its head
[2:30] Speaker 1: cut off and it regrows it. That's amazing, But we
[2:34] Speaker 1: don't tend to call that cognition. But what if we've
[2:37] Speaker 1: been looking at the whole notion of intelligence too narrowly.
[2:41] Speaker 1: What if intelligence isn't just about neurons and genes, but
[2:45] Speaker 1: it's about goals, and specifically, it's about the ability of
[2:49] Speaker 1: a system to navigate towards an objective, to adapt to
[2:54] Speaker 1: its circumstances, to make decisions.
[2:57] Speaker 2: Along the way.
[2:58] Speaker 1: That's a broader definition of intelligence. And if we apply it,
[3:02] Speaker 1: suddenly intelligence doesn't just belong to creatures with brains. It
[3:07] Speaker 1: becomes something that shows up in places we didn't expect.
[3:12] Speaker 1: Think about really simple creatures like a tadpole. It's millions
[3:16] Speaker 1: of cells collaborate and communicate and organize into an eye
[3:21] Speaker 1: and a spine and a heart without anybody orchestrating the
[3:25] Speaker 1: whole thing. There's no central planning, it's just a kind
[3:28] Speaker 1: of emergent intelligence at work. Or think about a flatworm
[3:33] Speaker 1: that can be cut into pieces and each piece regenerates
[3:36] Speaker 1: a complete, properly shaped body. How does each fragment know
[3:41] Speaker 1: what's missing? Where exactly is that information stored? What is
[3:45] Speaker 1: guiding the process? And as we ask these questions, that
[3:49] Speaker 1: leads us to ask how we can learn to talk
[3:52] Speaker 1: to these systems in the language that they understand, like
[3:55] Speaker 1: voltage gradients or bioelectric circuits or chemical signals. Can we
[3:59] Speaker 1: start reprogramming the goals of tissues? Can we tell a
[4:04] Speaker 1: clump of cells to build something new? And can we
[4:08] Speaker 1: use this kind of knowledge to regenerate organs, or repair
[4:13] Speaker 1: birth defects or.
[4:15] Speaker 2: Create entirely new forms of life.
[4:17] Speaker 1: These are the kinds of questions that today's guest has
[4:21] Speaker 1: spent his career exploring and his work leads us to
[4:24] Speaker 1: the conclusion that we're probably surrounded by minds, almost all
[4:29] Speaker 1: of which we don't recognize. Minds are pervasive, and they're
[4:33] Speaker 1: not restricted to brains, but spread across all kinds of
[4:37] Speaker 1: levels of organization, from single cells to societies. My guest
[4:45] Speaker 1: is Michael Levin. He's a distinguished professor of developmental and
[4:49] Speaker 1: Synthetic biology at Tufts University, and I've had him on
[4:52] Speaker 1: the podcast before because he's really one of my favorite
[4:55] Speaker 1: thinkers in the field. He's massively creative and always pulling
[4:59] Speaker 1: off amazing results that the frontier where biology meets information
[5:04] Speaker 1: theory or computation or philosophy, and as you're going to see,
[5:07] Speaker 1: his work always challenges our deepest intuitions about agency and
[5:12] Speaker 1: memory and selfhood. So you've heard of SETI, the Search
[5:16] Speaker 1: for Extraterrestrial Intelligence. Recently, Levin proposed SUTI, the search for
[5:21] Speaker 1: unconventional terrestrial intelligence. As we're about to hear, his position
[5:27] Speaker 1: is that right here on Earth, there are already aliens
[5:31] Speaker 1: among us that stretch and sometimes break. Are typical ways
[5:35] Speaker 1: of thinking about minds. So if you've ever wondered where
[5:40] Speaker 1: intelligence begins, how far it reaches, or whether you might
[5:44] Speaker 1: share more in common with blobs of cells than you think.
[5:48] Speaker 1: This episode is for you. Here's my interview with Mike Levin. So, Mike,
[5:56] Speaker 1: let's start by telling us how you define intelligence.
[6:01] Speaker 3: Okay, what I use is a definition that helps us
[6:04] Speaker 3: move forward in the lab. I do not claim that
[6:06] Speaker 3: it's the only definition or that it captures everything there
[6:09] Speaker 3: is to capture about intelligence. But I like William James's definition,
[6:13] Speaker 3: which is some degree of the ability to reach the
[6:15] Speaker 3: same goal by different means. So it's some level of
[6:18] Speaker 3: ingenuity to get your goals met when things change. That
[6:21] Speaker 3: doesn't capture play. It doesn't necessarily capture creativity, things other
[6:24] Speaker 3: than problem solving.
[6:25] Speaker 2: But this is what we focus on experimentally.
[6:27] Speaker 1: So typically when we think about intelligence, we think about
[6:30] Speaker 1: brains and nervous systems. But you think it doesn't even
[6:33] Speaker 1: necessarily require that.
[6:35] Speaker 3: Correct, Because if you're looking at it in this way,
[6:38] Speaker 3: that it's basically a functional capacity to navigate some kind
[6:41] Speaker 3: of problem space and meet specific goals under changing circumstances.
[6:45] Speaker 3: There are apparently a wide range of architectures that can
[6:48] Speaker 3: do this, and in order to see that what you
[6:50] Speaker 3: need to do is to relax some really constraining assumptions
[6:54] Speaker 3: that we often have about the problem space that we're
[6:56] Speaker 3: working in.
[6:56] Speaker 1: And so you often describe intelligence as scale free. So
[7:00] Speaker 1: just give us a sense what you mean by that.
[7:02] Speaker 3: Yeah, I mean that you know, as humans, we are
[7:05] Speaker 3: because of our own evolutionary firm where we are so
[7:07] Speaker 3: obsessed with three dimensional space and moving around in three
[7:10] Speaker 3: dimensional space, to the point where if people see some
[7:12] Speaker 3: sort of AI that isn't rolling around in some sort
[7:14] Speaker 3: of robotic body, they're going to say, this is not embody, right,
[7:16] Speaker 3: because they're expecting a body. People are expecting a body
[7:19] Speaker 3: that moves through three dimensional space. But actually the biology,
[7:23] Speaker 3: for example, has been solving problems and navigating all kinds
[7:26] Speaker 3: of spaces that are hard for us to visualize. So
[7:28] Speaker 3: the space of gene expression states, the space of physiological states,
[7:31] Speaker 3: the space of anatomical possible outcomes, and so on, and
[7:34] Speaker 3: so in order to understand how we navigate those spaces,
[7:38] Speaker 3: you have to think in other scales. Some of these
[7:40] Speaker 3: things happen very slowly, some of these things happen incredibly quickly.
[7:43] Speaker 3: Some of these things are very small, some of them
[7:45] Speaker 3: are very large, and we are just you know, focused
[7:49] Speaker 3: on medium sized objects moving at medium speeds. But you know,
[7:52] Speaker 3: I'm not claiming it's entirely scale free, but I'm saying
[7:55] Speaker 3: that there are very deep scale invariant principles that operate
[7:59] Speaker 3: at many different scales besides the ones we're used to.
[8:01] Speaker 1: So how have you gone about looking for intelligence at
[8:05] Speaker 1: other scales?
[8:06] Speaker 3: So one thing that you that you can do once
[8:09] Speaker 3: you've bought into the fact that we can't assume how
[8:13] Speaker 3: intelligent anything is or what kind of intelligence it has,
[8:15] Speaker 3: but you have to do experiments. Then then what it
[8:17] Speaker 3: turns out you have to do is you have to
[8:19] Speaker 3: posit some sort of problem space you have that the
[8:22] Speaker 3: system is working in. You have to posit some sort
[8:25] Speaker 3: of goal. That is, these are all hypotheses on some
[8:27] Speaker 3: sort of goal that it's it's trying to reach. And
[8:29] Speaker 3: then what you have to do is perturbational experiments to
[8:32] Speaker 3: prevent it from reaching its goal. And then you see
[8:34] Speaker 3: what how you know how how smart the system is
[8:36] Speaker 3: in getting around whatever you did to it, So you.
[8:38] Speaker 1: Knock it off track and then you see how it
[8:40] Speaker 1: comes back, or if it comes back.
[8:42] Speaker 3: Knock it off track is a good one add barriers
[8:45] Speaker 3: of some sort in whatever space you're working doesn't have
[8:47] Speaker 3: to be a physical barrier, but whatever space you're working in,
[8:49] Speaker 3: add a barrier, or in fact manipulate.
[8:51] Speaker 2: The system itself.
[8:52] Speaker 3: So change the system, right, It's not all about the environment,
[8:54] Speaker 3: so it can be about the system itself. And so
[8:56] Speaker 3: we've done this now in many different context Here are
[8:59] Speaker 3: a couple of favorites of ours. The biggest one that
[9:01] Speaker 3: we do most of our work in is morphogenesis. So
[9:04] Speaker 3: we all make a journey from a single cell to whatever.
[9:07] Speaker 3: You know, we're going to be a human, a giraffe, plant, whatever,
[9:09] Speaker 3: And that journey, as it turns out, as a matter
[9:12] Speaker 3: of experimental fact, it turns out that journey is not
[9:15] Speaker 3: a kind of open loop. What you know, the way
[9:17] Speaker 3: people model the emergence and complexity, lots of simple things
[9:20] Speaker 3: happening again and again, and ultimately some sort of complex
[9:23] Speaker 3: event happens.
