Ilya Sutskever: Deep Learning | Lex Fridman Podcast #94

the following is a conversation with elias discover co-founder and chief scientist of open ai one of the most cited computer scientists in history with over 165 000 citations and to me one of the most brilliant and insightful minds ever in the field of deep learning there are very few people in this world who i would rather talk to and brainstorm with about deep learning intelligence and life in general than ilia on and off the mic this was an honor and a pleasure this conversation was recorded before the outbreak of the pandemic for everyone feeling the

medical psychological and financial burden of this crisis i'm sending love your way stay strong we're in this together we'll beat this thing this is the artificial intelligence podcast if you enjoy it subscribe on youtube review it with five stars and have a podcast support it on patreon or simply connect with me on twitter at lex friedman spelled f-r-i-d-m-a-n as usual i'll do a few minutes of as now and never any ads in the middle that can break the flow of the conversation i hope that works for you and doesn't hurt the listening experience this show

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you were one of the three authors with alex kaczowski jeff hinton of the famed alex ned paper that is arguably the paper that marked the big catalytic moment that launched the deep learning revolution at that time take us back to that time what was your intuition about neural networks about the representational power of neural networks and maybe you could mention how did that evolve over the next few years up to today over the 10 years yeah i can answer that question at some point in about 2010 or 2011 i connected two facts in my mind

basically the realization was this at some point we realized that we can train very large i shouldn't say very you know they're tiny by today's standards but large and deep neural networks end to end with back propagation at some point different people obtained this result i obtained this result the first the first moment in which i realized that deep neural networks are powerful was when james martens invented the hessian-free optimizer in 2010 and he trained a 10-layer neural network end-to-end without pre-training from scratch and when that happened i thought this is it because if you

can train a big neural network a big neural network can represent very complicated function because if you have a neural network with 10 layers it's as though you allow the human brain to run for some number of milliseconds neuron firings are slow and so in maybe 100 milliseconds your neurons only fire 10 times so it's also kind of like 10 layers and in 100 milliseconds you can perfectly recognize any object so i thought so i already had the idea then that we need to train a very big neural network on lots of supervised data and

then it must succeed because we can find the best neural network and then there's also theory that if you have more data than parameters you won't overfit today we know that actually this theory is very incomplete and you want overfitting when you have less data than parameters but definitely if you have more data than parameters you want overfit so the fact that neural networks were heavily over parametrized wasn't discouraging to you so you you were thinking about the theory that the number of parameters the fact there's a huge number of parameters is okay it's gonna

be okay i mean there was some evidence before that it was okayish but the theory was most the theory was that if you had a big data set and a big neural net it was going to work the over parameterization just didn't really um figure much as a problem i thought well with images you're just going to add some data augmentation it's going to be okay so where was any doubt coming from the main doubt was can we train a bigger will we have enough computer trainer big enough neural net with back propagation back propagation

i thought would work this image wasn't clear would was whether there would be enough compute to get a very convincing result and then at some point alex krajewski wrote these insanely fast gooda kernels for training convolutional neural nets and that was bam let's do this let's get imaging that and it's going to be the greatest thing was your intuition most of your intuition from empirical results by you and by others so like just actually demonstrating that a piece of program can train a 10-layer neural network or was there some pen and paper or marker and

white board thinking intuition like because you just connected a 10 layer large neural network to the brain so you just mentioned the brain so in your intuition about neural networks does the human brain come into play as a intuition builder definitely i mean you you know you got to be precise with these analogies between neural artificial neural networks in the brain but there is no question that the brain is a huge source of intuition and inspiration for deep learning researchers since all the way from rosenblatt in the 60s like if you look at the the

whole idea of a neural network is directly inspired by the brain you had people like mccollum and pitts who were saying hey you got this these neurons in the brain and hey we recently learned about the computer and automata can we use some ideas from the computer and automata to design some kind of computational object that's going to be simple computational and kind of like the brain and they invented the neuron so they were inspired by it back then then you had the convolutional neural network from fukushima and then later yeah khan who said hey

if you limit the receptive fields of a neural network it's going to be especially suitable for images as it turned out to be true so there was there was a very small number of examples where analogies to the brain were successful and i thought well probably an artificial neuron is not that different from the brain if it's queen hard enough so let's just assume it is and roll with it so no we're now at a time where deep learning is very successful so let us squint less and say let's uh open our eyes and say

what to use an interesting difference between the human brain now i know you're probably not an expert neither in your scientist and your biologist but loosely speaking what's the difference between the human brain and artificial neural networks that's interesting to you for the next decade or two that's a good question to ask what is in what is an interesting difference between the neurons between the brain and our artificial neural networks so i feel like today artificial neural networks so we all agree that there are certain dimensions in which the human brain vastly outperforms our models

but i also think that there are some ways in which artificial neural networks have a number of very important advantages over the brain look looking at the advantages versus disadvantages is a good way to figure out what is the important difference so the brain uses spikes which may or may not be important yeah that's a really interesting question do you think it's important or not that's one big architectural difference between artificial neural networks and it's hard to tell but my prior is not very high and i can i can say why you know there are

people who are interested in spiking neural networks and basically what they figured out is that they need to simulate the non-spiking neural networks in spikes and that's how they're gonna make them work if you don't simulate the non-spike in neural networks in spikes it's not going to work because the question is why should it work and that connects to questions around back propagation and questions around deep learning you got this giant neural network why should it work at all why should the learning rule work at all it's not a self-evident question especially if you let's

say if you were just starting in the field and you read the very early papers you can say hey people are saying let's build neural networks that's a great idea because the brain is a neural network so it would be useful to build neural networks now let's figure out how to train them it should be possible to train them properly but how and so the big idea is the cost function that's the big idea the cost function is a way of measuring the performance of the system according to some measure by the way that is

a big actually let me think is that is that uh one a difficult idea to arrive at and how big of an idea is that that there's a single cost function let me sorry let me take a pause is supervised learning a difficult concept to come to i don't know all concepts are very easy in retrospect yeah that's what it seems trivial now but i so because because the reason i asked that and we'll talk about it because is there other things is there things that don't necessarily have a cost function maybe have many cost

functions or maybe have dynamic cost functions or maybe a totally different kind of architectures because we have to think like that in order to arrive at something new right so the only so the good examples of things which don't have clear cost functions are gans again you have a game so instead of thinking of a cost function where you want to optimize where you know that you have an algorithm gradient descent which will optimize the cost function and then you can reason about the behavior of your system in terms of what it optimizes with again

you say i have a game and i'll reason about the behavior of the system in terms of the equilibrium of the game but it's all about coming up with these mathematical objects that help us reason about the behavior of our system right that's really interesting yes again is the only one it's kind of a com the cost function is emergent from the comparison it's i don't i don't know if it has a cost function i don't know if it's meaningful to talk about the cost function of again it's kind of like the cost function of

biological evolution or the cost function of the economy it's you can talk about regions to which it will go towards but i don't think i don't think the cost function analogy is the most useful so if evolution doesn't that's really interesting so if evolution doesn't really have a cost function like a cost function based on its something akin to our mathematical conception of a cost function then do you think cost functions in deep learning are holding us back yeah i so you just kind of mentioned that cost function is a nice first profound idea do

you think that's a good idea do you think it's an idea will go past so self-play starts to touch on that a little bit uh in reinforcement learning systems that's right self-play and also ideas around exploration where you're trying to take action that surprise a predictor i'm a big fan of cos functions i think cost functions are great and they serve us really well and i think that whenever we can do things because with cost functions we should and you know maybe there is a chance that we will come up with some yet another profound

