Well good morning everyone. And good morning Dennis. I know it's like 1:00 a.m. in London. So we thank you for your solidarity
and actually being jet lagged with us. I hope you're fully caffeinated room here in Sanford Florida.
I'm good. Dennis let me just start off with you know when we talk about a guy in deep mind this is all about understanding
the human brain and actually how it works. How far along are we in that journey. How much do we still not understand that our
brains. So the mind way the way we approach building artificial
intelligence is we try and take some inspiration from how the brain works and what we mean by inspiration we're not trying to
copy how the brain works but we're sort of looking at the architectures and the algorithms and the reps patients the brain
uses. And then we're trying to get some hints and clues about how the brain produces intelligence. And then we're trying to
sort of recreate those capabilities in our machines so that effectively our systems can think and learn for themselves. So
there's enough known about the brain that we can do that. But we of course there are many open questions still about how the
brain works but there's enough clues that it's useful for building artificial intelligence. Should I look at it in
percentage wise or are we like 5 percent understanding of our brain or is it like 30 percent.
I would say you know it's it's probably 20 to 30 percent. But it's you know actually I think once we build A.I. it will be
helpful as a tool to finish understanding the brain. But I think that 20 30 percent is still quite indicative and quite useful.
After all the brain the human brain is the only existence proof we have in the universe that we know of that kind of general
intelligence is possible. I want to talk about biology in a second but how much do we actually interact with A.I. in our day
to day lives without even realizing sometimes. Yeah that's that's right. I mean artificial intelligence is embedded all
around us now. Every time you use your mobile phone you look at maps to find where you're going. You speak to it with your
voice. That's ISE underpinning all of those everyday things now. What's right you know recommended that you watch next on on
Netflix or for a streaming service. All of that has aspects of the A.I. underlying those algorithms.
So you know we're talking artificial intelligence you know pretty much every every minute by minute in our daily lives.
I want to talk a little bit about biology. So your system Alpha Field is the solution to the protein folding system. A grand
challenge in biology for the past 50 years. So why did you choose that thing to work on. Why biology and why now.
So when we started Deep Mine back in 2010 I was not fashionable like it is now if that was very much the opposite was very
difficult even to try to raise some seed funding. And what we decided to do that she initially was to prove our algorithmic
ideas these ideas about building general learning systems that can learn from themselves directly from data and experience. We
started to prove that our own gains because very convenient as its kind of testing ground for our ideas. So we would be the
world champion at go famously with our program our Figo and Chess and then stock a video game called Stop Loss. A teacher
can call Starcraft. And the idea was ultimately always to eventually apply those algorithmic ideas to real world massive
real world challenges in both in commercial challenges but also in science. And Alpha Forward as you say was our first big
breakthrough in science where we're able to predict the 3D structure of a protein which goes a long way to determining what
function it does in life and in our in our bodies. And we're able to predict the 3D structure directly from its one
dimensional genetic sequence which normally would take years and years of very painstaking experimental work too far. And now we
can do it in a matter of seconds. So is it Norfolk Labs. It is also your new company with a mission to reimagine the entire
drug discovery process. What can it achieve that humans can't. Is it pace or is it actually new discoveries.
Well we think that you know our alpha fold is a kind of proof of concept that some of the things some of these aspects of biology
and biochemistry that used to be done painstakingly in the lab in the wet lab and take many many years and be very difficult
types of experiments. A lot of these things we think are ready to be tackled computationally or A.I. fast. And and you know we
think that if it's not just the protein structure the structure of a protein which of course is only one small aspect of what
you need to do to to discover a new drug or cure for a disease. There are many many other aspects that need to be need to be
cracked. But we actually think a lot of those components in that drug discovery process
are potentially amenable to computation. When I first methods and what we want to do with the new company is kind of
accelerate investment in that area and really push that as far as it will go. And I think it could be very exciting time in the
next five plus years where we might see a new kind of revolution in the way some of those steps are tackled. Much much more
quickly and in the end delivered cures far faster than than the kind of 10 years average that it takes today. Is that for the
actual drug development or finding the drug or is it also safety. Because that's usually the process that takes a longer.
