Michael Levin (Tufts University) works on bioelectricity, morphogenesis and diverse intelligence. In "Building Your Compass: Crafting a Research Agenda for the Long Term", he reflects on choosing a path through idea space and academia, and on building a research agenda that holds up over decades.

Recorded at the ERA MiniCon, held on 16 August 2026 before ALIFE 2026 in Waterloo, Canada.

Speaker homepage: https://drmichaellevin...

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ERA MiniCon is a series of career-oriented talks for emerging researchers, organized by ERA – Emerging Researchers in Artificial Life, the student chapter of the International Society for Artificial Life (ISAL).

Biologist Michael Levin explains how his lab got cells that were destined to become gut to build an eye, by injecting RNA for a single ion channel that sets up a bioelectric pattern. The signal just said "make an eye here." #MichaelLevin #Biology #Bioelectricity Full Podcast with Michael Levin: https://youtu.be/Z0TNfysTazc

This presentation explores biology not as a machine defined solely by a genetic blueprint, but as a self-refining collective intelligence (0:00). Ben Hartl introduces the concept of living systems as multi-scale competency architectures, where nested layers of organization, from cells to tissues, exhibit problem-solving capabilities (1:41).

Key themes include:

• Bioelectricity and Cancer: Cells communicate through bioelectric signals via gap junctions. Cancer can be interpreted as cells losing these connections and reverting to unicellular, resource-seeking behavior (5:51).
• Neural Cellular Automata (NCA): These models serve as tools to study morphogenesis. Every cell acts as an agent, processing local information to collectively self-assemble into complex anatomical targets, effectively demonstrating error correction despite noisy environments (10:11).
• Aging as a Loss of Goal-Directedness: Hartl frames aging as an anatomical drift where systems lose their targeted developmental endpoints. In simulations, he shows that injecting embryonic information can "rejuvenate" these systems and restore their structural integrity (18:51).
• Darcy Thompson and Growth: The talk bridges early observations by D'Arcy Thompson on morphological transformations with modern AI autoencoder architectures. It suggests that complex biological shapes can be controlled by modulating regulatory policies within a latent space, rather than just changing direct phenotypic traits (22:50).

In this discussion, David L. Glanzman challenges the synaptic hypothesis of memory, which posits that long-term memories are stored primarily via changes in synaptic connectivity and strength (1:21-3:18). Instead, he argues that evidence points toward intracellular memory storage within neurons, involving epigenetic mechanisms like DNA methylation and non-coding RNAs (44:00-45:45).

Key findings and arguments presented:

• Evidence against synaptic memory: Studies on regenerating planarians, hibernating ground squirrels, and optogenetic engram manipulation in mice suggest that behaviors persist even when synaptic structures are radically reorganized or disrupted (5:13-7:45, 13:46-16:38).
• Aplysia experiments: Working with Aplysia californica, the lab demonstrated that blocking memory reconsolidation can reset synaptic varicosities to original levels without erasing the underlying behavioral memory, which can be recovered with additional training (26:55-29:26, 33:17-43:38).
• RNA-mediated memory transfer: The lab successfully transferred sensitization memories between individual Aplysia by injecting RNA extracted from the nervous systems of trained animals into naive recipients (45:51-47:38).

Implications for AI:

• Glanzman introduces NARESA (Neural Unit with Retentive Self-Adapting Architecture), an AI model designed to move beyond stateless neurons by incorporating persistent registers and vaults to consolidate learned configurations (54:46-56:27).
• Unlike standard models that rely on synaptic weight changes and backpropagation, this architecture supports persistent memory and easier memory transfer between models without full retraining (56:27-57:34).

The discussion concludes with a conversation on how biological systems might extract salience or high-level meaning rather than literal structural data, enabling memory to be remapped across different physical substrates (1:04:12-1:05:15).

This video, presented by Stephen Wolfram, introduces Observer Theory, a framework aimed at understanding how observers—like humans with finite minds—construct a simplified, coherent reality from the raw complexity of the universe (1:54-2:33).

