Theories of Everything with Curt Jaimungal - MIT Scientist on Unifying Cognition and Biology | Manolis Kellis

Episode Date: November 8, 2024

In today’s episode, MIT computational biologist Manolis Kellis dive into the hidden patterns linking DNA, evolution, and cognition, exploring a potential unifying theory that bridges biology, AI, and the essence of life. New Substack! Follow my personal writings and EARLY ACCESS episodes here: https://curtjaimungal.substack.com SPONSOR (THE ECONOMIST): As a listener of TOE you can get a special 20% off discount to The Economist and all it has to offer! Visit https://www.economist.com/toe LINKS MENTIONED: - Manolis Kellis’s Lab (website): https://compbio.mit.edu/ - Manolis Kellis’s profile: https://web.mit.edu/manoli/ - Curt’s article on language: https://curtjaimungal.substack.com/p/language-isnt-just-low-resolution - Chiara Marletto on TOE: https://www.youtube.com/watch?v=Uey_mUy1vN0 - Roger Penrose on TOE: https://www.youtube.com/watch?v=sGm505TFMbU TIMESTAMPS: 00:00 - Introduction 02:05 - The Scope of Biological Unification 06:02 - Biology vs. Physics 09:31 - DNA as Life’s Language 13:45 - The Universal Compatibility of DNA 16:55 - Evolutionary Trade-Offs and Isolation 20:17 - Layers of Abstraction in Biology 24:51 - Beyond DNA: The Role of Histones 30:30 - Protein Folding and Function 35:26 - How Cells Interpret DNA Signals 40:24 - The Creativity of Language and Miscommunication 44:55 - Teaching and Simplification 51:09 - Evolution of Cognition and Centralized Decision-Making 57:35 - Vertical vs. Horizontal Evolution 1:04:20 - Specialization and Society’s Role in Evolution 1:08:50 - The Future of Biological Understanding TOE'S TOP LINKS: - Support TOE on Patreon: https://patreon.com/curtjaimungal (early access to ad-free audio episodes!) - Listen to TOE on Spotify: https://open.spotify.com/show/4gL14b92xAErofYQA7bU4e - Become a YouTube Member Here: https://www.youtube.com/channel/UCdWIQh9DGG6uhJk8eyIFl1w/join - Join TOE's Newsletter 'TOEmail' at https://www.curtjaimungal.org Other Links: - Twitter: https://twitter.com/TOEwithCurt - Discord Invite: https://discord.com/invite/kBcnfNVwqs - iTunes: https://podcasts.apple.com/ca/podcast/better-left-unsaid-with-curt-jaimungal/id1521758802 - Subreddit r/TheoriesOfEverything: https://reddit.com/r/theoriesofeverything #science #sciencepodcast #physics #biology #consciousness Learn more about your ad choices. Visit megaphone.fm/adchoices

Red one we're coming at you is the movie event of the holiday season Santa Claus has been kidnapped You're gonna help us find you can't trust this guy. He's on the list. It's a naughty Lister naughty Lister Dwayne Johnson We got snowman Chris Evans, I might just go back to the car. Let's save Christmas I'm not gonna say that say it. All right Let's see Christmas. There it is. Only in theaters November 15th. This is mind boggling.

We now are sitting in this extraordinary convergence that allows us to peek into the building blocks of biology, into the building blocks of disease, into the heterogeneity of different individuals, different patients, different tissues, different patients, different tissues, different organs, different cell types at a scale that was unfathomable 20 years ago. I think ahead of us might be the most complex challenge yet.

Deep within the laboratories of MIT, a computational biologist has uncovered a pattern that appears everywhere in nature. From single cells to human societies, from bacterial evolution to artificial intelligence. Professor Manolis Kellis, a leader in computational biology at MIT and Harvard's Broad Institute, has spent decades piecing together a new type of unification theory, one that explains everything from biology to cognition to language and meaning. Now, this is remarkable, because unlike physics, which follows unchanging laws, biology seems to operate through endless tinkering, constantly rewriting its own rules. This unification task seems hopeless. However, today I have a treat as I travel to MIT to have

this in-depth conversation in person. Kellis reveals how this unifying principle could transform medicine, unleash new breakthroughs in AI, and fundamentally change our understanding of what makes life unique. With expert exposition, Kellis takes us on a journey through all adaptive systems from DNA to consciousness. Professor Manolis Kellis, we're here at MIT. Thank you for inviting me to your spacious office. It's a real pleasure to be here with you, Kurt. I've truly enjoyed our interaction so far. It's a real pleasure to be here with you, Kurt. I've truly enjoyed our interaction so far.

It's been a pleasure. I'm really excited to see where you're taking your podcast. I'm very excited to sort of see someone thinking deeply about this unification, not just from a shallow perspective, but the ability to dive in and that I'm very excited about. So that's why I'm excited to speak with you because on this channel, it's called theories of everything. And most people know theories of everything in physics means how do you merge general

relativity with the standard model. Okay, so that's something quite precise. But then there's also people in logic, people in philosophy who have their own ideas of what a theory of everything is because they just take it colloquially. Okay, a theory of everything, quote unquote. But what about biology? Biology is one of those things that doesn't really have theories.

So biology started with just the study of every single aspect of life. And the first biology department was at MIT. Before that, there was zoology, botanology, virology, and so on and so forth. And I feel that this concept of biology, of the unification of everything alive, is something that is a very recent concept, a concept that was fundamentally based on the genome. So the genome brings that initial unification of biology because of the central dogma of biology.

DNA makes RNA mix protein. So this concept that even though some organisms are animals, others are plants, others are viruses and bacteria, there is still some fundamental principle that biology can reveal. And that started with the elucidation of DNA as the basis of life and inheritance, and how it makes RNA and how it makes protein, basically this subdivision of labors, if you wish, between the information storage, the information transmission, and then the actual actualization,

the actual nation, actuation. And it has been dramatically transformed over the last few years. And the reason for that is that we went from simply knowing that DNA is around to actually sequencing DNA, to actually having a complete genome for one species and then a few species and then the human genome and now thousands of species. And what's really extraordinary there is that we can now start seeing some emerging principles. I wouldn't yet call them rules, unlike physics, which has sort of these laws of physics, but it's more like patterns of behavior,

patterns of emergence. And the reason is that biology is fundamentally different from physics. So physics started 13.7 billion years ago, let's say. So physics as we know it. So basically, there's the period immediately after the Big Bang where the laws of physics are still being written and changed and dramatically altered. And then there's, of immediately after the Big Bang where the laws of physics are still being written and changed and dramatically altered. And then there's of course the sort of corners of the universe that the physics as we know it breaks down, so black holes and emerging stars and so forth. And beyond that, every neighborhood speaks the same language. So Andromeda and Earth have the same physics.

Biology is very different. Biology in, I don't know, the thermal vents of the bottom of the ocean is very different than the biology in the Sahara Desert, very different than the biology in the Antarctic sub ice niches. Because biology creates niches and it shapes those niches and it adapts to niches. So there's a small number of rules for physics and they were written 13.8 billion years ago.

And there's a gazillion exceptions for biology, and they're still being written. And this is perhaps the first thing that we should embrace, that there are no rules, but then there are some emergent principles that keep coming up. So the principles with which the genome works, the principle with which evolution works, the principles with which the genome works, the principle with which evolution works, the principles with which cognition works, and the principles with which even AI works have a lot in common. And that starts sounding like rules, that starts sounding like laws, these fundamental recurrent patterns, if you wish. And those patterns are patterns of tunability, patterns of approximations, patterns of fitting curves and functions to data.

And I would say that bacteria are doing this, humans are doing this, brains are doing this, societies are doing this, computers are doing this. So this whole concept of instead of having a single function, F equals MA or E equals MC squared, that describes a big, big chunk of the observable universe, you have tunable parameters in the thousands that are slowly approximating these functions. So what's the difference between a rule and a principle or a pattern? Is it just

one is more flexible than the other? One can sometimes be broken but in physics there's no such thing as a law that has exceptions? I would say that the difference is that a rule was written before the observations. The law of physics was written before the observations. We have the laws and then we have a ton of observations. Whereas the patterns, the principles of biology of life are written after the observations. In other words, you have a landscape that you're trying to fit in terms of evolutionary fitness, in terms of response to a drug, in terms of response to a disease, in terms of aging or changing climate or environmental conditions or you name it.

So as these things are happening throughout evolution, species adapt and fit a landscape that's there. Physics doesn't care if the universe collapses. The laws were written without a goal in mind. Whereas the laws of biology are constantly rewritten to fit whatever evolutionary niche appears. So it seems like there's no hope then for unification in biology if there's such a plethora of species. Whereas in physics there's not that many entities. There's quarks, there's electrons and so on.

But as you mentioned, there are different species at the bottom of thermal vents in the ocean and that's different than those that are in Antarctica. If there are any that are on the space station right now, then that's going to be different. So how can you hope to unify them unless you're saying something so vague that it has little predictive power. So the building blocks are small and numbered. So DNA has four bases. Amino acids have 20 versions.

But the power in expressivity comes from the combinatorics. In other words, a single amino acid alone doesn't really mean much. It has some properties of its charge and its mass and its hydrophobicity and its interactions and what kind of bonds it can make. But in isolation, it doesn't really mean much. In the same way for a chemical,

a single carbon atom or a single nitrogen doesn't really mean much. In the same way for a chemical, a single carbon atom or a single nitrogen doesn't really mean much by itself. But when you have a molecule like caffeine, the combination of atoms have some emergent properties. In the code of DNA, the ACGTs alone are like zeros and ones in a computer. They don't mean anything. But it is their combinations that start meaning something. And now you start building up representations of the world.

