back to indexDaphne Koller: Biomedicine and Machine Learning | Lex Fridman Podcast #93
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The following is a conversation with Daphne Koller,
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a professor of computer science at Stanford University,
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a cofounder of Coursera with Andrew Ng,
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and founder and CEO of Incitro,
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a company at the intersection
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of machine learning and biomedicine.
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We're now in the exciting early days
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of using the data driven methods of machine learning
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to help discover and develop new drugs
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and treatments at scale.
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Daphne and Incitro are leading the way on this
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with breakthroughs that may ripple
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through all fields of medicine,
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including ones most critical for helping
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with the current coronavirus pandemic.
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This conversation was recorded
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before the COVID 19 outbreak.
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For everyone feeling the medical, psychological,
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and financial burden of this crisis,
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I'm sending love your way.
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at Lex Friedman, spelled F R I D M A N.
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and STEM education for young people around the world.
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And now here's my conversation with Daphne Koller.
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So you cofounded Coursera and made a huge impact
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in the global education of AI.
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And after five years in August, 2016,
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wrote a blog post saying that you're stepping away
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it is time for me to turn to another critical challenge,
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the development of machine learning
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and its applications to improving human health.
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So let me ask two far out philosophical questions.
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One, do you think we'll one day find cures
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for all major diseases known today?
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And two, do you think we'll one day figure out
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a way to extend the human lifespan,
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perhaps to the point of immortality?
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So one day is a very long time
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and I don't like to make predictions
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of the type we will never be able to do X
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because I think that's a smacks of hubris.
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It seems that never in the entire eternity
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of human existence will we be able to solve a problem.
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That being said, curing disease is very hard
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because oftentimes by the time you discover the disease,
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a lot of damage has already been done.
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And so to assume that we would be able to cure disease
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at that stage assumes that we would come up with ways
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of basically regenerating entire parts of the human body
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in the way that actually returns it to its original state.
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And that's a very challenging problem.
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We have cured very few diseases.
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We've been able to provide treatment
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for an increasingly large number,
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but the number of things that you could actually define
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to be cures is actually not that large.
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So I think that there's a lot of work
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that would need to happen before one could legitimately say
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that we have cured even a reasonable number,
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far less all diseases.
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On the scale of zero to 100,
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where are we in understanding the fundamental mechanisms
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of all of major diseases?
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What's your sense?
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So from the computer science perspective
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that you've entered the world of health,
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how far along are we?
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I think it depends on which disease.
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I mean, there are ones where I would say
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we're maybe not quite at a hundred
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because biology is really complicated
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and there's always new things that we uncover
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that people didn't even realize existed.
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But I would say there's diseases
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where we might be in the 70s or 80s,
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and then there's diseases in which I would say
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with probably the majority where we're really close to zero.
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Would Alzheimer's and schizophrenia
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and type two diabetes fall closer to zero or to the 80?
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I think Alzheimer's is probably closer to zero than to 80.
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There are hypotheses,
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but I don't think those hypotheses have as of yet
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been sufficiently validated that we believe them to be true.
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And there is an increasing number of people
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who believe that the traditional hypotheses
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might not really explain what's going on.
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I would also say that Alzheimer's and schizophrenia
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and even type two diabetes are not really one disease.
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They're almost certainly a heterogeneous collection
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of mechanisms that manifest in clinically similar ways.
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So in the same way that we now understand
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that breast cancer is really not one disease,
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it is multitude of cellular mechanisms,
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all of which ultimately translate
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to uncontrolled proliferation, but it's not one disease.
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The same is almost undoubtedly true
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for those other diseases as well.
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And that understanding that needs to precede
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any understanding of the specific mechanisms
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of any of those other diseases.
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Now, in schizophrenia, I would say
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we're almost certainly closer to zero than to anything else.
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Type two diabetes is a bit of a mix.
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There are clear mechanisms that are implicated
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that I think have been validated
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that have to do with insulin resistance and such,
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but there's almost certainly there as well
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many mechanisms that we have not yet understood.
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You've also thought and worked a little bit
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on the longevity side.
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Do you see the disease and longevity as overlapping
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completely, partially, or not at all as efforts?
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Those mechanisms are certainly overlapping.
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There's a well known phenomenon that says
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that for most diseases, other than childhood diseases,
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the risk for contracting that disease
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increases exponentially year on year,
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every year from the time you're about 40.
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So obviously there's a connection between those two things.
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That's not to say that they're identical.
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There's clearly aging that happens
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that is not really associated with any specific disease.
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And there's also diseases and mechanisms of disease
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that are not specifically related to aging.
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So I think overlap is where we're at.
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It is a little unfortunate that we get older
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and it seems that there's some correlation
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with the occurrence of diseases
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or the fact that we get older.
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And both are quite sad.
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I mean, there's processes that happen as cells age
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that I think are contributing to disease.
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Some of those have to do with DNA damage
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that accumulates as cells divide
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where the repair mechanisms don't fully correct for those.
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There are accumulations of proteins
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that are misfolded and potentially aggregate
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and those too contribute to disease
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and will contribute to inflammation.
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There's a multitude of mechanisms that have been uncovered
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that are sort of wear and tear at the cellular level
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that contribute to disease processes
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and I'm sure there's many that we don't yet understand.
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On a small tangent and perhaps philosophical,
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the fact that things get older
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and the fact that things die is a very powerful feature
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for the growth of new things.
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It's a learning, it's a kind of learning mechanism.
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So it's both tragic and beautiful.
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So do you, so in trying to fight disease
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and trying to fight aging,
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do you think about sort of the useful fact of our mortality
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or would you, like if you were, could be immortal,
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would you choose to be immortal?
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Again, I think immortal is a very long time
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and I don't know that that would necessarily be something
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that I would want to aspire to
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but I think all of us aspire to an increased health span,
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I would say, which is an increased amount of time
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where you're healthy and active
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and feel as you did when you were 20
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and we're nowhere close to that.
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People deteriorate physically and mentally over time
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and that is a very sad phenomenon.
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So I think a wonderful aspiration would be
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if we could all live to the biblical 120 maybe
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in perfect health.
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In high quality of life.
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High quality of life.
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I think that would be an amazing goal
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for us to achieve as a society
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now is the right age 120 or 100 or 150.
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I think that's up for debate
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but I think an increased health span
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is a really worthy goal.
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And anyway, in a grand time of the age of the universe,
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it's all pretty short.
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So from the perspective,
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you've done obviously a lot of incredible work
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in machine learning.
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So what role do you think data and machine learning
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play in this goal of trying to understand diseases
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and trying to eradicate diseases?