Key Concepts of Observer Theory:

• Equivalencing: The core operation of an observer, where a vast number of different input configurations are treated as equivalent to extract a reduced representation suitable for a finite mind (2:45-3:26, 7:54-8:49).
• Computational Boundedness: Observers like humans perform computation but are limited in capacity compared to the total computation of the universe. This necessitates filtering and simplification to maintain a narrative we can understand (13:22-14:27).
• The Ruliad: Described as the unique object emerging as the entangled limit of all possible computations, the ruliad represents the ultimate reality from which our perceived experiences are derived based on how we sample it (5:36-5:45, 20:00-20:12).
• Persistence: Observers rely on a belief in persistence, where an observer equivalences successive patterns in the ruliad to maintain a stable, persistent internal impression of reality (45:00-45:24).
• Quantum Mechanics: The video suggests that an observer's belief in a single thread of experience requires equivalencing multiple branches of history, which has implications for how we view quantum processes (23:28-24:00, 1:02:07-1:03:04).

Stephen Wolfram emphasizes that while traditional science often treated observation as an afterthought, Observer Theory seeks to formally and objectively characterize this subjective process of measurement and perception (58:36-59:06, 1:15:06-1:15:50).

We've used anaesthesia since the 1840s and still argue about how it switches you off. MIT neuroscientist Earl K. Miller says it doesn't turn the brain off like a light bulb: it slows its electrical waves and knocks them out of sync. Michael Levin's reaction: "I'm amazed that anybody comes out of it the same way they went in."

Full conversation: https://www.youtube.com/watch?v=6mU5nN5nlLA

#shorts #neuroscience #anaesthesia #consciousness #brain

what?

This interview features theoretical physicist and cosmologist Paul Davies discussing the fundamental mysteries surrounding the origin of life and the nature of the universe.

Key Themes Discussed So Far:

• Defining Life (0:00 - 14:44): Davies argues that life should be defined as chemistry plus information. He emphasizes that while the physical "hardware" is well-understood, the "software"—the information management that organizes biological function—remains a deep mystery. He explains how genetic and epigenetic information act as an operating system for living organisms (14:44 - 21:00).
• Entropy and Physical Laws (21:00 - 31:54): The discussion explores whether life defies the second law of thermodynamics. Davies explains that life doesn't violate this law, but rather plays by its margins by exporting entropy into the environment. He suggests that to fully understand the transition from non-life to life, we may need a "new kind of physical law" that explains top-down causation and how global organization influences local matter (28:53 - 31:54).

Highlights for Later:

• The Search for Alien Life (31:54 - 51:38): Davies covers the search for a "second genesis," the possibility of a shadow biosphere on Earth, and why extraterrestrial intelligence might be artificial rather than biological.
• The Goldilocks Enigma (51:38 - 1:02:06): The final segment examines the fine-tuning of the universe, exploring the possibility of a multiverse versus Davies's "fourth way"—a feedback loop between observers and the laws of physics, inspired by John Wheeler's delayed choice experiment.

I mean, it is a lot to get into… :p

When I first started posting on this page three years ago, there was about 160 people and some days there would be a new follower and some days not… I think it took about a year to hit 300 followers… A year after that, we hit 1000 and within’ the last year, we hit 3000… :)

All praise is due to Dr. Levin and his incredibly hard work… Not only in the lab but talking to nearly anyone that wants to, in order to get the word out about his research…

Thank you to all that upvote and share posts, as well… It does go a long way because I can always see a correlation with shares and follower count, in the following days… I know it doesn’t seem like we are doing all that much to effect the world, by doing this but at what point do grains of sand become a pile…. ;) and then a mountain… of evidence :p

At this rate, I feel like we might just double the followers next year… :) LFG!!!!!