You basically have from the raw ACGT of the genome, you start having patterns that mean something. For example, CGG spaced by 11 nucleotides from CCG suddenly becomes a motif. It is recognized by a protein. So in the same way that humans have language, we've come up with words that mean something. The word word means something and we both understand what it means. In the same way, the genome has come up with a language so that proteins can talk to each other and can talk with DNA. This CGGCCG attracts the regulator associated with binding that motif, Gal4, and it is involved

in turning on metabolism of galactose, for example, and shutting off glucose, and so on and so forth. So there are, there's a language. It's not a law, but there are rules in languages. There are communication agreements. And if you look at the genetic code, the way that ACGT is translated to the 20 amino acids. There's a table that translates every triplet into a different amino acid. There's 64 possible triplets. Three of them code for STOP, TAG, TGA, TAA,

and the others code for distinct amino acids. When viruses were exchanging DNA, and when bacteria were exchanging DNA with each other, a bacterium that didn't have the same genetic code would go extinct because it wouldn't be able to leverage the evolved proteins of other species that get passed on to it. So the genetic code started out with variations. But then those variations were eliminated because of software compatibility, because of that communication. Do we know that? Yeah.

Do we know that there are other competitors to A, T, C, G? So it's not that there's competitors to the four letters, but there's competitors to the translation table. I see. That gives us, you know, what does each amino acid code for? For example... Wait, wait, just a moment, because this is super interesting, because in English, we use an alphabet, and other languages use an alphabet, French and German and so on, and Chinese uses a different, or Mandarin uses a different alphabet, but let's just stick with what uses the alphabet. Okay, those are

different languages. So it sounds like you're saying the underlying letters are also common among species on Earth, and the language is also the same. So the answer is mostly yes. In other words, when the SARS-CoV-2 virus arrives with COVID-19 and it enters your cells. The RNA of that virus is fundamentally compatible with the machinery of the host. So the virus is throwing in a piece of code that you will run. It is literally a virus.

If I throw you a PC virus in a Mac, it will just not work. But if you take a virus from bacteria and you throw it into humans, it will work. Basically, that translation machinery is the same. So bacteria exchange DNA with each other. So for example, if you look at antibacterial resistance genes, you basically have genes that are passed on from one bacterium to the other. Basically saying, oh, I just figured out resistance. Here's how I did it. And they're like, oh, great code.

I will use it. And that code is immediately compatible. So the translation just works. So basically that translation table requires having a set of tRNAs. These are adapter molecules that basically take every triplet and then have bispecific translation. On one side they're specific to the three letters. On the other side they're specific to the amino acid that binds that RNA. So this was hypothesized decades before tRNAs were actually discovered,

where Francis Crick actually said, oh, whatever molecule is the adapter between DNA and protein must be an RNA. Because an RNA has a dual capability of A, binding the DNA with complementarity and binding the RNA. And on the other side, it has the ability of binding a protein based on 3D structure. And the only molecule that does that is basically RNA, because it has both a folding capability and a sequence complementarity capability. So that translation table is based on the set of tRNAs that we have. Now suppose that a species mutates one of its tRNAs and the A here changes to a T or

to a U. That U will no longer recognize the complementary base. It will now recognize a different base. That basically means that the genetic code itself has changed. Now that species can go off and recode all of its DNA. That's called a recoding event, which basically says that now I will translate this message from RNA into a different protein sequence. And there are examples of such recoding events.

In my own work, I ran into a species of candida that basically recoded its DNA, potentially to fight against viruses that would attack it. Oh, okay. Because I was about to say, it sounds like it's disadvantageous to not have the same... Correct. To not speak the same language. Correct. It's disadvantageous if you want to exploit your environment.

But if you're getting attacked by your environment, then by recording your genetic code, you basically have the advantage of now viruses can just simply throw in a piece of software that your cells will run because that piece of software will not make sense in the milieu of your cell. There must be some trade-off because otherwise there'd be more. Absolutely. Okay, so what's the trade-off? So the trade-off is of course isolation. You're now on your own. You're now doing your own thing.

And for example, if you look at mitochondria, mitochondria were engulfed very early in evolution. They were some protobacterium, if you wish. And it was engulfed by another proto-eukaryote that basically then became eukaryote, that basically now has an organelle that initially was a free-living organism and eventually shed most of its genes, except for about 11 genes, that are now coding for the electron transport chain at the very core of energy metabolism in every one of our cells. And that engulfment froze in time the mitochondrial genome. So the mitochondrial genome now has its own translation table, has its own code, if you wish.

So basically being able to now look back in time at that fusion event that gave rise to the lineage of eukaryotes that we're part of. And there's another, of course, fusion event with chloroplasts that basically the plants underwent that allowed them to now metabolize the energy from the sun. So again, it's one of those things where it was invented once and it was co-opted. But instead of co-opting it as a piece of DNA, it was co-opted as an entire species that became part of your core. And now every eukaryotic species has mitochondria

that are the descendants of this original engulfment, encapsulation event. So I want to get back to this analogy about language and math we can see as the language of physics. Is it then correct to say that the ATCG is the language of biology? Or would it be more correct to say that that table is the language of biology or something else? So, I want to distinguish different things here. So first of all, math is what humans use to model physics. And that's why we call it the language of physics, because it's sort of what humans use to model physics. And that's why we call it the language of physics, because it's sort of what humans use to code it.

But the building blocks of physics are basically the elementary particles of the standard model. And in the same way, the building blocks of biology are ACGT and the 20 amino acids. So these are the building blocks. Now, how are they put together? What are the rules that govern this putting together? Well, these are rules that were invented or evolved in the case of biology, and these are rules that arose in the early universe in the case of physics. These are the building blocks and the connectivity pattern between them

that give rise to the higher levels of abstraction. So basically you can think of these building blocks as the quarks and then the quarks sort of fit together to build photons and electrons and protons and neutrons and so on and so forth. And basically you have this layer of abstraction. You're starting from the fundamental particles and then you're building up combinations that we now call atoms. And then these atoms are not so indivisible as the etymology would suggest, but they are

sort of building blocks at another layer of abstraction. Whether there's another layer below quarks, we might never know. But there might be. You know, nothing prevents there from being one. And now from those basic atoms, we then have molecules. And then from these molecules, these are the building blocks that then make another layer of abstraction where even though from individual atoms you can make almost any molecule, there's some molecules that are agreed upon as the standard of communication

and these are amino acids and these are nucleotides and so on and so forth. So these are the building blocks of life if you wish. And there are maybe four or five layers of abstraction away from the fundamental laws of physics. Okay? Now, does the nucleotide care about quantum effects? Most of the time, no. Most of the time, no. Most of the time, no. That's the beauty of these layers of abstraction. You can abstract

away. Abstraction means you're separating, you're not looking at the details anymore. So right now, life basically speaks ACGT. It doesn't care that there's subtle variations. So basically, this variability at the lowest level is encapsulated in the knowledge and the reading machine that now reads these ACGTs and then does something with them. Wait, I'm just confused about what the subtle variation would be on an ATCG. So, for example, a C, most of the time is a C, but it can also undergo methylation. For example, you can add a CH3 group to your C and now it's a

methyl C. Now the methyl C most of the time will be read as a C. It still binds a G, it still has the three hydrogen bonds, but on the side it, you know, bulges a little. Now there are some regulators that will recognize C, some regulators that will recognize methyl C, and some regulators that will recognize either form without problem. So for those regulators that recognize either form, the SEGT with a methyl C is still the same. But for those that recognize the methylated form, they will only bind that one. And genomes have figured out a way to use methyl C as a repressive mark.

That basically will shut off some regions of the DNA that will now be bound by regulators that are repressive when they see the methyl C, and will not be bound by the activators because the methyl C will no longer be recognized. So that's a type of modification. So that's a type of modification. So there's a type of modulation. You can have other types of modifications that are sort of well defined chemically, but you can also have bending, for example, properties of the DNA itself. And then a particular regulator might bind DNA that is squished versus DNA that is looser, and so on and so forth.

These are additional subtle properties that are not fully captured in that ACGT digital abstraction. But very rarely do you break these levels of abstraction to basically start worrying about quantum effect at the level of amino acids. Does that make sense? Yeah. An analogy would be this has a certain font and sure I can still read it and often you don't notice the font, you don't notice the font change.

So if this C was written in a cursive C, I would read it the same. I wouldn't even notice it most of the time. And sometimes it can get so baroque that it would then cause me to pause and I'd have to reread or maybe I would skip the word because it's well that rarely happens but imagine it caused some error in me that I had to skip the word. Exactly, exactly it's exactly the same way. So basically the genome has tuning like italics or bolding or underlining that can give slightly different meanings to different words and sometimes can turn them off altogether.

For example, strike through. This is a font modification, if you wish, that basically causes you to actually skip a word. Basically say, oh, you know, I should not be reading this. And over parentheses or a dash at the end of a sentence, and so on and so forth. So basically all of these things are figures of the language that we have evolved,

but biology has done so a few billion years before us. Are there any other modifications like bioelectric fields or something else atop just squiggles on the CTAG? So the DNA is not swimming around naked inside our cells. It is packaged in nucleosomes. Nucleosomes are made out of eight histone proteins. Most of the time, H2A, two copies, H2B, two copies,

H3, two copies, H4, two copies. And there's about 150 nucleotides that are wrapped around every single one of these nucleosomes. Now, that packaging itself can basically undergo acetylation or methylation or ubiquitination or you know, post-relation. Ubiquitination? Yeah. What's that?

It's basically adding different types of modifications, adding additional chemical modifications to the histone proteins that hold together DNA when it's packaged up. And now the number of acetyl groups, for example, that you have can influence the compactness of DNA. If you add more acetyl groups, it becomes looser. If you remove them, it becomes tighter. And that has physical properties, not just discrete quantitative properties, in the interpretation of the DNA itself.

