Lex Fridman Podcast - #99 – Karl Friston: Neuroscience and the Free Energy Principle

Episode Date: May 28, 2020

Karl Friston is 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 the fascinating idea of the free-energy principle for action and perception. Support this podcast by signing up with these sponsors: – Cash App – use code "LexPodcast" and download: – Cash App (App Store): https://apple.co/2sPrUHe – Cash App (Google Play): https://bit.ly/2MlvP5w EPISODE LINKS: Karl's Website: https://www.fil.ion.ucl.ac.uk/~karl/ Karl's Wiki: https://en.wikipedia.org/wiki/Karl_J._Friston This conversation is part of the Artificial Intelligence podcast. If you would like to get more information about this podcast go to https://lexfridman.com/ai or connect with @lexfridman on Twitter, LinkedIn, Facebook, Medium, or YouTube where you can watch the video versions of these conversations. If you enjoy the podcast, please rate it 5 stars on Apple Podcasts, follow on Spotify, or support it on Patreon. Here's the outline of the episode. On some podcast players you should be able to click the timestamp to jump to that time. OUTLINE: 00:00 - Introduction 01:50 - How much of the human brain do we understand? 05:53 - Most beautiful characteristic of the human brain 10:43 - Brain imaging 20:38 - Deep structure 21:23 - History of brain imaging 32:31 - Neuralink and brain-computer interfaces 43:05 - Free energy principle 1:24:29 - Meaning of life

The following is a conversation with Carl Friston, one of the greatest neuroscientists in history. Sighted 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 and 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 and never any ads in the middle that can break the flow of the conversation. I hope that works for you and doesn't hurt the listening experience. This show is presented by CashApp. The number one finance app in the App Store. When you get it, use code Lex Podcast. Cash app lets you send money to friends by bitcoin and investing the stock market with as little as $1. Since cash app allows you to send and receive money digitally, let me mention a surprising

fact related to physical money. Of all the currency in the world, roughly 8% of it is actual physical money. The other 92% of money only exists digitally. So again, if you get cash from the App Store, Google Play, and use the code Lex Podcast, you get $10 and cash app will also donate to $10 first, an organization that is helping to advance robotics as some education for young people around the world. And now, here's my conversation with Karl Fiston. How much of the human brain do we understand from the low level of urinal 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 in that century. How far we've got to go, I think, is almost an unanswerable question. So you'd have to set the parameters, you know, 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 in right down to the microcircuitry in 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 no bounds on that. It's a really interesting question. And 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 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, you know, just looking at the whole person. So again, it comes back to what level do you want understanding. I think there are principle 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 understanding, 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 of 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 on some, but I'd look at that collective behaviour. On the other hand, if you go to course grain, you're going to miss some basic canonical principles of connectivity and architectures. 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 Seven Tessla. 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 conical microcircuitry that are replicated throughout the brain. They may tell us something fundamental about message passing in the brain and these density dynamics of neuronal, somal population dynamics, the underwriter, our brain function. So somewhere between a millimeter and a meter. Lingerying for a bit and 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 of 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 purely, it's anatomy and it's 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 bichemistry and homeostasis and you're 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 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.

So I always thought it's just like a giant mess. Interconnected mess was very difficult to figure anything out. But in what sense do you see the brain as hierarchical? Well, I see it's not a magic soup. Yeah. It's a... Which of course is what I used to think when I was before I studied men's 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. Well, your sense is there is some. Oh, absolutely. Yeah. In virtue of the fact that there is a sparsity of connectivity, not necessarily of a qualitative source, but certainly for a quantitative source. So it is demonstrably so that further apart to parts of the brain are, the less likely they are to be wired to possess

external 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 under writes a high a high rock on a very structured sort of connectivity that might be best understood like, I look a bit like an onion, you know, that there is a concentric, sometimes referred to as centripetal by people like Marcel Mezulem, hierarchal 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 center 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 hierarchal structure that tells one a lot about the kinds of representations and messages that come 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 dictates 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 imaging human, near imaging, that you might see in science journals, that speaks to the way the brain works, measures brain activity over time. So, you know, that's the first thing to say that we're effectively looking at fluctuations in neuronal responses, usually in response to some sensory importer, some instruction, some task. Not necessarily, there's a lot of interest in just looking at the brain in terms of resting state and dodging us or intrinsic activity.

