Michael Levin: Hidden Reality of Alien Intelligence & Biological Life | Lex Fridman Podcast #486

Well, the fundamental tension is in the first-person, the second-person, and third-person descriptions of mind. So, in third-person, we want to understand how do we recognize them, and how do we know, looking out into the world, what degree of agency there is, and how best to relate to the different systems that we find. And are our intuitions any good when we look at something and it looks really stupid and mechanical, versus it really looks like there's something cognitive going on there? How do we get good at recognizing them? Then there's the second-person, which is the control, and that's both for engineering but also for regenerative medicine, when you want to tell the system to do something, right? What kind of tools are you going to use? And this is a major part of my framework, is that all of these kinds of things are operational claims. Are you going to use the tools of hardware rewiring, of control theory and cybernetics, of behavior science, of psychoanalysis and love and friendship? Like, what are the interaction protocols that you bring, right? And then in first-person, it's this notion of having an inner perspective and being a system that has valence and cares about the outcome of things. Makes decisions and has memories and tells a story about itself and the outside world. And how can all of that exist and still be consistent with the laws of physics and chemistry and various other things that we see around us? So that, that I find to be maybe the most interesting and the most important mystery for all of us to both on the science and also on the personal level. So that's what I'm interested in.

Yeah, although, actually I would turn that upside down. I think that pyramid is backwards, and I think it's behavior science at the bottom. I think it's behavior science all the way. I think in certain ways, even math is the behavior of a certain kind of being that lives in a latent space, and physics is what we call systems that at least look to be amenable to a very simple, low agency kind of model, and so on. But that's what I'm interested in, is understanding that and developing applications. Because it's very important to me that what we do is transition deep ideas and philosophy into actual practical applications that not only make it clear whether we're making any progress or not, but also allow us to relieve suffering and make life better for all sentient beings, and enable us and others to reach their full potential. So these are very practical things, I think.

The idea basically is that where something is on that spectrum, and I've called it the spectrum of persuadability. You could call it the spectrum of intelligence or agency or something like that. I like the notion of the spectrum of persuadability, because it's an engineering approach. It means that these are not things you can decide or have feelings about from a philosophical armchair. You have to make a hypothesis about which tools, which interaction protocols you're going to bring to a given system, and then we all get to find out how that worked out for you, right? So you could be wrong in many ways, in both directions. You can guess too high or too low, or wrong in various ways, and then we can all find out how that's working out. And so, I do think that the behavior of certain objects is well-described by specific formal rules, and we call those things the subject of mathematics. And then there are some other things whose behavior really requires the kinds of tools that we use in behavioral cognitive neuroscience, and those are other kinds of minds that we think we study in biology or in psychology or other sciences.

Well—

Yeah, the beginning of my work is very much in regenerative medicine, in bioengineering, things like that. So for those kinds of systems, the question is always, how do you get the system to do what you want it to do? So there are cells, there are molecular networks, there are materials, there are organs and tissues and synthetic beings and biobots and whatever. So the idea is, if I want your cells to regrow a limb, for example, if you're injured and I want your cells to regrow a limb, I have many options. Some of those options are I'm going to micromanage all of the molecular events that have to happen, right? And there's an incredible number of those. Or maybe I just have to micromanage the cells and the stem cell kinds of signaling factors. Or maybe actually I can give the cells a very high-level prompt that says, "You really should build a limb," and convince them to do it, right? And so which of those is possible? I mean, clearly people have a lot of intuitions about that. If you ask standard people in regenerative medicine and molecular biology, they're going to say, "Well, that convincing thing is crazy. What we really should be doing is talking to the cells, or better yet, the molecular networks." And in fact, all the excitement of the biological sciences today are at single molecule approaches and big data and genomics and all of that. The assumption is that, going down is where the action's going to be, going down in scale, and... I think that's wrong. But the thing that we can say for sure is that you can't guess that. You have to do experiments and you have to see because you don't know where any given system is on that spectrum of persuadability. And it turns out that every time we look and we take tools from behavioral science, so learning different kinds of training, different kinds of models that are used in active inference and surprise minimization and perceptual multi-stability and visual illusions and all these kinds of interesting things. Stress perception and memory, active memory reconstruction. All these interesting things. When we apply them outside the brain to other kinds of living systems, we find novel discoveries and novel capabilities, actually being able to get the material to do new things that nobody had ever found before. And precisely because I think that people didn't look at it from those perspectives, they assumed that it was a low-level kind of thing. So when I say persuadability, I mean different types of approaches, right? And we all know if you want to persuade your wind-up clock to do something, you're not going to argue with it or make it feel guilty or anything. You're going to have to get in there with a wrench and you're gonna have to, you know, tune it up and do whatever. If you want to do that same thing to a cell or a thermostat or an animal or a human, you're going to be using other sets of tools that we've given other names to. And so that's... Now, of course, that spectrum, the important thing is that as you get to the right of that spectrum, whereas the agency of the system goes up, it is no longer just about persuading it to do things. It's a bidirectional relationship, what Richard Watson would call a mutual vulnerable knowing. So the idea is that on the right side of that spectrum, when systems reach the higher levels of agency, the idea is that you are willing to let that system persuade you of things as well. You know, in molecular biology, you do things, hopefully the system does what you want to do, but you haven't changed. You're still exactly the way you came in. But on the right side of that spectrum, if you're having interactions with even cells, but certainly, you know, dogs, other animals, maybe other creatures soon, you're not the same at the end of that interaction as you were going in. It's a mutual bidirectional relationship. So it's not just you persuading something else, it's not you pushing things. It's a mutual bidirectional set of persuasions, whether those are purely intellectual or of other kinds.

Yeah. Richard, you should talk to Richard as well. He's an amazing guy and he's got some very interesting ideas about the intersection of cognition and evolution. But I think what you bring up is very important because there has to be a kind of impedance match between what you're looking for and the tools that you're using. I think the reason physics always sees mechanism and not minds is that physics uses low agency tools. You've got voltmeters and rulers and things like this. And if you use those tools as your interface, all you're ever going to see is mechanisms and those kinds of things. If you want to see minds, you have to use a mind, right? You have to have some degree of resonance between your interface and the thing you're hoping to find.

Here's the issue. Everything here hangs on what it means to understand, okay? For me, because to understand doesn't just mean have some sort of pleasing model that seems to capture some important aspect of what's going on. It also means that you have to be generative and creative in terms of capabilities. So for me, that means if I tell you this is what I think about cognition in cells and tissues, it means, for example, that I think we're going to be able to take those ideas and use them to produce new regenerative medicine that actually helps people in various ways, right? It's just an example. So if you think as a physicist you're going to have a complete understanding of what's going on from that perspective of fields and particles, and, you know, who knows what else is at the bottom there. Does that mean then that when somebody is missing a finger or has a psychological problem, or you know, has these other high-level issues, that you have something for them, that you're going to be able to do something? Because my claim is that you're not going to, and even if, even if you have some theory of physics that is completely compatible with everything that's going on, that is... it's not enough. That's not specific enough to enable you to solve the problems you need to solve. In the end, when you need to solve those problems, the person you're going to go to is not a physicist. It's going to be either a biologist or a psychiatrist, or who knows, but it's not going to be a physicist. And the simple example is this. You know, let's say, let's say someone comes in here and tells you a beautiful mathematical proof, okay? It's just really, you know, deep and beautiful, and there's a physicist nearby, and he says, "Well, I know exactly what happened. There were some air particles that moved from that guy's mouth to your ear. I see what goes on. It moved the cilia in your ear and the electrical signals went up to your brain." I mean, we have a complete accounting of what happened, done and done. But if you want to understand what's the more important aspect of that interaction, it's not going to be found in the Physics Department. It's going to be found in the Math Department. So that's my only claim is that physics is an amazing lens with which to view the world, but you're capturing certain things, and if you want to stretch to sort of encompass these other things, it just, we just don't call that physics anymore, right? We call that something else.

Yeah, let's talk about it. Well, first of all, of course it can. I mean, it can help, meaning that I'm not saying physics is not helpful. Of course it's helpful. It's a very important lens on one slice of what's going on in any of these systems. But I think the most important thing I can say about that question is I don't believe in any such line. I don't believe any of that exists. I think there is a continuum. I think we as humans like to demarcate areas on that continuum and give them names because it makes life easier, and then we have a lot of battles over, you know, so-called category errors when people, they transgress those categories. I think most of those categories at this point, they may have done some good service at the beginning of when the scientific method was getting started and so on. I think at this point they mostly hold back science. Many, many categories that we can talk about are at this point very harmful to progress, because what those categories do is they prevent you from hoarding tools. If you think that living things are fundamentally different from non-living things, or if you think that cognitive things are these like advanced brainy things that are very different from other kinds of systems, what you're not going to do is take the tools that are appropriate to these kind of cognitive systems, right? So the tools that have been developed in behavioral science and so on, you're never going to try them in other contexts because you've already decided that there's a categorical difference, that it would be a categorical error to apply them. And people say this to me all the time is that you're making a category error, as if these categories were given to us from on high, and we have to obey them forevermore. The categories should change with the science. So yeah, I don't believe in any such line, and I think a physics story is very often a useful part of the story, but for most interesting things, it's not the entire story.

