Full interview: "Godfather of artificial intelligence" talks impact and potential of AI

how would you describe this current moment in AI machine learning whatever we want to call it I think it's a pivotal moment chat GPT has shown that these big language models can do amazing things and the general public has suddenly caught on yeah we have because Microsoft released something and they're suddenly aware of stuff that people of the big companies have been aware of for the last five years yeah what did you think the first time you used chat GPT um it's well I've used lots of things that came before chat gpg that were quite similar so chat GPD itself didn't amaze me much gpt2 which was one of the earlier language Brands amazed me and a model at Google amazed me that could actually explain why a joke was funny oh really yeah in just natural language it'll tell you yeah you tell it a joke not for all jokes but for quite a few of them it can tell you why it's funny okay and that you it seems very hard to say it doesn't understand when it can tell you why a joke's funny so if chat GPT wasn't all that surprising or impressive were you surprised by The public's reaction to it because the reaction was big yes I think everybody was a bit surprised by how big the reaction was that it was the sort of fastest growing up ever yeah um maybe we shouldn't have been surprised but people the researchers had kind of got used to the fact that these things actually worked yeah you were famously like half a century ahead of the curve on this AI stuff go ahead correct me go ahead not really not really because there were two schools of thought in AI um there was mainstream Ai and then there was years but it was all about friends yeah that thought it was all about reasoning and logic and then there was neural Nets which won't call AI then um which thought that you better study biology because those were the only things that really worked and so mainstream AI based its theories on reasoning and logic and we based our theories on the idea that connections between neurons change and that's how you learn and it turned out in the long run um we came up trumps um but in the short term it looked kind of hopeless well looking back knowing what you know now do you think there's anything you could have said then that would have convinced people I could have said it then but it wouldn't have convinced people and what I could have said then is the only reason that neural networks weren't working really well in the 1980s was because the computers weren't fast enough and the data sets weren't big enough but back in the 80s the big issue was could you expect a big neural network with lots of neurons in it compute nodes and connections between them that learns by just changing the strengths of the connections could you expect that to just look at data and with no kind of innate prior knowledge learn how to do things and people in mainstream AI thought that was completely ridiculous it sounds a little ridiculous it is a little ridiculous but it works and how did you know or why did you Intuit that it would work because the brain works because that's you have to explain how come we can do things and how come we can do things like we didn't evolve for like reading reading's much too recent for us to have had significant evolutionary input to it but we can learn to do that and Mathematics we can learn that so there must be a way to learn in these neural networks yesterday Nick Frost who used to work with you told us that you are not really that interested in creating AI your core interest is just in understanding how the brain works yes I'd really like to understand how the brain works obviously if your failed theories of how the brain works lead to good technology you cash in on that and it gets get grants and things but um I really would like to know how the brain works and I think there's currently a Divergence between the artificial neural networks that are the basis of all this new Ai and how the brain actually works I think they're going different routes now so we're still not going about it the right way that's what I believe this is my personal opinion but all of the big models now use a technique called back propagation which you helped popularize popularize in the 80s very good um and I don't think that's what the brain is doing explain why okay there's a fundamental difference between two different there's two different paths to intelligence so One path is a biological path where you have Hardware that's a bit flaky an analog so what we have to do is communicate by using natural language also by showing people how to do these imitation and things like that but instead of being able to communicate a hundred trillion numbers we can only communicate what you could say in a sentence which is not that many bits per second yeah and so we're really bad at communicating compared with these current computer models that run on digital computers it's almost infinite they're able to that's a communication band with this huge yeah because they're exactly the same model they're clones of the same model running on different computers and because of that they can see huge amounts of data because different computers can see different datas and then they can combine what they learned more than any person could ever comprehend far more than any person could have become and yet somehow we're smarter than them still okay so they're like idiot savants right chat GPT knows much more than any one person if you had a competition about you know how much you know it would just wipe out any one person it was amazing at bar trivia yes it would do amazing it would do me and it can do all you can write poems it can you know um they're not so good at reasoning we're better at reasoning we have to extract our knowledge from much less data so we've got a hundred trillion connections most of which we learn but we only live for a billion seconds which isn't