Karl Friston: Neuroscience and the Free Energy Principle | Lex Fridman Podcast #99
karl friston neurobiology machine learning deep learning brain imaging artificial intelligence agi ai ai podcast artificial intelligence podcast lex fridman lex podcast lex mit lex ai lex jre mit ai
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.

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EPISODE LINKS:
Karl's Website: https://www.fil.ion.ucl.ac.uk/~karl/
Karl's Wiki: https://en.wikipedia.org/wiki/Karl_J._Friston

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OUTLINE:
0:00 - Introduction
1:50 - How much of the human brain do we understand?
5:53 - 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

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 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 five 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 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 Cash App, the number one finance app in the app store. When you get it, use code LexPodcast. Cash App lets you send money to friends, buy Bitcoin, and invest in 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 App from the app store, Google Play, and use the code LexPodcast, you get $10, and Cash App will also donate $10 to First, an organization that is helping to 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 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 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. And 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 gonna give you the right kind of information to understand the computational anatomy. Then you'd choose to be a microscopist and you would study little cubic millimeters of brain for the rest of your life. If on the other hand, you were interested in holistic functions and a sort of functional anatomy of the sort that a neuropsychologist would understand, you'd study brain lesions and strokes, just looking at the whole person. So again, it comes back to at what level do you want understanding? I think there are principled reasons not to go too far. If you commit to a view of the brain as a machine that's performing a form of inference and representing things, that 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 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 their collective behavior. On the other hand, if you go too coarse grain, you're gonna miss some basic canonical principles of connectivity and architectures. I'm thinking here, it's a bit colloquial, but there's current excitement about high field magnetic resonance imaging at seven Tesla. Why? Well, it gives us for the first time the opportunity to look at the brain in action at the level of a few millimeters that distinguish between different layers of the cortex that may be very important in terms of evincing generic principles of canonical microcircuitry that are replicated throughout the brain that may tell us something fundamental about message passing in the brain and these density dynamics of neuronal or some of the population dynamics that underwrite our brain function. So somewhere between a millimeter and a meter. Lingering for a bit on the big questions, if you allow me. What to you is 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 dull minded way from the point of view of purely its anatomy and its structural aspects. I mean, there are many marvelous organs in the body. Let's take your liver, for example. Without it, you wouldn't be around for very long. And it does some beautiful and delicate biochemistry and homeostasis and evolved with a finesse that would easily parallel the brain, but it doesn't have a beautiful anatomy. It has a simple anatomy, which is attractive in a minimalist sense, but it doesn't have that crafted structure of sparse connectivity and that recurrence and that specialization that the brain has. So you said a lot of interesting terms here. So the recurrence, the sparsity, but you also started by saying hierarchical. So I've never thought of our brain as hierarchical. Sort of, I always thought it's just like a giant mess, 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 it's not a magic soup. Of course, is 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. I'm not committing to any particular form of hierarchy. 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 sort, but certainly for 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 Masoulom, hierarchical organization 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, to the deepest part of the onion. That's what I mean by a hierarchical organization. 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 earlier question, is this about the representational capacity or is it about the anatomy? Well, one underwrites the other. If one simply thinks of the brain as a message passing machine, a process that is in the service of doing something, then the circuitry and the connectivity that shape that message passing also dictate its function. So you've done a lot of amazing work in a lot of directions. So let's look at one aspect of that, of looking into the brain and trying to study this onion structure. What can we learn about the brain by imaging it, which is one way to sort of look at the anatomy of it, broadly speaking. What are the methods of imaging, but even bigger, what can we learn about it? Right, so well, most imaging, 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 dodging this 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 complimentary, 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 static viewing a static image, and then compare that to the responses or fluctuations in the brain, when you were exposed to a moving image, say a flying bird, we'd expect to see restricted segregated differences in activity. And those are basically the hotspots that you see in the in statistical parametric maps that test for the significance of the responses that are circumscribed. So now basically we're talking about some people have 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, is that such a beautiful sort of ideal to strive for? We humans scientists would like to hope that there's a beautiful structure to this whereas like you said, there are segregated regions that are responsible for the different function. How much hope is there to find such regions in terms of looking at the progress of studying the brain? Oh, I think enormous progress has been made in the past 20 or 30 years. So this is beyond incremental. You know, 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 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 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 interrupt but it's a 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 further away you go from a human brain, the greater the differences, but not as remarkable as you might think. And 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 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 sort of patches of cortex that may do different things than they 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 answer questions about human anatomy. But I think most people assume 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, so that was the early sort of ideas of studying the functional regions of the brain by if there's some damage to it to try to infer that there's that part of the brain might be somewhat responsible for this type of function. So what does that lead us? What are the next steps beyond that? Right, well, this actually is 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 show the same kinds of deficits. So it was very difficult to infer functional specialization purely on the basis of lesion deficit studies. But once we had the opportunity to look at the brain lighting up in its, literally, its 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 positron emission tomography, and then functional magnetic resonance imaging came along in the early 90s. And since that time, there has been an explosion of discovery, refinement, confirmation. 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 hotspots 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. 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 can really get at the core of the function by just deeply understanding the structure. Where do you sit on that? I think it's got some mileage to it, yes. So it's a worthy pursuit of studying, through imaging and all the different methods to actually study the structure. No, absolutely. 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, you're 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. Yep. Yeah. So deep inside the onion is the center of your soul. Beautifully put. Maybe briefly, if you could paint a picture of the kind of methods of neuroimaging, maybe the history, which you were a part of, from statistical parametric mapping. I mean, just what's out there that's interesting for people maybe outside the field to understand of what are the actual methodologies of looking inside the human brain? Right, well, you can answer that question from two perspectives. Basically, it's the modality. What kind of signal are you measuring? And they can range from, and let's limit ourselves to sort of 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. And 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. The most prevalent of those fall into two camps. You've got these metabolic, sometimes hemodynamic, blood-related signals. So these metabolic and or hemodynamic signals are basically proxies for elevated activity and message passing and neuronal dynamics in particular parts of the brain. Characteristically though, the time constants of these hemodynamic or metabolic responses to neural activity are much longer than the neural activity itself. And this is referring, forgive me for the dumb questions, but this would be referring to blood, like the flow of blood. Absolutely. So there's a ton of, it seems like there's a ton of blood vessels in the brain. Yeah. So, but what's the interaction between the flow of blood and the function of the neurons? Is there an interplay there? Yeah, yeah. And that interplay accounts for several careers of world-renowned scientists. Yes, absolutely. So this is known as neurovascular coupling is exactly what you said. It's how does the neural activity, the 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 anatomical, anatomically quite a useful metaphor. All the action, all the heavy lifting in terms of neural computation is done on the surface of the brain. And then the interior of the brain is constituted by fatty wires, essentially 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 neuro pill, which does the computation constituted largely by neurons. 