Free Lunches: Model Systems for Studying the Agential Gifts from the Platonic Space by Michael Levin

[00:00:02]and much of what I'm going to tell you today, although not everything because this needs to be significantly updated.

[00:00:07]I wrote this almost a year ago. So, um you can take a look in this paper, but I'm going to upload a new version probably uh next week. So, the first thing I want to say is that uh I run a a wet lab. Um we do experiments at the intersection of uh bioysics, computer science, and cognitive science. And uh what I what I think we do you know on our best day occasionally what what works out is that we can take some deep ideas from philosophy and actually push them all the way through practical applications. So the outputs of our lab are things like um discoveries for regenerative medicine, birth defects, uh organ regeneration, cancer, uh AI, bioengineering. These are these are things that that come out. And so everything that I'm going to tell you is aimed at uh experiment and application.

[00:00:52]The idea is that we have to have new research programs. That is everything.

[00:00:56]And so so um someone asked a moment ago around you know whether these things have to make make contact with with predictive experiments. Absolutely. Not not only to explain things that people have already seen but actually to give rise to new research programs that I think is absolutely critical. Not just looking backwards because looking backwards you can epicycle almost anything into a conventional paradigm.

[00:01:18]The question is does it does it lead you to do new experiments and find new things with new applications. So that that is kind of the the the good news is that I'm going to show you lots of lots of experiments and lots of model systems for studying these questions. The bad news that it prevents me from saying some very sort of you know cosmic kinds of things that people often ask me about and I typically don't talk about those things because unless we have either data or a way a way to address them experimentally I I you know I I don't talk about it in public. So uh one of the things that that people sometimes say we do is bioinspired engineering.

[00:01:53]Now the way typically people think of bioinspired is that they take things the biology is doing and then they try to make technology for example AI that's going to use those principles. I actually think that it looks something like this. Actually bioinspired doesn't mean you take inspiration from biology.

[00:02:09]Bioinspire to me is figuring out what is the biology inspired by and thus what is the engineering going to be inspired by.

[00:02:16]So these things are lateral. And so now here's the question. What what is it inspired by? So I'm going to uh try to tell you uh what I think at this moment.

[00:02:22]So so a summary of the whole thing goes like this. Um I'm going to first uh argue that patterns in particular patterns of form, patterns of behavior, patterns of physiology, patterns of computation, all kinds of things are actually part of the same. They're they're kind they're the same thing. So so so morphagenesis and behavior in threedimensional space are not different. uh they are they are just uh just different aspects of of of different kinds of patterns and I think it's a critical invariant that goes across biology, cognitive science and many other disciplines. Now we're interested in knowing what determines these patterns where do they quote unquote come from. I'm going to address this but I will tell you right now that I think select se selection and environment or history you know biology and physics are not sufficient to answer this question. I'm also going to say that physicalism has been dead for a long time. Uh mostly we knew this because of mathematics and I think you know Pythagoras and probably long before was already aware of this. But it's not just about that. My hypothesis is that this latent space that we're going to talk about contains a very wide range of patterns that include the kinds of things mathematicians study but also highly active complex high agency patterns that the behavior scientists would recognize as kinds of minds. Uh now the reason I didn't talk about this prior to 2025 is that we didn't have a research program for it. You know lots of um as was just said you know lots of uh kind of deep thinkers over the millennia have said vaguely things like this but but that's different from having a research program to actually do do experiments and now we do. We now have model systems which I'll describe to you where we can actually quantify identify and quantify ways in which our current frameworks are broken. In particular, what's broken is our accounting of the effort we put in and what we get out. What we get out is often much more than what we've put in.

[00:04:15]The delta, the difference there is what you can call free lunches in the physicist sense tells us uh about the space the structure latent space from which these things come and uh and we can now do experiments to find out why it comes into certain physical interfaces and not others. Well, actually all of them but but in different ways. So uh I'm also going to argue at the end that that we we we do receive so-called inspirations from that scale from that space across scales. So so your molecular networks get them, your cells get them, tissues, organs, they we all get them. But but but don't get don't don't don't feel too proud of yourselves because the quote unquote machines get them too. This is this is uh well whatever this is basically soaks into absolutely everything as far as I can tell. And finally, and I won't have time to talk about too much, but but but we can talk in the Q&A. I don't think we are physical beings that occasionally get affected by patterns from the space.

[00:05:09]I I I I think we are patterns. We we are the patterns looking out into a uh into a physical interface. So, so one thing that's uh that's interesting about all of these kinds of questions that we're asking is that we can start to uh um delete certain sharp categories that have been with us for a long time. you know, from pre-scientific times. And I think these categories are now doing way more harm than good. And so, I'm going to just point out a couple. One is this so-called difference uh or distinction between magical living beings on one end and dumb machines on the other end. And so, we we know from a developmental scale and also an evolutionary scale that we were all single cells once, you know, little blobs of chemistry and physics, unfertilized oases. Eventually, slowly and gradually, we became the subjects of behavior, science, psychoanalysis, love, all of those kinds of things. Uh but not not only that, there's another continuum, another gradual continuum in which both biological changes and technological changes, many of which are already happening, uh are telling us that it's really very non-trivial to to write down a description of what you think a machine actually is. And and the implications of this are going to be clear in a moment that it's actually a bad category that that leads us astray.

