Google Research Head Yossi Matias: AI For Cancer Research, Quantum's Progress, Researchers' Future

Channel: Alex Kantrowitz

Published at: 2025-10-28

YouTube video id: rnq9kQCclvo

Source: https://www.youtube.com/watch?v=rnq9kQCclvo

Hey everyone, I'm Alex Caneritz. I'm the
host of Big Technology Podcast and I'm
thrilled to be here for a conversation
with the head of Google research, Yosi
Matias, about the future of research and
how it intersects with product. Yosi,
great to see you.
>> Well, thanks for being here, Alex.
>> Definitely. So, there's been a lot of
noise in the AI world recently. A lot of
noise. uh but recently Google has come
up with a hypothesis about cancer cell
behavior with an LLM uh that was then
proven out in a living cell. So can you
talk a little bit about the significance
of this and how it came about? Is this
the beginning of generative AI being
used to potentially cure cancer or was
it lucky? What should we think about it?
Yeah, first I think that obviously the
we see the progress on AI is
transformative and one of the areas that
AI can probably do more impact than
anything is a healthcare because
healthcare is really about information
based kind of science. Now when you
bring together disciplines then
obviously you unlock new opportunities
and with AI models generative AI we now
have better understanding to understand
patterns and by all means this is one is
in a sequence of a lot of research work
and um you know a lot of magic happens
with collaborations. So this one for
example on the cell to sentence is a
collaboration with DL researchers and uh
researchers from Google research and
Google deep mind looking into how to
leverage foundation models in
combination with the data that we have
on cells. So I think that it's a step
towards obviously some of the biggest
challenges that we have on healthcare.
Uh there's a lot of more work to do.
It's part of a journey. I mean we're
looking into how to use uh generative AI
models actually for a few years now. how
to adapt them to models, how to have
help them with diagnostics, how to
actually empower researchers with the
like of AI coscientist which if think
about it is really using AI agents to
help out sift through the information
and do the kind of work that in the past
only uh you know very sophisticated
people could do and now we can actually
unlock these opportunities and empower
the researchers to do to ask even bigger
questions.
>> Right? I did the reading. It seems like
uh what what happened with this model is
that it found a a no a substance that
hadn't been used to treat cancer cells.
They could basically get them to raise
their hands to the immune system which
is pretty amazing.
>> Yeah. If you think about it, there's so
much information there that we have yet
to unlock. Actually, in many cases, we
don't know what we don't know. That's
why the scientific process of looking
for hypothesis. Uh and again, by the
way, this is the the basic for
aoscientist which is about how to help
out with generate these hypothesis. And
um but when you think about uh projects
and efforts such as the cell to
sentence, it's really about how do we
actually leverage an AI AI on the cell
information in this case to actually
identify the kind of patterns that may
be hidden out there and again under the
assumption that you know there are hints
all over the place. One of our effort on
the scientific process is to uncover
identify these hints test them validate
them. This all takes a lot of effort and
time and AR is really empowering that
research and accelerates it.
>> Okay, so let's talk about quantum
briefly. Google uh this week had a
quantum breakthrough where the quantum
chip was able to do uh complete an
algorithm 13,000 times faster than a
traditional supercomput. It's one of
those headlines that we see all the time
about quantum. Maybe it's, you know, to
the public it seems more frequent than
it does when you're actually doing the
research, but we see like these
breakthrough headlines about quantum
frequently. And then when you ask, well,
how far away are we from quantum
computing? It's always 5 10 years uh
maybe longer. So, can you explain that
disconnect and how real uh we should
think quantum is today.
>> So, first quantum computing is a very
long-term quest, right? I mean, if you
[clears throat] look into some of the
basic research, a lot of that goes back
to the 80s. In fact, we're very thrilled
just recently to um have our very own
Michelle Devor recognized with his
colleagues John Clark and um and uh and
John Martinez uh with and being a Nobel
laureate for their work from the 80s and
and Michelle and colleagues are actually
working in our fabulous AI quantum lab
in actually building on some of those um
you know early scientific breakthroughs
and building what we believe is going to
a practical quantum computing. Now, of
course, it's a long-term effort unlike
many of the uh research efforts that
sometimes will take months or a few
years. This one really goes back. But,
you know, back in um uh 2018 12, we
actually started we actually decided
that this is time to invest in that and
we have a very steady progress on very
measurable timeline and uh very clear
milestones. So, and of course everything
is validated. This announcement of
yesterday is a paper in nature that um
that actually shows the first verifiable
practical application advantage of a
quantum computer over classical
computer. And if you think about it,
this unlocks potential opportunities uh
future opportunities on better
understanding of molecules in so many
different applications. So we see a
steady state. Obviously there's a lot of
more work to be done. The important
thing is actually to make sure that
we're having these milestones and I'm
quite optimistic that we are going to
see these real life applications in the
in the framework of about five years.
