#153 – Dmitry Korkin: Evolution of Proteins, Viruses, Life, and AI

Dmitry Korkin is a professor of bioinformatics and computational biology at WPI. Please support this podcast by checking out our sponsors: – Brave: https://brave.com/lex – NetSuite: http://netsuite.com/lex to get free product tour – Magic Spoon: https://magicspoon.com/lex and use code LEX to get $5 off – Eight Sleep: https://www.eightsleep.com/lex and use code LEX to get special savings EPISODE LINKS: Dmitry’s Website: http://korkinlab.org/ Dmitry’s Twitter: https://twitter.com/dmkorkin PODCAST INFO: Podcast website: https://lexfridman.com/podcast Apple Podcasts: https://apple.co/2lwqZIr Spotify: https://spoti.fi/2nEwCF8 RSS: https://lexfridman.com/feed/podcast/ YouTube Full Episodes: https://youtube.com/lexfridman YouTube Clips: https://youtube.com/lexclips SUPPORT & CONNECT: – Check out the sponsors above, it’s the best way to support this podcast – Support on Patreon: https://www.patreon.com/lexfridman – Twitter: https://twitter.com/lexfridman – Instagram: https://www.instagram.com/lexfridman – LinkedIn: https://www.linkedin.com/in/lexfridman – Facebook: https://www.facebook.com/LexFridmanPage – Medium: https://medium.com/@lexfridman OUTLINE: Here’s the timestamps for the episode. On some podcast players you should be able to click the timestamp to jump to that time. (00:00) – Introduction (07:21) – Proteins and the building blocks of life (14:23) – Spike protein (21:11) – Coronavirus biological structure explained (26:09) – Virus mutations (32:39) – Evolution of proteins (42:25) – Self-replicating computer programs (50:02) – Origin of life (57:34) – Extraterrestrial life in our solar system (59:31) – Joshua Lederberg (1:05:30) – Dendral (1:08:24) – Why did expert systems fail? (1:10:35) – AlphaFold 2 (1:32:13) – Will AI revolutionize art and music? (1:39:12) – Multi-protein folding (1:43:39) – Will AlphaFold 2 result in a Nobel Prize? (1:46:10) – Will AI be used to engineer deadly viruses? (2:01:17) – Book recommendations (2:11:00) – Family (2:13:38) – A poem in Russian

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Lex Fridman?

  The following is a conversation with Dmitry Korkin, his second time on the podcast. He's a professor of bioinformatics and computational biology at WPI, where he specializes in bioinformatics of complex disease, computational genomics, systems biology, and biomedical data analytics. He loves biology, he loves computing, plus he is Russian and recites a poem in Russian at the end of the podcast. What else could you possibly ask for in this world? Quick mention of our sponsors: Brave Browser, NetSuite Business Management Software, Magic Spoon Low Carb Cereal, and Eight Sleep Self-Cooling Mattress.

• 0:42Lex Fridman?

  So the choice is browsing privacy, business success, healthy diet, or comfortable sleep. Choose wisely, my friends, and if you wish, click the sponsor links below to get a discount and to support this podcast. As a side note, let me say that to me, the scientists that did the best, apolitical, impactful, brilliant work of 2020 are the biologists who study viruses without an agenda, without much sleep, to be honest, just a pure passion for scientific discovery and exploration of the mysteries within viruses. Viruses are both terrifying and beautiful. Terrifying because they can threaten the fabric of human civilization, both biological and psychological.

• 1:27Lex Fridman?

  Beautiful because they give us insights into the nature of life on Earth and perhaps even extraterrestrial life of the not-so-intelligent variety that might meet us one day as we explore the habitable planets and moons in our universe. If you enjoy this thing, subscribe on YouTube, review it on Apple Podcasts, follow on Spotify, support on Patreon, or connect with me on Twitter at Lex Fridman. As usual, I'll do a few minutes of ads now and no ads in the middle. I try to make these interesting, but I give you timestamps. So go ahead and skip if you must, but please still check out the sponsors by clicking the links in the description. It is in fact, the best way to support this podcast.

• 2:10Lex Fridman?

  This show is sponsored by Brave, a fast, privacy preserving browser that feels like Google Chrome, but without ads or the various kinds of tracking that ads can do. I love using it more than any other browser, including Chrome. If you like, you can import bookmarks and extensions from Chrome, just as I did. The Brave browser is free, available on all platforms. It's actively used by over 20 million people. Speedwise, it just feels more responsive and snappier than other browsers. So I can tell there's a lot of great engineering behind the scenes. It has a lot of privacy related features that Chrome doesn't have, like it includes options such as private window with Tor for those seeking advanced privacy and safety.

• 2:55Lex Fridman?

  The tech behind Tor, in fact, is pretty fascinating and I'm sure I will explore it on a future podcast. Get this awesome browser at brave.com/lex and it might become your favorite browser as well. That's brave.com/lex. This show is also sponsored by NetSuite. This one's for the business owners. Running a business is hard. If you own a business, don't let QuickBooks and spreadsheets make it even harder than it needs to. You should consider upgrading to NetSuite. It allows you to manage financials, human resources, inventory, e-commerce, and many more business related details, all in one place. I dislike the bureaucracy that companies sometimes build up around this.

• 3:40Lex Fridman?

  NetSuite can probably help, but I'm sure bureaucracies can still flourish if you're not careful. To me at least, efficiency and excellence are essential, NetSuite or not. Anyway, whether you're doing a million or hundreds of millions of revenue, save time and money with NetSuite. 24,000 companies use it. Let NetSuite show you how they'll benefit your business with a free product tour and netsuite.com/lex. My reading engine is not functioning properly today. It requires more coffee. If you own a business, try them out. Schedule your free product tour right now, in all caps, at netsuite.com/lex.

• 4:22Lex Fridman?

  Right now, because they want to create an artificial sense of urgency. Netsuite.com/lex, go there. This episode is also sponsored by Magic Spoon Low Carb Keto Friendly Cereal. This is one of the more fun and colorful sponsors this podcast has. I've been on a mix of keto carnivore diet for a long time now. That means very few carbs. I do unfortunately binge eat cherries or apples sometimes and regret it later, but love it in the moment. Just like I used to regret eating cereal, because most have crazy amounts of sugar, which is terrible for you. But Magic Spoon is a totally new thing. Zero sugar, 11 grams of protein, and only three net grams of carbs.

• 5:05Lex Fridman?

  I personally like to celebrate little accomplishments in productivity with a snack of Magic Spoon. It feels like a cheat meal, but it is not. It tastes delicious. It has many flavors, including cocoa, fruity, frosted, and blueberry. I think they've been adding some new ones. To me, they're all delicious, but if you know what's good for you, you'll go with cocoa, my favorite flavor, and the flavor of champions. Just now realizing that cocoa reminds me of Joey Coco Diaz, who perhaps might be fun to have on this podcast one day. Click the magic spoon.com/lex link in the description and use code LEX at checkout for free shipping. Finally, this episode is also sponsored by Eight Sleep and its Pod Pro mattress,

• 5:50Lex Fridman?

  a product I enjoy every single day, sometimes in the afternoon as well. It controls temperature with an app, is packed with sensors, and can cool down to as low as 55 degrees on each side of the bed separately. It's been a game changer for me. I just enjoy sleep and power naps way more. I feel like I fall asleep faster and get more restful sleep. Combinations of cool bed and warm blanket is amazing. Now, if you love your current mattress, but are still looking for temperature control, Eight Sleep's new Pod Pro cover adds dynamic cooling and heating capabilities onto your current mattress. This thing can cool down to as low as 55 degrees or heat up to 110 degrees.

• 6:32Lex Fridman?

  The latter feature I do not use, but I know some of you probably will. And you could do this kind of cooling and heating on each side of the bed separately. Also, it can track a bunch of metrics like heart rate variability. But honestly, cooling alone is worth the money. Go to asleep.com/lex and when you buy stuff there, you'll get special savings as listeners of this podcast. Once again, that's asleep.com/lex. And now here's my conversation with Dmitry Korkin.

• 7:21Lex Fridman?

  It's often said that proteins and the amino acid residues that make them up are the building blocks of life. Do you think of proteins in this way, as the basic building blocks of life?

• 7:34Speaker 01

  Yes and no. So the proteins indeed is the basic unit, biological unit, that carries out important function of the cell. However, through studying the proteins and comparing the proteins across different species, across these different kingdoms, you realize that proteins are actually a much more complicated. So they have so-called modular complexity. And so what I mean by that is an average protein consists of several structural units.

• 8:17Speaker 01

  So we call them protein domains. And so you can imagine a protein as a string of beads, where each bead is a protein domain.

• 8:28Speaker 01

  And you know, in the past 20 years, scientists have been studying the nature of the protein domains. Because we realize that it's the unit. Because if you look at the functions, right, so many proteins have more than one function. And those protein functions are often carried out by those protein domains. So we also see that in the evolution, those proteins domains get shuffled. So they act actually as a unit, also from the structural perspective, right?

• 9:08Speaker 01

  So you know, some people think of a protein as a sort of a globular molecule. But as a matter of fact, is the globular part of this protein is a protein domain. So we often have this, you know, again, the collection of this protein domains align on a string as beads.

• 9:37Lex Fridman?

  And the protein domains are made up of amino acid residues.

• 9:40Speaker 01

  Yeah.

• 9:41Lex Fridman?

  So this is the basic, so you're saying the protein domain is the basic building block of the function that we think about proteins doing. So of course, you can always talk about different building blocks. It's turtles all the way down, but there's a point where there is, at the point of the hierarchy, where it's the most, the cleanest element block based on which you can put them together in different kinds of ways to form complex function. And you're saying protein domains, why is that not talked about as often in popular culture?

• 10:18Speaker 01

  Well, you know, there are several perspectives on this. And one, of course, is the historical perspective, right? So historically, scientists have been able to structurally resolve to obtain the 3D coordinates of a protein for, you know, for smaller proteins. And smaller proteins tend to be a single domain protein. So we have a protein equal to a protein domain. And so, so because of that, the initial suspicion was that the, the, the proteins are, they have globular shapes and the more of smaller proteins you obtain structurally,

• 10:59Speaker 01

  the more you were, you became convinced that that's, that's the, the case. And only later when we had, we started having, um, you know, uh, alternative approaches. So, you know, the, the, the traditionals or the traditional ones are X-ray crystallography and NMR spectroscopy. So this is sort of the, the, the two main techniques that give us the 3D coordinates. But nowadays, uh, there is huge breakthrough in cryo-electron microscopy. So the, the more advanced methods that allow us to, uh, you know, to get into the,

• 11:40Speaker 01

  uh, you know, 3D shapes of much larger molecules, molecular complexes. To give you, uh, one of the common examples, uh, for this year, right? So, so the, the first experimental structure of a SARS COVID-2 protein was the cryoium structure of the S-protein, so the spike protein. And so, uh, it was solved very quickly and the reason for that is the advancement of the, uh, of this technology is, is pre-spectacular.

• 12:17Lex Fridman?

  How many domains does the, uh, is it more than one domain?

• 12:20Speaker 01

  Oh, yes. Oh, yes. I mean, so, so it's, it's a very complex structure and, and

• 12:24Lex Fridman?

  It's complex.

• 12:25Speaker 01

  Uh, we, you know, on top of the complexity of a single protein, right? So this, this structure is actually is a complex, is a trimer. So it needs to form a trimer in order to function properly.

• 12:40Lex Fridman?

  What's a complex?

• 12:41Speaker 01

  So a complex is agglomeration of multiple proteins. And so we can have the same protein.

• 12:50Speaker 01

  Copied in multiple, you know, made up in multiple copies and forming something that we called a homoalligomer. Homo means the same, right? So, so in this case, so the spike protein is the, is an example of a homo tetramor, homo trimer, sorry.

• 13:09Lex Fridman?

  So it needs three copies of itself?

• 13:11Speaker 01

  Three copies.

• 13:12Lex Fridman?

  In order to?

• 13:13Speaker 01

  Exactly. We have the, these three chains, the, the three molecular chains coupled together and performing the, the function. That's what, when, when you look at this protein from, from the top, you see a perfect triangle.

• 13:27Lex Fridman?

  Yeah.

• 13:28Speaker 01

  So, uh, but other, uh, you know, so other complexes are made up of, um, you know, different proteins. Uh, some of them are completely different, some of them are similar, the, the hemoglobin molecule, right? So it's actually, it's a protein complex. It's made of four basic subunits, two of them, uh, are identical to each other, two other are identical to each other, but they are also similar to each other, which sort of, uh, gives us some ideas about the evolution of this, uh, you know, uh, of this, uh, molecule. And perhaps one of the hypotheses is that, you know, in the past, it was just a homo tetramor, right?

• 14:11Speaker 01

  So four identical copies and then it became, you know, uh, sort of, uh, modify, it, it became mutated, uh, over the time and, and became more specialized.

• 14:23Lex Fridman?

  Can we linger on the spike protein for a little bit? Is, is there something interesting or like beautiful you find about it?

• 14:29Speaker 01

  I mean, first of all, it's an incredibly challenging protein. And so we, as a part of, uh, our sort of, um, research to understand the structural basis of this virus, to sort of decode, structure decode every single protein, uh, in its proteome, um, which, you know, we've been working on this spike, uh, protein and, uh, one of the main challenges was that, um, the, uh,

• 15:00Speaker 01

  CryoYum data allows us to reconstruct or to obtain the 3D coordinates of roughly two thirds of the protein. The rest of the one third of this protein, it's a part that is buried into the membrane of the virus and of the viral envelope. And it also has a lot of unstable structures around it.

