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

1. Biological intelligence

You write that the central question at the heart of your work, from biological systems to computational ones, is: “How do embodied minds arise in the physical world, and what determines the capabilities and properties of those minds?” Can you unpack that question for us, and maybe begin to answer it?

The fundamental tension is in the first-person, second-person, and third-person descriptions of mind. In the third person, we want to understand how we recognize minds and how 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. Are our intuitions any good when we look at something and it looks really stupid and mechanical, versus when 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 about control, both for engineering and for regenerative medicine, when you want to tell the system to do something. What kind of tools are you going to use? This is a major part of my framework: all of these kinds of things are operational claims. Are you going to use the tools of hardware rewiring, control theory and cybernetics, behavioral science, psychoanalysis, love, and friendship? What are the interaction protocols that you bring?

And then, in the first person, there’s this notion of having an inner perspective and being a system that has valence and cares about the outcome of things. It makes decisions, has memories, and tells a story about itself and the outside world. How can all of that exist and still be consistent with the laws of physics and chemistry and the various other things that we see around us?

That, I find to be perhaps the most interesting and important mystery for all of us, both on the scientific and personal level. So that’s what I’m interested in.

So your work is focused on starting at physics and going all the way to friendship, love, and psychoanalysis.

Yeah, although actually, I would turn that upside down. I think that pyramid is backwards, and I think behavioral science is at the bottom. I think it’s behavioral science all the way. 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: understanding that and developing applications. It’s very important to me that what we do transitions deep ideas and philosophy into actual practical applications that not only make it clear whether we’re making any progress, but also allow us to relieve suffering, make life better for all sentient beings, and enable us and others to reach our full potential. These are very practical things, I think.

Behavioral science, I suppose, is more subjective, and mathematics and physics are more objective. Would that be the clear difference?

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 and 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.

You could be wrong in many ways, in both directions. You can guess too high or too low, or be wrong in various ways, and then we can all find out how that’s working out.

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. Then there are some other things whose behavior really requires the kinds of tools that we use in behavioral and cognitive neuroscience. Those are other kinds of minds that we think we study in biology, psychology, or other sciences.

Why are you using the term “persuadability”? Who are you persuading, and of what?

The beginning of my work is very much in regenerative medicine, bioengineering, and things like that. For those kinds of systems, the question is always: How do you get the system to do what you want it to do?

There are cells, molecular networks, materials, organs and tissues, synthetic beings, biobots, and so on. 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 involve micromanaging all of the molecular events that have to happen. There are an incredible number of those events. Or maybe I just have to micromanage the cells and the stem-cell signaling factors. Or maybe I can give the cells a very high-level prompt that says, “You really should build a limb,” and convince them to do it.

Which of those is possible? 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, “That convincing thing is crazy. What we really should be doing is talking to the cells, or better yet, the molecular networks.”

In fact, all the excitement in the biological sciences today is around single-molecule approaches, big data, genomics, and all of that. The assumption is that going down is where the action is going to be—going down in scale. I think that’s wrong.

The thing that we can say for sure is that you can’t guess that. You have to do experiments and see, because you don’t know where any given system is on that spectrum of persuadability.

It turns out that every time we look and take tools from behavioral science—learning, different kinds of training, different kinds of models used in active inference and surprise minimization, perceptual multistability, visual illusions, and all these kinds of interesting things—we find novel discoveries and novel capabilities when we apply them outside the brain to other kinds of living systems.

We’re actually able to get the material to do new things that nobody had ever found before, precisely because I think people didn’t look at it from those perspectives. They assumed that it was a low-level kind of thing.

When I say “persuadability,” I mean different types of approaches. We all know that 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. You’re going to have to get in there with a wrench, tune it up, and do whatever is necessary.

If you want to do that same thing to a cell, a thermostat, an animal, or a human, you’re going to be using other sets of tools that we’ve given other names to.

Now, of course, the important thing about that spectrum is that as you get to the right of it, as the agency of the system goes up, it is no longer just about persuading it to do things. It becomes a bidirectional relationship—what Richard Watson would call “mutual vulnerable knowing.”

The idea is that on the right side of that spectrum, when systems reach higher levels of agency, you are willing to let that system persuade you of things as well. In molecular biology, you do things, and hopefully the system does what you want it to do, but you haven’t changed. You’re still exactly the way you were when you came in.

But on the right side of that spectrum, if you’re having interactions with even cells, and certainly with dogs, other animals, or 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. It’s not just you persuading something else or pushing things. It’s a mutual, bidirectional set of persuasions, whether those are purely intellectual or of other kinds.

In order to be effective at persuading an intelligent being, you yourself have to be persuadable. The closer in intelligence you are to the thing you’re trying to persuade, the more persuadable you have to become. Hence, “mutual vulnerable knowing.” What a term.

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, rulers, and things like this. 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. You have to have some degree of resonance between your interface and the thing you’re hoping to find.

2. Living vs non-living organisms

You said this about physics before. Can you linger on that and expand on what you mean—why physics is not enough to understand life, to understand mind, to understand intelligence? You make a lot of controversial statements with your work. That's one of them, because there are a lot of physicists who believe they can understand life, the emergence of life, the origin of life, and the origin of intelligence using the tools of physics.

In fact, all the other tools are a distraction to those folks. If you want to fundamentally understand anything, you have to start with physics, according to them. And you're saying, “No, physics is not enough.”

Here's the issue. Everything here hangs on what it means to understand. For me, to understand doesn't just mean having 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.

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. 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 who knows what else is at the bottom there, does that mean then that when somebody is missing a finger, has a psychological problem, or has these other high-level issues, you have something for them? Are you going to be able to do something?

Because my claim is that you're not going to. And even if you have some theory of physics that is completely compatible with everything that's going on, it's not enough. It'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. Let's say someone comes in here and tells you a beautiful mathematical proof. It's really 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.” We have a complete accounting of what happened. Done and done.

But if you want to understand what the more important aspect of that interaction is, it's not going to be found in the physics department. It's going to be found in the math department. That's my only claim: physics is an amazing lens with which to view the world, but you're capturing certain things. If you want to stretch it to encompass these other things, we just don't call that physics anymore. We call that something else.

Okay. But you're speaking about super-complex organisms. Can we go to the simplest possible thing where you first take a step over the line—the Cartesian cut, as you've called it—from the non-mind to mind, from the non-living to living? The simplest possible thing—isn't that in the realm of physics to understand?

How do we understand that first step where you say, “That thing has no mind, is probably non-living, and here's a living thing that has a mind”? That line. I think that's a really interesting line. Maybe you can speak to the line as well. Can physics help us understand it?

Yeah, let's talk about it. First of all, of course it can. 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 that 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 so-called category errors when people transgress those categories.

I think most of those categories may have done some good service at the beginning, 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.

What those categories do is prevent you from borrowing tools. If you think that living things are fundamentally different from non-living things, or if you think that cognitive things are 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 kinds of cognitive systems.

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. People say this to me all the time: “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.

3. Origin of life

Okay. So if there's no line, is it still useful to talk about things like the origin of life? That's one of the big open mysteries before us as a human civilization, as scientifically minded, curious Homo sapiens. How did this whole thing start? Are you saying there is no start? Is there a point where you could say that event right there was the start of it all on Earth?

My suggestion is that, in my experience, it's much better not to try to define any kind of line, because inevitably—I've never found one yet that really shoots that down. People try to come up with one all the time when I make my continuum claim. They say, “Okay, well, what about this?” And I haven't found one yet where you can't zoom in and say, “Yeah, okay, but right before then this happened, and if we really look closely, 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 as things scaled up?

I'll give you a really dumb example. We always get into this because people often really don't like this continuum view. The word “adult”—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 hides is the fact that, first of all, nothing happens on your 18th birthday that's special, right?

Second, if you actually look at the data, the car rental companies have a much better estimate because they actually look at the accident statistics, and they'll say 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 the scaling of personal responsibility, decision-making, and judgment is. These are deep, fundamental questions.

Nobody wants to get into that every time somebody has a traffic ticket. So we've decided that there's this adult idea. 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 yesterday. Me today—this is me…”

Right? So we get into these very deep questions that are completely glossed over by this idea of an adult. I think once you start scratching the surface, most of these categories are like that. They're convenient and they're good.

I get into this with neurons all the time. I'll ask people, “What's a neuron? What's really a neuron?” If you're in neurobiology 101, of course you just say, “This is 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, neurons develop from other types of cells, and that was a slow and gradual process. Most of the cells in your body do the things that neurons do. So what really is a neuron, right?

Once you start scratching at this, this happens. 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—boom, this is it.

There are innovations that allow you to scale in an amazing way, for sure. There are lots of people who study those—things like thermodynamic and metabolic processes, 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.

4. The search for alien life (on Earth)

...the scaling process, but then there is more rapid scaling and slower scaling. Innovation and invention, I think, are useful to understand so you can predict how likely they are on other planets, for example, or describe the likelihood of these kinds of phenomena happening in certain kinds of environments.

Again, specifically in answering how many alien civilizations there are, that's why it's useful. But it is also useful on a scientific level to have categories, not just because it makes us feel good and fuzzy inside, but because it makes conversation possible and productive, I think.

If everything is a spectrum, it becomes difficult to make concrete statements, I think. We even use the terms “biology” and “physics.” Those are categories. Technically, it's all the same thing, really. Fundamentally, it's all the same. There's no difference between biology and physics, but it's a useful category.

If you go to the physics department and the biology department, those people are different in some kind of categorical way. So somehow, I don't know what the chicken or the egg is, but the categories—maybe the categories create themselves because of the way we think about them and use them in language—but it does seem useful.

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: 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 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. That's what I'm afraid is happening in a lot of different ways.

Look, I'm very interested in life beyond Earth and all these kinds of things, so we should also talk about what I call SUTI—S-U-T-I—the search for unconventional terrestrial intelligences. I think we've 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. 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 what we're really talking about is the spectrum of cognition.

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 are extremely interesting. 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.

For example, a lot of people will say, “Well, this is intelligent and this isn't,” right? There's a binary thing. That's useful occasionally; that's useful for some things. I would like to say, instead of that, let's admit that we have a spectrum.

But instead of just saying, “Oh, look, everything's intelligent,” 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 are they 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. We can even name them, right? That's fine. We can name different types of cognition. This is doing predictive processing. This can't do that, but it can form memories. What kind? 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 it is that you are claiming this thing does. It works in both directions.

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 the problem-solving that it's doing. Tell me why that doesn't match.”

Or in the opposite direction, somebody comes to me and says, “You're right, you're right. The whole solar system, man. It's just this amazing...” I'm like, “Whoa, okay. Well, what is it doing? Tell me what tools of cognitive and behavioral science you are using to reach that conclusion.”

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.”

To have a bit of a meta-conversation about the conversation, I should say that part of the persuadability argument that we, two intelligent creatures, are making is me playing devil's advocate every once in a while. You did the same, which is kind of interesting: taking the opposite view and seeing what comes out.

Because you don't know the result of the argument until you have the argument, it seems productive to just take the other side of the argument.

For sure. It's a very important thinking aid. First of all, what they call steelmanning: trying to make the strongest possible case for the other side and asking yourself, “Okay, what are all the places that I am glossing over because I don't know exactly what to say? Where are all the holes in the argument, and what would a really good critique look like?”

Yeah.

Sorry to go back there and linger on the term because it's so interesting: persuadability. Did I understand correctly that you mean it's kind of synonymous with intelligence? So it's an engineering-centric view of an intelligence system. If it's persuadable, you're more focused on, how can I steer the goals of the system or the behaviors of the system?

Meaning, an intelligence system may be a goal-oriented, goal-driven system with agency. When you call it persuadable, you're thinking more like, “Okay, here's an intelligence system that I'm interacting with that I would like to get to accomplish certain things.” But fundamentally, are persuadability and intelligence synonymous or correlated?

They're definitely correlated. So let me preface this with one thing. When I say it's an engineering perspective, I don't mean that the standard tools we use in engineering, and this idea of enforced control and steering, are how we should view all of the world. I'm not saying that at all.

I want to 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 human conversations.” My whole point is not that at all. 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 went to a bioengineering department or a genetics department and I started talking about high-level 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, regrow new organs, defeat aging, or do these specific things—you are leaving too much on the table by making an unwarranted assumption that the low-level tools we have, the rules of chemistry and molecular rewiring, are going to be sufficient to get to where you want to go.

It's an assumption only, and it's an unwarranted assumption. We've done experiments now, so this is not philosophy but real experiments, showing that if you take these other tools, you can, in fact, persuade the system in ways that have never been done before. 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—because I keep talking about scaling, so what is it that's scaling?—is something I call the cognitive light cone.

The cognitive light cone is the size of the biggest goal state that you can pursue. This doesn't mean how far your senses reach. This doesn't mean how far you can affect something.

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.

I do think it's a useful concept to enable us to think about very different types of agents of different composition and provenance—engineered, evolved, hybrid, whatever—all in the same framework.

