0:00
have you reflected a lot on how to select Talent or has that mostly been like
0:07
intuitive to you Ilia just shows up and you're like this is a clever guy let's let's work together or have you thought
0:13
a lot about that can we are we recording should we should we roll This yeah let's roll this okay we're good yeah
0:20
yeah okay s is working
0:30
so I remember when I first got to K melon from England in England at a Research Unit it would get to be 6:00
0:36
and you'd all go for a drink in the pub um at Caril melon I remember after I've been there a few weeks it was Saturday
0:43
night I didn't have any friends yet and I didn't know what to do so I decided I'd go into the lab and do some
0:48
programming because I had a list machine and you couldn't program it from home so I went into the lab at about 9:00 on a
0:53
Saturday night and it was swarming all the students were there and they were
0:59
all there because what they were working on was the future they all believed that what they did next was going to change
1:05
the course of computer science and it was just so different from England and so that was very refreshing take me back
1:12
to the very beginning Jeff at Cambridge uh trying to understand the brain uh
1:18
what was that like it was very disappointing so I did physiology and in
1:24
the summer term they were going to teach us how the brain worked and it all they taught us was how neurons conduct action
1:30
potentials which is very interesting but it doesn't tell you how the brain works so that was extremely disappointing I
1:36
switched to philosophy then I thought maybe they'd tell us how the mind worked um that was very disappointing I
1:42
eventually ended up going to Edinburgh to do Ai and that was more interesting at least you could simulate things so
1:48
you could test out theories and did you remember what intrigued you about AI was
1:53
it a paper was it any particular person that exposed you to those ideas I guess
1:59
it was a book I read by Donald Hebb that influenced me a lot um he was very
2:05
interested in how you learn the connection strengths in neural Nets I also read a book by John Fon neyman
2:11
early on um who was very interested in how the brain computes and how it's
2:16
different from normal computers and did you get that conviction that this ideas
2:22
would work out at at that point or what would was your intuition back at the
2:27
Edinburgh days it seemed to me there has to be a way that the brain
2:33
learns and it's clearly not by having all sorts of things programmed into it
2:39
and then using logical rules of inference that just seemed to me crazy from the outset um so we had to figure
2:46
out how the brain learned to modify Connections in a neural net so that it could do complicated things and Fon
2:53
noyman believed that churing believed that so Forman and churing were both pretty good at logic but they didn't
2:58
believe in this logical approach and what was your split between studying the ideas from from
3:05
neuroscience and just doing what seemed to be good algorithms for for AI how
3:11
much inspiration did you take early on so I never did that much study of Neuroscience I was always inspired by
3:17
what I'd learned about how the brain works that there's a bunch of neurons they perform relatively simple
3:23
operations they're nonlinear um but they collect inputs they wait them and then
3:29
they an output that depends on that weighted input and the question is how do you change those weights to make the
3:34
whole thing do something good it seems like a fairly simple question what collaborations do you remember from from
3:41
that time the main collaboration I had at Carnegie melon was with someone who wasn't at carnegy melon I was
3:47
interacting a lot with Terry sinowski who was in Baltimore at John Hopkins and about once a month either he would drive
3:53
to Pittsburg or I would drive to Baltimore it's 250 miles away and we would spend a weekend together working on boltimore machines that was a
4:00
wonderful collaboration we were both convinced it was how the brain worked that was the most exciting research I've
4:05
ever done and a lot of technical results came out that were very interesting but I think it's not how the brain works um
4:11
I also had a very good collaboration with um Peter Brown who was a very good
4:17
statistician and he worked on speech recognition at IBM and then he came as a
4:22
more mature student to kind melon just to get a PhD um but he already knew a lot he taught me a lot about spee
4:30
and he in fact taught me about hidden Markov models I think I learn more from him than he learned from me that's the
4:35
kind of student you want and when he Tau me about hidden Markov models I was
4:41
doing back propop with hidden layers only they weren't called hidden layers then and I decided that name they use in
4:47
Hidden Markov models is a great name for variables that you don't know what they're up to um and so that's where the
4:54
name hidden in neur NS came from me and P decided that was a great name for the hidden hidden L and your all Nets um but
5:03
I learned a lot from Peter about speech take us back to when Ilia showed up at
5:08
your at your office I was in my office I probably on a Sunday um and I was
5:14
programming I think and there was a knock on the door not just any knock but it won't cutter it's sort of an urgent knock so I
5:21
went and answer to the door and this was this young student there and he said he was cooking Fries over the summer but
5:27
he'd rather be working in my lab and so I said well why don't you make an appointment and we'll talk and so Ilia
5:32
said how about now and that sort of was Ila's character so we talked for a bit
5:38
and I gave him a paper to read which was the nature paper on back propagation and we made another meeting
5:45
