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 right 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 the 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 and 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,
there are that level of understanding is necessarily cast in terms of probability
densities and ensemble densities, distributions. And 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
non-equilibrium 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 behaviour.
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 7T. Why? Well,
it gives us for the first time the opportunity to look at the brain in action
at the level of a few millimetres that distinguish between different layers of the cortex that may
be very important in terms of evincing generic principles of conical 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 millimetre and a metre.
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 del-minded way,
from the point of view of purely its anatomy and its structural aspects. I mean, there are many
marvellous 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 specialisation 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 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 as 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 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 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 coming back to
your early 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 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 hot spots that you
see in the 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 neocartography. 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 to hope that there's a beautiful structure
to this. 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 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
tiny little tangent, you said animal models, 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 far 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
non-human primates to transfer questions about human anatomy. But I think most people assume that
that most of the important principles are conserved in a continuous way, right from,
well, yes, worms right through to you and me. So now returning to this, so that was the early
ideas of studying the functional regions of the brain, if there's some damage to it to try to
infer that that part of the brain might be somewhat responsible for this type of function.
So where does that lead us? What are the next steps beyond that?
Right. Well, this actually is to reverse a bit, come back to your sort of notion that the brain
is a magic soup. That was actually a very prominent idea at one point, 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 showed the same kinds of deficits. So 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, and 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,
shortly before with the advent of positon 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. 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
hot 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,
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. So it's a worthy pursuit of going,
of studying, of through imaging, and all the different methods to actually study the structure.
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.
So deep inside the onion is the center of your soul, let's say.
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
that 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.
Yeah. What kind of signal are you measuring? And they can range from, and let's limit ourselves to
some imaging-based non-invasive 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. You can make lots of inferences about
the structure of the organ of the sort that you might have to use from an x-ray, but a very
nuanced x-ray that is looking at this kind of property or that kind of property.
So looking at the anatomy non-invasively 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
and 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 it seems like there's a ton of blood vessels
in the brain. But 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. It's exactly what you
said. It's how does the neural activity, the neuronal 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 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,
axonal 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
neuropil, 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 a big ball of connections like spaghetti,
very carefully structured with sparse connectivity that preserve this deep hierarchal
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, 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 and of itself, the
neural activity and start to see the succession or cascade of hierarchical recurrent message
passing evoked by 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 hypothesis 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. It's like 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, you know, 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, mathematically speaking.
Yes, absolutely. Well-circumstried, well-defined 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. If you were a map maker of America and you did not know its structure,
the first thing you were 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 data features would evidence that particular
thing that you wanted to put on the map? And they normally are characterised in terms of
literally these blobs, or these sort of another way of looking at this is 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.
You describe these two methodologies. One is temporally noisy, one is spatially noisy,
and you have to play and figure out what can be useful. It'd be great if you can 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 dreams, but one of them is to understand
the brain by getting in there past the so-called factory wall, getting in there, be able to listen,
communicate both directions. What are your thoughts about the future of this kind of
technology of brain computer interfaces, to be able to now 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?
Ambivalent. 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 the
ways we understand the brain pride in your imaging. It wasn't just lesion deficit studies.
Some of the early work, in fact, literally a hundred yards from where we're sitting at the
institution 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, then 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.
Positive attention.
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 and the lie
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-fought interventions do change things. A little bit like banging the TV,
the valves aren't working properly, but it still works. So there is a long history of
brain computer interfacing or BCI. I think it 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 embodied brain engages with the world.
I'm thinking here of sensory substitution, 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. I think the 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. After a few weeks, they take it for granted. They integrate it,
they embody it, 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
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. Then we come to the more negative part of my
ambivalence. 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. 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 curing people with paraplegia or replacing sight, despite heroic
efforts. 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, but at the moment
there may be fundamental reasons to assume that it's 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 non-linear, that rests upon the kind of non-equilibrium
steady states and dynamics that the brain does, the weather. So imagine you had some
very aggressive satellites that could produce signals that could perturb some little parts 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 evolve 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 come back to your magic soup, 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 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 a hope that machine learning methods will help us play in this soup.
But perhaps you're right that it is a biology in the brain is just an incredible system
that may be almost impossible to get in. But for me, what seems impossible 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 just talking to brilliant neurosurgeons and the roboticists
that it 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 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.
Could 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 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
for any system that manages to survive in a changing world 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 characterise
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 minimise
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 maximise or it will look as if all your dynamics are trying to maximise the compliment
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 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 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 exist?
And so you can look at the brain, you can look at parts of the brain, or 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 endeavour. And it's nice that there's at least some, from some perspective,
a clean answer. So maybe can you talk about that optimisation view of it? So what's trying to be
minimised to maximise a system that's alive? What is it trying to minimise? Right. You've made a big
move there. Sorry. It's good to make big moves. But you've assumed that the things, the thing
exist in a state that could be living or non-living. So I may ask you, well, what
licences 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 study state physics,
that enable you to interpret their existence in terms of this optimisation procedure.