• 15:29Lex Fridman?

  So it's chemically interacting somehow with whatever the heck is connecting.

• 15:33Speaker 01

  Yeah, so people are still trying to understand. So the nature of and the role of this, you know, of this one third. Because the top part, you know, the primary function is to get attached to the ACE2 receptor, human receptor. There is also beautiful mechanics of how this thing happens, right? So because there are three different copies of this chains, you know, there are three different domains, right? So we're talking about domains. So this is the receptor binding domains, RBDs,

• 16:12Speaker 01

  that gets untangled and get ready to get attached to the receptor. And now, they are not necessarily going in a sync mode. As a matter of fact.

• 16:28Lex Fridman?

  They synchronous?

• 16:29Speaker 01

  So, yes. So, and this is where, you know, another level of complexity comes into play. Because right now, what we see is we typically see just one of the arms going out and getting ready to be attached to the ACE2 receptors. However, there was a recent mutation that people studied in that spike protein. And a very recently, a group from UMass Medical School, we happened to collaborate with groups. So this is a group of Jeremy Lubin and a number of other faculty.

• 17:14Speaker 01

  They actually solved the mutated structure of the spike. And they showed that actually, because of these mutations, you have more than one.

• 17:29Speaker 01

  Arms opening up. And so now, so the frequency of two arms going up increase quite drastically.

• 17:41Lex Fridman?

  Interesting. Does that change the dynamics somehow?

• 17:44Speaker 01

  It potentially can change the dynamics of because now you have two possible opportunities to get attached to the ACE2 receptor. It's a very complex molecular process, mechanistic process. But the first step of this process is the attachment of this spike protein, of the spike trimer to the human ACE2 receptor. So this is a molecule that sits on the surface of the human cell. And that's essentially what initiates, what triggers the whole process of encapsulation.

• 18:21Lex Fridman?

  If this was dating, this would be the first date. So this is the...

• 18:26Speaker 01

  In a way, yes.

• 18:28Lex Fridman?

  So is it possible to have the spike protein just floating about on its own? Or does it need that interactability with the membrane?

• 18:37Speaker 01

  Yeah, so it needs to be attached, at least as far as I know. But when you get this thing attached on the surface, there is also a lot of dynamics on where, how it sits on the surface. So for example, there was a recent work in, again, where people use the cryolectromicroscopy to get the first glimpse of the overall structure. It's a very low res, but you still get some interesting details about the surface, about what is happening inside. Because we have literally no clue until recent work about how the capsid is organized.

• 19:17Lex Fridman?

  What's a capsid?

• 19:18Speaker 01

  So capsid is essentially, it's the inner core of the viral particle, where there is the RNA of the virus, and it's protected by another protein, N protein, that essentially acts as a shield.

• 19:36Speaker 01

  But you know, now we are learning more and more. So it's actually, it's not just this shield. It's you is potentially is used for the stability of the outer shell of the of the virus. So it's it's pretty complicated.

• 19:50Lex Fridman?

  And I mean, understanding all of this is really useful for trying to figure out like developing a vaccine or some kind of drug to attack any aspects of this, right?

• 19:59Speaker 01

  So I mean, there are many different implications to that. And first of all, you know, it's it's important to understand the virus itself, right? So, you know, in order to to understand how it acts, what is the overall mechanistic process of this virus replication, of this virus proliferation to the cell, right? So so that's one aspect. The other aspect is, you know, designing new treatments. And so one of the possible treatments is, you know, designing nanoparticles. And so nanoparticles that will resemble the viral shape that would have the spike integrated

• 20:42Speaker 01

  and essentially would act as a competitor to the real virus by blocking the ACE2 receptors and thus preventing the real virus entering the cell.

• 20:55Speaker 01

  Now, there are also, you know, there is a very interesting direction in looking at the at the membrane, at the envelope portion of the protein and attacking its M protein. So so there are, you know, to give you a, you know, sort of a brief overview, there are four structural proteins. These are the proteins that made up a structure of the virus. So spike, S protein, that acts as a trimer, so it needs three copies.

• 21:30Speaker 01

  E envelope protein that acts as a pantomime. So it needs five copies to act properly. M is a membrane protein. It forms dimers and actually it forms beautiful lattice. And this is something that we've been studying and we are seeing it in simulations. It actually forms a very nice grid or, you know, threads, you know, of different dimers attached next to each other. There's a bunch of copies of each other and they naturally, when you have a bunch of copies of each other, they form an interesting lattice. Exactly. And, and you know, if you think about this, right? So, so, so the, this complex, you know, the viral shape

• 22:12Speaker 01

  needs to be organized somehow, self-organized somehow, right? So it, you know, if it was a completely random process, you know, you probably wouldn't have the, the, the envelope shell of the ellipsoid shape. You know, you would have something, you know, pre-random, right, shape. So there is some, you know, regularity in how this, you know, how this M dimers get to attach to each other in a very specific directed way. Is that understood at all?

• 22:46Speaker 01

  It's not understood. We are now, we, we've been working in the past six months since, you know, we, we met. Actually, this is where, where we started working on, on trying to understand the overall structure of the envelope and the, the key components that made up this, you know, structure. Wait, does the envelope also have the lattice structure or no? So, so the envelope is essentially is the outer shell of the viral particle. The N, the nucleocapsid protein, is something that is inside. Got it. But get that, the N is likely to interact with M. Does it go M and E, like where's the E and? So, so E, those different proteins, they occur in different copies

• 23:31Speaker 01

  on the viral particle. So, so E, this pantomime complex, we only have two or three maybe per each particle. Okay? We have thousand or so of M dimers that essentially made up, that makes up the entire.

• 23:52Speaker 01

  You know, outer shell.

• 23:53Lex Fridman?

  So most of the outer shell is the M.

• 23:56Speaker 01

  M-dimer.

• 23:57Lex Fridman?

  It's the M protein. When you say particle, that's the viron, the virus, the individual virus.

• 24:03Speaker 01

  It's a single, yes.

• 24:04Lex Fridman?

  Single element of the virus, it's a single virus.

• 24:06Speaker 01

  Single virus, right. And we have about, you know, roughly fifty to ninety spike trimers, right? So, so, so when you, you know, when you show a.

• 24:16Lex Fridman?

  Per, per virus particle.

• 24:18Speaker 01

  Per virus particle.

• 24:19Lex Fridman?

  Sorry, what did you say, fifty to ninety?

• 24:21Speaker 01

  Fifty to ninety, right? So, so this is how this thing is organized. And so now typically, right? So you see these, uh, uh, the, the antibodies that target, you know, spike proteins, certain parts of the spike protein. But there could be some, uh, also some treatments, right? So, so this, you know, this are small molecules that bind strategic parts of these proteins, disrupting its function. So one of the promising directions, uh, it's one of the newest directions is actually

• 24:59Speaker 01

  targeting the M-dimer of the protein, targeting the proteins that make up this outer shell. Because if you're able to destroy the outer shell, you're essentially destroying the, the, the viral particle itself. So preventing it from, from, you know, functioning at all.

• 25:19Lex Fridman?

  So that's, you think is, uh, from a sort of cybersecurity perspective, virus security perspective, that's the best attack vector is, uh, or like that's a promising attack vector?

• 25:31Speaker 01

  I would say yes. So I mean, it's still tons of research needs to be, you know, to be done. But, uh, yes, I think, you know, so.

• 25:39Lex Fridman?

  There's more attack surface, I guess.

• 25:41Speaker 01

  More attack surface, but, you know, from, from our analysis, from other evolutionary analysis, this protein is evolutionarily more stable.

• 25:51Speaker 01

  Compared to the, say, to the spike protein.

• 25:54Lex Fridman?

  And stable means a more static target?

• 25:58Speaker 01

  Well, yeah, so it doesn't change, it doesn't evolve from the evolutionary perspective so drastically as, for example, the spike protein.

• 26:09Lex Fridman?

  There's a bunch of stuff in the news about mutations of the virus in the United Kingdom.

• 26:14Speaker 01

  Yeah.

• 26:14Lex Fridman?

  I also saw in South Africa, something, maybe that was yesterday. You just kind of mentioned about stability and so on. Which aspects of this are mutatable and which aspects, if mutated, become more dangerous? And maybe even zooming out, what are your thoughts and knowledge and ideas about the way it's mutated, all the news that we've been hearing? Are you worried about it from a biological perspective? Are you worried about it from a human perspective?

• 26:44Speaker 01

  So, I mean, you know, mutations are sort of a general way for these viruses to evolve. Right? So, so it's, you know, it's, essentially, this is the way they evolve. This is the way they were able to jump from, you know, one species to another. We also see, you know, some recent jumps. There were some incidents of this virus jumping from human to dogs. So, you know, there is some danger in those jumps because, you know, every time it jumps, it also mutates. Right? So, so it, when it jumps to the, to the species and jumps back, right?

• 27:29Speaker 01

  So it acquires some mutations that are sort of

• 27:34Speaker 01

  driven by the environment of a new host.

• 27:38Lex Fridman?

  Yeah.

• 27:39Speaker 01

  Right? And it's different from the human environment. And so we don't know whether the mutations that are acquired in the new species are neutral with respect to the human host or maybe, you know, maybe damaging.

• 27:55Lex Fridman?

  Yeah, change is always scary, but so are you worried about? I mean, it seems like because the spread is during winter now, seems to be exceptionally high. And especially with a vaccine just around the corner already being actually deployed. Is there some worry that there is this puts evolutionary pressure selective pressure on the virus? And for to to mute for you to mutate. Is that a source of worry?

• 28:23Speaker 01

  Well, I mean, there is always this thought, you know, in in in the scientist mind, you know, what hap- what will happen? Right? So I know there've been there've been discussions about sort of the arms race between the, you know, the ability of of the of the humanity to, you know, to get vaccinated faster than the virus. You know, essentially, you know, becomes, you know, resistant to the vaccine.

• 28:58Speaker 01

  I I mean, I don't worry that much, simply because, you know, there is not that much evidence to that.

• 29:10Lex Fridman?

  To aggressive mutation around the vaccine.

• 29:13Speaker 01

  Exactly. You know, obviously, there are mutations around the vaccine, you know, there are vaccines. So the reason we get vaccinated every year against the season of new.

• 29:24Lex Fridman?

  Because there's mutations.

• 29:25Speaker 01

  Right? But, you know, I think it's important to study it. No doubts, right? So so I think one of the, you know, to me, and again, I might be biased. Because, you know, we we've been trying to to do that as well. So but one of the critical directions in understanding the virus is to to understand its evolution. In order to sort of understand the mechanisms, the key mechanisms that lead the virus to jump. You know, the Nordic viruses to jump from species from species to another. That the mechanisms that lead the virus to become resistant to vaccines, also to treatments.

• 30:09Speaker 01

  Right? And hopefully, that knowledge was will enable us to sort of forecast the evolutionary traces, the future evolutionary traces of this virus.

• 30:21Lex Fridman?

  I mean, what from a biological perspective, this might be a dumb question, but is there parts of the virus? That if souped up like through mutation could make it more effective at doing its job. We're talking about this specific coronavirus like because we were talking about the different like the membrane, the M protein, the E protein, the N and the S. The spike, is there some?

• 30:49Speaker 01

  And there are twenty or so more in addition to that.

• 30:53Lex Fridman?

  But is that is that a dumb way to look at it like which of these if mutated could have the greatest impact? Potentially damaging impact on the effectiveness of the virus?

• 31:06Speaker 01

  So it's actually, it's a very good question because and the short answer is we don't know yet. But of course, there is capacity of this virus to to become more efficient. The reason for that is, you know, so if you look at the virus, I mean, it's it's a machine, right? So it's a machine that does a lot of different functions. And many of these functions are sort of nearly perfect, but they're not perfect. And those mutations can make those functions more perfect. For example, the attachment to ACE2 receptor, right of the spike, right? So, you know, is it?

• 31:48Speaker 01

  Has this virus reached the efficiency in which the attachment is carried out? Or there are some mutations that that still to be discovered, right? That will make this attachment sort of stronger? Or, you know, something more, in a way, more efficient? From the point of view of this virus functioning? That's that's sort of the obvious example. But if you look at each of these proteins, I mean, it's there for a reason. It performs certain function. And it could be that certain mutations will, you know, enhance this function.

• 32:31Speaker 01

  It could be that some mutations will make this function much less efficient. So that's that's also the case.

• 32:39Lex Fridman?

  Let's, since we're talking about the evolutionary history of a virus, let's zoom back out and look at the evolution of proteins. I glanced at this 2010 Nature paper on the quote, "ongoing expansion of the protein universe." And then, you know, it kind of implies and talks about that proteins started with a common ancestor, which is, you know, kind of interesting. It's interesting to think about like, even just like the first organic thing that started life on Earth. And from that, there's now, you know, what is it? 3.5 billion years later, there's now millions of proteins and they're still evolving.

• 33:24Lex Fridman?

  And that's, you know, in part, one of the things that you're researching. Is there something interesting to you about the evolution of proteins from this initial ancestor to today? Is there something beautiful and insightful about this long story?

