Kind: captions
Language: en
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
Leaven.
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
psychoanalysis.
>> 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
sciences.
>> Why why are you using the term
persuadability? Who are you persuading
and of what?
>> Well,
>> 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, [clears throat] 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
process
>> 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
describe
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
terms
>> to have a bit of a meta conversation
about the conversation I should say that
part of the persuadability argument that
we two intelligent creatures are 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
persuadability.
>> 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
that
the cognitive light cone of that thing
is bigger than that of its parts. So, in
other words, rocks aren't very exciting
because the things it knows how to do
are the things that its parts already
know how to do, which is follow
gradients and 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
immediately.
>> 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
range.
>> 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
civilization.
Because if you look at the, you know,
bacteria
>> 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
find?
>> 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
anthropomorphization
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
algorithms
>> 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
that
>> you can start to do things like uh
trying to do psychedelics with a
sorting.
>> Yeah.
>> And what does that even look like?
[snorts]
>> 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
everywhere
>> an experimentally grounded framework for
understanding diverse bodies and minds
Could you tell me about this framework
and maybe can you tell me about figure
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
engineering
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
influence.
>> 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.
Right?
>> So you don't need a wrench to convince
me.
>> 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 [snorts] 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
embodied
>> 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 [clears throat] are we doing
together?
>> 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
15.
He doesn't understand geometry at all.
All he understands is arithmetic. You
don't think about arithmetic. You think
geometry. The reason you guys are
playing the same game is that there's
this magic square, right, that somebody
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
ac
>> 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
mind
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
thing?
>> 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. [snorts] 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.
Sure.
>> 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
is
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
mechanism.
>> How do you find where the goal is
encoded?
>> 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
to
>> 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?
It is a pattern. It is anformational
pattern that was continuously reinforced
within one cognitive system. And now
here I am. I'm this memory. What do I
need to do to persist into the future?
Well, now I'm facing the paradox of
change. If I if I try to remain the
same, I'm gone. There's no way the
butterfly is going to retain me in in
the original form that I'm in now. What
I need to do is is change, adapt, and
morph. Now, you might say, well, that's
kind of crazy. Uh, well, how are you
taking the perspective of a d of of a
pattern within an excitable medium,
right? Agents are physical things.
You're talking about the you talking
about information, right? So, so let me
let me tell you another um quick science
fiction story. Imagine that uh some
creatures come out from the center of
the earth. They live down in the core.
They're super dense. Okay? They're
incredibly dense because they live down
in the core. They have gammaray vision
for, you know, for and so on. So, they
come out to the surface. What do they
see? Well, all of this stuff that we're
seeing here, this is like a thin plasma
to them. They they are so dense. None of
this is is is is solid to them. They
they don't see any of this stuff. So,
they're walking around, you know,
they're the planet is sort of, you know,
covered in this like thin gas, you know,
and one of them is a scientist and he's
and he's taking measurements of the gas
and he says to the others, you know,
I've been watching this gas and they're
like little whirlpools in this gas and
they almost look like agents. They
almost look like they're doing things.
They they're moving around. They kind of
hold themselves together for a little
bit and they're trying to make stuff
happen. And and the others say, "Well,
that's crazy. Patterns in the gas can't
be agents. We are agents. We're we're
solid. This is just patterns in an
excitable medium." And by the way, how
long do they hold together? He says,
"Well, about a hundred years." Well,
that's crazy. Nothing, you know, no real
agent can can exist to be be that
dissipate that fast. Okay, we are all
metabolic patterns among other things,
right? And so, one of the things that
and so you see what I'm warming up to to
here. So, so one of the things that
we've been trying to uh dissolve and
this is like some work that I've done
with Chris Fields and others is this
distinction between thoughts and
thinkers. So, uh all agents are patterns
within some excitable medium. We could
talk about what what that is and they
can spawn off others. And now you can
have a really interesting spectrum.
Here's the here's the spectrum. Um you
can have fleeting thoughts which are
like waves in in in the ocean when you
throw a rock in. you know, they sort of
they sort of go through the excitable
medium and then they're gone. They pass
through and they're gone, right? So
those are those are kind of fleeting
thoughts. Then you can have patterns
that have a degree of persistence. So
they might be hurricanes or solitons or
persistent thoughts or earworms or
depressive thoughts. Those are harder to
get rid of. They they stick around for a
little while. They often do a little bit
of niche construction. So they change
the actual brain to have to make it
easier to have more of those thoughts,
right? Like that's a that's a thing. And
so they they they stay around longer.
Now, uh what's what's further than that?
Well, fragments, personality fragments
of a dissociative personality disorder,
they're more more stable and they're not
just on autopilot. They have goals and
they can do things. And then past that
is a full-blown human personality. And
who the hell knows what's past that?
maybe some sort of transhuman you know
transpersonal like I don't know right
but but this idea again I'm back to this
notion of a spectrum it's there is not a
sharp distinction between you know we
are real agents and then we have these
these these thoughts yeah patterns can
be agents too but again you don't know
until you do the experiment so if you
want to know whether a solaton or a
hurricane or a thought within a
cognitive system is its own agent do the
experiment see what it can do it can it
learn from experience does it have
memories does it have goal states does
that you know what what can it do right
does it have language so so coming back
to then during your original question
yeah we can definitely apply this
methodology to ideas and concepts and
and and social um uh you know whatevers
but you've got to do the experiment
that's such a challenging
thought experiment of like thinking
about memories from the caterpillar to
the butterfly as an organism I think at
the very basic level intuitively we
think
organisms as hardware.
>> Yeah.
>> And uh software is not possibly being
able to be organisms. But what you're
saying is that it's all just patterns in
an excitable medium. And we it doesn't
really matter what the pattern is. We
need to and and what the excitable
medium is. We need to do the testing
avoid how persistent is it? How goal
oriented is it? And there are certain
kind of tests to do that. And you can
apply that to memories. You can apply
that to ideas. You can apply that to
anything really. I mean, you could
probably think about like
consciousness. You could there's really
no um boundary to what you can imagine.
Probably really really wild things could
be could be minds.
>> Yeah. Stay tuned. I mean, this is
exactly what we're doing. We're getting
progressively like more and more
unconventional. I mean, so, so this, so
this whole distinction between software
and hardware, I think I think it's a
super important uh concept to think
about and and yet the way we've mapped
it onto the world, I I I would I I would
like to blow that up in in the in the
following way. Um, and and again, I want
to point out so so I'll tell you what
the what the practical um consequences
are because this is not just, you know,
fun stories that we tell each other.
These have really important research um
implications. Think about a touring
machine. So one thing you can say is the
machine's the agent. It has passive data
and it operates on the data and that's
it. The story of agency is the story of
whatever that machine can and can't do.
The data is passive and it moves it
around. You can tell the opposite story.
You can say look the patterns on the
data are the agent. The machine is a
stigmic scratch pad in the world of the
data doing what data does. The machine
is just the consequence is the scratch
pad of it working itself out. And both
of those stories make sense depending on
what you're trying to do. Here's the um
the biomedical side of things. So our
bio our our program in bielectrics and
aging. Okay. One model you could have is
the physical organism is the agent and
the the cellular collective has
pattern memories. Specifically what I
was saying before goals anatomical
goals. If you want to if you want to
persist for 100 plus years your cells
better remember what your correct shape
is and where the new cells go. Right? So
there are these pattern memories. They
exist during embryogenesis, during
regeneration, during resistance to
aging. We can see them. We can visualize
them. One thing you can imagine is fine
the physical body, the cells are the
agent. The electrical pattern memories
are just uh data. And what might happen
during aging is that uh the the data
might get uh degraded. They might get
fuzzy. And so what we need to do is
reinforce the data, reinforce the
memories, reinforce the pattern
memories. That's one that's one specific
research program. And we're doing that.
But that's not the only research program
because the other thing you might
imagine is that what if the patterns are
the agent in exactly the same sense as
we think in our brains. It's the uh
patterns of electrophysiological
um you know computations whatever else
that is the agent, right? And that what
they're doing in the brain are the side
effects of the patterns working
themselves out. And those side effects
might be to fire off some muscles and
some glands and some other things. From
that perspective, maybe what's actually
happening is maybe the agent's finding
it harder and harder to be embodied in
the physical world. Why? Because the
cells might get less um responsive. In
other words, there the cells are
sluggish. The patterns are fine. They're
having a harder time making the cells do
what they need to do. And that maybe
what you need to do is not reinforce the
memories. Maybe what you need to do is
make the cells more responsive to them.
And that is a different research agenda.
So which which we are also doing. We
have evidence for that as well actually
now. And then we we published it
recently. And so my point here is when
we tell these crazy sci-fi stories, the
only worth to them and the only reason
I'm talking about them now and I hadn't
been up you know a year ago I wasn't
talking about this stuff is because
these are now actionable in terms of
specific experimental research agendas
that are heading to the clinic. I hope
in uh in some of these biomedical
approaches. And so now here we can go
beyond this and we can say okay so up
until now we've considered what are
disease states well we know there's
organic disease something is physically
broken we can see the tissue is breaking
down there's this damage in the joint
you know whether the liver is doing what
you know we can see these things but
what about disease states that are not
physical states they're physiological
states orformational states or cognitive
problems so in other words in all of
these other spaces And you can start to
ask what's a barrier in gene expression
space? What's a local minimum uh that
traps you in physiological state space?
And what is a stress pattern that keeps
itself together, moves around the body,
causes damage, tries to keep itself
going, right? What what level of agency
does it have? This suggests an entirely
different uh set of approaches to to
biio medicine. And you know, anybody who
who's, let's say, in the uh alternative
medicine community is is probably
yelling at the screen saying, "We've
been saying this for hundreds of years."
And yeah, but but and and I'm I'm well
aware these are not the ideas are not
new. What's new is being able to now
take this and make them actionable and
say, "Yeah, but we can image this now. I
can now actually see the biomectric
patterns and why they go here and not
there. And we have the tools that now
hopefully will get us to to to
therapeutics." So this is this is very
actionable stuff and it all leans on not
assuming we know minds when we see them
because we don't and we have to do
experiments to return back to the
software hardware distinction. You're
saying that we can see the software as
the organism and the hardware is just
the uh scratch pad
or you can see the hardware as the
organism and the software is the thing
that the hardware generates. And in so
doing, we can decrease the amount of
importance we assign to something like
the human brain or it could be the
activations. It could be the electrical
signals that are the organisms. And then
the brain is the scratch pad. And by
saying scratch pad, I don't mean it's
not important. When we get to talking
about the platonic space, we we have to
talk about how important the interface
actually is. It's it's the scratch pad
isn't unimportant. The scratch pad is
critical. It's just that my only point
is that when we have these uh formalisms
of software, of hardware, of other
things, the way we map those formalisms
onto the world is not obvious. It's not
given to us. We we get used to certain
things, right? But but who's the
hardware, who's the software, who's the
agent, and who's the who's the excitable
medium is is to be determined.
>> So, this is the good place to talk about
the increasingly radical weird ideas
that you've been writing about. You've
mentioned it a few times, the platonic
space.
So there's this ingressing minds paper
where you describe the platonic space.
You mentioned there's an asynchronous
conference happening uh which is a
fascinating concept because it's
asynchronous people are just
contributing asynchronously.
>> So what happened was this crazy notion
which I'll describe momentarily. I have
given a couple talks on it. I then found
a couple papers in the machine learning
community called uh the platonic
representation hypothesis and I said
that's pretty cool. These guys are
climbing up to the same point where I'm
getting at it from biology and
philosophy and whatever they're getting
there from computer science and machine
learning. We'll take a couple hours.
I'll give a talk. They'll give a talk.
We'll talk about it. I I thought there
were going to be three talks at this
thing. Once I started reaching out to
people for this, everybody sort of said,
you know, I know somebody who's really
into this stuff, but they never talk
about it because there's no audience for
this. So, I reached out to them and then
they said, "Yeah, oh yeah, I know this
this mathematician or I know this uh,
you know, uh, economist, whatever who
has these ideas and there's nowhere we
can ever talk about them." So, I got
this whole list and it became completely
obvious that we can't do this in a
normal, you know, it's we're now booked
up through through December. So every
week in our in our center, somebody
gives a talk. We we kind of discuss it.
It all goes on this thing. I I'll give
you a link to it and then there's a
there's a huge running discussion after
that and then in the end we're all going
to get together for an actual real time
discussion section and talk about it.
But there's going to be probably 15 or
so talks about this from from all kinds
of disciplines. It's blown up in a way
that I didn't realize how much
undercurrent uh of these ideas had
already um existed that were already
like now now is the time. And I think I
this is like I've been thinking about
these things for I don't know 30 plus
years. I never talked about them before
because they weren't actionable before.
There wasn't a way to actually make
empirical progress with this now. You
know, this is something that Pythagoras
and Plato and probably many people
before them talked about. But now we're
to the point where we can actually do
experiments and they're making a
difference in in our research program.
>> You can just uh look at Platonic Space
uh conference. There's a there's a bunch
of different fascinating talks. Yours
first on the patterns of forms and
behavior beyond emergence, then uh
radical platonism and radical empiricism
from Joe Ojits and uh patterns and
explanatory gaps in psychotherapy.
Does God play dice from Alexi Toljinski
and so on. So let's talk about it. What
is it? And it's it's fascinating that
the origins of some of these ideas are
connected to
um ML people thinking about
representation space.
>> Yeah. The first thing I want to say is
that while I'm currently calling it the
Platonic space,
I'm in no way trying to stick close to
the things that Plato actually thought
about it. In fact, to whatever extent we
even know what that is, I I think I
depart from that in quite in some ways.