So it's good you introduce the word optimisation. So what the free energy principle in its most
ambitious but also most deflationary and simplest says is that if something exists,
then it must by the mathematics of non-equilibrium study state exhibit properties
that make it look as if it is optimising 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 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 study 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 more specific case study? I'm imagining a range of systems, but at its
simplest, a single celled organism that can be identified from its eco-nissue 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 dissolve into solvent? So what special properties of the
exchange between the surface of the oil drop and the external states in which it's immersed,
if your physicists 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 is 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?
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. Sorry, how would you describe things that configure themselves
out of nothing, where 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, but 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. 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 kind of use it to reason,
to think about, like, study a particular system and say, dude, 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 non-living things.
Yes. Well, actually, sorry. So yeah, maybe we can go there. You kind of drew a line and forgive
me for the stupid questions, but you kind of drew a line between living and existing.
Is there an interesting sort of distinction? Yeah, 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,
we'll go along the following lines. First of all, you have to define what it means to exist,
which now, as you've rightly pointed out, you have to define what probabilistic properties
must the states of something possess so it knows where it finishes. And then you write that down
in terms of statistical independences, 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 is as hierarchical,
but 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 apologise, this is the beautiful idea.
But it came out of California, so that's...
I changed my mind. I take it all back. So anyway, 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
independences and what different states, internal or blanket or external states,
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 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, 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 can 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? So let me ask you, what's the difference between an oil drop and a little tadpole or a
little lava or a 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 you 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 is sitting there
at the bottom on 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 en masse 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, and 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 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 manifest if we realized in terms of
essentially swimming, essentially moving, changing one's shape, a morphogenesis that is dynamic
and possibly adaptive. So that's what I was trying to get up between the difference from the oil
drop and the little tadpole. 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,
they data impressed upon their surface or the blanket states, but they are actively
re-sampling those data by moving. They're moving towards, say, 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
and test for chemical gradients, or then you start to move to much more lifelike form.
This is all fun and 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.
Yes. So what that means, this sort of 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.
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, 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?
I think it's possible to think about it, whether you'll get any words or the question.
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 a minimal
consciousness or even less than a minimal selfhood. 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 the future. Whereas you might anant. You might
think anant'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'd 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 talked 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. 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, can be 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 mark of 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 generalized, I don't know. 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 variational autoencoder that is thinking about the future conditioned upon different
courses of action. Now that brings a number of things to the table, which now you start to think,
well, those who've got all the right ingredients 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 were ever to exist. I don't
think it gets you quite as far as self-aware, though. I think and then 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 form of 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 favourite 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. Yeah.
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,
well, 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. Well,
what's the free energy principle good for? Is it just a pretty theoretical exercise to explain
non-equilibrium study 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 nature, the survival of the fittest. What's the fittest,
the survival? Why are the cycles the fittest? It just goes around in 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 minimize their free
energy. Why do they minimize their free energy? Because they exist. And you just keep on going
round and round and round. But what 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 that look as though they're
optimizing a variational free energy. And a variational free energy has to be a functional
of a generative model, a 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, possible 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 artifacts where you have supplied the objective function that supplies the gradients,
that supplies the self-organizing 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 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, not a lot of hard work,
but you would 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 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 artifact probabilistic
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 perform 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 artifact that you will create, or indeed, the kind of artifact 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 I was on Mars by myself, or if I was in the jungle as a feral child.
If there's nothing like me around, there would be no need to have an
inference that a hypothesis, ah 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 are equipped with, possibly by
evolution, generative models that allow for the fact 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, and 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. Well, 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,
which is probably not, 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. 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?
Blob. 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 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. At the very early age, usually in the former bedtime stories or
fairy stories, that I'm a princess and I'm going to be a beast who's going to transform.
So 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, not only am I modeling my
environment, my equinish, 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 time scales. So the question
now is for me being very selfish, what scripts were I given? It basically was a mixture between
Einstein and Chark Holmes. So I smoke as heavily as possible, tried to avoid too much interpersonal
contact, enjoy the fantasy that you're a popular scientist who's going to make a
difference in a slightly quirky way. So that's where I grew up. My father was an engineer and
loved science and he loved things like Sir Arthur Edington's Space, Time and Gravitation,
which was the first understandable version of general relativity. So all the fairy stories
I was told as I was growing up, all about these characters, I'm keeping the hobbit out of this
because that was quite fitting my narrative. It was a journey of exploration. It was a source.
So yeah, I've just grown up to be what I imagine a mild-managed Sherlock Holmes slash
Albert Einstein would do in my shoes. And you did it elegantly and beautifully. Carl is 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
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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 and
hope to see you next time.