[00:06:22]And so the the thing is that the problem we are all going to face in society is this kind of thing. It's not the language models and it's not the you know the AIs that everybody likes to say are not like humans. Uh that's fine.

[00:06:35]This is what we're dealing with. You know your neighbor is going to have some percentage of their brain and body replaced whether for medical reasons or just because of freedom of embodiment.

[00:06:43]they're going to uh look very different in the future. And basically across this spectrum, okay, the spectrum of intelligence all the way from very simple kinds of things uh that that can only be uh interacted with by physical rewiring versus the tools of cybernetics and control theory versus behavior science versus language and friendship and and and so on. This is a this is a spectrum and we really need to start to understand uh our own origins and our own possibilities in light of the spectrum of possibilities of what's going on. And I think our language really does us a disservice here. We have we have two two words to describe things who and what. Yeah. If you want to describe, you know, a noun, a system, you can either say you can either call it a who or you can call it a what. And I think this is a fundamental problem that is uh forces us to think in binary terms. I don't know what the answer is, but maybe we can have something like this, like a who with a little exponent, and the exponent tells you to what degree where you are, where that system is on the on the, you know, on the edge where, you know, some some way of letting our language catch up to actually the the discoveries of a diverse intelligence research and away from ancient categories where we really did only have, you know, two two categories. So, so um so one thing one thing to keep in mind uh is is that uh even even when you're talking about traditional brainy organisms and I'm going to talk about cell intelligence and things like this in a minute but even in traditional brainy organisms already you can see that things are not simple the standard mapping of the hardware that's required to maintain a certain amount of um e efficacious uh functionality let's say a normal human IQ and a normal human personality. There's already some some weird mismatches and Karina Kaufman and I reviewed them in this paper where there are certain individuals with massive reductions of brain volume but normal or above normal intell functional intelligence. Some of these people don't even know they're missing most of the brain. So what's going on here? Why why is there I mean this is not common by any means but it doesn't take too many of these clinical cases to understand that something is wrong in our accounting of what the mapping should be between the hardware uh and the and the um the performance that you get out of it.

[00:08:57]The other thing that I will point out is that life has embodiment and and uh traverses many different kinds of spaces besides the familiar threedimensional space. So we should remember as as humans with our own evolutionary history, our own kind of obsession with vision and threedimensional space that uh while we are okay at recognizing intelligence when it moves in the same 3D space that we do roughly same time scale roughly same um uh uh uh spatial scales biology has been solving problems aka intelligent behavior in many many spaces before nerve and muscle appeared long before. So highdimensional transcriptional spaces or gene expression spaces, physiological state spaces and anatomical morphus space which is mostly what we work on in the lab. Uh biology navigates all these spaces making decisions taking uh sensory readings uh doing goal directed functionalities and and this has this has lots of uh lots of implications for what we're going to talk about. So, um, another thing to keep in mind is that we are all collective intelligences, not just the beehives and the ant colonies, but all of us. We're all made of things like this. Okay, so this is a single cell, no brain, no nervous system, but you can see it's very competent in its own local cognitive light cone here.

[00:10:15]It's doing everything it needs to do.

[00:10:17]This is the kind of thing that that we are made of. And so, our bodies are kind of an ecosystem of diverse intelligence all the way from molecular networks up through all the different components and layers. All of these things have agendas. They all solve problems. Most of them can learn and have learning capacity. So, so this becomes this becomes also important that that this notion that we the verbal intelligence that you can have a conversation with is the is the one inhabitant of a body is absolutely wrong. And that's before you even get into microbiome and things like this. Just just the layers of your actual body is are already uh an ecosystem of minds that live that live with you. Now, now let's let's uh let's uh lay down some uh some some fundamentals here. First of all, um so as was just mentioned, uh we know this amazing process, you start with a with an egg, you eventually very reliably end with something like this. But I want to be clear for a number of reason for a number of reasons that I'll mention the the the standard concept of emergence.

[00:11:16]So lots of simple local dumb rules, rules that are well described by chemistry in which systems have no goals. they don't know anything. They just sort of roll on roll forward and eventually emergence, you know, something complex happens. You can get complexity that way. It's very clear that that by following simple rules, you can get very complex outcomes and in fact reliable outcomes. But that isn't what's going on here. And um the thing is that uh this this idea that we can only use models of chemistry where the components have no goals and don't know anything is an axiom. It is not a result. It is not something that was was shown or derived or proven to be successful. And in fact, it's a terrible axiom. I understand it might have been useful when the only options were dumb like rocks versus smart like like humans. But that's no longer the case.

[00:12:03]Since the 1940s, we've had cybernetics and we've had uh lots of rigorous theory around how systems whether natural or engineered can have goals. And it doesn't mean human metacognitive size goals. It means uh there's a there's a spectrum of of techniques that you can use to understand how systems have goals. And so I you know teology shouldn't have been taboo past the 1940s. And I think actually nothing in biology can be properly handled without understanding that sticking to this low lowest uh rung of the cybernetic ladder is simply uh leaving too many useful tools on the table. It's and evolution certainly didn't do that. Didn't didn't stay at this at this low level. So so it is not about emergence. Why? Because one of the things that we see in these complex systems is that they pursue a goal. They they they don't merely have complex outcomes. They go toward a specific pattern. Now, how do you know?

