"Diverse Intelligence: new frontier for Origins, Possibilities, and Diseases of Minds by" M. Levin

[00:00:00]and thank you for that extremely kind introduction and for giving me an opportunity to talk to this uh audience.

[00:00:05]Um if anybody wants to chase down some of the things that I'm only going to briefly mention, this is our website that has all the primary papers, the data sets, the software, everything is here. And then this is my personal blog where I write around um what I think some of these things mean. So um what I would like to do today is four things.

[00:00:22]First of all, just briefly introduce myself and and why uh why I think some of these things are of relevance to you.

[00:00:29]And then I'm going to go over a framework that we've been developing that uh is designed to uh really advance a diverse intelligence research and applications. Um and I'm going to show numerous examples uh that are directly uh grounded in in neuroscience and and and you know capabilities of brains and so on. But I'm going to take it in a much much farther direction. Um I will show you one model system that I think is a good stepping stone for communicating with semi-alien minds and that is morphagenesis. the ability of individual cells to work together as a as a collective intelligence that solves problems in anatomical space and then at the end um I'll say some of the most speculative things about what I think is the future of of the sciences of minds.

[00:01:12]So, so the first thing I would point out is that um both in basic science but also for me the mental health profession I think um your client list is your client list is about to get very very strange and um I think that uh you know this this this this notion of non-neurotypical doesn't even begin to cover what's what's going to happen. Uh basically, um as I'll point out, there are tremendous changes that are going to be made both to the um Yeah, I'm I'm sorry. There's a there's a some kind of a a noise coming through. Um I don't know if that's Yeah. Okay. Um so so basically what's going to happen is that uh there going to be great changes both in the physical embodiment and uh the cognitive capabilities of various beings. we're going to need uh different degrees of of um of of help. Uh and uh we really have to start to understand how we're going to relate to them. And and and I'll talk more about this. And and and we're on both sides of this door, right? As as for for you as as mental health professionals, you need to know how to uh how to relate to new clients that are basically not the same standard humans that have been here for, you know, thousands of years. and and again for for for all of us um uh entering this this new world things are things are going to really change. So the background to these claims are basically the following that the road to transformative regenerative medicine I'm going to show you um how we're doing this uh meaning that uh the ability to induce growth and form and repair on demand leads directly to something I call freedom of embodiment. In other words, I don't think there's any way to avoid it. I don't think we want to avoid it. But but it's very clear that this these kind of um modifications to our to our minds and bodies are not some separate thing that we can choose to to address or not. Basically our efforts at uh at at radical healing are going to lead to this. They're going to lead to this capability of basically having whatever whatever kind of body and possibly whatever kind of mind you want.

[00:03:14]Um I don't believe there is any timeline in which humanity simply keeps the same form factor of of hominids that we were uh sort of left with by by the various actions of cosmic rays hitting our ourselves over the evolutionary time scale. This is this is the embodiment that we have. I don't see any future in which we just leave it exactly as that as the as as humanity moves forward into a mature species. So this is happening now. This is not some future sci-fi world. This is actually happening right now. And I think the other the other uh step to becoming a mature species is to overcome what I think of as significant mind blindness. We have tremendous difficulties visualizing um the possibilities of minds and unconventional embodiment never mind communicating with them or or relating to them but even even to visualize the the minds at other scales of time of space operating in other problem spaces.

[00:04:08]So the thing the thing to keep in mind is that all of us are part of a continuum. We started out as a single cell both developmentally and evolutionarily. There was a slow gradual set of transformations. There is no magic lightning bolt during development at which you used to be the province of uh chemistry and physics. But now you're the subject of behavior science and psychiatry. This is this is a a um a slow and gradual process. And not only uh did it happen on on these time scales naturally, we now have another continuum that we are at the center of where through technological change and biological change, all of these things are now up for grabs. And so this is this is really fundamental to to questions of the mind. You know, whatever you think it is that that a modern adult human has in terms of hopes and dreams and personal responsibilities and and all of those kinds of things. um we have to ask ourselves where on this timeline did that appear right we typically don't talk that way about cells but eventually we we do and so what is the story of transformation that we're telling here and and and that that transformation is is going to have a huge um opening in terms of um capabilities so you know we've all heard issues with with AI and language models and and uh and and trying to compare them to to humans and so on but I I I think there are two things to know here one is that none of this is really about language models or AI sitting in a box somewhere. This is this is first going to come forward when your neighbor has some percentage of their brain or body replaced with novel technologies with engineered systems with smart implants and all of that. Again, these are these already exist and people are already getting them for therapeutics, but there's also going to be a great variety of optional uh optional modifications that of course people are going to want to take. And so trying to, you know, trying to understand whether somebody's somebody is a a proper human or a quote unquote machine. I think that that whole distinction is not going to survive the decade. Um, this is this these binary distinctions are simply not going to do.

[00:06:09]And I think that most of the issues brought up by AI are basically reflections of fundamental unsolved questions about our ourselves, our own origins, and our potential. So uh what I'm going to tell you today is basically this that intelligence and diseases of cognition that are not well captured by models of organic disease. They are not all hardware issues are way way older than neurons and brains. The kinds of things that you all study are of intense uh and and and profound relevance to problems in developmental biology and evolution. Um none of this is specific to uh to neurons or to or to brains. And what we've been doing is borrowing uh borrowing tools of uh of of behavior science uh of of cognitive science and so on uh are um we've we've been borrowing these tools and and really applying them to uh to all kinds of other problems because the the the the youth that uh that I'm speaking to right now that exists in your body is not the only intelligence that's present in your body. And the 3D behavior of you moving around in the the the outside world is not the only kind of behavior. And so what I think there is um the possibility of now is a very rich kind of virtuous cycle of positive feedback loop between the pro progress in cognitive science and um what I can only summarize as multiscale biology.

