Stuart Russell: Long-Term Future of Artificial Intelligence | Lex Fridman Podcast #9
the following is a conversation with Stuart Russell he's a professor of computer science at UC Berkeley and a co-author of a book that introduced me and millions of other people to the amazing world of AI called artificial intelligence a modern approach so it was an honor for me to have this conversation as part of MIT course and artificial general intelligence and the artificial intelligence podcast if you enjoy it please subscribe on youtube itunes or your podcast provider of choice or simply connect with me on twitter at Lex Friedman spelled Fri D and now here's my conversation with Stuart Russell so you've mentioned in 1975 in high school you've created one year first AI programs that play chess were you ever able to build a program that beat you a chess or another board game so my program never beat me at chess I actually wrote the program at Imperial College so I used to take the bus every Wednesday with a box of cards this big and shove them into the card reader and they gave us eight seconds of CPU time it took about five seconds to read the cards in and compile the code so we had three seconds of CPU time which was enough to make one move you know with a not very deep search and then we would print that move out and then we'd have to go to the back of the queue and wait to feed the cards in again how do you post a search well I would talk to no I think we got we got an eight move eight you know depth eight with alpha beta and we had some tricks of our own about move ordering and some pruning of the tree and we were still able to beat that program yeah yeah I I was a reasonable chess player in my youth I did Anna fellow program and a backgammon program so when I go to Berkley I worked a lot on what we call meta reasoning which really means reasoning about reasoning and in the case of a game playing program you need to reason about what parts of the search tree you're actually going to explore because the search tree is enormous or you know bigger than the number of atoms in the universe and the way programs succeed and the way humans succeed is by only looking at a small fraction of the search tree and if you look at the right fraction you play really well if you look at the wrong fraction if you waste your time thinking about things that are never gonna happen the moves that no one's ever gonna make then you're gonna lose because you you won't be able to figure out the right decision so that question of how machines can manage their own computation either how they decide what to think about is the meta-reasoning question we developed some methods for doing that and very simply a machine should think about whatever thoughts are going to improve its decision quality we were able to show that both for a fellow which is a standard to play game and for backgammon which includes dice for also it's a two-player game with uncertainty for both of those cases we could come up with algorithms that were actually much more efficient than the standard alpha beta search which chess programs at the time we're using and that those programs could beat me and I think you can see same basic ideas in alphago and alpha zero today the way they explored the tree is using a former meta reasoning to select what to think about based on how useful it is to think about it is there any insights you can describe without Greek symbols of how do we select which paths to go down there's really two kinds of learning going on so as you say alphago learns to evaluate board position so it can it can look at a go board and it actually has probably a superhuman ability to instantly tell how promising that situation is to me the amazing thing about alphago is not that it can be the world champion with its hands tied behind his back but the fact that if you stop it from searching altogether so you say okay you're not allowed to do any thinking ahead right you can just consider each of your legal moves and then look at the resulting situation and evaluate it so what we call a depth one search so just the immediate outcome of your moves and decide if that's good or bad that version of alphago can still play at a professional level right and human professionals are sitting there for five ten minutes deciding what to do and alphago in less than a second instantly into it what is the right move to make based on its ability to evaluate positions and that is remarkable because you know we don't have that level of intuition about go we actually have to think about the situation so anyway that capability that alphago has is one big part of why it beats humans the other big part is that it's able to look ahead 40 50 60 moves into the future mm-hmm and you know if it was considering all possibilities 40 or 50 or 60 moves into the future that would be you know 10 to the 200 possibility so wait way more than you know atoms in the universe and and so on so it's very very selective about what it looks at so let me try to give you an intuition about how you decide what to think about it's a combination of two things one is how promising it is right so if you're already convinced that a move is terrible there's no point spending a lot more time convincing yourself that it's terrible because it's probably not gonna change your mind so the the real reason you think is because there's some possibility of changing your mind about what to do mm-hmm right and is that changing your mind that would result then in a better final action in the real world so that's the purpose of thinking is to improve the final action in the real world and so if you think about a move that is guaranteed to be terrible you can convince yourself is terrible and you're still not gonna change your mind all right but on the other hand you I suppose you had a choice between two moves one of them you've already figured out is guaranteed to be a draw let's say and then the other one looks a little bit worse like it looks fairly likely that if you make that move you're gonna lose but there's still some uncertainty about the value of that move there's still some possibility that it will turn out to be a win all right then it's worth thinking about that so even though it's less promising on average than the other move which is guaranteed to be a draw there's still some purpose in thinking about it because there's a chance that you will change your mind and discover that in fact it's a better move so it's a combination of how good the move appears to be and how much I'm certainty there is about its value the more uncertainty the more it's worth thinking about because there's a higher upside if you want to think of it that way and of course in the beginning especially in the alphago 0 formulation it's everything is shrouded in uncertainty so you're really swimming in a sea of uncertainty so it benefits you too I mean actually following the same process as you described but because you're so uncertain about everything you you basically have to try a lot of different directions yeah so so the early parts of the search tree a fairly bushy that it will when looking a lot of different possibilities but fairly quickly the degree of certainty about some of the moves I mean if a movies are really terrible you'll pretty quickly find out right you lose half your pieces or half your territory and and then you'll say okay this this is not worth thinking about any more and then so a further down the tree becomes very long and narrow and you're following various lines of play you know 10 20 30 40 50 moves into the future and you know that's again it's something that human beings have a very hard time doing mainly because they just lacked the short-term memory you just can't remember a sequence of moves that's 50 movies long and you can't you can't imagine the board correctly for that money moves into the future of course the top players I'm much more familiar with chess but the top players probably have they have echoes of the same kind of intuition instinct that in a moment's time alphago applies when they see a board I mean they've seen those patterns human beings have seen those patterns before at the top at the Grandmaster level it seems that there is some similarities or maybe it's it's our imagination creates a vision of those similarities but it feels like this kind of pattern recognition that the alphago approaches are using is similar to what human beings at the top level or using I think there's there's some truth to that but not entirely yeah I mean I think the the extent to which a human Grandmaster can reliably wreak