Regina Barzilay: Deep Learning for Cancer Diagnosis and Treatment
嘉宾：Regina Barzilay
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§01Transcript · 00:00–09:57
The following is a conversation with Regina Barzilay.
She's a professor at MIT and a world class researcher
in natural language processing
and applications of deep learning to chemistry and oncology
or the use of deep learning for early diagnosis,
prevention and treatment of cancer.
She has also been recognized for teaching
of several successful AI related courses at MIT,
including the popular Introduction
to Machine Learning course.
This is the Artificial Intelligence podcast.
If you enjoy it, subscribe on YouTube,
give it five stars on iTunes, support it on Patreon
or simply connect with me on Twitter
at Lex Friedman spelled F R I D M A N.
And now here's my conversation with Regina Barzilay.
In an interview you've mentioned
that if there's one course you would take,
it would be a literature course with a friend of yours
that a friend of yours teaches.
Just out of curiosity, because I couldn't find anything
on it, are there books or ideas that had profound impact
on your life journey, books and ideas perhaps
outside of computer science and the technical fields?
I think because I'm spending a lot of my time at MIT
and previously in other institutions where I was a student,
I have limited ability to interact with people.
So a lot of what I know about the world
actually comes from books.
And there were quite a number of books
that had profound impact on me and how I view the world.
Let me just give you one example of such a book.
I've maybe a year ago read a book
called The Emperor of All Melodies.
It's a book about, it's kind of a history of science book
on how the treatments and drugs for cancer were developed.
And that book, despite the fact that I am in the business
of science, really opened my eyes on how imprecise
and imperfect the discovery process is
and how imperfect our current solutions
and what makes science succeed and be implemented.
And sometimes it's actually not the strengths of the idea,
but devotion of the person who wants to see it implemented.
So this is one of the books that, you know,
at least for the last year, quite changed the way
I'm thinking about scientific process
just from the historical perspective
and what do I need to do to make my ideas really implemented.
Let me give you an example of a book
which is not kind of, which is a fiction book.
It's a book called Americana.
And this is a book about a young female student
who comes from Africa to study in the United States.
And it describes her past, you know, within her studies
and her life transformation that, you know,
in a new country and kind of adaptation to a new culture.
And when I read this book, I saw myself
in many different points of it,
but it also kind of gave me the lens on different events.
And some of it that I never actually paid attention.
One of the funny stories in this book
is how she arrives to her new college
and she starts speaking in English
and she had this beautiful British accent
because that's how she was educated in her country.
This is not my case.
And then she notices that the person who talks to her,
you know, talks to her in a very funny way,
in a very slow way.
And she's thinking that this woman is disabled
and she's also trying to kind of to accommodate her.
And then after a while, when she finishes her discussion
with this officer from her college,
she sees how she interacts with the other students,
with American students.
And she discovers that actually she talked to her this way
because she saw that she doesn't understand English.
And I thought, wow, this is a funny experience.
And literally within few weeks,
I went to LA to a conference
and I asked somebody in the airport,
you know, how to find like a cab or something.
And then I noticed that this person is talking
in a very strange way.
And my first thought was that this person
have some, you know, pronunciation issues or something.
And I'm trying to talk very slowly to him
and I was with another professor, Ernst Frankel.
And he's like laughing because it's funny
that I don't get that the guy is talking in this way
because he thinks that I cannot speak.
So it was really kind of mirroring experience.
And it led me think a lot about my own experiences
moving, you know, from different countries.
So I think that books play a big role
in my understanding of the world.
On the science question, you mentioned that
it made you discover that personalities of human beings
are more important than perhaps ideas.
Is that what I heard?
It's not necessarily that they are more important
than ideas, but I think that ideas on their own
are not sufficient.
And many times, at least at the local horizon,
it's the personalities and their devotion to their ideas
is really that locally changes the landscape.
Now, if you're looking at AI, like let's say 30 years ago,
you know, dark ages of AI or whatever,
what is symbolic times, you can use any word.
You know, there were some people,
now we're looking at a lot of that work
and we're kind of thinking this was not really
maybe a relevant work, but you can see that some people
managed to take it and to make it so shiny
and dominate the academic world
and make it to be the standard.
If you look at the area of natural language processing,
it is well known fact that the reason that statistics
in NLP took such a long time to become mainstream
because there were quite a number of personalities
which didn't believe in this idea
and didn't stop research progress in this area.
So I do not think that, you know,
kind of asymptotically maybe personalities matters,
but I think locally it does make quite a bit of impact
and it's generally, you know,
speeds up the rate of adoption of the new ideas.
Yeah, and the other interesting question
is in the early days of particular discipline,
I think you mentioned in that book
is ultimately a book of cancer.
It's called The Emperor of All Melodies.
Yeah, and those melodies included the trying to,
the medicine, was it centered around?
So it was actually centered on, you know,
how people thought of curing cancer.
Like for me, it was really a discovery how people,
what was the science of chemistry behind drug development
that it actually grew up out of dying,
like coloring industry that people
who developed chemistry in 19th century in Germany
and Britain to do, you know, the really new dyes.
They looked at the molecules and identified
that they do certain things to cells.
And from there, the process started.
And, you know, like historically saying,
yeah, this is fascinating
that they managed to make the connection
and look under the microscope and do all this discovery.
But as you continue reading about it
and you read about how chemotherapy drugs
which were developed in Boston,
and some of them were developed.
And Farber, Dr. Farber from Dana Farber,
you know, how the experiments were done
that, you know, there was some miscalculation,
let's put it this way.
And they tried it on the patients and they just,
and those were children with leukemia and they died.
And then they tried another modification.
You look at the process, how imperfect is this process?
And, you know, like, if we're again looking back
like 60 years ago, 70 years ago,
you can kind of understand it.
But some of the stories in this book
which were really shocking to me
were really happening, you know, maybe decades ago.
And we still don't have a vehicle
to do it much more fast and effective and, you know,
scientific the way I'm thinking computer science scientific.
So from the perspective of computer science,
you've gotten a chance to work the application to cancer
and to medicine in general.
From a perspective of an engineer and a computer scientist,
how far along are we from understanding the human body,
biology of being able to manipulate it
in a way we can cure some of the maladies,
some of the diseases?
So this is very interesting question.
And if you're thinking as a computer scientist
about this problem, I think one of the reasons
that we succeeded in the areas
we as a computer scientist succeeded
is because we don't have,
we are not trying to understand in some ways.
Like if you're thinking about like eCommerce, Amazon,
Amazon doesn't really understand you.
And that's why it recommends you certain books
or certain products, correct?
