Manolis Kellis: Human Genome and Evolutionary Dynamics | Lex Fridman Podcast #113
the following is a conversation with manolis kellis he's a professor at mit and head of the mit computational biology group he's interested in understanding the human genome from a computational evolutionary biological and other cross-disciplinary perspectives he has more big impactful papers and awards than i can list but most importantly he's a kind curious brilliant human being and just someone i really enjoy talking to his passion for science and life in general is contagious the hours honestly flew by and i'm sure we'll talk again on this podcast soon quick summary of the ads three sponsors blinkist eight sleep and masterclass please consider supporting this podcast by going to blinkist.com lex 8sleep.com lex and signing up at masterclass.com lex click the links buy the stuff get the discount it's the best way to support this podcast if you enjoy this thing subscribe on youtube review it with five stars in apple podcast support it on patreon or connect with me on twitter at lex 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computer we are the descendants of the first digital computer basically life is digital and that's absolutely beautiful about life the fact that at every replication step you don't lose any information because that information is digital if it was analog it was just protein concentrations you'd lose it after a few generations it would just dissolve away and that's what the ancients didn't understand about inheritance the first person to understand digital inheritance was mendel of course and his theory in fact stayed in a bookshelf for like 50 years while darwin was getting famous about natural selection but the missing component was this digital inheritance the mechanism of evolution that mendel had discovered so that aspect in my view is the most beautiful aspect but it transcends all of life and can you elaborate maybe the inheritance part what was the what was the key thing that the ancients didn't understand so the very theory of inheritance uh as discrete units you know throughout the life of mendel and well after his writing people thought that his p experiments were just a little fluke that they were just you know a little exception that would normally not even apply to humans that basically what they saw is this continuum of eye color this continuum of skin color this continuum of hair color this continuum of height and all of these continuums did not fit with a discrete type of inheritance that mendel was describing but what's unique about genomics and what's unique about the genome is really that there are two copies and that you get a combination of these but for every trait there are dozens of contributing variables and it was only ronald fisher in the 20th century that basically recognized that even five mendelian traits would add up to a continuum-like inheritance pattern and he you know wrote a series of papers that still are very relevant today about sort of this mendelian inheritance of continuum like traits and i think that that was the missing step in inheritance so well before the discovery of the structure of dna which is again another amazingly beautiful aspect the double helix what i like to call the most noble molecule over time is uh you know holds within it the secret of that discrete inheritance but the conceptualization of discrete you know elements is something that precedes that so even though it's discrete when it uh materializes itself into actual traits that we see it can be continuous it can basically arbitrarily rich and complex so if you have five genes that contribute to human height and there aren't five there's a thousand if there's only five genes and you inherit some combination of them and everyone makes you two inches taller or two inches shorter it'll look like a continuum trait a continuous trait but instead of five there are thousands and every one of them contributes to less than one millimeter we change in height more during the day than each of these genetic variants contributes so by the evening you're shorter than you were you woke up with isn't it weird then that we're not more different than we are why are we all so similar if there's so much possibility to be different yeah so so there are selective advantages to being medium if you're extremely tall or extremely short you run into selective disadvantages so you have trouble breathing you have trouble running you have trouble sitting if you're too tall if you're too short you might i don't know have other selective pressures are acting against that if you look at natural history of human population there's actually selection for height in northern europe and selection against height in southern europe so there might actually be advantages to actually being not not super tall and if you look across the entire human population you know for many many traits there's a lot of push towards the middle uh balancing selection is you know the usual term for selection that sort of seeks to not be extreme and to sort of have a combination of alleles that sort of you know keep recombining and if you look at you know mate selection super super tall people will not tend to sort of marry super super tall people very often you see these couples that are kind of compensating for each other and the best predictor of the kids age is very often just take the average of the two parents and then adjust for sex and boom you get it it's extremely heritable let me ask uh you kind of uh took a step back to the genome outside of just humans but is there something that you find beautiful about the human genome specifically so i think that genome if more people understood the beauty of the human genome there would be so many fewer wars so much less anger in the world i mean what's really beautiful about the human genome is really the variation that teaches us both about individuality and about similarity so any two people on the planet are 99.9 identical how can you fight with someone who's 99.9 identical to you it's just counterintuitive and yet any two siblings of the same parent differ in millions of locations so every one of them is basically two to the million unique from any pair of parents let alone any two random parents on the planet so that's i think something that teaches us about sort of the nature of humanity in many ways that every one of us is as unique as any star and way more unique in actually many ways and uh yet we're all brothers and sisters and yeah just like stars most of it is just uh fusion uh reactions yeah you only have a few parameters to describe stars you know mass size initial size and you know stage of life whereas for humans it's you know thousands of parameters scattered across our genome so the other thing that makes humans unique the other things that makes inheritance unique in humans is that most species inherit things vertically basically instinct is a huge part of their behavior the way that you know i mean with my kids we've been watching this nest of birds with two little eggs you know outside our window for the last few months uh for the last few weeks as they've been growing and there's so much behavior that's hard-coded birds don't just learn as they grow they don't you know there's no culture like a bird that's born in boston will be the same as a bird that's born in california so there's not as much um inheritance of ideas of customs a lot of it is hard-coded in their genome what's really beautiful about the human genome is that if you take a person from today and you place them back in ancient egypt or if you take a person from ancient egypt and you place them here today they will grow up to be completely normal that is not genetics this is the other type of inheritance in humans so on one hand we have genetic inheritance which is vertical from your parents down on the other hand we have horizontal inheritance which is the ideas that are built up at every generation are horizontally transmitted and the huge amount of time that we spend in educating ourselves a concept known as niotini neo for newborn and then tenney for holding so if you look at humans i mean the little birds they were you know eggs two weeks ago and that now one of them has already flown off the other one's ready to fly off in two weeks they're ready to just fend for themselves humans 16 years 18 years 24 getting out of college i'm still learning so so that's so fascinating the this picture of a vertical in the horizontal i when you talk about the horizontal is it in the realm of ideas exactly okay so it's the actual social interactions and that's exactly right that's exactly right so basically the concept of neotimi is that you spend acquiring characteristics from your environment in an extremely malleable state of your brain and the wiring of your brain for a long period of your life compared to primates we are useless you take any primate at seven weeks and in human at seven weeks we lose the battle but at eighteen years you know all bets are off like we basically our brain continues to develop in an extremely malleable form until very late and this is what allows education this is what allows the person from egypt to do extremely well now and the reason for that is that the wiring of our brain and the development of that wiring is actually delayed so you know the longer you delay that the more opportunity you have to pass on knowledge to pass on concepts ideals ideas from the parents to the child and what's really absolutely beautiful about humans today is that that lateral transfer of ideas and culture is not just from uncles and aunts and teachers at school but it's from wikipedia and review articles on the web and thousands of journals that are sort of putting out information for free and podcasts and videocasts and all of that stuff where you can basically learn about any topic pretty much everything that would be in any super advanced textbook in a matter of days instead of having to go to the library of alexandria and sail there to read three books and then sail for another few days to get to athens and et cetera et cetera so the democratization of knowledge and the spread the speed of spread of knowledge is what defines i think the human inheritance pattern so you sound excited about it about it are you also a little bit afraid or you're more excited by the power of this kind of distributed spread of information so you put it very kindly that most people are kind of using the internet in uh you know looking wikipedia reading articles reading papers and so on but uh if we if we're honest most people online especially when they're younger probably looking at five second clips on tick tock or whatever the new social network is are you um given this power of horizontal inheritance are you optimistic or a little bit pessimistic about the this new effect of the internet and democratization of knowledge on our on our what would you call this this geno like would you would you use the term genome by the way yeah i think um you know we use the genome to talk about dna but very often we say you know i mean i'm greek so people ask me hey what's in the greek genome and i'm like well yeah what's in the greek genome is both our genes and also our ideas and our ideals and our culture so the poetic meaning of the word exactly exactly yeah yeah so i think that um there's a beauty to the democratization of knowledge the fact that you can reach as many people as you know any other person on the planet and it's not who you are it's really your ideas that matter is a beautiful aspect of the internet the [Music] i think there's of course a danger of my ignorance is as important as your expertise the fact that uh with this democratization comes the abolishment of respecting expertise just because you've spent you know 10 000 hours of your life studying i don't know human brain circuitry why should i trust you i'm just going to make up my own theories and they'll be just as good as yours it's an attitude that that sort of counteracts the beauty of the democratization and i think that within our educational system and within the upbringing of our children we have to not only teach them knowledge but we have to teach them the means to get to knowledge and that you know it's very similar to sort of you fish you catch a fish for a man for one day you fed them for one day you teach them how to fish you fed them for the rest of their life so instead of just gathering the knowledge they need for any one task we can just tell them all right here's how you google it here's how to figure out what's real and what's not here's how you check the sources here's how you form a basic opinion for yourself and i think that inquisitive nature is paramount to being able to sort through this huge wealth of knowledge so you need a basic educational foundation based on which you can then add on the sort of domain specific knowledge but that basic educational foundation should just just not just be knowledge but it should also be epistemology the way to acquire knowledge i'm not sure any of us know how to do that in this modern day we're actually learning one of the big surprising thing to me about the the coronavirus for example is that twitter has been one of the best sources of information basically like building your own network of experts of of uh you know as opposed to the traditional centralized expertise of the who and the cdc and the or um or maybe any one particular respectable person at the top of a department in some kind of institution you instead look at a you know 10 20 hundreds of people some of whom are young kids with just that are incredibly good at aggregating data and plotting and visualizing that data that's been really surprising to me i don't know what to make of it i don't know i don't know how that matures into something stable you know i don't know if you have ideas like what if you were to try to explain to your kids of how where should you go to learn about the about coronavirus what would you say it's such a beautiful example and i think uh the current pandemic and the the speed at which the scientific community has moved in the current pandemic i think exemplifies this horizontal transfer and the speed of horizontal transfer of information the fact that you know the genome was first sequenced in early january the first sample was obtained december 29 2019 a week after the publication of the first genome sequence moderna had already finalized his vaccine design and was moving to production i mean this is uh phenomenal the fact that we go from not knowing what the heck is killing people in wuhan to wow it's starscore v2 and here's the set of genes here's the genome here's the sequence here the polymorphisms et cetera in the matter of weeks is phenomenal in that incredible pace of transfer of knowledge there have been many mistakes so you know some of those mistakes may have been politically motivated our other mistakes may have just been innocuous errors others may have been misleading the public for the greater good such as don't wear masks because we don't want the mask to run out i mean that was very silly in my view and a very big mistake but the the spread of knowledge from the scientific community was phenomenal and some people will point out to bogus articles that snuck in and made the front page yeah they did but within 24 hours they were debunked and went out of the front page and i think that's that's the beauty of science today the fact that it's not oh knowledge is fixed it's the ability to embrace that nothing is permanent when it comes to knowledge that everything is the current best hypothesis and the current best model that best fits the current data and the willingness to be wrong the expectation that we're going to be wrong and the celebration of success based on how long was i not proven wrong for rather than wow i was exactly right because no one is going to be exactly right with partial knowledge but the arc towards perfection i think so much more important than how far you are on your first step and i think that's what sort of the current pandemic has taught us the fact that yeah no of course we're gonna make mistakes but at least we're going to learn from those mistakes and become better and learn better and spread information better so if i were to answer