for a week later and he came back and he said I didn't understand it and I was very disappointed I thought he seemed
5:50
like a bright guy but it's only the chain rule it's not that hard to understand and he said oh no no I
5:56
understood that I just don't understand why you don't give the gradient to a sens a sensible function
6:02
Optimizer which took us quite a few years to think about um and it kept on
6:07
like that with a he had very good his raw intuitions about things were always very good what do you think had enabled
6:14
those uh those intuitions for for Ilia I don't know I think he always thought for
6:19
himself he was always interested in AI from a young age um he's obviously good
6:24
at math so but it's very hard to know and what was that collaboration between
6:29
between the two of you like what part would you play and what part would Ilia play it was a lot of fun um I remember
6:37
one occasion when we were trying to do a complicated thing with producing maps of
6:43
data where I had a kind of mixture model so you could take the same bunch of similarities and make two maps so that
6:50
in one map Bank could be close to Greed and in another map Bank could be close to River um cuz in one map you can't
6:57
have it close to both right cuz River and greed along wayon so we'd have a mixture maps and we were doing it in mat
7:05
lab and this involved a lot of reorganization of the code to do the right Matrix multiplies and only got fed
7:10
up with that so he came one day and said um I'm going to write a an interface for
7:15
mat lab so I program in this different language and then I have something that just converts it into Matlab and I said
7:21
no Ilia um that'll take you a month to do we've got to get on with this project don't get diverted by that and I said
7:28
it's okay I did it this morning and that's that's quite quite
7:34
incredible and throughout those those years the biggest shift wasn't
7:40
necessarily just the the algorithms but but also the the skill how did you sort
7:45
of view that skill uh over over the years Ilia got that intuition very early
7:51
so Ilia was always preaching that um you just make it bigger and it'll work better and I always thought that was a
7:58
bit of a copout do you going to have to have new ideas too it turns out I was basically right new ideas help things
8:04
like Transformers helped a lot but it was really the scale of the data and the scale of the computation and back then
8:11
we had no idea computers would get like a billion times faster we thought maybe they' get a 100 times faster we were
8:17
trying to do things by coming up with clever ideas that would have just solved themselves if we had had bigger scale of the data and computation in about
8:25
2011 Ilia and another graduate student called James Martins and had a paper using character level
8:32
prediction so we took Wikipedia and we tried to predict the next HTML character
8:39
and that worked remarkably well and we were always amazed at how well it worked and that was using a fancy Optimizer on
8:47
gpus and we could never quite believe that it understood anything but it looked as though it
8:53
understood and that just seemed incredible can you take us through how do models trained to predict the next
9:02
word and why is it the wrong way of of thinking about them okay I don't
9:08
actually believe it is the wrong way so in fact I think I made the first
9:13
neuronet language model that used embeddings and back propagation so it's very simple data just
9:19
triples and it was turning each symbol into an embedding then having the
9:25
embeddings interact to predict the embedding of the next symbol and from that predic next symbol and then it was
9:31
back propagating through that whole process to learn these triples and I showed it could generalize um about 10
9:38
years later Yoshua Benji used a very similar Network and showed it work with real text and about 10 years after that
9:44
linguist started believing in embeddings it was a slow process the reason I think it's not just predicting the next symbol
9:52
is if you ask well what does it take to predict the next symbol particularly if you ask me a question and then the first
9:59
word of the answer is the next symbol um you have to understand the question so I
10:06
think by predicting the next symbol it's very unlike oldfashioned
10:11
autocomplete oldfashioned autocomplete you'd store sort of triples of words and then if you saw a pair of words you see
10:18
how often different words came third and that way you can predict the next symbol and that's what most people think auto
10:23
complete is like it's no longer at all like that um to predict the next symbol
10:28
you have to understand what's been said so I think you're forcing it to understand by making it predict the next symbol and I think it's understanding in
10:36
much the same way we are so a lot of people will tell you these things aren't like us um they're just predicting the
10:42
next symbol they're not reasoning like us but actually in order to predict the next symbol it's have going to have to
10:48
do some reasoning and we've seen now that if you make big ones without putting in any special stuff to do
10:53
reasoning they can already do some reasoning and I think as you make them bigger they're going to be able to do more and more reasoning do you think I'm
11:00
doing anything else than predicting the next symbol right now I think that's how you're learning I think you're
11:06
predicting the next video frame um you're predicting the next sound um but
11:11
I think that's a pretty plausible theory of how the brain's learning what enables these models to learn such a wide
11:19
variety of of fields what these big language models are doing is they looking for common structure and by
11:25