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Up until now, I don't think it's played
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very much of a significant role
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because largely the data sets that one really needed
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to enable a powerful machine learning methods,
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those data sets haven't really existed.
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There's been dribs and drabs
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and some interesting machine learning
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that has been applied, I would say machine learning
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slash data science,
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but the last few years are starting to change that.
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So we now see an increase in some large data sets
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but equally importantly, an increase in technologies
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that are able to produce data at scale.
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It's not typically the case that people have deliberately
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proactively used those tools
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for the purpose of generating data for machine learning.
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They, to the extent that those techniques
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have been used for data production,
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they've been used for data production
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to drive scientific discovery
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and the machine learning came as a sort of byproduct
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second stage of, oh, you know, now we have a data set,
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let's do machine learning on that
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rather than a more simplistic data analysis method.
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But what we are doing in Citro
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is actually flipping that around and saying,
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here's this incredible repertoire of methods
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that bioengineers, cell biologists have come up with,
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let's see if we can put them together in brand new ways
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with the goal of creating data sets
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that machine learning can really be applied on productively
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to create powerful predictive models
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that can help us address fundamental problems
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So really focus to get, make data the primary focus
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and the primary goal and find,
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use the mechanisms of biology and chemistry
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to create the kinds of data set
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that could allow machine learning to benefit the most.
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I wouldn't put it in those terms
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because that says that data is the end goal.
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Data is the means.
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So for us, the end goal is helping address challenges
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in human health and the method that we've elected to do that
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is to apply machine learning to build predictive models
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and machine learning, in my opinion,
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can only be really successfully applied
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especially the more powerful models
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if you give it data that is of sufficient scale
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and sufficient quality.
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So how do you create those data sets
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so as to drive the ability to generate predictive models
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which subsequently help improve human health?
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So before we dive into the details of that,
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let me take a step back and ask when and where
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was your interest in human health born?
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Are there moments, events, perhaps if I may ask,
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tragedies in your own life that catalyzes passion
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or was it the broader desire to help humankind?
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So I would say it's a bit of both.
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So on, I mean, my interest in human health
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actually dates back to the early 2000s
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when a lot of my peers in machine learning
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and I were using data sets
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that frankly were not very inspiring.
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Some of us old timers still remember
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the quote unquote 20 news groups data set
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where this was literally a bunch of texts
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from 20 news groups,
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a concept that doesn't really even exist anymore.
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And the question was, can you classify
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which news group a particular bag of words came from?
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And it wasn't very interesting.
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The data sets at the time on the biology side
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were much more interesting,
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both from a technical and also from
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an aspirational perspective.
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They were still pretty small,
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but they were better than 20 news groups.
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And so I started out, I think just by wanting
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to do something that was more, I don't know,
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societally useful and technically interesting.
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And then over time became more and more interested
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in the biology and the human health aspects for themselves
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and began to work even sometimes on papers
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that were just in biology
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without having a significant machine learning component.
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I think my interest in drug discovery
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is partly due to an incident I had with
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when my father sadly passed away about 12 years ago.
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He had an autoimmune disease that settled in his lungs
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and the doctors basically said,
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well, there's only one thing that we could do,
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which is give him prednisone.
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At some point, I remember a doctor even came and said,
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hey, let's do a lung biopsy to figure out
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which autoimmune disease he has.
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And I said, would that be helpful?
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Would that change treatment?
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He said, no, there's only prednisone.
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That's the only thing we can give him.
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And I had friends who were rheumatologists who said
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the FDA would never approve prednisone today
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because the ratio of side effects to benefit
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is probably not large enough.
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Today, we're in a state where there's probably four or five,
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maybe even more, well, it depends for which autoimmune disease,
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but there are multiple drugs that can help people
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with autoimmune disease,
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many of which didn't exist 12 years ago.
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And I think we're at a golden time in some ways
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in drug discovery where there's the ability to create drugs
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that are much more safe and much more effective
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than we've ever been able to before.
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And what's lacking is enough understanding
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of biology and mechanism to know where to aim that engine.
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And I think that's where machine learning can help.
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So in 2018, you started and now lead a company in Citro,
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which is, like you mentioned,
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perhaps the focus is drug discovery
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and the utilization of machine learning for drug discovery.
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So you mentioned that, quote,
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we're really interested in creating
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what you might call a disease in a dish model,
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disease in a dish models,
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places where diseases are complex,
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where we really haven't had a good model system,
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where typical animal models that have been used for years,
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including testing on mice, just aren't very effective.
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So can you try to describe what is an animal model
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and what is a disease in a dish model?
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So an animal model for disease
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is where you create effectively,
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it's what it sounds like.
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It's oftentimes a mouse where we have introduced
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some external perturbation that creates the disease
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and then we cure that disease.
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And the hope is that by doing that,
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we will cure a similar disease in the human.
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The problem is that oftentimes
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the way in which we generate the disease in the animal
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has nothing to do with how that disease
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actually comes about in a human.
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It's what you might think of as a copy of the phenotype,
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a copy of the clinical outcome,
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but the mechanisms are quite different.
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And so curing the disease in the animal,
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which in most cases doesn't happen naturally,
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mice don't get Alzheimer's, they don't get diabetes,
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they don't get atherosclerosis,
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they don't get autism or schizophrenia.
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Those cures don't translate over
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to what happens in the human.
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And that's where most drugs fails
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just because the findings that we had in the mouse
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don't translate to a human.
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The disease in the dish models is a fairly new approach.
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It's been enabled by technologies
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that have not existed for more than five to 10 years.
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So for instance, the ability for us to take a cell
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from any one of us, you or me,
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revert that say skin cell to what's called stem cell status,
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which is what's called the pluripotent cell
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that can then be differentiated
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into different types of cells.
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So from that pluripotent cell,
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one can create a Lex neuron or a Lex cardiomyocyte
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or a Lex hepatocyte that has your genetics,
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but that right cell type.
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And so if there's a genetic burden of disease
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that would manifest in that particular cell type,
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you might be able to see it by looking at those cells
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and saying, oh, that's what potentially sick cells
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look like versus healthy cells
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and then explore what kind of interventions
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might revert the unhealthy looking cell to a healthy cell.
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Now, of course, curing cells is not the same
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And so there's still potentially a translatability gap,
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but at least for diseases that are driven,
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say by human genetics and where the human genetics
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is what drives the cellular phenotype,
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there is some reason to hope that if we revert those cells
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in which the disease begins
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and where the disease is driven by genetics
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and we can revert that cell back to a healthy state,
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maybe that will help also revert
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the more global clinical phenotype.
