The following is a conversation with Carl Friston, one of the greatest neuroscientists in history,

cited over 245,000 times, known for many influential ideas in brain imaging,

neuroscience, and theoretical neurobiology, including especially

the fascinating idea of the free energy principle for action and perception.

Carl's mix of humor, brilliance, and kindness to me are inspiring and captivating. This was a

huge honor and a pleasure. This is the Artificial Intelligence Podcast. If you enjoy it, subscribe

on YouTube, review it with 5 stars on Apple Podcasts, support on Patreon, or simply connect with me

on Twitter. Alex Friedman, spelled F R I D M A N. As usual, I'll do a few minutes of ads now,

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advance robotics and STEM education for young people around the world. And now, here's my

conversation with Carl Friston. How much of the human brain do we understand from

the low level of neuronal communication to the functional level to the highest level,

maybe the psychiatric disorder level? Well, we're certainly in a better position than we were last

century. How far we've got to go, I think, is almost an answerable question. So you'd have to

set the parameters, what constitutes understanding, what level of understanding do you want.

I think we've made enormous progress in terms of broad brush principles, whether that affords a

detailed cartography of the functional anatomy of the brain and what it does, and write down to

the microcircuitry and the neurons, that's probably out of reach at the present time.

So the cartography, so mapping the brain. Do you think mapping of the brain,

the detailed, perfect imaging of it, does that get us closer to understanding of the mind

of the brain? So how far does it get us if we have that perfect cartography of the brain?

I think there are lower bounds on that. It's a really interesting question. It would determine

this sort of scientific career you'd pursue if you believe that knowing every dendritic connection,

every sort of microscopic synaptic structure, right down to the molecular level, was going to

give you the right kind of information to understand the computational anatomy. Then you'd

choose to be a microscopist and you would study little cubic millimeters of brain for the rest

of your life. If on the other hand you were interested in holistic functions and a sort

of functional anatomy of the sort that a neuropsychologist would understand, you'd study

brain lesions and strokes, just looking at the whole person. So again, it comes back to at what

level do you want understanding. I think there are principled reasons not to go too far. If you

commit to a view of the brain as a machine that's performing a form of inference and representing

things, that level of understanding is necessarily cast in terms of probability densities and ensemble

densities, distributions. What that tells you is that you don't really want to look at the atoms

to understand the thermodynamics of probabilistic descriptions for how the brain works.

So I personally wouldn't look at the molecules or indeed the single neurons in the same way if I

wanted to understand the thermodynamics of some nonequilibrium steady state of a gas or an active

material. I wouldn't spend my life looking at the individual molecules that constitute that ensemble.

I'd look at that collective behavior. On the other hand, if you go to coarse grain, you're going to

miss some basic canonical principles of connectivity and architectures. I'm thinking here

this bit colloquial, but there's current excitement about high field magnetic resonance imaging at

7 Tesla. Why? Well, it gives us for the first time the opportunity to look at the brain in action

at the level of a few millimeters that distinguish between different layers of the cortex that may

be very important in terms of evincing generic principles of canonical microcircuitry that

are replicated throughout the brain that may tell us something fundamental about message passing in

the brain and these density dynamics or neuronal ensemble population dynamics that underwrite

our brain function. So somewhere between a millimeter and a meter.

Yeah. Lingering for a bit on the big questions, if you allow me. What to use the most beautiful

or surprising characteristic of the human brain? I think it's hierarchical and recursive aspect,

it's recurrent aspect. Of the structure or of the actual representation or power of the brain?

Well, I think one speaks to the other. I was actually answering in a delminded way from the

point of view of purely its anatomy and its structural aspects. I mean, there are many

marvelous organs in the body. Let's take your liver, for example. Without it, you wouldn't

be around for very long and it does some beautiful and delicate biochemistry and homeostasis and

evolved with a finesse that would easily parallel the brain, but it doesn't have a beautiful anatomy.

It has a simple anatomy, which is attractive in a minimalist sense, but it doesn't have that

crafted structure of sparse connectivity and that recurrence and that specialization

that the brain has. So you said a lot of interesting terms here. So the recurrence,

the sparsity, but you also started by saying hierarchical. So I've never thought of our

brain as hierarchical. I always thought it's just like a giant, interconnected mess where

it's very difficult to figure anything out. But in what sense do you see the brain as hierarchical?

Well, I see it. It's not a magic soup. Of course, it's what I used to think when I was

before I studied medicine and the like. So a lot of those terms imply each other.

So hierarchies, if you just think about the nature of a hierarchy, how would you actually

build one? And what you would have to do is basically carefully remove the right connections

that destroy the completely connected soups that you might have in mind. So a hierarchy is

in and of itself defined by a sparse and particular connectivity structure.

And I'm not committing to any particular form of hierarchy.

But your sense says there is some. Oh, absolutely. Yeah. In virtue of the fact that there is a

sparsity of connectivity, not necessarily of a qualitative sort, but certainly of a quantitative

sort. So it is demonstrably so that the further apart two parts of the brain are, the less likely

that they are to be wired to possess axonal processes, neuronal processes that directly

communicate one message or messages from one part of that brain to the other part of the brain.

So we know there's a sparse connectivity. And furthermore, on the basis of anatomical

connectivity and tracer studies, we know that that has that sparsity underwrites a hierarchical

and very structured sort of connectivity that might be best understood like a little bit like

an onion. There is a concentric, sometimes referred to as centripetal by people like

Marcel Mesulam, hierarchical organisation to the brain. So you can think of the brain as in a rough

sense like an onion and all the sensory information and all the afferent outgoing messages that supply

commands to your muscles or to your secretory organs come from the surface. So there's a massive

exchange interface with the world out there on the surface. And then underneath, there's a little

layer that sits and looks at the exchange on the surface. And then underneath that, there's a layer

right the way down to the very centre through the deepest part of the onion. That's what I mean by

a hierarchical organisation. There's a discernible structure defined by the sparsity of connections

that lends the architecture, a hierarchical structure that tells one a lot about the kinds

of representations and messages. So going back to your earlier question, is this about the

representational capacity? Or is it about the anatomy? Well, one underwrites the other.

If one simply thinks of the brain as a message passing machine, a process that is in the service

of doing something, then the circuitry and the connectivity that shape that message passing

also dictate its function. So you've done a lot of amazing work in a lot of directions.

