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Discussion with Michael Johnson 2

Thoughtforms Life Podcast / Michael Levin

July 9, 2025 · 55 min

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Today, I was interested in broaching a new topic and thinking a little bit more about consciousness.

We've discussed this a little bit.

We talked about vasocomputation last time.

I guess what I want to say today is maybe a potential meet-the-middle approach or merging

what I would say Michael Levin thought with Michael Johnson thought and see where that

can go.

Sure.

That's great.

Yeah, let's do it.

Yeah, awesome.

So, yeah, just a few.

I just have some notes here.

I'll just read from them.

Just a few notes on my approach to consciousness, dealing a lot with formalism, structuralism,

symmetry, valence, physics, and what I'm calling strong monism.

And then vasocomputation as the neural system and the vasomuscular system coordinating on

patterns.

And I guess what I would say that a big theme is how do we get to a proper science of consciousness?

And maybe there are certain levels of organization that have somewhat unique affordances for understanding

consciousness.

And then I think that to put some words in your mouth here.

I think that I agree with Michael.

I think that we have to be very, very careful about how we read your work.

And it's like, there's this sort of beautiful, multi-scale approach to everything.

And so collective intelligence and diverse systems having agents and goals, or agency

and goals.

So, TAME, and I've heard you mentioned polycomputing, and sort of everything is doing some sort

of processing and whatnot.

Stressors, surprising competencies.

So, that's a good point.

And then sort of diverse systems having predictive models of the world, predictive world models,

and sort of this focus on emergence.

So yeah, I want to pause here.

Anything else that you would add?

Well, we can also talk about some of the latest things that I've been talking about as far

as the role of, for lack of a better word, platonic space and so on.

And I think, you know, also, just to mention that I don't, the majority of my work is not

about consciousness per se.

I've been talking about it more recently.

And in fact, just this morning, a new talk has gone up, which was a talk I gave at a

consciousness conference of last week.

So I've said a few things about it, but you know, I haven't made any strong claims about

it really.

And certainly, I don't yet have my own theory of consciousness to put out there, but I do

think about it a bit.

So, yeah.

I'm happy to, you know, sort of play off of whatever you want to say about it.

Yeah.

Great.

So I guess I kind of want to talk about, are cells conscious?

And so you've dug pretty deeply into the biochemistry and sort of electrical profile of cells.

And it's sort of one claim that I'd make here is that there are a lot of different sort

of theories of consciousness out there.

You know, that approach, you know, do systems have a world model?

Do systems have integrated information?

Markup blankets, quantum coherence, quantum decoherence?

Are they sort of EM pockets?

What is their shape in sort of space, I would say.

I would say that cells are sort of this sort of interesting system where basically any,

any theory of consciousness you come to the topic with, cells sort of check the box.

So they're pretty interesting.

Like I think that, like I think cells are conscious and I think you think cells are

conscious and it would be interesting to sort of explore the biochemistry of that.

Yeah.

Well, I guess the first question we should talk about is, do you think that's a, is that

a binary question that we're asking?

In terms of things either are or are not conscious?

Is that, how you're thinking about it or more of a continuum

view? Right, right. Yeah. One second. I'm getting a little hot. I'll open the window here. Sure. Yeah, that's a good question. I think, you know, I think of

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linked to space-time and sort of as having boundaries in space-time.

And this may be a difference in sort of how we approach this.

And I think like my sort of kind of beat in the middle approach is like, I think that we,

like both our sort of themes around consciousness would sort of identify the cell as sort of a

plausible conscious system. And I think that like there's going to be like a lot of edge cases.

And then like we can pretty confidently say, okay, like healthy neurons,

like are generally pretty conscious.

Yeah. So, so, you know, specifically, I think that it is,

it is,

I think the question is what kind and how much, and I suspect that, yeah, I mean, I do think that

well, what, what, what we don't know yet is to what extent consciousness tracks intelligence.

Are they, are they, they're not the same thing for sure. You know,

Anil Seth has this diagram where they're pretty much orthogonal, you know, he's got two,

two, two perpendicular axes before. I don't know if they're completely orthogonal or if they sort of,

tend to tend to track each other. I suspect they do, but, but I, I think any, any system that

has that has goal directedness and is putting forth effort to try to reach particular States,

as opposed to other States is going to have an inner perspective that, that matters.

