Dr. Michael Levin — Reprogramming Bioelectricity

Cancer fundamentally involves an electrical disregulation among cells. It's basically a dissociative identity disorder on the part of the cells. It's literally a disorder of the cognitive glue that binds individual cells towards large scale purpose, where large scale purpose I mean building organs and tissues and things like that, as opposed to being amoebas and doing amoeba level things. And we've shown in these animal models both that we can detect incipient tumor information and we can prevent and normalize tumors after they form by restoring, not by fixing the DNA if there is any DNA issue, not by killing the cells with chemotherapy, but by electrically reconnecting them to the group such that they can form again a memory of what they're supposed to be doing.

Yeah, wonderful.

Of course. Yeah, thanks for having me.

Yeah.

I think it is.

I'm going to guess it's The Body Electric.

Yeah, it's very relevant. I discovered it in an old bookstore that my dad and I visited when I was in Vancouver, Canada for the World's Fair in '86. And I found this thing and it's kind of a patchwork of a number of different things, right? Like he was into applied field dangers and things like that. But I was just stunned with all the references to prior work that revealed to me that the kinds of things I'd been thinking about were actually real and that people had investigated it.

Well, bioelectricity in general is the way that living systems exploit physics, in particular the physics of electricity, to do the amazing things that living systems do. And there are roughly speaking two kinds of bioelectricity. There's the familiar kind which is studied by neuroscience, and so this is the electrical activity of the cells in your brain. And I think everyone has a rough understanding of the fact that the reason you know things that your individual neurons don't know, and that you have beliefs and the preferences and so on that are more than just any of the neurons in your head, is through this amazing cognitive glue that electricity provides, right? It binds your neurons into a collective intelligence that underlies our mind. So that's the bioelectricity that everybody's familiar with. And then there's the other kind, also called developmental bioelectricity, which you can get to by asking about where did the brain come from and where did it learn those amazing tricks. And very quickly you realize that wow, some of these things have been around for a very long time, long before we had brains and neurons, and that the question of what does your body think about before it has a brain, how does it use electricity, is the study of developmental bioelectricity.

When we look at biology, we see lots of amazing things. For example, in a salamander, if they lose a limb, they regenerate the limb and they stop when it's complete. Right? And in fact, there are many other interesting kinds of things that when anybody looks at it, the first thing they ask is how does it know to do that?

And one of the things I discussed in that video was if you scramble the craniofacial organs of a tadpole, they still make a pretty normal frog. All they sort themselves out, they move in new paths until they get to a normal frog face and then they stop. So anybody sees that and immediately the question is okay, but how do they know what a proper frog face look like? And if you do know, then how do you know how to get from here to there, right? How do you navigate? So the way we're all taught in biology is that that's a bad question. We are told none of these things know anything. They are mechanical machines that sort of roll forward according to rules of chemistry and in the end some cool stuff happens and we'll call it emergence and things like that and complexity science will sort of catalog them. But don't worry, none of these things actually know anything. That's just what they do. And so what I was trying to describe in that talk is this idea that well actually the idea that chemical processes can in fact know things. It's not magic. It's not mysterionism. We are chemical processes that know things. And we've had for many decades mature science of including cybernetics and control theory and things like that, a mature science of figuring out how it is that machines of all different kinds can know things and they can have goals and so on. So what I tried to show in that talk are some examples by which the living tissues, for example, platforms that are cut into pieces and every piece has to figure out how many heads should I have, where do the heads go, what should the shape of my face be, these kinds of things that in fact they do know. And the way they know is because they store memories. And maybe not shockingly, although it's certainly shocking to a lot of folks, the way those memories are stored is in an electrical network that is very similar to the way that we store our goal directed behavioral repertoires in our brain, and that these things are sort of widely spread. And so regeneration, cancer suppression and cancer repair and remodeling, birth defects and birth defect repair, all of these things are extensively using electrical pattern memories. And we now have a way to rewrite those pattern memories.