very long whereas things like chat GPT have run for much more time than that to absorb all this data but on many different computers 1986 you publish a thing in nature that is the idea we're going to have a sentence of words and it'll predict the last word yes that was the first language model that's basically what we're doing now yes and no 1986 was a long time ago why still did people not say oh okay I think he's on to something oh because back then if you asked how much data I trained that model on I had a little um simple world of just family relationships there were 112 possible sentences and I trained it on 104 of them and checked out whether it got the last eight right okay and how would it do it got most of the last eight right okay it did better than symbolic AI so it's just that the computers weren't powerful enough at the time the computers we have now are millions of times faster they're parallel but they can do millions of times more competition so I did a little computation if I'd taken the um computer I had back in 1986 and I started learning something on it it would still be running now and not have got there huh um and that's stuff that would now take a few seconds to learn did you know that's what was holding you back I didn't know it I believe that might be what was holding us back but people sort of made fun of the idea that the claim that well you know if I just had a much bigger computer and much more data everything would work and the reason it doesn't work now is because we haven't got enough data on enough compute that's seen as a sort of lame excuse for the fact that your thing doesn't work was it hard in the 90s doing this work in the 90s computers were improving but um yes so there were other learning techniques that are on small data sets worked at least as well as neural networks and were easier to explain and had much fancier mathematical theory behind them and so people within computer science lost interest in neural networks within psychology they didn't because within psychology they're interested in how people might actually learn and these other techniques looked even less plausible than back propagation here which is an interesting part of your background you came to this not because you were interested in computers necessarily but because you were interested in the brain yes I sort of decided I was interested in Psychology originally then I decided we were never going to understand how people work without understanding the brain the idea that you could do it without worrying about the brain that was a sort of fashionable idea back in the 70s but um I decided that wasn't on you had to understand how the brain worked so we fast forward now to the 2000s is there a key moment you think back to is a turning point when it's like okay our side is going to Prevail in this around 2006 we started doing what we call Deep learning um before then it had been hard to get to neural Nets with many layers of representation to learn complicated things and we find better ways of doing it better ways of initializing the networks called pre-training and the p in chat gbt stands for pre-training okay um and the t is Transformer and G is generative and it was actually generative models provided this better way of pre-training neural ads so the seeds of it were there in 2006 in by 2009 we'd already produced something that was better than the best speech recognizers and recognizing which phoneme you were saying using different technology than all the other speech recognizers were then then the standard approach which you've been tuned for 20 for 30 years there were other people using neural Nets but they weren't using deep neural ads and then there's a big thing happens in 2012. yes to actually two big things okay one is that the research we'd done in 2009 done by two of my students over a summer that led to better speech recognition that got disseminated to all the big speech recognition Labs that Microsoft and IBM and Google and in 2012 Google was the first to get into a product and suddenly speech recognition on the Android became as good as Siri if not better so that was a deployment of deep neural Nets applied to speech recognition three years earlier at the same time as that happened within a few months of that happening two other students of mine developed an object recognition system that would look at images and tell you what the object was and it worked much better than previous systems how did this system work okay there was someone called Faith a Lee in her collaborators that created a big database of images like a million images of a thousand different categories you'd have to look at an image and give your best guess about what the primary object was in the image so the images would typically have one object in the middle yeah and I didn't have to say things like bullet train or Husky or and the other systems were getting like 25 errors and we were getting like 15 errors okay within a few years that 15 went down to three percent which was about human level and can you explain in a way people would understand the difference between the way they were doing it and the way your team did it I can try that's all we can hope for okay so suppose you wanted to recognize a bird in an image okay the image itself let's suppose it's a 200 by 200 image that's got 200 times 200 pixels and each pixel has three values for the three colors RGB and so you've got 200 by 200 by 3 numbers in the computer it's just numbers in the computer right and the job is to take those numbers in the computer and convert them to a string that says bird so how would you go about doing that and for 50 years people in standard AI tried to do that and couldn't got a bunch of numbers into a label that says bird so here's a way you might go about it at the first level of features you might make feature detectors things that you take little combinations of pixels okay so you might make a feature detector that