Metabolic Machines: Computation as a Living Process

In this episode, Dr.Mihaela Ulieru sits down with Anthony Finbow an engineer, law and philosophy scholar, turned venture capitalist and serial deep-tech entrepreneur. Drawing from his origins in cybernetics, signal processing, and control engineering, Anthony presents his groundbreaking computational framework: "Metabolism as Inference."

Moving beyond traditional silicon-based models and the standard neuromorphic approach of mimicking neurons, Anthony explores the cognitive capabilities of mitochondria. By mapping the mitochondrial electron transport chain and ATP synthase to control systems like phase-locked loops and operational amplifiers, he argues that true cognition cannot abstract away from resource and energy constraints.

Join us as we explore the nexus of biology and AI, active inference, autopoiesis, substrate dependency, and the future of embodied robotics.

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SPEAKER_00 0:00

I am absolutely wearing my investor hat in trying to explore the domain so that I can see what I think is the more exciting, compelling future beyond where we are. So if I had uh money, I'd be investing uh uh at the intersection of all of those domains active inference, bioelectricity, and um um um um uh energy-based uh compute models. And I'd be trying to do that with one foot in biology and one foot in AI because it it will transform our understanding of biological systems if we get it right on the compute side. And so that's that's one of the other reasons I'm interested because I see this huge focus of the superscaling AI companies trying to put their LLMs to work to solve biology. But uh the LLMs are fantastic um induction machines, uh, and traditional good old-fashioned AI is fantastic deductive machinery, but um they are not anticipatory systems and they can't project outcomes into the future and then direct themselves towards achieving those outcomes. The biological systems that I'm describing are anticipatory systems and they can do that, and so I think we're seeing a potential, a potentially much more compelling pathway towards uh a more exciting AGI beyond where we currently are, and of course that that's why I'm excited by your work. The more I look at the mirrored uh architecture and the machinery, uh horizontally opposed machinery on the mitochondrial crystal, the more it looks like a complementary transistor amplifier, amplifier architecture to me. And as I mentioned, each of those complexes that's um pumping protons or allowing electron flux through the membrane has a metal center. And so I think that is the transistor moment. We are looking at an architecture in a biological system that is equivalent to, although two, three billion years uh prior to, the complementary transistor amplifier circuit, which was the foundation for the modern computing industry. Um, that's a claim, that's a bold claim, that's an irrational claim, that's a um foolhardy claim, possibly. Uh, but I've I spend my time you know trying to push the boundaries here, really, so that we can, you know, a challenge attack, but first I think, uh, and this is what I love about Mike Levin's work, first, don't attack the straw man, build the steel man, and then attack the steel man. I think that's what is appropriate, necessary, and I think um will yield incredible uh new insights. But I think um this is the transistor moment because the system can be characterized, even if it's an abstract, highly stylized model I'm building, it will enable uh uh experimental scrutiny, but also it will describe uh ways for us to explain biological phenomena, I hope, in a fundamentally different way.

SPEAKER_02 3:09

And uh if your computing paradigm or you know this approach would succeed, uh what are the implications? But you know, um I also I think time because we mentioned it, time plays a role here because you know uh if it will succeed before the singularity or after, I think it's of the essence as well. Or maybe you think we cannot achieve the singularity without your approach. I mean, tell me what are your thoughts there with relative to what's happening today with uh AGI and all these exponential developments relative to your paradigm?

SPEAKER_00 3:49

Well, I would say um, and and and by the way, you know, this is your area of expertise, Mikhaila, um, not really mine, uh, but uh, you know, umbodied con embodied cognition, you know, soft robotics. Um uh I don't think we're gonna achieve the singularity without uh embodiment, uh, if indeed it's ever possible, and I'm skeptical. Uh uh, but actually, when we think about embodiment, embodiment is not the current approach, which is you know the pre-training moment in robotic systems. We're not downloading LLM propositions onto uh onto uh robotic structures and thinking of them as embodied. Embodiment means autopoesis, self-instantiation, self-maintenance, self-improvement uh over time scales that might not be perceptible uh to us as humans, considering about evolution, considering evolution, and this is you know really just briefly referencing the amazing work of the Agential Biology Institute, trying to understand evolutionary paradigms beyond the neo-Darwinist uh genome first, genome is everything proposition. Um, but I think that uh if you consider every single cell of an embodied agent as its own computational uh uh standalone agential computational architecture, and then you think about the uh individual cell as having 10, a hundred, a thousand, tens of thousands of mitochondria in it, and then you think about the activation function that is performed by an ecology of mitochondria within a cell, the complexity, and then you consider that you know we have hundreds of trillions of cells in our body and hundreds of trillions of mitochondria in our body, then we're looking at orders of scale very different from the orders of scale we're looking at, and we're looking at each individual uh embodied component of the system being itself a cognizing agent, interacting with its other agents. Then all of a sudden, I think we start to see a different pathway towards embodied cognition, uh, and and who knows, over time perhaps towards the singularity.

SPEAKER_02 6:01

Metabolism's inference means Hello everyone, and welcome to our Dream Wonders series on Mindplex. Our guest today is Anthony Finbo, who has followed an extraordinary intellectual and professional trajectory, if I may call it like that, because you know, his background is in aircraft engineering of all. But his background originally is in cybernetics and control engineering, which is also my own undergrad and graduate studies. Um, the intellectual lineage, actually, from which much of modern artificial intelligence emerged. And uh, as I mentioned, he began his career in aircraft engineering and subsequently moved through law, banking, and venture capital into an impressive career, building, scaling, and successfully exiting several Nasdaq listed deep tech businesses. And today he works uh on his passion across scientific research, entrepreneurship, and investment, pursuing ideas at the frontier of biological and artificial intelligence. And this is where his engineering origins return in a fascinating way that we are about to explore today. So, Anthony, welcome to the podcast.

SPEAKER_00 7:36

Michaela, thank you very much for inviting me to speak today. Delighted to um speak with someone with such an august and exciting background as you uh indeed. And thank you very much for uh wonderful introduction. Uh, I I feel like I recognize myself in something you said.

SPEAKER_02 7:52

Thank you, thank you. You know, I'm trying because I mean uh we will dive deeper into your amazing background, of course, during the conversation. And I wanted to keep that uh short, and I know it didn't do complete justice, but okay. So it didn't work uh what actually drew my interest in this conversation was your research work on a novel computing paradigm, which you coin metabolism as inference, and in which you ask, what have we lost by abstracting computation away from the physical substrate in which it occurs? And you know, you are proposing a path to a more biologically plausible AI by looking at the deep fundamentals of biology, and what I find really stunning, yeah, you are exploring the cognitive capabilities of mitochondria. So let's I'd like to start, you know, first of all, uh with a clarifying or a few clarifying questions. So, what is the ultimate goal of your pursuit? And why do you think a new computing paradigm is needing to needed today?