And the way I, and then we'll, we'll get to, we'll get to the platonic space stuff later,

I guess. But the way I like to think about it is that in certain kinds of systems, the, the, the,

the way of looking at it at the world from its perspective is different. So if you have a bumpy

landscape and you're looking and there's a bowling ball on this landscape, your view as an outside

observer basically tells you everything you need to know. You can, you know, exactly what's going

to happen as a third person observer. But if you have a mouse on that same landscape, your view of

the landscape is kind of irrelevant. What matters is the mouse's view of that landscape, because it

could be completely different. He might've been rewarded and punished at certain areas. He might

have different, different attention, you know, all kinds of things. And so it's all very, very

preference, whatever. So the degree to which you have to adopt the perspective of the agent in

order to know what's going on is relevant to how much of a first person perspective they will have.

And so, you know, I think being able to recognize that as a two way IQ test,

you know, if we don't know how to take that first person perspective, and I think we're

really bad at it. So people argue with me all the time, they say, you know, your liver can't be

conscious, I'm conscious. And, you know, nobody actually has a story to tell why the electrical

networks of the liver are somehow barred from the things that they think the electrical networks of

the brain are doing, that there is no story like that. But everybody assumes there is. And they

take their native, you know, certainty about these things, which just, you know, the priors that we

got from our evolutionary history, they sort of people often mistake that from some kind of a good

argument. So to the extent that sells, I think,

absolutely.

Absolutely navigate spaces with with valence and reward functions. And they have, you know,

they have all the same mechanisms and the same evolutionary history and the same kinds of

behavioral repertoires that we see complex organisms doing at least to a smaller extent,

but a lot of the same stuff shows up. Yeah, I see. I see absolutely no, no, no reason why you

wouldn't, you wouldn't think that they have a degree of consciousness. Now, personally, I think

it goes far below that. I don't think you need to be alive or anything like a cell.

I think you need to be alive or anything like a cell to be on that to get onto that spectrum. But

but but anyway, the important thing is, I do think it's a it's a it's a spectrum.

Yeah, nice. Yeah, that makes a lot of sense. And I think that, um, like the sort of perspective of

it's the perspective of the like, agent like process that matters. I think that's, that's very

right. And I guess I'm, I think that there might be an opportunity to sort of figure out like, okay,

what is the typology of

a cells world model, like a cell state, world model, like what sorts of things cells might

sense, and

overall metaphor that I'm going to is cells as qualia pixels in our canvas of experience.

So much as we are sort of a conglomeration of cells, our experience is also a conglomeration

of many cellular microstates. And so then you sort of can dig into, okay, what kind

of values can these pixels take? And then what's happening when a cell depolarizes?

And maybe the sort of intensity of the pixel is the B-mem of the cell. And then from sort

of the

cells are sort of faced with many, many informational imperatives, we can say, where like they

had to understand, okay, like, is my environment dangerous? Is it acidic? And then like some

cells like, you know, in an organism, some cells specialized in sort of detecting, you

know, hydrogen ions in the environment. And they sort of turned into like sour taste buds.

And likewise, you know, other cells specialized in, you know, is there umami in the environment?

Is there like amino acids with hydrophilic side chains that could be useful, nutritious?

So I guess I want to say that there's, it looks like to me that there's an interesting

sort of typology of cell microstates.

Yeah.

And that's where, which could sort of very cleanly map potentially to micro sensations.

Like if you took a, so basically if you just kind of categorized all the ways that sort

of all the different cell types, you would get a list of different possible types of

quality values.

Yeah.

Does that make sense?

Yeah. Yeah. I see. And these, these, um, equalia values are qualia of the cell or,

or when you say they're pixels, you think they, um, they somehow add up to the qualia

of the animal that they're, that they're part of?

Uh, I think the cell actually is conscious of that. Like that is what the cell

feels like. And then we are sort of a super set of, uh, of these cells.

I see. I see. I see.

Yeah. Yeah. Yeah. I mean, um, I, I, I think, I think it's, it's reasonable though. So certainly

the first part is reasonable fit. Try trying to, trying to figure out what the world of

a cell looks like based on the things that cares about in physiological space in the

transcriptional space. I mean, there's a long history of this, this business of the

umwelt, right. And trying to, trying to get inside a creature's head by, by asking yourself,

what does it, you know, what, what does it do? What does it do? What does it do? What,

what matters to it? I think that's reasonable. Um, I, I, so, so while, while I am a panpsychist

in that sense, I don't actually think that we are trying to solve, uh, the combination

problem here. That is, I don't think that our consciousness is some sort of aggregate

or, or amalgam of our components consciousnesses. I think that we have this, this, the cells

inside us have, have some degree of consciousness, the tissues and the organs do as well. And

so do we, but, but.