the question of where would you go to learn about coronavirus first textbook it all starts with a textbook just open up a chapter on virology and how coronaviruses work then some basic epidemiology and sort of how pandemics have worked in the past what are the basic principles surrounding these first wave second wave why do they even exist then understanding about growth understanding about the are not and rt at you know various time points and then understanding the means of spread how it spreads from person to person then how does it get into your cells from when it gets into the cells what are the paths that it takes what are the cell types that express the particular h2 receptor how is your immune system interacting with the virus and once your immune system launches your defense how is that helping or actually hurting your health what about the cytokine storm what are most people dying from why are the comorbidities and these risk factors even applying what makes obese people respond more or elderly people respond more to the virus while kids are completely you know you know very often not even aware that they're spreading it so the you know i think there's some basic questions that you would start from and then i'm sorry to say but wikipedia is pretty awesome yeah google is pretty awesome so it used to be a time it used to be a time maybe five years ago i forget i forget when but people kind of made fun of wikipedia for being an unreliable source i never quite understood it i thought from the early days it was pretty reliable or better than a lot of the alternatives but at this point it's kind of like a solid accessible survey paper on every subject ever the there's an ascertainment bias and a writing bias so so i think this this is related to sort of people saying oh so many nature papers are wrong and they're like why would you publish in nature so many nature papers are wrong and my answer is no no no so many nature papers are scrutinized and just because more of them are being proven wrong than in other articles is actually evidence that they're actually better papers overall because they're being scrutinized at a rate much higher than any other journal so if you basically uh judge wikipedia by not the initial content by but by the number of revisions yeah then of course it's going to be the best source of knowledge eventually it's still very superficial you then have to go into the review papers etc etc but i mean for most scientific project topics it's extremely superficial but it is quite authoritative because it is the place that everybody likes to criticize you as being wrong you say that it's superficial on a lot of topics that i'm i've studied a lot of i find it i don't know if superficial is the right word um because superficial kind of implies that it's not correct no no i don't mean any implication of it not being correct it's just superficial it's basically only scratching the surface for depth you don't go to wikipedia you go to the review articles but it can be profound in the way that articles rarely one of the frustrating things to me about like certain computer science like in the machine learning world articles they they don't as often take the uh the bigger picture view you know there's a it's a kind of data set and you show that it works and you kind of show that here's an architectural thing that creates an improvement and so on and so forth but you don't say well like what does this mean for the nature of intelligence for future data sets we haven't even thought about or if you were trying to implement this like if we took this data set of uh a hundred thousand examples and scaled it to a hundred billion examples with this method like like look at the bigger picture which is what a wikipedia article would actually try to do which is like what does this mean in the context of computer the broad field of computer vision or something like that yeah yeah and no i i agree with you completely like but it depends on the topic i mean for some topics there's been a huge amount of work for other topics it's just a stub so you know i got it yeah well yeah actually the uh which we'll talk on genomics was not yeah it's great very shallow yeah yeah it's not wrong it's just shallow yeah every time i criticize something i should feel partly responsible basically if more people from my community went there and edited it would not be shallow it's just that there's different modes of communication in different fields and in some fields the experts have embraced wikipedia in other fields it's relegated and perhaps the reason is that if it was any better to start with people would invest more time but if it's not great to start with then you need a few initial pioneers who will basically go in and say ah enough we're just going to fix that and then i think it'll catch on much more so if it's okay before we go on to genomics can we linger a little bit longer on the beauty of the human genome you've given me a few notes what else what else do you find beautiful about the human genome so the last aspect of what makes a human genome unique in addition to the you know similarity and the differences and individuality is that so very early on people would basically say oh you don't do that experiment in human you have to learn about that in fly or you have to learn about that in yeast first or in mouse first or in a prime at first and the human genome was in fact relegated to sort of oh the last place that you you're going to go to learn something new that has dramatically changed and the reason that changed is human genetics we are these species in the planet that's the most studied right now it's embarrassing to say that but this was not the case a few years ago it used to be you know first viruses then bacteria then yeast then the fruit fly and the worm then the mouse and eventually human was very far last so it's embarrassing that it took us this long to focus on it or the uh it's embarrassing that the model organisms have been taken over because of the power of human genetics that right now it's actually simpler to figure out the phenotype of something by mining this massive amount of human data than by going back to any of the other species and the reason for that is that if you look at the natural variation that happens in a population of 7 billion you basically have a mutation in almost every nucleotide so every nucleotide you want to perturb you can go find a living breathing human being and go test the function of that nucleotide by sort of searching the database and finding that person wait why is that embarrassing it's a beautiful data set it's embarrassing for the for the model organism for the flies yeah exactly i i mean do you do you feel on a small tangent is there something of value in um in the genome of a fly and other these model organisms that you miss that we wish we would have uh would be looking at deeper so directed perturbation of course so i think the place where the the place where humans are still lagging is the fact that in an animal model you can go and say well let me knock out this gene completely and let me knock out these three genes completely and i said the moment you get into combinatorics it's something you can't do in the human because there just simply aren't enough humans on the planet and again let me be honest we haven't sequenced all seven billion people it's not like we have every mutation but we know that there's a carrier out there so if you look at the trend with and the speed with which human genetics has progressed we can now find thousands of genes involved in human cognition in human psychology in the emotions and the feelings that we used to think are uniquely learned turns out there's a genetic basis to a lot of that so the uh you know the the human genome has continued to elucidate through these studies of genetic variation so many different processes that we previously thought were you know something that like free will free will is this beautiful concept that humans have had for a long time you know in the end it's just a bunch of chemical reactions happening in your brain and the particular abundance of receptors that you have this day based on what you ate yesterday or that you have been wired with based on you know your parents and your upbringing etc determines a lot of that quote unquote free will component to you know sort of narrower and narrower scale you know sort of slices so how much uh on that point how much freedom do you think we have to escape the the constraints of our genome you're making it sound like more and more we're discovering that our genome is actually has the a lot of the story already encoded into it how much freedom do we have i uh so so let me let me describe what that freedom would look like that freedom would be my saying oh i'm gonna resist the urge to eat that apple because i choose not to but there are chemical receptors that made me not resist the urge to prove my individuality and my free will by resisting the apple so then the next question is well maybe now i'll resist the urge to resist the apple and i'll go for the chocolate instead to prove my individuality but then what about those other receptors that you know that that might be all encoded in there so it's kicking the bucket down the road and basically saying well your choice will may have actually been driven by other things that you actually are not choosing so that's why it's very hard to answer that question well it's hard to know what to do with that i mean if uh if the genome has if there's not much freedom it's uh it's the butterfly effect it's basically that in the short term you can predict something extremely well by knowing the current state of the system but a few steps down it's very hard to predict based on the current knowledge is that because the system is truly free when i look at weather patterns i can predict the next 10 days is it because the weather it has a lot of freedom and after 10 days it chooses to do something else or is it because in fact the system is fully deterministic and there's just a slightly different magnetic feel of the earth slightly more energy arriving from the sun a slightly different spin of the gravitational pull of jupiter that is now causing you know all kinds of tides and slight deviation of the moon etc maybe all of that can be fully modeled maybe the fact that china is emitting a little more carbon today is actually going to affect the weather in you know egypt in three weeks and all of that could be fully modeled in the same way if you take a complete view of a human being now you know i model everything about you the question is can i predict your next step probably but at how far and if it's a little further is that because of stochasticity and sort of chaos properties of unpredictability of beyond a certain level or was that actually true free will yeah then yeah so the number of variables might might be so you might need to uh build an entire universe to uh to be able to simulate a human and then maybe that human will be fully simulatable but maybe aspects of free will will exist and where's that free will coming from it's still coming from the same neurons or maybe from a spirit inhabiting these neurons but again you know it's very difficult empirically to sort of evaluate where does free will begin and sort of chemical reactions and electric signals and you know and so on that's on that topic let me ask the most absurd question uh that uh most mit faculty role their eyes on but uh do what do you think about the simulation hypothesis and the idea that we live in a simulation i think it's complete bs okay there's no empirical evidence no it's not absolutely not not in terms of empirical evidence or not but uh in terms of a thought experiment does it help you think about the universe i mean so if you look at the genome it's encoding a lot of the information that is required to create some of the beautiful human complexity that we see around us it's an interesting thought experiment how much you know uh parameters do we need to um have in order to model some you know this full human experience like if we were to build a video game yeah how hard it would be to build a video game that's like convincing enough and fun enough and you know uh it has consistent laws of physics all that stuff it's not interesting to use the stock experiment i i mean it's cute but you know it's all comes razor i mean what's what's more realistic the fact that you're actually a machine or that you're you know a person what's what's you know the fact that all of my experiences exist inside the chemical molecules that i have or that somebody's actually you know simulating all that i mean well you did refer to humans as a digital computer earlier so of course of course but that's not kind of a machine right i know i know but i i think the probability of all that is nil and let the machines wake me up and just terminate me now if it's not i challenge your machines they're gonna they're gonna wait a little bit to see what you're gonna do next it's fun it's fun to watch especially the clever humans what's the difference to you between the way a computer stores information and uh the human genome stores information so you also have roots and your work would you say you're when you introduce yourself at a bar um it depends who i'm talking would you say it's computational biology do you um do you reveal uh your expertise in computers it depends who i'm talking to truly i mean basically if i meet someone who's in computers i'll say oh i mean professor in computer science if i meet someone who's in engineering i say computer science and electrical engineering if i meet someone in biology i'll say hey i work in genomics if i meet someone in medicine i'm like hey i work on you know genetics so you're a fun person to meet at a bar i got you but so no no but i'm trying to say is that i i don't i mean there's no single attribute that i will define myself as you know there's a few things i know there's a few things i study there's a few things i have degrees on and there's a few things that i grant degrees in and you know i i publish papers across the whole gamut you know the whole spectrum of computation to biology etc i mean i the complete answer is that i use computer science to understand biology so i'm a you know i develop methods in ai and machine learning statistics and algorithms etc but the ultimate goal of my career is to really understand biology if these things don't advance our understanding of biology i'm not as fascinated by them although there are some beautiful computational problems by themselves i've sort of made it my mission to apply the power of computer science to truly understand the human genome health disease you know and then the whole gamut of how our brain works how our body works and all of that which is so fascinating so the dream there's not an equivalent sort of uh complementary dream of understanding human biology in order to create an artificial life an artificial brain artificial intelligence that supersedes the intelligence and the capabilities of us humans it's an interesting question it's a fascinating question so understanding the human brain is undoubtedly coupled to how do we make better ai because so much of ai has in fact been inspired by the brain it may have taken 50 years since the early days of neural networks till we have you know all of these amazing progress that we've seen with uh you know deep belief networks and uh you know all of these advances in go and chess in image synthesis and deep vagues in you name it and but but the underlying architecture is very much inspired by the human brain which actually pauses a very very interesting question why are neural networks performing so well and they perform amazingly well is it because they can simulate any possible function and the answer is no no they simulate a very small number of functions is it because they can simulate every possible function in the universe and that's where it gets interesting the answer is actually yeah a little closer to that and here's where it gets really fun uh if you look at human brain and human cognition it didn't evolve in a vacuum it evolved in a world with physical constraints like the world that inhabits us it is the world that we inhabit and if you look at our senses