at which point is the neural network a being versus a tool?
The following is a conversation with Oriel Vinialis, his second time in the podcast.
Oriel is the research director and deep learning lead at DeepMind and one of the most brilliant
thinkers and researchers in the history of artificial intelligence.
This is the Lex Friedman podcast.
If you'd like to support it, please check out our sponsors in the description and now
to your friends, here's Oriel Vinialis.
You are one of the most brilliant researchers in the history of AI, working across all kinds
of modalities, probably the one common theme is it's always sequences of data.
So we're talking about languages, images, even biology and games as we talked about
last time.
So, you're a good person to ask this, in your lifetime, will we be able to build an
AI system that's able to replace me as the interviewer in this conversation in terms
of ability to ask questions that are compelling to somebody listening?
And then further question is, are we close, will we be able to build a system that replaces
you as the interviewer in order to create a compelling conversation?
How far away are we, do you think?
It's a good question.
I think partly I would say, do we want that?
I really like when we start now with very powerful models, interacting with them and
thinking of them more closer to us, the question is, if you remove the human side of the conversation,
is that an interesting artifact?
And I would say probably not.
I've seen, for instance, last time we spoke, we were talking about StarCraft and creating
agents that play games, involves self-play.
But ultimately, what people care about was, how does this agent behave when the opposite
side is a human?
So without a doubt, we will probably be more empowered by AI.
Maybe you can source some questions from an AI system.
I mean, that even today, I would say, it's quite plausible that with your creativity,
you might actually find very interesting questions that you can filter.
We call this terry picking sometimes in the field of language.
And likewise, if I had now the tools on my side, I could say, look, you're asking this
interesting question from this answer, I like the words chosen by this particular system
that created a few words.
Completely replacing it feels not exactly exciting to me.
Although in my lifetime, I think, given the trajectory, I think it's possible that perhaps
there could be interesting, maybe self-play interviews, as you're suggesting, that would
look or sound quite interesting and probably would educate or you could learn a topic through
listening to one of these interviews at a basic level, at least.
So you said it doesn't seem exciting to you, but what if exciting is part of the objective
function the thing is optimized over?
So there's probably a huge amount of data of humans, if you look correctly, of humans
communicating online, and there's probably ways to measure the degree of, as they talk
about, engagement.
So you can probably optimize the question that's most created an engaging conversation
in the past.
So actually, if you strictly use the word exciting, there is probably a way to create
a optimally exciting conversations that involve AI systems, at least one side is AI.
Yeah, that makes sense.
I think maybe looping back a bit to games and the game industry, when you design algorithms,
you're thinking about winning as the objective, right, or the reward function.
But in fact, when we discuss this with Blizzard, the creators of StarCraft in this case, I
think what's exciting, fun, if you could measure that and optimize for that, that's probably
why we play video games or why we interact or listen or look at cat videos or whatever
on the internet.
So it's true that modeling reward beyond the obvious reward functions we've used to
in reinforcement learning is definitely very exciting.
And again, there is some progress actually into a particular aspect of AI, which is quite
critical, which is, for instance, is a conversation that or is the information truthful, right?
So you could start trying to evaluate these from, except from the internet, right, that
has lots of information.
And then if you can learn a function automated, ideally, so you can also optimize it more
easily, then you could actually have conversations that optimize for non-obvious things such
as excitement.
So yeah, that's quite possible.
And then I would say, in that case, it would definitely be fun, a fun exercise and quite
unique to have at least one site that is fully driven by an excitement reward function.
But obviously, there would be still quite a lot of humanity in the system, both from
who is building the system, of course, and also ultimately, if we think of labeling for
excitement, that those labels must come from us because it's just hard to have a computational
measure of excitement.
As far as I understand, there's no such thing.
Well, you mentioned truth also.
I would actually venture to say that excitement is easier to label than truth or perhaps has
lower consequences of failure.
But there is perhaps the humanness that you mentioned.
That's perhaps part of a thing that could be labeled.
And that could mean an AI system that's doing dialogue, that's doing conversations should
be flawed, for example.
Like, that's the thing you optimize for, which is have inherent contradictions by design,
have flaws by design.
Maybe it also needs to have a strong sense of identity.
So it has a backstory, it told itself that it sticks to, it has memories, not in terms
of how the system is designed, but it's able to tell stories about its past.
It's able to have mortality and fear of mortality in the following way, that it has an identity,
and if it says something stupid and gets canceled on Twitter, that's the end of that system.
So it's not like you get to rebrand yourself.
That system is, that's it.
So maybe the high stakes nature of it, because you can't say anything stupid now, or because
you'd be canceled on Twitter, and that there's stakes to that.
And then I think part of the reason that makes it interesting.
And then you have a perspective, like you've built up over time that you stick with, and
then people can disagree with you.
So holding that perspective strongly, holding sort of maybe a controversial, at least a
strong opinion.
All of those elements, it feels like they can be learned because it feels like there's
a lot of data on the internet of people having an opinion.
And then combine that with a metric of excitement.
You can start to create something that as opposed to trying to optimize for sort of grammatical
clarity and truthfulness, the factual consistency over many sentences, you're optimized for
the humanness.
And there's obviously data for humanness on the internet.
So I wonder if there's a future where that's part, I mean, I sometimes wonder that about
myself.
I'm a huge fan of podcasts, and I listen to some podcasts, and I think, what is interesting
about this?
What is compelling?
The same way you watch other games, like you said, watch, play StarCraft, or have Magnus
Carlson play chess.
So I'm not a chess player, but it's still interesting to me.
What is that?
That's the stakes of it, maybe the end of a domination of a series of wins.
I don't know.
There's all those elements somehow connect to a compelling conversation.
And I wonder how hard is that to replace?
Because ultimately, all of that connects to the initial proposition of how to test whether
in AI's intelligence or not with the Turing test, which I guess my question comes from
a place of the spirit of that test.
Yes.
I actually recall, I was just listening to our first podcast where we discussed Turing
test.
So I would say from a neural network, AI builder perspective, usually you try to map many of
these interesting topics you discuss to benchmarks, and then also to actual architectures on how
these systems are currently built, how they learn, what data they learn from, what are
they learning, right?
