Tom Mitchell: Welcome to machine learning. Tom Mitchell: How did we get here? Tom Mitchell: I'm Tom Mitchell. Tom Mitchell: Today's episode is an interview Tom Mitchell: with Geoff Hinton, one of the Tom Mitchell: pioneers in the field of neural Tom Mitchell: network learning. Tom Mitchell: Geoff started out early, as you'll hear, in the seventies, Tom Mitchell: nineteen seventies, and has continued working in neural Tom Mitchell: networks ever since.
Tom Mitchell: During the period of the Tom Mitchell: nineteen nineties and early two Tom Mitchell: thousand, when neural networks Tom Mitchell: were really in disfavor in the Tom Mitchell: field of machine learning, Geoff Tom Mitchell: nevertheless persisted, and he Tom Mitchell: co-led the triumphant return of Tom Mitchell: neural networks in the form of Tom Mitchell: deep networks in the twenty ten Tom Mitchell: ish period.
Tom Mitchell: In twenty eighteen, Geoff, along with Joshua Bengio and Yann Tom Mitchell: LeCun, received the Turing Award in Computer Science. Tom Mitchell: That's the highest award given Tom Mitchell: in the field of computer science Tom Mitchell: to researchers in twenty twenty Tom Mitchell: four.
Tom Mitchell: Jeff, along with John Hopcroft, Tom Mitchell: were awarded the twenty twenty Tom Mitchell: four Nobel Prize in Physics for Tom Mitchell: their work on artificial neural Tom Mitchell: networks. Tom Mitchell: I hope you enjoy the episode. Tom Mitchell: I'm pleased to have with me today Geoff Hinton, one of the Tom Mitchell: pioneers of machine learning. Tom Mitchell: Geoff, great to see you again. Geoffrey Hinton: Thanks for inviting me.
Tom Mitchell: What I'd like to do today is get two things. Tom Mitchell: Two types of things from you. Tom Mitchell: One is your own personal history and how you got into this field Tom Mitchell: and what happened after you did. Tom Mitchell: And the second is kind of your perspective on the whole field Tom Mitchell: of machine learning, AI, and how things are turning out.
Geoffrey Hinton: So when I was in high school, I had a very smart friend who was Geoffrey Hinton: a very good mathematician and read widely, unlike me.
Geoffrey Hinton: And he came into school one day Geoffrey Hinton: and talked about how memories Geoffrey Hinton: might be distributed over the Geoffrey Hinton: brain rather than localized in a Geoffrey Hinton: place like a hologram, because Geoffrey Hinton: this would have been nineteen Geoffrey Hinton: sixty six and holograms had just Geoffrey Hinton: come out. Geoffrey Hinton: And that got me interested in Geoffrey Hinton: how our memories represented in Geoffrey Hinton: the brain.
Geoffrey Hinton: And I've been interested in that ever since. Tom Mitchell: Now, when I met you, we were both at Carnegie Mellon. Tom Mitchell: It was nineteen eighty six when, Tom Mitchell: uh, we really got to do some Tom Mitchell: work together or teach a course Tom Mitchell: together. Tom Mitchell: What? Tom Mitchell: How did you get from nineteen Tom Mitchell: sixty six up till Eighteen Tom Mitchell: eighty six. Tom Mitchell: What was the path? Geoffrey Hinton: Slightly
Geoffrey Hinton: rocky. So I went to Geoffrey Hinton: university. I studied physics, chemistry and physiology, and in Geoffrey Hinton: physiology. In the last term, they're going to teach us, um, how the Geoffrey Hinton: central nervous system Geoffrey Hinton: worked. And I was very Geoffrey Hinton: excited. And they taught us how action potentials are conducted Geoffrey Hinton: along an axon, which wasn't what I meant by how it Geoffrey Hinton: worked. And so I switched to
Geoffrey Hinton: philosophy. That was even less Geoffrey Hinton: useful. And then I switched Geoffrey Hinton: to psychology, which was Geoffrey Hinton: completely Geoffrey Hinton: hopeless. Um, and then I became a Geoffrey Hinton: carpenter. And after I'd been a Geoffrey Hinton: carpenter for about nine months, I met Geoffrey Hinton: a Geoffrey Hinton: carpenter. And he was so much better than me, I decided it'd be easier Geoffrey Hinton: to be an
Geoffrey Hinton: academic. Um, so I went to graduate Geoffrey Hinton: school in Edinburgh, um, Geoffrey Hinton: with Longuet-higgins, who Geoffrey Hinton: had published interesting stuff Geoffrey Hinton: on, um, using neural nets for Geoffrey Hinton: a Geoffrey Hinton: memory. Unfortunately, around the time I arrived, Winograd's thesis Geoffrey Hinton: came out and he switched his allegiance to symbolic AI Geoffrey Hinton: and gave up on neural
Geoffrey Hinton: nets. And so I spent five years as Geoffrey Hinton: his graduate student with Geoffrey Hinton: him, trying to persuade me to give Geoffrey Hinton: up neural Geoffrey Hinton: nets. And he never Geoffrey Hinton: succeeded. Um, in the end, he was very helpful to Geoffrey Hinton: me. But for a long time, there was a lot of argument about how Geoffrey Hinton: I should really be doing symbolic AI, and all this neural Geoffrey Hinton: net stuff was complete
Geoffrey Hinton: nonsense. And everybody else in Geoffrey Hinton: Edinburgh believed that neural nets Geoffrey Hinton: were Geoffrey Hinton: nonsense. Um, we actually a couple of Geoffrey Hinton: exceptions. There was a post-doc called David Willshaw who'd Geoffrey Hinton: done associative memory, and he basically done something Geoffrey Hinton: quite like Hopfield
Geoffrey Hinton: nets. But a long time before Geoffrey Hinton: Hopfield and Aaron Sloman was a Geoffrey Hinton: visitor for a while, and he was Geoffrey Hinton: more Geoffrey Hinton: sympathetic. Um, but basically they all knew it was Geoffrey Hinton: rubbish. And they would explain to me Geoffrey Hinton: how neural nets can't even Geoffrey Hinton: do
Geoffrey Hinton: recursion. So because everything, Geoffrey Hinton: everybody believed in recursion, then, Geoffrey Hinton: um, I actually figured out how to Geoffrey Hinton: do true recursion in a Geoffrey Hinton: neural network and implemented it on Geoffrey Hinton: a machine Geoffrey Hinton: with. I think by then it had one hundred and ninety two Geoffrey Hinton: kilobytes of memory, and it was only shared by forty Geoffrey Hinton: people. Um, but it had a huge disk that had two
Geoffrey Hinton: megabytes. So you never ran out of your Geoffrey Hinton: memory? Um, because you used virtual Geoffrey Hinton: memory. And I actually implemented Geoffrey Hinton: a little neural net that did Geoffrey Hinton: true Geoffrey Hinton: recursion. That is, in the recursive call, it used the same neurons and Geoffrey Hinton: the same connection strings for the recursive call as it did for Geoffrey Hinton: the high level
Geoffrey Hinton: call. Now, if you do that, of course it had to offload all Geoffrey Hinton: the parameters of the high level call into some short term Geoffrey Hinton: memory onto a Geoffrey Hinton: stack. Eventually, and I figured Geoffrey Hinton: out how to implement a stack Geoffrey Hinton: with associative memory in a Geoffrey Hinton: neural
Geoffrey Hinton: net. Um, so I had this little neural net running that was doing Geoffrey Hinton: full recursion in neural nets, and that was the first talk I Geoffrey Hinton: gave. And people were very Geoffrey Hinton: puzzled. They said, why would you want to do recursion in a neural Geoffrey Hinton: net? I mean, it's so easy to do in pop two, which was our our Geoffrey Hinton: sort of, uh, unfortunate bastard child of Pascal and Geoffrey Hinton: Lisp. Um, although I don't think Pascal existed
Geoffrey Hinton: then. Um, Geoffrey Hinton: so. Geoffrey Hinton: Yeah. So I keep meaning to go back to Tom Mitchell: I was going to ask, is there a Tom Mitchell: future to recursion for neural Tom Mitchell: nets? Geoffrey Hinton: Oh, yes. Geoffrey Hinton: I mean, to do true recursion, Geoffrey Hinton: you have to use the same neurons Geoffrey Hinton: and weights for the recursive Geoffrey Hinton: call.