My suggestion is that much better than trying to... in my experience, much better than trying to define any kind of a line, okay, because inevitably I've never found, and the people try to... you know, we play this game all the time when I make my continuum claim. Then people try to come up, "Okay, well, what about this?" And I haven't found one yet that really shoots that down, that you can't zoom in and say, "Yeah, okay, but right before then this happened, and if we really look close, like here's a bunch of steps in between," right? Pretty much everything ends up being a continuum, but here's what I think is much more interesting than trying to make that line. I think what's really more useful is trying to understand the transformation process. What is it that happened to scale up? And I'll give you a really dumb example. And we always get into this 'cause people often really, really don't like this continuum view. The word adult, right? Everybody is going to say, "Look, I know what a baby is. I know what an adult is. You're crazy to say that there's no difference." I'm not saying there's no difference. What I'm saying is the word adult is really helpful in court because you just need to move things along, and so we've decided that if you're 18, you're an adult. However, what it completely conceals is the fact that first of all, nothing happens on your 18th birthday, right? That's special. Second, if you actually look at the data, the car rental companies actually have a much better estimate because they actually look at the accident statistics and they'll say it's about 25 is really what you're looking for, right? So theirs is a little better. It's less arbitrary. But in either case, what it's hiding is the fact that we do not have a good story of what happened from the time that you were an egg to the time that you're the supposed adult and what is the scaling of personal responsibility, decision-making, judgment. These are deep fundamental questions. Nobody wants to get into that every time somebody, you know, has a traffic ticket. So, okay, we've just decided that there's this adult idea. And of course, it does come up in court because then somebody has a brain tumor or somebody's eaten too many Twinkies or something has happened. You say, "Look, that wasn't me. Whoever did that, I was on drugs." "Well, why'd you take the drugs?" "Well, that was, you know, that was yesterday. Me today, this is I'm..." Right? So we get into these very deep questions that are completely glossed over by this idea of an adult. So I think once you start scratching the surface, most of these categories are like that. They're convenient and they're good. You know, I get into this with neurons all the time. I'll ask people, "What's a neuron? Like, what's really a neuron?" And yes, if you're in neurobiology 101, of course you just say like, "These are what neurons look like. Let's just study the neuroanatomy and we're done." But if you really want to understand what's going on, well, neurons develop from other types of cells and that was a slow and gradual process, and most of the cells in your body do the things that neurons do. So what really is a neuron, right? So once you start scratching this, this happens, and I have some things that I think are coming out of our lab and others that are very interesting about the origin of life. But I don't think it's about finding that one boon like this is. Yeah, there will be... There are innovations, right? There are innovations that allow you to scale in an amazing way, for sure. And there are lots of people that study those, right? So things like thermodynamic, kind of metabolic things and all kinds of architectures and so on. But I don't think it's about finding a line. I think it's about finding a scaling process.

Let me make the opposite argument. They're absolutely useful. They're useful specifically when you want to gloss over certain things. The categories are exactly useful when there's a whole bunch of stuff. And this is what's important about science, is like the art of being able to say something without first having to say everything, right? Which would make it impossible. So categories are great when you want to say, "Look, I know there's a bunch of stuff hidden here. I'm going to ignore all that and we're just going to like, let's get on with this particular thing." And all of that is great as long as you don't lose track of the stuff that you glossed over. And that's what I'm afraid is happening in a lot of different ways. And in terms of... Look, I'm very interested in life beyond Earth and all these kinds of things so that we should also talk about what I call SUTI, S-U-T-I, the search for unconventional terrestrial intelligences. I think we got much bigger issues than actually recognizing aliens off Earth. But I'll make this claim. I think the categorical stuff is actually hurting that search. Because, if we try to define categories with the kinds of criteria that we've gotten used to, we are going to be very poorly set up to recognize life in novel embodiments. I think we have a kind of mind blindness. I think this is really key. To me, the cognitive spectrum is much more interesting than the spectrum of life. I think really what we're talking about is the spectrum of cognition. And, it is... Well, I know it's weird as a biologist to say, I don't think life is all that interesting a category. I think the categories of different types of minds, I think is extremely interesting. And to the extent that we think our categories are complete and are cutting nature at its joints, we are going to be very poorly placed to recognize novel systems. So for example, a lot of people will say, "Well, this is intelligent and this isn't," right? And there's a binary thing. And that's useful occasionally; that's useful for some things. I would like to say, instead of that, let's make a... let's admit that we have a spectrum. But instead of just saying, "Oh, look, everything's intelligent," right? Because if you do that, you're right, you can't do anything after that. What I'd like to say instead is, no, you have to be very specific as to what kind and how much. In other words, what problem spaces they're operating in? What kind of mind does it have? What kind of cognitive capacities does it have? You have to actually be much more specific. And we can even name, right? That's fine. We can name different types of, I mean, this is doing predictive processing. This can't do that, but it can form memories. What kind? Well, habituation and sensitization, but not associative conditioning. It's fine to have categories for specific capabilities, but it actually makes for much more rigorous discussions because it makes you say what is it that you are claiming this thing does, and it works in both directions. So, some people will say, "Well, that's a cell. That can't be intelligent." And I'll say, "Well, let's be very specific. Here are some claims about... Here's some problem solving that it's doing. Tell me why that doesn't... you know, why doesn't that match?" Or in the opposite direction, somebody comes to me and says, "You're right, you're right. You know, the whole, the whole solar system, man. It's just like this amazing..." I'm like, "Whoa, okay. Well, what is it doing?" Like, "Tell me what tools of cognitive and behavioral science are you using to reach that conclusion," right? And so I think it's actually much more productive to take this operational stance and say, "Tell me what protocols you think you can deploy with this thing that would lead you to use these terms."

For sure. It's a very important thinking aid to first of all, you know, what they call steel manning, right? To try to make the strongest possible case for the other side and to ask yourself, "Okay, what are all the places that I am sort of glossing over because I don't know exactly what to say? And where are all the holes in the argument, and what would a really good critique really look like?" Yeah.

They're definitely correlated. So, let me... I wanna preface this with one thing. When I say it's an engineering perspective, I don't mean that the standard tools that we use in engineering and this idea of enforced control and steering is how we should view all of the world. I'm not saying that at all. And I wanna be very clear on that because people do email me and say, "This engineering thing. You're going to drain the life and the majesty out of these high-end, like, human conversation." My whole point is not that at all. It's that of course at the right side of the spectrum it doesn't look like engineering anymore, right? It looks like friendship and love and psychoanalysis and all these other tools that we have. But here's what I want to do. I want to be very specific to my colleagues in regenerative medicine and everything. Just imagine if I, you know, if I went to a bioengineering department or a genetics department and I started talking about high-level, you know, cognition and psychoanalysis, right? They don't want to hear that. So I focus on the engineering approach... Because I want to say, look, this is not a philosophical problem. This is not a linguistics problem. We are not trying to define terms in different ways to make anybody feel fuzzy. What I'm telling you is, if you want to reach certain capabilities, if you want to reprogram cancer, if you want to regrow new organs, you want to defeat aging, you want to do these specific things, you are leaving too much on the table by making an unwarranted assumption that the low-level tools that we have, so these are the rules of chemistry and the kind of molecular rewiring, that those are going to be sufficient to get to where you want to go. It's an assumption only, and it's an unwarranted assumption, and actually, we've done experiments now, so not philosophy, but real experiments, that if you take these other tools, you can in fact persuade the system in ways that has never been done before. And we can unpack all that. But it is absolutely correlated with intelligence, so let me flesh that out a little bit. What I think is scaling in all of these things, right? Because I keep talking about the scaling, so what is it that's scaling? What I think is scaling is something I call the cognitive light cone, and the cognitive light cone is the size of the biggest goal state that you can pursue. This doesn't mean how far do your senses reach? This doesn't mean how far can you affect it? So the James Webb Telescope has enormous sensory reach, but that doesn't mean that's the size of its cognitive light cone. The size of the cognitive light cone is the scale of the biggest goal you can actively pursue, but I do think it's a useful concept to enable us to think about very different types of agents of different composition, different provenance, you know, engineered, evolved, hybrid, whatever, all in the same framework. And by the way, the reason I use light cone is that it has this idea from physics that you're putting space and time in the same diagram, which I like here. So if you tell me that all your goals revolve around maximizing the amount of sugar, the amount of sugar in this, you know, 10-20 micron radius of spacetime and that you have, you know, 20 minutes memory going back and maybe five minutes predictive capacity going forward, that tiny little cognitive light cone, I'm gonna say probably a bacterium. And if you say to me that, "Well, I'm able to care about several hundred yards sort of scale, I could never care about what happens three weeks from now, two towns over, just impossible," I would say you might be a dog. And if you say to me, "Okay, I care about really what happens, you know, the financial markets on Earth, you know, long after I'm dead and this and that," I'd say you're probably a human. And if you say to me, "I care in the linear range, I actively, I'm not just saying it, I can actively care in the linear range about all the living beings on this planet," I'm gonna say, "Well, you're not a standard human. You must be something else." Because humans, I don't know, standard humans today, I don't think can do that. You must be some kind of a bodhisattva or some other thing that has these massive cognitive light cones. So I think what's scaling from zero, and I do think it goes all the way down, I think we can talk about even particles doing something like this. The cognitive light cone. And so now this is an interesting... here, I'll try for a definition of life or whatever, for whatever it's worth. I spent no time trying to make that stick, but if we want it to, I think we call things alive to the extent that the cognitive light cone of that thing is bigger than that of its parts. So, in other words, rocks aren't very exciting because the things it knows how to do are the things that its parts already know how to do, which is follow gradients and things like that. But living things are amazing at aligning their competent parts so that the collective has a larger cognitive light cone than the parts. I'll give you a very simple example that comes up in biology and that comes up in our cancer program all the time. Individual cells have little tiny cognitive light cones. What are their goals? Well, they're trying to manage pH, metabolic state, some other things. There are some goals in transcriptional space, some goals in metabolic space, some goals in physiological state space, but they're generally very tiny goals. One thing evolution did was to provide a kind of cognitive glue, which we can also talk about, that ties them together into a multicellular system. And those systems have grandiose goals. They're making limbs, and if you're a salamander limb and you chop it off, they will regrow that limb with the right number of fingers, then they'll stop when it's done. The goal has been achieved. No individual cell knows what a finger is or how many fingers you're supposed to have, but the collective absolutely does. And that process of growing that cognitive light cone from a single cell to something much bigger, and of course the failure mode of that process, so cancer, right? When cells disconnect, they physiologically disconnect from the other cells. Their cognitive light cone shrinks. The boundary between self and world, which is what the cognitive light cone defines, shrinks. Now they're back to an amoeba. As far as they're concerned, the rest of the body is just external environment, and they do what amoebas do. They go where life is good. They reproduce as much as they can, right? So that cognitive light cone, that is the thing that I'm talking about that scales. So when we are looking for life, I don't think we're looking for specific materials. I don't think we're looking for specific metabolic states. I think we're looking for scales of cognitive light cone. We're looking for alignment of parts towards bigger goals in spaces that the parts could not comprehend.