So let's look at one aspect of that, of looking into the brain and trying to study this onion

structure. What can we learn about the brain by imaging it? Which is one way to sort of look

at the anatomy of it, broadly speaking. What are the methods of imaging, but even bigger? What can

we learn about it? Right. So well, most human neuroimaging that you might see in science journals

that speaks to the way the brain works, measures brain activity over time. So that's the first

thing to say that we're effectively looking at fluctuations in neuronal responses,

usually in response to some sensory input or some instruction, some task. Not necessarily,

there's a lot of interest in just looking at the brain in terms of resting state and dodgeness or

intrinsic activity. But crucially, at every point, looking at these fluctuations either induced or

intrinsic in the neural activity and understanding them at two levels. So normally, people would

recourse to two principles of brain organization that are complementary. One, functional specialization

or segregation. So what does that mean? It simply means that there are certain parts of the brain

that may be specialized for certain kinds of processing, for example, visual motion. Our

ability to recognize or to perceive movement in the visual world. And furthermore, that

specialized processing may be spatially or anatomically segregated, leading to functional

segregation, which means that if I were to compare your brain activity during a period of

viewing a static image, and then compare that to the responses of fluctuations in the brain

when you were exposed to a moving image, so a flying bird, we would expect to see restricted,

segregated differences in activity. And those are basically the hotspots that you see in the

in statistical parametric maps that test for the significance of the responses that are circumscribed.

So now, basically, we're talking about some people of perhaps unkindly called a neo cartography.

This is a phrenology augmented by modern day neuroimaging, basically finding

blobs or bumps on the brain that do this or do that. I'm trying to understand the cartography

of that functional specialization. So how much is there such a beautiful sort of ideal to strive

for? We humans scientists would like this to hope that there's a beautiful structure to this,

whereas, like you said, there are segregated regions that are responsible for the different

function. How much hope is there to find such regions in terms of looking at the progress

of studying the brain? Oh, I think enormous progress has been made in the past 20 or 30 years.

So this is beyond incremental. At the advent of brain imaging, the very notion of functional

segregation was just a hypothesis based upon a century, if not more, of careful neuropsychology,

looking at people who had lost via insult or traumatic brain injury, particular parts of

the brain, and then saying, well, they can't do this or they can't do that. For example,

losing the visual cortex and not being able to see or usually losing particular parts of the

visual cortex or regions known as V5 or the middle temporal region,

MT, and noticing that they selectively could not see moving things. And so that created the

hypothesis that perhaps movement processing, visual movement processing, was located in

this functionally segregated area. And you could then go and put invasive electrodes in animal

models and say, yes, indeed, we can excite activity here, we can form receptive fields that

are sensitive to or defined in terms of visual motion. But at no point could you exclude the

possibility that everywhere else in the brain was also very interested in visual motion.

By the way, I apologize to Interoppa's a tiny little tangent. He said animal models

are just out of curiosity from your perspective. How different is the human brain versus the

other animals in terms of our ability to study the brain?

Well, clearly, the further away you go from a human brain, the greater the differences,

but not as remarkable as you might think. So people will choose their level of

approximation to the human brain, depending upon the kinds of questions that they want to answer.

So if you're talking about canonical principles of microcircuitry, it might be perfectly okay

to look at a mouse, indeed, you could even look at flies, worms. If on the other hand,

you wanted to look at the finer details of organization of visual cortex and V1, V2,

these are designated patches of cortex that may do different things, indeed do.

You probably want to use a primate that looked a little bit more like a human,

because there are lots of ethical issues in terms of the use of nonhuman primates to

transfer questions about human anatomy. But I think most people assume that most of the

important principles are conserved in a continuous way, right from worms right through to you and me.

So now returning to this, so that was the early, the ideas of studying the functional

regions of the brain by, if there's some damage to it to try to infer that there's

that part of the brain might be somewhat responsible for this type of function.

So what, where does that lead us? What are the next steps beyond that?

Right, well, this actually is a reverse a bit, come back to your sort of notion that the brain

is a magic zoo, but that was actually a very prominent idea at one point to notions such as

Lashley's law of mass action inherited from the observation that for certain animals,

if you just took out spoonfuls of the brain, it didn't matter where you took these spoonfuls

out, they always show the same kinds of deficits. So, you know, it was very difficult to infer

functional specialization pure on the basis of lesion deficit studies. But once we had the

opportunity to look at the brain lighting up in its, it's literally it's sort of excitement,

neuronal excitement. When looking at this versus that, one was able to say, yes, indeed, these

functionally specialized responses are very restricted and they're here or they're over there.

If I do this, then this part of the brain lights up. And that became doable in the early 90s.

In fact, you know, shortly before with the advent of positron emission tomography,

and then functional magnetic resonance imaging came along in the early 90s. And since that time,

there has been an explosion of discovery, refinement, confirmation. You know, there are

people who believe that it's all in the anatomy. If you understand the anatomy, then you understand

the function at some level. And many, many hypotheses were predicated on a deep understanding

of the anatomy and the connectivity. But they were all confirmed and taken much further

with neuroimaging. So that's what I meant by we've made an enormous amount of progress

in this century, indeed, and in relation to the previous century by looking at these

functionally selective responses. But that wasn't the whole story. So there's this sort of near

phrenology, but finding bumps and hops spots in the brain that did this or that. The bigger

question was, of course, the functional integration, how all of these regionally specific responses

were orchestrated, how they were distributed, how did they relate to distributed processing and,

indeed, representations in the brain. So then you turn to the more challenging issue of the

integration of the connectivity. And then we come back to this beautiful sparse, recurrent,

hierarchical connectivity that seems characteristic of the brain and probably not many other organs.

And, but nevertheless, we come back to this challenge of trying to figure out how everything

is integrated. But what's your feeling? What's the general consensus? Have we moved away from

the magic soup view of the brain? So there is a deep structure to it. And then maybe

a further question, you said some people believe that the structure is most of it,

that you could really get at the core of the function by just deeply understanding the structure.

Where do you sit on that? Do you? I think it's got some mileage to it. Yes. Yeah.

So it's a worthy pursuit of going, of studying through imaging and all the different methods

to actually study the structure. No, absolutely. Yeah. Yeah. Sorry, I'm just noting you were accusing

me of using lots of long words. And then you introduced one there, which is deep, which is

interesting. Because deep is the sort of millennial equivalent of hierarchical. So if you put deep

in front of anything, not only are you very millennial and very trending, but you're also

implying a hierarchical architecture. So it is a depth, which is for me the beautiful thing.

That's right. The word deep kind of, yeah, exactly. It implies hierarchy. I didn't even think about

that. That indeed the implicit meaning of the word deep is a hierarchy. Yeah. Yeah. So deep

inside the onion is the center of your soul. Let's see. Beautifully put. Maybe briefly, if you

can paint a picture of the kind of methods of neuroimaging, maybe the history, which you were

a part of from statistical parametric mapping. I mean, just what's out there that's interesting

for people maybe outside the field to understand of what are the actual methodologies of looking

inside the human brain? Right. Well, you can answer that question from two perspectives. Basically,

it's the modality. What kind of signal are you measuring? And they can range from,

let's limit ourselves to sort of imaging based noninvasive techniques. So you've essentially

got brain scanners. And brain scanners can either measure the structural attributes,

the amount of water, the amount of fat or the amount of iron in different parts of the brain.