way of looking at things that will involve cost functions in a less central way but i don't know i think cost functions are i mean i would not better guess against cost functions is there other things about the brain that pop into your mind that might be different and interesting for us to consider in designing artificial neural networks so we talked about spiking a little bit i mean one one thing which may potentially be useful i think people neuroscientists figured out something about the learning rule of the brain or i'm talking about spike time independent

elasticity and it would be nice if some people were to study that in simulation wait sorry spike time independent plasticity yeah what's that std it's a particular learning rule that uses spike timing to figure out how to to determine how to update the synapses so it's kind of like if the synapse fires into the neuron before the neuron fires then it strengthens the synapse and if the synapse fires into the neurons shortly after the neuron fire then it weakens the synapse something along this line i'm 90 sure it's right so if i said something wrong

here don't don't get too angry but you sounded brilliant while saying it but the timing that's one thing that's missing the the temporal dynamics is not captured i think that's like a fundamental property of the brain is the timing of this of the signals well your recurrent neural networks but you you think of that as i mean that's a very crude simplified uh what's that called uh there's a clock i guess to uh recurring neural networks it's this it seems like the brain is the general the continuous version of that the the generalization where all

possible timings are possible and then within those timings this contains some information you think recurrent neural networks the recurrence in recurrent neural networks can capture the same kind of phenomena as the timing that seems to be important for the brain in the in the firing of neurons in the brain i i mean i think i think regarding neurons recurrent neural networks are amazing and they can do i think they can do anything we'd want them to if we'd want a system to do right now recurrent neural networks have been superseded by transformers but maybe one

day they'll make a comeback maybe they'll be back we'll see let me uh in a small tangent say do you think they'll be back so so much of the breakthroughs recently that we'll talk about on uh natural language processing and language modeling has been with transformers that don't emphasize your currents do you think recurrence will make a comeback well some kind of recurrence i think very likely recurrent neural networks for pros as they're typically thought of for processing sequences i think it's also possible what is to you a recurrent neural network and generally speaking i

guess what is a recurrent neural network you have a neural network which maintains a high dimensional hidden state and then when an observation arrives it updates its high dimensional hidden state through its connections in some way so do you think you know that's what like expert systems did right symbolic ai uh the knowledge based growing a knowledge base is is maintaining a hidden state which is its knowledge base and is growing it by sequential processing do you think of it more generally in that way or is it simply is it the more constrained form that

of of a hidden state with certain kind of gating units that we think of as today with lstms and that i mean the hidden state is technically what you described there the hidden state that goes inside the lstm or the rnn or something like this but then what should be contained you know if you want to make the expert system um analogy i'm not i mean you could say that the knowledge is stored in the connections and then the short term processing is done in the in the hidden state yes could you say that yeah

so sort of do you think there's a future of building large scale knowledge bases within the neural networks definitely so we're going to pause on that confidence because i want to explore that well let me zoom back out and ask back to the history of imagenet neural networks have been around for many decades as you mentioned what do you think were the key ideas that led to their success that image in that moment and beyond the success in the past 10 years okay so the question is to make sure i didn't miss anything the key

ideas that led to the success of deep learning over the past 10 years exactly even though the fundamental thing behind deep learning has been around for much longer so the key idea about deep learning or rather the key fact about deep learning before deep learning started to be successful is that it was underestimated people who worked in machine learning simply didn't think that neural networks could do much people didn't believe that large neural networks could be trained people thought that well there was lots of there was a lot of debate going on in machine learning

about what are the right methods and so on and people were arguing because there were no there were there were no there was no way to get hard facts and by that i mean there were no benchmarks which were truly hard that if you do really well in them then you can say look here is my system that's when you switch from that's when this field becomes a little bit more of an engineering field so in terms of deep learning to answer the question directly the ideas were all there the thing that was missing was

a lot of supervised data and a lot of compute once you have a lot of supervised data and a lot of compute then there is a third thing which is needed as well and that is conviction conviction that if you take the right stuff which already exists and apply and mix it with a lot of data and a lot of compute that it will in fact work and so that was the missing piece it was you had the you need the data you needed the compute which showed up in terms of gpus and you needed

the conviction to realize that you need to mix them together so that's really interesting so uh i i guess the presence of compute and the present supervised data allowed the empirical evidence to do the convincing of the majority of the computer science community so i guess there was a key moment with uh jitendra malik and uh alex alyosha afros who were very skeptical right and then there's a jeffrey hinton that was the opposite of skeptical and there was a convincing moment and i think emission had served as that moment that's right and they represented this

kind of were the big pillars of computer vision community kind of the the wizards got together and then all of a sudden there was a shift and it's not enough for the ideas to all be there and the computer to be there it's for it to convince the cynicism that existed that it's interesting that people just didn't believe for a couple of decades yeah well but it's more than that it's kind of been put this way it sounds like well you know those silly people who didn't believe what were they what were they missing but

in reality things were confusing because neural networks really did not work on anything and they were not the best method on pretty much anything as well and it was pretty rational to say yeah this stuff doesn't have any traction and that's why you need to have these very hard tasks which are which produce undeniable evidence and that's how we make progress and that's why the field is making progress today because we have these hard benchmarks which represent true progress and so and this is why we are able to avoid endless debate so incredibly you've contributed

some of the biggest recent ideas in ai in in computer vision language natural language processing reinforcement learning sort of everything in between maybe not gans is there there may not be a topic you haven't touched and of course the the fundamental science of deep learning what is the difference to you between vision language and as in reinforcement learning action as learning problems and what are the commonalities do you see them as all interconnected are they fundamentally different domains that require different approaches okay that's a good question machine learning is a field with a lot of

unity a huge amount of unity what do you mean by unity like overlap of ideas overlap of ideas overlap of principles in fact there is only one or two or three principles which are very very simple and then they apply in almost the same way in almost the same way to the different modalities to the different problems and that's why today when someone writes a paper on improving optimization of deep learning and vision it improves the different nlp applications and it improves the different reinforcement learning applications reinforcement learn so i would say that computer vision

and nlp are very similar to each other today they differ in that they have slightly different architectures we use transformers in nlp and use convolutional neural networks in vision but it's also possible that one day this will change and everything will be unified with a single architecture because if you go back a few years ago in natural language processing there were a huge number of architectures for every different tiny problem had its own architecture today this is just one transformer for all those different tasks and if you go back in time even more you had

even more and more fragmentation and every little problem in ai had its own little sub specialization and sub you know little set of collection of skills people who would know how to engineer the features now it's all been subsumed by deep learning we have this unification and so i expect vision to become unified with natural language as well or rather i shouldn't say expect i think it's possible i don't want to be too sure because i think on the commercial neural net is very computationally efficient rl is different rl does require slightly different techniques because

you really do need to take action you really do need to do something about exploration your variance is much higher but i think there is a lot of unity even there and i would expect for example that at some point there will be some broader unification between rl and supervised learning where somehow the rl will be making decisions to make the supervised learning go better and it will be i imagine one big black box and you just throw every you know you shovel travel things into it and it just figures out what to do with

whatever you shovel it i mean reinforcement learning has some aspects of language and vision combined almost there's elements of a long-term memory that you should be utilizing and there's elements of a really rich sensory space so it seems like the it's like the union of the two or something like that i'd say something slightly differently i'd say that reinforcement learning is neither but it naturally interfaces and integrates with the two of them do you think action is fundamentally different so yeah what is interesting about what is unique about policy of learning to act well so

one example for instance is that when you learn to act you are fundamentally in a non-stationary world because as your actions change the things you see start changing you you experience the world in a different way and this is not the case for the more traditional static problem where you have at least some distribution and you just apply a model to that distribution do you think it's a fundamentally different problem or is it just a more difficult general it's a generalization of the problem of understanding i mean it's it's it's a question of definitions almost