[00:07:32]Yeah. So which which I'll show you momentarily. But basically this this idea that that um cognitive and behavioral sciences have a lot to say about things that are not neurons or brains. And conversely uh we can we on on the on the biology and bioengineering side the things that we discover can actually feed back and maybe maybe help um your community understand uh the origins of the kind of uh cognitive systems that you're all dealing with.

[00:07:58]Okay. So uh so this is this is this is why I think uh I think these these things kind you know kind of come together and so so now let's take a look at a framework for trying to um understand what I mean by diverse intelligence. So uh what I would like to do is to create a way to think about all kinds of different beings on the same sort of on the same sort of scale regardless of their origin story or their composition. Okay? It shouldn't matter what you're made of or how you got here, whether it was uh natural evolution or or or engineering or some combination thereof. We should be able to think about what do all significant minds have in common. And this means being able to think about not just familiar creatures such as primates, birds, maybe an octopus or a whale, you know, uh but also weird kinds of things like like beehives and synthetic biology, which I'm going to show you some examples of engineered new life forms. AIS, whether software or robotic, maybe someday exobiological agents as well. I'll say that there are a lot of people thinking about um connecting to life off off planet. And I'll just say that if we can't wrap our heads around what's going on uh with the with the um the with the cognition inside our body organs and our cells, there's no way we're going to be able to connect to actual aliens. You know, this is this is a stepping stone. And so, uh this of course this this idea is is not new to me. So here's Rosen Luth Weiner and Bigalow in 1943 trying to paint a picture of the spectrum, you know, how you get from from from passive matter uh all the way up to human level metacognition and those kinds of things and what and what the steps are um in between. This is a very um sort of cybernetic view and I think I think a good one and the framework that I'm interested in it's laid out here in detail has to move experimental work forward. It cannot just be philosophy or linguistics. It has to move experimental work forward. It has to have implications that drive new discoveries in biio medicine and synthetic morphology, but also I think it has massive implications for ethics and for how we relate to these beings. And I see it kind of like what's happened in math.

[00:10:01]So, so we started out with natural natural numbers here and then progressively by by breaking rules, breaking arbitrary uh arbitrary limitations on what we thought these objects were, we discovered yet more and more other different kinds of uh of of important u objects out there. And and and that always happens when you when you examine your assumptions and you examine your categories and you say, "Okay, but what what happens if I give up this axiom or that axiom? What what can I discover?" And so this is uh this is kind of the the trajectory that we've been on. I'm not going to get a chance to talk about any of this, but basically, you know, looking at uh beyond just brainy animals, what what does the material of life actually offer? Um and then and then some other some other things that are kind of combinations of evolve, designed, and hybrid agents. And for each of these, we have to think about what is the conceptual leap that's needed and what can it what can it do for us? And then how do we actually relate to these novel beings that we're um that we're discovering? And I think the set of uh the set of other minds all around us that are difficult to notice right now is is going to grow um enormously. I think it's it's going to expand greatly.

[00:11:12]So So let's just look um you know when we when we look at things like this, right? So so brainy mammals. So here he is. He's setting up a little a little accident scene. Uh he's got a very good theory of mind about his owners. He knows what's going to happen when they see this, right? like very clever. He's even going to look to see like make sure that make sure that they're watching and things like that. These kinds of things are are are so obvious to us because it occurs on the same spatial temporal scale as we do, right? So the behavior in the 3D world, same scale of space and time as us say similar goals of of safety and and all of that. Okay, fine.

[00:11:45]But even even with brains, things are not simple. for example, and and and I reviewed a number of Karina Kaufman and I reviewed a number of cases where there's a really radical mismatch. These are rare clinical cases where there's a radical mismatch between the amount of human brain tissue that you would expect to be present and the performance the cognitive performance of that individual. Okay. So, so, so it it already we know there's there's I mean this is not common, but it does occur and so we know there's something there's something going on here and you can try to patch it up with the talk of redundancies and things like that but uh fundamentally the models we have do not predict that things like this will happen. They can be you know maybe shoehorned into it but they don't predict it and and then there are many other really interesting things that we don't understand. For example, this is a tadpole of the frogs and leavus. Here's the mouth. Here are the nostrils. Here's the brain, the spinal cord. And so what you'll notice we've done is we've made sure the primary eyes don't form, but we put a we put an eye on its tail. Okay?

[00:12:43]And this eye puts out an optic nerve.

[00:12:45]Here's the optic nerve. It does not connect to the brain. It synapses sometimes on the spinal cord, sometimes on the gut, sometimes nowhere at all.

[00:12:53]And then we built a machine uh this device to automate the behavioral training of these animals for visual cues. And what we discovered is that they can see, okay, they can they can learn in visual cues. Even though this thing that does not connect to the brain, even though they have the a completely different sensory motor architecture, immediately out of the box, this works. No new rounds of mutation, selection, adaptation to this completely different form factor of the um of the sensory and and processing systems. Uh yeah, no problem. Right out of the box, it works. Where does this incredible plasticity come from? Why is it so highly reconfigurable? Why don't you need rounds of of of selection to uh to make this thing work? We're starting out with a with a 100% normal genetically normal. We don't touch the genome, by the way. So 100% genetically normal animal. Why does this work? Um we should also talk about the ability of memories to move through tissues. So these are pleneria. You'll see some more of them in a minute. These are flatworms. Uh they are similar to our direct ancestor. They have a true centralized brain. Same neurotransmitters that you and I do.