And, you know, traditionally when people
were thinking about marketing, you know,
they divided the population to different kind of subgroups,
identify the features of this subgroup
and come up with a strategy
which is specific to that subgroup.
If you're looking about recommendation system,
they're not claiming that they're understanding somebody,
they're just managing to,
from the patterns of your behavior
to recommend you a product.
§02Transcript · 09:57–19:20
Now, if you look at the traditional biology,
and obviously I wouldn't say that I
at any way, you know, educated in this field,
but you know what I see, there's really a lot of emphasis
on mechanistic understanding.
And it was very surprising to me
coming from computer science,
how much emphasis is on this understanding.
And given the complexity of the system,
maybe the deterministic full understanding
of this process is, you know, beyond our capacity.
And the same ways in computer science
when we're doing recognition, when you do recommendation
and many other areas,
it's just probabilistic matching process.
And in some way, maybe in certain cases,
we shouldn't even attempt to understand
or we can attempt to understand, but in parallel,
we can actually do this kind of matchings
that would help us to find key role
to do early diagnostics and so on.
And I know that in these communities,
it's really important to understand,
but I'm sometimes wondering, you know,
what exactly does it mean to understand here?
Well, there's stuff that works and,
but that can be, like you said,
separate from this deep human desire
to uncover the mysteries of the universe,
of science, of the way the body works,
the way the mind works.
It's the dream of symbolic AI,
of being able to reduce human knowledge into logic
and be able to play with that logic
in a way that's very explainable
and understandable for us humans.
I mean, that's a beautiful dream.
So I understand it, but it seems that
what seems to work today and we'll talk about it more
is as much as possible, reduce stuff into data,
reduce whatever problem you're interested in to data
and try to apply statistical methods,
apply machine learning to that.
On a personal note,
you were diagnosed with breast cancer in 2014.
What did facing your mortality make you think about?
How did it change you?
You know, this is a great question
and I think that I was interviewed many times
and nobody actually asked me this question.
I think I was 43 at a time.
And the first time I realized in my life that I may die
and I never thought about it before.
And there was a long time since you're diagnosed
until you actually know what you have
and how severe is your disease.
For me, it was like maybe two and a half months.
And I didn't know where I am during this time
because I was getting different tests
and one would say it's bad and I would say, no, it is not.
So until I knew where I am,
I really was thinking about
all these different possible outcomes.
Were you imagining the worst
or were you trying to be optimistic or?
It would be really,
I don't remember what was my thinking.
It was really a mixture with many components at the time
speaking in our terms.
And one thing that I remember,
and every test comes and then you're saying,
oh, it could be this or it may not be this.
And you're hopeful and then you're desperate.
So it's like, there is a whole slew of emotions
that goes through you.
But what I remember is that when I came back to MIT,
I was kind of going the whole time through the treatment
to MIT, but my brain was not really there.
But when I came back, really finished my treatment
and I was here teaching and everything,
I look back at what my group was doing,
what other groups was doing.
And I saw these trivialities.
It's like people are building their careers
on improving some parts around two or 3% or whatever.
I was, it's like, seriously,
I did a work on how to decipher ugaritic,
like a language that nobody speak and whatever,
like what is significance?
When all of a sudden, I walked out of MIT,
which is when people really do care
what happened to your ICLR paper,
what is your next publication to ACL,
to the world where people, you see a lot of suffering
that I'm kind of totally shielded on it on daily basis.
And it's like the first time I've seen like real life
and real suffering.
And I was thinking, why are we trying to improve the parser
or deal with trivialities when we have capacity
to really make a change?
And it was really challenging to me because on one hand,
I have my graduate students really want to do their papers
and their work, and they want to continue to do
what they were doing, which was great.
And then it was me who really kind of reevaluated
what is the importance.
And also at that point, because I had to take some break,
I look back into like my years in science
and I was thinking, like 10 years ago,
this was the biggest thing, I don't know, topic models.
We have like millions of papers on topic models
and variation of topics models.
Now it's totally like irrelevant.
And you start looking at this, what do you perceive
as important at different point of time
and how it fades over time.
And since we have a limited time,
all of us have limited time on us,
it's really important to prioritize things
that really matter to you, maybe matter to you
at that particular point.
But it's important to take some time
and understand what matters to you,
which may not necessarily be the same
as what matters to the rest of your scientific community
and pursue that vision.
So that moment, did it make you cognizant?
You mentioned suffering of just the general amount
of suffering in the world.
Is that what you're referring to?
So as opposed to topic models
and specific detailed problems in NLP,
did you start to think about other people
who have been diagnosed with cancer?
Is that the way you started to see the world perhaps?
Oh, absolutely.
And it actually creates, because like, for instance,
there is parts of the treatment
where you need to go to the hospital every day
and you see the community of people that you see
and many of them are much worse than I was at a time.
And you all of a sudden see it all.
And people who are happier someday
just because they feel better.
And for people who are in our normal realm,
you take it totally for granted that you feel well,
that if you decide to go running, you can go running
and you're pretty much free
to do whatever you want with your body.
Like I saw like a community,
my community became those people.
And I remember one of my friends, Dina Katabi,
took me to Prudential to buy me a gift for my birthday.
And it was like the first time in months
that I went to kind of to see other people.
And I was like, wow, first of all, these people,
they are happy and they're laughing
and they're very different from these other my people.
And second of thing, I think it's totally crazy.
They're like laughing and wasting their money
on some stupid gifts.
And they may die.
They already may have cancer and they don't understand it.
So you can really see how the mind changes
that you can see that,
before that you can ask,
didn't you know that you're gonna die?
Of course I knew, but it was a kind of a theoretical notion.
It wasn't something which was concrete.
And at that point, when you really see it
and see how little means sometimes the system has
to have them, you really feel that we need to take a lot
of our brilliance that we have here at MIT
and translate it into something useful.
Yeah, and you still couldn't have a lot of definitions,
but of course, alleviating, suffering, alleviating,
trying to cure cancer is a beautiful mission.
So I of course know theoretically the notion of cancer,
but just reading more and more about it's 1.7 million
new cancer cases in the United States every year,
600,000 cancer related deaths every year.
So this has a huge impact, United States globally.
When broadly, before we talk about how machine learning,
how MIT can help,
when do you think we as a civilization will cure cancer?
How hard of a problem is it from everything you've learned
from it recently?
I cannot really assess it.
What I do believe will happen with the advancement
in machine learning is that a lot of types of cancer
we will be able to predict way early
and more effectively utilize existing treatments.