Geoffrey Hinton: That means you have to have a stack, something like a stack, Geoffrey Hinton: to store the parameters of the high level call. Geoffrey Hinton: That all works if you have fast weights. Geoffrey Hinton: So that was the first thing I did with fast weights in Geoffrey Hinton: nineteen seventy three. Geoffrey Hinton: I should say fast weights were invented by Schmidhuber in Geoffrey Hinton: nineteen ninety, something. Tom Mitchell: Fair enough.
Tom Mitchell: Okay, so then, um, you moved on from Edinburgh. Tom Mitchell: Did you come directly to Tom Mitchell: Carnegie Mellon from there, or Tom Mitchell: how did. Geoffrey Hinton: Oh, no. No. Um, I dropped out Geoffrey Hinton: again after I finished my Geoffrey Hinton: thesis. Geoffrey Hinton: I dropped out and became a Geoffrey Hinton: teacher in a free school in Geoffrey Hinton: London. Geoffrey Hinton: Um, it was voluntary, I was unpaid.
Geoffrey Hinton: They were rough, emotionally disturbed inner city kids. Geoffrey Hinton: And after a few months of that, Geoffrey Hinton: I again decided academia might Geoffrey Hinton: be easier. Geoffrey Hinton: Um, so I went back to a post-doc with Aaron Sloman in Sussex. Geoffrey Hinton: Higgins had moved from Edinburgh to Sussex and um, as I was Geoffrey Hinton: finishing my PhD, I got a post-doc with Aaron Sloman. Geoffrey Hinton: Um, and there were no proper faculty jobs in Britain.
Geoffrey Hinton: Then there was one job in the Geoffrey Hinton: whole of Britain which Alan Geoffrey Hinton: Bundy got, um, and so I applied Geoffrey Hinton: for jobs in the States, and I Geoffrey Hinton: got a job as a postdoc in UCSD, Geoffrey Hinton: um, with Don Norman and Dave Geoffrey Hinton: Rumelhart. Geoffrey Hinton: And I really got along very well with Dave Rumelhart, and that Geoffrey Hinton: made a huge difference.
Geoffrey Hinton: So I moved from a country where it was a sort of small country, Geoffrey Hinton: Britain, and there was only room for one ideology, and the Geoffrey Hinton: ideology was symbolic AI and neural nets was just rubbish. Geoffrey Hinton: And I moved to the states where, um, on the West coast, on the Geoffrey Hinton: East Coast, it was symbolic AI but on the West Coast they were Geoffrey Hinton: kind of more open.
Geoffrey Hinton: And in particular, Don Norman Geoffrey Hinton: and Dave Rumelhart thought Geoffrey Hinton: neural nets were worth Geoffrey Hinton: considering. Geoffrey Hinton: Um, so it was a huge liberation Geoffrey Hinton: to be in a place where neural Geoffrey Hinton: nets were regarded as not Geoffrey Hinton: obvious nonsense. Geoffrey Hinton: And. Geoffrey Hinton: Um, I went there. Geoffrey Hinton: I, I got to meet Terry Sinofsky, who I invited to a conference.
Geoffrey Hinton: And we'd been sort of lifelong friends and collaborators. Geoffrey Hinton: Um, I got to meet Francis Crick later on, who was there. Geoffrey Hinton: So I was there for a couple of years. Geoffrey Hinton: And then I got a job in Geoffrey Hinton: Cambridge in the Applied Geoffrey Hinton: Psychology Research Unit, um, Geoffrey Hinton: where I was meant to do applied Geoffrey Hinton: psychology.
Geoffrey Hinton: And I was strongly reminded of William James's comment about Geoffrey Hinton: applied psychology, which is to do applied psychology, you have Geoffrey Hinton: to have something to apply. Geoffrey Hinton: Um. But I actually did some interesting stuff.
Geoffrey Hinton: It was just around the time some Geoffrey Hinton: workstations were coming out, Geoffrey Hinton: and they had a contract with the Geoffrey Hinton: British Telephone Company to Geoffrey Hinton: help with network management and Geoffrey Hinton: network management. Geoffrey Hinton: Then was all done by hand.
Geoffrey Hinton: And you had information about, Geoffrey Hinton: um, the loads on various Geoffrey Hinton: switching centers, and the Geoffrey Hinton: information was on a huge wall Geoffrey Hinton: that was twenty feet high and Geoffrey Hinton: worked like you sometimes see at Geoffrey Hinton: train stations. Geoffrey Hinton: It was little flaps with white letters on that come down. Geoffrey Hinton: They sort of rotate around until you get the right flap.
Geoffrey Hinton: And so you could see all these numbers, um, that said how busy Geoffrey Hinton: each switching station was. Geoffrey Hinton: Um, and I figured a sun Geoffrey Hinton: workstation could do that and Geoffrey Hinton: would be a lot cheaper, and the Geoffrey Hinton: resolution wasn't that good Geoffrey Hinton: then.
Geoffrey Hinton: So I had to figure out if you could display the states of all Geoffrey Hinton: the switching stations in Britain on the screen of a sun Geoffrey Hinton: workstation, and you couldn't type the names, but if you had Geoffrey Hinton: two letters for each name, you could get two letters there. Geoffrey Hinton: And I worked on a display with Geoffrey Hinton: two letter names, and there were Geoffrey Hinton: a large number of switching Geoffrey Hinton: stations.
Geoffrey Hinton: There were hundreds. Geoffrey Hinton: And the question was could could Geoffrey Hinton: an operator remember which they Geoffrey Hinton: were? Geoffrey Hinton: So I actually taught myself to Geoffrey Hinton: remember all those two letter Geoffrey Hinton: names. Geoffrey Hinton: Um, they were in very small type to fit on, and I actually got a Geoffrey Hinton: serious migraine from looking at it too long.
Geoffrey Hinton: Um, that was my interaction with human factors, and then I. Tom Mitchell: It does remind me of that was around the time that Unix was Tom Mitchell: getting invented, and all the commands had no vowels in them. Tom Mitchell: So there was a theme there? Geoffrey Hinton: Yes. Um, so I wrote a report on Geoffrey Hinton: it and they said it was a very Geoffrey Hinton: nice report. Geoffrey Hinton: Thank you very much.
Geoffrey Hinton: And they weren't going to Geoffrey Hinton: implement it, even though it Geoffrey Hinton: would have been much more Geoffrey Hinton: efficient and much easier to Geoffrey Hinton: update. Geoffrey Hinton: And I said, why not?
Geoffrey Hinton: And they confidentially explained to me that, well, when Geoffrey Hinton: people come and visit the network control center, um, or Geoffrey Hinton: when actually when they visit the headquarters of British Geoffrey Hinton: Telecom, um, they have to have something to show them, like the Geoffrey Hinton: politicians have to see something, and they would always Geoffrey Hinton: show them this huge wall that displayed the state of all the
Geoffrey Hinton: networks, of all the switching stations, and they were very Geoffrey Hinton: impressed by that. Geoffrey Hinton: And if they got rid of the huge Geoffrey Hinton: wall and had just some Geoffrey Hinton: workstations, they were very Geoffrey Hinton: worried that network management Geoffrey Hinton: would get less funds from Geoffrey Hinton: British Telecom. Geoffrey Hinton: So they were going to keep their huge wall. Geoffrey Hinton: I learned a lot then about applied research.
