Unveiling the Mind-Blowing Biotech of Regeneration: Michael Levin

Today's guest is Professor Michael Levin, a developmental biologist and synthetic biologist at Tufts University. In my opinion, his work is Nobel Prize-worthy, as it is the equivalent of discovering DNA as the basis of genetic memory, although instead of a biochemical code, he has found a bioelectrical one. And instead of genetic memory at the low level, it is large-scale anatomical structures at the high level. Click on the timestamp in the description if you wish to skip this introduction. Michael Levin's work has direct implications for cancer research, limb regeneration, the possible regeneration of tissue in general, and thus can help with Alzheimer's research, the creation of engineered life that can scour and remove areas for toxins, the creation of an entirely new drug market based on non-neural bioelectrical manipulation, and even the recovery of traits that were in species that went extinct millions of years ago that live within us via mechanisms that we are only now beginning to understand due to the work of Michael and his team and his collaborators' teams. Truly, truly groundbreaking. For those new to this channel, my name is Curt Jaimungal, I am a filmmaker with a background in mathematical physics who is dedicated to explaining what are called theories of everything from a theoretical physics perspective, but as well as outlining possible consciousness. have to the fundamental laws of nature, provided these laws exist at all and are knowable to us. 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You can even learn group theory, which is what is referred to when you hear that the standard model depends on U1 x SU2 x SU3. It is technically called Lie groups, and they are local symmetries. Visit brilliant.org slash TOE, that is TOE, and I think you will be greatly surprised by the ease with which you can understand subjects that you previously found difficult. Do not stop before four lessons. Thank you, and enjoy this non-plus-full and wonderfully eye-opening conversation with Professor Michael Levin. I think what you do is Nobel Prize-winning work. Well, thank you very much. That's very thoughtful. Thank you. I'm very, very much looking forward to this. Thank you. When I was researching you, you would say about every 10 minutes or so, some innocuous remark that would floor me because of its implications, and then you would move on. And then you would say what trumps what came before, and this happened over and over again. So why don't you start with what non-neural bioelectrical states are, their relation to anatomical outcomes, and then we can later compare and contrast with standard developmental biology for decades, which is the genome teaching, constructing, perhaps what is epigenetic. So we'll start with what bioelectrical non-neural bioelectrical states are and their relation to anatomical outcomes. Certainly. Okay. Well, so non-neural bioelectrical states are simply the fact that all cells in your body, not just neurons, have the same machinery that's normally associated with neural cells. So ion channels, electrical synapses, neurotransmitter pathways, all of these things are much older than nervous systems. And every cell in your body creates an electrical potential across its membrane. Most cells in your body communicate those states to their neighbors via these electrical synapses. And on the one hand, people are often surprised to hear this. On the other hand, if you ask yourself where did neurons and brains come from, they didn't just pop out of nowhere. Evolution basically accelerated optimized processes that were already there around the time of bacterial biofilms. They're ancient. These things are ancient. And so if you just trace the phylogeny of nervous systems and neural cells, you find that all cells have this. And in fact, we've had discussions, very long discussions at some of these basal cognition meetings in terms of what is a neuron? People will say, well, here are neurons. And I'll say, by the way, what is a neuron? And so they'll write four or five criteria on the board for what they think a neuron is. And then they'll say, well, every cell does this. And so there are a few differences, but most things are pretty universal. And so then the question becomes, we'll find what is it used for, what is it used for? So in the brain, what you have is a system where an electrical network processes information to direct muscles that move your body through three-dimensional space. That's behavior, nervous system control of behavior. And before that, what these systems were used for was to generate signals that controlled cell behavior to move your body configuration through anatomical space. That's the space of possible anatomical configurations. So I think, and I'm working on more things on this now in terms of broadening this idea, but I think what evolution did was to repurpose some of the same tricks across different spaces. So electrical networks were used to control, to traverse first in evolution, probably first metabolic spaces and then physiological spaces and then transcriptional spaces and then morphe spaces and finally three-dimensional space when muscles came on the scene and animals could run around and do things like that. But that's what electrical networks were thinking about before they were thinking about behavior in three-dimensional space. They were thinking about navigating other kinds of spaces. Now, how does that compare to the standard view, which is just our DNA programming us? The standard view is correct in the following sense. What the DNA specifies is the structure of the micro hardware of your cells. So the DNA gives you the protein level sequence, which means the structure of the proteins that each cell has. That's the hardware. So the DNA is what specifies the hardware of the cells. Now, it turns out that that hardware is fantastic. It's wonderful in the following way. When you put that hardware together, it doesn't just have specific kinds of default behaviors, but it also has computational capability and it's reprogrammable. That's actually one of the most exciting parts of it. But the standard view is that you should be able to go very directly from what's in the genome to the anatomical structures. And I think what that view is missing is a very important middle layer that sits between the hardware of the anatomy, the hardware of the genome rather, and the final outcome, this anatomical homeostasis that we see in rebirth and development and so forth. And that layer is the software, it's the physiological software that connects those two things. Can you tell me about the experiments that you've done? Take the audience through a few of the experiments. So one, you amputate a frog's leg and then you can regenerate it, so on and so forth. Sketch maybe three that you find the most astonishing. Sure, sure. Okay, let's see. So here's one. If you use a voltage-sensitive fluorescent dye, which basically just reports, you flood your tissue with it and it just reports with different types of fluorescence, it reports where the different voltage values are. So for example, if you look at the early embryo as it's putting its face together, you'll see something that we call the electrical face. And this was discovered in work with my colleague, Danny Adams, where we found this thing called the electrical face, which is basically this before all the genes are turned on that are necessary to make different facial components and so forth, and certainly before the anatomy is there, there's an electrical pre-pattern that you see in that region that basically looks like a face. You can see where the eyes are going to be, because that's where the voltage difference is. You can see where the mouth is going to be. You can see where the placodes on the side of the head are going to be. And so you see this electrical face and it raised the obvious question, which is that if that pattern is instructive, then you should be able to do two things. You should be able to mess with it and thereby disrupt that electrical pattern and thereby induce defects in craniofacial patterns. And you absolutely can do that. And in fact, there are even human channelopathies where people have mutations and ion channels that give them craniofacial birth defects and defects of limb and brain and other things. So that's true. But the other thing, the more exciting thing that you might be able to do is to take some of those electrical patterns and move them somewhere else. And you can say, okay, if this is the type of pattern that tells the cells, build an eye here, can we move that electrical state somewhere else? Not move the cells, but move the electrical pattern, re-establish that electrical pattern somewhere else and get it to build an eye. And so this is actually we've done this. This is some of our earlier work, you know, around 2007 or so we discovered this. Basically, you take one ion channel that's capable of inducing a specific electrical pattern in a region of cells and you inject RNA that codes for that ion channel into another part of the embryo that's going to be gut. Let's say it's going to make endoderm, it's going to make gut cells. And sure enough, and so and so three things are significant about what happens. The first is that you get an eye and you get an eye in the middle of the gut. You get an eye that's built out of cells that would have been gut. So that's remarkable because if you look at developmental biology textbooks, what you'll see is that they say that cells outside of the anterior and ectoderm are not competent to become eyes. They're not supposed to be able to make eyes. And this is true if you use the biochemical master eye gene, PAK6. If you try to induce eyes with PAK6, it's true. It doesn't form them. You can't get ectopic eyes anywhere outside of the head. But by introducing this bioelectrical pattern, you can. And so this is the first thing that you can, you can go beyond the known competence limits by using this very upstream kind of master regulator, this electrical pattern. The second thing that's interesting is that the information content that we provide by putting in this channel is extremely low. We don't micro-specify the details of how to make an eye. You know, an eye has a dozen cell types all arranged in a specific way. We don't know how to do that. We couldn't possibly do that. You know, we couldn't possibly do that. In fact, what we do instead is provide a very simple signal that looks to a programmer basically like a subroutine call. Right. It's a trigger. It's a trigger for a cascade that the animal already knows how to do. It already knows how to make eyes. We don't say how to make an eye. What we say is make an eye here by activating that eye-building module, which includes all the gene expression, everything else that's downstream. So that modularity, that that incredible engineering feat that says that you can call once you know how to do something, you can reuse it in other places. The fact that these bioelectrical states are triggers of developmental subroutines. Okay. So that's sort of the second cool thing about it. The third cool thing about it is that if you label the cells into which you inject the channel RNA with some with a color, so that you can see which cells actually got the extra channel and then you look and then at that eye that you created. What you'll see is that half of the eye, for example, will often have the channel that you put in. The other half of the eye doesn't have it. This means that the cells that you affected, what they did, they recruited their normal neighbors, didn't they? Which in themselves were never modified by you. They were completely wild type. And yet they were recruited by their neighbors to be part of this thing. So there are two levels of instruction here. There's instruction by us that says to a region, you make an eye. And then there's a secondary instruction by those cells that says, oh, and by the way, I'm going to need more cells. You guys over here, you're going to be part of this lens. And they all come and it's even those that we never directly touched. And so this third part is cool, because, well, the second part is cool. The fact that it's modular and a trigger is cool, because that means that you can achieve regenerative medicine outcomes, things that are completely too complex for us to micromanage by using triggers. If we can identify the triggers of the subroutines that we want, make an eye, make a limb, make a liver, then we can activate those things long before we actually know all the details about how to micromanage them. Right. So part of part of reverse engineering, and I see this a lot as a reverse engineering task, part of reverse engineering is to figure out all the cool hooks in the system that are already there for you. Not that you have to assemble them from scratch, but they're already there for you. We know what the trigger is that it's the building and I subroutine. What other subroutines are there? Right. That's what's part of our work. The other thing that's cool about that third part is that it's non-cell-autonomous, which means that you can exert effects on cells without directly touching them because cells communicate with each other. So by convincing a bunch of cells over here that they should make an eye, you're actually affecting a bunch of other cells and causing them to be part of that