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

The following is a conversation with Michael Leaven,

his second time on the podcast.

He is one of the most fascinating

and brilliant biologists and scientists

I've ever had the pleasure of speaking with.

He and his labs at Tus University

study and build biological

systems that help us understand

the nature of intelligence,

agency, memory, consciousness, and

life in all of its forms here on Earth and beyond.

This is the Lex Freedman podcast.

To support it, please check out our sponsors in the description

where you can also find

links to contact me,

ask questions, give feedback, and so on.

And now, dear friends,

here's Michael You write that the central question at the heart of your work

from uh 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 uh begin to answer it?

>> Well, the fundamental tension

is in both the first person,

the second person and third person descriptions of mind.

So, so in third person,

we want to understand how do we recognize them and how do we know

looking out into the world what degree of agency there is and how best

to relate to the different

systems that we find and uh are our intuitions

any good when we look at something and it looks really

stupid and mechanical versus

uh it really looks like there's something

cognitive going on there.

How do we get good at recognizing them?

Then there's the second person which is the control

and that's both for engineering but also for regenerative medicine.

when you want to tell the system to do something

right, what kind of tools are you going to use?

And this is a major part of my framework is that all of these

kinds of things are operational claims.

Are you going to use

the tools of hardware rewiring,

of control theory and cybernetics,

of behavior science, of psychoanalysis

and love and friendship?

Like what are the interaction protocols that you bring, right?

And then in first person, it's this notion of having an inner perspective

and being a system that has veilance and cares about the outcome of things,

makes decisions and has memories and tells a story about itself and the outside world.

And how can all of that exist and still be consistent with the laws

of physics and chemistry and various other things that that we see around us?

So that that I find to be maybe the most interesting and the most

important mystery for all of us to uh

both on the science and also on the personal level.

So that's that's what I'm interested in.

So your work is focused on

starting at the physics

going all the way to friendship and love and >> Yeah.

Although although actually I would turn that upside down.

I I think that pyramid is backwards and I think it's behavior science at the bottom.

I think it's behavior science all the way.

I think in certain ways even math is the behavior of a certain kind

of being that lives in a latent space.

And physics is what we call systems that at least look to be amendable

to a very uh simple low agency kind of model. and so on.

But uh but that's what I'm interested in is understanding that and developing applications

because it's very important to me that

uh what we do is transition

deep ideas and philosophy

into actual practical applications that not only make it clear whether we're making any

progress or not but also allow us to relieve suffering and make life better

for all sensient beings and and enable to uh you know enable us and

others to reach their full potential.

So these are these are very practical things.

I think behavioral science I suppose is more subjective and mathematics

and physics is more objective.

Would that be the 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 from a philosophical armchair.

You have to make a hypothesis

about which tools, which interaction protocols

you're going to bring to a given system and then we all get to

find out how that worked out for you, right?

So, so you could be wrong in many ways in both directions.

You can guess too high or too low or wrong in various ways and

then we can all find out how that's working out.

And so I do think that

the behavior of certain objects

is well described by specific formal

formal rules and we call those things the the subject of mathematics.

And then there are some other things whose behavior really requires

the kinds of uh tools that we use in in behavioral cognitive neuroscience.

And those are other kinds of minds that that we think we study

in biology or in psychology or other

>> Why why are you using the term persuadability?

Who are you persuading and of what?

>> in this context, >> yeah, the beginning of my work is very much in regenerative

medicine, in uh in

bioengineering, things like that.

So, for those kinds of systems, the re the question is always

how do you get the system to do what you want it to do?

So, there are cells, there are molecular networks, there are materials,

there are organs and tissues and synthetic

beings and biobots and whatever.

And so the idea is if I want your cells to regrow a limb,

for example, if you're injured and I want your cells to regrow a limb,

I have many options.

Some of those options are I'm going to micromanage

all of the molecular

uh events that have to happen, right?

And there's an incredible number of those.

Or maybe I just have to micromanage

the cells and the stem cell

kinds of signaling factors.

or maybe actually I can give the cells a very high level

uh prompt that says you really should build the limb and convince them to

do it right and so where

um what which of those is possible

I mean clearly people have a lot of intuitions about that if you ask

standard people in regenerative medicine and molecular biology

they're going to say well that convincing thing is crazy

what we really should be doing is talking to the cells or better yet

the molecular networks and in fact all the excitement

of the biological sciences today are at at you know single molecule approaches and

big data and and and genomics and all of that.

The assumption is that

uh going down is where the action is going to be going down in

scale and and I think that's I think that's wrong.

But the but the thing that we can say for sure is that

you can't guess that

you you have to do experiments and you have to see because you don't

know where any given system is on that spectrum of persuadability.

And it turns out that every time we look

and we take tools

from behavioral science, so learning

different kinds of training, different kinds of

models that are used in uh active inference and

surprise minimization and uh perceptual multi-stability

and visual illusions and all all these kinds of interesting things, you know, stress

perception and and memory

um active memory reconstruction.

and all these interesting things

when we apply them outside the brain to other kinds of living systems we

find novel discoveries and novel capabilities

actually being able to get the material to do new things that nobody had

ever found before and and precisely

because I think that

uh people didn't didn't look at it from from those perspectives

they they assumed that it was a low-level kind of thing so when I

say persuadability I mean

different types of approaches

right and we all and we all know if you want to if you

want to persuade your windup clock to do something

you not going to argue with it or make it feel guilty or anything.

You're going to have to get in there with a wrench and you're going

to have to, you know, tune it up and do whatever.

If you want to do that same thing to a cell or a thermostat

or an animal or a human, you're going to be using other sets of

tools that we've given other names to.

And so that's now now of course that spectrum, the important thing is that

as you get to the right of that spectrum, as the agency of the

system goes up, it is no longer just about persuading it to do things.

It's a birectional relationship,

what Richard Watson would call a mutual vulnerable knowing.

So the idea is that

on the right side of that spectrum,

when systems reach the higher levels of agency,

the idea is that

you're willing to let that system persuade you of things as well.

You know, in molecular biology, you do things hopefully the system does what you

want to do, but you haven't changed.

You're still you're still exactly the way you you came in.