>> Right. Uh can I ask you briefly how does
quantum change the world if it works?
Well, the fact that we are going to be
able to ask question and get answers on
the kind of u you know information that
is practically out of reach today that's
going to be material change because it's
better understanding of the materials of
molecules. Um, and it's also going to
accelerate AI itself because suddenly
we're actually going to have more, you
know, if you think about it, AI today is
built on knowledge that we accumulate
and uh and build with computation and
then we take it and build the models
based on that. Now, just imagine that
now you're going to have the capability
to create new insights into um into the
world that can then be fed and amplified
with AI. So I think it's going to be
material change um no pun intended and
um
exciting thing about research and about
this domain as well is that a lot of the
important things which are going to
happen we're not even aware of because
once you uncover opportunity suddenly it
creates the kind of thing that perhaps
you did not anticipate right I mean
think about AI and what we can do today
that for many of us seemed like science
fiction just a few years ago and it's
just accelerating so quantum is going to
open up more and think about the world
where we're going to have many more
smart people actually working on that.
That's going to open up new insights,
new novelty, new innovation, and I'm
sure new worldwide world impact.
>> So, you're of the belief that if you
bring product and research closer
together, you actually end up getting
more research breakthroughs faster.
>> One thing that so first I'm I'm kind of
both excited about deep research and
intellectual curiosity and scientific
research as well. I'm a product guy in
the Google. I was actually over a decade
on search leadership working uh you know
actually leading autocomplete in search
and sports experience and trends and and
so forth. So on the other hand of course
today especially today it was always the
case that research is a driver for
everything that we do. Um but today it's
more than ever because when you think
about innovation a lot of it is built on
unlocking capabilities that uh we should
actually solve the research problem and
then it goes back this goes to what I'm
really excited about which I call the
magic cycle of research something I
always was excited about in fact even as
a you know early on my career when I was
in Bellabs in their heydays um my most
theoretical research was motivated by
real world examples and then actually
taking the results and applying them
back was to me the most fascinating
aspect. Today, that's what we do all the
time because all of our research
projects and efforts are motivated by
problems in the real world that if we
solve it, it would actually unlock
opportunities. Some of them longer term,
some of them would take years, many of
them are actually within months. Now
this magic cycle is about how to drive
breakthrough research motivated by real
world problems then solving the problem
the research problem quite often
publishing it you know that's why it's
so important to actually have the
validation the peer-reviewed and
everything
>> that's good
>> and then taking it back to applying it
back to real world applications on
products on businesses on science and
society and this generates the next
questions now this cycle one of the
magical things about Google research is
that we are actually working through the
entire cycle. And the same team quite
often that actually um had the
breakthrough research is the team that
would actually then bring it together
with product teams and others partners
to actually reality and go back to the
next big questions and accelerate that.
>> But but let me ask you isn't there a
danger of bringing product and research
too close? I mean you could have the
researchers motivated to get into the
product cycle and product oftentimes
it's evaluated by growth quarter to
quarter and you really want a long-term
focus on research. So how do you think
about that?
>> Well first it's true that in any
development environment one of the
important things is to have this balance
between what you need to do tomorrow and
how to invest in the future. the
innovation cycle right I mean innovation
dilemma in product development and
businesses of course is well known
research is no different in the sense
that we need to manage those priorities
all the time so it's a judgment call
when is the time to actually focus on
the breakthrough and quite often it's
for the long term quite often actually
you don't exactly know how it's going to
be applied you actually know that this
is an important thing right you know
that well if you if I can make LLMs more
efficient I know it's going to be
important if I can actually have better
prediction for floods. Oh, there's going
to be a way for me actually to bring it
to reality or if I'm going to have
better understanding of healthcare or
genomes, there's a way to do that. Then
you when you work with the product
teams, one important thing of course is
to know how to do that in an effective
way. And by the way, quite often people
are so excited about actually bring it
to reality that I need sometimes to say,
hey, it's time actually to go back to
the next question, right? and uh because
both product and research are so
exciting and having the right timing and
the right judgment is always one of the
you know decisions we need to do.