finding common structure they can encode things using the common structure and that more efficient so let me give you
11:31
an example if you ask gbd4 why is a compost heap like an atom
11:36
bomb most people can't answer that most people haven't thought they think atom bombs and compost seeps are very
11:42
different things but gp4 will tell you well the energy scales are very different and the time scales are very
11:48
different but the thing that's the same is that when the compost Heep gets hotter it generates heat faster and when
11:55
the atom bomb produces more NE neutrons it produces more neutrons faster and so it gets the idea of a chain
12:02
reaction and I believe it's understood they're both forms of chain reaction it's using that understanding to
12:08
compress all that information into its weights and if it's doing that then it's going to be doing that for hundreds of
12:15
things where we haven't seen the analogies yet but it has and that's where you get creativity from from
12:20
seeing these analogies between apparently very different things and so I think gp4 is going to end up when it
12:25
gets bigger being very creative I think this idea that it's just just regurgitating what it's learned just
12:31
pasing together text it's learned already that's completely wrong it's going to be even more creative than
12:37
people I think you'd argue that it won't just repeat the human knowledge we've
12:43
developed so far but could also progress beyond that I think that's something we haven't quite seen yet we've started
12:51
seeing some examples of it but to a to a large extent we're sort of still at the
12:56
current level of of of science what do you think will enable it to go beyond that well we've seen that in more
13:01
limited context like if you take Alpha go in that famous competition with Leo
13:08
um there was move 37 where Alpha go made a move that all the experts said must have been a mistake but actually later
13:15
they realized it was a brilliant move um so that was created within that limited domain um I think we'll see a lot more
13:22
of that as these things get bigger the difference with alphao as well was that
13:28
it was using reinforcement learning that that subsequently sort of enabled it to to go beyond the current state so it
13:35
started with imitation learning watching how humans play the game and then it would through selfplay develop Way
13:42
Beyond that do you think that's the missing component of the I think that may well be a missing component yes that
13:48
the the self-play in Alpha in Alpha go and Alpha zero are are a large part of
13:54
why it could make these creative moves but I don't think it's entirely necessary
13:59
so there's a little experiment I did a long time ago where you your training in neuronet to recognize handwritten digits
14:06
I love that example the mest example and you give it training data where half the answers are
14:12
wrong um and the question is how well will it
14:17
learn and you make half the answers wrong once and keep them like that so it
14:23
can't average away the wrongness by just seeing the same example but with the right answer sometimes and the wrong answer sometimes when it sees that
14:29
example half half of the examples when it sees the example the answer is always wrong and so the training data has 50%
14:37
error but if you train up back propagation it gets down to 5% error or
14:44
less other words from badly labeled data
14:49
it can get much better results it can see that the training date is wrong and that's how smart students can be smarter
14:55
than their advisor and their advisor tells them all this stuff and for half of what their advisor tells
15:01
them they think no rubbish and they listen to the other half and then they end up smarter than the advisor so these
15:06
big neural Nets can actually do they can do much better than their training data and most people don't realize that so
15:13
how how do you expect this models to add reasoning in into them so I mean one
15:19
approach is you add sort of the heuristics on on top of them which a lot of the research is doing now where you
15:25
have sort of Shan of thought you just feedback it's reasoning um in into itself and another way would be in the
15:32
model itself as you scale scale scale it up what's your intuition around that so
15:38
my intuition is that as we scale up these models I get better at reasoning and if you ask how people work roughly
15:44
speaking we have these intuitions and we can do reasoning and
15:50
we use the reasoning to correct our intuitions of course we use the intuitions during the reasoning to do
15:55
the reasoning but if the conclusion of the reasoning conflicts with our in itions we realize the intuitions need to
16:00
be changed that's much like in Alpha go or Alpha zero where you have an
16:06
evaluation function um that just looks at a board and says how good is that for me but then you do the Monte Cara roll
16:13
out and now you get a more accurate idea and you can revise your evaluation
16:18
function so you can train it by getting it to agree with the results of reasoning and I think these large language models have to start doing that
16:26
they have to start training their Raw intuitions about what should come next by doing reasoning and realizing that's
16:32
not right and so that way they can get more training data than just mimicking
16:37
what people did and that's exactly why alphao could do this creative move 37 it
16:43
had much more training data because it was using reasoning to check out what the right next move should have been and
16:49
what do you think about multimodality so we spoke about these analogies and often
16:54
the analogies are Way Beyond what we could see it's discovering analogy that are far beyond humans and at maybe
17:01
abstraction levels that we'll never be able to to to understand now when we introduce images to that and and video