Geoffrey Hinton: It's not about whether it works, Geoffrey Hinton: it's whether about the company Geoffrey Hinton: likes it. Tom Mitchell: Fair enough, fair enough. Geoffrey Hinton: Then after that, um, I went back to sea to San Diego for six Geoffrey Hinton: months, and that's when we worked on the PDP books with Geoffrey Hinton: Dave Rumelhart and McClelland. Geoffrey Hinton: Um, I was one of the authors Geoffrey Hinton: until almost when they were Geoffrey Hinton: published.
Geoffrey Hinton: And the last minute I dropped Geoffrey Hinton: out because at that point I Geoffrey Hinton: decided Boltzmann machines were Geoffrey Hinton: the future. Geoffrey Hinton: Boltzmann machines was just a much better idea than back prop, Geoffrey Hinton: and back prop was a silly idea. Geoffrey Hinton: Boltzmann machines were a much better idea. Geoffrey Hinton: Um, and there was no point being an author of a book where the Geoffrey Hinton: main thing was, um, back prop.
Geoffrey Hinton: Uh, that was a mistake. Geoffrey Hinton: Um. But in nineteen eighty four, I figured out. Geoffrey Hinton: Yeah, in nineteen eighty two, then I applied to CMU, and Geoffrey Hinton: because it was a private university, um, you could just Geoffrey Hinton: they didn't have to sort of advertise very widely. Geoffrey Hinton: Um, and Scott Fahlman was sort of my, um, host.
Geoffrey Hinton: He sort of interacted with him Geoffrey Hinton: at many workshops, and we got Geoffrey Hinton: along well, and he pushed hard Geoffrey Hinton: to get for me to get them to go Geoffrey Hinton: there. Geoffrey Hinton: And I had a very funny interview. Geoffrey Hinton: So I went there.
Geoffrey Hinton: On the first day there, I gave a Geoffrey Hinton: talk into computer science, and Geoffrey Hinton: then Scott Farmer took me out Geoffrey Hinton: for lunch at a place it might Geoffrey Hinton: have been called the oh, I can't Geoffrey Hinton: remember what it was called, but Geoffrey Hinton: it had a motto, which is if you Geoffrey Hinton: don't get sick, you got a bad Geoffrey Hinton: one. Geoffrey Hinton: Um, and I got terribly sick.
Geoffrey Hinton: And the next day I had acute diarrhea. Geoffrey Hinton: I couldn't eat anything. Geoffrey Hinton: I was living on coffee and Coca-Cola. Geoffrey Hinton: Um, I gave a talk in psychology, um, about mental imagery and my Geoffrey Hinton: theory of mental imagery. Geoffrey Hinton: And then there was someone at the end who, um, asked a Geoffrey Hinton: question which I didn't understand to begin with.
Geoffrey Hinton: And then I realized the question he was asking was, did I believe Geoffrey Hinton: the theory of someone called Marcel just about how you Geoffrey Hinton: weren't really rotating an image in your mind, you were just Geoffrey Hinton: looking backwards and forwards between two things. Geoffrey Hinton: Um, and in my reply I said, oh, Geoffrey Hinton: I see you mean that silly theory Geoffrey Hinton: by Marcel. Geoffrey Hinton: Marcel?
Geoffrey Hinton: Just where I didn't realize it was Marcel. Geoffrey Hinton: Just asking the question. Geoffrey Hinton: Um, and after that, I got a Geoffrey Hinton: request to go and see Nico Geoffrey Hinton: Habermann. Geoffrey Hinton: Now, Nico and I were always Geoffrey Hinton: great friends, even though we Geoffrey Hinton: were politically extremely Geoffrey Hinton: different.
Geoffrey Hinton: I was a sort of leftie, nineteen sixties radical with long hair Geoffrey Hinton: and rather disheveled. Geoffrey Hinton: Niko was a European gentleman who was very nicely dressed, Geoffrey Hinton: worked with the Defense Department, set up an institute Geoffrey Hinton: I wasn't allowed to go to because I was a foreigner. Geoffrey Hinton: Um, but we got along very well, and I think it was because of Geoffrey Hinton: our initial interview.
Tom Mitchell: So Niko was the department head in computer science? Geoffrey Hinton: Yes. And so in the initial Geoffrey Hinton: interview, he said, so we've Geoffrey Hinton: decided to offer you the Geoffrey Hinton: position. Geoffrey Hinton: And I said, oh, oh, there's something you should know. Geoffrey Hinton: And he said, oh what's that? Geoffrey Hinton: And I said, well, I don't Geoffrey Hinton: actually know any computer Geoffrey Hinton: science.
Geoffrey Hinton: And he said, it's okay here. Geoffrey Hinton: It's okay. Geoffrey Hinton: We have people here who do. Geoffrey Hinton: So I said, okay. Geoffrey Hinton: So I said, okay. Geoffrey Hinton: In that case I accept. Geoffrey Hinton: And Niko said, don't you think Geoffrey Hinton: perhaps we should talk about the Geoffrey Hinton: salary? Geoffrey Hinton: And I said, oh no, I'm not interested in salary. Geoffrey Hinton: You can pay me whatever you like.
Geoffrey Hinton: I'm not doing it for the money. Geoffrey Hinton: And he said, well, how does Geoffrey Hinton: twenty six thousand sound to Geoffrey Hinton: you? Geoffrey Hinton: So if that sounds fine, um, I Geoffrey Hinton: discovered I was being paid ten Geoffrey Hinton: thousand than the next lowest Geoffrey Hinton: paid professor. Geoffrey Hinton: Ten thousand less.
Geoffrey Hinton: Um, but every year I got a big pay rise, and me and Niko got Geoffrey Hinton: along very well after that, because he knew I wasn't doing Geoffrey Hinton: it for the money. Geoffrey Hinton: Change. Geoffrey Hinton: Things have changed so much. Tom Mitchell: That's. That's fantastic. Tom Mitchell: Okay, so now we're up to the mid Tom Mitchell: eighties when really neural nets Tom Mitchell: are reborn. Tom Mitchell: Is that the right word? Tom Mitchell: How would you.
Geoffrey Hinton: Yes. We back we back propagation. Geoffrey Hinton: I mean, we didn't invent it. Geoffrey Hinton: We invented by several different Geoffrey Hinton: groups, but we showed that it Geoffrey Hinton: really worked to learn Geoffrey Hinton: representations. Geoffrey Hinton: And as you know, sort of one of the big problems in AI is how do Geoffrey Hinton: you learn new representations? Geoffrey Hinton: How do you avoid having to put them all in by hand?
Geoffrey Hinton: Um, and my particular example, Geoffrey Hinton: which was the family trees Geoffrey Hinton: example, where you take all the Geoffrey Hinton: information in some family Geoffrey Hinton: trees, you convert it into Geoffrey Hinton: triples of symbols like John has Geoffrey Hinton: Father Mary. Geoffrey Hinton: Um, and then you train a neural Geoffrey Hinton: net to predict the last term in Geoffrey Hinton: a triple given the first two Geoffrey Hinton: terms.