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, 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, 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 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 a particular stimulus. But the problem is you're looking at electromagnetic signals that have passed through an enormous amount of magic soup or spaghetti of connectivity and through the scalp and the skull, and it's become spatially very diffused. So 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, be it at a when, or you've got these electromagnetic EEG, MEG setups that tell you to within a few milliseconds when something has responded, be it at a where. So you've got these two complimentary 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, from the outside, what are the big ways of using this technology? So once you've chosen the kind of neuroimaging that you want to use to answer your questions, and sometimes it would have to be both, then you've got a whole raft of analysis, time series analysis 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, this dialectic between specialization and integration, differentiation and integration. So it's the cartography, the blobology analysis. I apologize. I probably shouldn't interrupt so much, but just heard a fun word, the blah. Blobology. Blobology. It's a neologism, which means the study of blobs. So nothing. Are you being witty and humorous or is there an actual, does the word blobology ever appear in a textbook somewhere? It would appear in a popular book. It would not appear in a worthy specialist journal. But it's the fond word for the study of literally little blobs on brain maps showing activations. So the kind of thing that you'd see in the newspapers on ABC or BBC reporting the latest finding from brain imaging. Interestingly though, the maths involved in that stream of analysis does actually call upon the mathematics of blobs. So seriously, they're actually called Euler characteristics and they have a lot of fancy names in mathematics. We'll talk about it, but your ideas in free energy principle, I mean, there's a echoes of blobs there when you consider sort of entities, mathematically speaking. Yes, absolutely, yeah. Well, if that's circumscribed, well-defined, you entities of, well, from the free energy point of view, entities of anything, but from the point of view of the analysis, the cartography of the brain, these are the entities that constitute the evidence for this functional segregation. You have segregated this function in this blob and it is not outside of the blob. And that's basically the, if you were a map maker of America and you did not know its structure, the first thing you're doing constituting or creating a map would be to identify the cities, for example, or the mountains or the rivers, all of these uniquely spatially localizable features, possibly topological features have to be placed somewhere because that requires a mathematics 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 evidence that particular thing that you wanted to put on the map? And they normally are characterized in terms of literally these blobs or these sort of, now the way looking at this is a certain statistical measure of the degree of activation crosses a threshold in crossing that threshold in the spatially restricted part of the brain, it creates a blob. And that's basically what statistical parametric mapping does. It's basically mathematically finessed blobology. Okay, so those, you kind of described these two methodologies for one is a temporally noisy, one is spatially noisy, and you kind of have to play and figure out what can be useful. It'd be great if you can sort of comment. I got a chance recently to spend a day at a company called Neuralink that uses brain computer interfaces and their dream is to, well, there's a bunch of sort of dreams but one of them is to understand the brain by sort of getting in there past the so-called sort of 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 some of the ways we understand the brain prior to neuroimaging. It wasn't just lesion deficit studies. Some of the early work, in fact, literally 100 years 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 either with their muscles or with their salivation and imputing what that partial the brain must be doing. If I stimulate it and I vote this kind of response, then that tells me quite a lot about the functional specialization. So there's a long history of brain stimulation which continues to enjoy a lot of attention nowadays. 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 brain's function. Understand the neuronal dynamics underlie, movement disorders like Parkinson's disease. Even interest in magnetic stimulation, stimulating the magnetic fields and will it work in people who are depressed, for example. Quite a crude level of understanding what you're doing, but there is historical evidence that these kinds of brute force interventions do change things. They, you know, a little bit like banging the TV when the valves aren't working properly, but it still, it works. So, you know, there is a long history. Brain-computer interfacing or BCI, I think is a beautiful example of that. It's sort of carved out its own niche and its own aspirations. And there've been enormous advances within limits. Advances in terms of our ability to understand how the brain, the embodied brain engages with the world. I'm thinking here of sensory substitution, the augmenting our sensory capacities by giving ourselves extra ways of sensing and sampling the world, ranging from sort of trying to replace lost visual signals through to giving people completely new signals. So the, one of the, I think, most engaging examples of this is equipping people with a sense of magnetic fields. So you can actually give them magnetic sensors that enable them to feel, should we say, tactile pressure around their tummy, where they are in relation to the magnetic field of the earth. It's incredible. And after a few weeks, they take it for granted. They integrate it, they imbibe it, they assimilate it. They incorporate 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. So then we come to the more negative part of my ambivalence, the other side of it. So I don't want to be deflationary because much of my deflationary comments is probably a large out of ignorance than anything else. But generally speaking, the bandwidth and the bit rates that you get from brain computer interfaces as we currently know them, we're talking about bits per second. So that would be like me only being able to communicate with any world or with you using very, very, very slow Morse code. And it is not in the, even with the brain, even within an order of magnitude near what we actually need for an inactive realization of what people aspire to when they think about sort of curing people with paraplegia or replacing sight despite heroic efforts. So one has to ask, is there a lower bound on the kinds of recurrent information exchange between a brain and some augmented or artificial interface? And then we come back to interestingly what I was talking about before, which is, if you're talking about function in terms of inference, and I presume we'll get to that later on in terms of the free energy principle. And at the moment there may be fundamental reasons to assume that is the case. We're talking about ensemble activity. We're talking about basically, for example, let's paint the challenge facing brain computer interfacing in terms of controlling another system that is highly and deeply structured, very relevant to our lives, very nonlinear that rests upon the kind of non-equilibrium steady states and dynamics that the brain does, the weather, 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. And then what you're asking now is, can I meaningfully get into the weather and change it meaningfully and make the weather respond in a way that I want it to? You're talking about chaos control on a scale which is almost unimaginable. So there may be fundamental reasons why BCI, as you might read about it in a science fiction novel, aspirational BCI may never actually work in the sense that to really be integrated and be part of the system is a requirement that requires you to have evolved with that system. You have to be part of a very delicately structured, deeply structured dynamic ensemble activity that is not like rewiring a broken computer or plugging in a peripheral interface adapter. It is much more like getting into the weather patterns or, 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. 