17:09
and sound how do you think that will change the models and uh how do you
17:14
think it will change the analogies that it will be able to make um I think it'll
17:19
change it a lot I think it'll make it much better at understanding spatial things for example from language alone
17:26
it's quite hard to understand some spatial things although remarkably gp4 can do that even before it was
17:32
multimodal um but when you make it multimodal if you have it both doing
17:38
vision and reaching out and grabbing things it'll understand object much better if it can pick them up and turn
17:44
them over and so on so although you can learn an awful lot from language it's
17:50
easier to learn if you multimodal and in fact you then need less language and
17:55
there's an awful lot of YouTube video for predicting the next frame so or something like that so I think these
18:01
multimodal models are clearly going to take over um you can get more data that way they need less language so there's
18:08
really a philosophical point that you could learn a very good model from language alone but it's much easier to
18:14
learn it from a multimodal system and how do you think it will impact the model's reasoning I think it'll make it
18:21
much better at reasoning about space for example reasoning about what happens if you pick objects up if you actually try picking objects up you're going to get
18:27
all sorts of training data that's going to help do you think the human brain evolved to work well with with language
18:35
or do you think language evolved to work well with the human brain I think the question of whether language evolved to
18:41
work with the brain or the brain evolved to work with language I think that's a very good question I think both happened
18:48
I used to think we would do a lot of cognition without needing language at all um now I've changed my mind a bit so
18:57
let me give you three different views of language um and how it relates to cognition there's the oldfashioned
19:03
symbolic view which is cognition consists of having strings of symbols in
19:10
some kind of cleaned up logical language where there's no ambiguity and applying rules of inference and that's what
19:15
cognition is it's just these symbolic manipulations on things that are like strings of language symbols um so that's
19:22
one extreme view an opposite extreme view is no no once you get inside the head it's all vectors so symbols come in
19:30
you convert those symbols into big vectors and all the stuff inside's done with big vectors and then if you want to
19:36
ruce Output you produce symbols again so there was a point in machine translation in about
19:42
2014 when people were using neural recurrent neural Nets and words will keep coming in and then have a hidden
19:48
State and they keep accumulating information in this hidden state so when they got to the end of a sentence that
19:55
have a big hidden Vector that captures the meaning of that sentence that could then be used for producing the sentence
20:00
in another language that was called a thought vector and that's a sort of second view of language you convert the
20:05
language into a big Vector that's nothing like language and that's what cognition is all about but then there's
20:12
a third view which is what I believe now which is that you take these
20:20
symbols and you convert the symbols into embeddings and you use multiple layers
20:25
of that so you get these very rich embeddings but the embeddings are still to the symbols in the sense that you've
20:30
got a big Vector for this symbol and a big Vector for that symbol and these vectors interact to produce the vector
20:36
for the symbol for the next word and that's what understanding is understanding is knowing how to convert
20:42
the symbols into these vectors and knowing how the elements of the vector should interact to predict the vector
20:47
for the next symbol that's what understanding is both in these big language models and in our brains and that's an example which is
20:55
sort of in between you're staying with the symbols but you're interpreting them as these big vectors and that's where
21:02
all the work is and all the knowledge is in what vectors you use and how the elements of those vectors interact not
21:08
in symbolic rules um but it's not saying that you get away from the symbols all together
21:14
it's saying you turn the symbols into big vectors but you stay with that surface structure of the symbols and
21:20
that's how these models are working and that's I seem to be a more plausible model of human thought too you were one
21:26
of the first folks to get idea of using gpus and I know yansen loves you for
21:34
that uh back in 2009 you mentioned that you told yansen that this could be a quite good idea um for for training
21:41
training neural Nets take us back to that early intuition of of using gpus for for training neural Nets so actually
21:48
I think in about 2006 I had a former graduate student called Rick zisy who's a very good
21:55
computer vision guy and I talked to him and a meeting and he said you know you
22:00
ought to think about using Graphics processing cards because they're very good at Matrix multiplies and what
22:05
you're doing is basically all matric multiplies so I thought about that for a bit and then we learned about these
22:11
Tesla systems that had um four gpus in and initially we just got um gaming gpus
22:21
and discovered they made things go 30 times faster and then we bought one of these Tesla systems with 4 gpus and we
22:27
did speech on that and it worked very well then in 2009 I gave a talk at nips
22:34
and I told a thousand machine learning researches you should all go and buy Nvidia gpus they're the future you need
22:39
them for doing machine learning and I actually um then sent mail to Nvidia