[9:24] Speaker 2: That isn't how it works. It's very contact sensitive.
[9:27] Speaker 3: If you try to prevent let's say, embryos or regenerating limbs,
[9:31] Speaker 3: or or you know, in any you know, metamorphosis, any
[9:33] Speaker 3: of these processes, you try to prevent them from reaching
[9:36] Speaker 3: their goal. They often have extremely ingenious ways to get
[9:39] Speaker 3: there anyway, okay, And you can quantify this, and you
[9:42] Speaker 3: can say, what are kinds of perturbations that it's able
[9:46] Speaker 3: to deal with? And does it have delayed gratification? Does
[9:48] Speaker 3: it have planning, does it have a representation of the
[9:52] Speaker 3: of counterfactual states? Does it have learning and memory? Does
[9:55] Speaker 3: it store you know, a map of its environment? You
[9:57] Speaker 3: can you can test all of these things empirically.
[9:59] Speaker 1: So I mean in terms of let's say an embryo developing,
[10:03] Speaker 1: what we think traditionally in textbooks is that the genetics
[10:06] Speaker 1: somehow gives a blueprint and the whole thing just donepacks.
[10:10] Speaker 1: But you're asking is how is the system intelligent if
[10:13] Speaker 1: we knock it off track or put barriers in the way,
[10:15] Speaker 1: how does it figure out how to come together correctly? So,
[10:19] Speaker 1: what's the specific example of something you've done here? Here
[10:22] Speaker 1: there are some of my favorites. These first two are
[10:24] Speaker 1: not my work. These this is like classic, classic work
[10:26] Speaker 1: in the field.
[10:27] Speaker 3: So if you take normally, imagine cutting a cross section
[10:31] Speaker 3: through the kidney tubule of a newt. Normally what you'd
[10:34] Speaker 3: find is like eight to ten cells and they work
[10:36] Speaker 3: together to build to build this tube like structure. So
[10:41] Speaker 3: what you can do is you can make polyploid neutes
[10:42] Speaker 3: that have multiple copies of their chromosomes, which means their
[10:45] Speaker 3: cells have to get bigger. Those newts are still the
[10:47] Speaker 3: same correct size. So that's the first interesting thing. Wow,
[10:50] Speaker 3: well the cells get bigger, the thing scales down. How
[10:53] Speaker 3: does it do it by using fewer but bigger cells
[10:56] Speaker 3: to make the exact same structure.
[10:57] Speaker 2: So that's an adjustment, right, never mind the environment.
[11:00] Speaker 3: Your own parts are changing, and this thing is figuring
[11:01] Speaker 3: out how to get to the exact same goal, the
[11:03] Speaker 3: same neud same shape, same size, fewer of these bigger cells.
[11:06] Speaker 2: So let me ask you a quick question.
[11:08] Speaker 1: Is this analogous to the fact that a mouse's heart
[11:12] Speaker 1: and an elephant's heart are doing the same thing, but
[11:15] Speaker 1: they're made of a completely different number of cells. It's
[11:19] Speaker 1: a massive heart in an elephant, very tiny mouse, but
[11:21] Speaker 1: it's doing the same function.
[11:24] Speaker 3: It's similar, but there's only but there's one major difference
[11:27] Speaker 3: both in a mouse and in an elephant. What people
[11:29] Speaker 3: will say is, well, both of those have had long,
[11:32] Speaker 3: long history of being what they are, and so this
[11:34] Speaker 3: is just mechanical.
[11:35] Speaker 2: It just does what it does.
[11:36] Speaker 3: My example is different because you've done something to this
[11:39] Speaker 3: new that it does.
[11:40] Speaker 2: Not normally do.
[11:41] Speaker 3: You've given it a completely novel circumstance, and then it
[11:45] Speaker 3: adjusts in a new way. And the craziest thing happens
[11:48] Speaker 3: when you make the cells really gigantic. Okay, these are
[11:51] Speaker 3: like I think six or eight and the newts so
[11:53] Speaker 3: massive polyplaty. What happens is the cells are so big
[11:57] Speaker 3: there's no room for more than one cell.
[11:58] Speaker 2: One cell will wrap around und itself and give you
[12:01] Speaker 2: the lumen of the tubule in the middle.
[12:03] Speaker 3: Now, now this is crazy because because that is that's
[12:06] Speaker 3: a different molecular mechanism that side of skeletalle bending before
[12:09] Speaker 3: it was sell to sell communication and tubulo genesis. So
[12:12] Speaker 3: think about what this means. If you're a nude coming
[12:13] Speaker 3: into the world. Never mind the environment. You don't really
[12:16] Speaker 3: know what your environment's going to be. You don't know
[12:17] Speaker 3: how many copies of your chromosomes you're going to have,
[12:20] Speaker 3: you don't know how big your cells are going to be.
[12:22] Speaker 3: You don't know which of your many genetic affordances you
[12:25] Speaker 3: can use. Right, you have different molecular mechanisms you can use.
[12:28] Speaker 3: You have to figure out what to do in a
[12:29] Speaker 3: novel circumstance and still get the job done. I mean
[12:32] Speaker 3: this sounds like every IQ test you've ever heard of
[12:34] Speaker 3: when people show you, here's a little box, some tax
[12:36] Speaker 3: and a candle, and I want you to, you know,
[12:38] Speaker 3: solve this this particular problem. Right, Yeah, you have genetic affordances,
[12:42] Speaker 3: and then that morphogenetic process is not just doing the
[12:45] Speaker 3: same thing every single time.
[12:47] Speaker 2: You have to solve these problems. That's one of my
[12:49] Speaker 2: favorite examples.
[12:50] Speaker 3: Another one that we discovered is tadpoles become frogs, and
[12:53] Speaker 3: in order to do that, they have to rearrange their
[12:55] Speaker 3: face because their face looks actually quite different from and
[12:58] Speaker 3: you know, so the eyes, the jaws of reading has
[12:59] Speaker 3: to has to move.
[13:00] Speaker 2: What people thought was that this is a hardwired process.
[13:02] Speaker 3: Basically, somehow the genetics just tells every organ how far
[13:05] Speaker 3: to moving, what direction, and then you get from a
[13:07] Speaker 3: normal tadpole to a normal frog.
[13:09] Speaker 2: So we decided to test that.
[13:10] Speaker 3: Because you can't assume these things, you have to test
[13:12] Speaker 3: and so what we created was something called Picasso tadpoles,
[13:16] Speaker 3: so basically scrambled all the initial positions, so the mouth
[13:20] Speaker 3: is off to the side, the eyes on the back
[13:22] Speaker 3: of the head like everything is completely scramble. And what
[13:25] Speaker 3: we find is that they make pretty normal frogs because
[13:28] Speaker 3: all of these things will move in novel paths to
[13:31] Speaker 3: reach the correct goal, and then they stop. Actually, sometimes
[13:33] Speaker 3: they go too far and they have to double back
[13:35] Speaker 3: and stop. This is another example. You start them off
[13:36] Speaker 3: in the wrong position, they don't just blindly go the
[13:38] Speaker 3: same distance. They actually go until they meet their goal.
[13:41] Speaker 3: And you know, it's a goal.
[13:42] Speaker 2: And when I say goal, I don't mean it's a human.
[13:44] Speaker 3: Level, like I know what my goal is. That's a
[13:46] Speaker 3: kind of metacognition that I'm not claiming here. I'm saying
[13:48] Speaker 3: it's in the cybernetic sets like your thermostat has goals.
[13:51] Speaker 3: It's a set point, and now how clever are you
[13:54] Speaker 3: to be able to reach that set point when things change?
[13:58] Speaker 1: As an interesting analogy, what's going on at the level
[14:00] Speaker 1: of brain plasticity. We tend to think that, let's say,
[14:04] Speaker 1: a dog's brain is pre wired to drive a dog's body.
[14:09] Speaker 1: But one of the examples that I talked about in
[14:11] Speaker 1: my book Live Wired was this dog who was born
[14:14] Speaker 1: without front legs, and so she just walks bipedally and
[14:18] Speaker 1: she moves all around and.
[14:20] Speaker 2: Gets by that way.
[14:21] Speaker 1: Why, because she needed to get to her her dog
[14:25] Speaker 1: bowl and her water and other dogs and so on,
[14:28] Speaker 1: and so she just figured out. It turns out it's
[14:30] Speaker 1: not that hard for a dog to walk on back legs.
[14:32] Speaker 1: And the question is could all dogs walk on their
[14:35] Speaker 1: back legs? Presumably, but they don't have the proper motivation
[14:39] Speaker 1: to do so. But the point is that the dog's
[14:43] Speaker 1: body is very flexible.
[14:44] Speaker 2: It meets the goals of the world.
[14:46] Speaker 1: Another analogy is the world's best archer, as in he's
[14:51] Speaker 1: got the world record for the longest accurate shot in archery.
[14:55] Speaker 1: Is a guy named Matt Stutsman who happens to have
[14:58] Speaker 1: no arms, and he got interest in archery and figured
[15:01] Speaker 1: out how to pull the bow with his legs, and
[15:05] Speaker 1: so he shoots with his legs and became a great
[15:07] Speaker 1: archer that way.