SPEAKER_00 9:02

Well, that's a great question. What's the ultimate goal? I think uh uh you describe uh in very gracious terms, Mihala, my career uh as a super interesting trajectory. I'd describe it perhaps more like a random walk. Um, and uh I continue on that random path, just uh, you know, exploring um things that are exciting and interesting. Um but I think uh what I would say is, if I may, just to kind of tune a little bit for the listener, uh, why it is that I find myself exploring these concepts uh at the nexus of biology and um artificial intelligence. Um you right you rightly say that my backgrounds in cybernetics, control engineering, and uh, and uh, in fact, avionics in aircraft engineering, signal processing. That really characterizes the first 10 years of my career. And the last 10 years I've been focused on trying to build software models of microbiome and host microbiome interactions. Um, broadly for the use uh for the uh for the listeners who are not familiar, the microbiome is that ecology of microorganisms. Uh, you know, we we when we talk about it generally, we talk about the ecology of organisms in the gut, the hundreds of trillions of bacteria, archaea, fungi, etc., that uh cohabit in our our guts and um metabolize quite a lot of our foods and provide some of the substrate we require to subsist. And uh I was trying to model with an amazing team uh the interactions of that ecology of microorganisms, which you can describe describe as a point cloud or a distributed um uh um liquid brain almost, um, uh the interactions of that system with a host organism. And the host could be a human host. They were talking about microbiome in the gut and the human host. It could be the scalp, it could be the underarm, it could be the oral or indeed genital um uh areas where niche microbiomes uh evolve. Um it could be a plant, root, and the soil microbiome, it could be an animal gut and the animal interactions. And um I was modeling those uh interactions and working with an amazing team of engineers to model those interactions using higher order network science.

SPEAKER_02 11:24

But sorry to interrupt, I just want to know what brought you to that uh microbiome exploration? Because the question was mainly why it's uh you know, I know it's uh it's random path, and you didn't have an ultimate goal, maybe, from what I understand, uh from what you shared so far, but but why microbiome from engineering and aircraft engineering?

SPEAKER_00 11:52

You know how um throughout our lives we have uh opportunities, we also have trials. Well, um, you know, I was building technology businesses, I was building um software businesses in the uh communications space, uh uh, you know, through the internet boom and beyond. Um and you know, practically killing myself, to be honest, uh, on an airplane all the time, buying businesses, uh uh, building downsizing, trying to survive the uh internet bust. Um in my first CEO role uh within the tech business, um, uh a business that was listed, dual listed on the London Stock Exchange and the Nasdaq, just at the most difficult of times. You remember the times, perhaps. Um anyway, I I I ended up getting sick uh with a life-threatening uh autoimmune condition, um, which quite literally took me to within uh you know hours of expiration. And I made a full recovery, uh, I think. Uh, but um on the out, so to speak, uh, I had completely lost interest in man-made machines and was interested in uh biological systems. And uh and indeed uh when I was recovering in in my hospital bed, uh I picked up a copy just by chance of The Economist and and read an article about uh the microbiome. And what I read in there was fascinating because it talked about uh the article talked about um a particular bacterium, Helicobacter pillori, uh, and how uh people with you know a preponderance of that um bacterium in their in their duodenum uh you know were prone to duodenal ulcers. And the um therapeutic that was discovered uh to solve for that challenge uh was antibiotic therapies. And people then with the who'd made the recovery and had the antibiotic therapies often experienced autoimmune challenges subsequently. And in that article, I read that um, you know, once the Helicobacter pylori has been eradicated, then all of a sudden there's some correlation with the detuning of the immune response. And and that I found absolutely fascinating. Um and and then I didn't know what I was gonna do with that, but as I recovered, um uh I got opportunity, I didn't know what I was gonna do to be frank, uh, but uh um after a sort of um exploratory couple of years when I worked in uh Venture Capital for a period and didn't really like it, I'll be honest. Um I I got the opportunity within the space of a week to join two companies that were focused on biology. One was uh an atomic force microscope manufacturer that was looking at um mechanoforces at the nanoscale. Uh, and the other was a company called Eagle Genomics, which at the time uh was uh a services business looking at uh wrapping services around some open source software that had been developed by the European Bioinformatics Institute to explore variant calling in human DNA. Um and uh I pretty quickly realized um, and I was by the way introduced and parachuted into that business when it was in a challenging period. Um uh uh I was parachuted in uh first as a consultant, then as chairman, and I realized there was just no way to kind of compete with the large, you know, well-capitalized venture-backed US businesses that were trying to build out uh uh you know human DNA exploration software products. Um and I vectored the business there to the microbiome, not uh selfishly because of my interest, but because there were customers and potential customers that were very interested in trying to understand that microbiome challenge. And um it just seemed to me to be, you know, strategically speaking, the most proximate uh uh um path to take to you know try and build a sustainable, sustainable business. Which we did and uh you know, built it over a number of years, built some fantastic product uh and built some wonderful customers, uh, but uh ultimately through COVID uh was challenged with uh building out the capability. And then, of course, the LLM phenomenon came, and all of a sudden, all attention vectored from higher order uh hypergraph and simplicial uh systems and uh persistent homology uh in exploration of higher order networks to straightforward LLMs, and I couldn't get interest in what it was that we were building, so I I couldn't get a scale up capital. Uh and so I stepped back um from that about uh two and a half, three years ago, and you know, speaking very frankly, Mikhaila, I had no clue what to do. Uh I was exhausted again and uh I sat for a period of months just uh staring out of the window in front of me at the moment, just not knowing what to do. And then all of a sudden a wave of the learnings from the first part of the career just came flooding back into my mind, uh the signal processing, and um and I just happened to be uh looking at some images of the mitochondrial uh um electron transport chain and ATP synthase uh system. And I became interested, I bought the book on bioelectrics, uh, you know, um uh um and uh started to read up on the just out of curiosity, started to read up on the structure and and and then instead of seeing uh as was characterized in the text um a turbine or a pump um uh as a characterization of an ATP synthase enzyme, I saw a voltage-controlled oscillator. And then I worked backwards from that towards the electron transport chain, and all of a sudden I I saw a cascading operational amplifier structure, and not the biological um uh uh complexes described in the um in the in the text. And so I just thought, well, hang on a second, if I've got these two components, then I think I can see a phase lock loop. What if this is a phase lock loop? Uh and and then I said, well, a phase lock loop is a clock. Uh and it's the fundamental clock structure that used in any von Neumann compute architecture. So what if there are parallels between what I think I can see here and the compute architecture? And so that's the kind of genesis. And then for the last two and a half years, obsessively, I've been just exploring all of the components of the system, learning uh from whichever angle I can what uh has been written about the uh structures, and then just trying to build the model that is then worthy of further exploration, and um and then now trying to move towards uh empirically validating some of the uh some of the conjectures.