said look if all these pixels are dark and all these pixels are bright I'm going to turn on okay and so that feature detector would represent an edge here okay a vertical Edge you might have another one that said if all these pixels are bright and all these picks as a dark I'll turn on that would be if each detector that represent in a horizontal Edge okay and you can have others for edges of different organs we had a lot of work to do all we've done is made a box right so we've got to have a whole lot of feature settings like that and that's what you actually have in your brain okay so if you look in a cattle monkey cortex it's got feature detectors like that um then at the next level you might say if you were worried up by hand you would create all these little feature detectors at the next level you would say um okay suppose I have two two Edge detectors that join at a fine angle that could just be a beak so the next level up will have a feature detector that detects two of the lower level detectors joining a fine angle okay we might also notice a bunch of edges that sort of form a circle we might have a detector for that okay then the next level up we might have a detector that says hey I found this beak like thing and I find a circular thing in roughly the right spatial relationship to make the eye and the beak of a bird and so at the next level up you'd have a bird detector that says if I see those two there I think it might be a bird okay and you could imagine wiring all that up by hand okay and so the idea of back propagation is just put in random weights to begin with and now the featured textures would just be rubbish whether it be garbage okay okay but look to see what it predicts and if it happened to predict bird it wouldn't but if it happened to leave the weights alone um you got it right the connection strings but if it predicts cat then what you do is you go backwards through the network and you ask the following question and you can ask this with a branch of mathematics called calculus but you just need to think about the question and the question is how should I change this connection strength so it's less likely to say cat I'm more likely to say bird that's called the ER the error the discrepancy right okay and you figure out for every connection strength how I should change a little bit to make it more likely to say bird and less likely to say cat and a person's figuring that out or the algorithm is set to work a person has said this is a bird so a person looked at the image and said it's a bird it's not a cat it's a bird so that's a label supplied by a person but then the algorithm back propagation is just a way of figuring out how to change every connection strength to make it more likely to say burden less likely to say cat it just keeps trying keep turning it just keeps doing that and now if you showed enough birds and enough cats when you showed a bird it'll say burden when you showed a cat it'll say cat and it turns out that works much much better than trying to wire everything by hand and that's what your students did on this image database that's why they did on the image check device yes and they got it to work really well now they were very clever students in fact one of them Ilya sutskova is also one of the main people buying chat gbt so that was a huge moment in Ai and chat gbt was another huge moment and he was actually involved in both of them yeah yeah I don't know maybe it's cold in the room you got to the end of the story I go shivers the idea that you do this little dial thing and it says bird it feels like just an amazing breakthrough yeah I it was um mainly because the other people in computer vision thought okay so these neural Nets they work for simple things like recognizing a handwritten digit but that's not a real complicated image with sort of natural background with stuff it's never going to work for these big complicated images and then suddenly it did and to their credit the people who've been really staunch critics of neural Nets and said these things are never going to work when they worked they did something that scientists don't normally do which she said oh it worked we'll do that people see it as a huge shift yes it was quite impressive that they flipped very fast because they saw that it worked better than what they were doing yeah you make this point that when people are thinking both about their machines and about ourselves in the way we think we think language in language out must be language in the middle yes and this is an important misunderstanding yeah can you just explain that I think that's complete rubbish yeah um so if that were true and it were just language in the middle you'd have thought that approach which is called symbolic AI yeah would have been really good at doing things like machine translation which is just taking English in and producing French art or something your thought manipulating symbols was the right approach for that but actually neural networks work much better than Google translate when they switched from doing that kind of approach to using your alerts really much better what I think you've got in the middle is you've got millions of neurons and some of them are active and some of them aren't and that's what's in there the only place you'll find the symbols are at the input and at the output we're not exactly at the University of Toronto we're close to University of Toronto at universities here and around the world we're teaching a lot of people to code does this still make sense to be teaching so many people to code um I don't know the answer to that in about 2015 I famously said it didn't make sense to be teaching Radiologists to recognize things in images and because within the next five years um computers will be better at it yeah are we all about to be Radiologists though well then Coopers are not better I was wrong it's going to take 10 years not five I wasn't wrong in spirit I just got I factor