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 in this 10–20-micron radius of spacetime, and that you have 20 minutes of memory going back and maybe 5 minutes of predictive capacity going forward, that tiny little cognitive light cone, I'm going to say, “Probably a bacterium.”

And if you say to me, “Well, I’m able to care about several hundred yards’ worth of space, but I could never care about what happens 3 weeks from now, 2 towns over—just impossible,” I would say you might be a dog. If you say to me, “Okay, I care about what happens in the financial markets on Earth long after I’m dead,” I’d say you’re probably a human. If you say to me, “I care in the linear range—I’m not just saying it; I can actively care in the linear range about all the living beings on this planet,” I’m going to say, “Well, you’re not a standard human. You must be something else.”

Because 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 whatever is scaling from zero—and I do think it goes all the way down—we can talk about even particles doing something like this: the cognitive light cone.

So now, here’s an interesting definition of life, for whatever it’s worth. I spent no time trying to make it 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.

Rocks aren’t very exciting because the things they know how to do are the things their parts already know how to do: 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. Here’s a very simple example that comes up in biology and in our cancer program all the time.

Individual cells have little tiny cognitive light cones. What are their goals? They’re trying to manage pH, metabolic state, and some other things. There are some goals in transcriptional space, some goals in metabolic space, and some goals in physiological state space, but they’re generally very tiny goals. One thing evolution did was provide a kind of cognitive glue, which we can also talk about, that ties them together into a multicellular system.

Those systems have grandiose goals. They’re making limbs. If you’re a salamander and you chop off a limb, it will regrow that limb with the right number of fingers, and then it will 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.

That process of growing the cognitive light cone from a single cell to something much bigger—and, of course, the failure mode of that process—is cancer. When cells disconnect, when 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 and reproduce as much as they can. So that cognitive light cone is the thing I’m talking about that scales.

When we’re 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 toward bigger goals in spaces that the parts could not comprehend.

And so cognitive light cone, just to make clear, is about goals that you can actively pursue now. You said “linear,” like we’re within reach immediately.

No, I didn’t. Sorry, I didn’t mean that. First of all, the goal is often necessarily removed in time. 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, isn’t true right now. All your actions are going to be around reducing that error. 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 10 people,” and you have some degree of activation about it, and then I say, “No, no, no, actually it was 100, 10,000 people,” you’re not 1,000 times more activated about it. You’re somewhat more activated, but it’s not 1,000 times.

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. If you think about that curve, we 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: Can you grow your cognitive system to the point that it really isn’t just your family group, and it really isn’t just the 100 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, the whole ecosystem, or the whole whatever?

Can you actually care about that in exactly the same way that we now care about a much smaller set of people? That’s what I mean by linear range.

But this is separated by time, like a thermostat, but a bacterium—if you zoom out far enough, a bacterium could be formulated to have a goal state of creating human civilization. If you look at it, bacteria have a role to play in the whole history of Earth. If you anthropomorphize the goals of a bacterium enough, it has a concrete role to play in the history of the evolution of human civilization.

So when you define a cognitive light cone, you do need to look at direct short-term behavior.

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 that you have to do experiments.

The way you do experiments is you put barriers between it and its goal. You have to do interventional experiments. You have to put barriers between it and its goal, and you have to ask what happens. Intelligence is the degree of ingenuity that it has in overcoming barriers between it and its goal.

Now, this is a totally doable but impractical and very expensive experiment. You could imagine setting up a scenario where bacteria were blocked from becoming more complex. You could ask whether they would try to find ways around it, or whether their goals are actually metabolic. As long as those goals are met, they’re not actually going to 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 weird systems that you wouldn’t think are goal-driven systems. I’m sure we’ll get to this later, but what it allows us to do is get beyond just the anthropomorphizing claims of saying, “Oh, yeah, I think this thing is trying to do this or that.” The question is: Let’s do the experiment.

One other thing I want to say about anthropomorphizing is that people say this to me all the time: “I don’t think that exists.” I think it’s like heresy, or other terms that aren’t really a thing. I’ll tell you why.

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 that 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 humans separate because there’s some bright line.

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 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 intelligence.” Fine. Done.

So that’s really all I’m arguing for: an empirical approach. Then things like anthropomorphism go away. It’s just a matter of whether you’ve done the experiment and what you found.

And that’s actually one of the things you’re saying: if you remove the categorization of things, you can use the tools of one discipline on everything to try and then see.

You could try.

That’s the underpinning of the criticism of anthropomorphization, because what is that? It’s like psychoanalysis of another human; it could technically be applied to robots, to AI systems, to more primitive biological systems, and so on. Try.

Yeah. We’ve used everything from basic habituation and conditioning all the way through anxiolytics, hallucinogens, and all kinds of cognitive modification across a range of things that you wouldn’t believe.

By the way, I’m not the first person to come up with this. There was a guy named Jagadish Chandra Bose well over 100 years ago who was studying how anesthesia affected animals and animal cells, and drawing specific curves around electrical excitability. He then went and did it with plants and saw some very similar phenomena.

Being the genius that he was, he then said, “Well, how do I know when to stop?” Everybody thinks we should have stopped long before plants because 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?

The interesting thing is that 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.

Yeah, we'll probably get to it. You've been doing recent work on looking at computational systems, even trivial ones like algorithms, sorting algorithms, and analyzing them in a behavioral kind of way—to see if there are minds inside those sorting algorithms.

And, of course, let me make a pothead statement/question here: you could start to do things like trying to do psychedelics with a sorting algorithm. What does that even look like? It looks like a ridiculous question that'll get you fired from most academic departments, but if you take it seriously, you could try and see if it applies.

If a thing could be shown to have some kind of cognitive complexity, some kind of mind, why not apply to it the same kind of analysis and the same kind of tools, like psychedelics, that you would to a complex human mind? At least, it might be a productive question to ask. You've seen spiders on psychedelics, like more primitive biological organisms on psychedelics. Why not try to see what an algorithm does on psychedelics? Anyway.

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 1 example on Earth and we know what to expect from cells to 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 are so bad that to really think that we know enough not to try things at this point is, I think, really shortsighted.

Before we talk about the Platonic space, let's lay out some foundations. I think one useful one comes from the paper “A Technological Approach to Mind Everywhere: An Experimentally Grounded Framework for Understanding Diverse Bodies and Minds.” Could you tell me about this framework, and maybe can you tell me about Figure 1 from this paper that has a few components?

One is the tiers of biological cognition that goes from group to whole organism to whole tissue or organ, down to neural network, down to cytoskeleton, down to genetic network. Then there's layers of biological systems from ecosystem, down to swarm, down to organism, tissue, and then finally cell. So, can you explain this figure, and can you explain the TAME, so-called, framework?

So, this is 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 2 is basically this idea that there is a spectrum, and I'm just showing 4 waypoints along that spectrum. As you move to the right of that spectrum, a couple of 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 know in order to exert the influence. 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.

By the way, a quick pause just for people who are listening: let me describe what's in the figure. There are 4 different systems going up the scale of persuadability. The first system is a mechanical clock, then it's a thermostat, then it's a dog that gets rewards and punishments, Pavlov's dog, and then finally a bunch of very smart-looking humans communicating with each other and arguing, persuading each other using reasons.

There are arrows below that showing persuadability going up as you go up these systems from the mechanical clock to a bunch of Greeks arguing, and then going down as the effort needed to exert influence, and once again going down as mechanism knowledge needed to exert that influence.

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? 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 going to 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 multiscale agential material to take care of the chemistry underneath, all right?

So you don't need a wrench to convince me?

Correct. I don't need physics to convince you, and I don't need to know how you work. I don't need to understand all of the steps. What I do need to have is trust that you are a multiscale 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 of all of these things—the way that you regulate yourself—is not by sitting there and thinking, “Oh my God, wow, I really have to push some sodiums across this membrane.” All of that happens automatically, and that's the incredible benefit of these multiscale 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 that at every level of biological organization—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, spaces that are very hard for us to imagine. Because of our own evolutionary history, we humans are so obsessed with movement in 3D 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 there's this distinction: 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 going to 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.

Is it useful, as a thought experiment, as an experiment of radical empathy, to try to put ourselves in the space of the different minds at each stage of the spiral? What state space is human civilization, as a collective, embodied in? What does it operate in? Individual humans operate in 3D space. That's what we understand. But when there's a bunch of us together, what are we doing together?

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

But there is such a thing?

There may well be. We have to do experiments. I don't know.

Here's an example: Somebody will say to me, “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. 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 we're actively trying to build now to enable radically different agents to communicate. We are doing this using AI and other tools to try to get this kind of communication going across very different spaces.

I'll just give you a very dumb example of how that might be. Imagine that you're playing tic-tac-toe against an alien. You're in a room, and you don't see him. You draw the tic-tac-toe 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. Sometimes you win, sometimes you lose. It's obvious that, in that one little segment of activity, you guys are sharing a world.

What's happening in the other room next door? Let's say the alien doesn't know anything about geometry. He doesn't understand straight lines. What he's doing is looking through a box full of billiard balls, each one of which has a number on it. All he's doing is finding 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 about geometry. The reason you guys are playing the same game is that there's this magic square that somebody constructed. It's basically a 3-by-3 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.

Okay, so there's a mapping between the 2 different ways of seeing the world that allows you to communicate with each other.

Of seeing a thin slice of the world.

A thin slice of the world. How do you find that mapping? You're saying we're trying to figure out ways of finding that mapping for different kinds of systems. What's the process for doing that?

The process is 2-fold. One is to get a better understanding of what space the system is navigating, what goals it has, and what level of ingenuity it has to reach those goals.

For example, xenobots. We make xenobots. These are biological systems that have never existed on Earth before. We have no idea what their cognitive properties are. We're learning. We've 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.

Can you actually explain briefly what a xenobot is and what an anthrobot is?

5. Creating life in the lab - Xenobots and Anthrobots

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, “Why does it have certain forms of behavior, certain forms of anatomy, and certain forms of physiology? Why does it have those?” the answer is always the same: There's a history of evolutionary selection, and there's a long history of adaptation going back. There are certain environments, and this is what survived, so that's why it has those characteristics.

What I wanted to do was break out of that mold and force us, as a community, to dig deeper into where these things come from. That means taking away the crutch where you just say, “It's evolutionary selection. 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. In fact, they never existed. So you can't tell the same kind of story.

What I mean is, we can take epithelial cells off of an early frog embryo, and we don't change the DNA. There are no synthetic biology circuits, no material scaffolds, no nanomaterials, and no strange 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.

When you do that, normally these cells are bullied by their neighboring cells into having a very boring life. They become a 2-dimensional outer covering for the embryo, they keep out the bacteria, and that's that. So you might ask, “What are these cells capable of when you take them away from that influence?”

When you do that, they form another little life form we call a xenobot. 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. It 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 it can 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.

We did that and said, “Look, here are some amazing features of this novel system. Let's try to understand where they came from.” Some people said, “Maybe it's a frog-specific thing. Maybe this is just something unique to frog cells.” So we said, “What's the furthest you can get from frog embryonic cells? How about human adult cells?”

We took cells from adult human patients who were donating tracheal epithelia for biopsies and things like that. Those cells, again, had no genetic change. They self-organized into something we call anthrobots. Again, it's a self-motile little creature with 9,000 different gene expressions. About half the genome is now different.

They have interesting abilities. For example, they can heal human neural wounds. In vitro, if you plate some neurons and put a big scratch through them so you damage them, anthrobots can sit down and spontaneously—without us having to teach them to do it—try to knit the neurons back together.

What is this video that we're looking at here?

This is an anthrobot. Often, when I give talks about this, I show people this video and say, “What do you think this is?” People will say, “It looks like some primitive organism you got from the bottom of a pond somewhere.” I'll say, “What do you think the genome would look like?” And they say, “The genome would look like some primitive creature.”

If you sequence that thing, you'll get 100% Homo sapiens. 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. 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 kinds of systems—

Trying to talk to it.

We're trying to talk to it. That's exactly right. And not just to this. We're trying to talk to molecular networks.

We found a couple of years ago 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. 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, your potassium levels are this and that, and I don't feel good for these reasons. You should be able to talk to these things, and there should be an interface that allows us to communicate, right? I think AI is going to be a huge component of that interface, allowing us to talk to these systems. It's a tool to combat our mind blindness, to help us see the diverse, very unconventional minds that are all around us.

Can you generalize that? Let's say we meet an alien or an unconventional mind here on Earth. Think of it as a black box. You show up. What's the procedure for trying to get some hooks into a communication protocol with the thing?

Yeah. That is exactly the mission of my lab: 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. We're made of them. We have many things in common. There's some hope of understanding them.

You're talking about xenobots and anthrobots?

Xenobots, anthrobots, cells, and everything else. But they're alien in a couple of important ways. One is that 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 a snake or a giraffe or whatever, or a human, whatever we're going to be. I'm specifically telling you that this general idea, when people model that with cellular-automata-type ideas, this open-loop kind of thing where everything just follows local rules and eventually there's complexity—here you go, now you've got a giraffe or a human—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. When faced 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, because they're not infinitely intelligent—you will either get birth defects or creative problem-solving, such as what you're seeing here with xenobots and anthrobots. If you can't be a human, you'll find another way to be. You can be an anthrobot, for example, or you'll be something else.