SPEAKER_02 18:57

Amazing, really, and you already answered uh one of my next questions, which was what was the aha moment that led you or triggered this idea? And so this is really fascinating always. We like to hear such stories because many times it's like inspiration, you know, and at our AGI 26 conference, actually, Mike Levin. Mike Levin spoke about inspiration and and how it comes from his platonic spaces and so on. It's fascinating. Anyway, uh, yes, so so uh I don't want to call it divine, but by the way, it really was an aha moment, and it was an aha moment, and uh I felt the hairs on the back of my neck, uh the apocryphal story uh uh stand up.

SPEAKER_00 19:42

And um, and then over a period of about a month, uh successively, generally on a Friday morning after a shower, uh answers would come to the questions I hadn't even posed. But um uh you know, successively, uh over a period of months I started to piece the architecture together. And then I thought to myself, well, you know what, you're not a researcher, you're not a biologist, um, uh, your signal processing uh knowledge is tired. Um uh you better you know brush it all off. But at the same time, I thought I need to try and attract the interest of um bona fide scientists and researchers in the various fields that can help me uh actually evidence uh what I what I'm describing here. And um I I uh I I engaged uh uh I met um uh a researcher called Thomas Parr uh at a at a conference on active inference. I became very interested in active inference because it it looked to me to be the worthy successor, the natural successor of cybernetics. Um and I met Thomas um at a conference and started talking to him about my ideas and and and we developed a conversation over a period of months. Um and Thomas, by the way, wrote um the first uh definitive text uh on active inference uh with Carl Friston. Um and um so I I became very familiar with that book and um and then um cascading series events led me to present my first uh scruffy presentation deck on the conjectures to the Active Inference Institute's theoretical neurobiology group. Um, and Carl was in attendance, and uh he very kindly gave me some pointers and said, Well, I think you need to be speaking with Mike Levin and Nick Lane. Uh we collectively are working on a model trying to understand the dynamical causal model of the mitochondrion. It looks like you've got a model. Um you know, uh w you should engage with the other two uh um researchers. So all of a sudden I found myself in the mix uh speaking with some of the world leading scientists at this uh uh you know Nexus most compelling, exciting uh domain.

SPEAKER_02 21:59

I can say. Yeah, like like you were guided to actually end up where you ended up. And I think it's really fascinating. And thank you for really sharing this story anyway. I'm gonna get back to that because I have a thread here which I like to help our audience to follow. But um, you know, this is we will also post, if you allow us, is a link to that conversation which you had with Carl Freestone's group. And of course, you had a presentation there. We'll post that in the uh podcast description when we release it. Uh, but uh so you know what I'd like to uh dive a bit into right now is how is your approach different from how we treat computation today?

SPEAKER_00 22:49

Well uh you know, the remarkable thing is I don't know that it's different. I think uh but but I'll say that there are some fundamental challenges and problems um uh with the current approach to evolving uh artificial intelligence models. And the fundamental challenges.

SPEAKER_02 23:10

What is your contribution, yes, uh from from what we already have today?

SPEAKER_00 23:17

So I would say this uh uh distilling it uh to its most essential metabolism's inference means actually considering resource, food and energy and oxygen, as transposable immediately into compute. Whereas in the current architectures, resource is ignored. Uh and that has led to the psychosis, which is that we're building the ever larger data centers uh to uh enable the models to run and energy and constraints, you know, the energy constraint is not taken into account in describing compute function. And as long as that's the case, in my opinion, we won't have rational compute. And so that's my contribution essentially, trying to evidence that uh the incredibly efficient structures in the natural world, uh, particularly those machines, if I dare say machines, and I'm I'm not a sort of computational functionalist, uh, so I use that term um with uh I use that term tentatively. Uh but um unless we understand how nature has achieved this incredible efficiency in transposing resource into compute capability, we are missing the primary vector I think that we should be pursuing in order to us for us to move towards this more biologically plausible artificial intelligence.

SPEAKER_02 24:45

Uh and this is really very uh kind of the crux of the matter here, and and an answer to my question, I'd like to dive a bit deeper into that, you know. Like um coming back to the engineering thing, what does a cell have to sense about itself in order to remain itself? That's one question. But on the other side, you know, for engineers, your phrase metabolism as inference immediately evokes feedback control, for me at least, you know, uh immediately resonates with my own academic routine, dynamical systems, control theory, self-organization. Um, I know you also bring in Boltzmann machines from having listened to your presentation. But I want to tell you that my PhD advisor prevented me from taking biological systems because he thought they are too difficult because the biological system is simultaneously controller and sensors and even capable of rebuilding the very machinery performing the control. So, you know, a living system uh observes perturbations, estimates hidden states, anticipates demand, acts, and continually returns towards a viable region of state space. But could I formulate metabolism as a non-linear adaptive control problem? I mean, is this kind of your approach? Or please explain to us what is actually the approach.

SPEAKER_00 26:10

Are you sure, Mihaila, you didn't uh do the biology electives? You just characterized the system exactly as I would uh myself. Uh it is an adaptive nonlinear control system.

SPEAKER_02 26:21

Well, you know, with my engineering control engineering mind and listening to your presentation, I think I kind of figured out a bit, but thank you. Go ahead.