No, I didn't. Sorry, I didn't mean that. First of all, the goal necessarily is often removed in time. So, in other words, when you're pursuing a goal, it means that you have a separation between current state and target state, at minimum. Your thermostat, right? Let's just think about that. There's a separation in time because the thing you're trying to make happen, so that the temperature goes to a certain level, is not true right now. And all your actions are going to be around reducing that error, right? That basic homeostatic loop is all about closing that gap. When I said linear range, this is what I meant. If I say to you, "This terrible thing happened to, you know, ten people." And you have some degree of activation about it. And then I say, "No, no, no, actually it was 100, you know, 10,000 people." You're not a thousand times more activated about it. You're somewhat more activated, but it's not a thousand. And if I say, "Oh my God, it was actually 10 million people," you're not a million times more activated. You don't have that capacity in the linear range. You sort of... Right? If you think about that curve, we sort of reach a saturation point. I have some amazing colleagues in the Buddhist community with whom we've written some papers about this, the radius of compassion is like, "Can you grow your cognitive system to the point that, yeah, it really isn't just your family group, it really isn't just the hundred people you know in your circle? Can you grow your cognitive light cone to the point where, no, no, we care about the whole, whether it's all of humanity or the whole ecosystem, or the whole whatever? Can you actually care about that the exact same way that we now care about a much smaller set of people?" That's what I mean by linear range.

Well, no. How do you know what the cognitive light cone of something is? Because as you've said, it could be almost anything. The key is you have to do experiments. And the way you do experiments is you put barriers. You have to do interventional experiments. You have to put barriers between it and its goal, and you have to ask what happens. And intelligence is the degree of ingenuity that it has in overcoming barriers between it and its goal. Now, if it were to be that, now, this is, I think, a totally doable but impractical and very expensive experiment. But you could imagine setting up a scenario where the bacteria were blocked from becoming more complex. And you can ask if they would try to find ways around it, or whether their goals are actually metabolic. And as long as those goals are met, they're not going to actually get around your barrier. The business of putting barriers between things and their goals is actually extremely powerful because we've deployed it in all kinds of... I'm sure we'll get to this later, but we've deployed it in all kinds of weird systems that you wouldn't think are goal-driven systems. And what it allows us to do is to get beyond just the anthropomorphizing claims of saying, "Oh, yeah, I think this thing is trying to do this or that." The question is, well, let's do the experiment. And one other thing I want to say about anthropomorphizing is people say this to me all the time. I don't think that exists. I think that's kind of like, you know. And I'll tell you why. I think it's like heresy or like other terms that aren't really a thing. Because if you unpack it, here's what anthropomorphism means. Humans have a certain magic, and you're making a category error by attributing that magic somewhere else. My point is, we have the same magic that everything has. We have a couple of interesting things besides the cognitive light cone and some other stuff, and it isn't that you have to keep the humans separate because there's some bright line. It's just that same old... All I'm arguing for is the scientific method, really. That's really all this is. All I'm saying is you can't just make pronouncements such as, "Humans are this," and let's not sort of push that. You have to do experiments. After you've done your experiments, you can say either, "I've done it, and I've found... Look at that. That thing actually can predict the future for the next, you know, 12 minutes. Amazing." Or you say, "You know what? I've tried all the things in the behaviorist handbook, they just don't help me with this. It's a very low level of..." Like, that's it. It's a very low level of intelligence. Fine, right? Done. So that's really all I'm arguing for is an empirical approach, and then things like anthropomorphism go away. It's just a matter of, have you done the experiment, and what did you find?

You could try.

Yeah. We've used everything from basic habituation conditioning all the way through anxiolytics, hallucinogens, all kinds of cognitive modification on the range of things that you wouldn't believe. And by the way, I'm not the first person to come up with this. So there was a guy named Bose well over 100 years ago who was studying how anesthesia affected animals and animal cells, and drawing specific curves around electrical excitability. And he then went and did it with plants and saw some very similar phenomena. And being the genius that he was, he then said, "Well, how do I... I don't know when to stop, but there's no... You know, everybody thinks we should have stopped long before plants 'cause people made fun of him for that. And he's like, "Yeah, but the science doesn't tell us where to stop. The tool is working, let's keep going." And he showed interesting phenomena on materials, metals, and other kinds of materials, right? And so the interesting thing is that, yeah, there is no generic rule that tells you when you need to stop. We make those up. Those are completely made up. You have to just do the science and find out.

Well, the thing to remember is we don't have a magic sense or really good intuition for what the mapping is between the embodiment of something and the degree of intelligence it has. We think we do because we have an N of one example on Earth and we know what to expect from cells, snakes to primates, but we really don't. We don't have, and this is, we'll get into more of the stuff on the platonic space, but our intuitions around that stuff is so bad that to really think that we know enough not to try things at this point is, I think, really shortsighted.

So, this is the version 1.0 and there's a kind of update, a 2.0 that I'm writing at the moment, trying to formalize in a careful way all the things that we've been talking about here, and in particular this notion of having to do experiments to figure out where any given system is on a continuum. Let's just start with Figure 2 for a second, then we'll come back to Figure 1. First, just to unpack the acronym, I like the idea that it spells out TAME because the central focus of this is interactions and how do you interact with a system to have a productive interaction with it? The idea is that cognitive claims are really protocol claims. When you tell me that something has some degree of intelligence, what you're really saying is, "This is the set of tools I'm going to deploy and we can all find out how that worked out for you." And so, technological, because I wanted to be clear with my colleagues that this was not a project in just philosophy. This had very specific, empirical implications that are going to play out in engineering and regenerative medicine and so on. A technological approach to mind everywhere, this idea that we don't know yet where different kinds of minds are to be found, and we have to empirically figure that out. So, what you see here in figure two is basically this idea that there is a spectrum, and I'm just showing four waypoints along that spectrum. As you move to the right of that spectrum, a couple things happen: persuadability goes up, meaning that the systems become more reprogrammable, more plastic, more able to do different things than whatever they're standardly doing. So, you have more ability to get them to do new and interesting things. The effort needed to exert influence goes down, that is, autonomy goes up. To the extent that you are good at convincing or motivating the system to do things, you don't have to sweat the details as much, right? This also has to do with what I call engineering agential materials. When you engineer wood, metal, plastic, things like that, you are responsible for absolutely everything because the material is not going to do anything other than hopefully hold its shape. If you're engineering active matter, or you're engineering computational materials, or better yet, agential materials like living matter, you can do some very high-level prompting and let the system then do very complicated things that you don't need to micromanage. We all know that that increases when you're starting to work with intelligent systems like animals and humans and so on. The other thing that goes down as you get to the right is the amount of mechanism or physics that you need to exert the influence goes down. So, if you know how your thermostat is to be set as far as its set point, you really don't need to know much of anything else, right? You just need to know that it is a homeostatic system and that this is how I change the set point. You don't need to know how the cooling and heating plant works in order to get it to do complex things.

Yeah. I'll give you an example about that, panel C here with the dog. Isn't it amazing that humans have been training dogs and horses for thousands of years knowing zero neuroscience? What's amazing is that when I'm talking to you right now, I don't need to worry about manipulating all of the synaptic proteins in your brain to make you understand what I'm saying and hopefully remember it. You're gonna do that all on your own. I'm giving you very thin, in terms of information content, very thin prompts, and I'm counting on you as a multi-scale agential material to take care of the chemistry underneath, all right?