eye without directly touching them. And this comes up. Sorry, this is what you meant when you were talking about recruitment earlier. Precisely. Precisely. Right. Precisely. Right. Yeah. So that's so that's sort of the first is it's the first example I would want to talk about. The second example I would talk about has to do with cancer. And so, Michael, is it okay if we stick to the cancer outcome? Because what you said was so profound and I want to unpack it. Okay, sure, sure. Yeah, well, stick around. Tell me if that's generally correct. Let's imagine I'm a frog. What a frog is developing. Before an eye or a stomach or a throat or whatever it is, is made, you see a little adabration, a little adumbration, some electrical adumbration, like a hint of it. And what you can then do is you can say, well, there's some pattern. Let's imagine it's a circle. To be simplistic, there's a circular voltage gradient. And that means I. So what if I induce that? What if I induce a voltage gradient over here near the heart? I know you said something, but whatever. Here. What will then happen is instead, normally, we would think, well, you'd have to micromanage that eye. Every molecule, it's extremely difficult to make an eye. We don't actually know how to make an eye from scratch molecularly, but we see this pattern. What if we put that pattern on the heart or the stomach? Oh, lo and behold, a little while later than an eye is born. Is that correct? Yes, that's what's correct. And the only thing I would add to that, first, is that the reason why I told you about that electrical face pattern is because it's sort of the most obvious one in the sense that the electrical face pattern actually looks like a face. You can't miss it. It just looks like a face. But not all of them are that simple. Some of them are really sort of they're more deeply encoded so that by staring at them, you can't tell what it's going to be. Right. So so, for example, there are other patterns that we've seen where the only way we know what they are is by looking at what they make. Us that you couldn't guess, you know, so some of them are very direct, almost a paint by numbers. You can sort of see what's going to happen and others are really complicated and you need computational tools to unfold what you're looking at to figure out what it's going to be. So so not all of them are as clear as the electrical face pattern. Right. Yeah. One of the questions I had was, why wasn't this discovered before? Was there a technological limitation or did they just not look at cells with the dye that gives an indication of voltage gradients or optogenetic technologies and so forth? Yeah. So so that so so that's an interesting question. Why? Why? Why? Why not before? I mean, on the one hand, everything has to have a beginning at some point. Right. So so whenever that was, it would have, you know, you could have asked, well, like, why not before? Right. But hindsight is always obvious. You can always you can always say that. But let's just let's delve into it a bit. On the one hand, there was a conceptual leap that held this back quite a bit. I mean, let's be clear. I'm not the first person to talk about the importance of bioelectrical signals. People have been studying endogenous bioelectricity since before 1900. So so it had certainly occurred to people that electrical signals might be important in development, regeneration. All of my work, I was, I was incredibly inspired by work that was done in this in the 60s, 70s and 80s by a bunch of people who worked very hard on this stuff. The reason why it didn't go far enough was two reasons. Number one, the tools weren't there. So these dyes didn't exist. All they had was traditional electrophysiology. Traditional electrophysiology, you have one electrode and you stick it into cells. And if you want a picture of what's going on, you have to stick all the cells. And that's just completely impractical. Right. These dyes didn't exist. The conceptual thing was that biochemistry and molecular biology at the time that this stuff started to take off with the use of electrodes and such. And the thing why molecular biology grabbed all the attention was because you could do molecular biology and biochemistry in dead, fixed tissue. So you could kill your cells and fix them and you could sequence the DNA. You could sequence the RNA. You could get a proteome. You could get a you know, all of these all of these kinds of things that you could do. None of this is possible with bioelectrical. So the moment your cell is dead, it all goes away. So none of the typical omics approaches work. So it's it's it's so much harder. And so it really lagged behind because all the interest went into the molecular, you know, sort of molecular biochemistry. And and it had to wait for some of these tools to come up. The third thing, the third thing is, is that I think while people did think about the importance of these bioelectrical gradients, nobody, to my knowledge, before we did, really thought of it as the origin of the nervous system and to really put that computational spin on it. The fact that this thing is really like a neural network doing computations on development. I think that's new. I would say Harold Burr, who was this guy, worked in the 30s, 1930s, 40s and 50s. Okay, he had one of the first good voltmeters and he went around measuring things, you know, elm trees and rabbits and tumors and embryos and all sorts of things. Based on this, he wrote a wonderful book that basically said most of the things that we're discovering now. The guy had a crystal ball. It's incredible. It's absolutely incredible. So he could see a lot of this stuff clearly. The thing that he didn't see, because at the time it didn't exist, was the computational aspects and the and the actual link to, you know, to here as a kind of neuroscience being done in a different space. and more for space. That I think is new. But people already had these ideas and it needed the technology to really make it to really prove it and to really see how it works. I'll show people some overlays of some of the cells with the blue and green and red voltage colors. Now, voltage is actually pretty abstract for most people. But what isn't abstract is something like an electron. People can realize that it has a certain charge. So when someone looks at these videos of voltage gradients and they're colored, what are they seeing? Essentially, I'm asking you to simply explain what voltage is. But in terms of electrons, in terms of something that people can understand. Yeah. To understand both, you need to understand potential and you also need to understand fields technically, if you're going to understand it properly. Yeah, it's not that bad. You don't really have to do much with fields in this one. Oh, because because all the things that I'm talking about are really not fundamentally fields per se. They're just spatial distributions of voltage gradients. And to understand a voltage gradient is pretty simple. Instead of electrons, life uses, I mean, life uses electrons too. But mostly the kind of stuff we're talking about uses a different charged particle. They use potassium, chloride, sodium, and protons. Okay, but otherwise, the same analogy. And so any cell has a cell membrane around it, the outer surface. And it has these ion channels, which are these little gates, these little proteins. They can open and close and let specific ions like potassium or sodium in and out. This potassium, so potassium and sodium are both positively charged. So you can imagine if you're a cell and you let a bunch of your positively charged potassium out, you can have an imbalance, more positive out here, less positive inside. So now there's going to be a voltage gradient, basically a battery in effect. I mean, that's basically what a battery is, right? It's a membrane with a with a charge imbalance across it. That's all it is. So every cell is a battery. It achieves this by using energy to pump ions in a particular direction. And as a result, if you were to take a small voltmeter and put it across and people, this is exactly what electrophysiologists do. If you put it across that cell membrane, you're going to read some sort of voltage. You know, it's usually somewhere between, I don't know, between 20 millivolts and 70 millivolts, something like that. Right. That's all it is. And now imagine doing this for every cell in the tissue that you're looking at. And you're just going to color each cell depending on how large that voltage difference is. You're going to color it red if the voltage difference is pretty small. It's called being depolarized, which means there's just not that much of an imbalance. It's pretty much the same. You know, the ions inside and outside are pretty similar. So you're going to color that red. And then the ones that are really different where there's a bunch of positive charges that have been kicked out. So the cell is really pretty negative. It compared to the outside space you're going to color them, you're going to color that blue. And that's and that's what and that's what you're looking at. Great. Okay. So now we have a bit of background on what DNA calls for proteins. And you can think of that as a low level. And then what you discovered and you and your teams and the teams that you work with discovered is that there are these non-neural biological signals and these are somewhat like large-scale code. Some of the implications are limb regeneration that's barely touched. That's right. We've touched regeneration of. Actual generation of eyes, not rebirth of eyes. And then you were about to get into cancer. So do you mind? Yeah. So so so the net very quickly to say the analogy is that I think a good analogy is this. The DNA is what codes for the hardware. Okay. And the electrical dynamics is the software. Now, a lot of people get upset about this because they say, oh, living things aren't like a computer. So I'm certainly not claiming that living things are like the kinds of computers that you and I use on a daily basis. Right. This architecture is not what life uses. But the deeper concept of computer science, which is the idea of reprogrammable hardware and the idea of software, multi-layered software where you can program at the machine code level or you can look for higher-level subroutines and higher-level languages. That I think is quite, quite realistic. And I think what we're looking for here is to basically understand to find the best representation of that software so that we can manipulate it to basically take advantage of and to understand how evolution manipulates it. And so and so the other you know, you asked for three examples. So so there are there are three basic examples I wanted to give. So the electrical face was one. There's an example of cancer. And then there's an example of flatworm revival. So the cancer, the cancer example would look like this. One of the things about cancer, the one way to think about cancer is to ask the question, why is there ever anything other than cancer? In other words, individual cells like amoebas are extremely capable on their own. They handle single-cell level goals pretty well. Why do they ever come together to form something like a kidney or a liver? Because when you when there's when there's a cancer, what you see is a deviation from that process. You see cells that normally should be working to make a nice organ in the shop, maintain a nice organ in an adult. Instead, they go off and they basically revert to a single cell kind of existence. They basically become like an amoeba. They treat the rest of the body as just environment. It's like external environment. So you can think of that computational boundary between self and world can shrink. It can grow when a bunch of amoebas, a bunch of amoeba-like cells come together and they build something like an organ or an entire body that grows that computational boundary. But you also shrink because an individual cell can say, I'm not working on this anymore. I'm just an amoeba and I'm going to do what amoebas do. What do they do? They become two amoebas and two amoebas become four and so on. They proliferate and they go where the living is good. So they metastasize wherever they want to go. So that's cancer. So if you think about it like that, that cancer it's it's like deviation from multicellular cooperation. You can ask yourself, okay, so what is the process that normally harnesses them to specific goals? And so if you ask yourself, what what what do we know is a process that harnesses individual capable subunits for larger scale goals? It's very clear. It's neural like processing, because you have individual neurons, which are cells, but you connect them together in a network. And this wonderful computation starts to happen that can do things like plan for the future and have memories and preferences and goals at a large scale. You know, you as an organism can have goals and memories that your individual cells don't have. So so we know that electrical networks are really good at binding small capable subunits into larger scale computational agents. We use it in computer science. Evolution uses it to make neurons. So we asked the following question: Okay, could this be the basis of cancer? And now I have to say that we're not the first to have this idea. Okay, again, Harold Burr said this back in the 30s. So we did three things. We said: Okay, first, when this process happens, can