At every point, I don't think it's created by summing up the parts. I think the larger

scale allows, it allows a better interface for an aggression of a more complicated consciousness

that actually comes from this, this platonic space. Right. I think, I think our, our, our,

our physical bodies, including embryos, you know, biobots, uh, whatever, um, robots, whatever

are all sort of haunted by these patterns in the same way that triangular objects are

haunted by the truths of mathematics that pertain to triangles and to, um, you know,

prime numbers and then all of these kinds of things. It's, it's kind of like, it's like

that, I think. So, so I'm not trying to, I'm not trying to do any kind of a summation of

consciousness of the parts, but, but, but I do think that the cells have it in quite

probably the components within the cells as well, actually, um, from, from what we can

see.

Sure. Nice. Yeah. Um, so I want to talk a little bit about the, the platonic realm.

Uh,

Although just to, to sort of, uh, close this loop. Um, I think that like my expectation

is that the, the body sort of, if you look at it in four dimensions,

sort of three dimensions plus time.

Like consciousness is sort of, you know, these sort of, you know, probably dominantly affected

by the EM field.

And it may sort of...

There's probably one sort of biggest chunk of consciousness.

And then we call that our consciousness.

consciousness. But there may be smaller chunks in four dimensions. And like, for example,

the liver may have its own sort of pocket of consciousness, which we don't really have

direct access to. And so we can interface with, but not really in control. So I guess

I was thinking about the platonic mind hypothesis that, you know, we sort of are tapped into

this sort of larger and almost more beautiful space of sort of dynamics of possibilities

of these platonic forms of shapes. And you've written about this. And I guess I'm wondering,

to what degree could they be considered symmetry groups?

Yeah.

Well, my current model and all of this is up for grabs, of course, but my current model is that platonic space has levels or domains, parts of which are occupied by things that we recognize from math.

So this is where like the low agency version of things like the truths of number theory and things like that live there.

And then there are similar regions for things, more abstract things like symmetry groups, possibly, you know, the kinds of things that Plato and others talked about, you know, beauty and things like that, right, that may be related.

And then there are regions that are also occupied by more complex dynamic forms that we would typically recognize as behavioral propensities or kinds of minds.

And, you know, are they the same as symmetry groups?

I don't know.

I probably wouldn't think so, but we don't know.

How do you see it?

Yeah.

I mean, I think that past a certain point, you get to sort of this, you know, a lot of things work out to be equivalent.

So like the platonic shapes, you know, are equivalent to like some sort of mathematical classes and so on.

So I don't know whether to sort of anchor this.

To symmetry groups or to sort of a more general platonic frame.

I guess to touch, like to take this back to the sort of sensation stuff, you know, I'm, I know is I'm in awe of chemistry.

That there's this, this sort of, we can say mesoscale structure where it's, you know, chemistry is not necessarily sort of inherently in the laws of physics.

It's emergent from the laws of physics.

And, you know, one of your colleagues, Eric, he all has this wonderful causal emergence 2.0 paper just kind of talking about how real our, our various things.

And sort of can the whole be more real than the sum of its parts and so on.

And I guess I would just observed that chemistry is surprisingly real.

That, you know, okay, maybe what really exists is like electrons or something like that.

or fields, or strings, or strands, or, you know, there's many sort of approaches in physics

to sort of what really exists. But chemistry is surprisingly real. It's sort of a way of

course-gaining reality that is surprisingly sturdy, stable, predictive, descriptive. And

I was looking for, okay, what could analogous structures look like in consciousness? And

so how can we course-grain sensations in a similar way? You know, I've thought about,

okay, is there like a periodic table of qualia to be found and so on. But you know, it's

the periodic table is based on this harmonic structure and valence shells and so on. But

I guess like, you know, I'm not sure if I can answer that question. But I think it's

like, the move that I would want to make is something like maybe sort of these atomic

sensations that we have as humans, sourness, bitterness, sweetness,

smell of citrus.

I think David Guinty has written about 15 to 18 different types of touch receptors and done some great work there.

And, you know, can we understand each sort of, each of these as a different sort of symmetry group or symmetry breaking event?

And then if we could do that, I feel like then we could sort of slowly build up this basic alphabet of human sensation based on sort of what's happening in the cells themselves.

Yeah, interesting. Do you know if, have any aspects of chemistry or chemical reactions

been analyzed?

For example, from the perspective of causal information theory or anything like that?

Because we just did something like that that's coming out in a couple weeks.

But have you seen anything like that?