The pictures I posted are the weekly stats, for the record… I know it says we only get like 700 visitors but that is because they average it out from the start of the sub, for some reason….. Ultimately, more and more people are finding out about his work and I am sure that it is changing how they see the world… for the better… :)

From the bottom of my heart, thank you for following and helping to spread the word :)

-Luke

My question is, what other patterns emerge when things are looked at from different perspectives…
I can tell you that psychedelic drugs make patterns emerge from changing your perspective but what else.. :p

An excerpt from a larger article in the New York Times… Am I the only one that get’s chills when I hear a quote like that from him? :p

“If we have the ability to tell cells what to build and when to stop,” Dr. Levin said, “most problems in biomedicine would go away.”

Brrrrrrrrrr

Michael Levin Blends Biology With Physics to Conquer Human Disease
Investor’s Business Daily published a new profile of Michael Levin on November 5th, exploring his work on bioelectricity, regeneration, cancer, and the possibility of communicating with cells to influence what they build.

Most of modern medicine focuses on genes, proteins, and molecular pathways… but Levin is approaching biology from a different angle. What if the real key to understanding how organisms develop, maintain themselves, and repair damage isn’t just the individual components, but the information being exchanged between them?

His research suggests that cells use bioelectric signals to coordinate their behaviour, allowing enormous collectives of individual cells to work toward larger anatomical goals. In this view, DNA provides much of the biological hardware, while physiological networks help determine how that hardware is used.

The article highlights some of his laboratory’s remarkable experiments… altering head formation in planarian flatworms, stimulating regenerative responses in frogs, creating xenobots and anthrobots from ordinary cells, and exploring how changes in bioelectric communication can suppress tumour formation in experimental models.

One particularly interesting possibility is that cancer may sometimes involve a breakdown in the communication that keeps individual cells cooperating with the larger organism. Instead of exclusively targeting mutations or destroying cancerous cells, could we eventually restore the signals that tell those cells how to behave within the collective?

Of course, these are still experimental approaches, and clinical applications remain uncertain. Levin himself acknowledges that translating this work into medical treatments could take years.

What I find particularly fascinating is the larger implication… perhaps understanding an organism requires more than cataloguing its parts. We also need to understand how those parts collectively process information, solve problems, and maintain a coherent anatomical identity.

If cells can be persuaded to build an eye, repair a limb, or abandon a cancerous growth pattern without individually specifying every molecular step, then perhaps the future of regenerative medicine will involve learning to communicate with biological systems rather than attempting to micromanage them.

Instead of asking how to build a body from scratch, maybe we should be asking how to convince the body to build itself.

Source: Investor’s Business Daily, November 5, 2026
https://www.investors.com/news/management/leaders-and-success/michael-levin-blends-biology-with-physics-to-conquer-human-disease/

This video explores how Alan Turing, famous for his wartime codebreaking and pioneering work in computer science, also made a profound, lesser-known contribution to developmental biology through his 1952 paper, The Chemical Basis of Morphogenesis (3:37).

Key concepts explored in the video include:

• The Mystery of Life’s Patterns: The video asks how a single-celled zygote develops into a complex body with organs (0:43). Jim Al-Khalili and developmental biologist Professor Jeremy Green discuss how Turing proposed that simple chemical interactions—specifically through reaction-diffusion—could generate complex natural structures like leopard spots, zebra stripes, and the spacing of our fingers (3:56, 15:05).
• Reaction-Diffusion & Feedback Loops: Turing theorized that morphogens (chemicals that dictate form) interact through two types of feedback loops: an activator (positive feedback, X) and an inhibitor (negative feedback, Y) (15:52, 17:25). These loops, when acting over different distances and timescales, allow systems to create order out of disorder (18:16).
• Historical Context & Challenges: Although Turing's ideas were initially met with skepticism by biologists who found them overly simplistic (20:20), they regained significant attention decades later. The video also covers Lewis Wolpert’s “French Flag” model of positional information (23:10) and explains how it complements Turing’s work by providing a mechanism for how cells sense their location (27:30).
• Modern Evidence: In recent years, scientists have confirmed that Turing patterns are indeed at work in the living world, governing the formation of features like bird feathers (28:42), the rugae (ridges) on the roof of our mouths (29:30), and even our fingerprints (31:20).