22:45
saying I told a thousand machine learning researchers to buy your boards could you give me a free one and they said no actually they didn't say no they
22:51
just didn't reply um but when I told Jensen this story later on he gave me a free
22:57
one that's uh that's very very good I I think what's interesting is um as well
23:02
is sort of how gpus has evolved alongside the the field so where where
23:07
do you think we we should go go next in in the in the computer so my last couple
23:13
of years at Google I was thinking about ways of trying to make analog computation so that instead of using
23:19
like a megawatt we could use like 30 Watts like the brain and we could run these big language models in analog
23:26
hardware and I never made it work and but I started really
23:32
appreciating digital computation so if you're going to use that low power
23:38
analog computation every piece of Hardware is going to be a bit different and the idea
23:43
is the learning is going to make use of the specific properties of that hardware and that's what happens with people all
23:49
our brains are different um so we can't then take the weights in your brain and
23:54
put them in my brain the hardware is different the precise properties of the individual idual neurons are different the learning used to make has learned to
24:01
make use of all that and so we're mortal in the sense that the weights in my brain are no good for any other brain
24:07
when I die those weights are useless um we can get information from one to another rather
24:13
inefficiently by I produce sentences and you figure out how to change your weight so you would have said the same thing
24:20
that's called distillation but that's a very inefficient way of communicating knowledge and with digital systems
24:27
they're immortal because once you got some weights you can throw away the computer just store the weights on a
24:32
tape somewhere and now build another computer put those same weights in and if it's digital it can compute exactly
24:39
the same thing as the other system did so digital systems can share weights and
24:45
that's incredibly much more efficient if you've got a whole bunch of digital systems and they each go and do a tiny
24:51
bit of learning and they start with the same weights they do a tiny bit of learning and then they share their weights again
24:58
um they all know what all the others learned we can't do that and so they're far superior to us in being able to
25:04
share knowledge a lot of the ideas that have been deployed in the field are very
25:10
old school ideas uh it's the ideas that have been around the Neuroscience for
25:15
forever what do you think is sort of left to to to apply to the systems that we develop so one big thing that we
25:23
still have to catch up with Neuroscience on is the time scales for changes so in
25:31
nearly all the neural Nets there's a fast time scale for changing activities so input comes in the activities the
25:38
embedding vectors all change and then there's a slow time scale which is changing the weights and that's
25:43
long-term learning and you just have those two time scales in the brain there's many time scales at which
25:49
weights change so for example if I say an unexpected word like cucumber and now
25:56
5 minutes later you put headphones on there's a lot of noise and there's very faint words you'll be much better at
26:03
recognizing the word cucumber because I said it 5 minutes ago so where is that knowledge in the brain and that
26:10
knowledge is obviously in temporary changes to synapsis it's not neurons are going cucumber cucumber cucumber you
26:16
don't have enough neurons for that it's in temporary changes to the weights and you can do a lot of things with
26:22
temporary weight changes fast what I call fast weights we don't do that in these neural models and the reason we
26:28
don't do it is because if you have temporary changes to the weights that
26:33
depend on the input data then you can't process a whole bunch of different cases
26:38
at the same time at present we take a whole bunch of different strings we stack them stack them together and we
26:45
process them all in parallel because then we can do Matrix Matrix multiplies which is much more efficient and just
26:51
that efficiency is stopping us using fast weights but the brain clearly uses fast weights for temporary memory and
26:59
there's all sorts of things you can do that way that we don't do at present I think that's one of the biggest things we have to learn I was very hopeful that
27:04
things like graph core um if they went sequential and did just online learning
27:11
then they could use fast weights um but that hasn't worked out yet I
27:16
think it'll work out eventually when people are using conductances for weights how has knowing how this models
27:23
work and knowing how the brain works impacted the way you you think I think
27:29
there's been one big impact which is at a fairly abstract level which is that
27:35
for many years people were very scornful about the idea of having a big random neural
27:42
net and just giving a lot of training data and it would learn to do complicated things if you talk to
27:47
statisticians or linguists or most people in AI they say that's just a pipe
27:53
dream there's no way you're going to learn to really complicated things without some kind of innate knowledge without a lot of architectural
27:59
restrictions it turns out that's completely wrong you can take a big random neural network and you can learn
28:04
a whole bunch of stuff just from data um so the idea that stochastic gradient
28:10
descent to adjust the repeatedly adjust the weights using a gradient that will
28:16
learn things and we'll learn big complicated things that's been validated
28:21