And I'm I'm going to have to change the
name at some point. The reason I'm using
the name now is because I wanted to be
clear about a particular connection to
mathematics which which a lot of
mathematicians would call themselves
platonists because what they think
they're doing is discovering uh not not
inventing as a human construction but
discovering an or a structured ordered
space of truths. Let's put it this way.
um in biology as in physics there's
something very curious that happens that
if you keep asking why
the then some something interesting goes
on let's let's uh well I'll give you two
examples first of all imagine um cicas
so the cicas come out at 13 years and 17
years okay and so if you're a biologist
and you say so why is that and then you
get this explanation for well it's
because they're trying to be offcycle
from their predators because if it was
12 years then every 2 year, every three
year, every four year, every six year
predator would would would eat you when
you come out, right? So, and you say,
"Okay, okay, cool. Um, that makes sense.
What's special about 13 and 17?" Oh,
they're prime. Uh-huh. And why are they
prime? Well, now you're in the math
department. You're no longer in the
biology department. You're no longer in
the physics department. You've now
you're now in the math department to
understand why the distribution of
primes is what it is. Another example
and I'm not a physicist but what I see
is every time you you talk to a
physicist and you say hey uh why do the
you know lepttons do this or that or the
firmians are doing whatever eventually
the answer is oh because there's this
mathematical you know this SU8 group or
whatever the heck it is and it has
certain symmetries and these certain
structures yeah great once again you're
in the math department so so something
interesting happens is that there are
facts that you come across many of them
are very surprising you don't get to
design them you get more out than you
put in in a certain way because you make
very minimal assumptions and then
certain facts are thrust upon you for
example the value of fen bomb's constant
the value of natural logarithm e these
things you sort of discover right and
the salient fact is this if those facts
were different then biology and physics
would be different right so they matter
they they impact instructively
functionally they impact the physical
world if the distribution of primes was
something else well then the teicas
would have been coming out at different
times but the reverse isn't true what I
mean is there is nothing you can do in
the physical world to change e as far as
I know to change e or to change fen
bomb's constant you could have swapped
out all the constants at the big bang
right you can change all the different
things you are not going to change those
things so so this I think Plato and
Pythagoras understood very clearly that
there is a set of truths which impact
the physical world But they themselves
are not defined by and determined by
what happens in the physical world. You
can't change them by things you do in
the physical world. Right? And so I'll
make a couple claims about that. One
claim is I think we call physics those
things that are constrained by those
patterns. When you say hey why is this
the way it is? Ah it's because this is
how symmetry symmetries or or you know
topology or whatever. Biology
are the things that are enabled by
those. They're free lunches. there.
Biology exploits these kinds of truths
and uh and really it enables biology and
and evolution to do amazing things
without having to pay for it. I think
there's a lot of free free lunches going
on here. And so I show you a Zenobot or
an anthropot and uh I say, "Hey, look,
here are some amazing things they're
doing that tissue has never done before
in their history." You say, first of
all, where did that come from and when
did we pay the computational cost for
it? Because we know when we paid the
computational cost to design a frog or a
human. It was for the eons that the
genome was bashing against the
environment getting selected. Right? So
we pay the computational cost of that.
There's never been any anthropots.
There's never been any zenobots. When do
we pay the computational cost for
designing kinematic self-replication and
you know all these things that they're
able to do. So there's two things people
say. One is well it's sort of you got it
at the same time that they were being
selected to be good humans and good
frogs. Now the problem with that is it
kind of undermines the point of
evolution. The point of evolutionary
theory was to have a very tight
specificity between what how you are now
and the history of selection that got
you here, right? The history of
environments that got you to this point.
If you say, "Yeah, okay. So this is what
your environmental history was and by
the way, you got something completely
different. You you got you got these
other skills that you didn't know about
that. That's really strange, right?" And
so then what people say is, "Well, it's
emergent." And they say, "What's that?
What does that mean?" and they say well
besides the fact that you got surprised
right emergence is often just means I
didn't see it coming you know there was
something happened I didn't know that
was going to happen so uh they what does
it mean it's emergent and people say
well and there are many emergent things
like this for example the fact that gene
regulatory networks can do associative
learning like that's amazing and you
don't need evolution for that even
random genetic regulatory networks can
do associative learning I say why why
why does that happen and he said well
it's just a fact that holds in the world
just a fact that holds so so now you
have a you Yeah, you have an option. You
can go one of two ways. You can either
say, "Okay, look. I like my sparse
ontology. I don't want to think about
weird platonic spaces. I'm a physicist.
I want the physical world. Nothing
more." So, what we're going to do is
when we come across these crazy things
that are very specific, like, you know,
anthrobots have four specific behaviors
that they switch around. Why? Why four?
Why not 12? Why not one? Like four? Why
four? when we come across these things
just like when we come across the value
of E or Fen B's number or whatever, what
we're going to do is we're going to
write it down in our big book of
emergence and that that that's it. We're
just going to have to live with it. This
is this is what happens. We're just, you
know, the there's some cool surprises.
You know, when we come across them,
we're going to write them down. Great.
It's a random grabag of stuff and when
we come across them, we'll write them
down. That's that's one. The upside is
you get to be a physicist and you get to
keep your your sparse ontology. The
downside is I find it incredibly um
pessimistic and mysterian because you're
basically then just willing to uh make a
catalog of these of these amazing
patterns. Why not instead and this is
why I started with this with this
platonic uh terminology. Why not do what
the mathematicians already do? A huge
number of them say we are going to make
the same optimistic assumption that
science makes that there's an underlying
structure to that latent space. This is
not a random grabag of stuff. There's a
space to it which where these patterns
come from. And by studying them
systematically, we can get from one to
another. We can map out the space. We
can we can find out the relationships
between them. We can get an idea of
what's in that space. And we're not
going to assume that it's just random.
We're going to assume there's some kind
of structure to it. And you'll see all
kinds of people, I mean, you know,
well-known mathematicians that talk
about this stuff, you know, Penrose and
and lots of other people who will say
that, yeah, there's another space phys
physically and it has it has spatial
structure. it has components to it and
so on. We can traverse that space in
various ways. Uh and then and then
there's the physical space. So I I find
I find that much more um appealing
because it suggests a research program
which we are now undergoing in our lab.
The research program is everything that
we make cells, embryos, robots, biobots,
language models, simple machines, all of
it they are interfaces. They are inter
all physical things are interfaces to
these patterns. You build an interface.
Some of those patterns are going to come
through that interface depending on what
you build. Some patterns versus others
are going to come through. The research
program is mapping out that relationship
between the physical pointers that we
make and the patterns that come through
it. Right? Understanding what is the
structure of that space, what exists in
that space and what do I need to make
physically to make certain patterns come
through. Now when I say patterns now,
now we have to ask what kinds of things
live in that space. Well, the
mathematicians will tell we already
know. We have a whole list of objects.
you know, the amplrins and the, you
know, all this crazy stuff that lives in
that space. Yeah, I think that's one
layer of stuff that lives in that space.
But I think those patterns are the lower
agency kinds of things that are
basically studied by mathematicians.
What also lives in that space are much
more active, more complex, higher agency
patterns that we recognize as kinds of
minds that behavioral scientists would
look at that pattern and say, "Well, I
know what that is. that's the competency
for delayed gratification or problem
solving of certain kinds or whatever.
And so, so what I end up with right now
is a model in which that latent space
contains things that come through
physical objects. So, simple simple
patterns, right? So, so facts about
triangles and and Fibonacci, you know,
patterns and fractals and things like
that. But also if you make more complex
interfaces such as biologicals and and
but but importantly not just biologicals
but let's say cells and embryos and
tissues what you will then pull down is
much more complex patterns that we say
ah that's a that's a that's a mind
that's a human mind or that's a you know
snake mind or whatever. So I think the
mindb brain relationship is exactly the
kind of thing that the math physics
relationship is that in some very
interesting way there are truths of
mathematics that become embodied and
they kind of haunt physical objects
right in a very specific functional way.
And in the exact same way there are
other patterns that are much more um
complex higher agency patterns that
basically uh in form in form living
things that we see as as obvious
embodied minds. Okay. Given how weird
and complicated this uh we describing is
we'll talk about it more but you got to
eli the basics to a person has never
seen this. So again you mentioned things
like pointers. So the physical object
themselves or the brain is a pointer to
that platonic space.
What is in that platonic space? What is
the platonic space? What is the
embodiment? What is the pointer?
>> Yeah. Um okay, let's let's try let's try
it this way. Um there are certain facts
of mathematics. So the distribution of
prime numbers, right, that if you map
them out, they make these nice spirals.
And there's an image that I often show
which is a very particular kind of um
fractal.
>> Uh and that fractal is the Halley map
which is it's it's pretty awesome that
it actually looks very organic. It looks
very biological. So if you look at that
thing that image which has very specific
complex structure it's a map of a very
compact mathematical object. That
formula is like you know Z cub + 7. It's
something like that. That's it. So now,
so now you look at that structure and
you say, where does that actually come
from? It's definitely not packed into
the ZQ plus 7. It's not there's not
enough bits in that to give you all of
that. There's no fact of physics that
determines this. There's no evolutionary
history. It's not like we selected this
based on some, you know, from from a
larger set over time. Where does this
come from? Or or the fact that I think
about think about the way that biology
exploits these things. Imagine imagine a
world in which the highest fitness
belonged to a certain kind of triangle.
Right? So evolution cranks a bunch of
generations and it gets the first angle
right. Then cranks a bunch more
generations gets the second angle right
now. There's now there's something
amazing that happens. Doesn't need to
look for the third angle because you
already know. If you know to you get
this magical free gift from geometry
that says why I already know what the
third one should be. You don't have to
go look for it. Or as evolution if you
invent a voltage gated ion channel which
is basically a transistor, right? And
you can make a logic gate then all the
truth tables and the fact that NAND is
special and all these other things you
don't have to evolve those things you
get those for free you inherit those
where do all those things live these
mathematical truths that you come across
that you don't have any choice about you
can't you know uh once you've committed
to certain axioms there's a whole bunch
of other stuff that is now just it is
what it is and so what I'm saying is and
this is this is what what what
Pythagoras was was saying I think that
there is a whole space of these kinds of
uh truths. Now he was focused on on
mathematical ones but but he was
embodying them in music and in geometry
and then things like that. There are the
space of patterns and uh and they make a
difference in the physical world to
machines to sound to things like that.
What I I'm extending it and what I'm
saying is yeah and so far we've only
been looking at the low agency
inhabitants of that world. there are
other patterns that we would recognize
as kinds of minds and that you don't see
them in this space until there's an
interface until there's a way for them
to come through the physical world. That
interface the same the same way that you
have to make a triangular object before
you can actually see the rule you know
what what you're going to gain right out
of out of the rules of geometry and
whatever or you have to actually do the
computation on the fractal before you
actually see that pattern. If you want
to see some of those minds, you have to
build an interface, right? At least at
least if you're going to interact with
them in the physical world, the way we
normally do science. As Darwin said,
mathematicians have their own new sense,
like a different sense than the rest of
us. And so that's right. You you know,
mathematicians can can perhaps interact
with these uh with these patterns
directly in that space. But for the rest
of us, we have to make interfaces. And
when we make interfaces, which might be
cells or robots or the, you know,
embryos or whatever, what we are pulling
down are minds that are fundamentally
not produced by physics. So I don't
believe that. I don't know if we're
going to get into the whole
consciousness thing, but I I don't
believe that we create consciousness,
whether we make babies or whether we
make robots. No, nobody's creating
consciousness. What you create is an
interface, a physical interface through
which specific patterns which we call
kinds of minds are going to ingress,
right? And and and consciousness is what
it looks like from that direction
looking out into the world. It's it's
what we call the view from the
perspective of the platonic patterns.
Just to clarify, what you're saying is a
pretty radical idea here. So if uh
there's a mapping from mathematics to
physics, okay, that's understandable,
intuitive as you've described.
But what you're suggesting is there's a
mapping from some kind of abstract mind
object
to an embodied brain that we think of as
a mind.
>> Yeah.
>> As us fellow humans, what is that? What
exactly? Because you said interface,
you've also said pointer.
>> So the brain and I think you said
somewhere a thin interface.
>> A thin client. Yeah. The brain the brain
and brain is a thin client. Yeah.
>> Thin client. Okay. So you're [laughter]
>> a brain is a thin client to this other
world.
>> Yeah.
>> Can you just lay out very clearly how
radical the idea is? Sure.
>> Cuz you're kind of dancing around. I
think you also uh point to Donald
Hoffman and kind of who [snorts]
speaks of an interface
uh to a world. So we've only interact
with the quote unquote real world
through an interface. What is the
connection here?
>> Yeah. Um okay, a couple of things. First
of all, when you said it makes sense for
physics, I want to show that it's not as
simple as it sounds. Because what it
means is that even in Newton's
boring uh sort of classical universe
long before quantum anything Newton's
world physicalism was already dead in by
in Newton's world. I mean think about
what that means. This is this is nuts
because because already he knew
perfectly well. I mean Pythagoras and
Plato knew that even in a a totally
classical deterministic
world already you have the ingression of
information that determines what happens
in what's possible and what's not
possible in that world from a space that
is itself not physical. In other words,
something like the natural logarithm E,
right? Nothing in Newton's world is set
the value of E. There is nothing you
could do to set the value of E in that
world. And yet that fact that it was
that and not something else governed all
sorts of properties of things that
happened. His that classical world was
already haunted by patterns from outside
that world. That's this this should be
like this is this is this is wild. This
is this is not saying that okay
everything was was cool. Physicalism was
great up until you know maybe we got
quantum interfaces or we got you know
consciousness or whatever but but
originally it was fine. No, this is
saying that it was that that worldview
was already uh impossible really since
from from a very long time ago we
already knew that there are non-physical
properties that matter in the physical
world.
>> This is a chicken or the egg question.
You're saying Newton's laws are
creating the physical world.
>> That is a that is a very deep followon
question that that I we'll we'll we'll
come back to in a minute. What I all I
was saying about Newton is that in the
you don't need quantum anything. You
don't need to think about consciousness.