instantly recognize the right move instantly recognize the value of a position I think that's a little bit overrated but if you sacrifice a queen for exam I mean there's these there's these beautiful games of chess with Bobby Fischer somebody where it's seeming to make a bad move and I'm not sure there's a a perfect degree of calculation involved were they've calculated all the possible things that happen but there's an instinct there right that somehow adds up to the yeah so I think what happens is you you you get a sense that there's some possibility in the position even if you make a weird-looking move that it opens up some some lines of of calculation that otherwise would be definitely bad and and is that intuition that there's something here in this position that might might yield a win down the side and then you follow that right and and in some sense when when a chess player is following a line and in his or her mind they're they mentally simulating what the other person is gonna do while the opponent is gonna do and they can do that as long as the moves are kind of forced right as long as there's a you know there's a fourth we call a forcing variation where the opponent doesn't really have much choice how to respond and then you see if you can force them into a situation where you win you know we see plenty of mistakes even even in Grandmaster games where they just miss some simple three four five move combination that you know wasn't particularly apparent in in the position but we're still there that's the thing that makes us human yeah so when you mentioned that in a fellow those games were after some meta reasoning improvements and research I was able to beat you how did that make you feel part of the meta reasoning capability that it had was based on learning and and you could sit down the next day and you could just feel that it had got a lot smarter boom you know and all the sudden you really felt like you sort of pressed against the wall because it was it was much more aggressive and was totally unforgiving of any minor mistake that you might make and and actually it seemed understood the game better than I did and you know Gary Kasparov has this quote weary during his match against deep blue he said he suddenly felt that there was a new kind of intelligence across the board do you think that's a scary or an exciting possibility that's prevent for yourself in in the context of chess purely sort of in this like that feeling whatever that is I think it's definitely an exciting feeling you know this is what made me work on AI in the first place was as soon as I really understood what a computer was I wanted to make it smart you know I started out with the first program I wrote was for the sinclair programmable calculator and i think you could write a 21 step algorithm that was the biggest program you could write something like that and do little arithmetic calculations so I say think I implemented Newton's method for square roots and a few other things like that um but then you know I thought okay if I just had more space I could make this thing intelligent and so I started thinking about AI and and I think the the the thing that's scary is not is not the chess program because you know chess programs they're not in they're taking over the world business but if you extrapolate you know there are things about chess that don't resemble the real world right we know we know the rules of chess chess board is completely visible to the programmer of course the real world is not most you most the real world is not visible from wherever you're sitting so to speak and to overcome those kinds of problems you need qualitatively different algorithms another thing about the real world is that you know we we regularly plan ahead on the timescales involving billions or trillions of steps now we don't plan that was in detail but you know when you choose to do a PhD at Berkeley that's a five-year commitment and that amounts to about a trillion motor control steps that you will eventually be committed to including going up the stairs opening doors drinking water type yeah I mean every every finger movement while you're typing every character of every paper and the thesis and everything else so you're not commuting in advance to the specific motor control steps but you're still reasoning on a timescale that will eventually reduce to trillions of motor control actions and so for all these reasons you know alphago and and deep blue and so on don't represent any kind of threat to humanity but they are a step towards it right near that and progress in AI occurs by essentially removing one by one these assumptions that make problems easy like the assumption of complete observability of the situation right we remove that assumption you need a much more complicated kind of a computing design and you need something that actually keeps track of all the things you can't see and tries to estimate what's going on and there's inevitable uncertainty in that so it becomes a much more complicated problem but you know we are removing those assumptions we are starting to have algorithms that can cope with much longer timescales they can cope with uncertainty they can cope with partial observability and so each of those steps sort of magnifies by a thousand the range of things that we can do with AI systems so the way I started me I wanted to be a psychiatrist for long time to understand the mind in high school and of course program and so on and then I showed up University of Illinois to an AI lab and they said okay I don't have time for you but here's a book AI a modern approach I think was the first edition at the time mmm here go go learn this and I remember the lay of the land was well it's incredible that we solve chess but we'll never solve go I mean it was pretty certain that go in the way we thought about systems that reason was impossible to solve and now we've solved this as a very I think I would have said that it's unlikely we could take the kind of algorithm that was used for chess and just get it to scale up and work well for go and at the time what we thought was that in order to solve go we would have to do something similar to the way humans manage the complexity of go which is to break it down into kind of sub games so when a human thinks about a go board they think about different parts of the board as sort of weakly connected to each other and they think about okay within this part of the board here's how things could go and that part about his how things could go and now you try to sort of couple those two analyses together and deal with the interactions and maybe revise your views of how things are going to go in each part and then you've got maybe five six seven ten parts of the board and that actually resembles the real world much more than chess does because in the real world you know we have work we have home life we have sport you know whatever different kinds of activities you know shopping these all are connected to each other but they're weakly connected so when I'm typing a paper you know I don't simul taneous Li have to decide which order I'm gonna get the you know the milk and the butter you know that doesn't affect the typing but I do need to realize okay better finish this before the shops closed because I don't have anything you don't have any food at home all right right so there's some weak connection but not in the way that chess works where everything is tied into a single stream of thought so the thought was that go just sort of go we'd have to make progress on stuff that would be useful for the real world and in a way alphago is a little bit disappointing right because the the program designed for alphago was actually not that different from from deep blue or even from Arthur Samuels checker playing program from the 1950s and in fact the so the two things that make alphago work is one one is is amazing ability ability to evaluate the positions and the other is the meta-reasoning capability which which allows it to to explore some paths in the tree very deeply and to abandon other paths very quickly so this word meta-reasoning while technically correct inspires perhaps the the wrong degree of power that alphago has for example the word reasonings as a powerful word let me ask you sort of so you were part of the symbolic AI world for a while like whatever the AI was there's a lot of excellent interesting ideas there that unfortunately met a winter and so it do you think it really emerges well I would say yeah it's not quite as simple as that so the the AI winter so for the first window that was actually named