[15:09] Speaker 2: Amazing, amazing. Yeah, yeah, the plasticity is incredible.
[15:12] Speaker 3: And you know, the earlier the earliest example that I
[15:15] Speaker 3: know of the of this hind leg thing is is
[15:17] Speaker 3: Slipper's Goat, which was this I think it was in
[15:20] Speaker 3: the forties. This guy Slipper, a published study of a
[15:23] Speaker 3: goat who, again born without four legs, learned to walk
[15:26] Speaker 3: on its hind legs. When they dissected the goat, they
[15:28] Speaker 3: found out that a lot of the adjustments that you
[15:31] Speaker 3: need for bipedal locomotion, right, So things about the hips,
[15:34] Speaker 3: you know, the spine, all kind of stuff, we're all
[15:36] Speaker 3: there right as opposed to what you normally think of
[15:38] Speaker 3: for the evolution of modern humans as you know how
[15:40] Speaker 3: I mean, many hundreds of thousands of years you need
[15:42] Speaker 3: for that. And this is what's really interesting about this
[15:45] Speaker 3: plasticity is that you can project it into other spaces.
[15:47] Speaker 3: So so, as you pointed out, you know, can a
[15:50] Speaker 3: dog brain run an upright body?
[15:52] Speaker 2: Right?
[15:53] Speaker 3: Now?
[15:53] Speaker 2: Look at individual cells?
[15:55] Speaker 3: Can the same genome run a completely different anatomy and
[15:58] Speaker 3: set of behaviors? And this is what we've I mean,
[16:01] Speaker 3: other people have shown other examples of this, but for example,
[16:03] Speaker 3: in our lab, xenobots anthwrobots, right, these living constructs that
[16:07] Speaker 3: have a completely different body than what they normally do.
[16:10] Speaker 3: They have a different behavioral repertoire, no genetic change, same
[16:13] Speaker 3: gene regulatory networks are running a completely different body.
[16:17] Speaker 1: For the listenership, can you define anthrobots and xenobots which
[16:20] Speaker 1: you've built?
[16:20] Speaker 2: Sure, let's start with the zenobots.
[16:22] Speaker 3: So in the cases of zennabots, what our team did,
[16:25] Speaker 3: and this is in collaboration with Josh bond Guard's lab
[16:28] Speaker 3: at University the e Vermont and this is Doug Blackiston
[16:30] Speaker 3: in my group and Sam Kregman did a lot of
[16:32] Speaker 3: the computational work for it. What happens is that when
[16:35] Speaker 3: you liberate some epithelial cells from an early frog embryo,
[16:37] Speaker 3: normally what they do is they form this like two
[16:40] Speaker 3: dimensional outer covering of an embryo and the outer skin layer,
[16:43] Speaker 3: and they do that because they're induced to do that by.
[16:46] Speaker 2: The other cells.
[16:47] Speaker 3: Well, if you get them away from the other cells,
[16:49] Speaker 3: you sort of liberate, then find out what they really
[16:50] Speaker 3: want to do on their own and what they do.
[16:52] Speaker 2: They could do many things.
[16:53] Speaker 3: They could crawl away from each other, they could die,
[16:55] Speaker 3: they could make a flat layer like cell culture. What
[16:58] Speaker 3: to actually do is they form this little ball with
[17:00] Speaker 3: cilia that are on the outside. He's a little moving
[17:02] Speaker 3: hairs and they organize them so that the thing can
[17:05] Speaker 3: swim and it starts swimming around.
[17:07] Speaker 2: It has all sorts of interesting behaviors.
[17:08] Speaker 3: A couple of years ago we show that they do
[17:10] Speaker 3: kinematic cell for replication, which is that if you sprinkle
[17:12] Speaker 3: a bunch of loose skin cells in their environment, they
[17:14] Speaker 3: will collect them into little balls and guess what, those
[17:17] Speaker 3: become the next generation of zenobots. Right, So they can
[17:19] Speaker 3: do this weird kinematic replication that, as far as we know,
[17:22] Speaker 3: no other creature does. They express hundreds of genes differently
[17:25] Speaker 3: than they do within the embryo. No genetic change. By
[17:29] Speaker 3: the way, this is we're not adding anything. There are
[17:31] Speaker 3: no scaffolds, no, no synthetic circuits. But they use their
[17:34] Speaker 3: transcriptional affordances differently. They turn to hundreds of new genes
[17:39] Speaker 3: and among other things, it turns out they're sensitive to
[17:42] Speaker 3: acoustic vibrations.
[17:43] Speaker 2: That's the latest thing that just came out a month ago.
[17:45] Speaker 3: Is that we get because we found they were turning
[17:47] Speaker 3: out a bunch of genes related to hearing.
[17:48] Speaker 2: And we said, is it possible at these things we hear?
[17:50] Speaker 3: And so Vipofpie my group put a speaker under them
[17:53] Speaker 3: and showed that, yeah, there's actually sounds you can send
[17:55] Speaker 3: them that they will respond to.
[17:57] Speaker 2: So that's zenobots.
[17:58] Speaker 3: Anthrobots are a similar story because when we first did it,
[18:02] Speaker 3: some people said, well, you know, they're embryonic cells and
[18:05] Speaker 3: amphibia are plastic. Maybe that's why this is like a
[18:07] Speaker 3: frog embryology thing. You know, this is specific to and centebat.
[18:10] Speaker 3: So I said, okay, what's the furthest you can get
[18:12] Speaker 3: from an embryonic frog.
[18:13] Speaker 2: Well, I'll be an adult human.
[18:14] Speaker 3: And so we went and we took trachael epithelial cells
[18:18] Speaker 3: from adult human patients and we showed and this is
[18:21] Speaker 3: Gizem Gumushke's work, PhD student in my group.
[18:23] Speaker 2: Who developed a protocol whereby.
[18:26] Speaker 3: Again simply by taking the cells out of their normal context,
[18:29] Speaker 3: you get to release their the various possible outcomes that
[18:33] Speaker 3: they can do, and they make anthrobots. It's a little
[18:36] Speaker 3: round little thing that zips around. It has a couple
[18:39] Speaker 3: of interesting properties. First of all, it can heal neural wounds.
[18:43] Speaker 3: So if you played a dish of human neurons and
[18:45] Speaker 3: you put a big scratch through it with a scalpel,
[18:47] Speaker 3: they will. When they find the scratch, they settle down,
[18:49] Speaker 3: a bunch of them. We call it a superbot cluster.
[18:51] Speaker 3: They settle down and within about four days, if you
[18:54] Speaker 3: lift them up, you see that what they did. For
[18:55] Speaker 3: what they did meanwhile is they healed across the They
[18:57] Speaker 3: healed across the gap. Okay, who would have thought that
[19:00] Speaker 3: your trachular ethelial cells that sit there quietly dealing with
[19:03] Speaker 3: you know, mucus and and and the air particles have
[19:06] Speaker 3: the ability to to to to heal neurons. And these
[19:09] Speaker 3: guys express about nine thousand genes differently than right, so
[19:13] Speaker 3: what almost half the genome they express differently than than
[19:17] Speaker 3: they do in the body. They're, by the way, younger
[19:20] Speaker 3: than the patient, than the than the cells that they
[19:22] Speaker 3: came from. So so actually that process of becoming an
[19:24] Speaker 3: answer what actually rolls back the epigenetic clock.
[19:27] Speaker 2: So so they're they're they're a bit younger.
[19:29] Speaker 3: This is fascinating, you know, behaviors and and all of
[19:31] Speaker 3: this is run by that standard controller. So so that's
[19:34] Speaker 3: kind of my point is that is that there's amazing
[19:36] Speaker 3: plasticity in the brain and nervous system. But this goes
[19:39] Speaker 3: all the way down. This is not just for you know,
[19:41] Speaker 3: fancy fancy brains.
[19:57] Speaker 1: So we think about this as problems solved by the system.
[20:02] Speaker 1: And what's interesting, let's just come back for a second
[20:04] Speaker 1: to the dog or the goat without fore limbs. We
[20:08] Speaker 1: generally assume Okay, Look, if you're born with the typical
[20:12] Speaker 1: structure of the animal, then you just develop in this way.
[20:15] Speaker 1: But otherwise there's a lot of deep problem solving that
[20:18] Speaker 1: has to go on. But I know that you think
[20:21] Speaker 1: about it as, Hey, maybe the system is always problem solving.
[20:24] Speaker 2: Maybe it's problem solving no matter what if you have
[20:27] Speaker 2: front legs or not. It's just figuring out what to
[20:30] Speaker 2: do to get to the goals. Yeah, I think I
[20:33] Speaker 2: think that's right.
[20:34] Speaker 3: And and you know, in the last couple of years
[20:36] Speaker 3: we've really emphasized this and started to develop this idea
[20:40] Speaker 3: that you know, you can think about it as beginner's
[20:42] Speaker 3: mind basically, the way that the reason that all these
[20:44] Speaker 3: incredible plastic the plasticities exist. You know, when when we
[20:48] Speaker 3: make a Doug Blackiston years ago in our lab, many
[20:51] Speaker 3: tadpoles with eyes on their tails, and these these guys
[20:54] Speaker 3: could see they were.