I think, I hope at least that with all the advancements
in AI and drug discovery, we would be able
to much faster find relevant molecules.
What I'm not sure about is how long it will take
the medical establishment and regulatory bodies
to kind of catch up and to implement it.
And I think this is a very big piece of puzzle
that is currently not addressed.
§03Transcript · 19:20–29:07
That's the really interesting question.
So first a small detail that I think the answer is yes,
but is cancer one of the diseases that when detected earlier
that's a significantly improves the outcomes?
So like, cause we will talk about there's the cure
and then there is detection.
And I think where machine learning can really help
is earlier detection.
So does detection help?
Detection is crucial.
For instance, the vast majority of pancreatic cancer patients
are detected at the stage that they are incurable.
That's why they have such a terrible survival rate.
It's like just few percent over five years.
It's pretty much today the sentence.
But if you can discover this disease early,
there are mechanisms to treat it.
And in fact, I know a number of people who were diagnosed
and saved just because they had food poisoning.
They had terrible food poisoning.
They went to ER, they got scan.
There were early signs on the scan
and that would save their lives.
But this wasn't really an accidental case.
So as we become better, we would be able to help
to many more people that are likely to develop diseases.
And I just want to say that as I got more into this field,
I realized that cancer is of course terrible disease,
but there are really the whole slew of terrible diseases
out there like neurodegenerative diseases and others.
So we, of course, a lot of us are fixated on cancer
because it's so prevalent in our society.
And you see these people where there are a lot of patients
with neurodegenerative diseases
and the kind of aging diseases
that we still don't have a good solution for.
And I felt as a computer scientist,
we kind of decided that it's other people's job
to treat these diseases because it's like traditionally
people in biology or in chemistry or MDs
are the ones who are thinking about it.
And after kind of start paying attention,
I think that it's really a wrong assumption
and we all need to join the battle.
So how it seems like in cancer specifically
that there's a lot of ways that machine learning can help.
So what's the role of machine learning
in the diagnosis of cancer?
So for many cancers today, we really don't know
what is your likelihood to get cancer.
And for the vast majority of patients,
especially on the younger patients,
it really comes as a surprise.
Like for instance, for breast cancer,
80% of the patients are first in their families,
it's like me.
And I never saw that I had any increased risk
because nobody had it in my family.
And for some reason in my head,
it was kind of inherited disease.
But even if I would pay attention,
the very simplistic statistical models
that are currently used in clinical practice,
they really don't give you an answer, so you don't know.
And the same true for pancreatic cancer,
the same true for non smoking lung cancer and many others.
So what machine learning can do here
is utilize all this data to tell us early
who is likely to be susceptible
and using all the information that is already there,
be it imaging, be it your other tests,
and eventually liquid biopsies and others,
where the signal itself is not sufficiently strong
for human eye to do good discrimination
because the signal may be weak,
but by combining many sources,
machine which is trained on large volumes of data
can really detect it early.
And that's what we've seen with breast cancer
and people are reporting it in other diseases as well.
That really boils down to data, right?
And in the different kinds of sources of data.
And you mentioned regulatory challenges.
So what are the challenges
in gathering large data sets in this space?
Again, another great question.
So it took me after I decided that I want to work on it
two years to get access to data.
Any data, like any significant data set?
Any significant amount, like right now in this country,
there is no publicly available data set
of modern mammograms that you can just go
on your computer, sign a document and get it.
It just doesn't exist.
I mean, obviously every hospital has its own collection
of mammograms.
There are data that came out of clinical trials.
What we're talking about here is a computer scientist
who just wants to run his or her model
and see how it works.
This data, like ImageNet, doesn't exist.
And there is a set which is called like Florida data set
which is a film mammogram from 90s
which is totally not representative
of the current developments.
Whatever you're learning on them doesn't scale up.
This is the only resource that is available.
And today there are many agencies
that govern access to data.
Like the hospital holds your data
and the hospital decides whether they would give it
to the researcher to work with this data or not.
Individual hospital?
Yeah.
I mean, the hospital may, you know,
assuming that you're doing research collaboration,
you can submit, you know,
there is a proper approval process guided by RB
and if you go through all the processes,
you can eventually get access to the data.
But if you yourself know our OEI community,
there are not that many people who actually ever got access
to data because it's very challenging process.
And sorry, just in a quick comment,
MGH or any kind of hospital,
are they scanning the data?
Are they digitally storing it?
Oh, it is already digitally stored.
You don't need to do any extra processing steps.
It's already there in the right format is that right now
there are a lot of issues that govern access to the data
because the hospital is legally responsible for the data.
And, you know, they have a lot to lose
if they give the data to the wrong person,
but they may not have a lot to gain if they give it
as a hospital, as a legal entity has given it to you.
And the way, you know, what I would imagine
happening in the future is the same thing that happens
when you're getting your driving license,
you can decide whether you want to donate your organs.
You can imagine that whenever a person goes to the hospital,
they, it should be easy for them to donate their data
for research and it can be different kind of,
do they only give you a test results or only mammogram
or only imaging data or the whole medical record?
Because at the end,
we all will benefit from all this insights.
And it's not like you say, I want to keep my data private,
but I would really love to get it from other people
because other people are thinking the same way.
So if there is a mechanism to do this donation
and the patient has an ability to say
how they want to use their data for research,
it would be really a game changer.
People, when they think about this problem,
there's a, it depends on the population,
depends on the demographics,
but there's some privacy concerns generally,
not just medical data, just any kind of data.
It's what you said, my data, it should belong kind of to me.
I'm worried how it's going to be misused.
How do we alleviate those concerns?
Because that seems like a problem that needs to be,
that problem of trust, of transparency needs to be solved
before we build large data sets that help detect cancer,
help save those very people in the future.
So I think there are two things that could be done.
There is a technical solutions
and there are societal solutions.
So on the technical end,
we today have ability to improve disambiguation.
Like, for instance, for imaging,
it's, you know, for imaging, you can do it pretty well.
What's disambiguation?
And disambiguation, sorry, disambiguation,
removing the identification,
removing the names of the people.
There are other data, like if it is a raw tax,
you cannot really achieve 99.9%,
but there are all these techniques
that actually some of them are developed at MIT,
how you can do learning on the encoded data
where you locally encode the image,
you train a network which only works on the encoded images
and then you send the outcome back to the hospital
and you can open it up.
So those are the technical solutions.
There are a lot of people who are working in this space
where the learning happens in the encoded form.
We are still early,
but this is an interesting research area
where I think we'll make more progress.
There is a lot of work in natural language processing
community how to do the identification better.