So the beauty of the digital code of ACGT is that it is transmitted unaltered from generation to generation. In other words, a thousand generations later, it's not that the A has now diffused into some barely readable form of A. Every generation is replicated as an A. And if a mistake is introduced, like a T, now that T will be the new normal. It's not going to be somewhere between an A and a T. So basically, that aspect of inheritance is completely discrete.

But then there's the interpretation, the utilization of DNA, which in our somatic cells, for example, gives rise to brains and hands and eyes and senses and so on and so forth. That interpretation is by reading the DNA, making something of it, the same way that you were describing reading a piece of paper. That interpretation is where these marks are the most active. Can you talk about if there's a correspondence here between syntax and semantics,

or if that will take us off field, then we can go back to cognition and AI? So you can think of syntax as simply the fact that every gene starts with ATG and it ends with one of the three stop codons. So that's the syntax. You can also think of syntax as splicing, basically where a single protein is made out of multiple segments of RNA, which are then joined together. They're transcribed as one, but then they're spliced together in a process known as splicing that basically follows again very regimented rules about you know the donor and acceptor sites that are basically bringing them together. So this is all syntax. Now semantics, gosh where do I start? It is so, so beautiful. There's layers and layers and layers again of semantics. Basically the way that meaning comes out of DNA.

So let's distinguish two classes of functions. One of them is coding for a protein and one of them is coding for the regulation, the control of all of the proteins. So 1.5 all of the proteins. So 1.5% of the human genome codes for protein. 1.5% tiny fraction. 98.5% does not code proteins.

Somewhere in that 98.5 is a bunch of garbage, a bunch of spacer, a bunch of repeat elements that are just replicating for themselves, and a bunch of control regions that govern where the proteins will bind to turn on other genes, where the silencers will bind, how to organize the structure of the DNA, where to place nucleosomes, how to displace them when you're turning on transcription and all kinds of other stuff. So the regulation, the control of DNA lies within that 98%. And the proteins lie in the 1.5%.

Let's talk about the 1.5% first. So the way that meaning arises is when the code, the ACGT, is translated into amino acids. And those amino acids take shape. They fold onto themselves. Now we only have four nucleotides, but 20 amino acids. The 20 amino acids have side chains that have very different properties. Some are small, some are big.

Some will bind with each other. Some will basically interact loosely with each other. What they call side chains? Side chains. So basically there's the backbone, the amino acid backbone, and then there's the side chains, which is one of 20 options. So for example, tryptophan or methionine, you name it. So basically these are side chains, these are different amino acids.

So the alphabet of 20 amino acids is basically what allows you to have the diversity of functions. Oh, anyway, so we have four letters which give rise to the alphabet of 20 backbones of a protein? Yeah, 20 building blocks of a protein. And the backbone is? And the backbone is just a phosphate backbone.

Okay. For the DNA, and it's backbone. Okay. For the DNA. And it's an amino acid backbone for the proteins. But basically the code is not in the backbone. The code is in the bases in the case of DNA. And it's in the amino acids in the. Got it. Residues in the case of proteins.

Okay? So now where does the meaning arise? The meaning arises in the folds that proteins. So now, where does the meaning arise? The meaning arises in the folds that the protein makes. So basically, a set of amino acids, a sequence of amino acids, will basically fold in three dimensions. That fold will determine its structure, and its structure will determine its function.

So now the meaning, the semantics arises from that fold. And if you want something that will, for example, bind sugar, bind glucose, then there's a fold for binding glucose. If you want to transport cholesterol, there's a fold for transporting cholesterol. Can you make a different fold? The answer is yes, evolution has done that. Can you modify the fold while still preserving the cholesterol transport capabilities? Yeah, absolutely. Evolution has done us that well. So basically, you now have the function which is encoded in its shape complementarity, if you wish, with everything else that it interacts with.

And you can think of evolution, you can think of all of this tinkering, this tuning that evolution does, as basically exploring the landscape of shapes, if you wish. And this landscape of shapes will basically sometimes preserve the function, sometimes break the function, sometimes improve the function. And now whatever code change led to that change in shape and that change in function will be selected for or against. So that's why it's so beautiful because you basically have this extraordinary

synthesis of all of the rules of physics, all of the rules of chemistry, all of the rules of nature and gravity and light and dark and temperature and, you know, van der Waals forces and quantum effects that are constantly subjected to this tinkering across thousands of interacting parts all the time. Okay, I think this will bridge us beautifully to cognition and AI. So syntax in logic is usually thought of as the rules and then semantics is usually thought of as quote unquote meaning. Now here when people hear that they're like, what the heck is the meaning and you take the CTAGs and then you translate them to the proteins.

Where's the meaning in that? So why don't you talk about what meaning is, how that has anything to do with meaning, and then lead us into cognition. There are two sides to McDonald's new Cajun ranch McRisbee. On the one hand, it's a masterpiece centered around that crispy, juicy, tender seasoned chicken. On the other hand, McRispy fans are going to love the bold, tangy taste of the new Cajun Ranch flavors.

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That's the powerful backing of American Express. Terms and conditions apply. Visit mx.ca.com. So how do we make sense now of all of these different layers? So we talked about the protein side of things where constructs take meaning as you start going up the chain from nucleotides to amino acids to folds to structures and eventually to sort of all of the interactions. On the non-coding side of things, the meaning arises from the surfaces, the landing sites, if you wish, that every piece of DNA code provides to the corresponding proteins.

So DNA is a double helix. The bases, ACGT, are basically stacked in pairs, AT, CG, GC, GA, oh sorry, not GA, but CG, and so on and so forth. And these bases form the core of the ladder. Now when a protein binds this ladder, it binds from the outside. It binds the atoms that are facing outside. So the backbone of the DNA? No, because the backbone has no sequence specificity. The backbone of the DNA is just a phosphate backbone, and they all look like phosphates.

So transcription factors, regulators, have an affinity for the backbone just to recognize that there's DNA there, but then the affinity that gives rise to the recognition of a sequence pattern comes from feeling the sides of the bases. So basically there's, you can like, let's say there's an iron ladder and the bases themselves are made of wood, it's the wood that gets recognized. And the shape of different stacked base pairs is different. If I have an AT or a GC, the sequence that comes from that gives me specific atoms

that will then be recognized by specific protein. So now when we think of ACGT in the DNA, this is bound by specific regulators that will then give rise to meaning based on the set of regulators that are bound in a particular region. So basically, if three different regulators, let's call them proteins, I don't know, John, Jack and Jill. Okay? So if John, Jack and Jill bind a particular region of DNA, that region will take a new meaning because these proteins are bound. And then the translation between the binding sites, the motifs, the sequence patterns that are recognized and the set of proteins that are bound, that's where the meaning comes,

if you wish. And then some of those will influence the way that DNA folds over thousands of bases, sometimes millions of bases, letters, and that will basically give rise to a gene regulatory meaning, okay? Now, if we think like computer scientists, how do we make sense of all of this?

Where does the meaning come for us? Then we were talking earlier about how math is what we use to understand physics. How, like what do we use to understand this giant evolved tinkering result? We need a way to discover patterns in what was evolved. We need a way to discover not the 10 or 20 or a thousand rules, but the billions of rules that make biology function.

And in those billions of complementarities of shape matches, et cetera, we can find some common repeating recurrent patterns, some design principles, if you wish. And now those design principles are not unlike some of the design principles that we humans have come up with, both in our society, as well as in our artificial intelligence systems, as well as in our own cognition that interestingly

evolved from the same tinkering of ACGT. Like the most beautiful relationship in the universe perhaps is the relationship between the fact that the DNA that has its own language and code evolved a construct that has its own language and code that is now a learning construct that can adapt to learn almost any language and code. And that in that language and code, we develop programming languages and human languages. And in those languages, we wrote constructs that now give rise to AI systems, which themselves can now develop their own learning and code and patterns about the natural world.

So these patterns about patterns about patterns and this translation across all of these different languages, initially from the atoms and the quarks and the molecules, and ultimately the abstraction layer of ACGT, the abstraction layer of the 20 amino acids, and the abstraction layer of folds and proteins, and eventually the abstraction layer of I'm going to basically now build a learning system by evolving neurons and having projections come across them and synapses that are now able to sort of sense sensory signals and store them as memories and build inferences in patterns of thinking and start thinking about logic.

And now that becomes the language of cognition. And that language of cognition, which initially was just there to sort of make sense of our senses, to find food, to reproduce, to sort of take care of the young, to sort of make new generations, to build shelter, and so on and so forth, eventually developed human language. And that human language now has its own constructs from the simplest languages to the most elaborate grammars and syntax and meaning and semantics. And from that, the language of mathematics

with which we humans evolved ways to now represent the world around us, to represent shapes and geometry and to represent abstract patterns and to represent physics and so forth. And from that, being able to master electricity, being able to now make transistors, and being able to build modern computers, and from that building these programming languages that we use today, which themselves have layers of abstractions from the zeros and ones of the electric fields, to the assembly code,

to the interpreted code, to the more and more abstract languages that we have today, to then being able to actually speak with an AI interpreter that will then write the code for you, to then code up a whole other stack starting from the inferences of individual neurons in a neural network. And then the transformer architectures and being able to sort of lower rewire attention edges. And then from that making sense of the world around us. I mean, this is just like the most beautiful story of abstraction and of languages and translation

and sort of building up all of civilization on layers and layers of, you know, his basic principle. When we first talked a few days ago, we bonded over how most people have a diminution of language. They curtail it. They will say something that is repudiating of language. Language is just, I have some perfect qualia in me or some meaning in me, some intent and all I must do is convey it to you and I can never do so precisely. Therefore, language

is low resolution communication and all you get is broken telephone and if only we could communicate more precisely then we would have less misunderstandings and everything would be much better and oh, isn't language just a poor tool? Something like that. That's how many people think. And I wrote this article about my views on language, where I argue something that's akin to the opposite of that.