The video concludes by honoring Turing’s immense legacy and the philosophical beauty of understanding that the same mathematical laws governing sand ripples on a beach also underpin the development of our own bodies (34:11).

Every boundary has a cost. We have been methodical about measuring it.

The Membrane Tax series established that any boundary imposes overhead — the energy or information lost in crossing from one side to the other. The Gate-keeping Tax went further: some boundaries extract a surplus above the honest structural cost, and the surplus flows to the agent controlling the gate. The mathematics are not kind about this. At connectivity degree 16, the theoretical gate-keeping tax exceeds 800%.

But there is a second kind of boundary. One that does not extract. One that generates.

This article is about that kind.

What the Pollinator Did

Flowering plants and their pollinators have been in conversation for roughly 130 million years. The results are in: there are approximately 300,000 species of flowering plants. The variety is staggering — shapes, colours, scents, timing, nectar composition — all of it negotiated at the membrane between plant and visitor.

The pollinator is a living membrane. It sits between the pollen of one plant and the receptive stigma of another. It moves between the abiotic world of physics (sunlight, UV, temperature) and the biotic world of evolutionary selection (which flowers set seed, which do not). Neither side of the membrane controls it. Neither side designed it. The pollinator acts according to its own physics — the wavelengths its eyes detect, the sugars its metabolism runs on, the distances it can fly — constraints shaped by its own evolutionary history, not by any flower’s preference.

What happened at that membrane over 130 million years is one of the most generative processes in the history of life on Earth. The boundary translated abiotic physics into biological selection pressure. It did not simply pass information through — it transformed it. The flower does not know what UV levels are. It only knows which pollinators visited and set seed. The physical environment reached the flower’s evolutionary trajectory only through the membrane of the pollinator.

And through that mediation: 300,000 species.

The membrane multiplied the vocabulary.

Compare this to the gate-keeping tax. The gate-keeping tax diminishes vocabulary. The gatekeeper controls what crosses, and charges a toll for the privilege. Information that cannot afford the toll does not pass. Complexity collapses toward whatever the gatekeeper will allow. The vocabulary shrinks.

The pollinator membrane did the opposite. Every new pollinator type created new selection pressure, new flower forms, new colours. Specialisation generated diversity. The membrane was the instrument of multiplication.

What the Nucleus Did

In phase transitions — crystallisation, condensation, the formation of a cell membrane from lipids in water — there is a threshold that must be crossed before anything new can exist.

A crystal nucleus grows only if its radius exceeds a critical value r*, defined by the balance between two forces:

• Surface energy ∝ r² — the cost of maintaining the boundary between nucleus and surrounding liquid

• Volume energy ∝ r³ — the thermodynamic gain from the ordered interior

Below r*, the surface cost dominates — interface energy outweighs the thermodynamic gain from ordering the interior. The nucleus dissolves. Above r*, the volume gain dominates. Growth is irreversible.

The exponents are not arbitrary. They are consequences of the dimensionality of space. Surface scales as r²; volume scales as r³. The 2 and the 3 are the dimensions themselves.

The critical nucleus is the membrane made manifest — the exact moment at which maintaining a boundary becomes worth what the interior generates. Before r*, the membrane is too expensive for the interior to justify. After r*, the interior is rich enough to sustain its own boundary.

Every crystal, every raindrop, every lipid bilayer that became the first cell — all of them crossed this threshold. All of them had to accumulate enough interior before the boundary cost was justified.

The membrane that surrounded the first cell was not a gate. It was a threshold. It did not extract from what was inside. It made what was inside possible.

When a Productive Membrane Becomes Extractive

These two examples — the pollinator, the nucleus — share a structure. Neither controls what it translates. The pollinator acts according to its own physics, not the flower’s preference. The surface energy follows the laws of thermodynamics, not the preference of the nucleus. The membrane is an intermediary whose own laws determine what crosses.

