DeepMind’s Demis Hassabis on its breakthrough scientific discoveries | WIRED Live

i'm delighted this morning to be joined by dennis asabis the ceo and co-founder of deepmind dennis has led an absolutely fascinating career he's the former chess prodigy the recipients of a double first at the university of cambridge a five-time world mind sports olympiad champion an mit and harvard alumnus and a teenage entrepreneur entrepreneur today deepmind is one of the world's leading ai research companies and is best known for developing alphago the first program to be a world champion at go deepmind's published over a thousand research papers including more than a dozen in nature and science and achieved breakthrough results in many challenging ai domains demis welcome great to have you with us today thanks for coming in in person problem it's great to be here thank you so much so um last time we met we talked a lot about the way that deep mind is now moving into science and is now thinking about how it can impact how ai can impact scientific research um so i'm interested really can you just describe that sort of that move to us yeah well i mean it's always when we started deepmind and actually even before deepmind for me the ultimate vision of building ai was to try and use it as a tool to understand the world around us better that's what i was dreaming about when my teenage years when i first got into ai and i've been working towards that my whole career and obviously deep minded for the last 10 years and what's exciting now is that maybe we've got to the point finally where our algorithms are powerful enough and mature enough that we can actually apply it to big scientific challenges and maybe help accelerate scientific discovery so you know we're kind of most famous for our work on games things like alphago as you mentioned but really that was always just a proving ground for developing and testing and sort of um proving out these algorithms efficiently and then the idea was to transfer them to things like science and how do you think that scientific research is best organized in order to get optimal results well i think there's you know there's different ways to try and organize scientific research the main one academia uh which you know obviously i spent quite a lot of time in is mostly bottom up i would say so the creativity sort of bubbles up from phd students postdocs and so on um and it's kind of uh a creative chaos let's say um and then on the other hand you've got companies startups the best startups which are mostly top down and the great thing about those is they come with a lot of energy and pace and focus and intensity that you get with the best startups as you know well and i've always wondered why and i've been might be lucky enough to be in both worlds why you couldn't combine the best of those two worlds and have a kind of you know blue sky research group or division but with the same intensity you get from a startup kind of mentality and that's what we tried to do at deepmind so in academia generally you know you you get you hire the smartest or you you you you recruit the smartest people you possibly can you put them in a lab uh you uh say see you in five years for the best close the door and when you open it five years later maybe you have something maybe you don't yeah and uh you know it's not very coordinated so it's not that efficient in many ways because you as you say in in the top places top universities you have some of the smartest people in the world there so the ingredients are there and you give them time to think and so on but there's no sort of macro coordination between you know beyond the kind of small lab level like each lab is maybe coordinated but there's no coordination on a bigger level than that and in fact it's designed for there not to be yeah and so um you know it makes it hard to really go after massive breakthroughs in an intense way over multiple years especially if it's interdisciplinary yeah so there's some things like that that are quite hard to do actually in academia i would say i think the interdisciplinary bit's interesting because as far as i can understand what you're doing at deepmind is trying to build teams that have you know multiple sort of areas domains of expertise absolutely so obviously our core thing is machine learning yeah but we also have neuroscientists we have mathematicians physicists uh and um you know of course engineering yeah and um engineering is our kind of workbench if you like if you if you you know the engineering is it makes an empirical science and obviously we have some of the world's top engineers but in academia for example if you're working in computer science um there isn't actually weirdly there isn't a career path for engineers you have to be a research scientist and then you've got the you know traditional phd student postdoc and then you've got to try and make it to system prof but but but if you want to be a career engineer in academia there isn't really a sort of defined career path so you obviously end up losing some of the best engineers out of that um so there's many many sort of strange things like that that would be odd to somebody who's not used to academia like why does it work like that it's just kind of the way it's always worked and how do you get those individuals in those very who have deep expertise of a particular domain to kind of communicate with each other yeah a biologist isn't always going to be able to speak to a chemist or a mathematician or a computer scientist right that's the hard absolute hardest thing so first of all um you need to hire people with with curiosity yeah and also i would say a bit of humbleness because it it it takes some humility to approach someone else you're say you're a world expert in one of those domains right and then you're but you're you know relative beginner in these other domains and you just take some vulnerability and humility to go to someone else's super expert in the other domain and kind of you know explain you don't know that much about that when you're used to being the one that explains your area sure and i feel like that's one big reason why there aren't that many true interdisciplinary people because it's it it's quite hard to the ego to go and do that i mean i experienced that when i had a you know whole first career in computer science and then i went back to university to my phd in neuroscience and i was starting as a beginner again and and and you know sort of the bottom of the pile after after seven years of running my own games company and so it's quite