And you can make lots of inferences about the structure of the organ of the sort that you

might abduce from an x ray, but a very nuanced x ray that is looking at this kind of property

and that kind of property. So looking at the anatomy noninvasively is would be the first

sort of neuroimaging that people might want to employ. Then you move on to the kinds of measurements

that reflect dynamic function. The most prevalent of those fall into two camps, you've got these

metabolic sometimes hemodynamic blood related signals. So these metabolic and or hemodynamic

signals are basically proxies for elevated activity and message passing and neuronal dynamics,

in particular parts of the brain. Characteristically though, the time constants of these

hemodynamic or metabolic responses to neural activity are much longer than the neural activity

itself. And this is referring, forgive me for the dumb questions, but this would be referring to

blood like the flow of blood. Absolutely. So there's a ton of, it seems like there's a ton

of blood vessels in the brain. So what's the interaction between the flow of blood and the

function of the neurons? Is there an interplay there? And that interplay accounts for several

careers of world renowned scientists. Yes, absolutely. So this is known as neurovascular

coupling is exactly what you said. It's how does the neural activity, the neural infrastructure,

the actual message passing that we think underlies our capacity to perceive and act?

How is that coupled to the vascular responses that supply the energy for that neural processing?

So there's a delicate web of large vessels, arteries and veins, that gets progressively

finer and finer in detail until it perfuses at a microscopic level, the machinery where

little neurons lie. So coming back to this sort of onion perspective,

we were talking before using the onion as a metaphor for a deep hierarchical structure,

but also I think it's just anatomical, anatomically quite a useful metaphor. All the action,

all the heavy lifting in terms of neural computation is done on the surface of the brain.

And then the interior of the brain is constituted by fatty wires, essentially external processes

that are enshrouded by myelin sheaths. And these give the, when you dissect them, they look fatty

and white. And so it's called white matter, as opposed to the actual neuro pill, which does the

computation constituted largely by neurons. And that's known as gray matter. So the gray matter

is a surface or a skin that sits on top of this big ball, now we are talking magic soup, but it's

big ball of connections like spaghetti, very carefully structured with sparse connectivity

that preserves this deep hierarchical structure. But all the action takes place on the surface,

on the cortex of the onion. And that means that you have to supply the right amount of blood flow,

the right amount of nutrient, which is rapidly absorbed and used by neural cells that don't

have the same capacity that your leg muscles would have to basically spend their energy budget and

then claim it back later. So one peculiar thing about cerebral metabolism, brain metabolism,

is it really needs to be driven in the moment, which means you basically have to turn on the taps.

So if there's lots of neural activity in one part of the brain, a little patch of a few millimeters,

even less possibly, you really do have to water that piece of the garden now and quickly. And

that by quickly, I mean within a couple of seconds. So that contains a lot of that.

Hence the imaging could tell you a story of what's happening. Absolutely. But it is slightly

compromised in terms of the resolution. So the deployment of these little micro vessels that

water the garden to enable the activity to the neural activity to play out. The spatial resolution

is in order of a few millimeters. And crucially, the temporal resolution is the order of a few

seconds. So you can't get right down and dirty into the actual spatial and temporal scale of

neural activity in and of itself. To do that, you'd have to turn to the other big imaging

modality, which is the recording of electromagnetic signals as they're generated in real time.

So here the temporal bandwidth, if you like, or the low limit on the temporal resolution

is incredibly small. You're talking about, you know, Nala seconds, milliseconds. And then you

can get into the phasic fast responses that is in of itself the neural activity and start to

see the succession or cascade of hierarchical recurrent message passing evoked by a particular

stimulus. But the problem is you're looking at electromagnetic signals that have passed through

an enormous amount of magic soup or spaghetti of collectivity and through the scalp and the skull.

And it's become spatially very diffuse. It's very difficult to know where you are.

So you've got this sort of catch 22. You can either use an imaging modality. It tells you

within millimeters, which part of the brain is activated, we don't know when. Or you've got these

electromagnetic EEG MEG setups that tell you to within a few milliseconds when something has

responded, be aware. So you've got these two complementary measures, either indirect via

the blood flow or direct via the electromagnetic signals caused by neural activity. These are

the two big imaging devices. And then the second level of responding to your question, what are

the, you know, from the outside, what are the big ways of using this technology? So once you've

chosen the kind of neuroimaging they want to use to answer your set questions, and sometimes it would

have to be both. Then you've got a whole raft of analyses, time series analyses, usually that you

can bring to bear in order to answer your questions or address your hypotheses about those data.

And interestingly, they've both fallen to the same two camps we're talking about before,

you know, this dialectic between specialization and integration, differentiation and integration.

So it's the cartography, the blobology analyses.

I apologize. I probably shouldn't interrupt so much, but just heard a fun word.

The blobology. It's a neologism, which means the study of blobs.

Are you being woody and humorous? Or is there an actual, does the word blobology ever appear

in a textbook somewhere? It would appear in a popular book. It would not appear in a worthy

specialist journal. But it's the fond word for the study of literally little blobs on

brain maps showing activations. So the kind of thing that you'd see in the newspapers on

ABC or BBC reporting the latest finding from brain imaging. Interestingly, though, the maths

involved in that stream of analysis does actually call upon the mathematics of blobs.

So seriously, they're actually called Euler characteristics and they have a lot of fancy

names in mathematics.

We'll talk about it, but your ideas in free energy principle, I mean,

there's a echoes of blobs there when you consider sort of entities, so mathematically speaking.

Yes, absolutely. Well, circumscribed, well defined. You entities of,

well, from the free energy point of view, entities of anything, but from the point of view of

the analysis, the cartography of the brain. These are the entities that constitute the

evidence for this functional segregation. You have segregated this function in this blob,

and it is not outside of the blob. And that's basically the, if you were a map maker of America

and you did not know its structure, the first thing you're doing constituting or creating a map

would be to identify the cities, for example, or the mountains or the rivers. All of these

uniquely spatially localizable features, possibly topological features, have to be placed somewhere

because that requires a mathematics of identifying what does a city look like on the satellite image

or what does a river look like or what does a mountain look like. What would it, what data

features would it, would evidence that particular top, that particular thing that you wanted to

put on the map. And they normally are characterized in terms of literally these blobs or these sort of,

now the way of looking at this is that a certain statistical measure of the degree of activation

crosses a threshold and in crossing that threshold in the spatially restricted part of the brain,

it creates a blob. And that's basically what statistical parametric mapping does. It's basically

mathematically finessed blobology. Okay, so those, you kind of describe these two methodologies for

one is temporally noisy, one is spatially noisy, and you kind of have to play and figure out what

can be useful. It'd be great if you can sort of comment, I got a chance recently to spend a day

at a company called Neuralink that uses brain computer interfaces. And their dream is to,

well, there's a bunch of sort of dreams, but one of them is to understand the brain by sort of,

you know, getting in there past the so called sort of factory while getting in there and be

able to listen, communicate both directions. What are your thoughts about this, the future of this

kind of technology of brain computer interfaces to be able to now have a, have a window or direct

contact within the brain to be able to measure some of the signals to be able to send signals to

understand some of the functionality of the brain. I'm bivalent. My sense is ambivalent. So it's a

mixture of good and bad. And I acknowledge that freely. So the good bits, if you just look at

the legacy of that kind of reciprocal but invasive brain stimulation, I didn't paint a complete picture

when I was talking about some of the ways we understand the brain prior to neuroimaging.