of two computers are now comparable with Radiologists at a lot of medical images yeah they're not way better at all of them yet but they will get better yeah so I think there'll be a while when it's still worth having coders and I don't know how long that'll be but we'll need less of them yeah maybe or we'll need the same number and they'll be able to achieve a whole lot more um was talking about cohere we went over and visited them yesterday you're an investor uh in them maybe maybe the question is just like how they convince you what was the pitch that convinced you I want to invest in this so they're good people um and I've worked with several of them yeah and they were one of the first companies to realize that you need to take these big language models being developed to places like Google and other places open Ai and um make them available to companies so there's going to be it's going to be enormously valuable to companies to be able to use these big language models um and so that's what they've been doing and they've got a significant lead in that so that's why I think they're going to be successful another thing you've said that I just find fascinating so I want to get you to talk about it is the idea that there'll be kind of a new kind of computer that will be sent to this problem what is that idea so there's the biological route to intelligence where every brain is different and we have to communicate Knowledge from one to another by using language and there's the current AI version of neural Nets where you have identical models running on different computers and they can actually share the connection strength so they can share billions of numbers this is how we make a bird yeah so they can share all the connection strengths for recognizing a bird and one can learn to recognize cats and the other can learn to recognize birds and they can share their connection strengths and now each of them can do both things right and that's what's happening in these big language models they're sharing but that only works in digital computers because they have to be able to do identical things and you can't make different biological brains behave identically so you can't share the connections yeah but why wouldn't we stick with digital computers because of the power consumption you need a lot of power it's getting less as chips get better but um you need a lot of power to do this to run a digital computer you have to run it at such high power that it pays exactly in the right way whereas if you're willing to run at much lower power like the brain is then you'll allow a bit of noise and so on but that particular system will adapt to the kind of noise in that particular system and the whole thing will work even though you're not running it at such high power that it pays exactly as you intended and the difference is the brain runs on 30 Watts a big AI system needs like a megawatt so we're training on 30 watts and these big a systems are using because they've got lots of copies of the same thing they're using like a megawatt so you know you're talking factor of the order of a thousand in the power requirements and so I think there's going to be a phase when we train on digital computers but once something's trained we run it on very low Power Systems so if you want your toaster to be able to have a conversation with you and you want to chip in it that only costs a couple of dollars but can do chat gbt that'd better be a low power animal chip what are kind of like the next things you think this technology will do that will impact people's lives it's hard to pick one thing I think this it's going to be everywhere right it's already sort of sort of getting to me everywhere chat GPT is just made a lot of people realize it it's going to be everywhere but it's already you know when Google does search it uses big neural Nets to help decide what's the best thing to show you we're at a transition point now where chat gbt is this kind of idiot savant and it's also doesn't really understand about truth is being trained on lots of inconsistent data it's trying to predict what someone will say next on the web yeah and people have different opinions and it has to have a kind of blend of all these opinions so that it can model what anybody might say it's very different from a person who tries to have a consistent world view yeah particularly if you want to act in the world um it's good to have a consistent world view and I think what's good one thing that's going to happen is we're going to move towards systems that um can understand Different World Views and can understand that okay if you have this world view then this is the answer and if you have this other world view then that's the answer we get our own truths well that's the problem right because what you and I probably believe unless you're an extreme relativist is that actually is a truth to the matter certainly on many topics on many topics or even most topics yeah like the Earth is actually not flat it just looks flat right yeah so do we really want a model that says well for some people like we don't know that's going to be a big issue and we don't know we don't know how to deal with other present yeah and I don't think Microsoft knows how to deal with it either they don't and it seems to be a huge governance challenge who makes these decisions it's very tricky things you don't want some big for-profit company deciding what's true but they're controlling how we turn the neurons Google is very careful not to do that at present um what Google will do is refer you to relevant documents which will have all sorts of opinions in them well they haven't released their chat product at least as we speak right um but we've seen at least the people that have released chat products feel like there are certain things they don't want to be said by their voice right so they go in there and meddle with it so it won't say offensive things yeah but there's a limit to