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