[00:13:57]They're highly regenerative. So you can chop them into pieces. Each piece regenerates, but they're also pretty smart. You can train them, for example, place conditioning and these feed them in these bumpy little um little circles.

[00:14:07]And what you find is that if you chop off their heads, the tail sits there doing nothing. Eventually, it regrows a brand new brain. And now behavior kicks in and you can you you by testing them in that same machine that I just showed you, you find out that these animals remember the original training. So what's happened here is that the memory was somewhere. It was not just in the brain. That's clear. It was imprinted onto the new brain. The new brain has access to it. Okay. After it appears. So this new tissue. So the new tissue has access to these memories formed by the old tissue. So this has this has implications for understanding what's going to happen when we trigger regenerative uh responses in brains.

[00:14:46]Yeah. So so for for patients longevity um even even augmentation. If for example the algorithms that power hearing can take over visual processing areas in the blind, what else can your thought patterns uh inhabit? You know what what other real estate can they take over if we if we grew you a new hemisphere? Would they would they take over? Would they move into that to that space? Right? So the ability this this this intersection between behavior and morphagenesis when when novel novel tissue be it brain or not grows is extremely interesting. We can also think about examples like this where caterpillars turn into butterflies. In order to do this, they rip up their brain, basically dissolve most of the connections, kill off most of the cells, build a completely new brain suitable for a very different um kind of lifestyle. But one thing that has been found is that if you train the caterpillars, the memories uh are recovered in the butterfly or moth. Not only do they persist, I mean that's that's a big enough issue right there is how how do memories persist when all the synaptic connections are broken, right?

[00:15:47]So so that has implications for theories of where memory is and things like that.

[00:15:50]But but the bigger issue is that the actual memories of the caterpillar are of no use to the butterfly. In other words, butterflies don't eat the leaves that that the caterpillars got as a reward. They don't crawl the way that that the that the caterpillars behaved.

[00:16:04]It has to be remapped. the memories have to be remapped onto completely new architecture. And so again, that that plasticity that's that's that's baked in to um to to to living tissue is uh is is there for us to to think about and perhaps take advantage of in terms of how do you re remap the kinds of cognitive um capabilities that you have including your memories and your personality and so onto new hardware whether that be new hardware of the body or um or of the brain. And there are lots of interesting things we can talk about in terms of taking the perspective of the caterpillar facing the singularity. The perspective of the butterfly that's saddled with some memories that has no idea where they came from because the it wasn't part of the training in this embodiment and yet it has those memories and also perhaps of the memory itself which has to be remapped. We can we can talk about that.

[00:16:52]So so this plasticity I think I think is very interesting and it's telling us something important. Um here here's another example that I like a lot. If if I said to you, uh, I would like to take a reptile, a shy, slowmoving turtle. I wanted to I wanted to work at a at a cat-like speed of life. I wanted to be playful. I wanted to move at the speed of a cat. What would you have to do? And you might think that, wow, uh, millions of years of evolution or perhaps some sort of um, neuroengineering that we have no idea how to how to do. Actually, turns out you don't have to do much. So, here here's this guy. He put his turtle on the little on the little skateboard and immediately unlocked this this incredible um change in behavior. The the cat looks puzzled. That makes sense.

[00:17:33]We should all be puzzled at this. Um the the turtle has no problem moving at the at the relevant speed. He clearly wants to play. He's he's keeping up with the animal. What what was the latent space of possibilities? What else could you unlock with with really very minimal changes to the uh to the embodiment? you know, have these turtles been around for millions of years just uh you know, capable of of all of this and and not being able to do it because their body didn't let them and what is going to be unlocked in in humans when we have uh things that are things that are much more capable than than than a skateboard for us to connect to. So um that kind of plasticity of of form of function of in fact of integrated form and function asks us to really uh understand what's what's happening when when cells build and accommodate new structures. And so what I'm going to do for the next uh let's say uh 15 minutes is to talk about morphagenesis as a model system. So so let's talk about the cells that we're all made of to understand what I think of as the agential material of life.

[00:18:32]this this uh this this multiscale intelligence that that underlies our our behavior and our cognition. So first uh just to remind us that not just beehives and ant colonies but all of us are collective intelligences. We are all made of parts. Um everything everything that we are able to do is uh underwritten by the co cognitive glue by the policies and mechanisms that keep our parts aligned towards specific goals. This is the kind of thing we're made of. Now, this is a free-living organism called the lacrimaria, but you get the point. It's got no brain, no nervous system, extremely competent in its own little environment doing the things that it needs to do to survive.

[00:19:10]And these are the kinds of things that work together to build our bodies. Now, you might when I when I ask people, could you reward or punish something like this? Most people say yes. You know, you you they they they feel you could. Um if I ask you, could you reward or punish a chemical network? Most people say no. You chemicals don't care what happens. you can't reward or punish them. But of course, what is this made of, right? That this this is that's exactly what this is made of is is a chemical network. So what happened?

[00:19:35]What's happening from here to there? And what I want to point out is that even even chemical networks, in fact, small chemical networks, the smallest one is just four nodes that can do Pavlovian conditioning. Chemical networks, as we've now discovered, can do at least five or six different kinds of learning.

[00:19:53]They can do habituation, sensitization, um associative conditioning. They can count to small numbers. Um Walter Fontana showed they can do probabilistic inference. All of this not not brains, not neurons, not even cells. Small chemical networks. The material that we are all made of that our cells are made of is already cognitive right at the beginning. It can do primitive kinds of um operations that are that are easily recognizable to any behavioral scientist. Now, there's a couple of interesting things that I'll I'll just point out about this work. One is that um this is a kind of molecular placebo because when you train this network in an associative conditioning context when I when I hit it with a drug that causes some sort of strong outcome and then another drug impaired presentation where the where the neutral stimulus normally doesn't do anything. If they're applied together the system learns and then the neutral stimulus becomes the condition stimulus and activates the response.