We're talking about weights of a mathematical function.
And then looking at the current state of the game, maybe what do we need leaps forward
to get to the ultimate stage of all these experiences, lifetime experience, fears, like
words that currently barely we're seeing progress just because what's happening today is you
take all these human interactions, it's a large vast variety of human interactions online,
and then you're distilling these sequences, right, going back to my passion, like sequences
of words, letters, images, sound, there's more modalities here to be at play.
And then you're trying to just learn a function that will be happy that maximizes the likelihood
of seeing all these through a neural network.
Now, I think there's a few places where the way currently we train these models would clearly
like to be able to develop the kinds of capabilities you say, I'll tell you maybe a couple.
One is the lifetime of an agent or a model, so you learn from this data offline, right?
So you're just passively observing and maximizing this, you know, it's almost like a landscape
of mountains, and then everywhere there's data that humans interacted in this way, you're
trying to make that higher and then, you know, lower where there's no data.
And then these models generally don't then experience themselves, they just are observers,
right?
They're observers of the data.
And then we're putting them to then generate data when we interact with them.
But that's very limiting, the experience they actually experience when they could maybe
be optimizing or further optimizing the weights, we're not even doing that.
So to be clear, and again, mapping to AlphaGo, AlphaStar, we train the model.
And when we deploy it to play against humans, or in this case, interact with humans like
language models, they don't even keep training, right?
They're not learning in the sense of the weights that you've learned from the data, they don't
keep changing.
Now, there's something a bit more feels magical, but it's understandable if you're into Neuronet,
which is, well, they might not learn in the strict sense of the words, the weights changing,
maybe that's mapping to how neurons interconnect and how we learn over our lifetime.
But it's true that the context of the conversation that takes place when you talk to these systems,
it's held in their working memory, right?
It's almost like you start a computer, it has a hard drive that has a lot of information,
you have access to the internet, which has probably all the information.
But there's also a working memory where these agents, as we call them, or start calling them,
build upon.
Now, this memory is very limited, I mean, right now we're talking to be concrete about
2,000 words that we hold, and then beyond that, we start forgetting what we've seen.
So you can see that there's some short-term coherence already, right, with when you said,
I mean, it's a very interesting topic, having sort of a mapping, an agent to like have consistency,
and if you say, oh, what's your name, it could remember that, but then it might forget beyond
2,000 words, which is not that long of context, if we think even of these podcast books are
much longer.
So technically speaking, there's a limitation there, super exciting from people that work
on deep learning to be working on, but I would say we lack maybe benchmarks and the technology
to have this lifetime-like experience of memory that keeps building up.
However, the way it learns offline is clearly very powerful, right?
So you asked me three years ago, I would say, oh, we're very far.
I think we've seen the power of this imitation, again, on the internet scale that has enabled
this to feel like at least the knowledge, the basic knowledge about the world now is
incorporated into the weights, but then this experience is lacking.
And in fact, as I said, we don't even train them when we're talking to them other than
their working memory, of course, is affected.
So that's the dynamic part, but they don't learn in the same way that you and I have
learned from basically when we were born and probably before.
So lots of fascinating, interesting questions you asked there.
I think the one I mentioned is this idea of memory and experience versus just kind of
observe the world and learn its knowledge, which I think for that I would argue lots
of recent advancements that make me very excited about the field.
And then the second maybe issue that I see is all these models, we train them from scratch.
That's something I would have complained three years ago or six years ago or 10 years
ago, and it feels if we take inspiration from how we got here, how the universe evolved
us and we keep evolving, it feels that is a missing piece, that we should not be training
models from scratch every few months, that there should be some sort of way in which
we can grow models much like as a species and many other elements in the universe is
building from the previous sort of iterations.
And that's from just purely neural network perspective, even though we would like to
make it work, it's proven very hard to not throw away the previous weights, right?
This landscape we learned from the data and refresh it with a brand new set of weights
given maybe a recent snapshot of these data sets we train on, et cetera, or even a new
game we're learning.
So that feels like something is missing fundamentally.
We might find it, but it's not very clear how it will look like.
There's many ideas and it's super exciting as well.
Yes, just for people who don't know, when you approach a new problem in machine learning,
you're going to come up with an architecture that has a bunch of weights and then you initialize
them somehow, which in most cases is some version of random.
So that's what you mean by starting from scratch and it seems like it's a waste.
Every time you solve the game of go and chess, Starcraft, protein folding, surely there's
some way to reuse the weights as we grow this giant database of neural networks that have
solved some of the toughest problems in the world.
So some of that is, what is that, methods, how to reuse weights, how to learn, extract,
was generalizable, or at least has a chance to be and throw away the other stuff.
And maybe the neural network itself should be able to tell you that, like what ideas
do you have for better initialization of weights?
Maybe stepping back, if we look at the field of machine learning, but especially deep learning,
at the core of deep learning, there's this beautiful idea that is a single algorithm
can solve any task.
So it's been proven over and over with more increasing set of benchmarks and things that
were thought impossible that are being cracked by this basic principle, that is, you take
a neural network of uninitialized weights, so like a blank computational brain, then
you give it, in the case of supervised learning, a lot, ideally, of examples of, hey, here
is what the input looks like, and the desired output should look like this, I mean, image
classification is very clear example, images to maybe one of a thousand categories, that's
what ImageNet is like, but many, many, if not all problems can be mapped this way.
And then there's a generic recipe, right, that you can use, and this recipe with very
little change, and I think that's the core of deep learning research, right, that what
is the recipe that is universal, that for any new given task, I'll be able to use without
thinking, without having to work very hard on the problem at stake.