you see using the voltage dyes? Can you see the cells of the electrical network defect? And actually, it's the end. In fact, you can. So what you can what you can do is you before it happens. So you can inject a human oncogene, which will form a tumor in a tadpole. You inject it into you inject it into a into a tadpole. They make a they make a they make a tumor. And even before that tumor becomes visible, you can see with a voltage dye, you can see that those cells are highly depolarized, they electrically uncouple from the rest of the tissue and they go on their way and they just treat them. The rest of the animal is just external environment at that point. So they become electrically uncoupled. And that's the first thing that those oncogenes do is to electrically isolate the cell from its neighbors, from that collection of signals that normally tell the cell what to do in a larger context. So that's the first thing we did. The second thing we did, we said, well, if this is a potential cause of cancer, can we then cause cancer just by disrupting the electrical communication directly? No oncogenes, no carcinogens, no DNA damage, no mutations. Nothing wrong with the cells that any molecular biology test could see. And can we cause cancer? Because remember, the standard model in the field for years has been that cancer is basically caused by genetic damage. Right. That it's that it's a genetic disruption that makes a rogue cell that has other mutations and so on. So we said, okay, no, we're going to take completely normal cells, nothing wrong with them. And we're just going to prevent them from talking electrically to other cells. Okay, we're just going to manipulate that. And so we did. And sure enough, we made metastatic metastatic melanoma in tadpoles. So that tells you that there doesn't have to be anything wrong with the hardware to have cancer. It can be a purely physiological phenomenon. It can be caused at the software level, which a lot of people who study stress-induced cancers and things like this, they sort of already knew. But but actually, the paradigm was that there must be some genetic defect at the root of this. Okay. And then the third thing that we found, which is of course the most exciting thing, which is that you can go in the opposite direction. You can inject a very potent human gene like a P53 mutation. Oncogene, for those listening, is just a gene that causes cancer. Yes, an oncogene is a is a mutation in a in the normal gene that causes it to be what is thought to cause cancer transformation to cancer. Yes. Okay. So you can inject that. And then if you do that in a tadpole. If at the same time you inject an ion channel that forces the cell to stay in an electrical state where it's coupled to its neighbors and it doesn't depolarize, then there, even if that oncogene is blazing strongly expressed, no, you will not have a tumor, because you're ignoring that there's a hardware problem. But it doesn't matter, because you're ignoring it and saying, yes, I know you want to depolarize, but you can't. You have to stay coupled to these neighbors and you're just going to be part of this. And and and we can ignore a variety of different types of oncogenes that way. Okay. So I just had an analogy in my head. It's almost like thinking you have these kids and they're misbehaving. So you can say, well, it's the kids that are the problem and they're causing havoc in the house. But if you have an adult that's strict enough, they can ignore the misbehaving kids. If you leave the kids without the adult, then the house is in ruins. In this analogy, the adult is like the electrical communication. So you can force that electrical communication, that standard adaptive electrical communication. Yes, yes, you can think of it like that. I mean, the the the the the the the the the the the the the the the the thing where I think it breaks down a bit is that we're not introducing an extra element that keeps everyone else in line. Right. We're we're in effect. It's like, you know, it's like you have a bunch of kids who know what to do and you have one wearing dark glasses and

You cannot see, and it causes all sorts of trouble because you cannot see what it is doing. Well, you can, you can sort of, you can put a camera on it or take off the glasses or something, so that it just goes back into the, into the interactions with, you know, into the, into the with the rest of the, you know, the rest of the group. It's about, it's about communication. It's about binding individual competent subunits to a larger goal. Your goal is not just to grow as much as possible. Like an amoeba is, your goal is to build a hand or a liver or or an eye or something else. It's really about scaling goals. And electrical networks are wonderful for this. Does your work have any implications for what it means to have an identity? So now you've talked about cancer as if it's distancing itself from the larger cell. Yes. And then there are gap junctions, which you've referred to in your other work. And they effectively create an equivalence class between signals that I generate as a cell or my environment or signals from coupled cells. I cannot distinguish the difference between them. Yes. Because I cannot distinguish the difference between myself and my neighbor. It's as if I am identified with them. So it has a lot of traction. At least I see it has a lot of influence on what it means for the eye or the ego in a non-pejorative way. So what are the implications of your work for the concept of identity? Yes. Yes. No, you've put your finger right on it. So. So. So two, two years ago, I wrote this paper called On the Border of the Self. And it's precisely this idea. It's the way to do to define what a self is at different scales. And how does the border, the size of that self change over time? And this is precisely the sort of thing you're talking about. It's having communication channels that partially erase the metadata on information so that I no longer know if it's coming from you or if it's coming from me. Right. Give us a partial thought, because it's really difficult to maintain an identity now. If I can't tell what my memories are and what your memories are, it's really difficult for us to maintain clear identities. We become partially unified. And this is precisely the sort of process that evolution exploits to build larger selves out of small, competent ones. Another astonishing experiment of yours, I'm not sure if I should call it an experiment, was where you took frog skin cells and then they moved. Can you outline what the hell you did there and why it's important? Yes. Yes. No, it's definitely an experiment. So, so you're talking about our xenobots, I think. And the question was, the question that we're interested in is basically this. Where do anatomical goals come from? And to illustrate why that's even a good question, I just want to talk about planaria for a moment and then, and then you'll see why it's important for the xenobots. We are, we are used to each species having a specific form associated with it, and that frog eggs make frogs and zebrafish eggs make zebrafish, and so on. So we're sort of, we're sort of used to that. But the actual question, how do cellular collectives decide what they're going to build and when do they stop building? That's a very open question. And so, one way you can see how far we are from a good understanding of this is in a very simple, very simple experiment. There is, there is. So planaria are these flatworms that regenerate when you cut them into pieces, each piece builds whatever is missing and they regenerate. Okay, that's planaria. You can cut them into pieces and each piece repairs itself into a normal planarian. So there are species of planaria that have round heads and those cells are really good at building a round head and then stopping. So they stop when a round head is completed. Okay, then you have another species of planaria that has a very pointy head, a sort of a triangular head. And those cells are very good at making a triangular head. If you cut it off, it makes a triangular head and then it stops. So I have a simple question. If I take a bunch of the cells from the round-headed one and I put them into the body of the triangular one and I let them sort of get comfortable and sit for a while, then I cut off the head. What head shape are we going to have? Are we going to have one of the head shapes that is predominant for the other? Are we going to have an intermediate shape, or are we going to have a flat shape that never stops regenerating because neither of the cells is ever happy about the shape? The stop condition is never set. Okay, so now, so now look, the most important thing is not the answer. The most important thing is, despite all the papers in Nature and Science about the molecular pathways of control of stem cells and planaria and all this sort of high-resolution transcriptomics and all this stuff, there isn't a single model in the field that makes a prediction about this experiment. Why? Because every piece of data out there now addresses the hardware that enables individual cells to do cell things. Okay. But we have no understanding of what happens when the cells aggregate into a larger scale self that makes large-scale decisions about head shape, head number, things like that in morphospace, navigating morphospace by making these large-scale decisions. We have absolutely no idea how those algorithms work. And the fact that we know how the stem cells work and a lot of molecular biology about it hasn't really influenced this other question much. We just have no, no models for this. Because it's too computationally complex or some other principled reason? I don't think it's that, no, I don't think it's because it's too complex, although it is too complex to directly compute. I think it's because we conceptually haven't found the right, we haven't found the right way to think about how cellular collectives make decisions. It's a collective intelligence problem. It's not a molecular biology problem. We've been thinking about this as a molecular biology problem. That's not what it is. It's trying to read the minds of a collective intelligence. Now, people think of collective intelligences as exotic things like ant hills and bee colonies and things like that. Those are collective intelligences. I want to remind everyone that we are all collective intelligences. We are all bags of cells. No, there is no cognitive agent that is like this single diamond that doesn't consist of parts. It's sort of immutable. We are all made of parts. Any cognitive agent consists of parts. And so your goal is to ask, how do those parts bind together to make decisions as a collective? Individual cells don't know what a head is. They don't know what round means. They don't know what triangular means. But the collective definitely does. And so the collective is able to navigate morphospace in this way that we don't understand the algorithm. So if we don't even do that, we don't even know how to think about this. Okay, so, so, so this is very isomorphic to problems in neuroscience, to problems in artificial intelligence. It's trying to understand the scale of thought. And in the attempt to do so, we pose the following problem. Okay, standard tadpoles make, you know, standard frog eggs make, they make frog embryos. And everyone's pretty used to that, although, you know, you might recall that I always remind my students, did you realize that if I give you the frog genome, you can't tell me what a frog looks like? Right. You can compare it to other genomes. You knew what it was. That's cheating. So, so, so. Okay, so we asked, we asked a simple question. Where does the goal of making a frog or a tadpole really come from? And how hardwired is it? So what we did was we took a skin that we scraped off of an early frog embryo. We put it aside in a different environment. And we said, okay, now you are free to reboot your multicellularity. You are here. We have, we have, we have removed all the constraints of the rest of the embryo. You are no longer receiving instructive signals from, from endoderm, from mesoderm, from all these other things. You are no longer subject to all these other signals. What are you going to do? What is, what is your new, you know, what do you want to do? And there are a few different options that could have happened. The cells could have died. They could have drifted off and sort of gone to each cell going its own way. They could have made a monolayer of cells in a dish like you get in cell culture, all sorts of things they could have done. They did none of that. What they instead did was combine together and form a ball that grew cilia, these little moving hairs on the outer surface. Now, cilia are usually on the outside of embryos and they're there to redistribute the mucus and to move the pathogens and not stick to the skin. They're used to, they're used to keeping the surface of the tadpole clean. But instead, these cells basically reused that genetically encoded hardware. The cilia themselves are genetically encoded. All the proteins that are needed to make a cilium are in the genome. And so they already have that. But what they did was they. Gathered themselves into a new, into a new sort of, a new sort of architecture, which is this spherical thing, which and then, and then they use the cilia to propel themselves. So they started running around, they started moving