Correct. I don't need, and I don't need physics to convince you, and I don't need to know how you work. Like, I don't need to understand all of the steps. What I do need to have is trust that you are a multi-scale cognitive system that already does that for yourself, and you do. This is an amazing thing. I know people don't think about this enough, I think. When you wake up in the morning and you have social goals, research goals, financial goals, whatever it is that you have, in order for you to act on those goals, sodium and calcium and other ions have to cross your muscle membranes. Those incredibly abstract goal states ultimately have to make the chemistry dance in a very particular way, right? Our entire body is a transducer of very abstract things. And by the way, not just our brains, but our organs have anatomical goals and other things that we can talk about, because all of this plays out in regeneration and development and so on. But the scaling, right, of all of these things, the way that you regulate yourself is not by, "Oh my God," you don't have to sit there and think, "Wow, I really have to push some sodiums across this membrane." All of that happens automatically, and that's the incredible benefit of these multi-scale materials. So what I was trying to do in this paper is a couple of things. All of these were, by the way, drawn by Jeremy Gay, who's this amazing graphic artist that works with me. First of all, in panel A, which is the spiral I was trying to point out, is that at every level of biological organization, like we all know we're sort of nested dolls of organs and tissues and cells and molecules and whatever. But what I was trying to point out is that this is not just structural. Every one of those layers is competent and is doing problem-solving in different spaces, and spaces that are very hard for us to imagine. We humans are, because of our own evolutionary history, we are so obsessed with movement in three-dimensional space. Even in AI you see this all the time. They say, "Well, this thing doesn't have a robotic body, it's not embodied." Yeah, it's not embodied by moving around in 3D space, but biology has embodiments in all kinds of spaces that are hard for us to imagine, right? So your cells and tissues are moving in high-dimensional physiological state spaces, in gene expression state spaces, in anatomical state spaces. They're doing that perception, decision-making, action loop that we do in 3D space when we think about robots wandering around your kitchen. They're doing those loops in these other spaces. And so the first thing I was trying to point out is that every layer of your body has its own ability to solve problems in those spaces. And then on the right, what I was saying is that this distinction between, you know, people say, "Well, there are living beings and then there are engineered machines," and then they often follow up with all the things machines are never gonna be able to do and whatever. And so what I was trying to point out here is that it is very difficult to maintain those kinds of distinctions, because life is incredibly interoperable. Life doesn't really care if the thing it's working with was evolved through random trial and error or was engineered with a higher degree of agency, because at every level within the cell, within the tissue, within the organism, within the collective, you can replace and substitute engineered systems with naturally evolved systems. And that question of, "Is it real, is it biology or is it technology?" I don't think is a useful question anymore. So I was trying to warm people up with this idea that what we're going to do now is talk about minds in general, regardless of their history or their composition. It doesn't matter what you're made of. It doesn't matter how you got here. Let's talk about what you're able to do and what your inner world looks like. That was the goal of that.

It's really hard, and you have to do experiments, which at larger scales are really difficult.

There may well be. We have to do experiments. I don't know. Here's an example: Somebody will say to me, "Well, you know, with your kind of panpsychist view, you might as well think the weather is agential too." It's like, "Well, I can't say that, but we don't know, but have you ever tried to see if a hurricane has habituation or sensitization?" Maybe. We haven't done the experiment. It's hard, but you could, right? And maybe weather systems can have certain kinds of memories. I have no idea. We have to do experiments. So I don't know what the entire human society is doing, but I'll just give you a simple example of the kinds of tools, and we're actively trying to build tools now to enable radically different agents to communicate. So, we are doing this using AI and other tools to try and get this kind of communication going across very different spaces. I'll just give you a very kind of dumb example of how that might be. Imagine that you're playing tic-tac-toe against an alien, so you're in a room. You don't see him. And so you draw the tic-tac-toe thing on the board, on the floor. And you know what you're doing. You're trying to make straight lines with Xs and Os, and you're having a nice game. It's obvious that he understands the process. Like, sometimes you win, sometimes you lose. Like, it's obvious. In that one little segment of activity, you guys are sharing a world. What's happening in the other room next door? Well, let's say the alien doesn't know anything about geometry. He doesn't understand straight lines. What he's doing is he's got a box, and it's full of basically billiard balls, each one of which has a number on it. And all he's doing is he's looking through the box to find billiard balls whose numbers add up to 15. He doesn't understand geometry at all. All he understands is arithmetic. You don't think about arithmetic, you think geometry. The reason you guys are playing the same game is that there's this magic square, right, that somebody constructed that basically is a three-by-three square, where if you pick the numbers right, they add up to 15. He has no idea that there's a geometric interpretation to this. He is solving the problem that he sees, which is totally algebraic. You don't know anything about that. But if there is an appropriate interface like this magic square, you guys can share that experience. You can have an experience. It doesn't mean you start to think like him. It means that you guys are able to interact in a particular way.

Of seeing a thin slice of the world.

So, the process is twofold. One is to get a better understanding of what the system... what space is the system navigating, what goals does it have, what level of ingenuity does it have to reach those goals. For example, xenobots, right? We make xenobots. This is... Or anthropods. These are biological systems that have never existed on Earth before. We have no idea what their cognitive properties are. We're learning. We found some things. But you can't predict that from first principles, because they're not at all what their past history would inform you of.

So one of the things that we've been doing is trying to create novel beings that have never been here before. The reason is that typically when you have a biological system, an animal or a plant, and you say, "Hey, why does it have certain forms of behavior, certain forms of anatomy, certain forms of physiology? Why does it have those?" The answer is always the same. Well, there's a history of evolutionary selection, and there's a long, long history going back of adaptation, and there's certain environments, and this is what survived, and so that's why it has. So what I wanted to do was break out of that mold, and to basically force us as a community to dig deeper into where these things come from. And that means taking away the crutch where you just say, "Well, it's evolutionary selection that's... That's why it looks like that." So in order to do that, we have to make artificial synthetic beings now. To be clear, we are starting with living cells, so it's not that they had no evolutionary history. The cells do. They had evolutionary history in frogs or humans or whatever. But the creatures they make and the capabilities that these creatures have were never directly selected for. And in fact, they never existed. So you can't tell the same kind of story. And what I mean is, we can take epithelial cells off of an early frog embryo, and you don't change the DNA. No synthetic biology circuits, no material scaffolds, no nanomaterials, no weird drugs, none of that. What we're mostly doing is liberating them from the instructive influences of the rest of the cells that they were in in their bodies. And so when you do that, normally these cells are bullied by their neighboring cells into having a very boring life. They become a two-dimensional outer covering for the embryo, and they keep out the bacteria, and that's that. So you might ask, "Well, what are these cells capable of when you take them away from that influence?" So when you do that, they form another little life form we call a xenobot. And it's this self-motile little thing that has cilia covering its surface. The cilia are coordinated so they row against the water, and then the thing starts to move, and has all kinds of amazing properties. It has different gene expression, so it has its own novel transcriptome. It's able to do things like kinematic self-replication, meaning make copies of itself from loose cells that you put in its environment. It has the ability to respond to sound, which normal embryos don't do. It has these novel capacities. And we did that, and we said, "Look, here are some amazing features of this novel system. Let's try to understand where they came from." And some people said, "Well, maybe it's a frog-specific thing, you know? Maybe this is just something unique to frog cells." And so we said, "Okay, what's the furthest you can get from frog embryonic cells? How about human adult cells?" And so we took cells from adult human patients who were donating tracheal epithelia for biopsies and things like that, and those cells, again, no genetic change, nothing like that. They self-organized into something we call anthropods. Again, self-motile little creature. 9,000 different gene expressions. So about half the genome is now different. And they have interesting abilities. For example, they can heal human neural wounds. So in vitro, if you plate some neurons and you put a big scratch through it so you damage them, anthropods can sit down, and they will, they will spontaneously, without us having to teach them to do it, they will spontaneously try to knit the neurons across.

So this is an anthropod. So often when I give talks about this, I show people this video, and I say, "What do you think this is?" And people will say, "Well, it looks like some primitive organism you got from the bottom of a pond somewhere." And I'll say, "Well, what do you think the genome would look like?" And they say, "Well, the genome would look like some primitive creature." Right? If you sequence that thing, you'll get 100% Homo sapiens. And that doesn't look like any stage of normal human development. It doesn't act like any stage of human development. It has the ability to move around. It has, as I said, over 9,000 differential gene expressions. Also interestingly, it is younger than the cells that it comes from. So it actually has the ability to roll back its age, and we could talk about that and what the implications of that are. But to go back to your original question, what we're doing with these kind of systems...

We're trying to talk to it. That's exactly right. And not just to this. We're trying to talk to molecular networks. So, we found a couple years ago, we found that gene regulatory networks, never mind the cells, but the molecular pathways inside of cells can have several different kinds of learning, including Pavlovian conditioning. And what we're doing now is trying to talk to it. The biomedical applications are obvious. Instead of, "Hey, Siri," you want, "Hey, liver, why do I feel like crap today?" And you want an answer. "Well, you know, your potassium levels are this and that, and I don't feel good for these reasons." And you should be able to talk to these things, and there should be an interface that allows us to communicate, right? And I think AI is gonna be a huge component of that interface of allowing us to talk to these systems. It's a tool to combat our mind blindness, to help us see diverse other... very unconventional minds that are all around us.

Yeah. That is exactly the mission of my lab. It is to enable us to develop tools to recognize these things, to learn to communicate with them, to ethically relate to them. And in general, to expand our ability to do this in the world around us. I specifically chose these kinds of things because they're not as alien as proper aliens would be. So we have some hope. I mean, we're made of them. We have many things in common. There's some hope of understanding them.

Xenobots and anthropods and cells and everything else. But they're alien in a couple of important ways. One is the space they live in is very hard for us to imagine. What space do they live in? Well, your body, your body cells, long before we had a brain that was good for navigating three-dimensional space, was navigating the space of anatomical possibilities. It was going from, you start as an egg, and you have to become, you know, a snake or a giraffe or whatever, or a human, whatever we're gonna be. And I am specifically telling you that this general idea when people model that with cellular automata type of ideas, this open loop kind of thing where, well, everything just follows local rules and eventually there's complexity, and here you go. Now, you've got a giraffe or a human. I'm specifically telling you that that model is totally insufficient to grasp what's actually going on. What's actually going on, and there have been many experiments on this, is that the system is navigating a space. It is navigating a space of anatomical possibilities. If you try to block where it's going, it will try to get around you with things it's never seen before, it will try to come up with a solution. If you really defeat its ability to do that, which you can. You know, they're not infinitely intelligent, so you can defeat them. You will either get birth defects or you will get creative problem-solving such as what you're seeing here with xenobots and anthropods. If you can't be a human, you'll find another way to be. You can be an anthropod, for example, or you'll be something else.