But on the right side of that spectrum, if you're having interactions

with even cells, but certainly, you know, uh dogs, other other

animals, maybe maybe other other creatures soon,

you're not the same at the end of that interaction as you were going in.

It's a mutual birectional relationship.

So it's not just you persuading something else.

It's not you pushing things.

It's a it's a mutual birectional

set of uh set of persuasions

whether those are purely intellectual or of other kinds.

>> So in order to be

effective at persuading an intelligent

being, you yourself have to be persuadable.

So the closer in intelligence you are to the thing you're trying to persuade,

the more persuadable you have to become.

Hence the mutual vulnerable knowing. What a term. >> Yeah. Yeah.

Richard, yeah, you should you should talk to Richard as well.

He's he's an amazing guy and he's got some very interesting ideas about at

the intersection of cognition and um evolution.

But I, you know, I think I think what you bring up is is

very important because um

there has to be a kind of impedance

match between what you're looking for and the tools that you're using.

I think the reason physics

always sees mechanism and not minds is that physics uses low agency tools.

You've got voltmeters and and rulers and things like this and and if you

use those tools as your interface,

all you're ever going to see is mechanisms

and and those kinds of things.

If you want to see minds,

you have to use a mind, right?

You have to have there has to be some degree of resonance between your

interface and the thing you're hoping to find.

>> You said this about physics before.

Can you just linger on that?

Like expand on it.

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's a lot of physicists that believe they can

understand life, the emergence of life, the origin of life,

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 understand fundamentally

anything, you have to start a physics to them.

And you're saying, "No, physics is not enough."

>> Here's here's the issue.

Everything here hangs on what it means to understand. Okay?

in for for me because

understand doesn't just mean

uh have some sort of uh

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.

And so for me that means if I tell you this is what I

think about cognition in cells and tissues,

it means for example that

uh I think we're going to be able to take those ideas and use

them to produce new regenerative

medicine that actually helps people in various ways. Right?

is 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

uh perspective of of fields and particles and then you know who knows what

what else is at the bottom there.

Does that mean then that when somebody is missing a finger or has a

psychological problem or or or or

you know has these other highle issues that you have something for them that

you're going to be able to do something because my claim is that you're

not going to and even even if even if you you have some theory

of physics that is completely compatible

with everything that's going on that is it's not enough that's not specific enough

to enable you to solve the problems you need to solve.

In the end when you need to solve those problems

the the person you're going go to is not a physicist.

It's going to be either a biologist

or a psychiatrist or

who knows but but it's not going to be a physicist.

And and the simple example is this,

you know, let's say let's say someone uh comes in here and tells you

a beautiful mathematical proof.

Okay, it's just really, you know, deep and beautiful.

And there's a physicist nearby and he says, "Well, I know exactly what happened.

I there were some air particles that moved from from from

that guy's mouth to your ear.

I see what goes on.

It moved your uh the psyia

um in your ear and the and the electrical signals went up to your brain.

I mean we have a complete accounting of what happened done and done.

But if you want to understand what's the more important aspect of that interaction,

it's not going to be found in the physics department.

It's going to be found in the math department.

So that's my only claim is that is that physics is an amazing lens

with which to view the world, but you're capturing certain things

and and if you want to stretch

to sort of encompass these other things,

it it's just we just don't call that physics anymore, right?

That's we we call that something else. >> Okay.

But you're kind of speaking about the

uh super complex organisms.

Can we go to the simplest possible thing

where you first take a step over the line,

the cartisian cut as you've called it from the

non- mind to mind,

from the non-living to living is simplest possible thing.

Isn't that in the realm of physics to understand?

How do we understand that first step

where you're like that thing is

no mind 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 and can physics help us understand it >> yeah let's talk

about well first of all of of course it can

mean it can help meaning that I'm not saying physics is not helpful of

course it's helpful it's 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 um that question

is I I don't believe in any such line.

I don't believe any of that exists.

I think uh I think there is a um

I think it's a continuum.

I think we as humans like to

uh demarcate areas on that continuum and give them names because it makes life

easier and then we have a lot of battles over

uh you know so-called category

errors when people transgress those those categories.

I think most of those categories

at this point they they may have done some some good service at the

beginning of when the scientific method was getting started and so on.

I think at this point uh they mostly hold back science.

Many many categories that we can talk about are at this point

very harmful to progress

because what those categories do is they prevent you from porting tools.

If you think that

uh living things are fundamentally

different from non-living things or if you think that cognitive

things are these like advanced

brainy things that are very different from other kinds of systems,

what you're not going to do is take the tools that are appropriate to

these to to these kind of uh cognitive systems, right?

So the so the tools that have been developed in in behavioral science and

so on, you're never going to try them in other contexts

because because you've already decided that there's a categorical

difference that it would be a

categorical error to apply them and and people say this to me all the

time is that you're making a category

error and as as if these categories were given to us, you know, from

from from on high and we have to we have to obey them forever more.

The category should change with the science.

So um yeah I don't believe in any such line and I think 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. Okay.

So if there's no line

is it still useful to talk about things like the origin of life.

That's the the one of the big open mysteries

before us as a human civilization,

as uh 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 invention right there

was the start of it all on Earth?

My suggestion is that

much better than trying to

in in in my experience much better than trying to define any kind of a line. Okay?

Because because inevitably I've never I've never found and people try to you know

we play this game all the time when I make my continuum claim then

people try to come up okay well what about this?

You know what about this?

And I haven't found one yet that really shoots that down that that you

can't zoom in and say yeah okay but right before then this happened and

then if we really look close like here's a bunch of steps in between right?

pretty much everything ends up being a continuum.

But here's what I think is much more interesting than trying to make that line.

I think what's what's really

uh more useful is trying to understand the transformation process.

What is it that happened to scale up?

And I'll give you a really dumb example

and we and we always get into this because people people often

really really don't like this continuum view.

The word adult, right?

Everybody is going to say, "Look, I know what a baby is.

I know what an adult is.

You're crazy to say that there's no difference."

Not saying there's no difference.

What I'm saying is the word adult is really helpful in court

because because because you just need to move things along.

And so we've decided that

uh if you're 18, you're an adult.

However, what it hides

is is what what it completely conceals is the fact that

first of all, nothing happens on your 18th birthday, right? That's that's special.