as such was the one in the late 80s and that came about because in the mid 80s there was a really a concerted attempt to push AI out into the real world using what was called expert system technology and for the most part that technology was just not ready for primetime they were trying in many cases to do a form of uncertain reasoning judge you know judgment combinations of evidence diagnosis those kinds of things which was simply invalid and when you try to apply invalid reasoning methods to real problems you can fudge it for small versions of the problem but when it starts to get larger the thing just falls apart so many companies found that the stuff just didn't work and they were spending tons of money on consultants to try to make it work and there were you know other practical reasons like you know they they were asking the companies to buy incredibly expensive lisp machine workstations which were literally between fifty and a hundred thousand dollars in you know in 1980s money which was would be like between a hundred and fifty and three hundred thousand dollars per workstation in current prices so then the bottom line they weren't seeing a profit from it yeah they in many cases I think there were some successes there's no doubt about that but people I would say over invested every major company was starting an AI department just like now and I worry a bit that we might see similar disappointments not because the technology is invalid but it's limited in its scope and it's almost the the dual of the you know the scope problems that expert systems had so what have you learned from that hype cycle and what can we do to prevent another winter for example yeah so when I'm giving talks these days that's one of the warnings that I give to to pot warning slide one is that you know rather than data being the new oil data is the new snake oil that's a good line and then and then the other is that we might see a kind of very visible failure in some of the major application areas and I think self-driving cars would be the flagship and I think when you look at the history so the first self-driving car was on the freeway driving itself changing lanes overtaking in 1987 and so it's more than 30 years and that kind of looks like where we are today right you know prototypes on the freeway changing lanes and overtaking now I think significant progress has been made particularly on the perception side so we worked a lot on autonomous vehicles in the early mid 90s at Berkley you know and we had our own big demonstrations you know we we put congressmen into yourself driving cars and and had them zooming along the freeway and the problem was clearly perception at the time the problem that perception yeah so in simulation with perfect perception you could actually show that you can drive safely for a long time even if the other cars are misbehaving and and so on but simultaneously we worked on machine vision for detecting cars and tracking pedestrians and so on and we couldn't get the reliability of detection and tracking up to a high enough particular level particularly in bad weather conditions nighttime rainfall good enough for demos but perhaps not good enough to cover the general the general yeah the thing about driving is you know suppose you're a taxi driver you know and you drive every day eight hours a day for ten years right that's a hundred million seconds of driving you know and any one of those seconds you can make a fatal mistake so you're talking about eight nines of reliability right now if your vision system only detects ninety eight point three percent of the vehicles right and that's sort of you know one on a bit nines and reliability so you have another seven orders of magnitude to go and and this is what people don't understand they think oh because I had a successful demo I'm pretty much done but you know you're not even within seven orders of magnitude of being done and that's the difficulty and it's it's not there can I follow a white line that's not the problem right we follow a white line all the way across the country but it's the it's the weird stuff that happens it's some of the edge cases yeah the edge case other drivers doing weird things you know so if you talk to Google right so they had actually very classical architecture where you know you had machine vision which would detect all the other cars and pedestrians and the white lines and the road signs and then basically that was fed into a logical database and then you had a classical 1970s rule-based expert system telling you okay if you're in the middle lane and there's a bicyclist in the right lane who is signaling this then then then don't need to do that yeah right and what they found was that every day they go out and there'd be another situation that the rules didn't cover you know so they they come to a traffic circle and there's a little girl riding a bicycle the wrong way around a traffic circle okay what do you do we don't have a rule oh my god okay stop and then you know they come back and had more rules and they just found that this was not really converging and and if you think about it right how how do you deal with an unexpected situation meaning one that you've never previously encountered and the sort of the the reasoning required to figure out the solution for that situation has never been done it doesn't match any previous situation in terms of the kind of reasoning you have to do well you know in chess programs this happens all the time you're constantly coming up with situations you haven't seen before and you have to reason about them you have to think about okay here are the possible things I could do here the outcomes here's how desirable the outcomes are and then pick the right one you know in the 90s we were saying okay this is how you're gonna have to do automated vehicles they're gonna have to have a look ahead capability but the look ahead for driving is more difficult than it is for chess because Huysmans the other right there's humans and they're less predictable than just a standard well then will you have an opponent in chess who's also somewhat unpredictable but for example in chess you always know the opponent's intention they're trying to beat you right whereas in driving you don't know is this guy trying to turn left or has he just forgotten to turn off his tone signal or is he drunk or is he you know changing the channel on his radio or whatever it might be you got to try and figure out the mental state the intent of the other drivers to forecast the possible evolutions of their trajectories and then you've got to figure out okay which is the directory for me that's going to be safest and those all interact with each other because the other drivers going to react to your trajectory and so on so you know they've got the classic merging onto the freeway a problem where you're kind of racing a vehicle that's already on the freeway and you are you gonna pull ahead of them or you're gonna let them go first and pull in behind and you get this sort of uncertainty about who's going first so all those kinds of things mean that you need decision-making architecture that's very different from either a rule-based system or it seems to me a kind of an end-to-end neural network system you know so just as alphago is pretty good when it doesn't do any look ahead but it's way way way way better when it does I think the same is going to be true for driving you can have a driving system that's pretty good when it doesn't do any look ahead but that's not good enough you know and we've already seen multiple deaths caused by poorly designed machine learning algorithms that don't really understand what they're doing yeah and on several levels I think it's on the perception side there's mistakes being made by those algorithms were the perception is very shallow on the planning side to look ahead like you said and the thing that we come come up against that's really interesting when you try to deploy systems in the real world is you can't think of an artificial intelligence system as a thing that responds to the world always you have to realize that it's an agent that others will respond to as well so in order to drive successfully you can't just try to do obstacle avoidance you can't pretend that you're invisible thank you right you're the invisible car right just look that way I mean but you have to assert yet others have to be scared of you just we're all there's this tension there's this game so if we studied a lot of work with pedestrians if you approach pedestrians as purely an obstacle avoidance so you either doing look ahead