a you know it takes a certain um sort of psyche to be able to deal with that and then on the other thing is what we look for actually at deep mind is and and i try and hire for is what i call sort of affectionately glue people which is people that really are sitting that intersection of one or more subject areas or disciplines and can do that translation spot the connections and then what you don't need everyone to be like that you just need a few people like that and who can who can operate at the level where they can understand and explain things to the other world-class experts in their more narrow domains and then make the connections for them yeah and so say ah you know you you should talk to this person really because what you're actually talking about is similar and i'm i'm that's the one of the things i do actually at deepmind is is to try and be a generalist and yeah and make those connections yeah i was about to say they're many glue people out there they must be quite hard to find they're really hard i would say we have you know maybe out of a thousand people like um a couple of dozen of those right so they're they're very rare yeah and it's rare because it's it's a bit like a decathlon sort of they it's hard it's it the whole way that academia is structured it's hard for those interdisciplinary people because usually when you get evaluated for a position or a grant normally you're you're judged by an expert panel in your nominal subject and they don't care or know about these other extraneous things that you know about which is interdisciplinary part so you sort of have to compete on the narrow domain with the other people who are only doing that whilst keeping your general interest going so it's actually it's quite a lot more work and a lot harder to do that so one of the areas that you really working hard on i know at the moment we talked about it uh when we last met you know to discuss the the wide feature was protein folding yeah uh just give us a sense maybe for the audience on why you felt that particular challenge was one that you wanted to sort of like apply you know deepmind's resources to sure well we when we when we go after a big problem like that we there's a lot of evaluation we do beforehand so partly one of the main things first of all starting points is to make sure the problem is a big enough impact if you were to solve it if you're going to spend three four five years trying to solve something you better make sure that it's it's it's something that would unlock a lot of new potential and um secondly we also look for things like properties of the problem do they suit the types of algorithms we're building so the kinds of things we look for is there is there enough training data or is there even better is there simulations we can create more synthetic data from um is there a clear objective function that you're trying to optimize um so you know something that some clear metric that your ai system can can can um a hill climb towards uh and then we also look for things like clear um external benchmarks maybe a competition or something that runs by the community that you're going into that has that you can clearly measure your progress against and protein folding ticked all of those boxes so it has um it has all you know has some data it has um some great competitions this thing called casp every two years which is a community run competition where you have to predict the structure of a protein before it's revealed by experimentalists so it's a fantastic competition and in terms of how important it is i mean proteins are essential to every function in your body so it's key for disease um and so if we could understand the shape they fold into then maybe we could accelerate things like drug discovery so clearly if we could crack that problem it should have a lot of downstream impact you mentioned uh casp i know there's one going on at the moment clearly you can't sort of like talk about you know any kind of you know insider knowledge you might have about what's going on but like that's a really important proving point isn't it because it shows the research going in the right direction that's right and and casp is this incredible it's in the 14th edition now so it's been going since um mid 90s yeah and uh and and yes we we we've sort of you know the announcement is that the results are going to come out next week and you know we think we've done pretty well but obviously we can't you know it's confidential until the results are officially announced um but it's a great example i think in science of one of these really rigorous benchmarks that have been run and um and it's fantastic for uh uh push you know allowing the field to progress and making sure that you you you really are making progress um towards the ultimate goal not not kind of kidding yourself on the way somehow that you know your your your kind of your internal metrics are one thing obviously you always you're always measuring how good are your how good are your algorithms internally and your own benchmarks yeah but there's nothing better than like an external measure that's in by judged by an independent assessors to really test your mettle well we're going to be looking out for those results next week we can't have a conversation in 2020 obviously without talking about coronavirus so i'd love to get your thoughts on whether deepmind's been working on on this particular challenge and how machine learning as far as you know it has kind of played out and had an impact on drug discovery or what we understand about the pandemic yeah i mean so so us particularly i've been i mean i've been working on it both professionally at deepmind and also on a personal level as a scientist um where deepmind what we've done is uh actually when where very early on um the the covid virus was sequenced by i think some chinese researchers genetically sequenced and so um what we did is we used an earlier version of alpha fold which we thought was pretty good already in march to um to find to basically uh look at the structures of some of the some of the proteins in the virus that were understudied a few of them we already know the structure experimentally but some of them we didn't and we put our best predictions out open source out to the community straight away so the um people could use it and and maybe target against that um but we didn't make a big um claim about that because at the time obviously um the cast competition hadn't been run yet that would run over the summer so we hadn't had external validation of how good our system was we