Kind: captions
Language: en
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
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here's my conversation with manolis
kellis
what to use the most beautiful aspect of
the human genome
don't get me started
so we got time um the first answer is
that the beauty of genomes transcends
humanity so it's not just about the
human genome
genomes in general are amazingly
beautiful and again i'm obviously biased
so um in my view uh
the way that i like to introduce the
human genome and the way that i'd like
to introduce genomics to my class
is by telling them you know we're not
the inventors of the first digital
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
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 th