there is a huge you know there's a huge amount of commonality for sure you take gradients you try you take gradients we try to approximate gradients in both cases in some get in the case of reinforcement learning you have some tools to reduce the variance of the gradients you do that there's lots of commonality use the same neural net in both cases you compute the gradient you apply atom in both cases so i mean there's lots in common for sure but there are some small differences which are not completely insignificant it's really just a matter

of your point of view what frame of reference you what how much do you want to zoom in or out as you look at these problems which problem do you think is harder so people like no chomsky believe that language is fundamental to everything so it underlies everything do you think language understanding is harder than visual scene understanding or vice versa i think that asking if a problem is hard is slightly wrong i think the question is a little bit wrong and i want to explain why so what does it mean for a problem to

be hard okay the non-interesting dumb answer to that is there's this there's a benchmark and there's a human level performance on that benchmark and how there's the effort required to reach the human level okay benchmark so from the perspective of how much until we get to human level on a very good benchmark yeah like some i i understand what you mean by that so what i was going i'm going to say that a lot of it depends on you know once you solve a problem it stops being hard and that's all that's always true and

so whether something is hard or not depends on what our tools can do today so you know you say today true human level language understanding and visual perception are hard in the sense that there is no way of solving the problem completely in the next three months right so i agree with that statement beyond that i'm just i'll be my my guess would be as good as yours i don't know oh okay so you don't have a fundamental intuition about how hard language understanding is i think i i know i changed my mind let's say

language is probably going to be harder i mean it depends on how you define it like if you mean absolute top-notch 100 language understanding i'll go with language so but then if i show you a piece of paper with letters on it is that you see what i mean it's uh you have a vision system you say it's the best human level vision system i show you i open a book and i show you letters will it understand how these letters form into words and sentences and meaning is this part of the vision problem where

does vision end and language begin yeah so chomsky would say it starts at language so vision is just a little example of the kind of uh structure and you know fundamental hierarchy of ideas that's already represented in our brain somehow that's represented through language but where does vision stop and language begin that's a really interesting question it so one possibility is that it's impossible to achieve really deep understanding in either images or language without basically using the same kind of system so you're going to get the other for free i think i think it's pretty

likely that yes if we can get one we prob our machine learning is probably that good that we can get the other but it's not 100 i'm not 100 sure and also i think a lot a lot of it really does depend on your definitions definitions of like perfect vision because really you know reading is vision but should it count yeah to me so my definition is if a system looked at an image and then the system looked at a piece of text and then told me something about that and i was really impressed that's

relative you'll be impressed for half an hour and then you're gonna say well i mean all the systems do that but here's the thing they don't do yeah but i don't have that with humans humans continue to impress me is that true well the ones okay so i'm a fan of monogamy so i like the idea of marrying somebody being with them for several decades so i i believe in the fact that yes it's possible to have somebody continuously giving you uh pleasurable interesting witty new ideas friends yeah i think i think so they continue

to surprise you the surprise it's um you know that injection of randomness seems to be uh it seems to be a nice source of yeah continued uh inspiration like the the wit the humor i think yeah that that the that would be a it's a very subjective test but i think if you have enough humans in the room yeah i i understand what you mean yeah i feel like i i misunderstood what you meant by impressing you i thought you meant to impress you with its intelligence with how how with how good well it understands

um an image i thought you meant something like i'm going to show it a really complicated image and it's going to get it right and you're going to say wow that's really cool systems of you know january 2020 have not been doing that yeah no i i think it all boils down to like the reason people click like on stuff on the internet which is like it makes them laugh so it's like humor or wit yeah or insight i'm sure we'll get it as get that as well so forgive the romanticized question but looking back

to you what is the most beautiful or surprising idea in deep learning or ai in general you've come across so i think the most beautiful thing about deep learning is that it actually works and i mean it because you got these ideas you got the little neural network you got the back propagation algorithm and then you got some theories as to you know this is kind of like the brain so maybe if you make it large if you make the neural network lodge and you train it a lot of data then it will do the

same function of the brain does and it turns out to be true that's crazy and now we just train these neural networks and you make them larger and they keep getting better and i find it unbelievable i find it unbelievable that this whole ai stuff with neural networks works have you built up an intuition of why are there little bits and pieces of intuitions of insights of why this whole thing works i mean sums definitely while we know that optimization we now have good you know we've take we've had lots of empirical you know huge

amounts of empirical reasons to believe that optimization should work on all most problems we care about did you have insights of what so you just said empirical evidence is most of your sort of empirical evidence kind of convinces you it's like evolution is empirical it shows you that look this evolutionary process seems to be a good way to design organisms that survive in their environment but it doesn't really get you to the insides of how the whole thing works i think it's a good analogy is physics you know how you say hey let's do some

physics calculation and come up with some new physics theory and make some prediction but then you gotta run the experiment you know you gotta run the experiment it's important so it's a bit the same here except that maybe some sometimes the experiment came before the theory but it still is the case you know you have some data and you come up with some prediction you say yeah let's make a big neural network let's train it and it's going to work much better than anything before it and it will in fact continue to get better as

you make it larger and it turns out to be true that's that's amazing when a theory is validated like this you know it's not a mathematical theory it's more of a biological theory almost so i think there are not terrible analogies between deep learning and biology i would say it's like the geometric mean of biology and physics that's deep learning the geometric meaning of biology and physics i think i'm going to need a few hours to wrap my head around that because just to find the geometric just to find uh the set of what biology

represents well biology in biology things are really complicated theories are really really it's really hard to have good predictive theory and if in physics the theories are too good in theory in physics people make these super precise theories which make these amazing predictions and in machine learning mechanics in between kind of in between but it'd be nice if machine learning somehow helped us discover the unification of the two as opposed to some of the in-between but you're right that's you're you're kind of trying to juggle both so do you think there's still beautiful and mysterious

properties in your networks that are yet to be discovered definitely i think that we are still massively underestimating deep learning what do you think it will look like like what if i knew i would have done it yeah so uh but if you look at all the progress from the past 10 years i would say most of it i would say there have been a few cases where some were things that felt like really new ideas showed up but by and large it was every year we thought okay deep learning goes this far nope it

actually goes further and then the next year okay now you now this is this is peak deep learning we are really done nope goes further it just keeps going further each year so that means that we keep underestimating we keep not understanding it as surprising properties all the time do you think it's getting harder and harder to make progress need to make progress it depends on what we mean i think the field will continue to make very robust progress for quite a while i think for individual researchers especially people who are doing um research it

can be harder because there is a very large number of researchers right now i think that if you have a lot of compute then you can make a lot of very interesting discoveries but then you have to deal with the challenge of managing a huge compute a huge classic compute cluster trying to experiment so it's a little bit harder so i'm asking all these questions that nobody knows the answer to but you're one of the smartest people i know so i'm going to keep asking the so let's imagine all the breakthroughs that happen in the

next 30 years in deep learning do you think most of those breakthroughs can be done by one person with one computer sort of in the space of breakthroughs do you think compute will be compute and large efforts will be necessary i mean i can't be sure when you say one computer you mean how large uh you're uh you're clever i mean one can one gpu i see i think it's pretty unlikely i think it's pretty unlikely i think that there are many the stack of deep learning is starting to be quite deep if you look

at it you've got all the way from the ideas the systems to build the data sets the distributed programming the building the actual cluster the gpu programming putting it all together so now the stack is getting really deep and i think it becomes it can be quite hard for a single person to become to be world class in every single layer of the stack what about the what like vladimir vapnik really insist on is taking mnist and trying to learn from very few examples so being able to learn more efficiently do you think that's there'll