what do they perceive they perceive different you know parts of the electromagnetic spectrum you know the hearing is just different movements in air the the touch etc i mean all of these things we've built intuitions for the physical world that we inhabit and our brains and the brains of all animals evolved for that world and the ai systems that we have built happen to work well with images of the type that we encounter in the physical world that we inhabit whereas if you just take noise and you add random signal that doesn't match anything in our world neural networks will not do as well and that actually um basically has this whole loop around this which is this was designed by studying our own brain which was evolved for our own world and they happen to do well in our own world and they happen to make the same types of mistakes that humans make many times and of course you can engineer images by adding just the right amount of you know sort of pixel deviations to make a zebra look like a bamboo and stuff like that or like a table but ultimately the undoctored images at least are very often you know mistaken i don't know between muffins and dogs for example in the same way that humans make those mistakes so it's it's on you know there's no doubt in my view that the more we understand about the tricks that our human brain has evolved to understand the physical world around us the more we will be able to bring new computational primitives in our ai systems to again better understand not just the world around us but maybe even the world inside us and maybe even the computational problems that arise from new types of data that we haven't been exposed to but are yet inhabiting the same universe that we live in with a very tiny little subset of functions from all possible mathematical functions yeah and that small subset of functions all that matters to us humans really that's what makes it's all that has mattered so far and even within our scientific realm it's all that seems to continue to matter but i mean i always like to think about our senses and how much of the physical world around us we perceive and if you look at the um ligo experiment over the last you know year and a half has been all over the news what what did lago do it created a new sense for human beings a sense that has never been sensed in the history of our planet gravitational waves have been traversing the earth since its creation a few billion years ago life has evolved senses to sense things that were never before sensed light was not perceived by early life no one cared and eventually photoreceptors evolved and you know the ability to sense colors by sort of catching different parts of that electromagnetic spectrum and hearing evolved and touch evolved etc but no organism evolved a way to sense neutrinos floating through earth or gravitational waves flowing through earth etc and i find it so beautiful in the history of not just humanity but life on the planet that we are now able to capture additional signals from the physical world than we ever knew before and axions for example have been all over the news in the last few weeks the concept that we can capture and perceive more of that physical world is as exciting as the fact that we are we were blind to it is traumatizing before right because that also tells us how you know we're in 2020 picture yourself in 30 20 or in 20 you know what new senses why might we discover is it you know could it be that we're missing physics that like there's a lot of physics out there that we're just blind to completely oblivious to it yeah and yet they're permeating us all the time yes it might be right in front of us so so when you're thinking about premonitions yeah yeah a lot of that is ascertainment bias like yeah every you know every now and then you're like oh i remember my friend and then my friend doesn't appear and i'll forget that i remember my friend but every now and then my friend will actually appear i'm like oh my god i thought about you a minute ago you just called me that's amazing so you know some of that is this but some of that might be that there are within our brain sensors for waves that we emit that we're not even aware of and this whole concept of when i hug my children there's such an emotional transfer there that we don't comprehend i mean sure yeah of course we're all like hardwired for all kinds of touchy-feely things between parents and kids it's beautiful between partners it's beautiful etc but then there are intangible aspects of human communication that i don't think it's unfathomable that our brain has actually evolved ways and sensors for it that we just don't capture we don't understand the function of the vast majority of our neurons and maybe our brain is already sensing it but even worse maybe our brain is not sensing it at all and we're in oblivious to this until we build a machine that suddenly is able to sort of capture so much more of what's happening in the natural world so what you're saying is we're going physics is going to discover a sensor for love for and maybe maybe dogs are off scale for that and we've been oh you know we've been oblivious to it the whole time because we didn't have the right answer yeah and now you're gonna have a little wrist that says oh my god i feel all this love in the house i see i sense a disturbance in the force all around us and dogs and cats will have zero none none but let's take a step back to our unfortunately one of the 400 topics that we had actually planned [Laughter] but to our sad time in 2020 when we only have just a few sensors and uh very primitive early computers so in your you you have a foot in computer science and a floating biology in your sense how do computers represent information differently than like the genome or biological systems so first of all let me uh let me uh correct that no we're in an amazing time in 2020 computer science is totally awesome and physics is totally awesome and we have understood so much of the natural world than ever before so i am extremely grateful and feeling extremely lucky to be living in the time that we are because you know first of all who knows when the asteroid will hit [Laughter] and second um you know of all times in humanity this is probably the best time to be a human being and this might actually be the best place to be a human being so anyway you know for for anyone who loves science this is this is it this is awesome it's a great time at the same time just a swift comment all i meant is that uh if we look several hundred years from now and we end up somehow not uh destroying the uh ourselves yeah people will probably look back at this time in computer science and uh at your work of minos at mit i like to joke very often with my students that you know we've written so many papers we've published so much we've been cited so much and every single time i tell my students you know the best is ahead of us what we're working on now is the most exciting thing i've ever worked on so in a way i do have this sense of yeah even the papers i wrote 10 years ago they were awesome at the time but i'm so much more excited about where we're heading now and i don't mean to minimize any of the stuff we've done in the past but you know there's just this sense of excitement about what you're working on now that as soon as a paper is submitted it's like ugh it's old like you know i can't talk about that anymore at the same time you're not you probably are not going to be able to predict what are the most uh impactful papers and ideas when people look back 200 years from now at your work what would be the most exciting papers and it may very well be not the thing that you expected or yeah the things you got awards for or you know this might be true in some fields i don't know i feel slightly differently about it in our field i feel that i kind of know what what are the important ones and there's a very big difference between what the press picks up on and what's actually fundamentally important for the field and i think for the fundamentally important ones we kind of have a pretty good idea what they are and it's hard to sometimes get the press excited about the fundamental advances but you know we we take what we get and celebrate what we get and sometimes you know one of our papers which was in a minor journal made the front page of reddit and suddenly had like hundreds of thousands of views even though it wasn't a minor journal because you know somebody pitched it the right way that it suddenly caught everybody's attention whereas other papers that are sort of truly fundamental you know we have a hard time getting the editors even excited about them when so many hundreds of people are already using the results and building upon them so i do i do appreciate that there's a discrepancy between the perception and the perceived success and the awards that you get for various papers but i think that fundamentally and know that you know some people i'm so so so when you're writing that you're most proud you know you just you trapped yourself no no no no i mean is there a line of work that you you have a sense uh is really powerful that you've done today you've done so much work in so many directions which is interesting um is there something where you you think is quite special i i mean it's like asking me to say which of my three children i love best i mean exactly so i mean and it's such a give me question that it's so so difficult not to brag about the awesome work that my team and my students have done um and i'll i'll just mention a few of the top of my head i mean basically there's a few landmark papers that i think have shaped my scientific path and you know i like to somehow describe it as a linear continuation of one thing led to another led to another led to another and you know it kind of all started with skip skip skip skip skip let me try to start somewhere in the middle so my first phd paper was uh the first comparative analysis of multiple species so multiple complete genomes so for the first time we we basically con developed the concept of genome-wide evolutionary signatures the fact that you could look across the entire genome and understand how things evolve and from these signatures of evolution you could go back and study any one region and say that's a protein coding gene that's an rna gene that's a regulatory motif that's a you know binding site and so forth so sorry so comparing different different species of the same so so i think human mouse rat and dog you know they're all animals they're all mammals they're all performing similar functions with their heart with their brain with their lungs etc etc so there's many functional elements that make us uniquely mammalian and those mammalian elements are actually conserved 99 of our genome does not code for protein one percent codes for protein the other 99 we frankly didn't know what it does until we started doing these comparative genomic studies so basically these series of papers in in my career have basically first developed that concept of evolutionary signatures and then apply them to yeast apply them to flies apply them to four mammals apply them to 17 fungi apply them to 12 drosophila species apply them to them 29 mammals and now 200 mammals so sorry so can we so the evolutionary signatures this seems like a such a fascinating idea uh and we're probably gonna linger in your early phd work for two hours but uh what is how can you reveal something interesting about the genome by looking at the uh multiple multiple species and looking at the evolutionary signatures yeah like so so um you basically uh align the matching regions so everything evolved from a common ancestor way way back and mammals evolved from a common ancestor about 60 million years back so after you know the meteor that killed off the dinosaurs landed a legend near machu picchu we know the crater it didn't allegedly land that was the aliens okay no just slightly north of machu picchu in the gulf of mexico there's a giant hole that that meteorite by the way sorry is that uh definitive to people have people um um conclusively uh figured out what killed the dinosaurs i think so so it was media well you know for volcanic activity all kinds of other stuff is coinciding but the meteor is pretty unique and we know how terrifying i wouldn't if i we still have a lot of 20 20 left so if i think no no but think about it this way so the the dinosaurs ruled the earth for 175 million years we humans have been around for what less than one million years if you're super generous about what you call humans and you include gems basically so so uh we are just getting warmed up and you know we've ruled the planet much more ruthlessly than tyrannosaurus rex [Laughter] t-rex had much less of an environmental impact than we did yeah and um if you if you give us another 154 million years you know humans will look very different if we make it that far so i think dinosaurs basically are much more of life history on earth than we are in all respects but look at the bright side when they were killed off another life form emerged mammals and that's that whole the evolutionary uh branching that's happened so you you kind of have uh when you have these evolutionary signatures you see is there basically a map of how the genome changed yeah exactly exactly so now you can go back to this early mammal that was hiding in caves and you can basically ask what happened after the dinosaurs were wiped out a ton of evolutionary niches opened up and the mammals started populating all of these niches and in that diversification there was room for expansion of new types of functions so some of them populated the air with bats flying a new evolution of light some populated the oceans with dolphins and whales going off to swim etc but we all are fundamentally mammals so you can take the genomes of all these species and align them on top of each other and basically create nucleotide resolution correspondences what my phd work showed is that when you do that when you line up species on top of each other you can see that within protein coding genes there's a particular pattern of evolution that is dictated by the level at which evolutionary selection acts if i'm coding for a protein and i change the third codon position of a triplet that codes for that amino acid the same amino acid will be encoded so that basically means that any kind of mutation that preserves that translation that is invariant to that ultimate functional assessment that evolution will give is tolerated so for any function that you're trying to achieve there's a set of sequences that encode it you can now look at the mapping the you know graph isomorphism if you wish between all of the possible dna encodings of a particular function and that function and instead of having just that exact sequence at the protein level you can think of the set of protein sequences that all fulfill the same function what's evolution doing evolution has two components one component is random blind and stupid mutation the other component is super smart ruthless selection that's my mom calling from greece yes i might be a fully grown man [Laughter] did you just cancel the call wow i know i'm in trouble she's gonna be calling the cops [Laughter] so so yeah so there's a lot of encoding for the same kind of function yeah so so you now have this mapping between all of the set of functions that could all encode the same all of the set of sequences that can all encode the same function what evolutionary signatures does is that it basically looks at the shape of that distribution of sequences that all encode the same thing and based on that shape you can basically say ooh proteins have a very different shape than rna structures than regulator motifs etc so just by scanning a sequence ignoring the sequence and just looking at the patterns of change i'm like wow this thing is evolving like a protein and that thing is evolving like a motif and that thing is evolving so that's exactly what we just did for covid so our paper that we posted about our archive about coronavirus basically took this concept of evolutionary signatures and applied it on the sarsko v2 genome that is responsible for the carbon-19 pandemic uh and comparing it to 44 cerbicovirus species so this is the beta word did you just use cervical sarbic virus sars