That language is not only a process of transmission, but it's also of creation and excavation. And then you had your own views on language. Please tell us what language is and how you see its place. So I sympathize with both views. On one hand, language is something that we have to convert our thoughts to. And therefore it's such a low resolution interpretation of what we think, which is sometimes abstract. Other times, language becomes a tool of formulation and it gives us

primitives within which we can now start interpreting the world. Language gives you the primitives. Language gives you primitives with which you can then build layers of abstraction above that. In other words, I could be the most intelligent being on the planet. So imagine the most intelligent being on the planet, like basically, like this random human that is born in the middle of the Amazon forest, they have extraordinary cognition. Will they be able to formulate the laws of physics starting from nothing without language?

I think language gives us stepping stones that with which we're able to understand more and more and more complex concepts. And then those concepts basically take life and take new meaning by forgetting the primitives at some point with which we kind of started. And you can now start elaborating more and more abstract types of operations. Basically, you know, we're learning exponentiation with my kids right now. And I explained to them that exponentiation is repeated multiplication.

Multiplication is repeated addition. And they're asking, okay, is there something more complex than multiplication? Like, of course, tetration is repeated exponentiation. Is there something simpler than addition? Yes, addition is repeated next. So basically, successor function. So eight plus 13 is just simply applying

the successor function 13 times. So this layer of complexity that you can build more complex operations from simpler ones, the tration would make no sense if they didn't understand addition. So in the same way with language, we can build much more complex functions from these much

more elaborate building blocks. But there's a beauty also in the misunderstandings of communication as well. So by misunderstanding each other, we are creating new meanings that might be much more interesting than the meanings that I had in my thoughts. Interesting. And this happens twice. This happens in me trying to take my abstract thoughts and formulate them down to language. And this happens in you taking these formulated language and abstracting it back up into your

own brain. So there's two levels of mixed communication. There's how I misspeak and how you mishear what I say. And both of them are part of the creative process. And when I meet with my own students, I'm basically telling them, oh, please write down your questions. Like before our meeting, we have very short meetings, right before our meeting, have all of your questions written out,

have all of your slides there with legends, with explanations, with whole sentence interpretations of what you're seeing. Because the act of doing so will often make you think about the problem that you're having much more deeply and sometimes you will solve it just by formulating it. And there's a programming principle that basically says that one of the tools that programmers can use is the rubber duckling. The rubber duckling is exactly that, it's just a Robert Ducklin, nothing intelligent about it. But by explaining to the Robert Ducklin the problem that you're having, you're formulating

it, and sometimes you're solving it. And I'm sometimes using chat GPT as the Robert Ducklin. I'm basically telling it, oh listen, I have to create a presentation and I want to start with this and I want to sort of continue with that. I want to create a presentation and I want to start with this and I want to sort of continue with that and I want to touch on the ideas of this and this and this and that and then link it up this way and that way. And it's not a rubber duckling, it's much more intelligent than a rubber duckling, but it is still serving its function of me synthesizing and concretizing my thoughts into language. And sometimes the output will just be junk.

But what I wrote has already got the advantage of forcing me to formulate my thoughts, you know, one after the other. So in some ways, ChachiBT for me is the ultimate anti-procrastination device, because I can basically just ask somebody to do the stuff that I want it to do, but I can ask it so precisely that I'm basically doing it. Right, right. So this is one of the advantages to writing books. For people who are more technical, you would think, okay, why don't you write another textbook?

Why are you writing for a popular audience? And people that I've interviewed like Chiara Marletto of Constructor Theory and Roger Penrose and so on, even though they're writing to a popular audience, they say that they understand even their more technical ideas more precisely because they've had to reformulate them from another perspective. It's not always that I have to simplify it to the common denominator, it's just the act of formulating it.

I was invited to give a TEDx talk in 2008. And I was given a list of words that I couldn't use. And those were basically all of the words that I needed to explain what I'm working on. It was like playing taboo with science. But the act of now thinking about what is the more broad principle with which I can explain what I'm doing

to a much broader audience. So what I tell my students every time is, write your computational biology results as if you're speaking to the smartest physicist on the planet. Namely, somebody who's extraordinarily smart. But not in your field. But not in your field. But not in your field.

Right, interesting. So I say, assume infinite intelligence, zero knowledge. And that combination is sort of what we're aiming for. In other words, give everything for people to understand what you're saying without the jargon. And I think that's the beauty of these democratization books, these dissemination books,

these books that reach a broader audience, that they force us to now start thinking about how other fields will interpret what we're saying. And these are not like, you know, it's not like, oh, say to me as a five-year-old, but basically, you know, say to me like I'm a Nobel Prize in economics. Right. So it's a very different type of vulgarization, if you wish.

I love that. So I love this as an alternative to the colloquial, explain it like you're five. And then some people like to say, well, if you can't explain it like you're explaining it to someone who's five-year-old, then you must not understand it. But what they secretly mean is, I don't understand it like you're explaining it to someone who's five-year-old, then you must not understand it. But what they secretly mean is, I don't understand it, I feel bad. So I'm going to say that you must have some misunderstanding if I'm unable to get it. So I'm a researcher, and that takes the vast majority of my time, but I'm also a teacher. So I'm constantly teaching. The beauty of modern-day academia is that you have the two coexisting with each other. And why is that so beautiful? That's beautiful

because I am constantly forced to go back to basic principles. I'm constantly forced to go back to, hey, why does this make sense at all? Or if I'm an outsider, how will I understand this field? And that basically pushes me to number one, re-understand all the foundations that I maybe learned 20 years ago, update my knowledge of the broad field beyond just the depth of my own research every single year to sort of re-give the same lectures but now with all of the new advances, and understand all of the gaps in my explanations that will basically then allow a newcomer to the field to enter it. And I think that that advances my own research.

I've spoken with many researchers who say, oh, I wanna be in a research lab, not academia, because I hate teaching. I'm like, teaching is part of the deal. Teaching is part of what sort of forces you to sort of have that reach to a broader audience, that welcoming to someone new. And of course, it works both ways. These people now come with their own baggage, their own ideas, their own ways of thinking that

brings fresh wind and fresh air and fresh blood and fresh thought to your existing fields. So I think that this dissemination is part of what makes academia so extraordinarily creative and successful. The fact that we're teaching, you know, I'm teaching intro to machine learning. I'm teaching genetics or personal genomics or drug development. And by forcing myself to be not just conversant in all of these fields, but deeply insightful about all these fields in my own teaching to others, I'm basically now creating new representations of my mind for how I should

be disseminating this. And in doing so, I'm understanding it in fundamentally new ways, because every teacher is constantly looking for new abstractions, for new ways of conveying that knowledge to the next newcomer. And that is part of making that understanding so much deeper. So it's not that you don't understand it. The act of teaching it forces you to understand it at a level beyond what you would need to just work with it. And I think that whole, if I teach it, then I understand it deeper, is very, very fundamental. If I teach it, then I understand it deeper is very, very fundamental. Now when students ask a question in the class, sometimes those questions show a complete

lack of understanding. Those are the most powerful questions. Because if they're misunderstanding, chances are there's like 20 other people who are misunderstanding. And that basically says, no, no, the way that I interpret this question is entirely my fault. Oh, I understand why you're not understanding. And the beauty of it is that it's not, oh, I'm imagining that these students are the smartest people in the world.

No, they are. The beauty of teaching at MIT and in these kinds of institutions is that these are some of the smartest people on the planet. If they're misunderstanding what I'm saying, it's fundamentally about me. And fundamentally something what I'm saying, it's fundamentally about me.

And fundamentally something that I'm constantly placing myself in their eye to try to see what did I say that was ambiguous that they would have interpreted to go down that other path. And that's why I'm saying that those questions are the most beautiful because they teach me the limitations of my own speaking. Of sort of, oh, wow, you know, I can go back, you know, three, four sentences. I'm like, oh, that whole thing could be interpreted in a different way. Let me now adjust course to sort of correct that misunderstanding.

Yes. Something you said that was extremely interesting was that there are two levels of a misunderstanding. There's one that of you miss speaking and then other miss hearing or miss modeling or what have you. Yeah. And then you said that there's something creative in that. Yeah. Okay. I didn't hear what's creative in that. So explain. Yeah. Yeah. Beautiful. So, so we have research meetings with my students all the time. This room fills up with people and we're all brainstorming. And a student will start saying something. And as they start saying something,

every one of us is thinking. They're like, we're taking the same concepts and like spinning them in our heads. And I will start understanding the beginning of their sentence and I will go off on my own tangent for what they could be possibly saying. And sometimes I will be so enthralled with what I'm saying, and all of our meetings are constantly recorded, so I don't feel bad saying, okay, okay, I didn't exactly hear what you

said, but here's what I think you said, you know, based on the first few things that I hear. And now I go down one path. So that's part of the creative process because I'm constantly trying to piece a mental model of their idea as it's being formulated. And sometimes that beginning will now take me a different path. And that path might be just as interesting. I see.

And sometimes even more interesting. I see. So imagine two people are playing tennis, which is what we think a conversation is supposed to be. But sometimes you can hit the ball instead of toward the person over there into an interesting part of the woods. Now you have to go fetch the ball. You would never have gotten there if you were just exchanging it with one another. That's exactly right. That's exactly right. And now let me talk about a different aspect. So this was on the receiving end. So I start hearing what you're saying and I'm going one way with my brain. And then I'm like, oh, that was really interesting, but that's not where you went.