what you can do that way there's always going to be things you didn't think of right yeah so I think Google is going to be far more careful than Microsoft when it does release the chatbot yeah and it'll probably um come with lots of warnings this is just a chatbot and and don't necessarily believe what it says careful in the labeling or carefullying in the way they meddle with it so it doesn't do lousy things all of those things careful in how they present it as a product and careful in how they train it yeah um and do a lot of work to prevent it from saying bad things and well who gets to decide what a bad thing is some bad things are fairly obvious but many of the most important ones are not yes so that is a big open issue at present I think Microsoft was extremely Brave to release chat GPT yeah do you see this as like a larger some people see this as a larger societal thing we need either regulation or big public debates about how we handle these issues well when it comes to the issue of what's true I mean do you want the government to decide what's true speak problem right yeah you don't want the government doing it either I'm sure you've thought deeply on this question for a long time how do we navigate the line between you just send it out into the world and we find ways to curate it like I say I don't know the answer and I don't believe anybody really knows how to handle these issues we're going to have to learn quite fast how to handle these issues because it's a big problem with president but yeah how how it's going to be done I don't know but I suspect as a first step at least these big language models are going to have to understand that there are different points of view and that completions it makes a relative to a point of view some people are worried that this could take off very quickly and we just might not be ready for that does that concern you it does a bit until quite recently I thought it was going to be like 20 to 50 years probably have general purpose AI yeah and now I think it may be 20 years or less so okay some people think it could be like five is that silly I wouldn't completely rule that possibility out now and where's pre a few years ago I would have said no way okay and then some people say AGI could be massively dangerous to humanity because we just don't know what a system that's so much smarter than us will do do you share that concern I do a bit um I mean obviously what we need to do is make this synergistic have it so it helps people and I think the main issue here well one of the main issues is the political systems we have so I'm not confident that President Putin is going to use AI in ways to help people like even if say the US and Canada and a bunch of countries say okay we're going to put these guard rails up then how do you yeah it's it's pretty for things like autonomous lethal weapons okay we'd like to have something like Geneva conventions like chemical weapons people decided they were so nasty they weren't going to use them except just occasionally but I mean basically they don't use them people would love to get a similar treaty for autonomous lethal weapons but I don't think there's any way they're going to get that I think if Putin had an autonomous lethal weapons he would use them right away this is like the most pointed version of the question and you can just laugh it off or not answer it if you want but what do you think the chances are of AI just wiping out Humanity can we put a number on that it's somewhere between 100 percent okay I think I think it's not inconceivable okay that's all I'll say I think if we're sensible we'll try and develop it so that it doesn't but what worries me is the political Citizens We're in yeah where it needs everybody to be sensible there's a massive political challenge it seems to me and there's a massive economic challenge in that you can have a whole lot of individuals who pursue the right course and yet the profit motive of Corporations may not be as cautious as the individuals who work for them Maybe I mean I only really know about Google that's the only Corporation I've worked in and they've been among the most cautious they're extremely cautious about AI because they've got this wonderful search engine that gives you the answers you want to see and they can't afford to risk that yeah whereas Microsoft has Bingham well if being if being disappeared in Microsoft would hardly notice yeah but it was easy for Google to take it slow when there wasn't someone nipping at their heels and this seems to be exactly yeah so Google has actually been in the lead I mean Transformers were invented at Google right the big language models early ones were at Google but and they kind of kept it in your lab they're being much more conservative and I think it might be so yes but now they feel this pressure yeah and so they're trying to they're developing a system called bad that they're going to put out there and they're doing lots and lots of testing of it um but they're going to be I think a lot more cautious than Microsoft you mentioned autonomous weapons let me give you a chance just tell the story what's the connection between that and how you ended up in Canada okay there were several reasons I came to Canada but one of them was certainly not wanting to take money from the U.S defense department this was at the time of Reagan when they were mining the harbors in Nicaragua and it was interesting I was at a big university in Pittsburgh and I was one of the few people there who thought that mining the harbors in Nicaragua was really wrong [Music] um so I felt like a fish out of water and you saw that this was where the money was coming from for this kind of work so that department almost all lemon came from the Transformer you started to talk about the concerns that bringing this technology to Warfare could present what what are your concerns oh that um the Americans would like to replace their soldiers by autonomous by