thought it was pretty good but we couldn't we didn't want people necessarily to rely on that until it was externally validated yeah so god forbid if something like that happens again you know in the future profoundly three four five years i would imagine ai playing a much bigger part than it did this time around where i think in some senses it came a little bit too early for ai to be at the forefront of obviously what the great scientific effort that's going on and we've seen a lot of you know new hope in the last few weeks with the vaccines and so on so obviously the whole scientific community's come together and i think ai will be just one component of something like that in the future but maybe you know a much more important component there was this time around uh and then on a personal level you know i've worked with the royal society to try and put out studies on things like masks and the effect of opening schools and other things bring together some of the top scientists in the uk to help write white papers to advise government so let's talk a little bit about this discussion around algorithmic sort of bias and data bias obviously ai systems can amplify this in some ways how do you think about that in terms of what you know deepmind can do to sort of combat that yeah so this is a hugely important question obviously um we have a whole research team looking at this you know we kind of put it under the rubric of fairness bias and interpretability yeah these are all key things that we need um to understand about systems before you deploy them um a lot of what we do is pure research so it's not um uh uh it's it's it's you know i think it becomes more pressing when you actually create a product out of your research and then it's used in the real world to do something that affects people's lives so um a lot more research has to be done on that we work on that we also collaborate with places like turing institute and other places to sort of look into that further and i think the next phase of um development is going to be uh building analysis tools and and visualization tools to look inside these sort of so-called black boxes of these neural network systems and understanding better what they do and i think there we can take inspiration from how we analyze the brain using neuroscience and things like fmi machines and so on what's the equivalent of that for for ai systems um and just on the optimistic side of of this this problem is that of course humans are biased in many ways ourselves and if we build it the ai systems wrong it will amp you know they will amplify the biases we already have all that all designers and other people already have if we build it right then potentially the ai systems could be less biased than we are as as individuals um so i think it's sort of it's a bifurcation if we do well with it i think it could actually make the societal problem of fairness and bias better um than it currently is so clearly we've had a global pandemic ai is also a kind of like a a global technology and to some degree there's a lot of competition going on to sort of you know advance research as quickly as possible we have obviously china and the united states the uk it's going to be very important obviously for us you know as we kind of embark on our post-brexit sort of like journey um how how do you where do you think we are in the uk and and how best can we kind of push sort of you know deep tech forward and really become a center of excellence do you think i mean i think that the uk you know we've always punched punched well above our weight in terms of pure research so um we've got world-class universities you know we always have several in the top 10 in the world um and if you look at other measures like nobel prizes or or citations and and big papers um we do phenomenally well i think the the uk needs to really cherish that and build on that for starters um and that includes like post brexit making sure we're still plugged into all the all the european sort of um frameworks on research frameworks because we we generally get more grants than we we put money in so that needs to continue somehow and also work keep welcoming um the top talent here um in terms of ai obviously um you know deep mine's still here we've got we're located here with pretty much all of our staff um there are many you know i think we've helped create a big ecosystem in the uk now of of ai startups so i think we do very well on that so i think there's um a lot going well we just need to carry on building on that momentum and investing in things like um scholarships for underrepresented groups to bring to broaden access to ai technologies and to deepen where we're strong you know certain universities maybe increase grants to those and so on from a government level we're doing that as deepmind like sponsoring a lot of master scholarships and also i do that philanthropically as well uh in my own personal uh way so i think all of those things are gonna add and i think in the future you know you've got the uk you've got places like canada and france where there's actually a lot of good ai work going on that maybe it could act as a counterweight to the two superpowers i think in general let's say you take canada uk and france together um probably you know that that is as there's a much going on in those three countries put together as there is in in those two superpowers so i think there's a lot of opportunity for the uk actually to have a big um voice at the table a big say on how this goes it's good to hear that optimism so let's maybe um take a look at some of the questions uh coming in from the audience is uh we've been talking a lot this morning about sustainability um how do you think ai and deepmind in particular can kind of have an impact on on clean tech which seems to be an area that you know is really developing very quickly yeah i i think it's one of my passion areas actually is for for i think ai has a lot to play and we've done um quite a few thing projects already actually are more sort of applied projects um two of the better known ones are uh um applying our ai systems actually very similar ones to to alphago um to control the cooling systems in data centers and data centers use you know large amount of energy um actually google's ones are mostly renewable energy now which is great but even still we save 30 of the of the of the energy used in the data center by just more efficiently operating the cooling systems you know there's hundreds of different switches and pumps and fans and things you can turn on and and ai is perfectly