Geoffrey Hinton: So it's just like the big language models. Geoffrey Hinton: You're predicting the next word given the context. Geoffrey Hinton: It's just much simpler. Geoffrey Hinton: I had one hundred and twelve Geoffrey Hinton: total examples, of which one Geoffrey Hinton: hundred and four were training Geoffrey Hinton: examples and eight were test Geoffrey Hinton: examples, which is a bit less Geoffrey Hinton: than the trillion examples they Geoffrey Hinton: have nowadays.
Geoffrey Hinton: Um, but it was the same idea. Geoffrey Hinton: You convert a symbol into a feature vector. Geoffrey Hinton: You then have the feature vectors of the context interact Geoffrey Hinton: um, via a hidden layer.
Geoffrey Hinton: They then predict the features Geoffrey Hinton: of the next symbol, and from Geoffrey Hinton: those features you guess what Geoffrey Hinton: the next symbol should be, and Geoffrey Hinton: you try and maximize the Geoffrey Hinton: probability of predicting the Geoffrey Hinton: next symbol. Geoffrey Hinton: And you then backpropagate through the feature interactions Geoffrey Hinton: and through the process that converts a symbol into features.
Geoffrey Hinton: And that way you learn, um, feature vectors to represent the Geoffrey Hinton: symbols and how these vectors should interact to predict the Geoffrey Hinton: features of the next symbol. Geoffrey Hinton: And that's what these big language models do, except it's Geoffrey Hinton: a bit more complicated. Geoffrey Hinton: The feature interactions are much more complicated.
Geoffrey Hinton: They have many more layers of Geoffrey Hinton: interaction, so they can Geoffrey Hinton: disambiguate ambiguous symbols Geoffrey Hinton: and get refine the shade of Geoffrey Hinton: meaning of things where the Geoffrey Hinton: meaning depends a lot on the Geoffrey Hinton: context. Geoffrey Hinton: Um, but it's basically an extremely simple version of the Geoffrey Hinton: current large language models.
Geoffrey Hinton: I called it a tiny language Geoffrey Hinton: model, and that convinced the Geoffrey Hinton: editors of nature that we really Geoffrey Hinton: could learn interesting Geoffrey Hinton: representations, because the Geoffrey Hinton: vectors I learned for the Geoffrey Hinton: symbols, which were people and Geoffrey Hinton: relationships, they had six Geoffrey Hinton: components.
Geoffrey Hinton: And if you used weight decay, you could interpret what all Geoffrey Hinton: those components were. Geoffrey Hinton: And they were sensible semantic features. Geoffrey Hinton: They were the nationality of the person and the generation of the Geoffrey Hinton: person, and which branch of the family tree they were in.
Geoffrey Hinton: And so it would learn things Geoffrey Hinton: like the relationship uncle Geoffrey Hinton: requires the output person to be Geoffrey Hinton: one generation older than the Geoffrey Hinton: input person. Geoffrey Hinton: And so it would have generations for people. Geoffrey Hinton: And if the input person was a Geoffrey Hinton: generation two, it would predict Geoffrey Hinton: that the output person would be Geoffrey Hinton: generation one.
Geoffrey Hinton: Um, so it was actually learning Geoffrey Hinton: a whole bunch of little rules Geoffrey Hinton: just probabilistically. Geoffrey Hinton: And the people interested in rule based induction got Geoffrey Hinton: interested in it because they said, oh, we can do that too. Geoffrey Hinton: And it's true. Geoffrey Hinton: They could do that too, with Geoffrey Hinton: rules that weren't Geoffrey Hinton: probabilistic.
Geoffrey Hinton: The point about neural nets is Geoffrey Hinton: they can mimic something that Geoffrey Hinton: learns discrete rules, but they Geoffrey Hinton: can. Geoffrey Hinton: They're also perfectly happy if the rules are just usually true. Geoffrey Hinton: And they use the preponderance of the evidence then, which is Geoffrey Hinton: much harder to do in, um, logic.
Geoffrey Hinton: And so that it was that example which, curiously, was a little Geoffrey Hinton: language model, um, that convinced the editors of nature Geoffrey Hinton: to publish a paper. Geoffrey Hinton: I know because I talked to them later. Geoffrey Hinton: The referees, I talked to the referee, one of the referees Geoffrey Hinton: later, and he said, yeah, it was that example that did it. Geoffrey Hinton: And then we were all very excited.
Geoffrey Hinton: We thought, we can solve everything. Geoffrey Hinton: You just have to give it a lot Geoffrey Hinton: of training data and run Geoffrey Hinton: backprop, and it'll learn all Geoffrey Hinton: the representations you need and Geoffrey Hinton: it'll learn to do parallel Geoffrey Hinton: computation. Geoffrey Hinton: Because at that time people were very interested in parallel Geoffrey Hinton: computation, but it was quite hard to program.
Geoffrey Hinton: And the idea was, well, this will have all these neurons Geoffrey Hinton: inside and they'll all be operating in parallel and it'll Geoffrey Hinton: figure out how to use them so there aren't any problems. Geoffrey Hinton: At that point, people were very interested in races and things Geoffrey Hinton: like that, and you didn't have to worry about any of that. Geoffrey Hinton: It was all synchronous and you Geoffrey Hinton: just they just learned what to Geoffrey Hinton: do.
Geoffrey Hinton: Um, so we thought we'd solved everything and little did we Geoffrey Hinton: know we had. Geoffrey Hinton: It's just we needed more data and more compute. Tom Mitchell: So then there's the long period Tom Mitchell: of waiting for more data and Tom Mitchell: more compute. Geoffrey Hinton: And yeah, not realizing that that was the main problem.
Geoffrey Hinton: Obviously, with other little problems, there were more Geoffrey Hinton: sensible kinds of neurons to use than more sensible ways to Geoffrey Hinton: regularize it and all that. Geoffrey Hinton: Um, and things like transformers Geoffrey Hinton: had to be invented to make it Geoffrey Hinton: really efficient. Geoffrey Hinton: Um, but basically backprop was the way to do it.
Geoffrey Hinton: And you couldn't convince Geoffrey Hinton: anybody when computers were Geoffrey Hinton: slow. Geoffrey Hinton: It will work for little problems. Geoffrey Hinton: It will work for slightly bigger Geoffrey Hinton: problems, like a few years Geoffrey Hinton: later. Geoffrey Hinton: Yang got it working for for mNIST, recognizing digits. Geoffrey Hinton: But all the vision people said, Geoffrey Hinton: you know, that's not real Geoffrey Hinton: vision.
Geoffrey Hinton: Um, you're never going to do it with real images that are high Geoffrey Hinton: resolution on the web. Geoffrey Hinton: And so it wasn't until about twenty twelve that they had to Geoffrey Hinton: eat their words. Tom Mitchell: That's right. Tom Mitchell: That was the year when. Tom Mitchell: Well, you tell this story. Tom Mitchell: You were the first person. Geoffrey Hinton: Uh, well, I was the advisor of the first two people.
Geoffrey Hinton: Now, it's not quite fair, because Jan had already Geoffrey Hinton: basically shown that they worked for real images. Geoffrey Hinton: Um, and Jan realized when Feifei Geoffrey Hinton: came up with the ImageNet Geoffrey Hinton: dataset. Geoffrey Hinton: Jan realized they could win that Geoffrey Hinton: competition, and he tried to get Geoffrey Hinton: graduate students and postdocs Geoffrey Hinton: in his lab to do it, and they Geoffrey Hinton: all declined.
Geoffrey Hinton: Um, and Ilya, Ilya Sutskever realized that, um, backprop Geoffrey Hinton: would just kill ImageNet. Geoffrey Hinton: Um, and he wanted Alex to work on it. Geoffrey Hinton: And I didn't really want to work on it. Geoffrey Hinton: Um, Alex had already been Geoffrey Hinton: working on small images and Geoffrey Hinton: recognizing small images in Geoffrey Hinton: c410. Geoffrey Hinton: Um, and you pre-processed Geoffrey Hinton: everything for Alex to make it Geoffrey Hinton: easy.