Just to clarify, what's the difference between cellular-automata-type action, where you're just responding to your local environment and creating some kind of complex behavior, and operating in the space of anatomical possibilities?

Sure.

So there's a kind of goal, I guess, you're articulating—

Yes.

There is some kind—

Yes.

—of thing. There's a will to do something.

The will thing, let's put that aside—

Okay, sorry.

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

There I go, anthropomorphizing. I just always love to quote Nietzsche, so there we go.

Yeah, yeah. 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 control. 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? The Game of Life, all those kinds of cool things, right? You can get complexity. 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 in basic cell biology and developmental biology classes.

When you first 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 four fingers instead of seven?" What they tell you is, "It doesn't know anything." Make sure that's very clear. They all insist, when we learn these things, "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 has happened—those kinds 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. In particular, working them backward, 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.

The thing with those kinds of open-loop models is that 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 2 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.

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 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, 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. Once you can find it, read it, interpret it, and rewrite it, it means that by any engineering standard, you're dealing with a homeostatic mechanism.

How do you find where the goal's encoded?

Through lots and lots of hard work.

The barrier thing is part of that? Creating barriers and observing?

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

So step 1, when you approach an agentic system, is to create a barrier of different kinds until you see how persistent it is at pursuing the thing it seemed to have been pursuing originally. Then you know, okay, this thing has agency, first of all. And then, second of all, you start to build the intuition about exactly which goal it's pursuing.

Yes. The first couple of steps are all imagination. You have to ask yourself, "What space is this thing even working in?" You really have to stretch your mind, because we can't imagine all the spaces that systems work in, right?

Step 1 is: What space is it? Step 2 is: What do I think the goal is? And let's not mistake step 2—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, you do the experiments. You say, "Okay, if I want to block it from reaching its goal, how do I do that?" 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." That's a high level of intelligence. You will find out what the answer is.

6. Memories and ideas are living organisms

Another pothead question: Speaking of unconventional organisms, and going to Richard Dawkins, for example, with memes, is it possible to think of things like ideas? How weird can we get? Can we look at ideas as organisms, then create barriers for those ideas and see if the ideas themselves—if you take the individual ideas and try to empathize and visualize what kind of space they might be operating in—can be seen as organisms that have a mind?

Yeah. Okay, if you want to get really weird, we can get really weird here. Think about the caterpillar-butterfly transition, okay?

You've got a caterpillar, a soft-bodied creature, which has a particular controller that's suitable for running a soft body—a robot. It has a brain for that task, and then it has to become this butterfly, a hard-bodied creature that flies around.

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 colored disk, then it crawls over and eats some leaves—

It 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 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.

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

But there's a third perspective that I think is really interesting and useful. The third perspective is from the memory itself. If you take the 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, 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?" 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 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 is like a thin plasma to them. They are so dense that none of this is solid to them. They don't see any of this stuff.

They're walking around because the planet is covered in this thin gas. 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 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. No real agent can exist and dissipate that fast."

We are all metabolic patterns, among other things, right? You see what I'm warming up to here. 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.

All agents are patterns within some excitable medium. We could talk about what that is, and they can spawn off others. 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. They go through the excitable medium and then they're gone. Those are fleeting thoughts.

Then you can have patterns that have a degree of persistence, so they might be hurricanes, solitons, persistent thoughts, 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. They stay around longer.

Now, what's further than that? Well, personality fragments of dissociative identity disorder are more stable. They're not just on autopilot. They have goals and they can do things. Then past that is a full-blown human personality. Who the hell knows what's past that? Maybe some sort of transhuman, transpersonal thing. I don't know, right?

But this idea—again, I'm back to this notion of a spectrum—means there is not a sharp distinction between "we are real agents" and "we have these thoughts." 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? Does it have language?

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

That's such a challenging thought experiment, thinking about memories from the caterpillar to the butterfly as an organism. I think at the very basic level, intuitively, we think of organisms as hardware and software as not possibly being able to be organisms. But what you're saying is that it's all just patterns in an excitable medium, and it doesn't really matter what the pattern is or what the excitable medium is.

We need to do the testing: How persistent is it? How goal-oriented is it? There are certain kinds of tests to do that, and you can apply that to memories. You can apply that to ideas. You can apply that to anything, really. You could probably think about consciousness. There's really no boundary to what you can imagine. Probably really, really wild things could be minds.

Yeah. Stay tuned. This is exactly what we're doing. We're getting progressively more and more unconventional. This whole distinction between software and hardware, I think, is 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.

Again, I want to point out what the practical consequences are, because this is not just fun stories that we tell each other. These have really important research implications. Think about a Turing machine.

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 the machine 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. Both of those stories make sense, depending on what you're trying to do.

Here's the biomedical side of things. Our program in bioelectrics and aging. One model you could have is that 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, and 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: 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 are the agent, right? And what they're doing in the brain are the side effects of the patterns working themselves out. Those side effects might be to fire off some muscles, glands, and other things.

From that perspective, maybe what's actually happening is that 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. 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—when 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, where the liver is doing what; 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.

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 take these ideas 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.

To return to the software–hardware distinction, you're saying that we can see the software as the organism and the hardware as just the scratchpad. Or you could see the hardware as the organism and the software as the thing that the hardware generates. In so doing, we can decrease the importance we assign to something like the human brain. Or it could be the activations, the electrical signals, that are the organisms, and then the brain is the scratchpad.

And by saying scratchpad, I don't mean it's not important. When we get to talking about 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.

7. Reality is an illusion: The brain is an interface to a hidden reality

So this is a good place to talk about the increasingly radical, weird ideas that you've been writing about. You've mentioned it a few times: the Platonic space. So there's this Ingressing Minds paper where you described the Platonic space. You mentioned there's an asynchronous conference happening, which is a fascinating concept because it's asynchronous. People are just contributing asynchronously.

What happened was this crazy notion, which I'll describe momentarily. I have given a couple of talks on it. I then found a couple of 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 of hours: I'll give a talk, they'll give a talk, and we'll talk about it. I thought there were going to be 3 talks at this thing.

Once I started reaching out to people for this, everybody said, "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, "I know this mathematician," or, "I know this economist, whatever, who has these ideas and there's nowhere we can have her talk about them."

I got this whole list, and it became completely obvious that we can't do this in a normal way. We are now booked up through December. So every week in our center, somebody gives a talk. We 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. 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 made me realize how much of an undercurrent of these ideas had already existed and was ready—like, now is the time. I've been thinking about these things for 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.

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.

You can just look it up: Platonic Space Conference. There's a bunch of different fascinating talks. Yours first, on "The Patterns of Forms and Behavior: Beyond Emergence," then "Radical Platonism and Radical Empiricism" from Joel Dietz, and "Patterns and Explanatory Gaps in Psychotherapy: Does God Play Dice?" from Alexey Tolchinsky, and so on.

So let's talk about it. What is it? And it's fascinating that the origins of some of these ideas are connected to machine learning people thinking about representation space.

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 a few 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. 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. If you keep asking why, then something interesting goes on. I'll give you 2 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: "Well, it's because they're trying to be off-cycle from their predators. Because if it was 12 years, then every 2 years, every 3 years, every 4 years, every 6 years, 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 that every time you talk to a physicist and say, "Hey, why do the leptons do this or that, or the fermions are doing whatever?" eventually, the answer is, "Oh, because there's this mathematical, 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: there are facts that you come across, and 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 the natural logarithm, e. These things you discover, right?

And the salient fact is this: if those facts were different, biology and physics would be different, right? They impact it instructively and 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, 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're not going to change those things.

I think Plato and Pythagoras understood very clearly that there is a set of truths that impact the physical world, but they themselves are not defined by or determined by what happens in the physical world. You can't change them by things you do in the physical world, right? I'll make a couple of claims about that. One claim is that 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 of symmetry, symmetries, or topology, or whatever. Biology consists of the things that are enabled by those. They're free lunches. Biology exploits these kinds of truths, and it enables biology and evolution to do amazing things without having to pay for it. I think there are a lot of free lunches going on here.

I show you a xenobot or an anthrobot, and I say, “Hey, look, here are some amazing things they're doing that tissue has never done before in its history.” You say, first of all, “Where did that come from? And when did we pay the computational cost for it?” We know when we pay the computational cost to design a frog or a human: it was during the eons when the genome was bashing against the environment and getting selected, right? So you pay the computational cost of that.

There have never been any anthrobots. There have never been any xenobots. When did we pay the computational cost for designing kinematic self-replication and all these things that they're able to do? There are 2 things people say. One is, “Well, you got it at the same time that they were being selected to be good humans and good frogs.”

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

Then people say, “Well, it's emergent.” And I say, “What's that? What does that mean?” They say—besides the fact that you got surprised—emergence often just means, “I didn't see it coming.” Something happened. I didn't know that was going to happen. So what does it mean that it's emergent?

There are many emergent things like this. For example, the fact that gene regulatory networks can do associative learning. 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.”

Now you have an option, and you can go 1 of 2 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 when we come across these crazy things that are very specific—like anthrobots have 4 specific behaviors that they switch around—why 4? Why not 12? Why not 100? Four—why 4?

When we come across these things, just like when we come across the value of e or Feigenbaum's number or whatever, we're going to write it down in our big book of emergence. That's it. We're just going to have to live with it. This is what happens. There are some cool surprises; when we come across them, we'll write them down. Great. It's a random grab bag of stuff, and when we come across them, we'll write them down.

The upside is that you get to be a physicalist, and you get to keep your sparse ontology. The downside is that 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—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 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.”

You'll see well-known mathematicians like Penrose and lots of others who will say, “Yeah, there's another space, and it has spatial structure. It has components to it and so on. We can traverse that space in various ways.” Then there's the physical space. I find that much more appealing because it suggests a research program, which we are now undergoing in our lab.

The research program is that everything we make—cells, embryos, robots, biobots, language models, simple machines, all of it—is an interface. All physical things are interfaces to these patterns. You build an interface, and 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 the relationship between the physical pointers that we make and the patterns that come through them, right? Understanding the structure of that space, what exists in that space, and what I need to make physically to make certain patterns come through.

When I say “patterns,” now we have to ask, “What kinds of things live in that space?” The mathematicians will tell you, “Well, we already know. We have a whole list of objects—the amplituhedrons and all this crazy stuff that lives in that space.” I think that's 1 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. 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.

What I end up with right now is a model in which that latent space contains things that come through physical objects—simple patterns, right? Facts about triangles, Fibonacci patterns, fractals, and things like that. But if you make more complex interfaces, such as biologicals—and importantly, not just biologicals, but cells, embryos, and tissues—what you will then pull down are much more complex patterns that we say, “Ah, that's a mind. That's a human mind,” or, “That's a snake mind,” or whatever.

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

Okay, given how weird and complicated what you're describing is, we'll talk about it more, but you've got to ELI5 the basics for a person who's never seen this. Again, you mentioned things like pointers. So the physical object itself, or the brain, is a pointer to that Platonic space. What is in that Platonic space? What is the Platonic space? What is the embodiment? What is the pointer?

Yeah, okay. Let's try it this way. There are certain facts of mathematics. Take the distribution of prime numbers, right? If you map them out, they make these nice spirals. There's an image that I often show, which is a very particular kind of fractal. That fractal is the Hally map, which is pretty awesome because it actually looks very organic. It looks very biological.

If you look at that image, which has a very specific, complex structure, it's a map of a very compact mathematical object. That formula is like z cubed plus 7, something like that. That's it. Now you look at that structure and say, “Where does that actually come from?” It's definitely not packed into z cubed plus 7. There's not enough information 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 from a larger set over time. Where does this come from? 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?

Evolution cranks through a bunch of generations and gets the first angle right, then cranks through a bunch more generations and gets the second angle right. Now something amazing happens: it doesn't need to look for the third angle, because you already know. If you know 2, 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. Once you've committed to certain axioms, there's a whole bunch of other stuff that is now just what it is.

And so what I'm saying—and this is what Pythagoras was saying, I think—is that there is a whole space of these kinds of truths. He was focused on mathematical ones, but he was embodying them in music and in geometry and in things like that.

There is a 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, 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 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 is 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 out of the rules of geometry, 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. Mathematicians can perhaps interact with these patterns directly in that space. But for the rest of us, we have to make interfaces.

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. 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.

Just to clarify, what you're saying is a pretty radical idea here. If there's a mapping from mathematics to physics, that's understandable and intuitive, as you've described. But what you're suggesting is there's a mapping from some kind of abstract mind object to an embodied brain that we think of as a mind—as fellow humans. What is that? What exactly? You said “interface.” You've also said “pointer.” So the brain—and I think you said somewhere—a thin interface.

A thin client. Yeah, the brain—

Thin client.

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

Thin client. Okay. So a brain is a thin client to this other world. Can you just lay out very clearly how radical the idea is? Because you're kind of dancing around it. I think you could also point to Donald Hoffman, who speaks of an interface to a world. So we only interact with the “real world” through an interface. What is the connection here?