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So that's really what we're hoping to do.
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That step, that backward step, I was reading about it,
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the Yamanaka factor.
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So it's like that reverse step back to stem cells.
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Honestly, before that happened,
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I think very few people would have predicted
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that to be possible.
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Can you maybe elaborate, is it actually possible?
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Like where, like how stable?
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So this result was maybe like,
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I don't know how many years ago,
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maybe 10 years ago was first demonstrated,
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something like that.
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Is this, how hard is this?
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Like how noisy is this backward step?
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It seems quite incredible and cool.
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It is, it is incredible and cool.
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It was much more, I think finicky and bespoke
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at the early stages when the discovery was first made.
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But at this point, it's become almost industrialized.
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There are what's called contract research organizations,
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vendors that will take a sample from a human
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and revert it back to stem cell status.
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And it works a very good fraction of the time.
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Now there are people who will ask,
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I think good questions.
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Is this really truly a stem cell or does it remember
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certain aspects of what,
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of changes that were made in the human beyond the genetics?
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It's passed as a skin cell, yeah.
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It's passed as a skin cell or it's passed
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in terms of exposures to different environmental factors
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So I think the consensus right now
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is that these are not always perfect
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and there is little bits and pieces of memory sometimes,
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but by and large, these are actually pretty good.
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So one of the key things,
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well, maybe you can correct me,
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but one of the useful things for machine learning
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is size, scale of data.
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How easy it is to do these kinds of reversals to stem cells
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and then disease in a dish models at scale.
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Is that a huge challenge or not?
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So the reversal is not as of this point
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something that can be done at the scale
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of tens of thousands or hundreds of thousands.
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I think total number of stem cells or IPS cells
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that are what's called induced pluripotent stem cells
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in the world I think is somewhere between five and 10,000
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Now again, that might not count things that exist
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in this or that academic center
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and they may add up to a bit more,
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but that's about the range.
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So it's not something that you could at this point
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generate IPS cells from a million people,
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but maybe you don't need to
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because maybe that background is enough
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because it can also be now perturbed in different ways.
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And some people have done really interesting experiments
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in for instance, taking cells from a healthy human
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and then introducing a mutation into it
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using one of the other miracle technologies
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that's emerged in the last decade
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which is CRISPR gene editing
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and introduced a mutation that is known to be pathogenic.
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And so you can now look at the healthy cells
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and the unhealthy cells, the one with the mutation
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and do a one on one comparison
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where everything else is held constant.
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And so you could really start to understand specifically
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what the mutation does at the cellular level.
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So the IPS cells are a great starting point
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and obviously more diversity is better
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because you also wanna capture ethnic background
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and how that affects things,
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but maybe you don't need one from every single patient
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with every single type of disease
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because we have other tools at our disposal.
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Well, how much difference is there between people
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I mentioned ethnic background in terms of IPS cells?
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So we're all like, it seems like these magical cells
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that can do to create anything
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between different populations, different people.
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Is there a lot of variability between cell cells?
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Well, first of all, there's the variability,
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that's driven simply by the fact
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that genetically we're different.
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So a stem cell that's derived from my genotype
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is gonna be different from a stem cell
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that's derived from your genotype.
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There's also some differences that have more to do with
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for whatever reason, some people's stem cells
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differentiate better than other people's stem cells.
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We don't entirely understand why.
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So there's certainly some differences there as well,
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but the fundamental difference
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and the one that we really care about and is a positive
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is that the fact that the genetics are different
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and therefore recapitulate my disease burden
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versus your disease burden.
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What's a disease burden?
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Well, a disease burden is just if you think,
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I mean, it's not a well defined mathematical term,
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although there are mathematical formulations of it.
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If you think about the fact that some of us are more likely
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to get a certain disease than others
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because we have more variations in our genome
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that are causative of the disease,
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maybe fewer that are protective of the disease.
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People have quantified that
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using what are called polygenic risk scores,
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which look at all of the variations
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in an individual person's genome
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and add them all up in terms of how much risk they confer
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for a particular disease.
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And then they've put people on a spectrum
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of their disease risk.
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And for certain diseases where we've been sufficiently
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powered to really understand the connection
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between the many, many small variations
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that give rise to an increased disease risk,
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there's some pretty significant differences
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in terms of the risk between the people,
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say at the highest decile of this polygenic risk score
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and the people at the lowest decile.
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Sometimes those differences are factor of 10 or 12 higher.
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So there's definitely a lot that our genetics
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contributes to disease risk, even if it's not
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by any stretch the full explanation.
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And from a machine learning perspective,
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there's signal there.
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There is definitely signal in the genetics
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and there's even more signal, we believe,
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in looking at the cells that are derived
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from those different genetics because in principle,
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you could say all the signal is there at the genetics level.
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So we don't need to look at the cells,
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but our understanding of the biology is so limited at this
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point than seeing what actually happens at the cellular
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level is a heck of a lot closer to the human clinical outcome
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than looking at the genetics directly.
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And so we can learn a lot more from it
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than we could by looking at genetics alone.
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So just to get a sense, I don't know if it's easy to do,
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but what kind of data is useful
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in this disease in a dish model?
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Like what's the source of raw data information?
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And also from my outsider's perspective,
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so biology and cells are squishy things.
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And then how do you connect the computer to that?
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Which sensory mechanisms, I guess.
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So that's another one of those revolutions
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that have happened in the last 10 years
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in that our ability to measure cells very quantitatively
link |
has also dramatically increased.
link |
So back when I started doing biology in the late 90s,
link |
early 2000s, that was the initial era
link |
where we started to measure biology
link |
in really quantitative ways using things like microarrays,
link |
where you would measure in a single experiment
link |
the activity level, what's called expression level
link |
of every gene in the genome in that sample.
link |
And that ability is what actually allowed us
link |
to even understand that there are molecular subtypes
link |
of diseases like cancer, where up until that point,
link |
it's like, oh, you have breast cancer.
link |
But then when we looked at the molecular data,
link |
it was clear that there's different subtypes
link |
of breast cancer that at the level of gene activity
link |
look completely different to each other.
link |
So that was the beginning of this process.
link |
Now we have the ability to measure individual cells
link |
in terms of their gene activity
link |
using what's called single cell RNA sequencing,
link |
which basically sequences the RNA,
link |
which is that activity level of different genes
link |
for every gene in the genome.
link |
And you could do that at single cell level.
link |
So that's an incredibly powerful way of measuring cells.
link |
I mean, you literally count the number of transcripts.
link |
So it really turns that squishy thing
link |
into something that's digital.