SPEAKER_00 26:33

Well, uh I think um yes, uh, and of course the model I've built uh so far is a is a is a radically uh simplified uh abstracted stylized model of one uh of the uh structural elements of the mitochondrion that I think is um worthy of very much deeper exploration. Um you know, the mitochondrion, if I may, for your audience, just uh briefly uh you know I spent two and a half years learning about this, so I can uh perhaps you know simply just introduce the concept. The mitochondrion, you know, two two plus billion years ago was a standalone organism. It was a um a bacterium related to cyanobacteria. Um uh uh and it um uh supposedly uh with the great uh oxygen catastrophe, when well rather when cyanobacteria started to pump more oxygen as a byproduct, as a waste product into the atmosphere, uh at a certain point the levels of oxygen in the atmosphere became such that the previous life forms that were anaerobic uh uh were tested and stressed. Uh at which point probably there was some kind of conjugation of the archaeon and the bacterium to create the first universal common ancestor of what is now the basis for all life you can see, the eukaryotic cell, the cell with a nucleus. Um and uh so the question is um who's in charge, in my opinion? You know, is the cell the master of the mitochondrion or is the mitochondrion the master of the cell? And what is the function that the mitochondrion performs for the cell? Uh and it's you know generally characterized as uh the uh generating ATP, uh the energy currency of life, but in lots of ways that's a byproduct of the uh control system that's running, I think, uh, to generate energy for the cell. Um the amazing thing about mitochondrion and mitochondrial study is it's incredibly well characterized, but generally uh through a biochemical lens or through an electrostatic lens. And so I just wanted to think about the electrodynamic and indeed hydrodynamic phenomena that might be at work uh to enable the system to regulate its function. And so, yes, uh I am looking at a system which uh has got two components which probably independently arose but then came together uh on the mitochondrion. The ATP synthase, which is effectively the rotor, the spinning rotor that enables uh when it's operating in one direction, uh enables protons to be pumped out, and when it's operating in the other, uh enables them to flow back in. And then the electron transport chain, which uh allows electrons to flow from highly reduced chemical substrate NADH and FADH2. Uh electron donors uh donate electrons that are pulled through these successive complexes to oxygen at the other end with the byproduct that water and carbon dioxide are produced. That flux of electrons through the membrane results in protons being pumped onto the outer onto the uh surface of the inner membrane. And then they flow back through the ATP synthase. And so there you have these components of the control system, and they are, and what's also remarkable about the uh structures is the individual complexes within the electron transport chain, they they they all they they most of them have metal centers, so they look like semiconductors, of course, and they behave like semiconductors. The membrane itself, and you've you've you've um uh uh interviewed uh my wonderful uh collaborators um Marco Cavaglia and Tommaso Faro, they are looking at membrane dynamics with Jack Duchinski and um looking at and I have to say now that you mentioned them, uh that uh Marco and Tommaso have been three times with us on the podcast.

SPEAKER_02 30:53

And we will also post uh in the description my conversations with them. So thank you for mentioning that. Go ahead. Just want intervention for our audience that they find that in the description.

SPEAKER_00 31:05

Yes, so so they're looking at uh membrane dynamics and uh membrane uh oscillations and interactions of membranes with vicinal water in the cell. Uh and they're looking at the cellular level, and I'm looking at the mitochondrial level, and I reached out to them because I saw your podcast and just thought, well, you know what, uh there's huge affinity between what we're talking about here, and subsequently, we've been in a wonderful conversation uh since then. Um but um then I look more closely at the structure, and uh I as I said, I I my thesis was that this is um, you know, even the primitive uh circuit, the individual electron transport chain and an individual ATP synthase enzyme together uh is locked in a phase lock loop uh and if it is, it requires certain components in order for it to function. The phase lock loop uh in essence takes a reference signal, an oscillation, let's say a sine wave, and compares it with another signal uh which is produced later in the circuit by the voltage-controlled oscillator. If the two signals are in phase and of the same frequency, uh the next component of the system, the phase detector, generates no output. If the signals are of a different frequency and phase, it will generate an output. And that output is a pulse that is proportional, proportionate to the phase and frequency difference between the reference phase reference wave and the uh uh output wave. That pulse output is then passed through a low pass filter to generate a voltage output, a more or less smooth voltage output. And that voltage output is the input to the reference oscillator. So if a higher voltage, uh if there are more pulses because there's a difference between the uh um reference and the output frequency. If if there's a big difference, there'll be fatter pulses emanating from the phase detector. That means from the loop filter, the low pass filter, there'll be a higher voltage. That higher voltage will drive the driven voltage-controlled oscillator to increase its output frequency, which will be fed back to the phase detector, that will reduce the error signal and reduce the pulses out. And the system will come into equilibrium. Uh, interestingly enough, the system I've described uh is utilizing what's called in electronic equivalent circuit uh language a f a type 1 phase detector. Uh and um uh I actually believe that the system operating on the mitochondrial membrane utilizes a type 2 phase detector. And I'll perhaps uh if it's of interest, I'll explain why that is absolutely key and germane to the efficiency of the system and to its cognitive capabilities uh also.

SPEAKER_02 34:09

Yes, and and I don't want to run into the risk of getting too abstract here for my audience because we will post your conversation with Carl Freestone, which covers a lot of uh of this. You know, I'd like to kind of keep it uh as simple as possible and not too many details, if if you may.

SPEAKER_00 34:33

Let me Mahayla just uh and I didn't mean to interrupt you there, apologies. Let me just um make it relevant for your audience. So uh the difference between these two frequencies.

SPEAKER_01 34:45

Okay, thank you.

SPEAKER_00 34:48

The difference between these two frequencies and the effort to minimize the difference is the same as variational free energy minimization, which is the same as relative entropy minimization, which is the same as Kobeck-Libler divergence minimization. And so uh KL divergence or relative entropy is used in all of the models to describe how a probability distribution compared with another probability distribution uh generates uh you know some uh cross some some relative entropy, some um information that the system then seeks to minimize in uh in generating its output, generating its its result. Does that make it a little bit more relevant for the audience?

SPEAKER_02 35:40

Thank you so much. And um so my you know, because I have so many more questions and I'd like to cover a lot of stuff here. Uh so um, you know, you mentioned Carl Freestone, of course, and and your approach connects to his work in many ways. Like, you know, and I don't know if we will have time to cover his mortal computation because he referred to that paper during his conversation with with you and his work with Overbiam, who was also both Carl Freestone and Alexander were keynote speakers at the OAGI 26 conference. So, you know, your approach connects, however, to Carl's mark of blankets as well, and to nested agency. And you know, this nested agency, the holonic paradigm uh of mitochondria nested within cells, which you already mentioned, cells within tissues, tissues within organism, and how they actually the mitochondria are the engine and the energy source of the whole organism in that way. Uh, turtles all the way up and down, as uh Ken Wilber describes it. So, you know, I use such holonic models in my work, applying them from global supply chains to designing more organic ways of governance for societal systems. My question to you uh related to this is could intelligence be an emergent negotiation among nested computational agents rather than something localizing a single privileged processor?

SPEAKER_00 37:14

Uh I I I think uh my own my own uh construction, my own model is that that's absolutely the case. Um we have multi-scale uh synchronization endeavors uh uh and circular causation, circular feedback loops across the scales. Uh, uh, with the system trying to minimize phase discrepancies across the scales uh and across the harmonics as the clocks change their periodicity uh through the through the structures and the cycles. And and and the uh the fundamental requirement for the system is state maintenance under conditions of uncertainty uh and perturbation. Uh and uh error signals uh in clock synchronization across the scales results in a sort of nested computational um error signal uh that the system seeks to minimize. That's the kind of essential um model that I'm trying to describe. Um but I think um if you don't mind, I just want to revert back uh because you mentioned Alexander Leichner of Google DeepMind and his um wonderful paper on the abstraction fallacy.