Yeah, you're not wrong. I could list any number of scenarios that we and others have studied in which the genetics not only don't tell the whole story but in fact tell a fairly misleading story. And the way that I would describe it, and there are two pieces to this and I'll do the simpler piece first and then we can talk about the other piece. The simpler piece is we can get there by thinking about the distinction between software and hardware. And by the way I should preface this because some people get really upset about this. I am not saying that the current way that we think about software and hardware is sufficient to get everything we need from biology. It does not cover all of biology. It covers one important piece of biology. Reprogrammability is really critical. And so if you wanted to make that same movie about computers for example, you could make a movie that basically goes electric fields, silicon and germanium and transistors and the flow of energy through circuits. Done. Right? That could be your movie. And it's not an unimportant part of the story. It's a very important part of the story. But the critical part that doesn't get to is that's the hardware. And in fact, that's what the genome does. So the genome tells every cell what the hardware is going to be. So the genome gives every cell the little tiny sort of protein level hardware that it gets to have. But now comes the other interesting part which is the reprogrammability. And we've known for a very long time now that if your hardware is good enough and the biological hardware is more than good enough then that hardware is reprogrammable. So, what happens just as an example, what happens in these flatworms, these two-headed flatworms that you were referring to, the flatworm has a bioelectric memory in it that says, and we can see it. I'm saying these things because we can now see these memories and we can rewrite them at will. So, this is now actionable in the lab. It has a bioelectric memory that says one head. That memory is not genetically encoded. What is genetically encoded is a bunch of hardware that when you first turn on the juice, it basically acquires that memory as a default. It's the way when you buy a calculator from the store and you turn on the power, they all say zero, right? Reliably 100% of the time. They all say zero. Great. But that zero is not the only thing that that circuit can do, right? As you find out very quickly, they can store memory and do all these things. The genetic hardware of the worm is very good at making sure that every worm starts out with a very specific, it's a little bit I think related to instinct and how you know certain birds are born knowing how to make nests and things like that.

The hardware has defaults and by default one head but the hardware is reprogrammable. So what we were able to do is go in and identify the memory that actually says how many heads and we can change it. And when you change it you don't need to change the hardware. You don't need to change the genetics any more than when we form new memories. You don't need to change the genes in your brain to form new memories. I always say to people on your laptop if you want to go from Photoshop to Microsoft Word. You don't get out your soldering iron and start rewiring. It'd be laughable if you had to, but that's how we used to do it in the 40s and 50s. You program a computer by pulling and plugging wires. Well, you don't do that anymore because it's reprogrammable. And that's what the biology is. And so that's the first thing. And the second thing just very quickly and we can get into it if you want is that this cellular intelligence that exists not only is reprogrammable but it is actually creative in the sense that it interprets the DNA and we can talk about this. It doesn't blindly do what the DNA says and this is kind of a deep thing because it's the way our cognition works too. It interprets memories in a way that is improvisational. It does not simply follow what they say counter to what we all learn.

So what we can see directly are the bioelectrical properties of tissues. And we've developed tools using voltage sensitive fluorescent dyes. And so that means you take your embryo or your tissues or whatever you've got and you soak it in this special chemical that glows different degrees or different wavelengths depending on what the local voltage is. And so back in the olden days in electrophysiology, you had an electrode then you would have to poke like a little needle and you would poke every cell and you would get the voltage reading. We don't need, I mean of course we still do that for certain purposes but what you can now do is get a full map of the whole tissue all at once and in fact you can make movies of it and watch it change over time and we have these amazing videos of embryos changing their electrical activities over time. It's basically like what neuroscientists do when they do imaging in brains but we can do it in the rest of the body. So there what you see are the electrical patterns. Now from there you have to do a lot of experiments to prove that what you're looking at are in fact memories. And there are many different kinds of things we do, but functionally what you have to show is that you can decode the electrical pattern that you're seeing and show that what it encodes is the future set points towards which the cells will work. In other words, I can take a one-headed worm. I can change the voltage pattern. It's still a one-headed worm, but its internal representation of what a correct worm should look like now says two heads. You don't see it because it's a latent memory, but when you cut the thing into pieces, now what the cells do is consult the memory and they say, "Oh, two heads." And then they build two heads and you get your two-headed worm. So you don't know right away when you're first looking at it. You don't know that that's a memory. You have to do experiments to prove that that's what it actually is.