But even today, there are already a lot of data
which can be deidentified perfectly,
like your test data, for instance, correct,
where you can just, you know the name of the patient,
you just want to extract the part with the numbers.
The big problem here is again,
§04Transcript · 29:08–38:53
hospitals don't see much incentive
to give this data away on one hand
and then there is general concern.
Now, when I'm talking about societal benefits
and about the education,
the public needs to understand that I think
that there are situation and I still remember myself
when I really needed an answer, I had to make a choice.
There was no information to make a choice,
you're just guessing.
And at that moment you feel that your life is at the stake,
but you just don't have information to make the choice.
And many times when I give talks,
I get emails from women who say,
you know, I'm in this situation,
can you please run statistic and see what are the outcomes?
We get almost every week a mammogram that comes by mail
to my office at MIT, I'm serious.
That people ask to run because they need to make
life changing decisions.
And of course, I'm not planning to open a clinic here,
but we do run and give them the results for their doctors.
But the point that I'm trying to make,
that we all at some point or our loved ones
will be in the situation where you need information
to make the best choice.
And if this information is not available,
you would feel vulnerable and unprotected.
And then the question is, you know, what do I care more?
Because at the end, everything is a trade off, correct?
Yeah, exactly.
Just out of curiosity, it seems like one possible solution,
I'd like to see what you think of it,
based on what you just said,
based on wanting to know answers
for when you're yourself in that situation.
Is it possible for patients to own their data
as opposed to hospitals owning their data?
Of course, theoretically, I guess patients own their data,
but can you walk out there with a USB stick
containing everything or upload it to the cloud?
Where a company, you know, I remember Microsoft
had a service, like I try, I was really excited about
and Google Health was there.
I tried to give, I was excited about it.
Basically companies helping you upload your data
to the cloud so that you can move from hospital to hospital
from doctor to doctor.
Do you see a promise of that kind of possibility?
I absolutely think this is, you know,
the right way to exchange the data.
I don't know now who's the biggest player in this field,
but I can clearly see that even for totally selfish
health reasons, when you are going to a new facility
and many of us are sent to some specialized treatment,
they don't easily have access to your data.
And today, you know, we might want to send this mammogram,
need to go to the hospital, find some small office
which gives them the CD and they ship as a CD.
So you can imagine we're looking at kind of decades old
mechanism of data exchange.
So I definitely think this is an area where hopefully
all the right regulatory and technical forces will align
and we will see it actually implemented.
It's sad because unfortunately, and I need to research
why that happened, but I'm pretty sure Google Health
and Microsoft Health Vault or whatever it's called
both closed down, which means that there was
either regulatory pressure or there's not a business case
or there's challenges from hospitals,
which is very disappointing.
So when you say you don't know what the biggest players are,
the two biggest that I was aware of closed their doors.
So I'm hoping, I'd love to see why
and I'd love to see who else can come up.
It seems like one of those Elon Musk style problems
that are obvious needs to be solved
and somebody needs to step up and actually do
this large scale data collection.
So I know there is an initiative in Massachusetts,
I think, which you led by the governor
to try to create this kind of health exchange system
where at least to help people who kind of when you show up
in emergency room and there is no information
about what are your allergies and other things.
So I don't know how far it will go.
But another thing that you said
and I find it very interesting is actually
who are the successful players in this space
and the whole implementation, how does it go?
To me, it is from the anthropological perspective,
it's more fascinating that AI that today goes in healthcare,
we've seen so many attempts and so very little successes.
And it's interesting to understand that I've by no means
have knowledge to assess it,
why we are in the position where we are.
Yeah, it's interesting because data is really fuel
for a lot of successful applications.
And when that data acquires regulatory approval,
like the FDA or any kind of approval,
it seems that the computer scientists
are not quite there yet in being able
to play the regulatory game,
understanding the fundamentals of it.
I think that in many cases when even people do have data,
we still don't know what exactly do you need to demonstrate
to change the standard of care.
Like let me give you an example
related to my breast cancer research.
So in traditional breast cancer risk assessment,
there is something called density,
which determines the likelihood of a woman to get cancer.
And this pretty much says,
how much white do you see on the mammogram?
The whiter it is, the more likely the tissue is dense.
And the idea behind density, it's not a bad idea.
In 1967, a radiologist called Wolf decided to look back
at women who were diagnosed
and see what is special in their images.
Can we look back and say that they're likely to develop?
So he come up with some patterns.
And it was the best that his human eye can identify.
Then it was kind of formalized
and coded into four categories.
And that's what we are using today.
And today this density assessment
is actually a federal law from 2019,
approved by President Trump
and for the previous FDA commissioner,
where women are supposed to be advised by their providers
if they have high density,
putting them into higher risk category.
And in some states,
you can actually get supplementary screening
paid by your insurance because you're in this category.
Now you can say, how much science do we have behind it?
Whatever, biological science or epidemiological evidence.
So it turns out that between 40 and 50% of women
have dense breasts.
So about 40% of patients are coming out of their screening
and somebody tells them, you are in high risk.
Now, what exactly does it mean
if you as half of the population in high risk?
It's from saying, maybe I'm not,
or what do I really need to do with it?
Because the system doesn't provide me
a lot of the solutions
because there are so many people like me,
we cannot really provide very expensive solutions for them.
And the reason this whole density became this big deal,
it's actually advocated by the patients
who felt very unprotected
because many women went and did the mammograms
which were normal.
And then it turns out that they already had cancer,
quite developed cancer.
So they didn't have a way to know who is really at risk
and what is the likelihood that when the doctor tells you,
you're okay, you are not okay.
So at the time, and it was 15 years ago,
this maybe was the best piece of science that we had.
And it took quite 15, 16 years to make it federal law.
But now this is a standard.
Now with a deep learning model,
we can so much more accurately predict
who is gonna develop breast cancer
just because you're trained on a logical thing.
And instead of describing how much white
and what kind of white machine
can systematically identify the patterns,
which was the original idea behind the thought
of the cardiologist,
machines can do it much more systematically
and predict the risk when you're training the machine
to look at the image and to say the risk in one to five years.
Now you can ask me how long it will take
to substitute this density,
which is broadly used across the country
and really is not helping to bring this new models.
And I would say it's not a matter of the algorithm.
Algorithms use already orders of magnitude better
than what is currently in practice.
I think it's really the question,
who do you need to convince?
How many hospitals do you need to run the experiment?
What, you know, all this mechanism of adoption
and how do you explain to patients
and to women across the country
that this is really a better measure?
And again, I don't think it's an AI question.