You already long before you get to any
of that as as Pythagoras I think knew
already we have the idea that this
physical world is being strongly
impacted by truths that do not live in
the physical world. And when I
>> which truths are we referring to are we
talking about Newton's laws like
mathematical equations
>> mathematical mathematical facts. So for
example, the actual value of E or
>> oh like very primitive mathematical
facts.
>> Yeah. Yeah. I mean some of them are you
know I mean if you if you ask Don
Hoffman there's this like amplitude
thing that that is a set of mathematical
objects that determines all the
scattering amplitudes of the particles
and whatever. They don't have to be
simple. I mean the old ones were simple
now. They're like crazy. I can't I can't
imagine this amplitron thing. But may
maybe they can. But um but but all of
these are mathematical structures that
explain and determine facts about the
physical world. Right? If you ask
physicists hey why you know this many of
this type of particle ah because this
mathematical thing has these symmetries
that's why
>> so Newton is discovering these things
they're not he's not inventing
>> this is very controversial right and
there are of course physicists and
mathematicians who who disagree with
what I'm saying for for sure but what
I'm leaning on is simply this I don't
know of anything you can do in the
physical world at the big you're around
at the big bang you get to set all the
constants
>> set physics however you want. Can you
change E? Can you change Fenbomb's
constant? I don't think you can.
>> Is that an obvious statement? I I don't
even know what it means to change the
parameters at the start of the Big Bang.
>> So, physicists do this. They'll say,
"Okay, you know, if we made the if we
made the ratio between the the the you
know, the gravitation and and the
electromagnetic force different, would
we have matter? Would we how many
dimensions would we have? Would there be
inflation? Would there be this or that?"
Right? You can you can imagine playing
with there. There are however many um
unitless constants of physics. These are
the kind of like knobs on the universe
that that that you could have could in
theory be different and then you'd have
different phys you'd have different
physics you'd have different physical
properties.
>> You're saying that's not going to change
the axiomatic systems that mathematics
has.
>> What I'm not saying is that every alien
everywhere is going to have the exact
same math that we have. That's not what
I'm claiming. Although maybe, but that's
not what I'm claiming. What I'm saying
is you get more out than you put in once
you've made a choice. and maybe some
aliens somewhere made a different choice
of how they're going to do their math.
But once you've made your choice, then
you get saddled with a whole bunch of
new truths that you discover that you
can't do anything about. They are given
to you from somewhere and you can say
they're random or you can say no,
there's a space of these facts that
they're pulled from. There's a latent
space that of options that they come
from. So when you get so when your E is
exactly 2.718 and so on, there is
nothing you can do in physics to change
it.
>> And you're saying that space is
immutable. It's
>> I'm not saying it's immutable. So So I
think I Plato may or may or may not have
thought that these forms are eternal and
unchanging. That's one place we differ.
I actually think that space has some
action to it. Maybe even some
computation to it.
>> But we're we're just pointers.
Okay. That's
>> well so so let's Okay. So So I'll So
I'll circle I'll circle back around to
that to that whole thing. So So the the
only thing I was trying to do is blow up
the idea that we're cool with how it
works in physics. No problem there. I
like I don't like I think that's a much
bigger deal than than people normally
think it is. I think already there you
have this weird haunting of of the
physical world by patterns that are not
coming from the physical world. The
reason I emphasize this is because now
what I'm going to when I amplify this
into biology, I don't think it sort of
jumps as a new thing. I think it's just
a much more I think what we call biology
is our systems that exploit the hell out
of it. I think physics is constrained by
it, but we call biology those things
that make make use of those kinds of
things and and run with it. And so I
again I just think it's a scaling. I
don't think it's a brand new thing that
happens. I think it's a it's a scaling.
Right? So what I'm saying is we already
know from physics that there are
non-physical patterns and these are
generally patterns of form which is why
I call them low agency because they're
like fractals that stand still and
they're like prime number distributions.
Although there's a mathematician that's
talking in our symposium that's telling
me that actually I'm too chauvinistic
even there that actually even those
things have more more oomph than even I
gave him credit for which I which I
love. So uh so what I'm saying is those
kind of static patterns are things that
we typically see in physics
but they're not the full extent of what
lives in that space. That space is also
home to some patterns that are very high
agency. And if we give them a body, if
we build a body that they can inhabit,
then we get to see different behavioral
competencies that the behavior
scientists say, "Oh, I know what that
looks like. That's a this this kind of
behavioral, you know, this kind of mind
or that kind of mind." In a certain
sense, I mean, yes, what I'm saying is
extremely radical, but it is a very old
idea. It's an old idea of a dualistic
worldview, right? where the mind was not
in the physical body and that it in some
way interacted with the physical brain.
So I just want to be clear. I'm not
claiming that this is fundamentally a
new idea. This has been around for
forever. However, it's mostly been
discredited and uh it's a very unpopular
view nowadays. There are very few people
in the for example cognitive science
community or or anywhere else in science
that like this kind of view primarily
and already Decart was getting getting
crap for this when he first trotted out
is this interaction problem right so the
idea was okay well if you have this
non-physical mind and then you have this
brain that presumably obeys conservation
of mass energy and things like that how
are you supposed to inter you know how
are you supposed to interact with it
there are many other problems there so
uh what I'm trying to point out is that
first of all physics already had this
problem you didn't have to wait till you
had biology and and and cognitive
science to to ask about it. And what I
think is happening in the way the the
way we need to think about this is
coming back to to my my point that I
think the mind brain relationship is
basically of the same kind as the math
physics relationship. The same way that
non-physical facts of physics haunt
physical objects is basically how I
think different kinds of patterns that
we call kinds of minds are manifesting
through our through interfaces like
brains. How do we prove or disprove the
existence of that world? Cuz it's a
pretty radical one.
>> Yeah.
>> Cuz this physical world we can poke.
It's there. It feels like all the
incredible things like consciousness and
cognition and all the goal oriented
behavior and agency all seems to come
from this
3D entity. Yeah.
>> And so like we can test it, we can poke
it, we can hit it with a stick.
>> Yeah. Sort of
>> makes noises
>> sort of. I mean the one so so Deart got
some stuff wrong. I think but one thing
that he did get right the fact that
actually you don't know what you can
poke and what you can't poke. The only
thing you actually know are the contents
of your mind and everything else might
be and and in fact what we know from
Anneil Seth and Don Hoffman and various
other people is definitely a construct.
You might be on drugs and you might wake
up tomorrow and say my god I had the
craziest dream of being Lex Freedman.
Amazing.
>> It's a nightmare. [laughter]
>> Yeah that who know
>> right? But um but but you see uh I I you
know it's it's not clear at all that
that the po that the physical poking is
your primary reality. That's not clear
to me at all.
>> I don't know. That's a obvious thing
that a lot of people can show is true to
take a step to the the the cart. I think
therefore I am that's the only thing you
know for sure and everything else could
be an illusion or a dream. That's
already a leap. I think from a basic
caveman science perspective, the
repeatable experiment
>> is the one that most of intelligence
comes from here. the reality is exactly
as it is. To take a step towards the the
Donald Hoffman worldview takes a lot of
uh guts and imagination and uh stripping
away of the ego and all these kinds of
processes.
>> I I think you can get there more easily
by synthetic bio-engineering in the
following in the following sense. Do you
feel a lack of X-ray perception? Do you
feel blind in the X-ray spectrum or or
in the ultraviolet? I mean, you don't
you have absolutely no clue that stuff
is there. And uh all of your um your
your reality as you see it is shaped by
your evolutionary history. It's shaped
by the cognitive structure that you
have. Right? There are tons of stuff
going on around us right now that we of
which we are completely oblivious.
There's equally all kinds of other stuff
which we construct and this is just this
is just modern cognitive science that
says that a lot of what we think is
going on is is is a total fabrication
constructed by us. So I I think this is
not a I don't think this is a philos got
there from a philosophical point. That's
not what I'm that's not the leap I'm
asking us to make. I'm saying that
depending on your embodiment, depending
on your interface, and this is incre
this this is um increasingly going to be
more relevant as we make f first
augmented humans that have sensory
substitution. You're going to be walking
around your friends going to be like,
"Ah man, I have this primary perception
of the solar weather and the stock
market because I got those implants and
what do you see?" Well, I see the, you
know, the traffic of the internet
through the, you know, uh transpacific
channel. We're all going to be living in
somewhat different worlds. That's the
first thing. The second thing is we're
going to become better attuned to other
beings, whether they be cells, tissues,
you know, what's what's it like to be a
cell living in a 20,000dimensional
transcriptional space, okay? To novel
beings that have never been here before
that have all kinds of uh crazy spaces
that they live in. And that might be
AIS, it might be cyborgs, it might be
hybrids, it might be all sorts of
things. So this idea that we have a
consensus reality here that's
independent of some very specifically
chosen aspects of our brain and and our
interaction, we we're going to have to
give that up no matter what to relate to
these other beings.
>> I think the tension is uh and absolutely
and this idea that you're talking about
of sort of almost I think you've termed
it cognitive prosthetics which is
different ways of perceiving and
interacting with the world. But I guess
the question is, is our human
experience, the direct human experience,
is that just a slice of the real world
or is it a pointer to a different world?
That that's what I'm trying to uh
>> figure out because the claim you're
making is a really fascinating one,
compelling one. There's a pretty strong
one which is there's another world into
which our brain is an interface to which
means you could theoretically map that
world systematically.
>> Yeah. Which which is exactly what we're
trying to do. I mean
>> but it's not clear that that world
exists.
>> Yeah. Yeah. Okay. I mean so so that's
the beautiful part about this and this
is why I'm talking about this now where
I wasn't you know about a year ago. Up
until a year ago, I was never talking
about this because I think this is now
actionable. So, there's this diagram
that's called a map of mathematics and
they basically try to uh show how all
the different pieces of math linked
together and there's a bunch of
different versions of it. So, there's
two features to this one is that what is
it a map of? Well, it's a map of various
truths. It's a map of facts that are
that are that are thrust on you. You
don't have a choice once you've picked
some axioms. you just, you know, here's
some surprising facts that that are just
going to be given to you. But the other
key thing about this is that it has a
metric. It's not just a random heap of
facts. They are all connected to each
other in a particular way. They they
literally make a space. And so when I
say it's a space of patterns, what I
mean is it is not just a random bag of
patterns such that when you have one
pattern, you are no closer to finding
any other pattern. I'm saying that
there's a some kind of a metric to it so
that when you find one others are closer
to it and then you can get there. So
that's the claim and obviously this is
not not everybody buys this and and and
so on. This is one idea. Now how do we
know that this exists? Well, I'll say a
couple of things. If that didn't exist,
what is that a map of?
If there is no space, if if if you don't
want to call it a space, that's okay.
But you can't get away from the fact
that as a matter of research there are
patterns that relate to each other in a
particular way
what what what you know the final step
of calling it a space is minimal. The
bigger the bigger issue is what the hell
is it a map of then if it's not a space.
So that's so that's the first thing. Now
that that's that's how it plays out I
think in math and physics. Now in
biology here's here's how we're going to
know if this makes any sense.
What we are doing now is trying to map
out that space by saying look we took we
we we we know that that the frog genome
maps to one thing and that's a frog.
Turns out that exact same genome if you
if you just if you just take a take the
slightest step with the exact same
genome but you just take some cells out
of that environment they can also make
zenobots with very specific different
transcripttos very specific behaviors
very specific shapes. It's not just oh
well you know they do whatever and then
they have very specific behaviors just
like the frog had very specific
properties. We can start to map out what
all those are right make that ba
basically try to try to um draw the
latent space of from which those things
are pulled and one of two things is
going to happen in the future. And so
this is you know come back in 20 years
and we'll and we'll see how this worked
out. One thing that could happen is that
we're going to see, oh yeah, just like
the map of mathematics, we we we made
the we made a map of the space and we
know now that if I want a system that
acts like this and this, here's the kind
of body I need to make for it because
those are the patterns that exist. The
anthropots have four different
behaviors, not seven and not one. And so
that's what I can pull from. These are
the options I have. Is there is it
possible u that there's varying degrees
of grandeur to the to the space that
you're thinking about mapping? Meaning
it could be just just like with the
space of mathematics. Might it strictly
be just the space of biology versus a
space of
like minds which feels like it could
encompass
a lot more than the just biology.
>> Yeah. except that I I don't see how I
don't see how it would be separate
because I'm not just talking about an
anatomical shape and transcriptional
profile. I'm also talking about
behavioral competencies. So when we make
something and we find out that okay it
does habituation sensitization, it does
not do Pavlovian conditioning and it
does do delayed gratification and it
doesn't have language that is a very
specific cognitive profile. That's a
region of that space and there's another
region that looks different because I
don't make a sharp distinction between
biology and cognition. If you want to
explain behaviors, they are drawn from
some distribution as well. So, so I
think in 20 years or however long it's
going to take, one of two things will
happen. either we and other people who
are working on this are going to
actually produce a map of that space and
say, "Here's why you've gotten um
systems that that work like this and
like this and like this, but you've
never seen any that work like that."
Right? or we're going to find out that
I'm wrong and that basically it's not
worth calling it a space because it is
so random and so jumbled up that there
is we've been able to make zero progress
in linking the uh the embodiment that we
make to the patterns that come through.
>> Yeah. Just just to be clear, I mean from
your uh blog post on this from the paper
I mean we're talking about space that
includes a lot of stuff.
>> Yeah. Yeah. includes human what is it
meditating Steve hello my name is Steve
AI systems so all the space of
computational systems objects
biological systems concepts it includes
everything
>> well it includes specific patterns that
we have given names to some of those
patterns we've named mathematical
objects some of those patterns we made
we've named anatomical outcomes some of
those patterns we've made psychological
types so every entry in an encyclopedia.