[00:20:47]It's a kind of placebo effect.

[00:20:48]Basically, at that point, the system reacts to the to the inert drug as if it were something else because it's past history of experience has convinced that that that's what this is, right? So, you can you can already start to see uh a kind of uh the the the scale down of the things we're all used to in in in human medicine now scales down to these kinds of things. The other interesting thing about this is that we found that if you look at causal emergence, so these are metrics that uh basically tell you the degree to which the whole is more than the sum of its parts. So people use this to distinguish between locked in patients versus comeosse patients versus just a pile of neurons. You know the question of is somebody home? So Julio toni and others use use these these kind of metrics. What we've shown is that if you if you use them to look at these chemical networks, you will find that that quite often uh with training the causal emergence goes up. So so their integrated agency goes up the more you train them. But also the more it goes up, the better they get at learning. And there's this there's this incredible feedback loop which which actually is asymmetric. It points in one direction because if you force them to forget, you do not lose the gains that you made in causal emergence. That's very interesting. It it it it's um it means that that uh what happens in these networks is they're primed for higher intelligence and higher um higher agency. Yeah. The it's it's not a symmetrical thing where it's just as easy to fall back down. And if you ask where does that come from? It doesn't come from physics. It doesn't come from selection. This is a property of of mathematics. Actually, it's a free gift for mathematics that the properties of these kinds of things are uh aimed is are are such that they're aimed upwards in terms of intelligence and integrated agency. So, so we we looked at the molecular networks that are inside of cells. Now, now we go to the cellular level and we look at an early embryo.

[00:22:39]So, here's a blasterm um maybe few hundred thousand cells at this point. We look at that and we say there's an embryo. What what is there one of? When we say there's one embryo, what is there one of? Well, what there's one of is a kind of um collective model. They all all of the cells are uh connected and are aligned in both physically and uh and physiologically they're aligned towards the same journey in anatomical space. They're going to undertake actions that bring them closer to a specific uh a species specific target morphology. Now, if you take if you take a little needle and put some scratches into this blaster, each one of these little islands before they heal up is going to make its own embryo. And so that's how you get conjoint twins and triplets and so on. And so now some very interesting questions arise. You know the number of beings, the number of individuals inside this embryo is not known in advance. It is not fixed by the genetics. It is um an outcome of the physiological events that that take place. We can ask how many agents per cubic millimeter of embryionic tissue like what's the carrying capacity. For computer science, we think we know how to how to calculate those kinds of things based on the number of um number of logic gates you have and so on. But for biology, we really don't. Uh and of course, of course, you all deal with these kinds of things all the time. You know, split brain patients, dissociative disorders, how many personalities can you actually fit in a uh standard uh standard amount of brain? This is these are all these are all open questions.

[00:24:03]And so the bodies of which we are made are a kind of u multiscale competency architecture. Each level of organization from the molecular networks on up has its own agendas. They solve problems in various spaces and they have learning capacity. They have different ways of uh of sensing and and acting and and so on at every level. Okay, this is and this is this is one difference between how we currently make robotics and so on. we are we are a multiscale intelligence um that is collective at every at every step. Now I will also point out that um what's really important to know is that uh these kind of systems have embodiment way outside a familiar three-dimensional space. We are pretty good at recognizing intelligence as movement in 3D space but um biology has been solving problems long before nerve and muscle showed up.

[00:24:51]So cells have been navigating the space of gene expression highdimensional difficult space like 20,000 dimensions in some cases. Uh anatomical space, physiological state space. So you know when you're looking at when you're looking at an organoid that's just sitting there not moving around and you say well it has no embodiment or in fact a language model you say it has no embodiment. Yeah, maybe it doesn't have embodiment in as as movement in 3D space, but there are many other spaces in which agents do this perception, decision-m, action loop, active inference, all these things. They're doing this in all kinds of other spaces.

[00:25:24]So, what we've been studying is the symmetry between behavioral science and developmental biology. I like this. I like James' sort of definition of intelligence. Um, the ability to navigate spaces towards the same goals.

[00:25:36]And what we've been studying is the hypothesis that morphagenesis, the creation and and and repair of bodies is a collective intelligence that exerts its behavioral competencies in a different space in anatomical space.

[00:25:48]Whereas you you and I are collective intelligences that uh uh in the sense that our our neurons and other and other cell types work together to help us navigate 3D space. Um there are there are there are some some pivots that you can make around time scale and so on.

[00:26:03]And actually the symmetry is incredibly strong. And we've we've actually made tools where you can paste in a neuroscience paper and it sort of swaps the word milliseconds for minutes and neurons for the word cells and then you have yourself a developmental biology paper. So there are there are really really strong uh similarities and that's because as I'll point out in a minute the underlying evolutionary origin is is is common. These are not um these are not sort of uh really really distinct things that I'm trying to pull together.

[00:26:34]they actually have uh the same mechan this the same underlying molecular mechanisms. So I'll show you a couple of a couple of interesting examples. I could talk for an hour just just on that um on that subject alone. But uh but let's let's talk about the kind ofum top down control. So so here's an experiment. We take we take a um an amphibian like this and this is not our work. This is back from the 50s. They would graft a tail onto the side here, the flank and this thing slowly trans uh transforms into a limb. Why? uh because the system as a as a whole uh has a uh has a has a stored representation as it turns out of what a correct system is supposed to look like and when there's a when there are errors it tries to to uh to to repair them and and to move towards the right the right location.