• 33:41Speaker 01

  So I think, you know, if, if I were to pick a single keyword about protein evolution, I would pick modularity, something that we talked about in the, in the beginning. And that's the fact that the proteins are no longer considered as, you know, as a sequence of letters. There are hierarchical complexities in the way these proteins are organized. And this complexities are actually going beyond the protein sequence. It's actually going all the way back to the, to the gene, to the nucleotide sequence.

• 34:23Speaker 01

  And so, you know, again, these protein domains, they are not only functional building blocks. They are also evolutionary building blocks. And so what we see in the sort of in the later stages of evolution, I mean, once this stable structurally and functionally building blocks were discovered, they essentially, they stay, those domains stay as such. So that's why if you start comparing different proteins, you will see that many of them will have similar fragments. And those fragments will correspond to something that we call protein domain families.

• 35:05Speaker 01

  And so, so they are still different because you, you still have mutations and, and, and, and, you know, the, you know, different mutations are attributed to, to, you know, diversification of the function of this, you know, protein domain. However, you don't, you very rarely see, you know, the, the evolutionary events that would split this domain into fragments. Because, and it's, you know, once you have the, the, the, the domain split, you actually, you, you know, you can completely cancel out its function or at the very least you can reduce it.

• 35:49Speaker 01

  And that's not, you know, efficient from the point of view of the, you know, of the cell function. So, so the, the, the protein domain level is a very important one. Now, on top of that, right? So if you look at the proteins, right? So you have this structural units and they carry out the function. But then, much less is known about things that connect these protein domains. Something that we called linkers. And those linkers are completely flexible, you know, parts of the protein that nevertheless carry out a lot of Function. It's like little tails, little heads.

• 36:31Speaker 01

  So, so, so we, we do have tails, so they're called termini, C and N termini. So these are things right on the, on, on, uh, on, uh, one and another ends of the protein sequence. So they are also very important. So they, they attribute it to, to very specific, uh, interactions between the proteins. So. But you're referring to the links between domains. That connect the domains. And, you know, apart from the, just the, the, uh, simple perspective, if you have, you know, a very short domain, you have, uh, sorry, a very short linker, you have two domains next to each other. They are forced to be next to each other. If you have a very long one, you have the domains that are extremely flexible

• 37:15Speaker 01

  and they carry out a lot of sort of spatial reorganization, right? That's awesome. But on top of that, right? Just this linker itself, because it's so flexible, it actually can adapt to a lot of different shapes. And therefore it's a, it's a very good interactor when it comes to interaction between this protein and other protein. All right, so these things also evolve, you know, uh, and they, in a way, have different law, sort of, uh, uh, laws of, uh, uh, or the driving laws that, uh, underlie the, the evolution because they no longer need to, uh, to, uh, preserve certain structure, right?

• 37:59Speaker 01

  Uh, unlike protein domains. And so now on top of that, you have, uh, something that is even less studied. And this is something that, uh, uh, attribute to, to the concept of alternative splicing. So alternative splicing, so it's a, it's a very cool concept. It's something that, uh, uh, we've been fascinated about for, you know, over a decade, uh, in my lab and trying to do research with that. But so, you know, so typically, you know, a, a simplistic perspective is that one gene is equal one protein product. All right, so you have a gene, you know, you transcribe it and, and, and translate it

• 38:44Speaker 01

  and you, it becomes a protein.

• 38:47Speaker 01

  In reality, when we talk about eukaryotes, especially sort of more recent eukaryotes that are very complex, the gene is no longer equal to one protein. It actually can produce multiple functionally, you know, active protein products. And each of them is, you know, is called an alternatively spliced product. The reason it happens is that if you look at the gene, it actually has, it has also blocks.

• 39:28Speaker 01

  And the blocks, some of which, and it essentially, it goes like this. So we have a block that will later be translated, we call it exon. Then we'll have a block that is not translated, cut out, we call it intron. So we have exon, intron, exon, intron, et cetera, et cetera, et cetera, right? So and sometimes you can have, you know, dozens of these exons and introns. So what happens is during the process when the gene is converted to RNA,

• 40:00Speaker 01

  we have things that are cut out, the introns that are cut out, and exons that now get assembled together. And sometimes we will throw out some of the exons. And the remaining protein product will become

• 40:17Lex Fridman?

  Still be the same.

• 40:18Speaker 01

  Different.

• 40:18Lex Fridman?

  Oh, different.

• 40:19Speaker 01

  Right? So so now you have fragments of the protein that no longer there. They were cut out with the introns.

• 40:26Lex Fridman?

  Okay.

• 40:26Speaker 01

  Sometimes you will essentially take one exon and replace it with another one, right?

• 40:32Lex Fridman?

  So there's some flexibility in this process.

• 40:35Speaker 01

  So so that creates a whole new level of complexity. Because now-

• 40:40Lex Fridman?

  Is this random though? Is it random?

• 40:42Speaker 01

  It's not random. We, and this is where I think now the appearance of this modern single cell and before that tissue level sequencing, next generation sequencing techniques such as RNA-seq allows us to see that these are the events that often happen in response in, it's a dynamic event that happens in response to disease or in response to certain developmental stage of a cell. And this is an incredibly complex layer that also undergoes, I mean, because it's at the gene level, right?

• 41:24Speaker 01

  So it undergoes certain evolution, right? And now we have this interplay between what is happening in the protein world and what is happening in the gene and RNA world. And for example, you know, it's often that we see that the boundaries of these exons coincide with the boundaries of the protein domains. Right? So there is, you know, close interplay to that. It's not always, I mean, you know, otherwise it would be too simple, right? But we do see the connection between those sort of machineries.

• 42:08Speaker 01

  And obviously, the evolution will pick up this complexity and, you know-

• 42:14Lex Fridman?

  Select for whatever is successful, whatever is interesting.

• 42:17Speaker 01

  Yeah, we see that complexity in play and makes this question, you know, more complex, but more exciting.

• 42:25Lex Fridman?

  A small detour, I don't know if you think about this, into the world of computer science, there's Douglas Hassetter, I think, came up with the name of Quine.

• 42:37Lex Fridman?

  Which are, I don't know if you're familiar with these things, but it's computer programs that have, I guess Exxon and Intron, and they copy, the whole purpose of the program is to copy itself. So it prints copies of itself, but can also carry information inside of it. So it's a very kind of crude, fun exercise of, can we sort of replicate these ideas from cells? Of can we have a computer program that when you run it, just prints itself? The entirety of itself? And does it in different programming languages and so on? I've been playing around and writing them. It's a kind of fun little exercise.

• 43:16Speaker 01

  You know, when I was a kid, so, so you know, it, it was essentially one of the, of the sort of main stages in, in Informatics Olympias that you have to reach in order to be any so good. Is you should be able to write a program that replicates itself. And so the task then becomes even, you know, sort of more complicated. So what is the shortest?

• 43:45Lex Fridman?

  What is the shortest?

• 43:47Speaker 01

  And of course, it's, it's, you know, it's a function of a programming language. But yeah, I, I remember, you know, a long, long, long time ago, when we tried to, you know, to, to make it short and short and find the, the, the shortcuts.

• 43:59Lex Fridman?

  There's actually on Stack Exchange, there's an entire site called Code Golf, I think, where the entirety is just a competition. People just come up with whatever task. I don't know, like, write code that reports the weather today. And the competition is about, in whatever programming language, what is the shortest program? And it makes you actually, people should check it out because it makes you realize there's some, some weird programming languages out there. But, you know, just to dig on that a little deeper. Do you think, you know, in computer science, we don't often think about programs.

• 44:42Lex Fridman?

  There's like the machine learning world now, that's still kind of basic programs. And then there's humans that replicate themselves, right? And there's these mutations and so on. Do you think we'll ever have a world where there's programs that kind of have an evolutionary process? So I'm not talking about evolutionary algorithms, but I'm talking about programs that kind of mate with each other and evolve and like on their own replicate themselves. So this is kind of the idea here is, you know, that's how you can have a runaway thing. So we think about machine learning as a system that gets smarter and smarter and smarter and smarter.

• 45:24Lex Fridman?

  At least the machine learning systems of today are like, it's a program that you can like turn off. As opposed to throwing a bunch of little programs out there and letting them like multiply and mate and evolve and replicate. Do you ever think about that kind of world? You know, when we jump from the biological systems that you're looking at to to artificial ones?

• 45:50Speaker 01

  I mean, it's almost like you you take the the sort of the area of intelligent agents, right? Which are essentially the the independent sort of codes that run and interact and exchange the information. Right? So I I don't see why not. I mean, I, you know, it could be sort of a natural evolution in in this, you know, area of computer science.

• 46:16Lex Fridman?

  I think it's kind of interesting possibility. It's terrifying too, but I think it's a really powerful tool. Like to have like agents that enter, you know, we have social networks with millions of people and they interact. I think it's interesting to inject into that was already injected into that bots, right? But those bots are pretty dumb. You know, they're they're probably pretty dumb algorithms.

• 46:39Lex Fridman?

  You know, it's interesting to think that there might be bots that evolve together with humans. And there's the sea of humans and robots that are operating first in the digital space. And then you can also think, I love the idea. Some people worked, I think, at Harvard, at Penn, there's robotics labs that, you know, built it takes a fundamental task to build a robot that given extra resources can build another copy of itself. Like in the physical space, which is a super difficult to do, but super interesting. As I remember, there's like research on Robots that can build a bridge, so they make a copy of themselves and they connect themselves.

• 47:21Lex Fridman?

  And so it's like self-building bridge based on building blocks. You can imagine like a building that self-assembles. So it's basically self-assembling structures from robotic parts. But it's interesting to, within that robot, add the ability to mutate. And do all the interesting, like, little things that you're referring to in evolution. To go from a single origin protein building block to like this weird complexity.

• 47:52Speaker 01

  And if you think about this, I mean, you know, the bits and pieces are there, you know. So you mentioned evolutionary algorithm, right? You know, so this is sort of, and maybe sort of the goal is in a way different, right? So the goal is to, you know, to essentially to optimize your search, right? So, but sort of the ideas are there. So people recognize that, you know, that the, you know, recombination events lead to global changes in the, in the search trajectories. The mutations event is a more refined, you know, step in the search.

• 48:33Speaker 01

  Then you have, you know, other sort of nature-inspired algorithm, right? So one of the reasons that, that I, you know, I think it's, it's one of the funnest one is the slime-based algorithm, right? So that it's, I think the first was introduced by the Japanese group, but where it was able to solve some, some pre, you know, complex problems. So that's, and then I think there are still a lot of things we've yet to, to, you know, borrow from the nature, right? So there are a lot of sort of ideas that nature.

• 49:17Speaker 01

  Gets to offer us that, you know, it's up to us to grab it and to, to, to, you know, get the best use of it.

• 49:25Lex Fridman?

  Including neural networks, you know, we have a very crude inspiration from nature on neural networks. Maybe there's other inspirations to be discovered in the brain or other aspects of the various systems, even like the immune system, the way it interplays. I recently started to understand that the, like, the immune system has something to do with the way the brain operates. Like, there's multiple things going on in there, which, all of which are not modeled in artificial neural networks. And maybe if you throw a little bit of that biological spice in there, you'll come up with something, something cool. I, I'm not sure if you're familiar with the Drake equation, that, I just did a video on it yesterday

• 50:09Lex Fridman?

  because I wanted to give my own estimate of it. It's, it's an equation that combines a bunch of factors to estimate how many alien civilizations are in the galaxy.

• 50:19Speaker 01

  Oh, yeah. I've heard about it. Yes.

• 50:21Lex Fridman?

  So one, one of the interesting parameters, you know, it's like, how many stars are born every year? How many planets are on average per star for this? How many habitable planets are there? And then the, the one that starts being really interesting is the probability that life emerges on a habitable planet. So like, I don't know if you think about, you certainly think a lot about evolution, but do you think about the thing which evolution doesn't describe, which is like the beginning of evolution, the origin of life? I think I put the probability of life developing a habitable planet at 1%. This is very scientifically

• 51:06Lex Fridman?

  rigorous. Okay. Well, first, at a high level for the Drake equation, what would you put that percent at on Earth? And in general, do you have something, do you have thoughts about how life might have started? You know, like the proteins being the first kind of, one of the early jumping points?

• 51:26Speaker 01

  Yeah. So, so I think back in 2018, there was a very exciting paper published in Nature where they found one of the simplest amino acids, glycine.

• 51:43Speaker 01

  In a comet dust.

• 51:46Lex Fridman?

  Hmm.

• 51:46Speaker 01

  So, so this is, uh, and I apologize if I don't pronounce. It's a Russian named comet. It's, I think, Chugryumov-Gerasimenko. This is the comet where, and there was this, uh, um, mission to, to get and, uh, get close to this comet and get the, the, uh, stardust from, from its tail. And, uh, when scientists analyzed it, they actually found traces of, uh, you know, uh, of glycine, which, you know, makes up, you know, the, well, it's one of the basic, uh, one of the 20 basic, uh, amino acids that makes up proteins. Right? So, uh, so that was kind of

• 52:31Lex Fridman?

  That's exciting.

• 52:32Speaker 01

  Very exciting, right? But, you know, it's, the question is very interesting, right? So what, uh, you know, what, if there is some alien life, is it gonna be made of proteins?

• 52:45Lex Fridman?

  Yeah.

