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: 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?
Speaker 3: Is produced by the Stanford Digital Economy Lab.
Speaker 3: If you enjoyed this episode,
Speaker 3: subscribe wherever you listen to
Speaker 3: podcasts.