Old school Britannica
is a pointer
to this space. There is a set of things
that I feel very strongly about because
the research is telling us that that's
what's going on. And then there's a
bunch of other stuff that I see as
hypotheses for next steps that guide
experiment. So what I'm about to tell
you, I I don't, you know, these are
things I don't actually know. These are
just uh guesses that that you know you
need to make some guesses to make
progress. I I I don't think that there
are specific or I don't know but it
doesn't mean that there are going to be
specific platonic patterns for this is
the Titanic and this is the sister of
the Titanic and this is some other kind
of boat. This is not what I'm saying.
What I'm saying is in some way that we
absolutely need to work out when we make
minimal interfaces we get more than we
than we put in. We get behaviors. We get
shapes. We get mathematical truths. And
we get all kinds of patterns that we did
not have to create. We didn't
micromanage them. We didn't know they
were coming. We didn't have to put any
effort into making them. They come from
some distribution that seems to exist
that we don't have to create. And
exactly whether that space is sparse or
dense, I don't know. So, for example, if
there is um you know, some kind of um
you know, a platonic form for the movie
The Godfather, if it's surrounded by a
bunch of crappy versions and then
crappier versions still, I have no idea,
right? I don't know if the space is
sparse or not. Um I you know, I don't
know if uh if it's finite or infinite.
These are all things I don't know. What
I do know is that it seems like physics
and for sure biology and cognition are
the benefits of ingressions that are are
free lunches in some sense. We we did
not make them. Calling them emergent
does nothing for a research program.
Okay, that just means you got surprised.
I I think I think it's much better if
you say if you make the optimistic
assumption that they come from a
structured space that we have a prayer
in hell of actually exploring and in
some decades if I'm wrong and it says
you know what we tried. It looks like it
really is random too bad. Fine.
>> Is there a difference between like can
we one day prove the existence of this
world? Is and is there a difference
between it being a really effective
model
for connecting things,
explaining things versus like an actual
place where the information about these
distributions that we're sampling
actually exists
that we can hit with a stick. you yeah
you can you can try to make that
distinction
>> but I think I think modern cognitive
neuroscience will tell you that whatever
you think this is at at most it is a
very effective model for predicting the
future experiences you're going to have
>> so all of this that we think about as
physical reality is just a is a nice
convenient model
>> I mean that's not me that's predictive
processing and and active in like that's
modern neuroscience telling you this
that that this isn't anything that I
that I'm particularly coming up with All
I'm saying is uh the distinction the
distinction you're trying to make which
is like an old school like realist you
know kind of view that uh is it is it is
it is it metaphorical or is it real all
we have in science are metaphors I think
and the only question is how good are
your metaphors and I think as agents act
living in a world all we have are models
of what we are and what the outside
world is that's it and the question is
how good is it a model and and and my
claim about this is in some small number
of decades either this will either give
rise to a very enabling uh mapping of
the space for for AI for bioengineering
for you know biology whatever or we are
going to find out that it really sucks
because it really is a random grabag of
stuff and we tried the optimistic
research program it failed and we're
just going to have to live with surprise
I mean I doubt that's going to happen
but it's a possible outcome
>> but do you think it's there's some place
where the information is stored about
these distributions that were being
sampled through the thin interfaces like
actual place.
>> Place is weird because it isn't the same
as our physical spaceime. Okay, I don't
think it's that. So, calling it a place
is a little a little weird.
>> No, but like uh physics, general
relativity describes a spaceime.
>> Could other physics theories be able to
describe this other space where
information is stored that we can apply?
may be different but uh in the same
spirit laws about yes information.
>> I definitely think they're going to be
systematic laws. I don't think they're
going to look anything like physics. You
can call it physics if you want but I
think it's going to be so different that
that probably just you know cracks the
word. Um and whether information is
going to survive that I'm not sure. But
I but I definitely think that it's going
to be there are going to be laws. But I
think they're going to look a lot more
like um aspects of of of psychology and
cognitive science than they're going to
look like physics. That's my guess.
>> So what does it look like to prove that
world exists?
>> What it looks like is a successful
research program that explains how you
pull particular patterns when you need
them and why some patterns come and
others don't and show that they come
from an ordered space
>> across a large number of organisms.
Well, it's not just organisms. I mean, I
think I think it's going to end up and I
mean, you can talk to the machine
learning people about how they got to
this point again because this is this is
not just me. There's a bunch of there
are a bunch of different disciplines
that are converging on this now
simultaneously. Um, you're going to
you're going to find um again just like
in mathematics where from from from from
different directions, everybody sort of
is looking at different things. Oh my
god, this is one underlying structure
that seems to like inform all of this.
Uh so in in physics, in mathematics, in
uh computer science, machine learning,
possibly in economics, uh certainly in
biology, possibly in you know cognitive
science, we're going to find these
structures. It was already obvious in
Pythagoras's time that that there are
these patterns. The only remaining
question is are they part of an ordered
structured or you know space and are we
up to the task of mapping out the
relationship between what we build and
the patterns that come through it. So
from the machine learning perspective,
is it then the case that the even
something as simple as LLMs
are sneaking up onto this world that the
representations that they form are
sneaking up to it? when I I've g I've
given this talk to to to some audiences
and especially in the organicist um
community, people like the first part
where it's like, okay, now there's an
idea for what the magic quote unquote is
that's uh that's special about the
living things and and so on. Now, now if
we could just stop there, we would have
dumb machines that just do what the
algorithm says and we have these magical
living interfaces that can be the
recipient for these. Cool, right? We can
cut up the world in this way. [sighs] Uh
unfortunately or or fortunately um I
think that's not the case. And I think
that even even simple uh minimal
computational models are to some extent
beneficiaries of these free lunches. I
think that um
the theories we have and this this goes
back to the to the thin client interface
kind of idea. The theories we have of
both of physics and computation. So
theory of algorithms, you know, touring
machines, all all that good stuff, those
are all good theories of the front-end
interface
and they're not complete theories of the
whole thing. They capture the front end,
which is why they get surprised, which
is why these things are surprising when
they happen. I think that when we see
embryos of different species, we are
pulling from welltrodden familiar
regions of that space and we know what
to expect. Frog, you know, snake,
whatever. When we make cyborgs and
hybrids and biobots, we are pulling from
new regions of that space that look a
little weird and they're unexpected, but
you know, we can still kind of get our
get our mind around them. When we start
making AIs, like proper AIs, we are now
fishing in a region of that space that
we may that that may never have had
bodies before. It may have never been
embodied before. And what we get from
that is going to be extremely
surprising. And um the final um thing
just to mention on that is that because
of this because of the inputs from this
platonic space some of the really
interesting things that um artificial
constructs can do are not because of the
algorithm they're in spite of the
algorithm. They are filling up the
spaces in between. There's what the
algorithm is forcing you to do and then
there's the other cool stuff it's doing
which is nowhere in the algorithm. And
if that's true and we think it's true
even of very minimal systems, then this
whole business of of um of language
models and AIs in general,
watching the language part may be a
total red herring because the language
is what we force them to do. The
question is what what what else are they
doing that we are not we are not good at
noticing. And this is you know this this
this is something that we are I think um
as a as a kind of a existential um step
for humanity is to is to be become
better at this because we are not good
at recognizing these things. Now
>> you got to tell me more about uh this
behavior that is observable that is
unrelated to the explicitly stated goal
of a particular algorithm. So you looked
at a simple algorithm of uh sorting. Can
you explain what was done?
>> Sure. First just the goal of the study.
There are two things that people
generally assume. One is that we have a
pretty good intuition about what kind of
systems are going to have competencies.
So from observing biologicals, we're not
terribly surprised when biology does
interesting things. Everybody always
says, well, it's biology, you know, of
course it does all this cool stuff.
And yeah, but but we have these machines
and the whole point of having machines
and dumb and algorithms and so on is
they do exactly what you tell them to
do, right? And and people feel pretty
strongly that that's a binary
distinction and that that's what uh
that's we we can carve up the world in
that way. So I I wanted to do two
things. I wanted to first of all explore
that and hopefully break the assumption
that we're good at seeing this because I
think we're not and I think it's
extremely important that we understand
very soon that uh we need to get much
better at uh at at knowing when to uh
when to expect these things and the
other thing I wanted to do was to find
out uh you know mo most mostly people
assume that you need a lot of complexity
for this so when somebody says well
the capabilities of my mind are not
properly um encompassed by the rules of
biochemistry. Everybody's like, "Yeah,
that makes sense where, you know, you're
very complex and okay, you know, your
mind does things that that you can't you
could you didn't see that coming from
the rules of biochemistry, right? Like
we we know that." Um so mostly people
think that has to do with complexity and
and what I would like to find out is as
as part of understanding what kind of
interfaces give rise to what kind of is
it really about complexity? How much
complexity do you actually need? Is
there some threshold after which this
happens? Is it really specific
materials? Is it biologicals? Is it
something about evolution? Like what is
it about these kinds of things that
allows this this this surprise, right?
Allows this idea that we are more than
the sum of our parts. And so and and and
I had a strong intuition that none of
those things are actually required. That
this is this kind of magic, so to speak,
seeps into pretty much everything. And
uh and so to to look at that I wanted
also to uh have an example that had
significant shock value because the
thing with biology is
there's always more mechanism to be
discovered right like there's infinite
depth of what the materials are doing
what the you know somebody will always
say what there's a mechanism I just
haven't found it yet so I wanted an
example that was simple transparent so
you could see all the stuff there was
nowhere to hide I wanted it to be
deterministic because I don't want it to
be something around unpredictability or
stochasticity and uh and I wanted to be
uh something familiar to people minimal
and I wanted to use it as a model system
for honing our abilities to take a new
system and looking at it with fresh eyes
and that's because these sorting
algorithms have been studied for over 60
years. We all think we know what they do
and what their properties are. The
algorithm itself is just a few lines of
code you know you can you can see
exactly what's there. It's deterministic
and that's that's so that that's why
that's why right I wanted I wanted the
most shock value out of a system like
that if we were to find anything and to
use it as an example of taking something
minimal and and and seeing what can be
gotten out of it. So I'll I'll describe
two interesting things about it and then
we have lots of other work coming uh in
the next in the next year about even
simpler systems. I mean it's actually
crazy. Um so the so the very first thing
is this the standard sorting. So let's
let's take bubble sort, right? And and
and all these sorting algorithms, you
know, what you're starting out with is
an array of jumbled up digits, okay? So
integers. It's an array of mixed up
integers. And what the algorithm is
designed to do is to eventually arrange
them all into order. And what it does
generally is compare some pieces of that
array and and based on which one is
larger than which it swaps them around.
And you can imagine that if you just
keep doing that and you just keep
comparing and swapping, then eventually
you can get all the digits in the same
order. So the first thing I decided to
do and this is uh this is the work of uh
my student tening Jiang and then Adam
Goldstein on this paper. This goes back
to our original discussion about putting
a barrier between it and its goals. And
the first thing I said, okay, how do how
do we put a barrier in? Well, how about
this? The traditional algorithm
assumes that the hardware is working
correctly. So if you have a seven and
then the five and you tell them to swap
the the lines swap the swap the five and
the seven and then you go on you never
check did it swap because you assume
that that that it's reliable hardware.
Okay.
So what we decided to do was to break
one of the digits so that it doesn't
move. When you tell it to move doesn't
move. We don't change the algorithm.
That's really key. We do not put
anything new in the algorithm that says
what do you do if the damn thing didn't
move. Okay, just run it exactly the same
way. What happens? Turns out something
very interesting happens. Still works.
It still so it still sorts it. Uh but it
it eventually sort sorts it by moving
all the stuff around the broken number.
Okay, that makes sense. But here's
something interesting. Suppose we
suppose we plot at any given moment we
plot the degree of sortedness of the
string as a function of time. If you run
the normal algorithm, it's sort and it
gets it's guaranteed to get where it's
going. That's the, you know, it's got to
it's got to sort and it will always
reach the end. But when it encounters
one of the broken digits, what happens
is the actual sortedness goes down
>> Mhm.
>> in order to then recoup and get better
order later. What it's able to do is to
go against the thing that it's trying to
do
>> Mhm.
>> to go around in order to meet its goal
later on. Now if I didn't if if if I
showed this to a behavior scientist and
I didn't tell them what this what system
was doing is they will say well we know
what this is this is delayed
gratification this is the ability of a
system to go against its gradient and
get what it needs to do. Now imagine two
magnets imagine you take two magnets and
you put a piece of wood between them and
they're like this. What the magnet is
not going to do is to go around the
barrier and get to its goal. The two
they're not smart enough to go against
their gradient. They're just going to
like keep doing this. Some animals are
smart enough, right? They'll go around
and the sorting algorithm is smart
enough to do that. But the trick is
there are no steps in the algorithm for
doing that. You could stare at the
algorithm all day long. You would not
see that this thing can do delayed
gratification. It isn't there. Now,
there's two ways to look at this. On the
one hand, you could say, so the the
reductionist physics approach, you could
say, did it did it follow all the steps
in the algorithm? Yeah, it did. Well,
then uh there's nothing to see here.
There's no magic. this is, you know, it
it does what it does that it didn't it
didn't disobey the algorithm, right? I'm
not claiming that this is a miracle. I'm
not saying it disobys the algorithm. I'm
saying it's not failing to sort. I'm
saying it's not doing some sort of, you
know, crazy quantum thing. Not saying
any of that. What I'm saying is other
people might call it emergent. What it
has is are properties that are not
complexity, not unpredictability, not
perverse instantiation as in sometimes
in in a life. What it has are unexpected
competencies recognizable by behavioral
scientists meaning meaning different
types of cognition primitive. Well, we
wanted primitive. So there you go. It's
simple. Uh that you didn't have to code
into the algorithm. That's very
important. You get more than you start
with you than you put in. You didn't
have to do that. You get these
surprising behavioral competencies, not
just complexity. That's the first thing.