We have not found this recipe, but I think the field is excited to find less tweaks or
tricks that people find when they work on important problems specific to those, and
more of a general algorithm, right, so at an algorithmic level, I would say we have something
general already, which is this formula of training a very powerful model on neural network
on a lot of data, and in many cases, you need some specificity to the actual problem you're
solving, protein folding being such an important problem has some basic recipe that is learned
from beyond before, right, like transformer models, graph neural networks, ideas coming
from NLP, like, you know, something called BERT, that is a kind of loss that you can
in place to help the model, model knowledge distillation is another technique, right,
so this is the formula, we still had to find some particular things that were specific
to AlphaFold, right, that's very important because protein folding is such a high value
problem that as humans, we should solve it no matter if we need to be a bit specific,
and it's possible that some of these learnings will apply them to the next iteration of
this recipe that deep learners are about, but it is true that so far, the recipe is
what's common, but the weights, you generally throw away, which feels very sad, although
maybe in the last, especially in the last two, three years, and when we last spoke,
I mentioned this area of meta learning, which is the idea of learning to learn, that idea
and some progress has been had starting, I would say, mostly from GPT-3 on the language
domain only, in which you could conceive a model that is trained once, and then this
model is not narrow in that it only knows how to translate a pair of languages, or it
only knows how to assign sentiment to a sentence, these, these actually, you could teach it
by a prompting it's called, and this prompting is essentially just showing it a few more
examples, almost like you do show examples, input-output examples, algorithmically speaking
to the process of creating this model, but now you're doing it through language, which
is very natural way for us to learn from one another, I tell you, hey, you should do this
new task, I'll tell you a bit more, maybe you ask me some questions, and now you know
the task, right, you didn't need to retrain it from scratch, and we've seen these magical
moments almost, in this way to do a few short prompting through language on language only
domain, and then in the last two years we've seen this expanded to beyond language, adding
vision, adding actions and games, lots of progress to be had, but this is maybe, if you
ask me about how are we going to crack this problem, this is perhaps one way in which
you have a single model, the problem of this model is it's hard to grow in weights or capacity,
but the model is certainly so powerful that you can teach it some tasks, right, in this
way that I could teach you a new task now if we were, oh, let's, a text-based task or
a classification, a vision-style task, but it still feels like more breakthroughs should
be had, but it's a great beginning, right, we have a good baseline, we have an idea that
this maybe is the way we want to benchmark progress towards AGI, and I think in my view
that's critical to always have a way to benchmark the communities sort of converging to these
overall, which is good to see, and then this is actually what excites me in terms of also
next steps for deep learning is how to make these models more powerful, how do you train
them, how to grow them if they must grow, should they change their weights as you teach
it the task or not, there's some interesting questions, many to be answered.
Yeah, you've opened a door, about two, a bunch of questions I want to ask, but let's first
return to the, to your tweet and read it like a Shakespeare.
You wrote, Godo is not the end, it's the beginning, and then you wrote Meow, and then an emoji
of a cat.
So first, two questions, first, can you explain the Meow and the cat emoji, and second, can
you explain what Godo is and how it works?
Right, indeed, I mean, thanks for reminding me that we're all exposing on Twitter and
it's...
Permanently there.
Yes, permanently there.
One of the greatest AI researchers of all time, Meow and Cat Emoji, yes, there you go.
Right, so...
Can you imagine like Turing, tweeting, Meow and Cat, probably he would, probably he would.
So yeah, the tweet is important actually, you know, I put thought on the tweets, I hope
people do as well.
Which part do you think, okay, so there's three sentences, Godo's not the end, Godo's
the beginning, Meow, Cat Emoji, okay, which is the important part.
The Meow, no, definitely that it is the beginning, I mean, I probably was just explaining a bit
where the field is going, but let me tell you about Godo.
So first the name Godo comes from maybe a sequence of releases that the mind had that
named, like used animal names to name some of their models that are based on this idea
of large sequence models, initially their only language, but we're expanding to other
modalities.
So we had, you know, we had Gopher, Chinchilla, these were language only, and then more recently
we released Flamingo, which adds vision to the equation, and then Godo, which adds vision
and then also actions in the mix, right?
As we discussed actually actions, especially discrete actions like up, down, left, right,
I just told you the actions, but they're words.
So you can kind of see how actions naturally map to sequence modeling of words, which these
models are very powerful.
So Godo was named after, I believe, I can only, from memory, right, these, you know,
these things always happen with an amazing team of researchers behind.
So before the release, we had a discussion about which animal would we pick, right?
And I think because of the word general agent, right, and this is a property quite unique
to Godo, we kind of were playing with the GA words, and then, you know, Godo.
And Godo is obviously a Spanish version of God.
I have nothing to do with it, although I'm from Spain.
Oh, how do you, wait, sorry, how do you say cat in Spanish?
Gato.
Gato.
Yeah.
Okay, okay, I see, I see, I see you.
Now it all makes sense.
Okay, so.
How do you say meow in Spanish?
No, that's probably the same.
I think you say it the same way, but you write it as M-I-A-U.
Okay, it's universal.
Yes.
All right, so then how does the thing work?
So you said general is, so you said language, vision, and action.
How does this, can you explain what kind of neural networks are involved, what does the
training look like, and maybe what do you or some beautiful ideas within this system?
Yeah.
So, maybe the basics of Gato are not that dissimilar from many, many work that come.
So here is where the sort of the recipe, I mean, hasn't changed too much.
There is a transformer model that's the kind of recurrent neural network that essentially
takes a sequence of modalities, observations that could be words, could be vision, or could
be actions.
And then its own objective that you train it to do when you train it is to predict what
the next anything is.
And anything means what's the next action.
If this sequence that I'm showing you to train is a sequence of actions and observations,
then you're predicting what's the next action and the next observation, right?
So you think of these really as a sequence of bytes, right?
So take any sequence of words, a sequence of interleaf words and images, a sequence
of maybe observations that are images and moves in a target up, down, left, right.
And these you just think of them as bytes and you're modeling what's the next byte going
to be like.
And you might interpret that as an action and then play it in a game or you could interpret
it as a word and then write it down if you're chatting with the system and so on.
So Gato basically can be thought as inputs, images, text, video, actions.
It also actually inputs some sort of proprioception sensors from robotics because robotics is
one of the tasks that it's been trained to do.
And then at the output, similarly, it outputs words, actions.
It does not output images.
That's just by design, we decided not to go that way for now.
That's also in part why it's the beginning because there's more to do clearly.