But crucially, at every point, looking at these fluctuations either induced or intrinsic in your activity, and understanding them at two levels. So normally people would recourse to two principles of brain organization that are complementary, one functional specialisational segregation, so what does that mean? It simply means that there are certain parts of the brain that may be specialised

for certain kinds of processing, you know, for example, visual motion, our ability to recognise or to perceive movement in the visual world. And furthermore, that specialised 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 I'm kindly called a neocartography. This is a a phonology augmented by modern day near imaging 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 is 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, particularly parts of the brain, and then say, well, they can't do this, who had lost via insult or traumatic brain injury, particular parts of the brain, and then say, 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 using particular parts of the visual cortex or regions known as V5 or the middle temporal region, MT, noting that they selectively could not see moving things. So that created the hypothesis that perhaps movement processing, visual movement processing was located in this functionally segregated area, and you could then put go

and put invasive electrons in animal models and say yes, indeed we can excite, activate 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 interrupt, but it's a tiny little tangent. You said animal models just add a 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 difference is. 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 sort of canonical principles of micro-circuitry, 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 sort of 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 most of the important principles are conserved in a continuous way, served in a continuous way, you know, from right from, well, yes, worms right through the way through to you and me. So now returning to, so that was the early, so if ideas are studying the, the, the, the region, 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 is that lead us? What are the next steps beyond that? Right. Well, this actually is a bit reverse of it. Come back to your notion that the brain is a magic soup. But that was actually a very prominent idea at one point, notions such as a notion such as a nationalist law of mass action inherited from the observation that four 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, you know, it was very difficult to infer functional specialization, pure and 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 post-torn 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 newer imaging. 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 function selective responses. But that wasn't the whole story. So there's this sort of near-ferinology but finding bumps and hopspots in the brain that did this or that. The bigger question was of course the functional integration. How all of these region 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,arse 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. 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 can really get at the core of the function, but just deeply understanding the structure. Where do you sit on that? Do you? I think it's got some knowledge to it, yes. So the worthy

pursuit of going, of studying, of through imaging and all the different methods to actually study. No, absolutely. The structure. Yeah. Sorry, I'm just, I'm just not a you, you were accusing me of using lots of long words and then you introduced one that which is deep, which is interesting. And then you introduce one that 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, you're very millennial and it's 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. Yep. Yeah. So deep inside the onion is the center of your soul. Let's see. Beautifully put. Maybe briefly, if you pay in a picture of the kind of methods of neuroimaging, maybe the history, which you were a part of, you know, from statistical parametric

mapping, I mean, just what's out there that's interesting for people to 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, you know, what kind of signal are you measuring, and they can range from, and let's limit ourselves to sort of imaging based, non-invasive techniques. So you 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 used from an x-ray, but a very nuanced x-ray that is looking at this property, that kind of property. So looking at the anatomy, non-invasively, 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, sometimes hemodynamic blood related signals. So these metabolic and or hemodynamic signals are basic 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. Yeah.

So what's the interaction between the flow of blood and the function of the new neurons? Is there an interplay there? Yeah. Yep. And that interplay accounts for several careers of world renown scientists. Yes, absolutely. So this is known as neurovascular coupling, is exactly what you said. How does a 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 were little neurons live. 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 an anatomical, anatomical quite a useful metaphor. All the action, all the heavy

lifting in terms of neural computations 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, when you dissect them, they look fatty and white, and so it's called white matter, as opposed to the actual neuropill, 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 a 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 information that 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 the deployment of these little micro vessels that water the garden to enable the activity to to the newer activity to play out. The spatial resolution is in order of a few millimeters 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 neuronal

activity in and of itself. To do that you'd have to turn to the other big imaging medallity 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, nanoseconds, 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, with a millimeter 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 it unaware. 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 mirror imaging they want to use to answer your 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 both fall into the same two camps we're talking about before. You know, this dialect between specialization and integration, differentiation and integration. So it's the cartography, the blobology analyses.

I probably shouldn't have thought so much, but just heard a fun word. The blobology. Blobology. It's a neologism, which means the study of blobs. So, nothing but... Are you being Whitty and Humors? Or is there an actual... Does the word, blah, blah,

do you ever appear in a textbook somewhere? It would appear in a popular book. It would not appear in a worthy, especially as 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 actually call it oil accatristics and they have a lot of fancy names in mathematics. We'll talk about it by your ideas in free energy principle.