>> So we've talked before and one of the
things that you brought up to me was
something kind of counterintuitive
because we hear or maybe not surprising
to me we hear these terms tossed out
invention, innovation, research,
breakthrough, breakthrough. Uh but you
think there's a real difference between
an actual breakthrough and what
innovation is. So can you just describe
a little bit about what the difference
between innovation and a breakthrough
is?
>> Well, first innovation is something that
uh we're doing all the time. We should
do that uh on on product development on
uh on the next generation of what we're
going to build. I think that innovation
is actually accelerating around the
world with new capabilities. When I
think about research breakthroughs, this
is about problems that currently we
don't know how to solve in principle and
we need to somehow make this dent. Now
sometimes some of the applied research
is actually to bring together things
that are known. Innovation is something
that we apply both on product but also
on the research itself because asking
the right questions is one of the most
important thing in any research. But
also I mentioned earlier the magic
cycle. When you think about the magic
cycle, it's not uh you know, I don't
like the term technology transfer
because life is never you build
something, oh, let's transfer it and
make it in use. It's always this cycle.
It's always this making the judgment
call. What how can I take what I've
already built and see and test it and
have a pilot or test it out and then ask
the next question. So this I think this
is part of the innovation applied to the
magic cycle itself and some of the
innovation is really understanding that
oh if this capability is unlocked with
research this opens up all these new
opportunities. I mean think about
conversational AI right some of it is
really about uh early on it was asking
can I actually have a conversation and
then the next one how can I actually use
it and then it brings back to the
question of what is actually the
capabilities that I need to drive here
and building on that and it's really a
combination of both research and
innovation in this case
>> so how important then is the long-term
research that is detached from the need
to innovate right now
>> first no research is detached research
again as I mentioned the best research
is research that is motivated by either
a need that you already know or by
exploring the art of the possible and
when you think about exploring the art
of the possible it's motivated by saying
we know if I manage to solve it that is
going to unlock things that are actually
going to be meaningful for my business
for my products for capabilities so it's
always connected to your question the
importance of long-term research is
greater than is is more than ever. And
here's why. We are actually when you
think about our job is really to drive
breakthrough research that is going to
be transformative
that could enable actually products and
capabilities and experience and science
and uh all societal challenges to
actually be solved in a way that is
materially better than we can do today.
Now some of it is something you can
actually innovate and find the kind of
uh the shorter term research. A lot of
it is really to find entirely new
paradigms to think about. I mean, think
about the transformers that you know
developed by Google research back in
2017. It was a new paradigm that once
done it actually created a lot of the
industry or thinking about some of the
work we're doing on genomics or quantum.
Quantum of course is a very long term as
as we know. So in many areas actually I
can see this um combination of things
that are we can do that very quickly
because with breakthroughs and research
and we can um you know have a new
algorithm and then apply it very
quickly. Speculative decoding is a great
example. You know, once we had the right
insights, we could very quickly actually
apply it and then it got its own kind of
um impact across the industry in
industry standard uh as well and many
variations and there are things that you
need to actually think through new
architectures, new capabilities, new
ways in which to do generative AI or
healthcare or earth AI for example that
is built on years of actually research.