But that's kind of what Gato is, is this brain that essentially you give it any sequence
of these observations and modalities and it outputs the next step.
And then off you go, you feed the next step into and predict the next one and so on.
Now it is more than a language model because even though you can chat with Gato, like you
can chat with Chinchilla or Flamingo, it also is an agent.
So that's why we call it A of Gato, the letter A, and also it's general.
It's not an agent that's been trained to be good at only StarCraft or only Atari or only
Go.
It's been trained on a vast variety of data sets.
What makes an agent, if I may interrupt, the fact that it can generate actions?
So when we call it, it's a good question, right?
When do we call a model, I mean, everything is a model, but what is an agent, in my view,
is indeed the capacity to take actions in an environment that you then send to it and
then the environment might return with a new observation and then you generate the next
action and so on.
This actually, this reminds me of the question from the side of biology, what is life?
Which is actually a very difficult question as well.
What is living when you think about life here on this planet Earth and a question interesting
to me about aliens, what is life when we visit another planet, would we be able to recognize
it?
And this feels like, it sounds perhaps silly, but I don't think it is, at which point is
the neural network a being versus a tool?
And it feels like action, ability to modify its environment, is that fundamental leap?
Yeah, I think it certainly feels like action is a necessary condition to be more alive,
but probably not sufficient either.
So sadly I...
It's a full consciousness thing, whatever.
Yeah, yeah, we can get back to that later.
But anyways, going back to the meow and the gato, right?
So one of the leaps forward and what took the team a lot of effort and time was, as you
were asking, how has gato been trained, so I told you gato is this transformer neural
network, models actions, sequences of actions, words, etc.
And then the way we train it is by essentially pulling data sets of observations, right?
So it's a massive imitation learning algorithm that it imitates obviously to what is the
next word that comes next from the usual data sets we used before, right?
So these are these web-scale style data sets of people writing on webs or chatting or whatnot,
right?
So that's an obvious source that we use on all language work.
But then we also took a lot of agents that we have at DeepMind.
I mean, as you know, DeepMind, we're quite interested in learning reinforcement learning
and learning agents that play in different environments.
So we kind of created a data set of these trajectories, as we call them, or agent experiences.
So in a way, there are other agents we train for a single-mind purpose to, let's say, you
know, control a 3D game environment and navigate a maze.
So we had all the experience that was created through the one agent interacting with that
environment and we added this to the data set, right?
And as I said, we just see all the data, all these sequences of words or sequences of these
agents interacting with that environment or, you know, agents playing Atari and so on.
We see that as the same kind of data.
And so we mix these data sets together and we train GATO.
That's the G-part, right?
It's general because it really has mixed, it doesn't have different brains for each modality
or each narrow task.
It has a single brain.
It's not that big of a brain compared to most of the neural networks we see these days.
It has 1 billion parameters.
Some models we're seeing get in the trillions these days and certainly 100 billion feels
like a size that is very common from when you train this job.
So the actual agent is relatively small, but it's been trained on a very challenging, diverse
data set, not only containing all of the internet, but containing all these agent experience
playing very different distinct environments.
So this brings us to the part of the tweet of, this is not the end, it's the beginning.
It feels very cool to see GATO in principle is able to control any sort of environments
that, especially the ones that it's been trained to do, these 3D games, Atari games, all sorts
of robotics tasks and so on.
But obviously it's not as proficient as the teachers it learned from on these environments.
It's not obvious, it's not obvious that it wouldn't be more proficient.
It's just the current beginning part is that the performance is such that it's not as
good as if it's specialized to that task.
So it's not as good, although I would argue size matters here.
So the fact that-
I would argue size always matters, that's a different conversation.
But for neural networks, certainly size does matter.
So it's the beginning because it's relatively small, so obviously scaling this ID app might
make the connections that exist between text on the internet and playing Atari and so on
more synergistic with one another and you might gain.
And that moment we didn't quite see, but obviously that's why it's the beginning.
That synergy might emerge with scale.
Right, might emerge with scale and also I believe there's some new research or ways in
which you prepare the data that you might need to sort of make it more clear to the
model that you're not only playing Atari and it's just you start from a screen and here
is app and a screen and down.
Maybe you can think of playing Atari as there's some sort of context that is needed for the
agent before it starts seeing, oh, this is an Atari screen, I'm going to start playing.
You might require, for instance, to be told in words, hey, this is in this sequence that
I'm showing, you're going to be playing an Atari game.
So text might actually be a good driver to enhance the data.
So then these connections might be made more easily.
That's an idea that we start seeing in language, but obviously beyond this is going to be effective.
It's not like, I don't show you a screen and you from scratch, you're supposed to learn
a game.
There is a lot of context we might set.
So there might be some work needed as well to set that context, but anyways, there's
a lot of work.
So that context puts all the different modalities on the same level ground.
Exactly.
If you provide the context best.
So maybe on that point, so there's this task which may not seem trivial of tokenizing
the data, of converting the data into pieces, into basic atomic elements that then could
cross modalities somehow.
So what's tokenization?
How do you tokenize text?
How do you tokenize images?
How do you tokenize games and actions and robotics tasks?
Yeah, that's a great question.
So tokenization is the entry point to actually make all the data look like a sequence because
tokens then are just kind of these little puzzle pieces.
We break down anything into these puzzle pieces and then we just model what's this puzzle
look like when you make it lay down in a line, so to speak, in a sequence.
So in Gato, the text, there's a lot of work.
You tokenize text usually by looking at commonly used substrings.
So there's ING in English is a very common substring, so that becomes a token.
There's quite a well-studied problem on tokenizing text, and Gato just used the standard techniques
that have been developed from many years, even starting from Ngram models in the 1950s
and so on.
Just for context, how many tokens, like what order, magnitude, number of tokens is required
for a word?
Yeah.
But what are we talking about?
Yeah, for a word in English, every language is very different.
The current level or granularity of tokenization generally means it's maybe two to five.
I don't know the statistics exactly, but to give you an idea, we don't tokenize at the
level of letters, then it would probably be like, I don't know what the average length
of a word is in English, but that would be the minimum set of tokens you could use.