[20:55] Speaker 2: Not connected to the brain.
[20:56] Speaker 3: They make an optic nerve that connects sometimes to the
[20:58] Speaker 3: spinal corse, sometimes to the gut, sometimes no where. They
[21:00] Speaker 3: can see it, and they can learn visual tasks. Why
[21:03] Speaker 3: does that work out of the box. Why don't you
[21:05] Speaker 3: need you know, new rounds of selection mutation? You know,
[21:08] Speaker 3: basically adaptation.
[21:09] Speaker 2: All of these things.
[21:10] Speaker 3: Plasticities work, I think because it never expected everything to
[21:14] Speaker 3: be in the right place to begin with. It has
[21:16] Speaker 3: to solve the problem from scratch every single time. And
[21:19] Speaker 3: that goes back to the idea that biology is fundamentally
[21:22] Speaker 3: dealing with an unreliable medium. Think about the way we
[21:25] Speaker 3: build computers today. So we have error correcting codes, we
[21:28] Speaker 3: have abstraction layers.
[21:29] Speaker 2: Right.
[21:30] Speaker 3: The reason that we do, you know, our microchips can't
[21:32] Speaker 3: can't scale down easily, is because you don't want the
[21:35] Speaker 3: data interfering with each other. Right when you get to
[21:37] Speaker 3: that atomic limit that you know, the memory, the bits
[21:41] Speaker 3: that are in there are stars starting to you know,
[21:43] Speaker 3: interact with each other, and you don't want that.
[21:44] Speaker 2: All of all of our current.
[21:46] Speaker 3: Computer technology is built around the fidelity of the data.
[21:49] Speaker 3: And that's because the interpreter of that data is us,
[21:52] Speaker 3: the user. We don't you know, the computer has no issues,
[21:54] Speaker 3: It doesn't need to interpret the data. We interpret the data,
[21:57] Speaker 3: so all the computer has to do is keep the
[21:59] Speaker 3: data still. Biology is exactly the opposite. First of all,
[22:02] Speaker 3: you have no hope of keeping anything still in biology.
[22:04] Speaker 3: You have no idea, never mind your environment but you're
[22:06] Speaker 3: going to mutate as a lineage, you're going to mutate.
[22:08] Speaker 2: You can't count on your parts.
[22:09] Speaker 3: You can't count on, you know, knowing how many copies
[22:12] Speaker 3: of any protein you're going to have. Things degrade, the environment,
[22:15] Speaker 3: you know, internal millire plus or minus, you know whatever, homeostasis.
[22:18] Speaker 3: But things are always changing. So I think what biology
[22:22] Speaker 3: really cranks on. And we've done computational simulations showing how
[22:25] Speaker 3: this happens. That the minute you have this kind of
[22:28] Speaker 3: problem solving material, I call it an agential material because
[22:31] Speaker 3: it's not just the computational material, it's actually an agential material.
[22:34] Speaker 3: And the minute you have that material, evolution it starts
[22:36] Speaker 3: to hide information from selection because you're not looking at
[22:39] Speaker 3: the genome. You're looking at what's going on after you've
[22:42] Speaker 3: you've solved the problem using whatever.
[22:44] Speaker 2: Tools the genome has given you.
[22:45] Speaker 3: And that means that evolution starts to spend a lot
[22:47] Speaker 3: of its time cranking on that problem solving capacity.
[22:50] Speaker 2: It spends you know, less of its time on the.
[22:54] Speaker 3: On the hardwired mechanisms, and more of its time on
[22:56] Speaker 3: that creative, confabulatory problem solving.
[22:59] Speaker 2: So I see all of these.
[23:00] Speaker 3: Things, you know, behavioral memories, genetic memories, meaning you know
[23:04] Speaker 3: your genome, of your lineage. All of these things are
[23:07] Speaker 3: basically messages. They're messages from your past self. They're prompts,
[23:11] Speaker 3: but at any given moment it's up to you how
[23:12] Speaker 3: you're going to interpret them. And the biological material has
[23:16] Speaker 3: eons of pressure to learn to tell good stories with
[23:20] Speaker 3: whatever it's given, whatever information it's given.
[23:22] Speaker 2: And that's morphogenesism, behavior and so on.
[23:26] Speaker 1: Yeah, you know, as an analogy, this is exactly the
[23:29] Speaker 1: argument that I made in Live Wired, is that the
[23:32] Speaker 1: genes do not specify the blueprints for making the brain. Instead,
[23:37] Speaker 1: it's just specifying how to build this problem solving organism.
[23:41] Speaker 1: And as you know, you know, children can get a
[23:43] Speaker 1: hemisphere ectomy, which means half of their brain is removed.
[23:47] Speaker 2: For example, if they.
[23:47] Speaker 1: Have an epilepsy that affects an entire half of the brain,
[23:51] Speaker 1: So the surgeon removes half the brain and the kids
[23:53] Speaker 1: grow up to be just fine because the other half
[23:56] Speaker 1: that remains takes over the missing functions.
[24:00] Speaker 3: So it's actually I wanted to ask you about that.
[24:02] Speaker 3: I want to see what your take on it is.
[24:04] Speaker 3: So we reviewed recently. I've got this Karina Coffin that
[24:07] Speaker 3: I reviewed these cases where people have massive amounts of
[24:11] Speaker 3: brain missing, like the part that's left is incredibly small.
[24:15] Speaker 3: So most of them, of course have very reduced function.
[24:16] Speaker 3: But the interesting cases, and there are some amazing cases
[24:20] Speaker 3: where it's a massive reduction on both sides, right, so
[24:22] Speaker 3: it's not a hemisphere, I mean, and yet they have
[24:24] Speaker 3: normal or in some cases above normal intelligence. What do
[24:27] Speaker 3: you think is going on in these in these you know,
[24:29] Speaker 3: fairly unique but still have to be explained cases, What's
[24:32] Speaker 3: going on there?
[24:32] Speaker 2: Yeah?
[24:33] Speaker 1: So this is the uh, this is the magic of
[24:35] Speaker 1: live weiring. I think one of the cases that used
[24:37] Speaker 1: in that paper was people with hydrocephalis, which means you
[24:41] Speaker 1: get this build up of this pressure in the ventricles,
[24:44] Speaker 1: these fluid filled spaces in the brain, and the whole
[24:46] Speaker 1: ring gets squished up against the sides of the skull.
[24:49] Speaker 1: So when you look at it on MRI, it looks
[24:52] Speaker 1: like it's essentially empty space and the little bit of
[24:56] Speaker 1: brain is squished up against it. What that demonstrates is
[24:59] Speaker 1: exactly what you I both love, which is how flexible
[25:02] Speaker 1: this material is. Because you know, you can't run over
[25:06] Speaker 1: half your laptop and expected to still function, but you
[25:10] Speaker 1: can squish this stuff anyway you want, and it just
[25:13] Speaker 1: figures out how to accomplish the goals, in this case,
[25:17] Speaker 1: the cognitive and movement goals that it needs to do.
[25:20] Speaker 1: There's one of the cases in the medical literature this
[25:23] Speaker 1: guy who was forty years old and he went to
[25:25] Speaker 1: the doctor because he was having a little bit of
[25:27] Speaker 1: leg pain. And the doctor couldn't figure out why the
[25:30] Speaker 1: guy's leg was hurting, so he said, hey, why don't
[25:32] Speaker 1: we just take a brain scan, And that's when they
[25:35] Speaker 1: discovered that most of the brain scan just looks like
[25:38] Speaker 1: empty or fluid filled space.
[25:40] Speaker 2: But you know, he was married, he had a job,
[25:43] Speaker 2: normal IQ.
[25:44] Speaker 1: It's quite remarkable how different this livewear is from the
[25:50] Speaker 1: way that we think about building things in Silicon Valley.
[25:52] Speaker 1: And of course you're what you do which is so
[25:55] Speaker 1: remarkable is is look at all the cells, the whole system,
[25:59] Speaker 1: and this massive flexibility and the collective intelligence of all
[26:03] Speaker 1: the pieces and parts all the way up. Tell me
[26:05] Speaker 1: you think this is a good analogy about collective intelligence.
[26:08] Speaker 1: I was thinking about I was just trying to think
[26:09] Speaker 1: of an analogy, and I was thinking about with Wikipedia,
[26:13] Speaker 1: everybody's doing their little thing depending on their own expertise,
[26:17] Speaker 1: they put in some things, and nobody who's doing this
[26:21] Speaker 1: knows the giant shape of the full Wikipedia.
[26:24] Speaker 2: It's much too big for any given human.
[26:28] Speaker 1: But nonetheless everyone's doing their things, and what you get
[26:31] Speaker 1: is this collective intelligence out of it. And I was
[26:35] Speaker 1: thinking about whether I could stretch its analogy. You know,
[26:38] Speaker 1: if some part of Wikipedia got cut off, like an
[26:42] Speaker 1: Axi Lotel's limb, it would grow back and it would
[26:45] Speaker 1: take the right shape again, because that knowledge is somehow
[26:49] Speaker 1: stored in all the individuals who are writing the stuff.