It wasn't just lesion deficit studies. Some of the early work, in fact, literally 100 yards from

where we're sitting at the Institute of Neurology was done by stimulating the brain of, say, dogs

and looking at how they responded with their muscles or with their salivation. And imputing

what that part of the brain must be doing that if I stimulate it and I vote this kind of response,

then that tells me quite a lot about the functional specialization. So there's a long

history of brain stimulation, which continues to enjoy a lot of attention nowadays.

Oh, yes, absolutely. Deep brain stimulation for Parkinson's disease is now a standard treatment

and also a wonderful vehicle to try and understand the neuronal dynamics underlie movement disorders

like Parkinson's disease. Even interest in magnetic stimulation, stimulating the magnetic

fields and will it work in people who are depressed, for example? Quite a crude level

of understanding what you're doing. But there is historical evidence that these kinds of

brute thought interventions do change things. A little bit like banging the TV when the valves

aren't working properly, but it still works. So there is a long history. Brain computer

interfacing or BCI, I think is a beautiful example of that. It's sort of carved out its own

niche and its own aspirations and there have been enormous advances within limits.

Advances in terms of our ability to understand how the brain, the embodied brain,

engages with the world. I'm thinking here of sensory substitution, the augmenting our sensory

capacities by giving ourselves extra ways of sensing and sampling the world, ranging from

sort of trying to replace lost visual signals through to giving people completely new signals.

One of the, I think, most engaging examples of this is equipping people with a sense of

magnetic fields. So you can actually give them magnetic sensors that enable them to feel,

should we say, tactile pressure around their tummy, where they are in relation to the magnetic

field of the earth. And after a few weeks, they take it for granted, they integrate it,

they embody this, simulate this new sensory information into the way that they literally

feel their world, but now equipped with this sense of magnetic direction. So that tells you

something about the brain's plastic potential to remodel and its plastic capacity to suddenly

try to explain the sensory data at hand by augmenting the sensory sphere and the kinds of

things that you can measure. Clearly, that's purely for entertainment and understanding the

other nature and the power of our brains. I would imagine that most BCI is pitched at solving

clinical and human problems such as locked in syndrome, such as paraplegia, or replacing lost

sensory capacities like blindness and deafness. So then we come to the more negative part of my

ambivalence, the other side of it. So I don't want to be deflationary because

much of my deflationary comments are probably large out of ignorance than anything else. But

generally speaking, the bandwidth and the bit rates that you get from

brain computer interfaces as we currently know them, we're talking about bits per second.

So that would be like me only being able to communicate with any world or with you using very,

very, very slow Morse code. And it is not even within an order of magnitude near what we actually

need for an inactive realization of what people aspire to when they think about sort of curing

people with paraplegia or replacing sight, despite heroic efforts. So one has to ask,

is there a lower bound on the kinds of recurrent information exchange between a brain and some

augmented or artificial interface? And then we come back to, interestingly, what I was talking

about before, which is if you're talking about function in terms of inference, and I presume

we'll get to that later on in terms of the free energy principle, then the moment there may be

fundamental reasons to assume that is the case, we're talking about ensemble activity, we're

talking about basically, for example, let's paint the challenge facing brain computer

interfacing in terms of controlling another system that is highly and deeply structured,

very relevant to our lives, very nonlinear, that rests upon the kind of non equilibrium

steady states and dynamics that the brain does, the weather. So good example, yeah.

Imagine you had some very aggressive satellites that could produce signals that could perturb

some little parts of the of the weather system. And then what you're asking now is,

can I meaningfully get into the weather and change it meaningfully and make the weather

respond in a way that I want it to, you're talking about chaos control on a scale, which is almost

unimaginable. So there may be fundamental reasons why BCI, as you might read about it in a science

fiction novel, aspirational BCI may never actually work in the sense that to really be integrated

and be part of the system is a requirement that requires you to have evolved with that system.

You have to be part of a very delicately structured, deeply structured dynamic ensemble

activity that is not like rewiring a broken computer or plugging in a peripheral interface

adapter. It is much more like getting into the weather patterns or a come back to your magic

soup is getting into the active matter and meaningfully relate that to the outside world.

So I think there are enormous challenges there. So I think the example of the weather is a brilliant

one. And I think you paint a really interesting picture. And it wasn't as negative as I thought.

It's essentially saying that it's it might be incredibly challenging, including the low bound

of the bandwidth and so on. So just to full disclosure, I come from the machine learning world.

So my natural thought is the hardest part is the engineering challenge of controlling the

weather, of getting those satellites up and running and so on. And once they are, then the rest

is fundamentally the same approaches that allow you to win in the game of go will allow you to

potentially play in this soup in this chaos. So I have I have a hope that sort of machine

learning methods will will help us play in this soup. But perhaps you're right that it is a biology

in the brain is just an incredible, incredible system that may be almost impossible to get in.

But for me, what seems impossible is is the incredible mess of blood vessels that you also

described without, you know, we also value the brain. You can't make any mistakes. You can't

damage things. So to me, that engineering challenge seems nearly impossible. One of the things I was

really impressed by at Neuralink is just just talking to brilliant neurosurgeons and the roboticists

that made me realize that even though it seems impossible, if anyone can do it, it's some of

these world class engineers that are trying to take it on. So so I think the conclusion of our

discussion here is of this part is basically that the problem is really hard, but hopefully not

impossible. Absolutely. So if it's okay, let's start with the basics. So you've also formulated a

fascinating principle, the free energy principle. Can we maybe start at the basics and what is

the free energy principle? Well, in fact, the free energy principle

inherits a lot from the building of these data analytic approaches to these very high dimensional

time series you get get from the brain. So I think it's interesting to acknowledge that. And in

particular, the analysis tools that try to address the other side, which is the functional

integrations and the connectivity analysis. On the one hand, but I should also acknowledge it

inherits an awful lot from machine learning as well. So the free energy principle is just a

formal statement that the existential imperatives are any system that manages to survive in a

changing world is can be cast as an inference problem, in the sense that you can interpret

the probability of existing as the evidence that you exist. And if you can write down that problem

of existence as a statistical problem, then you can use all the maths that has been developed

for inference to understand and characterize the ensemble dynamics that must be in play in the

service of that inference. So technically, what that means is you can always interpret anything

that exists in virtue of being separate from the environment in which it exists as trying to

minimize variational free energy. And if you're from the machine learning community, you will

know that as a negative evidence lower bound or a negative elbow, which is the same as saying

you're trying to maximize or it will look as if all your dynamics are trying to maximize the

complement of that which is the marginal likelihood or the evidence for your own existence. So that's

basically the free energy principle. But to even take a sort of a small step backwards,

you said the existential imperative. There's a lot of beautiful poetic words here, but to put

it crudely, it's a fascinating idea of basically just of trying to describe if you're looking at a

blob, how do you know this thing is alive? What does it mean to be alive? What does it mean to

be to exist? And so you can look at the brain, you can look at parts of the brain or you this is

just a general principle that applies to almost any system. That's just a fascinating sort of

philosophically at every level question and a methodology to try to answer that question.