It was bigger than letters smaller than words.
Yes, yes, and you could think of very, very common words like D. I mean, that would be
a single token, but very quickly you're talking two, three, four, four tokens or so.
Have you ever tried to tokenize emojis?
Emojis are actually just sequences of letters.
Maybe to you, but to me, they mean so much more.
Yeah, you can render the emoji, but you might, if you actually just...
This is a philosophical question.
Is emojis an image or a text?
The way we do these things is they're actually mapped to small sequences of characters, so
you can actually play with these models and input emojis, it will output emojis back,
which is actually quite a fun exercise.
You probably can find other tweets about these out there.
But yeah, so anyway, text, it's very clear how this is done.
And then in Gato, what we did for images is we map images to essentially...
We compressed images, so to speak, into something that looks more like less like every pixel
with every intensity.
That would mean we have a very long sequence, right?
Like if we were talking about 100 by 100 pixel images, that would make the sequences far
too long.
So what was done there is you just use a technique that essentially compresses an image into
maybe 16 by 16 patches of pixels, and then that is mapped, again tokenized, you just
essentially quantize this space into a special word that actually maps to this little sequence
of pixels.
And then you put the pixels together in some raster order, and then that's how you get
out or in the image that you're processing.
But there's no semantic aspect to that.
So you're doing some kind of...
You don't need to understand anything about the image in order to tokenize it currently.
No, you're only using this notion of compression.
So you're trying to find common, it's like JPG or all these algorithms, it's actually
very similar at the tokenization level.
All we're doing is finding common patterns and then making sure in a lossy way we compress
these images, given the statistics of the images that are contained in all the data
we deal with.
So you could probably argue that JPG does have some understanding of images, because
visual information, maybe color, compressing based, crudely based on color does capture
some something important about an image that's about its meaning, not just about some statistics.
Yeah, I mean, JP, as I said, the algorithms look actually very similar to...
They use the cosine transform in JPG, the approach we usually do in machine learning
when we deal with images and we do this quantization step is a bit more data driven.
So rather than have some sort of Fourier basis for how frequencies appear in the natural
world, we actually just use the statistics of the images and then quantize them based
on the statistics, much like you do in words.
So common substrings are allocated a token and images is very similar.
But there's no connection, the token space, if you think of, oh, the tokens are an integer
at the end of the day.
So now we work on, maybe we have about, let's say, I don't know the exact numbers, but let's
say 10,000 tokens for text, certainly more than characters, because we have groups of
characters and so on.
So from one to 10,000, those are representing all the language and the words we'll see.
And then images occupy the next set of integers.
So they're completely independent, right?
So from 10,000 one to 20,000, those are the tokens that represent these other modality
images.
And that is an interesting aspect that makes it orthogonal.
So what connects these concepts is the data, right?
Once you have a data set, for instance, that captions images that tells you, oh, this is
someone playing a frisbee on a green field, now the model will need to predict the tokens
from the text green field to then the pixels, and that will start making the connections
between the tokens.
So these connections happen as the algorithm learns.
And then the last, if we think of these integers, the first few are words, the next few are
images in GATO, we also allocated the highest order of integers to actions, right?
Which we discretize.
And actions are very diverse, right?
In Atari, there's, I don't know, 17 discrete actions.
In robotics, actions might be torques and forces that we apply.
So we just use kind of similar ideas to compress these actions into tokens.
And then we just, that's how we map now all the space to these sequence of integers.
But they occupy different space, and what connects them is then the learning algorithm.
That's where the magic happens.
So the modalities are orthogonal to each other in token space.
So in the input, everything you add, you add extra tokens.
And then you're shoving all of that into one place.
Yes, the transformer.
And that transformer tries to look at this gigantic token space and tries to form some
kind of representation, some kind of unique wisdom about all of these different modalities.
How's that possible?
If you were to put your psychoanalysis hat on and try to psychoanalyze this neural network,
is it schizophrenic?
Does it try to give very few weights, represent multiple disjoint things, and somehow have
them not interfere with each other?
Or is it some kind of building on the joint strength, on whatever is common to all the
different modalities?
If you were to ask a question, is it schizophrenic or is it of one mind?
I mean, it is one mind.
And it's actually the simplest algorithm, which that's kind of in a way how it feels
like the field hasn't changed since back propagation and gradient descent was purpose for learning
neural networks.
So there is obviously details on the architecture.
This has evolved.
The current iteration is still the transformer, which is a powerful sequence modeling architecture.
But then the goal of this, you know, setting these weights to predict the data is essentially
the same as basically, I could describe, I mean, we describe a few years ago, Alpha
Star, language modeling, and so on, right?
We take, let's say, an Atari game, we map it to a string of numbers that will all be
probably image space and action space interleaved.
And all we're going to do is say, OK, given the numbers, you know, 1001, 1004, 1005,
the next number that comes is 2006, which is in the action space.
And you're just optimizing these weights via very simple gradients, like, you know, mathematical
is almost the most boring algorithm you could imagine.
We settle the weights so that, given this particular instance, these weights are set
to maximize the probability of having seen this particular sequence of integers for this
particular game.
And then the algorithm does this for many, many, many iterations, looking at different
modalities, different games, right?
That's the mixture of the data set we discussed.
So in a way, it's a very simple algorithm.
And the weights, right, they're all shared, right?
So in terms of, is it focusing on one modality or not?
The intermediate weights that are converting from these input of integers to the target
integer you're predicting next, those weights certainly are common.
And then the way that tokenization happens, there is a special place in the neural network,
which is we map this integer, like, number 1001, to a vector of real numbers, like,
real numbers, we can optimize them with gradient descent, right?
The functions we learn are actually surprisingly differentiable.
That's why we compute gradients.
So this step is the only one that this orthogonality you mentioned applies.
So mapping a certain token for text or image or actions, each of these tokens gets its
own little vector of real numbers that represents this.
If you look at the field back many years ago, people were talking about word vectors or
word embeddings.
These are the same.
We have word vectors or embeddings.
We have image vector or embeddings and action vector of embeddings.