related beta corona virus it's a port ponto so that one family of viruses yeah so it was that family by the way we have 44 species that or 24 species in the fam yeah virus is a clever no no but but there's just 44 and again we don't call them species in in viruses we call them strange but anyway there's 44 strains and that's a tiny little subset of you know maybe another 50 strains that are just far too distantly related most of those only infect bats as the host and a subset of only four or five have ever infected humans and we basically took all of those and we aligned them in the same exact way that we've aligned mammals and then we looked at what proteins are you know which of the currently hypothesized genes for the coronavirus genome are in fact evolving like proteins and which ones are not and what we found is that orf10 the last little open reading frame the last little gene in the genome is bogus that's not a protein at all what is it it's an rna structure that doesn't have a it doesn't get translated into amino acids and that's so it's important to narrow down to basically discover what's useful and what's not exactly basically what are what is even the set of genes the other thing that these evolutionary signatures showed is that within or 3a lies a tiny little additional gene encoded within the other gene so you can translate a dna sequence in three different reading frames if you start in the first one it's you know atg et cetera if you start on the second it's tgc etc and with there's a there's a gene within a gene so there's a whole other protein that we didn't know about that might be super important so we don't even know the building blocks of sarsko v2 so if we want to understand coronavirus biology and eventually find it successfully we need to even have the set of genes and and these evolutionary signatures that are developed in my phd work we just recently used you know what let's uh let's run with that tangent for a little bit if it's okay uh is uh can we talk about uh the the kovic 19 a little bit more like how what's your sense about the the genome the proteins the functions that we understand about covet 19 where do we stand in in your sense what are the big open problems and and also you know you you kind of said it's important to understand what are the like the the important proteins and like why is that important so what else does the comparison of these species tell us what it tells us is how fast are things evolving it tells us about at what level is the acceleration or deceleration pedal set for every one of these proteins so the genome has you know 30 some genes some genes evolve super super fast others evolve super super slow if you look at the polymerase gene that basically replicates the genome that's a super slow evolving one if you look at the nuclear capsid protein that's also super slow evolving if you look at the spike one protein this is the part of the spike protein that actually touches the h2 receptor and then enables the virus to attach to your cells that's the thing that gives it that that visual yeah the corona look basically the coronal look yeah so basically the spike protein sticks out of the virus and there's a first part of the protein s1 which basically attaches to the h2 receptor and then s2 is the latch that sort of pushes and channels the fusion of the membranes and then the incorporation of the um viral rna inside our cells which then gets translated into all of these 30 proteins so the s1 protein is evolving ridiculously fast so if you look at the stop professor's gas pedal the gas pedal is all the way down or 8 is also evolving super fast and or six is evolving super fast we have no idea what they do we have some idea but nowhere near what s1 is so what the isn't that terrifying that s1 is evol that means that's a really useful function and if it's evolving fast doesn't that mean new strains could be created or it does something that means that it's searching for how to match how to best match the host so basically anything in in general in evolution if you look at genomes anything that's contacting the environment is evolving much faster than anything that's internal and the reason is that the environment changes so if you look at um the evolution of these cervical viruses the s1 protein has evolved very rapidly because it's attaching to different hosts each time we think of them as bats but there's thousands of species of bats and to go from one species of bat to another species of bat you have to adjust one to the new ace2 receptor that you're going to be facing in that new species sorry quick tangent yeah is it fascinating to you that viruses are doing this i mean it feels like they're this intelligent organism i mean is it like does that give you pause how incredible it is that they're the the evolutionary dynamics that you're describing is actually happening and they're freaking out figuring out how to jump from bass to humans all in this distributed fashion and then most of us don't even say they're alive or intelligent whatever so intelligence is in the eye of the beholder you know stupid is a stupid dose as forest gum would say yes and intelligence is as intelligent does so basically if the virus is finding solutions that we think of as intelligent yeah it's probably intelligent but that's again in the eye of the beholder do you think viruses are intelligent of course not really no because so incredible so remember remember when i was talking about the two components of evolution one is the stupid mutation yeah which is completely blind and the other one is the super smart selection which is ruthless so it's not viruses who are smart it's this component of evolution that's smart so it's evolution that that sort of appears smart and how is that happening by huge parallel search across thousands of you know parallel infections throughout the world right now yes but so to perfect on that so yes so then the the intelligence is in the mechanism but then uh by that argument uh viruses would be more intelligent because there's just more of them so the search they're basically the the brute force search that's happening with viruses because there's so many more of them than humans then they're taken as a whole are more intelligent i mean so you don't think it's possible that i i mean who runs would we even be here with if viruses weren't i mean who runs this thing so survivors so let me answer yeah let me answer your your question um so um we would not be here if it wasn't for viruses yes and part of the reason is that if you look at mammalian evolution early on in this mammalian radiation that basically happened after the death of the dinosaurs is that some of the viruses that we had in our genome spread throughout our genome and created binding sites for new classes of regulatory proteins and these binding sites that landed all over our genome are now control elements that basically control our genes and sort of help the complexity of the circuitry of mammalian genomes so you know everything's co-evolution and we're working together yeah but and yet you saw they just don't care they don't care another thing oh is the virus trying to kill us no it's not the virus is not trying to kill you it's true it's not it's actually actively trying to not kill you so when you get infected if you die palmer i killed him is what the reaction of the virus will be why because that virus won't spread many people have a misconception of oh viruses are smart or oh viruses are mean they don't care it like you have to clean yourself of any kind of anthropomorphism out there i don't know oh yes so there's a there's a sense when taken as a whole that there's it's in a eye of the beholder stupid is a stupid does intelligent injustice intelligence does so if you want to call them intelligent that's fine then because and the end result is that they're finding amazing solutions right i mean i mean but they're all they're so dumb about it they're just doing dumb they don't care they're not dumb and they're not interested they don't care they care the care word is really interesting exactly i mean there could be an argument that they're conscious they're just dividing they're not they're just dividing they're just a little entity which happens to be dividing and spreading it does doesn't want to kill us in fact it prefers not to kill us it just wants to spread and when i say once again i'm anthropomorphizing but it's just that if you have two versions of a virus one acquires a mutation that spreads more that's going to spread more one acquires a mutation that's pressed less that's going to be lost yes one acquires a mutation that enters faster that's going to be kept one requires a mutation that kills you right away it's going to be lost so over evolutionary time the viruses that spread super well but don't kill the host are the ones that are going to survive yeah but so you see you brilliantly describe the basic mechanisms of how it all happens but when you zoom out and you see the uh you know the entirety of viruses maybe across different strains of viruses it seems like a living organism i am in awe of biology i find biology amazingly beautiful i find the design of the current coronavirus however lethal it is amazingly beautiful the way that it is encoded the way that it tricks your cells into making 30 proteins from a single rna human cells don't do that human cells make one protein from each rna molecule they don't make two they make one we are hardwired to make only one protein from every rna molecule and yet this virus goes in throws in a single messenger rna just like any messenger rna we have tens of thousands of messenger rnas in our cells in any one time in every one of our cells it throws in one rna and that rna is so i'm going to use your word here not my word intelligent yeah that it hijacks the entire machinery of your human cell yeah it basically has at the beginning a giant open reading frame that's a giant protein that gets translated two-thirds of that rna make a single giant protein that single protein is basically what a human cell would make it's like oh here's a start codon i'm going to start translating here human cells are kind of dumb i'm sorry again this is not the word that we normally use but the human cell basically is oh this is an rna it must be mine let me translate and it starts translating it and then you're in trouble why because that one protein as it's growing gets cleaved into about 20 different peptides the first peptide and the second peptide start interacting and the third one and the fourth one and they shut off the ribosome of the whole cell to not translate human rnas anymore so the virus basically hijacks your cells and it cuts it cleaves every one of your human rnas to basically say to the ribosome don't translate this one junk don't look at this one junk and it only spares its own rnas because they have a particular mark that it spares then all of the ribosomes that normally make protein in your human cells are now only able to translate viral rnas and make more and more and more and more of them that's the first 20 proteins in fact halfway down about protein 11 between you know 11 and 12 you basically have a translational slippage where the ribosome skips reading frame and it translates from one reading frame to another reading frame that means that about half of them are going to be translated from 1 to 11 and the other half are going to be translated from 12 to 16. wow it's gorgeous and then then you're done then that mrna will never translate elastin proteins but spike is the one right after that one so how does spike even get translated this positive strand rna virus has a reverse transcriptase which is an rna-based reverse transcriptase so from the rna on the positive strand it makes an rna of the negative strand and in between every single one of these genes these open reading frames there's a little signal aac gca or something like that that basically loops over to the beginning of the rna and basically instead of sort of having a single full negative strand or an a it basically has a partial negative strand rna that ends right before the beginning of that gene and another one that ends right before the beginning of that gene these negative strand rnas now make positive strand rnas that then look to the human whole cell just like any other human mrna it's like oh great i'm going to translate that one because it doesn't have the cleaving that the virus has now put on all your human genes and then you've lost the battle that cell is now only making proteins for the virus that will then create the spike protein the envelope protein the membrane protein the nucleocapsid protein that will package up the rna and then sort of create new viral envelopes and these will then be secreted out of that cell in new little packages that will then infect the rest of the cells and repeat the whole process beautiful right it's hard not to anthropomorphize it oh but it's so gorgeous so there is a beauty to it is there is it is it terrifying to you so this is something that has happened throughout history humans have been nearly wiped out over and over and over again and yet never fully wiped out so i'm yeah i'm not concerned about the human race i'm not even concerned about you know the impact on sort of our our survival as a species um this is absolutely something i mean you know human life is so invaluable and every one of us is so invaluable but if you think of it as sort of is this the end of our species by by no means basically so so let me explain the black death killed what 30 of europe that has left a tremendous imprint uh you know a huge hole a horrendous hole in the genetic makeup of humans there's been series of wiping out of huge fractions of entire species or just entire species all together and that has a consequence on the human immune repertoire if you look at how europe was shaped and how africa was shaped by malaria for example all the individuals that carry a mutation that protected from malaria were able to survive much more and if you look at the frequency of sickle cell disease and the frequency of malaria the maps are actually showing the same pattern the same imprint on africa and that basically led people to hypothesize that the reason why sickle cell disease is so much more frequent in americans of african descent is because there was selection in africa against malaria leading to sickle cell because when the cells sickle malaria is not able to you know replicate inside your cells as well and therefore you protect against that so if you look at human disease all of the genetic associations that we do with human disease you basically see the imprint of these waves of selection killing off gazillions of humans and there's so many immune processes that are coming up as associated with so many different diseases the reason for that is similar to what i was describing earlier where the outward facing proteins evolve much more rapidly because the environment is always changing but what's really interesting the human genome is that we have co-opted many of these immune genes to carry out non-immune functions for example in our brain we use immune cells to cleave off neuronal connections that don't get used this whole user will lose it we know the mechanism it's microglia the cleave of neuronal synaptic connections that are just not utilized when you utilize them you mark them in a particular way that basically when the microglia come tell it don't kill this one it's it's used now and the microwave will go off and kill once you don't use this is an immune function which is co-opted to do non-immune things if you look at our adipocytes m1 versus m2 macrophages inside our fat will basically determine whether you're obese or not and these are again immune cells that are resident and living within these tissues so many disease associations that we co-opt these kinds of things for incredibly uh complicated functions exactly evolution works in so many different ways which are all beautiful and mysterious it's not intelligent not intelligent it's in the eye of the beholder [Laughter] but but but the the the point that i'm trying to make is that if you look at the imprint that kovit will have hopefully it will not be big hopefully the u.s will get attacked together and stop the virus from