• 52:45Speaker 01

  Right? Or maybe RNAs, right? So we see that, you know, the, the RNA viruses are certainly, you know, very well established sort of, uh, you know, group of molecular machines. Right? So, um, so yeah, it's, it's, it's a very interesting question.

• 53:04Lex Fridman?

  What, what probability would you put? Like, how hard is this job? Like, how unlikely just on Earth do you think this whole thing is that we got going? Like, is that, are we really lucky or is it inevitable? Like, what's your sense when you sit back and think about life on Earth? Is it higher or lower than 1%? Well, cause 1% is pretty low, but it's still like, damn, that's a pretty good chance.

• 53:28Speaker 01

  Yes, it's, it's a pretty good chance. I mean, I, I, I would personally, but again, you know, uh, I'm, um, you know, probably not the best person to, to, to, to do such estimations, but, uh, I would, you know, intuitively, I would probably put it lower.

• 53:45Lex Fridman?

  Lower.

• 53:46Speaker 01

  But still, I mean, you know,

• 53:47Lex Fridman?

  You're really lucky here on Earth.

• 53:50Speaker 01

  I mean.

• 53:51Lex Fridman?

  Or the conditions are really good.

• 53:53Speaker 01

  It's, you know, I think that there was, everything was right in a way, right? So, so we still, it's not, the, the conditions were not like ideal. If you try to, to look at, you know, what was, you know, several billions of years ago, when the life emerged.

• 54:11Lex Fridman?

  So there, there is something called the rare earth hypothesis that, you know, in counter to the Drake equation says that the, you know, the conditions of earth, if you actually were to describe earth, it's quite a special place. So special, it might be unique in our galaxy and potentially, you know, close to unique in the entire universe. Like it's very difficult to reconstruct those same conditions. And what the rare earth hypothesis argues is all those different conditions are essential for life. And so that's the, it's sort of the counter, you know, like all the things we,

• 54:52Lex Fridman?

  thinking that earth is pretty average. I mean, I can't really, I'm trying to remember to, to go through all of them, but just the fact that it is shielded from a lot of asteroids. The, obviously the distance to the sun, but also the fact that it's, it's like a perfect balance between the amount of water and land and all those kinds of things. I don't know, there's a bunch of different factors that I don't, I don't remember. There's a long list, but it's fascinating to think about if, if, if in order for something like proteins and then DNA and RNA to, to emerge, you need, and basic living organisms,

• 55:33Lex Fridman?

  you need to be a very close and earth-like planet, which would be sad.

• 55:40Lex Fridman?

  Or exciting, I don't know which.

• 55:42Speaker 01

  If you ask me, I, you know, in a way, I put a parallel between, you know, between our own research. And I mean, from the, from the intuitive perspective, you know, you have those two extremes. And the reality is never, very rarely falls into the extremes. It's always the optimus always reached somewhere in between. So, so I would, so, and that's what I tend to think. I think that, you know, we're probably somewhere in between. So they were not unique, unique, but again, the chances are, you know, reasonably small.

• 56:25Lex Fridman?

  The problem is we don't know the, the other extreme is like, I tend to think that, that we don't actually understand the basic mechanisms of like, what this is all originated from. Like, it seems like we think of life as this distinct thing, maybe intelligence is a distinct thing. Maybe the physics that from which planets and suns are born is a distinct thing. But that could be a very, it's like the Stephen Wolfram thing. It's like the, from simple rules emerges greater and greater complexity. So, you know, I tend to believe that just life finds a way. It like, we don't know the extreme of how common life is, because it could be life is like everywhere.

• 57:08Lex Fridman?

  Like, like so everywhere that it's almost like laughable. Like that we're such idiots to think, or you, like, it's, it's like ridiculous to even like think. It's like ants thinking that their little colony is the unique thing and everything else doesn't exist. I mean, it's also very possible that that's, that's the extreme. And we're just not able to maybe comprehend the nature of that life. I just to stick on alien life for just a brief moment more. There is some signs of, signs of life on Venus in gaseous form.

• 57:45Lex Fridman?

  There's hope for life on Mars, probably extinct. We're not talking about intelligent life. Although that has been in the news recently. We're talking about basic like, you know, bacteria.

• 58:00Speaker 01

  Proter bacteria.

• 58:00Lex Fridman?

  Yeah. And then also, I guess, there's a couple moons that I guess.

• 58:05Speaker 01

  Europe.

• 58:05Lex Fridman?

  Yeah, Europa, which is Jupiter's moon. I think there's another one. Are you, is that exciting? Or is it terrifying to you that we might find life? Do you hope we find life?

• 58:17Speaker 01

  I certainly do hope that we find life. I mean, it was very exciting to, to hear about, you know, this news about the, the possible life on Venus.

• 58:32Lex Fridman?

  It'd be nice to have hard evidence of something with, which is what the hope is for, for Mars and, and Europa. But do you think those organisms would be similar biologically? Or would they even be sort of carbon based if we do find them?

• 58:48Speaker 01

  I would say they, they would be carbon based. How similar? It's a big question, right? So it's, it's the moment we discover things outside Earth, right? Even if it's a tiny little single cell. I mean, there's so much.

• 59:08Lex Fridman?

  Just imagine that, that would be so.

• 59:10Speaker 01

  Yeah, I, I think that that would be another turning point for, for the science, you know?

• 59:15Lex Fridman?

  And if, especially if it's different in some very new way. That's exciting because that says, that's a definitive state, not a definitive, but a pretty strong statement that life is everywhere in the, in the, in the universe. To me at least, that's, that's really exciting. You brought up Joshua Lederberg in an offline conversation. I think I'd love to talk to you about Alpha Fold. And this might be an interesting way to enter that conversation because, so he won the 1958 Nobel Prize in Physiology and Medicine for discovering that bacteria can mate and exchange genes. But he also did a ton of other stuff like, like we mentioned, helping NASA find life on Mars and.

• 1:00:02Lex Fridman?

  The uh.

• 1:00:04Speaker 01

  Dendral.

• 1:00:04Lex Fridman?

  Dendral, the the chemical expert system, expert systems, remember those?

• 1:00:11Lex Fridman?

  Do you, what do you find interesting about this guy and his his ideas about artificial intelligence in general?

• 1:00:18Speaker 01

  So I have a kind of personal story to to share. So I started my PhD in in Canada back in two thousand. And so essentially, my my PhD was, so we were developing sort of a new language for symbolic machine learning. So it's different from the feature based machine learning. And and the one of the sort of cleanest applications of this, you know, of this approach, of this formalism, was to cheminformatics and computer aided drug design, right? So so essentially, we were, you know, as a part of my research,

• 1:00:56Speaker 01

  I developed a system that essentially looked at chemical compounds of, say, the same therapeutic category, you know, male hormones, right? And tried to figure out the structural fragments that are the structural building blocks that are important, that define this class versus structural building blocks that are there just because, you know, they're to complete the structure. But they are not essentially the ones that make up the the chemical, the the key chemical properties of this therapy category. And and, you know, for me, it was something new.

• 1:01:40Speaker 01

  I was I was trained as an applied mathematician, you know, as with some machine learning background. But, you know, computer aided drug design was completely a completely new territory. So because of that, I often find myself asking lots of questions on one of these sort of central forums. Back then, there were no no Facebooks or stuff like that.

• 1:02:04Lex Fridman?

  What's a forum?

• 1:02:06Speaker 01

  You know, it's a forum, it's it's essentially, it's like a bulletin board.

• 1:02:09Lex Fridman?

  Yeah.

• 1:02:09Speaker 01

  Right?

• 1:02:10Lex Fridman?

  On the internet.

• 1:02:11Speaker 01

  Yeah, so the. You essentially, you have a bunch of people and you post a question and you get, you know, an answer from, you know, different people. And, and, and back then, this one of the most popular forums was CCL. Think computational chemistry, not library, but something like that, but CCL, that, that was the, the forum. And there I, I, you know, I.

• 1:02:35Lex Fridman?

  Asked a lot of dumb questions.

• 1:02:37Speaker 01

  Yes, I asked questions, also shared some, some, you know, some information about our formulas and how we do. And whether, what, whatever we do makes sense. And so, you know, and I remember that one of these posts, I mean, I still remember, you know, I, I would call it desperately looking for, for a chemist advice, something like that, right? And so, so I posed my question, I explained, you know, how, how my, our formalism is, what is, what it does? And what kind of applications I'm planning to, to do. And, you know, and it was, you know, in the middle of the night and, you know, I went back, you know, to bed.

• 1:03:22Speaker 01

  And, and next morning, have a phone call from my advisor, who also looked at this forum. It's like, you won't believe who replied to you. And, and it's like, who? And he said, well, you know, there is a message to you from Joshua Lederberg.

• 1:03:42Speaker 01

  And my reaction was like, who is Joshua Lederberg?

• 1:03:47Lex Fridman?

  Your advisor hung up.

• 1:03:50Speaker 01

  So, and essentially Joshua wrote me that we, we had conceptually similar ideas in, in the Dendral project. You may wanna look it up. And.

• 1:04:03Lex Fridman?

  And we, we should also, sorry, and as a side comment, say that even though he. He won the Nobel Prize at a really young age, in fifty-eight, but so.

• 1:04:12Speaker 01

  He was, I think, he was, what, thirty-three?

• 1:04:15Lex Fridman?

  Yeah, it's just crazy.

• 1:04:16Speaker 01

  Yeah.

• 1:04:17Lex Fridman?

  So anyway, so that's, so Hans, Hans in the nineties responding to young whippersnappers on the, on the CCL forum.

• 1:04:25Speaker 01

  And, and, and so, so, so back then, he was already very senior. I mean, he unfortunately passed away back in two thousand eight. Uh, but, you know, uh, back in two thousand one, he was, I mean, he, he was a professor emeritus at Rockefeller University. And, you know, that was actually, believe it or not, one of the, one of the, uh, of, uh, of the reasons. I decided to join, uh, you know, as a postdoc, uh, the, the group of Andre Saleh, who was at Rockefeller University. With the hope that, you know, that I could actually, you know, uh, have a chance, uh, to meet Joshua in person. And I met him very briefly, right? The, uh, well, just because he was walking.

• 1:05:07Speaker 01

  You know, uh, there, there is a little bridge that connects the, sort of the research ca- campus with the, um, with the, uh, sort of skyscraper that Rockefeller owns, the where, you know, uh, postdocs and faculty and graduate students live. And so, so I met him, you know, and had a very short, you know, conversation, you know. But, uh, so I, I started, you know, reading about dendral and I was amazed. You know, it's, we're talking about nineteen sixty.

• 1:05:37Lex Fridman?

  Yeah.

• 1:05:37Speaker 01

  Right? The ideas were so profound.

• 1:05:42Lex Fridman?

  Well, what's the fundamental ideas of it?

• 1:05:44Speaker 01

  The, the reason to make this is even crazier. So, so, so Lederberg wanted to make a system that would help him study the, um, extraterrestrial molecules.

• 1:06:01Speaker 01

  Right, so, so the idea was that, you know, the, the way you study the extraterrestrial molecules is you do the mass spec analysis, right? And so the mass spec gives you sort of bits, numbers about essentially, uh, gives you the ideas about the possible fragments or, you know, atoms, uh, you know, and, and, and, and maybe little fragments, pieces of this molecule that make up the molecule, right? So now you need to sort of, to, uh, decompose this information and to figure out what was the whole before, you know, it became, became, uh, fragments, bits and pieces, right?

• 1:06:40Speaker 01

  So, so in order to make this, uh, you know, to have this tool, uh, the idea of Lederberg was to connect chemistry, computer science, and to design this so-called expert system that looks, that takes into account, that takes as an input the mass spec data, the possible, the database of possible molecules, and essentially try to, uh, sort of induce the molecule that would correspond to this spectrum.

• 1:07:18Speaker 01

  Or, you know, essentially, the, what this project ended up being, uh, was that, you know, it would provide a list of candidates, that then a chemist would look at and, and, and make final decision, so.

• 1:07:35Lex Fridman?

  But the original idea supposedly is to solve the entirety of this problem automatically.

• 1:07:39Speaker 01

  Yes, yes. So, so, so he, uh, you know, so, uh, so he, uh, back then, uh, he.

• 1:07:47Lex Fridman?

  Sixties.

• 1:07:47Speaker 01

  Yes, believe that. You know, it's, it's amazing. I mean, it still blows my mind, you know, that it's, that's, it's, and this was essentially the, the, the origin of the modern bioinformatics, cheminformatics, you know, back in the sixties.

• 1:08:05Lex Fridman?

  Yeah.

• 1:08:05Speaker 01

  Right, so that's, that's, you know. So every time you you you you deal with with projects like this, with the, you know, research like this, you just, you know, so the the the power of of the of the, you know, intelligence of these people is is just, you know, overwhelming.

• 1:08:24Lex Fridman?

  Do you think about expert systems, is there, and why they kind of didn't become successful, especially in the space of bioinformatics, where it does seem like there is a lot of expertise in humans, and, you know, it's it's possible to see that a system like this could be made very useful and be built up?