So can you achieve that quicker to make sure that it's safe. And so maybe cut. Yes actually both those things. So. So part of it
is is identifying the right target then it's sort of you know designing the right compound the right chemical compound to bind
to that target and then also testing its properties. So making sure it's not toxic and has the right solubility and thermal
properties and so on. And we think that I could potentially predict many of those properties. So then when it goes to you
know to the point where it's going to technical trial then you have a much higher confidence and percentage chance of getting
through that current clinical trial. And of course that would extend it would also accelerate that whole discovery process as
well by potentially many years. And then as people who work in technology or like health tech often say about a culture clash
between software developers that are interactive want to go fast and maybe drug development which has more checks and balances.
How do you make sure that there's not a culture clash. Yeah. No. So I think of course there are many reasons. So for
Medicine Man and pharma to be like it is and obviously you know you have to carefully check all of your experiments before you
distribute anything. But I think there are many steps in in the early discovery process before you're anywhere close to getting
to markets or doing the clinical trials and and regulations that come with that and all those kinds of things where it's purely
sort of scientific in the lab. And it's those steps that I think we're looking to see if they can be accelerated and enhanced and
helped with very sophisticated deep computational and I methods much like our footfalls.
Google's previous format for a you know have struggled. I'm thinking of the partnership with Sanofi-Aventis fizzling in
2019. There was the contact lenses that can actually check whether we had diabetes or not. What will you do differently
with this project. This is a very different sort of project to other things.
Actually I think that's going on in farmer in general where we're really focusing on the computational side of things.
That's our expertise from deep mind. That's what the expertise of the new company ISE Labs will have is really doubling down on
this idea of modelling aspects of biology. How much of biology can we computationally model. Can we understand with information
systems. I think it is at its most fundamental level. Biology is an information processing system. You know you can obviously DNA
has often been called as a sort of code you know really like a code.
But I think many other aspects of biology our cells proteins and other things can actually best be understood as information
processing systems. And if that's the that's the right hunch then a lot of these systems might be multilevel and predictable
with machine learning methods. So it's a really different kind of approach to say voting devices or building wet labs and other
more traditional approaches to things like drug discovery and medicine. There are people like Deep Mind concerned about the
powerful A.I. that you're creating being controlled by a single entity. Do you feel comfortable with that. And what are the
checks and balances that that you want to make sure always in place.
Yeah we're very comfortable. I mean we've been now parts of the alphabet group for nearly eight years. We're proud of that. They
give us a ton of support when independently sort of run subsidiary. So we have our own roadmap our own research agenda
our own culture and all of that. And you know we're based in London and so we have a lot of freedom to operate how we think
best to sort of deliver our mission of solving intel projects and using it to advance science and benefiting humanity. And so
we're sort of laser focused on that and we sort of see as the best of all worlds really. So we get to still sort of be like a
nimble. I still regard us as a startup even though we're a dozen years in still have plenty to do in our mission but we're backed
by this really big entity supportive entity. And another thing is we have a lot of collaborations with Google on the commercial
side in terms of our technology being embedded now in hundreds of Google products. I mean pretty much any Google products you
use will have some aspects of deep mines technology under that under the hood. And in terms of sort of ethics questions and
other things we've always had the ethical use of A.I. as a fundamental building block of deep months in the very beginning.
We plan for success. So we thought even back in 2010 when no one else pretty much was working on A.I. we already envisaged how
powerful the technology could be. An extraordinary powerful technology with extra opportunities but also some risks that
come with that. And so we had that in mind in the beginning and we've embedded that in everything we do. You know we have
internal ethics committees and other things that check and look through
with the research is in partnership with them. Every research that we work on and deploy and Alpha Bank do that itself has
also publicly declared that that's what I principles which we were a big part of helping draft. And and obviously we subscribe
to this principle as well. Are there lessons to be learned actually from the social media industry about risks and
reputation and is regarding holding back. Are we do we fear it or are we curious about it.