spreading further but if it doesn't it's having an imprint on individuals who have particular genetic repertoires so if you look at now the genetic associations of blood type and immune function cells etc there's actually association genetic variation that basically says how much more likely am i or you to die if we contact the virus and it's it's through these rounds of shaping the human genome that humans have basically made it so far and uh selection is ruthless and it's brutal and it only comes with a lot of killing but this is the way that viruses and environments have shaped the human genome basically when you go through periods of famine you select for particular genes and what's left is not necessarily better it's just whatever survived and it may have been the surviving one back then not because it was better maybe the ones that ran slower survived i mean you know again not necessarily better but the surviving ones are basically the ones that then are shaped for any kind of subsequent evolutionary condition and environmental condition but if you look at for example obesity obesity was selected for basically the genes that now predisposes to obesity were at two percent frequency in africa they rose to 44 frequency in europe wow that's fascinating because you basically went through the ice ages and there was a scarcity of food so you know there was a selection to being able to store every single calorie you consume eventually environment changes so the better allele which was the fat storing allele became the worst allele because it's the fat storing allele it still has the same function so if you look at my genome speaking of mom calling mom gave me a bad copy of that gene these fto locus basically has to do with the obesity or the obesity yeah i basically now have a bad copy from mom that makes me more likely to be obese and i also also have a bad copy from dad that makes me more likely to be obese i'm homozygous and that's the allele it's still the minor allele but it's at 44 frequency in southeast asia 42 frequency in europe even though it started at 2 it was an awesome allele to have 100 years ago right now it's pretty terrible so the other concept is that diversity matters if we had a hundred million nuclear physicists living the earth right now we'd be in trouble you need diversity you need artists and you need musicians and you need mathematicians and you need you know politicians yes even those and you need like it's not it's not get crazy enough but so because then if uh virus comes along or whatever exactly exactly so no there's two reasons number one you want diversity and immune repertoire and we have built in diversity so basically they're they are the most diverse basically if you look at our immune system there's layers and layers of diversity like the way that you create your cells generates diversity because of the selection for the vdj recombination that basically eventually leads to a huge number of repertoires but that's only one small component of diversity the blood type is another one the major histogram histocompatibility complex the hla alleles are you know another source of diversity so the immune system of humans is by nature incredibly diverse and that basically leads to resilience so basically what i'm saying that i don't worry for the human species because we are so diverse immunologically we are likely to be very resilient against so many different attacks like this current virus so you're saying natural pandemics may not be something that you're really afraid of because of the diversity in our genetic makeup what about engineered pandemics do you have fears of us messing with the makeup of viruses or well yeah let's say with the makeup of viruses to create something that we can't control and we'd be much more destructive than it would come about naturally remember how we were talking about how smart evolution is humans are much dumber so you mean like human scientists yeah humans humans just humans overall yeah but i mean even you know the sort of synthetic biologists um you know basically if you were to create a you know virus like sars that will kill other people you would probably stars start with stars so whoever you know would like to design such a thing would basically start with stars tree or at least some relative of stars the source genome for the current virus was something completely different it was something that has never infected humans no one in their right mind would have started there oh but when you say source is like the nearest the nearest relative relative he's in a whole other branch no species of which has ever infected humans in that branch so you know let's put this to rest this was not designed by someone to kill off the human race so you don't you don't believe it was engineered the likely yeah the the path to engineering a deadly virus would not come from this strain that got it that was used uh moreover there's been various um claims of haha this was mixed and matched in lab because the s1 protein has three different components each of which has a different evolutionary tree so you know a lot of popular press basically said aha this came from pangolin and this came from you know all kinds of other species this is what has been happening throughout the coronavirus tree so basically the s1 protein has been recombining across species all the time remember when i was talking about the positive strand the negative strands sub genomic rnas these can actually recombine and if you have two different viruses infecting the same cell they can actually mix and match between the positive strand and the negative strand and basically create a new hybrid virus with recombination that now has the s1 from one and the rest of the genome from another and this is something that happens a lot in s1 you know fade etc and that's something that's true of the whole training for the whole family exactly viruses so it's not like someone has been messing with this for millions of years and you know changing this happens naturally that's again beautiful that that somehow happens that they recombine in the so two different strands can affect the body and recombine so all of this actually magic happens inside uh hosts like all like yeah yeah that way that's why classification wise virus is not thought to be alive because it doesn't self-replicate it's not autonomous it's something that enters a living cell and then co-opts it to basically make it its own but by itself people ask me how do we kill this bastard i'm like you stop it from replicating it's not like a bacterium that will just live in a you know puddle or something it's a virus viruses don't live without their host and they only live in their house for very little time so if you stop it from replicating it'll stop from spreading i mean it's not like hiv which can stay dormant for a long time basically coronaviruses just don't do that they're not integrating genomes there are any genomes so if it's not expressed it degrades rna degrades it doesn't just stick around well let me ask also um about the immune system you mentioned a lot of people kind of ask you know um how can we strengthen the immune system to respond to this particular virus but the viruses in general do you have from a biological perspective thoughts on what we can do as humans uh too if you look at our traits across different countries people with less vaccination have been dying more if you look at north italy the vaccination rates are abysmal there and a lot of people have been dying if you look at greece very good vaccination rates almost no one has been dying so yes there's a policy component so italy reacted very slowly greece reacted very fast so yeah many fewer people died in greece but there might actually be a component of genetic immune repertoire basically how did people die off you know in the history of the greek population versus the italian population there's a that's interesting to think about uh and then there's a component of what vaccinations did you have as a kid and what are the off-target effects of those vaccinations so basically a vaccination can have two components one is training your your immune system against that specific insult the second one is boosting up your immune system for all kinds of other things if you look at allergies northern europe super clean environments tons of allergies southern europe my kids grew up eating dirt no allergies so growing up i never had even heard of what allergies are like really allergies and the reason is that i was playing in the garden i was putting all kinds of stuff in my mouth from you know all kinds of dirt and stuff tons of viruses there tons of bacteria there you know my immune system was built up so the more you protect your immune system from exposure the less opportunity it has to learn about non-self repertoire in a way that prepares it for the next insult so it's a horizontal thing too like the says throughout your lifetime in the lifetime of the of the people that uh your ancestors yeah that kind of thing yeah what about the so again it returns against free will on the free will side of things is there something we could do to strengthen our immune system in 2020 is there like uh you know exercise diet all that kind of stuff so it's kind of funny um there's a cartoon that basically shows uh two windows with a teller in each window one has a humongous line and the other one has no one the one that has no one above says health no it says exercise and diet and the other one says pill yeah and there's a huge line for pill so we're looking basically for magic bullets for sort of ways that we can you know beat cancer and beat coronavirus and beat this and beat that and it turns out that the window with like just diet and exercise is the best way to boost every aspect of your health if you look at alzheimer's exercise and nutrition i mean you're like really for my brain neurodegeneration absolutely if you look at cancer exercise and nutrition if you look at coronavirus exercise and nutrition every single aspect of human health gets improved and one of the studies we're doing now is basically looking at what are the effects of diet and exercise how similar are they to each other we're basically taking diet intervention and exercise intervention in human and in mice and we're basically doing single cell profiling of a bunch of different tissues to basically understand how are the cells both the stromal cells and the immune cells of each of these tissues responding to the effect of exercise what are the communication networks between different cells where with the muscle that exercises sends signals through the bloodstream through the lymphatic system through all kinds of other systems that give signals to other cells that i have exercised and you should change in this particular way which basically reconfigure those receptor cells with the effect of exercise how well understood is the those reconfigurations very little we're just starting now basically is there is the hope there uh to understand the effect on uh so like the effect on the immune system on the immune system the effect on brain the effect on your liver on your digestive system on your adipocytes adipose you know the most misunderstood organ everybody thinks oh fat terrible no fat is awesome your fat cells is what's keeping you alive because if you didn't have your fat cells all those lipids and all those calories would be floating around in your blood and you'd be dead by now your adipocytes are your best friend they're basically storing all these excess calories so that they don't hurt all of the rest of the body and they're also fat burning in many ways so you know again when you don't have the homozygous version that i have your cells are able to burn calories much more easily by sort of flipping a master metabolic switch that involves this fto locus that i mentioned earlier and its target genes irx3 and rx5 that basically switch your adipocytes during their three first days of differentiation as they're becoming mature dipocytes to basically become either fat burning or fat storing fat cells and the fat burning fat cells are your best friends they're much closer to muscle than they are to white egg boss eyes is there a lot of difference between people like that you could give science could eventually give advice that is very generalizable or is our differences in our genetic makeup like you mentioned is that going to be basically something we have to be very specialized individuals any advice we give in terms of diet like we were just talking about believe it or not the most personalized advice that you give for nutrition don't have to do with your genome they have to do with your gut microbiome with the bacteria that live inside you so most of your digestion is actually happening by species that are not human inside you you have more non-human cells and you have human cells you're basically a giant bag of bacteria with a few human cells along and those do not necessarily have to do with your genetic makeup they interact with your genetic makeup they interact with your ruby genome they interact with your nutrition they interact with your environment they're you know basically an additional source of variation so when you're thinking about sort of personalized nutritional advice part of that is actually how do you match your microbiome and part of that is how do we match your genetics but again you know this is a very diverse set of um you know contributors and the effect sizes are not enormous so i think the science for that is not fully developed yet speaking of dyes because i've wrestled in combat sports with sports my whole life or weight matters so you have to cut and all that stuff one thing i've learned a lot about my body and which seems to be i think true about other people's bodies is that you can adjust to a lot of things that's the miraculous thing about this biological system is um like i fast often i used to eat like five six times a day and thought that was absolutely necessary how could you not eat that often and then when i started fasting your body adjusts to that and you learn how to not eat you know and it's it was uh if you just give it a chance for a few weeks actually over a period of a few weeks your body can adjust to anything yeah and that's a miraculous that's such a beautiful thing so i'm a computer scientist and i've basically gone through periods of 24 hours without eating or stopping and you know then i'm like oh must eat and i eat a ton i used to order two pizzas just with my brother and you know like so i i've gone through these extremes as well and i've gone the whole intermittent fasting thing so i can sympathize with you both on the seven meals a day to the zero meals a day um so i think when i say everything in moderation i i actually think your body responds interestingly to these different changes in diet i think part of the reason why we lose weight with pretty much every kind of change in behavior is because our epigenome and the set of proteins and enzymes that are expressed and our microbiome are not well suited to that nutritional source and therefore they will not be able to sort of catch everything that you give them and then you know a lot of that will go undigested and that basically means that your body can then you know lose weight in the short term but very quickly will adjust to that new normal and then we'll be able to sort of perhaps gain a lot of weight yeah from the diet so anyway i mean there's also studies in um factories where basically people you know dim the lights and then suddenly everybody started working better it was like wow that's amazing three weeks later they made the lights a little brighter everybody started working better so any kind of intervention has a placebo effect of wow now i'm healthier and i'm going to be running more often etc so it's very hard to uncouple the placebo effect of wow i'm doing something to intervene on my diet from the wow this is actually the right thing for me so you know yeah from the perspective from nutrition science psychology both things i'm interested in especially psychology it seems that it's extremely difficult to do good science because uh there's so many variables involved it's so difficult to control the variables so difficult to do sufficiently large-scale experiments