be breakthroughs in that space that would may not need the huge compute i think it will be a very i think there will be a large number of breakthroughs in general that will not need a huge amount of compute so maybe i should clarify that i think that some breakthroughs will require a lot of compute and i think building systems which actually do things will require a huge amount of compute that one is pretty obvious if you want to do x right an x requires a huge neural net you got to get a huge neural

net but i think there will be lots of i think there is lots of room for very important work being done by small groups and individuals you may be sort of on the topic of the the science of deep learning talk about one of the recent papers that you released sure that deep double descent where bigger models and more data hurt i think it's really interesting paper can you can you describe the main idea and yeah definitely so what happened is that some over over the years some small number of researchers noticed that it is

kind of weird that when you make the neural network larger it works better and it seems to go in contradiction with statistical ideas and then some people made an analysis showing that actually you got this double descent bump and what we've done was to show that double descent occurs for all for pretty much all practical deep learning systems and that it'll be also so can you step back uh what's the x-axis and the y-axis of a double descent plot okay great so you can you can look you can do things like you can take a

neural network and you can start increasing its size slowly while keeping your data set fixed so if you increase the size of the neural network slowly and if you don't do early stopping that's a pretty important detail then when the neural network is really small you make it larger you get a very rapid increase in performance then you continue to make it large and at some point performance will get worse and it gets and and it gets the worst exactly at the point at which it achieves zero training error precisely zero training loss and then

as you make it large it starts to get better again and it's kind of counter-intuitive because you'd expect deep learning phenomena to be monotonic and it's hard to be sure what it means but it also occurs in in the case of linear classifiers and the intuition basically boils down to the following when you when you have a lot when you have a large data set and a small model then small tiny random so basically what is overfitting overfitting is when your model is somehow very sensitive to the small random unimportant stuff in your data set

in a training day in the training data set precisely so if you have a small model and you have a big data set and there may be some random thing you know some training cases are randomly in the data set and others may not be there but the small mod but the small model is kind of insensitive to this randomness because it's the same you there is pretty much no uncertainty about the model when it is that it's large so okay so at the very basic level to me it is the most surprising thing that

neural networks don't overfit every time very quickly uh before ever being able to learn anything the huge number of parameters so here so there is one way okay so maybe so let me try to give the explanation maybe that will be that will work so you got a huge neural network let's suppose you've got a you are you have a huge neural network you have a huge number of parameters and now let's pretend everything is linear which is not let's just pretend then there is this big subspace where a neural network achieves zero error and

sdgt is going to find approximately the point that's right approximately the point with the smallest norm in that subspace okay and that can also be proven to be insensitive to the small randomness in the data when the dimensionality is high but when the dimensionality of the data is equal to the dimensionality of the model then there is a one-to-one correspondence between all the data sets and the models so small changes in the data set actually lead to large changes in the model and that's why performance gets worse so this is the best explanation more or

less so then it would be good for the model to have more parameters so to be bigger than the data that's right but only if you don't really stop if you introduce early stop in your regularization you can make the double asset descent bump almost completely disappear what is early stop early stopping is when you train your model and you monitor your test your validation performance and then if at some point validation performance starts to get worse you say okay let's stop training if you're good you're good you're good enough so the the magic happens

after after that moment so you don't want to do the early stopping well if you don't do the early stop and you get this very you get a very pronounced double descent do you have any intuition why this happens double descent oh sorry are you stopping you no the double descend so that oh yeah so i try let's see the intuition is basically is this that when the data set has as many degrees of freedom as the model then there is a one-to-one correspondence between them and so small changes to the data set lead to

noticeable changes in the model so your model is very sensitive to all the randomness it is unable to discard it whereas it turns out that when you have a lot more data than parameters or a lot more parameters than data the resulting solution will be insensitive to small changes in the data set so it's able to that's nicely put discard the small changes the the randomness exactly the the the spurious correlation which you don't want jeff hinton suggested we need to throw back propagation we already kind of talked about this a little bit but he

suggested that we just throw away back propagation and start over i mean of course some of that is a little bit um and humor but what do you think what could be an alternative method of training neural networks well the thing that he said precisely is that to the extent you can't find back propagation in the brain it's worth seeing if we can learn something from how the brain learns but back propagation is very useful and we should keep using it oh you're saying that once we discover the mechanism of learning in the brain or

any aspects of that mechanism we should also try to implement that in neural networks if it turns out that we can't find back propagation in the brain if we can't find bad propagation in the brain well so i guess your answer to that is back propagation is pretty damn useful so why are we complaining i mean i i personally am a big fan of back propagation i think it's a great algorithm because it solves an extremely fundamental problem which is finding a neural circuit subject to some constraints and i don't see that problem going away

so that's why i i really i think it's pretty unlikely that we'll have anything which is going to be dramatically different it could happen but i wouldn't bet on it right now so let me ask a sort of big picture question do you think can do you think neural networks can be made to reason why not well if you look for example at alphago or alpha zero the neural network of alpha zero plays go which which we all agree is a game that requires reasoning better than 99.9 of all humans just the neural network without

this search just the neural network itself doesn't that give us an existence proof that neural networks can reason to push back and disagree a little bit we all agree that go is reasoning i think i i agree i don't think it's a trivial so obviously reasoning like intelligence is uh is a loose gray area term a little bit maybe you disagree with that but yes i think it has some of the same elements of reasoning reasoning is almost like akin to search right there's a sequential element of stepwise consideration of possibilities and sort of building

on top of those possibilities in a sequential manner until you arrive at some insight so yeah i guess playing go is kind of like that and when you have a single neural network doing that without search that's kind of like that so there's an existent proof in a particular constrained environment that a process akin to what many people call reasoning exist but more general kind of reasoning so off the board there is one other existence oh boy which one us humans yes okay all right so do you think the architecture that will allow neural networks

to reason will look similar to the neural network architectures we have today i think it will i think well i don't want to make two overly definitive statements i think it's definitely possible that the neural networks that will produce the reasoning breakthroughs of the future will be very similar to the architectures that exist today maybe a little bit more current maybe a little bit deeper but but these these new lines are so insanely powerful why wouldn't they be able to learn to reason humans can reason so why can't neural networks so do you think the

kind of stuff we've seen neural networks do is a kind of just weak reasoning so it's not a fundamentally different process again this is stuff we don't nobody knows the answer to so when it comes to our neural networks i would think which i would say is that neural networks are capable of reasoning but if you train a neural network on a task which doesn't require reasoning it's not going to reason this is a well-known effect where the neural network will solve exactly the it will solve the problem that you pose in front of it

in the easiest way possible right that takes us to the to one of the brilliant sort of ways you describe neural networks which is uh you refer to neural networks as the search for small circuits and maybe general intelligence as the search for small programs which i found is a metaphor very compelling can you elaborate on that difference yeah so the thing which i said precisely was that if you can find the shortest program that outputs the data in you at your disposal then you will be able to use it to make the best prediction

possible and that's a theoretical statement which can be proven mathematically now you can also prove mathematically that it is that finding the shortest program which generates some data is not it's not a computable operation no a finite amount of compute can do this so then with neural networks neural networks are the next best stain that actually works in practice we are not able to find the best the shortest program which generates our data but we are able to find you know a small but now now that statement should be amended even a large circuit which

fits our data in some way well i think what you meant by this small circuit is the smallest needed circuit well i see the thing the thing which i would change now back back then i really have i haven't fully internalized the over parameter the over parameterized results the the things we know about over parameters neural nets now i would phrase it as a large circuit that con whose weights contain a small amount of information which i think is what's going on if you imagine the training process of a neural network as you slowly transmit