Yeah I think you know if you look at the eye obviously it's a very different field from the from the social media
field and industry. But there are some lessons there which is I think we should take on board about building very powerful
technologies in perhaps building them with good intent in mind and and some good theories at the outset of what what they would
be capable of. But then when actually you realize your ambitions you also need to think at that point but before it
happens what the unintended consequence might be. And I think for some if some for powerful things like social media but also
like A.I. you know you need to think about that ahead of time. I don't
think it's a good idea to sort of tease an anachronism of like you know move fast and break things you know that's maybe
appropriate for things like games or apps. So you know things like that. But I think for when you're talking about powerful
new technologies and soft and powerful science I think a scientific method a scientific approach is sort of thoughtful
reflection thoughtful progress critiquing everything you do submitting your work to peer review
outside peer review which is obviously the norm scientific method. All of those things help science to be very robust and
why it works so brilliantly. And I think the scientific method is more the correct method and approach I think for building
these types of powerful technologies. How much can I actually contribute to clinic goals.
Yeah. So actually we I've just come back from COP 26. Many of you and I was talking there about the many ways I think I can
actually be a really big contributor to helping with the climate crisis. I think there's at least three ways. One is optimizing
existing infrastructure. So we did that already at Google where we reduced the amount of cooling
the energy the cooling systems within the Google data center is used by about 30 percent. We've now generalized that to and is
available on to account for all building controls. And we're running a bunch of pilots and it looks like we can save 20 to 30
percent of the energy used by an office building three more efficiently controlling the air conditioning and heating and so
on. So you know that's could be huge if you think about rolling that out globally. It's about 6 percent of the world's emissions
are building office emissions. So I think I could optimize many existing infrastructure which would almost be for free because
it's just software on top of existing infrastructure. Secondly I think it's like better making better predictive models the
climate and the weather. We've just built a rainfall prediction system that's better than anything the UK met office has. And
then thirdly it would be accelerating new technologies. So things like material science and
also protein. What what's we on protein could help potentially and things even like fusion and plasma containment we've worked
on. So I think all of those three areas could be accelerated by and is what can contribute most of the acceleration of A.I.
adoption and capability. And I don't know whether for example for Alpha Folds or is a more fit labs you have a timeframe on
which you want to you know solve certain drug situations. Yeah we always have like we always have sort of near-term plans
medium plans and then very long term plans we've had. That's when they're getting we had a kind of 20 year plan when we
started with the mileage. About halfway through now. And I'm saying that ISE thick with ice with wet alfalfa. We've already
open source that and released it free for any use commercial or academic because we felt that was actually the quickest way to
get the maximum benefit for the scientific community and humanity.
And we actually partnered up with the European Biomass Institute based in Cambridge and they built the best database tools for
the biological research community. And we hosted all the predictions for all 20000 proteins in the human body. So that's
the human proteomics code is sort of equivalent to the human genome. But with proteins and that's all available along with 20
other model organisms. So over every 300 structure predictions are on that database. And we've been really amazed and
pleasantly sort of surprised by how quickly the entire biological research community has adopted that. Three hundred
thousand scientists have already access the database which I think it is. I think there's only about 300000 biological
researchers in the world. So perhaps every single one of them has already looked up their favorite protein in our database. So
it's amazing to see that kind of impact. And then ISE is going to sort of look more carefully specifically a drug discovery
process. So things like small molecule design binding affinities small directly to do with the drug discovery process for example
fold was more about the fundamental biological research questions and that was how a final question. How does I first of
all fit in the metaverse and what's your utopia in five years. Yeah. So I either you know metaverse is actually a funny thing
because I feel it's come full circle with the best of us. I used to started my career before the mind designing computer games
and writing a ISE for computer games. And obviously the metaverse is a sort of next evolution of gaming in some ways. I
think I've got a big place to play that in terms of creating compelling avatars or characters within the metaverse world that
you can converse with and interact with and perhaps advance stories and other things with. So I think actually there's a
there's a huge amount that A.I. is gonna bring to the metaverse. And ultimately you know I hope my hope for ISE that we'll we'll
use it to over the next decade. We'll start seeing it really materially contributing to some of the biggest grand challenges
facing us as a society from disease to planet demons. Thank you so much for joining us today. Thank you. Thanks for
having me.