Geoffrey Hinton: And I bought Alex two Nvidia Geoffrey Hinton: GPUs to have in his bedroom at Geoffrey Hinton: home. Geoffrey Hinton: Um, And Alex, then get on with get on with it. Geoffrey Hinton: And he was an absolutely wizard programmer. Geoffrey Hinton: He wrote amazing code on Geoffrey Hinton: multiple GPUs to do convolution Geoffrey Hinton: really efficiently. Geoffrey Hinton: Much better code than anybody else had ever written.
Geoffrey Hinton: Um, I believe and so it's a combination of Ilya realizing we Geoffrey Hinton: really had to do this, and Ilya was involved in the design of Geoffrey Hinton: the net and so on, but Alex's programming skills and then I Geoffrey Hinton: added a few ideas, like use rectified linear units instead Geoffrey Hinton: of sigmoid units and use little patches of the images.
Geoffrey Hinton: I mean big patches of the Geoffrey Hinton: images, so you can translate Geoffrey Hinton: things around a bit to get some Geoffrey Hinton: translation invariance, as well Geoffrey Hinton: as using convolution, um, and Geoffrey Hinton: use dropout. Geoffrey Hinton: So that was one of the first applications of dropout. Geoffrey Hinton: And that helped about one percent. Geoffrey Hinton: It really helped. Geoffrey Hinton: And then we beat the best vision systems.
Geoffrey Hinton: The best vision systems were sort of plateauing at twenty Geoffrey Hinton: five percent errors. Geoffrey Hinton: That's errors for getting the right answer in the top in your Geoffrey Hinton: top five bets. Geoffrey Hinton: Um, and we got like fifteen percent, fifteen or sixteen Geoffrey Hinton: depending on how you count it. Geoffrey Hinton: So we got almost half the error rate.
Geoffrey Hinton: And what happened then was what Geoffrey Hinton: ought to happen in science but Geoffrey Hinton: seldom does. Geoffrey Hinton: So our most vigorous opponents, like Jitendra Malik and Geoffrey Hinton: Zisserman Andrew Zisserman, looked at these results and Geoffrey Hinton: said, okay, you were right. Geoffrey Hinton: That never happens in science. Geoffrey Hinton: And slightly irritating the Andrew Zisserman then switched Geoffrey Hinton: to doing this.
Geoffrey Hinton: He had some very good postdocs or students working with him. Geoffrey Hinton: Simonyan um, and um, after about a year, they were making better Geoffrey Hinton: networks than us. Geoffrey Hinton: But that was really the. Geoffrey Hinton: As far as the general public was Geoffrey Hinton: concerned, that was the start of Geoffrey Hinton: this big swing towards deep Geoffrey Hinton: learning in twenty twelve when Geoffrey Hinton: we really nailed computer Geoffrey Hinton: vision.
Geoffrey Hinton: But it actually happened before that. Geoffrey Hinton: It happened in two thousand and nine when we showed how you Geoffrey Hinton: could do speech recognition, or rather the acoustic modeling Geoffrey Hinton: part of speech recognition. Geoffrey Hinton: We showed how you could do that Geoffrey Hinton: a bit better than the best Geoffrey Hinton: technology.
Geoffrey Hinton: And that influenced all the big speech groups, the big speech Geoffrey Hinton: groups that IBM and Microsoft, um, and somewhere else. Geoffrey Hinton: Google. Geoffrey Hinton: Yes. Geoffrey Hinton: Um, all switched to doing neural nets for acoustic modeling. Geoffrey Hinton: And so by twenty ten, it was Geoffrey Hinton: clear that neural nets were the Geoffrey Hinton: right way to do acoustic Geoffrey Hinton: modeling. Geoffrey Hinton: And we had lots of people onside.
Geoffrey Hinton: Um, and but in twenty twelve, it Geoffrey Hinton: actually came out for the Geoffrey Hinton: Android, and suddenly the Geoffrey Hinton: Android caught up with Siri in Geoffrey Hinton: speech recognition. Geoffrey Hinton: So really we demonstrated it for speech before that, but that Geoffrey Hinton: didn't make a big impact. Geoffrey Hinton: The reason it worked for speech was they had a big data set. Geoffrey Hinton: They had millions of examples.
Geoffrey Hinton: They were one of the areas. Geoffrey Hinton: Unlike vision, they had big data Geoffrey Hinton: sets because of the DARPA speech Geoffrey Hinton: project. Geoffrey Hinton: Um, because they really wanted to be able to benchmark systems. Geoffrey Hinton: Um, also, speech is easier than vision. Geoffrey Hinton: Speech is just vision with either one or two pixels. Geoffrey Hinton: It's just they change rather fast. Geoffrey Hinton: Um, and.
Geoffrey Hinton: So we demonstrated for speech when we did it for vision. Geoffrey Hinton: The big companies already knew it worked for speech and they Geoffrey Hinton: saw it work for vision. Geoffrey Hinton: And so they realized it was sort of universal. Geoffrey Hinton: Um, it wasn't just a specific trick for a specific domain. Geoffrey Hinton: It will work for perception in general.
Geoffrey Hinton: They didn't realize at that Geoffrey Hinton: point it would work for Geoffrey Hinton: language. Geoffrey Hinton: And nor really did we, even though our very first impressive Geoffrey Hinton: example was for language. Geoffrey Hinton: Um. Geoffrey Hinton: Yeah. Geoffrey Hinton: So in twenty twelve, there was this big swing to neural Geoffrey Hinton: networks And that's when Jensen at Nvidia realized he finally Geoffrey Hinton: realized those Nvidia boards weren't just for gaming.
Geoffrey Hinton: They were supercomputers for doing machine learning. Geoffrey Hinton: Now, I actually gave a talk at NIPS in two thousand and nine Geoffrey Hinton: when I told a thousand people this was about speech, I told a Geoffrey Hinton: thousand people, if you want to do machine learning now, you Geoffrey Hinton: have to buy Nvidia GPUs.
Geoffrey Hinton: Nvidia GPUs will make your program go about thirty times as Geoffrey Hinton: fast because they're relatively easy to utilize parallelism. Geoffrey Hinton: They're just right for neural nets. Geoffrey Hinton: It was Rick Zelinsky who was a student of mine at CMU, who told Geoffrey Hinton: me that in about two thousand and six, um, and it was true. Geoffrey Hinton: And, um, I sent mail to Nvidia Geoffrey Hinton: saying, how about giving me a Geoffrey Hinton: free one?
Geoffrey Hinton: Because I told a thousand machine learning researchers to Geoffrey Hinton: buy your boards. Geoffrey Hinton: And they declined. Geoffrey Hinton: Um. Years later, Jensen came to Toronto and gave a talk and Geoffrey Hinton: mentioned how Toronto, you know, was the place where they Geoffrey Hinton: convinced him that Nvidia GPUs were good for AI. Geoffrey Hinton: Um, and that it all happened in Geoffrey Hinton: twenty twelve, and I couldn't Geoffrey Hinton: resist it.
Geoffrey Hinton: At the end. Geoffrey Hinton: I said, well, I told you in two Geoffrey Hinton: thousand and nine that you Geoffrey Hinton: ignored me. Geoffrey Hinton: And what he should have said was, well, you're very silly. Geoffrey Hinton: You should have bought stock in two thousand and nine. Geoffrey Hinton: If I'd done that, I'd be a billionaire. Geoffrey Hinton: Um, but, um, instead, he gave me.