isn't modeling the intent that you're you they're not going to they're going to take advantage of you they're not going to respect you at all there has to be a tension a fear some amount of uncertainty that's how we have create we or at least just a kind of a resoluteness right so you have you have to display a certain amount of resoluteness you can't you can't be too tentative and yeah so the right the the solutions then become pretty complicated right you get into game theoretic yes analyses and so we're you know Berkeley now we're working a lot on this kind of interaction between machines and humans and that's exciting yeah and so my colleague and could drag an actually you know if you if you formulate the problem game theoretically and you just let the system figure out the solution you know it does interesting unexpected things like sometimes at a stop sign if no one is going first right the car will actually back up a little all right and just to indicate to the other cars that they should go and that's something it invented entirely by itself that's interesting you know we didn't say this is the language of communication at stop signs it figured it out that's really interesting so let me one just step back for a second just this beautiful philosophical notion so Pamela I'm a quartic in 1979 wrote AI began with the ancient wish to forge the gods so when you think about the history of our civilization do you think that there is an inherent desire to create let's not say gods but to create super intelligence is it inherent to us is it in our genes that the natural arc of human civilization is to create things that are of greater and greater power and perhaps no echoes of ourselves so to create the gods as Pamela said if the maybe I mean you know we're all we're all individuals certainly we see over and over again in history individuals who thought about this possibility hopefully when I'm not being too philosophical here but if you look at the arc of this you know where this is going and we'll talk about AI safety we'll talk about greater and greater intelligence do you see that there in when you created the earth Allah program and you felt this excitement what was that excitement was it excitement of a tinkerer who created something cool like a clock or was there a magic or was it more like a child being born that yeah you know yeah so I mean I certainly understand that viewpoint and if you look at the light he'll report which was commit so in the 70s there was a lot of controversy in the UK about AI and you know whether it was for real and how much the money money the government should invest and there was a lot long story but the government commissioned a report by by light Hill who was a physicist and he wrote a very damning report about AI which I think was the point and he said that that these are you know frustrated men who unable to have children would like to create and you know create life you know as a kind of replacement you know which I which I think is really pretty unfair but there is I mean there there is a kind of magic I would say you when you you build something and what you're building in is really just you're building in some understanding of the principles of learning and decision-making and to see those principles actually then turn into intelligent behavior in in specific situations it's an incredible thing and you know that is naturally going to make you think okay where does this end and so there's a there's magical optimistic views of word and whatever your view of optimism is whatever your view of utopia is it's probably different for everybody yeah but you've often talked about concerns you have of how things might go wrong so I've talked to max tegmark there's a lot of interesting ways to think about AI safety you're one of the seminal people thinking about this problem among sort of being in the weeds of actually solving specific AI problems you also think about the big picture of where we're going so can you talk about several elements of it let's just talk about maybe the control problem so this idea of losing ability to control the behavior and of a AI system so how do you see that how do you see that coming about what do you think we can do to manage it well so it doesn't take a genius to realize that if you make something that's smarter than you you might have a problem you know in Turing Alan Turing you know wrote about the gave lectures about this you know 19 1951 painted a lecture on the radio and he basically says you know once the machine thinking method stops you know very quickly they'll outstrip humanity and you know if we're lucky we might be able to I think he says if we may be able to turn off the power at strategic moments but even so a species would be humbled yeah you can actually I think was wrong about that right here is you you know if it's a sufficiently intelligent machine is not gonna let you switch it off so it's actually in competition with you so what do you think is meant just for a quick tangent if we shut off this super intelligent machine that our species will be humbled I think he means that we would realize that we are inferior right that we we only survive by the skin of our teeth because we happen to get to the off switch just in time you know and if we hadn't then we would have lost control over the earth so do you are you more worried when you think about this stuff about super intelligent AI or are you more worried about super powerful AI that's not aligned with our values so the paperclip scenario is kind of I think so the main problem I'm working on is is the control problem the the problem of machines pursuing objectives that are as you say not aligned with human objectives and and this has been it has been the way we've thought about I eyes since the beginning you you build a machine for optimizing and then you put in some objective and it optimizes right and and you know we we can think of this as the the King Midas problem right because if you know so King Midas put in this objective right everything I touch you turned to gold and the gods you know that's like the machine they said okay done you know you now have this power and of course his food and his drink and his family all turned to gold and then he's sighs misery and starvation and this is you know it's it's a warning it's it's a failure mode that pretty much every culture in history has had some story along the same lines you know there's the the genie that gives you three wishes and you know third wish is always you know please undo the first two wishes because I messed up and you know and when author Samuel wrote his chest his checkup laying program which learned to play checkers considerably better than Martha Samuel could play and actually reached a pretty decent standard Norbert Wiener who was a one of the major mathematicians of the 20th century sort of a father of modern automation control systems you know he saw this and he basically extrapolated you know as Turing did and said okay this is how we could lose control and specifically that we have to be certain that the purpose we put into the machine as the purpose which we really desire and the problem is we can't do that right you mean we're not it's a very difficult to encode so to put our values on paper is really difficult or you're just saying it's impossible your line is writing this so it's it theoretically it's possible but in practice it's extremely unlikely that we could specify correctly in advance the full range of concerns of humanity that you talked about cultural transmission of values I think is how humans to human transmission of values happens right what we learned yeah I mean as we grow up we learn about the values that matter how things how things should go what is reasonable to pursue and what isn't reasonable to pursue machines can learn in the same kind of way yeah so I think that what we need to do is to get away from this idea that you build an optimizing machine and you put the objective into it because if it's possible that you might put in a wrong objective and we already know this is possible because it's happened lots of times alright that means that the machine should never take an objective that's given as gospel truth because once it takes them the the objective is gospel truth alright then it's the leaves that whatever actions it's taking in pursuit of that objective are the correct things to do so you could be jumping up and down and saying no you know no no no you're gonna destroy the world but the machine knows what the true objective is and it's pursuing it and tough luck to you you