[26:52] Speaker 1: But again, nobody knows what they're doing. Everyone's just contributing
[26:56] Speaker 1: where they see a gap. Does that seem like an
[26:58] Speaker 1: interesting analogies?
[26:59] Speaker 3: It is and what it suggests to me, And I'll
[27:01] Speaker 3: actually I'll actually talk to Eric Hole about this and
[27:03] Speaker 3: see I see if this is a good analysis to do.
[27:06] Speaker 3: Because there are now computational tools from from information theories
[27:11] Speaker 3: and then Eric Hole was one of the one of
[27:12] Speaker 3: the key developers of some of this where you can
[27:15] Speaker 3: actually in a specific given circumstance, you can actually ask
[27:18] Speaker 3: whether the higher level has caught more causal power than
[27:21] Speaker 3: the lower level. And so so it's actually an amazing
[27:24] Speaker 3: advance because it means that questions that before used to
[27:26] Speaker 3: be philosophy, and people argued about this for you know,
[27:28] Speaker 3: probly thousands of years, whether the reductionism or you know,
[27:32] Speaker 3: was was all you need or whether sometimes you have
[27:34] Speaker 3: these higher level things that are causally powerful. Now, now
[27:36] Speaker 3: now there's actual maths to answer that question. It's it's
[27:38] Speaker 3: it's quite amazing. And so so you know, there are
[27:40] Speaker 3: Python toolkits now to estimate in your given system, is
[27:43] Speaker 3: everything explainable by the lower levels or is there a
[27:47] Speaker 3: higher level that does something that the lower levels don't do.
[27:50] Speaker 3: And actually, people like Julio Tononi and Laris Albontakis in
[27:54] Speaker 3: his group, they apply this to all kinds of human patients. So,
[27:57] Speaker 3: so coma you know, locked in a sleep, anesthetize the wake, right,
[28:01] Speaker 3: are you dealing with a pile of neurons or is
[28:03] Speaker 3: there a human.
[28:03] Speaker 2: Being in there, you know, some kind of collector?
[28:05] Speaker 1: Right.
[28:05] Speaker 3: So, so now what you're making me think, is this whole,
[28:08] Speaker 3: this whole process of Wikipedia, we could we could in
[28:12] Speaker 3: theory apply those tools and and and really empirically asked
[28:15] Speaker 3: the question is there a collective there that's bigger than
[28:18] Speaker 3: just the individual processes? That go on when people get
[28:21] Speaker 3: on and and edit those you know, at those those
[28:24] Speaker 3: Wikipedia entries.
[28:25] Speaker 2: Let's do this experiment. I love it.
[28:26] Speaker 1: I want to make sure that we have enough time
[28:28] Speaker 1: to talk about diverse intelligences. So one of your interests
[28:34] Speaker 1: is in not just looking at human brains and thinking about, okay,
[28:38] Speaker 1: how is this intelligence? So on, but saying, what are
[28:41] Speaker 1: other systems that are intelligence? So let's let's dive into that.
[28:44] Speaker 1: Tell us about how you think about diverse cognition.
[28:47] Speaker 3: Yeah, so so because of some of the things that
[28:50] Speaker 3: we've already discussed, meaning that problem solving is something that
[28:53] Speaker 3: biology has to grapple with at the very beginning, you know,
[28:55] Speaker 3: at the very origin of life, and in fact, probably
[28:58] Speaker 3: long before that. You know, you can't afford to be
[29:01] Speaker 3: like a Laplacian demon that's going to track micro states.
[29:03] Speaker 3: You have to coarse grain your environment. You have to
[29:05] Speaker 3: start telling yourself kind of agential stories about what's going on.
[29:10] Speaker 3: In other words, you have to make models of the
[29:11] Speaker 3: environment where you course grain a whole bunch of different
[29:14] Speaker 3: things that are happening, and you say, okay, I'm going
[29:16] Speaker 3: to treat all those as one thing, and this is
[29:18] Speaker 3: danger or this is food, or this is a conspecific
[29:20] Speaker 3: or this is you know, low pH you know for
[29:22] Speaker 3: an ambarrow or whatever it is. So that kind of thing,
[29:25] Speaker 3: having to having to tell these kind of agential stories
[29:28] Speaker 3: that you can then eventually turn on yourself and say wait.
[29:30] Speaker 3: And I also am an agent that does things and
[29:33] Speaker 3: the need to improvise, continuously improvise meaning for the information
[29:37] Speaker 3: that you get, because you're not told nobody's going to
[29:39] Speaker 3: interpret anything for you. You have to interpret your own genome,
[29:41] Speaker 3: your own physiological states, your own memories. And so I'm
[29:44] Speaker 3: really interested in the different ways that this gets amplified
[29:46] Speaker 3: in evolution. And of course, you know brains, you know,
[29:49] Speaker 3: the familiar brains are one way that that happens, but
[29:51] Speaker 3: there are many other ways that that happens. And I
[29:53] Speaker 3: want to want to just briefly give you two quick
[29:55] Speaker 3: analogies that I think illustrate some of the aspects of
[29:58] Speaker 3: what the field of diverse and tell legence is about,
[30:00] Speaker 3: at least the way I see it. First of all,
[30:03] Speaker 3: think about the electromagnetic spectrum. So back in the day
[30:05] Speaker 3: when we didn't have a proper theory of electromagnetism, we
[30:08] Speaker 3: had lightning and static electricity, and light and magnets and.
[30:13] Speaker 2: Various things like that, and we thought those were all
[30:15] Speaker 2: different things.
[30:15] Speaker 3: We thought they were all categories, like sharp crisp categories.
[30:19] Speaker 3: Nobody thought that light and magnets were the same, and
[30:21] Speaker 3: those are you know, we have categories for all those things.
[30:25] Speaker 2: And also, so that's the first thing. We thought these
[30:27] Speaker 2: were all distinct and because of our own.
[30:29] Speaker 3: Evolutionary history, we were only sensitive to a tiny part
[30:32] Speaker 3: of that spectrum. There were huge examples of this phenomena
[30:35] Speaker 3: that we were completely blind to. And then we eventually
[30:39] Speaker 3: we ended up with a good theory of electromagnetism.
[30:40] Speaker 2: We did two things.
[30:41] Speaker 3: First of all, unified it says, no, these are all
[30:43] Speaker 3: actually in a very meaningful way. They are all examples
[30:47] Speaker 3: of the same underlying phenomena. Okay, so a deep unification,
[30:50] Speaker 3: so that's great.
[30:51] Speaker 2: And two, they allowed us to make technology useful, technology
[30:54] Speaker 2: that allows.
[30:55] Speaker 3: Us to operate across the spectrum, to be able to
[30:57] Speaker 3: detect and modulate things that before were completely in visible
[31:00] Speaker 3: to us, and meaning we didn't think they existed, but
[31:02] Speaker 3: now we know better. So something like this is what
[31:04] Speaker 3: I think is going to happen for cognition. I think
[31:07] Speaker 3: we are sensitive to an extremely narrow spectrum among the
[31:10] Speaker 3: gigantic space of possible minds. I think they are all
[31:14] Speaker 3: around us, but we are totally mind blind to most
[31:17] Speaker 3: of them, you know. I think that's a good term
[31:19] Speaker 3: mind blindness, is that we just don't recognize these things
[31:22] Speaker 3: because we don't have a good theory that explains why
[31:25] Speaker 3: the problem solving of an amoeba, of a thermostat, of
[31:29] Speaker 3: you know, of an organ of a human, of a
[31:31] Speaker 3: collection of humans doing Wikipedia whatever, why these are all
[31:34] Speaker 3: actually on the same spectrum. We don't have a good
[31:37] Speaker 3: theory yet. And and the second thing is we don't
[31:40] Speaker 3: have the technology. And this is something else that I
[31:42] Speaker 3: think we have a lot to talk about in terms
[31:44] Speaker 3: of prosthetics, Okay, cognitive and physical, bodily prosthetics that would
[31:47] Speaker 3: allow us to interact with these other beings that are
[31:49] Speaker 3: all around us.
[31:52] Speaker 1: So let's dive into some examples of diverse intelligence. Sure,
[31:55] Speaker 1: so let's just start from from the beginning and work
[31:57] Speaker 1: our way up. So, so brains, okay, we all know brain.
[32:00] Speaker 1: Any kinds of animals exist.
[32:02] Speaker 3: Then, because of what we understand about navigating other biological spaces,
[32:06] Speaker 3: we can think about plants, and we can think about cells,
[32:09] Speaker 3: and we can think about tissues and organs, which also
[32:13] Speaker 3: solve problems.
[32:14] Speaker 2: They store memories, they can learn, they can be communicated with.
[32:17] Speaker 3: This is what all of the biomedical efforts in my
[32:19] Speaker 3: lab are pointed at, which is learning through in particular
[32:23] Speaker 3: bioelectrical interface. They're all oriented towards communicating our goals to
[32:28] Speaker 3: cells and tissues. So for full on regenerative medicine, it
[32:31] Speaker 3: is not going to be sufficient to try to micromanage
[32:33] Speaker 3: the receptors or genetic states. We are going to have
[32:37] Speaker 3: to get the buy in of the cells, respecify their
[32:39] Speaker 3: goals at a high level, and get them to do
[32:41] Speaker 3: these complicated things that we can't possibly micromanage.