Second, if you actually look at the data, the car rental companies actually have

a much better estimate because they actually look at the accident statistics and they'll

say it's about 25

is is 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 this supposed adult.

And what is the scaling

of re personal responsibility,

decisionm judgment, like these are deep fundamental

cont, you know, questions.

Nobody wants to get into that every time

somebody uh, you know, has a traffic ticket.

And so, okay, so so we've just decided that there's this adult idea.

How and and and of course it does come up in court because then

somebody has a brain tumor or somebody's eaten too many Twinkies

or or something has happened.

You say, "Look, that wasn't me. Whoever did that?

I was on drugs."

Well, why'd you take the drugs?

Well, that was, you know, that was yesterday, me today.

This is some, right?

So, so we get into these very deep questions

that are completely glossed over by this idea of an adult.

So, so I think once you start scratching the surface,

most of these categories are like that.

They're convenient and they're good.

It it's, you know, I get into this with neurons all the time.

I I'll ask people what's what's a neuron?

Like what's really a neuron?

And yes, if you're if you're in

neurobiology 101, of course, you just say, "Look, these are what neurons look like.

Let's just study the neuro anatomy and we're done."

But if you really want to understand what's going on,

well, neurons develop from

other types of cells

and that was a slow and gradual process

and most of the cells in your body do the things that neurons do.

So, what really is a neuron, right?

So, so once you start scratching this, this this happens and

I have some things that I think are coming out of our lab and

others that are I think very interesting about the origin of life.

But I don't think it's about finding that one boom like this is yeah

there'll be there there are innovations

right there are there are innovations

that that um allow you to uh scale in a in an amazing way

for for sure and and there are lots of people that study those right

so so things that thermodynamic

kind of metabolic things and and 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

>> the scaling process but then

there is more rapid scaling and there's slower scaling so innovation

invention I think is useful to understand

so you can predict how likely it is on other planets for example

or uh to be able to

the likelihood of these kinds of phenomena

happening in certain kinds of environments

again specifically in answering how many alien civilizations

there are you that's why it's useful

but it's also useful on a scientific level

to have categories not just cuz it makes us feel good and fuzzy side

but because it makes conversation possible and productive.

I think if everything is a spectrum is it it becomes

um difficult to make concrete statements.

I think like we even use the terms of 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 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.

Ex the categories are exactly useful when there's a whole bunch of stuff.

And this is this is what's important about science is like the art of

being able to say something without first having to say everything, right?

Which would make it impossible.

So, so categories are great when you when you want to say, look, I

I I know there's a bunch of stuff hidden here.

I'm going to ignore all that and we're just going to like let's get

on with this particular thing.

And all of that is great

as long as you don't lose track of the stuff that you glossed over.

And that was what I'm afraid is happening in a lot of different ways.

And in terms of look, I'm I'm I'm very interested in in in life,

you know, beyond Earth and all all of these kinds of things.

Although we should also talk about what I call suti sui,

the search for unconventional terrestrial intelligences.

I think I think I think we got much bigger issues than than actually

recognizing aliens off Earth.

But I'll make this claim.

I think the categorical stuff is actually hurting that search

because because if we try to define categories

uh 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 embodiment.

I think we have a kind of mind blindness.

I think this is really key.

It's much to to me to me

um the cognitive spectrum is much more interesting than the spectrum of life.

I think really what we're talking about is a spectrum of cognition.

And uh it it's 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 of different types of minds I think is extremely interesting.

And to the extent that we think our categories

are complete and are cutting nature at its joints, we are going to be

very poorly placed to recognize novel systems.

So for example, a lot of people will say, well, this is intelligent

and this isn't, right?

and there's a binary thing and and and that's useful in occasionally

that's useful for some things.

I would like to say instead of that, let's make us let's let's let's admit

that we have a spectrum.

But instead of just saying, oh look, everything's intelligent, right?

Because if you do that, you're right.

You can't you can't do anything after that.

What I'd like to say instead is no, no, you have to be very

specific as to what kind and how much.

In other words, what problem space is it operating in?

What kind of mind does it have?

What kind of cognitive capacities does it have?

You have to actually be much more specific.

And and we can even name, right? That's fine.

We can name different types of I mean this is doing predictive processing.

this can't do that but it can't form memories. What kind?

Well, habituation and sensitization

but not associative conditioning.

Like it's fine to have categories for specific capabilities.

But it's it's uh

it actually I think it actually makes makes for much more rigorous discussions because

it makes you say what is it that you're claiming this thing does?

And it works in both directions.

So So some people will say well that's a that's a cell that can't be intelligent.

And I say well let's be very specific.

Here are some claims about here's some problem solving that it's doing.

tell me why that doesn't

you know why doesn't that match

or in the opposite direction somebody comes to me and says you're right you're

right you know the whole the whole solar system and it's just like this

amazing like okay what is it doing like tell me tell me what what

tools of cognitive and behavioral science are you using to to to reach that

conclusion right and so I think I think it's actually much more productive to

take this operationalist stance and say tell tell me what protocols

you think you can deploy with this thing

that would lead you to to to use these

>> 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 doing is uh me playing devil's advocate every once in a while

and you did the same

which is kind of interesting taking the opposite view you see what comes out

>> cuz you don't know the result of the argument until you have the argument

and it's seems productive to just take the other side of the argument >> for

sure it's a very important

uh thinking aid to

first of all you know what they call steel manning right to try to

try to make the strongest possible case for the other side and to ask

yourself, okay, what are all the what are all the places that I am

sort of glossing over because I don't know exactly what to say and where

all the where are all the holes in the argument and what would what

would a you know a really good critique really look like? Yeah.

>> Sorry to go back there just to linger on the term because it's so

interesting >> Did I understand correctly that you mean that it's kind of synonymous with intelligence?

So it's an engineering

centric view of an intelligence

system because if it's persuadable

you're more focused on

how can I steer

the goals of the system

the behaviors of the system

which meaning an intelligence

system maybe is a is a goal

oriented goal- driven system

with agency and when you call it persuadable

you're thinking more like

okay here's an intelligent system that I'm interacting

with that I would like to get it to accomplish certain things,

but fundamentally they're synonymous

or correlated persuadability and intelligence. >> They're definitely correlated.

So, so let me I want to I want to um preface this with with one thing.