around. And so we have these amazing videos of them moving around alone, moving around in groups, interacting, going through a maze, back and forth in different configurations. They have all sorts of behaviors. They have all sorts of types that they can regenerate if you cut them almost completely in half, they will rejoin and make a, you know, make a, make a, make a xenobot again. And the, the, and so, and so the coolest thing about them is, and by the way, we don't know, we don't know their cognitive abilities yet. Okay, we haven't, we've only just started to begin to see, can they learn? Can they have preferences? All these sorts of things we don't know yet. But the coolest thing about them is that, as far as I know, they are the only being on the planet that doesn't really have an evolutionary background. The individual cells do. Okay, the cells have a long evolutionary history on Earth, but they were selected for that genome was selected for the ability of these cells to sit still on the outside of the frog and keep out the pathogens. They were not specifically selected to be able to come together and run around in a separate, in a separate configuration away from the embryo. Where did all this come from? And, to be honest, one of the, one of the things about it is people often say, well, you know, when are you going to buy when you're going to design these things, you know, plug in various synthetic biology circuits, right, let them do things. We will absolutely do that. But my goal before we do any of that was my goal was to show people what can happen, while the diversity that can happen from precisely the same genome without any manipulations whatsoever. Because if you, because the thing about these inabouts is they have just a normal frog genome. They have no other, they have no transgenes, no genomic editing. There's nothing else to them. This is what we're seeing is the plasticity of this collective intelligence that is capable of making a new functional proto-organism in a new way out of precisely the same parts. So there's no genomic editing there. Have you manipulated its morphogenetic code, the electrical signals at all? Not yet. No, we're going to, we're definitely. That's all to come. No, at this stage, we haven't done that. This is, this is, this is purely indigenous plasticity. This is what these cells already know how to do. We, we scrape them, we scrape them off the frog and we put them in small holes, small, small, small sort of depressions. And I have to back up. There are two types of xenobots. The one I'm describing now, we've literally done almost nothing. You scrape them off the embryo, you put them aside and you say, okay, now free from all the signals that would have turned you into various different things. What do you want to do? And this is what they do on their own. There is another type of xenobot, which is actually the one we started with, where we sculpted them a bit. We, and this is, by the way, this is all work done in collaboration with Bongard Lab at the University of Vermont. And Sam Kriegman did all the computational modeling for this. And then Doug Blackiston did all the microsurgery and everything. We sculpted them a bit to give them legs. So you're basically just, it's subtractive, like subtractive sculpture. You just cut away a few things so that you have an ottoman that has, like, four, four legs, you know, and, and we, and we put in a bit of, a bit of muscle and then it learned to walk. So the muscle would contract and the thing would basically walk. These are the first set of xenobots that we made. The second one has no muscles. It has no nerves. It's just the skin. And they can move around entirely on their own using the cilia. And there are interventions are extremely minimal. Is it important that it was taken from embryos or does it matter if you took it much later in development from a frog? We didn't, we didn't take it later in development from a frog, but we have, we have other evidence that I can't really talk about yet that suggests that it doesn't matter that it's a frog and it doesn't matter that it's an embryo. We have, we have the data now on adult cells. What comes to mind for me is. I wonder if this has implications for what it means to be alive in colloquial terms, so forget about in the biological sense, so we think of our skin as dead and our bones are dead and maybe dead isn't the right term. But let's say animated, animated life, along with them, with brio. Yet you've shown that you can still activate vim and brio in some way, not through electrical manipulation. I thought that was it. But does it have any bearing on what we consider alive, or is it a, or is it unrelated? I'll tell you that one of the things that this kind of work does is it really illustrates the inadequacy of our vocabulary. So people often reason, for example, I mean, alive is a funny thing. I don't know, I don't really know what alive is, I don't have a good definition. These cells and these organisms are for sure alive in the traditional sense. I mean, the cells are alive. There is no, there is no perishing. But, but people will often argue, for example, are they robots? Are they organisms? Are they, you know, these are the machines, these sorts of things? And I and Josh Bongard wrote a paper that addressed this question and basically pointed out that although that terminology is now almost useless. It was, it was great 50 years ago when you could, when it was really easy to distinguish machines from what was boring, predictable. They were designed and living things that were surprising and interesting and warm and wet and and developed. Those things are now so mixed up that with modern, with modern techniques of digital evolution and bioengineering and synthetic morphology, that distinction no longer exists. And so it used to be that you could sort of like you could tap on something. And if you heard a hollow metallic sound and you said, oh, yeah, that came from a factory, it's a machine. And I'm morally okay with taking it apart and doing what I want with it. And if you were to do that and it was sort of soft and squishy, then you would say it developed and it's alive. And I better, I should be okay with that. Right. The easy distinction is just that it no longer exists. So we need a better vocabulary. I mean, they are alive for sure. But if you want to ask questions about whether they are machines or robots or living organisms or that it's all that stretches the vocabulary, which is no longer up to the task. Is there a connection between perception and this morphogenetic code? And I know I keep using that word morphogenetic code and yes, forgive me if I misuse the terminology, but is there a connection between perception and morphogenetic code? I'll give you my reasoning behind it. At the moment, what I see is that I recognize a monitor. I see a microphone. I see you. You have eyebrows. I see large-scale structures. Then the question is, well, is there anything special about your eyebrows? Well. Besides you being a handsome man, physically speaking, let's say, physics would say there's nothing special about this microphone or 10 percent of the microphone or 10 percent of the microphone, plus the air slightly around it, it's more of a pragmatic matter that it's a practical that it matters that I can use it. However, when you talk about this non-neural bioelectric code, it's as if these large-scale structures that we recognize as noticeable and meaningful, like a low-resolution facet, like a child's drawings of eyebrows, nose, head attachment, and so on, that it's in the code. So what we perceive is also what is encoded. And I'm curious, well, is there a confounding factor that influences both, or does the morphogenetic code influence our perceptions? Perhaps I can give a better analogy for a computer science analogy where we have machine code and then you have like you build a top that builds top libraries, jQuery and on HTML5 and so on. So we have a function bounce, which takes an image and bounces it or texts and bounces it or one that plays MPEG. So the question is, well, what makes bouncing or MPEG play more fundamental? Well, to me, I would say nothing. It's us. It's what we use. However, it's strange that objectively there is this code for it. Yes. Yes. Well, there is a, there is a lot in what you just said. Perception is definitely part of this whole process, because to have this sort of anatomical homeostasis where you, you, you, you, you, you, you damage an organism like a salamander, which can regenerate most of its organs or a planarian, which can regenerate all of its organs, you, you damage it. And then it grows, it grows back. You, you damage it and then it grows over the right thing. And then it stops when it's done. That loop, that homeostatic loop must have a perception component, because it must be able to recognize when it's done. So it must be able to perceive, am I a correct planarian or not? And if I'm not, I'm going to keep remodeling until I am. And at that point, it's a error minimization scheme. And to achieve that error minimization, you have to be able to perceive around you in anatomical space. And to say, am I in the right area of space, is my head the right size, is it the right number of eyes, all of that, you have to perceive that and people, people like Grossberg and, at BU, wrote years ago about the relationship between retinal information processing and development. And I actually think he was, he was really onto something in the sense that I think most epithelia are basically like a large retina and what they do is they, they are, they are constantly surveying the rest of the animal. Uh, the rest of the body, uh, and making decisions about large-scale features. So not just individual pixels, but things like in the, in the retina, you would talk about edge detection, motion, um, things like that. And this is what they do. They look at large-scale features that individual cells cannot detect. So one way to look at this is that we have a precedent for this from neuroscience and from visual, from the science of visual processing, how likely the more accurate way to look at it is yes. That guess where the retina learned its tricks, right. And guess where, where brains learn their visual processing tricks through many older mechanisms of cellular groups, by navigating morphospace that it is, well, that's what it was before it, um, the, the, before it became vision, basically. Do you believe that the problem of aging, to the extent that it can be called a problem, is largely a disruption of this electrical blueprint rather than oxidative stress and damage to DNA and so on, telomere length, which people think. Um, I don't have any evidence yet that there's a bioelectrical component to this. I mean, I suspect there is, but, but we have no evidence for it. We haven't really worked on aging per se. I would say that I don't think it's anything as fundamental as, uh, this sort of thermodynamic decay or something like that, because the planaria are immortal. They don't have a lifespan limit. They, they live forever. And so they tell us that it's possible to be a complex regenerative organism with a learning ability and so on and not age. So, so, so it's clearly possible. So, so the rest is details, right? The rest is, uh, I don't think it's anything as fundamental as the, the, the, the theories that say, well, look, if you copy things, you inevitably make mistakes. So eventually, well, where is, so where is out, if that were true, you wouldn't have planaria, so, so I don't think, I don't think it's anything like that. I think it's something much more contingent, much more specific. Uh, and therefore I'm optimistic that we can overcome it. So there was a work that you outlined in a previous talk, uh, like I think it was a few years ago when it was an undergraduate student, her name was Maya, although I don't remember her last name. And she switched between three types of planarian heads. Like I think it was Felina, Mediterranean, Doro. I can't remember how to pronounce it, but there were about ten million years apart evolutionarily. And then it implies to me that there might be structures that are unfathomably from our past embedded in us. And then I'm curious if there's a connection between this, between these, let's say these electrical blueprints, even if it's a, whatever these electrical blueprints and union archetypes are to go on a, on a big speculative leap, is the relationship between them. Son, uh, that's, that's, that's a good, that's a wonderful question. Um, I think that, uh, if, if one were, if, let's run it backwards, let's say, let's say that we've understood, we have a conception of Jungian archetypes for neuroscience and psychology. And now someone could say, yes, but you're saying that all, you know, neuropsychology comes from earlier somatic bioelectrics. What would the Jungian, what would that look like then? And that right, in that case, what would the Jungian archetypes look like in this other pre-neural type of, of, of bioelectrics, because we do this all the time, we, we ask things like, what does memory look like before it was brain memory, what does, um, through stable visual illusions before they were, before there were brains, all these things that we see in neuroscience, you can ask what the, what the older somatic equivalent looks like. So you can do the same thing here. I mean, that's an interesting question. I've never