link |
Another tremendous data source that's emerged
link |
in the last few years is microscopy
link |
and specifically even super resolution microscopy,
link |
where you could use digital reconstruction
link |
to look at subcellular structures,
link |
sometimes even things that are below
link |
the diffraction limit of light
link |
by doing a sophisticated reconstruction.
link |
And again, that gives you a tremendous amount of information
link |
at the subcellular level.
link |
There's now more and more ways that amazing scientists
link |
out there are developing for getting new types
link |
of information from even single cells.
link |
And so that is a way of turning those squishy things
link |
into digital data.
link |
Into beautiful data sets.
link |
But so that data set then with machine learning tools
link |
allows you to maybe understand the developmental,
link |
like the mechanism of a particular disease.
link |
And if it's possible to sort of at a high level describe,
link |
how does that help lead to a drug discovery
link |
that can help prevent, reverse that mechanism?
link |
So I think there's different ways in which this data
link |
could potentially be used.
link |
Some people use it for scientific discovery
link |
and say, oh, look, we see this phenotype
link |
at the cellular level.
link |
So let's try and work our way backwards
link |
and think which genes might be involved in pathways
link |
that give rise to that.
link |
So that's a very sort of analytical method
link |
to sort of work our way backwards
link |
using our understanding of known biology.
link |
Some people use it in a somewhat more,
link |
sort of forward, if that was a backward,
link |
this would be forward, which is to say,
link |
okay, if I can perturb this gene,
link |
does it show a phenotype that is similar
link |
to what I see in disease patients?
link |
And so maybe that gene is actually causal of the disease.
link |
So that's a different way.
link |
And then there's what we do,
link |
which is basically to take that very large collection
link |
of data and use machine learning to uncover the patterns
link |
that emerge from it.
link |
So for instance, what are those subtypes
link |
that might be similar at the human clinical outcome,
link |
but quite distinct when you look at the molecular data?
link |
And then if we can identify such a subtype,
link |
are there interventions that if I apply it
link |
to cells that come from this subtype of the disease
link |
and you apply that intervention,
link |
it could be a drug or it could be a CRISPR gene intervention,
link |
does it revert the disease state
link |
to something that looks more like normal,
link |
happy, healthy cells?
link |
And so hopefully if you see that,
link |
that gives you a certain hope
link |
that that intervention will also have
link |
a meaningful clinical benefit to people.
link |
And there's obviously a bunch of things
link |
that you would wanna do after that to validate that,
link |
but it's a very different and much less hypothesis driven way
link |
of uncovering new potential interventions
link |
and might give rise to things that are not the same things
link |
that everyone else is already looking at.
link |
That's, I don't know, I'm just like to psychoanalyze
link |
my own feeling about our discussion currently.
link |
It's so exciting to talk about sort of a machine,
link |
fundamentally, well, something that's been turned
link |
into a machine learning problem
link |
and that says can have so much real world impact.
link |
That's how I feel too.
link |
That's kind of exciting because I'm so,
link |
most of my day is spent with data sets
link |
that I guess closer to the news groups.
link |
So this is a kind of, it just feels good to talk about.
link |
In fact, I almost don't wanna talk about machine learning.
link |
I wanna talk about the fundamentals of the data set,
link |
which is an exciting place to be.
link |
It's what gets me up in the morning.
link |
It's also what attracts a lot of the people
link |
who work at InCetro to InCetro
link |
because I think all of the,
link |
certainly all of our machine learning people
link |
are outstanding and could go get a job selling ads online
link |
or doing eCommerce or even self driving cars.
link |
But I think they would want, they come to us
link |
because they want to work on something
link |
that has more of an aspirational nature
link |
and can really benefit humanity.
link |
What, with these approaches, what do you hope,
link |
what kind of diseases can be helped?
link |
We mentioned Alzheimer's, schizophrenia, type 2 diabetes.
link |
Can you just describe the various kinds of diseases
link |
that this approach can help?
link |
Well, we don't know.
link |
And I try and be very cautious about making promises
link |
about some things that, oh, we will cure X.
link |
People make that promise.
link |
And I think it's, I tried to first deliver and then promise
link |
as opposed to the other way around.
link |
There are characteristics of a disease
link |
that make it more likely that this type of approach
link |
can potentially be helpful.
link |
So for instance, diseases that have
link |
a very strong genetic basis are ones
link |
that are more likely to manifest
link |
in a stem cell derived model.
link |
We would want the cellular models
link |
to be relatively reproducible and robust
link |
so that you could actually get enough of those cells
link |
and in a way that isn't very highly variable and noisy.
link |
You would want the disease to be relatively contained
link |
in one or a small number of cell types
link |
that you could actually create in an in vitro,
link |
in a dish setting.
link |
Whereas if it's something that's really broad and systemic
link |
and involves multiple cells
link |
that are in very distal parts of your body,
link |
putting that all in the dish is really challenging.
link |
So we want to focus on the ones
link |
that are most likely to be successful today
link |
with the hope, I think, that really smart bioengineers
link |
out there are developing better and better systems
link |
all the time so that diseases that might not be tractable
link |
today might be tractable in three years.
link |
So for instance, five years ago,
link |
these stem cell derived models didn't really exist.
link |
People were doing most of the work in cancer cells
link |
and cancer cells are very, very poor models
link |
of most human biology because they're,
link |
A, they were cancer to begin with
link |
and B, as you passage them and they proliferate in a dish,
link |
they become, because of the genomic instability,
link |
even less similar to human biology.
link |
Now we have these stem cell derived models.
link |
We have the capability to reasonably robustly,
link |
not quite at the right scale yet, but close,
link |
to derive what's called organoids,
link |
which are these teeny little sort of multicellular organ,
link |
sort of models of an organ system.
link |
So there's cerebral organoids and liver organoids
link |
and kidney organoids and.
link |
Yeah, brain organoids.
link |
It's possibly the coolest thing I've ever seen.
link |
Is that not like the coolest thing?
link |
And then I think on the horizon,
link |
we're starting to see things like connecting
link |
these organoids to each other
link |
so that you could actually start,
link |
and there's some really cool papers that start to do that
link |
where you can actually start to say,
link |
okay, can we do multi organ system stuff?
link |
There's many challenges to that.
link |
It's not easy by any stretch, but it might,
link |
I'm sure people will figure it out.
link |
And in three years or five years,
link |
there will be disease models that we could make
link |
for things that we can't make today.
link |
Yeah, and this conversation would seem almost outdated
link |
with the kind of scale that could be achieved
link |
in like three years.
link |
That would be so cool.