When I say it's an engineering perspective,

I don't mean that the standard

uh tools that we use in engineering

and this idea of of enforced

control and steering is how we should view all of the world.

I'm not saying that at all and and and I want to be very

clear on the because because because because because

people do email me and say nah this engineering thing you're going to drain

the you know the life and the majesty out of these high-end like human conversation.

My whole my whole point is not that at all.

It's that uh of course at the right side of the spectrum it doesn't

look like engineering anymore right it looks like 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 just imagine if I you know if I if I went to

a bioengineering department or a genetics department and I started talking about highle

you know cognition and psychoanalysis

right they don't want to hear that so so I I bring my

I focus on the engineering approach because I I want to say look

>> this is not a philosophical

problem this is not a linguistics

problem we are not trying to uh define terms in different ways to make

anybody feel fuzzy what I'm telling you is if you want to reach certain capab capabilities.

If you want to reprogram

cancer, if you want to regrow new organs, you want to defeat aging, you

want to do these specific things,

you are leaving too much on the table by making an unwarranted

assumption that the low-level

tools that we have, so these are the rules of chemistry and the kind

of remlecular rewiring that those are going to be sufficient to get to where

you want to go.

It's a it's a it's an assumption only and it's an unwarranted

assumption and actually we've done experiments now.

So, so not philosophy but real experiments that if you take these other tools

you can in fact persuade the system in ways that has never been done

before and and and we can we can unpack all of that but it

is it is absolutely

um correlated with intelligence.

So let me um flesh that out a little bit.

Um what I think is scaling in all of these things right because I

keep talking about the scaling.

So what is it that's scaling?

What I think is scaling

is something I call the cognitive ly cone.

And the cognitive lyone

is the size of

the biggest goal state

that you can pursue.

This doesn't mean how far do your senses reach.

This doesn't mean how far can you affect it.

So the James Web telescope has enormous sensory reach.

But that doesn't mean that's that's the size of its cognitive ly.

The size of the cognitive ly

is the scale of the biggest goal you can actively pursue.

But I do think it's a useful concept to enable us to think about

very different types of agents

of different composition, different provenence,

you know, engineered, evolved,

hybrid, whatever, all in the same framework.

And by the way, the reason I use Lyone

is that it has this idea from physics that you're putting space and time

kind of in the same diagram, which is which which I like here.

So if you tell me that

all your goals revolve

around maximizing the amount of sugar con the amount of sugar in this in

this you know 10 20 micron

radius of spacetime and that you have you know 20 minutes memory going back

and maybe 5 minutes predictive capacity going forward that tiny little cognitive light I'm

going to say probably a bacterium

and if you say to me that well I care I'm able to care

about several hundred yards

sort of scale I could never care about what happens 3 weeks from now

two towns over just impossible.

I'm say you might be a dog

and if and if you say to me okay I care about

uh really what happens you know the financial markets on earth the you know

long after I'm dead and this and that say you're probably a human

and if you say to me

I care in the linear range I actively not 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 humans I don't

these standard humans today I don't think can do that you you must be

some kind of a body or some other thing that has these massive cognitive icons.

So I think what's scaling

from zero and I do think it goes all the way down.

I think we can talk about um

uh even even particles

doing something like this.

I think what scales

is the size of the cognitive icon.

And so now this is an interesting here.

I'll I'll try for a definition of life or whatever for whatever it's worth.

I spent no time trying to make that stick, but if we wanted to,

uh, I think we call

things alive to the extent

the cognitive light cone of that thing is bigger than that of its parts.

So, in other words, rocks aren't very exciting because the things it knows how

to do are the things that its parts already know how to do, which

is follow gradients and and things like that.

But living things are amazing at aligning their

their competent parts so that the collective has a larger cognitive lie than the parts.

I'll give you a very simple example that comes up in in biology and

it comes up in our cancer um program all the time.

Individual cells have little tiny cognitive lyones.

They what are their goals?

Well, they're trying to manage pH,

metabolic state, some other things.

There are some goals in transcriptional

space, some goals in

uh metabolic space, some goals in

uh physiological state space, but but they they're generally very tiny goals.

One thing evolution did was to provide a kind of cognitive glue, which we

can also talk about

that ties them together

into a multisellular system.

And those systems have grandiose goals. They're making limbs.

And and if you're a salamander

limb and you chop it off, they will regrow that limb with the right number of fingers.

Then they'll stop when it's done.

the goal has been achieved.

No individual cell knows what a finger is or how many fingers you're supposed

to have, but the collective absolutely does.

And that process of growing that cognitive ly from a single cell to something

much bigger and of course the

failure mode of that process. So cancer, right?

When cells disconnect, they physiologically

disconnect from the other cells, their cognitive ly shrinks.

The boundary between self and world, which is what the cognitive ly defines, uh 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 amibbas do.

They go where life is good.

They reproduce as much as they can. Right?

So that that cognitive lie

that that that is the thing that I'm talking about that scales.

And so when we're looking for life,

I 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 lone.

We're looking for alignment

of parts towards bigger goals in spaces that the parts could not comprehend.

And so cognitive ly cone just to

uh make clear is about goals that you can actively pursue now.

You said linear like within reach >> No, I didn't.

Sorry, I didn't mean that.

First of all, the goal necessarily

is is often removed in time.

So in other words, when you're pursuing a goal, it means that you have

a separation between current state and target state at minimum your your thermostat, right?

Let's just think about that.

there there is a separation in time because

the thing you're trying to make happen so that the temperature goes to a

certain level is not true right now and all your actions are going to

be around reducing that error right that basic homeostatic

loop is all about closing that that gap when I meant when I said

linear range this is what I meant

uh if I say to you

this this terrible thing happened to

uh you know 10 people

and and you know you have some some degree of activation about it and

then they say no no no actually it was 100

you know 10,000 You're not a thousand times more activated about it.

You're somewhat more activated, but but it's not a thousand.

And if I say, "Oh my god, it was actually 10 million people."

You're not a million times more activated.

You you don't have that capacity in the linear range, you sort of you sort of, right?

If you think about that curve, we sort of 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 like, can you grow your cognitive

system to the point that yeah, it really isn't just your family group.

It really isn't just the hundred people you know in your in your you know circle.