What does it mean to be alive? Yes. So that's a huge endeavor and it's nice that there's at least

some from some perspective a clean answer. So maybe can you talk about that optimization

view of it? So what's trying to be minimized to maximize a system that's alive? What is it trying

to minimize? Right, you've made a big move there. First of all, it's good to make big moves.

But you've assumed that the things, the thing exists in a state that could be living or non

living. So I may ask you, what licenses you to say that something exists? That's why I use the

word existential. It's beyond living, it's just existence. So if you drill down onto the definition

of things that exist, then they have certain properties. If you borrow the maths from non

equilibrium steady state physics that enable you to interpret their existence in terms of this

optimization procedure. So it's good you introduce the word optimization. So what the free energy

principle in its sort of most ambitious but also most deflationary and simplest says is that if

something exists, then it must buy the mathematics of non equilibrium steady state exhibit properties

that make it look as if it is optimizing a particular quantity. And it turns out that particular

quantity happens to be exactly the same as the evidence lower bound in machine learning,

or Bayesian model evidence in Bayesian statistics, or I can list a whole other list of ways of

understanding this key quantity, which is abound on surprises, self information, if you

know information theory, there are a number of different perspectives on this quantity.

It's just basically the log probability of being in a particular state. I'm telling this story

as an honest attempt to answer your question. And I'm answering it as if I was pretending

to be a physicist who was trying to understand the fundamentals of non equilibrium steady state.

And I shouldn't really be doing that because the last time I was taught physics, I was in my 20s.

What kind of systems when you think about the free energy principle, what kind of systems

are you imagining as a sort of more specific kind of case study?

Yeah, I'm imagining a range of systems, but at its simplest, a single celled organism

that can be identified from its eco niche or its environment. So at its simplest,

that's basically what I always imagined in my head. And you may ask, well, how on earth

can you even elaborate questions about the existence of a single drop of oil, for example?

But there are deep questions there. Why doesn't the thing, the interface between the drop of oil

that contains an interior and the thing that is not the drop of oil, which is the solvent in

which it is immersed? How does that interface persist over time? Why doesn't the oil just dissolve

into solvent? So what's special properties of the exchange between the surface of the oil drop

and the external states in which it's immersed? If you're a physicist, say it would be the heat

path. You've got a physical system, an ensemble again, we're talking about density dynamics,

ensemble dynamics, an ensemble of atoms or molecules immersed in the heat path. But the

question is, how did the heat path get there? And why is it not dissolved?

How's it maintaining itself? Exactly. What actions is it? I mean, it's such a fascinating idea of a

drop of oil and I guess it would dissolve in water. It wouldn't dissolve in water. So what?

Precisely. So why not? So why not? Why not? And how do you mathematically describe it? I mean,

it's such a beautiful idea and also the idea of where does the drop of oil end and where does it

begin? Right. So I mean, you're asking deep questions, deep in a non millennial sense here.

But what you can do, so this is a deflationary part of it. Can I just qualify my answer by

saying that normally when I'm asked this question, I answer from the point of view of a psychologist

when we talk about predictive processing and predictive coding and the brain as an inference

machine. But you have asked me from that perspective, I'm answering from the point of view of a

physicist. So the question is not so much why, but if it exists, what properties must it display?

So that's the deflationary part of the free energy principle. The free energy principle

does not supply an answer as to why. It's saying, if something exists, then it must display these

properties. That's the thing that's on offer. And it so happens that these properties, it must

display are actually intriguing and have this inferential gloss, this sort of self evidencing

gloss that inherits on the fact that the very preservation of the boundary between the oil

drop and the not oil drop requires an optimization of a particular function or a functional

that defines the presence of the existence of this oil drop, which is why I started with

existential imperatives. It is a necessary condition for existence that this must occur,

because the boundary basically defines the thing that's existing. So it is that self assembly

aspect that you were hinting at in biology, sometimes known as auto poesis in computational

chemistry with self assembly. What does it look like? Sorry, how would you describe things that

configure themselves out of nothing? The way they clearly demarcate themselves from the states or the

soup in which they are immersed. So from the point of view of computational chemistry, for example,

you would just understand that as a configuration of a macromolecule to minimize its free energy,

its thermodynamic free energy. It's exactly the same principle that we've been talking about,

that thermodynamic free energy is just the negative elbow. It's the same mathematical construct.

So the very emergence of existence, of structure, of form that can be distinguished from the environment

or the thing that is not the thing necessitates the existence of an objective function that it

looks as if it is minimizing. It's finding a free energy minima. And so just to clarify,

I'm trying to wrap my head around, so the free energy principle says that if something exists,

these are the properties it should display. So what that means is we can't just go into a soup

and there's no mechanism. A free energy principle doesn't give us a mechanism to find the things

that exist. Is that what's implying is being applied that you can use it to reason, to think about

study a particular system and say, does this exhibit these qualities?

That's an excellent question. But to answer that, I'd have to return to your previous question

about what's the difference between living and nonliving things.

Yes, actually, sorry. So yeah, maybe we can go there. You drew a line, and forgive me for

the stupid questions, but you drew a line between living and existing. Is there an

interesting distinction? I think there is. So things do exist, grains of sand, rocks on the

moon, trees, you. So all of these things can be separated from the environment in which

they are immersed. And therefore, they must at some level be optimizing their free energy,

taking this sort of model evidence interpretation of this quantity that basically means they're

self evidencing. Another nice little twist of phrase here is that you are your own existence

proof. And statistically speaking, which I don't think I said that, somebody did, but I love that

phrase. You are your own existence proof. Yeah. So it's so existential, isn't it?

I'm going to have to think about that for a few days. That's a beautiful line.