Geoffrey Hinton: He opened his briefcase and gave me their very special, very Geoffrey Hinton: latest GPU, of which they'd only made a few that had twice as Geoffrey Hinton: much memory as any other GPU. Geoffrey Hinton: So that was a nice move by Jensen. Tom Mitchell: That's a great story too. Tom Mitchell: So then in the twenty tens, things really just kind of rapid Tom Mitchell: fire started taking off. Tom Mitchell: Take us through that. Geoffrey Hinton: So speech worked.
Geoffrey Hinton: Um, I we got a good collaboration between the Geoffrey Hinton: research groups at IBM and Google and, um. Geoffrey Hinton: Toronto and Microsoft. Geoffrey Hinton: Yeah. Geoffrey Hinton: Um, we actually published a joint paper, which is sort of Geoffrey Hinton: quite rare in this stuff about the sort of new view of how to Geoffrey Hinton: do acoustic modeling.
Geoffrey Hinton: Um. And then we did vision, and then, um, I started getting lots Geoffrey Hinton: of requests from big companies who wanted to. Geoffrey Hinton: By me or by me and Alex and Ilya or fund our company or get us to Geoffrey Hinton: come work for them. Geoffrey Hinton: Um, and I realized this stuff was probably valuable. Geoffrey Hinton: We had no idea how much it was worth.
Geoffrey Hinton: Um, so Craig Butler, who was the Geoffrey Hinton: chair of the Department of Geoffrey Hinton: Computer Science, was an expert Geoffrey Hinton: on auctions. Geoffrey Hinton: And he said, you know, you Geoffrey Hinton: should actually, since you have Geoffrey Hinton: no idea what it's worth, but Geoffrey Hinton: there's people, many people Geoffrey Hinton: interested, you should set up an Geoffrey Hinton: auction.
Geoffrey Hinton: So at Lake Tahoe, which seemed like the appropriate place, um, Geoffrey Hinton: in a casino, um, a casino hotel. Geoffrey Hinton: In twenty twelve, Alex and I set up a little company for the sole Geoffrey Hinton: function of doing an aqua hire. Geoffrey Hinton: And there was an auction between, um, Microsoft and Geoffrey Hinton: Google and DeepMind and Baidu. Geoffrey Hinton: Um, DeepMind dropped out fairly early.
Geoffrey Hinton: Um, and on the ground floor, Geoffrey Hinton: they had all these people at Geoffrey Hinton: slot machines with cigarettes Geoffrey Hinton: hanging out the corner of their Geoffrey Hinton: mouth, just pulling these Geoffrey Hinton: levers. Geoffrey Hinton: And every so often they made like a thousand dollars and Geoffrey Hinton: lights would flash, and we were upstairs having an auction where Geoffrey Hinton: you had to raise by a million. Geoffrey Hinton: Um, that was fun.
Geoffrey Hinton: And the auction went on for quite a long time. Geoffrey Hinton: We were completely amazed when it got to forty four million. Geoffrey Hinton: It was so much money that we couldn't imagine that any more Geoffrey Hinton: money would be useful. Geoffrey Hinton: I mean, that seemed like as much Geoffrey Hinton: money as anybody could possibly Geoffrey Hinton: want.
Geoffrey Hinton: Um, and so we then became much Geoffrey Hinton: more concerned about who we Geoffrey Hinton: worked for, and I wouldn't have Geoffrey Hinton: been able to get to China Geoffrey Hinton: because I couldn't fly at that Geoffrey Hinton: time. Geoffrey Hinton: And I'd spent the summer of twenty twelve working with Jeff Geoffrey Hinton: Dean at Google. Geoffrey Hinton: And I got along really well with Jeff Dean.
Geoffrey Hinton: It was a really nice group, and Geoffrey Hinton: I figured it was much more Geoffrey Hinton: important to work in a really Geoffrey Hinton: nice place than to get more Geoffrey Hinton: money. Geoffrey Hinton: So we actually terminated the auction. Geoffrey Hinton: We told Baidu we got an offer he couldn't refuse, and the offer Geoffrey Hinton: we couldn't refuse was the chance to work at Google with Geoffrey Hinton: Jeff Dean, and that all worked out very well.
Geoffrey Hinton: So then I was off to Google and while we were there a year, um, Geoffrey Hinton: along with Shockley and Yoshua and Bodner in Montreal, um, they Geoffrey Hinton: developed, uh, attention language models with attention, Geoffrey Hinton: which was a precursor of Transformers, and showed that Geoffrey Hinton: language models actually work well for machine translation.
Geoffrey Hinton: And I think that was the final Geoffrey Hinton: nail in the coffin of symbolic Geoffrey Hinton: AI, because if anything was Geoffrey Hinton: going to be good for symbolic Geoffrey Hinton: AI, it was converting symbol Geoffrey Hinton: strings in one language into Geoffrey Hinton: symbol strings in another Geoffrey Hinton: language. Geoffrey Hinton: The idea that you might do that by taking symbol strings and Geoffrey Hinton: manipulating them actually sounded quite plausible.
Geoffrey Hinton: Um, but that's not the way to do it. Geoffrey Hinton: The way to do it is to understand what's being said in Geoffrey Hinton: one language, by associating big vectors with words appropriately Geoffrey Hinton: vectors, and then convert that to the other language. Geoffrey Hinton: Um, so it was clear by about Geoffrey Hinton: twenty fifteen that neural nets Geoffrey Hinton: were going to do everything, Geoffrey Hinton: including language.
Geoffrey Hinton: That's the point at which Gary Marcus published a book chapter Geoffrey Hinton: saying neural nets were okay. Geoffrey Hinton: Maybe they could do object recognition, but they'd never do Geoffrey Hinton: language because language involved novel sentences. Geoffrey Hinton: They were already doing it. Tom Mitchell: Well. So that was twenty fifteen. Tom Mitchell: You were still at Google?
Geoffrey Hinton: I was at Google. And Ilya then Geoffrey Hinton: moved to OpenAI, um, around Geoffrey Hinton: twenty fifteen, uh, maybe twenty Geoffrey Hinton: fourteen, I can't remember the Geoffrey Hinton: year. Geoffrey Hinton: And, um. Geoffrey Hinton: And then OpenAI did rather well. Geoffrey Hinton: Um, Over an hour.
Geoffrey Hinton: I basically just took stuff that had been done at Google on Geoffrey Hinton: Transformers and put a nicer interface on it, and realized Geoffrey Hinton: which Google hadn't realized that if you did human Geoffrey Hinton: reinforcement learning, you didn't need that many examples Geoffrey Hinton: to make it behave nicer. Geoffrey Hinton: Um, you didn't need like one hundred million examples which Geoffrey Hinton: you might have thought you could do it with.
Geoffrey Hinton: Like some fraction of a million examples would already make it Geoffrey Hinton: behave a lot better. Geoffrey Hinton: So you could actually train it up to have nicer behavior. Geoffrey Hinton: And that was ChatGPT. Geoffrey Hinton: Um, Google was then in the classic situation of not wanting Geoffrey Hinton: to interfere with search, which was its moneymaker. Geoffrey Hinton: So it was in this difficult situation. Geoffrey Hinton: Do they do they release chatbots or not?
Geoffrey Hinton: But when Microsoft teamed up with OpenAI, they basically had Geoffrey Hinton: to release them. Geoffrey Hinton: Um, but they lost a few years. Geoffrey Hinton: I think it was partly because search was working so well, and Geoffrey Hinton: it was obvious search would be better if instead of using Geoffrey Hinton: keywords, it used what you meant, which would mean it had Geoffrey Hinton: to understand what you meant.