We can work more and make the algorithm even better,
but I don't think that this is the current, you know,
the barrier, the barrier is really this other piece
that for some reason is not really explored.
It's like anthropological piece.
And coming back to your question about books,
there is a book that I'm reading.
It's called American Sickness by Elizabeth Rosenthal.
And I got this book from my clinical collaborator,
Dr. Connie Lehman.
§05Transcript · 38:53–48:00
And I said, I know everything that I need to know
about American health system,
but you know, every page doesn't fail to surprise me.
And I think there is a lot of interesting
and really deep lessons for people like us
from computer science who are coming into this field
to really understand how complex is the system of incentives
in the system to understand how you really need to play
to drive adoption.
You just said it's complex,
but if we're trying to simplify it,
who do you think most likely would be successful
if we push on this group of people?
Is it the doctors?
Is it the hospitals?
Is it the governments or policymakers?
Is it the individual patients, consumers?
Who needs to be inspired to most likely lead to adoption?
Or is there no simple answer?
There's no simple answer,
but I think there is a lot of good people in medical system
who do want to make a change.
And I think a lot of power will come from us as consumers
because we all are consumers or future consumers
of healthcare services.
And I think we can do so much more
in explaining the potential and not in the hype terms
and not saying that we now killed all Alzheimer
and I'm really sick of reading this kind of articles
which make these claims,
but really to show with some examples
what this implementation does and how it changes the care.
Because I can't imagine,
it doesn't matter what kind of politician it is,
we all are susceptible to these diseases.
There is no one who is free.
And eventually, we all are humans
and we're looking for a way to alleviate the suffering.
And this is one possible way
where we currently are under utilizing,
which I think can help.
So it sounds like the biggest problems are outside of AI
in terms of the biggest impact at this point.
But are there any open problems
in the application of ML to oncology in general?
So improving the detection or any other creative methods,
whether it's on the detection segmentations
or the vision perception side
or some other clever of inference?
Yeah, what in general in your view are the open problems
in this space?
Yeah, I just want to mention that beside detection,
not the area where I am kind of quite active
and I think it's really an increasingly important area
in healthcare is drug design.
Absolutely.
Because it's fine if you detect something early,
but you still need to get drugs
and new drugs for these conditions.
And today, all of the drug design,
ML is non existent there.
We don't have any drug that was developed by the ML model
or even not developed,
but at least even knew that ML model
plays some significant role.
I think this area with all the new ability
to generate molecules with desired properties
to do in silica screening is really a big open area.
To be totally honest with you,
when we are doing diagnostics and imaging,
primarily taking the ideas that were developed
for other areas and you applying them with some adaptation,
the area of drug design is really technically interesting
and exciting area.
You need to work a lot with graphs
and capture various 3D properties.
There are lots and lots of opportunities
to be technically creative.
And I think there are a lot of open questions in this area.
We're already getting a lot of successes
even with kind of the first generation of these models,
but there is much more new creative things that you can do.
And what's very nice to see is that actually
the more powerful, the more interesting models
actually do do better.
So there is a place to innovate in machine learning
in this area.
And some of these techniques are really unique to,
let's say, to graph generation and other things.
So...
What, just to interrupt really quick, I'm sorry,
graph generation or graphs, drug discovery in general,
how do you discover a drug?
Is this chemistry?
Is this trying to predict different chemical reactions?
Or is it some kind of...
What do graphs even represent in this space?
Oh, sorry, sorry.
And what's a drug?
Okay, so let's say you're thinking
there are many different types of drugs,
but let's say you're gonna talk about small molecules
because I think today the majority of drugs
are small molecules.
So small molecule is a graph.
The molecule is just where the node in the graph
is an atom and then you have the bonds.
So it's really a graph representation.
If you look at it in 2D, correct,
you can do it 3D, but let's say,
let's keep it simple and stick in 2D.
So pretty much my understanding today,
how it is done at scale in the companies,
without machine learning,
you have high throughput screening.
So you know that you are interested
to get certain biological activity of the compound.
So you scan a lot of compounds,
like maybe hundreds of thousands,
some really big number of compounds.
You identify some compounds which have the right activity
and then at this point, the chemists come
and they're trying to now to optimize
this original heat to different properties
that you want it to be maybe soluble,
you want it to decrease toxicity,
you want it to decrease the side effects.
Are those, sorry again to interrupt,
can that be done in simulation
or just by looking at the molecules
or do you need to actually run reactions
in real labs with lab coats and stuff?
So when you do high throughput screening,
you really do screening.
It's in the lab.
It's really the lab screening.
You screen the molecules, correct?
I don't know what screening is.
The screening is just check them for certain property.
Like in the physical space, in the physical world,
like actually there's a machine probably
that's actually running the reaction.
Actually running the reactions, yeah.
So there is a process where you can run
and that's why it's called high throughput
that it become cheaper and faster
to do it on very big number of molecules.
You run the screening,
you identify potential good starts
and then when the chemists come in
who have done it many times
and then they can try to look at it and say,
how can you change the molecule
to get the desired profile
in terms of all other properties?
So maybe how do I make it more bioactive and so on?
And there the creativity of the chemists
really is the one that determines the success
of this design because again,
they have a lot of domain knowledge
of what works, how do you decrease the CCD and so on
and that's what they do.
So all the drugs that are currently
in the FDA approved drugs
or even drugs that are in clinical trials,
they are designed using these domain experts
which goes through this combinatorial space
of molecules or graphs or whatever
and find the right one or adjust it to be the right ones.
It sounds like the breast density heuristic
from 67 to the same echoes.
It's not necessarily that.
It's really driven by deep understanding.
It's not like they just observe it.
I mean, they do deeply understand chemistry
and they do understand how different groups
and how does it changes the properties.
So there is a lot of science that gets into it
and a lot of kind of simulation,
how do you want it to behave?
It's very, very complex.
So they're quite effective at this design, obviously.
Now effective, yeah, we have drugs.
Like depending on how do you measure effective,
if you measure it in terms of cost, it's prohibitive.
If you measure it in terms of times,
we have lots of diseases for which we don't have any drugs
and we don't even know how to approach
and don't need to mention few drugs
or neurodegenerative disease drugs that fail.
So there are lots of trials that fail in later stages,
which is really catastrophic from the financial perspective.
So is it the effective, the most effective mechanism?
Absolutely no, but this is the only one that currently works.
And I was closely interacting
with people in pharmaceutical industry.
I was really fascinated on how sharp
and what a deep understanding of the domain do they have.
It's not observation driven.
There is really a lot of science behind what they do.