Sure.

Yes.

Yes.

The will thing, let's put that aside—

because that's a... Well, it's fine too.

Yeah. Yeah, yeah. And I'm not saying that's wrong. I'm just saying I don't have data for that one, but I'll tell you the stuff that I'm quite certain of. There are a couple of different formalisms that we have in control theory. One of those formalisms is open-loop complexity. In other words, I've got a bunch of subunits, like a cellular automaton. They follow certain rules, and you turn the crank, time goes forward, whatever happens, happens. Clearly you can get complexity from this. Clearly you can get some very interesting-looking things, right? So the game of life, all those kinds of cool things, right? You can get complexity. No, no, no problem. But the idea that that model is going to be sufficient to explain and control things like morphogenesis is a hypothesis. It's okay to make that hypothesis, but we know it's false despite the fact that that is what we learned, you know, in basic cell biology and developmental biology classes. When the first time you see something like this, inevitably, especially if you're an engineer in those classes, you go, "Hey, how does it know to do that? How does it know, you know, four fingers instead of seven?" What they tell you is, "It doesn't know anything." Make sure. That's very clear. They all insist, like, when we learn these things, they insist, "Nothing here knows anything. There are rules of chemistry, they roll forward, and this is what happens." Okay. Now, that model is testable. We can ask, "Does that model explain what happens?" Here's where that model falls down. If you have that model and situations change, either there's damage or something in the environment that's happened, those kind of open-loop models do not adjust to give you the same goal by different means. This is William James' definition of intelligence: the same goal by different means. And in particular, working them backwards, let's say you are in regenerative medicine and you say, "Okay, but this is the situation now. I want it to be different." What should the rules be? It's not reversible. So the thing with those kind of open-loop models is they're not reversible. You don't know what to do to make the outcome that you want. All you know how to do is roll them forward, right? Now, in biology, we see the following: If you have a developmental system and you put barriers between... So, I'm going to give you two pieces of evidence that suggest that there is a goal. One piece of evidence is that if you try to block these things from the outcome that they normally have, they will do some amazing things. Sometimes very clever things, sometimes not at all the way that they normally do it, right? So this is William James' definition. By different means, by following different trajectories, they will go around various local maxima and minima to get to where they need to go. It is navigation of a space. It is not blind, turn the crank, and wherever we end up is where we end up. That is not what we see experimentally. And more importantly, I think, what we've shown, and this is something that I'm particularly happy with in our lab, over the last 20 years, we've shown the following: We can actually rewrite the goal states because we found them. We have shown through our work on bioelectric imaging and bioelectric reprogramming, we have actually shown how those goal memories are encoded, at least in some cases. We certainly haven't got them all, but we have some. If you can find where the goal state is encoded, read it out, and reset it, and the system will now implement a new goal based on what you just reset, that is the ultimate evidence that your goal-directed model is working. Because if there was no goal, that shouldn't be possible. Right? Once you can find it, read it, interpret it, and rewrite it, it means that by any engineering standard, it means that you're dealing with a homeostatic mechanism.

So, through lots and lots of hard work.

The barrier thing tells you that you should be looking for a goal.

Yes. The first couple of steps are all imagination. You have to ask yourself, "What space is this thing even working in?" And you really have to stretch your mind, because we can't imagine all the spaces that systems work in, right? So, step one is, what space is it? Step two, what do I think the goal is? And let's not mistake step two, you're not done. Just because you have made a hypothesis, that doesn't mean you can say, "Well, there, I see it doing this, therefore that's the goal." You don't know that. You have to actually do experiments. Now, once you've made those hypotheses, now you do the experiments. You say, "Okay, if I want to block it from reaching its goal, how do I do that?" And this, by the way, is exactly the approach we took with the sorting algorithms and with everything else. You hypothesize the goal, you put a barrier in, and then you get to find out what level of ingenuity it has. Maybe what you see is, "Well, that derailed everything, so probably this thing isn't very smart." Or you say, "Oh, wow, it can go around and do these things." Or you might say, "Wow, it's taking a completely different approach using its affordances in novel ways, like that's a high level of intelligence." You will find out what the answer is.

Yeah. Okay, if you want to get really weird, we can get really weird here. Think about the caterpillar-butterfly transition, okay? So, you've got a caterpillar, a soft-bodied creature, which has a particular controller that's suitable for running a soft body, you know, a robot. It has a brain for that task, and then it has to become this butterfly, a hard-bodied creature that flies around. Okay. During the process of metamorphosis, its brain is basically ripped up and rebuilt from scratch, right? Now, what's been found is that if you train the caterpillar, so you give it a new memory, meaning that if the caterpillar sees this color disc, then it crawls over and eats some leaves. Turns out, the butterfly retains that memory. Now, the obvious question is, how do you retain memories when the medium is being...

refactored like that? Let's put that aside. I'm going to get somewhere even weirder than that. There's something else that's even more interesting than that.

It's not just that you have to retain the memory. You have to remap that memory onto a completely new context, because guess what? The butterfly doesn't move the way the caterpillar moves, and it doesn't care about leaves. It wants nectar from flowers. And so, if that memory is going to survive, it can't just persist. It has to be remapped into a novel context.

Now, here's where things get weird. We can take a couple of different perspectives here. We can take the perspective of the caterpillar facing some sort of crazy singularity and say, 'My God, I'm going to cease to exist, but, you know, I'll sort of be reborn in this new higher-dimensional world where I'll fly.' Okay, so that's one thing.

We can take the perspective of the butterfly and say that, 'Well, here I am, but, you know, I seem to be saddled with some tendencies and some memories, and I don't know where the hell they came from, and I don't remember exactly how I got them, and they seem to be a core part of my psychological makeup, and, you know, they come from somewhere. I don't know where they come from.' Right? So you can take that perspective.

But there's a third perspective that I think is really interesting and useful. The third perspective is out of the memory itself. If you take a perspective of the memory, what is a memory? It is a pattern. It is an informational pattern that was continuously reinforced within one cognitive system, and now here I am on this memory. What do I need to do to persist into the future?

Well, now I'm facing the paradox of change. If I try to remain the same, I'm gone. There's no way the butterfly is going to retain me in the original form that I'm in now. What I need to do is change, adapt, and morph. Now, you might say, 'Well, that's kind of crazy. How are you taking the perspective of a pattern within an excitable medium?' Right? Agents are physical things. You're talking about information, right?

So let me tell you another quick science fiction story. Imagine that some creatures come out from the center of the Earth. They live down in the core. They're super dense, okay? They're incredibly dense because they live down in the core. They have gamma ray vision and so on. So they come out to the surface. What do they see? Well, all of this stuff that we're seeing here, this is like a thin plasma to them. They are so dense.

None of this is solid to them. They don't see any of this stuff. So they're walking around, you know, because the planet is covered in this thin gas, you know. And one of them is a scientist, and he's taking measurements of the gas, and he says to the others, 'You know, I've been watching this gas, and there are, like, little whirlpools in this gas, and they almost look like agents. They almost look like they're doing things. They're moving around. They kind of hold themselves together for a little bit, and they're trying to make stuff happen.'

And the others say, 'Well, that's crazy. Patterns in a gas can't be agents. We're agents. We're solid. This is just patterns in an excitable medium. And by the way, how long do they hold together?' He says, 'Well, about 100 years.' 'Well, that's crazy. Nothing... You know, no real agent can exist to dissipate that fast.'

Okay. We are all metabolic patterns, among other things, right? And so one of the things that... And so you see what I'm warming up to here. So one of the things that we've been trying to dissolve, and this is some work that I've done with Chris Fields and others, is this distinction between thoughts and thinkers. So, all agents are patterns within some excitable medium. We could talk about what that is, and they can spawn off others.

And now you can have a really interesting spectrum. Here's the spectrum. You can have fleeting thoughts, which are like waves in the ocean when you throw a rock in. You know, they sort of go through the excitable medium and then they're gone. They pass through and they're gone, right? So those are kind of fleeting thoughts.

Then you can have patterns that have a degree of persistence, so they might be hurricanes or solitons or persistent thoughts or earworms or depressive thoughts. Those are harder to get rid of. They stick around for a little while. They often do a little bit of niche construction, so they change the actual brain to make it easier to have more of those thoughts, right? Like, that's a thing. And so they stay around longer.

Now, what's further than that? Well, fragments, personality fragments of a dissociative personality disorder, they're more stable. And they're not just on autopilot. They have goals and they can do things. And then past that is a full-blown human personality.

And who the hell knows what's past that? Maybe some sort of trans-human, you know, transpersonal, like, I don't know, right? But this idea, again, I'm back to this notion of a spectrum. There is not a sharp distinction between, you know, we are real agents and then we have these thoughts. Yeah, patterns can be agents too, but again, you don't know until you do the experiment.

So, if you want to know whether a soliton or a hurricane or a thought within a cognitive system is its own agent, do the experiment. See what it can do. Can it learn from experience? Does it have memories? Does it have goal states? What can it do, right? Does it have language?

So, coming back to your original question, yeah, we can definitely apply this methodology to ideas and concepts and social, you know, whatevers, but you've got to do the experiment.