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—in Newton's world—physicalism was already dead.

In Newton's world, 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 the 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 is wild. This is not saying that everything was cool—physicalism was great up until maybe we got quantum, interfaces, or consciousness or whatever. But originally it was fine. No, this is saying that worldview was already impossible, really, from a very long time ago. We already knew that there are non-physical properties that matter in the physical world.

This is the chicken-or-the-egg question. You're saying Newton's laws are creating the physical world?

That is a very deep follow-on question that 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.

Wait, which truths are we referring to? Are we talking about Newton's laws, like mathematical equations?

No, mathematical facts. For example, the actual value of e.

Oh, like very primitive mathematical facts.

Yeah. Some of them are—I mean, if you ask Don Hoffman, there's this 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. The old ones were simple. Now they're 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.

So Newton is discovering these things. He's not inventing.

This is very controversial, right? 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.

You're around at the Big Bang, and you get to set all the constants. Change physics however you want. Can you change e? Can you change Feigenbaum's constant? I don't think you can.

Is that an obvious statement? I don't even know what it means to change the parameters at the start of the Big Bang.

So physicists do this. They'll say, “Okay, if we changed the ratio between gravitation and the other forces, would we have matter? How many dimensions would we have? Would there be inflation? Would there be this or that?” You can imagine playing with it.

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

You're saying that's not going to change the axiomatic systems that mathematics has?

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.

And you're saying that space is immutable?

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.

But we're just pointers.

Okay, so I'll circle back around to that whole thing. 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 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 when I amplify this into biology, I don't think it sort of jumps as a new thing. 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, 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 who's speaking at our symposium who's telling me that, actually, I'm too chauvinistic even there.

Even those things have more oomph than I gave them credit for, which I love. What I'm saying is that those kinds 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. 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 behavioral scientists say, “Oh, I know what that looks like.”

That's this kind of behavior, this kind of mind, or that kind of mind. In a certain sense, yes, what I'm saying is extremely radical, but it is a very old idea. It's an old idea of a dualistic worldview, where the mind was not in the physical body and, in some way, interacted with the physical brain. 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 who like this kind of view. Primarily, Descartes was already getting crap for this when he first tried it out as an interaction problem. The idea was, okay, if you have this nonphysical 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? There are many other problems there.

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. 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 nonphysical facts of physics haunt physical objects is basically how I think different kinds of patterns that we call kinds of minds are manifesting through interfaces like brains.

How do we prove or disprove the existence of that world? It's a pretty radical one. This physical world, we can poke. It's there. It feels like all the incredible things—consciousness, cognition, all the goal-oriented behavior, and agency—seem to come from this 3D entity.

We can test it. We can poke it. We can hit it with a stick. It makes noises.

Yeah, sort of. Descartes got some stuff wrong, I think. But one thing that he did get right is the fact that you actually 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—

It's a nightmare.

Yeah, well, that—who knows? But—

It's a ride.

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

I don't know. That's an obvious thing that a lot of people can show is true. To take a step toward Descartes: “I think, therefore I am.” That's the only thing you know for sure, and everything else could be an illusion or a dream. That's already a leap.

I think, from a basic caveman science perspective, the repeatable experiment is the one that most intelligence comes from here. Reality is exactly as it is. To take a step toward the Donald Hoffman worldview takes a lot of guts and imagination, and stripping away the ego and all these kinds of processes.

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? 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.

There are tons of things going on around us right now of which we are completely oblivious. There are equally all kinds of other things that we construct, and this is just modern cognitive science, which says that a lot of what we think is going on is a total fabrication constructed by us. So I don't think this is a philosophical leap. Descartes got there from a philosophical point, but 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 the first augmented humans that have sensory substitution—you're going to be walking around, and 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 traffic or the internet through the Trans-Pacific Channel.”

We're all going to be living in somewhat different worlds. That's the first thing. The second thing is that we're going to become better attuned to other beings, whether they be cells or tissues. What's it like to be a cell living in a 20,000-dimensional transcriptional space?

Then there will be novel beings that have never been here before, that have all kinds of crazy spaces that they live in. They might be AIs, cyborgs, hybrids, or 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.

I think the tension is this: You're talking about what I think you've termed cognitive prosthetics, which are different ways of perceiving and interacting with the world. But I guess the question is, is our human experience—the direct human experience—just a slice of the real world, or is it a pointer to a different world? That's what I'm trying to figure out, because the claim you're making is a really fascinating and compelling one.

It's a pretty strong one: there's another world into which our brain is an interface, which means you could theoretically map that world systematically.

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

Right, right, but it's not clear that that world exists.

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

There's this diagram called the Map of Mathematics, and it tries to show how all the different pieces of math link together. There are a bunch of different versions of it. There are 2 features to this. One is: what is it a map of? 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 hear some surprising facts that are 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. So when I say it's a space of patterns, what I mean is that 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. That's the claim.

Obviously, not everybody buys this and so on. This is one idea. Now, how do we know that this exists? I'll say a couple of things. If that didn't exist, what is that a map of? If there is no space, and 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. The final step of calling it a space is minimal. The bigger issue is: what the hell is it a map of, then, if it's not a space? That's the first thing.

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 know that the frog genome maps to one thing, and that's a frog.”

It turns out that, with that exact same genome, if you just take some cells out of their environment, they can also make xenobots with very specific, different transcriptomes, very specific behaviors, and very specific shapes. It's not just, “Oh, well, 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.

One of 2 things is going to happen in the future, so 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. 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 4 different behaviors, not 7 and not 1. Those are the options I have. That's what I can pull from.

Is it possible that there's varying degrees of granularity to the space that you're thinking about mapping? Meaning, it could be, just like with the space of mathematics, strictly the space of biology, or is this a space of minds, which feels like it could encompass a lot more than just biology?

Yeah.

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 find out that it does habituation and sensitization, does not do Pavlovian conditioning, does delayed gratification, and 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 we’ve been able to make zero progress in linking the embodiments that we make to the patterns that come through it.

Yeah, just to be clear, from your blog post on this and from the paper, we’re talking about a space that includes a lot of stuff.

Yeah, yeah.

It includes humans—what is it?—meditating? Steve: “Hello, my name is Steve.” AI systems, all those basic computational systems, objects, biological systems, and concepts. It includes everything.

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.

So every entry in an encyclopedia—old-school Britannica—is a pointer to this space.

There is a set of things that I feel very strongly about because the research is telling us that’s what’s going on, and then there’s a bunch of other stuff that I see as hypotheses for next steps that guide experiments. So what I’m about to tell you consists of things I don’t actually know. These are just guesses that you need to make to make progress.

I don’t know, but that doesn’t mean there are going to be specific Platonic patterns for, “This is the Titanic, and this is the sister of the Titanic, and this is some other kind of boat.” This is not what I’m saying. What I’m saying is, in some way that we absolutely need to work out, when we make minimal interfaces, we get more than we put in. We get behaviors, shapes, mathematical truths, and all kinds of patterns that we did not have to create.

We didn’t micromanage them. We didn’t know they were coming. We didn’t have to put any effort into making them. They come from some distribution that seems to exist, that we don’t have to create. Exactly whether that space is sparse or dense, I don’t know.

So, for example, if there is some kind of a Platonic form for the movie The Godfather, if it’s surrounded by a bunch of crappy versions and then crappier versions still, I have no idea. I don’t know if the space is sparse or not. I don’t know if it’s finite or infinite. These are all things I don’t know.

What I do know is that it seems like physics—and for sure biology and cognition—are the beneficiaries of ingressions that are free lunches in some sense. We did not make them. Calling them emergent does nothing for a research program. That just means you got surprised.

I think it’s much better if you make the optimistic assumption that they come from a structured space that we have a prayer in hell of actually exploring. In some decades, if I’m wrong and it says, “You know what? We tried. It looks like it really is random. Too bad,” fine.

Is there a difference between proving the existence of this world and it being a really effective model for connecting things and explaining things versus an actual place where the information about these distributions that we’re sampling actually exists, that we can hit with a stick?

Yeah, you can try to make that distinction. But I think modern cognitive neuroscience will tell you that whatever you think this is, at most, it is a very effective model for predicting the future experiences you’re going to have.

So all of this that we think about as physical reality is a nice, convenient model.

That’s not me. That’s predictive processing and active inference. That’s modern neuroscience telling you this; this isn’t anything that I’m particularly coming up with. All I’m saying is, the distinction you’re trying to make—which is an old-school realist kind of view—is: Is it metaphorical, or is it real?

All we have in science are metaphors, I think, and the only question is how good are your metaphors. As agents act, living in a world, all we have are models of what we are and what the outside world is. That’s it. The question is, how good is it as a model?

My claim about this is that in some small number of decades, this will either give rise to a very enabling mapping of the space for AI, for bioengineering, for biology, whatever. Or we are going to find out that it really sucks, because it really is a random grab bag of stuff, and we tried the optimistic research program, it failed, and we’re just going to have to live with surprise. I doubt that’s going to happen, but it’s a possible outcome.

But do you think there is some place where the information is stored about these distributions that are being sampled through the thin interfaces? An actual place?

Place is weird because it isn’t the same as our physical space-time. I don’t think it’s that. Calling it a place is a little weird.

No, but physics—general relativity—describes a space-time. Could other physics theories be able to describe this other space where information is stored, so that we can apply—maybe differently, but in the same spirit—laws about—

Yes.

—information?

I definitely think there are going to be systematic laws. I don’t think they’re going to look anything like physics. You can call it physics if you want, but I think it’s going to be so different that that probably just cracks the word. Whether information is going to survive that, I’m not sure.

But I definitely think there are going to be laws. I think they’re going to look a lot more like aspects of psychology and cognitive science than they’re going to look like physics. That’s my guess.

So what does it look like to prove that world exists?

What it looks like is a successful research program that explains how you pull particular patterns when you need them, why some patterns come and others don’t, and shows that they come from an ordered space.

Across a large number of organisms?

Well, it’s not just organisms. I think it’s going to end up—and you can talk to the machine learning people about how they got to this point—because this is not just me. There are a bunch of different disciplines that are converging on this now simultaneously.

You’re going to find, again, just like in mathematics, where from different directions everybody is looking at different things and saying, “Oh my God, this is one underlying structure that seems to inform all of this.” In physics, in mathematics, in computer science, machine learning, possibly in economics, certainly in biology, and possibly in cognitive science, we’re going to find these structures.

It was already obvious in Pythagoras’s time that there are these patterns. The only remaining question is, are they part of an ordered, structured space, and are we up to the task of mapping out the relationship between what we build and the patterns that come through it?

So from the machine learning perspective, is it then the case that even something as simple as LLMs are sneaking up onto this world, that the representations they form are sneaking up to it?

When I’ve given this talk to some audiences, especially in the organicist community, people like the first part, where it’s like, “Okay, now there’s an idea for what the magic, quote-unquote, is that’s special about living things,” and so on.

If we could just stop there, we would have dumb machines that just do what the algorithm says, and we would have these magical living interfaces that can be the recipients for these ingressions. Cool, right? We can cut up the world in this way.

Unfortunately or fortunately, I think that’s not the case. I think that even simple, minimal computational models are to some extent beneficiaries of these free lunches. This goes back to the thin-client interface idea.

The theories we have of both physics and computation—the theory of algorithms, Turing machines, all that good stuff—are all good theories of the front-end interface, and they’re not complete theories of the whole thing. They capture the front end, which is why they get surprised, which is why these things are surprising when they happen.

I think that when we see embryos of different species, we are pulling from well-trodden, familiar regions of that space, and we know what to expect: frog, snake, whatever. When we make cyborgs, hybrids, and biobots, we are pulling from new regions of that space that look a little weird and are unexpected, but we can still kind of get our minds around them.

When we start making proper AIs, we are now fishing in a region of that space that may never have had bodies before. It may never have been embodied before. What we get from that is going to be extremely surprising.

And the final thing to mention is that, because of the inputs from this Platonic space, some of the really interesting things that artificial constructs can do are not because of the algorithm; they're in spite of the algorithm. They are filling up the spaces in between. There's what the algorithm is forcing you to do, and then there's the other cool stuff it's doing, which is nowhere in the algorithm. If that's true—and we think it's true even of very minimal systems—then this whole business of language models and AI in general, watching the language part may be a total red herring, because the language is what we force them to do. The question is: What else are they doing that we are not good at noticing? And this is something that we need to become better at, I think, as an existential step for humanity, because we are not good at recognizing these things now.

8. Unexpected intelligence of sorting algorithms

You got to tell me more about this behavior that is observable, that is unrelated to the explicitly stated goal of a particular algorithm. So you looked at a simple sorting algorithm. Can you explain what was done?

Sure. First, just the goal of this study. There are 2 things that people generally assume. One is that we have a pretty good intuition about what kind of systems are going to have competencies. From observing biologicals, we're not terribly surprised when biology does interesting things. Everybody always says, “Well, it's biology. Of course it does all this cool stuff.” But do we have these intuitions with machines? The whole point of having machines and algorithms is that they do exactly what you tell them to do, right? People feel pretty strongly that that's a binary distinction, and that's how we can carve up the world in that way.