uh both sort of in terms of number of subjects and temporal like how long you do the study for that uh it just seems like it's not even a real science for now like nutrition science i want to jump into the whole placebo effect for a little bit here and basically talk about the implications of that if i give you a sugar pill and tell you it's a sugar pill you won't get any better but if i tell you sugar appeal and tell you and i tell you wow this is an amazing drug it actually will stop your cancer your cash will actually stop with much higher probability what does that mean that's so amazing that means that if i can trick your brain into thinking that i'm healing you your brain will basically figure out a way to heal itself to heal the body and that tells us that there's so much that we don't understand in the interplay between our cognition and our biology that if we were able to better harvest the power of our brain to sort of you know impact the body through the placebo effect we would be so much better in so many different things just by tricking yourself into thinking that you're doing better you're actually doing better so there's something to be said about sort of positive thinking about optimism about sort of you know just getting your brain and your mind into the right mindset that helps your body and helps your entire biology yeah from a science perspective that's just fascinating i obviously most things about the brain is a total mystery for now but that's a fascinating interplay that the brain yeah that the brain can reduce uh the brain can help cure cancer as a i don't even know what to do with that i mean the way to think about that is the following the converse of the equation is something that we are much more comfortable with like oh if you're stressed then your heart right might rise and all kinds of sort of toxins might be released and that can have a detrimental effect in your body etc so maybe it's easier to understand your body healing from your mind by your mind is not killing your body or at least it's killing it less so i think the you know that aspect of the stress equation is a little easier for most of us to conceptualize but then the healing part is you know perhaps the same pathways perhaps different pathways but again something that is totally untapped scientifically i think we try to bring this question up a couple of times but let's return to it again is what do you think is the difference between the way a computer represents information the human genome represents and stores information like what and maybe broadly what is the difference between how you think about computers and how you think about biological systems so i made a very provocative claim earlier that we are a digital computer like that at the core lies a digital code and that's true in many ways but surrounding that digital core there's a huge amount of analog if you look at our brain it's not really digital if you look at our sort of rna and all of that stuff inside our cell it's not really digital it's really analog in many ways but let's start with the code and then we'll expand to the rest so the code itself is digital so there's genes you can think of the genes as i don't know the procedures the functions inside your language and then somehow you have to turn these functions on how do you call a gene how do you call that function the way that you would do it in old programming languages is go to address whatever in your memory and then you start running from there and you know modern programming languages have encapsulated this into functions and objects and all of that and it's nice and cute but in the end deep down there's still an assembly code that says go to that instruction and it runs that instruction if you look at the human genome and you know the genome of pretty much most species out there it's there's no go-to function you just don't start in you know transcribing in position thirteen hundred five you know thirteen thousand five hundred twenty seven in chromosome 12. you instead have content based indexing so at every location in the genome in front of the genes that need to be turned on i don't know when you drink coffee there's a little coffee marker in front of all of them and whenever your cells that metabolize coffee need to metabolize coffee they basically see coffee and they're like oh let's go turn on all the coffee marked jeans so there's basically these small motifs these small sequences that we call regulatory motifs they're like patterns of dna they're only eight characters long or so like gat gca et cetera and these motifs work in combinations and every one of them has some recruitment affinity for a different protein that will then come and bind it and together collections of these motifs create regions that we call regulatory regions that can be either promoters near the beginning of the gene and that basically tells you where the function actually starts where you call it and then enhancers that are looping around of the dna that basically bring the machinery that binds those enhancers and then bring it onto the promoter which then recruits the right sort of the ribosome and the polymerase and all of that thing which will first transcribe and then export and then eventually translate in the cytoplasm you know whatever rna molecule so the beauty of the way that the digital computer that's the genome works is that it's extremely fault tolerant if i took your hard drive and i messed with twenty percent of the letters in it of those zeros and ones and i flipped them you'd be in trouble if i take the genome and i flip 20 of the letters you probably won't even notice and that resilience that's fascinating again is a key design principle and again i'm triple morphizing here but it's a key driving principle of how biological systems work they're first resilient and then anything else and when you look at this incredible beauty of life from the most i don't know beautiful i don't know human genome maybe of humanity and all of the ideals that should come with it to the most terrifying genome like i don't know kovit-19 sarsko v2 and the current pandemic you basically see this elegance as the epitome of clean design but it's dirty it's a mess it's you know the the way to get there is hugely messy and that's something that we as computer scientists don't embrace we like to have clean code you know as like in engineering they teach you about compartmentalization about sort of separating functions about modularity about hierarchical design none of that applies in bio testing [Laughter] testing sure yeah biology does plenty of that but i mean through evolutionary exploration but um if you look at biological systems first they are robust and then they specialize to become anything else and if you look at viruses the reason why they're so elegant when you look at the design of this you know genome it seems so elegant and the reason for that is that it's been stripped down from something much larger because of the pressure to keep it compact so many compact genomes out there have ancestors that were much larger you don't start small and become big you go through a loop of add a bunch of stuff increase complexity and then you know slim it down and one of my early papers was in fact on genome duplication one of the things we found is that baker's yeast which is the you know yeast that you use to make bread but also the yeast that you use to make wine which is basically the dominant species when you go in the fields of tuscany and you say you know what's out there it's basically saccharomyces cerevisiae or the way my italian friends say saccharomyces so um uh which means what oh sakura okay i'm sorry i'm i'm greek so yeah zacharo mickeys zacharo is sugar minky's is fungus yes cerevisiae cerveza beer so so it means the sugar fungus of beer yeah you know less less sounding to the still poetic yeah so anyway uh saccharomyces cerevisiae basically the major baker's yeast out there is the descendant of a whole genome duplication why would a whole genuine duplication even happen when it happened is coinciding with about 100 million years ago and the emergence of fruit-bearing plants why fruit-bearing plants because animals would eat the fruit and would walk around and poop huge amounts of nutrients along with a seed for the plants to spread before that plants were not spreading through animals they were spreading through wind and all kinds of other ways but basically the moment you have fruit-bearing plants the the the these plants are basically creating this abundance of sugar in the environment so there's an evolutionary niche that gets created and in that evolutionary niche you basically have enough sugar that a whole genome duplication which initially is a very messy event allows you to then you know relieve some of that complexity so to pause what does genome duplication mean that basically means that instead of having eight chromosomes you can now have 16 chromosomes so but with the duplication at first when you have six when you go to 16 you're not using that oh yeah you are yeah so basically from one day to the next you went from having eight chromosomes to having 16 chromosomes probably a non-disjunction event during a duplication during a division so you basically divide the cell instead of half the genome going this way and half the genome going the other way after duplication of the genome you basically have all of it going to one cell and then there's a sufficient messiness there that you end up with slight differences that make most of these chromosomes be actually preserved it's a long story short but it's a big upgrade right so that's not necessarily because what happens immediately thereafter is that you start massively losing tons of those duplicated genes so ninety percent of those genes were actually lost very rapidly after holding duplication and the reason for that is that biology is not intelligent it's just ruthless selection random mutation so the ruthless selection basically means that as soon as one of the random mutations hit one gene ruthless selection just kills off that gene it's just you know you you know if you have a pressure to maintain a small compact genome you will very rapidly lose the second copy of most of your genes and a small number 10 were kept in two copies and those had to do a lot with environment adaptation with the speed of replication with the speed of translation and with sugar processing so i'm making a long story short to basically say that evolution is messy the only way like so so you know the example that i was giving of messing with 20 of your bits in your computer totally bogus duplicating all your functions and just throwing them out there in the same you know function just totally bogus like this would never work in an engineer system but biological systems because of this content-based indexing and because of this modularity that comes from the fact that the gene is controlled by a series of tags and now if you need this gene in another setting you just add some more tags that will basically turn it on also in those settings so this gene is now pressured to to do two different functions and it builds up complexity i see a whole term duplication and gene duplication in general as a way to relieve that complexity so you have this gradual buildup of complexity as functions get past get sort of added on to the existing genes and then boom you duplicate your your workforce and you now have two copies of this gene one will probably specialize to do one and the other one will specialize to do the other or one will maintain the ancestral function the other one will sort of be free to evolve and specialize while losing the ancestral functions and so forth so that's how genomes evolve they're they're just messy things but they're extremely fault tolerant and they're extremely able to deal with mutations because that's the very way that you generate new functions so new functionalization comes from the very thing that breaks it so even in the current pandemic many people are asking me which mutations matter the most and what i tell them is well we can study the evolutionary dynamics of the current genome to then understand which mutations have previously happened or not and which mutations happen in genes that evolve rapidly or not and one of the things we found for example is that the genes that evolved rapidly in the past are still evolving rapidly now in the current pandemic the genes have evolved slowly in the past are still evolving slowly which means that they're useful which means that they're under the same evolutionary pressures but then the question is what happens in specific mutations so if you look at the d614 gene mutation that's been all over the news so in position 614 in the amino acids and harvest 14 of the s protein there's a d to g mutation that that sort of has creeped over the population mutation we found out through my work disrupts a perfectly conserved nucleotide position that has never been changed in the history of millions of years of equivalent mammalian evolution of these viruses that basically means that it's a completely new adaptation to human and that mutation has now gone from one percent frequency to 90 frequency in almost all outbreaks so this mutation i like how you say in the mu the 416 what was it okay yes 6 on 14 sorry 614 right that d614g dc so so literally so what you're saying is it's like a chess move yeah so it's just mutated one letter to another exactly and that hasn't happened before yeah and and this somehow this mutation is really useful uh it's really useful in the current environment of the genome which is moving from human to human when it was moving from bad to bad it couldn't care less for that mutation but it's environment specific so now that it's moving from human to human whoo-hoo it's moving way better like by orders of magnetism what do you okay so so you're like tracking this evolutionary dynamics which is fascinating but what do you do with that so what does that mean what does this mean what do you make what do you make of this mutation in uh trying to anticipate i guess is is the is one of the things you're trying to do is anticipate where how this unrolls into the future this this evolutionary dynamics such a great question so so there's there's two things remember when i was saying earlier mutation is the path to new things but also the path to break old things so what we know is that this position was extremely preserved through gazillions of mutations that mutation was never tolerated when it was moving from best to bats so that basically means that that contain that position is extremely important in the function of that protein that's the first thing it tells the second one is that that position was very well suited to bat transmission but now is not well suited to human transmission so it got rid of it and it now has a new version of that amino acid that basically makes it much easier to transmit from human to human so in terms of the evolutionary history teaching us about the future it basically tells us here's the regions that are currently mutating here's the regions that are most likely to imitate going forward as you're building a vaccine here's what you should be focusing on in terms of the most stable regions that are the least likely to mutate or here's the newly evolved functions that are most likely to be important because they've overcome this local maximum that it had reached in the in the bat transmission so anyway it's a tangent to basically say that evolution works in messy ways and the thing that you would break is the thing that actually allows you to first go through a lull and then reaching new local maximum and i often like to say that if engineers had basically designed evolution we would still be perfectly replicating bacteria because it's by making the bacterium worse that you allow evolution to reach a new optimum that's just a pause on that that's so profound the the that's so profound for the entirety of um this scientific and engineering disciplines exactly we as engineers need to embrace breaking things we as engineers need to embrace robustness as the first principle beyond perfection because nothing is going to ever be perfect and when you're sending a satellite to mars when something goes wrong it'll break down