The process of regeneration and repair in general is kind of homeostatic process. So, it's like a thermostat. You have a set point. If the temperature gets too low, it tries to go up. If it gets too high, it tries to come down. It tries to keep a certain, that is exactly what happens in the body which is anatomical homeostasis. So cells come and go all the time, right? So the ship, we're kind of a ship of Theseus, right? In many ways. So cells and materials come and go. Sometimes drastic kinds of injuries for animals that regenerate past them. Embryogenesis. I mean look, half our population can regenerate an entire body from one cell. I mean that's amazing, right? That's an amazing like development. And embryonic development is an incredible example of regeneration. The whole body regenerating from just one egg cell. And in all of those cases, what needs to happen is just like a thermostat has to remember what's the right set point. There has to be a memory mechanism that stores it. And so the electric circuits in the body that store these patterns, they have a memory property as well such that when you change it, it stays. Now sometimes there are multiple memories. And so we've done things like, for example, in these flatworms, there are different species that have different shaped heads, round ones, triangular ones, flat ones. We've shown that you can take a worm, change the biological signaling, and get it to grow a head of a different species. But the fun thing about that is it grows the head of a different species. You haven't touched the genetics, by the way. Again, the genome is totally wild type.

Right? But it'll grow the head of a different species and it'll stay there for about 30 days and then it goes back to its original. It's not permanent unlike the two-headed thing is permanent.

But the head shape after about 30 days they go back. And so clearly there are multiple, there's more than one. There's sort of some kind of metacognitive thing that says yeah you know I know you thought that was your memory but actually that's wrong. It sort of overwrites some kind of error correction thing which that one we haven't cracked yet. So there are kind of layers upon layers.

He's incredibly well cited.

Played a part after his experiences with patients with septic shock identifying TNF alpha and a lot of subtleties around that.

And has developed hardware in this case. I mean they're programmable but for vagus nerve stimulation predominantly for at this point autoimmune disorders like rheumatoid arthritis and so on. But you can see some incredible, incredible clinical effects and we're just touching the tip of the iceberg.

Yeah. No, and that's a great connection. Yeah, Kevin's work is amazing. I was just talking to him a couple weeks ago. It's awesome stuff.

Yeah, he really is. So, a couple of things to explain why this is relevant to humans and then I'll give you like three broad areas of application. The reason it's absolutely relevant to humans is that we are all basically built on fundamentally the same principles. People have this idea that well frogs are sort of a lower creature, but you know, we're mammals and once you get past yeast and things like that, we are all roughly the same as far as this stuff goes. These kind of electrical signals were evolution discovered them around the time of bacterial biofilms like very long ago. And so this is all very well conserved and for that reason for example there are human mutations in ion channels that are birth defects. So if you mutate ion channels in humans you get birth defects just like we see in frog and chick and zebrafish and things like that. So those are all well conserved. And with David Kaplan who's a collaborator of mine at Tufts we've done a bunch of work on bioelectrics of human mesenchymal stem cells. This stuff works for humans as well. It is not some like frog or flatworm specific thing. This is very very broad. I should say this is a disclaimer I always have to do. You mentioned morphaceuticals. So there are a couple of spin-off companies that have licensed some of this technology. So I need to sort of say that as a disclosure. So one is specifically called Morphaceuticals. There's a company that is pushing forward our limb regeneration work in bioelectrics. And then there's also this other company called Astonishing Labs that is doing some of the stuff in aging and so on. Having said all that, I firmly believe that these things are heading for clinical application in humans and probably not that far off. I hope you know...