Let me show you that part of the woods. And then let's come back and now I want to see your part of the woods. Now that's one direction. The other is in my own formulation of my ideas. So I have this giant headset that I constantly put on when I'm thinking. And I basically say, okay, okay, okay, give me like 10 seconds. And I'm like thinking there and very often I realize that I'm put on when I'm thinking. And I basically say, okay, okay, okay, give me like 10 seconds. And I'm like thinking there and very often

I realize that I'm moving my hands and I'm sort of taking different parts and sort of putting them together. And what I'm doing there is that I'm taking ideas and just like expanding them out. And sort of every idea lives somewhere in that hyperspace. And what I'm doing there is that I am trying to, without language, I'm pretty sure without language,

I'm trying to sort of take these variables and sort of align them, organize them, think about them, find patterns between them, et cetera. And in forcing myself to put them into words, there's a massive amount of compression that needs to happen from this sort of high dimensional space. And in that compression, I will sometimes create one thing and sometimes create another thing

and sometimes create another thing. So basically these are projections of this higher dimensional world of ideas that each of us is having. And in making these projections, I will change or alter a little bit that projection, that initial idea, because the projection doesn't work in that way. And I'm basically saying, okay, let me now repeat what I just said, but in a slightly different way, or let me now think about it from a different angle.

Or I'm like, okay, I have three ideas but they're all living together, you know, let me try to sort of get them out. So there's an error that gets made from this high dimensional to the spoken word and that reduction to language is, you know, part of the altering of the ideas and then you re-expanding that into your own high-dimensional view might take us to a different representation and so on and so forth. So anyway, I think that that's absolutely part of the creative process. So professor, I want to know if you have a biological theory of everything that also includes cognition and AI, and if it has a biological theory of everything that also includes cognition and AI?

And if it has a name? So, we started out with these building blocks of quarks into atoms, into building blocks of physics, into chemistry, eventually DNA, amino acids, biology, and life. And what's extraordinary with that progression is that life itself now became self-reproducing. Why is that so exciting? Because then you kickstart the process of evolution and you now can tinker. You can now play the numbers. You can basically just make a billion copies of your organism and start tinkering and basically,

the fittest will survive for every evolutionary niche. These organisms initially are simply taking on energy, making more of themselves and reproducing. But to become fitter than their competitors, they need to start chasing chemicals, figuring out where is the highest concentration of sugar, also known as chemotaxis. They're able to align themselves to chemical gradients. That's the beginning of sensing. And sensing can be super, super simple. Basically, you just make more of your cell turn towards the direction of higher

concentration of sugar. At some point, you need to start sensing light to start knowing which way is up, which way is down, which way is warm, which way is cold. And chemotaxis becomes a little more sophisticated. Can you spell that? Chemo for chemical, taxis for aligning, like taxonomy. I see. So you align yourself to the chemical.

So this chemotaxis now allows you to now start sensing from your environment. Initially you sense chemicals, you can sense heat, you can sense pressure, you can sense light, you can sense heat, you can sense pressure, you can sense light, you can sense sound eventually. And you can now start saying, okay, I have multiple streams of information, which one should I believe? Which one should I believe? Which one of the different streams of information should a cell believe?

So I understand which one should I act on, given that I have plenty, but what do you mean which one I should believe in? We have conflicting information coming to us all the time. Our brain is constantly taking multiple hypotheses and choosing the ones that is more compatible with, say, the vast majority of the sources or with the most reliable of those sources. When you look at optical illusions, you know, your eye keeps choosing between different representations of reality.

So which one should I believe basically means that there will sometimes be conflicting information. I'm looking for the most food. Should I go towards the light? Should I go to a little gradient this way that might take me away from the real source of food? Mosquitoes floating around, they're basically sensing CO2 gradients. They're looking for light. They're looking for heat. But that still sounds like which one should I do? What actions should I take?

First you build a model of the world, and from that model of the world, you need to move towards the source according from that model of the world you need to move towards the source according to that model. Okay, so the belief is the instantiation of the model? Correct. I see. Correct. So then you act based on that model.

That's exactly right. Got it. Yeah. So the concept of multiple streams of information, if they all agree, no problemo. Then I have different regulatory processes that basically will respond to this one, respond to that one, respond to this one. So basically, initially, if you think about life,

evolving new senses, if you wish, and then having actuators that based on those senses starts moving in the direction that that sense tells it, you now have five different senses and they're all pulling in the same direction, no problemo. You can basically build independent streams of response that a cell will respond to all of them at once. But sometimes it will be conflicting. The chemical information might be pulling me this way and the light information might be pulling me that way.

And the heat information a third way. Am I responding to all three of them? So that's the point in evolution where you have cognition arising. The concept of a central nervous system. The concept of I can integrate that information to build a model of the world and then act upon a single decision for that model of the world. Does that make sense? Somewhat. So it sounds like what you're saying is we have touch, we have sense, we have hunger,

we have temperature sensors, interoception, what have you. Okay. We want to take all of that and then decide what is the world like? And that what is the world like is the same as a model which if I understand correctly is the same as belief. Based on that you then act. But what I want to know is can people act in contradiction to their belief? Because it sounds like there's a one-to-one correspondence between... not one-to-one but model leads to an action. But it's not like if you were to develop a model and it says you must take this action that you can do another action.

If you were to do another action, that to me would imply you have a different model. So I'm actually slowly getting there. I'm sort of building up layers and I'm about to get there. And I think a lot of your conflicts will be resolved as soon as I complete. But so far, am I roughly following that? You are, but you are two steps ahead. I'm going to break it down to much simpler. So we might even splice all of this back and forth for a little bit.

And let me explain why. So to go back to what I was saying, I now have multiple streams of information, and I could have a decentralized control which reacts to every one of these streams of information. But I still have a single goal. My goal is to just get more sugar. So I have chemical sensors, I have visual sensors, I have heat sensors, and all of these I'm just trying to make a simple decision for a simple goal of more sugar. So I'm still a bacterium and I'm now integrating information. And now that's where cognition starts.

Cognition starts with having some kind of integration of the multiple streams of information to make a decision based on that. And that common decision with a single goal is fairly simple. You're just trying to say which of these lines of evidence do I believe in sort of going that direction. Now with central nervous systems the type of integration is much more complex because you now have multiple senses, you have multiple lines of evidence and you have a much more complex model of the world. And that model of the world is not only dealing about one goal, but multiple goals at the

same time. I'm thirsty. I'm hungry. I'm late for class. I'm cold. I forgot my jacket. All kinds of things. So I'm making a decision based on multiple streams of information,

based on multiple, sometimes conflicting goals, based on local tactics, based on global strategies, short-term, long-term, and so on and so forth. And in my view, that's one of the most beautiful transitions in this, you know, history that I'm painting of complexity immersion. So the concept of the emergence of cognition, the emergence of integration. Now you can think of life on the planet as having these very, very simple rules, simple goals. Reproduction, nutrition, and shelter, and so so on so forth. But it all boils down to reproduction.

Namely, if a species is able to make more of itself, it will survive. Evolution doesn't care if that species is beautiful, intelligent, sort of, you know, useful. No, nothing matters. The only thing that matters, will there be more of this thing? Okay. You can now start thinking about why would a species, for example, develop a central nervous system?

And the answer is, oh, because it gets better at, for example, reproduction, or it's better at survival. Or it's better at survival. So all of these are boiling down to, can I better interpret the world around me, make better decisions, and ultimately arrive at my goal of making more of myself? The place where things get a little weirder is humans. Because nearly every other species, you can explain its behaviors as purely natural selection. You can explain it as, you know, dogs are basically being kind to humans and we will breed them more. You know, plants are being kind to animals and they will eat them more.

They will eat their fruit and poop their seed, you know, to spread the plant. So nearly everything in evolution you can explain as some kind of symbiotic relationship or some kind of, I'm fitting in my evolutionary niche and I'm providing a service to whoever will help me reproduce. Humans is a little weird because we have basically conquered natural selection. We have overcome the burden of reproduction and shelter. And in some ways you could say that we stopped evolving, at least in the genetic sense. That basically humans is no longer about being the strongest or being the smartest or being

the fittest or you name it. Right now, humanity has taken a new trajectory. And that trajectory is not one of genetic evolution, but perhaps one of cultural evolution. This is what I call horizontal evolution instead of vertical evolution. So vertical evolution is passing on your genes. And with humans, vertical evolution also included passing on your knowledge to your village. Every village had its own vertical lineage of knowledge. And at some point, culture started spreading horizontally. A great idea in Mesopotamia would basically spread across the world. A great idea in Chinaopotamia would basically spread across the world.

A great idea in China would spread, you know, perhaps separated by the Himalayas or, you know, spreading all kinds of cultural ways. And part of what has led to the acceleration of civilization is the fact that we have unified the world. The trade routes of being able to sort of take silk and you know trade it with glass or you name it basically means that you are now exchanging information much much more and therefore a new idea will be adopted and spread much more rapidly. So it might just see this as the reason you call this horizontal is because

there's horizontal gene transfer with bacteria. So this is like horizontal meme transfer with people. That's exactly right. That's exactly right. So, bacteria are able to spread their genes this way. That's horizontal gene transfer. That's basically saying, I found an antibiotic resistance solution. I'm going to dissipate it in the environment, those genes that get randomly dissipated could be any of these genes.