[00:12:59]You can't just assume this. You can't just say this because you see them reliably doing it. You have to do perturbative experiments. So, for example, if we scramble the face of this tadpole, it doesn't make a scrambled frog, which it would if all of the organs were just following their standard rules of of how far to go in what direction. So these kind of Picasso like scrambled tackles, nope. They make pretty normal frogs because all the different organs will move in different novel paths and they will sometimes go too far and have to come back and they all rearrange themselves and they make pretty pretty normal frog faces and then they stop and then they stop moving.

[00:13:35]Same thing here. If you have an axelottle, you amputate the limb, these cells will grow really quickly but then they stop. The most amazing part of regeneration is that it stops. When does it stop? It stops when the correct pattern has been completed. So all of this again uh doesn't you know ever since ever since we've understood about homeostats doesn't need to be magical.

[00:13:54]It basically is a system that represents the the journey that it's taking in morphus space in anatomical space. It navigates to that position using now well-known mechanisms. And when it gets there uh then then it stops like any other kind of goal seeking behavior. uh and in fact now so so so how does it know what the what the patterns are? I I ask that question absolutely literally because the way that these systems know where they are going is exactly the same mechanisms by which you know where you're going when you execute goals. Uh the same way that your collective intelligence uses electrical processes in your brain, ion channels, neurotransmitters, electrical synapses in electrical networks of the brain to to remember goals and to guide goal directed behavior toward those goals.

[00:14:43]That exactly same system is evolutionarily ancient. It was used long before you anybody had neurons to uh to remember what our shape was supposed to look like. Back in 2000 or so, we developed the first uh molecular tools to read and write these these patterns.

[00:15:00]So, we can literally see this is this is not AI or simulations or anything like that. These are these are actual uh data from an early frog embryo and from the cells in culture looking at now we can read in the vivo in the living state. We can now read the the electrical um memory patterns that non-neural tissue drives. And so this is a particular pattern here. This is we call this the electric face. This is a pattern that shows up before any of the genes turn on to regionalize the the tadpole face. So it already tells you everything you need to know. Here's where the the animal's right eye is going to be. Here's where the mouth is going to be. Here are the plaodes. What you're seeing here, by the way, is not, you know, some some mystical energy. What you're seeing here are just voltage gradients between the inside and the outside of the cell plasma membrane. Just like in your brain, that's what the neurons are doing. But this network is much slower.

[00:15:50]It doesn't operate at milliseconds. It operates in minutes and and hours and what it likes to think about is shape.

[00:15:56]And so having done having seen this, one of the things you might do is try to establish the same biological pattern somewhere else and see what happens. And if you do that, so here's a tadpole, here's the here's its normal eye.

[00:16:07]There's the other ones on the other side. So here's the mouth, the brain as up here, the gut. So So if we establish by using ion channel injections this bioelectrical state somewhere else, here it is on the gut. The cells know exactly what this means. They interpret it as build an I here and they build an I. So what I'm telling you here which will be important momentarily is that we now have the ability to read and and write and rewrite at least in some cases specific patterns that serve as the goal for a bonafide goal directed system which basically works to reduce error.

[00:16:39]It uses an electrical network to gauge the delta between how are we now versus how are we supposed to be and try to reduce that delta. Here's another example of this in these plenarium.

[00:16:50]Normally nice uh one-headed flatworm.

[00:16:52]You cut it into pieces many times as you want. Very reliably you get a worm with one head, one tail. How do they know how many heads they're supposed to have? The answer is not genetics. This this tissue is reprogrammable. And so you can take the exact same genetics and you can take this biological pattern that tells the animal one head, one tail and actually uh enforce change it to be two heads. And what that says to the tissue is, oh, a correct plenarian should have two heads. And when you do this, guess what they build?

[00:17:21]Here's a nice two-headed worm. The p the memory is stable. If I keep cutting it, the pattern actually remaps itself onto onto new sizes of tissue. So, you will continue to get two-headed worms as many times as you want in in in the future.

[00:17:35]Here they are. You can see them hanging out. And uh again, this is this has nothing to do with the genetics. What the genetics does for you is to provide hardware that is reprogrammable. But but after that as we all know from using computers the hardware are just the beginning of the story. Then the question is what are the uh the the theformational goal states that that can propagate through that excitable medium.

[00:17:58]Now one of the things this hardware is good at and here's where we're actually going to get into this issue of the uh the kind of the most controversial part.

[00:18:06]every everything that I've said showed you now is pretty much against the standard paradigm, but I mean it's already in the text. It's in the developmental biology textbook. Uh you know, this stuff is uh is is is pretty pretty solid now for decades. I'm going to show you the the you know, the weirdest things next. But one of the things this this hardware is good at is visiting other types of attractors in morphospace. So if you visualize anatomical morphospace with different shaped heads, flatheads, round heads, you know, triangular heads, different shaped heads. What you can ask is uh uh normally there are different species that live in this that live in these different attractors. So this is where they go. Um could we ask a different species to go into these attractors?