Nice. No, I haven't.

Yeah, I hadn't either. But what we did was, this is Federico Pagosi's work in my group.

It's really, really wild. Basically, well, I'll take a step back.

You know, when

you have a rat and let's say you do some associative conditioning.

So if the rat presses the lever, it gets the reward.

Well, we know that no individual cell has both experiences, right?

So the foot of the rat touches the lever, the gut gets the delicious sugar.

But you know, in order to have that associative memory, you have to be a collective intelligence.

You have to have an integration that allows the rat to know things that none of the individual cells know.

Yeah.

Clear enough.

What I wanted to know was, what are the things that the rat can do to help the individual cells?

Does it work in the opposite direction? That is, if you train something,

does it become more of an integrated agent by virtue of being trained?

In other words, does forming new memories raise your causal emergence?

And so we looked at it in the context of models of gene regulatory networks.

So this is just chemistry. There's no cell. There's no, you know, there's nothing.

All there is, is a set of differential equations that control how certain chemicals turn other

chemicals on or off. That's it.

And previously, we have a few papers previously showing that when you have a system like that,

it can learn. It can do about six different kinds of learning. It can do habituation,

sensitization, associative Pavlovian conditioning, and so on. So what Federico did was he looked at

a measure of Phi D, of causal emergence as we train these things. And he found that these networks

divide into several different categories. We don't have a good name for it yet.

But in some of these categories, so not all networks, but some and many,

the more you train them, the higher the causal emergence goes.

Oh, wow.

Yeah, it's pretty wild. They do become, I wrote a blog post about it. And at the end,

I have a diagram of Pinocchio. And, you know, he was told, if you want to be a real boy,

you got to go to school. And that's the thing, right? It like reifies the process of learning

new things as a collective. Right? So, yeah, that's a good point. I think that's a good point.

reifies the agent as a collective intelligence. And you can quantitatively, you can watch it

happen. And there's some other interesting aspects to it. But, yeah, I mean, chemistry, apparently,

does already have these features, you don't need to be a cell to do this. And, yeah, we have some

other stuff that isn't public yet, that it takes it one step further, and, you know, the origin of

these things, and so on. So, yeah, yeah, I agree with you, chemistry is already, you know, I don't

know, I don't know what we could do below that, if there's anything, anything, you know, in the at

the particle level that could be analyzed this way, but the chemistry is already doing it.

Right. Nice. Yeah, I mean, it does seem like you're, you're sort of putting some

optimization pressure on, on the integration term. And I guess what comes to mind is like,

Zurich has this quantum Darwinism brain that like, even at physics, like physics is the product of,

of some natural selection for patterns that can persist and copy themselves into the environment.

When he says physics, does he mean specific physical phenomena? Or does he mean the laws

of physics? Like, is he talking about a small and kind of multiple universes thing that's Darwinian?

Or does he mean, within our universe, the patterns are the physical instances are trying to persist?

I believe his work deals with the patterns in

our universe. So kind of motifs in the, I guess the formal term would be like motifs in the Hamiltonian.

I wouldn't want to define that. Nice. Interesting. One thing that comes to mind here, there's always these questions of everything can be multiscale. And then the next question

is, what are the sweet spots to coarse-grain the system on? To say, okay, this is a really interesting phenomenon that doesn't necessarily happen in the same way at other scales, but it does happen at this scale.

And so I guess my sort of attention on the cell is this sort of ping pong between hyperpolarization and depolarization. I mean, neurons and muscle cells and some obscure immune system cells and so on, they sort of obsolete between a polarized and depolarized state.

And so I'm sort of imagining sort of a

sort of

computational thing. And there are like cool things to do with how muscles move and how

neurons fire. So movement and communication sort of arise from this dance between the

sort of really charged state and the state which happens when that charge kind of gets

released and collapses. And the question that I'm looking at right now is sort of what

happens to the cell's internal structure when it depolarizes. And I guess to just say a

few words there, I'm looking at depolarization as a symmetry breaking.

So, you know, you pump energy into it and you sort of create some symmetries and that's

sort of, we can say the, for most cell types, that's the neutral state. And then you break

that symmetry and it sort of, maybe physically, but more so electrically, it sort of collapses

into a more high entropy, more directional state. And I'm just wondering, like, what

your intuitions are in terms of, like, if there's some sort of origami, like cells origami,

and if you pump up their energy, they sort of unfold. And then when you release the energy,

they sort of collapse. What's that look like?