When you think about it, Earth AI is
about taking all our geospatial models
that we developed over the years to
tackle various problems and take those
state-of-the-art problems with a lot of
other models that we developed over the
years. Then leverage machine uh
generative AI on top of that and enable
anybody to ask any question about Earth
and planet in plain language and
suddenly get the result which actually
is based on combination of all these
models. Now if you think about it, this
is a long-term research that is based on
various components that each of them was
a pretty long-term research itself,
right? I mean a work on flood
forecasting started in 2017. Now we have
a global model serving two billion
people. Two billion people in 150
countries. It took us years of of of you
know magic cycle iterations to get
there. And now this comes with other
models such as storms um you know
weather now casting population dynamics
etc. along with agentic layer of AI to
actually enable and unlock new
opportunities. If you think about this
dynamics of this to get to the point
that now businesses, organizations
um can actually use it to solve their
problems, uh it actually was a pretty
long cycle, but there were many
milestones in between. So I'm a great
believer that in many cases you take a
very long-term vision on something that
looks very audacious but then you
actually unpack it into tangible
milestones. Some of them are research
milestones, some of them are product
milestones that actually helps you get
into that kind of uh you know what
you're trying to get into this uh
mountain that you try to climb.
>> All right. So let's take it on a
practical level now. I mean you're very
close to what's happening in generative
AI. You're looking at the latest
breakthrough research. Where is the next
breakthrough coming from?
>> Beautiful thing about research is that
it's really exploring in many cases
exploring the unknown. And one thing
that we are all need to be very humbled
about is that in any given moment we
don't know what we don't know. And the
exciting thing is to actually explore
that terrain. Of course, it does it's
not at random. We don't don't just try
to bump into opportunities. We try to be
intentional about it. We try to take
some bets.
So the most exciting thing are the
things that we don't know yet. Now
obviously we want to look into new
architectures. We want to do new
insights. We want to be inspired by you
know a lot of what we do is really
inspired by the human brain and and
people and animals and how we see
behavior and we know there are gaps. We
know that certain you know people or
animals can do things much much more
efficient than we can do as humans. This
is actually a proof of existence. So in
research quite often what you do you
first want to understand that if
something is possible and I've yet to
see something that is not to be honest.
Uh and then if you know it's possible
the question is how do I get there and
what are the steps so I think there's a
lot that we're going to unlock to
uncover that we're not even aware of.
>> Briefly do you think the majority of
progress in generative AI is going to
come from algorithms or just more
compute?
>> I think it's going to be combination.
Obviously a lot of the um you know
progress that we've done we've seen
actually you know even going back the to
the early days of um I mean the the new
revolution of deep learning was taking
some ideas that were there before and
suddenly when you put enough computing
power and enough data suddenly it has a
phase transition in terms of utility and
what it can do so it's always a
combination I mean think about um we
discussed earlier about the cellto
sentence so a lot of the material and
knowledge ology is there but then when
you take a big model you put this 27
billion par you know parameter model out
there and you build on that suddenly it
unlocks new opportunities when you take
medgema and you put some capabilities on
medical information and suddenly you can
unlock new opportunities that you don't
know so some of it is about scale but
then there's a layer of reasoning that
we have for a coscientist for example
it's not only about doing the search out
there it's really about applying the
kind of reasoning that typically you'd
expect researchers to do which is to
form hypothesis to actually then go
through test ways of testing them and
then measuring them. So or think about
our work on empirical software to help
model building. You know when a lot in
the scientific process some of the um
biggest hurdles is really you have a
problem you want to build a model you
actually have a bunch of models just
testing and see what's the best and then
trying to get the the answers. It's very
tedious work with this empir AI based
empirical software that can actually
build and help you select the right
model for that it accelerates the entire
so obviously this combination of um not
only stronger models but more
intelligent models with better reasoning
and thinking as well as the power to do
that is one approach
>> right
>> on the other hand algorithmic innovation
and you know anybody who's been long
enough in research knows that there are
some problems that at some point
somebody comes with this innovation that
is an aha moment and oh I can actually
solve it in a way that was previously un
poss impossible think about the
transformers there are going to be more
algorithmic innovations that are going
to make breakthroughs um some of them
are already in the work and I'm really
excited when I look into some of the
work our teams are doing on algorithmic
innovation I'm excited about what I see
from you know the ecosystem the academic
community research communities and other
companies and uh but but I think the
best is yet to
So you're the head of Google research.
How do you convince researchers to work
on something that's not generative AI
related?
>> You know, when you ask yourself what
drives researchers, I would say it's a
combination of
um working on interesting problems that
you know, typically when you have a
problem that nobody could solve, that
makes it interesting, right? It's real.