[32:44] Speaker 2: So give us some specific examples.
[32:46] Speaker 3: So one of the things that we have learned to
[32:49] Speaker 3: do is much like neuroscientists read electrical patterns in the
[32:52] Speaker 3: brain and they try to decode them.
[32:54] Speaker 2: So this is neural.
[32:55] Speaker 3: Decoding, where people want to read the electrophysiology of your
[32:59] Speaker 3: brain and say here's your memories or goals or preferences
[33:01] Speaker 3: and be able to read that out. We've learned to
[33:03] Speaker 3: do that, and we developed the first tools to do
[33:05] Speaker 3: it in the early two thousands for the rest of
[33:07] Speaker 3: the body. So when I say that the early embryo
[33:10] Speaker 3: navigates anatomical MorphOS space to the shape of whatever it's
[33:14] Speaker 3: going to be, and that it is an active agent
[33:17] Speaker 3: that has a memory of where it's going, it has
[33:18] Speaker 3: a representation of where it's going, that's a very big claim.
[33:21] Speaker 3: You then have to say, well, what's the mechanism for
[33:23] Speaker 3: storing the representation where is it?
[33:25] Speaker 2: Can you decode it? And can you rewrite it? And
[33:28] Speaker 2: so this is what we've done. We've developed tools to
[33:30] Speaker 2: read the electrical memories of collections of cells. This goes
[33:33] Speaker 2: right back to what you said.
[33:34] Speaker 3: No individual cell knows what a face is, or what
[33:36] Speaker 3: an eye is, or how many fingers you're supposed to have,
[33:38] Speaker 3: but the collective absolutely knows. And we can read this
[33:41] Speaker 3: out now. In a few cases, we can literally see
[33:44] Speaker 3: the in images and videos, the memory, the electrical pattern
[33:48] Speaker 3: that is of the future shape that is guiding the
[33:50] Speaker 3: sell activity. Moreover, it serves as a kind of cognitive
[33:53] Speaker 3: glue that binds all the cells towards one story, one
[33:56] Speaker 3: story of what a correct embryo is supposed to look like.
[33:59] Speaker 3: This is why you say it's an embryo and not
[34:00] Speaker 3: a pile of cells because they've all committed to the
[34:02] Speaker 3: same journey in that space. This actually, this idea is
[34:06] Speaker 3: at least as old as Harold Burr in the thirties.
[34:08] Speaker 3: He without anything other than a good vaultmeter, he was
[34:11] Speaker 3: able to kind of already figure this out.
[34:13] Speaker 2: Amazing.
[34:14] Speaker 3: And so now we can read those memories, we can
[34:17] Speaker 3: decode those memories, and we can rewrite those memories.
[34:20] Speaker 2: Because if I take.
[34:20] Speaker 3: A plenarian flatworm and I say, oh, look, this is
[34:23] Speaker 3: where it says that you should have two heads if
[34:25] Speaker 3: you're injured, we can rewrite that. And this is Falon
[34:28] Speaker 3: Durant's work when she was a PhD student in my group.
[34:30] Speaker 3: We can rewrite that electrical pattern, no genetic modification, just
[34:36] Speaker 3: brief application only takes about three hours, a brief application
[34:39] Speaker 3: of ion channel drugs that we've chosen specifically in tune
[34:42] Speaker 3: with a computational model of how you would.
[34:44] Speaker 2: Do that, and we change that pattern. Instead of saying
[34:46] Speaker 2: one head and now it says two.
[34:48] Speaker 3: Now that becomes a false memory because the worm currently
[34:51] Speaker 3: doesn't have to It has one and it'll sit there
[34:53] Speaker 3: perfectly happy. The anatomy does not match the memory. It's
[34:55] Speaker 3: a latent memory until you injure the thing, and when
[34:58] Speaker 3: you cut it, bang, that's when the cells consult the
[35:00] Speaker 3: memory and memories says, build two heads. Well, that's their
[35:02] Speaker 3: ground truth. I don't know any different, and so they
[35:04] Speaker 3: will go ahead and they will build this new vision
[35:06] Speaker 3: of what a worm is.
[35:07] Speaker 2: And it's a memory because it is permanent.
[35:09] Speaker 3: If you take two headed animals and keep cutting them,
[35:11] Speaker 3: they will continue regenerating as two headed, even though their genome.
[35:15] Speaker 2: Is a perfectly standard genome.
[35:16] Speaker 3: If you were to sequence that, you would have been
[35:18] Speaker 3: none the wiser that this thing has two heads. So
[35:20] Speaker 3: this kind of thing, the ability to put new goals
[35:23] Speaker 3: into the mind of the collective is the kind of
[35:26] Speaker 3: an earliest example of communicating with it because we can,
[35:29] Speaker 3: we can in some cases, we can train it. Another
[35:31] Speaker 3: thing we're really working on is to actually ask it questions.
[35:34] Speaker 3: That would be really cool because sells have all kinds
[35:36] Speaker 3: of problem solving capacities.
[35:37] Speaker 2: I would love to be able to actually ask them
[35:39] Speaker 2: questions in a way. And AI is a.
[35:41] Speaker 3: Very powerful tool that we're now using to start to
[35:43] Speaker 3: communicate with these things. So that's kind of the first
[35:46] Speaker 3: weird kind of mind, meaning in our body we have
[35:49] Speaker 3: I can't you know, I don't think you can count them.
[35:51] Speaker 3: I think that you know, it's not probably not really infinite,
[35:53] Speaker 3: but but a very large number of different cognitive units
[35:57] Speaker 3: inside your body, solving their own problems in their own
[35:59] Speaker 3: time scales and so on.
[36:00] Speaker 2: But you can get weirder than that. Which is which
[36:02] Speaker 2: is this? You know?
[36:05] Speaker 3: I'll start with a very quick story, and this goes
[36:07] Speaker 3: back to us, to US sci fi story from from
[36:09] Speaker 3: that I read years ago. Imagine, these creatures come from
[36:12] Speaker 3: the core of the earth. They live, they live in
[36:13] Speaker 3: the center of the earth. They're super dense. They come
[36:15] Speaker 3: up to the surface. What do they see, Well, they
[36:18] Speaker 3: don't see physical objects as far as they're concerned.
[36:20] Speaker 2: Everything here is like a thin gas. It's like a plasma.
[36:22] Speaker 2: They are so dense.
[36:23] Speaker 3: They walk right through us the way that we walk
[36:25] Speaker 3: through you know, patterns of pollen in the garden, and
[36:27] Speaker 3: we don't even we don't even notice it. And so
[36:29] Speaker 3: one of them is a scientist and he's looking and
[36:30] Speaker 3: he says, you know, this gas that we're that we're
[36:32] Speaker 3: walking through. I kind of if you actually look at
[36:35] Speaker 3: patterns within the gas, it almost looks like they're doing something.
[36:38] Speaker 3: It almost looks like they're agential. They like these patterns,
[36:40] Speaker 3: you know, they walk around, they have behaviors, they're doing stuff,
[36:43] Speaker 3: and and the others say, well, that's crazy.
[36:44] Speaker 2: We're real physical beings. Patterns can't be agents.
[36:47] Speaker 3: Patterns, you know, patterns and an excitable medium can't have,
[36:50] Speaker 3: you know, their own their own memories and their own goals.
[36:52] Speaker 3: And by the way, how long these patterns last? He says, well,
[36:54] Speaker 3: they dissipate after about one hundred years. He's like, yeah, no,
[36:56] Speaker 3: it's not anything, right, So okay, So so what that
[36:58] Speaker 3: reminds us of is that the distinction between you know,
[37:02] Speaker 3: we too our patterns, right, we're metabolic patterns that hold
[37:04] Speaker 3: ourselves together for some amount of time and then we dissipate.
[37:07] Speaker 3: And this distinction between patterns and objects is in the
[37:11] Speaker 3: eye of the beholder. And so that leads you to ask,
[37:13] Speaker 3: what are the things that we think of as mere
[37:16] Speaker 3: patterns and an excitable medium that might be agents themselves.
[37:19] Speaker 2: And so that's the second that's the next kind of
[37:21] Speaker 2: level is can we communicate?
[37:22] Speaker 3: Can we can we recognize and communicate with patterns, patterns
[37:26] Speaker 3: of gene expression, patterns of bioelectric state. You know, this
[37:30] Speaker 3: whole thoughts are thinker's idea from William James.
[37:50] Speaker 1: So you look around, you see these patterns everywhere, and
[37:52] Speaker 1: you think, which of these are agential, which have what
[37:56] Speaker 1: we might call intelligence unpack the thoughts or things idea
[38:00] Speaker 1: for us?