The second thing which which is also
crazy but it it requires a little bit of
a little bit of explanation. The second
thing that we said is okay what if
instead of in the typical sorting
algorithm you have a single controller
top down. I'm I'm sort of godlike
looking down at the numbers and I'm
swapping them according to the
algorithm. What if and this goes back to
actually the title of the paper talks
about agential data self-sorting
algorithms. This is back to like what's
who's the pattern and who's the agent,
right? He said what if we give the
numbers a little bit of agency. Here's
what we're going to do. We're not going
to have any kind of top- down sort.
Every single number knows the algorithm
and he's just going to do whatever the
algorithm says. So if I'm a five, I'm
just going to execute the algorithm and
the algorithm will try to make sure that
to my right is the six and to my left is
a four. That's that's it. So every digit
is so it's like a distributed as you
know it's like an ant colony. There is
no central planner. Everybody just does
their own algorithm. Okay, we're just
going to do that once you've done that.
And by the way, one of the values of
doing that is that you can simulate
biological processes because in biology,
you know, if I have like a frog face and
I scramble it with all the different
organs, every every tissue is going to
rearrange itself so that ultimately you
have, you know, nose, eyes, head, you
know, you're going to have an or right?
So you can do that. But um, okay, fine.
But you can do something else cool once
you've done that. You can do something
cool that you can't do with a standard
algorithm. You can make a chimeic
algorithm. What I mean is not all the
cells have to follow the same algorithm.
Some of them might follow bubble sort.
Some of them might follow selection
sort. It's like in biology what we do
when we make chimeas. We make
frogalottles. So frogalottles have some
frog cells. They have some axelottle
cells. What is that going to look like?
Does anybody know what a frogalottle is
going to look like? It's actually really
interesting that despite all the
genetics and the and the developmental
biology, you have the genomes. You have
the frog genome. You have the axel
genome. Nobody can tell you what a
frogalottle is going to look like. Even
though you have Yeah, this is this is
the this is back to your question about
physics and chemistry. Like yeah, you
can know everything there is to know
about how you know how the physics and
the and the genetics work, but the
decision- making, right, is like baby
baby axelottles have legs. Tadpoles
don't have legs. Is a frogalottle going
to have legs, right? Can you predict
that from from understanding the physics
of transcription and all of that?
Anyway,
>> so so we made some uh so so you you see
this is like an intersection of biology,
physics, cognition. So we made chimeic
algorithms and we said okay half the
digits randomly. We assign them
randomly. So half the digits are
randomly doing bubble sort, half the
digits are randomly doing selection sort
or something.
>> But that once you choose bubble sort,
that digit is sticking with bubble sort.
>> It's sticking. We haven't done the thing
where they can swip swap between. No,
they're they're sticking to it, right?
You label them and they're sticking to
it. The first thing we learned is that
the first thing we learned is that
distributed sorting still works. It's
amazing. You don't need a central
planer. When when every when every
number is doing its whole thing, still
gets sorted. That's cool. The second
thing we found is that when you make a
chimeic algorithm where actually the
algorithms are not even matching that
works too is the thing still gets
sorted. That's cool. But the most
amazing thing is when we looked at
something that had nothing to do with
sorting and that is we asked the
following question. We defined um Adam
Goldstein actually named this property
and I think it's it's well named. We
defined the algo type of a single cell.
It's not the genotype. It's not the
phenotype. It's the algae. The algae is
simply this. What algorithm are you
following? Which one are you? Are you a
are you a selection sort or bubble sort?
Right? That's it. There's two algo
types. And we simply ask the following
question. During that process of
sorting,
what are the odds that whatever algo
type you are, the guys next to you are
your same type.
It's it's not the same as asking how the
numbers are sorted because it's got
nothing to do with the numbers. It's
actually it's just whatever type you
are.
>> It's more about clustering than sorting.
>> Clustering. Well, that's exactly what we
call it. We call it clustering. And at
first, so so now think of what happens.
And that's and you can see this on that
graph. It's the red. You start off the
clustering is at 50%, because as I told
you, we assign the alot types randomly.
So the odds that the guy next to you is
the same as you is half 50%. Right?
There's only two algo types. In the end,
it is also 50%. Because the thing that
dominates is actually the sorting
algorithm. And the sorting algorithm
doesn't care what type you are. You got
to get the numbers in order. So by the
time you're done, you're back to random
algo types because because you have to
get the number sorted. Mhm. But in
between in between you get some amount
of increased very significant cuz look
at look at the control is in the middle.
The pink is in the middle. Uh in in
between you get significant amounts of
clustering meaning that certain algo
types like to hang out with their
buddies for as long as they can. Now now
now here's here's the one more thing and
then I'll kind of give up the
philosophical significance of this. And
so we saw this and I said that's nuts
because the algorithm doesn't have any
provisions for asking what algo type am
I? What algo type is my is my neighbor?
If we're not the same, I'm going to move
to be next to like if you wanted to
implement this, you would have to write
a whole bunch of extra steps. There
would have to be a whole bunch of
observations that you would have to take
of your neighbor to see how he's acting.
Then you would infer what algo type he
is. Then you would go stand next to the
one that seems to have the same algo
type as you. You would have to take a
bunch of measurements to say, "Wait, is
that guy doing bubbles? Is he doing
selection?" Right? Like if you wanted to
implement this, it's a whole bunch of
algorithmic steps. None of that exists
in our algorithm. You don't have any way
of knowing what algo type you are or
what anybody else is. Okay, we didn't
have to pay for that at all. So notice
notice a couple of interesting things.
The first interesting thing is that this
was not at all obvious from the uh from
the algorithm itself. Algorithm doesn't
say anything about algo types. Second
thing is we paid computationally for all
the steps needed to have the numbers
sorted, right? because we know, you
know, you pay you pay for certain
computation cost. The clustering was
free. We didn't pay for that at all.
There were no extra steps. So, this gets
back to your other question of how do we
know there's a platonic space? And this
is kind of like one of the craziest
things that we're doing. I actually
suspect we can get free compute out of
it. I suspect that one of the things
that we can do here is use these in a
useful way that don't require you to pay
cost to pay physical cost, right? Like
we we know every every bit has a has a
an energy cost that you have to get. The
clustering was free. Nothing extra was
done.
>> Yeah. Just uh the this plot for people
who are just listening on the x- axis is
the percentage of completion of the
sorting process. The y ais is the
sortedness of the list of numbers. And
then also in the red line is basically
the degree to which they're clustered.
And uh you're saying that there's this
unexpected competence
of clustering.
And I should comment that I'm sure
there's a theoretical computer scientist
listening to this saying I can model
exactly what is happening here and prove
that the clustering increases decreases.
So taking the specific instantiation of
the
thing you've experimented with and and
prove certain uh properties of this. But
the point is that there's a more general
pattern here of probably other that you
haven't discovered unexpected
competencies that emerge from this that
you can could get free computation out
of this thing.
>> So this goes back to the very first
thing you said about uh physicists
thinking that physics is enough. You're
100% correct that somebody could look at
this and say, "Well, I see exactly why
this is happening. We can track we can
track through the algorithm." Yeah, you
can. There's no miracle going on here,
right? I'm the hardware isn't doing some
crazy thing that it wasn't supposed to
do. The point is that despite following
the algorithm to do one thing, it is
also at the same time doing other things
that are neither prescribed nor
forbidden by the algorithm. It's the
space between between uh the chance and
necessity which is how a lot of people
you know see these things. It's that
it's that free space. We don't really
have a good vocabulary for it where the
interesting things happen. And to
whatever extent it's doing other things
that are useful, that stuff is is is
computationally without extra cost. Now,
there's one other cool thing about this
and this is the beginning of a lot of um
thinking that I've done about um this
this relates to AI and stuff like that.
Intrinsic motivations.
>> The sorting of the digits is what we
forced it to do. The clustering is an
intrinsic motivation. We didn't ask for
it. We didn't expect it to happen. We
didn't uh we didn't explicitly forbid it
but we didn't you know we didn't know.
This is a great definition of the
intrinsic motivation of a system. So
when people say oh that's a machine it
only does what you programmed it to do.
I you know I as a human have intrinsic
motivation you know uh I'm creative and
I have intrinsic motivation. Machines
don't do that. Even even even this
minimal thing has a minimal kind of
intrinsic motivation which is something
that is not forbidden by the algorithm
but isn't prescribed by the algorithm
either. And I think I think that's an
important you know third thing besides
chance and necessity. Something
something else that's that's fun about
this is uh when you think about
intrinsic motivations think think about
a child uh if you make him sit in math
class all day you're never going to know
what the other intrinsic motivations are
that he might be doing right who knows
what else he might be interested in. So
we so I wanted to ask this question. I
said if we let off the pressure on the
sorting,
what would happen? Now that's hard
because because if you mess with the
algorithm, now it's no longer the same
algorithm. So you don't want to do that.
So we did something that I think was was
kind of clever. We allowed repeat
digits. So if you allow repeat digits in
your in your array, you can still have
all the fives can still be after all the
fours and after all the sixes, but you
can keep them as clustered as you want.
So this thing at the end where they have
to get declustered in order for the
sorting to happen. We thought maybe we
could let off the pressure a little bit.
If you do that, all you do is allow some
extra repeat digits, the clustering gets
bigger. It will cluster as much as you
let it. The clustering is what it wants
to do. The sorting is what we're forcing
it to do. And my only point is if if the
if the bubble sword which has been gone
over and gone over how many times has
these kinds of things that we didn't see
coming what about the AIS the language
mod everything else not because not
because they talk not because they say
that they're you know have an inner
perspective or any of that but just from
the fact that this thing is even even
the most minimal system surprises with
what happens and I frankly when I see
this tell me if this doesn't sound like
all of our existential story for the
brief time that we're here. The universe
is going to grind us into dust
eventually, but until then, we get to do
some cool stuff that is intrinsically
motivating to us that is neither
forbidden by our by the laws of physics
nor determined by the laws of physics,
but eventually it it kind of comes to an
end. So I I I think that that aspect of
it right that um there are spaces even
in algorithms there are spaces in which
you can do other new things not just
random stuff not just complex stuff but
things that are easily recognizable to a
behavior scientist you see that's the
point here and I think that kind of
intrinsic motivation is what's telling
us that this idea that we can carve up
the world we can say okay look biology
is complex cognition who knows what's
respons responsible for that. But at
least we can take a chunk of the world
aside and we can we can cut it off and
we can say these are the dumb machines.
These are just this algorithms. Whereas
we know the rules of biochemistry don't
explain everything we want to know about
how psychology is going to go. But at
least the rules of algorithms tell us
exactly what the machines are going to
do. Right? We have we have some hope
that we've we've carved off a little
part of the world and everything is nice
and simple and it is exactly what we
said it was going to be. I think that
failed. I think it was a good try. I
think we have good theories of
interfaces, but even even the simplest
algorithms have have these kinds of
things going on and and so that's that
that's why I think something like this
is significant. [clears throat]
>> Do you think that there is going to be
in all kinds of systems of varying
complexity things that the system wants
to do and things that is forced to do?
So are there these unexpected
competencies to be discovered in
basically all algorithms and uh all
systems?
>> That's my suspicion and I think that is
extremely important for us to as as
humans to have a research program to
learn to recognize and predict and
recognize. We make things never mind
something as simple as this. We make we
make you know social structures,
financial structures, internet of
things, um robotics, AI. But we make all
this stuff and we think that the thing
we make it do is the main show. And I I
think it is very important for us to
learn to recognize the the the kind of
stuff that that sneaks in into the
spaces.
>> What what it's a very counterintuitive
notion. Yeah.
>> By the way, I like the word emergent. I
hear your criticism and it's a really
strong one that emergent is like you
toss your hands up. I don't know the the
process, but it's just a beautiful word
because it is I guess it's a synonym for
surprising
and I mean this is very surprising
but just because it's surprising doesn't
mean there's not a mechanism that
explains it.
>> Mechanism and explanation are both uh
not all they're cracked up to be in the
sense that you know anything you and I
do. We could we could come up with the
most beautiful theory. We paint a
painting. anything we do. Somebody could
say, "Well, I was watching the
biochemistry and the and the and the
Schroinger equation playing out." And it
was to it totally described everything
that was happening. You didn't break you
didn't break even a single law of
biochemistry. Nothing to see here.
Nothing to see, right? Like, okay, you
know, consistent with the with the
low-level rules. You can do the same
thing here. You can look at the machine
code and say, "Yeah, this thing is just
executing machine code." You can go
further and say, "Oh, it's it's quantum
foam. It's just doing the thing that
quantum foam does
>> that that you're saying that's what
physicists miss.
>> And I'm not saying they're unaware of
that. I'm I mean they're generally a
pretty sophisticated bunch. I just think
they've picked a level and they're going
to discover what is to be seen at that
level, which is a lot. And my point is
the stuff that the the the behavior
scientists are interested in shows up at
a much lower level than you think. How
often do you think there's a
misalignment of this kind between the
thing that a system is forced to do and
what it wants to do? I particularly I'm
thinking about various levels of
complexity of AI systems.
>> Yeah. So right now we've looked at like
five other systems. That's a small N.
Okay. But but just looking at that, I I
would find it very uh surprising if
Bubbles was able to do this and then
there was some sort of valley of death
where nothing showed up and then blah
blah living things like I can't imagine
that. I I'm going to say that if
something and we and we actually have a
system that's even simpler than this,
which is one dellular automata that's
doing some weird stuff. If if these
things are to be found in this kind of
simple system, I I I mean they just have
to be showing up in in these other more
complex AIs and things like that. The
only thing what what we don't know, but
we're going to find out is to what
extent there is interaction between
these. So I call these things side
quests, you know, it's like they're like
like like in a game, you know, where the
main thing you're supposed to do and
then as long as as long as you still do
it. The thing about this is you have to
sort you have to sort. which is no
miracle you're going to sort. But but
no, but but as long as you can do other
stuff while you're sorting, it's not
forbidden. And what we don't know is to
what extent are the two things linked?