Here are the three applications. So the first application is birth defects. So we have shown that we can repair a number of different birth defects of the brain, the face, the heart, what else? The gut, these kinds of things by restoring correct biological patterns in vivo. And so this is now in animal models. We are moving of course to more clinical kinds of things and I hope in the future this will absolutely be of human application. So birth defects is one. Regeneration is another. The name of the game here is communicating with the cells. This is not about stem cells or gene therapy or scaffolds made of nanomaterials. Like those are all tools that might be useful. But the real trick here is to communicate to a group of cells what do you want them to build? And that's what the bioelectric code is all about. It's about communicating to the collective, to the cellular collective. And so we've done work on limb regeneration. We've done work on inducing whole organ formation, eyes and things like this. So I think there are going to be massive applications hopefully clinically in restoring the damaged and missing limbs and other structures like that. And then the third thing is going to be cancer. So something else and we can get into what the kind of more profound aspect is but the bottom line is that cancer fundamentally involves an electrical disregulation among cells. I'll just say it and we can unpack it later, but it's basically a dissociative identity disorder on the part of the cells. It's literally a disorder of the cognitive glue that binds individual cells towards large scale purpose where large scale purpose I mean building organs and tissues and things like that as opposed to being amoebas and doing amoeba level things. So cancer is another thing and we've shown again in these animal models both that we can detect incipient tumor formation and we can prevent and normalize tumors after they form by restoring, not by fixing the DNA if there is any DNA issue which there doesn't have to be, not by killing the cells with chemotherapy but by electrically reconnecting them to the group such that they can form again a memory of what they're supposed to be doing. So those three things, regeneration, birth defects, and cancer, I think are going to be of great value in humans. Now, there's also issues of aging. So we also have an aging program in our lab and looking at why it is that over time cells forget how to upkeep a proper organism and we have some interesting thoughts about that as well.

First of all, one of the things that we've seen is that over time and by the way this is fairly recent work. So this is in no way is this the final story. This is just kind of what we know now. I'm sure this will be updated over time. The electrical prepatterns that tell the cells and tissues what large scale structure we're supposed to look like, they get fuzzy. They degrade over time. And so much like what we do with birth defects is we try to reinforce the correct patterns. And this is one of the ways we're addressing aging as well is by reinforcing these patterns. Now, one question you might ask is why over time are these things getting fuzzy, right? What's going on? And there are a couple of schools of thought. One is that this is the consequence of accumulated noise and damage. So molecular damage entropy basically right over time you just accumulate damage and everything kind of gets degraded over time. And then there's also these kind of what they call programmatic theories where basically the idea is that you're programmed to age for whatever reason evolution has favored a decline and death. So we have an interesting third alternative to offer which is the following. We did a simulation experiment where we had a kind of a virtual body where the cells cooperate together to build an embryo. Okay? And so they work really hard to work together. They build to a particular pattern memory. So you know this thing I've been telling you about. And then I said, "Let it run. Just leave it alone and let it run." And so what you see is something very interesting. They work really hard together and they make the correct body. Then it sort of stays that way as they defend it and then it falls apart and it begins to degrade. Now what's interesting is that in our simulation there was no evolution for a limited lifespan. There was no noise. There was no damage. It was sort of perfect. You know everything was perfect and still it degraded. Why would it do that? I had this interesting thought and I'll back into it this way. Just imagine this standard Judeo-Christian version of heaven, right? So you get to heaven and you get there. Let's say your pet snake and your dog get to heaven. So okay, everything is great. There's no more damage. There's no decay. Nothing is damaged. Everything is great. Everything's fantastic for the next trillion years. What happens? So, the snake may be fine doing snake things for every day is the same as every other day. Maybe fine. The dog, not sure. Probably okay chasing rabbits on the farm, you know, like maybe fine for forever. Basically, the human though, what do you think? I'd be interested in your thoughts like what are the odds that a human cognitive system can be sane for an infinite like okay I'll keep myself busy for the first 10,000 years maybe 100 thousand years but like a billion years in are we still sane like what happens? What do you think like what do you think would happen?

So maybe that's not the claim I was going to make but it's not impossible.

What I took away from that work that we did was the following. You have a goal seeking system that has met its goal. Yeah, it's achieved the goal. It made the body it was supposed to make. The error falls to zero. Everything is great. Hangs out there for a while. But what does a goal seeking system do when there are no new goals? Because we're looking at a system that may or may not be able to give itself new goals. I mean, cognitively I think we can, but it's not clear yet that this system can do that. And so what we were able to do is we were able to give it new goals by having interventions and going back in and saying okay now this is your new pattern and it will do that. But I think you know part of the you could call it the boredom theory of aging basically not cognitively semantically like if your body cells over a long period of time they've completed their job they've created a body during adulthood but at some point they start to degrade the cells don't degrade the collective does the cohesion the alignment between them because there's no longer a common goal I mean this is what makes for an embryo or a body as opposed to just a billion independent cells is they're all aligned towards the same set point towards the same goal. And so when that isn't there, regeneration, repair, maybe remodeling becomes something else. I don't know how, you know, maybe you need to sort of change up the body every once in a while. That's also a possibility. Planaria, do a planaria are immortal.