[38:01] Speaker 3: Yeah, yeah, so so this is uh and and I
[38:03] Speaker 3: admit I haven't I haven't looked for the actual reference,
[38:06] Speaker 3: but but I'm pretty sure I saw this in in
[38:08] Speaker 3: James's book, where what he's pointing out is that, look,
[38:12] Speaker 3: you have fleeting thoughts. They come and they go, right,
[38:15] Speaker 3: they sort of run through your your your memory medium,
[38:17] Speaker 3: and then they and then they go. Then you have
[38:19] Speaker 3: persistent thoughts, and these are a little harder to get
[38:21] Speaker 3: rid of. They do a little niche construction, as you know,
[38:23] Speaker 3: they they kind of change some of your brain to
[38:25] Speaker 3: enable it to be to make it easier for them
[38:27] Speaker 3: to to persist. Right, these these intrusive, persistent, you know
[38:30] Speaker 3: kinds of thoughts. And then you have you go further
[38:33] Speaker 3: on the spectrum and you have personality fragments like from
[38:35] Speaker 3: a you know, like from a dissociated identity kind of situation.
[38:39] Speaker 3: And then you keep going and then you have a
[38:40] Speaker 3: full coherent human personality and you say Okay, well that's
[38:43] Speaker 3: the thing we're we're kind of used to. But but
[38:45] Speaker 3: it's on a spectrum. And then and then who knows, right,
[38:47] Speaker 3: some people claim there's like a superhuman you know, sort
[38:50] Speaker 3: of a larger, larger superman mind and so on.
[38:53] Speaker 2: I don't know. So that's the idea. And so there
[38:55] Speaker 2: are two there are two ways to to think.
[38:57] Speaker 3: About any of these situations that were sort of given
[38:59] Speaker 3: to us by by the by the touring paradigm. You
[39:02] Speaker 3: can say that the cells those that's that's your touring machine.
[39:07] Speaker 3: That's your that's your machine. That's the real agent. And
[39:10] Speaker 3: the patterns that move through it, the information, the energy
[39:13] Speaker 3: slash information patterns that.
[39:14] Speaker 2: Move through it. They're they're just they're just patterns.
[39:16] Speaker 3: They're passive data and and and it's the agent that
[39:19] Speaker 3: processes the data. Right we you know, our brain moves
[39:21] Speaker 3: around the information that moves the energy, and our body
[39:24] Speaker 3: does the same thing.
[39:25] Speaker 2: Okay.
[39:26] Speaker 3: Or you can flip the whole thing, which is what
[39:27] Speaker 3: we're working on now, which is to say, what if
[39:30] Speaker 3: it's the patterns that are the agents and everything that
[39:32] Speaker 3: happens to the machine, meaning all the outcomes of gene expression,
[39:36] Speaker 3: of protein movement, of cell behavior of morphogenesis. What if
[39:39] Speaker 3: that's those things are just a scratch pad. It's the
[39:41] Speaker 3: it's kind of a stigma gee the way that any
[39:43] Speaker 3: ant colony will eventually you know, particles and pheromones and
[39:46] Speaker 3: things will move around because the ant colony mind is
[39:48] Speaker 3: kind of doing its thing as the ants, you know,
[39:50] Speaker 3: send messages to each other.
[39:51] Speaker 2: What if the the.
[39:52] Speaker 3: The anatomy and physiology that we see and and and
[39:56] Speaker 3: the and the body of the touring machine is the
[39:57] Speaker 3: scratch pad of of the actual age, which are the patterns,
[40:01] Speaker 3: you know, working out their dynamics in the physical world.
[40:05] Speaker 3: And it actually has some real implications just very quickly,
[40:09] Speaker 3: for example, in our program on aging, right, so we're
[40:11] Speaker 3: trying to understand an address agent. So imagine the classic
[40:15] Speaker 3: way of thinking about aging from a bioelectric standpoint is
[40:18] Speaker 3: we know that during embryogenesis there's a bielectric pattern that
[40:20] Speaker 3: guides morphogenesis. And so probably what happens is that those
[40:25] Speaker 3: memories become fuzzy in adulthood, and as the age, they
[40:28] Speaker 3: just get fuzzy and fuzzier.
[40:29] Speaker 2: Their cells have no idea what to do.
[40:30] Speaker 3: The memory degrades, and the agent, the physical body doesn't
[40:34] Speaker 3: know what to do anymore.
[40:35] Speaker 2: That's the standard approach, and that's what you know. That's
[40:37] Speaker 2: one thing we're doing.
[40:38] Speaker 3: But you can flip it and you can say, what
[40:40] Speaker 3: if the agent is actually the pattern that it's trying
[40:42] Speaker 3: to the vocabulary kind of fails us year, but it's
[40:45] Speaker 3: trying to ingress into the physical world through our medium.
[40:49] Speaker 3: And maybe what happens as we age is that the
[40:52] Speaker 3: cells become less and less able to implement it, They
[40:54] Speaker 3: become unresponsive, the machine slows down. Maybe the mind of
[40:59] Speaker 3: the agent, of the morphogenetic intelligence is perfectly fine, but
[41:02] Speaker 3: the machine doesn't respond. And so those are experimentally distinguishable,
[41:06] Speaker 3: and we're doing those experiments. We actually have some data
[41:08] Speaker 3: for this now and so those are those are just
[41:10] Speaker 3: very different. And the way you then would address aging
[41:12] Speaker 3: from two different from those two different viewpoints is quite different.
[41:16] Speaker 3: So that's what we would love to do, is to
[41:17] Speaker 3: is to learn to recognize and communicate with other kinds
[41:21] Speaker 3: of agents that are not even physical objects as such.
[41:24] Speaker 2: They are they are.
[41:25] Speaker 3: Persistent patterns that may have all kinds of energe, you know,
[41:29] Speaker 3: their own agendas.
[41:31] Speaker 1: So let me ask you a couple of rapid fire questions.
[41:34] Speaker 1: If they're diverse intelligences everywhere. If we can start understanding
[41:38] Speaker 1: these patterns around us as being cognitions of their own,
[41:42] Speaker 1: what does this mean for ethics?
[41:44] Speaker 3: Yeah, this is a huge problem. This is an absolutely
[41:47] Speaker 3: huge problem. I think that it is foundational to the
[41:50] Speaker 3: development of ethics as a mature species to learn to
[41:54] Speaker 3: recognize and ethically relate to minds that are nothing like ours,
[41:58] Speaker 3: that are basically not on the you know, at least
[42:01] Speaker 3: in some cases, because you can actually believe or now
[42:03] Speaker 3: you could get much even weirder than this pattern thing
[42:06] Speaker 3: that I'm talking about, And so it's certainly above my
[42:09] Speaker 3: remit to try and formulate the ethics. But what is
[42:12] Speaker 3: very clear is that we need to learn to recognize them,
[42:15] Speaker 3: we need to learn to communicate with them, and we
[42:17] Speaker 3: need to start thinking about what do we owe other
[42:19] Speaker 3: beings that live with us, that live in spaces and
[42:22] Speaker 3: have goals that are really hard for us to visualize.
[42:25] Speaker 2: What are the.
[42:25] Speaker 1: Implications for AI at this moment that we're in.
[42:29] Speaker 3: People tend to have a very kind of a binary
[42:32] Speaker 3: view on this. They will either say, oh, yeah, it
[42:35] Speaker 3: talks like us and therefore it's like a human brain,
[42:37] Speaker 3: or they'll say, oh no, this thing is a machine,
[42:39] Speaker 3: and therefore it's nothing like us.
[42:40] Speaker 2: So I think both of those are terrible.
[42:43] Speaker 3: And first of all, because in order to be intelligent
[42:46] Speaker 3: and have meaningful cognition and maybe moral worth, you don't
[42:49] Speaker 3: need to be like a human mind.
[42:51] Speaker 2: There are many minds that are nothing like a human mind.
[42:54] Speaker 2: You don't have to be like humans.
[42:55] Speaker 3: And I don't believe at this point, as far as
[42:56] Speaker 3: I know, we don't have any ais that are like
[42:58] Speaker 3: a human mind, but that doesn't mean they're not minds.
[43:01] Speaker 3: And the other problem is that there is no such
[43:03] Speaker 3: thing as a machie. And if you believe that algorithms
[43:06] Speaker 3: and the facts of physics around the silicon and copper,
[43:11] Speaker 3: and the kinds of things we make computers out of,
[43:13] Speaker 3: if you think that those things tell the entire story
[43:15] Speaker 3: of artificial intelligence, then you should think that the story
[43:19] Speaker 3: of biochemistry is everything you need to know about the
[43:22] Speaker 3: human mind. And that's you know, I think that's blatantly false.
[43:24] Speaker 3: And so for both in both cases, I think we
[43:27] Speaker 3: have to be extremely open to the idea that we
[43:29] Speaker 3: do not understand how different kinds of minds ingress into
[43:33] Speaker 3: the world through different interfaces. And I realized this is
[43:36] Speaker 3: a weird way of putting it. This is not the standard,
[43:38] Speaker 3: the kind of neuroscience way where intelligence is created by
[43:41] Speaker 3: the hardware.
[43:42] Speaker 2: I don't actually believe that's true.
[43:44] Speaker 3: I think I think consciousness is separate and what we
[43:47] Speaker 3: see what we provide when we make you know, ais, robots, embryos,
[43:52] Speaker 3: the biobots, all of this stuff. We make interfaces different
[43:54] Speaker 3: interfaces for it, and we are currently very bad at
[43:58] Speaker 3: guessing ahead of time what is going to appear when
[44:02] Speaker 3: we make certain kinds of interfaces. And you know, I
[44:05] Speaker 3: think I think one of the most relevant pieces of
[44:09] Speaker 3: our work for this is the stuff that we detaining.
