>> Narrator: Live from
Stanford University,
it's theCUBE covering Stanford
Women in Data Science 2020,
brought to you by SiliconANGLE Media.
(inquisitive music)
>> Hi, and welcome to theCUBE.
I'm your host, Sonia Tagare,
and we're live at Stanford
University covering WiDS,
Women in Data Science
Conference, the fifth annual one.
And joining us today, is Daphne Koller,
who is the CEO and Founder of insitro.
Daphne, welcome to theCUBE.
>> Nice to be here, Sonia,
thank you for having me.
>> So tell us a little bit about insitro,
how you got it founded,
and more about your role.
>> So I've been working
in the intersection
of machine learning and biology and health
for quite a while.
And it was always a bit
of an interesting journey
in that the data sets were
quite small and limited.
We're now in a different
world where there is tools
that are allowing us to create
massive biological data sets
that I think can help us solve
really significant societal problems.
And one of those problems that
I think is really important
is drug discovery and development,
where, despite many
important advancements,
the costs just keep
going up and up and up.
And the question is can
we use machine learning
to solve that problem better.
>> And you talked about
this more in your keynote.
So give us a few highlights
of what you talked about.
>> So in the last, you can
think of drug discovery
and development in the last 50 to 70 years
as being a bit of a glass
half full, glass half empty.
The glass half full is the
fact that there is diseases
that used to be a death sentence,
or of sentence to a lifelong
of pain and suffering
that are now addressed by some
of the modern day medicines
and I think that's absolutely amazing.
The other side of it is that the cost
of developing new drugs has
been growing exponentially
in what's come to be known as Eroom's law,
being the inverse of Moore's law,
which is the one we're all familiar with,
because the number of drugs
approved per billion US dollars,
just keeps going down exponentially.
So the question is, can
we change that curve.
>> And you talk in your keynote
about the interdisciplinary culture,
so tell us more about that.
>> I think in order to address
some of the critical
problems that we're facing,
one needs to really build a culture
of people who work together
from different disciplines,
each bringing their own insights
and their own ideas into the mix.
So at insitro, we actually have a company
that's half life scientists,
many of whom are producing data
for the purpose of driving
machine learning models,
and the other half are
machine learning people
and data scientists who
are working on those,
but it's not a handoff where
one group produces the data
and the other one consumes
it, interprets it,
but really they start
from the very beginning
to understand what are the problems
that one could solve together,
how do you design the experiment,
how do you build the model,
and how do you drive insights from that,
that can help us make
better medicines for people.
>> And I also wanted to ask
you, you co-founded Coursera,
so tell us a little bit
more about that platform.
>> So I founded Coursera
as a result of work
that I'd been doing at Stanford,
working on how technology
can make education better
and more accessible.
This was a project that I did here,
a number of my colleagues as well,
and at some point in the fall of 2011,
there was an experiment of
let's take some of the content
that we've been developing within Stanford
and put it out there for
people to just benefit from,
and we didn't know what would happen.
Would it be a few thousand people?
But within a matter of weeks,
with minimal advertising,
other than one "New York
Times" article that went viral,
we had 100,000 people in
each of those courses.
And that was a moment in
time where we looked at this
and said, can we just go
back to writing more papers
or is there an incredible
opportunity to transform
access to education to
people all over the world,
and so I ended up taking
what was supposed to be
a two-year leave of absence from Stanford,
to go and co-found Coursera.
And I thought I'd go back after two years,
but at the end of that two-year period,
there was just so much more to be done
and so much more impact
that we could bring
to people all over the world,
people of both genders,
people of different socio-economic status,
every single country around the world,
I just felt like this was
something that I couldn't not do.
>> And why did you decide to
go from an educational platform
to then going into machine
learning and biomedicine?
>> So I've been doing Coursera
for about five years, in 2016,
and the company was on a great trajectory,
but it's primarily a content company.
And around me, machine learning
was transforming the world
and I wanted to come
back and be part of that.
And when I looked around,
I saw machine learning
being applied to e-commerce
and the natural language,
and to self-driving cars,
but there really wasn't a
lot of impact being made
on the life science
area and I wanted to be
part of making that happen,
partly because I felt like,
coming back to our earlier comment,
that in order to really have that impact,
you need to have someone
who speaks both languages.
And while there's a new
generation of researchers
who are bilingual in biology
and in machine learning,
there's still a small group in there,
very few of those in
kind of my age cohort,
and I thought that I would
be able to have a real impact
by building a company in this space.
>> So it sounds like your
background is pretty varied.
What advice would you give to women
who are just starting college now,
who may be interested
in this similar field,
would you tell them, they
have to major in math,
or do you think that maybe
there's some other majors
that may be influential as well?