by these big models and that's a very important thing to know about the brain it doesn't have to have all this inate
28:27
structure now obviously it's got a lot of innate structure but it certainly
28:32
doesn't need innate structure for things that are easily learned and so the sort of idea coming
28:37
from Chomsky that you won't you won't learn anything complicated like language unless it's all kind of wired in already
28:43
and just matures that idea is now clearly nonsense I'm sure shumsky would
28:49
appreciate you calling his ideas nonsense well I think actually I think a
28:54
lot of chs's political ideas are very sensible and I'm was struck by how how come someone with such sensible ideas
29:00
about the Middle East could be so wrong about Linguistics what do you think would make these
29:06
models simulate consciousness of of humans more effectively but imagine you
29:12
had the AI assistant that you've spoken to in your entire life and instead of that being know like shat today that
29:20
sort of deletes the memory of the conversation and you start fresh all of the time okay it had
29:26
self-reflection at some point you you pass away and you tell that to to the
29:32
assistant do you think I me not me somebody else tells that toist yeah you
29:38
would it would be difficult for you to tell that to the assistant um do you think that assistant would would feel at
29:44
that point yes I think they can have feelings too so I think just as we have this inner theater model for perception
29:51
we have an inthat model for feelings they're things that I can experience but other people can't um
29:59
I think that model is equally wrong so I think suppose I say I feel like punching
30:04
Gary on the nose which I often do let's try and Abstract that away from the idea
30:10
of an in a theater what I'm really saying to you is um if it weren't for
30:16
the inhibition coming from my frontal loes I would perform an action so when we talk about feelings we really talking
30:22
about um actions we would perform if it weren't for um con straints and that
30:29
really that's really what feelings are the actions we would do if it weren't for constraints um so I think you can give
30:36
the same kind of explanation for feelings and there's no reason why these things can't have feelings in fact in
30:42
1973 I saw a robot having an emotion so in Edinburgh they had a robot with two
30:49
grippers like this that could assemble a toy car if you put the pieces separately
30:54
on a piece of green felt um but if you put them in a pile his vision wasn't
31:01
good enough to figure out what was going on so it put his gripers together went whack and it knocked them so they were
31:06
scattered and then it could put them together if you saw that in a person you say it was crossed with the situation
31:11
because it didn't understand it so it destroyed it that's profound you uh we spoke previously you
31:19
described sort of humans and and and and the llms as analogy missiones what do
31:24
you think has been the most powerful analogies that you found throughout your
31:30
life oh in throughout my life um woo I
31:36
guess probably an a sort of weak analogy that's influenced me a lot is um the
31:45
analogy between religious belief and between belief in symbol processing so when I was very young I
31:52
was confronted I came from an atheist family and went to school and was confronted with religious belief and it
31:58
just seemed nonsense to me it still seems nonsense to me um and when I saw symbol processing as an explanation how
32:04
people worked um I thought it was just the same
32:10
nonsense I don't think it's quite so much nonsense now because I think actually we do do symbol processing it's
32:17
just we do it by giving these big embedding vectors to the symbols but we are actually symbol processing um but
32:24
not at all in the way people thought where you match symbols and the only thing is symbol has is it's identical to
32:29
another symbol or it's not identical that's the only property a symbol has we don't do that at all we use the context
32:35
to give embedding vectors to symbols and then use the interactions between the components of these embedding vectors to
32:41
do thinking but there's a very good researcher at Google called Fernando
32:46
Pereira who said yes we do have symbolic reasoning and the only symbolic we have
32:52
is natural language natural language is a symbolic language and we reason with it and I believe that now you've done
32:58
some of the most meaningful uh research in the history of of computer science
33:03
can you walk us through like how do you select the right problems to to work on well first let me correct you me and my
33:11
students have done a lot of the most meaningful things and it's mainly been a very good collaboration with students
33:17
and my ability to select very good students and that came from the fact that were're very few people doing
33:22
neural Nets in the 70s and ' 80s and 90s and 2000s and so the few people doing
33:28
your nets got to pick the very best students so that was a piece of luck but my way of selecting problems is
33:35
basically well you know when scientists talk about how they work they have theories about how they work which
33:41
probably don't have much to do with the truth but my theory is that I look for something where everybody's
33:48
agreed about something and it feels wrong just there's a slight intuition there something wrong about it and then
33:54
I work on that and see if I can elaborate why it is I think it's wrong and maybe I can make a little demo with
34:00
a small computer program that shows that it doesn't work the way you might expect
34:06
so let me take one example um most people think that if you add noise to a neural net is going to work worse um if
34:14
for example each time you put a training example through you make half of the neurons be silent