• 1:08:47Speaker 01

  Right, so it's it's actually, it's a it's a great question, and and this is something so, you know, so, you know, at my university, I teach artificial intelligence, and, you know, we start the my first two lectures are on the history of AI, and there we, you know, we try to, you know, go through the main stages of AI, and so, you know, the question of why expert systems failed or became obsolete is actually a very interesting one, and there are, you know, if you try to read the, you know, the historical perspectives, there are

• 1:09:26Speaker 01

  actually two lines of thoughts. One is that the they were essentially not up to the expectations, and so therefore they were replaced, you know, by by other things, right? The other one was that completely opposite one, that they were too good, and and as a result, they essentially became sort of a household name, and then essentially they they got transformed. I mean, they they in both cases, sort of the outcome was the same, they evolved into something.

• 1:10:05Lex Fridman?

  Yeah.

• 1:10:06Speaker 01

  Right? And that's what I, you know, if if I look at this, right? So the modern machine learning, right?

• 1:10:13Lex Fridman?

  So there's echoes in in the modern machine learning.

• 1:10:16Speaker 01

  I think so. I think so, because, you know, if if you think about this, you know, and how we design, you know, The most successful algorithms, including AlphaFold, right? You built in the knowledge about the domain that you study. Alright, so you built in your expertise.

• 1:10:35Lex Fridman?

  So speaking of AlphaFold, so DeepMinds AlphaFold 2 recently was announced to have quote unquote "solved protein folding." How exciting is this to you? It seems to be one of the exciting things that have happened in 2020. It's incredible accomplishment from the looks of it. What part of it is amazing to you? What part would you say is overhyped or maybe misunderstood?

• 1:11:02Speaker 01

  It's definitely a very exciting achievement. To give you a little bit of perspective, right? So in bioinformatics, we have several competitions. And so the way, you know, you often hear how those competitions have been explained to sort of to non-bioinformaticians is they, you know, they call it Bioinformatics Olympic Games. And there are several disciplines, right? So the historically, one of the first one was the discipline in predicting the protein structure, predicting the 3D coordinates of the protein. But there are some others. So the predicting protein functions, predicting effects of mutations on protein functions, then predicting protein-protein interactions.

• 1:11:48Speaker 01

  So the original one was CASP, or Critical Assessment of Protein Structure.

• 1:11:56Speaker 01

  And typically, what happens during these competitions is, you know, scientists, experimental scientists solve the structures, but don't put them into the protein data bank, which is a centralized database that contains all these 3D coordinates. Instead, they hold it and release protein sequences.

• 1:12:25Speaker 01

  And now the challenge of the community is to predict the 3D structures of these proteins and then use the experimental cell structures to assess which one is the closest one.

• 1:12:39Lex Fridman?

  Right, and this competition, by the way, just a bunch of different tangents. And maybe you can also say, what is protein folding? Then this competition, CasP competition is has become the gold standard. And that's what was used to say that protein folding was solved. So I just add a little, um, yeah, just a bunch. So if you can, whenever you say stuff, maybe throw in some of the basics for the folks that might be outside of the field. Anyway, sorry.

• 1:13:05Speaker 01

  So, so, yeah, so, you know, so the reason it's, it's, um, you know, it's relevant to our understanding of protein folding is because, you know, we, we, we've yet to learn how the folding mechanistically works, right? So there are different hypotheses. Wha- what happens to this fold? For example, uh, there, there is a hypothesis that the folding happens by, you know, in, in, also in the modular fashion, right? So that, you know, we, we have protein domains that get folded independently because their structure is stable. And then the whole protein structure gets formed.

• 1:13:46Speaker 01

  But, you know, within those domains, we also have so-called secondary structure, the small alpha helices, beta sheets. So these are, you know, as, uh, elements that are structurally stable. And so, and the, the question is, you know, when they, when do they get formed? Because some of the secondary structure elements, you have to have, uh, you know, a fragment in the beginning and say the fragment in the middle, right? So, so you cannot potentially start. Having the full fold from the get-go, right? So it's still, you know, it's still a big enigma. What happens? We know that it's an extremely efficient and stable process.

• 1:14:28Lex Fridman?

  Right, so there's this long sequence and the fold happens really quickly.

• 1:14:32Speaker 01

  Exactly.

• 1:14:33Lex Fridman?

  So that's really weird, right? And it happens like the same way almost every time.

• 1:14:38Speaker 01

  Exactly, exactly.

• 1:14:39Lex Fridman?

  That's really weird. That's freaking weird.

• 1:14:42Speaker 01

  It's, yeah, that's why it's such an amazing thing. But most importantly, right? So it's, you know, so when you see the, you know, the translation process, right? So when you don't have the whole protein translated, right? It's still being translated, you know, getting out from the ribosome. You already see some structural, you know, fragmentation. So folding starts happening before the whole protein gets produced, right? And so this is, this is obviously, you know, one of the biggest questions in, you know, in modern molecular biology.

• 1:15:23Lex Fridman?

  Not like maybe what happens? Like that's not, that's bigger than the question of folding. That's the question of like, something like deeper fundamental idea of folding.

• 1:15:35Speaker 01

  Yes.

• 1:15:35Lex Fridman?

  Behind folding.

• 1:15:36Speaker 01

  Exactly, exactly. So, you know, so obviously if we are able to predict the end product of protein folding, we are one step closer to understanding sort of the mechanics of the protein folding. Because we can then potentially look and start probing what are the critical parts of this process and what are not so critical parts of this process? So we can start decomposing this, you know, so, so, so in a way, this protein structure prediction algorithm can be

• 1:16:14Speaker 01

  Can be used as a tool, right? So, so you change the, you know, you modify the protein. You get back to this tool. It predicts, okay, it's completely, it's completely unstable. Yeah, which, uh, which aspects of the input will have a big impact on the output? Exactly, exactly. So, so what happens is, you know, we typically have some sort of incremental advancement. You know, each stage of this casp competition, you have groups with incremental advancement. And, you know, historically, uh, the top performing groups were, uh, you know, they were not using machine learning.

• 1:16:57Speaker 01

  They were using, uh, very advanced biophysics, combined with bioinformatics, combined with, you know, the, the data mining. Uh, and that was, uh, you know, that would enable them to obtain, uh, protein structures of those proteins that don't have any structure-y solved relatives. Because, you know, if we have, uh, another protein, say the same protein, but coming from a different species. Uh, we could potentially derive some ideas, and that's so-called homology or comparative modeling, where we'll derive some ideas from the previously known structures.

• 1:17:40Speaker 01

  And that would help us tremendously in, uh, you know, in, uh, reconstructing the 3D structure, uh, overall. But what happens when we don't have these relatives? This is when it becomes really, really hard, right? So that's so-called de novo, uh, you know, uh, de novo protein structure prediction. And in this case, those methods were, uh, traditionally very good. But what happened in the, in the last year? The original alpha fold came into.

• 1:18:13Speaker 01

  And all of a sudden, it's much better than everyone else.

• 1:18:19Lex Fridman?

  This is two thousand eighteen.

• 1:18:20Speaker 01

  Yeah.

• 1:18:21Lex Fridman?

  Oh, and the competition is only every two years, I think.

• 1:18:25Speaker 01

  And then, so, you know, it was sort of kind of of a shockwave to the bioinformatic community that, you know, we have like a state of the art machine learning system that does, you know, structured prediction. And essentially what it does, you know, so, you know, if you look at this, it actually predicts the context. So, you know, so the process of reconstructing the three D structure starts by predicting the the context between the different parts of the protein. And the context essentially is the part of the proteins that are in a close proximity to each other.

• 1:19:06Lex Fridman?

  Right, so it actually, the machine learning part seems to be estimating, you can correct me if I'm wrong here, but it seems to be estimating the distance matrix, which is like the distance between the different parts.

• 1:19:19Speaker 01

  Yeah, so we call it a contact map.

• 1:19:21Lex Fridman?

  Contact map.

• 1:19:21Speaker 01

  Right, so once you have the contact map, the reconstruction is becoming more straightforward.

• 1:19:27Lex Fridman?

  Yeah.

• 1:19:27Speaker 01

  Right, but so the contact map is the key. And so, so, you know, so that what happened. And now we started seeing in this current stage, right, where in the in the most recent one, we started seeing the emergence of these ideas in others' people works, right? But yet, here's, you know, Alpha Fall two.

• 1:19:52Lex Fridman?

  Yeah.

• 1:19:52Speaker 01

  That's again outperforms everyone else. And also by introducing yet another wave of the of the, you know, machine learning ideas.

• 1:20:01Lex Fridman?

  Yeah, there does seem to be also an incorporation, first of all, the paper is not out yet, but there's a bunch of ideas already out. There does seem to be an incorporation of this other thing. I don't know if it's something that you can speak to, which is like the incorporation of like other.

• 1:20:19Lex Fridman?

  Structures like evolutionary similar.

• 1:20:23Speaker 01

  Yes.

• 1:20:24Lex Fridman?

  Structures that are used to kind of give you hints.

• 1:20:26Speaker 01

  Yes, so evolutionary similarity is something that we can detect at different levels, right? So we know, for example, that the structure of proteins is more conserved than the sequence. The sequence could be very different, but the structural shape is actually still very conserved. So that's that's sort of the intrinsic property that, you know, in a way related to protein folds, you know, to the evolution of the, you know, of the protein, of proteins and protein domains, etc. But we know that, I mean, there've been multiple studies. And, you know, ideally, if you have structures, you know, you should use that information.

• 1:21:11Speaker 01

  However, sometimes we don't have this information. Instead, we have a bunch of sequences, sequences we have a lot, right? So we have, you know, hundreds, thousands of, you know, different organisms sequenced, right? And by taking the same protein, but in different organisms and aligning it, so making it, you know, making the corresponding positions aligned, we can actually say a lot about sort of what is conserved in this protein and therefore, you know, structurally more stable, what is diverse in this protein.

• 1:21:51Speaker 01

  So on top of that, we could provide sort of the information about the sort of the secondary structure of this protein, etc., etc. So this information is extremely useful and it's already there. So while it's tempting to, you know, to do a complete ab initio, so you just have a protein sequence and nothing else, the reality is such that we are overwhelmed with this data. So why not use it? And so, yeah, so I'm looking forward to reading this paper.

• 1:22:24Lex Fridman?

  It does seem to like they've, in the previous version of AlphaFold, they didn't. For this evolutionary similarity thing, they didn't use machine learning for that.

• 1:22:36Lex Fridman?

  Or they rather they used it as like the input to the entirety of the neural net. Like the features derived from the similarity. It seems like there's some kind of quote unquote iterative thing where it seems to be part of the part of the learning process is the incorporation of this evolutionary similarity.

• 1:22:57Speaker 01

  Yeah, I don't think there is a bio archive paper, right?

• 1:23:00Lex Fridman?

  There's nothing.

• 1:23:00Speaker 01

  Nothing.

• 1:23:01Lex Fridman?

  There's a blog post that's written by a marketing team essentially.

• 1:23:05Speaker 01

  Yeah.

• 1:23:05Lex Fridman?

  Which, you know, it has some scientific

• 1:23:10Lex Fridman?

  similarity probably to the the actual methodology used, but it could be it's like interpreting scripture. It could it could be just poetic interpretations of the actual work as opposed to direct connection to the work.

• 1:23:25Speaker 01

  So now, speaking about protein folding, right? So so so, you know, in order to answer the question whether or not we we have solved this, right?

• 1:23:32Lex Fridman?

  Yeah.

• 1:23:32Speaker 01

  So we need to go back to to the beginning of our conversation. You know, with the realization that, you know, an average protein is that typically what the the casp has been focusing on is the, you know, the this competition has been focusing on the single, maybe two domain proteins that are still very compact. And even those ones are extremely challenging to to solve, right? But now we talk about, you know, an average protein that has two, three protein domains. If you look at the proteins that that are in charge of the, you know, of the process with the neural system, right?

• 1:24:14Speaker 01

  Well, perhaps one of the of the most recently evolved sort of systems in in in the organism, right? All of them, well, the majority of them are highly multi domain proteins. So they are, you know, some of them have five, six, seven, you know, and more domains, right? And, you know, we are very far away from understanding how these proteins are folded.

• 1:24:45Lex Fridman?

  So the complexity of the protein matters here, the complexity, the complexity of the protein modules or the the protein domains.

• 1:24:55Lex Fridman?

  So you're saying solved, so the definition of solved here is particularly the cast competition achieving human level, not human level, achieving

• 1:25:05Speaker 01

  Experimental level.

• 1:25:06Lex Fridman?

  Experimental level performance on these particular sets of proteins that have been used in these competitions.

• 1:25:13Speaker 01

  Well, I mean, you know, I, I, I do think that, you know, especially with, with regards to the Alpha fold, you know, it is able to, you know, to solve, you know, at the near experimental level, a pretty big majority of the, of the more compact proteins like, or protein domains, because again, in order to understand how the overall protein, you know, multi-domain protein fold, we do need to understand the structure of its individual domains.

• 1:25:51Lex Fridman?

  I mean, unlike, if you look at Alpha Zero, like Mu Zero, if you look at that work, you know, there, it's nice, reinforcement learning, self-playing mechanisms are nice because it's all in simulation, so you can learn from just huge amounts, like you don't need data, it was like the problem with proteins, like the size, I forget how, how many 3D structures have been mapped, but the training data is very small, no matter what, it's like millions, maybe a one or two million, something like that, but some very small number, but like, it doesn't seem like that's scalable, there has to be, I don't know,

• 1:26:32Lex Fridman?

  it feels like you want to somehow 10x the data, or 100x the data somehow.