So the step through to answer your question about what's it good for,

well, we go along the following lines. First of all, you have to define what it means to exist,

which down as you've rightly pointed out, you have to define what probabilistic properties

must the states of something possess so that it has so it knows where it finishes. And then you

write down that down in terms of statistical independence is again sparsity. Again, it's not

what's connected or what's correlated or what depends upon what it's what's not

correlated and what doesn't depend upon something. Again, it comes down to the deep structures,

not in this instance hierarchical, but the certainly the structures that emerge from removing

connectivity and dependency. And in this instance, basically being able to identify the surface of

the oil drop from the water in which it is immersed. And when you do that, you start to realise, well,

there are actually four kinds of states in any given universe that contains anything,

the things that are internal to the surface, the things that are external to the surface and

the surface in and of itself, which is why I use a metaphor, a little single celled organism that

has an interior and exterior and then the surface of the cell. And that's mathematically a Markov

blanket. Just to pause, I'm in awe of this concept that there's the stuff outside the surface stuff

inside the surface and the surface itself, the Markov blanket. It's just the most beautiful

kind of notion about trying to explore what it means to exist, mathematically. I apologize,

this is the beautiful idea. I came out of California. I changed my mind. I take it all back. So

you were just talking about the surface, about the Markov blanket.

So this surface or this blanket, these blanket states that are the

because they are now defined in relation to these dependencies and what different states internal

or blanket or external states can, which ones can influence each other and which cannot influence

each other, you can now apply standard results that you would find in non equilibrium physics

or steady state or thermodynamics or hydrodynamics, usually out of equilibrium solutions and apply

them to this partition. And what it looks like is if all the normal, normal gradient flows that

you would associate with any non equilibrium system, apply in such a way that two part of the

Markov blanket and the internal states seem to be hill climbing or doing a gradient descent on

the same quantity. And that means that you can now describe the very existence of this oil drop.

You can write down the existence of this oil drop in terms of flows, dynamics, equations of motion

where the blanket states or part of them, we call them active states and the internal states

now seem to be and must be trying to look as if they're minimizing the same function,

which is a lot of probability of occupying these states. The interesting thing is that

what would they be called if you were trying to describe these things? So

what we're talking about are internal states, external states and blanket states. Now let's

carve the blanket states into two sensory states and active states. Operationally, it has to be

the case that in order for this carving up into different sets of states to exist, the active

states, the Markov blanket cannot be influenced by the external states. And we already know

that the internal states can't be influenced by the external states because the blanket separates

them. So what does that mean? Well, it means the active states, the internal states are now

jointly not influenced by external states. They only have autonomous dynamics. So now you've got

a picture of an oil drop that has autonomy. It has autonomous states. It has autonomous states

in the sense that there must be some parts of the surface of the oil drop that are not influenced

by the external states and all the interior. And together, those two states endow even a

little oil drop with autonomous states that look as if they are optimizing their variational free

energy or their negative elbow, their model evidence. And that would be an interesting

intellectual exercise. And you could say you could even go into the realms of panpsychism,

that everything that exists is implicitly making inferences on self evidencing.

Now, we make the next move. But what about living things? I mean, so let me ask you,

what's the difference between an oil drop and a little tadpole or a little lava or plankton?

The picture was just painted of an oil drop. Just immediately, in a matter of minutes,

took me into the world of panpsychism, where you just convinced me, made me feel like an

oil drop is a living, certainly an autonomous system, but almost a living system. So it has

a sensor capabilities and acting capabilities and it maintains something. So what is the

difference between that and something that we traditionally think of as a living system?

That it could die? Or it can't, I mean, yeah, mortality? I'm not exactly sure. I'm not sure

what the right answer there is, because it can move, like movement seems like an essential

element to being able to act in the environment, but the oil drop is doing that. So I don't know.

Is it? The oil drop will be moved, but does it in and of itself move autonomously?

Well, the surface is performing actions that maintain its structure.

Yeah, you're being too clever. I had in mind a passive little oil drop that's sitting there.

Yeah. At the bottom of the top of a glass of water.

Sure, I guess.

What I'm trying to say is you're absolutely right. You've nailed it. It's movement.

So where does that movement come from? If it comes from the inside,

then then you've got, I think, something that's living.

What do you mean from the inside?

What I mean is that the internal states that can influence the active states,

where the active states can influence, but they're not influenced by the external states,

can cause movement. So there are two types of oil drops, if you like.

There are oil drops where the internal states are so random that they average themselves away,

and the thing cannot balance on average when you do the averaging move.

So a nice example of that would be the sun. The sun certainly has internal states.

There's lots of intrinsic autonomous activity going on, but because it's not coordinated,

because it doesn't have the deep in the millennial sense, a hierarchical structure that the brain

does, there is no overall mode or pattern or organization that expresses itself on the surface

that allows it to actually swim. It can certainly have a very active surface, but on mass at the

scale of the actual surface of the sun, the average position of that surface cannot in itself move,

because the internal dynamics are more like a hot gas. They are literally like a hot gas,

whereas your internal dynamics are much more structured and deeply structured,

and now you can express on your mark of, on your active states with your muscles and

your secretory organs, your autonomic nervous system, and its effectors.

You can actually move, and that's all you can do. And that's something which, you know,

if you haven't thought of it like this before, I think it's nice to just realize there is no other

way that you can change the universe other than simply moving. Whether that moving is

articulating my, with my voice box, or walking around, or squeezing juices out of my secretory

organs, there's only one way you can change the universe. It's moving. And the fact that you do

so non randomly makes you alive. Yeah. So it's that non randomness. And that would be manifested,

we realize in terms of essentially swimming, essentially moving, changing one shape, a morphogenesis

that is dynamic, and possibly adaptive. So that that's what I was trying to get up between the

difference from the oil drop and the little tadpole, that the tadpole is moving around.

Its active states are actually changing the external states. And there's now a cycle,

an action perception cycle, if you like, a recurrent dynamic that's going on that depends upon this

deeply structured autonomous behavior that rests upon internal dynamics that are not only

modeling, the data impressed upon their surface or the blanket states, but they are actively

resampling those data by moving, they're moving towards chemical gradients and chemotaxis.

So they've gone beyond just being good little models of the kind of world they live in. For

example, an oil droplet could, in a panpsychic sense, be construed as a little being that has

now perfectly inferred. It's a passive non living oil drop living in a bowl of water. No problem.

But to now equip that oil drop with the ability to go out and test that hypothesis about different

states of beings, so you can actually push its surface over there, over there, and test for

chemical gradients, or then you start to move to much more lifelike form. This is all fun,

theoretically interesting, but it actually is quite important in terms of reflecting what I

have seen since the turn of the millennium, which is this move towards an inactive and embodied

understanding of intelligence. And you say you're from machine learning. So what that means,

the central importance of movement, I think has yet to really hit machine learning. It certainly

has now diffused itself throughout robotics. And perhaps you could say certain problems in

active vision where you actually have to move the camera to sample this and that. But machine

learning of the data mining deep learning sort simply hasn't contended with this issue. What

it's done instead of dealing with the movement problem and the active sampling of data, it's

just said, we don't need to worry about we can see all the data because we've got big data.