34:22
it'll work worse actually we know it'll generalize better if you do that
34:28
and you can demonstrate that um in a simple example that's what's nice about
34:34
computer simulation you can show you know this idea you had that adding noise is going to make it worse and sort of
34:39
dropping out half the neurons will make it work worse which it will in the short term but if you train it with like that
34:45
in the end it'll work better you can demonstrate that with a small computer program and then you can think hard about why that is and how it stops big
34:53
elaborate co- adaptations um but that I think that that's my method of working
34:59
find something that sounds suspicious and work on it and see if you can give a simple demonstration of why it's wrong
35:06
what sounds suspicious to you now well that we don't use fast weight sounds suspicious that we only have these two
35:12
time scales that's just wrong that's not at all like the brain um and in the long run I think we're going to have to have
35:18
many more time scans so that's an example now and if you had if you had
35:23
your group of of students today and they came to you and they said so the Hamming question that we talked about previously you know what's the most important
35:29
problem in in in your field what would you suggest that they take on and work on on next we spoke
35:36
about reasoning time scales what would be sort of the highest priority Problem that that you'd give them for me right
35:43
now it's the same question I've had for the last like 30 years or so which is
35:48
does the brain do back propagation I believe the brain is getting gradients if you don't get gradients your learning
35:54
is just much worse than if you do get gradients but how is the brain getting gradients and is it
36:01
somehow implementing some approximate version of back propagation or is it some completely different technique
36:06
that's a big open question and if i' kept on doing research that's what I would be doing research on and when you
36:12
look back at at your career now you've been right about so many things but what
36:18
were you wrong about that you wish you sort of spent less time pursuing a certain direction okay those are two
36:24
separate questions one is what were you wrong about and two do you wish you'd less spent less time on it I think I was
36:31
wrong about Boltz machines and I'm glad I spent a long time on it there are much more beautiful theory of how you get
36:37
gradients than back propagation back propagation is just ordinary and sensible and it's just a chain rule B of
36:42
machines is clever and it's a very interesting way to get gradients and I
36:47
would love for that to be how the brain works but I think it isn't did you spend much time imagining what would happen
36:54
post the systems developing as as well did you have an idea that okay if we could make these systems work really
37:00
well we could you know democratize education we could make knowledge way more accessible um we could solve some
37:07
tough problems in in in medicine or was it more to you about understanding the
37:13
Brin yes I I sort of feel scientists ought to be doing things that are going to help Society but actually that's not
37:22
how you do your best research you do your best research when it's driven by curiosity you just have to understand
37:28
something um much more recently I've realized these things could do a lot of
37:33
harm as well as a lot of good and I've become much more concerned about the effects they're going to have on society
37:39
but that's not what was motivating me I just wanted to understand how on Earth can the brain learn to do things that's
37:45
what I want to know and I sort of failed as a side effect of that failure we got some nice engineering
37:51
but yeah it was a good good good failure for the world if you take the lens of the things that could go really right
37:59
what what do you think are the most promising applications I think healthc care is
38:05
clearly uh a big one um with healthc care there's almost no end to how much
38:11
Health Care Society can absorb if you take someone old they could use five
38:17
doctors fulltime um so when AI gets better than people at doing things um
38:24
you'd like it to get better in areas where you could do with a lot more of that stuff and we could do with a lot
38:31
more doctors if everybody had three doctors of their own that would be great and we're going to get to that point um
38:38
so that's one reason why Healthcare is good there's also just a new engineering
38:44
developing new materials for example for better solar panels or for super conductivity or for just understanding
38:51
how the Body Works um there's going to be huge impacts there those are all
38:57
going to be be good things what I worry about is Bad actors using them for bad things we've facilitated people like
39:04
Putin or Z or Trump using AI for Killer Robots or for manipulating public
39:11
opinion or for Mass surveillance and those are all very worrying things are you ever concerned that slowing down the
39:19
field could also slow down the positives oh absolutely and I think there's not
39:25
much chance that the field will slow down partly because it's International and if one country slows down the other
39:31
countries aren't going to slow down so there's a race clearly between China and the US and neither is going to slow down
39:39
so yeah I don't I mean there was this partition saying we should slow down for six months I didn't sign it just because I thought it was never going to happen I
39:46
maybe should have signed it because even though it was never going to happen it made a political point it's often good to ask for things you know you can't get
39:53
just to make a point um but I didn't think we're going to slow down and how did do you think that it will impact the