• 1:26:38Speaker 01

  Yes, but we also can take advantage of, of homology models, right, so the models that are of very good quality, because they are essentially obtained based on the evolutionary information, right, so you can, there is a potential to enhance this information and, you know, use it again to, to empower the, the, the training set. And it's, I think, I, I am actually very optimistic, I think it's been one of this sort of

• 1:27:23Speaker 01

  You know, uh, turning events where you have a system that is, you know, a machine learning system that is truly better than the sort of the more conventional biophysics based methods.

• 1:27:41Lex Fridman?

  That's a huge leap. This is one of those fun questions, but where would you put it in, in the ranking of the greatest breakthroughs in artificial intelligence history?

• 1:27:53Speaker 01

  Oh, oh, oh.

• 1:27:54Lex Fridman?

  So like, okay, so let's, let's see who's in the running. Maybe you can correct me. So you got like AlphaZero and AlphaGo beating, you know, beating the world champion at the game of Go, thought to be impossible like 20 years ago, or at least the AI community was highly skeptical. Then you got like also D-Blue, original Kasparov. You have deep learning itself, like the, maybe, what would you say, the AlexNet image in that moment? So the first neural network achieving human level performance, super not, that's not true, achieving like a big leap in performance on the computer vision problem.

• 1:28:37Lex Fridman?

  There is OpenAI, the whole like GPT-3, that whole space of transformers and language models just achieving this incredible performance of application of neural networks to language models. Boston Dynamics, pretty cool, like robotics, even though people are like, there's no AI. No, no, there's no machine learning currently, but AI is much bigger than machine learning.

• 1:29:07Speaker 01

  Yes.

• 1:29:07Lex Fridman?

  So, so that just the engineering aspect, I would say is one of the greatest accomplishments in engineering side, engineering, meaning like mechanical engineering of robotics ever. Then of course autonomous vehicles, you can argue for Waymo, which is like the Google self-driving car, or you can argue for Tesla, which is like actually being used by hundreds of thousands of people on the road today, machine learning system.

• 1:29:36Lex Fridman?

  And I don't know if you can, what else, what else is there? But I think that's it. So, and then AlphaFold, many people are saying is up there, potentially number one. Would you put them at number one?

• 1:29:47Speaker 01

  Well, in terms of the impact.

• 1:29:51Speaker 01

  On the science and on the society beyond is definitely, you know, to me would be one of the, you know.

• 1:30:00Lex Fridman?

  Top three.

• 1:30:02Speaker 01

  Maybe, I mean, I'm, I'm, I, I probably not the best person to, to, to, to answer that, you know, but, you know, I, you know, I, I do have, I, I remember my, you know, uh, back in, I think nineteen ninety seven, when Deep Blue, that Kasparov, it was, I mean, it was a shock. I mean, it was, and I think for the, for the, you know, uh,

• 1:30:31Speaker 01

  for the, you know, uh, pre substantial part of the world, that especially people who have some, uh, you know, uh, some experience with chess, right? And realizing how incredibly human this game, how, you know, how much of a brain power you need, you know, to, to reach those, you know, those levels of grandmasters, right, level.

• 1:30:59Lex Fridman?

  Yeah, and it's probably one of the first time, and how good Kasparov was.

• 1:31:02Speaker 01

  And, and again, yeah, so Kasparov is arguably one of the best ever, right? And you get a machine that beats him, right? So it's, it's.

• 1:31:11Lex Fridman?

  First time a machine probably beat a human at that scale of a thing, of anything.

• 1:31:16Speaker 01

  Yes, yes. So that was, to me, that was like, you know, one of the groundbreaking events in the history of AI.

• 1:31:23Lex Fridman?

  Yeah, that's probably number one. That's probably, like, we don't, it's, it's hard to remember. It's like Muhammad Ali versus, uh, I don't know, any of the Mike Tyson, something like that. It's like, nah, you gotta put Muhammad Ali at number one. Uh, same with, same with Deep Blue, even though it's not machine learning based.

• 1:31:42Speaker 01

  Uh, still, it, it uses advanced search and search is the integral part of AI, right?

• 1:31:47Lex Fridman?

  So it's not, it's, people don't think of it that way now, at this moment, in vogue currently. Search is not seen as a, as a fundamental aspect of intelligence, but it very well, it very likely is. In fact, I mean, that's what neural networks are, is there's just performing search on the space of parameters. And it's all search.

• 1:32:08Lex Fridman?

  All of intelligence is some form of search and you just have to become clever and clever at that search problem.

• 1:32:13Speaker 01

  And I also have another one that you didn't mention that's that's that's one of my favorite ones is so you've probably heard of this it's I think it's called Deep Rembrandt. It's the project where they they trained I think there was a collaboration between the sort of the experts in in Rembrandt painting in Netherlands and a group an artificial intelligence group where they train an algorithm to replicate the style of the Rembrandt and they actually printed a a portrait that never existed before in the style of Rembrandt they they I think they printed it on

• 1:32:57Speaker 01

  the on the sort of on the canvas that you know using pretty much same types of paints and stuff. To me it was mind blowing.

• 1:33:07Lex Fridman?

  Yeah.

• 1:33:07Speaker 01

  It's.

• 1:33:08Lex Fridman?

  And the space of art that's interesting there hasn't been um I was maybe that's that's it but I I think there hasn't been an image in that moment yet in the space of art. You haven't been able to achieve superhuman level performance in the space of art even though there was you know there's this big famous thing where there was a piece of art was purchased I guess for a lot of money.

• 1:33:31Speaker 01

  Yes.

• 1:33:32Lex Fridman?

  Yeah, but it's still you know people are like in the space of music at least.

• 1:33:38Lex Fridman?

  That's you know it's clear that human created pieces are much more popular. So there hasn't been a moment where it's like oh this is we're now I would say in the space of music what makes a lot of money we're talking about serious money it's music and movies or like shows and so on and entertainment there hasn't been a moment where AI created AI was able to create a piece of music or a piece of cinema.

• 1:34:11Lex Fridman?

  Like Netflix show that is, you know, that's sufficiently popular to make a ton of money.

• 1:34:18Speaker 01

  Yeah.

• 1:34:18Lex Fridman?

  And that moment would be very, very powerful because that's like, that's an AI system being used to make a lot of money. And like direct, of course, AI tools, like even Premiere, audio editing, all the editing, everything I do to edit this podcast. There's a lot of AI involved. I won't, actually, there's this program, I want to talk to those folks just because I want to nerd out. It's called Isotope. I don't know if you're familiar with it. They have a bunch of tools of audio processing and they have, I think they're Boston based. Just, it's so exciting to me to, to use it like on the audio here because it's all machine learning. It's not, because most, most audio production stuff is like any kind of processing you do is very basic signal processing.

• 1:35:02Lex Fridman?

  And your tuning knobs and so on. They have all of that, of course, but they also have all of this machine learning stuff. Like where you actually give it training data, you select parts of the audio you train on. You, you, you train on it and it, it figures stuff out. It's, it's great. It's able to detect. Uh, like the, the ability of it to be able to separate voice and music, for example, or voice and anything is incredible. Like it, it just, it's clearly exceptionally good at, uh, you know, applying these different neural networks models to, to, to separate the different kinds of signals from the audio that, that, uh, okay. So that's really exciting.

• 1:35:45Lex Fridman?

  Photoshop, Adobe people also use it, but to generate a piece of music.

• 1:35:50Speaker 01

  Yeah.

• 1:35:51Lex Fridman?

  That will sell millions, a piece of art. Yeah.

• 1:35:55Speaker 01

  No, I, I agree. And you know, it's, uh, that's, that's, you know, uh, I, as I mentioned, I offer my, my AI class. And you know, an integral part of this is a project, right? So it's, it's my favorite, ultimate favorite part. Because typically we have these, you know, project presentations, the last two weeks of the classes, right before, you know, the, the Christmas break. And it's, it's sort of. It adds this cool excitement and every time I'm amazed, you know, with with some some projects that people, you know, come up with. And so, and quite a few of them are actually, you know, they some have some link to

• 1:36:39Speaker 01

  to to arts. I mean, you know, I think last year

• 1:36:44Speaker 01

  we had a group who designed NAI producing Hokus, Japanese poems.

• 1:36:51Lex Fridman?

  Oh, wow.

• 1:36:52Speaker 01

  Uh, so, and some of them, so, you know, it got trained on the on the English base. Hikers, hikers, right? So, um, and and some of them, you know, they get to present like the the top selection. They were pretty good. I mean, you know, I mean, of course, I'm not I'm not a specialist, but you you read them and you see.

• 1:37:13Lex Fridman?

  It seems profound.

• 1:37:14Speaker 01

  Yes, yeah, it it seems reasonable. So it's it's kind of cool. We we also had a couple of projects where people tried to to teach AI how to play like rock music, classical music, uh, I think, and and and popular music.

• 1:37:31Lex Fridman?

  Yeah.

• 1:37:33Speaker 01

  Interestingly enough, uh, you know, classical music was among the most difficult ones. And and and, you know, of course, if you if, you know, uh, you know, if if you look at the, you know, the uh, like grand masters of music like Bach, right? So there is a lot of, uh, there is a lot of almost math.

• 1:37:57Lex Fridman?

  Yeah, well, he's very mathematical.

• 1:37:59Speaker 01

  Exactly. So so this is, I would imagine that at least some style of this music could be picked up. But then you have such completely different spectrum of of, you know, classical composers. And so, you know, it it and, you know, it's almost like, you know, you don't have to sort of look at the data. You just listen to it and say, nah, that's that's that's not it, not yet.

• 1:38:24Lex Fridman?

  That's not it.

• 1:38:24Speaker 01

  Yeah.

• 1:38:25Lex Fridman?

  That's that's how I feel too. This OpenAI has, I think, OpenMuse or something like that, the system. It's cool, but it's like, eh, it's not compelling for some for some reason. It could be a psychological reason too. Maybe we need to have a human being, a tortured soul behind the music. I don't know.

• 1:38:43Speaker 01

  Yeah, no, that absolutely. I completely agree. But yeah, whether or not we'll have, one day we'll have, you know, a song written by an AI engine to be in like in top charts.

• 1:38:59Lex Fridman?

  Yeah.

• 1:38:59Speaker 01

  Musical charts, I wouldn't be surprised. I wouldn't be surprised.

• 1:39:06Lex Fridman?

  I wonder if we already have one and it just hasn't been announced.

• 1:39:11Lex Fridman?

  We wouldn't know. How hard is the multi-protein folding problem? Is that kind of something you've already mentioned, which is baked into this idea of greater and greater complexity of proteins? Like multi-domain proteins, is that basically become multi-protein complexes?

• 1:39:31Speaker 01

  Yes, you got it right. So it's sort of, it has the components of both of protein folding and protein-protein interactions. Because in order for these domains, I mean, many of these proteins, actually, they never form a stable structure. You know, one of my favorite proteins, you know, and pretty much everyone who works in the I know, whom I know who works in, you know, with proteins, they always have their favorite proteins. So one of my favorite proteins, probably my favorite protein, the one that I worked when I was a post-doc,

• 1:40:14Speaker 01

  is so-called postsynaptic density ninety-five, PSD ninety-five protein. So it's one of the key actors in the majority of neurological processes at the molecular level. So it's a, and it essentially, it's a, it's a key player in the postsynaptic density. So this is the crucial part of the synapse where, you know, a lot of these key molecular processes are happening. So it has five domains, right? So five protein domains, you know, large proteins, I think six hundred something, I mean, it's

• 1:40:58Speaker 01

  but, you know, the way it's organized itself, it's flexible, right? So it acts as a scaffold. So it is used to bring in other proteins. So they start acting in the orchestrated manner. Right, so and the the type of the shape of this protein, it's in a way, there are some stable parts of this protein, but there are some flexible. And this flexibility is built in into the protein in order to become sort of this multifunctional machine.

• 1:41:36Lex Fridman?

  So do you think that kind of thing is also learnable through the AlphaFold two kind of approach?

• 1:41:42Speaker 01

  I mean, the time will tell.

• 1:41:45Lex Fridman?

  Is it another level of complexity? Is it, is it like how big of a jump in complexity is that whole thing?

• 1:41:51Speaker 01

  To me, it's it's yet another level of complexity. Because when we talk about protein-protein interactions, and there is actually a different challenge for this called CAPRi. And so this that is focused specifically on macromolecular interactions, protein-protein, protein, DNA, etc. So, but it's, you know, there are different mechanisms that govern molecular interactions and that need to be picked up, say, by a machine learning algorithm. Interestingly enough, we actually, we participated for a few years in this competition.

• 1:42:34Speaker 01

  We typically don't participate in competitions. I don't know, don't have enough time, you know, because it's very intensive. It's a very intensive process. But we participated back in, you know, about 10 years ago or so. And the way we entered this competition, so we designed a scoring function, right? So the function that evaluates whether or not your protein-protein interaction is supposed to look like experimentally solved, right? So the scoring function is very critical part of the of the model prediction. So we designed it to be a machine learning.

• 1:43:15Speaker 01

  And so it was one of the first machine learning based scoring function used in Capri. And you know, we essentially, you know, learned what should contribute, what are the critical components contributing into the protein protein interactions.

• 1:43:33Lex Fridman?

  So this this could be converted into a learning problem and thereby it could be it could be learned.