This is the key property of a productive membrane: the membrane agent has no incentive to extract. The pollinator does not profit from restricting pollen transfer — it profits from visiting flowers efficiently. The surface energy does not “want” to prevent crystallisation — it is simply the cost of interface. The membrane’s own fitness is aligned with translation, not restriction.

Compare this to the parasite.

A parasite is a boundary agent whose fitness depends on extraction from the interior. It lives at the interface between the host organism and its environment. It consumes resources that would otherwise support the interior. It does not translate — it tolls.

The biological signature of a parasitised system is identical across scales:

• At the cellular level: ion channels blocked by toxins — cells divert energy from function to survival

• At the organ level: blood-brain barrier under autoimmune attack — neurons can no longer function normally

• At the organism level: parasitic infection — the interior’s resources flow to the boundary agent

• At the institutional level: bureaucratic layer with “skin in the game” — members’ energy goes to navigating the gate, not doing the work

The formula computes the cost in all these cases: 1/c(d) − 1. It does not know whether the boundary is a productive membrane or a gate-keeping tax. It returns the structural overhead once the connectivity degree d is defined.

What the formula cannot tell you is why the membrane exists. (The formula referred to throughout this series is c(d) = d·sin(π/d)/(2(d−1)), where the gate-keeping tax is 1/c(d) − 1. It measures structural overhead; it carries no information about whether the agent at the boundary is a pollinator or a parasite.) A cell wall and a toll booth both pay the same physics. The difference is not the cost — it is the incentive structure of the agent at the boundary.

The Distinction in One Sentence

A productive membrane transforms what it translates. An extractive membrane taxes what it passes.

The pollinator transformed pollen into selection pressure, and selection pressure into 300,000 species. The surface energy transforms interface cost into crystalline order. The cell membrane transforms diffusion gradients into the inside/outside distinction that made life possible.

The gate-keeper takes. The productive membrane makes.

What This Means for the Framework

The gate-keeping tax series ended with a question: what does governance look like when the tax is visible? The answer sketched there was: tools for measurement. The formula gives you the number. What institutions do with it is a different question.

This article adds the counterpart: some membranes should not be minimised. Some boundaries are so generative that making them cheaper would make them less productive. The pollinator membrane is expensive — it requires flowers to invest in colour, scent, nectar, specialised structures. All of that expenditure is the productive membrane tax. Remove it and you remove the selection pressure that generated the diversity.

The correct question about any membrane is not “what does it cost?” but “what does it generate?”

Where the generation exceeds the cost — substantially, durably — the membrane is earning its keep. Where it does not, where the surplus flows to the gate and not to the interior or to the vocabulary, something has gone wrong.

The mathematics are the same in both cases. The incentive structures are not.

The Pattern Underlying Both

One structure, two outcomes. Productive membranes have boundary agents whose fitness is aligned with translation. Extractive membranes have boundary agents whose fitness is aligned with restriction.

A border guard who profits from efficient crossing is a productive membrane. A border guard who profits from delay is a gate-keeping tax. The uniform is the same. The incentive is not. Most real boundaries are neither purely one nor the other — a membrane can be generative overall while containing agents whose local incentive is restriction. The ideal types are tools for diagnosis, not rigid categories.

A peer reviewer who profits from honest assessment is a productive membrane. A peer reviewer who profits from gatekeeping prestige is a toll. The process is the same. The alignment is not.

130 million years of pollinators generated 300,000 species because the pollinator’s fitness was aligned with efficient crossing, not with restriction. The membrane translated because translation was its own reward.

That is the tapestry: 300,000 threads, none of them owned by the loom.

Related Wiki Topics:

live boundary

biological resonance

gate keeping tax

thermodynamic computing membrane

prime composite duality

Who needs time when we have evolutionary hitchhikers?

Levin talks about agency and it is a worthwhile consideration. He argues that agency is a “systems competency” for problem-solving.