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 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 sort of 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, 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 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 of this part is basically 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. Can we maybe start at the basics and what is the free energy principle? Well, in fact, the free energy principle inherits a lot from the building of these data analytic approaches to these very high dimensional time series you get 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 integration, so 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 characterize the ensemble dynamics that must be in play in the service of that inference. So technically what that means is you can always interpret anything that exists in virtue of being separate from the environment in which it exists as trying to minimize variational free energy. And if you're from the machine learning community, you will know that as a negative evidence lower bound or a negative elbow, which is the same as saying you're trying to maximize, or it will look as if all your dynamics are trying to maximize the complement of that, which is the marginal likelihood or the evidence for your own existence. So that's basically the free energy principle. But to even take a sort of a small step backwards, you said the existential imperative. There's a lot of beautiful poetic words here, but sort of to put it crudely, it's a fascinating idea of basically of trying to describe if you're looking at a blob, how do you know this thing is alive? What does it mean to be alive? What does it mean to 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 endeavor, and it's nice that there's at least some, from some perspective, a clean answer. So maybe can you talk about that optimization view of it? So what's trying to be minimized and maximized? A system that's alive, what is it trying to minimize? Right, you've made a big move there. Apologize. No, no, it's good to make big moves. But you've assumed that the things, the thing exists in a state that could be living or non-living. So I may ask you, what licenses you to say that something exists? That's why I use the word existential. It's beyond living, it's just existence. So if you drill down onto the definition of things that exist, then they have certain properties if you borrow the maths from non-equilibrium steady state physics that enable you to interpret their existence in terms of this optimization procedure. So it's good you introduce the word optimization. So what the free energy principle in its most ambitious but also most deflationary and simplest says is that if something exists, then it must, by 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 a surprise or self-information if you have 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, 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 steady state. And I shouldn't really be doing that because the last time I was taught physics, I was in my twenties. What kind of systems, when you think about the free energy principle, what kind of systems are you imagining as a sort of more specific kind of case study? Yeah, I'm imagining a range of systems, but at its simplest, a single celled organism that can be identified from its eco-niche or its environment. So at its simplest, that's basically what I always imagined in my head. And you may ask, well, is there any, how on earth can you even elaborate questions about the existence of a single drop of oil, for example? Yeah, but there are deep questions there. Why doesn't the oil, why doesn't the thing, the interface between the drop of oil that contains an interior and the thing that is not the drop of oil, which is the solvent in which it is immersed, how does that interface persist over time? Why doesn't the oil just dissolve into solvent? So what 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 know, 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 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, 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? 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? Right, so I mean, you're asking deep questions, deep in a non-millennial sense here. No, in a hierarchical sense. No, but what you can do, you see, so this is the 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 haven't 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 sort of 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 autopoiesis 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 macromolecule to minimize its free energy, its thermodynamic free energy. It's exactly the same principle that we've been talking about that thermodynamic free energy is just the negative elbow. It's the same mathematical construct. So the very emergence of existence of structure of form that can be distinguished from the environment or the thing that is not the thing necessitates the existence of an objective function that it looks as if it is minimizing. It's finding a free energy minima. And so just to clarify, I'm trying to wrap my head around. So the free energy principle says that if something exists, these are the properties 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 implied that you can kind of use it to reason, to think about like study a particular system and say, does this exhibits 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 that 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 between the two. 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 that self-evidencing. Another nice little twist of phrase here is that you are your own existence proof. 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? We're gonna have to think about that for a few days. Yeah, that's a beautiful line. So the step through to answer your question about what's it good for, we go along the following lines. First of all, you have to define what it means to exist, which now as you 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 is, 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. 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 realize, well, there are actually four kinds of states in any given universe that contains anything. The things that are internal to the surface, the things that are external to the surface and the surface in and of itself, which is why I use a metaphor, a little single celled organism that has an interior and exterior and then the surface of the cell. And that's mathematically a Markov blanket. Just to pause, I'm in awe of this concept that there's the stuff outside the surface, stuff inside the surface and the surface itself, the Markov blanket. It's just the most beautiful kind of notion about trying to explore what it means to exist mathematically. I apologize, it's just a beautiful idea. It came out of California. I changed my mind, I take it all back. So anyway, so what you were just talking about, the surface about the Markov blanket. So this surface or these blanket states that are the, because they are now defined in relation to these independencies 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. Interesting thing is that what would they be called if you were trying to describe these things? So what we're talking about are internal states, external states and blanket states. Now let's carve the blanket states into two, sensory states and active states. Operationally, it has to be the case that in order for this carving up into different sets of states to exist, the active states, the Markov blanket cannot be influenced by the external states. And we already know that the internal states can't be influenced by the external states because the blanket separates them. So what does that mean? Well, it means the active states, the internal states are now jointly not influenced by external states. They only have autonomous dynamics. So now you've got a picture of an oil drop that has autonomy. It has autonomous states. It has autonomous states in the sense that there must be some parts of the surface of the oil drop that are not influenced by the external states and all the interior. And together those two states endow even a little oil drop with autonomous states that look as if they are optimizing their variational free energy or their negative elbow, their model evidence. And that would be an interesting intellectual exercise. And you could say, you could even go into the realms of panpsychism that everything that exists is implicitly making inferences or self-evidencing. Now we make the next move, but what about living things? I mean, so let me ask you, what's the difference between an oil drop and a little tadpole or a little lava or 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've 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 capability, sensory capabilities and acting capabilities and maintain something. So what is the difference between that and something that we traditionally think of as a living system? That it could die or it can't, I mean, yeah, mortality. I'm not exactly sure. I'm not sure what the right answer there is because 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? Will the surface is performing actions that maintain its structure. You're being too clever. I was, I had in mind a passive little oil drop is sitting there at the bottom of the top of a glass of water. Sure, I guess. What I'm trying to say is you're absolutely right. You've nailed it. It's movement. So where does that movement come from? If it comes from the inside, then 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. I see. And the thing cannot on 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 Markov 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, 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. So that's what, so is, and that would be manifested, we realize in terms of essentially swimming, essentially moving, changing one shape, a morphogenesis that is dynamic and possibly adaptive. So that's what I was trying to get at 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 the data impressed upon their surface or the blanket states, but they are actively resampling 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 it can actually push its surface over there, over there, and test for chemical gradients or their properties then you start to move to much more lifelike form. This is all fun, theoretically interesting, but it actually is quite important in terms of reflecting what I have seen since the turn of the millennium, which is this move towards an inactive and embodied understanding of intelligence. And you say you're from machine learning. So what that means, 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 is to 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 that it will ever need to be exposed to as opposed to the tadpoles 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, like, yeah, 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. Get it anywhere else, another question. And again, I'm not sure that I'm licensed to answer that question. I think you'd have to speak to a qualified philosopher to get a definitive answer there. But certainly there's a lot of interest in using not just these ideas, but related ideas from information theory to try and tie down the maths and the calculus and the geometry of consciousness, either in terms of sort of a minimal consciousness, and even less than a minimal selfhood. And what I'm talking about is the ability effectively to plan, to have agency. So you could argue