link |
So you've cofounded Coursera with Andrew Ng
link |
and were part of the whole MOOC revolution.
link |
So to jump topics a little bit,
link |
can you maybe tell the origin story of the history,
link |
the origin story of MOOCs, of Coursera,
link |
and in general, your teaching to huge audiences
link |
on a very sort of impactful topic of AI in general?
link |
So I think the origin story of MOOCs
link |
emanates from a number of efforts
link |
that occurred at Stanford University
link |
around the late 2000s
link |
where different individuals within Stanford,
link |
myself included, were getting really excited
link |
about the opportunities of using online technologies
link |
as a way of achieving both improved quality of teaching
link |
and also improved scale.
link |
And so Andrew, for instance,
link |
led the Stanford Engineering Everywhere,
link |
which was sort of an attempt to take 10 Stanford courses
link |
and put them online just as video lectures.
link |
I led an effort within Stanford to take some of the courses
link |
and really create a very different teaching model
link |
that broke those up into smaller units
link |
and had some of those embedded interactions and so on,
link |
which got a lot of support from university leaders
link |
because they felt like it was potentially a way
link |
of improving the quality of instruction at Stanford
link |
by moving to what's now called the flipped classroom model.
link |
And so those efforts eventually sort of started
link |
to interplay with each other
link |
and created a tremendous sense of excitement and energy
link |
within the Stanford community
link |
about the potential of online teaching
link |
and led in the fall of 2011
link |
to the launch of the first Stanford MOOCs.
link |
By the way, MOOCs, it's probably impossible
link |
that people don't know, but it's, I guess, massive.
link |
Open online courses. Open online courses.
link |
We did not come up with the acronym.
link |
I'm not particularly fond of the acronym,
link |
but it is what it is. It is what it is.
link |
Big bang is not a great term for the start of the universe,
link |
but it is what it is. Probably so.
link |
So anyway, so those courses launched in the fall of 2011,
link |
and there were, within a matter of weeks,
link |
with no real publicity campaign, just a New York Times article
link |
that went viral, about 100,000 students or more
link |
in each of those courses.
link |
And I remember this conversation that Andrew and I had.
link |
We were just like, wow, there's this real need here.
link |
And I think we both felt like, sure,
link |
we were accomplished academics and we could go back
link |
and go back to our labs, write more papers.
link |
But if we did that, then this wouldn't happen.
link |
And it seemed too important not to happen.
link |
And so we spent a fair bit of time debating,
link |
do we wanna do this as a Stanford effort,
link |
kind of building on what we'd started?
link |
Do we wanna do this as a for profit company?
link |
Do we wanna do this as a nonprofit?
link |
And we decided ultimately to do it as we did with Coursera.
link |
And so, you know, we started really operating
link |
as a company at the beginning of 2012.
link |
And the rest is history.
link |
But how did you, was that really surprising to you?
link |
How did you at that time and at this time
link |
make sense of this need for sort of global education
link |
you mentioned that you felt that, wow,
link |
the popularity indicates that there's a hunger
link |
for sort of globalization of learning.
link |
I think there is a hunger for learning that,
link |
you know, globalization is part of it,
link |
but I think it's just a hunger for learning.
link |
The world has changed in the last 50 years.
link |
It used to be that you finished college, you got a job,
link |
by and large, the skills that you learned in college
link |
were pretty much what got you through
link |
the rest of your job history.
link |
And yeah, you learn some stuff,
link |
but it wasn't a dramatic change.
link |
Today, we're in a world where the skills that you need
link |
for a lot of jobs, they didn't even exist
link |
when you went to college.
link |
And the jobs, and many of the jobs that existed
link |
when you went to college don't even exist today or are dying.
link |
So part of that is due to AI, but not only.
link |
And we need to find a way of keeping people,
link |
giving people access to the skills that they need today.
link |
And I think that's really what's driving
link |
a lot of this hunger.
link |
So I think if we even take a step back,
link |
for you, all of this started in trying to think
link |
of new ways to teach or to,
link |
new ways to sort of organize the material
link |
and present the material in a way
link |
that would help the education process, the pedagogy, yeah.
link |
So what have you learned about effective education
link |
from this process of playing,
link |
of experimenting with different ideas?
link |
So we learned a number of things.
link |
Some of which I think could translate back
link |
and have translated back effectively
link |
to how people teach on campus.
link |
And some of which I think are more specific
link |
to people who learn online,
link |
more sort of people who learn as part of their daily life.
link |
So we learned, for instance, very quickly
link |
that short is better.
link |
So people who are especially in the workforce
link |
can't do a 15 week semester long course.
link |
They just can't fit that into their lives.
link |
Sure, can you describe the shortness of what?
link |
The entirety, so every aspect,
link |
so the little lecture, the lecture's short,
link |
the course is short.
link |
We started out, the first online education efforts
link |
were actually MIT's OpenCourseWare initiatives.
link |
And that was recording of classroom lectures and,
link |
Hour and a half or something like that, yeah.
link |
And that didn't really work very well.
link |
I mean, some people benefit.
link |
I mean, of course they did,
link |
but it's not really a very palatable experience
link |
for someone who has a job and three kids
link |
and they need to run errands and such.
link |
They can't fit 15 weeks into their life
link |
and the hour and a half is really hard.
link |
So we learned very quickly.
link |
I mean, we started out with short video modules
link |
and over time we made them shorter
link |
because we realized that 15 minutes was still too long.
link |
If you wanna fit in when you're waiting in line
link |
for your kid's doctor's appointment,
link |
it's better if it's five to seven.
link |
We learned that 15 week courses don't work
link |
and you really wanna break this up into shorter units
link |
so that there is a natural completion point,
link |
gives people a sense of they're really close
link |
to finishing something meaningful.
link |
They can always come back and take part two and part three.
link |
We also learned that compressing the content works
link |
really well because if some people that pace works well
link |
and for others, they can always rewind and watch again.
link |
And so people have the ability
link |
to then learn at their own pace.
link |
And so that flexibility, the brevity and the flexibility
link |
are both things that we found to be very important.
link |
We learned that engagement during the content is important
link |
and the quicker you give people feedback,
link |
the more likely they are to be engaged.
link |
Hence the introduction of these,
link |
which we actually was an intuition that I had going in
link |
and was then validated using data
link |
that introducing some of these sort of little micro quizzes
link |
into the lectures really helps.
link |
Self graded as automatically graded assessments
link |
really helped too because it gives people feedback.
link |
See, there you are.
link |
So all of these are valuable.
link |
And then we learned a bunch of other things too.