[00:27:20]Now look the cells at the tip of this tail are tail tip cells where they belong. There's nothing wrong with them.

[00:27:26]They don't have any damage. There's no injury and yet they turn into fingers.

[00:27:29]Why why is this happening? because the system is primed to for a transduction from a large-scale highly abstract uh piece of information about what the layout of a of a typical um amphibian like that looks like that has to filter down to the molecular events of cells.

[00:27:45]Cells don't have any idea what a limb is or how many fingers you're supposed to have. The the the collective does, but the individual cells have to be instructed as the system transduces down into the chemical signals that actually are required to make this happen. Where have we seen this before? Well, basically voluntary motion. When you get up in the morning and you have these incredibly abstract goals around, you know, financial goals, research goals, whatever, in order for you to actually move on to to act on any of those goals, the chemistry has to change. Ions have to cross your muscle membranes for this to happen. And our bodies are basically an amazing system for transducing very abstract uh kinds of goals in in in in very um high level spaces down into actually making the chemistry and physics uh act in accordance and and of course vice versa. And then and then we have these kind of creative problem solving capacities where if you take a normal uh N that has eight to 10 cells that normally make up its kidney tubules, we can artificially make NES where the cells are gigantic and uh so so FHauser did this years ago. If you do that, the cells automatically adjust fewer cells uh but but the exact same large scale structure. If you keep pushing it and make truly enormous cells, just one cell will bend around itself leaving a space in the middle.

[00:29:04]Give you the exact same structure. So two things happening here. One is uh the ability to call up different molecular mechanisms. This is cellto cell communication. This is cytokeleletal bending. Call up different mechanisms uh to solve a problem. Right? That's they have those on every IQ test. Here's a set of objects. Use them to solve this problem. Here are the affordant the molecular affordances you have. um use them to solve a problem you've never seen before. So, kind of creative problem solving in anatomical space. And and of course, because you can't even count on your parts as a as a as a as a new embryo coming into the world. Not only do you not know what the environment is like, you don't know how many copies of your genome you're going to have. You don't know what size your cells are going to be. You you you have to get the job done uh under under these kind of very unreliable medium. You know, biology is an unreliable medium.

[00:29:52]And it drives the the the of feedback loop of of creative intelligence that was there long before um neurons appeared.

[00:30:01]And so um you know this this so so so so what I've shown you is is is uh these these ideas about top- down control and and and problem solving. And one one other thing uh to sort of cement together the cognitive science and and uh and and morphagenesis and evolution is the is the following. uh none of us have access to our past. What you have access to are the memory engrams that past versions of you have uh have have created based on experiences that they had. So basically the memories that you have are uh messages. They are um uh they're communications from a past self that has left specific compressed highly compressed um uh kinds of representation somewhere in your brain or body. And then so so that's what happens in the past. Here's our now moment. every every cognitive system is in charge of interpreting uh these these steps into the this this information into the future. Now, because this compression process and gen the compression and generalization inevitably throws away information, you don't have all the details nor nor could you function if you had all the details. But what it means is that this is a creative process. As you know, you guys know better than anybody, um recall of memories has a lot of reconstruction associated with it. Your job at at any given moment is to figure out what your own memories mean. And you don't have you don't have necessarily an allegiance to how past your past self interpreted.

[00:31:25]You are trying to tell the most adaptive story going forward into the future. So this is a this is a a creative improvisational process and that is exactly what evolution does. So, so you get handed a set of uh genetic affordances which are compressed representations of the experience of your of your um of your ancestors. But, but now as an embryo undergoing morphagenesis, your goal is to uh reconstruct the the most uh adaptive meaning not not what it used to mean, but but because you have actually no idea what it used to mean. What what you have to do now is to con is to use them as as as best as you can to construct a good story going forward. And this is why the plasticity is such the all these examples why this is why this thing works. Yeah. The the the eye on the tail and why these guys can do it and and and many other kind examples of amazing plasticity because the whole process of building a body is not the thing that that we were told for decades which is you have the DNA and the DNA tells you what you're going to be. That isn't it at all. The the genome is basically a set of um a set of uh engrams or prompts and it is an active intelligent process by groups of cells to interpret it and to do something coherent. And I'm going to show you some very um unusual um things that they do in in a moment. So So how so how does all this how does all this work? How does all this intelligence work? Well, in the brain we think what's happening is that you have the hardware. I don't have to tell this group um what the hardware looks like.

[00:32:51]It's an electrical network uh employing both chemical and electrical synapses.

[00:32:56]Um and what allows that network to do is to do information processing that can do things like move you through three-dimensional space and then people who work on neural decoding will try to read these electrophysiological states and infer what the what the what the subject was thinking about you know memories goals that kind of stuff. So it turns out that this this system here is incredibly ancient. It goes back to the time of bacterial biofilms. Okay. So it is it is uh th this is this is what uh evolution co-opted and sped up to make nervous systems. Every cell in your body has ion channels. Most cells in your body have electrical synapses known as gap junctions to their neighboring cells. They make electrical networks.