Geoffrey Hinton: Um, but they didn't want to undermine their moneymaker. Geoffrey Hinton: No, that's based not on any inside information. Geoffrey Hinton: It just seems obvious. Tom Mitchell: Pretty amazing. Tom Mitchell: So. So here we are now. Tom Mitchell: And you were famously on record, uh, warning people about some of Tom Mitchell: the risks of AI.
Tom Mitchell: Um, what should what should Tom Mitchell: people who are working in this Tom Mitchell: area do in response to that Tom Mitchell: risk? Geoffrey Hinton: Okay, so I didn't actually talk Geoffrey Hinton: much about the risks until I Geoffrey Hinton: left Google.
Geoffrey Hinton: I realized in the beginning of Geoffrey Hinton: twenty twenty three there was a Geoffrey Hinton: huge existential threat I hadn't Geoffrey Hinton: fully appreciated, because it's Geoffrey Hinton: a better form of intelligence Geoffrey Hinton: than us, and it's better because Geoffrey Hinton: it can share so different copies Geoffrey Hinton: of the same neural net, can look Geoffrey Hinton: at different data and share the Geoffrey Hinton: gradient, and then update all
Geoffrey Hinton: their weights in sync and stay Geoffrey Hinton: the same so they can keep doing Geoffrey Hinton: that. Geoffrey Hinton: And when they share the gradient, they're sharing Geoffrey Hinton: information they got from different data sets. Geoffrey Hinton: Um, out of the order of a Geoffrey Hinton: trillion bits per episode of Geoffrey Hinton: sharing. Geoffrey Hinton: If they've got a trillion weights.
Geoffrey Hinton: Whereas what we're doing now is Geoffrey Hinton: sharing the information and Geoffrey Hinton: maybe one hundred bits per Geoffrey Hinton: sentence. Geoffrey Hinton: Um, so a few bits per second, maybe if we're lucky, we're Geoffrey Hinton: sharing it ten bits per second. Geoffrey Hinton: Um, and so you're comparing like Geoffrey Hinton: trillions of bits with hundreds Geoffrey Hinton: of bits. Geoffrey Hinton: There are billions of times better than us at sharing.
Geoffrey Hinton: And that's why if I'm running on different hardware, they can Geoffrey Hinton: learn so much more than us. Geoffrey Hinton: They can learn from the whole internet. Geoffrey Hinton: It doesn't all have to go through one piece of hardware, Geoffrey Hinton: and it's going to get more important that effect as we go Geoffrey Hinton: to AI agents that operate in the real world in real time.
Geoffrey Hinton: Most AI you images, you can just speed them up and send them Geoffrey Hinton: through one network very fast. Geoffrey Hinton: Um, because obviously computers operate, you know, thousands of Geoffrey Hinton: times faster than a brain. Geoffrey Hinton: But, um, if you're operating in Geoffrey Hinton: the real world, you can't get Geoffrey Hinton: experience faster. Geoffrey Hinton: Um, because the real world has an actual time scale.
Geoffrey Hinton: If you're interacting with other Geoffrey Hinton: agents who take a little while Geoffrey Hinton: to reply, um, then this Geoffrey Hinton: advantage that different copies Geoffrey Hinton: of the same neural net can share Geoffrey Hinton: will be an even bigger Geoffrey Hinton: advantage.
Geoffrey Hinton: So at that point, I decided Geoffrey Hinton: there's all these short term Geoffrey Hinton: threats, and it wasn't really my Geoffrey Hinton: intention to warn about those, Geoffrey Hinton: but I got sucked into warning Geoffrey Hinton: about those because journalists Geoffrey Hinton: always confuse the existential Geoffrey Hinton: threat with all the other Geoffrey Hinton: threats. Geoffrey Hinton: They just muddle all the threats together.
Geoffrey Hinton: They move seamlessly from joblessness to fake videos to Geoffrey Hinton: cyber attacks to lethal autonomous weapons, as if Geoffrey Hinton: they're all the same thing. Geoffrey Hinton: Um, so I had to sort of clarify Geoffrey Hinton: a lot of those threats, but my Geoffrey Hinton: main worry was the much longer Geoffrey Hinton: term threat, but not long enough Geoffrey Hinton: that they will be much smarter Geoffrey Hinton: than us.
Geoffrey Hinton: It's not necessarily the case, Geoffrey Hinton: but I think most people, most Geoffrey Hinton: neural net experts, believe that Geoffrey Hinton: within twenty years we'll have Geoffrey Hinton: superintelligent AI. Geoffrey Hinton: We vary, you know, Demis thinks it'll be about ten years. Geoffrey Hinton: I think it may be as long as twenty years. Geoffrey Hinton: And it'll very likely be more than five years. Geoffrey Hinton: Um, Dario thinks it'll be three years.
Geoffrey Hinton: Um, but then he runs a company. Geoffrey Hinton: Um, so any. Geoffrey Hinton: Ilya thinks it'll be sooner than ten years. Geoffrey Hinton: Um, we all think it's probably going to happen.
Geoffrey Hinton: So the question is, what happens Geoffrey Hinton: when AI is a lot smarter than us Geoffrey Hinton: And when it's our agents that Geoffrey Hinton: are smart enough so they're also Geoffrey Hinton: more powerful than us, they can Geoffrey Hinton: collaborate with other AI Geoffrey Hinton: agents, get stuff done even if Geoffrey Hinton: they can't sort of fire guns or Geoffrey Hinton: pull switches. Geoffrey Hinton: They can persuade people.
Geoffrey Hinton: And we know AI is already very Geoffrey Hinton: good at persuasion and will soon Geoffrey Hinton: be much better than people at Geoffrey Hinton: persuasion, like in ten years Geoffrey Hinton: time. Geoffrey Hinton: And so they'll be able to persuade people to do things Geoffrey Hinton: just like Trump persuaded people to invade the capital. Geoffrey Hinton: Um, so they don't actually have to be able to do anything Geoffrey Hinton: themselves except talk.
Geoffrey Hinton: So most of the tech bros are Geoffrey Hinton: thinking they have a model, Geoffrey Hinton: which is I'm the CEO, you're the Geoffrey Hinton: secretary. Geoffrey Hinton: You're much smarter than me. Geoffrey Hinton: Um, but I can always fire you, Geoffrey Hinton: and you'll make my life really Geoffrey Hinton: easy. Geoffrey Hinton: Because whatever I want to Geoffrey Hinton: happen, I'll sort of be like Geoffrey Hinton: Star Trek.
Geoffrey Hinton: I'll say, make it so and it will happen. Geoffrey Hinton: Um. and I don't really have to understand it. Geoffrey Hinton: I'll still get the credit for it because I said make it. Geoffrey Hinton: So, um, I think that's their model, and I just don't think Geoffrey Hinton: that's going to work. Geoffrey Hinton: I think the big problem is how Geoffrey Hinton: do we prevent these things ever Geoffrey Hinton: wanting to take control or to Geoffrey Hinton: take over?
Geoffrey Hinton: They may have control, but they Geoffrey Hinton: may still not want to replace Geoffrey Hinton: us. Geoffrey Hinton: And so I've fallen back on the only example I know of a less Geoffrey Hinton: intelligent thing controlling a more intelligent thing. Geoffrey Hinton: And that's a baby controlling a mother. Geoffrey Hinton: And evolution has put a huge amount of work into that.