And the beauty here is that as you train this model, if you visualize these little vectors,
it might be that they start aligning, even though they're independent parameters.
There could be anything.
But then it might be that you take the word ghetto or cat, which maybe is common enough
that it actually has its own token.
And then you take pixels that have a cat, and you might start seeing that these vectors
look like they align.
So by learning from this vast amount of data, the model is realizing the potential connections
between these modalities.
Now I will say there would be another way, at least in part, to not have these different
vectors for each different modality.
For instance, when I tell you about actions in certain space, I'm defining actions by
words.
So you could imagine a world in which I'm not learning that the action app in Atari
is its own number.
The action app in Atari maybe is literally the word or the sentence app in Atari.
And that would mean we now leverage much more from the language.
This is not what we did here, but certainly it might make these connections much easier
to learn and also to teach the model to correct its own actions and so on.
So all this to say that GATO is indeed the beginning, that it is a radical idea to do
this this way.
But there's probably a lot more to be done, and the results to be more impressive, not
only through scale, but also through some new research that will come hopefully in the
years to come.
So just to elaborate quickly, you mean one possible next step or one of the paths that
you might take next is doing the tokenization fundamentally as a kind of linguistic communication.
So you convert even images into language, so doing something like a crude semantic segmentation,
trying to just assign a bunch of words to an image that have almost like a dumb entity
explaining as much as it can about the image.
And so you convert that into words, and then you convert games into words, and then you
provide the context in words and all of it eventually getting to a point where everybody
agrees with Noam Chomsky that language is actually at the core of everything.
It's the base layer of intelligence and consciousness and all that kind of stuff.
You mentioned early on, it's hard to grow.
What did you mean by that?
Because we're talking about scale might change.
There might be, and we'll talk about this too, there's a emergent, there's certain
things about these neural networks that are emergent, so certain performance we can see
only with scale, and there's some kind of threshold of scale.
So why is it hard to grow something like this Meow network?
So the Meow network is not hard to grow if you retrain it, what's hard is, well we have
now one billion parameters, we train them for a while, we spend some amount of work towards
building these weights that are an amazing initial brain for doing this kind of task
we care about.
Could we reuse the weights and expand to a larger brain?
And that is extraordinarily hard, but also exciting from a research perspective and a
practical perspective point of view.
So there's this notion of modularity in software engineering, and we're starting to see some
examples and work that leverages modularity.
In fact, if we go back one step from GATO to a work that I would say train much larger,
much more capable network called Flamingo, Flamingo did not deal with actions, but it
definitely dealt with images in an interesting way, kind of akin to what GATO did, but slightly
different technique for tokenizing, but we don't need to go into that detail.
But what Flamingo also did, which GATO didn't do, and that just happens because these projects,
you know, they're different, you know, it's a bit of like the exploratory nature of research
which is great.
And the research behind these projects is also modular.
Yes, exactly.
And it has to be, right?
We need to have creativity, and sometimes you need to protect pockets of people, researchers,
and so on.
By we human humans.
Yes.
Okay.
And also in particular, researchers, and maybe even further, you know, DeepMind or other
such labs.
And then they act in neural networks themselves.
So it's modularity all the way down.
All the way down.
The way that we did modularity very beautifully in Flamingo is we took Chinchilla, which is
a language only model, not an agent if we think of actions being necessary for agency.
So we took Chinchilla, we took the weights of Chinchilla, and then we froze them.
We said, these don't change.
We train them to be very good at predicting the next word is a very good language model,
state of the art at the time you release it, et cetera, et cetera.
We're going to add a capability to see, right?
We are going to add the ability to see to this language model.
So we're going to attach small pieces of neural networks at the right places in the model.
It's almost like injecting the network with some weights and some substructures in the
ways, in a good way, right?
So you need the research to say, what is effective?
How do you add this capability without destroying others, et cetera?
So we created a small sub network, initialized not from random, but actually from self-supervised
learning, a model that understands vision in general.
And then we took data sets that connect the two modalities, vision and language.
And then we froze the main part, the largest portion of the network, which was Chinchilla,
that is 70 billion parameters.
And then we added a few more parameters on top, trained from scratch, and then some others
that were pre-trained with the capacity to see.
It was not tokenization in the way I described for Gato, but it's a similar idea.
And then we trained the whole system, parts of it were frozen, parts of it were new.
And all of a sudden, we developed Flamingo, which is an amazing model that is essentially,
I mean, describing it is a chatbot where you can also upload images and start conversing
about images, but it's also kind of a dialogue style chatbot.
So the input is images and text, and the output is text.
Exactly.
And how many parameters?
You said 70 billion.
70 billion.
For Chinchilla.
Yeah.
Chinchilla is 70 billion.
And then the ones we add on top, which is almost like a way to overwrite its little activations
so that when it sees vision, it does kind of a correct computation of what it's seeing,
mapping it back towards, so to speak, that adds an extra 10 billion parameters, right?
So it's total 80 billion, the largest one we released.
And then you train it on a few data sets that contain vision and language.
And once you interact with the model, you start seeing that you can upload an image
and start sort of having a dialogue about the image, which is actually not something.
It's very similar and akin to what we saw in language only.
These prompting abilities that it has, you can teach it a new vision task, right?
It does things beyond the capabilities that, in theory, the data sets provided in themselves,
but because it leverages a lot of the language knowledge acquired from Chinchilla, it actually
has this few-shot learning ability and these emerging abilities that we didn't even measure
once we were developing the model.
Once developed, then as you play with the interface, you can start seeing, wow, okay,
yeah, it's cool.
We can upload, I think, one of the tweets talking about Twitter was this image from
Obama that is placing a weight and someone is kind of weighting themselves and it's
kind of a joke-style image.
And it's notable because, I think, Andrew Karpati a few years ago said, no computer
vision system can understand the subtlety of this joke in this image, all the things
that go on and so what we try to do, and it's very anecdotally, I mean, this is not a proof
that we solved this issue, but it just shows that you can upload now this image and start
conversing with the model trying to make out if it gets that there's a joke because the
person weighting themselves doesn't see that someone behind is making the weight higher
and so on and so forth.