[00:33:35]And basically we can take all the tools of of neuroscience and behavioral science and if you just make a few pivots, right? If you if you're if you're willing to talk about morphus space instead of 3D space and you slow the scale down to minutes and hours instead of milliseconds everything works because that is what that is basically what happened. All of the same kinds of mechanisms the g the the the ability to create voltage potentials to propagate them through paths through tissue to use them to compute to integrate information uh to store memories and counterfactuals and so on. All of this is is is extremely ancient. Um we developed the first tools to read and write these electrical pattern memories. You know if you because because we we have some idea of what uh the electrical networks in your brain like to think about. What did these electrical networks think about before uh before there were brain before there were neurons and muscles able to move you around? Well, it turns out it turns out that what they thought about was shape. That is what these networks originally were for is to process movement through anatomical space to to move your configuration from a single cell to that of a of a human or whatever else. Um that that is the navigation that they used to think about. And so we developed some tools using voltage sensitive fluorescent eyes here to for example to see all of the conversations that uh the the electrical conversations that these cells are having in an early frog embryo. Um you can track it at the individual cell level here. um we do a lot of um computational modeling to to do kind of a multiscale model all the way down to the transcription of the channel genes and all the way up to network properties such as inpainting outpainting the kinds of things that you see in in regeneration and so on. And so now for the next few minutes I want to show you what does this give us. You know this this figuring out this this this um symmetry between between uh the the the algorithms that uh that are that are carried out by cognitive systems and the the the ones that build your body in addition to the ones that build your mind. Um what does that give us? Well, one of the things we can do is we can start to communicate with this with this sematic intelligence, the problem solving uh system that I've been showing you up till now that that has this incredible plasticity and the ability to solve various problems. What we can do now that we understand the medium in which it records its goals, it's basically the same medium as as as in the brain. So these electrophysiological networks once we can read and write information there we can use that as an interface to communicate novel goals not to micromanage them not to uh you know the the most interesting thing I think about uh about neuroscience is that it it helps us to understand this kind of a multiscale phenomenon where when I'm talking to you now I don't need to worry about what your synaptic proteins are doing you will take care of all of that I'm giving you high level um high prompts through a through a thin language interface. And your system will then transduce this down to the chemistry that's needed to to make things happen. It's the reason humans were able to uh to train dogs and horses for thousands of years before we knew any any neuroscience at all. But this works all the way down. And so we can make a very simple prompt to a bunch of cells here in the early embryo and say make an eye. Okay, we don't have to tell it how to make an eye. We we have no idea how to make an eye. But what we do know is is a little bit of the electrical language. And this is what we're trying to do is to crack this code such that we can inject ion channels that induce a particular voltage state in these cells. And we now know that the cells um interpret that particular voltage pattern. It's a multisellular voltage pattern. Uh that the cells interpret that as oh we should build an eye here. And they build an eye. Here's one's um sitting in the gut. All lens, retina, optic nerve, all the stuff it's supposed to have. If there's too few of them, they actually, so the blue cells are the ones we we um we injected with the channels, they will actually uh recruit their neighbors to help them build the eye. They know there's not enough of them, so they recruit their neighbors. Uh here's, you know, ants and termites and things do exactly the same thing. This is a common common feature of collective intelligences. Um what happens then is that you can you can induce these ectopic fosi but there's a battle that goes on because the surrounding cells they they know perfectly well there should not be an eye there and as a cancer suppression mechanism they try to uh remove they try to normalize the voltage. So you may not get three or four eyes here. You may only get one or or none because there's a battle for the future of of the morphagenetic journey. Some of them say go to the eye fate. Some of them are saying no you should stay skin or or whatever you are. And so we can actually watch these conversations take place in those flatworms that I showed you. If you if you cut them into pieces, every piece makes one head and one tail. And you might think that it's the genetics that tells every piece how how many heads to have. But of course, remember that the genome doesn't say anything about heads. The genome, if you actually read it, it refers to proteins. And so we still have this this question about um how how does the piece know how many heads it's supposed to have? Turns out there's a there's a voltage gradient that you can read. And the voltage gradient says one head, one tail. And if we rewrite that gradient and say, "No, you should have two heads." And then after the fact cut the animal, so here's a perfectly normal body with a with an a perfectly normal molecular marker expression, but it has an aberrant memory of what a correct plenarian is supposed to look like. Now, that memory is latent, doesn't do anything. It just sits there until we cut. And at that point, all the cells consult that memory and they say, "Ah, two heads fine." And they built this two-headed worm. Um, this is not the AI or Photoshop or anything like that. These are actual animals. Uh, a single body can can hold up to, you know, probably more, but but at least two different representations of what a plenarian should look like.

[00:39:16]This, I think, is the origin of mental time travel that we can all do and counterfactual thought because this this creature is holding a pattern that is not correct right now. I only have one head right now, but my memory of what to do if I get injured in the future potentially. And so that's the counterfactual part. this is what I would build in the future and and that's in fact what they do. So um lots of lots of interesting neuroscience to be done on these animals with two heads. Uh again reminder we don't touch the genome. This is not about the hardware whatsoever. Um the plenarian genome builds a system that by default makes a single head but it is highly reprogrammable. That is not it is not nailed down. You can also ask them to make heads of other species. So here's a a dazzphila with a nice triangular head.

[00:40:01]They can make worms of flatheads, round heads, and so on. Not just the head shape, but the the shape of the brain and the distribution of stem cells. Just like these other species, 100 to 150 million years of evolutionary distance.

[00:40:14]No genetic change. The same hardware is perfectly happy to visit the attractors in morphospace that are normally occupied by these other systems. We can use the same thing to repair. Uh I won't I won't go into the details, but basically this is our program in birth defect repair. So if you uh if you do a dominant mutation in a gene called notch, you probably all know that what this is very important neurogenesis gene, the brain is completely wrecked.