So we need to ignore movement. So that, for me, is an important omission in current machine

learning. The current machine learning is much more like the oil drop. Yes. But an oil drop that

enjoys exposure to nearly all the data it will ever need to be exposed to, as opposed to the

tapels swimming out to find the right data. For example, it likes food. That's a good hypothesis.

Let's test it out. Let's go and move and ingest food, for example, and see what that is that

evidence that I'm the kind of thing that likes this kind of food. So the next natural question,

and forgive this question, but if we think of sort of even artificial intelligence systems,

which has just painted a beautiful picture of existence and life. So do you ascribe,

but do you find within this framework a possibility of defining consciousness or exploring the idea

of consciousness, like what self awareness and expanded to consciousness? How can we start

to think about consciousness within this framework? Is it possible?

Well, I think it's possible to think about it, whether you'll get anywhere. It's another question.

And again, I'm not sure that I'm licensed to answer that question. I think you'd have to

speak to a qualified philosopher to get a definitive answer there. But certainly,

there's a lot of interest in using not just these ideas, but related ideas from information theory

to try and tie down the maths and the calculus and the geometry of consciousness,

either in terms of sort of a minimal consciousness, even less than a minimal

selfhood. And what I'm talking about is the ability effectively to plan, to have agency.

So you could argue that a virus does have a form of agency in virtue of the way that it selectively

finds hosts and cells to live in and moves around. But you wouldn't endow it with the capacity to think

about planning and moving in a purposeful way where it countens as the future. Whereas you might

an ant, you might think an ant's not quite as unconscious as a virus. It certainly seems to

have a purpose. It talks to its friends on route during its foraging. It has a different kind of

autonomy, which is biotic, but beyond a virus.

So there's something about, so there's some line that has to do with the complexity of planning

that may contain an answer. I mean, it would be beautiful if we can find a line beyond which

we can say a being is conscious. These are wonderful lines that we've drawn with existence,

life and consciousness. It will be very nice. One little wrinkle there, and this is something

I've only learned in the past few months, is the philosophical notion of vagueness.

So you're saying it would be wonderful to draw a line. I had always assumed that that line at some

point would be drawn until about four months ago, and the philosopher taught me about vagueness.

So I don't know if you've come across this, but it's a technical concept and I think most

revealingly illustrated with at what point does a pile of sand become a pile?

Is it one grain, two grains, three grains or four grains? So at what point would you draw

the line between being a pile of sand and a collection of grains of sand? In the same way,

is it right to ask, where would I draw the line between conscious and unconscious?

And it might be a vague concept. Having said that, I agree with you entirely.

So systems that have the ability to plan. So just technically what that means is

your inferential self evidencing by which I simply mean the dynamics, literally the

thermodynamics and gradient flows that underwrite the preservation of your oil droplet like form

are described as an optimization of log Bayesian model evidence, your elbow.

That self evidencing must be evidence for a model of what's causing the sensory impressions on the

sensory part of your surface or your Markov blanket. If that model is capable of planning,

it must include a model of the future consequences of your active states or your action just planning.

So we're now in the game of planning as inference. Now notice what we've made though.

We've made quite a big move away from big data and machine learning because again,

it's the consequences of moving. It's the consequences of selecting those data or those

data or looking over there. And that tells you immediately that even to be a contender for

a conscious artifact or a strong AI or general, then you've got to have movement in the game.

And furthermore, you've got to have a generative model of the sort you might find in say a variation

autoencoder that is thinking about the future conditioned upon different courses of action.

Now that brings a number of things to the table, which which now you start to think,

well, those have got all the right ingredients to talk about consciousness. I've now got to

select among a number of different courses of action into the future as part of planning.

I've now got free will. The act of selecting this course of action or that policy or that

policy or that action suddenly makes me into an inference machine, a self evidencing artifact

that now looks as if it's selecting amongst different alternative ways forward as I actively

swim here or swim there or look over here, look over there. So I think you've now got to a situation

if there is planning in the mix, you're now getting much closer to that line if that line

wherever to exist. I don't think it gets you quite as far as self aware, though. I think

and then you're you have to I think grapple with the question.

How would formally write down a calculus or a maths of self awareness? I don't think it's

impossible to do. But I think you would be pressure on you to actually commit to a formal

definition of what you mean by self awareness. I think most people that I know would probably

say that a goldfish, a pet fish was not self aware. They would probably argue about their

favorite cat, but would be quite happy to say that their mum was self aware.

But that might very well connect to some level of complexity with planning. It seems like

self awareness is essential for complex planning.

Yeah. Do you want to take that further? I think you're absolutely right.

Again, the line is unclear, but it seems like integrating yourself into the world,

into your planning, is essential for constructing complex plans.

Yes.

So mathematically describing that in the same elegant way as you have with the free energy

principle might be difficult.

Well, yes and no. I don't think that perhaps we should just, can we just go back? That's

a very important answer you gave. And I think if I just unpacked it, you'd see the truisms

that you've just exposed for us. But let me, sorry, I'm mindful that I didn't answer your

question before. What's the free energy principle good for? Is it just a pretty

theoretical exercise to explain nonequilibrium in steady states? Yes, it is. It does nothing

more for you than that. It can be regarded, it's going to sound very arrogant, but it is of the

sort of theory of natural selection or a hypothesis of natural selection.

Beautiful, undeniably true, but tells you absolutely nothing about why you have legs

and eyes. It tells you nothing about the actual phenotype and it wouldn't allow you to build

something. So the free energy principle by itself is as vacuous as most

tautological theories. And by tautological, of course, I'm talking to the theory of

natural survival of the fittest. What's the fittest survival? Why are the cycles the fitter?

It discorrected circles. In a sense, the free energy principle has that same deflationary

tautology under the hood. It's a characteristic of things that exist. Why do they exist? Because

they minimise their free energy. Why do they minimise their free energy? Because they exist.

And you just keep on going round and round and round. But the practical thing which you

don't get from natural selection, but you could say has now manifest in things like differential

evolution or genetic algorithms and MCMC, for example, in machine learning. The practical

thing you can get is if it looks as if things that exist are trying to have density dynamics

and look as though they're optimising a variational free energy. And a variational free energy has

to be a functional of a generative model, probabilistic description of causes and

consequences, causes out there, consequences in the sensorium on the sensory parts of the

Markov Planckin. Then it should, in theory, impossible to write down the generative model,

work out the gradients, and then cause it to autonomously self evidence. So you should be

able to write down oil droplets. You should be able to create artefacts where you have supplied

the objective function that supplies the gradients that supplies the self organising dynamics to

non equilibrium steady state. So there is actually a practical application, the free energy principle,

when you can write down your required evidence in terms of, well, when you can write down the

generative model, that is the thing that has the evidence, the probability of these sensory data

or this data, given that given that model is effectively the thing that the elbow of the

variational free energy bounds or approximates. That means that you can actually write down the

model. And the kind of thing that you want to engineer the kind of AGI artificial general

intelligence that you want to manifest probabilistically. And then you engineer a robot and a computer

to perform a gradient descent on that objective function. So it does have a practical implication.