I wanted to do 2 things. I wanted, first of all, to explore that and hopefully break the assumption that we're good at seeing this, because I think we're not. I think it's extremely important that we understand very soon that we need to get much better at knowing when to expect these things. The other thing I wanted to do was to find out—mostly, people assume that you need a lot of complexity for this. When somebody says, “The capabilities of my mind are not properly encompassed by the rules of biochemistry,” everybody's like, “Yeah, that makes sense. You're very complex, and your mind does things that you didn't see coming from the rules of biochemistry.” We know that.

What I would like to find out is, as part of understanding what kind of interfaces give rise to what kind of emergences, is it really about complexity? How much complexity do you actually need? Is there some threshold after which this happens? Is it really specific materials? Is it biologicals? Is it something about evolution? What is it about these kinds of things that allows this surprise, that allows this idea that we are more than the sum of our parts?

I had a strong intuition that none of those things are actually required, that this kind of magic, so to speak, seeps into pretty much everything. To look at that, I also wanted to have an example that had significant shock value. The thing with biology is that there's always more mechanism to be discovered. There's infinite depth to what the materials are doing. Somebody will always say, “Well, there's a mechanism for that. You just haven't found it yet.”

So I wanted an example that was simple and transparent, so you could see all the stuff. There was nowhere to hide. I wanted it to be deterministic, because I didn't want it to be something around unpredictability or stochasticity, and I wanted it to be something familiar to people and minimal. I also wanted to use it as a model system for honing our abilities to take a new system and look at it with fresh eyes.

That's because sorting algorithms have been studied for over 60 years. We all think we know what they do and what their properties are. The algorithm itself is just a few lines of code. You can see exactly what's there. It's deterministic.

So I wanted the most shock value out of a system like that, if we were to find anything, and to use it as an example of taking something minimal and seeing what can be gotten out of it. I'll describe 2 interesting things about it, and then we have lots of other work coming in the next year about even simpler systems. It's actually crazy.

The standard sorting algorithm—let's say bubble sort. In all these sorting algorithms, what you're starting with is an array of jumbled-up digits, integers. It's an array of mixed-up integers, and what the algorithm is designed to do is eventually arrange them all into order. What it generally does is compare some pieces of that array and, based on which one is larger than the other, swap them around. You can imagine that if you just keep doing that, comparing and swapping, eventually you can get all the digits in the same order.

The first thing I decided to do—and this is the work of my student Kaining Zhang and then Adam Goldstein on this paper—goes back to our original discussion about putting a barrier between it and its goals. The first thing I said was, “Okay, how do we put a barrier in?” The traditional algorithm assumes that the hardware is working correctly. So if you have a 7 and then a 5, and you tell them to swap, the line swaps the 5 and the 7, and then you go on; you never check, “Did it swap?” because you assume that it's reliable hardware.

What we decided to do was to break one of the digits so that it doesn't move. When you tell it to move, it doesn't move. We don't change the algorithm. That's really key. We do not put anything new in the algorithm that says, “What do you do if the damn thing didn't move?” Just run it exactly the same way. What happens?

It turns out something very interesting happens. It still works. It still sorts the array, but it eventually sorts it by moving all the stuff around the broken number. That makes sense, but here's something interesting. Suppose we plot, at any given moment, the degree of sortedness of the string as a function of time. If you run the normal algorithm, it's guaranteed to get where it's going. That's it; it's got to sort, and it will always reach the end.

But when it encounters one of the broken digits, what happens is the actual sortedness goes down in order to then recoup and get better order later. What it's able to do is go against the thing that it's trying to do, to go around in order to meet its goal later on. If I showed this to a behavioral scientist and didn't tell them what system was doing, they would say, “Well, we know what this is. This is delayed gratification.” This is the ability of a system to go against its gradient and do what it needs to do.

Imagine 2 magnets. Imagine you take 2 magnets and put a piece of wood between them, and they're like this. What the magnet is not going to do is go around the barrier and get to its goal. They're not smart enough to go against their gradient. They're just going to keep doing this. Some animals are smart enough, right? They'll go around, and the sorting algorithm is smart enough to do that.

But the trick is that there are no steps in the algorithm for doing that. You could stare at the algorithm all day long; you would not see that this thing can do delayed gratification. It isn't there.

There are 2 ways to look at this. On the one hand, you could take the reductionist physics approach and say, “Did it follow all the steps in the algorithm?” You say, “Yeah, it did.” Well, then there's nothing to see here. There's no magic. It does what it does. It didn't disobey the algorithm, right?

I'm not claiming that this is a miracle. I'm not saying it disobeys the algorithm. I'm saying it's not failing to sort. I'm saying it's not doing some sort of crazy quantum thing. I'm not saying any of that. What I'm saying is that other people might call it emergent.

What it has are properties that are not complexity, not unpredictability, not perverse instantiation, as sometimes happens in ALife. What it has are unexpected competencies recognizable by behavioral scientists, meaning different types of cognition. Primitive. We wanted primitive, so there you go. It's simple, and you didn't have to code it into the algorithm. That's very important. You get more than you start with, more than you put in. You didn't have to do that. You get these surprising behavioral competencies, not just complexity. That's the first thing.

The second thing, which is also crazy but requires a little bit of explanation, is this. What if, instead of having a single top-down controller in the typical sorting algorithm, I'm godlike, looking down at the numbers and swapping them according to the algorithm? What if—and this goes back to the title of the paper, “Agential Data: Self-Sorting Algorithms”—we give the numbers a little bit of agency? This is back to, “Who's the pattern and who's the agent?” Right?

Here's what we're going to do: we're not going to have any kind of top-down sort. Every single number knows the algorithm, and it's just going to do whatever the algorithm says. So if I'm a 5, I'm just going to execute the algorithm, and the algorithm will try to make sure that to my right is the 6 and to my left is a 4. That's it.

It's like a distributed system, like an ant colony. There is no central planner. Everybody just does their own algorithm. Once you've done that—and one of the values of doing that is that you can simulate biological processes—you can see how this works in biology. If I have a frog face and I scramble it with all the different organs, every tissue is going to rearrange itself so that ultimately you have a nose, eyes, and a head.

You're going to have an order, right? So you can do that. But you can do something else cool. Once you've done that, you can make a chimeric algorithm. What I mean is, not all the cells have to follow the same algorithm. Some of them might follow bubble sort; some might follow selection sort.

It's like in biology: when we make chimeras, we make frogolotls. Frogolotls have some frog cells and some axolotl cells. What is that going to look like? Does anybody know what a frogolotl is going to look like? It's actually really interesting that, despite all the genetics and developmental biology—you have the genomes, the frog genome, the axolotl genome—nobody can tell you what a frogolotl is going to look like, even though you have the frog genome and the axolotl genome.

This is back to your question about physics and chemistry. You can know everything there is to know about how the physics and genetics work, but the decision-making, right? Baby frogs and baby axolotls have legs. Tadpoles don't have legs. Is a frogolotl going to have legs? Can you predict that from understanding the physics of transcription and all of that?

So you see this as an intersection of biology, physics, and cognition?

So we made chimeric algorithms, and we said, "Okay, assign half the digits randomly." Half the digits are randomly doing bubble sort, and half are randomly doing, I don't know, selection sort or something.

But once you choose bubble sort, that digit is sticking with bubble sort.

It's sticking. We haven't done the thing where they can swap between algorithms, no. But they're sticking to it, right? You label them, and they're sticking to it.

The first thing we learned is that distributed sorting still works. It's amazing. You don't need a central planner. When every number is doing its own thing, it still gets sorted. That's cool. The second thing we found is that when you make a chimeric algorithm where the algorithms aren't even matching, that works too. The thing still gets sorted.

But the most amazing thing is when we looked at something that had nothing to do with sorting. We asked the following question. We defined the algotype of a single cell. Adam Goldstein actually named this property, and I think it's well-named. It's not the genotype, it's not the phenotype, it's the algotype.

The algotype is simply this: What algorithm are you following? Which one are you? Are you a selection sort or a bubble sort? That's it. There are 2 algotypes. We simply ask the following question: During that process of sorting, what are the odds that, whatever algotype you are, the guys next to you are the same type as you?

It's more about clustering than sorting.

Clustering. Well, that's exactly what we call it. We call it clustering.

At first, think of what happens—you can see this on that graph, in the red. You start off with clustering at 50% because, as I told you, we assign the algotypes randomly. The odds that the guy next to you is the same as you are half, 50%, because there are only 2 algotypes.

In the end, it is also 50% because what dominates is actually the sorting algorithm, and the sorting algorithm doesn't care what type you are. You've got to get the numbers in order. By the time you're done, you're back to random algotypes because you have to get the numbers sorted.

But in between, you get a significant increase in clustering. Look at the control; it's in the middle. The pink is in the middle. In between, you get significant amounts of clustering, meaning that certain algotypes like to hang out with their buddies for as long as they can.

Now, here's one more thing, and then I'll give you the philosophical significance of this. We saw this and I said, "That's nuts, because the algorithm doesn't have any provisions for asking, 'What algotype am I? What algotype is my neighbor? If we're not the same, I'm going to move to be next to the one that seems to have the same algotype as me.'"

If you wanted to implement this, you would have to write a whole bunch of extra steps. There would have to be a whole bunch of observations that you would have to take of your neighbor to see how he's acting. Then you would infer what algotype he is. Then you would go stand next to the one that seems to have the same algotype as you.

You would have to take a bunch of measurements to say, "Wait, is that guy doing bubble sort or is he doing selection sort?" If you wanted to implement this, it's a whole bunch of algorithmic steps. None of that exists in our algorithm. You don't have any way of knowing what algotype you are or what anyone else is. We didn't have to pay for that at all.

Notice a couple of interesting things. The first interesting thing is that this was not at all obvious from the algorithm itself. The algorithm doesn't say anything about algotypes. The second thing is that we paid computationally for all the steps needed to have the numbers sorted, because you pay for a certain computation cost. The clustering was free. We didn't pay for that at all. There were no extra steps.

So this gets back to your other question of how we know there's a Platonic space. This is one of the craziest things that we're doing. I actually suspect we can get free compute out of it. I suspect that one of the things we can do here is use these emergent properties in a useful way that doesn't require you to pay physical costs, because we know every bit has an energy cost that you have to pay. The clustering was free. Nothing extra was done.

This plot, for people who are just listening, has the percentage of completion of the sorting process on the X-axis and the sortedness of the listed numbers on the Y-axis. The red line is basically the degree to which they're clustered. You're saying that there's this unexpected competence of clustering.

I should comment that I'm sure there's a theoretical computer scientist listening to this saying, "I can model exactly what is happening here and prove that the clustering increases and decreases." They could take the specific instantiation of the thing you've experimented with and prove certain properties of it.

But the point is that there's a more general pattern here of probably other things that you haven't discovered—unexpected competencies that emerge from this—and that you can get free computation out of this thing.

So this goes back to the very first thing you said about physicists thinking that physics is enough. You're 100% correct that somebody could look at this and say, "I see exactly why this is happening. We can track through the algorithm." You can. There's no miracle going on here, right? The hardware isn't doing some crazy thing that it wasn't supposed to do.

The point is that, despite following the algorithm to do one thing, it is also doing other things at the same time that are neither prescribed nor forbidden by the algorithm. It's the space between chance and necessity, which is how a lot of people see these things. It's that free space. We don't really have a good vocabulary for it. That's where the interesting things happen.

To whatever extent it's doing other things that are useful, that stuff is computationally without extra cost. Now, there's one other cool thing about this, and this is the beginning of a lot of thinking that I've done about this. This relates to AI and things like intrinsic motivations.

The sorting of the digits is what we forced it to do. The clustering is an intrinsic motivation. We didn't ask for it. We didn't expect it to happen. We didn't explicitly forbid it, but we didn't know it would happen. This is a great definition of the intrinsic motivation of a system.

When people say, "Oh, that's a machine. It only does what you programmed it to do," as a human, I have intrinsic motivation. I'm creative, and I have intrinsic motivation. Machines don't do that. Even this minimal thing has a minimal kind of intrinsic motivation, which is something that is not forbidden by the algorithm but isn't prescribed by the algorithm either.

I think that's an important third thing besides chance and necessity. Something else that's fun about this is, when you think about intrinsic motivations, think about a child. If you make him sit in math class all day, you're never going to know what other intrinsic motivations he might have. Who knows what else he might be interested in?

So I wanted to ask this question: If we let off the pressure on the sorting, what would happen? That's hard because if you mess with the algorithm, now it's no longer the same algorithm, so you don't want to do that.

We did something that I think was kind of clever. We allowed repeat digits. If you allow repeat digits in your array, you can still have all the 5s after all the 4s and before all the 6s, but you can keep them as clustered as you want. We thought maybe we could let off the pressure a little bit—the pressure at the end, where they have to get declustered in order for the sorting to happen.