So it's a fascinating capability and it comes from this key idea of modularity where we
took a frozen brain and we just added a new capability.
So the question is, should we, so in a way you can see even from DeepMind we have Flamingo
that this modular approach and thus could leverage the scale a bit more reasonably because
we didn't need to retrain a system from scratch and on the other hand we had Gato which used
the same data sets but then it trained it from scratch, right?
And so I guess big question for the community is should we train from scratch or should
we embrace modularity and this lies like this goes back to modularity as a way to grow
but reuse seems like natural and it was very effective, certainly.
The next question is if you go the way of modularity, is there a systematic way of freezing
weights and joining different modalities across, you know, not just two or three or four networks
but hundreds of networks from all different kinds of places, maybe open source network
that looks at weather patterns and you shove that in somehow and then you have networks
that, I don't know, do all kinds of the Plague Starcraft and play all the other video games
and you can keep adding them in without significant effort, like maybe the effort scales linearly
or something like that as opposed to like the more network you add the more you have
to worry about the instabilities created.
Yeah, so that vision is beautiful.
I think there's still the question about within single modalities like Chinchilla was
reused but now if we train an X iteration of language models, are we going to use Chinchilla
or not?
Yeah, how do you swap out Chinchilla?
Right.
So there's still big questions but that idea is actually really akin to software engineering
which we're not re-implementing, you know, libraries from scratch.
We're reusing and then building ever more amazing things including neural networks with software
that we're reusing.
So I think this idea of modularity, I like it, I think it's here to stay and that's
also why I mentioned it's just the beginning, not the end.
You mentioned meta learning.
So given this promise of ghetto, can we try to redefine this term that's almost akin to
consciousness because it means different things to different people throughout the history
of artificial intelligence but what do you think meta learning is and looks like now
in the five years, ten years, will it look like system like ghetto but scaled?
What's your sense of, what does meta learning look like?
Do you think with all the wisdom we've learned so far?
Yeah, great question.
Maybe it's good to give another data point looking backwards rather than forward.
So when we talk in 2019, meta learning meant something that has changed mostly through
the revolution of GPT-3 and beyond.
So what meta learning meant at the time was driven by what benchmarks people care about
in meta learning and the benchmarks were about a capability to learn about object identities.
So it was very much overfitted to vision and object classification and the part that was
meta about that was that, oh, we're not just learning a thousand categories that ImageNet
tells us to learn.
We're going to learn object categories that can be defined when we interact with the model.
So it's interesting to see the evolution.
The way this started was we have a special language that was a dataset, a small dataset
that we prompted the model with, saying, hey, here is a new classification task.
I'll give you one image and the name, which was an integer at the time of the image and
a different image and so on.
So you have a small prompt in the form of a dataset, a machine learning dataset.
And then you got then a system that could then predict or classify these objects that
you just defined kind of on the fly.
So fast forward, it was revealed that language models are future learners.
That's the title of the paper.
So very good title.
Sometimes titles are really good.
So this one is really, really good because that's the point of GPT-3 that showed that,
look, sure, we can focus on object classification and what meta learning means within the space
of learning object categories.
This goes beyond or before rather to also Omniglot before ImageNet and so on.
So there's a few benchmarks.
To now all of a sudden, we're a bit unlocked from benchmarks.
And through language, we can define tasks.
So we're literally telling the model some logical task or little thing that we wanted
to do.
We prompted much like we did before, but now we prompted through natural language.
And then, not perfectly, I mean, these models have failure modes and that's fine, but these
models then are now doing a new task.
So they meta-learn this new capability.
Now that's where we are now.
Flamingo expanded this to visual and language, but it basically has the same abilities.
You can teach it, for instance, an emergent property was that you can take pictures of
numbers and then do arithmetic with the numbers just by teaching it, oh, that's, I mean, when
I show you three plus six, you know, I want you to output nine and you show it a few examples
and now it does that.
So it went way beyond the, oh, this ImageNet sort of categorization of images that we were
a bit stuck maybe before this revelation moment that happened in 2000, I believe it was 19,
but it was after we checked.
And that way, it has solved meta-learning as was previously defined.
Yes, it expanded what it meant.
So that's what you say.
What does it mean?
So it's an evolving term.
But here is maybe now looking forward, looking at what's happening, you know, obviously in
the community with more modalities, what we can expect.
And I would certainly hope to see the following.
And this is a pretty drastic hope, but in five years, maybe we chat again.
And we have a system, right, a set of weights that we can teach it to play StarCraft.
Maybe not at the level of AlphaStar, but play StarCraft a complex game.
We teach it through interactions to prompting.
You can certainly prompt a system that's what Gato shows to play some simple Atari games.
So imagine if you start talking to a system, teaching it a new game, showing it examples
of, you know, in this particular game, this user did something good.
Maybe the system can even play and ask you questions, say, hey, I played this game.
I just played this game.
Did I do well?
Can you teach me more?
So five, maybe to 10 years, these capabilities or what meta learning means will be much more
interactive, much more rich, and through domains that we were specializing, right?
So you see the difference, right?
We built AlphaStar specialized to play StarCraft.
The algorithms were general, but the weights were specialized.
And what we're hoping is that we can teach a network to play games, to play any game,
just using games as an example, through interacting with it, teaching it, uploading the Wikipedia
page of StarCraft, like this is in the horizon, and obviously their details need to be filled
and research need to be done.
But that's how I see meta learning above, which is going to be beyond prompting.
It's going to be a bit more interactive.
It's going to, you know, the system might tell us to give it feedback after it maybe makes
mistakes or it loses a game.
But it's nonetheless very exciting because if you think about this this way, the benchmarks
are already there.
We just repurposed the benchmarks, right?
So in a way, I like to map the space of what maybe AGI means to say, okay, like, we went
101% performance in Go, in Chess, in StarCraft.
The next iteration might be 20% performance across, quote, unquote, all tasks, right?
And even if it's not as good, it's fine, we actually, we have ways to also measure progress
because we have those special agents, specialized agents, and so on.
So this is to me very exciting.