Now, why am I wittering on about that? It did seem relevant to, yes. So what kinds of, so the

answer to, would it be easy or would it be hard? Well, mathematically, it's easy. I've just told

you all you need to do is write down your perfect artefact probabilistically in the form of a

probabilistic generative model, a probability distribution over the causes and consequences

of the world in which this thing is immersed. And then you just engineer a computer and a robot

to form a gradient descent on that objective function. No problem. But of course, the big

problem is writing down the generative model. So that's where the heavy lifting comes in.

So it's the form and the structure of that generative model, which basically defines the

artefact that you will create or indeed the kind of artefact that has self awareness.

So that's where all the hard work comes in very much like natural selection doesn't tell you in

the slightest why you have eyes. So you have to drill down on the actual phenotype, the actual

generative model. So with that in mind, what did you tell me that tells me immediately the kinds

of generative models I would have to write down in order to have self awareness? What you said to me

was I have to have a model that is effectively fit for purpose for this kind of world in which I

operate. And if I now make the observation that this kind of world is effectively largely populated

by other things like me, i.e. you, then it makes enormous sense that if I can develop a hypothesis

that we are similar kinds of creatures, in fact, the same kind of creature, but I am me and you

are you, then it becomes again mandated to have a sense of self. So if I live in a world

that is constituted by things like me, basically a social world or community,

then it becomes necessary now for me to infer that it's me talking and not you talking. I wouldn't

need that if it was on Mars by myself or if I was in the jungle as a feral child. If there was

nothing like me around, there would be no need to have an inference that a hypothesis, oh yes,

it is me that is experiencing or causing these sounds and it is not you. It's only when there's

ambiguity in play induced by the fact that there are others in that world. So I think that the

special thing about self aware artifacts is that they have learned to or they have acquired,

or at least not equipped with, possibly by evolution, generative models that allow for the

fat. There are lots of copies of things like them around and therefore they have to work out it's

you and not me. That's brilliant. I've never thought of that. I never thought of that,

that the purpose of the really usefulness of consciousness or self awareness in the context

of planning existing in the world is so you can operate with other things like you. It doesn't

have to necessarily be human, it could be other kind of similar creatures. Absolutely. Well,

we view a lot of our attributes into our pets, don't we? Or we try to make our robots humanoid.

And I think there's a deep reason for that, that it's just much easier to read the world

if you can make the simplifying assumption that basically you're me and it's just your turn to

talk. I mean, when we talk about planning, when you talk specifically about planning, the highest

if like manifestation or realization of that planning is what we're doing now. I mean,

the human condition doesn't get any higher than this talking about the philosophy of existence

and the conversation. But in that conversation, there is a beautiful art of turn taking

and mutual inference, theory of mind. I have to know when you want to listen,

I have to know when you want to interrupt, I have to make sure that you're online,

I have to have a model in my head of your model in your head. That's the highest,

the most sophisticated form of generative model, where the generative model actually has a

generative model, somebody else's generative model. And I think that and what we are doing now

evinces the kinds of generative models that would support self awareness. Because without that,

we'd both be talking over each other, or we'd be singing together in a choir. That's not a

brilliant analogy for what I'm trying to say, but we wouldn't have this discourse.

Yeah, the dance of it. Yeah, that's right. You get to have as I interrupt. I mean, that's

beautifully put. I'll re listen to this conversation many times. There's so much

poetry in this and mathematics. Let me ask the silliest or perhaps the biggest question

as a last kind of question. We've talked about living in existence and the objective function

under which these objects would operate. What do you think is the objective function of our

existence? What's the meaning of life? What do you think is the for you perhaps, the purpose, the

source of fulfillment, the source of meaning for your existence as one blob in the soup?

I'm tempted to answer that again as a physicist. Free energy, I expect consequent upon my behavior.

So technically, we can get a really interesting conversation about

what that comprises in terms of searching for information, resolving uncertainty about the

kind of thing that I am. But I suspect that you want a slightly more personal and fun answer.

But which is can be consistent with that. And I think it's reassuringly simple and

harps back to what you were taught as a child, that you have certain beliefs about the kind of

creature and the kind of person you are. And all that self evidencing means, all that minimizing

variational free energy in an inactive and embodied way means is fulfilling the beliefs

about what kind of thing you are. And of course, we're all given those scripts, those narratives

very early age, usually in the former bedtime stories or fairy stories that I'm a princess

and I'm going to make a beast who's going to transform. The narratives are all around you

from your parents to the friends, to the society feeds these stories. And then your objective

function is to fulfill. Exactly. That narrative that has been encultured by your immediate family,

but as you say also, the sort of the culture in which you grew up. And you create for yourself.

I mean, again, because of this active inference, this inactive aspect of self evidencing, you know,

not only am I modeling my environment, my economy, my my external states out there,

but I'm actively changing them all the time. And external states are doing the same back,

we're doing it together. So there's a synchrony that means that I'm creating my own culture

over different timescales. So the question now is for me being very selfish, what scripts were I

given? It basically was a mixture between Einstein and shark homes. So I smoke as heavily as possible,

try to avoid too much interpersonal contact. Yeah, enjoy the fantasy that you're a popular

scientist who's going to make a difference in a slightly quirky way. So that's what I grew up on.

My father was an engineer and loved science and he loved, you know, sort of things like Sir Arthur

Edwards, Space, Time and Gravitation, which was the first understandable version of general

relativity. And so all the fairy stories I was told as I was growing up were all about these

characters. I'm keeping the hobbit out of this because that was quite fit my narrative. There's

a journey of exploration, I suppose, of sorts. So yeah, I've just grown up to be what I imagine

a mild mannered Sherlock Holmes slash Albert Einstein would would do in my shoes.

And you did it elegantly and beautifully. Carl was a huge honor talking today. It was fun.

Thank you so much for your time. Thank you, Shane. Thank you for listening to this conversation

with Carl Friston. And thank you to our presenting sponsor, Cash App. Please consider supporting

the podcast by downloading Cash App and using code Lex podcast. If you enjoy this podcast,

subscribe on YouTube, review it with five stars and Apple podcast, support on Patreon,

or simply connect with me on Twitter at Lex Friedman. And now let me leave you with some words

from Carl Friston. Your arm moves because you predict it will. And your motor system seeks

to minimize prediction error. Thank you for listening. I hope to see you next time.