[00:18:48]Turns out you can. So we can take a nice triangular headed dugia dorado cut off the head perturb the bioelectric network topology and it it can grow uh flatheads like a pilina round heads like an S Mediterranean about 100 to 150 million years distance between these animals and these and yet the hardware has no problem visiting these other attractors forming shapes of the brain distribution of stem cells exactly like these other species. So the plasticity is is is is is remarkable.

[00:19:18]Now this is what's going to what's going to lead us to um to the model systems that I was talking about. So far what I've shown you is plasticity of standard forms. So I've showed you the ability to make a standard frog eye in the wrong location but a standard frog eye. An ectopic normal plenarian head. Wrong location wrong number but normal plenarian head. And here the heads of other species. So let's go further.

[00:19:42]Let's start looking at shapes that and and configurations that have never existed before. And the reason we're going to do this is to really hammer this uh this issue of how much effort did you put in and what did you get out because up until now everything that I've told you could all of these stories could be told in a fairly standard evolutionary kind of tale that that why this why the materials are reprogrammable, why these signals work.

[00:20:06]I can tell a very standard um sort of Darwinian explanation for that. But but we're going to reach the limits of that.

[00:20:12]So So here's a few a few examples just for fun. Um if I told you that I want to make it I wanted to make a tadpole uh that would not have any eyes in the head. I want a tadpole with an eye on its tail. Furthermore, I don't want this eye connected to the brain. I want the optic nerve to stop somewhere on the on the spinal cord or in the gut or somewhere like that. Um and uh and I want this thing to be able to see out of that eye. Even though the eye is in a weird location, it's not connected to the brain. and I still wanted to be able to see what would you need to do. You might think that wow very difficult lots of rounds of mutation selection adaptation maybe some kind of crazy engineering uh of the nervous system that you would have to do. Turns out you don't have to do anything. When we create these animals and we made a machine this is an automated uh system that trains and tests these animals for vision for they learn and they learn visual tasks. Turns out that yeah, when you when you when you force the cells on the back of a of a of a tail to make an eye and you track the optic nerve and doesn't connect to anything, certainly not to the brain, in fact, they can still see. Didn't take any new rounds of of of um uh evolution, of selection, of adaptation to to take a novel sensory motor architecture and make it absolutely functional. Why does that work? That's really weird. And just for fun, if you wonder what happens to these as the tail disappears, um the tail is killed off by by programmed signals that that kill off the tail and for a frog, which isn't supposed to have a tail. The uh the eye completely ignores all of those signals. It it will not kill itself. It rides back as the tail disappears and eventually lands on the on the behind of the of the frog. If if nothing else today, you've seen a frog with an eye on its butt. So So there is that.

[00:21:55]>> Um so, okay. So that's so that's that already we're starting to see there's something there's something fishy here.

[00:22:00]Uh why does this work out of the box immediately with no no accommodations needed? Let me ask you another question.

[00:22:06]Uh if I if I had a turtle, so this is a a slow, shy reptile. And what I want is to have a playful cat-like speed of its life. I want it to be like a cat. What would we have to do? Now you might think, wow, millions of years of evolution or maybe some kind of crazy neural engineering that nobody knows how to do. Turns out you don't have to do much. So, here's this guy that put a turtle on a little skateboard. And immediately this this prosthetic, it didn't take a long time for the turtle to first of all uh move at uh at this at a speed sufficient to play with this cat. He wants to play with the cat. The cat, I guess, doesn't know what to make of it, but the turtle is perfectly able to keep up with him.

[00:22:47]>> So, so what's going on here? This a very small adjustment to the physical uh embodiment unlocks a new cognitive domain. I mean, have turtles for millions of years been wanting to do this and they just couldn't? I have no idea. But it makes you wonder, right? It makes you wonder what would we be capable of with really fairly small tweaks. But but again, that that delta between what you put what you put in and and what happened? What what is the latent space of uh the possibilities for this for this system? These kind of engineering changes doing these weird things. And I'm going to show you some much weirder things, but but putting a turtle on a skateboard is basically uh like a periscope to find these these additional things that you did not see coming. So, uh here's here's another interesting example of these of these free gifts. And so now now we're really going to ratchet this up. Um, we showed a few years ago that if you take small networks, molecular networks, so not even a cell, never mind a brain or a neuron, not even a whole cell, just molecular networks, and they don't have to be large networks, the smallest one that can do this has just four nodes. If you if you take molecular networks, you can train them. In other words, even even simple molecular networks can do habituation, sensitization, associative conditioning. It is very clear how it works. It's dynamical system learning.