as opposed to building systems that tolerate failure and are resilient to that and in fact get better through that so the spacex approach versus nasa for the for example is there something we can learn about the incredible uh take lessons from the incredible biological systems in their resilience in their in the mushiness the messiness to uh to our computing systems to uh to our computers it would basically be starting from scratch in many ways it would basically be building new paradigms that don't try to get the right answer all the time but try to get the right answer most of the time or a lot of the time do you see deep learning systems in the whole world of machine learning is kind of taking a step in that direction absolutely absolutely basically by allowing this much more natural evolution of these parameters you basically and then if you look at sort of deep learning systems again they're not inspired by the genome aspect of biology they're inspired by the brain aspect of biology and again i want you to pause for a second and realize the complexity of the entire human brain with trillions of connections within our you know neurons with millions of cells talking to each other is still encoded within that same genome that same genome encodes every single freaking cell type of the entire body every single cell is encoded by the same code and yet specialization allows you to have this single viral-like genome that self-replicates the single module modular automaton work with other copies of itself it's mind-boggling create complex organs through which blood flows and what is that blood the same freaking genome create organs that communicate with each other and what are these organs the exact same genome create a brain that is innervated by massive amounts of blood pumping energy to it 20 of our energetic needs to the brain from the same genome and all of the neuronal connections all of the auxiliary cells all of the immune cells the astrocytes the ligand size the neurons the excitatory the inhibitory neurons all of the different classes of parasites the blood-brain barrier all of that same genome one way to see that in a sad so this one is beautiful the sad thing is thinking about the trillions of organisms that died to create that you mean on the evolutionary path and the evolutionary path to humans that's crazy there's two descendants of apes just talking on the podcast okay this is so mind-boggling just just to boggle our minds a little bit more yeah us talking to each other we are basically generating a series of vocal utterances through our pulsating of vocal chords received through this the people who listen to this are taking a completely different path to that information transfer yet through language but imagine if we could connect these brains directly to each other the amount of information that i'm condensing into a small number of words is a huge funnel which then you receive and you expand into a huge number of thoughts from that small funnel in many ways engineers would love to have the whole information transfer just take the whole set of neurons and throw them away i mean throw them to the other person this might actually not be better because in your misinterpretation of every word that i'm saying you are creating new interpretation that might actually be way better than what i meant to the first place the ambiguity of language perhaps might be the secret to creativity every single time you work on a project by yourself you only bounce ideas with one person and your neurons are basically fully cognizant of what these ideas are but the moment you interact with another person the misinterpretations that happen might be the most creative part of the process with my students every time we have a research meeting i very often pause and say let me repeat what you just said in a different way and i sort of go on and brainstorm with what they were saying but by the third time it's not what they were saying at all and when they pick up what i'm saying you're like oh well now they they've sort of learned something very different from what i was saying and that is the same kind of messiness that i'm describing in the genome itself it's sort of embracing the messiness and that's a feature not a book exactly and in the same way when you're thinking about sort of these deep learning systems that will allow us to sort of be more creative perhaps or learn better approximations of these complex functions again tuned to the universe that we inhabit you have to embrace the breaking you have to embrace the you know how do we get out of these local optima and a lot of the design paradigms that have made deep learning so successful are ways to get away from that ways to get better training by sort of sending long range messages these lstm models and the you know sort of feed forward loops that you know sort of jump through layers of a convolutional neural network all of these things are basically ways to push you out of this local maxima and that's sort of what evolution does that's what language does that's what conversation and brainstorming does that's what our brain does so you know this design paradigm is something that's pervasive and yet not taught in schools not taught in engineering schools where everything is minutely modularized to make sure that we never deviate from you know whatever signal we're trying to emit as opposed to let all hell breaks loose because that's the way that's the path of paradise the path to paradise yeah i mean it's difficult to know how to teach that and uh what to do with it i mean it's um it's difficult to know how to build up a sign the scientific method around messiness you i mean it's not all messiness we need we need some cleanness and going back to the example with mars that's probably the place where i want to sort of moderate error as much as possible and sort of control the environment as much as possible but if you're trying to repopulate mars well maybe messiness is a good thing then on that uh you quick you quickly mentioned this in terms of us using our vocal cords to speak on a podcast um so elon musk and neurolink are working on trying to plug as per discussion with computers and biological systems to connect the two he's trying to con connect our brain to a computer to create a brain computer interface where they can communicate back and forth on this line of thinking do you think this is uh possible to bridge the gap between our engineered computing systems and the messy biological systems my answer would be absolutely we you know there's no doubt that we can understand more and more about what goes on in the brain and we can sort of train the brain i mean i don't know if you remember the palm pilot yeah palm pilot yeah remember this whole sort of alphabet that they had created am i thinking of the same thing um it's basically you had you had a little pen and for every character you had a little scribble that was unique that the machine could understand and that instead of trying the machine trying to teach the machine to recognize human characters you had basically they figured out that it's better and easier to train humans to create human-like characters that the machine is better at recognizing so in the same way i think what will happen is that humans will be trained to be able to create the mind pattern that the machine will respond to before the machine truly comprehends our thoughts so the first human brain interfaces will be tricking humans to speak the machine language where with the right set of electrodes i can sort of trick my brain into doing this and this is the same way that many people teach like learn to control artificial limbs you basically try a bunch of stuff and eventually you figure out how your limbs work that might not be very different from how humans learn to use their natural limbs when they first grow up basically you have these you know neoteny period of you know this puddle of soup inside your brain trying to figure out how to even make your own connections before you're born and then learning sounds in utero of you know all kinds of echoes and you know eventually getting out in the real world and i don't know if you've seen newborns but they just stare around a lot you know one way to think about this as a machine learning person is oh they're just training their edge detectors and eventually they figure out how to train their edge detectors they work through the second layer of the visual cortex and the third layer and so forth and you basically have this um learning how to control your limbs that probably comes at the same time you're sort of you know throwing random things there and you realize that oh wow when i do this thing my limb moves let's do the following experiment take a breath what muscles did you flex now take another breath and think what muscles do i flex the first thing that you're thinking when you're taking a breath is the impact that he has on your lungs you're like oh i'm now going to increase my lungs or i'm not going to bring air in but what you're actually doing is just changing your diaphragm yeah that's not conscious of course you never think of the diaphragm as a thing yeah and why is that that's probably the same reason why i think of moving my finger when i actually move my finger i think of the effect instead of actually thinking of whatever muscle is twitching that actually causes my finger to move so we basically in our first years of life build up this massive lookup table between whatever neuronal firing we do and whatever action happens in our body that we control if you have a kid grow up with a third limb i'm sure they'll figure out how to control them probably at the same rate as their natural limbs and uh a lot of the work would be done by the so if the third limb is the computer you kind of have a uh not a faith but a thought that um the brain might be able to figure out like if the plasticity would come from the brain yeah like the brain would be cleverer than the machine at first when i talk about a third limb that's exactly what i'm saying an artificial limb that basically just controls your mouse while you're typing you know perfectly natural thing i mean again you know in a few hundred years maybe sooner than that but but basically there's as long as the machine is consistent in the way that it will respond to your brain impulses you'll figure out how to control that and you could play tennis with your third limb and let me go back to consistency people who have dramatic accidents that basically take out a whole chunk of their brain can be taught to co-opt other parts of the brain to then control that part you can basically build up that tissue again and eventually train your body how to walk again and how to read again and how to play again and how to think again how to speak a language again etc so there's a massive amount of malleability that happens you know naturally in our way of controlling our body our brain or thoughts or vocal cords or limbs etc and human machine interfaces are inevitable if we sort of figure out how to read these electric impulses but the resolution at which we can understand human thought right now is nil is ridiculous so how are human thoughts encoded it's basically combinations of neurons that co-fire and these create these things called engrams that eventually form memories and so so forth we know nothing of all that stuff so before we can actually read into your brain that you want to build a program that does this anytime it's on that we need a lot of neuroscience well so uh to push back on that do you think it's possible that without understanding the functionally about the brain or the from the neuroscience or the cognitive science or psychology whichever level of the brain will look at do you think if we just connect connect them just like per your previous point if we just have a high enough resolution between connection between uh wikipedia and your brain the brain will just figure it out with us understanding um because that's one of the innovations of neural link is they're increasing the number of connections to the brain to like several thousand which before was you know in the dozens or whatever you're still off by a few orders of magnets right but the the thing is the hope is if you increase that number more and more and more maybe you don't need to understand anything about the actual how human thought is represented in the brain you could just let it let it figure it out by itself well uh cannery is waking up and saying i know yeah exactly exactly so yeah sure you don't have faith in the plasticity of the brain to that degree it's not about brain plasticity it's about the input aspect basically i think on the output aspect being able to control a machine is something that you can probably train your neural impulses that you're sending out to sort of match whatever response you see in the environment if this thing moved every single time i thought a particular thought then i could figure out i could hack my way into moving this thing with just a series of thoughts i could think guitar piano tennis ball and then this thing would be moving and then you know i would just have the series of thoughts that would sort of result in the impulses that will move this thing the way that i want it and then eventually it'll become natural because i won't even think about it um i mean the same way that we control our limbs in a very natural way but babies don't do that babies have to figure it out and you know some of it is hard-coded but some of that is actually learned based on the whatever soup of neurons you ended up with whatever connections you pruned them to and eventually you were born with you know a lot of that is coded in the genome but a huge chunk of that is stochastic instead of the way that you sort of create all these neurons they migrate they form connections they sort of you know spread out they have particular branching patterns but then the connectivity itself unique in every single new person all this to say that on the output side absolutely i'm very very you know um hopeful that we can have machines that read thousands of these neuronal connections on the output side but on the input side oh boy i don't expect any time in the near future we'll be able to sort of send a series of impulses that will tell me oh earth to sun distance 7.5 million et cetera like nowhere i mean i think language will still the be the input way rather than sort of any kind of more complex it's a really interesting notion that the ambiguity of language is a feature yeah and we evolved for millions of years to uh to take advantage of that ambiguity exactly and yet no one teaches us the subtle differences between words that are near cognates and yet evoke so much more than you know one from the other and yet you know when you're choosing words from a list of 20 synonyms you know exactly the connotation of every single one of them and that's something that you know is there so so yes there's ambiguity but there's all kinds of connotations and in the way that we select our words we have so much baggage that we're sending along the way that we're emoting the way that we're moving our hands every single time we speak the you know the pauses the eye contact etc so much higher baud rate than just a vocal you know string of characters well let me just take a small tangent on that oh tangent we haven't done that yet and i haven't done an idea uh we'll return to the origin of life so i mean you're greek but i'm i'm going on this personal journey uh i'm going to paris for the explicit purpose of talking to one of the most famous uh a couple who's a famous translators of russian literature dostoyevsky tolstoy yeah and they go that's their art is the translation and um everything i've learned about the translation art it makes me feel um it's so profound in a way that's so much more profound than the natural language processing papers i read in the machine learning community that there's such depth to language that um i don't know what to do with i don't know if you've experienced that in your own life with knowing multiple languages um i don't know what to i don't know how to make sense of it but there's so much loss in translation between russian and english and getting a sense of that like for example there's like just taking