entropy from the data set to the parameters then somehow the amount of information in the weights ends up being not very large which would explain why they generalized so well so that's that the large circuit might be one that's helpful for the regulation for the generalization yeah some of this but do you see their do you see it important to be able to try to learn something like programs i mean if you can definitely i think it's kind of the answer is kind of yes if we can do it we should do things that we

can do it it's it's the reason we are pushing on deep learning the fundamental reason the cause the the root cause is that we are able to train them so in other words training comes first we've got our pillar which is the training pillar and now we are trying to contort our neural networks around the training pillar we got to stay trainable this is an invo this is an invariant we cannot violate and so being trainable means starting from scratch knowing nothing you can actually pretty quickly converge towards knowing a lot or even slowly but

it means that given the resources at your disposal you can train the neural net and get it to achieve useful performance yeah that's a pillar we can't move away from that's right because if you can whereas if you say hey let's find the shortest program but we can't do that so it doesn't matter how useful that would be we can't do it so we want so do you think you kind of mentioned that the neural networks are good at finding small circuits or large circuits do you think then the matter of finding small programs is

just the data no so the sorry not not the size or character the qual the the type of data sort of ask giving it programs well i think the thing is that right now finding there are no good precedence of people successfully finding programs really well and so the way you'd find programs is you'd train a deep neural network to do it basically right which is which is the right way to go about it but there's not good uh illustrations that it has hasn't been done yet but in principle it should be possible can you

elaborate in a little bit you what's your insight in principle and put another way you don't see why it's not possible well it's kind of like more it's more a statement of i think that it's i think that it's unwise to bet against deep learning and if it's a if it's a cognitive function that humans seem to be able to do then it doesn't take too long for some deep neural net to pop up that can do it too yeah i'm i'm i'm there with you i can i've i've stopped betting against neural networks at

this point because they continue to surprise us what about long-term memory can neural networks have long-term memory or something like knowledge bases so being able to aggregate important information over long periods of time that would then serve as useful sort of representations of state that uh you can make decisions by so have a long-term context based on what you make in the decision so in some sense the parameters already do that the parameters are an aggregation of the day of the neural of the entirety of the neural nets experience and so they count as the

long as long form long-term knowledge and people have trained various neural nets to act as knowledge bases and you know investigated with invest people have investigated language tomorrow's knowledge basis so there is work there is work there yeah but in some sense do you think in every sense do you think there's a it's it's all just a a matter of coming up with a better mechanism of forgetting the useless stuff and remembering the useful stuff because right now i mean there's not been mechanisms that do remember really long-term information what do you mean by that

precisely i like i like the word precisely so i'm thinking of the kind of compression of information the knowledge bases represent sort of creating a now i apologize for my sort of human-centric thinking about what knowledge is because neural networks aren't interpretable necessarily with the kind of knowledge they have discovered but a good example for me is knowledge bases being able to build up over time something like the knowledge that wikipedia represents it's a really compressed structured knowledge base obviously not the actual wikipedia or the language but like a semantic web the dream that semantic

web represented so it's a really nice compressed knowledge base or something akin to that in the non-interpretable sense as neural networks would have well the neural networks would be non-interpretable if you look at their weights but their outputs should be very interpretable okay so yeah how do you make very smart neural networks like language models interpretable well you ask them to generate some text then the text will generally be interpretable do you find that the epitome of interpretability like can you do better like can you uh because you can't okay i'd like to know what

does it know and what doesn't know i would like the neural network to come up with examples where it it's completely dumb and examples where it's completely brilliant and the only way i know how to do that now is to generate a lot of examples and use my human judgment but it would be nice if a neonatal had some aware self-awareness about it yeah 100 i'm a big believer in self-awareness and i think that i think i think neural net self-awareness will allow for things like the capabilities like the ones you describe like for them

to know what they know and what they don't know and for them to know where to invest to increase their skills most optimally and to your question of interpretability there are actually two answers to that question one answer is you know we have the neural net so we can analyze the neurons and we can try to understand what the different neurons and different layers mean and you can actually do that and openai has done some work on that but there is a different answer which is that i would say this is the human-centric answer where

you say you know you look at a human being you can't read you know how how do you know what a human being is think and you ask them you say hey what do you think about this what do you think about that and you get some answers the answers you get are sticky in the sense you already have a mental model you already have an uh yeah mental model of that human being you already have an understanding of like a a big conception of what it of that human being how they think what they

know how they see the world and then everything you ask you're adding on to that and that stickiness seems to be that's one of the really interesting qualities of the the human being is that information is sticky you don't you seem to remember the useful stuff aggregate it well and forget most of the information that's not useful that process but that's also pretty similar to the process that neural networks do is just that neural network so much crappier at it at this time it doesn't seem to be fundamentally that different but just to stick on

reasoning for a little longer he said why not why can't i reason what's a good impressive feat benchmark to you of reasoning that you'll be impressed by if you don't know what we're able to do is that something you already have in mind well i think writing writing really good code i think proving really hard theorems solving open-ended problems with out-of-the-box solutions and uh sort of theorem type mathematical problems yeah i think though those ones are a very natural example as well you know if you can prove an unproven theorem then it's hard to argue

don't reason and so by the way and this comes back to the point about the hard results you know if you got a heart if you have machine learning deep learning as a field is very fortunate because we have the ability to sometimes produce these unambiguous results and when they happen uh the debate changes the conversation changes it's a conversa we have the ability to produce conversation changing results conversation and then of course just like you said people kind of take that for granted and say that wasn't actually a hard problem well i mean at

some point we'll probably run out of heart problems yeah that whole mortality thing is kind of kind of a sticky problem that we haven't quite figured out maybe we'll solve that one i think one of the fascinating things in your entire body of work but also the work at open ai recently one of the conversation changers has been in the world of language models can you briefly kind of try to describe the recent history of using neural networks in the domain of language and text well there's been lots of history i think i think the

elman network was was this was was a small tiny recurrent neural network applied to language back in the 80s so the history is really you know fairly long at least and the thing that started the thing that changed the trajectory of neural networks and language is the thing that changed the trajectory of deep learning and that's data and compute so suddenly you move from small language models which learn a little bit and with language models in particular you can there's a very clear explanation for why they need to be large to be good because they're

trying to predict the next word so we don't when you don't know anything you'll notice very very broad stroke surface level patterns like sometimes there are characters and there is a space between those characters you'll notice this pattern and you'll notice that sometimes there is a comma and then the next character is a capital letter you'll notice that pattern eventually you may start to notice that there are certain words occur often you may notice that spellings are a thing you may notice syntax and when you get really good at all these you start to notice

the semantics you start to notice the facts but for that to happen the language model needs to be larger so that's let's linger on that because that's where you and noam chomps could disagree so you think we're actually taking uh incremental steps a sort of larger network larger compute will be able to get to the semantics to be able to understand language without what gnome likes to sort of think of as a fundamental understandings of the structure of language like imposing your theory of language onto the learning mechanism so you're saying the learning you can

learn from raw data the mechanism that underlies language well i think i think it's pretty likely but i also want to say that i don't really know precisely what is what chomsky means when he talks about him you said something about imposing your structure and language i'm not 100 sure what he means but empirically it seems that when you inspect those larger language models they exhibit signs of understanding the semantics whereas the smaller language models do not we've seen that a few years ago when we did work on the sentiment neuron we trained the small