Yeah, that's an interesting way of thinking about it. You know, what we see from our work in

non-neural cells is that, you know, we're not going to be able to see the energy that's going to

voltage change is slow and gradual. Now, all

be coming out of the cell. All of these things are relative because it has a unit associated

with it. So they seem slow or not to us, but it's all relative, of course, but slower than what you

see and much slower than what you see in neuroscience. And the symmetry breaking that we see is spatial

at the level of a multicellular collective. So you have an initial homogenous pattern

of cells and you've got these, just for example, you could set up these local amplification

long range inhibition loops that basically symmetry will break a certain cell will well

let's say depolarize and become an organizer or something and it will automatically tell everybody

else basically you you don't do it i'm doing it it will suppress everybody else right so that's a

that's an example um of uh of that kind of thing and and but but in any case much like with touring

patterns you can have uh symmetry breaking and spontaneous pattern formation in electrical

with with no underlying hardware differences you know so purely at the level of the physiology

that can happen so so yeah so so we see that as a multi-scale a kind of thing uh well one one thing

that i've always wanted to do and i have a student that's actually gonna gonna try it finally is uh

do some of the voltage mapping uh okay we've already found we've already mapped in in within

individual cells the voltage is not homogeneous so we already know there are patterns within single

cells but but

most single cells are kind of featureless in the plane so we so what i want to do is work um examine

some some very highly patterned cells so some ciliates you know we're talking paramecium

luckily you know this this kind of thing that has that has very very uh very complex patterns

yeah, so what does the voltage look like, right, within a single cell? Are there regions?

I'm almost certain. We did a little bit of Stentor, I think, in an old Danny Adams paper from my group

like a while back, but there needs to be a lot more of this done.

Nice, nice. Yeah, that seems really interesting. And I think like one question that comes up in

thinking about this a lot is like, you know, how do you proxy the internal structure of the cell?

Like, you

And what do we even mean by sort of internal structure? So, just in terms of like

cell membrane polarity, Nick Lane has some great pieces. I think he gave a talk about what is a

feeling in biophysical terms and like talked about sort of different, like,

what are the different places on the membrane would be the configuration of the electrical

membrane would correspond to sort of how the cell might feel. And I thought that was a really clever

approach. Another sort of cluster of ideas, and I know that, so I've been speaking with Ben Anderson

and his team.

and a friend.

Yeah, I've been talking to a friend, Nick Ford, about this a lot. And it's basically this idea of

is the water within the cell structured? And this, you know, gets into Gilbert Ling's work,

Albert St. Georgi and so on. And like this could be like an interesting proxy for what

else is happening in the cell, but it also could be sort of causal in this, in a sense. But anyway,

it should be a very sensitive topic. And I think that's a really good point. I think it's a really

sensitive thing. And so I guess, have you spent much time thinking about what could be happening

with like the water and the hydration shells around proteins and so on and so on?

No, I haven't. I mean, it's certainly an interesting thing. You know, Jerry Pollack has

written about this kind of stuff a lot. I'm sure there's something to it. We have not studied it

much. We have not. Okay.

Yeah.

Yeah. It's just beyond, you know, I've got my hands full at this point with all the stuff we do,

and I don't have any expertise in that anyway. But there are a number of people looking at it,

and I'm sure there's something there.

Yeah. Yeah. Cool. Yeah. I mean, Martin Picard has also written about sort of Christie alignment. I

might be pronouncing it wrong, but basically how mitochondria in the cell kind of align or

can get disordered as well.

I guess like my optimistic hope here is that a lot of these metrics might sort of overlap.

That, you know, if you can measure Christie alignment, you're also proxying water structure,

and you're also proxying EM fields, you're also proxying, you know, anything that sort of matters.

But that's very weakly held.

Yeah.

Yeah. Yeah. I tend to think that pretty much all the materials inside a cell are A, being hacked by

all the stuff around them. They're being used as a memory medium. They're being manipulated

and conversely have their own, some degree of an agenda of what they're going to do in terms of

various ends to the goal states they're trying to achieve. I would think that water was probably

part of that.

Right. Right. Yeah.

Yeah. I guess like to sort of put a...

To sort of try to say something real about sort of sensation and cells and whatnot. I

think it's very important to think about the kinds of possible ways that cells can sort

of depolarize or collapse into a sort of less lower charge state.

When you say low to mid hundreds of ways, do you mean the channels that are causing

it or do you mean the specific physiological states that they can then occupy?

The specific physiological states, which will definitely be like correlated with

the channels.

So if we pretend the whole membrane has one value, then...