§06Transcript · 48:00–57:10
But if you ask me, can machine learning change it,
I firmly believe yes,
because even the most experienced chemists
cannot hold in their memory and understanding
everything that you can learn
from millions of molecules and reactions.
And the space of graphs is a totally new space.
I mean, it's a really interesting space
for machine learning to explore, graph generation.
Yeah, so there are a lot of things that you can do here.
So we do a lot of work.
So the first tool that we started with
was the tool that can predict properties of the molecules.
So you can just give the molecule and the property.
It can be by activity property,
or it can be some other property.
And you train the molecules
and you can now take a new molecule
and predict this property.
Now, when people started working in this area,
it is something very simple.
They do kind of existing fingerprints,
which is kind of handcrafted features of the molecule.
When you break the graph to substructures
and then you run it in a feed forward neural network.
And what was interesting to see that clearly,
this was not the most effective way to proceed.
And you need to have much more complex models
that can induce a representation,
which can translate this graph into the embeddings
and do these predictions.
So this is one direction.
Then another direction, which is kind of related
is not only to stop by looking at the embedding itself,
but actually modify it to produce better molecules.
So you can think about it as machine translation
that you can start with a molecule
and then there is an improved version of molecule.
And you can again, with encoder translate it
into the hidden space and then learn how to modify it
to improve the in some ways version of the molecules.
So that's, it's kind of really exciting.
We already have seen that the property prediction
works pretty well.
And now we are generating molecules
and there is actually labs
which are manufacturing this molecule.
So we'll see where it will get us.
Okay, that's really exciting.
There's a lot of promise.
Speaking of machine translation and embeddings,
I think you have done a lot of really great research
in NLP, natural language processing.
Can you tell me your journey through NLP?
What ideas, problems, approaches were you working on?
Were you fascinated with, did you explore
before this magic of deep learning reemerged and after?
So when I started my work in NLP, it was in 97.
This was very interesting time.
It was exactly the time that I came to ACL.
And at the time I could barely understand English,
but it was exactly like the transition point
because half of the papers were really rule based approaches
where people took more kind of heavy linguistic approaches
for small domains and try to build up from there.
And then there were the first generation of papers
which were corpus based papers.
And they were very simple in our terms
when you collect some statistics
and do prediction based on them.
And I found it really fascinating that one community
can think so very differently about the problem.
And I remember my first paper that I wrote,
it didn't have a single formula.
It didn't have evaluation.
It just had examples of outputs.
And this was a standard of the field at the time.
In some ways, I mean, people maybe just started emphasizing
the empirical evaluation, but for many applications
like summarization, you just show some examples of outputs.
And then increasingly you can see that how
the statistical approaches dominated the field
and we've seen increased performance
across many basic tasks.
The sad part of the story maybe that if you look again
through this journey, we see that the role of linguistics
in some ways greatly diminishes.
And I think that you really need to look
through the whole proceeding to find one or two papers
which make some interesting linguistic references.
It's really big.
Today, yeah.
Today, today.
This was definitely one of the.
Things like syntactic trees, just even basically
against our conversation about human understanding
of language, which I guess what linguistics would be
structured, hierarchical representing language
in a way that's human explainable, understandable
is missing today.
I don't know if it is, what is explainable
and understandable.
In the end, we perform functions and it's okay
to have machine which performs a function.
Like when you're thinking about your calculator, correct?
Your calculator can do calculation very different
from you would do the calculation,
but it's very effective in it.
And this is fine if we can achieve certain tasks
with high accuracy, doesn't necessarily mean
that it has to understand it the same way as we understand.
In some ways, it's even naive to request
because you have so many other sources of information
that are absent when you are training your system.
So it's okay.
Is it delivered?
And I would tell you one application
that is really fascinating.
In 97, when it came to ACL, there were some papers
on machine translation.
They were like primitive.
Like people were trying really, really simple.
And the feeling, my feeling was that, you know,
to make real machine translation system,
it's like to fly at the moon and build a house there
and the garden and live happily ever after.
I mean, it's like impossible.
I never could imagine that within, you know, 10 years,
we would already see the system working.
And now, you know, nobody is even surprised
to utilize the system on daily basis.
So this was like a huge, huge progress,
saying that people for very long time
tried to solve using other mechanisms.
And they were unable to solve it.
That's why coming back to your question about biology,
that, you know, in linguistics, people try to go this way
and try to write the syntactic trees
and try to abstract it and to find the right representation.
And, you know, they couldn't get very far
with this understanding while these models using,
you know, other sources actually capable
to make a lot of progress.
Now, I'm not naive to think
that we are in this paradise space in NLP.
And sure as you know,
that when we slightly change the domain
and when we decrease the amount of training,
it can do like really bizarre and funny thing.
But I think it's just a matter
of improving generalization capacity,
which is just a technical question.
Wow, so that's the question.
How much of language understanding can be solved
with deep neural networks?
In your intuition, I mean, it's unknown, I suppose.
But as we start to creep towards romantic notions
of the spirit of the Turing test
and conversation and dialogue
and something that maybe to me or to us,
so the humans feels like it needs real understanding.
How much can that be achieved
with these neural networks or statistical methods?
So I guess I am very much driven by the outcomes.
Can we achieve the performance
which would be satisfactory for us for different tasks?
Now, if you again look at machine translation system,
which are trained on large amounts of data,
they really can do a remarkable job
relatively to where they've been a few years ago.
And if you project into the future,
if it will be the same speed of improvement, you know,
this is great.
Now, does it bother me
that it's not doing the same translation as we are doing?
Now, if you go to cognitive science,
we still don't really understand what we are doing.
I mean, there are a lot of theories
and there's obviously a lot of progress and studying,
but our understanding what exactly goes on in our brains
when we process language is still not crystal clear
and precise that we can translate it into machines.
What does bother me is that, you know,
again, that machines can be extremely brittle
when you go out of your comfort zone
of when there is a distributional shift
between training and testing.
And it have been years and years,
every year when I teach an LP class,
now show them some examples of translation
from some newspaper in Hebrew or whatever, it was perfect.
And then I have a recipe that Tomi Yakel's system
sent me a while ago and it was written in Finnish
of Karelian pies.
And it's just a terrible translation.
You cannot understand anything what it does.
It's not like some syntactic mistakes, it's just terrible.
And year after year, I tried and will translate
and year after year, it does this terrible work
because I guess, you know, the recipes
§07Transcript · 57:10–1:06:27
are not a big part of their training repertoire.
So, but in terms of outcomes, that's a really clean,
good way to look at it.