thought about it that way before, but you can, you can ask that question. If you ask that question, you arrive at precisely the sort of thing you're talking about and what I would say is, well, probably in morphospace, there are these stable attractors that correspond to different types of shapes of heads, different numbers of eyes, different planar body plans, different, all sorts of different things. And what you can do is you can dial in those different. Uh, stable attractors by, by, by shifting, in, in the state space of the electrical circuit, by, by shifting those electrical circuit states. So, uh, yes, I think, I think that's a fair, I think that's a, that's a completely fair way to think about it. Your work is so, it's like the discovery of DNA to me and perhaps you're too modest to accept it as a compliment, but I see it as, as that seminal and I want, well, I'm going to put it out there and I'm going to start by saying, I think this guy is going to win a Nobel Prize. He didn't say it, like, there's not, he's saying that I'm saying it. Well, thank you very much. Yes. That's, uh, that's, that's very kind. Um, there are many people. I mean, I, I think it's important to say that, uh, yes, this is not, not an effort. Right. Well, it's on, on two levels. So, so, so first, none of this, uh, came out of thin air. I didn't think of any of this stuff just, you know, out of nothing. I, I, I, I, I built these ideas, uh, on, on, on, on many other ideas from, from really, uh, sort of, uh, really pioneering people who have been working for years. And many of them, um, haven't really, uh, you know, gotten much, uh, acceptance from the community. So it's, it's important to say is that there's a lot of that out there. And, uh, and of course, the people in our lab, right. The, the, the postdocs and, and, and, uh, PhD students and technicians who do the work. I mean, it's, it's definitely not, not just me doing all this work. There are many people in this field and many people in my group. So, um, many people, um, contribute to pushing all of this forward. We'll take questions from the audience. Okay. So this one comes from Rupert Sheldrake. How does he, how does he think his conception of morphogenetic fields relates to mine, referring to Rupert? Yes. Uh, interesting question. Um, so basically our morphogenetic fields that we're working with are, uh, entirely physical. In other words, they take place entirely within the body of the organism. They are generated by the cells. We can measure them using current technology. Um, I don't know if that's true of the sort of things Rupert is talking about. I suspect quite a bit that it would have quite different properties, but just to be clear, our fields are, uh, and in fact, the things we're working with are, strictly speaking, not even fields, right? So we're working with spatial distributions of resting potential. So it's not clear to me that these are actually fields in the mathematical sense of, of, of the word field. But they are distributions of electrical potentials of living cells in, in a specific body. So they are, they are, they are very physical, they are local, they are, you know, sort of quite, fairly traditional distributions of voltage. Another application that I've heard you mention a few times, it was, I don't know if it was more on the speculative side or if you've developed it, it was some organism or the potential to create some organism that spontaneously and temperamentally goes out and cleans up the environment designed to remove certain toxins. Can you talk more about that? Yes, that was likely referring to our xenobots. So we have this, uh, this technology where we are, we're creating synthetic living proto-organisms made in this, in this case, made from frog skin. So these are frog skin cells that have self-organized in different environments to be these little mobile creatures. And at least one of many possible applications in the future is to program them for some sort of a collection task so that they would go out and perhaps collect, um, useful, useful molecules, or perhaps they would clean up toxins. Perhaps they would detect various, various other chemicals in the environment that you would want to know about. So these are all potential applications of, of the practical sort of use of these sorts of synthetic living machines. You've mentioned a few times that it's important when you're dealing with the manipulation of this electrical field or voltage gradient that you're not using external electrical fields. You're actually manipulating the cellular ion channels directly. Yes. Okay. So then I was wondering, does that mean, you know how some people say 5G that we should be afraid of 5G, because, well, for various reasons, but then other people say it's non-ionizing and that's, and that's all that matters. Well, is that all that matters? Is there some validity to being concerned about 5G? Um, so, so I think, I think both of those positions are a bit off and the truth is somewhere in the middle. So, so let's, let's just start with the ionizing business. So, so I think the evidence is very clear that electromagnetic radiation doesn't have to be ionizing and in fact, it doesn't have to be particularly strong to affect living cells in some way. So living things, so living things are sensitive to all sorts of electromagnetic radiation, uh, in many ways that don't require ionization or heat or anything like that. At the same time, uh, I think, uh, I have no reason to be concerned about 5G. First of all, the sort of things that we study. These bioelectrical signaling pathways are not particularly affected by external electromagnetic fields. If they were, we would be using these sorts of things in the lab to manipulate the electrical, uh, the electrical signal. It's just not, it's not a good way to control bioelectrical signaling within tissue. It just doesn't do a very good job there. So I have no specific reason to be concerned about 5G. I have a feeling that, uh, for most people who are concerned about it, you have much greater dangers and stressors in your life. You know, uh, if you eat certain things, if you, uh, engage in certain behaviors, those are statistically much more of a problem for you than 5G ever will be. So, so I'm, I'm not particularly concerned about 5G on a, on a practical level, in the, in the grand scheme of things that I'm concerned about and the things that we all do in our lives that are sort of not optimal for health. I think 5G is probably way down the list of things you should be worried about. However, I think it's, it's not true to say that, uh, because it's non-ionizing, we don't have to worry about it. I think that's actually false. Okay. Speaking of diet, you mentioned eating and then in one of your talks, you also mentioned that there's a connection between the microbiome and this morphogenetic field, but I haven't heard further elaboration on that. So if you don't mind elaborating, that would be great. Yes. Well, the general point is that any sort of, uh, sort of, any, any sort of modality that controls the behavior of cells in an organism automatically becomes the target of an evolutionary arms race by other organisms that live near you, on you, inside you, and so on, because they can potentially manipulate those controls to make specific things happen in the body. Right? So, so anything, including chemical signaling neurotransmitters, we already know that the microbiome influences mood and behavior and things like that by tapping into the neurotransmitter pathways. So there's this gut-brain axis and so on. So the same is true of bioelectrics. So in general, we can definitely assume that various microbes that live in the body and various other types of parasites would have ways to adapt ion channel activity, which means that it's likely using some sort of chemicals that they would put out to manipulate your tissues in ways that would be evolutionarily advantageous to them. Now, coincidentally, we have a practical example of this that we studied a few years ago in planaria, in which this was done in collaboration with Ben Wolfe's laboratory at Tufts, where we showed that there are bacteria that live on these planaria and these bacteria are actually capable of manipulating the worms to, for example, change the structure of their visual system, to have multiple heads, and so on. And this is because these, these bacteria are capable of adapting the same sorts of controls that the worm tissues use in the first place to make decisions about how many heads you're supposed to have, what your visual system should look like. So this is, this is, this is on the one hand, sort of astonishing that these microbes have a say in the structure of these sorts of organisms in which they live. On the other hand, from

From an evolutionary perspective, it is entirely expected that they would have discovered ways to do this. Earlier in our talk, you mentioned that when you were looking for these voltage gradients, when you did this dye, the voltage dye, you saw something that was a striking sight on the frog. And then you also mentioned, well, you don't imagine that the code would be so obvious for the majority of what we care about, especially for humans. How do you go about figuring out or decoding this code? And also what other factors matter? Is it the, pulse they the voltage pulses? And then, so the frequency of pulsing matters. Is the movement, what are the factors involved in determining the code? And how do you then decode it? Yes. We don't know a lot of the things about that. So for example, at the moment, it doesn't look like there's pulsing and that the temporal aspects of it are particularly critical, but that's probably more a function of the fact that we haven't really dug into it yet, but it's entirely possible that when we do dig into the temporal aspects, we'll find out that the time-dependent changes are really important. It's possible. At the moment, we're entirely focused on the spatial aspects and it doesn't look, at least to our technology, it doesn't look like it's pulsing in that particular way, but we might have simply not found it yet. In terms of, in terms of how do you, how do you crack the code? So there are a few, there are a few pieces to this. One piece is simply observation, right? So it was almost everything in science started with some sort of observation and to actually just get a database or a profile of different tissues under different conditions, a bio-electric profile of different tissues under different, different conditions will be absolutely essential to decode this, because we need in the same way that we currently have databases of, of gene expression, of proteomics, of all, all these sorts of biochemical and genetic profiling of tissues and health and diseases and different cells of the body and so on. We need exactly the same thing for bio-electric. So we need some sort of physiomic profiling where there should be a database where we can go and say that this particular tissue under these conditions in this, we have this bio-electric pattern and here's the sort of normal range. And here's how a difference between, between humans and between organisms in different conditions and so on. So that, that's, that's the first thing. And so we only have that for a very small number of cases. We certainly don't have anything like a complete physiomic profile yet. What you then have to do is you have to build computational models that help you explain why the electrical pattern is as it is given the different channels and pumps that are, that are expressed in that tissue. And then you start with the hard work of functional experiments. So you open and close some channels, you observe what happens and you build a theory based on an improved computational model of how that particular bio-electric pattern causes the downstream effects that it has. So for example, we now know there's a pattern that corresponds to making an eye. And there's another pattern that corresponds to building everything that's going to happen at the site of a wound. We have a pattern that says build a nice sharp brain, the edge of a brain. Then we have a pattern that says don't be a tumor, be normal tissue and so on. These sorts of these sorts of patterns need to be identified. And that can only be done with this sort of mapping of a pattern to the specific outcome. And then there's a lot of computational work that goes in between. Do you think psychedelics have any role to play in changing or altering the morphogenetic morphological code? What I mean is, you know, after a single initial dose, there's substantially greater openness. And I wonder, well, is that because there's a change in some non-neuronal bio-electric activity? I have no idea. I have no expertise in psychedelics whatsoever. I can tell you that, just like in the brain, there's a very good connection between neurotransmitter activity in the rest of the body and the electrical signals that move these neurotransmitters around. So I would not be shocked at all if there was some connection. And in fact, we've certainly used several compounds that are normally used to target brains. And so you have things like anxiolytics and SSRIs and various sorts of drugs that are normally used in the nervous system. We've used them in embryogenesis, in regeneration to try to alter some of these pathways outside of the nervous system. So that's certainly something that you can do. Whether