that a virus does have a form of agency in virtue of the way that it selectively finds hosts and cells to live in and moves around, but you wouldn't endow it with the capacity to think about planning and moving in a purposeful way where it countenances the future. Whereas you might an ant. You might think an ant's not quite as unconscious as a virus. It certainly seems to have a purpose. It talks to its friends on route during its foraging. It has a different kind of autonomy, which is biotic, but beyond a virus. So there's something about, so there's some line that has to do with the complexity of planning that may contain an answer. I mean, it'd be beautiful if we can find a line beyond which we could say a being is 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 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. I think it's a system that have the ability to plan. So just technically what that means is your inferential self-evidencing, by which I simply mean the dynamics, literally the thermodynamics and gradient flows that underwrite the preservation of your oil droplet-like form are described as a, 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 Markov blanket. If that model is capable of planning, it must include a model of the future consequences of your active states or your action, just planning. So we're now in the game of planning as inference. Now notice what we've made though. We've made quite a big move away from big data and machine learning, because again, it's the consequences of moving. It's the consequences of selecting those data or those data or looking over there. And that tells you immediately that even to be a contender for a conscious artifact or a strong AI or generalized AI, 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 to talk about consciousness, I've now got to select among a number of different courses of action into the future as part of planning. I've now got free will. The act of selecting this course of action or that policy or that policy or that action suddenly makes me into an inference machine, a self-evidencing artifact that now looks as if it's selecting amongst different alternative ways forward as I actively swim here or swim there or look over here 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, I think, 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 there would be pressure on you to actually commit to a formal definition of what you mean by self-awareness. I think most people that I know would probably say that a goldfish, a pet fish was not self-aware. They would probably argue about their favorite cat, but would be quite happy to say that their mom was self-aware. So. I mean, 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. So mathematically describing that in the same elegant way as you have with the free energy principle might be difficult. Well, yes and no. I don't think that, well, perhaps we should just, can we just go back? Because 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 steady-states? Yes, it is. It does nothing more for you than that. It can be regarded, it's gonna sound very arrogant, but it is all the sort of theory of natural selection or a hypothesis of natural selection. Beautiful, undeniably true, but tells you absolutely nothing about why you have legs and eyes. It tells you nothing about the actual phenotype and it wouldn't allow you to build something. So the free energy principle by itself is as vacuous as most tautological theories. And by tautological, of course, I'm talking to the theory of natural, the survival of the fittest. What's the fittest of the survival? Why do the cycles, the fitter? 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 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 the practical thing, which you don't get from natural selection, but you could say has now manifested things like differential evolution or genetic algorithms or MCMC, for example, in machine learning. The practical thing you can get is if it looks as if things that exist are trying to have density dynamics and look as though they're 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 blanket. Then it should in theory be 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 of 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 or 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 wittering on about that? It did seem relevant to, yes. So what kinds of, so the answer to, would it be easier 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 probabilistically in the form of a probabilistic generative model, a probability distribution over the causes and consequences of the world in which this thing is immersed, and then you just engineer a computer and a robot to 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. It 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, this in fact, the same kind of creature, but I am me and you are you, then it becomes a game 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, a community, then it becomes necessary now for me to infer that it's me talking and not you talking. I wouldn't need that if it was on Mars by myself, or if I was in the jungle as a feral child. If there was nothing like me around, there would be no need to have an inference that a hypothesis, oh yes, it is me that is experiencing or causing these sounds and it is not you. It's only when there's ambiguity in play, induced by the fact that there are others in that world. So I think that the special thing about self-aware artifacts is that they have learned to, or they have acquired, or at least 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 like you could, 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. And I mean, when we talk about planning, when you talk specifically about planning, the highest, if you 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. And in that conversation, there is a beautiful art of turn-taking and mutual inference, theory of mind. I have to know when you wanna listen, I have to know when you want to interrupt, I have to make sure that you're online. I have to have a model in my head of your model in your head. That's the highest, the most sophisticated form of generative model, where the generative model actually has a generative model of 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. We wouldn't have this. 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 in this soup? I'm tempted to answer that again as a physicist and tell you 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 a slightly more personal and fun answer, but which can be consistent with that. And I think it's reassuringly simple and hops 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 form of bedtime stories or fairy stories that I'm a princess, I'm gonna meet a beast who's gonna transform and it's gonna be a prince. 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 Econish, my external states out there, but I'm actively changing them all the time. And it's still says I'm 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 Schalke Holmes. So I smoke as heavily as possible, tried to avoid too much interpersonal contact. Yeah, enjoy the fantasy that you're a popular scientist who's gonna make a difference in a slightly quirky way. So that's where I grew up. My father was an engineer and loved science and loved sort of things like Sir Arthur Eddington'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 were all about these characters, keeping the hobbit out of this because that was quite fitting my narrative. But it was a journey of exploration, I suppose, of sorts. So yeah, I've just grown up to be what I imagine a mild-mannered Schalke Holmes slash Albert Einstein would do it in my shoes. And you did it elegantly and beautifully. Carl, it was a huge honor talking to you today. It was fun. Thank you so much for your time. No, thank you. Appreciate it. Thank you for listening to this conversation with Carl Friston, and thank you to our presenting sponsor, Cash App. Please consider supporting the podcast by downloading Cash App and using code LEXPODCAST. If you enjoy this podcast, subscribe on YouTube, review it with five stars on Apple Podcast, 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.
 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,
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 Alex Friedman, spelled F-R-I-D-M-A-N.
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 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 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 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.
 And 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 gonna give you the right kind of information
 to understand the computational anatomy.
 Then you'd choose to be a microscopist
 and you would study little cubic millimeters of brain
 for the rest of your life.
 If on the other hand, you were interested
 in holistic functions and a sort of functional anatomy
 of the sort that a neuropsychologist would understand,
 you'd study brain lesions and strokes,
 just looking at the whole person.
 So again, it comes back to
 at what level do you want understanding?
 I think there are principled reasons not to go too far.
 If you commit to a view of the brain
 as a machine that's performing a form of inference
 and representing things,
 that 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 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 their collective behavior.
 On the other hand, if you go too coarse grain,
 you're gonna miss some basic canonical principles
 of connectivity and architectures.
 I'm thinking here, it's a bit colloquial,
 but there's current excitement about high field
 magnetic resonance imaging at seven Tesla.
 Why?
 Well, it gives us for the first time the opportunity
 to look at the brain in action at the level
 of a few millimeters that distinguish
 between different layers of the cortex
 that may be very important in terms of evincing
 generic principles of canonical microcircuitry
 that are replicated throughout the brain
 that may tell us something fundamental
 about message passing in the brain
 and these density dynamics of neuronal
 or some of the population dynamics
 that underwrite our brain function.
 So somewhere between a millimeter and a meter.