[44:12] Speaker 3: Zhang and Adam Goldstein and I wrote this paper on
[44:15] Speaker 3: unexpected competencies in sorting algorithms like bubblesort. These are things
[44:20] Speaker 3: that that computer science students have been studying, you know,
[44:22] Speaker 3: in first year CS for I don't know, sixty years,
[44:24] Speaker 3: I guess, and nobody had actually looked at it the
[44:27] Speaker 3: way that we had looked at it, and we found
[44:29] Speaker 3: this thing has delayed gratification and has these weird little
[44:32] Speaker 3: side quests that it goes on that are not in
[44:34] Speaker 3: the algorithm at all. In other words, if you just
[44:36] Speaker 3: stare at the algorithm. You know, it's six lines of code.
[44:38] Speaker 3: It's a deterministic algorithm. There's no magic that, there's no
[44:40] Speaker 3: new biology to be found. You know exactly what it's doing,
[44:42] Speaker 3: and yet it does things that we do not expect
[44:45] Speaker 3: it to do in the algorithm, not just randomness, not
[44:47] Speaker 3: just complexity, not just unpredictability, but things you would recognize
[44:50] Speaker 3: as cognitive competencies.
[44:52] Speaker 2: And that means that if we don't, if we can't,
[44:55] Speaker 2: you know.
[44:55] Speaker 3: Sometimes people say me, well, I build I build language models.
[44:59] Speaker 3: It's just linear alogib I know what they're doing. There's
[45:00] Speaker 3: nothing as if look, we don't even know what bubble
[45:02] Speaker 3: story is doing. If you can't, if you don't know
[45:04] Speaker 3: what bubble story is doing, you sure as hell don't
[45:06] Speaker 3: know what these language models are doing. And so we
[45:08] Speaker 3: need to treat all of these things as empirical questions,
[45:11] Speaker 3: not philosophical decisions that we can make, and we have
[45:14] Speaker 3: to get much better at understanding how new minds ingress
[45:17] Speaker 3: even in tiny interfaces like low complexity, very simple kinds
[45:21] Speaker 3: of interfaces.
[45:26] Speaker 1: That was my interview with Mike Levin, biologist at Tufts.
[45:30] Speaker 1: Every time I talk with Mike, it's hard to look
[45:32] Speaker 1: at the world the same way. So we started by
[45:35] Speaker 1: asking what is intelligence? But instead of finding a crisp,
[45:40] Speaker 1: singular answer, we were handed something far more powerful, which
[45:44] Speaker 1: is a new lens, a new way of thinking about intelligence,
[45:48] Speaker 1: not as a static property that some lucky creatures have
[45:52] Speaker 1: and others lack, but as a multi dimensional space of
[45:57] Speaker 1: gold directed behavior, shaped by evolution and context and purpose.
[46:02] Speaker 1: And this is of course a reframing of life itself
[46:06] Speaker 1: because through this lens, intelligence isn't only confined to a cranium.
[46:11] Speaker 1: It's not restricted just to animals with brains. Instead, it's
[46:15] Speaker 1: something that shows up wherever you have systems that are
[46:19] Speaker 1: solving problems and adapting to things they didn't expect and
[46:23] Speaker 1: correcting errors. Wherever there are goals, there may be something
[46:28] Speaker 1: in play that is like a mind. And when we
[46:31] Speaker 1: look through this lens, the universe becomes alive in strange
[46:35] Speaker 1: and beautiful ways. Cells are more than dumb building blocks.
[46:40] Speaker 1: We can see them as decision makers. Organs are more
[46:44] Speaker 1: than machine units that are chugging along. We can see
[46:47] Speaker 1: them as negotiating parties in their own societies. A regenerating
[46:52] Speaker 1: flatworm is more than a textbook collection of cells. It's
[46:56] Speaker 1: an entity that knows what it's missing and takes action
[47:00] Speaker 1: to restore itself. In a sense, it remembers what it
[47:03] Speaker 1: used to be, and it holds that shape in its
[47:06] Speaker 1: future and moves towards it. So, Mike Levin's work suggests
[47:09] Speaker 1: that the basic machinery of cognition, like memory and problem
[47:14] Speaker 1: solving and preferences, this all might emerge way earlier in
[47:19] Speaker 1: evolution then we've assumed. Because cognition might not require neurons,
[47:24] Speaker 1: it might not even require consciousness in any familiar sense.
[47:27] Speaker 1: What it does require is something more basic that you
[47:31] Speaker 1: can achieve with lots of architectures, a capacity to act
[47:36] Speaker 1: in service of a goal. And this raises all kinds
[47:38] Speaker 1: of great questions. If we accept that intelligence exists in
[47:42] Speaker 1: a multi dimensional space, what else around us counts as intelligent?
[47:48] Speaker 1: How about a tree sending resources through its root network.
[47:52] Speaker 1: How about a colony of ants adjusting its forging behavior.
[47:56] Speaker 1: How about your immune system adapting to a virus.
[48:00] Speaker 2: How about a cluster of.
[48:01] Speaker 1: Engineered cells navigating a maze. And one thing I think
[48:05] Speaker 1: is really important here is thinking about what all this
[48:08] Speaker 1: means for the future of AI. At the moment, we're
[48:11] Speaker 1: only building machines inspired by brains. But I think when
[48:15] Speaker 1: we look back in twenty years, that will seem quaint,
[48:18] Speaker 1: and we will be seeing a lot more emulation of
[48:21] Speaker 1: the distributed, adaptive self regulating qualities of other more spread
[48:27] Speaker 1: out and sometimes more creative biological systems. Could we design
[48:32] Speaker 1: machines that do physical things, not just like minds, but
[48:36] Speaker 1: like cell assemblies and bodies. And finally, with everything that
[48:40] Speaker 1: we talked about today, what does this all say about.
[48:43] Speaker 2: Who you really are?
[48:45] Speaker 1: Because when we're being honest, we are not individuals in
[48:49] Speaker 1: the traditional sense.
[48:50] Speaker 2: We are collectives.
[48:52] Speaker 1: We are billions of cells and trillions of microbes, all
[48:57] Speaker 1: operating with partial autonomy some goals, and this vast ballgame,
[49:04] Speaker 1: which is much larger than we can conceive, is somehow
[49:07] Speaker 1: coordinated into the illusion of a unified self. The story
[49:14] Speaker 1: of you is a kind of consensus reality emerging from
[49:19] Speaker 1: many smaller parts, most of which have no idea you exist.
[49:24] Speaker 2: To my mind, this is a call for awe.
[49:27] Speaker 1: I don't know why we'd only talk about this stuff
[49:29] Speaker 1: occasionally on a podcast. Why aren't airplanes flying around with
[49:33] Speaker 1: banners celebrating this kind of stuff? Why aren't we talking
[49:36] Speaker 1: about this on CNN instead of local political cycles, because
[49:40] Speaker 1: the lesson from today's episode is that intelligence is probably
[49:43] Speaker 1: not rare but common. We always look at it as
[49:47] Speaker 1: a strange exception to the rules of nature, but maybe
[49:49] Speaker 1: it is the rule. And if this is the right
[49:52] Speaker 1: lens to look through, what it means for us is
[49:55] Speaker 1: that the world is full of minds, strange and ancient,
[50:00] Speaker 1: and in many ways alien minds, some fast, some slow,
[50:04] Speaker 1: some huge, some microscopic, some we've built ourselves, and most
[50:09] Speaker 1: that have.
[50:09] Speaker 2: Been here all along waiting.
[50:12] Speaker 1: For us to notice. We're just starting to map this territory.
[50:15] Speaker 1: And I think one of the lessons from Levin's lab
[50:18] Speaker 1: is that the boundary between mind and matter is more
[50:21] Speaker 1: porous than we generally assume.
[50:23] Speaker 2: And the more we study this, the.
[50:24] Speaker 1: More we're going to need to update our science textbooks.
[50:28] Speaker 2: But more importantly, we're going to need to update our.
[50:30] Speaker 1: Intuitions about what it means to be alive and to
[50:34] Speaker 1: be intelligent.
[50:36] Speaker 2: We'll need to tune into the.
[50:37] Speaker 1: Fact that the whole world around us might be more alive,
[50:40] Speaker 1: more curious, more goal seeking than we thought to imagine.
[50:45] Speaker 1: In that light, the story of intelligence isn't a peak
[50:48] Speaker 1: that we have reached, but a vast landscape where agency
[50:52] Speaker 1: is common, and every living system, no matter how small
[50:56] Speaker 1: or strange, might be solving problems that we have yet
[51:01] Speaker 1: to understand. Go to eagleman dot com slash podcast for
[51:09] Speaker 1: more information and to find further reading. Join the weekly
[51:13] Speaker 1: discussions on my substack, and check out and subscribe to
[51:16] Speaker 1: Inner Cosmos on YouTube for videos of each episode and
[51:20] Speaker 1: to leave comments until next time. I'm David Eagleman, and
[51:24] Speaker 1: this is Inner Cosmos

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