I mean, there's echoes of blobs there when you consider sort of entities mathematically speaking. Yes, absolutely. Anyway, well, circumstride, well-defined. Yes. You enter 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. That's basically the, if you are 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 root mountains, or the rivers. All of these uniquely spatially localizable features, possibly topological features have to be placed somewhere, because that requires a mathematics to identify what does a city look like on a 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 thing that you wanted to put on the map. 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, in crossing that threshold in a spatially restricted part of the brain, it creates a blob. And that's basically what citical parametric mapping does. It's basically mathematically for nest blobology. Okay, so you kind of describe these two methodologies for one is temporarily 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 could 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 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. 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 on how I was talking about so the ways we understand the brain pride in your imaging. It wasn't just Leesian deficit studies, some of the early work, in fact, literally 100 years from where we're sitting at the institution neurology. and 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 the muscles or with the salivation. And imputing what that part of the brain must be doing

that if I stimulate it, then you're, 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 positive attention oh yes absolutely you know deep brain stimulation for Parkinson's diseases now standard treatment and also a wonderful vehicle to try and understand the neuronal dynamics and the lie movement disorders, light Parkinson's disease, even interest in magnetic stimulation, stimulating with magnetic fields and working 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-fort interventions do change things, a little bit like buying the TV, the valves aren't working properly, but it's still, it works. So there is a long history, brain computer interfacing, or BCI, I think is a beautiful example of brain computer interfacing a BCI. I think is a beautiful example of that. It's carved out its own niche, 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 of 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. So, 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, to say tactile pressure around their tummy, where they are in relation to the magnetic field of the earth. It's incredible. And after a few weeks, they take it for granted. They integrate it, they abide, they assimilate 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 augmentating the sensory sphere, 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 capacitors like blindness and deafness. So then we come to the more

I'm the negative part of my ambivalent side of it side of it. So I, you know, I don't want to be deflation 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 braincomputer 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 in the, 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 site, 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 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-anger principle, and the moment there may be fundamental reasons to see 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. Right? So good example, yeah.

Imagine you had some very aggressive satellites that could produce signals, that could perturb some little parts of the weather system. 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 an requirement

that requires you to have evolved with that system. You have to be part of a very delicately structured, deeply structured, dynamic on somal 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 combative or 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. I think you paint a really interesting picture. And it wasn't as negative as they thought.

It's essentially saying that it might be incredibly challenging, including the low bound of the bandwidth and so on. I kind of, 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 approach is that allow you to be 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 help us play in this soup. But perhaps you're right that it is a biology and the brain is just an incredible, 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 talking to brilliant

neurosurgeons and their 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 that the problem is really hard, but hopefully not impossible. Absolutely. If it's okay, let's start with the basics. So you've also formulated a fascinating principle, the free energy principle. 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 a functional integrations and the connectivity analyses. On the one hand, but I should also acknowledge that it inherits an awful lot from machine learning as well. 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 though. 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 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. And 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 you can talk about that optimization view of it. So what's trying to be minimized, the maximized, a system that's alive, what is it

trying to minimize? Right, you've made a big move there. First of all, I guess it's good to make big moves. But you've assumed that the things, the thing exists before the in a state that could be living or non-living. So I may ask you, or what slices is you to say that something exists? That's why I use word existential. It's beyond living. It's just existence. 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 optimization procedure. So it's could you introduce a 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 and then I can list a whole other list of ways of understanding this key quantity which is a bound on surprises, self-information, if you don't know, you know, information theory.

There are a whole, there are a number of different perspectives on this country. It's just basically the log probability of being in a particular state. I'm telling this story as an honest, an 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? It's a more specific kind of case study. Yeah, I'm imagining a range of systems, but you know, at its simplest, a single-celled organism that can be identified from its econy sure its environment. So at its simplest that's basically what I always imagined in my head. And you may ask, well, is there any, how on earth can you even elaborate questions about the existence of a single drop of oil, for example? the existence of a single drop of oil, for example. But there are 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? 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 bath.

You've got a physical system, an ensemble again, we talk about densities dynamics, ensemble dynamics, and ensemble of atoms or molecules immersed in the heat bath. But the question is, how did the heat bath get there and why is it not dissolved? How is it maintaining itself? Exactly. What actions is it? I mean, such a fascinating idea of a drop of oil and

I guess it would solve water, it wouldn't dissolve in water. So what precisely? So why not? So why not? Why not? And how do you mathematically describe? I mean, it's such a beautiful idea. And also the idea of like, where does the thing, where does the drop of oil end? And where does it begin? 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. No, of course. But what you can do, you see, so this is a deflationary part of it.