If you do that, all you do is allow some extra repeated digits, and the clustering gets bigger. It will cluster as much as you let it. The clustering is what it wants to do. The sorting is what we're forcing it to do.

My only point is, if bubble sort, which has been gone over and over so many times, has these kinds of things that we didn't see coming, what about the AIs, the language models, and everything else? Not because they talk, not because they say that they have an inner perspective or any of that, but just from the fact that even the most minimal system surprises us with what happens.

And frankly, when I see this, tell me if this doesn't sound like all of our existential story. For the brief time that we're here, the universe is going to grind us into dust eventually, but until then, we get to do some cool stuff that is intrinsically motivating to us, that is neither forbidden by the laws of physics nor determined by the laws of physics. Eventually, it kind of comes to an end.

I think that aspect of it—that there are spaces, even in algorithms, in which you can do other new things, not just random stuff, not just complex stuff, but things that are easily recognizable to a behavior scientist—you see, that's the point here. I think that kind of intrinsic motivation is what's telling us that this idea that we can carve up the world and say, “Okay, look, biology is complex. Cognition, who knows what's responsible for that, but at least we can take a chunk of the world aside and cut it off and say, ‘These are the dumb machines.’”

These are just algorithms. Whereas we know the rules of biochemistry don't explain everything we want to know about how psychology is going to go, at least the rules of algorithms tell us exactly what the machines are going to do, right? We have some hope that we've carved off a little part of the world and everything is nice and simple, and it is exactly what we said it was going to be.

I think that failed. I think it was a good try. I think we have good theories of interfaces, but even the simplest algorithms have these kinds of things going on. That's why I think something like this is significant.

Do you think that there are going to be, in all kinds of systems of varying complexity, things that the system wants to do and things that it's forced to do? Are there unexpected competencies to be discovered in basically all algorithms and all systems?

That's my suspicion, and I think that it is extremely important for us as humans to have a research program to learn to recognize and predict. We make things—never mind something as simple as this. We make social structures, financial structures, the Internet of Things, robotics, AI. We make all this stuff, and we think that the thing we make it do is the main show.

I think it is very important for us to learn to recognize the kind of stuff that sneaks into the spaces.

It's a very counterintuitive notion. By the way, I like the word “emergent.” I hear your criticism, and it's a really strong one, that “emergent” is like you toss your hands up: “I don't know the process.” But it's just a beautiful word because it is, I guess, a synonym for surprising. This is very surprising, but just because it's surprising doesn't mean there's not a mechanism that explains it.

Mechanism and explanation are both not all they're cracked up to be, in the sense that anything you and I do, we could come up with the most beautiful theory. We paint a painting—anything we do. Somebody could say, “Well, I was watching the biochemistry and the Schrödinger equation playing out, and it totally described everything that was happening. You didn't break even a single law of biochemistry. Nothing to see here, nothing to see, right?”

You can do the same thing here. You can look at the machine code and say, “Yeah, this thing is just executing machine code.” You can go further and say, “Oh, it's quantum foam. It's just doing the thing that quantum foam does.”

You're saying that's what physicists miss.

Well, I'm not saying they're unaware of that. They're generally a pretty sophisticated bunch. I just think they've picked a level, and they're going to discover what is to be seen at that level, which is a lot. My point is, the stuff that the behavior scientists are interested in shows up at a much lower level than you think.

How often do you think there's a misalignment of this kind between the thing that a system is forced to do and what it wants to do? I'm particularly thinking about various levels of complexity of AI systems.

So right now, we've looked at 5 other systems. That's a small N, okay? But just looking at that, I would find it very surprising if bubble sort was able to do this, and then there was some sort of valley of death where nothing showed up, and then living things. I can't imagine that.

We actually have a system that's even simpler than this, which is 1D cellular automaton that's doing some weird stuff. If these things are to be found in this kind of simple system, they just have to be showing up in these other, more complex AIs and things like that.

The only thing we don't know, but we're going to find out, is to what extent there is interaction between these. I call these things side quests. It's like in a game, with the main thing you're supposed to do. As long as you still do it, the thing about this is, you have to sort. You have to sort. There's no miracle: you're going to sort. But as long as you can do other stuff while you're sorting, it's not forbidden.

What we don't know is to what extent the two things are linked. If you do have a system that's very good at language, are the other side quests that it's capable of related to language whatsoever? We don't know the answer to that.

The answer might be no, in which case all of the stuff that we've been saying about language models because of what they're saying could be a total red herring and not really important. The really exciting stuff is what we never looked for. Or, in complex systems, maybe those things become linked.

In biology, they're linked. In biology, evolution makes sure that the things you're capable of have a lot to do with what you've actually been selected for. In these things, I don't know. We might find out that they actually do give language some sort of leg up, or we might find that language is just not the interesting part.

Also, it is an interesting question of this intrinsic motivation of clustering. Is this a property of the particular sorting algorithms? Is this a property of all sorting algorithms? Is this a property of all algorithms operating on lists, on numbers? How big is this?

For example, with LLMs, is it a property of any algorithm that's trying to model language, or is it very specific to transformers? That's all to be discovered.

We're doing all that. We're testing this stuff in other algorithms, and we're developing suites of code to look for other properties. To some extent, it's very hard because we don't know what to look for, but we do have a behaviorist handbook that tells you all kinds of things to look for: delayed gratification, problem-solving—we have all that.

I'll tell you an N of 1 of an interesting biological intrinsic motivation. In the alignment community and stuff, there's a lot of discussion about what the intrinsic motivations of AIs are going to be. What are their goals going to be? What are they going to want to do?

Just as an N of 1 observation, with anthrobots, the very first thing we checked for—this is not experiment number 972 out of 1,000 things—was to put them on a plate of neurons with a big wound through them, a big scratch. The first thing they did was heal the wound.

It's an N of 1, but I like the fact that the first intrinsic motivation that we noticed out of that system was benevolent and healing. I thought that was pretty cool. We don't know. Maybe the next 20 things we find are going to be some sort of damaging effects. I can't tell you that. But the first thing that we saw was a positive one, and that makes me feel better.

9. Can aging be reversed?

What was the thing you mentioned with the anthrobots that they can reverse aging?

There's a procedure called an epigenetic clock. You can look at particular epigenetic states of cells and compare them to a curve built from humans of known age. You can guess what the age is.

This is Steve Horvath's work, and many other people have done similar work. When you take a set of cells, you can guess what their biological age is. We make the anthrobots from cells that we get from human tracheal epithelium.

We collaborated with Steve's group, the Clock Foundation. We sent them a bunch of cells, and we saw that if you check the anthrobots themselves, they are roughly 20% younger than the cells they come from. That's amazing, and I can give you a theory of why that happens, although we're still investigating. Then I could tell you the implications for longevity and things like that.

My theory for why it happens—I call this “age evidencing”—is that what's happening here, like with a lot of biology, is that cells have to update their priors based on experience. I think they come from an old body. They have a lot of priors about how many years they've been around and all that, but their new environment screams, “I'm an embryo,” basically.

There are no other cells around. You're being bent into a pretzel. They actually express some embryonic genes. They say, “You're an embryo.” I think it's not enough new evidence to roll them all the way back, but it's enough to update them to about 28% back.

Yeah, it's similar to when an older adult gives birth to a child. You're saying you could just fake it till you make it with age? The environment convinces the cell that it's young?

Well, first of all, yes. That's my hypothesis.

That's nice.

We have a whole bunch of research being done on this. There was a study where they went into an old-age home and redid the décor in a 1960s style, when all these folks were really young.

And they found all kinds of improvements in blood chemistry and stuff like that, because, they say, it was sort of mentally taking them back to when they were the way they were at that time. I think this is a basal version of that. Basically, if you're finding yourself in an embryonic environment, what's more plausible: that you're young, or what? I think this is the basic feature of biology: to update priors based on experience.

Do you think that's actually actionable for longevity? Can you convince cells that they're younger and thereby extend their lifespan?

This is what we're trying to do, yeah.

Could it be as simple as that?

Well, that's not simple. That is in no way simple. But, again, all of the regenerative medicine stuff that we do balances on one key thing, which is learning to communicate to the system.

When we make gut tissue into an eye, for example, you have to convince those cells that their priors—that we are gut precursors—are wrong, and that they should adopt this new worldview that they're going to be an eye. Being convincing, figuring out what kinds of messages are convincing to cells, learning how to speak their language, and figuring out how to make them take on new beliefs, literally, is at the root of all of these future advances in birth defects, regenerative medicine, and cancer. That's what's going on here. I'm not saying it's simple, but I can see the path.

10. Mind uploading

Going back to the Platonic space, I have to ask: if our brains are indeed thin-client interfaces to that space, what does that mean for our mind? Can we upload the mind? Can we copy it? Can we ship it over to other planets? What does that mean for exactly where the mind is stored?

A couple of things. We are now beyond anything that I can say with any certainty. This is total conjecture, okay? Because we don't yet know. The whole point of this is that we actually don't really understand very well the relationship between the interface and the thing.

And the thing you're currently working on is to map—

Correct.

—this space?

Correct. And we are beginning to map it, but this is a massive effort.

One of the conjectures here is that I strongly suspect that the majority of what we think of as the mind is the pattern in that space. One of the interesting predictions from that model, which is not a prediction of modern neuroscience, is that there should be cases where there is very minimal brain and yet normal IQ function. This has been seen clinically. Corina Kaufman and I reviewed this in a paper recently: a bunch of cases of humans where there's very little brain tissue, and they have normal or sometimes above-normal intelligence.

Now, things are not simple because that obviously doesn't happen all the time. Most of the time it doesn't happen. So, what's going on? We don't understand. But it is a very curious thing that is not a prediction of modern neuroscience. I'm not saying it can't be accommodated. You can take modern neuroscience and sort of bend it into a pretzel to accommodate it. You can say, “Well, there are these redundancies and things like this,” right? So you can accommodate it, but it doesn't predict this. There are these incredibly curious cases.

Now, do I think you can copy it? No, I don't think you can, because what you're going to be copying is the interface, the front end—the brain or whatever. The action is actually the pattern in the Platonic space. Are you going to be able to copy that? I doubt it. But what you could do is produce another interface through which that particular pattern is going to come through. I think that's probably possible. I can't say anything at this point about what that would take, but my guess is that that's possible.

Is your guess, your gut, that that process, if possible, is different than copying? Like, does it look more like creating a new thing versus copying the interface?

So, here's my prediction for the Star Trek transporter. For whatever reason, right now, your brain and body are very attuned and attractive to a particular pattern, which is your set of psychological propensities. If we could rebuild that exact same thing somewhere else, I don't see any reason why that same pattern wouldn't come through it the same way it comes through this one. That's a guess.

I think what you will be copying is the physical interface, while hoping to maintain whatever it is about that interface that was appropriate for that pattern. We don't really know what that is at this point.

When we've been talking about mind, in this particular case, it's the most important to me because I'm a human. Does self come along with that? Does the feeling, “This mind belongs to me,” come along with all minds? Not the subjective experience—the subjective experience is important too, consciousness—but the ownership.

I suspect so, and I think so because of the way we come into being. One of the things that I should be working on is this paper called “Booting Up the Agent,” and it talks about the very earliest steps of becoming a being in this world.

It's kind of like you can do this for a computer, right? Before you switch the power on, it belongs to the domain of physics. It obeys the laws of physics. You switch the power on, and some number of nanoseconds or microseconds—I don't know—later, you have a thing that, oh, look, it's taking instructions off the stack and doing them. So now it's executing an algorithm. How did you get from physics to executing an algorithm? What was happening during the boot-up exactly before it starts to run code or whatever? We can ask that same question in biology. What are the earliest steps of becoming a being?

Yeah, that's a fascinating question. Through embryogenesis, at which point are you booting up? Do you have any hope of an answer to that?

I think so, in 2 ways. The first thing is just physically what happens. I think that your first task as a being—and, again, I don't think this is a binary thing—is to enter into a positive feedback loop that sort of cranks on up and up. Your first task as a being coming into this world is to tell a very compelling story to your parts.

As a biological, you are made of agential parts. Those parts need to be aligned, literally, into a goal they have no comprehension of. If you're going to move through anatomical space by means of a bunch of cells which only know physiological and metabolic spaces and things like that, you're going to have to develop a model and bend their action space. You're going to have to deform their option space with signals, with behavior-shaping cues, with rewards and punishments—whatever you've got.

Your job as an agent is ownership of your parts and alignment of your parts. I think that fundamentally is going to give rise to this ability. Now, that also means having a boundary saying, “Okay, this is the stuff I control. This is me. This other stuff over here is the outside world.” I have to figure out where that is. You don't know where that is, by the way. You have to figure it out.

In embryogenesis, it's really cool. As a grad student, I used to do this experiment with duck embryos, which are a flat blastodisc. You can take a needle and put some scratches into it, and every island you make, for a while until they heal up, thinks it's the only embryo. There's nothing else around, so it becomes an embryo. Eventually, you get twins and triplets and quadruplets and things like that.