But if the genes that get randomly dissipated are antibacterial resistance genes, then those bacteria will be selected for and will continue reproducing and so on and so forth. So basically there's, again, you know, it's not teleological, it's not based on the goal, it's simply purely based on survival. But with humans, it's different. With humans, there's this new layer of evolution, which is about cognitive evolution and cultural evolution

and idea evolution, meme evolution, if you wish. And now those memes live in different niches. cultural evolution and idea evolution, meme evolution if you wish. And now those memes live in different niches and those niches themselves are attracting different people. And there's this coevolution of people and niches. And there's the evolution of the ideas, there's the evolution of the genes that are sort of starting to blend in some ways. And the very exciting part about all this is that we are now creating societies where we take care

of each other. Whereas, you know, if you don't have good eyesight, no problemo, we're going to help you. If you are having trouble running, no problemo. You don't need to sort of chase your own food. If you have trouble, you know, for anything, we are there to help. So we as a society are there to help. And the beauty of that is that you can now allow humans to excel in thousands of different directions. And basically you can have someone who's extraordinary at physics, but can't cook their own meal. No problem.

In today's society, we're basically constantly trying to force everybody to learn math. Why? Why? We don't need everybody to learn math. We're forcing everybody to do sports. Why? Not everybody needs to do sports. I think it's okay to do specialization. That's what made our society work.

Like the concept that every one of us has to be good at everything is ridiculous. I think it's okay to let humans specialize. Now in science, does that mean that you should stop looking at stuff outside your field? No, no, not at all, because being better at understanding physics might give you new ideas for biology and vice versa. Or AI to physics, we can talk about the Nobel Prize soon.

So, the advantage of basically being a professor in the department of basically being a professor in the department or of artificial intelligence while working on biology, while interacting with students from physics and from all of these different disciplines is that we constantly have this interbreeding of ideas. Instead of basically doing specialization in this way, you basically are bringing all of these different ways of thinking together. And I think that the next generation of science comes from taking all of these different specialties and having people collaborate together. Right. So not getting rid of the specialties, even going further in your specialty, but

more cross- cross pollination. That's exactly right. That's exactly right. And I don't want just interdisciplinary scientists all coming to my lab. No, I want the pure physicist and I want the pure mathematician and I want the pure biologist and I want the pure chemist and I want the pure experimentalist all working together under one roof. Because diversity is the power of humanity. Diversity is the power of pushing things forward in a multiplicity of solutions. So that's sort of part of the beauty of all of that. So now if you ask me, what is that unifying theory,

if you wish, so we now have this spread of ideas. We have these layers that I mentioned earlier of building up all of these different components to eventually create these artificial systems that are able to do cognition. What is your unifying view of biology, cognition, and AI? So, underlying all three of them is this fundamental concept of fitting functions.

So if you look at the genome, you basically have millions of gene regulatory constructs of elements that will basically govern the regulatory networks, the circuitry of those genomes. And by tuning those networks, you can adapt to different kinds of environments, different niches. This is basically a function fitting operation. This is tuning your gene regulatory circuitry to whatever evolutionary niche you're currently occupying. So that tinkering is at the basis of genomes, it's at the basis of gene regulation, and it's at the basis of evolution. Evolution doesn't work by giant, you know, knobs that you're turning one way or another

way. Evolution works by being able to tinker with subtle functions. And this tinkering is what I call evolvability. In other words, you get good at evolving, you get good at adapting. And this evolvability is something that, in my view, took a long time to get at. In other words, if you look at the history of life on this planet, it took a billion years for multicellular life to even appear.

And then you would expect that with genetic algorithms, increasing complexity will require more time. But in fact, we see exactly the opposite. That basically, after you have multicellularity, you start sort of seeing extraordinarily complex body plants very, very rapidly. And after you have those, you start seeing increasing cognition very, very rapidly. Like basically, the human brain evolved in a blink of an eye in evolutionary terms. This expansion of the neocortex is something that happened faster than almost anything in evolution.

And that in my view is reflective of. Evolvability it's reflective of, we get better at modularity. We get better at hierarchical organization. We get better at all of these different things. So as you start increasing that complexity, you see an acceleration, not a deceleration. This is mind-boggling. And again, it sort of teaches us how powerful this tinkering is. So that's in the realm of gene regulation.

But evolution is tinkering with gene regulation. So in a way, this adaptability of these thousands of little parameters appears to be an emerging property, maybe even a fundamental necessity of both evolution and of gene regulation. And if you start looking at the shapes of proteins, it's exactly the same thing. They're trying to match a particular function.

You're tinkering with these 20 amino acids as your building blocks, but then you have thousands of copies of these and you're constantly adapting. So the same kind of principle of this tinkering applies to protein structure. It applies to chemical structure with the same type of tinkering of individual atoms. It also applies to AI. It also applies to cognition. How? If you look at our neurons, we basically have a very simple developmental program that gives

rise to our entire brain. Basically simply says, just create these layers of these neurons. As they build the layers, the neurons are basically specializing. They're communicating with each other. They're sort of having both within layer communication and across layer communication. You basically have the signaling itself shaping the connections. You basically have synapses that are reinforced or weakened based on the signals that they receive

and the feedbacks that they receive. So it is an adaptable neural network based on the information that it is processing. As children grow up in more and more complex environments, their brains are actually adapting to these environments during their early years. And you know even during gestation the signals that you are exposed to are altering the neuronal processes that you have. Why is it so hard to learn a language after the age of 12? Because a lot of your

neuronal connections have now been fixed, they've been pruned. The sounds that you could hear as a child have now been pruned away because you're not responding to those sounds. Same with olfaction, same with all kinds of aspects of our adaptation to different types of environments. So again, the neuronal aspect is very much about these thousands of parameters, millions of parameters, billions of parameters, constantly getting tuned to fit whatever functions.

And on the AI side, it's exactly the same thing. Modern AI, like all of these representation learning, all of this generative AI, this is built on the same fundamental principle of I'm starting with thousands of parameters and I'm sort of building them up to fit functions. And in all of these different layers, you have one property that is unifying them all

if you wish, and that property is the property of abstraction. It's the property of basically being able to build building blocks upon which you will then build the next layer. If you look at human language, that same concept applies. You basically can take a word, which is a composite word, for example, atom, it means uncuttable. Tomi, bectomography in Greek means to cut. Atom means uncuttable.

But you now have the concept of an atom that you can build upon and you can sort of build layers and layers of understanding on that part. So it is always this tuning of functions, these building blocks, these abstraction layers that you're constantly able to recombine concepts and then build upon them. In biology now you have, for example, a protein domain that you have created, you have a fold. That fold will get reused and reused and reused and then recombined with other folds to sort of now create a new primitive. And that new primitive will now get, again, replicated in different places and reused. You not only have vertical evolution of a gene changing, but you also have duplications of these genes where you now have two copies that you can continue tinkering with.

So I think this beauty of creating building blocks in layers of abstraction is what makes language work. It's what makes language work, it's what makes evolution work, it's what makes gene regulation work, it's what makes brains work, it's what makes AI work. So modern AI is dramatically different from classical AI. So classical AI initially was about rule-based systems.

And then neural networks were, you know, sort of invented, modeled very much after the brains of mammals. They studied how our brains fit in functions, and they said, okay, great, let's make this sort of function-fitting operation. So classical AI was all about designing systems that appear intelligent. It was all about sort of building expert systems where the expert would come in with all of the knowledge, all of the rules, all of the decision, and we would simply build a sensory part where the AI will observe what's happening in the

world and then make decisions that are predetermined where the flow chart was already determined by a human expert. Like if-then statements. That's exactly right. The advance of neural networks is that you could suddenly start modeling the world using this type of tinkering. It was very much modeled after the human brain. And then the goal was to be able to approximate functions, to be able to have these thousands of parameters

being updated through multiple layers of these systems, which could then build functions that are nested, functions that are reusing other functions. But that only took us so far. Modern AI and modern generative AI, in my view, started with image and vision, where the concept of a convolution was developed. In my view, the concept of a convolution is this dramatic transition to a new type of AI.

That transition is about building representations. The concept that you're not letting every neuron in isolation recognize a different part of an image, but instead you're learning these convolutional filters, these operations that you can then apply at every part of the image. Just a moment. So are you saying that convolution means that there's no grandmother neuron that says, this is what a grandmother is?

It's a set of neurons? Are you saying you still have to look at the whole brain? It's a horizontal. Slice? Horizontally applied operation, where one part of the image says, ooh, I like a filter that recognizes diagonal edges. And you can now use that filter, you can use that kernel,

you can use that primitive, you can use that pattern, you can use that building block everywhere in the image. That you're sharing parameters across many parts of your image. That you're basically learning this convolution of filters at the lowest level, that are basically learning how to recognize edges. Right. And then at the level above that, you're building on the representations of the layer before. So, recall 10 years ago or so when Google Dream or so came up, something like that.

And there was these trippy psychedelic images where a dog became eyes and they were all eyes for 10 seconds, and then it became something else. Okay, are you saying that in that eyes where it became all eyes that it's as if you can have a part of your neurons or what have you that select for the diagonal lines, there may be another one that are selecting eyes or representing eyes and those ones were somehow triggered more? What's going on there? Yeah, so what's going on there is that you can basically

ask can I represent the pixels associated with this image triggered more, what's going on there? Yeah, so what's going on there is that you can basically ask, can I represent the pixels associated with this image using primitives from another domain? For example, you want to represent giraffe pictures, but your primitives are all about eyes, so you're not gonna have giraffes built of eyes. So this idea is that you're taking these primitives, you're taking these primitives, you're taking these building blocks, and then

you're combining them to create higher level abstractions. And in the world of images, this was about going from the pixels to the lines to the shapes to the eyes to the ears and noses and eventually people and scenes. In the realm of genomes, this is about going from DNA to patterns and motifs, and then building them up into grammars and regulatory programs. In the realm of chemistry, this is about building up representations of your chemicals from the individual atoms to the connections that these atoms make together and then the patterns of connectivity that you can then start representing as primitives.