SPEAKER_02 38:25

Um, I I actually meant meant uh sorry, I I I'll get I'll get to that as well, but I mentioned Alex Ororbia who co-authored with Scuspensing about the the mortal computation, and of course if you want to link uh your work uh with that, how does it actually relate uh with the mortal computing what you do.

SPEAKER_00 38:52

Um uh that's a multidimensional answer, but um you know uh uh what I know of Alex's work, and Carl you know suggested that um you know he had written a wonderful paper in collaboration with Carl on mortal computation. What what Alex has done that?

SPEAKER_02 39:08

She showed that paper during the uh during your conversation, and I thought uh you kind of delved into it. And if you didn't, that's fine. We can move on to another question.

SPEAKER_00 39:17

No, but I I did read it. Um I read it um uh uh last year. Um so my my knowledge is a bit rusty, but he provides a wonderful exposition of all the approaches to describe, you know, uh the kind of um essential requirement for us to focus on mortal computation. Um what I'll say is that you know, we are born and we live and we die. Uh mitochondria uh come into existence and they have a lifespan of five to twenty days. Uh and cells come into existence and they have lifespans uh of um uh you know months or possibly years, but then they expire. And if we think about uh mortal computation, the system that you know if you can describe the human as a system, then there are life timescales, clock frequencies, and lifespans that are differing all the way down in this organism. So, in some ways, I think one has to think about that in terms of mortal computation. Uh, that the uh uh but then I think uh what's really important also is that one has to consider autopoesis, uh, which for you know those in your audience that maybe are not familiar with that term is a term of art that was uh uh and of science, of course, that was first um first introduced by Umberto Materano and uh Francisco Varela, cyberneticians um uh from the Chile school, uh, to describe a system that um instantiates or you know um builds its own components to instantiate processes that build its own components, that instantiate processes, etc., etc., etc. Um, and that's an important um uh kind of component of what is next, because I think uh you know self-organizing cognitive systems probably need to be self-instantiating. Uh and I say that because all of a sudden there's a big focus on recursive self-improvement. Um uh but what is what is the self and what is recursive and what is improving? My point would be in order for a system to be recursively self-improving, it has to first recursively self-instantiate, then it has to recursively self-maintain, and then it can self-improve. And so I think autopoesis is necessary for us to consider in that context. I think then that I have to refer to the um uh the work of uh Robert Rosen, mathematical biologist or biological mathematician, uh, with his metabolism repair framework, um, which was truly groundbreaking uh and quite you know separately in parallel, evolved uh along the uh trajectory to describe something similar, of course, to uh autopoesis. Um and um uh you know Robert Rosen also comes out of the cybernetic tradition, uh he was a student of Rashevsky, um, and um and so you know he had this uh feedback uh uh negative feedback uh sort of framework in mind, I think, as he described um his metabolism repair framework. But um if I may, I just before I seek to describe that framework, one has to go to uh what he utilizes in order for him to start to describe his work in the book Life Itself that he wrote. And he he references um uh Aristotle's uh causal framework. Um and and just in its most simple form, the causal framework uh is uh that you have a range of causes. You have a material cause, a formal cause, an efficient cause, and a final cause. And if we just take the example of the table, you and I are both sitting at tables having this conversation. My table is a wooden table. So the material cause of my table is wood. The formal cause of my table is the blueprint that described the components that had to be fabricated and how those components fabricated should be assembled to create the table. The efficient cause was the carpenter that read the blueprint that fashioned the and fabricated the components from the wood and then assembled them into the table. And then the final cause is that Mikhaila and Anthony have a conversation on mortal computation uh um uh across the Atlantic using um um uh communications paradigm that was never imagined possible by Aristotle. The most important point there is that there is no entailment. Of the final cause in the structure and the framework that Aristotle has described there. That's really important because it is effectively saying what Martin Heidegger said when he said we don't build models of the world in our heads, which is a kind of good old-fashioned AI proposition, using predicate calculus, structuring relations between objects in order to build a model of our world. Rather, we interact with the world, and the world, the best model of the world is the world itself. The point is we see affordances in the world and we operate to identify and then exploit those affordances. So we have to consider the world and the environment that we're in. Now that takes me to Rosen's proposition, which was that the efficient cause has to be entailed in the system, in order for the system to be a living system. And that living system will also be an anticipatory system. And of course, this was essentially what Alexander Lechner, whom we mentioned, I accidentally mentioned when I thought you were talking about him. You were talking about Alexander.

SPEAKER_02 45:13

No, no, no, but that's also what Aurorbia is doing with predictive coding, of course. Yes, so it is all about prediction and all this kind of stuff. And we will get to Alexander. Of course, also I have I have some questions about that. But this is indeed, I think this is the crux of the matter. I don't know if you read uh Frederico Fajin's uh irreducible. He is the inventor of the microprocessor, speaking of computing and all that. And he says exactly, I mean, the his whole point is we cannot control reality, we cannot control. I mean, exactly the point you make here. It's we can only participate with it one moment at a time, and and what will happen? Aristotle couldn't anticipate that.

SPEAKER_00 46:02

But okay, so you it's so interesting you bring up Frederico Fajin. I uh you know, I love listening to his lectures, and he he did, of course, incredible work um in the sort of foundational stage in the semiconductor industry. Um, but I don't think I share his idealist perspectives. I'm actually a devotee of his uh co-collaborator Calver Mead.

SPEAKER_02 46:24

And I know that, and I'm gonna I'm gonna touch on that later on when we discuss about Beck Gut versus Alexander Lesnar. But uh but let me I don't know if you wanted to close the loop here because I have more questions, or can I move on?

SPEAKER_00 46:39

I just wanted to say that um in order for a system to be a living system, just to re-emphasize, according to Rosen, it has to be uh closed to efficient causation. So as as I've described um using my Aristotelian uh table uh example, the carpenter has to be entailed in the system in order for the system to be characterized as a living system. So if the carpenter is not part of the system, the system itself can't be living. You have to consider the uh carpenter and the table that's been fabricated as the system. And that, of course, is effectively also what the second order cyberneticians were uh thinking about as they described second-order cybernetics as the science of observing systems.

SPEAKER_02 47:33

Yes, and I actually was uh uh editor of the first journal on cybernetics and human knowing, uh journal of second order cybernetics, and of course Maturana Varela, they are the pioneers, uh, with Professor Suren Breer from Denmark. He is actually he was uh founder of this journal. That was when I was doing my PhD several decades ago. But yes, I I can relate to that as well, and that's a very important point. But if you don't mind, I'd like to return, unless you want to make more more uh details here. I think it's a few important okay.