that has anything to do with the mental states that are caused by these sorts of compounds in humans, I have no idea. I know these questions are superfluous. They're going from topic to topic. That's just how my notes are. But anyway, regardless, you once mentioned, I think it was to Sean Carroll, that you can use Daniel Dennett's way of talking about intentionality. That is, I believe it was to give as much intentionality as you want to a system to explain what's going on. When I say intentionality, I mean act as if it's prepared to do something. And then you also mentioned that when one scales down your theological projection to smaller particles like panpsychics might do, then it naturally leads to quantum indeterminacy and the principle of least action. OK, so let's start from the beginning. The Dan Dennett's intentional stance is basically the following idea that the real answer to or to how much intentionality, he usually talks about consciousness, but intentionality, cognition, intelligence, whatever you're interested in, the real question of how much of that a specific system has, is not to be found through some sort of armchair philosophy, right? To just assume and people say this all the time. They say things like, well, thermostats can't possibly have any intentionality or, you know, so it's a decision that someone has basically just made by fiat. And Dan's point is very important. It's that it's an empirical question. You can't just decide. And the way you discover it is simply this. You take a specific stance and you say, here's my system. I think it has this much intelligence or I think it's capable of learning or I think it can have preferences or I think it's a goal-directed system, whatever you choose to start on that continuum. And by using that stance, you do empirical experiments to see how well that stance helps you understand what and whatever you're dealing with. And so the point is that we can't simply assume that something is a non-intelligent system because of how it's made or because of how it looks. You actually have to ask, what is the optimal way to look at that system? So just to give you a simple analogy, if you have a ball on top of a hill, you're pretty good, you're going to do pretty well using Newton's laws to ask how it's going to roll down the hill. And if you have additional theories about the hopes and dreams of this ball as it rolls down the hill, it's not going to do you much good, right? They're not going to give you any improved ability to understand and control what's going to happen. On the other hand, if you start with a live mouse on top of a hill and you think you're going to apply Newton's laws, you're not going to do very well, because you're going to need some other laws. And so you can decide that the system is minimally intelligent and see how you do. You can decide that the system is highly intelligent and that it has memories of what happened when you put it on the hill last week, and it might do something different. The point is that it's an empirical experiment. You can't just decide what it's going to be. You have to choose a level of abstraction of some sort of learning tool, maybe very little, maybe quite a lot, and see how you do. So that's the intentional stance, is that everything should be determined by the quality of the predictions and the amount of control you gain by viewing your system in a particular light, right? So everything is in a sense observer-dependent and it's dependent on the experimental context in which you want to investigate the system. You know, a human brain is very intelligent in a certain context. It also makes a good paperweight. And if that's how you choose to look at it, then you don't have to ascribe much intelligence to it if you're investigating the problem space of holding some papers in a wind, then it doesn't come up. So that's it. And so where I cross this is that I basically have a system that's very abstract, and I basically point out that it's a very essential way to look at things when traditional phylogenetics is not a good guide. And that means that when we are confronted with new beings, they can be new bio-engineered beings, they can be chimeras, they can be something that you find somewhere in space, some exobiological agent, they can be artificial intelligences that we create, whatever. it is, when you are confronted with something that you cannot simply place on the known evolutionary tree of life and on Earth and say, oh, yeah, this thing is now related to a fish. Therefore, I'm going to, I'm going to assume it has roughly the cognition of other fish that I knew. So when you either create new beings or reverse engineer, the intentional stance is entirely essential because you cannot a priori know what the cognitive capabilities of this thing are going to be. And that might lead you to ascribe a lot of cognition to it, or perhaps none at all, depending on how it works for you in terms of empirical success. So that said, then the natural question might arise, is there a zero on the scale? So if you have a scale, a continuum of cognition or of intelligence, is it a smooth gradient where different types of systems can land. Right. And so the question is, is there in fact a zero? And so what I said is simply this, that if you as someone as someone said to me, what would be the absolute minimum? What would you have to have an absolute minimum to be on this scale at all? Right. So so to be somewhere on the scale of cognitive beings, what is the what is the basement? Right. What is the minimum minimal version you would have to have? I would say probably the minimum you would have to have two things. You would have to have some ability to do goal-directed behavior. So you would have to have some ability to pursue goal states. And you would have to have some sort of internal control so that your your behavior and the things that you do are not perfectly described by all the external influences around you at any given time. Right. In other words, in other words, if I can if I can look at all the forces that are impinging on you and know exactly what's going to happen, then you're probably a marble rolling down, you know, some sort of an inclined plane. Otherwise, otherwise, if you are if you are more complex than that, then I would have to take into account things that happened previously, things that might happen in the future, all sorts of things that are not immediately what is what is what is there. So, what it comes down to is some sort of internally internally initiated action, some what I would call quotation marks freedom. And it's you know, it's a whole different story to really dive into that. But this idea that you would be able to initiate things on your own, you are not just a responder, you are not you are not just a passive responder to forces around you at that time. And so after that's said, after you've said those two things, you realize that particles already have those two things, because because particles already exhibit quantum indeterminacy, where they do things that are that are fundamentally not caused by any of the things around them. Right. It's entirely sort of indeterminate. And they have the ability to pursue goals in a very primitive way, which is the principle of least action. And so one of the advantages of people often say that that panpsychism is a bad theory because because it leads nowhere. It gives you nothing. And I think there's certainly some truth to that. But there's also the fact that if you were to ask the question, I think that even if you said that I think even particles should have some degree of goal-directed activity, you might make a prediction of something like the principle of least action existing. And then you would be right. You would find that you know that that model actually makes a prediction that is entirely not obvious. It's not obvious that when you have a beam of light going through a bunch of lenses, it's not obvious that you can actually forgo the calculations of how the light will interact with the glass at every point along the path and simply say, you know what, I think I think it wants to get where it's going with the least amount of action. Right. And so and so you can make the correct prediction of where it's going to go simply by assuming that the light likes to get where it's going by minimizing and maximizing certain things. And you can predict something like that if you had the idea that there would be some sort of goal-directed activity even at the very bottom. So if we ask what does it look like, what does agency and intelligence look like in the very minimal, the most minimal version possible? I think what you get is something like particles. So from that perspective, I suspect there is no zero on this scale, because even particles are already on the scale. OK, this zero was that the way I understand it is that it's like a scale of intelligence. Do you equate it with consciousness? Right. That's a good question. I would say that in my writing about all of this, I've almost entirely avoided consciousness. OK, I almost never talk about consciousness. I talk about cognition and and that's sort of deliberate. I don't have my views on consciousness are not to the point where I would be interested in talking about it, because I don't think I can add anything yet that many other smart people haven't already chewed on. . What I think I have something to contribute is to the questions of cognition and intelligence, because those things are empirically measurable. They are public behavior that is publicly observable. And we can have a research program that focuses around them, which I think is different from what other people have done. So that's what I've been talking about. Consciousness is different in the sense that I think a lot of the people who who who say they study consciousness are actually not studying consciousness. What they're studying at best are correlates of consciousness or often behaviors and properties that may or may not have anything to do with actual consciousness. And so I think it's very difficult to study actual consciousness. If you must. To study consciousness is a first-person activity, it's not a third-person activity, the way you would study anything in the external world, which means to study it externally outside of yourself. I think to fundamentally study consciousness requires that the subject of meaning that you or whoever is studying it, actually changes during that process. It's a whole different thing. So so I'm working on a few things in that direction. It's a bit early. It's a bit early to talk about it. Faraz Hanarvar asks, can the mapping and thereby the treatment of the signal that is this electrical signal, differ between individuals when we talk about humans? So is the code species dependent or can it actually differ based on humans? Yes, I don't think it's even species dependent, because we've seen that we can, let's say, cause one species of flatworm to form a head that belongs to a completely different species, simply by altering the distribution of gap junctions. . I suspect there are massive conservations in the same way that the biochemical and genetic codes are highly, highly conserved. Will there for sure be individual differences between patients? And we need to understand what that is. We don't know what that is yet. It's an important area for future research. But I think it's going to be conserved enough that we will be able to have general purpose electroceutical products. However, I think there's going to have to be some very serious computational modeling that will probably be personalized. That is, we'll have to take into account the patient's different physiological and genetic conditions in terms of do they have any ion channel mutations? What other sorts of physiological things are going on in their blood in terms of ion content and so on. In order to perfect some of these some of these treatments, I think it's going to be very personalized. But underlying all of that is going to be a highly conserved bio-electro code. You mentioned electroceutical products, which makes me think of your company Morphoceuticals. So if you're allowed to talk about it, what's the status of it? What's the goal of it? Yes, the goal of the goal is that Morphoceuticals Inc. is a new company that I founded with David Kaplan, who's the head of biomedical engineering at Tufts. He and I are partners in this work. We work very closely together. And at the moment, the mission of Morphoceuticals is focused on limb regeneration. So we're taking the things that we've learned from the frog in terms of how to achieve the regeneration of appendages in the frog and trying to move that to mammals so that it can go to humans someday. And so, you know, I can't really go into details about how it's going, but it's in its very early days. But I'm very optimistic that we'll actually have something useful. So that's what we're doing. Do you see more progress than you hoped for or do you see less or is it about as you expected? Basically going as I thought it would. It's in the we're on track. We're on track given the timeline that we've envisioned and the basic science that needs to be done. I mean, it needs to be clear. And I receive all sorts of emails and phone calls from people with with truly desperate medical conditions. And it's just incredible that the need is incredible. And unfortunately, I have to tell all these people