So when you now ask what is the similarity between these different chemicals, you now have the properties of individual atoms that are pieced together by combining the properties of their neighbors in the same realm. When you're looking about proteins and sort of the emergent properties of those proteins, what we're now doing in my lab, for example, is trying to see how do we translate structure into function. And how do we do that? By basically looking at what is the functional representation of a protein? What does it actually do? And there's many ways to capture that function. The highest impact of AI and perhaps the highest challenge of it all

will be understanding life and medicine, sort of improving human condition. So that's the final one and that's basically what my work is all about. So what distinguishes modern AI from classical AI is really these representations. The fact that the same way that I can start building up layers of complexity in an image using these abstractions, layers of complexity in a chemical, in a protein, I can do the same thing with language. I can basically start with these primitives of language. I can start with large language models that represent every word as a vector somewhere in space.

And this vector takes a new meaning from the words surrounding it. So the word apple in the context of a fruit basket is very different than the word apple in the context of a PC or a Macintosh. So the way that our brain understands these words individually has some type of projection and these words in context can shift these projections. And that concept of words taking meaning

with other words around them is identical to pixels in an image taking meaning based on the pixels around them. And atoms in a chemical taking meaning with the atoms around them. And the same way that we find across all of these evolved systems. So this is fundamentally different than the way physics works.

So physics again has these laws that were written 13.8 billion years ago. Biology is all about tinkering. And yet, within biology, we see that life took about a billion years to become multicellular. And from there on, you have this explosion. How is that possible? I think that's possible through the same layers of abstraction, the same building blocks, the same sort of reusability of these concepts that you learn by tinkering with little parameters. And then the way that you get to this exponentially faster growth

is when you stop tinkering with pixels, but you start tinkering with concepts. I see. And that's, in my view, this unifying emergent property of these evolved systems that is fundamentally different from how physics works, but appears to be somehow unified across AI, across cognition, across evolution, across genomes, across gene regulation, across every aspect of living adaptable things. And it doesn't stop at

the resolution of a single human. You can now start thinking about corporations. You can start thinking about societies. You can start thinking about research groups in the same type of building up layers of abstraction and building up complexity and sort of fitting functions and all of these types of emergent properties. Okay, let me see if I got a handle on it. I'll make the analogy with cognition

because I'm more familiar with that. Let me make an analogy with Lego first and then cognition. So let's say you have Lego and you're playing in your little kid in a room. Then you build a bridge in Lego and you also build a fort, a tower. What you could do is then the next day you start over and you build the bridge from scratch. But what if you could build a bridge again as if the bridge was its own Lego piece and the fort was its own Lego piece, the castle was its own Lego piece. So that's what we do with ideas because when we hear the word, when we hear about plants,

we don't have to reconstruct plant from stem with dirt and so on or however else we learned it before. We just got plants now. Now we can use that as a foothold and go farther. And so for life, you don't have to wait another billion years for a eukaryote to develop. You all of a sudden have life that has mitochondria in it already. And that's about building blocks. It's about primitives.

It's about re-use. It's about abstraction. That becomes now part of your language. And that goes back to why language is so extraordinary. Because these primitives allow us to now formulate much more complex ideas. And it appears everywhere, throughout the evolved world, throughout the world that adapts, throughout the adaptable world. The same type of principle is everywhere. That's a whirlwind tour. So, Manolis, what I want to know is what does the future, and people may not know this,

but you are also a professor of medicine, or at least you study medicine, you research medicine, what does the future of medicine look like? And feel free to integrate that with AI and biology and the rest of the concepts that we've been speaking about thus far? So, we are at a crossroads. We are in this extraordinary convergence of all of these different fields that have come together. One of them, of course, is AI, and of course, computer science as a much broader part,

a much broader field of which AI is one part. We also have miniaturization of technologies of being able to use microfluidics to manipulate individual cells, to be able to use imaging with expansion microscopy to be able to look inside individual cells to understand the properties of the cells. You have the extraordinary measurement capabilities that are allowing

us to start observing biomarkers, start observing imaging of the human brain. We have the ability to sequence genomes at this extraordinarily fast pace with these seven orders of magnitude reduction in price in the span of a decade. Wow. Which is unprecedented in the history of humanity of how fast this technology has evolved. So we are now at this crossroads of all of these different fields coming together, along with the ability to fold every protein using alpha fold and ESM fold and all of these extraordinary tools that are now using protein language models and geometric deep learning and graph

neural networks with constraints that are geometric on them. And we now have the ability to measure gene expression across millions of cells and thousands of individuals, every measurement being 20,000 genes at a time. That allows us to peek into the building blocks of biology, into the building blocks of disease, into the heterogeneity of different individuals, different patients, different tissues, different organs, different cell types, at a scale that was unfathomable 20 years ago.

Not just dream, not even conceptualizable. And we now are sitting in this extraordinary convergence, this crossroads, where we can now understand the function of a gene using all of these different facets. So what does a gene do? Well, you can look in the literature. But that literature is now approachable because we can use large language models to capture every single paper that has been written about that gene. We can now represent the function of a gene from the entire medical literature. We can represent the function of the same gene using knowledge graphs. And what are the diseases that this gene interacts with?

The individual chemicals. What are the functional annotations that this gene has been associated with, what are the anatomical regions where this gene is expressed, what are... So all of these different concepts of a gene, first through language, then through knowledge graph, represent a function of the same object. You can now think about that gene as a collection of amino acids and the structure that this gene has. And you can use protein language models and geometric deep learning to start understanding

the folds of that same gene. You can look at that same gene and look at its expression patterns across thousands of datasets with where it's expressed and how it's expressed in the brain and with all of the other genes in the body. And you can build these gene expression similarity networks. You can also build a protein-protein interaction network to basically see from experiments that pull down things that interact with each other what are all of the interactors of that gene. And you can use now these joint representations to unite the different spheres,

to basically start combining together knowledge graphs and latent representations and these sort of hierarchical AI-based representations of every aspect of how that one gene works. But you can do the same thing with every other node in this knowledge graph. You can take a disease and now start looking at that disease

from the set of all of the genes that it interacts with, from the drugs that are influencing that disease, from the genetic variants that are influencing that disease, and the genes that these genetic variants are connected with, from the imaging representations of the pathology of patients with that disease. And you can now build these convergent representations of every aspect of biology, from the proteins to the chemicals to the patients and understand a patient that carries that disease with the entire clinical record

of that patient, with all of the blood measurements and imaging measurements and clinical notes that every doctor has written for that patient across their lifetime. You can now start measuring all of the environmental variables that are interacting with that. So at some point, it becomes so complete and so complex of a picture that if we don't understand it at this point, it's our fault. We basically have more technologies and more data and more facets of function than we ever wished for. And at the same time, we have this extraordinary ability of AI to understand all of these concepts,

to sort of make these hierarchical representations and interconnect them with each other. And that's what I'm so, so excited about. I'm so excited about the fact that all of these separate projects in my group, my team has been working on all of these different aspects separately for decades from genetics to understand where the genes are. I started working on the human genome before there was a human genome. The tools that we wrote helped annotate where the genes even are in the human genome. The current gene set is based on the work that my team did. Annotating where the gene regulatory elements are and where the circuits are in the networks.

Understanding how the epigenome works. Understanding how to interpret genetic variation. All of these different things were separate research programs and they've all been now converging together. I have students who are working on understanding protein structure, understanding the structured function problem, understanding how to represent knowledge graphs,

understanding how to visualize the latent embeddings of different structures and different functions and different patients and patient trajectories and patient heterogeneity. And we've been working on electronic health records for many, many years now. And all of the different things used to be separate pieces of knowledge. And it seemed mad for any one research group to be working on all these different areas. But now there's method to the madness. It's all

coming together and it's coming together in the most beautiful integrative way. Because in order to understand the complexity of something like Alzheimer's or obesity or schizophrenia or cardiovascular disease or immune disorders or cancer or immunotherapy response, you can't separate all of that information. You have to be cognizant of all of the different parts. And all of them are influencing together your understanding of biology. So what we're doing now is we're integrating all of these massive, massive data sets that we ourselves have generated in some of these massive, massive data sets that we ourselves have generated

in some of these cases. So my lab has probably generated more single cell human brain data in dozens of different neurodegenerative and psychiatric disorders and neurodevelopmental disorders than any other lab in the whole world. And we are a computational lab. Why did we do that? We basically partnered with all of these extraordinary doctors, experimentalists, experts in each

of these different areas, and we worked together to generate this extraordinary map. Why? Because we can now leverage it to understand the diversity of human cognition, to understand the diversity of pathology, the diversity of pathology, the diversity of psychiatric disorders across thousands of individuals by breaking down all of these layers from

the phenotype all the way down to individual genes and all the way down to individual gene regulatory regions. So where I see biology and where I see medicine heading is that unification. It's that ability to now have AI systems that understand all of these parts together, that are able to help us as discovery partners to truly make sense of gene function at an unprecedented level of resolution and complexity and scale and facets and multimodality and to do the same thing with disease to do the same thing with every pathway underlying disease and basically what this is painting now is a unified view of

medicine and biology and therapeutics which is revealing the same type of modularity that we've been talking about earlier, applying now at the disease level, where we can now start understanding the complexity of Alzheimer's in terms of its building blocks, in terms of its hallmarks, if you wish. And instead of just saying it's a monolithic disorder and everybody has Alzheimer's, we can say, well, wait a minute, there's building blocks, there's cholesterol transport, there's lipid dysregulation, there's amyloid accumulation, there's tau pathology,

there's microglial clearance, there's neuroinflammation, there's neurovascular unit dysregulation. And all of these different components are now emerging as building blocks with which we can finally understand medicine in a unifying way. And why is that exciting? Because the same pathways that appear to be underlying

Alzheimer's are reused in different ways in cardiovascular disease. And they're reused in different ways in frontotemporal dementia and in schizophrenia. So we can now start understanding what are the points of convergence? What are these primitives? What are these building blocks of disease, of medicine, of biology,

of human well-being that we can now start tackling one at a time? And for personalized medicine to function both in terms of feasibility and in terms of economics. You cannot simply say I want a pill for a person because then we'll have a pill for Jeff Bezos, a pill for Bill Gates, a pill for Elon Musk and that's it because they are the only ones who can afford it. Instead we have to think about what is uphill for this pathway and that pathway and that pathway.