SPEAKER_00 48:10

I I I want to make perhaps one final point, which is um gold-directedness and teleology. Uh so what's interesting is um, as you know, I'm a great uh devotee of Mike Levin's work as well. Um Mike um talks about agency, and by the way, I'm also now uh I I want I'm very happy to say I've been invited by the Agential Biology Institute, uh uh, which is looking at agency in biological systems to join as uh entrepreneur and engineer in residence. So um that's hot off the press, and um and uh uh and and of course uh that's very proximate because uh Marco and uh Tomaso are also members of the um of the Agential Biology Institute, and and and Marco indeed is uh on the advisory board. Um but I want to talk about this uh concept of gold-directedness uh and teleology um because having described that framework, um the the point is in order to maintain state, the system has to cohere with its environment, and that means it has to have a repertoire or a variety of directions that it can take in order to maintain state. And according to you know the traditional cybernetic principles, the system that is modelling another system has to be imbued with the requisite variety. That means the complexity of the system here that's modelling the environment there has to have the same degrees of freedom or variety uh and complexity. The point being that in order to maintain state, there'd be m there may be many actions the system can take or must take in order to minimize that very uh that um uh uh relative entropy so that it maintains state uh in relation to its environment. So goal-directedness is um, you know, I think the industry generally has to think about what teleology goal-directedness and agency means. Mike, uh, I mean, you know, he he he talks about William James' definition of goal-directedness as um, or rather intelligence, as um being able to fulfill any goal by many means. Uh but um uh uh what I'm trying to describe is a variety of goals to maintain state as another way to characterize maintenance of state in the presence of uncertainty in the environment. Does that make sense?

SPEAKER_02 50:50

Especially to a control engineer, of course, it makes sense. I mean, this is systems science, right? Of course, this is our part of the code. I wish I'd met you 20 years later. You're talking, you're preaching to the choir here. So, you know, I'd like to continue the conversation um on the controversial matter which you are undertaking in this new research program. Uh, does substrate truly matter? And if intelligence is substrate dependent or independent, and this you know brings us directly to one of the deepest disputes in AGI. Uh and you you you mentioned uh uh Alexander Lechner, who I invited actually to give the keynote at the conference. And um I do not know if you uh watched uh uh our online uh conference, but there was a big debate between Dr. Ben Gertzel and Alexander. And Alexander is actually uh from Google Deep Mind, and he's an amazing researcher, and uh his abstraction fallacy is actually, you know, uh kind of uh uh very clearly uh combating, if I may say that, the idea that abstract causal topology alone uh could capture the relevant reality of intelligence. And your work seems on his side. Uh so if you you seem to, you know, maybe I don't ask anything, please elaborate. You seem to you want to jump in. Let me know what you think of that.

SPEAKER_00 52:33

Um yes, um you know we are um possibly communic communities separated by a common language. Um I I I think uh it was Churchill that said that you know Great Britain and America were um two peoples separated by a common language. Um what I find interesting is that um the substrate-dependent computationalists uh uh are just trying to describe the physics to describe the physics, and and I think you know my effort has been to try and do that to describe the physics that drive computation on the mitochondrial inner membrane. And um and um the uh uh substrate independent community are saying, well, you know, it's software that can run on any substrate. But you know, when we look at the OSI stack, you know, coming from the compute communications industry, you know, there's a stack of software.

SPEAKER_02 53:28

Sorry, may I correct something? They do not say uh no substrate, they say it can be any, you know, and and I think they also say like it's substrate independent, but I mean, you know, if you look um and you say you are familiar with Federico Fajin, and you also say you don't agree with everything, if you can answer the question from this perspective like Federico, Alexander, yes.

SPEAKER_00 53:55

Yes, um uh so I mean from a philosophical perspective, I'm not an idealist. Uh, you know, uh uh I am uh a um uh a dual aspect monist, uh, and what because we're seeing the physics encoding the information on membranes. Uh uh, or as uh Carl and uh Thomas Parr would say, uh a Markovian monist. Um and um so that's what I don't agree with uh uh Federico on. Uh uh, you know, I've I've had my dallions try and understand idealism. I studied philosophy also earlier in my career. Um uh I I actually detoured and studied law and philosophy along the path, so you know that was kind of several years wasted. But um, sorry, if I said uh no substrate, I meant to say on any substrate. I'm trying to characterize the uh substrate independent community. But if I look at um, you know, we now have, of course, the uh Institute for Machine Consciousness in the Silicon Valley, and Josh Bach is, you know, talks uh emphatically about uh software running on any substrate. Well, if I listen to him, uh incredibly capable uh uh and and you know uh interesting researcher, the self-organization.

SPEAKER_02 55:07

Articulate, I would say, very articulate, right?

SPEAKER_00 55:10

So in a second language, uh I'm in awe, yes, uh I have to say. Um but um uh the the approaches he's using to describe substrate independent computation reach into self-organization, and as I say, uh they will be focused on recursive self-improvement. So the question is what are the software programs they're gonna run to enable recursive self-improvement? They have to be those, as I've said earlier, that enable self-instantiation and self-maintenance first before they get to recursive self-improvement. So I think he's reaching, and that endeavor is reaching into the substrate-dependent world, and the substrate-dependent physics of computation world has to recognize that um there are programs inverted commas that can run on the biological substrate. Now, Gordon Pask, one of my heroes' second-order cybernetician, came up with a path to describe how we solve for this. He he he he uses the concept of an M individual, a mechanical individual, and a P individual. And he says, an M individual, which is a substrate, can run any number of P individuals on it. And indeed we do as individuals. We run, you know, we have we have uh cognitive processes that describe many personas that we could occupy in exploring a landscape as cognizing beings. Um, so my point is there is a bridge to be built and a requirement for an ontology, a common ontology, to enable the conversation between those that focus on the substrate and those that focus on substrate independence. Now, at the moment there's a category error, in my opinion, in the space, which is that people talk about carbon versus silicon-based cognition and computation. Well, it just so happens that the best example of a substrate-dependent cognizing system that we have access to is a biological system, which is carbon-based. But there's a requirement for an ontology for substrate dependency or substrate independency. And we're wasting time if we don't bridge the communities to solve for that. This happened in the last century. There was the Macy Conference, which gave birth to cybernetics, which was an interdisciplinary uh endeavor in the in the in, I think it was 56, uh, you know, the um the uh uh breakaway by the uh AI community, Minsky uh et al. Uh, primarily because they didn't really like uh Michellech and uh Norbert Wiener uh because of their personalities, more than anything. But we we are now at a point in time where the McCullough Pitts uh Neural Net has given us extraordinary advances, and so cybernetics is kind of in lots of ways uh uh at the heart of the most recent you know flourish in the AI revolution. But we now have communities that are diverging again, uh uh and I think there's an opportunity to bring communities together, to ground the conversation by agreeing a common ontology so that the conversations can be held. Instead of people saying, oh, they don't believe in substrate independency or the philosophers thinking about the physics of computation. I think it's an unnecessary uh argument that is a polarizing effect where we we could actually make really fast and significant advances by bringing the communities together to develop an ontology, a common ontology.