The flatworms. Yeah. Yeah. They're immortal. Every two weeks they rip themselves in half and regenerate. So they give themselves a challenge every two weeks. And so they've been, you know, they've been that way for half a billion years or so. We can see evidence of this. For example, if you look at there's a way to look at the age of certain genes, the evolutionary age of certain genes to see like when did they show up. The gene expression of a young person, all the cells are in all the different tissues have the same idea of what evolutionary stage they are, meaning a human. When you look at old tissue, and this is something we just published recently, when you look at we call it adivistic dissociation. When you look at the tissues of old age, the genes that they express start to float backwards in evolution and they're discordant. They're out of sync. So your liver versus your neurons, they may all have different start to get different ideas in terms of the genes they express of where on the evolutionary tree they are, right? It's like starts to float off and in the absence of a compelling set point or goal state, all the subunits start to sort of float off and do their own thing. And this is I think an important component of aging.

Tight deadlines for aging are tough because you're not going to know for decades whether your thing works. But I get the idea. This is what I would say. Fundamentally, I think that aging, cancer, birth defects, lack of regenerative repair throughout our lifespan, all of these kinds of things are downstream of one fundamental pressure point that if you solve that, all of these things get solved by sort of by side effect and that is regeneration. More specifically, that in turn is everything there hangs on the cognition of groups of cells. In other words, how do groups of cells know what to build, when to stop, how do we communicate with them, and what kind of intelligence do they have? And I'm being very specific about this. When I say they have intelligence, I don't mean complexity. I don't mean some sort of linguistic project where I'm going to take things that are beautiful and fascinating and I say, well, that's the intelligence of life. That's not what I mean. I'm using a very specific definition of intelligence which is what behavioral scientists use which is problem solving, memory, different degrees of a cognitive light cone of goal directed the size of your goals, things like that. So specifically figuring out what are the competencies of the living material that we're made of and how do you communicate new goals to them. There are lots of amazing people in the aging field doing interesting things and that's cool. If I had a lot of money specifically for aging, I would put everybody on that question. I would say you're not studying aging. What you're studying is the goal directedness of multisellular systems. Figure out how they know what to do and how we communicate goals with them. If you solve that, all of these other things get taken care of as a side effect.