[00:40:38]No forebrain, midbrain and hindbrain or a bubble. No behavior uh profound damage with a very simple modification of an HCN2 ion channel. We get back to a correct bioelectric pattern for the brain and then the cells um build to suit. So you get a normal brain, normal um phys normal gene expression, normal IQs, their learning rates which we measure go back to those of controls even though they have this profound dominant notch mutation. So at least some hardware defects can be fixed quote unquote in software at the level not not at the level of of the genetics but but at the level of the physiology that drives. We have a similar program in limb regeneration where we can take frogs which normally don't regenerate their legs as adults and give a simple 24-hour stimulus that then leads to a year and a half of leg growth. Just a very simple trigger at the very beginning say go down the leg building path not the scarring path and then you know you see by by by 45 days you've already got some toes and a toenail and then this thing grows for for a year and a half. So, so these are these are the uh you know what I'm showing you now are the consequences of of using this uh of pursuing this hypothesis that that uh this kind of multiscale problem solving and communication with these systems is not just for brains in in 3D space but is actually fundamentally baked into uh to to biology all the way to the bottom.

[00:42:02]And we can now take advantage of this.

[00:42:04]And the final example I'm going to show you uh has to do with uh has to do with a cancer problem. And you know what happens in uh in in evolution and development is that you start with single cells which have extremely tiny goals. So a single cell is only concerned about managing its own pH, hunger level, those kinds of things, right? So so very tiny cognitive lone uh very tiny um scalar kinds of goals. When you make a network, this network is able to uh store much larger uh set points for its homeostatic activity. So here's a uh an amphibian limb. If you amputate anywhere along here, the cells very quickly go back and they grow what's needed, then they stop. Why do they stop? Because this is a homeostatic process, they they they they note the error. They try to reduce the error. And when when the error is within acceptable limits, then then they stop. So, uh I've just been showing you in the case of the of the of the worm and and so on. The how do they know what the what the pattern is? It's it's stored biomectrically. There's a biological pre- pattern that tells them what the what the set point for this homeostatic activity is. But but note that collections collectivisms of cells can store enormous grandiose goals. You know, their cognitive lyones are huge.

[00:43:15]They can they can the the collective can store a memory of something like this, you know, this this large scale shape.

[00:43:21]But that of that system of course has a failure mode. And that failure mode is cancer. Um cancer cells aren't more selfish than normal cells. They just have smaller selves because what happens when a single cell disconnects from this electrical collective? It no longer has access to this enormous memory of what they were building. Now the border between it and the outside world shrinks, right? It's back to being a unicellular organism as far as it's concerned. The rest of the body is just external environment and it pursues its own tiny little goals which are eat as much as you want, um reproduce as much as you can, go where life is good, and that's metastasis.

[00:43:55]And so so we've been we've been using this idea of cancer as basically a sematic dissociative identity disorder, right? All of these things all of these things together have the identity that they're building a limb and they have ways of doing it. But individual cells once they disconnect have no access to that. So there's a diagnostic here. You can use these voltage dice to see when it's about to happen because what we do is we inject human anka genes into these tadpoles. Eventually they make tumors, but you can see when the cells are about to disconnect. And then most importantly, when you reconnect them, when you when you you don't we don't fix the anka gene. We don't kill the cells.

[00:44:30]This isn't a chemotherapy. We we we leave the cells alone, but we reconnect them. We forcibly reconnect them to the electrical network. Then even though the Anka protein is blazingly expressed here, there's no tumor because it's not the genetics that drives, it's the physiology that drives. And as long as the network is thinking about making large muscle and and you know large large scale structures like like muscle and and skin and so on uh then they they're kept away from these kind of single cell um kind of ancient unicellular behaviors. So, oops. Uh, so the the bottom line to all of this is that I I really think that uh the the control of of growth and form for therapeut for regenerative therapeutics, these are issues that that that affect most of the problems we have in medicine. Complementing the bottom up kind of stuff that everybody's been focused on for hundreds of of of years is this kind of top- down control that's taken straight from concepts in uh in in in behavioral and cognitive sciences.

[00:45:27]And I actually think that um future medicine is going to look a lot more like a kind of sematic psychiatry than it's going to look like chemistry. Okay?

[00:45:34]It's not going to be this kind of bottom-up intervention that that we try to do with drugs today. And and the bioelectric layer is a is a major it's not the only interface, but it's a it's a massively useful interface. So just the last couple of things I want to I want to point out. First of all, you know, we've been talking about this addressing things like birth defects, traumatic injury, cancer, aging, uh and and what we want is complete control. We want an anatomical compiler, right? All of these things would go away if we were able to communicate. So, this is not a 3D printer of some sort. It's it's a it's a way to communicate your goals as a bio-engineer to the goals of the cellular collective, right? So, maybe you draw this like three-headed flatworm and and and that is what that what what the compiler will do is give you the uh the exact stimuli that have to be given to cells to get them to build whatever you want them to build. So we are very far away from a complete version of this but we've taken the first steps. So that's so that's fine. We can rebuild uh the the near future will be to rebuild organs standard structures that that belong in your body. What's left? What what goes beyond that? And so here's the last the last part just just for a couple minutes. Um what's what's left is is this interesting question of where does your mental architecture come from?

[00:46:46]Well, mostly people would say evolution, right? It's been shaped by your evolutionary history. Okay, but what are going to be the goals, preferences, and capabilities of entirely novel beings that have never been here before, that don't have an evolutionary history that selected them for specific cognitive properties? What what what is the content of their mind going to be like?

[00:47:05]And so I want to show you two quick examples. The first we call zenobots.

[00:47:09]These are made of frog cells. We dissociate some epithelial cells from a frog embryo. This is just skin. There's no there's no neurons. There's nothing.

[00:47:16]What they do is they they combine into this little thing that um swims. It uses the psyia on the epithelial cells to to actually coordinate and and swimming activity. Here it's running this um traversing this this kind of maze. It turns around whenever it whenever it wants to. It has these amazing properties that if you give them loose epithelial cells, it will um it will collect them into little piles and compact them. And guess what the little piles turn into? They turn into the next generation of zenobot which then goes on to do the same thing and the same thing.