Yeah. Stay tuned. I mean, this is exactly what we're doing. We're getting progressively, like, more and more unconventional. I mean, so this whole distinction between software and hardware, I think it's a super important concept to think about. And yet, the way we've mapped it onto the world, I would like to blow that up in the following way.

And again, I want to point out what the practical consequences are because this is not just, you know, fun stories that we tell each other. These have really important research implications. Think about a Turing machine. So one thing you can say is the machine's the agent. It has passive data, and it operates on the data, and that's it. The story of agency is the story of whatever that machine can and can't do. The data is passive, and it moves it around.

You can tell the opposite story. You can say, 'Look, the patterns on the data are the agent. The machine is a stigmergic scratch pad in the world of the data doing what data does.' The machine is just the consequences, the scratch pad of it working itself out. And both of those stories make sense depending on what you're trying to do.

Here's the biomedical side of things. So our program in bioelectrics and aging, okay? One model you could have is the physical organism is the agent and the cellular collective has pattern memories, specifically what I was saying before, goals, anatomical goals. If you want to persist for 100 plus years, your cells better remember what your correct shape is and where the new cells go, right?

So there are these pattern memories. They exist during embryogenesis, during regeneration, during resistance to aging. We can see them. We can visualize them. One thing you can imagine is, fine, the physical body, the cells, are the agent. The electrical pattern memories are just data, and what might happen during aging is that the data might get degraded. They might get fuzzy.

And so what we need to do is reinforce the memories, reinforce the pattern memories. That's one specific research program, and we're doing that. But that's not the only research program because the other thing you might imagine is that, what if the patterns are the agent in exactly the same sense as we think in our brains?

It's the patterns of electrophysiological computations, whatever else, that is the agent, right? And that what they're doing in the brain are the side effects of the patterns working themselves out. And those side effects might be to fire off some muscles, glands, and other things.

From that perspective, maybe what's actually happening is, maybe the agent's finding it harder and harder to be embodied in the physical world. Why? Because the cells might get less responsive. In other words, the cells are sluggish. The patterns are fine. They're having a harder time making the cells do what they need to do.

And maybe what you need to do is not reinforce the memories. Maybe what you need to do is make the cells more responsive to them, and that is a different research agenda, which we are also doing. We have evidence for that as well, actually now. We published it recently.

So my point here is, when we tell these crazy sci-fi stories, the only worth to them, and the only reason I'm talking about them now, and a year ago I wasn't talking about this stuff, is because these are now actionable in terms of specific experimental research agendas that are heading to the clinic, I hope, in some of these biomedical approaches.

So now here we can go beyond this and say, 'Okay, up until now we've considered, what are disease states?' Well, we know there's organic disease, something that's physically broken. We can see the tissues breaking down. There's damage in the joint, you know, where the liver is doing what, you know, we can see these things.

But what about disease states that are not physical states? They're physiological states, informational states, or cognitive problems? So in all of these other spaces, you can start to ask, what's a barrier in gene expression space? What's a local minimum that traps you in physiological state space?

And what is a stress pattern that keeps itself together, moves around the body, causes damage, tries to keep itself going, right? What level of agency does it have? This suggests an entirely different set of approaches to biomedicine.

And, you know, anybody who's, let's say, in the alternative medicine community is probably yelling at the screen right now saying, 'We've been saying this for hundreds of years.' And yeah, I'm well aware these ideas are not new. What's new is being able to now take this and make them actionable and say, 'Yeah, but we can image this now. I can now actually see the bioelectric patterns and why they go here and not there.' And we have the tools that now hopefully will get us to therapeutics.

So this is very actionable stuff, and it all leans on not assuming we know minds when we see them, because we don't, and we have to do experiments.

And by saying scratch pad, I don't mean it's not important. When we get to talking Platonic space, we have to talk about how important the interface actually is. The scratchpad isn't unimportant; the scratchpad is critical.

It's just that my only point is that when we have these formalisms of software, of hardware, of other things, the way we map those formalisms onto the world is not obvious. It's not given to us. We get used to certain things, right? But who's the hardware, who's the software, who's the agent and who's the excitable medium is to be determined.

So what happened was this crazy notion, which I'll describe momentarily. I have given a couple talks on it. I then found a couple papers in the machine learning community called the Platonic Representation Hypothesis, and I said, 'That's pretty cool. These guys are climbing up to the same point where I'm getting at it from biology and philosophy and whatever. They're getting there from computer science and machine learning.'

We'll take a couple hours, I'll give a talk, they'll give a talk, we'll talk about it. I thought there were going to be three talks at this thing. Once I started reaching out to people for this, everybody sort of said, 'You know, I know somebody who's really into this stuff, but they never talk about it because there's no audience for this.' So I reached out to them.

And then they said, 'Yeah. Oh, yeah, I know this mathematician,' or, 'I know this, you know, economist, whatever, who has these ideas and there's nowhere we can have her talk about them.' So I got this whole list and it became completely obvious that we can't do this in a normal... We are now booked up through December.

So every week in our center, somebody gives a talk. We kind of discuss it. It all goes on this thing. I'll give you a link to it, and then there's a huge running discussion after that, and then in the end, we're all going to get together for an actual real-time discussion section and talk about it. But there's going to be probably 15 or so talks about this from all kinds of disciplines.

It's blown up in a way that I didn't realize how much undercurrent of these ideas had already existed that were ready, like now is the time. And I think... this is... like, I've been thinking about these things for, I don't know, 30-plus years.

I never talked about them before because they weren't actionable before. There wasn't a way to actually make empirical progress with this now. You know, this is something that Pythagoras and Plato and probably many people before them talked about, but now we're to the point where we can actually do experiments, and they're making a difference in our research program.

Yeah. The first thing I want to say is that while I'm currently calling it the Platonic space, I am in no way trying to stick close to the things that Plato actually thought about. In fact, to whatever extent we even know what that is, I think I depart from that in quite... in some ways, and I'm going to have to change the name at some point.

The reason I'm using the name now is because I wanted to be clear about a particular connection to mathematics, which a lot of mathematicians would call themselves Platonists because what they think they're doing is discovering... not inventing as a human construction, but discovering a structured ordered space of truths.

Let's put it this way: In biology, as in physics, there's something very curious that happens that if you keep asking why, you keep asking why, then something interesting goes on. Well, I'll give you two examples.

First of all, imagine cicadas. The cicadas come out at 13 years and 17 years, okay? And so if you're a biologist and you say, 'So why is that?' Then you get this explanation for, 'Well, it's because they're trying to be off-cycle from their predators. Because if it was 12 years, then every two year, every three year, every four year, every six year, a predator would eat you when you come out, right?'

So you say, 'Okay, cool. That makes sense. What's special about 13 and 17?' 'Oh, they're prime.' 'Uh-huh. And why are they prime?' Well, now you're in the math department. You're no longer in the biology department. You're no longer in the physics department. You're now in the math department to understand why the distribution of primes is what it is.

Another example, and I'm not a physicist, but what I see is every time you talk to a physicist and you say, 'Hey, why do the, you know, leptons do this or that, or the fermions are doing whatever?' Eventually, the answer is, 'Oh, because there's this mathematical, you know, this SU(8) group or whatever the heck it is, and it has certain symmetries in these certain structures.' 'Yeah, great. Once again, you're in the math department.'

So something interesting happens is that there are facts that you come across, many of them are very surprising. You don't get to design them. You get more out than you put in, in a certain way, because you make very minimal assumptions. And then certain facts are thrust upon you. For example, the value of Feigenbaum's constant, the value of natural logarithm E. These things you sort of discover, right?

And the salient fact is this: if those facts were different, then biology and physics would be different, right? They impact instructively, functionally; they impact the physical world. If the distribution of primes was something else, well then the cicadas would have been coming out at different times.

But the reverse isn't true. What I mean is, there is nothing you can do in the physical world to change E, as far as I know, to change E or to change Feigenbaum's constant. You could have swapped out all the constants at the Big Bang, right? You can change all the different things, you are not going to change those things.

So this, I think Plato and Pythagoras understood very clearly, that there is a set of truths which impact the physical world, but they themselves are not defined by and determined by what happens in the physical world. You can't change them by things you do in the physical world, right?

And so I'll make a couple claims about that. One claim is, I think we call physics those things that are constrained by those patterns. When you say, 'Hey, why is this the way it is?' Ah, it's because this is how symmetry, symmetries or topology or whatever. Biology are the things that are enabled by those. They're free lunches. Biology exploits these kinds of truths.

And really it enables biology and evolution to do amazing things without having to pay for it. I think there's a lot of free lunches going on here. And so I show you a xenobot or an anthropod, and I say, 'Hey, look, here are some amazing things they're doing,' that tissue has never done before in their history.

You say, first of all, where did that come from? And when did we pay the computational cost for it? Because we know when we pay the computational cost to design a frog or a human, it was for the eons that the genome was bashing against the environment getting selected, right? So you pay the computational cost of that.

There's never been any anthropods. There's never been any xenobots. When do we pay the computational cost for designing kinematic self-replication and, you know, all these things that they're able to do? So there's two things people say. One is, 'Well, it's sort of... you got it at the same time that they were being selected to be good humans and good frogs.'

Now, the problem with that is it kind of undermines the point of evolution. The point of evolutionary theory was to have a very tight specificity between what... how you are now and the history of selection that got you here, right? The history of environments that got you to this point.

If you say, 'Yeah, okay, so this is what your environmental history was. And by the way, you got something completely different. You got these other skills that you didn't know about,' that's really strange, right? And so then what people say is, 'Well, it's emergent.' And I say, 'What's that? What does that mean?'