Can you grow your cognitive

um lightco to the point where no no we care about the whole whether

it's all of humanity or the whole ecosystem or the whole whatever.

Can you actually care about that the exact same way

that we now care about a much smaller

um set of people.

That's what I mean by linear

>> But you say separated by time like a thermostat.

But a bacteria, I mean,

if you zoom out far enough, a bacteria

could be formulated to have a goal state of creating human

Because if you look at the, you know,

>> has a role to play in the whole history of Earth.

And so if you anthropomorphize

the goals of a bacteria

enough, I mean it has a concrete

role to play in the history

of the evolution of human civilization.

So you do need to

when you define a cognitive light cone,

you're looking at directly short-term behavior. >> Well, no.

How do you know what the

cognitive ly cone of something is?

Because as as you've said, it could be it could be almost anything.

The key is you have to do experiments

and the way you do experiments

is you put barrier you have to do interventional experiments.

You have to put barriers between it and its goal and you have to

ask what happens and intelligence

is the degree of ingenuity

that it has in overcoming

barriers between it and its goal.

Now if it were to be that now now this is the this this

is I think a totally

doable but but impractical

and very expensive experiment but you could imagine

setting up a scenario where the bacteria were blocked from

becoming more complex and you can ask if they would try to find ways

around it or whether it's actually nah their goals are actually metabolic and as

long as those goals are met they're not going to actually get around your barrier.

The the the this this this

business of putting barriers between things and their goals is actually extremely

powerful because we've deployed it in all kinds of and I'm sure I'm sure

we'll get to this later, but we've we've deployed it in all kinds of

weird systems that you wouldn't think are goal- driven systems.

And what it allows us to do is to get beyond

just the the the

what you call anthropomorphizing

claims of say you know saying oh yeah I think you know I think

this is thing is trying to do this or that.

The question is well let's do the experiment.

And one other thing I want to say about anthropomorphizing

is people people say this to me all the time.

Um I I I

don't think that exists.

I think that's kind of like

you know uh uh and I'll I'll tell you why.

I think it's like

heresy or like uh other other terms that

aren't really a thing because if you if you unpack it, here's here's what anthropomorphism means.

Humans have a certain magic

and you're making a category

error by attributing that magic somewhere else.

My point is we have the same magic that everything has.

We have a couple of interesting things besidg

and some other stuff.

And it isn't that you have to keep the humans

separate because there's some bright line.

It's just it's it's that same old

uh all all I'm 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 the humans are this and let's not uh 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

found look at that.

That thing actually can predict the future for the next, you know, 12 minutes. Amazing.

Or you say, you know what, I've tried all the things in the behaviorist handbook.

they just don't help me with this.

It's a very low level of like that's it.

It's it's a very low level of intelligence. Fine. Right. Done.

So that's really all I'm arguing for is an empirical approach and then things

like anthropomorphism go away.

It's just a matter of have you done the experiment and what did you

>> And that's actually one of the things you're saying that

uh if you remove the categorization

of things, you can use the tools

>> of one discipline on everything.

>> You can try >> to try and then see.

That's the underpaintings of the criticism

because uh what is that?

That's like psychoanalysis of another human

could technically be applied to

to robots to AI systems

to more primitive biological

systems and so on. try.

Yeah, we've used everything from

basic habituation conditioning all the way through

anxolytics, hallucinogens, all kinds of cognitive modification

on the range of things that you wouldn't believe.

And by the way, I'm not the first person to come up with this.

So, there was a guy named Bose

well over a hundred years ago,

who was studying how anesthesia

affected animals and animal cells

and drawing specific curves around electrical excitability.

And he then went and did it with plants

and saw some very similar phenomena.

And being the genius that he was, he then said, "Well, how do I

don't know when to stop, but there's no there's no, you know, everybody thinks

we should have stopped long before plants cuz people made fun of him for that."

And he's like, "Yeah, but 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 and and other kinds of materials, right?

And so uh the interesting thing is that

yeah there is no there is no

uh you know generic rule that tells you when

uh when do you need to stop.

We make those up.

Those are completely made up.

You have to just you have to do the science and find out. >> Yeah.

You uh we'll probably get to it.

Uh you've been doing recent work on looking at computational systems even trivial ones

like algorithms sorting >> and analyzing in the behavioral kind of way.

See if there's minds inside those sorting algorithms.

And it of course

let me make a pod

statement question here >> you can start to do things like

uh trying to do psychedelics

with a >> And what does that even look like?

>> It looks like a ridiculous question.

It'll get you fired from most academic

departments, but it may be if you take it seriously, you could try

>> and see if it applies. >> Yeah.

If it has 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 human mind that's a complex human mind.

It's at least might be a productive question to ask what cuz you've seen

like spiders on psychedelics

like more primitive biological organisms on psychedelics.

Why not try to see what what an algorithm does on psychedelics?

>> Well, well, yeah, because you see

the the thing to remember is

we don't have a magic sense

or a really good intuition

for what the mapping is between an the embodiment

of something and the degree of intelligence it has.

We we think we do because we have an N of one example on

Earth and we kind of know what to expect from cells, snakes,

uh you know, primates,

what but we really don't.

We don't have and this is we we'll get into more of the stuff

on the platonic space but I our intuitions

around that stuff is so bad

that to really think that we know enough not to try things at this

point is is I think really shortsighted

before we talk about the platonic space let's uh

let's lay out some foundations

I think one useful one comes from the paper

technological approach to mind

>> 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 one from this paper

that has a few components?

One is the tiers of biological cognition.

It goes from group to whole organism to whole tissue organ

down to neural network down to cytokeleton

down to genetic network

and then there's layers of biological

systems from ecosystem down to swarm

down to organism tissue and finally cell.

So can you explain this figure and can you explain

the tame so-called framework?

So this is the version 1.0

and there's a there's a kind of update of 2.0 that I'm writing at

the moment trying to

uh 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.

And we can let's let's just start with figure two maybe for a second

and then we'll come back to figure one.

And 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 um how do you interact with a system to have

a productive interaction with it?

And 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 um technological

because I wanted to be clear

uh with my colleagues that

this was not a pro 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.

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 uh empirically figure that out.

And so what you see here in figure two is basically this this idea

that there is a spectrum.