every day we're working as fast as we can, but it's still a basic science project. It's we're not in clinical trials. We're not dealing with human patients. It's still very basic science. However, it is now to the point where we have commercial investment and it's clear that it's going to be for patients at some point. So it's pretty much on track. The idea is pretty simple. A David's group makes these wearable bioreactors and you wear it on a limb amputation site and they basically produce a sort of protective aqueous environment around the wound. And then my group comes with the payload, which is the drugs. It's the electroceutical agents, which are different types of ion channel drugs and other other sorts of drugs that will set the wound cells to a decision of let's let's rebuild. Let's rebuild a limb. Okay, Nadia wants to know, will be very interested to learn how the actual algorithms work, if he's even allowed to share that information. I think it's some form of pattern recognition, but the details would be cool to learn. What what. Yes, I'm not sure what algorithm she's talking about. I think she means the algorithm from when we were talking about decoding. Oh, I see. So so like frogs face means frog face. Well, I see. And then she has a sub-question that might be related. Also, how do we know that we're actually learning the cells' language and not just observing cause and effect because we're just seeing their behavior on the outside? Well, I think to go in order, the algorithm is still very much under development. Part of the problem is that traditional machine learning algorithms require incredible amounts of data, meaning large numbers of examples to learn from. We don't we don't have that data. So it's very expensive and time consuming to get these images of the electrical pattern. So we can't deploy the typical sorts of algorithms that are used. So we're still we're still a lot of the early work is basically done by hand. And we're at the moment still still working on these algorithms. So it's still sort of a story that's ongoing. With regard to the second question, I think I'm not sure what the distinction would be. So if we understand the bio-electric signals sufficiently that we can send out any signal that we want and have the cells do what we want, I think by definition, that means that we have communicated correctly with the cells, which by definition means that we have learned their appropriate language. I'm not sure what it would mean to be able to do that and yet not really the language of the cells. Sam Thompson wants to know, do you think that biological self-organization and emergence could be proto-algorithms? And what would be the implications of that then? What would be the implications for science? I don't know what proto-algorithmic means in this context. I can take a stab at what I think might be an interesting sense of it, but I'm not sure that it captures what he asked. The sort of thing that I think is important to think about is where do the set points of various homeostatic systems come from? So whether you have physiological homeostasis or anatomical homeostasis, the ability of a system to return to the same state, even if it's perturbed, right? One can ask, where is that information? And an easy thing to say is that, well, it's evolution that provides it, because certain types of set points are adaptive and other types will not let you survive. And that's true, except that what we're seeing now with these synthetic organisms is that we, for example, with the xenobots, we can take these frog tadpole cells and put them in a new environment. And within 48 hours or so, they self-assemble into a new organism with a new anatomy, a new behavior, and several new capabilities. They've never existed before. They have no long history of selection on Earth. Right. The cells themselves evolved to be very good at sitting on the outside of a frog or a tadpole and keeping out bacteria. They did not evolve for the ability to come together and run around on their own and do various things. So that raises the interesting question of where does that actually come from? Clearly, there's incredible plasticity of the hardware that's encoded by the genome. It can do all sorts of new things. But where do the specific things come from? And I don't know if that's what he meant by proto-algorithmic, but you can sort of think about it. One of my favorite analogies is this thing called a Galton board. I don't know if everyone knows what that is, but imagine a vertical piece of wood like this. It's a vertical piece of wood. And then you hammer a bunch of nails into it at regular intervals. just hammer a bunch of nails into it. You take a bucket of marbles and you pour it in the top and they go boom, boom, boom, boom. They all go in. Each marble just sort of bounces back and forth stochastically. If you have enough marbles, the outcome will always be exactly the same. You'll get this beautiful bell curve. Right. If you visualize and actually have toys of this, you can actually buy one on Amazon. It's like Plinko from Prices, right? Yes. Yes. Yes. Very, very similar. Very much the same. But you pour a whole. You see, if you throw one marble, you have no idea where it's going to end up. But if you pour a lot of marbles in, then on average you're going to get this beautiful bell curve. And so you can ask a simple question. Where is the shape of this bell curve encoded? Was it in the description of the wood? No. Was it in the layout of the nails? No. You can put the nails almost any way you want. Was it in the recipe for making this thing? No. Where was it? And so you end up with this idea that's very much like and this is certainly not the first person to say this. It's a very old, you know, maybe Pythagoras or Plato had similar ideas where you would say that somewhere in an important sense there are laws, laws of mathematics, laws of computation that exist independently of them having an independent existence. And what happens is that when we build specific sorts of machines, we connect to those laws and we benefit from them. So, for example, if you build a machine that looks like a Galton board, you can connect to the rules of mathematics that give you this beautiful shape. You don't have to specify the shape beforehand. You get the shape for free by building a device that can connect to those laws. If you discover a transistor, which is basically just a voltage-gated current conduction, right? It's like a small synapse. You know, it's the same as a gap junction or an ion channel. Once you've made that little machine, you can connect to these wonderful computational laws that tell you, for example, that if you have a bunch of NAND gates, you can build anything. Well, where did that fact come from? You know, these truth tables or if you know two angles of a triangle, you automatically know the third. Where did that come from? So where is it? So maybe that's what he meant by proto algorithmic. But it's the idea that there are these laws and some of them are physics, some of them are mathematics, and some of them are computation. If you make the right sort of device, you can reap the benefits of some of those laws. And evolution does this all the time. Evolution discovers certain pieces of hardware that then let you do wonderful, wonderful things, because you're using these laws that are out there that are invisible to you until you've built the right hardware. Great. We'll just get to four more questions and hopefully they'll be quick. Thane, is the evolutionary suppression of regeneration in mammals a beneficial trait for memory accumulation? Gosh, I'm not sure about memory accumulation, I doubt it, because there are many creatures that can do perfectly well with memory that are highly regenerative. So I don't think that you need that for your. I don't think it's impossible to have a regenerative capacity and memory in the same animal. However, we can think about why don't you, why aren't humans regenerating their limbs, for example? So so nobody knows. But I'll tell you a plausible story that might be correct or not. Imagine you're the ancestor of mammals. You're a small thing that looks a bit like a mouse and you're running around the woods and someone bites your leg off. So the problem is that unlike a salamander, which can hang out in water and take a long time to heal, you have a fast metabolism, you have a fast heart rate and blood pressure, and you're going to bleed out long before you get a chance to recover. So your job, if you want to survive, is to form a scar and have an inflammatory response that's going to kill off the bacteria. You need to not bleed out. So you need to seal the wound immediately. You need to form a scar. And by the way, you're going to try to put weight on it, because you're walking on it. Unlike a salamander, which has the buoyancy of water to hold you up, you're going to try to put weight on it, which means that as soon as some sort of delicate blastema is formed and these cells start to grow, you're going to grind it into the forest floor. So that's not particularly conducive. Also, because you're in dry air instead of water, all the electrical currents that need to come out of that wound epithelium to drive the electrical conditions can't work, because the dry air is an insulator. So you can imagine that at that point you might as well switch to scarring because of regeneration. Now, that story has pros and cons. One nice thing about that story is that it fits, for example, with this very strange fact. Why are deer regenerative on their antlers? Why can deer regenerate large amounts of bone and blood vessels and innervation every year? I mean, what's interesting about deer is that they don't put weight on it. They carry it around and they never have to. It never has to worry about being disrupted while it's trying to grow. So that's one part, you know, that fits. What doesn't fit is questions like, well, OK, what explains why the limbs don't regenerate? What about internal organs? Why don't they regenerate? Right. And we don't know. So, you know, so nobody knows. And we can come up with a few ideas that sort of have pros and cons. Tom Carrick asks, well, say, fascinating. Are there overlaps with the field of quantum biology? What about or IF? That is, I'm sure you've heard of Stuart Hameroff and Penrose's orchestrated objective reduction. Yes. I don't know. I can't say too many useful things about it, but I'll say sort of one thing. I agree with Hameroff and Penrose on the idea that anesthesia in general is one of the most profound, perhaps perhaps the only profound tools that we have for studying actual consciousness. Right. It's that we don't have many other tools for studying consciousness, but anesthesia is pretty good. And the interesting thing about anesthesia is that most general anesthesia are generally gap junction disruptors. Now, this this this has like many facts, it has things that are easy to understand and some things that are deeply confusing. The sort of thing that makes perfect sense is this. These electrical networks in the body have manifested various cognitive abilities long before they were brains. So these gap junctions that enable body cells to form networks are critical for these networks to have memories, memories of body shape, to make decisions about what they're going to grow and so on. So so the use of gap junctions to make networks that can follow large-scale goals like making a limb and making an organ and so on, that's evolutionarily ancient. And it's not at all surprising that what evolution did when the nervous systems evolved was to use the same trick to create another type of cognitive agent, which is basically centrally located, centered in the brain and use exactly the same, exactly the same hardware for reuse for that. So that makes sense. And so it makes perfect sense that it goes away when those gap junctions are disrupted by a general anesthetic. It also makes perfect sense that if we want to change a planarian's head, turn its head into the head of another species of planarian, guess what we use? A general anesthetic called octanol. It's exactly the same thing. It's a gap junctional disruptor. So what you do is you basically disrupt that proto-cognitive agent, the collective intelligence of the body that normally remembers how to make a specific type of head. You basically disrupt it with this general anesthetic. Now, the wonderful thing about general anesthesia is that any of us ever come back as the same person. Think about it. You have this brain that you have, you have, right? It supports the cognitive structures of a very complex being. And then for a few hours, you simply disconnect most of the cells from being able to communicate electrically with each other. And then you let the connections reform and you just hope that everything comes back as it was. If I didn't know, you know, if we didn't know that general anesthetics work, someone told me that's their plan, I would say, well, you might get a living, living human out of it at the end, but it's definitely not going to be the patient that walked in. You know, you're going to be set up. Of course, you're going to completely destroy their mental state. And so, one thing that's wonderful is that most people actually come out as more or less the same person as they went in. But the other interesting thing is not everyone. So and in fact, that's why they don't give, they don't like to give general anesthesia if they can help it, because some people have permanent psychosis. Some people, in fact, many people have hallucinations on their way out, out of which eventually resolve as the brain sort of