 Lingering for a bit on the big questions, if you allow me.
 What to you is 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 dull minded way
 from the point of view of purely its anatomy
 and its structural aspects.
 I mean, there are many marvelous organs in the body.
 Let's take your liver, for example.
 Without it, you wouldn't be around for very long.
 And it does some beautiful and delicate biochemistry
 and homeostasis and evolved with a finesse
 that would easily parallel the brain,
 but it doesn't have a beautiful anatomy.
 It has a simple anatomy,
 which is attractive in a minimalist sense,
 but it doesn't have that crafted structure
 of sparse connectivity and that recurrence
 and that specialization that the brain has.
 So you said a lot of interesting terms here.
 So the recurrence, the sparsity,
 but you also started by saying hierarchical.
 So I've never thought of our brain as hierarchical.
 Sort of, I always thought it's just like a giant mess,
 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 it's not a magic soup.
 Of course, is 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.
 I'm not committing to any particular form of hierarchy.
 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 sort,
 but certainly for 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 Masoulom,
 hierarchical organization 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, to the deepest part of the onion.
 That's what I mean by a hierarchical organization.
 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 earlier question,
 is this about the representational capacity
 or is it about the anatomy?
 Well, one underwrites the other.
 If one simply thinks of the brain
 as a message passing machine,
 a process that is in the service of doing something,
 then the circuitry and the connectivity
 that shape that message passing also dictate its function.
 So you've done a lot of amazing work in a lot of directions.
 So let's look at one aspect of that,
 of looking into the brain
 and trying to study this onion structure.
 What can we learn about the brain by imaging it,
 which is one way to sort of look at the anatomy of it,
 broadly speaking.
 What are the methods of imaging, but even bigger,
 what can we learn about it?
 Right, so well, most imaging, 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 dodging this
 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 complimentary,
 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 static viewing a static image,
 and then compare that to the responses
 or fluctuations in the brain,
 when you were exposed to a moving image,
 say a flying bird,
 we'd expect to see restricted segregated differences
 in activity.
 And those are basically the hotspots
 that you see in the in statistical parametric maps
 that test for the significance of the responses
 that are circumscribed.
 So now basically we're talking about some people
 have 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,
 is that such a beautiful sort of ideal to strive for?
 We humans scientists would like to hope
 that there's a beautiful structure to this
 whereas like you said, there are segregated regions
 that are responsible for the different function.
 How much hope is there to find such regions
 in terms of looking at the progress of studying the brain?
 Oh, I think enormous progress has been made
 in the past 20 or 30 years.
 So this is beyond incremental.
 You know, 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 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 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 interrupt
 but it's a 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 further away you go from a human brain,
 the greater the differences,
 but not as remarkable as you might think.
 And 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 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 sort of patches of cortex
 that may do different things than they 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 answer questions
 about human anatomy.
 But I think most people assume 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, so that was the early
 sort of ideas of studying the functional regions
 of the brain by if there's some damage to it
 to try to infer that there's that part of the brain
 might be somewhat responsible for this type of function.
 So what does that lead us?
 What are the next steps beyond that?
 Right, well, this actually is 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 show the same kinds of deficits.
 So it was very difficult to infer functional specialization
 purely on the basis of lesion deficit studies.
 But once we had the opportunity to look at the brain
 lighting up in its, literally,
 its 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 positron emission tomography,
 and then functional magnetic resonance imaging came along
 in the early 90s.
 And since that time, there has been an explosion
 of discovery, refinement, confirmation.
 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 hotspots 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.
 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 can really get
 at the core of the function by just deeply understanding
 the structure.
 Where do you sit on that?
 I think it's got some mileage to it, yes.
 So it's a worthy pursuit of studying, through imaging
 and all the different methods to actually study
 the structure. No, absolutely.
 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,
 you're 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.
 Yep. Yeah.
 So deep inside the onion is the center of your soul.
 Beautifully put.
 Maybe briefly, if you could paint a picture of the kind
 of methods of neuroimaging, maybe the history,
 which you were a part of,
 from statistical parametric mapping.
 I mean, just what's out there that's interesting
 for people maybe outside the field to understand
 of what are the actual methodologies
 of looking inside the human brain?
 Right, well, you can answer that question
 from two perspectives.
 Basically, it's the modality.
 What kind of signal are you measuring?
 And they can range from, and let's limit ourselves
 to sort of 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.
 And 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.
 The most prevalent of those fall into two camps.
 You've got these metabolic, sometimes hemodynamic,
 blood-related signals.
 So these metabolic and or hemodynamic signals
 are basically proxies for elevated activity
 and message passing and neuronal dynamics
 in particular parts of the brain.
 Characteristically though, the time constants
 of these hemodynamic or metabolic responses
 to neural activity are much longer
 than the neural activity itself.
 And this is referring, forgive me for the dumb questions,
 but this would be referring to blood,
 like the flow of blood.
 Absolutely.
 So there's a ton of, it seems like there's a ton
 of blood vessels in the brain.
 Yeah.
 So, but what's the interaction between the flow of blood
 and the function of the neurons?
 Is there an interplay there?
 Yeah, yeah.
 And that interplay accounts for several careers
 of world-renowned scientists.
 Yes, absolutely.
 So this is known as neurovascular coupling
 is exactly what you said.
 It's how does the neural activity,
 the 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 anatomical,
 anatomically quite a useful metaphor.
 All the action, all the heavy lifting
 in terms of neural computation is done
 on the surface of the brain.
 And then the interior of the brain is constituted
 by fatty wires, essentially 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 neuro pill,
 which does the computation constituted largely by neurons.
 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, 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,
 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 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 a particular stimulus.
 But the problem is you're looking at electromagnetic signals
 that have passed through an enormous amount of magic soup
 or spaghetti of connectivity and through the scalp
 and the skull, and it's become spatially very diffused.
 So 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,
 be it at a when, or you've got these electromagnetic
 EEG, MEG setups that tell you to within a few milliseconds
 when something has responded, be it at a where.
 So you've got these two complimentary 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, from the outside,
 what are the big ways of using this technology?
 So once you've chosen the kind of neuroimaging
 that you want to use to answer your questions,
 and sometimes it would have to be both,
 then you've got a whole raft of analysis,
 time series analysis 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,
 this dialectic between specialization and integration,
 differentiation and integration.
 So it's the cartography, the blobology analysis.
 I apologize.
 I probably shouldn't interrupt so much,
 but just heard a fun word, the blah.
 Blobology. Blobology.
 It's a neologism, which means the study of blobs.
 So nothing.
 Are you being witty and humorous or is there an actual,
 does the word blobology ever appear in a textbook somewhere?
 It would appear in a popular book.