And they say... besides the fact that you got surprised, right? Emergence often just means I didn't see it coming. You know, there was something happened. I didn't know that was going to happen. So what does it mean that it's emergent?

And people say, 'Well,' and there are many emergent things like this. For example, the fact that gene regulatory networks can do associative learning. Like, that's amazing, and you don't need evolution for that. Even random genetic regulatory networks can do associative learning. I say, 'Why does that happen?' And they say, 'Well, it's just a fact that holds in the world. Just a fact that holds.'

So now you have an option, and you can go one of two ways. You can either say, 'Okay, look, I like my sparse ontology. I don't want to think about weird platonic spaces. I'm a physicalist. I want the physical world, nothing more.'

So what we're going to do is when we come across these crazy things that are very specific, like, you know, anthropods have four specific behaviors that they switch around. Why four? Why not 12? Why not 100? Like four, why four? When we come across these things, just like when we come across the value of E or Feigenbaum's number or whatever, what we're going to do is we're going to write it down in our big book of emergence. And that's it. We're just going to have to live with it.

This is what happens. We're just... you know, there's some cool surprises. When we come across them, we're going to write them down. Great. It's a random grab bag of stuff. And when we come across them, we'll write them down. That's one. The upside is you get to be a physicalist, and you get to keep your sparse ontology.

The downside is I find it incredibly pessimistic and mysterian because you're basically then just willing to make a catalog of these amazing patterns. Why not instead, and this is why I started with this Platonic terminology, why not do what the mathematicians already do?

A huge number of them say, 'We are going to make the same optimistic assumption that science makes, that there's an underlying structure to that latent space. It's not a random grab bag of stuff. There's a space to it which, where these patterns come from, and by studying them systematically, we can get from one to another. We can map out the space. We can find out the relationships between them. We can get an idea of what's in that space, and we're not going to assume that it's just random. We're going to assume there's some kind of structure to it.'

And you'll see all kinds of people, I mean, you know, well-known mathematicians that talk about this stuff. You know, Penrose and lots of other people who will say that, 'Yeah, there's another space physically, and it has spatial structure. It has components to it and so on. We can traverse that space in various ways.' And then there's the physical space.

So I find that much more appealing because it suggests a research program, which we are now undergoing in our lab. The research program is everything that we make, cells, embryos, robots, biobots, language models, simple machines, all of it, they are interfaces. All physical things are interfaces to these patterns.

You build an interface, some of those patterns are going to come through that interface. Depending on what you build, some patterns versus others are going to come through. The research program is mapping out that relationship between the physical pointers that we make, and the patterns that come through it, right? Understanding the structure of that space, what exists in that space, and what do I need to make physically to make certain patterns come through?

Now, when I say patterns, now we have to ask, 'What kinds of things live in that space?' Well, the mathematicians will tell you, 'Well, we already know. We have a whole list of objects. You know, the amplituhedrons and all this crazy stuff that lives in that space.' Yeah, I think that's one layer of stuff that lives in that space, but I think those patterns are the lower agency kinds of things that are basically studied by mathematicians.

What also lives in that space are much more active, more complex, higher agency patterns that we recognize as kinds of minds, that behavioral scientists would look at that pattern and say, 'Well, I know what that is. That's the competency for delayed gratification or problem-solving of certain kinds,' or whatever.

And so what I end up with right now is a model in which that latent space contains things that come through physical objects, so simple patterns, right? So facts about triangles and Fibonacci patterns and fractals and things like that.

But also, if you make more complex interfaces such as biologicals and, but importantly, not just biologicals, but let's say cells and embryos and tissues, what you will then pull down is much more complex patterns that we say, 'Ah, that's a mind. That's a human mind,' or, 'That's a, you know, snake mind,' or whatever.

So I think the mind-brain relationship is exactly the kind of thing that the math-physics relationship is, that in some very interesting way, there are truths of mathematics that become embodied, and they kind of haunt physical objects, right, in a very specific functional way.

And in the exact same way, there are other patterns that are much more complex, higher agency patterns that basically inform living things that we see as obvious embodied minds.

Yeah, okay. Let's try it this way. There are certain facts of mathematics. So the distribution of prime numbers, right? If you map them out, they make these nice spirals. And there's an image that I often show, which is a very particular kind of fractal.

And that fractal is the Hally map, which is pretty awesome that it actually looks very organic. It looks very biological. So if you look at that thing, that image, which has very specific complex structure, it's a map of a very compact mathematical object. That formula is like, you know, Z cubed plus seven. It's something like that. That's it.

So now you look at that structure and you say, 'Where does that actually come from?' It's definitely not packed into the Z cubed plus seven. There's not enough bits in that to give you all of that. There's no fact of physics that determines this. There's no evolutionary history. It's not like we selected this based on some, you know, from a larger set over time. Where does this come from?

Or the fact that... think about the way that biology exploits these things. Imagine a world in which the highest fitness belonged to a certain kind of triangle, right? So evolution cranks a bunch of generations and it gets the first angle right, then cranks a bunch more generations, gets a second angle right. Now, there's something amazing that happens.

It doesn't need to look for the third angle because you already know. If you know two, you get this magical free gift from geometry that says, 'Well, I already know what the third one should be.' You don't have to go look for it.

Or as evolution, if you invent a voltage-gated ion channel, which is basically a transistor, right, and you can make a logic gate, then all the truth tables and the fact that NAND is special and all these other things, you don't have to evolve those things. You get those for free. You inherit those.

Where do all those things live? These mathematical truths that you come across that you don't have any choice about. You know, once you've committed to certain axioms, there's a whole bunch of other stuff that is now just it is what it is.

And so what I'm saying is, and this is what Pythagoras was saying, I think, that there is a whole space of these kinds of truths. Now, he was focused on mathematical ones, but he was embodying them in music and in geometry and in things like that. There are the space of patterns, and they make a difference in the physical world, to machines, to sound, to things like that.

I'm extending it, and what I'm saying is, yeah, and so far we've only been looking at the low agency inhabitants of that world. There are other patterns that we would recognize as kinds of minds, and that you don't see them in this space until there's an interface, until there's a way for them to come through the physical world.

That interface, the same way that you have to make a triangular object before you can actually see the rule of what you're going to gain, right? Out of the rules of geometry and whatever. Or you have to actually do the computation on the fractal before you actually see that pattern.

If you want to see some of those minds, you have to build an interface, at least if you're going to interact with them in the physical world, the way we normally do science. As Darwin said, 'Mathematicians have their own new sense, like a different sense than the rest of us.' So that's right. You know, mathematicians can perhaps interact with these patterns directly in that space.

But for the rest of us, we have to make interfaces. And when we make interfaces, which might be cells, robots, embryos, or whatever, what we are pulling down are minds that are fundamentally not produced by physics.

So I don't believe that, I don't know if we're going to get into the whole consciousness thing, but I don't believe that we create consciousness, whether we make babies or whether we make robots. Nobody's creating consciousness. What you create is a physical interface through which specific patterns, which we call kinds of minds, are going to ingress, right?

And consciousness is what it looks like from that direction looking out into the world. It's what we call the view from the perspective of the Platonic patterns.

A thin client. Yeah. The brain—

The brain, a brain is a thin client. Yeah.

Sure.

Yeah. Okay, a couple of things. First of all, when you said it makes sense for physics, I want to show that it's not as simple as it sounds. Because what it means is that even in Newton's boring, classical universe, long before quantum anything, Newton's world, physicalism was already dead.

In Newton's world, I mean, think about what that means. This is nuts, because already he knew perfectly well, I mean Pythagoras and Plato knew, that even in a totally classical, deterministic world, already you have the ingression of information that determines what happens and what's possible and what's not possible in that world from a space that is itself not physical.

In other words, it's something like the natural logarithm E, right? Nothing in Newton's world is set to the value of E. There is nothing you could do to set the value of E in that world. And yet that fact that it was that and not something else governed all sorts of properties of things that happened. That classical world was already haunted by patterns from outside that world.

This, this should be like... this is wild. This is not saying that, 'Okay, everything was cool. Physicalism was great up until, you know, maybe we got quantum this, interfaces, or we got, you know, consciousness or whatever. But originally it was fine.' No, this is saying that it was... that worldview was already impossible really since... so from a very long time ago, we already knew that there are non-physical properties that matter in the physical world.

That is a very deep follow-on question that I... we'll come back to in a minute. All I was saying about Newton is that you don't need quantum anything. You don't need to think about consciousness. You already, long before you get to any of that, as Pythagoras, I think, knew, already we have the idea that this physical world is being strongly impacted by truths that do not live in the physical world.

No. Mathematical facts. So for example, the actual value of E or—

Yeah, yeah. I mean, some of them are, you know... I mean, if you ask Don Hoffman, there's this like, amplituhedron thing that is a set of mathematical objects that determines all the scattering amplitudes of the particles and whatever. They don't have to be simple. I mean, the old ones were simple. Now they're like crazy. I can't imagine this amplituhedron thing, but maybe they can.

But all of these are mathematical structures that explain and determine facts about the physical world, right? If you ask physicists, 'Hey, why this many of this type of particle?' 'Ah, because this mathematical thing has these symmetries.' That's why.

This is very controversial, right? And there are of course physicists and mathematicians who disagree with what I'm saying, for sure. But what I'm leaning on is simply this. I don't know of anything you can do in the physical world. At the big... you're around at the Big Bang, you get to set all the constants. Change set physics however you want. Can you change E? Can you change Feigenbaum's constant? I don't think you can.