Can I just call if I'm answered by saying, normally when I'm asked this question, I answer from the point of view of a psychologist, we talk about predictive processing and predictive coding and the brain as an inference machine. But you have not 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 deflation in the free-range principle. The free-range principle does not supply and answer us to why. It's saying, if something

exists, then it must display these properties. That's the sort of thing that's on offer. And it so happens that these properties it display, are actually intriguing and have this inferential gloss, this sort of self-evidenting 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 order, 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. It's that that you were hinting at, in biology, sometimes known as auto-poesis,

in computational chemistry with self-assembly. It's the, 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 macro-march, or to minimise 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 minimising. 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 that should display. So what that means is we can't just look, 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, does this exhibit these qualities? Well, so 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. Well, it's 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 distinction? Yeah, I think there is. So, you know, 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 optimising their free energy, taking this sort of model evidence interpretation of this quantity that basically means that self-evident thing. Another nice little twist of phrase here is that you are your own existence proof, you know, 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 wanted to think about that for a few days. That's a beautiful line. So the step through to answer your question about, you know, what's it good for? It will go on 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 that down in terms of statistical independence 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 structures that emerge from removing connectivity and dependency. In this instance, basically being able to identify the surface of the oil drop from the water in which it is immersed. 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 in exterior,

and then the surface of the cell. And that's mathematically a mark of blanket. Just to pause, I'm in all of this concept that there's the stuff outside the surface, inside the surface, in the surface itself, the mark of blanket. It's just the most beautiful kind of notion about trying to explore what it means to exist mathematically. Apologize 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 what you were just talking about the surface about the market?

Yeah, so this surface or this blanket states that are the... 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 were dissociate 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 it 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. Operation lead has to be the case that in order for this carving up into different sets of states to exist, the active states, the mark of 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 pancycism that everything that exists is implicitly making inferences on self-evident thing. Now we make the next move, but what about living things? I mean, so let me ask you The picture was just painted of an oil drop. Just immediately in a matter of minutes took me into the world of pancillism where you

just convinced me, it made me feel like an oil drop is a living, certainly an autonomous system, but almost the living system. So it has a capability capability sensor capabilities and acting capabilities and it maintains something. So what is the difference in 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 they 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. You're being too clever. I was out of sense.

I'm not a passive little oil drop. It's sitting there at the bottom of the top of a glass of water. Sure. I guess. Well, I'm trying to say, you're absolutely right. You've nailed it. It's movement. So when 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 will 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 millenial 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 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 realise 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 secretive 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. So that's what, so it would be manifest. It we realize in terms of essentially swimming, essentially moving, changing one shape, a morphogenesis that is dynamic and possibly adaptive. So that's what I was trying to get up between the difference between 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 behaviour 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 towards chemical gradients in chemo-taxis. 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 and 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 lifeline 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 saw 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 no movement. So that for me is, you know, 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 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, we just painted a beautiful picture of existence and life. So, do you, do you, describe, do you, do you find within this framework a possibility of defining consciousness or exploring the idea of consciousness, like what, you know, self-awareness and expanded to consciousness, like, yeah, how can we, how can we start to think about consciousness

within this framework? Is it possible? Yeah, I think it's possible to think about it, whether you'll get it in your ears. And again, I'm not sure that I'm licensed to do that. I'm still like 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 self-hood, and what I'm talking about is the ability effectively to plan, so 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 move around, but you wouldn't endow it with the capacity to think about planning and moving a purposeful way where it countenance is the future. Whereas you might an aunt, you might think an aunt'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 are being as conscious. Yes, it will be. These are wonderful lines that we've drawn with existence, life and consciousness. Yes. 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 what point would you draw the line between being a pile of sand and a collection of sand 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 your inferential self-evident thing by which I simply mean the dynamics literally the thermodynamics and gradient flows that underwrite the preservation of your oil droplet-like form

of your oil droplet-like form are described as an optimization of log-basium model evidence of your elbow. That self-evident thing must be evidence for a model of what's causing the sensory impressions on the sensory part of your surface or your mark of planking. If that model is capable of planning, it must include a model of the future consequences of your active state, 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 generalist, I don't know what to call that. Then you've got to have movement in the game. And furthermore, you've got to have a gerentum 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 more out. Those who've got all the right ingredients to talk about consciousness, I've now got a selector