39:59
AI research process uh having uh this assistance so I think it'll make it a
40:04
lot more efficient a research will get a lot more efficient when you've got these assistants that help you program um but
40:11
also help you think through things and probably help you a lot with equations too have you reflected much on the
40:17
process of selecting Talent has that been mostly intuitive to you like when Ilia shows up at the door you feel this
40:24
is smart guy let's work together so for selecting Talent um sometimes you just
40:30
know so after talking to Ilia for not very long he seemed very smart and then talking him a bit more he clearly was
40:36
very smart and had very good intuitions as well as being good at math so that
40:41
was a no-brainer there's another case where I was at a nips conference um we
40:47
had a poster and I someone came up and he started asking questions about the poster and every question he asked was a
40:54
sort of deep insight into what we'd done wrong um and after 5 minutes I offered him a postto position that guy was David
41:01
McKai who was just brilliant and it's very sad he died but he was it was very
41:07
obvious you'd want him um other times it's not so obvious and one thing I did
41:12
learn was that people are different there's not just one type of good
41:17
student um so there's some students who aren't that creative but are technically
41:23
extremely strong and will make anything work there's other students who aren't technically strong but are very creative
41:31
of course you want the ones who are both but you don't always get that but I think actually in the lab you need a
41:36
variety of different kinds of graduate student but I still go with my gut intuition that sometimes you talk to
41:43
somebody and they're just very very they just get it and those are the ones you want what do you think is the reason for
41:51
some folks having better intuition do they just have better training data than than others or how can you develop your
42:00
intuition I think it's partly they don't stand for nonsense so here's a way to
42:06
get bad intuitions believe everything you're told that's fatal you have to be
42:12
able to I think here's what some people do they have a whole framework for understanding reality and when someone
42:17
tells them something they try and sort of figure out how that fits into their framework and if it doesn't they just
42:24
reject it and that's a very good strategy um people who try and
42:30
incorporate whatever they're told end up with a framework that's sort of very fuzzy and sort of can believe everything
42:38
and that's useless so I think actually having a strong view of the world and
42:44
trying to manipulate incoming facts to fit in with your view obviously it can lead you into deep religious belief and
42:51
fatal flaws and so on like my belief in boltzman machines um but I think that's the way to go if you got good intuitions
42:58
you can trust you should trust them if You' got bad intuitions it doesn't matter what you do so you might as well
43:04
trust them a very very good very good point
43:09
when when you look at the the types of research that's that's that's being done
43:15
today do you think we're putting all of our eggs in one basket and we should diversify our ideas a bit more in in the
43:22
field or do you think this is the most promising Direction so let's go all in on it
43:28
I think having big models and training them on multimodal data even if it's
43:33
only to predict the next word is such a promising approach that we should go pretty much all in on it obviously
43:39
there's lots and lots of people doing it now and there's lots of people doing apparently crazy things and that's good
43:45
um but I think it's fine for like most of the people to be following this path because it's working very well do you
43:50
think that the learning algorithms matter that much or is it just a skill
43:56
are there basically millions of ways that we could we could get to human level in in intelligence or are there
44:03
sort of a select few that we need to discover yes so this issue of whether particular learning algorithms are very
44:10
important or whether there's a great variety of learning algorithms that'll do the job I don't know the answer it
44:16
seems to me though that back propagation there's a sense in which it's the correct thing to do getting the gradient
44:23
so that you change a parameter to make it work better that seems like the right thing to do and it's been amazingly
44:30
successful there may well be other learning algorithms that are alternative ways of getting that same gradient or
44:36
that are getting the gradient to something else and that also work um I think that's all open and a very
44:43
interesting issue now about whether there's other things you can try and maximize that will give you good systems
44:50
and maybe the brain's doing that cuz it's easier but backprop is in a sense the
44:55
right thing to do and we know that doing it works really well and one last question when when you
45:02
look back at your sort of Decades of research what are you what are you most proud of is it the students is it the
45:07
research what what makes you most proud of when you look back at at your life's work the learning algorithm for
45:14
boltimore machines so the learning algorithm for Boltz machines is beautifully elegant it's maybe hopeless
45:21
in practice um but it's the thing I enjoyed most developing that with Terry
45:27
and it's what I'm proudest of um even if it's [Music]
45:36
wrong what questions do you spend most of your time thinking about now is it
45:41
the um what what should I watch on Netflix

summarize the key points as per the transcript above. Give your answer with some details, not just surface level.