I find attraction that was previously there. But if you look, you can go to YouTube and you can watch some funny videos of people coming out of general anesthesia. Right. And, you know, people think they're pirates and they're gangsters and they, you know, they don't understand where they were and they have all these crazy stories about who they think they are. And eventually those kinds of solutions become. Not really. That part is not really surprising. I'm shocked anyone ever comes out of it properly. That's amazing. So the planaria. It's exactly the same thing. So when you disrupt their gap junctions, the first thing they do, they regenerate random heads that might belong to other species. Right. And then after about 30 days, those heads actually rebuild back to the correct species, a species-specific form. So they're not permanent. So for me, it looks exactly like what happens when you come out of general anesthesia. OK. Moflo wants to know, how does he see his work relating to David Sinclair's biological clock? Yes. Yes. Interesting. That's funny. I've talked a lot. I spoke with David recently and I've been thinking a lot about people asking me a lot about aging, bio-electrical of aging. I don't know. I don't know what the relationship between bio-electrical and aging really is. I can tell you that planaria, as far as we can see, do not age. There's no such thing as an old planarian. They live forever if if, you know, if they're not injured. And so I think what that tells us is that aging would be solved if we could increase the regenerative capacity to the point where we would constantly repair any cells that were aging. Right. Aging cells would just be regenerated like planaria would be regenerated. So my strong suspicion is that aging is a consequence of our poor ability to stay at the appropriate anatomical structure over long periods of time. And if we then clear up that problem, we will at the same time get the answer to aging, cancer, degenerative disease, and traumatic injury. I think it all comes down to the ability to defend a specific body plan over time. I know a lot of people, you know, they they they they they they they there is The origin of aging much better than that. I don't believe people say telomere length has to do with aging or the shortened telomere length. And you suggest, well, it might, but it's also related to this morphogenetic code that you're referring to. And yeah, I mean, are they somehow interrelated? Probably. I mean, I'm not an expert on telomeres. I have no idea what's going on with telomeres in the meeting. I assume someone is studying it. And the whole story of inevitable aging because you keep making copies of things and fundamentally things that are the information is degrading and eventually you don't have it anymore and it's degrading at the ends, because you know, because that's where you read it it clearly can't be the whole story, because planarian have permanently avoided. So whatever they're doing, that's the way to circumvent aging. It's more than a coincidence that the species that is immortal is also the species with the greatest regenerative capacity or let's say the set of species. So I don't think it's a it's a crazy coincidence that I think there's a reason for it. OK, the last question, Nate Grundman. Can you imagine that you're referring to, Michael, can you imagine a mental practice through which a person can influence the target state of the body? Joe Dispenza, for example, has made some claims that he healed his body in a way that doctors say is impossible. And also a question I had for you earlier, which is related to this, is how your work relates to the placebo effect. So whether you see the connection there or not, I'm also interested in the placebo effect. I'm trying to sneak in two questions for the price of one. Yes. Um, OK. So so I know nothing about Joe Dispenza. I know nothing about the claims that he's made or any specific, you know, sort of healing event. But I'll give you sort of a general thought on this. It's uncontroversial that your thoughts, whatever they may be, whether you know it or not, whatever you think thinking is, it's pretty uncontroversial that your thoughts influence the physiological functioning of your body. I mean, that's clear. If you if you want to get up and walk around, your thoughts have now activated various electrical pathways. They've caused a bunch of muscle movements. If you have a tendency to, you know, mentally work yourself up into an anxious state, you can definitely by your thinking ramp up various stress enzyme production in your body, right? We all know that you can do the opposite. If you if you've trained in techniques to calm yourself down under various circumstances, you can reduce the level of cortisol in your blood. You can reduce various different fight and flight responses. So it's it's not some strange, you know, voodoo sort of claim to say that your thoughts absolutely influence the biophysical processes of your body. We do it every day. If that weren't true, you couldn't get up in the morning when you wanted to get up and go to work. So it's so that part is totally obvious. So from there, it's a very short hop, skip and a jump to the idea that you can not only give commands to your muscles and your glands to produce various hormones, neurotransmitters, and muscle movement, but that you might be able to exert some influence over other cells, for example, skin cells in your, you know, by wounds and and and your liver, the way it processes information. I don't find that unlikely at all. So I don't again, I'm not commenting on any specific instance of someone healing themselves of anything. I'm just saying it's it's it's it's it's not a stretch to think that you can not only talk to your if I say talk, I mean, you know, exert influence over your various glands that they're putting out cortisol and adrenaline and various other things. Why? Why can't you send commands to other cells? It seems like it seems dumb to think that that's impossible. So having said all that, I think that the placebo effect is extremely profound. I think what it tells us is that there's communication across levels. So you have meaning that you have a level of organization that consists of your body cells and which has some cognition and some intelligence. But your body is also home to an additional intelligence, which is probably largely residing in the brain. And it appears that those two can communicate in various ways. And I can imagine that there's new, there's a lot of things to be discovered about ways to improve that communication and if, you know, we know we know there are certain practices where people can extend the amount of time they can put themselves underwater and and change their body temperature and change their heart rate and such things. I find it totally plausible that there are ways to communicate with other cells in the body in that way. There's also the field of hypnodermatology where people try through hypnosis to treat various skin diseases, some of which have a neural and neuro-immune component, some of which may not have a neuro-immune component. So the activity of the mind, which is simply the execution of of the physiological computations that happen in the brain, influences physiological computations that happen outside the brain. I don't think that's, I don't think that's a stretch at all. One of your goals is an anatomical assembler. And then what you just said made me think, well, some of these people who meditate or are on the more meditative side tend to work with thoughts to heal yourself. And then I wondered, I wonder if your anatomical assembler can advise some of the more thought-based healing practices. Here's an example, say you, well, if you were to think of this image, it's more likely to heal you than if you were to think of this other image. Do you think that's possible at all? Or is that too high level? I don't think that's I don't think that's impossible. No. Again, I'm not suggesting that there is that I'm not advocating a specific image of a healing one. But I don't think that's impossible at all. I mean, the and and there's been recent work on different kinds of pulsed light stimuli in the retina, which have some interesting neuro-neuro-protective effects in the brain and so on. Yes, all of that. It's a giant electrical network. All the cells communicate with each other. There's absolutely no reason why that couldn't work. But I think that, you know, to be clear, this anatomical assembler is not just us. You know, the anatomical assembler is a sort of practical embodiment of the goal that all of us in the community are going towards, which is the ability to control growth and form. Right. And when we have that ability, that's when the anatomical assembler becomes positive, possible. So it's not just something that we, you know, we particularly work on. But I think that it's part of all the things that you're discussing now is part of the deep reason why cognitive science and consciousness and all those sorts of things are deeply related to developmental biology and physiology. Right. They're absolutely interrelated because they're two sides of the same coin. Information processing in goal-directed hierarchical systems. And when you understand the more you understand of one, the better you are at managing the other. It's two sides of the same question. Where can people find out more about you and what's next for you? Well, they can find out. I have a website at drmike11.org. We have a center website, which is allencenter.tufts.edu. I have a Twitter feed, which is at Dr. Mike 11. And what's next? That's a good question. I don't know, I can't tell you exactly what's next, but I definitely know the things that we're trying to do and working on. And you can go to our website and see all sorts of projects that we're working on in the areas of trying to lay a better foundation for understanding basic cognition and understanding morphogenesis and developing applications and birth birth defects and regeneration and cancer. We're doing some work in machine learning and trying to close that loop and understand how we can use the principles that we've learned in biology to make better cognitive to make new and better cognitive systems that are going to help us in various ways. And the links to everything that Michael just mentioned will be in the description. So please check that out. You mentioned two sides of the same coin, but I didn't quite understand that. How is it that developmental biology and consciousness can be two sides? Because you mentioned one is first and third person is the other. So how are they two sides of the same coin? Well, in many ways, first, the fact that we all start life as a single cell and that cell-self coalesces into a creature that can later say, I am a centralized intelligence. I am a I am a I am a single I am a self. Yes, that might be. But you're made of a bunch of cells. And in fact, you were formerly one cell and then a ball of cells. And so that whole process of how it is that that that unified self is arranged and and supported by a collection of competent agents, these being cells, is very similar to how the body in the pattern of the body is arranged by the collective intelligence of cells. Morphogenesis is a collective intelligence problem. It's not a chemistry problem or a genetics problem. It's a problem of collective intelligence. And that same sort of problems arise when you try to understand any sort of human or any other centralized intelligence. How does the information processing and the capabilities of many independent subunits in the case of brains, that will be neurons. But in the case of the body, that will be other other types of cells. How do they work together to pursue goals and plans and have preferences that don't belong to any of the individual subunits themselves? Right. To make a limb is a goal that no individual cell can know. No individual cell knows what a limb is or can answer the question of, well, how many fingers are we supposed to have or how long is the finger supposed to be? That's a piece of information that only the cellular collective has. Right. So this ability of. To pursue large-scale goals to have collective information and to have that is more than the sum of its parts is exactly the same question of where does intelligence and sort of cognitive capacity come from. That's all they were those problems will be answered together. They will not be answered. If one of these things remains a mystery, we will not have an answer to the other. Thank you, sir. Thank you very much for spending so much time. Thank you very much. Yes. Thank you for your questions. I want to let everyone know that this, I think, is Nobel Prize-winning work. So I'll do my best to promote it and give you more attention, man. I hope so. Thank you. Thank you very much. That's very thoughtful. Thank you. I appreciate it. The podcast is now over. If you want to support conversations like this, please consider going to Patreon dot com slash curtjaimunga l. That's Curt Jaimungal. It's support from the patrons and from the sponsors that allows me to do this full time. Every dollar helps tremendously. Thank you.