But each one of them, at the border—they're joined—has to ask, “Where do I end and where does he begin?” You have to know what your borders are. So that action of aligning your parts and coming to be—I mean, I'm even going to say it—this emergence. We just don't have a good vocabulary for it. This emergence of a model that aligns all the parts is really critical to keep that thing going.

There's something else that's really interesting, and I was thinking about this in the context of this question of these beautiful kinds of ideas. There's this amazing thing that we found, and this is largely the work of Federico Pagosi in my group. A couple of years ago, we saw that networks of chemicals can learn. They have 5 or 6 different kinds of learning that they can do.

What I asked them to do was to calculate the causal emergence of those networks while they're learning. What I mean by that is this: if you're a rat and you learn to press a lever and get a reward, there's no individual cell that had both experiences. The cells at your paw had touched the lever; the cells in your gut got the delicious reward. No individual cell has both experiences. Who owns that associative memory? The rat. That means you have to be integrated, right? If you're going to learn associative memories from different parts, you have to be an integrated agent that can do that.

We can measure that now with metrics of causal emergence like phi and things like that. So we know that in order to learn, you have to have significant phi. But I wanted to ask the opposite question: what does learning do for your phi level? Does it do anything for your degree of being an agent that is more than the sum of its parts?

So we trained the networks, and sure enough, some of them—not all of them, but some of them—as you train them, their phi goes up. Basically, what we were able to find is that there is this positive feedback loop: every time you learn something, you become more of an integrated agent. And every time you do that, it becomes easier to learn.

It's a virtuous cycle.

It's a virtuous cycle. It's an asymmetry that points upward for agency and intelligence. Now, back to our Platonic space stuff: where does that come from? It doesn't come from evolution. You don't need to have any evolution for this. Evolution will optimize the crap out of it, for sure, but you don't need evolution to have this.

It doesn't come from physics. It comes from the rules of information, causal information theory, and the behavior of networks. They're mathematical objects. This is not anything that was given to you by physics or by a history of selection. It's a free gift from math. Those 2 free gifts from math lock together into a spiral that I think causes, simultaneously, a rise in intelligence and a rise in collective agency. I think that's just been amazing to think about.

Well, that free gift from math, I think, is extremely useful in biology. When you have small entities forming networks, a hierarchy that builds more and more complex organisms, that's obvious. I mean, this speaks to embryogenesis, which I think is one of the coolest things in the universe. In fact, you acknowledge its coolness in “The Ingression of Mind,” writing:

“Most of the big questions of philosophy are raised by the process of embryogenesis. Right in front of our eyes, a single cell multiplies and self-assembles into a complex organism, with order on every scale of organization and adaptive behavior. Each of us takes the same journey across the Cartesian cut, starting off as a quiescent human oocyte, a little blob thought to be well-described by chemistry and physics. Gradually, it undergoes metamorphosis and eventually becomes a mature human with hopes, dreams, and a self-reflective metacognition that can enable it to describe itself as a not-a-machine that's more than its brain, body, and underlying molecular mechanisms,” and so on.

What, in all of our discussion, can we say is the clear intuition for how it's possible to take a leap from a single cell to a fully functioning organism full of dreams and hopes and friends and love and all that kind of stuff? In everything we've been talking about, which has been a little bit technical, how do we understand that? That's one of the most magical things the universe is able to create, perhaps the most magical: from simple physics and chemistry, to create this—us 2, talking about ourselves.

I think we have to keep in mind that physics and chemistry are not real things. They are lenses that we put on the world. They are perspectives where we say, “For the time being, for the duration of this chemistry class or career or whatever, we are going to put aside all the other levels, and we're going to focus on this one level.”

What is fundamentally going on during that process is an amazing positive feedback loop of collective intelligence for the interface. It's the physical interface that's scaling; it's the cognitive light cone that it can support. So it's going from a molecular network. The molecular network can already do things like Pavlovian conditioning. You don't start with zero. When you have a simple molecular network, you are already hosting some patterns from the Platonic space that look like Pavlovian conditioning. You've already got that starting out.

That's just the molecular network. Then you become a cell, and then you're many cells. Now you're navigating anatomical morphospace, and you're hosting all kinds of other patterns. And I think, again, this is all the stuff that we're trying to work out now. There's a consistent feedback between the ingressions you get and the ability to have new ones, which, again, I think is this positive feedback cycle, where the more of these free gifts you pull down, they allow you physically to develop in ways where, “Oh, look, now we're suitable for more and higher ones.” This continuously goes and goes and goes until you're able to pull down a full human set of behavioral capacities.

What is the mechanism of such radical scaling of the cognitive cone? Is it just the same thing that you were talking about with the network of chemicals being able to learn?

I'll give you 2 mechanisms that we found. But again, just to be clear, these are mechanisms of the physical interface. What we haven't gotten is a mature theory of how they map onto the space; that's just beginning. But I will tell you what the physical side of things looks like.

The first one has to do with stress propagation. Imagine that you've got a bunch of cells, and there's a cell down here that needs to be up there. All of these cells are exactly where they need to go, so they're happy and their stress is low. Now, let's imagine stress is basically a physical implementation of the error function. It's basically the delta between where you are now and where you need to be.

Not necessarily in physical position; this could be in anatomical space, in physiological space, and in transcriptional space, whatever, right? It's just the delta from your set point. So you're stressed out, but these guys are happy and they're not moving. You can't get past them.

Now imagine if what you could do is leak your stress, whatever your stress molecule is. The cool thing is that evolution has actually conserved these. We're studying all of these things; they're highly conserved. If you start leaking your stress molecules, all of this stuff around here is starting to get stressed out.

When things get stressed, their temperature—in the sense of simulated annealing, not physical temperature—goes up. Their plasticity goes up. Because they're feeling stress, they need to relieve that stress, and because all the stress molecules are the same, they don't know it's not their stress. They are equally irritated by them as if it were their own stress, so they become a little more plastic. They become ready to adopt different fates.

You get up to where you're going, and then everybody's stress can drop. So notice what can happen by a very simple mechanism: just be leaky for your own stress. My problems become your problems, not because you're altruistic, not because you actually care about my problems. There's no mechanism for you to actually care about my problems, but that simple mechanism means that faraway regions are now responsive to the needs of other regions, such that complex rearrangements and things like that can happen. It's an alignment of everybody to the same goal through this very dumb, simple stress-sharing thing.

Via leaky stress.

Leaky stress, right? So there's another one, which I call memory anonymization. Imagine here are 2 cells. Imagine something happens to this cell, and it sends a signal over to this cell. Traditionally, you send a signal over and this cell receives it. It's very clear that it came from outside, so this cell can do many things. It could ignore it, take on the information, reinterpret it, or do whatever, but it's very clear that it came from outside.

Now imagine the kind of thing that we study, which is called gap junctions. These are electrical synapses that could directly link the internal milieus of 2 cells. If something happens to this cell—let's say it gets poked—and there's a calcium spike or something, that propagates through the gap junction here. This cell now has the same information, but this cell has no idea: “Wait a minute, was that my memory, or is that his memory?” Because it's the same, right? It's the same components. And so what you're able to do now is to have a mind meld.

You can have a mind meld between the 2 cells where nobody's quite sure whose memory it is. When you share memories like this, it's harder to say that I'm separate from you. If we share the same memories, we are kind of a— and I don't mean every single memory, right? They still have some identity, but to a large extent, they have a little bit of a mind meld. There are many complexities you can layer on top of it.

What it means is that if you have a large group of cells, they now have joint memories of what happened to us, as opposed to “what happened to you” and “I know what happened to me.” That enables a higher cognitive light cone because you have greater computational capacity and a greater area of concern, of things you want to manage. I don't just want to manage my tiny little memory states because I'm getting your memories. Now I know I've got to manage this whole thing.

Both of these things end up scaling the size of things you care about, and that is a major ladder for cognition: to scale the size of concern that you have.

It'd be fascinating to be able to engineer that scaling. Probably applicable to AI systems. How do you rapidly scale the cognitive cone?

Yeah. We have some collaborators—

Light cone.

—in a company called Softmax that we're working with to do some of that stuff in biology. That's our cancer therapeutic. What you see in cancer, literally, is that cells electrically disconnect from their neighbors when they were part of a giant memory that was working on making a nice organ.

Now they can't remember any of that. Now they're just amoebas, and the rest of the body is just an external environment. What we found is that if you then physically reconnect them to the network, you don't have to fix the DNA, and you don't have to kill the cells with chemo. You can just reconnect them, and they go back to what they were working on because they're now part of this larger collective.

And so, I think we can intervene at that scale.

11. Alien intelligence

Let me ask you more explicitly about SETI, the Search for Unconventional Terrestrial Intelligence. What do you hope to do there? How do you actually try to find unconventional intelligence all around us? First of all, do you think on Earth there are all kinds of incredible intelligence we haven't yet discovered?

Guaranteed. We've already seen it in our own bodies, and I don't just mean that we are host to a bunch of microbiome or any of that. I mean, your cells—and we have all kinds of footwork on this—traverse these alien spaces, 20,000-dimensional spaces, and other spaces every day. They solve problems. I think they suffer when they fail to meet their goals, and they have stress reduction when they meet their goals. These things are inside of us. They are all around us.

I think that we have an incredible degree of mind blindness to all of the very alien kinds of minds around us. Looking for aliens off Earth is awesome and whatever, but if we can't recognize the ones that are inside our own bodies, what chance do we have to really recognize the ones that are out there?

Do you think there could be a measure like IQ for mind? What would it be? Not mindedness, but intelligence that's broadly applicable to unconventional minds, that's generalizable to unconventional minds, where we could even quantify it, like, “Holy shit, this discovery is incredible because it has this IQ”?

Yeah, yes and no. The yes part is that, as we have shown, you can take existing IQ metrics—literally existing ways that people use to measure intelligence in animals and humans and whatever—and you can apply them to very weird things. If you have the imagination to make the interface, you can do it. We've done it, and we've shown creative problem-solving and all this stuff. So, yes.

However, we have to be humble about these things and recognize that all of those IQ metrics that we've come up with so far were derived from an N of 1 example of the evolutionary lineage here on Earth, and so we are probably missing a lot of them. I would say we have plenty to start. We have so much to start with. We could keep tens of thousands of people busy just testing things now, but we have to be aware that we're probably missing a lot of important ones.

What do you think has more interesting, intelligent, unconventional minds: the human body or, like we were talking off-mic, the Amazon jungle—nature, natural systems outside of the body? The sophisticated biological systems we're aware of?

We don't know, because it's really hard to do experiments on larger systems. It's a lot easier to go down than it is to go up. But my suspicion is, like the Buddhists say, “Innumerable sentient beings.” I think by the time you get to that degree of infinity, it kind of doesn't matter to compare. I suspect there are just massive numbers of them.

Yeah, I think it really matters which kinds of systems are amenable to our current methods of scientific inquiry. I spent quite a lot of hours just staring at ants when I was in the Amazon, and it's such a mysterious, wonderful collective intelligence. I don't know how amenable it is to research. I've seen some folks try. You can simulate, but I feel like we're missing a lot.

I'm sure we are. One of my favorite things about that kind of work—have you seen that there are at least 3 or 4 papers showing that ant colonies fall for the same visual illusions that we fall for? Not the ants, the colonies. If you lay out food in particular patterns, they'll do things like complete lines that aren't there. All the same shit that we fall for, they fall for. So I don't think it's hopeless, but I do think that we need a lot of work to develop tools.

Do you think all of the tooling that we develop and the mapping that we've been discussing will help us with the study part—finding aliens out there?

I think it's essential. We are so parochial in what we expect to find in terms of life that we are going to be completely missing a lot of stuff. If we can't even agree on—never mind definitions of life—what's actually important, I read a paper recently where they asked about 65 modern working scientists for a definition of life. We had so many different definitions across so many different dimensions. We had to use AI to make an amorphous space out of it, and there was zero consensus about what actually is important. If we're not good at recognizing it here, I just don't see how we're going to be good at recognizing it somewhere else.

Given how miraculous life is here on Earth, it's clear to me that we have so much more work to do. That said, would it be exciting to you if we found life on other planets in the solar system? What would you do with that information? Or is that just another life form that we don't understand?

I would be very excited about it because it would give us some more unconventional embodiments to think about. Right? A data point that's pretty far away from our existing data points, at least in this solar system. So that would be cool. I'd be very excited about it.

But I must admit that my level of surprise has been pushed so high at this point that it would have to be something really weird to make me shocked. The things that we see every day are just...

I think you've mentioned in a few places that the “Ingressing Minds” paper is not the weirdest thing you plan to write. How weird are you going to get? Maybe a better question is: in which direction of weirdness do you think you will go in your life? In which direction of the weird Overton window are you going to expand?

Yeah. The background to this is simply that I've had a lot of weird ideas for many, many decades, and my general policy is not to talk about stuff until it becomes actionable. The amazing thing—and I'm really just shocked—is that in my lifetime, the empirical work has gotten this far. I really didn't think we would get this far.