 It would not appear in a worthy specialist journal.
 But it's the fond word for the study of literally
 little blobs on brain maps showing activations.
 So the kind of thing that you'd see in the newspapers
 on ABC or BBC reporting the latest finding
 from brain imaging.
 Interestingly though, the maths involved
 in that stream of analysis does actually call upon
 the mathematics of blobs.
 So seriously, they're actually called Euler characteristics
 and they have a lot of fancy names in mathematics.
 We'll talk about it, but your ideas in free energy principle,
 I mean, there's a echoes of blobs there
 when you consider sort of entities,
 mathematically speaking.
 Yes, absolutely, yeah.
 Well, if that's circumscribed, well-defined,
 you entities of, well, from the free energy point of view,
 entities of anything, but from the point of view
 of the analysis, the cartography of the brain,
 these are the entities that constitute the evidence
 for this functional segregation.
 You have segregated this function in this blob
 and it is not outside of the blob.
 And that's basically the, if you were a map maker
 of America and you did not know its structure,
 the first thing you're doing constituting
 or creating a map would be to identify the cities,
 for example, or the mountains or the rivers,
 all of these uniquely spatially localizable features,
 possibly topological features have to be placed somewhere
 because that requires a mathematics 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 evidence
 that particular thing that you wanted to put on the map?
 And they normally are characterized
 in terms of literally these blobs
 or these sort of, now the way looking at this
 is a certain statistical measure
 of the degree of activation crosses a threshold
 in crossing that threshold
 in the spatially restricted part of the brain,
 it creates a blob.
 And that's basically what statistical parametric mapping does.
 It's basically mathematically finessed blobology.
 Okay, so those, you kind of described
 these two methodologies for one is a temporally noisy,
 one is spatially noisy, and you kind of have to play
 and figure out what can be useful.
 It'd be great if you can sort of comment.
 I got a chance recently to spend a day
 at a company called Neuralink
 that uses brain computer interfaces
 and their dream is to, well, there's a bunch of sort of dreams
 but one of them is to understand the brain
 by sort of getting in there past the so-called
 sort of 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
 some of the ways we understand the brain
 prior to neuroimaging.
 It wasn't just lesion deficit studies.
 Some of the early work, in fact, literally 100 years
 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 either with their muscles
 or with their salivation and imputing what that partial
 the brain must be doing.
 If I stimulate it and I vote this kind of response,
 then that tells me quite a lot
 about the functional specialization.
 So there's a long history of brain stimulation
 which continues to enjoy a lot of attention nowadays.
 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 brain's function.
 Understand the neuronal dynamics underlie,
 movement disorders like Parkinson's disease.
 Even interest in magnetic stimulation,
 stimulating the magnetic fields
 and will it work in people who are depressed, for example.
 Quite a crude level of understanding what you're doing,
 but there is historical evidence that these kinds
 of brute force interventions do change things.
 They, you know, a little bit like banging the TV
 when the valves aren't working properly,
 but it still, it works.
 So, you know, there is a long history.
 Brain-computer interfacing or BCI,
 I think is a beautiful example of that.
 It's sort of carved out its own niche
 and its own aspirations.
 And there've been enormous advances within limits.
 Advances in terms of our ability to understand
 how the brain, the embodied brain engages with the world.
 I'm thinking here of sensory substitution,
 the augmenting our sensory capacities
 by giving ourselves extra ways of sensing
 and sampling the world, ranging from sort of trying
 to replace lost visual signals
 through to giving people completely new signals.
 So the, one of the, I think, most engaging examples
 of this is equipping people with a sense of magnetic fields.
 So you can actually give them magnetic sensors
 that enable them to feel, should we say,
 tactile pressure around their tummy,
 where they are in relation to the magnetic field
 of the earth.
 It's incredible.
 And after a few weeks, they take it for granted.
 They integrate it, they imbibe it, they assimilate it.
 They incorporate 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.
 So then we come to the more negative part of my ambivalence,
 the other side of it.
 So I don't want to be deflationary
 because much of my deflationary comments
 is probably a large out of ignorance than anything else.
 But generally speaking, the bandwidth and the bit rates
 that you get from brain computer interfaces
 as we currently know them, we're talking about bits per second.
 So that would be like me only being able to communicate
 with any world or with you using very, very, very slow
 Morse code.
 And it is not in the, even with the brain,
 even within an order of magnitude
 near what we actually need for an inactive realization
 of what people aspire to when they think about
 sort of curing people with paraplegia or replacing sight
 despite heroic efforts.
 So one has to ask, is there a lower bound
 on the kinds of recurrent information exchange between a brain
 and some augmented or artificial interface?
 And then we come back to interestingly
 what I was talking about before, which is,
 if you're talking about function in terms of inference,
 and I presume we'll get to that later on
 in terms of the free energy principle.
 And at the moment there may be fundamental reasons
 to assume that is the case.
 We're talking about ensemble activity.
 We're talking about basically, for example,
 let's paint the challenge facing brain computer interfacing
 in terms of controlling another system
 that is highly and deeply structured,
 very relevant to our lives, very nonlinear
 that rests upon the kind of non-equilibrium steady states
 and dynamics that the brain does, the weather, 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.
 And then what you're asking now is,
 can I meaningfully get into the weather
 and change it meaningfully
 and make the weather respond in a way that I want it to?
 You're talking about chaos control on a scale
 which is almost unimaginable.
 So there may be fundamental reasons why BCI,
 as you might read about it in a science fiction novel,
 aspirational BCI may never actually work
 in the sense that to really be integrated
 and be part of the system is a requirement
 that requires you to have evolved with that system.
 You have to be part of a very delicately structured,
 deeply structured dynamic ensemble activity
 that is not like rewiring a broken computer
 or plugging in a peripheral interface adapter.
 It is much more like getting into the weather patterns
 or, 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.
 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 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 sort of 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,
 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 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
 of this part is basically 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.
 Can we maybe start at the basics
 and what is the free energy principle?
 Well, in fact, the free energy principle
 inherits a lot from the building
 of these data analytic approaches
 to these very high dimensional time series
 you get 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 integration,
 so 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 characterize
 the ensemble dynamics that must be in play
 in the service of that inference.
 So technically what that means is
 you can always interpret anything that exists
 in virtue of being separate from the environment
 in which it exists as trying to minimize
 variational free energy.
 And if you're from the machine learning community,
 you will know that as a negative evidence lower bound
 or a negative elbow, which is the same as saying
 you're trying to maximize, or it will look as if
 all your dynamics are trying to maximize
 the complement of that, which is the marginal likelihood
 or the evidence for your own existence.
 So that's basically the free energy principle.
 But to even take a sort of a small step backwards,
 you said the existential imperative.