So if you do have a system that's very
good at language, are the are the others
the the the side quests that it's
capable of, do they have anything to do
with language whatsoever? The the we
don't know the answer to that. The
answer might be no. In which case, all
of the stuff that we've been saying
about language models because of what
they're saying, all of that could be a
total red herring and not really
important and the really exciting stuff
is what we never looked for. Or in
complex systems, maybe those things
become linked. In biology, they're
linked. In biology, evolution makes sure
that that the things you're capable of
have a lot to do with what you've
actually been selected for. In these
things, I I don't know. And so we might
find out that that they actually do give
the language some sort of leg up or we
might find that the language is is just
uh you know that's not that's not the
interesting part.
>> Also it is an interesting question of um
this intrinsic motivation of clustering.
Is this a property of the particular
sorting algorithms? Is this a property
of all sorting algorithms? Is this a
property of all algorithms operating on
lists on numbers?
How big is this? So for example with
LLMs, is it a property of any algorithm
that's trying to model language or is it
very specific to transformers and that's
all to be discovered?
>> We're doing all that. We're doing all
that. We're testing. We're testing the
stuff in other algorithms. We're looking
for we're developing suites of code to
look for other properties. We, you know,
to some extent it's very hard because we
don't know what to look for. But we do
have a behaviorist handbook which tells
you what the the all all kinds of things
to look for. the the delayed
gratification, the you know problem
solving like we we have all that. I'll
tell you an end of one of an interesting
biological intrinsic motivation because
because people so so so in in like the
alignment community and stuff there's a
lot of discussion about like what are
the intrinsic motivations going to be of
AIS what are their goals going to be
right what are they going to want to do
uh just just as an NF1 observation
anthrobots the very first thing we
checked for so this is not experiment
number 972 out of a thousand things this
is the very first thing we checked for
we put them on a plate of neurons with a
big wound through them a big scratchm
first thing they did was heal the wound.
Okay, so it's an end of one, but I I I
like the fact that the first intrinsic
motivation that we noticed out of that
system was benevolent and healing. Like
I thought that was pretty cool. And we
don't know maybe that, you know, maybe
the next 20 things we find are going to
be some sort of, you know, damaging
effect. I I can't tell you that. But but
the first thing that we saw was was kind
of a positive one. And and I don't know
that makes me feel better.
>> What was the thing you mentioned with
the anthrobots that they can reverse
aging? There's a a procedure called an
epigenetic clock where what you can do
is look at a particular epigenetic
states of cells and compare to a a curve
that was built from humans of known age.
You can guess you can guess what the age
is. Okay. So so so we can take now and
this is Steve Hrath's work and many
other people that when you take a set of
cells you can guess what their
biological age is. Okay. So, we make the
anthrobots from cells that we get from
human tracheal epithelium.
We collaborated with with Steve's group,
the clock foundation. We sent them a
bunch of cells and we saw that if you if
you check the the anthrobots themselves,
they are roughly 20% younger than the
cells they come from. And so, that's
amazing. And I I can I can give you a
theory of why that happens. Although
we're still investigating and then I can
tell you the implications for um
longevity and things like that. My
theory for why it happens
uh I call this uh uh I call this age
evidencing. And I think that what's
happening here like with a lot of
biology is that cells have to update
their priors based on experience. And so
I think that they come from an old body.
They have a lot of priors about how many
years they've been around and all that,
but their new environment screams, "I'm
an embryo." Basically, there's no other
cells around. You're being bent into a
pretzel. They actually express some
embryionic genes. They say, "You're
you're an embryo." And I think it
doesn't it it's not enough new evidence
to roll them like all the way back, but
it's enough to update them to about 28%
back.
>> Yeah. So, it's similar to like uh when
older adult gives birth to a a child. So
you're you're you're saying you can just
fake it till you make it with uh with
age like the environment convinces the
cell that it's young.
>> Well, first of all, yes. Yes. And uh
that's that's that's my hypothesis and
we have a whole bunch of research uh
being done on this. There was a study
where they went into a um an old age
home and they redid the decor like 60s
style when all these folks were really
young and they they found all kinds of
improvements in blood chemistry and
stuff like that because they say it was
sort of mentally taking them back to
when you know when they were the way
they were at that time. I I I think this
is a basil version of that that
basically if if you're finding yourself
in an embryionic environment, what's
more plausible that that that you're
young or or what what you know like I
think I think this is this is the basic
feature of of biology is to is to update
priors based on experience.
>> Do you think that's actually actionable
for longevity? Like you can convince
cells that they're younger and thereby
extend
the lifespan.
>> This is what we're trying to do. Yeah.
>> Could it be as simple as that?
>> Why that's not si well I'm not claiming
it's simple that that is in no way
simple but because because again you
have to all all of this all of the
regenerative medicine stuff that we do
balances on one key thing which is
learning to communicate to the system.
We have to if you're going to convince
that system you know so so when we make
gut tissue into an eye you have to
convince those cells that their priors
about we are we are gut precursors those
priors are wrong and you should adopt
this new world view that you're going to
be you know you're going to be an eye so
being convincing and figuring out what
what kind of messages are convincing to
to cells and how to speak the language
and how to make them take on new uh new
beliefs literally is is at the root of
all of these future advances. in in
birth defects and regenerative medicine
and cancer and that's that's what's
going on here. So I'm not saying it's
simple but I can see the I can see the
path.
>> Uh going back to the platonic space I I
have to ask if uh if our brains are
indeed thin client interfaces
to that space. Uh what does that mean
for our mind? Like can we upload the
mind?
Can we copy it?
Can we uh ship it over to other planets?
Like how what does that mean for exactly
where the mind is stored?
>> Yeah. Couple of things. So we so we are
now beyond anything that I can say with
any certainty. This is total total
conjecture. Okay. So because we don't
know yet. The whole point of this is we
actually don't really understand very
well the relationship between the
interface and the thing
>> and the thing you're currently working
on is to map correct this space.
Correct. and we're and and we are
beginning to map it, but you know this
is this is a massive effort. So um so so
a couple of uh a couple of conjectures
here. One is that I I strongly suspect
that um the majority of what we think of
as the mind is is the pattern in that
space. Okay. And one of the interesting
predictions from that model, which is
not a prediction of modern neuroscience,
is that there should be cases where
there's very minimal brain and yet
normal IQ function. This has been seen
clinically. We we just Karina Kaufman
and I reviewed this in a paper recently,
a bunch of cases of humans where there's
very little brain tissue and they have
normal or in sometimes above normal
intelligence. Now things are not simple
because that obviously doesn't happen
all the time, right? Most of the time
that doesn't happen. So so what's going
on? We don't understand. But it is a
very curious thing that is not a
prediction of I'm not saying I'm not
saying it can't you know you can take
modern neuroscience and sort of bend it
into a pretzel to accommodate it. You
can say well there are these you know
kind of redundancies and things like
this right? So you can accommodate it
but it doesn't predict this. So uh there
there are these incredibly curious
cases. Now, do I think you can copy it?
No, I don't think you can because what
you're going to be copying is the is the
interface, the front end, the the brain
or the the you whatever you the act the
action is actually the pattern in the
platonic space. Are you going to be able
to copy that? I doubt it. But what you
could do is produce another interface
through which that particular pattern is
going to come through. I think that's
probably possible. I can't say anything
about at this point about what that
would take, but my guess is that that's
that that's possible.
>> Is your guess your gut is that that
process if possible is different than
copying? Like
it looks more like creating a new thing
versus copying
>> for the interface. So if you could So so
so um so here's my prediction for um
Star Trek Transporter. For whatever
reason, right now your brain and body
are very uh attuned and attractive to a
particular pattern which is your set of
psychological propensities. If we could
re if we could rebuild that exact same
thing somewhere else, I don't see any
reason why that same pattern wouldn't
come through it the same way it comes
through this one. That's that would be a
guess, you know. So, so I think what you
what you will be copying is the physical
interface and hoping to maintain
whatever it is about that interface that
was appropriate for that pattern. We we
don't really know what that is at this
point.
>> So, when we've been talking about mind
in this particular case, it's the most
important uh to me cuz I'm a human. Uh
does self come along with that
this
the feeling
like this mind belongs to me? Yeah.
>> So that come along with all minds the
the subjective
not the subjective experience the
subjective experience is important too
consciousness but like the ownership
>> I suspect so and I think so because of
the way we come into being. So, so one
of the things that um I should be
working on is uh this paper called
booting up the agent and it talks about
the very earliest steps of becoming a
being in this world. kind of like you
can do this for a computer, right? And
before you switch the power on, it
belongs to the domain of physics, right?
It obeys the laws of physics. You switch
the power on some number of what
nanconds, microsconds, I don't know,
later you have a thing that oh look,
it's taking instructions off the stack
and doing them right. So, so now you're
now it's executing an algorithm. How did
you get from from physics to executing
an algor what what what was happening
during the boot up exactly before it
starts to run code or whatever, right?
And so we can ask that same question uh
in biology. What are the earliest steps
of uh of becoming a being?
>> Yeah, that's a fascinating question.
Through embryogenesis, at which point is
the are you booting on?
>> Yeah. Yeah. Yeah. Yeah. Yeah. Exactly.
Do
>> you have a hope of an answer to that?
>> Well, I think I think so. I think so. In
in in two ways. Um the first thing is
just physically what what happens. So I
I think that the your your first task as
uh as a as a being and and I again I
don't think this is a binary thing. I
think this is a positive feedback loop
that sort of cranks on up up and up.
Your first task as a being coming into
this world is to tell a very compelling
story to your parts. As a biological you
are made of a gentle parts. Those parts
need to be aligned literally into a
goal. They have no comprehension of they
if you're going to move through
anatomical space by means of a bunch of
cells which only know physiological and
um you know metabolic spaces and things
like that you are going to have to
develop a model and give them uh bend
their action space. You're going to have
to deform their option space with
signals with uh behavior shaping cues
with rewards and punishments. whatever
you got your job as a as an agent is
ownership of your parts is alignment of
your parts. I I think that fundamentally
is going to give rise to this this this
ability. Now, now that also means having
a boundary saying, "Okay, this is the
stuff I control. This is me. This other
stuff over here is outside world. I have
to figure out." You don't know where
that is, by the way. You have to figure
it out. And in embryogenesis, it's
really cool. You can uh as a as a as a
grad student, I used to do this
experiment with duck embryos, which a
flat blast disc. You can take a needle
and and put some scratches into it. And
every every island you make for a while
until they heal up thinks it's the only
embryo. There's nothing else around. So,
it becomes an embryo. And eventually you
get twins and triplets and quadruplets
and things like that. But each one of
them at the border, you know, they're
joined. Well, where do I end and where
does where does he begin? You have to,
you know, you have to know what where
your borders are. So, um, that act that
action of aligning your parts and coming
to be this this this, uh, I mean, I'm
even going to say this emergence, we we
just don't have a good vocabulary for
it. This this this emergence of a model
that aligns all the parts is really
critical to keep that thing going.
There's something else that's really
interesting and uh I was thinking about
this in the context of of of this
question of like like you know these
these beautiful um kind of ideas you
know that uh there's there's this
amazing thing that we found and this is
this is largely the work of Federico
Piggoi in my group. So a couple years
ago we saw that networks of chemicals um
can learn. They have five or six
different kinds of learning that they
can do. And so what I asked them to do
was to calculate um the causal emergence
of those networks while they're
learning. And what I mean by that is is
this. If you're a rat and you learn to
press a lever and get a reward,
there's no individual cell that had both
experiences. The right the cells at your
at your paw had touched the lever. The
cells in your gut got the delicious
reward. No individual cell has that both
experiences. Who owns that associative
memory? Well, the rat. So that means you
have to be integrated, right? If you're
going to learn associative memories from
different parts, you have to be an
integrated agent that can do that. And
so we can measure that now with metrics
of causal emergence like FI and and
things like that. So we know that in
order to learn, you have to have
significant FI. But I wanted to ask the
opposite question. What does learning do
for your FI level? Does it do anything
for your degree of being an agent that
is more than the sum of its parts? So we
trained the networks and sure enough
some of them not all of them but some of
them as you train them the their their
fi goes up. Okay. And so basically what
we were able to find is that there is
this uh positive feedback loop between
every time you learn something you
become more of an integrated agent
>> and every time you do that it becomes
easier to learn. And so it's this
>> it's a virtuous cycle.
>> It's a virtuous cycle. It's an asymmetry
that points upwards for agency and
intelligence. And now back to our
Platonic space stuff. Where does that
come from? Doesn't come from evolution.
You don't need to have any evolution for
this. Evolution will optimize the crap
out of it for sure. But you don't need
evolution to have this. Doesn't come
from physics. It comes from the rules of
information, causal information theory,
and the behavior of networks. The
mathematical objects. It has it's this
is not anything that uh that was you
know was was given to you by physics or
by a history of selection. It's a free
gift from math and the and and and those
two free gift free uh gifts from math
lock together into a spiral that I think
causes simultaneously a rise in
intelligence and a rise in collective
agency. And I think that's just uh you
know that's been you know just just
amazing to think about. Well, that free
gift from I think is extremely useful in
biology.
>> Mhm.
>> When you have small entities forming
networks, hierarchy that builds more and
more complex organisms. That's that's
obvious. I mean, this speaks to
embryogenesis, which I think is one of
the coolest things in the universe. Uh
and in fact you acknowledge its coolness
in Ingressing May's paper writing quote
most of the big questions of philosophy
are raised by the process of
embryogenesis right in front of our eyes
a single cell multiplies and self
assembles into a complex organism with
order on every scale of organization and
adaptive behavior. Each of us takes the
same journey across the cartisian cut.
Starting off as a quiescent human
oasite, a little blob thought to be well
described by chemistry and physics.