know and this is not restricted to AI right this is you know I think many of the 20th century technologies right so in statistics you you minimize a loss function the loss function is exogenously specified in control theory you minimize a cost function in operations research you maximize a reward function and so on so in all these disciplines this is how we conceive of the problem and it's the wrong problem because we cannot specify with certainty the correct objective right we need uncertainty we the machine to be uncertain about a subjective what it is that it's post it's my favorite idea of yours I've heard you say somewhere well I shouldn't pick favorites but it just sounds beautiful we need to teach machines humility yeah I mean it's a beautiful way to put it I love it that they humble oh yeah they know that they don't know what it is they're supposed to be doing and that those those objectives I mean they exist they are within us but we may not be able to explicate them we may not even know you know how we want our future to go so exactly and the Machine you know a machine that's uncertain he's going to be deferential to us so if we say don't do that well now the machines learn something a bit more about our true objectives because something that it thought was reasonable in pursuit of our objectives turns out not to be so now it's learn something so it's going to defer because it wants to be doing what we really want and you know that that point I think is absolutely central to solving the control problem and it's a different kind of AI when you when you take away this idea that the objective is known then in fact a lot of the theoretical frameworks that we're so familiar with you know Markov decision processes goal based planning you know standard games research all of these techniques actually become inapplicable and you get a more complicated problem because because now the interaction with the human becomes part of the problem because the human by making choices is giving you more information about the 'true objective and that information helps you achieve the objective better and so that really means that you're mostly dealing with game theoretic problems where you've got the machine and the human and they're coupled together rather than a machine going off by itself with a fixed objective which is fascinating on the machine and the human level that we when you don't have an objective means you're together coming up with an objective I mean there's a lot of philosophy that you know you could argue that life doesn't really have meaning we we together agree on what gives it meaning and we kind of culturally create things that give why the heck we are in this earth anyway we together as a society create that meaning and you have to learn that objective and one of the biggest I thought that's what you were gonna go for a second one of the biggest troubles we've run into outside of statistics and machine learning and AI and just human civilization is when you look at I came from the south was born in the Soviet Union and the history of the 20th century we ran into the most trouble us humans when there was a certainty about the objective and you do whatever it takes to achieve that objective whether you talking about in Germany or communist Russia oh yeah I get the trouble I would say with you know corporations in fact some people argue that you know we don't have to look forward to a time when AI systems take over the world they already have and they call corporations right that corporations happen to be using people as components right now but they are effectively algorithmic machines and they're optimizing an objective which is quarterly profit that isn't aligned with overall well-being of the human race and they are destroying the world they are primarily responsible for our inability to tackle climate change right so I think that's one way of thinking about what's going on with with cooperations but I think the point you're making you is valid that there are there are many systems in the real world where we've sort of prematurely fixed on the objective and then decoupled the the machine from those that's supposed to be serving and I think you see this with government right government is supposed to be a machine that serves people but instead it tends to be taken over by people who have their own objective and use government to optimize that objective regardless of what people want do you have do you find appealing the idea of almost arguing machines where you have multiple I systems with a clear fixed objective we have in government the red team and the blue team that are very fixed on their objectives and they argue and it kind of maybe it would disagree but it kind of seems to make it work somewhat that the the duality of it okay let's go a hundred years back when there was still was going on or at the founding of this country there was disagreement and that disagreement is where so there's a balance between certainty and forced humility because the power was distributed yeah I think that the the the nature of debate and disagreement argument takes as a premise the idea that you could be wrong right which means that you're not necessarily absolutely convinced that your objective is the correct one right if you were absolutely Guiness there'll be no point in having any discussion or argument because you would never change your mind and there wouldn't be any any sort of synthesis or or anything like that so so I think you can think of argumentation as a as an implementation of a form of uncertain reasoning and you know I I've been reading recently about utilitarianism in the history of efforts to define in a sort of clear mathematical way a I feel like a formula for moral or political decision-making and it's really interesting that the parallels between the philosophical discussions going back 200 years and what you see now in discussions about existential risk because you it's almost exactly the same so someone would say okay well here's a formula for how we should make decisions right so utilitarianism you know each person has a utility function and then we make decisions to maximize the sum of everybody's utility mm-hmm right and then people point out well you know in that case the best policy is one that leads to the enormous lis vast population all of whom are living a life that's barely worth living right and this is called the repugnant conclusion and you know another version is you know that we we should maximize pleasure and that's what we mean by utility and then you'll get people effectively saying well in that case you know we might as well just have everyone hooked up to a heroin drip yeah you know and they didn't use those words but that debate you know what's happening in the 19th century as it is now about AI that if we get the formula wrong you know we're going to have AI systems working towards an outcome that in retrospect would be exactly wrong do you think there's it has beautifully put so the the echoes are there but do you think I mean if you look at sam Harris is our imagination worries about the AI version of that because of the speed at which the things going wrong in the utilitarian context could happen yeah is that is that a worry for you yeah I I think that you know it in most cases not in all but you know if we if we have a wrong political idea you know we see it starting to go wrong and we're you know we're not completely stupid and so we said okay that was maybe that was a mistake let's try something different and and also we're very slow and inefficient about implementing these things and so on so you have to worry when you have corporations or political systems that are extremely efficient but when we look at AI systems or even just computers in general right they have this different characteristic from ordinary human activity in the past so let's say you were a surgeon you had some idea about how to do some operation right well and let's say you were wrong all right that that way of doing the operation would mostly kill the patient well you'd find out pretty quickly like after three maybe three or four tries right but that isn't true for pharmaceutical companies because they don't do three or four operations they they manufacture three or four billion pills and they sell them and then they find out maybe six months or a year later that oh people are dying of heart attacks or getting cancer from this drug and so that's why we have the FDA right because of the scalability of pharmaceutical production and you know and there have been some unbelievably bad episodes in the