 There's a lot of beautiful poetic words here,
 but sort of to put it crudely,
 it's a fascinating idea of basically
 of trying to describe if you're looking at a blob,
 how do you know this thing is alive?
 What does it mean to be alive?
 What does it mean to 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 endeavor, and it's nice that there's
 at least some, from some perspective, a clean answer.
 So maybe can you talk about that optimization view of it?
 So what's trying to be minimized and maximized?
 A system that's alive, what is it trying to minimize?
 Right, you've made a big move there.
 Apologize.
 No, no, it's good to make big moves.
 But you've assumed that the things,
 the thing exists in a state
 that could be living or non-living.
 So I may ask you, what licenses you
 to say that something exists?
 That's why I use the word existential.
 It's beyond living, it's just existence.
 So if you drill down onto the definition of things
 that exist, then they have certain properties
 if you borrow the maths
 from non-equilibrium steady state physics
 that enable you to interpret their existence
 in terms of this optimization procedure.
 So it's good you introduce the word optimization.
 So what the free energy principle in its most ambitious
 but also most deflationary and simplest says
 is that if something exists, then it must,
 by 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 a surprise or self-information
 if you have 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,
 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 steady state.
 And I shouldn't really be doing that
 because the last time I was taught physics,
 I was in my twenties.
 What kind of systems, when you think about
 the free energy principle,
 what kind of systems are you imagining
 as a sort of more specific kind of case study?
 Yeah, I'm imagining a range of systems,
 but at its simplest, a single celled organism
 that can be identified from its eco-niche
 or its environment.
 So at its simplest, that's basically
 what I always imagined in my head.
 And you may ask, well, is there any, how on earth
 can you even elaborate questions about the existence
 of a single drop of oil, for example?
 Yeah, but there are deep questions there.
 Why doesn't the oil, why doesn't the thing,
 the interface between the drop of oil
 that contains an interior and the thing
 that is not the drop of oil,
 which is the solvent in which it is immersed,
 how does that interface persist over time?
 Why doesn't the oil just dissolve into solvent?
 So what 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 know, 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 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, 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? 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?
 Right, so I mean, you're asking deep questions,
 deep in a non-millennial sense here.
 No, in a hierarchical sense.
 No, but what you can do, you see,
 so this is the 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 haven't 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 sort of 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 autopoiesis
 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 macromolecule
 to minimize its free energy, its thermodynamic free energy.
 It's exactly the same principle that we've been talking about
 that thermodynamic free energy is just the negative elbow.
 It's the same mathematical construct.
 So the very emergence of existence of structure of form
 that can be distinguished from the environment
 or the thing that is not the thing
 necessitates the existence of an objective function
 that it looks as if it is minimizing.
 It's finding a free energy minima.
 And so just to clarify, I'm trying to wrap my head around.
 So the free energy principle says that if something exists,
 these are the properties 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 implied
 that you can kind of use it to reason,
 to think about like study a particular system
 and say, does this exhibits 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 that 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 between the two.
 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
 that self-evidencing.
 Another nice little twist of phrase here
 is that you are your own existence proof.
 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?
 We're gonna have to think about that for a few days.
 Yeah, that's a beautiful line.
 So the step through to answer your question about
 what's it good for, we go along the following lines.
 First of all, you have to define what it means
 to exist, which now as you 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 is,
 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.
 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 realize,
 well, there are actually four kinds of states
 in any given universe that contains anything.
 The things that are internal to the surface,
 the things that are external to the surface
 and the surface in and of itself,
 which is why I use a metaphor,
 a little single celled organism
 that has an interior and exterior
 and then the surface of the cell.
 And that's mathematically a Markov blanket.
 Just to pause, I'm in awe of this concept
 that there's the stuff outside the surface,
 stuff inside the surface and the surface itself,
 the Markov blanket.
 It's just the most beautiful kind of notion
 about trying to explore what it means
 to exist mathematically.
 I apologize, it's just a beautiful idea.
 It came out of California.
 I changed my mind, I take it all back.
 So anyway, so what you were just talking about,
 the surface about the Markov blanket.
 So this surface or these blanket states
 that are the, because they are now defined
 in relation to these independencies
 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.
 Interesting thing is that what would they be called
 if you were trying to describe these things?
 So what we're talking about are internal states,
 external states and blanket states.
 Now let's carve the blanket states into two,
 sensory states and active states.
 Operationally, it has to be the case
 that in order for this carving up into different sets
 of states to exist, the active states,
 the Markov blanket cannot be influenced
 by the external states.
 And we already know that the internal states
 can't be influenced by the external states
 because the blanket separates them.
 So what does that mean?
 Well, it means the active states, the internal states
 are now jointly not influenced by external states.
 They only have autonomous dynamics.
 So now you've got a picture of an oil drop
 that has autonomy.
 It has autonomous states.
 It has autonomous states in the sense that there must be
 some parts of the surface of the oil drop
 that are not influenced by the external states
 and all the interior.
 And together those two states endow even a little oil drop
 with autonomous states that look as if they are optimizing
 their variational free energy or their negative elbow,
 their model evidence.
 And that would be an interesting intellectual exercise.
 And you could say, you could even go into the realms
 of panpsychism that everything that exists
 is implicitly making inferences or self-evidencing.
 Now we make the next move, but what about living things?
 I mean, so let me ask you, what's the difference
 between an oil drop and a little tadpole
 or a little lava or 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've 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 capability, sensory capabilities
 and acting capabilities and maintain something.
 So what is the difference between that
 and something that we traditionally think of
 as a living system?
 That it could die or it can't, I mean, yeah, mortality.
 I'm not exactly sure.
 I'm not sure what the right answer there is
 because 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?
 Will the surface is performing actions
 that maintain its structure.
 You're being too clever.
 I was, I had in mind a passive little oil drop
 is sitting there at the bottom of the top of a glass of water.
 Sure, I guess.
 What I'm trying to say is you're absolutely right.
 You've nailed it.
 It's movement.
 So where does that movement come from?
 If it comes from the inside, then 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.
 I see.
 And the thing cannot on 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 Markov
 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,
 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.
 So that's what, so is, and that would be manifested,
 we realize in terms of essentially swimming,
 essentially moving, changing one shape,
 a morphogenesis that is dynamic and possibly adaptive.
 So that's what I was trying to get at
 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 the data impressed
 upon their surface or the blanket states,
 but they are actively resampling 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 it can actually push its surface over there, over there,
 and test for chemical gradients or their properties
 then you start to move to much more lifelike form.
 This is all fun, theoretically interesting,
 but it actually is quite important in terms of reflecting
 what I have seen since the turn of the millennium,
 which is this move towards an inactive
 and embodied understanding of intelligence.