Gradually, it undergoes metamorphosis
and eventually becomes a mature human
with hopes, dreams, and uh a
self-reflective
metacognition that can enable it to
describe itself as a not a machine. It's
more than its brain, body and underlying
molecular mechanisms and so on. What in
all of our discussion
can we say as the clear intuition how
it's possible to take a leap from uh a
single cell to a fully functioning
organism
full of dreams and hopes and friends and
love and all that kind of stuff. in
everything we've been talking about
which has been a little bit technical
like how what how do we understand
because that's one of the most magical
things the universe is able to create
perhaps the most magical
from simple physics and chemistry create
this
us two talking about ourselves I I I
think we have to keep in mind that
physics and chemistry are not real
things they are lenses that we put on
the world that that They they are uh
perspectives where we say we are for the
time being uh for the duration of this
chemistry class or career or whatever we
are going to put aside all the other
levels and we're going to focus on this
one level and that what is fundamentally
going on during that process is an
amazing positive feedback loop of
collective intelligence for the
interface. It's the physical interface
is scaling its uh the cognitive light
cone that it can support. So it's going
from a molecular network. The molecular
network can already do things like
Pavlovian conditioning. You don't start
with zero. When you have a simple
molecular network, you are already
hosting some patterns from the platonic
space that look like Pavlovian
conditioning. You you've already got
that in starting out. That's that's just
a molecular network. Then you become a
cell and then you're many cells and now
you're navigating anatomical morphus
space and you're hosting all kinds of
other patterns and eventually you and
and and I think again I think there's
and this is like what you know all the
stuff that we're trying to work out now
there's a consistent feedback between
the ingressions you get and the ability
to have new ones which again I think
it's this like positive feedback cycle
where the more of these free gifts you
pull down they allow you physically to
develop to a ways Oh, look now now now
we're suitable for for more and higher
ones. And this continuously goes and
goes and goes until, you know, until
you're able to pull down a full human
set of behavioral capacities.
>> What is the mechanism of uh such radical
scaling of the cognitive cone? Is it is
it just this kind of the same thing that
you were talking about with the network
of chemicals being able to learn?
>> I'll give you two two mechanisms that we
found, but again just to be clear, these
are mechanisms of the physical
interface.
what what we haven't gotten is a mature
theory of um how they map onto the
space. That's just like just beginning.
But I'll tell you I'll tell you what the
what the physical side of things look
like. The first one has to do with um
stress propagation. So imagine that um
you got a bunch of cells and there's a
cell down here that needs to be up
there. Okay. All of these cells are
exactly where they need to go. So
they're happy, their stress is low. this
cell the this now now let's imagine
stress stress is basically a uh uh a
it's a it's a it's a physical
implementation of the error function.
It's basically the amount of stress is
basically the delta between where you
are now and where you need to be. Not
necessarily in physical position. This
could be an anatomical space and
physiological space and in
transcriptional space whatever right?
It's just it's just the delta from your
set point. So So you're stressed out but
these guys are happy. They're not
moving. you you can't get past them.
Now, imagine if what you could do is you
could leak your stress, whatever your
stress molecule is. And the cool thing
is that evolution has actually conserved
these highly. So, these are all and
we're studying all of these things.
They're um they're actually highly
conserved. If you start leaking your
stress molecules, then all of this stuff
around here is starting to get stressed
out. When things get stress starting to
get stressed out, their temperature in
the not not physical temperature, but in
the sense of like simulated analing or
something, right? Their their ability to
their plasticity goes up because because
they're feeling stressed. They need to
relieve that stress. And because all the
stress molecules are the same, they
don't know it's not their stress. They
are equally irritated by them as if it
was their own stress. So they become a
little more plastic. They become ready
to kind of uh you know adopt different
fates. You get up to where you're going
and then everybody's stress can drop. So
So notice what can happen by a very
simple mechanism. Just be leaky for your
own stress. My problems become your
problems. Not because you're altruistic,
not because you actually care about my
problem. There's no mechanism for you to
actually care about my problems. But
just that simple mechanism means that
far away regions are now responsive to
the needs of other regions such that
complex rearrangements and things like
that can happen. It's a it's it's
alignment of everybody to the same goal
through this very dumb simple um stress
sharing thing
>> via leaky stress.
>> Leaky stress. Right? So there's another
one there's another one which I call
memory anonymization. So imagine um here
are two cells and imagine something
happens to this cell and uh it sends a
signal over to this cell. Traditionally
you send a signal over this cell
receives it. It's very clear that it
came from outside. So this cell can do
many things. It could ignore it. It
could believe you know it could take on
the information. It could just ignore
it. It could reinterpret it. It could do
whatever. But it's very clear that came
from outside. Now imagine the kind of
thing that we study which is uh called
um gap junctions. These are electrical
synapses that that could directly link
the internal millers of two cells. If
something happens to this cell, it gets
let's say it gets poked and there's a
calcium spike or something that
propagates through the gap junction
here. This cell now has the same
information, but this cell has no idea,
wait a minute, was that is that my
memory or is that his memory? Cuz it's
the same, right? It's the same it's the
same components. And so what you're able
to do now is to have a mind melt. You
can have a mind melt between the two
cells where nobody's quite sure whose
memory it is. And when you share
memories like this, it's harder to say
that I'm separate from you. If we share
the same memories, we're kind of and I
don't mean every single memories, right?
So, they still have some identity, but
to a large extent, they have a little
bit of a mind melt and there's many
complexities you can you can you can
lean on top of it. But what it means is
that if you have a large group of cells,
they now have joint memories of what
happened to us as opposed to you know
what happened to you and I know what
happened to me. And that enables a
higher cognitive ly cone because you
have greater computational capacity. You
have a greater area of concern of things
you want to manage. I don't just want to
manage my tiny little memory states
because I'm getting your memories now. I
know I got to manage this this whole
thing. So, so both of these things end
up scaling the size of things you care
about and that is a major ladder um for
cognition is is scale the the the degree
of you know the size of concern that you
have.
>> It'd be fascinating to be able to
engineer that scaling
>> probably applicable to AI systems. How
do you rapidly scale the cognitive cone?
>> Yeah. Yeah. We have some collaborators
in a company called Softmax that that
we're working with to do some of that
stuff. Um, in biology
that that's our cancer therapeutic,
which is that what you see what you see
in cancer literally is uh cells
electrically disconnect from their
neighbors when they were part of a giant
memory that was working on making a nice
organ. Well, now they can't remember any
of that. Now they're just amiebas and
the rest of the body is just external
environment. And what we found is if you
then physically reconnect them for to to
the to the network, you don't have to
fix the DNA. You don't have to kill the
cells with chemo. You can just reconnect
them and they go back to because they're
now part of this larger collective. They
go back to what they were working on.
And so so yeah, I I think we can
intervene at that at that scale.
>> Let me ask you more explicitly on the
search the Suri the search for
unconventional terrestrial intelligence.
What do you hope to do there? How do you
actually find
uh try to find unconventional
intelligence all around us? First of
all, do you think on Earth there is all
kinds of incredible intelligence we
haven't yet discovered?
>> I mean, guaranteed we've we've already
seen in our own bodies, and I don't just
mean that we are host to a bunch of
microbiome or any of that. I mean that
your your cells and and we have um all
kinds of work on this. Every day they
they traverse these alien spaces, 20,000
dimensional spaces and other spaces.
They solve problems. I I I think they
they they they have they suffer when
they fail to meet their goals. They have
stress reduction when they meet their
goals. These things are inside of us.
They are all around us. I think that we
are we have an incredible degree of mind
blindness to all of the very alien kinds
of minds around us. And I think that,
you know, looking for aliens off off the
earth is is awesome and whatever, but if
we can't recognize the ones that are
inside our own bodies, what what chance
do we have to really, you know, uh to
really recognize the ones that are out
there?
>> Do do you think that could be a measure
like IQ
for uh
for mind? What would it be? not
mindedness
but intelligence that's broadly
applicable to the unconventional minds
that's generalizable to unconventional
minds where we could uh even uh quantify
like holy this discovery is
incredible because it has this IQ yeah I
I yes and no um the the yes part is that
what as we have shown you can take
existing IQ metrics I mean literally
existing kinds of ways that that people
used to measure intelligence of animals
and humans or whatever and you can apply
them to very weird things if you have
the imagination to make the interface.
Um, you can do it and and we've done it
and we've shown creative problem solving
and and all this kind of stuff like so
so so yes,
>> however, we have to be humble about
these things and recognize that all of
those IQ metrics that we've come up with
so far were derived from an N of one
example of the evolutionary lineage here
on Earth. And so we are probably missing
a lot of them. So I would say we have
plenty to start. We have we have so much
to start with. We could keep, you know,
tens of thousands of people busy just
testing things now. But we have to be
aware that we're probably missing um a
lot of important ones.
>> What do you think has more interesting
uh intelligent unconventional minds
inside our body, the human body? or like
we were talking off mic the Amazon
jungle like nature natural systems
outside of uh like the sophisticated
biological systems were aware of.
>> Yeah, we don't know because it's really
hard to do experiments on larger
systems. It's a lot easier to go down
than it is to go up. But my suspicion
is, you know, uh like the Buddhists say,
uh innumerable sentient beings. I think
by the time you get to that degree of
infinity, it kind of doesn't matter to
compare. I suspect there's just uh
massive numbers of them.
>> Yeah, I think it really matters which
kind of systems are amendable to our
current methods of scientific inquiry.
>> I mean, I spent quite a lot of hours
just staring at ants
>> when I was in the Amazon and it's such a
mysterious, wonderful collective
intelligence. I don't know howable it is
to research. I've seen some folks try
you could simulate you could but I I
feel like we're missing a lot.
>> I'm sure we are. But but one of my
favorite things about that kind of work
um have you seen uh there's at least
three or four papers showing that ant
colonies fall for the same visual
illusions that we fall for? Not the not
the ants, the colonies. So the colonies.
So if you if you lay out food in
particular patterns, they'll do things
like complete lines that aren't there
and and like all the same that we
fall for, they fall. So, so you know, I
don't think it's hopeless, but I do
think that we need to a lot of work to
develop tools.
>> Do you think all the the tooling that we
develop and the mapping that we've been
discussing will help us uh do the SETI
part, finding aliens out there?
>> I think it's essential. I think it's
essential. I I we we are so parochial in
uh what we expect to find in terms of
life that we are going to be just
completely missing a lot of stuff if we
if we can't even if we can't even agree
on uh never mind definitions of life but
you know uh what's actually important I
I I I led a paper recently where I asked
whatever 65 or so uh modern working
scientists um for a definition of life
and and uh We had we had so many
different definitions across so many
different dimensions. We had to use AI
to make a morphus space out of it. And
and there was zero consensus about what
actually is important, you know, uh and
if if we're not good at recognizing it
here, I just don't see how we're going
to be good at recognizing it somewhere
else. So given how miraculous life is
here on Earth, so it's clear to me that
we have so much more work to do. That
said,
would that be exciting to you if we find
life on other planets in the solar
system? Like what would you do with that
information? Or is that just another
another life form that we don't
understand?
>> I would be very excited about it because
it would give us uh some more
unconventional embodiment to think
about, right? A data point that's pretty
far away from our existing data points,
at least in this solar system. So,
that'd be cool. I'd be I'd be very
excited about it, but I must admit that
my my level of uh my my set point for
surprise has been pushed so high at this
point that it would have to, you know,
it would have to be something really
weird to to make me shocked. I mean, I
the the things that we see every day is
just uh yeah,
>> I think you've mentioned in a few places
that uh
uh like you wrote that the Ingressing
Minds paper is not the weirdest thing
you plan to write. Yeah.
>> Um,
how weird are you going to get? Can you
hit maybe a better question is like in
which direction
of weirdness do you think you will go in
your in your life? In which direction of
the weird overtone window are you going
to expand?
>> Yeah. Well, the kind of background to
this is simply that
I' I've I've had a lot of weird ideas
for many many decades. And my general
policy is not to talk about stuff until
it becomes actionable. And the amazing
thing, I mean, I'm really kind of
shocked, uh, is that in my lifetime, the
empirical work, like I really didn't
think we would get this far. and the
knob. I have this like mental mental
knob of of what percentage of the weird
things I think do I actually say in
public, right? And and [snorts] every
few years when the when the um empirical
work moves forward, I sort of turn that
knob a little, right, as we keep going.
So, I have no idea if we'll continue to
be that fortunate or how long I can keep
doing this or however like I don't know.
Um just to give you um just to give you
a a direction for it. It's going to be
in the direction of what kinds of things
do we need to take seriously as other
beings with with which to relate to. So
I've already pushed it, you know, so
like we knew brainy things and and then
we said, well, it's not just brains and
then we said, well, it's not just so so,
you know, it's not just in physical
space and it's not just biologicals and
it's not just complexity. There there's
a couple of other steps to take that I'm
pretty sure are there, but but we're
going to have to do the the actual work
to make it actionable before, you know,
before we really talk about it. So that
that direction
>> I think it's fair to say you're one of
the more unconventional humans
scientists uh out there. Uh so the
interesting question is what's your
process of idea generation? What's your
process of discovery
from you you've done a lot of really
incredibly
interesting like you said actionable but
interesting out there
uh ideas that you've actually engineered
with xenobots
and anthrobots these kinds of things
like what when you uh go home tonight go
to the lab what's the process empty
sheet of paper when you're thinking
through it.
>> Well, the mental part is a lot of it uh
much like funny enough much like making
zenobots. You know, we we make zenobots
by releasing constraints, right? We we
don't do anything to them. We just
release them from the constraints they
already have and then we see
>> so a lot of it is releasing the
constraints that mentally have been
placed on us. And and part of it is my
my education has been a little weird
because I was a computer scientist first
and and only later biology. And so by
the time I heard all the biology things
that we typically just take on board, I
was already a little skeptical and
thinking a little differently, but um a
lot of it comes from releasing
constraints. And I very specifically
think about, okay, this is what we know.