suited to managing that and then we've developed that further recently as a service on cloud uh google cloud to uh we call it uh you know sort of building adaptive controls so big industrial buildings uh office buildings i guess not so much now during covid but but in normal times use a huge amount of energy again with the you know air conditioning and heating and all that stuff and um it's the same kind of principle that we use in the data centers but now in terms of controlling all the building uh systems uh uh that govern you know climate temperature and all that sort of stuff and that saves a huge amount of obviously money but also um energy by more efficiently uh running all those systems sure another question coming in from the audience there was one about neuropsychology how influential is it when developing ai solutions and an area i know that's a yes it did your heart yeah so it's actually very influential on a certain type of level so what we're not trying to do when we say we're neuroscience inspired is we're not trying to copy how the brain works right um like reverse engineering some people are trying to do that yeah but we don't think that's the right way to build ai because of course there's different massive differences obviously between a carbon-based system like our brains and a silicone-based system like a computer sure so the implementation details will be different but what you're interested in is the is the is the principles of general intelligence so the architecture the representations and the algorithms and so we call that systems neuroscience and that's where the level that we look at neuroscience and psychology out to debt to get inspiration okay thank you another question around how you balance uh you know the work for google and google products clearly you have to deliver sort of products for google um how best to balance that relationship yeah very big i mean you can think of it as their our biggest client in a way right if you think about all the the the the things different things we do and um and so it's obviously we put we've actually had 100 product launches now within google so when you use google devices you may not know it but most of the time you'll be using some of our tech will be under the hood probably the best example of that is wavenet which is the world's best text-to-speech system and we developed that a few years ago and scaled it up and now pretty much any device you speak to on android or google device anywhere the voice that's speaking back to you will be um we'll be waving at our technology so that's just one example um on the other hand obviously we do a lot of other partnerships um and there are a lot of other work that we do that's outside of google especially in the scientific realm so it's quite a nice balance and and one nice thing about google is that when you when you build a product when you build some research and you you discover something new you can straight away put it in a product that reaches a billion people so that's fantastic for impact uh and also for getting you know back sort of real world information about how how good are your algorithms really you know once they go out of the lab yeah absolutely one sponsor people actually using the product and using it at scale absolutely so a final question from the audience that synthetic data could be tricky how do you manage that avoid issue including biases yeah well synthetic actually interestingly i mean we're experts in synthetic data because we started with games and virtual worlds and and where we generate all of our own data actually and one reason i did that was when we were startup um obviously a small startup we had no customers and no data so how how would we compete with with you know big companies that had obviously all their consumer data and and one of the answers was to use games and then generate that ourselves with like atari games and run the simulators and then generate our own data so that was one of the i guess the the founding principles of deepmind one of the reasons we chose games obviously is also my background in games so um and so but of course you know as we've now matured you you've got to be careful with synthetic data you're not generating the invite in a biased way but just like we discussed with fairness and bias if you it's also the optimistic side of that is that if you generate synthetic data you can mathematically analyze it and make sure that it's not biased right which is which you can't really do with real data because you just have whatever data you have yeah so so i think synthetic data has the potential if you analyze it in the right way to be more balanced okay one final question uh your chess prodigy um have you been watching the queen's gambit so what do you think of it i have it seems like me and and then the rest of the world and i watched it as soon as it came out of course how could i not given it was about chess and and it was such a great story i thought it was fantastic and i actually um thought it was um extremely realistic trail in many ways of the of what it's like to be a child chess prodigy and the exhilaration of it but also the pressure perhaps minus the psychedelic drugs um but i i mean and the chest was extremely accurate which is unusual in films but i guess it's because gary kasparov i found out afterwards was a consultant on it so so i i guess he made sure that the chess part was was really accurate well but i recommend it well if people out there aren't watching the deep bind documentary on netflix that the queen's gambit is the second best demis thank you so much for joining us really delightful to have you here at wildlife thank you

Channel: WIRED UK

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Rating: 4.9578209 out of 5

Keywords: wired video, wired magazine, wired uk, wired, pop culture, science, politics, conde nast, health, technology, new technology, DeepMind, Demis Hassabis, DeepMind protein folding, Deepmind AI, machine learning, AI, Artificial intelligence, Demis hassabis interview, demis hassabis 2020, WIRED live, WIRED events, demis hassabis protein folding, demis hassabis talk

Id: 2WRow9FqUbw

Length: 22min 21sec (1341 seconds)

Published: Fri Mar 05 2021

Reddit Comments

[u/Thorusss]

Demis Hassabis is definitely worth listening to.

[u/sdzundercover]

I could listen to this legend talk all day. Proud to have him leading Britain’s A.I. Efforts

[u/[deleted]]

That was awesome. Thanks for sharing!

[u/boytjie]

I've always been an admirer. He dropped off the radar for awhile. Good to see him back.