So physicists do this. They'll say, 'Okay, you know, if we made the ratio between the, you know, the gravitation and... would we have matter? Would we... how many dimensions would we have? Would there be inflation? Would there be this or that?' Right? You can imagine playing with it.

There are however many unitless constants of physics. These are the kind of like knobs on the universe that could, in theory, be different, and then you'd have different physics, you'd have different physical properties.

What I'm not saying is that every alien everywhere is going to have the exact same math that we have. That's not what I'm claiming. Although, maybe. But that's not what I'm claiming.

What I'm saying is, you get more out than you put in. Once you've made a choice... and maybe some alien somewhere made a different choice of how they're going to do their math. But once you've made your choice, then you get saddled with a whole bunch of new truths that you discover that you can't do anything about. They are given to you from somewhere.

And you can say they're random, or you can say, 'No, there's this space of these facts that they're pulled from. There's a latent space of options that they come from.' So when your E is exactly 2.718 and so on, there is nothing you can do in physics to change it.

I'm not saying it's immutable. So I think Plato may or may not have thought that these forms are eternal and unchanging. That's one place we differ. I actually think that space has some action to it, maybe even some computation to it.

Well, so let's... okay, so I'll circle back around to that whole thing. So the only thing I was trying to do is blow up the idea that we're cool with how it works in physics. No problem there. I don't... I think that's a much bigger deal than people normally think it is.

I think already there, you have this weird haunting of the physical world by patterns that are not coming from the physical world. The reason I emphasize this is because now what I'm going to... when I amplify this into biology, I don't think it sort of jumps as a new thing.

I think it's just a much more... I think what we call biology is our systems that exploit the hell out of it. I think physics is so constrained by it, but we call biology those things that make use of those kinds of things and run with it. And so I, again, I just think it's a scaling. I don't think it's a brand new thing that happens. I think it's a scaling, right?

So what I'm saying is we already know from physics that there are non-physical patterns, and these are generally patterns of form, which is why I call them low agency, because they're like fractals that stand still, and they're like prime number distributions.

Although there's a mathematician that's talking in our symposium that's telling me that actually I'm too chauvinistic even there. That actually, even those things have more oomph than even I gave 'em credit for, which I love.

So what I'm saying is those kind of static patterns are things that we typically see in physics, but they're not the full extent of what lives in that space. That space is also home to some patterns that are very high agency.

And if we give them a body, if we build a body that they can inhabit, then we get to see different behavioral competencies that the behavior scientists say, 'Oh, I know what that looks like.' That's this kind of behavioral, you know... this kind of mind or that kind of mind.

In a certain sense, I mean, yes, what I'm saying is extremely radical, but it is a very old idea. It's an old idea of a dualistic worldview, right? Where the mind was not in the physical body, and that it in some way interacted with the physical brain.

So I just want to be clear. I'm not claiming that this is fundamentally a new idea. This has been around forever. However, it's mostly been discredited, and it's a very unpopular view nowadays. There are very few people in, for example, the cognitive science community or anywhere else in science that like this kind of view.

Primarily, and already Descartes was getting crap for this when he first tried it out as this interaction problem, right? So the idea was, okay, well, if you have this non-physical mind, and then you have this brain that presumably obeys conservation of mass energy and things like that, how are you supposed to interact with it? And there are many other problems there.

So what I'm trying to point out is that, first of all, physics already had this problem. You didn't have to wait until you had biology and cognitive science to ask about it. And what I think is happening and the way we need to think about this is coming back to my point that I think the mind-brain relationship is basically of the same kind as the math-physics relationship.

The same way that non-physical facts of physics haunt physical objects is basically how I think different kinds of patterns that we call kinds of minds are manifesting through our... through interfaces like brains.

Yeah, I mean...

Yeah, sort of.

Sort of. I mean, so Descartes got some stuff wrong, I think. But one thing that he did get right, the fact that you actually, you don't know what you can poke and what you can't poke. The only thing you actually know are the contents of your mind, and everything else might be...

And in fact, what we know from Anil Seth and Don Hoffman and various other people, it's definitely a construct. You might be on drugs, and you might wake up tomorrow and say, 'My God, I had the craziest dream of being Lex Fridman.' Amazing.

Yeah, well... yeah, that... who knows? But—

Right? But you see, I... you know, it's not clear at all that the physical poking is your primary reality. That's not clear to me at all.

I think you can get there more easily by synthetic bioengineering in the following sense. Do you feel a lack of x-ray perception? Do you feel blind in the x-ray spectrum or in the ultraviolet? I mean, you don't. You have absolutely no clue that stuff is there, and all of your reality as you see it is shaped by your evolutionary history.

It's shaped by the cognitive structure that you have, right? There are tons of stuff going on around us right now that we, of which we are completely oblivious. There's equally all kinds of other stuff which we construct, and this is just modern cognitive science that says that a lot of what we think is going on is a total fabrication constructed by us.

So I think this is not... I don't think this is a philo... I mean, Descartes got there from a philosophical point. That's not what I'm... that's not the leap I'm asking us to make. I'm saying that depending on your embodiment, depending on your interface, and this is increasingly going to be more relevant as we make first augmented humans that have sensory substitution.

You're going to be walking around. Your friend's going to be like, 'Oh, man, I have this primary perception of the solar weather and the stock market because I got those implants.' 'And what do you see?' 'Well, I see the, you know, the traffic or the internet through the, you know, Trans-Pacific Channel.' We're all going to be living in somewhat different worlds. That's the first thing.

The second thing is we're going to become better attuned to other beings, whether they be cells, tissues. You know, what's it like to be a cell living in a 20,000-dimensional transcriptional space, okay? To novel beings that have never been here before that have all kinds of crazy spaces that they live in, and that might be AIs. It might be cyborgs. It might be hybrids. It might be all sorts of things.

So this idea that we have a consensus reality here that's independent of some very specifically chosen aspects of our brain and our interaction. We're going to have to give that up no matter what to relate to these other beings.

Yeah, which is exactly what we're trying to do. I mean, we're—

Yeah, yeah, okay. I mean, so that's the beautiful part about this, and this is why I'm talking about this now, whereas I wasn't, you know, about a year ago. Up until a year ago, I was never talking about this because I think this is now actionable.

So there's this diagram that's called the Map of Mathematics, and they basically try to show how all the different pieces of math link together, and that there's a bunch of different versions of it. So there's two features to this.

One is, what is it a map of? Well, it's a map of various truths. It's a map of facts that are thrust on you. You don't have a choice. Once you've picked some axioms, you just, you know, hear some surprising facts that are just going to be given to you.

But the other key thing about this is that it has a metric. It's not just a random heap of facts. They're all connected to each other in a particular way. They literally make a space, and so when I say it's a space of patterns, what I mean is it is not just a random bag of patterns such that when you have one pattern, you are no closer to finding any other pattern.

I'm saying that there's some kind of a metric to it so that when you find one, others are closer to it, and then you can get there. So that's the claim. And obviously, this is... now, not everybody buys this and so on. This is one idea.

Now, how do we know that this exists? Well, I'll say a couple of things. If that didn't exist, what is that a map of? If there is no space, if you don't want to call it a space, that's okay, but you can't get away from the fact that as a matter of research, there are patterns that relate to each other in a particular way.

What's, you know, well, the final step of calling it a space is minimal. The bigger, bigger issue is what the hell is it a map of then if it's not a space? So that's the first thing.

Now, that's how it plays out, I think, in math and physics. Now in biology, here's how we're going to know if this makes any sense. What we are doing now is trying to map out that space by saying, 'Look, we took... we know that the frog genome maps to one thing and that's a frog.'

It turns out that exact same genome, if you just take the slightest step with the exact same genome, but you just take some cells out of their environment, they can also make xenobots with very specific different transcriptomes, very specific behaviors, very specific shapes.

It's not just, 'Oh, well, you know, they do whatever.' They have very specific behaviors, just like the frog had very specific properties. We can start to map out what all those are and basically try to draw the latent space from which those things are pulled.

And one of two things is going to happen in the future, so this is, you know, come back in 20 years and we'll see how this worked out. One thing that could happen is that we're going to see, 'Oh, yeah, just like the map of mathematics, we made a map of the space.'

And we know now that if I want a system that acts like this and this, here's the kind of body I need to make for it, because those are the patterns that exist. The Anthrobots have four different behaviors, not seven and not one. And so, that's what I can pull from. These are the options I have.

Yeah. And I don't see how it would be separate because I'm not just talking about an anatomical shape and transcriptional profile. I'm also talking about behavioral competencies.

So when we make something and we find out that, okay, it does habituation, sensitization, it does not do Pavlovian conditioning, and it does do delayed gratification, and it doesn't have language, that is a very specific cognitive profile. That's a region of that space, and there's another region that looks different, because I don't make a sharp distinction between biology and cognition.

If you want to explain behaviors, they are drawn from some distribution as well. So I think in 20 years, or however long it's going to take, one of two things will happen.

Either we and other people who are working on this are going to actually produce a map of that space and say, 'Here's why you've gotten systems that work like this and like this and like this, but you've never seen any that work like that.'

Or, we're going to find out that I'm wrong, and that basically it's not worth calling it a space, because it is so random and so jumbled up that there is, we've been able to make zero progress in linking the embodiments that we make to the patterns that come through.

Yeah, yeah.

Well, it includes specific patterns that we have given names to.

Some of those patterns we've named mathematical objects. Some of those patterns we've named anatomical outcomes. Some of those patterns we've named psychological types.