Because by building this modular view of personalized medicine, the economics work out because that pathway is shared by millions of people in different combinations. And we can now start thinking about personalized medicine in a way where I can take your own genome, add up all of the burden that you have in terms of dysregulation of every one of the genes in each of these pathways, and say, okay, well, I need to alter your cholesterol metabolism in this way and your microglial inflammation

that way. And for each of those, we can now have a modular combination that allows us to create for every patient their own pill, which will be a combination of all of these different tinkerings, if you wish. So that's why I'm so hopeful. That's why I'm so excited about where we're heading, because we're finally understanding the basic building blocks of life, and we're finally understanding how they fit together to sort of paint an actually feasible view

of personalized medicine. Manolis, it's been a pleasure. I'm extremely grateful that we've connected. We seem to have a passion for integrating and unification. I'm glad that yours has been decades in the making and that it's coming to fruition in the intersection now. I could not be more excited. I think this is an extraordinary time for our field. It's also an extraordinary time for AI. And we're basically seeing this impact of AI in every aspect of humanity, in every aspect

of society. But I think the biggest impact yet will be in understanding biology and in understanding medicine and in dramatically fundamentally changing the human condition. A lot of the early applications that we see now of AI in healthcare are basically just fancy versions of assistance. They're using language. Language with large language models has seen this extraordinary ability to sort of break down both the syntax and the semantics of language. And language is simple by

comparison. Language is something that evolved by humans talking to each other. It evolved with the limitations of our own evolved brains. So if we humans couldn't understand language, language would not be as it is now. It would be simpler. So language evolved in complexity up until our own comprehension. And it was limited by the fact that we need to develop our brain from a single cell through a series of divisions and developmental programs and not only that constraint, but also the constraint that it needs to build on however the chimp brain evolved and however the mammalian brain evolved and the vertebrate

brain evolved, etc. In other words, the type of cognition that we have is constrained by number one, evolution. You have to arrive at it by tiny adjustments of the previous version. And you also have to arrive at it from every generation from scratch to give rise to, again, the same neural network. AI is not constrained that way. AI has the ability to sort to start with dramatically different parameters in terms of the depth, the representation of neurons, the types of operations, and so on and so forth.

And right now we're using a very simple architecture for AI. Basically, this type of representation learning that AI is using is much more similar to say a single cell type, for example, only neocortex for the human brain. But the human brain also has all of these subcortical regions, all of these limbic system as we call it. And this is where all of the emotional part is. That's where all of the fear and fight or flight and so on and so forth.

Components of the brain are. And the brain is not just subject to traditional electric based computation. It's also of course subject to neurotransmitters and brain waves and hormonal interactions and so forth. And you could think of them as features or bugs. You could basically say, oh, we have this extraordinarily beautiful cognitive brain, but unfortunately it is influenced

by all of these other things. Or you could think of them as features. You could basically say we have this brain that is constantly pushed into different local optima or in different shapes and scales because of all of these things. And therefore, it can arrive at solutions that it wouldn't normally arrive at if it was just a neocortical brain. So you can basically think about what are the types of architectures that we can

now leverage from even just our evolved human brain to arrive at perhaps more efficient computation or more creative computation, more integrative computation for our engineered brains, if you wish. But you can also think that, wait a minute, we can bring in all kinds of other reasoning. Why do we have to use only traditional human-like reasoning? Humans are pretty bad at doing all kinds of math. Humans have no intuition about genomes or protein folding or chemical interactions at all. So we now have the ability with AI to to bring in a whole new class of computation, bring in all of these different ways

of understanding the natural world that are far beyond language. And when language has had such an impact in healthcare by being able to transcribe what doctors are saying or being able to summarize notes and being able to summarize information, et cetera, which is already having an impact in healthcare. Can we now blend that with symbolic reasoning,

with causality inferences, with structural and geometric and spatial representations of patient information and so on and so forth? So I would say that solving language is a much simpler problem than solving biology or solving medicine. So I think ahead of us might be the most complex challenge of AI yet by understanding not physics that has a relatively small number of laws, but understanding this tinkering, this extraordinary diversity in functions and adaptations, in niches that biology has, and tuning to it with these billions of parameters the same way that evolution itself and cognition and gene regulation is able to tune to the natural world.

So I think that if physics has as its language mathematics, maybe biology and medicine have as its language this tinkering that you can see in AI, that the same type of complexity has now finally found its match with both the most powerful cognition that we have ever built, combined of course with extraordinary capabilities of humans to be able to now build primitives based on these AI models of the world and start reasoning about them and guiding them, but now matching the complexity of medicine, the complexity of biology, the complexity of chemistry, and the complexity of the evolved world.

So I see this alignment coming together of these techniques that we have built initially for understanding images and for understanding language, but now for understanding a language that none of us speaks. Right. The language of medicine, the language of biology, the language of chemistry, and the language of the natural world. Super interesting.

Thank you for spending so much time with me. Thank you. Now we both got to get going in two minutes. So I want you to spend one minute and just address the audience who's watching and give your advice to new students who are watching, but also researchers are watching. Advice would be to... Very often people say, do what you're passionate about. Yeah, sure, that's great advice. But don't just do what you're passionate about. Build your foundational knowledge, build your foundational understanding of the world

and go and study all of these different disciplines. Soak them up. Train your brain to think in every single possible way that different fields of science have embraced. And the reason why I'm saying that is because you will need it. There's this unification that's happening across all of these different disciplines right now. And that unification, this ability to sort of bring all of these different ways of thinking together is basically what has allowed us to make

so, so much progress in so, so many different fields. So advice number one is learn how to code. There's just no excuse not to. Learn how to program fundamentally by reading tons of programs, by understanding the building blocks of the most complex AI systems. Learn how to tinker with these complex systems. Use ChatGPT as your partner to explore giant pieces of code that would seem indomitable. But ask it to break it, break them down for you. Basically be fearless, go out there and dive in to all of these different disciplines.

Because it is by becoming an expert, and frankly, we have no excuse anymore to not understand something. We have at our disposal YouTube and Wikipedia and bio archive and archive and Chachi PT where you can take any paper and just say, okay, break this down for me, explain it and sort of you can keep building and asking questions. And that fearlessness will translate into huge, huge dividends. Don't just say, oh, I'm passionate about this. Say, oh, I will do my homework. I will build up my work.

And right above the camera over there, it says effort equal interest times enjoyment. Skill equals talent times effort. Achievement equals skill times effort. And effort counts twice. Namely, as you start putting in the work, you achieve better, you understand better, you get things better. And that investment is multiplicative because your achievement is your skill that you build

from your effort times that effort. So basically effort along with talent gives you skill but skill again along with effort gives you achievement and achievement then feeds back into your enjoyment. So you have this extraordinary interplay that if you say, oh, I'm gonna take the easy way, yes, it will be easy for a little bit, but then you're stuck.

If instead you say, let me now understand the theory of everything, let me dive in, understand the physics, understand the chemistry, understand the biology, understand the chemistry, understand the biology, understand the, you know, chemistry and sort of how it builds up within the building blocks of life and do the same thing with mathematics. A lot of our teaching of mathematics is unfortunately stripping away all of the beauty that you only get to later on. But dive into YouTube channels

that sort of expand mathematics with extraordinary visualizations. I'm thinking about three brown, one blue. Yep. Three blue, one brown. Three blue, one brown. This does not exist when I was a student. And my eight-year-old is loving it

and truly understanding it and getting these concepts in a way that was not accessible before. And by seeing how beautiful the mathematics is later on, they're excited about the mathematics that they have to bear with today. And I think that ability, and I've met with students who basically tell me, oh, in the next four semesters I'm going to be taking all these classes to eventually take that class. I'm like, okay, go to that class today. Just spend a week in that class.

See if you like it. And then sometimes they come back and saying, yes, this is exactly what I want to do. Other times they come back and say, oh, that's horrible. Like, thanks for saving me like the next three years. And I think that the ability to start from complexity down rather than from simple up, I think it's something that is finally achievable by being able to have the world's most accomplished tutor, by having Chachi P.T. as your partner in all of your learning, by tackling the most complex paper and then saying, hey, break it down for me and sort of, hey, you know, break it down for me, you know,

and sort of, oh, tell me about that concept. And now you can sort of build it back up and sort of dive as far as the rabbit hole will go. So anyway, my advice is be fearless, go to the foundations, go to the building blocks, understand both deeply, but then be able to emerge back and see the bigger picture. And when you see something in another field that doesn't make sense, chances are there's some aspect of human cognition that you will be training by trying to understand that thing and watch channels like theory of everything.

And pause and go deep. Try to understand every one of these words because because if there's something hidden in there, there's a beauty of integration that comes to humans that I think you will truly benefit from. So that's my advice, best of luck with that. All right, thank you. Okay, thank you so much, such a pleasure. New update, start of a sub stack.

Writings on there are currently about language and ill-defined concepts, as well as some other mathematical details. Much more being written there. This is content that isn't anywhere else. It's not on Theories of Everything, it's not on Patreon. Also, full transcripts will be placed there at some point in the future. Several people ask me, Hey Kurt, you've spoken to so many people in the fields of theoretical physics, philosophy, and consciousness. What are your thoughts?

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