AI soldiers and they're trying to work towards that and what evidence do you see of them I'm on a mailing list from the U.S defense department I'm not sure they know I'm on the meeting list it's a big list they didn't notice you're there you might be off tomorrow I might be off tomorrow what's on the list oh they just describe various things they're going to do there's some disgusting things on there okay we'll discussed you the thing that disgusted me most was a proposal for a self-healing minefield so the idea is look at it from the point of view of the minefield when some silly civilian trespasses into the Minefield they get blown up and that makes a hole in the poor my field so it's got a gap in now so it's not fit for purpose yeah so the ideas may be nearby Minds could communicate or maybe they could move over a bit and they call that healing and it was just the idea of talking about healing for these things that blow the legs off children I mean and the healing being about the Minefield healing yeah that disgusted me there is this argument that though the autonomous systems might play a role in helping the Warfighter it's ultimately a human making the decision here's what worries me if you wanted to make an effective autonomous Soldier you'd need to give it you'd need to give it the ability to create sub goals in other words it has to realize things like okay I want to kill that person over there but to get over there how am I going to get over there and then it has to realize well if I could get to that road I could get there more quickly so it has a sub goal of getting to the road so as soon as you give it the ability to create his own sub goals it's going to become more effective and so people like Putin are going to want robots like that and but as soon as it's got an ability to create sub goals you have What's called the alignment problem which is how do you how are you sure it's not going to create sub goals that are going to be um not good for people not good for you who knows who's on that road who knows on that road and if these systems are being developed by the military the idea of wiring in some rule that says never hurt a person well that's they're being designed to eat at people yeah do you see any way out of this is it a treaty is it what is it I think the best batch is something like a Geneva Convention but it's going to be very difficult I think if there was a lot of public outcry that might persuade I can imagine the Biden Administration going for something like that with enough public outcry but then you have to deal with Putin yeah um okay we've covered so much I think I have like two more things there's one more thing I want to say yeah yeah go for it you can ask me the question some people say that these big models are just autocomplete well on some level the models are autocomplete we're told that the large language models they're just predicting the next word is that not so simple no that's true they are just predicting the next word and so they're just auto-complete but ask yourself the question of what do you need to understand about what's being said so far in order to predict the next word accurately and basically you have to understand what's being said to bring language so you're just already complete too um in the sentences they are you can predict the next word maybe not as well as chat gbt yeah but to do that you have to understand the sentence so let me give you a little example from translation it's a very Canadian example okay suppose I take the sentence the trophy would not fit in the suitcase because it was too big and I want to translate that into French well when I say the trophy would not fit in the suitcase because it was too big you assume the it refers to Trophy I do and in French trophy has a particular gender so you know what pronoun to use yeah but suppose I say the trophy would not fit in the suitcase because it was too small now you think that it refers to suitcase right and that has a different gender in French so in order to translate that sentence to French you have to know when it wouldn't fit in because it was too big it's the trophy that's too big and when it wouldn't fit in because it was too small it's a suitcase that's too small and that means you have to understand about spatial relations and containment and so on yeah so you have to understand just to do machine translation or to predict that pronoun if you want to predict that pronoun you've got to understand what's being said it's not enough just to treat it as a string of words yeah yeah I mean this gets me to another thing you've pointed out which is kind of a either exciting or troubling idea that you working intimately in this field for as long as anyone describe the progress as well we had this idea and we tried it and it worked and so we get a couple decades of back propagation we have this idea for a Transformer now we'll do some trick but it could there's hundreds of other ideas that haven't been tried out yes so I think even if we didn't have any new ideas just making computers go faster and getting more data will make all this stuff work better we've seen that as they scale up chat gbt it's not radically new ideas there I think it's just more connections and more data to train it with yeah but in addition to that there's going to be new ideas like Transformers and they're going to make it work much better are we close to the computers coming up with their own ideas for improving themselves um yes we might be and then it could just go fast that's an issue right we have to think hard about how to control that yeah can we we don't know we haven't been there yet but we can try okay that seems kind of concerning um yes do you have any you're seen as like a Godfather of this industry do you have any concern about what you've wrought I do a bit on the other hand I think whatever's going to happen is pretty much inevitable that is one person stopping doing research wouldn't