>> I think there is a lot of
ways to get into data science.
Math is one of them, but there's
also statistics or physics.
And I would say that,
especially for the field
that I'm currently in,
which is at the intersection
of machine learning data
science on the one hand,
and biology and health on the other,
one can get there from
biology or medicine as well.
But what I think is
important is not to shy away
from the more mathematically
oriented courses
in whatever major you're in
because that foundation
is a really strong one.
There's a lot of people
out there who are basically
lightweight consumers of data science
and they don't really
understand how the methods
that they're deploying, how they work,
and that limits them in their
ability to advance the field
and come up with new methods
that are better suited, perhaps,
to the problems that they're tackling.
So I think it's totally fine, and in fact,
there's a lot of value to
coming into data science
from fields other than
math or computer science,
but I think taking
courses in those fields,
even while you're majoring in
whatever field you're interested in,
is going to make you a much better person
who lives at that intersection.
>> And how do you think
having a technology background
has helped you in founding your company,
as in has helped you
become a successful CEO?
>> In companies that are
very strongly R&D-focused,
like insitro and others,
having a technical co-founder
is absolutely essential
because it's fine to
have an understanding of
whatever the user needs and so on,
and come from the business side of it,
and a lot of companies
have a business co-founder,
but not understanding what the
technology can actually do,
is highly limiting because
you end up hallucinating,
oh, if we could only do this,
and that would be great but you can't,
and people end up oftentimes
making ridiculous promises
about what the technology
will or will not do
because they just don't
understand where the landmines sit
and where you're going to hit
real obstacles in the path.
So I think it's really important to have
a strong technical foundation
in these companies.
>> And that being said,
where do you see insitro in the future
and how do you see it solving, say, NASH,
that you talked about in your keynotes?
>> So we hope that insitro
will be a fully integrated
drug discovery and development company
that is based on a completely
different foundation
than the traditional pharma company
where they grew up in the old approach
of that is very much a
bespoke scientific analysis
of the biology of different diseases
and then going after targets
or ways of dealing with the disease
that are driven by human intuition.
Where I think we have the
opportunity to go today,
is to build a very data-driven approach
that collects massive amounts of data
and then let analysis of those data
really reveal new hypotheses
that might not be the ones
that accord with people's preconceptions
of what matters and what doesn't.
And so hopefully, we'll
be able to, over time,
create enough data and
apply machine learning
to address key bottlenecks
in the drug discovery
and development process,
so we can bring better drugs to people,
and we can do it faster and
hopefully, at much lower cost.
>> That's great.
And you also mentioned in your keynote
that you think that 2020 is
like a digital biology year,
so tell us more about that.
>> So I think if you take a
historical perspective on science
and think back, you realize
that there's periods in history
where one discipline
has made a tremendous amount of progress
in relatively short amount of time,
because of a new technology or
new way of looking at things,
in the 1870s, that
discipline was chemistry,
with the understanding
of the periodic table,
and that you actually
couldn't turn lead into gold.
In the 1900s, that was
physics, with understanding
the connection between matter and energy,
and between space and time.
In the 1950s, that was
computing where silicon chips
were suddenly able to perform calculations
that up until that point only
people had been able to do.
And then in 1990s,
there was an interesting
bifurcation though.
One was the era of data,
which is related to computing
but also involves elements of statistics
and optimization and neuroscience.
And the other one was quantitative biology
in which biology moved
from a descriptive science
of taxonomizing phenomena
to really probing and measuring
biology in a very detailed
and high throughput way using
techniques like microarrays
that measure the activity
of 20,000 genes at once,
or the human genome,
sequencing of the human genome,
and many others.
But these two fields kind
of evolved in parallel,
and what I think is coming
now, 30 years later,
is the convergence of those
two fields into one field
that I like to think of as digital biology
where we are able, using
the tools that have,
and continue to be developed,
measure biology in entirely
new levels of detail,
of fidelity, of scale,
we can use the techniques
of machine learning
and data science to
interpret what we're seeing
and then use some of the
technologies that are also emerging
to engineer biology to do things
that it otherwise wouldn't do.
And that will have
implications in biomaterials,
in energy, in the
environment, in agriculture,
and I think also in human health.
And it's an incredibly exciting
space to be in right now
because just so much is happening
and the opportunities
to make a difference,
and make the world a better
place are just so large.
>> That sounds awesome.
Daphne, thank you for your insight
and thank you for being on theCUBE.
>> Thank you.
>> I'm Sonia Tagare.
Thanks for watching, stay tuned for more.
>> Daphne: Great.
(inquisitive music)