Uh it's kind of a math Olympiad type of
of challenge. problems that matter that
could make a difference and the
intersection of finding a problem that
is um going to be both interesting
exciting from a research point of view
then something that could be applied and
have a big impact is really the
motivation this is the research cycle
that I was talking about this is the
motivation for for the brightest uh
researchers the thing is that we have
that across the board I mean think about
just announcements
today um quantum Think about genomics.
Think about Earth AI. Now each of them
may have some some of them may have some
strong generative AI component and
generative AI is an amazing technology
that also you know brings up some
exciting questions. I mentioned uh
research on factuality. I mentioned on
efficiency. Um but there are so many
other disciplines that and and
ultimately people are excited to work on
things that matter and can actually
apply their uh brilliance and innovation
and uh and have breakthrough research.
So um we're at no lack of uh such
important problems and opportunities and
again uh I'd like to give a shout out to
the amazing team at Google research
brilliant researchers and I when we
bring together talents looking into the
different disciplines you know bring
people who understand languages and
health and climate and quantum and we
bring them all together then a lot of
the magic happens and it's quite quite
amazing to see how people actually also
quite often uh move between disciplines
and bring their insights from one to
another. So I think again the um the
exciting part of Google of of being in
research today is also that the fact
that we have the full stack of research
we have AI infrastructures great models
worldclass research products that we can
actually be inspired by and then apply
to. So this alto together enables us to
actually get uh really exciting research
on many disciplines. Anything from
machine learning foundations and
algorithms into systems into quantum
into science into um you know um
applying to societal problems.
>> Okay, I got one last one for you before
we have to go. um
the cancer research. One of the cool
things about that, if I get it right,
was that the actual the model went
through all these different uh potential
treatments that hadn't been tried yet
and actually found one that would work
better than the ones that humans had
uncovered. Um obviously this technology,
generative AI technology is going to be
applied in research all across the
board. Do you anticipate that it's going
to lessen the need for researchers or
are we going to have more? Well, we're
going to need many more researchers in
all disciplines. I mean, think about
what's the role of a researcher. It's
really to build on what we can and ask
the right questions and and and build
for the next one. Now, the only
situation where you need less
researchers is if you assume that we
practically almost answered all the
questions that we need to have. I don't
think anybody here in the audience would
think that uh we are only understanding
tiny bit of what we need to understand.
In fact, the opportunity that we have
with AI to empower researchers is going
to give opportunity not only for more
researchers but for each of them to ask
bigger bigger question move faster on
the research agenda have better results.
I mean think about alphafold which you
know uh my colleagues were recognized
with Nobel prize uh deis and John um I
mean we don't have less researchers
working on proteins we have actually
have many more right uh but now they
don't need to work on the protein
folding problem they're actually using
it for bigger questions with AI
coscientist again think about the fact
that every grad student every postoc
have now their own research lab which
can help them with literature search
looking hypothesis. So now they're going
to ask bigger questions. They are going
to ask the kind of questions that
previously we expected only very senior
scientists to do and we can actually
accelerate the kind of scientific
process. Similarly in healthcare,
similarly in climate, similarly in
education.
I mean with AI there's an opportunity
for more teachers to be more effective,
do more effective work with more
students. And again where no lack of
opportunity to actually have the next
generation be educated in a better way.
In fact, one of the things that are most
important in my opinion is how do we
actually uh empower the next generation
because the innovation is going to come
from them to unlock many of the other
problems. So the way I think about it,
we're so early on in our ability to
understand science, to understand
healthcare, to understand the world in a
way, for example, in crisis, our
northstar is nobody should ever be
surprised from a natural disaster coming
their way. And by using AI and having
the experts using that, we can actually
get closer to that. On health care,
there's no reason why anybody should be
surprised by a disease that is hitting
them. Right? So there's so much more
work to do. And I think about it as AI
as an amplifier of human ingenuity. It
really empowers the scientists, the
healthcare workers, the teachers, the
business people in our everyday life.
And the more we're making advancements
with AI, then the more we can actually
expect all these professions to actually
to do to take on bigger missions, to do
bigger progress for the benefit of
humanity makes me really optimistic
about our role at research and in
technology in general to actually play a
role in actually making this
amplification of human ingenuity with
AI.
>> Yosi, thank you so much.
>> Thank you very much, Alex.
>> Thank you everyone. [applause]