Yeah.

you're talking about a scale that basically goes from roughly zero to roughly minus 80,

something like that. And as far as we can tell,

the cells are only sensitive to plus or minus five millivolts,

any given cell.

Like it's probably not going to read any finer than that.

So that tells you, right, that you've got a small number of tens of distinct states.

However, the cell membrane is not a single value.

When we've looked at it, the domains that can be different voltages are about two to

five microns in size.

So potentially, potentially a cell could be like a soccer ball of different polygons or

whatever on it.

So that's a lot more.

And then, right, so that would be, you know, I don't know, probably in the thousands, I

guess.

Yeah. Interesting.

Because my guess is, and so we don't know how finely cells react to that, you know, how

finely do they read that whole manifold.

But my suspicion is that it can matter, that there is a code there that it can, you know,

that it can interpret.

Yeah. Interesting.

So I guess the follow up question there would be, it's like, if there are dangers to cells,

if like there's some acid or there's a predator, there's like some bad condition, you know,

somewhere, or there's some good conditions.

Yeah.

And then you know, close by and it's like what components of cells would the cell want to be

very protective of?

It's like, so just to like tell this sort of very simple story with water structure, you know,

Ling talks about how the water in the cell is sort of structured around proteins and sort of

proteins kind of get unfurled and then water sort of being a dipole molecule, it sort of

attaches to the charge sites and then other water attaches there and they sort of hold

the dipole such that it's a little bit more polarized. And in theory, you can get sort

of chains of water molecules, sort of hydrating proteins. And then just like trying to tell

the story about how Ling thought of this as like the living state and it's kind of a delicate

balance. And then if you have something like hydrogen ions kind of trying to bump into

this, it would disorder this system. And so it would be kind of a danger and kind of it

would lead to symmetry breaking of this water matrix.

In a specific sort of taste or flavor. Likewise, you'd have something like amino acids with

hydrophobic side chains. So things that taste bitter. And if this bumped into this water

matrix, it would also disorder. They would also sort of lead to symmetry breaking, but

in a different motif with sort of a different flavor.

So I guess I'm...

I'm looking at sort of cell microstates as sort of corresponding to various symmetry

breaks of this water matrix. Now, this is very loosely held, but I guess I think like

a big question is like, what is the cell trying to preserve? What is the cell trying to protect?

And like, this is one case where I'm like, I'm going to try to find out what the cell

candidate uh but it's like sensory states will sort of revolve around like the core

things that the cell wants to maintain we can say yeah yeah interesting um i think that's i think

that's a good uh that's a good thing to to think about this there's an there's another issue here

to think about which is in in induced versus intrinsic motivation so in our and this is just

the beginning so i i'm not certain about you know what what the what the bigger picture is going to

be but but in our in our work on sorting algorithms yeah these are short deterministic

algorithms to sort numbers what we found is that there's the thing that

we make it do via the algorithm which is to sort numbers and yeah sorts numbers all right but also

there are these weird side quests that it takes that are nowhere in the algorithm

They're not prohibited by the algorithm, but neither are they you know uh instantiated by it they're sort of and so and so you can loosely i so so i've been playing with this with this notion of there's the there's the reward function that we force on it but but then there's the intrinsic motivation so i think you can sort of see that in biology too so evolution would be

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say you have to do this and this but in the meantime there's some others that as long as

you don't interfere too badly with it too much of the time you're also free to do some other stuff

and what that other stuff you know we're used to i think we're very used to looking at biological

functions from that evolutionary lens and saying okay why is it doing that that's got to be good

for reproduction or it's got to be it's got to be a side effect of something else that's good

for reproduction or you know but i have a feeling that that there's also a bunch of other stuff that

things are doing even very simple things are probably doing other things that i don't think

are coming directly from any of their experiences in the physical world maybe you know whether we

can flesh out a theory of of platonic space for it or yeah i don't know but you know it's like

it's like some of the stuff that we see the xenobots and the anthropos doing like they do

things that were clearly evolutionally

important for their primary goal but or their primary lifestyle i should say but when you take

them out of that lifestyle then you get to find out here's what it would be doing if the other

cells didn't force it to be a two-dimensional skin layer on the outside of the embryo right

and normally all that is suppressed and it's it's sort of um uh you know it's like it's like uh

you know forcing a kid to sit in class and do math you don't get to find out what else you'd

be doing if you if you weren't doing that right but if you but if you let up to some extent then

then then you get to find out what the intrinsic motivation is and then possibly possibly you work

with that right so there are of course you know educational um uh philosophies that that target