[00:24:02]No, nobody had seen it before because nobody had thought to test to test these such simple models for um such a thing as learning. But it turns out that they do. And and here's here's the amazing thing. Oh, and we're using this in our in our lab. We're using this for for medical purposes like drug conditioning and so on. Turns out that if you can do Pavlovian conditioning in molecular networks, it's basically a molecular placebo, which means that you can associate powerful drugs that you don't really want to be taking with a uh with a with a neutral drug. Eventually, you know, you pair presentation and so on and eventually you can use neutral drugs to get the same effect. So, so that's the practical application, but there's something deeper very very deep here, which is this. If you use causal emergence metrics, so this is the mathematical tools pioneered by people like Julio Toni and Eric Oily and and many others to try to quantify the degree to which holes are more than the sum of their parts, right? So, so the integrated causal um identity of a level beyond the low-level parts. So, so there is math for this now. It's no longer just a philosophical debate about reductionism. There's actually math that tells you that some systems are absolutely not suitable for the reductionist assumption. There's something cool that happens. Um, networks with higher causal emergence are are better learners, but also when you train them, their causal emergence goes up. So, what's happening here is that every time you train them, they become more and more of an integrated agent, but that makes them better at learning and so on. So there's this there's this amazing uh there's this amazing positive feedback loop that we call the functional agency ratchet. Why is it a ratchet? Because if you force them to forget, and that's really important for medical reasons. You want your network sometimes to forget um physiological experiences. When you force them to forget, you do not erase the gains that they've made in becoming a higher level integrated agent. So it's an asymmetry that points uh upward in terms of agency and intelligence. It's an it's an it's asymmetric. It points upwards. But now so now you say okay where did this come from? Surely this was evolution did this evolution because I mean that kind of a thing is um uh very uh sort of you would have to select you know needle in a haststack for this kind of thing right you would have to select for networks. So it turns out that that ratchet if you look at uh random networks compared to biological networks what you see is that biology can improve it a little bit but the random networks already do this. They are they are I don't know if this is fine-tuning the way that we see in some of the parameters of the physical universe but in this is not a needle in a haststack situation. random networks are already optimized for this incredible ratchet. And so it's not about replicators or selection. There are no replicators in these systems.

[00:26:52]Nothing is replicating. Nothing is being selected for. It is it does not come from physics. It does not come from biology. It is a free gift from mathematics. If you want to know where this comes from, it comes from the way that causal emergence and network properties work. It doesn't rely on any facts of mathematics. It doesn't rely on a history of selection and so on. So this is so this is now kind of crazy.

[00:27:11]Biologists uh biologists love uh to be able to explain things in terms of either uh genetics or environment. In other words, a a history, right? Some kind of history of selection or physics.

[00:27:23]Those are the that those are supposed to be the sources. So we I think in in in looking at some of these things, I'm going to show you some some more examples in a minute. We have to start asking where else can information come from? Well, I just want to point out that so so if you look at something like this, this is the Halley plot of a simple function complex number. So Z is a complex number here. ZQ plus 7 something like that. If you plot it out, gives you this incredible order. And actually I can make a video of it, but when by changing these parameters just a tiny bit for every frame here, you can see this this amazing world. Um it's kind of cool. It doesn't hurt that these things look vaguely biological and organic. That that's kind of fun. Um but but the one thing you have to realize is that if I want to know why the pattern of this equation is exactly this, not something else but exactly this. I can't lean on physics. There is nothing about physics that explains this. I can't lean on selection or history. This was not selected to look like this. There's something else. So there are apparently there are facts that are not facts of physics. There are they are facts of we call it mathematics.

[00:28:27]And so um now we have to ask what does this have to do with biology? Does it have to do anything with biology? Could we fi could we go further? I mean I've shown you manipulation of standard forms. I've shown you some surprising sort of novel things you can do. Could we find living forms with no history of selection for their specific properties?

[00:28:46]So I'll just show you uh two. One we call zenobots. This is what happens when you take cells from a from the epithelium. So it's going to be skin from the epithelium of an early frog embryo. We we liberate them from the frog embryo. We dissociate them. We put them in a in a container by themselves.

[00:29:02]They could have died. They could have crawled away from each other. They could have made a flat monollayer like cell culture. Instead, what they do is this.

[00:29:10]Uh you can see it here. Each one of these things is a single cell. Here's a little group. They don't all look like a little horse. I just thought this was a cute example. Um they they they sort of move as a collective. They they they uh they assemble and they assemble into something we call a zenobot. What is it doing? Well, first of all, it has little hairs. The hairs are used by a frog to move mucus down the body and then get rid of pathogens and things like that.

[00:29:33]But these guys are using them to swim.

[00:29:34]They're using them to row against the the water. What you're seeing that this this has never existed before. This is a uh this is a patch of frog skin rebooting its multisellularity into a new way of life. They can move in circles. They can sort of patrol back and forth. They can have collective motion. um they can uh with with given neurons and you can this does not have any neurons but but my post colleague threw some neurons in there and they can do all kinds of interesting things.

[00:30:02]Here's one sort of swimming around. Um here's one traversing uh this this this maze structure. So it goes here. It's going to take this corner without bumping into the opposite wall. So it can it could it takes the corner then for some reason it turns around goes back where where it came from. Um, one of the most amazing things they do we call kinematic self-replication. So, look, we've made it impossible for these creatures to reproduce in the normal froggy fashion. We uh give them a bunch of loose epithelial cells. That's what this white uh dust is here that's sprinkled everywhere. These are loose cells. And what they do is they run around both collectively and individually. And they collect the cells into little little piles. The little piles mature into the next generation of zenobots. And guess what they do? uh they do exactly the same thing and they make the next generation and they make the next generation. So this ability it's kind of like Vonoyman's dream, right, of a robot that goes around making copies of itself from uh uh materials it finds in the environment uh is uh is is is something we you know there is no let's let's remember in these there's no genetic editing. We didn't touch the genome. There are no new synthetic circuits. We didn't do any synthetic biology. We didn't add any scaffolds. There are no nanom materials.