history of pharmaceuticals and and adulteration of of products and so on that that have killed tens of thousands or paralysed hundreds of thousands of people now with computers we have that same scalability problem that you can sit there and type for I equals 1 to 5 billion do right and all of a sudden you're having an impact on a global scale and yet we have no FDA right there's absolutely no controls at all it's over what a bunch of undergraduates with too much caffeine can do to the world and you know we look at what happened with Facebook well social media in general and click-through optimization so you have a simple feedback algorithm that's trying to just optimize click-through that sounds reasonable right because you don't want to be feeding people ads that they don't care about I'm not interested in and you might even think of that process as simply adjusting the the feeding of ads or news articles or whatever it might be to match people's preferences right which sounds like a good idea but in fact that isn't how the algorithm works right you make more money the algorithm makes more money if it could better predict what people are going to click on because then it can feed them exactly that right so the way to maximize click-through is actually to modify the people to make them more predictable and one way to do that is to feed them information which will change their behavior and preferences towards extremes that make them predictable now whatever is the nearest extreme or the nearest predictable point that's where you're going to end up the machines will force you there now and then I think there's a reasonable argument to say that this among other things is contributing to the destruction of democracy in the world and where was the oversight of this process where were the people saying okay you would like to apply this algorithm to five billion people on the face of the earth can you show me that it's safe can you show me that it won't have various kinds of negative effects no there was no one asking that question there was no one placed between you know the undergrads were too much caffeine and the human race well it's just they just did it and but some way outside the scope of my knowledge so economists would argue that the what is it the invisible hand so the the capitalist system it was the oversight so if you're going to corrupt society with whatever decision you make is a company then that's going to be reflected in people not using your product sort of one that's one model of oversight so we shall see but you know in the meantime you know that but you you might even have broken the political system that enables capitalism to function well you've changed it and so we should see yeah change changes often painful so my question is uh absolutely it's fascinating you're absolutely right that there is ZERO oversight on algorithms that can have a profound civilization changing effect so do you think it's possible I mean I haven't have you seen government so do you think it's possible to create regulatory bodies oversight over AI algorithms which are inherently such cutting edge set of ideas and technologies yeah but I think it takes time to figure out what kind of oversight what kinds of controls I mean took time to design the FDA regime you know and some people still don't like it and they want to fix it and I think there are clear ways that it could be improved but the whole notion that you have stage 1 stage 2 stage 3 and here are the criteria for what you have to do to pass a stage 1 trial right we haven't even thought about what those would be for algorithms so I mean I think there are there are things we could do right now with regard to bias for example we we have a pretty good technical handle on how to detect algorithms that are propagating bias that exists in data sets how to D by us those algorithms and and even what it's going to cost you to do that so I think we could start having some standards on that I think there are there are things to do with impersonation of falsification that we could we could work on so I thanks ya or you know in a very simple point so impersonation ISM is a machine acting as if it was a person I can't see a real justification for why we shouldn't insist that machines self-identify as machines you know where is the social benefit in in fooling people into thinking that this is really a person when it isn't you know I I don't mind if it uses a human-like voice that's easy to understand that's fine but it should just say I'm a machine in some some form people are speaking to that I would think relatively obvious factors I think mostly yeah I mean there is actually a law in California that bans impersonation but only in certain restricted circumstances so for the purpose of engaging in a for Geling transaction and for the purpose of modifying someone's voting behavior so those are those are the circumstances where machines have to self-identify but I think this is you know arguably it should be in all circumstances and then when you talk about deep fakes you know we're just beginning but already it's possible to make a movie of anybody saying anything in ways that are pretty hard to detect including yourself because you're on camera now and your voice is coming through with high resolution so you could take what I'm saying and replaces it with it pretty much anything else you wanted me to be saying yeah and even it will change my lips and expression expressions to fit and there's actually not much in the way of real legal protection against that I think in the commercial area you could say yeah that's you're using my brand and so on that there there are rules about that but in the political sphere I think it's at the moment it's you know anything goes so like that could be really really damaging and let me just try to make not an argument but try to look back at history and say something dark in essence is while regulation seems to be oversight seems to be exactly the right thing to do here it seems that human beings what they naturally do is they wait for something to go wrong if you're talking about nuclear weapons you can't talk about nuclear weapons being dangerous until somebody actually like the United States drops the bomb or Chernobyl melting do you think we will have to wait for things going wrong in a way that's obviously damaging to society not an existential risk but obviously damaging or do you have faith that I I hope not but I mean I think we do have to look at history and when you know so the two examples you gave nuclear weapons and nuclear power are very very interesting because you know in nuclear weapons we knew in the early years of the 20th century that atoms contained a huge amount of energy right we had e equals mc-squared we knew the the mass differences between the different atoms and their components and we knew that you might be able to make an incredibly powerful explosive so HG Wells wrote science fiction book I think in 1912 Frederick Soddy who was the guy who discovered isotopes so Nobel Prize winner he gave a speech in 1915 saying that this new explosive would be the equivalent of 150 tons of dynamite which turns out to be about right and you know Kenton this was in World War one right so he was imagining how much worse the world would be if we were using that kind of explosive but the physics establishment simply refused to believe that these things could be made including the people who are making it well so they were doing the nuclear physics I mean eventually were the ones who made it and Rockwell for me or whoever well so up to the the development was was mostly theoretical so it was people using sort of primitive kinds of particle acceleration and doing experiments at the at the level of single particles or collections of particles they they they want yet thinking about how to actually make a bomb or anything like that they but they knew the energy was there and they figured if they understood it better it might be possible but the physics establishment their view and I think because they did not want it to be true their view was that it could not be true that this could not provide a way to make a super weapon and you know there was this famous speech given by Rutherford who was the sort of leader of nuclear physics and I was on September 11th 1933 and he he said you know anyone who talks about the possibility of obtaining energy from transformation of atoms is talking complete moonshine and the next the next morning Leo Szilard read about that speech and then invented the nuclear chain reaction and so as soon as he invented he