And I'm just showing four way points along that spectrum.

And as you move to the right of that spectrum, a couple things happen.

Persuadability goes up, meaning that the systems become more reprogrammable,

more plastic, more able to do different things than whatever they're standardly doing.

So you have more ability to get them to do new and interesting things.

The effort needed to exert influence goes down.

That is autonomy goes up.

And 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?

And this also has to do with what I call engineering agential materials.

So when you engineer

um 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 uh active matter or you're engineering computational

materials or better yet um agential

materials like like living

matter, you can do some very high level

uh prompting and let the system then do very complicated things that you don't need to micromanage.

And we all we all know that that

increases when you're starting to work with intelligent

systems like animals and and humans and so on.

And the other thing that goes down as you get to the right is

the amount of mechanism

or physics that you need to exert the influence goes down.

So if you know how your thermostat

is to be set as far as its set point, you really don't need

to know much of anything else, right?

You 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 uh pause just for people who are listening.

Let me describe what's in the figure.

So there's four different systems going up the scale of persuadability.

So the first system is a mechanical clock, then it's a thermostat,

then it's a a dog that gets rewards and punishments.

Pavlov's dog, and then finally

a bunch of very smartl lookinging humans communicating with each other and arguing,

persuading each other using hashtag reasons.

And then uh there's 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 >> Yeah, I'll give you an example about that panel C here with the with the dog.

Isn't it amazing that

humans have been training dogs and horses for thousands of years knowing zero neuroscience?

Also amazing is that when I'm talking to you right now,

I don't need to worry about manipulating

all of the synaptic proteins in your brain to make you understand what I'm

saying and hopefully remember it.

You're going to do that all on your own.

I'm giving you very thin

in terms of information

uh content, very thin prompt

and I'm counting on you as a as a multiscale

agential material to take care of the chemistry underneath.

>> So you don't need a wrench to convince >> Correct.

I don't need and I don't need physics to convince you and I don't

need to know how you work.

like I 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 for yourself.

And you do like this is an amazing thing.

I don't people don't think about this enough.

I think uh when you wake up in the morning and you have

social goals, research goals, financial goals, whatever, 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?

Your your entire body is is is a transducer

of of very abstract things and and by the way not just our our

brains but other you know our organs have um

uh uh anatomical goals and other things that we can talk about because all

of this uh plays out in

uh in in in

regeneration and development and so on.

But that the scaling right of all of these things the way that the

way you regulate yourself

is not by oh my god you don't have to sit there and think

wow I really have to push some some you know some sodiums across this

membrane all of that happens automatically

and that's the that's the incredible benefit of these multiscale

materials so what I was trying to do in this paper is a couple

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 this spiral I was trying to point out is that at every level

of biological organization Like we all know we're sort of nested,

you know, organs and tissues and cells and molecules and whatever.

But what I was trying to point out is that this is not just structural.

Every one of those layers

is competent and is doing problem solving in different spaces

and spaces that are very hard for us to imagine.

We humans are because of our own evolutionary

history, we are so obsessed with movement in three-dimensional

space that even 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 embodiment in all kinds of spaces that are hard for us to imagine, right?

So, your cells and tissues

are moving in highdimensional

physiological state spaces in in uh trans

gene expression state spaces

in anatomical state spaces.

They're doing that perception

decisionm 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 yeah

every layer of your body has its own ability to solve problems in those spaces.

And then um on the right what I was saying is that this distinction

between you know people say well there are living beings and then there are

engineered machines and then they often follow up with all the things machines are

never 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 kind of distinctions

because life is incredibly interoperable.

uh life doesn't really care if if

um the thing it's working with

was evolved through random trial and error or was engineered with a higher degree

of of agency because at every level within the cell within the tissue within

the organism within the collective

you you can replace and substitute

engineered systems with the natural evolved systems

and that question of is it really you know 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.

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 the goal of that.

Uh is it useful to as a thought experiment,

as an experiment of radical empathy

to try to put ourselves

in the space of the different uh minds

at each stage of the spiral?

It's like what state space

is human and civilization

as a collective >> like what does it >> operate in?

So humans, individual organisms

operate in 3D space,

that's what we understand.

But when there's a bunch of us together,

>> what are we doing

>> It's really hard and you have to do experiments which at larger scales is

are, you know, really difficult.

>> But there is such a thing.

>> There may well be.

We have to do experiments.

I I don't know. Here's an example.

Somebody will say to me, well, you know, with your with your kind of

pansychist view, you might as you probably think the weather is uh is in is aial, too.

like well I can't say that but

we we don't know but have you ever tried to see if a hurricane

has habituation or sensitization

maybe we we haven't done the experiment it's hard but you could

right and maybe 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 but I'll

just give you a simple example of um

uh the kinds of tools and we're we're actively trying to build tools now

to enable radically different agents to communicate

so so we we we are doing this using using AI and other uh

other tools to try and uh try and get this kind of communication

going across very different spaces.

I'll just give you a very kind of dumb example of

of how how that might be.

Imagine that um you're playing tic-tac-toe against an alien.

So you're in a room, you don't see him

uh and so so you you draw the tic-tac-toe

thing on the board on the floor

and uh and you know what you're doing.

You're trying to uh you're trying to make straight lines with X's and O's

and you're having a nice game.

It's obvious that he understands the process.

It's like sometimes you win, sometimes you lose.

Like it's obvious in that in that one little little

segment of activity, you guys are sharing a world.

What what's happening in the other room next door?

Well, let's say the alien doesn't know anything about geometry.

He doesn't understand straight lines.

What he's doing is he's got a he's got a box

and it's full of uh basically billyard balls, each one of which has a number on it.

And all he's looking he's doing is he's looking through the box to find

billyard balls whose numbers add up to

He doesn't understand geometry at all.

All he understands is arithmetic.

You don't think about arithmetic. You think geometry.

The reason you guys are playing the same game is that there's this magic

square, right, that somebody could constructed

that basically is is a 3x3

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 he is solving the problem that that that he sees, which is

which is totally algebra.

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 two different

ways of seeing the world

that allows you to communicate with >> seeing a thin slice of the world.

>> Thin slice of the world.

How do you find that mapping?

So 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?

>> So, so the process the process is twofold.