stop this happening if my impact is to make it happen a month earlier that's about the limit of what one person can do there's this idea of the and I'm going to get it wrong the short Runway and the long takeoff maybe we need time to prepare or maybe it's better if it happens quickly because then people will have urgency around the issue rather than like creep creep creep do you have any like thoughts on this I think time for repair would be good and so I think it's very reasonable for people to be worrying about those issues now even though it's not going to happen in the next year or two yeah people should be thinking about those issues we haven't even touched on job displacement um which is just my mistake for not bringing it up is this just going to eat up just job after job after job after job I think it's going to make jobs different people are going to be doing the more creative end and less of the routine end but what's the creative if it can write the poem and make the movie and all of that well if you go back in history and look at ATMs these cash machines came along and people said that's the end of bank tellers it wasn't actually the end of Bank tell us um the bank tellers now deal with more complicated things and take coders so people say you know these things can do simple coding and usually get it right you just need to get it to write the program and then just check it so you'll be able to work 10 times as fast well either you could have 10 of the programmers well you could have the same number the program as producing 10 times as much stuff yeah and I think there's going to be a lot of trade-offs like that you'll the once these things start being creative they'll be hugely more stuff created this is the biggest technological advancement sense is this another Industrial Revolution what is this how should people think of it I think it's comparable in scale with the Industrial Revolution or electricity electricity maybe the wheel or maybe the wheel yeah that was earlier yeah okay so buckle up yeah one of the reasons I got a Toronto got a big lead in AI is because of the policies of the granting agencies in Canada which don't have much money but they use some of that money to support curiosity-driven basic research okay and so in the states the funding comes you have to say what what products you're going to produce with it and so on yeah yeah some of the government money quite a lot of it is given to professors to employ graduate students and other researchers to explore things they're curious about and if they seem to be good at that then they get more money three years later and that's what supported both Joshua Benja and me it was money for curing curiosity driven basic research and we've seen that before even through Decades of not being able to show much yes even through decades not being able too much so that's one thing that happened in Canada another thing that happened was there's a Canadian organization called the Canadian Institute for advanced research that provides extra money to professors in areas where Canada is good and provides money for professors to interact with each other when they're far apart like in Vancouver and Toronto but also to interact with researchers in other parts of the world um like America and Britain and Israel and so on and see far setup of programming AI is set at one originally in the 1980s which is the one that brought me to Canada which was in symbolic AI yet you came I was an oddball okay I was kind of weird because I did this stuff everybody else thought was nonsense they recognized that I was good at this kind of nonsense and so if anyone's going to do the nonsense it might as well be him one of my letters of recommendation said that it said you know I don't believe in this stuff but if you want somebody to do it Jeff engines to go okay um and then after that program finished I went back to Britain for a few years and then when I came back to Canada they decided to fund a program in deep learning essentially sentience I think you have complaints with the even just how you define that right yeah I when it comes to sentience I'm amazed that um people can confidently pronounce these things are not sentient and when you ask them what they mean by sentient they say well they don't really know so how can you be confident they're not sentient if you don't know what sentient means so maybe they are already who knows I think whether they're sentient or not depends on what you mean by sentient so you better Define what you mean by sentient before you try and answer the question are they sentient does it matter what we think or does it only matter whether it effectively acts as if it is sentient it's a very good question Matt and what's your answer I don't have one sure okay because if it's not sentient but it decides for whatever reason that it believes it is and it needs to achieve some goal that is contrary to our interests but it believes in its interests does it really matter if in any human I think a good a good context to think of this in is an autonomous Lethal Weapon yeah okay so it's all very well saying it's not sentient but when it's hunting you down to shoot you um yeah you're going to start thinking it's sentient we're not really caring not an important standard anymore the kind of intelligence we're developing is very different from our intelligence so it's this idiot savant kind of intelligence yes so it's quite possible as if it is a tool center is essentially in a somewhat different way from us but your goal is to make it more like us and you think we'll get there and my goal is to understand us oh okay no but yeah and I think I think the way you understand us is by building things like us okay so that's I mean the physics is called Richard Feynman said you can't you don't can't understand things unless you can build them that's the real test of do you understand it and so you've been building so I've been building yeah