link |
We did some really interesting experiments, for instance,
link |
on gender bias and how having a female role model
link |
as an instructor can change the balance of men to women
link |
in terms of, especially in STEM courses.
link |
And you could do that online by doing AB testing
link |
in ways that would be really difficult to go on campus.
link |
Oh, that's exciting.
link |
But so the shortness, the compression,
link |
I mean, that's actually, so that probably is true
link |
for all good editing is always just compressing the content,
link |
making it shorter.
link |
So that puts a lot of burden on the creator of the,
link |
the instructor and the creator of the educational content.
link |
Probably most lectures at MIT or Stanford
link |
could be five times shorter
link |
if the preparation was put enough.
link |
So maybe people might disagree with that,
link |
but like the Christmas, the clarity that a lot of the,
link |
like Coursera delivers is, how much effort does that take?
link |
So first of all, let me say that it's not clear
link |
that that crispness would work as effectively
link |
in a face to face setting
link |
because people need time to absorb the material.
link |
And so you need to at least pause
link |
and give people a chance to reflect and maybe practice.
link |
And that's what MOOCs do is that they give you
link |
these chunks of content and then ask you
link |
to practice with it.
link |
And that's where I think some of the newer pedagogy
link |
that people are adopting in face to face teaching
link |
that have to do with interactive learning and such
link |
can be really helpful.
link |
But both those approaches,
link |
whether you're doing that type of methodology
link |
in online teaching or in that flipped classroom,
link |
interactive teaching.
link |
What's that, sorry to pause, what's flipped classroom?
link |
Flipped classroom is a way in which online content
link |
is used to supplement face to face teaching
link |
where people watch the videos perhaps
link |
and do some of the exercises before coming to class.
link |
And then when they come to class,
link |
it's actually to do much deeper problem solving
link |
oftentimes in a group.
link |
But any one of those different pedagogies
link |
that are beyond just standing there and droning on
link |
in front of the classroom for an hour and 15 minutes
link |
require a heck of a lot more preparation.
link |
And so it's one of the challenges I think that people have
link |
that we had when trying to convince instructors
link |
to teach on Coursera.
link |
And it's part of the challenges that pedagogy experts
link |
on campus have in trying to get faculty
link |
to teach differently is that it's actually harder
link |
to teach that way than it is to stand there and drone.
link |
Do you think MOOCs will replace in person education
link |
or become the majority of in person of education
link |
of the way people learn in the future?
link |
Again, the future could be very far away,
link |
but where's the trend going do you think?
link |
So I think it's a nuanced and complicated answer.
link |
I don't think MOOCs will replace face to face teaching.
link |
I think learning is in many cases a social experience.
link |
And even at Coursera, we had people who naturally formed
link |
study groups, even when they didn't have to,
link |
to just come and talk to each other.
link |
And we found that that actually benefited their learning
link |
in very important ways.
link |
So there was more success among learners
link |
who had those study groups than among ones who didn't.
link |
So I don't think it's just gonna,
link |
oh, we're all gonna just suddenly learn online
link |
with a computer and no one else in the same way
link |
that recorded music has not replaced live concerts.
link |
But I do think that especially when you are thinking
link |
about continuing education, the stuff that people get
link |
when they're traditional,
link |
whatever high school, college education is done,
link |
and they yet have to maintain their level of expertise
link |
and skills in a rapidly changing world,
link |
I think people will consume more and more educational content
link |
in this online format because going back to school
link |
for formal education is not an option for most people.
link |
Briefly, it might be a difficult question to ask,
link |
but there's a lot of people fascinated
link |
by artificial intelligence, by machine learning,
link |
Is there a recommendation for the next year
link |
or for a lifelong journey of somebody interested in this?
link |
How do they begin?
link |
How do they enter that learning journey?
link |
I think the important thing is first to just get started.
link |
And there's plenty of online content that one can get
link |
for both the core foundations of mathematics
link |
and statistics and programming.
link |
And then from there to machine learning,
link |
I would encourage people not to skip
link |
to quickly pass the foundations
link |
because I find that there's a lot of people
link |
who learn machine learning, whether it's online
link |
or on campus without getting those foundations.
link |
And they basically just turn the crank on existing models
link |
in ways that A, don't allow for a lot of innovation
link |
and an adjustment to the problem at hand,
link |
but also B, are sometimes just wrong
link |
and they don't even realize that their application is wrong
link |
because there's artifacts that they haven't fully understood.
link |
So I think the foundations,
link |
machine learning is an important step.
link |
And then actually start solving problems,
link |
try and find someone to solve them with
link |
because especially at the beginning,
link |
it's useful to have someone to bounce ideas off
link |
and fix mistakes that you make
link |
and you can fix mistakes that they make,
link |
but then just find practical problems,
link |
whether it's in your workplace or if you don't have that,
link |
Kaggle competitions or such are a really great place
link |
to find interesting problems and just practice.
link |
Perhaps a bit of a romanticized question,
link |
but what idea in deep learning do you find,
link |
have you found in your journey the most beautiful
link |
or surprising or interesting?
link |
Perhaps not just deep learning,
link |
but AI in general, statistics.
link |
I'm gonna answer with two things.
link |
One would be the foundational concept of end to end training,
link |
which is that you start from the raw data
link |
and you train something that is not like a single piece,
link |
but rather towards the actual goal that you're looking to.
link |
From the raw data to the outcome,
link |
like no details in between.
link |
Well, not no details, but the fact that you,
link |
I mean, you could certainly introduce building blocks
link |
that were trained towards other tasks.
link |
I'm actually coming to that in my second half of the answer,
link |
but it doesn't have to be like a single monolithic blob
link |
Actually, I think that's not ideal,
link |
but rather the fact that at the end of the day,
link |
you can actually train something that goes all the way
link |
from the beginning to the end.
link |
And the other one that I find really compelling
link |
is the notion of learning a representation
link |
that in its turn, even if it was trained to another task,
link |
can potentially be used as a much more rapid starting point
link |
to solving a different task.
link |
And that's, I think, reminiscent
link |
of what makes people successful learners.
link |
It's something that is relatively new
link |
in the machine learning space.
link |
I think it's underutilized even relative
link |
to today's capabilities, but more and more
link |
of how do we learn sort of reusable representation?
link |
And so end to end and transfer learning.
link |
Is it surprising to you that neural networks
link |
are able to, in many cases, do these things?
link |
Is it maybe taken back to when you first would dive deep
link |
into neural networks or in general, even today,
link |
is it surprising that neural networks work at all
link |
and work wonderfully to do this kind of raw end to end
link |
and end to end learning and even transfer learning?