I'll give you an example and then we can talk about what the human case might look like. What we can do is we can take a frog embryo and induce a particular electrical pattern somewhere in the body that we already know that pattern codes for make an eye. That's how the other cells interpret that pattern. It means make an eye. Very interesting in the sense that we don't have to say which cells do what. We don't have to say which genes you need to turn on. These are all microlevel details. We don't need to worry about them because the material is competent. Just like when I'm talking to you, I don't need to worry about how your synaptic proteins are going to, you know, you're going to take care of all of that, right? All I need to do is give you the prompt and vice versa. And we're having this amazing conversation, but our hardware takes care of all the molecular detail. And the same thing here. So, we provide a biological pattern that says make an eye here and the cells make an eye. Now, the first thing that happens, it's interesting. The first thing that happens is there's a battle of world views that takes place. We inject a few cells. They tell their neighbors, "Let's make an eye." The neighbors actually say, "No, we're supposed to be skin or gut. Don't do it." And some cases, sometimes they win and sometimes we win. And so the goal of regenerative medicine is to be as convincing as possible so that you win 100% of the time. But in the cases where we are convincing, and we have amazing videos of cells like convincing each other to have different voltages and whatnot, they make an eye. And so what you've done is you've taken a bunch of cells that were going to be for example gut and you've now pushed them to be an eye at a very high level. We don't tell I don't know how to build an eye. I don't know all the genes that have to be turned on. You do that. I'm telling you something at the level of organs. This is going to be an eye. The eye is of the right size. It has all the right layers to it. It is functional, right? So you can see out of these ectopic eyes. It's really really amazing. And so that is an example of giving these cells a new goal. How do I know it's a goal? Because I did not micromanage you to do it. I was not there saying turn on this gene, turn on that gene. I gave you a faroff set point, by the way, in a wild space that no individual cell knows anything about. The anatomical space of organ structures, no individual cell knows what an eye is, but the collective does. And they stop when it's done. I don't need to be there to tell them to stop. They stop when it's done. And so this is autonomous goal-directed activity. It's a navigation of anatomical space. And so we can do this. And we can't make everything. We can make portions of the brain. We can make eyes. We can make in some cases limbs. We can make some other structures. So in the human you could imagine two ways to go and I don't know which is going to be correct and we need to do a lot of experiments in mammals to nail this down. One possibility is that it might be enough to simply reinforce the existing human pattern. Every so often you would get like a tuneup that reminds all the cellular collectives what we're supposed to look like. That's one possibility. There's another possibility and I don't know which is correct. I hope the first one is right but I think it wouldn't be the end of the world if it's the latter. Maybe it really does get too boring with the same pattern meaning that okay you can go a few hundred years with the reminding of the standard human pattern but eventually you have to do something unique. Now the planaria are telling us that actually it's hundreds of millions of years that you can make the same thing. So I'm kind of optimistic that you can do that but let's say that's not the case. If that's not the case in humans, maybe you have some number of the hundreds of years or whatever of the standard human body plan. But then if you want to keep going, you got to make some changes. What does that mean? Maybe you wanted some wings. Maybe you want some tentacles. Maybe you want a third hemisphere, you know, to crank your IQ. Maybe you want, I don't know, a third eye. Who knows? Sure. Sure. Sure. Infrared vision out the back of your head. I don't know. You know, people email me all the time asking for all kinds of weird...

peripherals. So maybe at some point it means that you really got to change things up a little bit. You know, caterpillar butterfly style.

Maybe.

There certainly are theories, reasonable theories of why evolution wants you dead, and there have been a number of them. Overall, I think there may well be tradeoffs of the kind that, for example, we're not going to put a lot of evolution, you know, would not put a lot of effort into maintaining something if something else is going to go off and you're going to die anyway, right? So, there are these ecological trade-offs. I'll give you an example of something like that. People ask, "Hey, why can't humans regenerate their limbs the way that, you know, axolotls can and things like that?" Nobody knows. But here's a plausible theory, right? Imagine you're an early mammal. You're running around the forest. Somebody bites your leg off. Now, you have high blood pressure. You're going to bleed out. If you don't bleed out, you're going to walk around and grind that thing into the forest floor. It's going to get infected. You're never going to have time to regenerate. What you might do is scar, seal the wound, inflammation, so that you might live, you know, to fight another day. But you're definitely not going to have time to regenerate the way that an axolotl might, you know, sort of floating around in water for three weeks or whatever. Basically, what you might say is that evolution just kind of decided that it's not worth it. It's never going to work. It's not worth it. Right? And by the way, deer antlers, deer antlers are the one amazing mammalian example of regeneration. Plus, the liver, I mean, liver regenerates, stupid, but deer antlers, right? Adult large adult mammal that regenerates this like huge structure of vasculature.

Centimeter and a half per day of new bone.

Nuts bone vasculature innervation and you don't put weight on it. It's not load-bearing. It's the one, you know, appendage that's not load-bearing. So anyway, why I'm saying that is because you can imagine evolutionary trade-offs like that where evolution just didn't bother optimizing for long age. You could, you can imagine that. But fundamentally, I do not believe that we are inevitably mortal. I think that at some point if we knew what we were doing, if we had appropriate regenerative medicine, I don't see any particular reason why we have to age and die. And then you face interesting questions about, for example, mental plasticity. We all know with advanced age people get a little, a little less plastic mentally, right? That, that kind of stuff. Is that a hardware problem or a software problem? We don't know. You know, if you had somebody with a physically young brain at, you know, at 100, would they be like an 18-year-old in terms of their ability to take on new ideas and, and focus and pay attention, whatever? Would that still stay or is there some kind of a cognitive tiredness that happens that is not a hardware issue? Like I don't, I don't think we know, but, but we need to find out.