SPEAKER_02 58:46

Amazing, yes. And but before we get into that, because I have many questions about that as well. Um I don't know if you can throw a punchline like what exactly does substrate the substrate contribute that uh cannot be captured by the abstract computational description.

SPEAKER_00 59:08

Well, I think um two things. So if one reads Alexander Lechner's uh paper, you know, he talks about the map and the map maker. So he's using the same kind of uh metaphor as I was using with my table metaphor. The carpenter has to be entailed, or the map maker has to be entailed in the system for us to just describe the system as a cognizing system. That's Roslin's work and that's uh Alexander Lechner's work. The point is the substrate independence at the moment at least uh is a mirror. The cognizing agent that, as Lechner said, alphabetizes the code that is then running in the software, uh has been built by the cognizing agent, which is the external human in the loop. Um and uh and until the human and the loop is entailed, or some equivalent of the human and the loop is entailed in the system such that it is close to efficient causation, the systems won't be cognizing systems. And that's my point about mitochondria and metabolism's inference, because effectively the uh it's not quite the case, but effectively the mitochondrion is an autonomous agent operating in the cell, interacting with the cell. So one could argue that the system, the cell, is close to efficient causation. So my point is if you can figure out how to build a system based on the model that I'm describing that enables you to evolve from where we are with substrate independency towards recursive self-improvement, and as I say, that requires first instantiation and then maintenance. Then I think we can move beyond the argument about whether or not substrate independency is the pathway to go. And I think uh Blaise Aguera Iarcus, um, also Google has done wonderful work here as well in describing endosymbiosis as a computational paradigm. Um, and and so I think what's interesting is the pathway is emerging for us to move towards a new computational paradigm based on a number of these ideas. If we can bring the communities together, then I think we can really make fast progress to describe the landscape that enables us to have a common conversation.

SPEAKER_02 1:01:31

Yes, and and I really salute you for this uh initiative because you are actually, I don't know if you want to talk about this. You kind of are alluding here that um you actually are setting up or in the process of setting up, um, let's say, I would call it a research program that ultimately would point to you know, the truth about that and and and develop new ways of computation. And my question, my initial question, you know, I will come back to it somehow, but different, phrase it different now, you know. I mean, you have sat on both sides of the table because I started asking you what's the point of all your this mitochondrial computing and so on. Why are you doing all this? So, you know, because you and you very nicely uh included this in your answers. I mean, you've sat at both sides of the table as inventor and investor. So I'm asking now the investor, what result would make you wearing your investor hat uh rather than the inventor hat, say now I would invest serious capital in this, in a program like this, and in this research. Because you know, if you and and as an inventor, if you had enough funding tomorrow, what are the two or three experiments you would fund that could most decisively falsify? So you know, you can you can switch heads here and and uh let us know your thoughts.

SPEAKER_00 1:03:06

Well, I think um uh what really attracted me and drew me into the space was Karl Frison Thomas Parr with uh active inference. Because it just looked like a more elegant way to describe uh how systems uh generally speaking might uh uh uh make sense of their world, and therefore it looked like an appropriate, interesting path. So that's one of the vectors I'm interested in pursuing. The second, of course, is Mike, Mike Levin's bioelectricity. You know, that has been uh you know groundbreaking stuff, and uh and and you know if we start to look at electrodynamics, hydrodynamics beyond electrostatics in biological systems, uh we we move into much more compelling, exciting territory and a research agenda for the 21st century. Also, along the way, I've been uh engaged deeply with um the Guy Foundation for Quantum Biology, and they are looking at quantum effects in biological systems, uh particularly mitochondria. They're looking orders of scale down from where I am building the system model, but I I've been tremendously excited by the work that they've done. Jeffrey Guy put the Guy Foundation together, and he's backing um he's backed research uh uh programs by Mike and by Nick Lane, who really you know, with his work, most incredibly titled work on the mitochondrion, which I read at the beginning of my effort, um Power, Sex and Suicide, um, really sort of described uh in the most compelling way the system that I've spent the last two and a half years from a computational perspective trying to characterize. So I think all of those things, together with the uh requirement for us to move to a different computational paradigm that doesn't ignore energy and the it doesn't violate the constraint, which is provide as much energy as is necessary in order to compute, but rather utilizes compute resource availability as part of the computational program. That's I think uh charts a pathway beyond the current uh scale is all you need, LLM uh um local minimum is how I describe it, uh, uh, towards a more compelling future. So if I am absolutely wearing my investor hat in trying to explore the domain so that I can see what I think is the more exciting, compelling future beyond where we are. So if I had uh money, I'd be investing uh at the intersection of all of those domains active inference, bioelectricity, and um um um um uh energy based uh compute models. And I'd be trying to do that with one foot in biology and one foot in AI because it it it will transform our understanding of biological systems if we get it right on the compute side. And so that's that's one of the other reasons I'm interested because I see this huge focus of the superscaling AI companies trying to put their LLMs to work to solve biology. But uh the LLMs are fantastic um induction machines. Uh and traditional good old-fashioned AI is fantastic deductive machinery, but um they are not anticipatory systems and they can't project outcomes into the future and then direct themselves towards achieving those outcomes. The biological systems that I'm describing are anticipatory systems and they can do that. And so I think we're seeing a potential, a potentially much more compelling pathway towards uh a more exciting AGI beyond where we currently are. And of course, that that's why I'm excited by your work.

SPEAKER_02 1:06:52

Fascinating. And you know, um computing history contains moments when an initially obscure phenomenon, in your case, now it's uh like uh let's say how the mitochondria works, suddenly becomes an engineering uh primitive, like the transistor, you know, is kind of the example.