• 1:43:38Speaker 01

  I believe so, yes.

• 1:43:40Lex Fridman?

  Do you think AlphaFold two or something similar to it from DeepMind or somebody else will be will result in a Nobel Prize or multiple Nobel Prizes? So like the, you know, obviously, maybe not so obviously, you can't give a Nobel Prize to a computer program. You, at least for now, give it to the designers of that program. But is, do you see one or multiple Nobel Prizes where AlphaFold two is like a large percentage of what that prize is given for? Would it lead to discoveries at the level of Nobel Prizes?

• 1:44:23Speaker 01

  I mean, I think we are definitely destined to see the Nobel Prize becoming sort of to be evolving with the evolution of science. And the evolution of science is such that it now becomes like really multifaceted, right? Where you have, you don't really have like a unique discipline. You have sort of the, the, a lot of cross disciplinary talks in order to achieve sort of, you know, really big advancements, you know? So I think, you know, the computational methods will be acknowledged in one way or another.

• 1:45:05Speaker 01

  And in, as a matter of fact, you know, they were first acknowledged back in two thousand and thirteen, right? Where, you know, the, the first three people were, you know, awarded the, the Nobel Prize for the product, for study the protein folding, right? The principle. And, you know, I think all three of them are computational biophysicists. And so, you know, that I think is, is unavoidable. You know, it will come with the time. The fact that You know, AlphaFold and, you know, similar approaches because again, it's a matter

• 1:45:48Speaker 01

  of time that people will embrace the, this, you know, principle and will see more and more such, you know, such tools coming into play. But, you know, these methods will be critical in, in a scientific discovery, no, no doubts about it.

• 1:46:09Lex Fridman?

  On the engineering side, maybe a dark question, but do you think it's possible to use these machine learning methods to start to engineer proteins? And the next question is something quite a few biologists are against, some are for, for study purposes, is to engineer viruses. Do you think machine learning, like something like AlphaFold could be used to engineer viruses?

• 1:46:37Speaker 01

  So to answering the first question, you know, it has been, you know, a part of the research in the protein science, the protein design is, you know, is a very prominent areas of research. Of course, you know, one of the pioneers is David Baker and Rosetta algorithm that, you know, essentially was doing the, the de novo design and was used to design new proteins, you know.

• 1:47:04Lex Fridman?

  And design of proteins means design of function. So like when you design a protein, you can control, I mean, the whole point of a protein with the protein structure comes a function, like it's doing something.

• 1:47:16Speaker 01

  Correct.

• 1:47:17Lex Fridman?

  So you can design different things.

• 1:47:19Speaker 01

  So you can, yeah, so you can, well, you can look at the proteins from the function perspective. You can also look at the proteins from the structural perspective, right? So the structural building blocks. So if you want to have a building block of a certain shape, you can try to achieve it.

• 1:47:33Lex Fridman?

  Yes.

• 1:47:33Speaker 01

  By, you know, introducing a new protein sequence and predicting, you know, how it will fold. So, so with that, I mean, it's, it's a natural, one of the, you know, natural applications of these algorithms. Now, talking about engineering a virus.

• 1:47:57Lex Fridman?

  With machine learning.

• 1:47:58Speaker 01

  With machine learning, right? So, so, well, um, you know.

• 1:48:03Speaker 01

  So luckily for us, I mean, uh, we don't have that much data. Right?

• 1:48:09Lex Fridman?

  Yeah.

• 1:48:10Speaker 01

  Uh, we actually, uh, right now, one of the projects that we are, uh, carrying on in the lab is we, we, we're trying to develop a machine learning al- algorithm that, uh, determines the, uh, whether or not the, the current strain is pathogenic. And

• 1:48:26Lex Fridman?

  The current strain of the coronavirus.

• 1:48:27Speaker 01

  Of, of the, of, of the virus. I mean, so, so there, there are applications to coronaviruses because we have strains of SARS-CoV-2, also SARS-CoV-1, MERS, that are pathogenic. But we also have strains of other coronaviruses that are, you know, not pathogenic. I mean, the, the common cold, uh, viruses and, uh, you know, and, and some other ones, right? So, so

• 1:48:49Lex Fridman?

  Uh, pathogenic meaning spreading.

• 1:48:51Speaker 01

  Uh, but, but the G A N I C means actually infli- inflicting

• 1:48:55Lex Fridman?

  Damage.

• 1:48:56Speaker 01

  Correct. Uh, there are also some, you know, seasonal versus pandemic strains of influenza. Right? And to determining the, what are the molecular determinant, right? So that are built in, into the protein sequence, into the gene sequence. Right? So, and, uh, whether or not the machine learning can determine those deter- uh, those components. Right?

• 1:49:21Lex Fridman?

  Oh, interesting. So like using machine learning to, that's really interesting to, to, to given, give the input is like, uh, what?

• 1:49:29Speaker 01

  Protein sequence.

• 1:49:30Lex Fridman?

  The, the protein sequence and then determine if this thing is going to be able to do damage to, to, uh, to a biological system.

• 1:49:37Speaker 01

  Yeah.

• 1:49:39Speaker 01

  So, so I mean

• 1:49:39Lex Fridman?

  Good machine learning. You're saying we don't have enough data for that?

• 1:49:42Speaker 01

  We, I mean, for, for this specific one, we do. Uh, we might actually, you know, have to back up on this. We, we're still in the process. There was, uh, one, uh, work, uh, that appeared in Bio Archive by Eugene Kuhnin, who is one of these, you know, uh, pioneers in, uh, in, in evolutionary genomics. Uh, and they tried to look at this, uh, but, uh, you know, the, the methods were sort of standard, uh, you know, supervised learning, uh, methods. Uh, and now the question is, you know, can you, you know? Advance it further by by using, you know, not so standard methods, you know. So there's obviously a lot of hope in in transfer learning,

• 1:50:25Speaker 01

  where you can actually try to transfer the information that the machine learning learns about the proper protein sequences, right? And, uh, you know, so so there is some promise in going this direction. But if we have this, it would be extremely useful because then we could essentially forecast the potential mutations that would make current strain more or less pathogenic.

• 1:50:50Lex Fridman?

  And anticipate, anticipate them from a vaccine development for the treatment, the anti uh, antiviral drug development.

• 1:50:57Speaker 01

  That that would be a very crucial task.

• 1:50:59Lex Fridman?

  But you could also use that system to then say, how would we potentially modify this virus to make it more pathogenic?

• 1:51:10Speaker 01

  This, that's true, that's true. I mean, uh, you know, the, again, the hope is, well, several things, right? So so one is that, you know, it's even if you design a, you know, a sequence, right? So to carry out the actual experimental biology, to ensure that all the common components working, you know, is is a completely different matter.

• 1:51:42Lex Fridman?

  Difficult process.

• 1:51:42Speaker 01

  Yes, uh, then there did, you know, uh, we've seen in the past, there there could be some regulation of the the moment the scientific community recognizes that it's now becoming no longer a sort of a a fun puzzle to, you know, for for machine learning.

• 1:51:59Lex Fridman?

  Could be a weapon.

• 1:52:00Speaker 01

  Yeah, so so then there might be some regulation. So I I think back in what, two thousand fifteen, there was, you know, the there was an issue on regulating the uh, the research on uh, on influenza strains, right? That there were, you know, several groups, uh, you know, use sort of the mutation analysis to to determine whether or not this strain will jump from one species to another. And I think there was like a half a year moratorium on the research on the paper published until, you know, scientists, you know, analyzed it and decided that it's actually safe.

• 1:52:39Lex Fridman?

  I forgot what that's called, something of function, test of function.

• 1:52:43Speaker 01

  Gain of function.

• 1:52:43Lex Fridman?

  Gain of function, yeah, gain of function, loss of function, that's right, sorry.

• 1:52:49Lex Fridman?

  It's like, let's watch this thing mutate for a while to see like, to see what kind of things we can observe. I guess, I'm not so much worried about that kind of research if there's a lot of regulation and if it's done very well and with competence and seriously. I am more worried about kind of this, you know, the, the, the underlying aspect of this question is more like 50 years from now.

• 1:53:16Lex Fridman?

  Speaking to the Drake equation, one, one of the parameters in the Drake equation is how long civilizations last. And that's, that seems to be the most important value, actually, for calculating if there's other alien intelligent civilizations out there. That's where there's most variability. Assuming, like, if life, if that percentage that life can emerge is like, not zero, like if we're a super unique, then it's the how long we last is, is basically the most important thing. So I'm, from, from a selfish perspective, but also from a Drake equation perspective, I'm worried about the last, our civilization lasting.

• 1:53:58Lex Fridman?

  And you kind of think about all the ways in which machine learning can be used to design greater weapons of destruction, right? And, I mean, one way to ask that, if you look sort of 50 years from now, 100 years from now, would you be more worried about natural pandemics or engineered pandemics?

• 1:54:22Lex Fridman?

  Like who's who's the better designer of viruses, nature or humans? If we look down the line?

• 1:54:29Speaker 01

  I I think uh my view, I would still be worried about the natural pandemics. Simply because, I mean, the the capacity uh of the nature producing.

• 1:54:43Lex Fridman?

  Yeah, it does a pretty good job, right?

• 1:54:45Speaker 01

  Yes.

• 1:54:46Lex Fridman?

  And the motivation for using virus, engineering viruses for uh as a weapon is a weird one because uh maybe you can correct me on this, but it's very, it seems very difficult to target a virus, right? The whole point of a weapon, the way a rocket works, you have a starting point, you have an endpoint and you you're trying to hit a target. To to hit a target with a virus is very difficult. It's basically just, right? It it's the the target would be the human species.

• 1:55:14Lex Fridman?

  Uh, man.

• 1:55:15Speaker 01

  Yeah.

• 1:55:16Lex Fridman?

  Yeah, I have a I have a hope in us. I'm forever optimistic that we will not, there's no, there's insufficient evil in the world to do to lead that to that kind of destruction.

• 1:55:27Speaker 01

  Well, you know, I also hope that, I mean, that's what we see. I mean, uh, with the way we are getting connected, the world is getting connected. I think it it helps for the world to become more transparent.

• 1:55:44Lex Fridman?

  Yeah.

• 1:55:45Speaker 01

  So so the information spread is, you know, I think it's one of the key things for the for the society to become more balanced.

• 1:55:58Lex Fridman?

  Yeah.

• 1:55:58Speaker 01

  One way or another.

• 1:55:59Lex Fridman?

  This is something that people disagree with me on, but I do think that the kind of secrecy the governments have. So you're kind of speaking more to the other aspects, like a research community being more open. Companies are being more open. Uh, government is still like, uh, uh, we're talking about like military secrets.

• 1:56:20Speaker 01

  Yeah.

• 1:56:20Lex Fridman?

  I think I think military secrets of the kind that could destroy the world will become also a thing of the 20th century. It'll become more and more open. Like, I, I think nations will lose power in the 21st century, like lose sufficient power towards secrecy. Transparency is more beneficial than secrecy. But of course, it's not obvious.

• 1:56:43Speaker 01

  Let's hope so. Let's hope so. That, that, you know, the, the, the, um, the governments will become more transparent.

• 1:56:54Lex Fridman?

  What, so we last talked, I think in March or April. What have you learned? How has your philosophical, psychological, biological worldview changed since then? Or you've been studying it nonstop from a computational biology perspective. How has your understanding and thoughts about this virus changed over those months, from the beginning to today?

• 1:57:17Speaker 01

  One thing that I was really amazed at how efficient the scientific community was. I mean, and, you know, even just judging on, on this very narrow domain of, you know, a protein structure, you know, understanding the structural characterization of this virus from the components point of view, you know, whole virus point of view, you know, if you look at, at SARS, right, the, the, something that happened, you know, oh, less than 20, but, you know, close enough, 20 years ago. And you see what, you know, when it happened,

• 1:58:01Speaker 01

  you know, what was sort of the response to, by the scientific community. You see that the, the structural characterization did a cure, but it took several years, right? Now, the things that took several years, it's a matter of months, right? So, so we, we see that, you know, the, the, the, the research pop up. We are at the unprecedented level in terms of the sequencing, right? Never before we had a single virus sequenced so many times, you know, so which allows us to actually to trace very precisely the

• 1:58:44Speaker 01

  sort of the evolutionary nature of this virus, what happens. And it's not just the, you know, This virus independently of everything is, you know, is the, you know, the, the sequence of this virus linked anchored to the specific geographic place.

• 1:59:02Lex Fridman?

  Yeah.

• 1:59:03Speaker 01

  To specific people because, you know, the, the, our genotype influences also, you know, the, the evolution of this, you know, it's, it's always a host pathogen co-evolution that, that, you know, occurs.

• 1:59:18Lex Fridman?

  It'd be cool if we also had a lot more data about, so that the spread of this virus, not maybe, well, it'd be nice if we had it for like contact tracing purposes for this virus, but it'd be also nice if we had it for the study for future viruses to be able to respond and so on.

• 1:59:36Speaker 01

  Well.

• 1:59:37Lex Fridman?

  But it's already nice that we have geographical data and like basic data from individual humans.

• 1:59:41Speaker 01

  Yeah, exactly. I know, I think contact tracing is, is, is obviously a key component in understanding the spread of this virus. There is also, there is a number of challenges, right? So X price is one of them. We, we, you know, just recently, you know, took a part of this competition. It's the prediction of the, of the number of infections in different regions.