[00:47:48]So this is kinematic replication. We've made it impossible for them to reproduce in the normal froggy fashion. They figured out another way of doing it. No other creature on earth that we know of does kinematic self-replication. They make copies of themselves from materials they find in the environment. If you if you track the calcium signaling, remember there's no neurons here. This is just epithelial cells. If you track this thing and you apply some metrics, so this is uh Thomas Farley's work from from Josh Bongard's lab, basically compared to null models, they do about as as as well as as as we see in fMRI.

[00:48:22]So this for of brains. So I'm not saying that these are brain-like. What I'm saying is that both brains and these kinds of simple systems are exploiting dynamics that are incredibly fundamental. And we can catch this with with metrics like like causal emergence metrics. Now, that might seem weird and maybe specific to amphibians. I would ask you, what what would your cells do if I liberated them from your body? So, you can look at something like this.

[00:48:47]This looks like we got this off the bottom of a pond somewhere. Actually, if you were to sequence it, 100% homo sapiens genome, completely normal human genome. These are made from adult cells taken from uh patients that go in for tracheal um biopsies. We buy the cells.

[00:49:02]We let them reboot their multisellularity. And they make these amazing little creatures known as anthrobots. Anthrobots have over 9,000 different gene expressions than they had when they were sitting in your in your trachea. Remember, we don't touch the genome. No synthetic biology, no scaffolds, no weird nanom materials.

[00:49:19]These are just new ways of new new lifestyles that your cells can adopt given the opportunity. One of the things they like to do is they like to heal neural wounds. So, for example, if we make a scratch here, throw a bunch of human neurons in a dish, these guys get together and uh and they will over the next four days, they will heal. They'll take the two edges of the wound and they will actually heal it together. We did not teach them to do that. This is not anything that's that's in their history.

[00:49:44]And so, uh this just the first thing we the first thing we looked for. And so now we have a we have a simple question with a with a very uh with a very difficult answer which is we know when we paid the computational cost to make a good frog and to have these nice developmental stages and some tatpoles with behaviors all the years the the eons of of this genome bashing against the environment that's when we paid that computational cost. When did we pay the computational cost to make a good zenobot or a good anthrobot? There's never been any zenobots or anthrobots.

[00:50:14]There's never been selection for those features. And we have to think really carefully about what it means if we're going to say that, well, at the same time that you selected for frogs, you also got kinematic replication and and hearing because actually zenobots can hear in ways that tapos don't. Um, and things like that. Yeah, he just sort of got them at the same time. That that that basically rips up the whole point of specificity between the history of environments and selection and what we get at the end, right? That was kind of the whole point of evolutionary theory.

[00:50:40]So, so this that kind of plasticity and the ability to just just you know out of the box have these capacities really ask us to think about where do they come from in the first place. Selection is only part of the story. Uh we need to be able to understand them because we make novel things all the time. Um internet of things, swarm robotics, social and financial structures, we have no idea how to how to predict. And so when we when we think about where do these things come from as biologists, we like to think that they come from genetics and environment. But I just want to point out and I'm just I'm just going to tease this last part. I've been doing a lot of work on this and and you know there's there's lots lots of things that that I've written on this. Just going to tease it here and to point out that there is another source of information.

[00:51:20]So this this pattern for example is what happens when you plot when you do a plot of this tiny little equation and complex numbers. All of this is hidden in there.

[00:51:29]Where is this specified? Like where does this come from? There is no no aspect of physics. there is no aspect of history or environment that tells you that this has the specificity of exactly this pattern. Where does it come from? And so I've actually been working on on a model in which uh these kinds of truths of mathematics are not just uh relevant to mathematical objects but actually to patterns of physiology, patterns of behavior aka kinds of minds. So we can we can talk about that. So, so the bottom line is this. Uh, pretty much any uh combination of evolved cellular material, engineered material and software is some kind of viable agent because of the plasticity of life. This tiny little corner of the space is everything that includes what Darwin called the endless forms most beautiful.

[00:52:14]Actually, the space is enormous cyborgs and hybr and and you know um combinatoral combinatoric explosion of different kinds of embodied minds. And I really do think we have to work very hard to be able to enter some kind of ethical synth symbiosis with the beings with which we're going to be sharing our world very very very soon. And so I'll just uh summarize here what I've told you today is that intelligence aka problem solving in different spaces is much older and much broader than brains.

[00:52:43]Morphogenesis is a model system for beginning to communicate with these alien intelligences. And the applications in regenerative medicine are basically the outcomes that are telling us we're we're on the right track. I think that um this this uh standard picture where it's a physics and then chemistry and then you know and behavior science is somewhere at the top. I think it's upside down. I think I think for for many reasons it's basically behavior science all the way down and certainly um certainly by the time you have molecular networks all of your behavior little textbooks are are relevant. Um and you know uh what's what's coming is that the the the field of diverse intelligence research is is really going to inform the fact that you know the there will be mental health diseases of novel beings that have completely different different life experiences. You'll have patients with with highly diverse umets with new sensory motor capabilities with connections to others. We're going to have to figure out what we do. For example, if you're analyzing dreams, what what do the dreams of novel beings look like? What do they mean? How do we how do we help uh beings that have never been here before that don't share the same evolutionary stream? How do we help them have a good and meaningful life?

[00:53:52]So, um I will um just stop here and thank the people who did the uh the empirical work that I showed you today and our our many many um collaborators.

[00:54:00]And here are some disclosures and these are these are companies that have licensed some of the IP that we've had.

[00:54:05]So, thank you so much. I'll stop here.