I guess the question I was asking is,
do you think, imagine a future,
do you think the current approaches can pass
the Turing test in the way,
in the best possible formulation of the Turing test?
Which is, would you wanna have a conversation
with a neural network for an hour?
Oh God, no, no, there are not that many people
that I would want to talk for an hour, but.
There are some people in this world, alive or not,
that you would like to talk to for an hour.
Could a neural network achieve that outcome?
So I think it would be really hard to create
a successful training set, which would enable it
to have a conversation, a contextual conversation
for an hour.
Do you think it's a problem of data, perhaps?
I think in some ways it's not a problem of data,
it's a problem both of data and the problem of
the way we're training our systems,
their ability to truly, to generalize,
to be very compositional.
In some ways it's limited in the current capacity,
at least we can translate well,
we can find information well, we can extract information.
So there are many capacities in which it's doing very well.
And you can ask me, would you trust the machine
to translate for you and use it as a source?
I would say absolutely, especially if we're talking about
newspaper data or other data which is in the realm
of its own training set, I would say yes.
But having conversations with the machine,
it's not something that I would choose to do.
But I would tell you something, talking about Turing tests
and about all this kind of ELISA conversations,
I remember visiting Tencent in China
and they have this chat board and they claim
there is really humongous amount of the local population
which for hours talks to the chat board.
To me it was, I cannot believe it,
but apparently it's documented that there are some people
who enjoy this conversation.
And it brought to me another MIT story
about ELISA and Weisenbaum.
I don't know if you're familiar with the story.
So Weisenbaum was a professor at MIT
and when he developed this ELISA,
which was just doing string matching,
very trivial, like restating of what you said
with very few rules, no syntax.
Apparently there were secretaries at MIT
that would sit for hours and converse with this trivial thing
and at the time there was no beautiful interfaces
so you actually need to go through the pain
of communicating.
And Weisenbaum himself was so horrified by this phenomenon
that people can believe enough to the machine
that you just need to give them the hint
that machine understands you and you can complete the rest
that he kind of stopped this research
and went into kind of trying to understand
what this artificial intelligence can do to our brains.
So my point is, you know,
how much, it's not how good is the technology,
it's how ready we are to believe
that it delivers the goods that we are trying to get.
That's a really beautiful way to put it.
I, by the way, I'm not horrified by that possibility,
but inspired by it because,
I mean, human connection,
whether it's through language or through love,
it seems like it's very amenable to machine learning
and the rest is just challenges of psychology.
Like you said, the secretaries who enjoy spending hours.
I would say I would describe most of our lives
as enjoying spending hours with those we love
for very silly reasons.
All we're doing is keyword matching as well.
So I'm not sure how much intelligence
we exhibit to each other with the people we love
that we're close with.
So it's a very interesting point
of what it means to pass the Turing test with language.
I think you're right.
In terms of conversation,
I think machine translation
has very clear performance and improvement, right?
What it means to have a fulfilling conversation
is very person dependent and context dependent
and so on.
That's, yeah, it's very well put.
But in your view, what's a benchmark in natural language,
a test that's just out of reach right now,
but we might be able to, that's exciting.
Is it in perfecting machine translation
or is there other, is it summarization?
What's out there just out of reach?
I think it goes across specific application.
It's more about the ability to learn from few examples
for real, what we call few short learning and all these cases
because the way we publish these papers today,
we say, if we have like naively, we get 55,
but now we had a few example and we can move to 65.
None of these methods
actually are realistically doing anything useful.
You cannot use them today.
And the ability to be able to generalize and to move
or to be autonomous in finding the data
that you need to learn,
to be able to perfect new tasks or new language,
this is an area where I think we really need
to move forward to and we are not yet there.
Are you at all excited,
curious by the possibility
of creating human level intelligence?
Is this, cause you've been very in your discussion.
So if we look at oncology,
you're trying to use machine learning to help the world
in terms of alleviating suffering.
If you look at natural language processing,
you're focused on the outcomes of improving practical things
like machine translation.
But human level intelligence is a thing
that our civilization has dreamed about creating,
super human level intelligence.
Do you think about this?
Do you think it's at all within our reach?
So as you said yourself, Elie,
talking about how do you perceive
our communications with each other,
that we're matching keywords and certain behaviors
and so on.
So at the end, whenever one assesses,
let's say relations with another person,
you have separate kind of measurements and outcomes
inside your head that determine
what is the status of the relation.
So one way, this is this classical level,
what is the intelligence?
Is it the fact that now we are gonna do the same way
as human is doing,
when we don't even understand what the human is doing?
Or we now have an ability to deliver these outcomes,
but not in one area, not in NLP,
not just to translate or just to answer questions,
but across many, many areas
that we can achieve the functionalities
that humans can achieve with their ability to learn
and do other things.
I think this is, and this we can actually measure
how far we are.
And that's what makes me excited that we,
in my lifetime, at least so far what we've seen,
it's like tremendous progress
across these different functionalities.
And I think it will be really exciting
to see where we will be.
And again, one way to think about it,
there are machines which are improving their functionality.
Another one is to think about us with our brains,
which are imperfect,
how they can be accelerated by this technology
as it becomes stronger and stronger.
Coming back to another book
that I love, Flowers for Algernon.
Have you read this book?
Yes.
So there is this point that the patient gets
this miracle cure, which changes his brain.
And all of a sudden they see life in a different way
and can do certain things better,
but certain things much worse.
So you can imagine this kind of computer augmented cognition
where it can bring you that now in the same way
as the cars enable us to get to places
where we've never been before,
can we think differently?
Can we think faster?
And we already see a lot of it happening
in how it impacts us,
but I think we have a long way to go there.
So that's sort of artificial intelligence
and technology affecting our,
augmenting our intelligence as humans.
Yesterday, a company called Neuralink announced,
they did this whole demonstration.
I don't know if you saw it.
It's, they demonstrated brain computer,
brain machine interface,
where there's like a sewing machine for the brain.
Do you, you know, a lot of that is quite out there
in terms of things that some people would say
are impossible, but they're dreamers
and want to engineer systems like that.
Do you see, based on what you just said,
a hope for that more direct interaction with the brain?
I think there are different ways.
§08Transcript · 1:06:27–1:16:08
One is a direct interaction with the brain.
And again, there are lots of companies
that work in this space
and I think there will be a lot of developments.
But I'm just thinking that many times
we are not aware of our feelings,
of motivation, what drives us.
Like, let me give you a trivial example, our attention.
There are a lot of studies that demonstrate
that it takes a while to a person to understand
that they are not attentive anymore.