I have this mental knob for what percentage of the weird things I think about I actually say in public. Every few years, when the empirical work moves forward, I turn that knob a little as we keep going. I have no idea if we'll continue to be that fortunate or how long I can keep doing this. I don't know.

But just to give you a direction for it, it's going to be in the direction of what kinds of things we need to take seriously as other beings with which to relate. I've already pushed it. We knew brainy things, and then we said, “Well, it's not just brains.” And then we said, “Well, it's not just...” So it's not just in physical space, it's not just biologicals, and it's not just complexity.

There are a couple of other steps to take that I'm pretty sure are there, but we're going to have to do the actual work to make it actionable before we really talk about it. So that's the direction.

I think it's fair to say you're one of the more unconventional humans and scientists out there. The interesting question is, what's your process of idea generation? What's your process of discovery? You've done a lot of really incredibly interesting—like you said, actionable—but interesting, out-there ideas that you've actually engineered with Xenobots and Anthrobots, these kinds of things.

When you go home tonight and go to the lab, what's the process? An empty sheet of paper when you're thinking through it?

The mental part is a lot like, funny enough, making Xenobots. We make Xenobots by releasing constraints, right? We don't do anything to them. We just release them from the constraints they already have. A lot of it is releasing the constraints that have been placed on us mentally.

Part of it is that my education has been a little weird because I was a computer scientist first and only later biology. By the time I heard all the biology things that we typically just take on board, I was already a little skeptical and thinking a little differently. A lot of it comes from releasing constraints.

I very specifically think: This is what we know. What would things look like if we were wrong? Or what would it look like if I was wrong? What are we missing? What is our worldview specifically not able to see, given whatever model I have?

Another way I often think is that I'll take 2 things that are considered to be very different things, and I'll say, “Let's just imagine those as 2 points on a continuum.” What does that look like? What does the middle of that continuum look like? What's the symmetry there? What's the parameter—the knob I can turn to get from here to there?

Those are the kinds of things I look for. I look for symmetries a lot. I'm like, okay, this thing is that way; in what way? What's the fewest number of things I would have to move to make this map onto that? Those are the kinds of mental tools.

The physical process for me is basically that I'm fortunate to have a lot of discussions with very smart people. In my group, I've hired some amazing people, so we have a lot of discussions, and some stuff comes out of that.

My process is that pretty much every morning I'm outside for sunrise, and I walk around in nature. There's really nothing better as inspiration than nature. I do photography, and I find that it's a good meditative tool because it keeps your hands and brain just busy enough. You don't have to think too much, but you're sort of twiddling, looking, and doing some stuff, and it keeps your brain off of the linear, logical, careful train of thought enough to release it, so that you can ideate a little more while your hands are busy.

So it's not even the thing you're photographing; it's the mechanical process of doing the photography.

And mentally, right? Because I'm not walking around thinking, "Okay, let's see. For this experiment, I have to get this piece of equipment and this..." That goes away, and it's, "Okay, what's the lighting? What am I looking at?" During that time, when you're not thinking about that other stuff, then I say, "Well, yeah, I got a notebook," and I'm like, "Look, this is what we need to do." So that kind of stuff.

And the actual idea-writing-down stuff, is it a notebook? Is it a computer? Are you super organized in your thinking, or is it just random words here and there, with drawings? What is the space of thoughts you have in your head? Is it amorphous—things that aren't very clear? Are you visualizing things? Is there something you can articulate there?

I tend to leave myself a lot of voicemails. As I'm walking around, I'm like, "Oh, man, this idea," so I'll just call my office and leave myself a voicemail to transcribe later. I don't have a good enough memory to remember any of these things, so what I keep is a mind map.

I have an enormous mind map. One piece of it hangs in my lab so people can see, "These are the ideas; this is how they link together. Here's everybody's project. I'm working on this. How does this attach to everybody else's so they can track it?"

The thing that hangs in the lab is about 9 feet wide. It's a silk sheet, and it's out of date within a couple of weeks of my printing it because new stuff keeps moving around. Then there's more that isn't for anybody else's view. I try to be very organized because otherwise I forget.

Everything is in the mind map. Things are in manuscripts. I have something like, right now, probably 163 or 62 open manuscripts that are in the process of being written at various stages. When things come up, I stick them in the right manuscript, in the right place, so that when I'm finally ready to finalize, I'll put words around it and whatever. But there are outlines of everything.

Ah.

I try to be organized because I don't have a good enough memory.

So there's a wide front of manuscripts of work that's being done, and it's continuously pushing toward completion, but you're not clear what's going to be finished, when, and how?

That's just the theoretical, philosophical stuff. The empirical work that we're doing in the lab—we know exactly what those are.

It's more focused. There's a specific set of questions.

We know this is anthrobot aging. This is limb regeneration. This is the new cancer paper. This is whatever. Those things are very linear.

Where do you think ideas come from when you're taking a walk that eventually materialize in a voicemail? Where's that? What is that? Is that from you? A lot of the most interesting people feel like they're channeling from somewhere else.

I hate to bring up the Platonic space again, but if you talk to any creative, that's basically what they'll tell you, right? Certainly, that's been my experience, so I feel like it's a collaboration. The way it feels to me is a collaboration.

Collaboration means I need to bust my ass and be prepared: A, to work hard to be able to recognize the idea when it comes, and B, to actually have an outlet for it so that when it does come, we have a lab and people who can help me do it, and then we can actually get it out, right?

That's my part: be up at 4:30 AM doing your thing and be ready for it. But the other side of the collaboration is that, when you do that, amazing ideas come. To say that it's me, I don't think would be right. I think it's definitely coming from other places.

12. Advice for young people

What advice would you give to scientists, PhD students, grad students, and young scientists who are trying to explore the space of ideas, given the very unconventional, nonstandard, unique set of ideas you've explored in your life and career?

The first and most important thing I've learned is not to take too much advice, so I don't like to give too much advice. But I do have one technique that I've found very useful, and this isn't for everybody. There's a specific demographic. A lot of unconventional people reach out to me, and I try to respond and help them.

This is a technique that I think is useful for some people. It's the act of bifurcating your mind, and you need to have 2 different regions. One region is the practical region of impact. In other words, how do I get my idea out into the world so that other people recognize it? What should I say? What are people hearing? What are they able to hear? How do I pivot it? What parts do I not talk about? Which journal am I going to publish this in? Is it time now? Do I wait 2 years for this?

All the practical stuff is about how it looks from the outside. All the stuff where I think, "I can't say this," or, "I should say this differently," or, "This is going to freak people out," or, "This is odd." This community wants to hear this, so I can pivot it this way. All that practical stuff has to be there; otherwise, you're not going to be in a position to follow up any of your ideas. You're not going to have a career. You're not going to have resources to do anything.

But it's very important that that can't be the only thing. You need another part of your mind that ignores all that completely, because this other part of your mind has to be pure. It has to be, "I don't care what anybody else thinks about this. I don't care whether this is publishable or describable. I don't care if anybody gets it. I don't care if anybody thinks it's stupid. This is what I think, and why." You have to give it space to grow.

If you try to mush them together, I find that impossible because the practical stuff poisons the other stuff. If you're too much on the creative end, you can be an amazing thinker; it's just that nothing ever materializes. But if you're very practical, it tends to poison the other stuff, because the more you think about how to present things so that other people get it, the more it constrains and bends how you start to think.

What I tell my students and others is that there are 2 kinds of advice. There's very practical, specific advice, like somebody says, "You forgot this control," or, "This isn't the right method," or, "You shouldn't be doing this." That stuff is gold, and you should take it very seriously and use it to improve your craft. That's super important.

But then there's the meta-advice, where people are like, "That's not a good way to think about it. Don't work on this. This isn't..." That stuff is garbage. Even very successful people often give very constraining, terrible advice.

One of my reviewers on a paper years ago said—I love this, the Freudian slip—he said he was going to give me constrictive criticism, right? And that's exactly what he gave me.

That's funny.

It was constrictive criticism. I was like, "That's awesome. That's a great typo."

It's very true. The bifurcation of the mind is beautifully put. I do think some of the most interesting people I've met sometimes fall short on the normie side, on the practical side: "How do I communicate this with people who have a very different worldview, who are more conservative and more conventional and fit into the norm?"

You have to be able to have the skill to fit in. Then you have to, again, beautifully put, be able to shut that off when you go on your own and think. Having those 2 skills is very important.

I think a lot of radical thinkers think that they're sacrificing something by learning the skill of fitting in, but if you want to have impact, if you want ideas to resonate and actually lead to something, first, you have to be able to build great teams that help bring your ideas to life. Second, for your ideas to have impact, scale, and resonate with a large number of people, you have to have that skill. Those are very different skills.

Let me ask a ridiculous question. You already spoke about it, but what, to you, is one of the most beautiful ideas that you've encountered in your various explorations? Maybe not just beautiful, but one that makes you happy to be a scientist, to be a curious human exploring ideas.

I must say that I sometimes think about these ingressions from this space as a kind of steganography. Steganography is when you hide data and messages within the bits of another pattern that don't matter, right? The rule of steganography is that you can't mess up the main thing. If it's a picture of a cat or whatever, you have to keep the cat. But if there are bits that don't matter, you can stick stuff in.

I feel like all these ingressions are a kind of universal steganography: patterns seep into everything, everywhere they can. They're kind of shy, meaning that they're very subtle, not invisible. If you work hard, you can catch them. They're hard to see.

The fact that I think they also affect, quote-unquote, machines as much as they certainly affect living organisms, I think is incredibly beautiful. Personally, I'm happy to be part of that same spectrum; that magic is applicable to everything.

A lot of people find that extremely disturbing, and that's some of the hate mail I get. It's like, “Yeah, we were with you on the majesty of life thing until you got to the fact that machines get it too.” And now, like, terrible, right? You're kind of devaluing the majesty of life.

I don't know. The idea that we're now catching these patterns and we're able to do meaningful research on the interfaces and all that is just, to me, absolutely beautiful. And that it's all one spectrum, I think, to me, is amazing. I'm enriched by it.

13. Questions for AGI

I agree with you. I think it's incredibly beautiful. I lied—there's an even more ridiculous question. It seems like we are progressing toward possibly creating a superintelligent system—an AGI, an ASI. If I had one, gave it to you, and put you in the room, what would be the first question you ask it? Maybe the first set of questions?

There are so many topics that you've worked on and are interested in. Is there a first question that, if you could get a solid answer, you really would want to ask?

I mean, the first thing I would ask is, “How much should I even be talking to you?” For sure. Because it's not clear to me at all that getting somebody to tell you an answer in the long run is optimal. It's the difference between when you're a kid learning math and having an older sibling who will just tell you the answers, right?

Sometimes it's like, “Come on, just give me the answer. Let's move on with this cancer protocol and whatever.” Great. But in the long run, the process of discovering it yourself—how much of that are we willing to give up? And by getting a final answer, how much have we missed of stuff we might have found along the way?

Now, I don't know. I don't think it's correct to say, “Don't do that at all. Take the time in all the blind alleys.” That may not be optimal either, but we don't know what the optimal is. We don't know how much we should be stumbling around versus having somebody tell us the answer.

That's actually a brilliant question to ask AGI, then.

I mean, if it's really—

That's a really—

If it's really an AGI.

I mean, that's a good first question.

Yeah, if it's really an AGI, I'm like, “Tell me what the balance is. How much should I be talking to you versus stumbling around in the lab and making all my own mistakes?” Is it 70/30? 10/90? I don't know. So that would be the first—

And then the AGI will say, “You shouldn't be talking to me.”

It may well be. It may say, “What the hell did you make me for in the first place? You guys are screwed.” That's possible.

You know, the second question I would ask is, “What's the question I should be asking you that I probably am not smart enough to ask you?” That's the other thing I would say.

This is really complicated. That's a really, really strong question. But again, there, the answer might be, “You wouldn't understand the question it proposes,” most likely.

For me, assuming you can get a lot of questions, I would probably go for questions where I would understand the answer. It would uncover some small mystery that I'm super curious about. Because if you ask big questions like you did, which are really strong questions, I just feel like I wouldn't understand the answer.

If you ask it, “What question should I be asking you?” it would probably say something like, “What is the shape of the universe?” And you're like, “What? Why is that important?” You would be very confused by the question it proposes.

I would probably want it to be nice for me to know, straight up, as a first question: How many living, intelligent alien civilizations are in the observable universe? Yeah, that would just be nice to know. Is it 0, or is it a lot? I just want to know that.

That's what I was about to say. My guess is it's going to be exactly the problem you said, which is it's going to say, “Oh my God. I mean, right in this room, you got—” You know, and like, “Oh, man.”

Yeah, yeah, yeah. Everything you need to know about alien civilizations is right here in this room. In fact, it's inside your own body.

Just for starters.

Thank you, AGI. Thank you. All right, Michael. One of my favorite scientists, one of my favorite humans, thank you for everything you do in this world.

Thank you so much.

Truly, truly fascinating work. Keep going for all of us. You're an inspiration.

Thank you so much. It's great to see you. Always a good discussion. Thank you so much. I appreciate it.

Thank you for this.

Thank you.