[00:31:17]There was no learning, you know, there was no training. There was no selection and uh there was no uh engineering of these things beyond taking the cells, liberating them from the other cells which typically bully them into having a boring life as a as a you know outer skin and letting them reboot into a new uh lifestyle. And here they are doing kinematic replication. Now you might say, okay, well maybe maybe amphibians are weird. Maybe this is some kind of amphibian like weird amphibian thing.

[00:31:44]And I would ask you, what what what do you think your cells would do if we liberated them? What would your cells do? Well, here's a little creature. Uh we call this an anthrobot. If you were to sequence it, you would find 100% homo sapiens. You would have zero clue from the genome that this is anything but a human being. Uh they also run around.

[00:32:04]They they have spontaneous spontaneous motion. You could not guess what genome it had by by by analyzing the their their shape or behavior. Um uh one thing that we discovered they do is they have this amazing healing function.

[00:32:19]If I plate a dish of human neurons and put a big scratch down the middle, the anthrobots will find the scratch. They drive down the the scratch here. They settle somewhere into in a group like this. And what they immediately start to do is knit the neurons across the gap.

[00:32:33]Okay, here they are. These cells come from uh adult not embryionic. There's no embryionic material here. These cells come from adult tracheal epithelial samples donated by by patients during biopsies. So we we buy the cells, they make these anthrobots. Who knew that your tracheal cells, which sit in your airway for very long periods of time, dealing with dust particles and and and so on, have the ability to assemble into a little creature. It has 9,000 differentially expressed genes. Uh so half the genome is differently expressed. It is actually younger than the cells they come from. So it actually rolls back its age by based on epigenetic clock data and they have this ability to heal neural wounds. Obvious applications here, right? This can in theory go into your body, do repair. You wouldn't need immunosuppression. These are your own cells, right? This, you know, they're not genetically engineered. They can go right into your right into your body. So, and they have this amazing amazing healing function which we did not select for, we did not train them for, and we did not engineer.

[00:33:30]So, so, so here here we go. This is the final the final piece of all of this. Uh the whole point of of evolution is that it was supposed to explain complexity with a high specificity for selection history. In other words, if you ask why a specific animal or plant looks the way it looks, acts the way it acts, you should be able to tell a story. You should be able to guess the environment and tell a story of the selection pressures that killed off everything else and and and left this. Okay, that's a story maybe you can tell for uh tadpo for the developmental stages of the frog and eventually the behavior of tadpoles.

[00:34:04]But what about the zenobots? There's never been any zenobots. There's never been selection to be a good zenobot or a good anthrobot. None of these things have been here before. No other creature on Earth does kinematic replication. And you can't really say that. Well, at the same time that um uh uh the frog genome was learning to make a good frog, it also learned to make zenobots. That isn't how this is supposed to work.

[00:34:26]There's supposed to be some degree of specificity. And in particular, computer science forces us to ask when did you pay the cost? Because computation and design always have a cost. When did you pay the cost of designing good zenobots?

[00:34:38]We know when you paid the cost to be a good frog or a good human. Eons of selection. Where does all this come from? When did you pay this cost? Right?

[00:34:46]So we have to so so we already know that our standard way of accounting for outcomes, right? So, so the the the the math that says you're supposed to put in some amount of effort. Effort comes in three forms. You either design it with an algorithm, you select it, be evolutionary algorithms, or you train it by learning. Those are the three ways we know how to put in effort. When you get something like this, none of those three things happened. And so, something is broken. There's some there's something there's there's something we're we're missing here. Um, and I would argue that that here and and and I'm about to talk about this platonic space. And my point isn't that I saw zenobots and anthrobots and immediately went went platonist.

[00:35:21]That that isn't it. I've been thinking about these things in biology for decades. But now we have a model system.

[00:35:26]In fact, two classes of model systems, the biological and the computational where we actually where this is now actionable, where we can actually do experiments. But but the fact I think we already knew long before this is that uh physicalism is not viable. Um, you know, pretty much anywhere you start in biology or physics, if you keep asking why, like a like a 5-year-old, right?

[00:35:48]You take a fact and you just go, "But why? Why? Why?" And you keep asking why.

[00:35:51]Eventually, you end up in the math department. If you want to know why the cicatas come out at 13 and 17 years, the biologist says, "Oh, it's so that the predators don't time their arrival and eat them." Ah, so why 13 and 170? Well, it's because those are prime. But why are those prime? Go talk to the mathematicians. Everything ends like this. And in fact um if you want to hear more talks about this we have a we have a symposium uh here there about 30 talks of some amazing people giving giving lectures about this. So, so here's here's what I think we have. What what we already know and and I could I could talk for an hour ju just examples just just facts of mathematics that are not underwritten by any facts of physics that cannot be changed by anything you do in the physical world that cannot be discovered through physics alone. You know, truths of number theory and the specific value of fenbounds constant and all this kind of stuff. Um, we already know there are facts that are not physical facts that's been known forever. We know that these things imp impact what happens in biology and they impact what happens in physics. If you want to know about particle properties, eventually it all boils down to the symmetries of certain mathematical objects. And so now you have a choice.