And we know that there are people
who really have strong capacity to hold attention.
There are other end of the spectrum people with ADD
and other issues that they have problem
to regulate their attention.
Imagine to yourself that you have like a cognitive aid
that just alerts you based on your gaze,
that your attention is now not on what you are doing.
And instead of writing a paper,
you're now dreaming of what you're gonna do in the evening.
So even this kind of simple measurement things,
how they can change us.
And I see it even in simple ways with myself.
I have my zone app that I got in MIT gym.
It kind of records, you know, how much did you run
and you have some points
and you can get some status, whatever.
Like, I said, what is this ridiculous thing?
Who would ever care about some status in some app?
Guess what?
So to maintain the status,
you have to do set a number of points every month.
And not only is that I do it every single month
for the last 18 months,
it went to the point that I was injured.
And when I could run again,
in two days, I did like some humongous amount of running
just to complete the points.
It was like really not safe.
It was like, I'm not gonna lose my status
because I want to get there.
So you can already see that this direct measurement
and the feedback is, you know,
we're looking at video games
and see why, you know, the addiction aspect of it,
but you can imagine that the same idea can be expanded
to many other areas of our life.
When we really can get feedback
and imagine in your case in relations,
when we are doing keyword matching,
imagine that the person who is generating the keywords,
that person gets direct feedback
before the whole thing explodes.
Is it maybe at this happy point,
we are going in the wrong direction.
Maybe it will be really a behavior modifying moment.
So yeah, it's a relationship management too.
So yeah, that's a fascinating whole area
of psychology actually as well,
of seeing how our behavior has changed
with basically all human relations now have
other nonhuman entities helping us out.
So you teach a large,
a huge machine learning course here at MIT.
I can ask you a million questions,
but you've seen a lot of students.
What ideas do students struggle with the most
as they first enter this world of machine learning?
Actually, this year was the first time
I started teaching a small machine learning class.
And it came as a result of what I saw
in my big machine learning class that Tomi Yakel and I built
maybe six years ago.
What we've seen that as this area become more
and more popular, more and more people at MIT
want to take this class.
And while we designed it for computer science majors,
there were a lot of people who really are interested
to learn it, but unfortunately,
their background was not enabling them
to do well in the class.
And many of them associated machine learning
with the word struggle and failure,
primarily for non majors.
And that's why we actually started a new class
which we call machine learning from algorithms to modeling,
which emphasizes more the modeling aspects of it
and focuses on, it has majors and non majors.
So we kind of try to extract the relevant parts
and make it more accessible,
because the fact that we're teaching 20 classifiers
in standard machine learning class,
it's really a big question to really need it.
But it was interesting to see this
from first generation of students,
when they came back from their internships
and from their jobs,
what different and exciting things they can do.
I would never think that you can even apply
machine learning to, some of them are like matching,
the relations and other things like variety.
Everything is amenable as the machine learning.
That actually brings up an interesting point
of computer science in general.
It almost seems, maybe I'm crazy,
but it almost seems like everybody needs to learn
how to program these days.
If you're 20 years old, or if you're starting school,
even if you're an English major,
it seems like programming unlocks so much possibility
in this world.
So when you interacted with those non majors,
is there skills that they were simply lacking at the time
that you wish they had and that they learned
in high school and so on?
Like how should education change
in this computerized world that we live in?
I think because I knew that there is a Python component
in the class, their Python skills were okay
and the class isn't really heavy on programming.
They primarily kind of add parts to the programs.
I think it was more of the mathematical barriers
and the class, again, with the design on the majors
was using the notation, like big O for complexity
and others, people who come from different backgrounds
just don't have it in the lexical,
so necessarily very challenging notion,
but they were just not aware.
So I think that kind of linear algebra and probability,
the basics, the calculus, multivariate calculus,
things that can help.
What advice would you give to students
interested in machine learning,
interested, you've talked about detecting,
curing cancer, drug design,
if they want to get into that field, what should they do?
Get into it and succeed as researchers
and entrepreneurs.
The first good piece of news is that right now
there are lots of resources
that are created at different levels
and you can find online in your school classes
which are more mathematical, more applied and so on.
So you can find a kind of a preacher
which preaches in your own language
where you can enter the field
and you can make many different types of contribution
depending of what is your strengths.
And the second point, I think it's really important
to find some area which you really care about
and it can motivate your learning
and it can be for somebody curing cancer
or doing self driving cars or whatever,
but to find an area where there is data
where you believe there are strong patterns
and we should be doing it and we're still not doing it
or you can do it better
and just start there and see where it can bring you.
So you've been very successful in many directions in life,
but you also mentioned Flowers of Argonon.
And I think I've read or listened to you mention somewhere
that researchers often get lost
in the details of their work.
This is per our original discussion with cancer and so on
and don't look at the bigger picture,
bigger questions of meaning and so on.
So let me ask you the impossible question
of what's the meaning of this thing,
of life, of your life, of research.
Why do you think we descendant of great apes
are here on this spinning ball?
You know, I don't think that I have really a global answer.
You know, maybe that's why I didn't go to humanities
and I didn't take humanities classes in my undergrad.
But the way I'm thinking about it,
each one of us inside of them have their own set of,
you know, things that we believe are important.
And it just happens that we are busy
with achieving various goals, busy listening to others
and to kind of try to conform and to be part of the crowd,
that we don't listen to that part.
And, you know, we all should find some time to understand
what is our own individual missions.
And we may have very different missions
and to make sure that while we are running 10,000 things,
we are not, you know, missing out
and we're putting all the resources to satisfy
our own mission.
And if I look over my time, when I was younger,
most of these missions, you know,
I was primarily driven by the external stimulus,
you know, to achieve this or to be that.
And now a lot of what I do is driven by really thinking
what is important for me to achieve independently
of the external recognition.
And, you know, I don't mind to be viewed in certain ways.
The most important thing for me is to be true to myself,
to what I think is right.
How long did it take?
§09Transcript · 1:16:08–1:17:28
How hard was it to find the you that you have to be true to?
So it takes time.
And even now, sometimes, you know,
the vanity and the triviality can take, you know.
At MIT.
Yeah, it can everywhere, you know,
it's just the vanity at MIT is different,
the vanity in different places,
but we all have our piece of vanity.
But I think actually for me, many times the place
to get back to it is, you know, when I'm alone
and also when I read.
And I think by selecting the right books,
you can get the right questions and learn from what you read.
So, but again, it's not perfect.
Like vanity sometimes dominates.
Well, that's a beautiful way to end.
Thank you so much for talking today.
Thank you.
That was fun.
That was fun.