soon as he had that idea that you could make a chain reaction with neutrons because neutrons were not repelled by the nucleus so they could enter the nucleus and then continue the reaction as soon as he has that idea he instantly realized that the world was in deep doo-doo because this is 1933 right you know Hitler had recently come to power in Germany Zil odd was in London and eventually became a refugee and and came to the US and the in the process of having the idea about the chain reaction he figured out basically how to make a bomb and also how to make a reactor and he patented the reactor 2:34 but because of the situation the great power conflict situation that he could see happening he kept that a secret and so between then and the beginning of World War two people were working including the Germans on how to actually create Neutron sources right what specific fission reactions would produce neutrons of the right energy to continue the reaction and and that was demonstrated in Germany I think in 1938 if I remember correctly the first nuclear weapon patent was 1939 by the French so this was actually you know this was actually going on you know well before World War two really got going and then you know the British probably had the most advanced capability in this area but for safety reasons among others and blush which is sort of just resources they moved the program from Britain to the US and then that became Manhattan Project so the the the reason why we couldn't have any kind of oversight of nuclear weapons and nuclear technology was because we were basically already in an arms race in a war and but you you've mentioned then in the 20s and 30s so what are the echoes yeah the way you've described this story I mean there's clearly echoes why do you think most a I researchers folks who are really close to the metal they really are not concerned about it and they don't think about it whether they don't want to think about it it's but what are the yeah why do you think that is what are the echoes of the nuclear situation to the current situation and what can we do about it I think there is a you know a kinda modak motivated cognition which is a term in psychology means that you believe what you would like to be true rather than what is true and you know it's it's unsettling to think that what you're working on might be the end of the human race obviously so you would rather instantly deny it and come up with some reason why it couldn't be true and the you know I have I collected a long list of reasons that extremely intelligent competent AI scientists have come up with for why we shouldn't worry about this you know for example calculators are super human at arithmetic and they haven't taken over the world so there's nothing to worry about well okay my five-year-old you know could have figured out why that was an unreasonable and and really quite weak argument you know another one was you know you while it's theoretically possible that you could have superhuman AI destroy the world you know it's also theoretically possible that a black hole could materialize right next to the earth and destroy humanity I mean yes it's theoretically possible quantum theoretically extremely unlikely that it would just materialize right there but that's a completely bogus analogy because you know if the whole physics community on earth was working to materialize a black hole in near Earth orbit right wouldn't you ask them is that a good idea is that gonna be safe you know what if you succeed all right right and that's the thing right the AI is sort of refused to ask itself what if you succeed and initially I think that was because it was too hard but you know Alan Turing asked himself that and he said we'd be toast right if we were lucky we might be able to switch off the power but probably we'd be toast but there's also an aspect that because we're not exactly sure what the future holds it's not clear exactly so technically what to worry about sort of how things go wrong and so there is something it feels like maybe you can correct me if I'm wrong but there's something paralyzing about worrying about something that logically is inevitable but you don't really know what that will look like yeah I think that's that's it's a reasonable point and you know the you know it's certainly in terms of existential risks it's different from you know asteroid collides with the earth right right which again is quite possible you know it's happened in the past it'll probably happen again we don't right we don't know right now but if we did detect an asteroid that was going to hit the earth in 75 years time we'd certainly be doing something about it well it's clear there's got big rocks we'll probably have a meeting you see what do we do about the big rock will they I write with a I I mean the very few people who think it's not gonna happen within the next 75 years I know rod Brooks doesn't think it's gonna happen maybe and ruing doesn't think it's happened but you know a lot of the people who work day-to-day you know as you say at the rock face they think it's gonna happen I think the median estimate from AI researchers is somewhere in forty to fifty years from from now or maybe a little you know I think in Asia they think it's gonna be even faster than that I am I'm a little bit more conservative I think probably take longer than that but I think it's you know as happened with nuclear weapons well I went overnight it can happen overnight that you have these breakthroughs and we need more than one breakthrough but you know the it's on the order of half a dozen this is a very rough scale but so half a dozen breakthroughs of that nature it would have to happen for us to reach the superhuman AI but the you know the AI research community is vast now the massive investments from governments from corporations tons of really really smart people you know you just have to look at the rate of progress in different areas of AI to see that things are moving pretty fast so to say oh it's just gonna be thousands of years I don't see any basis for that you know I see you know for example the the Stanford hundred year AI project right which is supposed to be sort of you know the serious establishment view their most recent report actually said it's probably not even possible Wow right which if you want a perfect example of people in denial that's it because you know for the whole history of AI we've been saying to philosophers who said it wasn't possible well you have no idea what you're talking about of course it's possible right give me an give me an argument for why it couldn't happen and there isn't one all right and now because people are worried that maybe a oh it might get a bad name or or I just don't want to think about this they're saying okay well of course it's not really possible you know and we imagine right imagine if you know the the leaders of the cancer biology community got up and said well you know of course curing cancer it's not really possible complete outrage and dismay and you know I I find this really a strange phenomenon so okay so if you accept it as possible and if you accept that it's probably going to happen the point that you're making that you know how does it go wrong a valid question without that without an answer to that question then you stuck with what I call the gorilla problem which is you know the problem that the gorillas face right they made something more intelligent than them namely us a few million years ago and now now they're in deep doo-doo yeah so there's really nothing they can do they've lost the control theater they failed to solve the control problem of controlling humans and so they've lost so we don't want to be in that situation and if the gorillas problem is is the only formulation you have there's not a lot you can do right other than to say okay we should try to stop you know we should just not make the humans or right in this case not make the AI and I think that's really hard to do - I'm not actually proposing that that's a feasible course of action I also think that you know if properly control a I could be incredibly beneficial so the but it seems to me that there's a there's a consensus that one of the major failure modes is this loss of control that we create AI systems that are pursuing incorrect objectives and because the AI system believes it knows what the objective is it has no incentive to listen to us anymore so to speak right it it's just carrying out the the strategy that it it has computed as being the optimal solution and you know it may be that in the process it needs to acquire more