[00:36:56]What what most people do with this currently is uh they say look these things are emergent. You know the reason you got zenobots is because emergence.

[00:37:05]And you say what does that mean? And they say well they're just specific facts that hold in our world. Let's let's have let's have monism. We're not going to have any any extra uh uh realms with with with structure in it. We're just gonna we're just going to say there's physics and occasionally interesting things emerge. And to me, this is this is incredibly pessimistic.

[00:37:24]This is a very mysterian view. I don't want to occasionally be surprised and write things down and in a big book of emergent surprises. I would rather uh assume that and this is a metaphysical stance. Of course, you can't prove this, but I would like to assume for the purposes of experiment that there is an ordered structured space of patterns that underwrites some of the things that we see. Okay. So, so the the Platonist mathematicians, and I realize this isn't all mathematicians, but a good chunk of the mathematicians agree with this. It's not random. These things aren't random.

[00:37:55]They've been studying this for for millennia. They've been building a map of mathematics. they already know there's a there's a set of uh truths that you cannot change or or uh uh or or or derive from the physical world and they are systematically studying these things. So, what I think is happening when we build these weird synthetic morphology creatures, but also when you do anything, when you make an embryo, when you build a a quote unquote machine, we'll get to that in a minute.

[00:38:21]Um, all of these things are kind of a window on this latent space of possibilities, um, this latent space of patterns. And, you know, at this point, people often say, I'm not I'm not sure about this audience, but but typical audiences I talk to are extremely uh, resistant to that that idea. They say we do not want another realm. We don't we don't want a realm. Even though I'm not arguing for a mysterious realm. I'm saying the exact opposite. I think I think emergence is is mysterion. I'm arguing for a latent space of that is structured that we are going to systematically investigate. But but people still don't want another realm.

[00:38:55]And so what they typically say are one of two things. They say, well, there are no separate math facts. Math facts are just facts of physics. Eventually we'll get to that. We'll we'll know that. and or or or and or sometimes people say math is an arbitrary human construction.

[00:39:08]You know, math doesn't exist outside of our our thinking about it. So, so you know, my my latest way of of addressing this kind of thing is I I I ask how you feel about two claims. If I claim that well the constants of physics change over time, most people kind of shrug their shoulder and say ah so the speed of light changes a little bit over you know um cosmological time scale. So the gravitational constant changes who no big deal. Who cares? Paul Drack already said this in 37 that some of these things might float a little bit. Now I tell now now let's try this. I tell you, hey the ma the the mathematical constants change over time, right? So here's the first part of the expansion of the natural logarithm e suppose I tell you that yeah I think like somewhere out here in the you know somewhere out in the 10,000th space where people don't really pay any attention to it. So we don't really collapse it, you know, in that in that sense. Maybe those things change over time. At this point, everybody I've not met anybody yet who said anything other than uh somewhere between incoherent and impossible. Definitely not. Right. So what if if that's the case, if you agree that this is possible and this is this is not possible, what you've already told me is three things. First of all, that mathematical facts and physical facts are are absolutely not the same kind of beast. In fact, you expect the mathematical facts to be more stable.

[00:40:24]you're far more freaked out if they if I suggest that they change versus versus versus facts of physics and that math is actually not arbitrary or changeable because uh you can't make it be whatever you want. you are forced once you start with like empty set and successor of empty set right so you start with basic logic or uh something like that then eventually you get you get to find out that E is 2.78 you know 718 whatever you don't get to to choose it you can't change it you you are given these these things so uh you know the characters in our novels can do whatever you want them to do e does whatever he wants to do you don't get a you don't get a choice about that so here's here's how I extend so so so far all of this is is straight up mathematical platism. Here's here's how I extend this for the purposes of this discussion. And um I just have a couple more things and then and then I'm done.

[00:41:16]Uh first of all, I think that the standard assumption that the that this latent space contains only the low agency static facts of mathematics and everything else is a product of emergence and can be handled by physicists. I think that's we we should we should loosen that assumption and we should perhaps say that maybe the space contains other patterns that that are of interest to uh developmental biologists, cognitive scientists in particular.

[00:41:39]Maybe some of these patterns are high agency things that we would recognize as kinds of minds. Maybe math is just the behavioral science of one layer of that space. I don't you know I I don't know if if E and things like this actually change. I don't have any claims on that.

[00:41:52]But but uh in in my in my current model all of these things are not eternal and are changing and unchanging. These are high high level uh the ones that we associate with with both morphology and behavior and and physiology and so on.

[00:42:05]These are uh absolutely uh uh dynamic things that can change but not only by sensitization which you you you just heard about. I suspect that um they use all of the toolbox and probably more that we haven't even thought of of the behavioral sciences. So, so sensitization uh you know pavlovian conditioning delayed gratification path planning language like all of these things are right there along with sensitization once you've once you've gone down this road and realize that what why should this space have only patterns that are that are amendable to the formal tools of mathematics? What about the rest of it? So, we can talk about this idea. Again, in most audiences at this point, people freak out and they say, "Look, you're basically saying that minds exist in a non-physical space, but they affect the physical world in the same way that the truths of about prime numbers affect what the teicas are and aren't going to do when they're when they evolve. Um, interactionism should be dead. You know, Decart, you know, had this problem with with interactionism, whatever." And my point is simply this. Whatever whatever the actual re resolution to this, it's been here for a really long time because we already know that there's a degree of interaction between non-physical truths and physical objects. The relationship between math and physics already tells us this. And maybe the relationship mind of minds and bodies and when I say minds, I don't just mean our human minds. I mean the minds that power the problem solving of morphagenesis, of physiological space navigation, of transcriptional space navigation. All of these things are diverse intelligences of different grade and different type.