that as opposed to trying to you know do a a strict reward function so so i wonder you know

when we look at these cells i wonder how much of that and and that also relates to some other

conversations that i've had with other people about whether problem solving so we've cashed out

all when we study intelligence i define it as a problem solving problem solving is a problem solving

it as problem solving so goal directed problem solving but that's just for convenience there are

of course other aspects of being cognitive that have nothing to do with with that they're you

know just play exploration right there's all this other stuff that isn't captured by the by this

kind of thing and so we talked about what does that like we all know what it looks like when

birds and mammals play so you can see crows doing these things you know they're sliding down roofs

on these on these little little flat things that they've you know found somewhere and they you know

clearly like they're just having fun you can you can see it it's not anything useful that they're

doing so so the question is what do you think about that and what do you think about that and

question is what what does it look like when cells do this so right so there's the evolutionary

extrinsic motivation like yeah you have to keep your ph in this level if you don't do that you're

going to die fine right but alongside of that what does what does play look like on the cellular scale

what what else are you doing and you know and and these you know some people say well cells are too

simple to do that if six if if bubble sword can do it i'm pretty sure cells can do it and and you

know and so i think i think we're just bad at noticing it is all it is and we need to uh

as much as we've been focusing on intelligence and problem solving we we or somebody needs to

needs to develop uh some tools to be able to recognize play and exploration in unconventional

embodiments yeah yeah nice that's great um i'm i'm such a fan of your your work on xenobots

yeah good stuff um and i guess like i was thinking about you know what is

you know if we if we take the perspective of um cells as quality pixels

um uh although i i keep wanting to use the word quarks as quality pixels

i'm getting some pushback on that but um but then uh uh you know taking a look at like what is like

a lone xenobot uh look like as a as sort of a dynamic quality pixel how does it how does its

value change uh in different environments and like is it this sort of unitary pixel

uh that the best way to look at it is like okay like there's the xenobot there's the cell and it

has a value or um is it heterogeneous and that you know maybe we could think of its mitochondria

as its pixels um so yeah yeah i don't have a clear answer there yeah yeah it's a good question

that's a good question uh

yeah yeah i don't know i guess i guess we'll have to we'll have to see uh

to what extent we end up needing to solve some kind of a summation function

Function or not, or whether it's just completely different types of consciousness that shows up when you make a particular interface. Yeah, I'm not sure how much, how constraining the parts are.

Yep.

For what you get. The causal architecture is clearly important in some way, but.

Right, right. Yeah, and I guess this gets into questions of,

does consciousness require definite extension and location in space and time?

Or can it be more of a logical computational thing?

Yeah. I mean, I tend to think that

a particular embodiment of consciousness

will have location in space and time. That location will be fuzzy to some extent because

there is no unified, there is no indivisible intelligence anywhere. We're all made of parts,

we're all collective intelligence. And so, I don't know, once you get to electrons or something,

I don't know what the deal is in physics. So it's going to be a little bit fuzzy.

But the other question that this brings up is, to what extent are there lateral interactions within

space in addition to, among things that are not currently coming through any interface?

Because if they are not static, which I strongly suspect is the case,

then there will be some sort of its own chemistry of patterns in that space that

are doing things regardless of their connection in the physical world.

Yeah.

And those things, then how much spatiality there is, I sort of, I can sort of imagine that it's not,

it's not spatial the way we're, it doesn't have a location the way we're used to,

but it's much more, it's almost like a, it's almost like a content addressable memory instead

of a location addressable, right? So instead of saying, this is where this information is,

it's like, well, what is this information about? Well, then it must be somewhere near this other

thing, which is about the same thing, right? So something like that.

Yeah, that makes sense. Interesting. I guess one, one sort of,

for this sort of analysis is that it's always a question of, for me, so I guess nine years ago now,

I had came out with this symmetry theory of valence. And sort of similar to what you've said

about sort of geometric frustration is real frustration. And it's sort of, if we had a

mathematical representation of an experience, the

symmetry of this representation would correspond to the pleasantness of the experience. So wrote a

short book on this. Yeah. And so it's sort of, it's speaking about, you know, a formalism of an

experience and, you know, not necessarily making a big claim in terms of how to create the formalism,

but if we had a formalism, how to interpret it. Yeah. And then, but I, I'm always eager

to, you know, to, you know, to, you know, to, you know, to, you know, to, you know, to, you know,

to try to apply it to biological systems. Yeah. And, you know, there's, there's been a lot of

questions about, well, how do you apply it to, to brain or to a nervous system? And I guess I'm,

I'm optimistic that it can be applied to like a cells symmetry group. Although there's a big

question of how do you coarse grain a cell symmetry group? I think, you know, I think

it'd be interesting to try to apply some of these things to,

data in, for example, transcriptional space, right. So omix data. What does, what does symmetry,

you know, beauty, what does all that stuff look like in the, in that space?