One is to get a better

um understanding of what the system what space is the system navigating.

What goals does it have?

What level of ingenuity does it have to reach those goals?

For example, zenobots, right? We make zenobots.

This is or anthrobots.

These are um biological

systems that have never existed on Earth before.

We have no idea

what their cognitive properties are. We're learning.

We found some things.

But you can't predict that from first principles because they're not at all what

their past history would would would inform you of.

>> Can you actually explain

briefly what a Zenobot

is and what an anthrobot is?

>> So, one of the things that we've been doing is

trying to create novel beings that have never been here before.

The reason is that typically

when you have a biological

system, an animal or a plant,

and you say, "Hey, why does it have certain forms of behavior,

certain forms of anatomy,

certain forms of physiology?

Why why does it have those?"

the answer is very

is is always the same.

Well, there's a history of

evolutionary selection and there's a long

long um history going going back of adaptation

and there are certain environments and this is what survived and so that's why

it has so uh what I what I wanted to do was was break

out of that mold and

to to to basically force us as a community

to to dig deeper into where these things come from.

And that means taking away the crutch where you just say well it's ev

it's it's evolutionary selection that's that that's why it looks like that.

So in order to do that we have to make artificial um synthetic beings.

Now to be clear we are starting with living cells.

So it's not that they had no evolutionary history.

The cells do they had evolutionary

history in frogs or humans or whatever.

But the creatures they make and the capabilities that these creatures have

were never directly selected for and in fact they never existed.

So you can't tell the same kind of story.

And what I mean is we can take epithelial

cells off of an early frog embryo

and we don't change the DNA.

No synthetic biology circuits,

no material scaffolds, no nano materials,

no weird drugs, none of that.

What we're mostly doing of is

liberating them from the

instructive influences of the rest of the cells that they were in in their bodies.

And so when you do that, right, normally these cells

are bullied by their neighboring cells into having a very boring life.

they become a two-dimensional

outer covering for the for the embryo and they keep out the bacteria and that's that.

So you might ask well what are these cells capable of when you take

them away from that influence.

So when you do that they form

another little um life form we call a zenobot

and it's this uh self motile little thing that has cyia covering its surface.

The cyia are coordinated so they row against the water and then the thing

starts to move and has all kinds of amazing properties.

It has different gene expression so it has its own novel transcryto.

It's able to do things like kinematic

self-replication, meaning make copies of itself

from loose cells that you could put in its environment.

It has the ability to respond to sound,

which normal embryos don't do.

It has these novel capacities.

And we did that and we said, "Look, here are some amazing features of this novel system.

Let's try to understand where they came from."

And some people said, "Well,

maybe it's a frog specific

thing, you know, uh maybe this is just something unique to frog cells."

And so we said, "Okay, what's the furthest you can get from from frog embryionic cells?

How about human adult cells?"

And so we took uh cells from adult human patients who were donating tracheal

epipthelia for um biopsies and things like that.

And those cells in again no genetic change, nothing like that.

They self-organized into something we call anthrobots.

Again, self-motile little creature.

9,000 different gene expressions.

So about half the genome is now different.

And uh they have interesting abilities.

uh for example they can heal human neural wounds.

So in vitro if you if you

plate some um some neurons and you put a big scratch through it so

you damage them anthrobots

can sit down and they will they will try they will spontaneously

without have us having to teach them to do it they will spontaneously

try to uh knit the neurons

>> so this is an anthrobot.

So often when I give talks about this, I show people this video and

I say, "What do you think this is?"

And people will say, "Well,

it looks like some primitive

organism you got from the bottom of a pond somewhere

and I'll say, "What do you think the genome would look like?"

And well, the genome would look like some primitive creature, right?

If you sequence that thing, you'll get 100% homo sapiens.

And that doesn't look like any stage of normal human development.

It doesn't act like uh like any stage of of human development.

It has the ability to move around.

It has, as I said, over 9,000 differential gene expressions.

Uh, also, interestingly, it is uh younger

um than the cells that it comes from.

So, it actually has the ability to roll back its age.

And we could we could talk about that and and what the implications of that are.

But, but to go back to your original question, what we're doing with these

kinds of systems, >> try and 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.

So gene so we found a couple years ago we found that gene regulatory

networks never mind the cells but the molecular pathways

inside of cells can have several different kinds of learning

including Pavlovian conditioning and what we're doing now is trying to talk to it

the biomedical applications are obvious

instead of hey Siri you want hey liver why do I feel like crap

today and you want an answer well you know your potassium levels are this

and that and I don't feel uh you know I don't feel good for

these reasons and you should be able to talk to these things and there

should be able to be an interface

that allows us to communicate, right?

And and I think AI is going to be a huge component of that

interface of allowing us to talk to these systems.

It's a it's a tool to combat our mind blindness to help us see

diverse other very unconventional

minds that are all around us.

>> Can you generalize that?

So, let's say we meet

an alien or an unconventional

uh here on Earth.

Think of it as a black box. You show up.

What's the uh procedure

for trying to get some

hooks into uh communication

protocol with the >> Yeah, that is exactly

the mission of of my lab.

It is it is to enable us to develop tools

to recognize these things to learn to

uh communicate with them to ethically

relate to them and in general to expand

our ability to uh to do this in the in the in the in

the world around us.

I specifically chose these kinds of things because

they're not as alien

as proper aliens would be.

So, we have some hope.

I mean, we're made of them.

We have some many things in common.

There's some hope of understanding them.

>> You're talking about xenobots and >> xenobots and anthropots and cells and everything else.

But, they're alien in a couple of important ways.

One is the space they live in is very hard for us to imagine.

What space do they live in?

Well, um, your body, your body cells,

long before we had a brain that was good for navigating threedimensional

space was navigating the space of anatomical possibilities.

It was going from you start as an egg

and you have to become,

you know, a snake or or,

you know, a giraffe or what or human, whatever, whatever we're going to be.

And I specifically am

telling you that this this this

general idea when people model that with

uh kind of cellular automata

type of ideas, this open loop kind of thing where well everything just follows

local rules and eventually there's complexity and and and here you go now now

you've got a now you've got a giraffe or a human.

Um I I'm specifically telling you that that model is totally insufficient

to grasp what's actually going on.

What's actually going on and there have been many 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.