[00:43:36]Maybe the relationship between those minds and bodies is exactly the same as between math and physics. So um so I'll I'll I'll wrap up here just to say that the the one thing some people like this and they say ah so so here's what it is.

[00:43:50]Biology is and its complexity are really primed for these kind of ingressions of minds from the from this from this space. And that's what distinguishes us from dead matter, from mere machines, all of that. And I will just um I'll just point out that that we we did work on I don't have time to go into all the details, but anybody who's interested, I have long discussions of this on online.

[00:44:12]Uh we a lot of humility is warranted here because what we found is that even extremely simple deterministic systems are susceptible to ingressions of not just complexity, not just unpredictability but competencies that are familiar to any behavioral scientist and they are not in the algorithm. The algorithm and the materials of even a simple machine tell you what the machine must do and it tells you what it cannot do. But between those two things is a massive amount of degrees of freedom.

[00:44:42]even in small deterministic things.

[00:44:44]Apparently, this this was a was a shocker to me and uh and those and and they can hold they can host interesting patterns from that from that space. So, um I don't think it takes life or cells or or large complexity to uh to start to become an interface. And I think that's what all physical systems are. They're interfaces for specific patterns from that from that space. And therefore we have to remember that as much as we all you know would like to think that the rules of of chemistry do not tell the entire story of the human mind I would say that those kind of things don't tell the proper story of machines either. I'm not sure at this point there is any dead matter or dumb machines. And we should just remember that when we say some nothing is a computer, nothing is a touring machine. There are just times at which you would like to take a formal model of computation or of touring machines or whatever and apply it to some system. And we all need to remember that when you do that, you do not capture the like our formal models never capture the whole thing. And we thought that was negligible for quote unquote machines. It is absolutely not negligible. And it has massive implications for how we do engineering, how we do AI and and so on. We could talk about that for a long time. And so we now have an actual research program on what you might consider inspiration.

[00:45:56]Inspiration is when you get patterns, useful ideas, patterns, symphonies, what whatever you know theorems like Rammenujan got all these things from this from some space that you did not put in the effort to meticulously create step by step. Neither created, you know, neither engineered, evolved or um or or learned. And so so we can now do this with very simple very simple systems by creating robots that are powered by uh mathematical objects but also by some other things and you know so mathematical objects are signals from bacteria cosmic microwave background believe it or else uh they can do interesting things when you give them bodies when you give them robotic bodies. So yes, we're the beneficiaries of ingressing patterns that we don't pay for in the conventional mo model of of of physics and computation, but uh actually even dumb machines get this too in a in a of course in a small way and also I think we actually are patterns.

[00:46:55]We're not just impinging upon by patterns. So so I'm just going to I'm just going to summarize here and then and then I'm done. Um basically I think these patterns of of of form of behavior and so on are ubiquitous. genetics and emergence is absolutely insufficient for the life sciences of the future, but also for the ethics and engineering of the future. Uh emergence is not going to do the do the trick. Uh and the free lunches are um they're they're most obvious in biologicals. They're the most amazing in biologicals, but you can't really quantify or prove anything in biologicals. They're too complex. So, but they can be quantified in minimal computational systems. And and so that's why um uh I'm not going to take the time to to to to go through all all of this stuff. Uh but that's why we have a research program. You know, this is not just philosophy. I have six people working on all this stuff in figuring out what does that space offer you? Does it offer you static patterns like like the digits of E? Yes. But also dynamic behavioral um policies, which is what allows our robots with no controller whatsoever, just the patterns to to do tasks. um and um so what's in the space?

[00:48:01]Why does do these patterns uh inhabit certain interfaces not others and so on.

[00:48:06]All of these are now tractable. We're actually doing experiments. There are couple papers out many more coming this summer and and fall. And this is critical because uh right now we live mostly with biologicals. But all of these kinds of things, pretty much any combination of evolved material, engineered material and software is some kind of a possible embodied agent that is going to host these patterns. And there are so many different kinds of minds, you know, cyborgs and hybrids and humans with altered I mean every imaginable being is going to be here with us. We need to figure out how to do an ethical synth biosis with them. We need to learn to live with creatures that are not like us. And this this machine life thing is not not going to do it. It is I I think factually wrong as a matter of empirical data. And I think this, you know, classic picture of Adam naming the animals in the Garden of Eden is going to look much more like this. And the idea is that it's going to be very uh weird and very, you know, and very diverse and we're going to be able to understand ourselves a lot better if we if we uh embrace the the research program of of diverse intelligence. So um I I will stop here just to thank the people who did all of the hard work that I showed you today. Lots of amazing collaborations. I have to do three disclosures, no four actually, of companies that have licensed some of the intellectual property that comes out of these kinds of things. And um you know I always like to thank the model systems because they do all the hard work in in teaching us about these things. So I will stop here.