link |
I think I was surprised by how well
link |
when you have large enough amounts of data,
link |
it's possible to find a meaningful representation
link |
in what is an exceedingly high dimensional space.
link |
And so I find that to be really exciting
link |
and people are still working on the math for that.
link |
There's more papers on that every year.
link |
And I think it would be really cool
link |
if we figured that out, but that to me was a surprise
link |
because in the early days when I was starting my way
link |
in machine learning and the data sets were rather small,
link |
I think we believed, I believed that you needed
link |
to have a much more constrained
link |
and knowledge rich search space
link |
to really make, to really get to a meaningful answer.
link |
And I think it was true at the time.
link |
What I think is still a question
link |
is will a completely knowledge free approach
link |
where there's no prior knowledge going
link |
into the construction of the model,
link |
is that gonna be the solution or not?
link |
It's not actually the solution today
link |
in the sense that the architecture of a convolutional
link |
neural network that's used for images
link |
is actually quite different
link |
to the type of network that's used for language
link |
and yet different from the one that's used for speech
link |
or biology or any other application.
link |
There's still some insight that goes
link |
into the structure of the network
link |
to get the right performance.
link |
Will you be able to come up
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with a universal learning machine?
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I wonder if there's always has to be some insight
link |
injected somewhere or whether it can converge.
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So you've done a lot of interesting work
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with probabilistic graphical models in general,
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Bayesian deep learning and so on.
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Can you maybe speak high level,
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how can learning systems deal with uncertainty?
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One of the limitations I think of a lot
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of machine learning models is that
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they come up with an answer
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and you don't know how much you can believe that answer.
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And oftentimes the answer is actually
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quite poorly calibrated relative to its uncertainties.
link |
Even if you look at where the confidence
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that comes out of say the neural network at the end,
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and you ask how much more likely
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is an answer of 0.8 versus 0.9,
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it's not really in any way calibrated
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to the actual reliability of that network
link |
and how true it is.
link |
And the further away you move from the training data,
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the more, not only the more wrong the network is,
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often it's more wrong and more confident
link |
in its wrong answer.
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And that is a serious issue in a lot of application areas.
link |
So when you think for instance,
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about medical diagnosis as being maybe an epitome
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of how problematic this can be,
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if you were training your network
link |
on a certain set of patients
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and a certain patient population,
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and I have a patient that is an outlier
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and there's no human that looks at this,
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and that patient is put into a neural network
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and your network not only gives
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a completely incorrect diagnosis,
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but is supremely confident
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in its wrong answer, you could kill people.
link |
So I think creating more of an understanding
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of how do you produce networks
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that are calibrated in their uncertainty
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and can also say, you know what, I give up.
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I don't know what to say about this particular data instance
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because I've never seen something
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that's sufficiently like it before.
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I think it's going to be really important
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in mission critical applications,
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especially ones where human life is at stake
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and that includes medical applications,
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but it also includes automated driving
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because you'd want the network to be able to say,
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you know what, I have no idea what this blob is
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that I'm seeing in the middle of the road.
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So I'm just going to stop
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because I don't want to potentially run over a pedestrian
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that I don't recognize.
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Is there good mechanisms, ideas of how to allow
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learning systems to provide that uncertainty
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along with their predictions?
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Certainly people have come up with mechanisms
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that involve Bayesian deep learning,
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deep learning that involves Gaussian processes.
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I mean, there's a slew of different approaches
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that people have come up with.
link |
There's methods that use ensembles of networks
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trained with different subsets of data
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or different random starting points.
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Those are actually sometimes surprisingly good
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at creating a sort of set of how confident
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or not you are in your answer.
link |
It's very much an area of open research.
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Let's cautiously venture back into the land of philosophy
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and speaking of AI systems providing uncertainty,
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somebody like Stuart Russell believes
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that as we create more and more intelligence systems,
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it's really important for them to be full of self doubt
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because if they're given more and more power,
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we want the way to maintain human control
link |
over AI systems or human supervision, which is true.
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Like you just mentioned with autonomous vehicles,
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it's really important to get human supervision
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when the car is not sure because if it's really confident
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in cases when it can get in trouble,
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it's gonna be really problematic.
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So let me ask about sort of the questions of AGI
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and human level intelligence.
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I mean, we've talked about curing diseases,
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which is sort of fundamental thing
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we can have an impact today,
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but AI people also dream of both understanding
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and creating intelligence.
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Is that something you think about?
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Is that something you dream about?
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Is that something you think is within our reach
link |
to be thinking about as computer scientists?
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Well, boy, let me tease apart different parts
link |
The worst question.
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Yeah, it's a multi part question.
link |
So let me start with the feasibility of AGI.
link |
Then I'll talk about the timelines a little bit
link |
and then talk about, well, what controls does one need
link |
when thinking about protections in the AI space?
link |
So, I think AGI obviously is a longstanding dream
link |
that even our early pioneers in the space had,
link |
the Turing test and so on
link |
are the earliest discussions of that.
link |
We're obviously closer than we were 70 or so years ago,
link |
but I think it's still very far away.
link |
I think machine learning algorithms today
link |
are really exquisitely good pattern recognizers
link |
in very specific problem domains
link |
where they have seen enough training data
link |
to make good predictions.
link |
You take a machine learning algorithm
link |
and you move it to a slightly different version
link |
of even that same problem, far less one that's different
link |
and it will just completely choke.
link |
So I think we're nowhere close to the versatility
link |
and flexibility of even a human toddler
link |
in terms of their ability to context switch
link |
and solve different problems
link |
using a single knowledge base, single brain.
link |
So am I desperately worried about
link |
the machines taking over the universe
link |
and starting to kill people
link |
because they want to have more power?
link |
Well, so to pause on that,
link |
so you kind of intuited that super intelligence
link |
is a very difficult thing to achieve.
link |
Even intelligence.