now 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-evident thing, 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 and 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 you have to grapple with the question, how would formally you write down a countless 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 petfish, was not self-aware. pet fish was not self-aware. They would probably argue about their favorite cat, but would be quite happy to say that their mom 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. You want to take that further, because 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, it might be difficult. Well, yes and no. I don't think that perhaps we

should just, can we just go back? But 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. But I'm mindful that I didn't answer your question before, what's the 300 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 hypothesis of natural selection. Beautiful, undeniably true, but tells you absolutely nothing about why you have legs and eyes and it tells you nothing about the actual phenotype and it wouldn't allow it to build something. So the free

energy principle by itself is as vacuous as most total logical theories and by total logical, of course, I'm talking to the theory of naturals, the survival of the fittest, what's the fittest of survival? Why did this cycle? So the fitter, it discour it circles. And in a sense, the free energy principle has that same deflationary tautology under the hood. It's a characteristic of things that exist, why they exist because they minimize their free energy, why 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 and look as as if they're optimizing a variational free energy, and a variational free energy has to be a functional of a gerontive model, probabilistic description of causes and consequences, causes out their consequences in the sensorium on the sensory parts of the Markov Plancki, then it should, in theory, possible to write down the genetic 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 of the free energy principle when you can write down your required evidence in terms of, well, when you can write down the gerative 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 or the variational frionary 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 agiot, artificial general intelligence that you want to engineer, the kind of AGI odd artificial general intelligence that you want to manifest probabilistically, and then you engineer, not a lot of 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 wiching on about that? It did seem relevant.

Yes. So what kinds of, so the answer to, would it be easy or it'd be hard? Well, mathematically it's easy. I've just told you, all you need to do is write down your, your perfect artifact, probabilistically, in the form of a probabilistic gerontimodel, a probabilistic 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 gerontimodel.

So that's where the heavy lifting comes in Yeah, so it's it's the form and the structure of that gerative 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 feeling type, the actual gerontum model. So with that in mind, what did you tell me that tells me immediately the kinds of gerontum 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, IEU, 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 of 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 the zombie guitier 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, gerative models that allow for the fact that 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've never thought of that that the purpose of the

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 like you couldn't, it doesn't have to necessarily be human, it could be other kind of similar creatures and some absolutely well. We've been 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.

And 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, most sophisticated, form of gerative model. Well, the gerative model actually has a gerative model of somebody else's gerative model. And I think that, and what we are doing now,

evinces the kinds of gerative 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, you know. That's probably not a brilliant analogy, if we're not trying to say that. And, you know, we wouldn't have this discourse. We wouldn't have the answer of it.

Yeah, that's right. You'd have to have as I interrupt. I mean, that's beautifully put. I'll really listen to this conversation many times. It's, uh, there's so much poetry in this and mathematics. Let me, um, ask the silliest or perhaps the biggest question as, uh, 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 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 and tell me the free energy I expect consequent upon my behavior. So technically that, and 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 to say 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-evident thing 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 form of bedtime stories or fairy stories that I'm a princess, and I'm going to meet a beast who's going to transform and be a prince.

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 which you grow up, and you create for yourself, I mean again, because of this active inference, this inactive aspect of self-evident thing, you're not only am I modeling my environment,

my e-conish, 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 a synchrony that means that I'm creating my own culture over different time skills. So the question now is for me being very selfish. What scripts were I given? It basically was a mystery between Einstein and Shorke Holmes. 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 and it's like a quirky way. So that's what I grew up on. My father, my father was an engineer, loved science, and he loved things like Sir Arthur Endham'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, keeping the hobbit out of this because that was quite fit by now. But it was a journey of exploration, it was a source. So yeah, I've just grown up to be what I imagine a mild-mannered Sherlock Holmes slash

Einstein would do in my shoes. And you did it elegantly and beautifully. Carl is a huge honor talking to you today. It was fun. Thank you so much for your time. No thank you, Shane. Thank you for listening to this conversation with Carl Friston. And thank you to our presenting sponsored cash app.

Please consider supporting the podcast by downloading cash app and using co-lex podcast. If you enjoy this podcast, subscribe on YouTube, review it with 5 stars and Apple podcasts, support it 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, and hope to see you next time. Thank you.