you know smaller shell stm to predict the next character in amazon reviews and we noticed that when you increase the size of the lstm from 500 lstm cells to 4000 lstm cells then one of the neurons starts to represent the sentiment of the article of story of the review now why is that sentiment is a pretty semantic attribute it's not a syntactic attribute and for people who might not know i don't know if that's a standard term but sentiment is whether it's a positive or negative review that's right like this is the person happy with

something is the person unhappy with something and so here we had very clear evidence that a small neural net does not capture sentiment while a large neural net does and why is that well our theory is that at some point you run out of syntax to models you start gotta focus on something else and with size you quickly run out of syntax to model and then you really start to focus on the semantics is would be the idea that's right and so i don't i don't want to imply that our models have complete semantic understanding

because that's not true but they definitely are showing signs of semantic understanding partial semantic understanding but the smaller models do not show that those signs can you take a step back and say what is gpt2 which is one of the big language models that was the conversation change in the past couple of years yes it's so gpt-2 is a transformer with one and a half billion parameters that was trained on upon about 40 billion tokens of text which were obtained from web pages that were linked to from reddit articles with more than three upvotes and

what's the transformer the transformer is the most important advance in neural network architectures in recent history what is attention maybe too because i think that's the interesting idea not necessarily sort of technically speaking but the idea of attention versus maybe what recurring neural networks represent yeah so the thing is the transformer is a combination of multiple ideas simultaneously which attention is one do you think attention is the key no it's a key but it's not the key the transformer is successful because it is the simultaneous combination of multiple ideas and if you were to remove

either idea it would be much less successful so the transformer uses a lot of attention but attention existed for a few years so that can't be the main innovation the transformer is designed in such a way that it runs really fast on the gpu and that makes a huge amount of difference this is one thing the second thing is the transformer is not recurrent and that is really important too because it is more shallow and therefore much easier to optimize so in other words it uses attention it is it is a really great fit to

the gpu and it is not recurrent so therefore less deep and easier to optimize and the combination of those factors make it successful so now it makes it makes great use of your gpu it allows you to achieve better results for the same amount of compute and that's why it's successful were you surprised how well transformers worked and gpt2 worked so you worked on language you've had a lot of great ideas before transformers came about in language so you got to see the whole set of revolutions before and after were you surprised yeah a little

a little yeah i mean it's hard it's hard to remember because you adapt really quickly but it definitely was surprising it definitely was in fact i'll you know what i'll i'll retract my statement it was it was pretty amazing it was just amazing to see generate this text of this and you know you got to keep in mind that we've seen at that time we've seen all this progress in gans in improving you know the samples produced by cans were just amazing you have these realistic faces but text hasn't really moved that much and suddenly

we moved from you know whatever gans were in 2015 to the best most amazing gans in one step right and i was really stunning even though theory predicted yeah you train a big language model of course you should get this but then to see it with your own eyes it's something else and yet we adapt really quickly and now there's uh sort of some cognitive scientists write articles saying that gpt2 models don't truly understand language so we adapt quickly to how amazing the fact that they're able to model the language so well is so what

do you think is the bar for what for impressing us that it i don't know do you think that bar will continuously be moved definitely i i think when you start to see really dramatic economic impact that's when i think that's in some sense the next barrier because right now if you think about the working ai it's really confusing it's really hard to know what to make of all these advances it's kind of like okay you got an advance and now you can do more things and you got another improvement and you got another cool

demo at some point i think people who are outside of ai they can no longer distinguish this progress anymore so we were talking offline about translating russian to english and how there's a lot of brilliant work in russian that the the rest of the world doesn't know about that's true for chinese that's true for a lot of for a lot of scientists and just artistic work in general do you think translation is the place where we're going to see sort of economic big impact i i don't know i i think i think there is a

huge number of i mean first of all i would want to i want to point out the translation already today is huge i think billions of people interact with uh big chunks of the internet primarily through translation so translation is already huge and it's hugely hugely positive too i think self-driving is going to be hugely impactful and that's you know it's it's unknown exactly when it happens but again i would i would not bet against deep learning so i so that's deep learning in general but you you keep learning for self-driving yes deep learning for

self-driving but i was talking about sort of language models let's see just to ch just spear it off a little bit just to check you're not seeing a connection between driving and language no no okay all right they both use neural nets they'll be a poetic connection i think there might be some like you said there might be some kind of unification towards uh a kind of multi-task transformers that can take on both language and vision tasks that'd be an interesting unification now let's see what can i ask about gpt2 more um it's simple it's

not much to ask it's so you take it you take a transform you make it bigger you give it more data and suddenly it does all those amazing things yeah one of the beautiful things is that gpg the transformers are fundamentally simple to explain to train do you think bigger will continue to show better results in language probably sort of like what are the next steps with gpt2 do you think i mean for i think for for sure seeing what uh larger versions can do is one direction also i mean there are there are many

questions there's one question which i'm curious about and that's the following so right now gpt2 so we feed all this data from the internet which means that he needs to memorize all those random facts about everything in the internet and it would be nice if the model could somehow use its own intelligence to decide what data it wants to study accept and what data it wants to reject just like people people don't learn all data indiscriminately we are super selective about what we learn and i think this kind of active learning i think would be

very nice to have yeah listen i love active learning so let me ask does the selection of data can you just elaborate that a little bit more do you think the selection of data is um like i i have this kind of sense that the optimization of how you select data so the active learning process is going to be a place for a lot of breakthroughs even in the near future because there hasn't been many breakthroughs there that are public i feel like there might be private breakthroughs that companies keep to themselves because the fundamental

problem has to be solved if you want to solve self-driving if you want to solve a particular task but do you what do you think about the space in general yeah so i think that for something like active learning or in fact for any kind of capability like active learning the thing that it really needs is a problem it needs a problem that requires it it's very hard to do research about the capability if you don't have a task because then what's going to happen is you will come up with an artificial task get good

results but not really convince anyone right like we're now past the stage where getting a result an mnist some clever formulation remnants will will convince people that's right in fact you could quite easily come up with a simple active learning scheme on amnesty and get a 10x speed up but then so what and i think that with active learning their needs they need active learning will naturally arise as there are as problems that require it pop up that's how i would that's my my take on it there's another interesting thing that openai has brought up

with gpt2 which is when you create a powerful artificial intelligence system and it was unclear what kind of detrimental once you release gpt2 what kind of detrimental effect it will have because if you have an a model that can generate pretty realistic text you can start to imagine that you know on the it would be used by bots and some some way that we can't even imagine so like there's this nervousness about what it's possible to do so you you did a really kind of brave and i think profound thing which you started a conversation

about this like how do we release powerful artificial intelligence models to the public if we do it all how do we privately discuss with other even competitors about how we manage the use of the systems and so on so from that this whole experience you released a report on it but in general are there any insights that you've gathered from just thinking about this about how you release models like this i mean i think that my take on this is that the field of ai has been in a state of childhood and now it's exiting

that state and it's entering a state of maturity what that means is that ai is very successful and also very impactful and its impact is not only large but it's also growing and so for that reason it seems wise to start thinking about the impact of our systems before releasing them maybe a little bit too soon rather than a little bit too late and with the case of gpt2 like i mentioned earlier the results really were stunning and it seemed plausible it didn't seem certain it seemed plausible that something like gpt2 could easily use to

reduce the cost of this information and so there was a question of what's the best way to release it and staged release seemed logical a small model was released and there was time to see the many people use these models in lots of cool ways they've been lots of really cool applications there haven't been any negative applications we know of and so eventually it was released but also other people replicated similar models that's an interesting question though that we know of so in your view stage release is uh at least part of the answer to

the question of how do we uh how what do we do once we create a system like this it's part of the answer yes is there any other insights like say you don't want to release the model at all because it's useful to you for whatever the business is well there are plenty plenty of people don't release models already right of course but is there some moral ethical responsibility when you have a very powerful model to sort of communicate like just as you said when you had gpt2 it was unclear how much it could be