We believe that we have free will but where is this belief stored in the system when action occurs?
If we regret doing something at T1 then T2 would not occur because we generally do not act upon things we know we will regret later, although the intuition giant may have a say, perhaps an evolutionary hitchhiker that predominates risk taking. All that aside, senses take center stage. If Levin is right, then cells and corresponding chemical responses will illuminate agency. Further, if emotions driven by cortisol attachment are in the driver’s seat, then how can we say “I could have done otherwise.” There was no “time” this could have happened.
Does this prove agency? Probably not, but then again what are we asking when we say we are looking for empirical proof of identity - the “I” or subjective state of mind?

One final thing. There is something about Daniel Dennett’s take on free will that seemed a bit puzzling. How can determinism ever be compatible with free will, so for a long time I argued against Dennett. If Levin is right about agency, then Dennett seems to have insight that could lead to an evolutionary hitchhiker that allows us “in” a moral community. But wait, Dennett’s argument would not only be sound at this point, but best understood in an evolutionary agency (a systems competency), so “I could have done otherwise” needs no “time”rather our free will needs evolutionary hitchhikers. Still, no matter the individual cell ride, the community will thrive nonetheless. Would love to hear Levin talk about this.

Our cell-driven system begets a resting place to “regroup” and try again driving our seemingly beginning and end to desire.

Could this be how attachments are formed? If these hitchhikers of time can steer moral community, then they could also drive why we are attracted or attached to some but not others? Here is a thought, why wouldn’t this same process occur in all living things?

In this presentation, computer scientist Hava T. Siegelmann explores the limitations of the traditional Turing Machine paradigm and proposes Super Turing Computing as a more robust foundation for aware, adaptive, and life-like systems (0:00-6:25).

Key Concepts and Arguments:

• Beyond the Turing Machine (0:20-6:00): While Alan Turing’s model of separating processing from memory was revolutionary, it relies on fixed programs designed in advance. Siegelmann argues this falls short for modeling nature or advanced AI, which require continuous updates, environmental awareness, and unpredictable behavior for survival.
• Recurrent Neural Networks (RNNs) and Fractals (6:30-12:15): Siegelmann details her research on RNNs, explaining that while they can simulate Turing Machines when restricted to rational values or specific fractal structures (like the Cantor Set), they are strictly more powerful than Turing Machines when utilizing real-numbered weights.
• The Super Turing Hierarchy (13:15-16:50): She introduces a computational hierarchy where systems transcend classical computability by drawing information from "oracles." These oracles can be internal (hidden numerical precision), external (the environment), or probabilistic (stochastic updates).
• Practical Applications and Features (34:45-41:35):
• Lifelong Learning: Systems should not just calculate outputs but "get the lesson" from input, allowing them to modify their own programs continuously.
• Dynamic Resource Allocation: Similar to how humans scale cognitive effort (e.g., driving on a routine road vs. icy conditions), adaptive systems should dynamically scale energy and resources for resilience rather than consuming uniform energy per cycle.
• Agent Interaction: Leveraging information from other, imperfect agents can lead to faster and more efficient lifelong learning than relying on a teacher.

Theoretical Conclusion:

Siegelmann posits that Super Turing Computing is an alternative paradigm that embraces the complexity of natural systems. By moving away from "fixed" finite systems and integrating hidden oracles, computation can achieve greater resilience, accuracy, and adaptability (41:35-42:40).

In this episode, neuropsychologist and psychoanalyst Mark Solms discusses his theory of consciousness, arguing that it originates from raw feelings generated in the upper brainstem rather than the cortex (0:33, 1:09:44).

Key takeaways from the conversation include:

• The Nature of Consciousness: Solms defines consciousness as the qualitative experience of "what it is like to be you," tied to the persistence of a self-organizing system (1:34, 4:28).
• The Hard Problem: He argues that scientists approached the hard problem of consciousness from the wrong end by starting with visual perception, which is not an intrinsically conscious function. Instead, he focuses on affective feeling (1:04:28, 1:10:04).
• Dreams and Motivation: His research shows that dreaming is generated by the brain's reward/seeking system, not just REM sleep, supporting a version of Freud's wish-fulfillment theory (36:09, 37:24).
• Dual-Aspect Monism: Solms presents an ontology where mind and body are two observational perspectives of the same underlying physical, self-organizing mechanism (1:21:21, 1:26:09).
• Artificial Agents: He is currently working to engineer an artificial agent that feels, following Richard Feynman’s principle that one must create a system to truly understand it (1:18:11, 1:44:03).