resources to increase the possibility of success or prevent various failure modes by defending itself against interference and so that collection of problems I think is something we can address yes that the other problems are roughly speaking you know misuse right so even if we solve the control problem we make perfectly safe controllable AI systems well why you know why does dr. evil going to use those right he wants to just take over the world and he'll make unsafe AI system said but then get out of control so that's one problem which is sort of a you know a partly a policing problem partly a-- a sort of a cultural problem for the profession of how we teach people what kinds of AI systems are safe you talk about autonomous weapon system and how pretty much everybody agrees there's too many ways that that can go horribly wrong if this great slaughter BOTS movie that kind of illustrates that beautifully I want to talk that's another there's another topic I I'm happy talking about the I just want to mention that what I see is the third major failure mode which is overuse not so much misuse but overuse of AI that we become overly dependent so I call this the wooly problems if you seen wall-e the movie all right all the humans are on the spaceship and the machines look after everything for them and they just watch TV and drink big gulps and they're all sort of obese and stupid and they sort of totally lost any notion of human autonomy and you know so a in effect right this would happen like the slow boiling frog right we would gradually turn over more and more of the management of our civilization to machines as we are already doing in this you know this if this process continues you know we sort of gradually switch from sort of being the Masters of Technology to just being the guests right so so we become guests on a cruise ship you know which is fine for a week but not not further the rest of eternity right you know and it's almost irreversible right once you once you lose the incentive to for example you know learn to be an engineer or a doctor or a sanitation operative or or any other of the the infinitely many ways that we maintain and propagate our civilization you know if you if you don't have the incentive to do any of that you won't and then it's really hard to recover and of course there's just one of the technologies that could that third failure mode result in that there's probably other technology in general detaches us from it does a bit but the the the difference is that in terms of the knowledge to to run our civilization you know up to now we've had no alternative but to put it into people's heads right and if you oh it's not we're with Google I mean so software in general so I probably if computers in general but but the you know the knowledge of how you know how a sanitation system works you know that's an the AI has to understand that it's no good putting it into Google so I mean we we've always put knowledge in on paper but paper doesn't run our civilization it only runs when it goes from the paper into people's heads again right so we've always propagated civilization through human minds and we've spent about a trillion person years doing that literature right you you can work it out yeah but right is about just over a hundred billion people who've ever lived and each of them has spent about ten years learning stuff and to keep their civilization going and so that's a trillion person years we put into this effort beautiful way to describe all of civilization and now we're you know we're danger of throwing that away so this is a problem that AI console it's not a technical problem it's a you know if we do our job right the AI systems will say you know the human race doesn't in the long run want to be passengers in a cruise ship the human race wants autonomy this is part of human preferences so we the AI systems are not going to do this stuff for you you've got to do it for yourself right I'm not going to carry you to the top of Everest in an autonomous helicopter you have to climb it if you want to get the benefit and so on so but I'm afraid that because we are short-sighted and lazy we're gonna override the AI systems and and there's an amazing short story that I recommend to everyone that I talk to about this called the machine stops written in 1909 by Ian Foster who you know wrote novels about the British Empire and sort of things that became costume dramas on the BBC but he wrote this one science fiction story which is an amazing vision of the future it has it has basically iPads it has video conferencing it has MOOCs it has computer and computer induced obesity I mean literally the whole thing it's what people spend their time doing is giving online courses or listening to online courses and talking about ideas but they never get out there in the real world that they don't really have a lot of face-to-face contact everything is done online you know so all the things we're worrying about now were described in this story and and then the human race becomes more and more dependent on the Machine loses knowledge of how things really run and then becomes vulnerable to collapse and so it's a it's a pretty unbelievably amazing story for someone writing in 1909 to imagine all this loss yeah so there's very few people that represent artificial intelligence more than you Russell so it's all my fault right you're often brought up as the person well Stuart Russell like the AI person is worried about this that's why you should be worried about it do you feel the burden of that I don't know if you feel that at all but when I talk to people like from you talk about set people outside of computer science when they think about this still Russell is worried about AI safety you should be worried too do you feel the burden of that I mean in a practical sense yeah because I'd yet you know a dozen sometimes 25 invitations a day to talk about it to give interviews to write press articles and so on so in that very practical sense I'm seeing that people are concerned and really interested about this are you worried that you could be wrong as all good scientists are of course I worry about that all the time I mean that's that's always been the way that I I've worked you know is like I have an argument in my head with myself right so I have some idea and then I think okay how could that be wrong or did someone else already have that idea so I'll go and you know search and as much literature as I can't to see whether someone else already thought of that or or even refuted it so you know I right now I'm I'm reading a lot of philosophy because you know in in the form of the debate so V over utilitarianism and other kinds of moral moral formulas shall we say people have already thought through some of these issues but you know what one of the things I'm I'm not seeing in a lot of these debates is this specific idea about the importance of uncertainty in the objective that this is the way we should think about machines that are beneficial to humans so this idea of provably beneficial machines based on explicit uncertainty in the objective you know it seems to be you know my gut feeling is this is the core of it it's gonna have to be elaborated in a lot of different directions and there are a lot of lis beneficial yeah but they're I mean it has to be right we can't afford you know hand-wavy beneficial yeah because there are you know whenever we do hand wavy stuff there are loopholes and the thing about super intelligent machines is they find the loopholes you know just like you know tax evaders if you don't write your tax law properly that people will find loopholes and end up paying no taxes and and so you should think of it this way and in getting those definitions right you know it is really a long process you know so you can you can define mathematical frameworks and within that framework you can prove mathematical theorems that yes this will you know this this theoretical entity will be proven beneficial to that theoretical entity but that framework may not match the real world in some crucial way so long process thinking through it of iterating and so on the last question yep you have ten seconds to answer it what is your favorite sci-fi movie about AI I would say interstellar has my favorite robots or beat it Space Odyssey yeah yeah yeah so so tars the robots one of the robots in interstellar is the way a robot should behave and I would say ex machina is in some ways the one like the one that makes you think in a nervous kind of way about a lot where we're going well Stuart thank you so much for talking today pleasure you