• 2:00:11Lex Fridman?

  Oh, sure.

• 2:00:11Speaker 01

  So, and, you know, obviously the, the AI is the main topic in, in those predictions.

• 2:00:19Lex Fridman?

  Yeah, but it's still the data. I mean, that's, that's a competition, but the, the data is weak on the training. Like it's, it's great. It's much more than probably before, but like it'd be nice if it was like really rich. I, I, I talked to Michael Mina from, from Harvard. I mean, he dreams that the community comes together with like a weather map to where of viruses, right? Like really high resolution sensors on like how from person to person, the viruses that travel, all the different kinds of viruses, right? Because there's, there's, there's a ton of them. And then you'd be able to tell the story that you've spoken about of the evolution

• 2:01:03Lex Fridman?

  of these viruses, like day to day mutations that are occurring. I mean, that'd be fascinating just from a perspective of study. And from the perspective of being able to respond to future pandemics. That's ultimately what I'm worried about. People love books. Is there, is there some three or whatever number of books, technical fiction, philosophical, that, that brought you joy in life, had an impact on your life? And maybe some that you would recommend others?

• 2:01:34Speaker 01

  So I'll give you three very different books, and I also have a special runner up. And

• 2:01:40Lex Fridman?

  Honorable mention.

• 2:01:41Speaker 01

  Yeah, it's, yeah, I wouldn't, I mean, it's, it's an audio book. And that's, that's, yeah, there's some specific reason behind it.

• 2:01:48Lex Fridman?

  Okay.

• 2:01:48Speaker 01

  So, you know, so the first book is, you know, something that, that sort of impacted my earlier stage of life. And I'm probably not going to be very original here. It's Bulgakov's Master and Margarita. So that's probably, you know.

• 2:02:04Lex Fridman?

  Well, not for Russian, maybe it's not super original, but it's, you know, it's, it's a really powerful book for even in English. So I read it in English, so.

• 2:02:12Speaker 01

  It is incredibly powerful. And I mean, it's the, the way it ends, right? So it's, it's, I, I, I still have goosebumps when I read the, the, the very last sort of, it's called prologue, where it is just so powerful.

• 2:02:28Lex Fridman?

  What impact did you, did it have on you? What ideas, what insights did you get from it?

• 2:02:32Speaker 01

  I was just taken by, you know, by the, the fact that you have those parallel lives apart from many centuries, right? And somehow they, they got sort of intertwined into one story. And, and that's, to me, was fascinating. And, you know, of course, the, you know, the romantic part of this book is like, you know, it's not just, you know, romance, it's like the romance empowered by sort of magic, right? And, and that, and, and, and maybe on top of that, you have some irony, which unavoidable, right?

• 2:03:16Speaker 01

  Because it, it was that, you know, the Soviet time.

• 2:03:19Lex Fridman?

  But it's very, it's very, it's deeply Russian. So that's the, the wit, the humor, the pain, the, the love, all of that is one of the books that kind of captures something about. Russian culture that people outside Russia should probably read.

• 2:03:35Speaker 01

  I agree.

• 2:03:36Lex Fridman?

  What's the second?

• 2:03:37Speaker 01

  So, so the second one is again another one that it happened, I read it later in my life. I think I read it first time when I was a graduate student. And that's Solzhenitsyn's cancer ward. That is amazingly powerful book.

• 2:03:59Lex Fridman?

  What is it about?

• 2:04:00Speaker 01

  It's about, I mean, essentially based on, you know, Solzhenitsyn was diagnosed with cancer when he was reasonably young and he made a full recovery. But, you know, so this is about a person who was sentenced for life in one of these, you know, camps. And he had some cancer. So he was, you know, transported back to one of these Soviet republics. I think, you know, South Asian republics.

• 2:04:36Speaker 01

  And the book is about, you know, his experience being a prisoner, being a, you know, a patient in the cancer clinic, in a cancer ward, surrounded by people, many of which die, right? But in a way, you know, the way, you know, it reads, I mean, first of all, later on, I read the accounts of the doctors who described this, you know, the experiences, you know, in the book

• 2:05:18Speaker 01

  by the patient as incredibly accurate, right? So, so, you know, I read that there was, you know, some doctors saying that, you know, every single doctor should read this book to understand what the patient feels. But, you know, again, as many of the Solzhenitsyn's books, it has multiple levels of complexity. And obviously, the, you know, if you look above.

• 2:05:47Speaker 01

  The cancer and the patient, I mean, the tumor that was growing and then disappeared in his body with some consequences. I mean, this is, you know, allegorically, the Soviet. And, you know, and he actually, he agreed, you know, when he was asked, he said that this is what make him think about this, you know, how to combine these experiences. Him being a part of the, you know, of the Soviet regime, also being a part of the, of the, you know, of someone sent to Gulag camp, right?

• 2:06:31Speaker 01

  And also someone who experienced cancer in his life. You know, the Gulag or Hippolago and this book, these are the works that actually made him, you know, receive a Nobel Prize. But, you know, to me, I've, you know, I've read other, you know, books by Solzhenitsyn. This one is, to me, is the most powerful one.

• 2:06:57Lex Fridman?

  And by the way, both this one and the previous one, you write in Russian?

• 2:07:00Speaker 01

  Yes, yes. So now there is, the third book is, is an English book, and it's completely different. So, so, you know, we're switching the gears completely. So this is the book which, it's not even a book, it's an essay by John von Neumann.

• 2:07:19Lex Fridman?

  Oh, wow.

• 2:07:20Speaker 01

  Called The Computer and the Brain. And that was the book he was writing, knowing that he was dying of cancer. So, so the book was released back, it's a very thin book, right? But the power, the intellectual power in this book, in this essay, is incredible. I mean, you probably know that von Neumann... Is considered to be one of these biggest thinkers, right? So the his intellectual power was incredible, right? And you can actually feel this power in this book where, you know, the person is writing knowing that he will be, you know, he will die.

• 2:08:04Speaker 01

  The book actually got published only after his death back in nineteen fifty eight. He died in nineteen fifty seven. And but so so he tried to put as many ideas that, you know, he still, you know, hadn't realized. And, you know, so so this book is very difficult to read. Because, you know, every single paragraph is just compact. You know, is is filled with these ideas and, you know, the ideas are incredible. Um, even nowadays, you know, so so he tried to put the parallels between the brain computing power, the neural system,

• 2:08:49Speaker 01

  and the computers, you know, as they were.

• 2:08:52Lex Fridman?

  Do you remember what year he was working on? It's like approximately?

• 2:08:55Speaker 01

  Fifty seven.

• 2:08:56Lex Fridman?

  Fifty seven.

• 2:08:56Speaker 01

  So so that was right during his, you know, when he was diagnosed with cancer and he was essentially.

• 2:09:02Lex Fridman?

  Yeah, he's one of those, um, there's a few folks people mention. I think Ed Witten is another that like everybody, everyone that meets them, they say he's just an intellectual powerhouse.

• 2:09:14Speaker 01

  Yes.

• 2:09:15Lex Fridman?

  Okay, so who's the honorable mention?

• 2:09:17Speaker 01

  So so so and this is, I mean, the reason I put it sort of in this separate section, because this is a book that I reasonably listened, uh, recently listened to. So so it's an audio book. And this is a book called Lab Girl by Hope Jaren. So Hope Jaren is, she is a scientist. She's a geochemist that essentially studies, um, the the fossil plants. And so so uses the this fossil, the the chemical analysis to understand what was the climate. Back in, you know, in thousand years, hundreds of thousand years ago.

• 2:10:03Speaker 01

  And so, something that incredibly touched me by this book. It was narrated by the author.

• 2:10:11Lex Fridman?

  Nice.

• 2:10:11Speaker 01

  And it's in incredibly personal story, incredibly. So, certain parts of the book, you could actually hear the author crying. And that to me, I mean, I, I never experienced anything like this, you know, reading the book. But it was like, you know, the, the connection between you and the author. And I think this is, you know, this is really a must read, but even better, a must listen to uh audiobook for anyone who wants to learn about sort of, you know, academia, science, research in general.

• 2:10:54Speaker 01

  Because it's a very personal account about her uh becoming a scientist.

• 2:11:01Lex Fridman?

  So uh, we're, we're just before New Years. You know, we talked a lot about some difficult topics of viruses and so on. Do you have some exciting things uh you're looking forward to in two thousand twenty one? Some New Year's resolutions, maybe silly or fun?

• 2:11:22Lex Fridman?

  Or uh, something very important and fundamental to the world of science or something completely unimportant?

• 2:11:30Speaker 01

  Well, well, I'm, I'm definitely looking for to, towards, you know, things becoming normal. Right? So uh, yes, so I, I really miss traveling. Every summer, I go to a international summer school. It's, it's called the School for Molecular and Theoretical Biology. It's held in Europe and organized by very good friends of mine. And this is the cool, the school for gifted kids uh from all over the worlds. And they're incredibly bright. It's like every time I go there, it's like, you know, it's, it's, it's, it's a highlight of the year. Uh, and you know, we couldn't make it this August.

• 2:12:14Speaker 01

  So we, we did this school remotely, but it's, it's different. So some, I am uh definitely looking forward next August, coming there.

• 2:12:24Speaker 01

  I also, I mean, you know, one of the, one of my, you know, personal resolutions, I, I realized that, you know, being in, you know, in-house and working from, from home, you know, I realized that, that actually I apparently missed a lot, you know, spending time with my family, believe it or not. So, so you, you typically, you know, with, with all the research and, you know, and, and teaching and everything related to the academic life. I mean, you get distracted and so, so, you know, you don't feel that,

• 2:13:06Speaker 01

  you know, the fact that you are away from your family doesn't affect you because you are, you know, naturally distracted by other things. And, you know, this time I, I realized that, you know, that, that's so important, right? Spending your time with the, with the family, with your kids. And so I'm, that, that would be my New Year resolution and actually trying to spend as much time as possible.

• 2:13:32Lex Fridman?

  Even when the world opens up, yeah, that's a, that's a beautiful message. That's a beautiful reminder. I asked you if, if there's a Russian poem you could read, that I could force you to read, and you said, okay, fine, sure.

• 2:13:49Lex Fridman?

  Do you mind, do you mind reading? And you're like, you said that no paper needed, so.

• 2:13:53Speaker 01

  Nope. So, yeah, so the, this poem was written by my namesake, another Dmitri, Dmitri Kemirfeldt. And is a, you know, it's a recent poem and it's, it's called Sorceress, Wiedma, in Russian, or actually Kaldunia. So that's sort of another sort of connotation of sorceress or witch. And I really like it and it's one of just a handful poems I actually can recall by heart. I also have a very strong association when I read this poem

• 2:14:33Speaker 01

  with Master Margarita, the, the, the main female character, Margarita.

• 2:14:41Speaker 01

  And also it's, you know, it's about, you know, it's happening about the same time we're talking now. So around New Year, around Christmas.

• 2:14:52Lex Fridman?

  You mind reading it in Russian?

• 2:14:56Speaker 01

  I'll give it a try. В белой вьюге, белой зыби, белой мгле, На распятии сопящих городов Ты, безумная, летала на метле, Взяв кое жесть от январьских холодов. Пах сочельник апельсином и треской, Ветер вялил в новогодние шары. И под ластной твоей силе колдовской Гибли души и крученились миры. Так ты узела глаза своей ерясь, Что любой, кому спускалась благодать, Был готов за эту ведьминскую связь Без оглядки душу дьяволу отдать. На погостах весерелось воронье,

• 2:15:39Speaker 01

  Ну, а я, без предрассудков и рубах, Выбегал, чтобы почувствовать твое Изумленное дыхание на губах. Чтобы кожей, языком, ребром крутым На чужой, уже на призрачной земле Вспоминать, как над землей летала ты В белой вьюге, белой зыби, белой мгле.

• 2:16:03Lex Fridman?

  That's beautiful. I love how it captures a moment of longing

• 2:16:09Lex Fridman?

  And maybe love even.

• 2:16:12Speaker 01

  Yes. To me, it has a lot of meaning About, you know, this Something that is happening, Something that is far away, But still very close to you. And, yes, it's the winter.

• 2:16:29Lex Fridman?

  There's something magical about winter, isn't it? What is the... Well, I don't know. I don't know how to translate it, but a kiss in winter is interesting. Lips in winter and all that kind of stuff is beautifully... I mean, Russian has a way. There's a reason Russian poetry is just... I'm a fan of poetry in both languages, but English doesn't capture Some of the magic that Russian seems to have. Thank you for doing that. That was awesome. Dmitry, it's great to talk to you again. You're... It's contagious how much you love what you do, how much you love life. So I really appreciate you taking the time to talk today.

• 2:17:07Speaker 01

  And thank you for having me.

• 2:17:09Lex Fridman?

  Thanks for listening to this conversation with Demetri Korkin, and thank you to our sponsors Brave Browser, NetSuite Business Management Software, Magic Spoon Low Carb Cereal, and Asleep Self Cooling Mattress. So the choice is browsing privacy, business success, healthy diet, or comfortable sleep. Choose wisely, my friends, and if you wish, click the sponsor links below to get a discount and to support this podcast. Now, let me leave you with some words from Jeffrey Eugenides. Biology gives you a brain. Life turns it into a mind. Thank you for listening and hope to see you next time.