We're already trying to think about, what does it look like to have

barriers? What does it look like to have, you know, what does a mirror test look like in,

in, in, you know, in transcriptional space? Yeah. It's been very hard to think about these things

cause we're so obsessed with the three-dimensional world and so on. But I feel like all this can be

defined and, and so I'm looking at it in a more qualitative and qualitative perspective.

and it would be interesting to see what does symmetry breaking look like in that,

you know, in these other spaces.

Right, right.

Well, yeah, one thing that comes to mind is I do think that symmetry breaking

is directional, which is, it's a very useful property.

So it's like you have the symmetries of a system,

and like a starfish is a pretty simple example where it's just basically

a ring of neurons.

And then

symmetry is the success condition. It's like homeostatic success. And then if a fish comes and starts nibbling on a leg, then the symmetry gets broken in a way that the different parts of an organism can tell where the problem is. There's kind of a lensing effect. And then the starfish can move or

adapt to that. And then once it's safe again, the symmetry gets restored. So I guess in terms of looking at transcription networks and so on, I don't know. But it does seem like there's some sort of directional high information perspective that the system can take, such that if you have a

symmetry and it gets broken, then every part of the system knows a little bit about where the problem is.

That's very interesting. So what I'm hearing is symmetry breaking as a cognitive glue. So some kind of non-locality that is... And yeah, I can see how that would be connected to geometric

illustration, right? And so, yeah. And you'd have some sort of propagation speed of light thing for getting it around. But yeah, I think that's very interesting. I think that's worth more development as symmetry breaking as a binding set of policies that create the collective intelligence. I think that's a cool idea.

Yeah, nice.

Yeah.

Nice.

Yeah.

Nice. Well, I'm mindful of your time. But yeah, any other cool things to talk about?

Let me think.

I was looking at my notes. We covered most of what I wanted. Yeah, let's go off and think about this symmetry breaking business.

And we could...

Yeah, I think we could look for it if we knew how to recognize it in the abstract, like in its general form.

Yeah.

Yeah.

Yeah.

Yeah. I mean, just a few words on that. So there are different numbers of symmetry in different dimensions. I think in 2D, there are 17 sort of wallpaper symmetry groups.

Like, you know, different ways things can...

get flipped or rotated. Frank Wilczek has this nice

um definition of symmetry as change without change yeah yeah anytime you can apply an

operation to a system but leave it yeah same thing yeah and then there there's something

like you know 300 plus symmetries in in 3D and I think almost 4,000 and 4D although you might

want to check my numbers on this uh but yeah um and then I guess I see in yeah go ahead so I was

just gonna say that that by itself is one of these you know people people often ask you what you know

what do you mean by by by facts that that don't have a physics I mean that right there like like

the number of these groups at under various circumstances that's just what it is yeah yeah

that's just that's just how it is that's it there's no you know there's no there's no fact

of physics there's no history there's nothing that's gonna that's gonna I mean you know underlie

that as a more reductive explanation it just is what it is yeah totally I think I think that's

really interesting yeah

nice yeah I I also see that uh so the the symmetries of a self-organizing system

um I almost see as sort of kaleidoscopic grooves that the system can sort of follow to get back

into a state of order um so my water has uh I believe tetrahedral symmetry uh so I I believe

that's exactly like 24 symmetry

um and

like

and like every symmetry is sort of a

every symmetry is sort of a

every symmetry is sort of a like I I think of them like as like grooves

like I I think of them like as like grooves

like I I think of them like as like grooves in a kaleidoscope that you can kind of

in a kaleidoscope that you can kind of

in a kaleidoscope that you can kind of um uh

um uh

um uh follow back to order

follow back to order

follow back to order and it would be interesting to do this

and it would be interesting to do this

and it would be interesting to do this sort of analysis for your Gene regulatory

sort of analysis for your Gene regulatory networks I I yeah I think I think it

networks I I yeah I think I think

the calculation. But I think that would be quite interesting. Yeah. Yeah. Yeah. You know, we have, of course there's genomic data, there's transcriptomic data. We have electrophysiological data. We have lots of simulations so we could look at it in, you know, sort of in silico

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that.