What would things look like if we were
wrong or what would it look like if I
was wrong? What are we missing? What is
our worldview specifically not able to
see? Right? whatever model I have. Or
another way I often think is uh I'll
take two things that are considered to
be very different things and I'll say
let's just imagine those as two points
on a continuum. What what does that look
like? What does the middle of that
continuum look like? What's the what's
the symmetry there? What's the what's
the parameter that I can you know what's
the knob I can turn from to get from
here to there. So those kinds of I I
look for symmetries a lot. I'm like,
okay, this thing is like that way in
what way? What's the what's the fewest
number of things I would have to move to
make this map onto that? Right? So, so
these so those are you know those are
kind of mental mental tools. The
physical process um for me is basically
uh I mean obviously I'm I'm fortunate to
have a lot of discussions with very
smart people and so so in my in my group
there are some you know I've hired some
amazing people so we of course have a
lot of discussions and some stuff comes
out of that. My process is uh I do
pretty much pretty much every morning um
or I I I'm outside for sunrise and I
walk around uh in nature. Um there's not
really anything anything better than an
in than as inspiration, right, than than
nature. I do um I do I do photography
and I find that it's a good meditative
tool because it keeps your hands and
brain just busy enough like you don't
have to think too much but you know
you're sort of twiddling and looking and
doing some stuff and it keeps your brain
off of the linear like logical like
careful train of thought enough to
release it so that you can ideulate a
little more while while your hands are
busy. So it's not even the the thing
you're photographing. It's the the
mechanical process of doing the
photography
>> and mentally, right? So I because I'm
not walking around thinking, okay, let's
see. So for this experiment, we got to,
you know, I got to get this piece of
equipment and this like that goes away
and it's like, okay, what's the lighting
and what's the what am I looking at? And
during that time when you're not
thinking about that other stuff, then
then they say, whoa, yeah, I got to get
a I got a notebook. And I'm like, look,
this is this is what we need to do. So
that that kind of stuff. And the actual
idea, writing down stuff, is it
notebook, is a computer? Uh, are you
super organized thinking or is it just
like random words here and there with
drawings and like what if and also like
what is the space of thoughts you have
in your head? Is this sort of amorphous
things that aren't very clear? Are you
visualizing stuff? Uh, is there is there
something you can articulate there?
>> I tend to leave myself a lot of
voicemails because as I'm walking
around, I'm like, "Oh man, this this
idea." And so I'll I'll just call my
office and leave myself a voicemail for
later to to to transcribe.
>> I I I don't have a good enough memory to
remember any of these things. And so
what I keep is a mind map. So I have a I
have an enormous mind map. One piece of
it hangs in my in my lab so that people
can see like these are the ideas. This
is how they link together. Here's
everybody's project. I'm working on
this. How the hell does this attach to
everybody else's so they can track it?
The thing that hangs in the lab is about
9 ft wide. It's a silk uh sheet and I,
you know, it's it's out of date within a
couple of weeks of of my of my printing
it because new stuff keeps moving
around. Um and then and then there's
more that isn't, you know, isn't for
anybody else's uh uh view. But um yeah,
I try I try to be very organized because
otherwise otherwise I I forget. So So
everything is in the mind map. Things
are in manuscripts. I have something
like at right now probably 163 62 um
open manuscripts that are in process of
being written at various stages and and
when things come up I stick them in the
right manuscript [clears throat] in the
right place so that when I'm finally
ready to finalize then then I'll put
words around it and whatever but there's
like outlines of everything. So I I try
to be organized because I can't I don't
have to you know
>> so there's a wide front of uh
manuscripts of work that's being done
and it's continuously like pushing
towards completion but you're not clear
where what's going to be finished when
and how and when
>> that's I mean that's yes but that's just
the that's just the theoretical
philosophical stuff the the empirical
work that we're doing with in the lab I
mean those are we know exactly you know
>> it's more focused
>> we know this is this is you know
anthropot aging This is limb
regeneration. This is the new cancer
paper. This is whatever. Yeah, those
things are very linear.
>> Where do you think ideas come from? When
you're taking a walk
that eventually materialize in a
voicemail. Where's that?
What is that from you? Is that, you
know, a lot of really some of the most
interesting people feel like they're
channeling from somewhere else? I mean,
I hate to bring up the Platonic Space
again, but but I mean, if you talk to
any creative, that's basically what they
what they'll tell you, right? And and
certainly that's been my experience. So,
I feel I feel like it's a uh the way the
way it feels to me is a collaboration.
So collab collaboration is I I I need to
bust my ass and be be prepped in in one
a to to to work hard to be able to
recognize the idea when it comes and b
to actually have an outlet for it so
that when it does come we have a lab and
we have people who can who can help me
do it and then we can actually get it
out. Right? So that's that's that's my
part is you know be be up at 4:30 a.m.
doing your thing and be ready for it.
But the other side of the collaboration
is that yeah when you do that like
amazing ideas come and you know to say
that it's me I don't think would be
would be right. I you know I think it's
it's definitely coming from from other
places.
>> What advice would you give to scientists
PhD students grad students young
scientists
that are trying to explore the space of
ideas
given the very unconventional
non-standard unique set of ideas you've
explored in your life and career. Um,
let's see. Uh, well, the the first and
most important thing I've learned is not
to take too much advice. And so, I don't
like to give too much advice, but um,
but I do have one technique that I found
very useful. And this isn't for
everybody, but there's a specific
demographic because a lot of a lot of
um, unconventional people reach out to
me and I try to um, respond and and help
them and so on. This is a technique that
I think is useful for some people.
How do I describe it? You need to uh
it's it's it's a it's the act of
bifurcating your mind and you need to
have two different regions.
One region is the practical region of
impact. In other words, how do I get my
idea in out into the world so that other
people recognize it? What should I say?
What are people hearing? What are they
able to hear? How do I pivot it? What
parts do I not talk about? Which journal
am I going to publish this in? Is it
time now? Do I wait two years for this?
Like all the practical stuff that is all
about how it looks from the outside,
right? All the stuff that I can't say
this or I should say this differently or
this is going to freak people out or
this is uh you know this community wants
to hear this so I can pivot it this way.
Like all that practical stuff, it's got
to be there. Otherwise, you're not going
to be in a position to follow up any of
your ideas. You're not going to have a
career. You can't you're not have
resources to do anything. But it's very
important that that can't be the only
thing. You need another part of your
mind that ignores all that
completely because this other part of
your mind has to be pure. It has to be I
don't care what anybody else thinks
about this. I don't care whether this is
publishable, describable. I don't care
if anybody gets it. I don't care if
anybody thinks it's stupid. This is this
is what I what I think and why and and
give it space to to sort of grow, right?
And if you keep the if you try to mush
them if you try to mush them together, I
I found that impossible because because
the practical stuff poisons the other
stuff. If you're if you're too much if
you're too much on the creative end, you
can be an amazing thinker. It just
nothing ever materializes. But if you're
very practical, it tends to poison the
other stuff because the more you think
about how to present things so that
other people get it, it constrains and
it and it bends how you start to think.
And you know, uh what I tell my students
and others is there's two kinds of
advice. There's very practical specific
things like somebody says, "Well, you
forgot this control or this isn't the
right method or you shouldn't be." That
stuff is gold and you should take that
very seriously and you should use it to
to improve your craft, right? And that's
like super important. But then there's
the meta advice where people like that's
not a good way to think about it. Don't
work on this. This isn't that that stuff
is is is garbage. And and even very
successful people often give very
constraining, terrible advice. Like one
of my one of my reviewers in a paper
years ago said I I love this Freudian
slip. He he said he's going to give me
constrictive criticism, right? And
that's exactly what he gave me was
constrictive criticism. I was like
that's awesome. That's a great typo.
>> Well, it's very true. I mean that that
second the bifurcation of the mind is
beautifully put. I do think some of the
most interesting people I've met are
sometimes uh
fall short on the on the normie side on
the practical
how do I having the emotional
intelligence of how do I communicate
this with people that have a very
different worldview that are more
conservative and more uh conventional
and more kind of fit into the norm. You
have to be able to have the skill to fit
in.
>> Yeah. And then you have to again
beautifully put be able to shut that off
when you go on your own and think and
having two skills is very important. I
think a lot of radical thinkers think
that they're sacrificing something by
learning the skill of fitting in. But I
think if you want to have impact, if you
want ideas to resonate and actually lead
to um first of all be able to build
great teams that help bring your ideas
to life and second of all for your ideas
to have impact and to scale and to uh
resonate with a large number of people,
you have to have that skill.
>> Yeah.
>> And those are those are very different.
Those are very different.
>> Yeah.
>> Uh let me ask a ridiculous question. and
you already spoke about it, but
uh
what to you is one of the most beautiful
ideas that you've encountered
in your various explorations
maybe maybe not just beautiful but one
that makes you happy to be a scientist
to be able to um be a curious humans
exploring ideas. I mean I must say that
you know I I sometimes think about um
these these ingressions from this from
this space as a kind of steganography
you know so so steganography is is when
you hide data and messages within the
the bits of another pattern that don't
matter right and the rule of
steganography is you can't mess up the
main thing you know if a picture of a
cat or whatever you got to keep the cat
but if there's bits that don't matter
you can kind of stick stuff so I feel
like I feel like all these aggressions
are a kind of universal steganography
that there's this like these patterns
seep into everything everywhere they
can. And they're kind of they're kind of
shy, meaning that they're they're very
subtle, not invisible. If you work hard,
you can catch them, but but they're not
invisible, but but they're hard to see.
And the fact that the fact that I think
they also affect quote unquote machines
as much as they certainly affect living
organisms, I think is incredibly
incredibly beautiful. And I personally
am happy to be part of that same
spectrum. And the fact that that that
magic is sort of uh applicable to
everything. I I a lot of people find
that extremely disturbing. And that's
that's some of the some of the hate mail
I get is like, yeah, we were with you,
you know, on the majesty of life thing
until you got to the fact that machines
get it, too. And now now that like
terrible, right? You're you kind of
devaluing the the the majesty of life.
And I don't I don't know. I I the the
idea that we're now catching these
patterns and we're able to do meaningful
research on the on the interfaces and
all that is just to me absolutely
beautiful and that that it's all one
spectrum I think to me is is amazing.
I'm I'm I'm I'm enriched by it.
>> I agree with you. I think it's
incredibly beautiful. I lied. There's an
even more in ridiculous question. Uh so
it it seems like we are progressing
towards possibly creating a super
intelligent system.
um and AGI and ASI.
Uh if I had one, gave it to you, put you
in the room, what would be the first
question you ask it? Maybe the first set
of questions like there's so many topics
that you've worked on and interested in.
Is is there like a first question you
really just if you can get an answer
solid answer? I mean the well the first
thing I would ask is uh how much should
I even be talking to you? Uh for sure
because
>> it's not clear to me at all that getting
somebody to tell you an answer in the
long run is optimal.
>> It's the difference between when you're
a kid learning math and having an older
sibling that'll just tell you the
answers, right? Like sometimes it's just
like come on just give me the answer.
Let's move on with this, you know,
cancer protocol and whatever.
Great. But in the long run, the process
of discovering it yourself, how much of
that are we willing to give up? And by
getting a final answer, how much have we
missed of of stuff we might have found
along the way? Now, I don't know what
the the thing is I, you know, I I don't
think it's correct to say don't do that
at all. You know, take take the time and
all the blind alleys and like that that
may not be optimal either, but we don't
know what the optimal is. We don't know
how much we should be stumbling
stumbling around versus having somebody
tell us the answer.
>> That's actually a brilliant question to
ask AGI then.
>> I I mean if it's really a question yeah
if it's really an AGI I'm like tell me
what the balance is like how much should
I be talking to you versus stumbling
around in the lab and making all my you
know all my my own mistakes. Was it 7030
you know 1090 I I don't know. So that
would be that would be
>> from the age I will say you shouldn't be
talking to me.
>> It may well be it the it may say what
the hell did you make me for in the
first place? You guys are screwed.
[laughter] like that's possible. Um
>> uh yeah,
>> you know, the second question I would
ask is uh what's the what's the answer I
should be what's the question I should
be asking you that I probably am not
smart enough to ask you? That's the
other thing I would say.
>> This is really complicated. That's it's
a really really strong question. Um
but again there the answer might be
you wouldn't understand the question it
proposes most likely. So I I think for
me I would probably assuming you can get
a lot of questions I would probably
go for questions where I would
understand the answer like it would
uncover some small mystery that I'm
super curious about.
>> Cuz if you ask big questions like you
did which is really strong questions I
just feel like I wouldn't understand the
answer. If you ask it, what question
should I be asking you? It will probably
say something like
uh it'll say something like what is the
shape of the universe? And you're like
what? Why is that important? Right. You
you would be very confused by the
question it proposes.
>> Yeah.
>> I I I would probably want to it would
just be nice for me to know straight up.
First question, how many living
intelligent alien civilizations are in
the observable universe?
>> Mhm.
>> Yeah. That would just be nice. Yeah.
>> To know if is it zero or is it a lot? I
just want to know that and then and
unfortunately it might answer it might
it might be a give me a like 11 answer.
>> That's that's what [laughter] I was
about to say is that my guess is you
it's going to be exactly the problem you
said which is is going to say oh my god
I mean right in this room you got you
know like oh man.
>> Yeah. Yeah. Yeah. Everything you need to
know about alien civilizations is right
here in this room. In fact, it's inside
your own body
>> just for starters.
>> AGI, thank you. [gasps]
>> All right, Michael. Dear one, one of my
favorite scientists, one of my favorite
humans. Thank you for everything you do
in this world.
>> Thank you so much.
>> Truly, truly fascinating work and keep
going for all of us. You're an
inspiration.
>> Thank you so much. It's great to see you
like always, always a good discussion.
Yeah, thank you so much. I appreciate
>> you for this.
>> Thank you.
>> Thanks for listening to this
conversation with Michael Leven. To
support this podcast, please check out
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you can also find links to contact me,
ask questions, get feedback, and so on.
And now, let me leave you with some
words from Albert Einstein.
The most beautiful thing we can
experience is the mysterious. It is the
source of all true art and science.
Thank you for listening. I hope to see
you next time.