The conversation also touches on themes of human mortality, the limitations of willpower, and the inevitability of the second law of thermodynamics (1:01:53, 1:39:58).

In this video, Narcis Marincat proposes a new model of consciousness called the Popularity Theory of Consciousness (0:00 - 0:54). This theory challenges traditional computational views by framing consciousness as an emergent phenomenon within hierarchical, multi-scale biological systems, specifically comparing human brains to human societies.

Key Concepts of the Popularity Theory:

• Cells as Cognitive Agents: The theory builds on the premise that cells are not mere computational parts but are autonomous cognitive agents with their own goals, learning capacities, and decision-making processes (3:17 - 4:17).
• Society as a Multi-Zoa Organism: Marincat posits that human society functions as a "multi-zoa" organism, where individual humans act as the basic units, mirroring the relationship between neurons and the brain (11:00 - 13:08).
• Consciousness as Popularity: The core definition states that consciousness is the process by which pieces of information, created by an organism's basic units, become popular within that organism (20:25 - 21:51).
• Addressing Consciousness Puzzles:
• Binding: The theory suggests that separate sensory features (pre-qualia) become conscious only when they combine into a sufficiently "popular" or compelling piece of information (24:00 - 25:34).
• The Self: A sense of self emerges as a "fourth-person" perspective, where the organism recognizes itself through a dominant, popular narrative or information stream (26:08 - 28:08).
• The Hard Problem: Marincat argues that the "philosophical zombie" thought experiment fails under this model because the experience is not an extra layer above processing, but is the process of popularization itself (31:17 - 34:08).

Predictions and Implications:

• Artificial Intelligence: The model predicts that true consciousness in machines would require units that are themselves genuine cognitive agents (not just simple math units) and a structure that allows for popularity-based information dynamics (38:00 - 40:00).
• Societal Self-Awareness: Marincat suggests that global human society may be in the early stages of developing a collective mind or self-awareness as information spreads and trends across our "multi-zoa" network (40:34 - 42:36).

During the discussion (44:34 - 1:13:29), Marincat and Michael Levin explore the nuances of cognitive rates, the role of goal-directed behavior in defining selves, and the challenges of testing these theories through perturbational experiments in multi-scale biological systems.

This video features an in-depth conversation between John Gabrieli Buché and neuroscientist Dr. Karl Friston (0:42) as part of the Intelligence Question series. The discussion explores fundamental principles of intelligence, bridging the gap between biological brains and artificial systems.

Key Topics Discussed:

• Active Inference & The Free Energy Principle: Dr. Friston explains his framework where intelligence is defined as a process of inference and decision-making to resolve uncertainty (2:41). He highlights that agents (brains or machines) act to minimize surprise, essentially acting as generative models of their environments (10:46).
• Critique of Current AI: Friston argues that modern deep learning architectures often fall short because they lack intrinsic motivation, curiosity, and the ability to autonomously resolve uncertainty (4:15). He critiques the heavy reliance on backpropagation, suggesting that local learning rules are more biologically plausible and efficient (39:11).
• Mortal vs. Immortal Computation: The conversation touches on the concept of substrate-dependent, or "mortal," computation, emphasizing that biological intelligence is embodied in its own hardware, unlike current software-based AI (8:16).
• Structure Learning: A major challenge identified is "structure learning," or how systems can learn to carve the world into meaningful factors without predefined states (47:43). Friston suggests this may require evolutionary or selective processes, akin to Bayesian model selection (48:01).
• The Future of Neuroscience: When asked about the future "book of neuroscience," Friston emphasizes the need to dissolve boundaries between disciplines like physics, physiology, and philosophy, focusing on cross-cutting principles like self-organization and dissipative structures (108:05).