If you try to challenge it with things it's never seen before, it will

try to come up with a with a solution.

If you if you really

uh defeat its ability to do that, which you can, you know, they're not infinitely intelligent.

So, you can you can defeat them.

You will either get birth defects or you will get creative problem solving such

as what you're seeing here with xenobots and anthrobots.

If you can't be a human,

you'll be some you can you'll find another way to be in.

You can be an anthrobot

for example or you'll be something else.

>> Just to clarify, what's the difference between

cellular automa type of action where you're just responding to your local environment and

creating some kind of complex behavior and

uh operating in the space of anatomical possibilities.

>> So there's a kind of

>> goal I guess you

there is some kind of thing.

There's a will >> to X something. >> The will thing.

Let's put that aside cuz that's it.

Well, it's it's fine.

>> I go anthropom I just always love to quote Nisha. So >> yeah. Yeah. Yeah.

And and I'm not saying 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 openloop complexity.

In other words, I've got a bunch of subunits

like a cellular automaton.

They follow certain rules

and you turn the crank, time goes forward. Whatever happens happens.

Now clearly you can get complexity from this.

Clearly you can get some very interesting looking things, right?

So the game of life, all all those kinds of cool things, right?

You can get complexity.

No, no, no problem.

But the idea that that

model is going to be sufficient

to uh explain and control

things like morphagenesis is a hypothesis.

It's it's okay to make that hypothesis, but we we know we know it's

false despite the fact that that is what what we learn, you know, in

in in basic um

uh cell biology and and developmental biology classes

when the first time you see something like this inevitably,

especially if you're an engineer in those classes, you raise your go, how does

it know to do that?

How does it know uh you know, four fingers instead of seven?

What they tell you is it doesn't know anything.

Make sure that that's that's very clear.

They all insist, right?

When we learn these things, they say

none 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 either there's damage

or some something in the

environment that h that's happened.

those kind of openloop

models do not adjust

to give you uh to give you the same goal by different means.

This is William James' definition of intelligence is same goal by different means

and in particular working them backwards.

Let's say you're in regenerative

medicine and you say okay but this is the situation now I want it to be different.

What should the rules be? It's not reversible.

So the thing with those kind of openloop models is they're not reversible.

You don't know what to do to make the outcome that you want.

all you know how to do is roll them forward. Right?

Now, in biology, we see the following.

Uh if if you have a

developmental system and you put barriers

between So, so I'm going to give you two pieces of evidence that suggest that

there is a goal.

One piece of evidence is that if you try to block these things

from the outcome that they normally have,

they will do some amazing things.

Uh 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 nod blind

turn the crank and wherever we end up is where we end up.

That is not what we see experimentally.

And more importantly I think what we've shown and this is this is um

uh something 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 we have shown through

uh through our work on biomectric

imaging and biomectric re reprogramming

we have actually shown how those goal memories are encoded

at least in some cases.

We certainly haven't got them all but we have some.

If you can find where the goal state is encoded,

read it out and reset it and the system will now implement a new

goal based on what you just reset.

That is the ultimate

uh evidence that that your goal

uh directed model is working.

Because if there was no goal that shouldn't be possible

you shouldn't right on once you can find it read it

uh inter interpret it and rewrite it

means that by by any engineering standard it means that you're dealing with a

homeostatic >> How do you find where the goal is

>> So through lots and lots of hard work >> the barrier thing is part of

that creating barriers and observing.

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

So step one when you approach a gentic system is create a barrier of

different kinds until you see how persistent it is at pursuing the thing it

seemed to have been pursuing originally

and then you know okay cool this is uh this thing has agency

first of all and then second of all like

>> 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 and

and you really have to stretch your mind because because we can't imagine all

the spaces that systems work in right so so step one is what space

is it step two what do I think the goal is and let's not

mistake step two you're not done just because you had 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 hypothesis

now you do the experiments you say okay if I want to block it

from reaching its goal how do I do that and this by the way

is exactly the the approach we took with the sorting algorithms and with everything

else you you 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 see, oh wow, it it can go around and do these things.

Or you might see, wow, it's taking a completely different approach using its

affordances in novel ways.

Like that's a high level of intelligence.

You you will find out what the what the answer is. >> Another pod question.

And is it possible to look at uh

speaking about unconventional organisms

and going to Richard Dawkins for example with memes,

is it possible to think of things like ideas

like how weird can we get?

Can we look at ideas as organisms

then creating barriers for those ideas

and seeing are the ideas themselves?

You take the actual individual

ideas and trying to empathize

and visualize what kind of space they might be operating in.

Can they be seen as organisms

that have a mind? >> Yeah. Um, okay.

If you want to get really weird, we can we can get we can

get really weird here.

Uh, think about the uh caterpillar butterfly transition. Okay.

So got a caterpillar softbodied

kind of creature has a particular controller that's suitable for running a softbodied

you know kind of robot.

It has a brain for that task and then it has to become this

butterfly hardbodied creature flies around.

Okay during the process of metamorphosis

its brain is basically

ripped up and rebuilt

from from 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 you if the caterpillar

sees this color disc then it crawls over and eats some leaves.

Turns out the butterfly retains that memory.

Now the obvious question is how the hell do you retain memories when the

medium is being refactored like that?

Let's put that aside.

That's I'm 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

uh retain the memory.

You have to remap

that memory onto a completely new context because guess what?

The butterfly doesn't move the way the caterpillar moves and it doesn't care about leaves.

It wants nectar from from flowers.

And so if you're going if that memory is going to survive,

it can't just persist.

It has >> be remapped.

>> Be remapped into a novel context.

Now, here's what I now now here's 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 I'm going to cease to exist, but

but you know, I'll sort of be reborn in this new higher dimensional

world where I'll fly."

Okay, so that's one thing.

We can take the perspective of the butterfly

and say that well here I am but you know I I seem to

be saddled with some some

tendencies and some memories and I don't know where the hell they came from

and and and I don't remember exactly how I got them and they seem to

be a core part of my psychological

makeup and and you know they're they're

they come from somewhere I don't know where they come from. Right?

So you can take that perspective

but there's a third perspective that I think is really interesting and useful

and the third perspective is that of the memory itself.

If you take a perspective of the memory which which so what is a memory?