SPEAKER_00 1:07:13

So the question would be what would be the equivalent transistor moment for your research, for your program, for your approach, however you want to call it well uh so I talked about you know first a voltage controlled oscillator, and then a cascading uh operational amplifier structure and then a phase lock loop. The more I look at the mirrored uh architecture and the machinery, uh horizontally opposed machinery on the mitochondrial crystal, the more it looks like a complementary transistor amplifier, amplifier architecture to me. And as I mentioned, each of those complexes that's um pumping protons or allowing electron flux through the membrane has a metal center. And so I think that is the transistor moment. We are looking at an architecture in a biological system that is equivalent to, although two, three billion years uh prior to, the complementary transistor amplifier circuit, which was the foundation for the modern computing industry. Um that's a claim, that's a bold claim, that's an irrational claim, that's a um foolhardy claim, possibly. Uh, but I've I spend my time you know trying to push the boundaries here, really, so that we can, you know, a challenge, attack, but first I think, uh, and this is what I love about Mike Levin's work, first, don't attack the straw man, build the steel man, and then attack the steel man. I think that's what is appropriate, necessary, and I think um will yield incredible uh new insights. But I think um this is the transistor moment because the system can be characterized, even if it's an abstract, highly stylized model I'm building, it will enable uh uh experimental scrutiny, but also it will describe uh ways for us to explain biological phenomena, I hope, in in a fundamentally different way.

SPEAKER_02 1:09:26

And uh if your computing paradigm or you know this approach would succeed, uh what are the implications? But you know, um I also I think time, because we mentioned it, time plays a role here, because you know, yes, uh if it will succeed before the singularity or after, I think it's of the essence as well. Or maybe you think we cannot achieve the singularity without your approach. I mean, tell me what are your thoughts there with relative to what's happening today with uh AGI and all these exponential developments relative to your paradigm?

SPEAKER_00 1:10:06

Well, I would say um, and um and by the way, you know, this is your area of expertise, Mikhaila, um, not really mine, uh, but uh, you know, umbodied con embodied cognition, you know, soft robotics. Um uh I don't think we're gonna achieve the singularity without uh embodiment, uh, if indeed it's ever possible, and I'm skeptical. Uh uh, but actually, when we think about embodiment, embodiment is not the current approach, which is you know the pre-training moment in robotic systems. We're not downloading LLM propositions onto uh onto uh robotic structures and thinking of them as embodied. Embodiment means autopoesis, self-instantiation, self-maintenance, self-improvement over time scales that might not be perceptible uh to us as humans, considering about evolution, considering evolution, and this is you know really just briefly referencing the amazing work of the Agential Biology Institute, trying to understand evolutionary paradigms beyond the neo-Darwinist uh genome first, genome is everything proposition. Um, but I think that uh if you consider every single cell of an embodied agent as its own computational uh uh standalone agential computational architecture, and then you think about the uh individual cell as having ten, a hundred, a thousand, tens of thousands of mitochondria in it, and then you think about the activation function that is performed by an ecology of mitochondria within a cell, the complexity, and then you consider that you know we have hundreds of trillions of cells in our body and hundreds of trillions of mitochondria in our body, then we're looking at orders of scale very different from the orders of scale we're looking at, and we're looking at each individual uh embodied component of the system being itself a cognizing agent, interacting with its other agents. Then all of a sudden I think we start to see a different pathway towards embodied cognition, uh, and and who knows, over time perhaps towards the singularity. But the models we're talking about are not embodied in any sense in the way I've described.

SPEAKER_02 1:12:22

Absolutely. And you know, now he you answered my initial question. Yes, I mean, actually, what would be the application? What is kind of this uh breakthrough useful for? And I agree because you know, now everybody's talking about physical intelligence and just putting LLMs inside the robots' brains and so on, brains, I mean, whatever, chips. So which which is definitely has nothing to do, and even designing humanoid robots. And um, I've been at the Davos Robot Summit in June, and uh they came, of course, with a lot of uh humanoid robots, even looking very nice and sexy and all that, but definitely there was nothing about embodied intelligence, they looked really creepy. And I agree. Because you know, if we really want a breakthrough in robotics, now I can see the the advantage of your paradigm. And actually, that may be it may be true, you know, that uh we cannot achieve that kind of embodiment without um uh your paradigm. Uh I just wonder if you, you know, because we reached kind of uh the end of our time here, but I you know I wonder if you have any final statements or any things which I didn't touch on. I know there's a lot that you are doing, but um a call to join your program or whatever you want to tell us that we didn't cover, please.

SPEAKER_00 1:13:52

Yeah, so I mean uh I'm I'm working with the agencial biology institute uh amongst amazing people, Charlie Munford and uh Richard Watson, uh the president and the chief science officer, and uh and an incredible group of people. Uh on the advisory board, we have people like Mike Levin, Josh Bongard, Stuart Kaufman, Dennis Noble, just incredible uh scientists uh, you know, who've been uh uh instrumental in shaping their respective fields. You know, uh I hope to work with the Agential Biology Institute to raise the funds, to help them raise the funds to explore my conjectures, but more important, their existing list of conjectures towards trying to explain agency and an agency-first evolutionary path. Um, and I think that will also uh bring incredible uh future value uh for the AI industry, but for biology more generally. Um, I'm also interested in working with um funders, uh venture funders, about the ideas I've described here towards um uh not if not necessarily neuromorphic uh hardware architectures, uh software emulation of hardware architectures uh in the first instance, um, uh, as a pathway to describing how we might achieve uh more compelling embodied soft robotics uh technologies, but also uh cognitive technologies uh beyond um beyond the induction and deduction mechanisms available uh with the existing AI paradigms towards an abductive uh you know uh type of system that can uh project and question future counterfactual pathways in a way that the LLMs can't currently.

SPEAKER_02 1:15:42

Thank you so much, Anthony. I mean, the engineer who once thought about how an aircraft determines its orientation in space is now asking how a living cell determines its own state and how that in turn might illuminate the past towards entirely new kinds of intelligent machines and the future of robotics. This is more than fascinating. I really, your past is so unique, and I so much appreciate uh you coming on the podcast. And also, of course, I look forward for us to forge a very powerful collaboration with our group, and uh I'd like to be involved, of course, in your in your program um that you are now uh crafting, and thank you for sharing that uh with me and and with the audience as well.

SPEAKER_00 1:16:33

Because um, if I may, I just want to say thank you. Thank you for noticing my work. And um, you know, I wish I'd met you 20 years ago because you know, we we share so much common heritage. Um it could have been an easier path.

SPEAKER_02 1:16:45

I know, it's like, where have you been? That's you know, sometimes you have this feeling, and and I had that uh as soon as I found you. I think I think I found you or you found me, and I think I think it was in the context of Alexander Lechner uh when he launched uh this uh abstraction fallacy paper. I don't actually remember how we met, but my feeling was exactly like that. Where have you been?

SPEAKER_00 1:17:12

Thank you. I and uh it it's it's it it's touched me, and it it's enchanting that you uh are enthusiastic about my work. Thank you so much.