 And you say you're from machine learning.
 So what that means, 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 is to 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 that it will ever need
 to be exposed to as opposed to the tadpoles 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,
 like, yeah, 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.
 Get it anywhere else, another question.
 And again, I'm not sure that I'm licensed
 to answer that question.
 I think you'd have to speak to a qualified philosopher
 to get a definitive answer there.
 But certainly there's a lot of interest
 in using not just these ideas, but related ideas
 from information theory to try and tie down the maths
 and the calculus and the geometry of consciousness,
 either in terms of sort of a minimal consciousness,
 and even less than a minimal selfhood.
 And what I'm talking about is the ability effectively
 to plan, to have agency.
 So you could argue that a virus does have a form of agency
 in virtue of the way that it selectively finds hosts
 and cells to live in and moves around,
 but you wouldn't endow it with the capacity
 to think about planning and moving in a purposeful way
 where it countenances the future.
 Whereas you might an ant.
 You might think an ant's not quite as unconscious
 as a virus.
 It certainly seems to have a purpose.
 It talks to its friends on route during its foraging.
 It has a different kind of autonomy,
 which is biotic, but beyond a virus.
 So there's something about,
 so there's some line that has to do
 with the complexity of planning
 that may contain an answer.
 I mean, it'd be beautiful if we can find a line
 beyond which we could say a being is 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 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.
 I think it's a system that have the ability to plan.
 So just technically what that means
 is your inferential self-evidencing,
 by which I simply mean the dynamics,
 literally the thermodynamics and gradient flows
 that underwrite the preservation
 of your oil droplet-like form
 are described as a,
 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 Markov blanket.
 If that model is capable of planning,
 it must include a model of the future consequences
 of your active states or your action, just planning.
 So we're now in the game of planning as inference.
 Now notice what we've made though.
 We've made quite a big move away from big data
 and machine learning,
 because again, it's the consequences of moving.
 It's the consequences of selecting those data
 or those data or looking over there.
 And that tells you immediately that even to be a contender
 for a conscious artifact or a strong AI or generalized AI,
 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
 to talk about consciousness,
 I've now got to select among a number of different courses
 of action into the future as part of planning.
 I've now got free will.
 The act of selecting this course of action
 or that policy or that policy or that action
 suddenly makes me into an inference machine,
 a self-evidencing artifact that now looks as if
 it's selecting amongst different alternative ways forward
 as I actively swim here or swim there
 or look over here 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, I think, 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 there would be pressure on you
 to actually commit to a formal definition
 of what you mean by self-awareness.
 I think most people that I know would probably say
 that a goldfish, a pet fish was not self-aware.
 They would probably argue about their favorite cat,
 but would be quite happy to say
 that their mom was self-aware.
 So.
 I mean, 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.
 So mathematically describing that in the same elegant way
 as you have with the free energy principle
 might be difficult.
 Well, yes and no.
 I don't think that, well, perhaps we should just,
 can we just go back?
 Because 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 steady-states?
 Yes, it is.
 It does nothing more for you than that.
 It can be regarded, it's gonna sound very arrogant,
 but it is all the sort of theory of natural selection
 or a hypothesis of natural selection.
 Beautiful, undeniably true,
 but tells you absolutely nothing about
 why you have legs and eyes.
 It tells you nothing about the actual phenotype
 and it wouldn't allow you to build something.
 So the free energy principle by itself is as vacuous
 as most tautological theories.
 And by tautological, of course,
 I'm talking to the theory of natural,
 the survival of the fittest.
 What's the fittest of the survival?
 Why do the cycles, the fitter?
 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 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 the practical thing,
 which you don't get from natural selection,
 but you could say has now manifested things
 like differential evolution or genetic algorithms
 or MCMC, for example, in machine learning.
 The practical thing you can get is if it looks
 as if things that exist are trying to have density dynamics
 and look as though they're 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 blanket.
 Then it should in theory be 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
 of 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 or 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 wittering on about that?
 It did seem relevant to, yes.
 So what kinds of, so the answer to,
 would it be easier 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 probabilistically
 in the form of a probabilistic generative model,
 a probability distribution over the causes and consequences
 of the world in which this thing is immersed,
 and then you just engineer a computer and a robot
 to 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.
 It 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,
 this in fact, the same kind of creature,
 but I am me and you are you,
 then it becomes a game 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, a community,
 then it becomes necessary now for me to infer
 that it's me talking and not you talking.
 I wouldn't need that if it was on Mars by myself,
 or if I was in the jungle as a feral child.
 If there was nothing like me around,
 there would be no need to have an inference
 that a hypothesis, oh yes, it is me that is experiencing
 or causing these sounds and it is not you.
 It's only when there's ambiguity in play,
 induced by the fact that there are others in that world.
 So I think that the special thing
 about self-aware artifacts is that they have learned to,
 or they have acquired, or at least 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 like you could, 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.
 And I mean, when we talk about planning,
 when you talk specifically about planning,
 the highest, if you 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.
 And in that conversation, there is a beautiful art
 of turn-taking and mutual inference, theory of mind.
 I have to know when you wanna listen,
 I have to know when you want to interrupt,
 I have to make sure that you're online.
 I have to have a model in my head of your model in your head.
 That's the highest, the most sophisticated form
 of generative model, where the generative model
 actually has a generative model
 of 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.
 We wouldn't have this.
 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 in this soup?
 I'm tempted to answer that again as a physicist
 and tell you 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 a slightly more personal
 and fun answer, but which can be consistent with that.
 And I think it's reassuringly simple
 and hops 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 form of bedtime stories or fairy stories
 that I'm a princess, I'm gonna meet a beast
 who's gonna transform and it's gonna be a prince.
 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 Econish,
 my external states out there,
 but I'm actively changing them all the time.
 And it's still says I'm 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 Schalke Holmes.
 So I smoke as heavily as possible,
 tried to avoid too much interpersonal contact.
 Yeah, enjoy the fantasy that you're a popular scientist
 who's gonna make a difference in a slightly quirky way.
 So that's where I grew up.
 My father was an engineer and loved science
 and loved sort of things like
 Sir Arthur Eddington'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
 were all about these characters,
 keeping the hobbit out of this
 because that was quite fitting my narrative.
 But it was a journey of exploration, I suppose, of sorts.
 So yeah, I've just grown up to be what I imagine
 a mild-mannered Schalke Holmes slash Albert Einstein
 would do it in my shoes.
 And you did it elegantly and beautifully.
 Carl, it was a huge honor talking to you today.
 It was fun.
 Thank you so much for your time.
 No, thank you. Appreciate it.
 Thank you for listening to this conversation
 with Carl Friston,
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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.