a single sentence from dostoyevsky and like there's a lot of them you could you could talk for hours about how to translate that sentence properly uh that captures the meaning the the the period the culture the humor the wit the suffering that was in the context of the time all of that it could be a single sentence uh you could you could talk forever about what it takes to translate that correctly i don't know what to do with that so being greek it's very hard for me to think of a sentence or even a word without going into the full etymology of that word breaking up every single atom of that that sentence and every single atom of these words and rebuilding it back up i have three kids and the way that i teach them greek is the same way that you know the documentary i was mentioning earlier about sort of understanding the deep roots of all of these you know words um and it's very it's very interesting that every single time i hear a new word that i've never heard before i go and figure out the etymology of that word because i will never appreciate that word without understanding how it was initially formed interesting but how does that help because that's that's not the full picture no no of course of course but what i'm trying to say is that knowing the components teaches you about the context of the formation of that word and sort of the original usage of that word and then of course the word takes new meaning as you create it you know from its parts and that meaning then gets augmented and two synonyms that that sort of have different roots will actually have implications that carry a lot of that baggage of the historical provenance of these words so before working on genome evolution my passion was evolution of language and sort of tracing cognates across different languages through their etymologies and that's fascinating that there's parallels between i mean of course the idea that there's evolutionary dynamics to our language yeah every single word that you utter parallels parallels what does parallels mean para means side by side alleles from alleles which means identical twins parallels i mean name any word and there's so much baggage so much beauty in how that word came to be and how this word took a new meaning than the sum of its parts yeah and that and those and they're just they're just words they don't have any physical exactly and now you take your words and you weave them into a sentence the emotional invocations of that weaving are fathomless and they're all all of those emotions all live in our in the brains of humans in the eye of the beholder no seriously you have to embrace this concept of the eye of the beholder it's it's the the conceptualization that nothing takes meaning with one person creating it everything takes meaning in the receiving end and the emergent properties of these communication networks where every single you know if you look at the network of our cells and how they're communicating with each other every cell has its own code this code is modulated by the epigenome this creates a bunch of different cell types each cell type now has its own identity yet they all have the common root of the stem cells that sort of led to them each of these identities is now communicating with each other they take meaning in their interaction there's an emergent property that comes from a bunch of cells being together that is not in any one of the parts if you look at neurons communicating again these engrams don't exist in any one neuron they exist in the connection in the combination of neurons and the meaning of the words that i'm telling you is empty until it reaches you and it affects you in a very different way then it affects whoever's listening to this conversation now because of the emotional baggage that i've grown up with that you've grown up with and that they've grown up with yeah and that's i think the magic of translation if you start thinking of translation as just simply capturing that emotional set of reactions that you have that you evoke you need a different set of words to evoke that same set of reactions to a french person than to a russian person because of the baggage of the culture that we grew up in yeah i mean there's so so basically you shouldn't find the best word sometimes it's a completely different sentence structure that you will need matched to the cultural context of the target audience that you have yeah the it's i mean you're just i usually don't think about this but right now there's this feeling as a reminder that it's just you and i talking but there's several hundred thousand people will listen to this there's some guy in russia right now running uh like in moscow listening to us and there's somebody in india i guarantee you there's somebody in china and south america there's somebody in texas and and they all have different emotional baggage they probably got angry earlier on about the whole discussion about coronavirus and uh about some aspect of it uh yeah it's and there's that network effect yeah yeah that's uh it's a beautiful thing and and this lateral transfer of information that's what makes the collective quote-unquote genome of humanity so unique from any other species so you somehow miraculously wrapped it back to the very beginning of when we were talking about the human the beauty of the human genome so i think this is the right time unless we want to go for a six to eight hour conversation we're gonna have to talk again but i think for now to wrap it up um this is the right time to talk about the uh the biggest most ridiculous question of all meaning of life off mike you mentioned to me that you um you had your 42nd birthday 40 a second being a very special absurdly special number uh and you had to kind of um get together with friends to discuss the meaning of life so let me ask you in your as a biologist as a computer scientist and as a human what is the meaning of life i've been asking this question for a long time ever since my 42nd birthday but well before that and even planning the meaning of life symposium and symposium means together posey actually means to drink together so symposium is actually a drinking party [Laughter] so can you actually elaborate about this meaning of life that you put together it's like the most genius idea i've ever heard so 42 is obviously the answer to life the universe and everything from the hitchhiker's guide to the galaxy and as i was turning 42 i've had the theme for every one of my birthdays when i was turning 32 it's one zero zero zero zero zero in binary so i celebrated my 100 000th binary binary birthday and i had a theme of going back 100 000 years you know let's dress something in the last hundred thousand years anyway it was we've i've always had these that's such an interesting human being okay that's awesome i've always had these sort of uh sort of numerology [Music] related announcements for my for my birthday party so what came out of that meaning of life symposium is that i basically asked 42 of my colleagues 42 my friends 42 of my you know collaborators to basically give seven minute species on the meaning of life each from their perspective and i really encourage you to go there because it's mind-boggling that every single person said a different answer every single person started with i don't know what the meaning of life is but and then give this beautifully eloquently answer eloquent answer and they were all different but they all were consistent with each other and mutually synergistic and together forming a beautiful view of what it means to be human in many ways some people talked about the loss of their loved one their life partner for many many years and how their life changed through that some people talked about the origin of life some people talked about the difference between purpose and meaning i'll you know maybe quote one of the answers which is this linguistics uh professor friend of mine at harvard who basically said that she was gonna she's greek as well and she said i will give a very pythian answer so pithia was the oracle of delphi who would basically give these very cryptic answers very short but interpretable in many different ways there was this whole set of priests who were tasked with interpreting what pethia had said and very often you would not get a clean interpretation but she said i will be like pethi and give you a very short and multiple interpretable answer but unlike her i will actually also give you three interpretations and she said the answer to the meaning of life is become one and the first interpretation is like a child become one year old with the excitement of discovering everything about the world second interpretation in whatever you take on become one the first the best excel drive yourself to perfection for every one of your tasks and become one when people are separate become one come together learn to understand each other damn that's an answer and one way to summarize this whole meaning of life symposium is that the very symposium was illustrating the quest for meaning which might itself be the meaning of life this constant quest for something sublime something human something intangible some you know aspect of what defines us as a species and as an individual both the quest of me as a person through my own life but the meaning of life could also be the meaning of all of life what is the whole point of life why life why life itself because we've been talking about the history and evolution of life but we haven't talked about why life in the first place is life inevitable is life part of physics does life transcend physics but fighting by fighting against entropy by compartmentalizing and increasing concentrations rather than diluting away is life um a distinct entity in the universe beyond the traditional very simple physical rules that govern gravity and you know electromagnetism and all of these forces is life another force is there a life force is there a unique kind of set of principles that emerge of course built on top of the hardware of physics but is it sort of a new layer of software or a new layer of a computer system so that's at the level of you know big questions there's another aspect of gratitude of basically what i you know what i like to say is during this pandemic i've basically worked from 6 a.m until 7 00 pm every single day non-stop including saturday and sunday i've basically broken all boundaries of where life personal life begins and work life you know ends and uh that has been exhilarating for me just just the intellectual pleasure that i get from a day of exhaustion where at the end of the day my brain is hurting i'm telling my wife wow i was useful today and there's a certain pleasure that comes from feeling useful and there's a certain pleasure that comes from feeling grateful so i've written this little sort of prayer for my kids to say at bedtime every night where they basically say thank you god for all you have given me and give me the strength to give unto others with the same love that you have given unto me we as a species are so special the only ones who worry about the meaning of life and maybe that's what makes us human and what i like to say to my wife and to my students during this pandemic work extravaganza is every now and then they ask me but how do you do this and i'm like i'm a workaholic i love this this is me in the most unfiltered way the ability to do something useful to feel that my brain is being used to interact with the smartest people on the planet day in day out and to help them discover aspects of the human genome of the human brain of human disease and the human condition that no one has seen before with data that we're capturing that has never been observed and there's another aspect which is on the personal life many people say oh i'm not going to have kids why bother i can tell you as a father they're missing half the picture if not the whole picture teaching my kids about my view of the world and watching through their eyes the naivete with which they start and the sophistication with which they end up they understanding that they have of not just the natural world around them but of me too the unfiltered criticism that you get from your own children that knows no bounds of honesty and i've grown components of my heart that i didn't know i had until you sense that fragility that vulnerability of the children that immense love and passion the unfiltered egoism that we as adults learn how to hide so much better it's just this back of emotions that tell me about the raw materials that make a human being and how these raw materials can be arranged with more sophistication that we learn through life to become truly human adults but there's something so beautiful about seeing that progression between them the complexity of the language growing as more neural connections are formed to to realize that the hardware is getting rearranged as their software is getting implemented on that hardware that their frontal cortex continues to grow for another 10 years these neuronal connections are continuing to form new neurons that actually get replicated and formed and it's it's just incredible that we have this not just you grow the hardware for 30 years and then you feed it all of the knowledge no no the knowledge is fed throughout and is shaping these neural connections as they're forming so seeing that transformation from either your own blood or from an adopted child is the most beautiful thing you can do as a human being and it completes you it completes that path that journey the create life oh sure that's at conception that's easy but create human life to add the human part that takes decades of compassion of sharing of love and of anger and of impatience and patience and as a parent i think i've become a very different kind of teacher because again i'm a professor my first role is to bring adult human beings into a you know more mature level of adulthood where they learn not just to do science but they learn the process of discovery and the process of collaboration the process of sharing the process of conveying the knowledge of encapsulating something incredibly complex and and sort of giving it up in sort of bite-sized chunks that the rest of humanity can appreciate i tell my students all the time if you you know like when an apple fall when when when a tree falls in the forest and no one's there to listen has it really fallen the same way you do this awesome research if you write an impenetrable paper that no one will understand it's as if you never did the awesome research so conveying of knowledge conveying this lateral transfer that i was talking about at the very beginning of sort of human humanity and sort of the sharing of information all of that has gotten so much more rich by seeing human beings grow in my own home because that that makes me a better parent and that makes me a better teacher and a better mentor to the nurturing of my adult children which are my research group first of all beautifully put connects beautifully to the vertical and the horizontal inheritance of ideas that we've talked about at the very beginning i don't think there's a better way to end it uh on this poetic and powerful note uh manolas thank you so much for talking there's a huge honor we have to talk again about the origin of life about epigenetics epigenomics and uh some of the incredible research you're doing truly an honor thanks so much for talking thank you such a pleasure it's such a pleasure i mean your questions are outstanding i've had such a blast here i can't wait to be back awesome thanks for listening to this conversation with manolas kellis and thank you to our sponsors blinkist eight sleep and masterclass please consider supporting this podcast by going to blinkist.com lex eightsleep.com lex and dot com slash lex click the links buy the stuff get the discount it's the best way to support this podcast if you enjoy this thing subscribe on youtube review 5 stars on apple podcast support on patreon or connect with me on twitter at lex friedman and now let me leave you with some words from charles darwin that i think manolis represents quite beautifully if i had my life to live over again i would have made a rule to read some poetry and listen to some music at least once every week thank you for listening and hope to see you next time you