Geoffrey Hinton: So evolution has made sure the mother cannot bear the sound of Geoffrey Hinton: the baby crying, and the mother gets huge rewards, um, for being Geoffrey Hinton: nice to the baby. Geoffrey Hinton: Um, lots of pleasurable Geoffrey Hinton: sensations and just generally Geoffrey Hinton: good feelings. Geoffrey Hinton: Um, and We need to do the same Geoffrey Hinton: for these eyes, for being nice Geoffrey Hinton: to us. Geoffrey Hinton: We're still making them.
Geoffrey Hinton: And if we could make an AI that was super intelligent but cared Geoffrey Hinton: more about us than it cared about about itself or other Geoffrey Hinton: superintelligent AIS, then we might be okay. Geoffrey Hinton: Um, but we have to accept that we're going to be the babies, Geoffrey Hinton: and they're going to be the mothers, and people aren't Geoffrey Hinton: prepared to accept that.
Geoffrey Hinton: Trump's not prepared to accept Geoffrey Hinton: that Trump would never accept Geoffrey Hinton: that. Geoffrey Hinton: Um, I think we have a lot more Geoffrey Hinton: hope of the Chinese Geoffrey Hinton: understanding it. Geoffrey Hinton: So I recently went to Shanghai Geoffrey Hinton: and talked to a member of the Geoffrey Hinton: Politburo. Geoffrey Hinton: Me and Eric Schmidt, who aren't natural allies.
Geoffrey Hinton: We're in terms of politics with a rather different Eric Schmidt, Geoffrey Hinton: for example, thinks Kissinger was a good guy. Geoffrey Hinton: Um, but we agree on this existential threat, and the Geoffrey Hinton: Chinese leadership will understand it much better than Geoffrey Hinton: any of the other leaderships, because many of them are Geoffrey Hinton: engineers and they actually understand how this stuff works.
Geoffrey Hinton: They understand the argument Geoffrey Hinton: that it's a better form of Geoffrey Hinton: intelligence. Geoffrey Hinton: But I think all the countries will collaborate on can we make Geoffrey Hinton: it so that it cares more about us than it does about itself? Geoffrey Hinton: Because there if any country Geoffrey Hinton: figured out how to do that, it'd Geoffrey Hinton: be very happy to tell the other Geoffrey Hinton: countries.
Geoffrey Hinton: That's like preventing a global nuclear war. Geoffrey Hinton: And there the USSR and America collaborated in the nineteen Geoffrey Hinton: fifties on that. Geoffrey Hinton: Um, the height of the Cold War. Geoffrey Hinton: They still collaborated to prevent that.
Geoffrey Hinton: So what I think we should have is research institutes in Geoffrey Hinton: different countries that get access to their own country's Geoffrey Hinton: super smart AI, which they're not going to give to any other Geoffrey Hinton: country and can do experiments on how to make it not want to, Geoffrey Hinton: how to make it care more about people than about itself.
Geoffrey Hinton: Um, and share with other countries how to do that, Geoffrey Hinton: because I believe the techniques for doing that will be roughly Geoffrey Hinton: orthogonal to the techniques for making it smarter. Geoffrey Hinton: They're not going to share the Geoffrey Hinton: techniques for making it Geoffrey Hinton: smarter, because they're all Geoffrey Hinton: doing cyber attacks on each Geoffrey Hinton: other, and they all know the Geoffrey Hinton: best.
Geoffrey Hinton: You want a better AI to do Geoffrey Hinton: better cyber attacks and better Geoffrey Hinton: fake videos and better Geoffrey Hinton: autonomous weapons. Geoffrey Hinton: They're never going to share that stuff. Geoffrey Hinton: They're anti-aligned. Geoffrey Hinton: But on not having AI replace us, Geoffrey Hinton: they're aligned so they will Geoffrey Hinton: collaborate.
Geoffrey Hinton: Now, one of the things you ought to mention, I ought to mention Geoffrey Hinton: Russ Salakhutdinov, um. Geoffrey Hinton: He was one of my best students. Geoffrey Hinton: Um, he came to Toronto, did his PhD at Toronto. Geoffrey Hinton: Um, he did a postdoc with Josh Tenenbaum, and then he wanted to Geoffrey Hinton: come back to Toronto, and he had a faculty offer from Harvard.
Geoffrey Hinton: And I really tried to get the Department of Computer Science, Geoffrey Hinton: which had an open position in machine learning, to give a job Geoffrey Hinton: to Russ and they refused. Geoffrey Hinton: Basically, this was about twenty eleven or twelve. Geoffrey Hinton: No, this was two thousand and Geoffrey Hinton: probably twenty twelve or Geoffrey Hinton: thirteen.
Geoffrey Hinton: My department was one of the last departments to accept that Geoffrey Hinton: neural networks really worked. Geoffrey Hinton: They had a big AI group, and the Geoffrey Hinton: big AI group said you had got Geoffrey Hinton: several people in neural Geoffrey Hinton: networks already. Geoffrey Hinton: That's your quota.
Geoffrey Hinton: We're short on people in knowledge representation, and we Geoffrey Hinton: need as many people in computational linguistics as we Geoffrey Hinton: do in neural networks. Geoffrey Hinton: Um, and they refused to give us a job. Geoffrey Hinton: So we eventually got a job in Geoffrey Hinton: statistics so that it could be Geoffrey Hinton: in Toronto. Geoffrey Hinton: And we were trying to negotiate Geoffrey Hinton: it for him to move to computer Geoffrey Hinton: science.
Geoffrey Hinton: And then CMU swooped in, and I think they offered him tenure at Geoffrey Hinton: CMU, and that was that. Tom Mitchell: Well, you have a whole cadre of former students who are, um, Tom Mitchell: really leading the charge, leading the way, and in a lot of Tom Mitchell: areas of neural nets, it's pretty amazing if you. Geoffrey Hinton: Well, it was luck. Geoffrey Hinton: It was luck basically. Geoffrey Hinton: There were so few people who believed in neural nets.
Geoffrey Hinton: Who was Yann? Geoffrey Hinton: There was Yoshua, there was me, the Schmidhuber. Geoffrey Hinton: Uh, there were a few other Geoffrey Hinton: people, but MIT didn't have Geoffrey Hinton: anybody. Geoffrey Hinton: Stanford didn't have anybody. Geoffrey Hinton: Um, Berkeley didn't have anybody. Geoffrey Hinton: Um, Mike Jordan made sure of that.
Geoffrey Hinton: And, um, so the few of us who believed in it got the really Geoffrey Hinton: good students who believed in it, and that was great. Tom Mitchell: It worked. Geoffrey Hinton: People like Russ and E and George Stahl and other people. Geoffrey Hinton: It was. Geoffrey Hinton: Yeah. Tom Mitchell: So if you could, uh, one final question. Tom Mitchell: If you could give advice to new PhD students now entering this Tom Mitchell: area, what would you say?
Geoffrey Hinton: Sometimes I'd say become a plumber. Geoffrey Hinton: You're too late. Geoffrey Hinton: Um. But actually, I say, if you're a CMU and you're doing Geoffrey Hinton: this, you may be in the small fraction of people who survive Geoffrey Hinton: in ER and don't get replaced, because for quite a while Geoffrey Hinton: there's going to be creative people making our work better. Geoffrey Hinton: And you've got a good chance of being one of those people if Geoffrey Hinton: you're at CMU.
Tom Mitchell: All right. Tom Mitchell: Well, we'll take that. Tom Mitchell: Uh, Jeff, thank you so much for spending the time sharing that. Tom Mitchell: Um, it's it's always great to catch up and, um. Tom Mitchell: Thank you. Geoffrey Hinton: Okay. Well, thank you for inviting me. Speaker 3: Tom Mitchell is the founders Speaker 3: university professor at Carnegie Speaker 3: Mellon University. Speaker 3: Machine learning. Speaker 3: How did we get here?
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