Channel: CBS Mornings

Views: 1,217,944

Keywords:

Id: qpoRO378qRY

Length: 42min 30sec (2550 seconds)

Published: Sat Mar 25 2023

Reddit Comments

[u/FomalhautCalliclea]

25:51:

Q: "Some people are worried that this could take off very quickly and we just might not be ready for that, does that concern you?"

A: "It does a bit. Until quite recently i thought it was gonna be like 20 to 50 years before we have general purpose AI, and, now i think it may be 20 years or less, so...

Q: "Some people think it could be like 5. Is that silly?"

A: "I wouldn't completely rule out this possibility out now. A few years ago i would have said no way."

Q: "And then some people say AGI could be massively dangerous to humanity cause we just don't know what a system that's so much smarter than us will do. Do you share that concern?"

A: "I do a bit. Obviously what we need to do is make this synergistic, have it so it helps people. I think the main issue here is, well one of the main issues is the political systems we have. I'm not confident that president Putin is gonna use AI in ways that help people."

EDIT:

35:50:

Q: "Are we close to the computers coming up with their own ideas for improving themselves?"

A: "Yes we might be."

Q: "And then it could just go fast."

A: "That's an issue, right, we have to think hard about how to control that."

37ish : job displacement won't be a problem because of huge increase in productivity (example of one programmer doing 10x more work than before and all programmers producing 10x more than before).

38:08:

Q: "Is this a another industrial revolution ?"

A: "I think it's comparable in scale with the industrial revolution or electricity. Or maybe the wheel."

(Questionner in awe).

[u/agonypants]

This interview is incredible. Hilton’s a master at explaining this complex topic for a general audience.

[u/7734128]

Great interview.

[u/cinematicallystupid]

This was really interesting

[u/hypnomancy]

I think it's kind of hilarious that he only got into AI specifically because he wanted to learn how the human brain worked better since going into that field might yield those types of results lol. He originally was in the psychology field but came to the conclusion it was worthless since we barely understood the human mind. Guy seems like a great dude though

[u/No_Ninja3309_NoNoYes]

I was planning to watch this tomorrow, but I saw a short summary. It doesn't seem that there's a clear path to AGI. Obviously we all agree that AI will have enormous impact only not on the time and implementation details. Society has some inertia, so I don't expect top politicians to talk about job losses this year. I think that it's an important question. When will AI be a mainstream topic?