used for misinformation it's an open question and getting an answer to that might require that you talk to other really smart people that are outside of uh outside your particular group have you please tell me there's some optimistic pathway for people across the world to collaborate on these kinds of cases or is it still really difficult from from one company to talk to another company so it's definitely possible it's definitely possible to discuss these kind of models with colleagues elsewhere and to get get their take on what's on what to do how hard is it

though i mean do you see that happening i think that's that's a place where it's important to gradually build trust between companies because ultimately all the ai developers are building technology which is bitcoin to be increasingly more powerful and so it's the way to think about it is that ultimately we're only together yeah it's uh i tend to believe in the the better angels of our nature but i do hope that um that when you build a really powerful ai system in a particular domain that you also think about the potential negative consequences of um

it's an interesting and scary possibility that it'll be a race for a ai development that would push people to close that development and not share ideas with others i don't love this i've been like a pure academic for 10 years i really like sharing ideas and it's fun it's exciting what do you think it takes to let's talk about agi a little bit what do you think it takes to build a system of human level intelligence we talked about reasoning we talked about long-term memory but in general what does it take you think well i

can't be sure but i think the deep learning plus maybe another small idea do you think self-play will be involved so like you've spoken about the powerful mechanism of self-play where systems learn by sort of uh exploring the world in a competitive setting against other entities that are similarly skilled as them and so incrementally improve in this way do you think self-play will be a component of building an agi system yeah so what i would say to build agi i think is going to be deep learning plus some ideas and i think self-play will be

one of those ideas i think that that is a very self play has this amazing property that it can surprise us in truly novel ways for example like we i mean pretty much every self-play system both are dotabot i don't know if openai had a release about multi-agent where you had two little agents who were playing hide and seek and of course also alpha zero they were all surprising behaviors they all produce behaviors that we didn't expect they are creative solutions to problems and that seems like an important part of agi that our systems don't

exhibit routinely right now and so that's why i like this area i like this direction because of its ability to surprise us to surprise us and an agr system would surprise us fundamentally yes but and to be precise not just not just a random surprise but to find a surprising solution to a problem that's also useful right now a lot of the self-play mechanisms have been used in the game context or at least in the simulation context how much how much do you how far along the path to egi do you think will be done

in simulation how much faith promise do you have in simulation versus having to have a system that operates in the real world whether it's the real world of digital real world data or real world like actual physical world of robotics i don't think it's an either or i think simulation is a tool and it helps it has certain strengths and certain weaknesses and we should use it yeah but okay i understand that that's um that's true but one of the criticisms of self-play one of the criticisms of reinforcement learning is one of the the its

current power its current results while amazing have been demonstrated in a simulated environments or very constrained physical environments do you think it's possible to escape them escape the simulated environments and be able to learn in non-simulated environments or do you think it's possible to also just simulate in the photorealistic and physics realistic way the real world in a way that we can solve real problems with self-play in simulation so i think that transfer from simulation to the real world is definitely possible and has been exhibited many times in by many different groups it's been especially

successful in vision also open ai in the summer has demonstrated a robot hand which was trained entirely in simulation in a certain way that allowed for cinderella transfer to occur is this uh for the rubik's cube that's right and i wasn't aware that was trained in simulation it was straining simulation entirely really so what it wasn't in the physical the hand wasn't trained no 100 of the training was done in simulation and the policy that was learned in simulation was trained to be very adaptive so adaptive that when you transfer it could very quickly adapt

to the physical to the physical world so the kind of perturbations with the giraffe or whatever the heck it was those weren't were those part of the simulation well the simulation was generally so the simulation was trained to be robust to many different things but not the kind of perturbations we've had in the video so it's never been trained with a glove it's never been trained with a stuffed giraffe so in theory these are novel perturbations correct it's not in theory in practice that those are novel probation well that's okay that's a clean small scale

but clean example of a transfer from the simulated world to the to the physical world yeah and i will also say that i expect the transfer capabilities of deep learning to increase in general and the better the transfer capabilities are the more useful simulation will become because then you could take you could experience something in simulation and then learn a moral of the story which you could then carry with you to the real world right as humans do all the time when they play computer games so let me ask sort of an embodied question staying

on agi for a sec do you think aj asks us that we need to have a body we need to have some of those human elements of self-awareness consciousness sort of fear of mortalities or self-preservation in the physical space which comes with having a body i think having a body will be useful i don't think it's necessary but i think it's very useful to have a body for sure because you can learn a whole new you you can learn things which cannot be learned without a body but at the same time i think that you

can if you don't have a body you could compensate for it and still succeed you think so yes well if there is evidence for this for example there are many people who were born deaf and blind and they were able to compensate for the lack of modalities i'm thinking about helen keller specifically so even if you're not able to physically interact with the world and if you're not able to i mean i actually was getting it maybe let me ask on the more particular i'm not sure if it's connected to having a body or not

but the idea of consciousness and a more constrained version of that is self-awareness do you think an egi system should have consciousness it's what we can't define kind of whatever the heck you think consciousness is yeah hard question to answer given how hard it is to find it do you think it's useful to think about i mean it's it's definitely interesting it's fascinating i think it's definitely possible that our assistants will be conscious do you think that's an emergent thing that just comes from do you think consciousness could emerge from the representation that's stored within

your networks so like that it naturally just emerges when you become more and more you're able to represent more and more of the world well i'd say i'd make the following argument which is humans are conscious and if you believe that artificial neural nets are sufficiently similar to the brain then there should at least exist artificial neurons you should be conscious too you're leaning on that existence proof pretty heavily okay but it's it's just that that's that's the best answer i can give no i i know i know i know uh there's still an open

question if there's not some magic in the brain that we're not i mean i don't mean a non-materialistic magic but that um that the brain might be a lot more complicated and interesting that we give it credit for if that's the case then it should show up and at some point at some point we will find out that we can't continue to make progress but i think i think it's unlikely so we talk about consciousness but let me talk about another poorly defined concept of intelligence again we've talked about reasoning we've talked about memory what

do you think is a good test of intelligence for you are you impressed by the test that alan turing formulated with the imitation game of that with natural language is there something in your mind that you will be deeply impressed by if a system was able to do i mean lots of things there's certain there's certain frontiers there is a certain frontier of capabilities today yeah and there exists things outside of that frontier and i would be impressed by any such thing for example i would be impressed by a deep learning system which solves a

very pedestrian you know pedestrian task like machine translation or computer vision task or something which never makes mistake a human wouldn't make under any circumstances i think that is something which have not yet been demonstrated and i would find it very impressive yeah so right now they make mistakes and differ they might be more accurate than human beings but they still they make a different set of mistakes so my my i would guess that a lot of the skepticism that some people have about deep learning is when they look at their mistakes and they say

well those mistakes they make no sense like if you understood the concept you wouldn't make that mistake and i think that changing that would be would would that would that would inspire me that would be yes this is this this is this is progress yeah that's that's a really nice way to put it but i also just don't like that human instinct to criticize a model is not intelligent that's the same instinct as we do when we criticize any group of creatures as the other because it's very possible that gpt2 is much smarter than human

beings and many things that's definitely true it has a lot more breadth of knowledge yes breadth knowledge and even and even perhaps depth on certain topics it's kind of hard to judge what depth means but there's definitely a sense in which humans don't make mistakes that these models do yes the same is applied to autonomous vehicles the same is probably going to continue being applied to a lot of artificial intelligence systems we find this is the annoying this is the process of in the 21st century the process of analyzing the progress of ai is the