My original background is in computer science. Computer scientists are amazing generally at compartmentalizing, coarse-graining, you know, sort of modularizing, like hiding details and asking, okay, but what's actually important here, you know, and, and like blackboxing things. Biologists generally think everything is important and if you ask a biologist you get a list of, you know, 30 genes like these are, you know, hard-won details, right? They're all important, but a computer scientist is like, okay, but, but what is it actually doing, you know, and that's really important. The most basic thing is this issue of reprogrammability, is that understanding that certain kinds of hardware is reprogrammable and why that, that I think is really key. The other thing that I wish, and there's not really time unfortunately for almost any biologist to do this, but one thing I really love for my students to do if they can is to take a course in programming languages. And here's why, not so they could code, that, that doesn't matter. It's not the coding aspect. What happens in a typical course of programming language is that, so let's say in a single semester you'll spend three weeks doing different languages and the thing about those languages, and maybe this is true of some human languages as well, but it's definitely true of computer languages, is that each language is a different way of looking at the world. You start off with something that makes sense and you're like, ah, step by step, you know, you sort of tell it what to do, okay, and then all of a sudden, bam, now there's this other thing where every piece of data, there's this language called Lisp where every piece of data is also instructions and you can execute any piece of data. Like, what? And then you get into this other thing and it's functional programming. Now there are no variables. You don't get to have any variables. You have to, like, everything is just a function call. And every time you do this, it sort of rips the foundation of your, of your world out from under you. And it says this universe works in a very different way than you thought before. Forget everything you knew before. Now you got to do this. And how are you going to solve this problem? Now there's recursion or now, you know, there's no global variables or whatever. And what it's really good for is that mental plasticity that reminds you that the way you think things are and the tools you think you have are not the only things in town. And so when you do that in a lightning and things go fast and then the final exam comes and that's this other thing you've never seen before, like being able to do that quickly, I think is super valuable. And I would love that to be more known in biology. But the final thing I'll say is, and this is, I think this is true, but, but just to be clear, this is very controversial and almost nobody else thinks this is true. So, you know, who knows, but the interesting thing that a lot of people, not just biologists but a lot of people think is, is something like this. Okay, there's something going on with humans, maybe other animals where biochemistry does not tell the whole story, right? You, you read the biochemistry textbook and you say, "Okay, that's, that's cool, but there's something about my mind and my, my ability to solve problems in abstract spaces and my inner perspective and all this stuff is just not captured in these low-level details." So, that's a little disturbing. It's like, what is that then? If it's not captured in the chemistry, like where is that coming from? But don't worry, we have this other thing over here which are machines. Dumb machines, dead matter, dumb machines, algorithms, computers, and those things do only exactly what the algorithm tells them to do. They are perfectly captured by our formal model. So, we have a formal model of, of chemistry and the rules of chemistry. And that we think does not capture all what it is to be an entire, you know, full-on human. But we have these other formal models of Turing machines and programming and code and, and, you know, mechanics and those things capture exactly what the machines do. Those get the whole thing. Okay. I think, and this is the part that's very sort of controversial and not a widely shared opinion, I think that's false. I think our formal models never capture all of what's going on. And some of the, some of the craziest stuff coming out, out of our lab recently is showing how much even in very simple sorts of machines, how much interesting novelty, not just complexity, not just unpredictability, but things that any behavioral scientist would recognize as some kind of a protocognitive capacity shows up in even minimal systems where you don't expect it. And so what I'd like the biologist to sort of eventually, you know, once we can show this, this widely, the biologist to understand is that biological systems are amazing and awesome, but it's a kind of a larger degree, not kind of what's already going on in inanimate systems. And for this reason, I, this is, this is also kind of a crazy claim, is that I think the circle, if you make a circle of cognitive things and living things, I think cognition is wider than life. I think cognition predates life and I think it's bigger than life. And normally people do that the other way around. They say here's the inanimate universe, some chunk of that is living and some tiny piece of that is intelligent. I think that's exactly backwards and that's something we need to understand both on the biology end and on the computer science end is like, is there a distinction between what people commonly think of as living things and machines? Like are there any actual machines in the sense that we like to think that there are? You know, that's a deep set of questions for both fields in the future.

Great.