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Intelligence, intelligence.
link |
Super intelligence, we're not even close to intelligence.
link |
Even just the greater abilities of generalization
link |
of our current systems.
link |
But we haven't answered all the parts
link |
and we'll take another.
link |
I'm getting to the second part.
link |
Okay, but maybe another tangent you can also pick up
link |
is can we get in trouble with much dumber systems?
link |
Yes, and that is exactly where I was going.
link |
So just to wrap up on the threats of AGI,
link |
I think that it seems to me a little early today
link |
to figure out protections against a human level
link |
or superhuman level intelligence
link |
where we don't even see the skeleton
link |
of what that would look like.
link |
So it seems that it's very speculative
link |
on how to protect against that.
link |
But we can definitely and have gotten into trouble
link |
on much dumber systems.
link |
And a lot of that has to do with the fact
link |
that the systems that we're building are increasingly
link |
complex, increasingly poorly understood.
link |
And there's ripple effects that are unpredictable
link |
in changing little things that can have dramatic consequences
link |
And by the way, that's not unique to artificial intelligence.
link |
I think artificial intelligence exacerbates that,
link |
brings it to a new level.
link |
But heck, our electric grid is really complicated.
link |
The software that runs our financial markets
link |
is really complicated.
link |
And we've seen those ripple effects translate
link |
to dramatic negative consequences,
link |
like for instance, financial crashes that have to do
link |
with feedback loops that we didn't anticipate.
link |
So I think that's an issue that we need to be thoughtful
link |
about in many places,
link |
artificial intelligence being one of them.
link |
And I think it's really important that people are thinking
link |
about ways in which we can have better interpretability
link |
of systems, better tests for, for instance,
link |
measuring the extent to which a machine learning system
link |
that was trained in one set of circumstances,
link |
how well does it actually work
link |
in a very different set of circumstances
link |
where you might say, for instance,
link |
well, I'm not gonna be able to test my automated vehicle
link |
in every possible city, village,
link |
weather condition and so on.
link |
But if you trained it on this set of conditions
link |
and then tested it on 50 or a hundred others
link |
that were quite different from the ones
link |
that you trained it on and it worked,
link |
then that gives you confidence that the next 50
link |
that you didn't test it on might also work.
link |
So effectively it's testing for generalizability.
link |
So I think there's ways that we should be
link |
constantly thinking about to validate the robustness
link |
I think it's very different from the let's make sure
link |
robots don't take over the world.
link |
And then the other place where I think we have a threat,
link |
which is also important for us to think about
link |
is the extent to which technology can be abused.
link |
So like any really powerful technology,
link |
machine learning can be very much used badly
link |
as well as to good.
link |
And that goes back to many other technologies
link |
that have come up with when people invented
link |
projectile missiles and it turned into guns
link |
and people invented nuclear power
link |
and it turned into nuclear bombs.
link |
And I think honestly, I would say that to me,
link |
gene editing and CRISPR is at least as dangerous
link |
as technology if used badly than as machine learning.
link |
You could create really nasty viruses and such
link |
using gene editing that you would be really careful about.
link |
So anyway, that's something that we need
link |
to be really thoughtful about whenever we have
link |
any really powerful new technology.
link |
Yeah, and in the case of machine learning
link |
is adversarial machine learning.
link |
So all the kinds of attacks like security almost threats
link |
and there's a social engineering
link |
with machine learning algorithms.
link |
And there's face recognition and big brother is watching you
link |
and there's the killer drones that can potentially go
link |
and targeted execution of people in a different country.
link |
One can argue that bombs are not necessarily
link |
that much better, but people wanna kill someone,
link |
they'll find a way to do it.
link |
So in general, if you look at trends in the data,
link |
there's less wars, there's less violence,
link |
there's more human rights.
link |
So we've been doing overall quite good as a human species.
link |
Are you optimistic?
link |
Surprisingly sometimes.
link |
Are you optimistic?
link |
Maybe another way to ask is do you think most people
link |
are good and fundamentally we tend towards a better world,
link |
which is underlying the question,
link |
will machine learning with gene editing
link |
ultimately land us somewhere good?
link |
Are you optimistic?
link |
I think by and large, I'm optimistic.
link |
I think that most people mean well,
link |
that doesn't mean that most people are altruistic do gooders,
link |
but I think most people mean well,
link |
but I think it's also really important for us as a society
link |
to create social norms where doing good
link |
and being perceived well by our peers
link |
are positively correlated.
link |
I mean, it's very easy to create dysfunctional norms
link |
in emotional societies.
link |
There's certainly multiple psychological experiments
link |
as well as sadly real world events
link |
where people have devolved to a world
link |
where being perceived well by your peers
link |
is correlated with really atrocious,
link |
often genocidal behaviors.
link |
So we really want to make sure
link |
that we maintain a set of social norms
link |
where people know that to be a successful member of society,
link |
you want to be doing good.
link |
And one of the things that I sometimes worry about
link |
is that some societies don't seem to necessarily
link |
be moving in the forward direction in that regard
link |
where it's not necessarily the case
link |
that being a good person
link |
is what makes you be perceived well by your peers.
link |
And I think that's a really important thing
link |
for us as a society to remember.
link |
It's really easy to degenerate back into a universe
link |
where it's okay to do really bad stuff
link |
and still have your peers think you're amazing.
link |
It's fun to ask a world class computer scientist
link |
and engineer a ridiculously philosophical question
link |
like what is the meaning of life?
link |
Let me ask, what gives your life meaning?
link |
Or what is the source of fulfillment, happiness,
link |
When we were starting Coursera in the fall of 2011,
link |
that was right around the time that Steve Jobs passed away.
link |
And so the media was full of various famous quotes
link |
that he uttered and one of them that really stuck with me
link |
because it resonated with stuff that I'd been feeling
link |
for even years before that is that our goal in life
link |
should be to make a dent in the universe.
link |
So I think that to me, what gives my life meaning
link |
is that I would hope that when I am lying there
link |
on my deathbed and looking at what I'd done in my life
link |
that I can point to ways in which I have left the world
link |
a better place than it was when I entered it.
link |
This is something I tell my kids all the time
link |
because I also think that the burden of that
link |
is much greater for those of us who were born to privilege.
link |
And in some ways I was, I mean, I wasn't born super wealthy
link |
or anything like that, but I grew up in an educated family
link |
with parents who loved me and took care of me
link |
and I had a chance at a great education
link |
and I always had enough to eat.
link |
So I was in many ways born to privilege
link |
more than the vast majority of humanity.
link |
And my kids I think are even more so born to privilege
link |
than I was fortunate enough to be.
link |
And I think it's really important that especially
link |
for those of us who have that opportunity
link |
that we use our lives to make the world a better place.
link |
I don't think there's a better way to end it.
link |
Daphne, it was an honor to talk to you.
link |
Thank you so much for talking today.
link |
Thanks for listening to this conversation
link |
with Daphne Koller and thank you
link |
to our presenting sponsor, Cash App.
link |
Please consider supporting the podcast
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link |
And now let me leave you with some words from Hippocrates,
link |
a physician from ancient Greece
link |
who's considered to be the father of medicine.
link |
Wherever the art of medicine is loved,
link |
there's also a love of humanity.
link |
Thank you for listening and hope to see you next time.