search for one case where the system fails in a big way where humans would not and then many people writing articles about it and then broadly as a com as a the public generally gets convinced that the system is not intelligent and we like pacify ourselves by thinking it's not intelligent because of this one anecdotal case and this can seems to continue happening yeah i mean there is truth to that though there is people also i'm sure that plenty of people are also extremely impressed by the system that exists today but i think this connects

to the earlier point we discussed that it's just confusing to judge progress in ai yeah and you know you have a new robot demonstrating something how impressed should you be and i think that people will start to be impressed once ai starts to really move the needle on the gdp so you're one of the people that might be able to create an agi system here not you but you and open ai if if you do create an ajax system and you get to spend sort of the evening with it him her what would you talk

about do you think the very first time first time well the first time i would just i would just ask all kinds of questions and try to make it to get it to make a mistake and i would be amazed that it doesn't make mistakes and just keep keep asking abroad okay what kind of questions do you think would they be factual or would they be personal emotional psychological what do you think all of that bob would you ask for advice definitely i mean why why would i limit myself talking to a system like this

now again let me emphasize the fact that you truly are one of the people that might be in the room where this happens so let me ask a sort of a profound question about um i've just talked to a stalin historian i've been talking to a lot of people who are studying power abraham lincoln said nearly all men can stand adversity but if you want to test a man's character give him power i would say the power of the 21st century maybe the 22nd but hopefully the 21st would be the creation of an agi system

and the people who have control direct possession and control of the agi system so what do you think after spending that evening having a discussion with the agi system what do you think you would do well the ideal world would like to imagine is one where humanity are like the board the board members of a company where the agi is the ceo so it would be i would like the picture which i would imagine is you have some kind of different entities different countries or cities and the people that live there vote for what the

agi that represents them should do and then age other represents them goes and does it i think a picture like that i find very appealing and you could have multiple you would have an agi for a city for a country and there would be it would be trying to in effect take the democratic process to the next level and the board can always fire the ceo essentially press the reset button and say re-randomize the parameters here well let me sort of that's actually okay that's a beautiful vision i think as long as it's possible to

con to press the reset button do you think it will always be possible to press the reset button so i think that it's def it's definitely be possible to build so you're talking so the question that i really understand from you is will reveal humans or humans people have control over the ai systems that they built yes and my answer is it's definitely possible to build ai systems which will want to be controlled by their humans wow that's part of their so it's not that just they can't help but be controlled but that's that's um

the they exist the one of the objectives of their existence is to be controlled in the same way that human parents generally want to help their children they want their children to succeed it's not a burden for them they are excited to help the children and to feed them and to dress them and to take care of them and i believe with highest conviction that the same will be possible for an agi it will be possible to program an agi to design it in such a way that it will have a similar deep drive that

it will be delighted to fulfill and the drive will be to help humans flourish but let me take a step back to that moment where you create the agi system i think this is a really crucial moment and between that moment and the the democratic board members with the agi at the head there has to be a relinquishing of power says george washington despite all the bad things he did one of the big things he did is he relinquished power he first of all didn't want to be president and even when he became president he

gave he didn't keep just serving as most dictators do for indefinitely do you see yourself being able to relinquish control over an agi system given how much power you can have over the world at first financial just make a lot of money right and then control by having possession as a gi system i i'd find it trivial to do that i'd find it trivial to relinquish this this kind of i mean you know the the kind of scenario you are describing sounds terrifying to me that's all i would absolutely not want to be in that

position do you think you represent the majority or the minority of people in the ai community well i mean open question an important one are most people good is another way to ask it so i don't know if most people are good but i think that when it really counts people can be better than we think that's beautifully put yeah are there specific mechanisms you can think of of aligning aig and values to human values is that do you think about these problems of continued alignment as we develop the eye systems yeah definitely in some

sense the kind of question which you are asking is so if you have to translate that question to today's terms yes it would be a question about how to get an rl agent that's optimizing a value function which itself is learned and if you look at humans humans are like that because the reward function the value function of humans is not external it is internal that's right and there are definite ideas of how to train a value function basically an objective you know and as objective as possible perception system that will be trained separately to

recognize to internalize human judgments on different situations and then that component would then be integrated as the value as the base value function for some more capable rail system you could imagine a process like this i'm not saying this is the process i'm saying this is an example of the kind of thing you could do so on that topic of the objective functions of human existence what do you think is the objective function that is implicit in human existence what's the meaning of life oh i think the question is is wrong in some way i

think that the question implies that the reason there is an objective answer which is an external answer you know your meaning of life is x right i think what's going on is that we exist and that's amazing and we should try to make the most of it and try to maximize our own value and enjoyment of a very short time while we do exist it's funny because action does require an objective function it's definitely theirs in some form but it's difficult to make it explicit and maybe impossible to make it explicit i guess is what

you're getting at and that's an interesting fact of an rl environment well but i was making a slightly different point is that humans want things and their ones create the drives that cause them to you know our wants are our objective functions our individual objective functions we can later decide that we want to change that what we wanted before is no longer good and we want something else yeah but they're so dynamic there's there's got to be some underlying sort of freud there's things there's like sexual stuff there's people who think it's the fear of

fear of death and there's also the desire for knowledge and you know all these kinds of things procreation the sort of all the evolutionary arguments it seems to be there might be some kind of fundamental objective function from from which everything else uh emerges but it seems because that's very important i think i think that probably is an evolutionary objective function which is to survive and procreate and make sure you make your children succeed that would be my guess but it doesn't give an answer to the question what's the meaning of life i think you

can see how humans are part of this big process this ancient process we are we are we exist on a small planet and that's it so given that we exist try to make the most of it and try to enjoy more and suffer less as much as we can let me ask two silly questions about life one do you have regrets moments that if you uh went back you would do differently and two are there moments that you're especially proud of that made you truly happy so i can answer that i can answer both questions

of course there are there's a huge number of choices and decisions that i've made that with the benefit of hindsight i wouldn't have made them and i do experience some regret but you know i try to take solace in the knowledge that at the time i did the best i could and in terms of things that i'm proud of there are i'm very fortunate to have things i'm proud to have done things i'm proud of and they made me happy for himself for some time but i don't think that that is the source of happiness

so your academic accomplishments all the papers you're one of the most excited people in the world all the breakthroughs i mentioned in computer vision and language and so on is what is the source of happiness and pride for you i mean all those things are a source of pride for sure i'm very ungrateful for having done all those things and it was very fun to do them but happiness comes from but you know you can happiness well my current view is that happiness comes from our to allow to a very large degree from the way

we look at things you know you can have a simple meal and be quite happy as a result or you can talk to someone and be happy as a result as well or conversely you can have a meal and be disappointed that the meal wasn't a better meal so i think a lot of happiness comes from that but i'm not sure i don't want to be too confident i being humble in the face of the uncertainty seems to be also a part of this whole happiness thing well i don't think there's a better way to

end it than uh meaning of life and discussions of happiness so ilya thank you so much you've given me a few incredible ideas you've given the world many incredible ideas i really appreciate it and thanks for talking today yeah thanks for stopping stopping by i really enjoyed it thanks for listening to this conversation with elias discoverer and thank you to our presenting sponsor cash app please consider supporting the podcast by downloading cash app and using code lex podcast if you enjoy this podcast subscribe on youtube review it with 5 stars in apple podcast support on

patreon or simply connect with me on twitter at lex friedman and now let me leave you with some words from alan turing on machine learning instead of trying to produce a program to simulate the adult mind why not rather try to produce one which simulates the child's if this were then subjected to an appropriate course of education one would obtain the adult brain thank you for listening and hope to see you next time