And these next iteration models are definitely hinting at that direction of progress, which
hopefully we can have.
There are obviously some things that could go wrong in terms of we might not have the
tools, maybe transformers are not enough, then we must, there's some breakthroughs to
come, which makes the field more exciting to people like me as well, of course.
But that's, if you ask me five to 10 years, you might see these models that start to look
more like weights that are already trained, and then it's more about teaching or make,
or meta-learn what you're trying to induce in terms of tasks and so on, well beyond the
simple now tasks we're starting to see emerge, like smaller, arithmetic tasks and so on.
So a few questions around that, this is fascinating.
So that kind of teaching, interactive, so it's beyond prompting, so it's interacting
within your own network, that's different than the training process.
So it's different than the optimization over differentiable functions.
This is already trained and now you're teaching, I mean, it's almost like akin to the brain,
the neurons already set with their connections.
On top of that, you're now using that infrastructure to build up further knowledge.
Okay.
So that's a really interesting distinction.
That's actually not obvious from a software engineering perspective, that there's a line
to be drawn, because you always think for a neural network to learn, it has to be re-trained,
trained and re-trained, but maybe, and prompting is a way of teaching a neural network, a little
bit of context about whatever the heck you're trying it to do, so you can maybe expand this
prompting capability by making it interact.
That's really, really interesting.
By the way, this is not, if you look at way back at different ways to tackle even classification
tasks, so this comes from long-standing literature in machine learning.
What I'm suggesting could sound to some like a bit like Neater's Neighbor.
So Neater's Neighbor is almost the simplest algorithm that does not require learning,
so it has this interesting like, you don't need to compute gradients, and what Neater's
Neighbor does is you quote-unquote have a data set or upload a data set, and then all
you need to do is a way to measure distance between points, and then to classify a new
point, you're just simply computing what's the closest point in this massive amount of
data, and that's my answer.
So you can think of prompting in a way as you're uploading not just simple points and
the metric is not the distance between the images or something simple, it's something
that you compute that's much more advanced, but in a way, it's very similar.
You simply are uploading some knowledge to this pre-trained system in Neater's Neighbor.
Maybe the metric is learned or not, but you don't need to further train it, and then now
you immediately get a classifier out of this.
Now it's just an evolution of that concept, very classical concept in machine learning,
which is just learning through what's the closest point, closest by some distance, and
that's it.
It's an evolution of that, and I will say how I saw meta-learning when we worked on
a few ideas in 2016 was precisely through the lens of Neater's Neighbor, which is very
common in computer vision community.
There's a very active area of research about how do you compute the distance between two
images, but if you have a good distance metric, you also have a good classifier.
All I'm saying is now these distances and the points are not just images, they're words
or sequences of words and images and actions that teach you something new, but it might
be that technique-wise, those come back.
I will say that it's not necessarily true that you might not ever train the weights
a bit further, some aspect of meta-learning, some techniques in meta-learning do actually
do a bit of fine-tuning, as it's called.
They train the weights a little bit when they get a new task.
As I call the how or how we're going to achieve this, as a deep learner, I'm very skeptic,
we're going to try a few things, whether it's a bit of training, adding a few parameters,
thinking of these as Neater's Neighbor, or just simply thinking of there's a sequence
of words, it's a prefix, and that's the new classifier we'll see, there's the beauty
of research, but what's important is that is a good goal in itself that I see as very
worthwhile pursuing for the next stages of not only meta-learning, I think this is basically
what's exciting about machine learning period to me.
Well, the interactive aspect of that is also very interesting, the interactive version
of Neater's Neighbor.
Yeah, to help you pull out the classifier from this giant thing.
Okay, is this the way we can go in five, 10 plus years from any task, sorry, from many
tasks to any task?
What does that mean?
What does it need to be actually trained on?
Which point is the network had enough?
What does a network need to learn about this world in order to be able to perform any task?
Is it just as simple as language, image, and action, or do you need some set of representative
images?
If you only see land images, will you know anything about underwater?
Is that some fundamentally different, I don't know?
Those are open questions, I would say, the way you put, let me maybe further your example.
If all you see is land images, but you're reading all about land and water worlds, but
in books, imagine, would that be enough?
Good question, we don't know, but I guess maybe you can join us if you want in our quest
to find this.
That's precisely.
Waterworld, yeah.
That's precisely, I mean, the beauty of research, and that's the research business we're in,
I guess, is to figure this out and ask the right questions, and then iterate with the
whole community, publishing findings and so on.
But yeah, this is a question, it's not the only question, but it's certainly, as you
ask, is on my mind constantly, right?
And so we'll need to wait for maybe the, let's say, five years, let's hope it's not
10, to see what are the answers.
Some people will largely believe in unsupervised or self-supervised learning of single modalities,
and then crossing them.
Some people might think end-to-end learning is the answer, modularity is maybe the answer,
so we don't know, but we're just definitely excited to find out.
But it feels like this is the right time, and we're at the beginning of this, we're
finally ready to do these kind of general, big models and agents.
What do you sort of, specific technical thing about Gato, Flamingo, Chinchilla, Gopher,
any of these that is especially beautiful, that was surprising, maybe?
Is there something that just jumps out at you?
Of course, there's the general thing of like, you didn't think it was possible, and then
you realize it's possible in terms of the generalizability across modalities and all
that kind of stuff, or maybe how small of a network, relatively speaking, Gato is, all
that kind of stuff.
But is there some weird little things that were surprising?
Look, I'll give you an answer that's very important, because maybe people don't quite
realize this, but the teams behind these efforts, the actual humans, that's maybe the surprising,
in an obviously positive way.
So anytime you see these breakthroughs, I mean, it's easy to map it to a few people,
there's people that are great at explaining things and so on, that's very nice.
But maybe the learnings or the meta learnings that I get as a human about this is, sure,
we can move forward, but the surprising bit is how important are all the pieces of these
projects, how do they come together?
So I'll give you maybe some of the ingredients of success that are common across these, but
not the obvious ones on machine learning, I can always also give you those.
But basically, engineering is critical, so very good engineering, because ultimately
we're collecting data sets, so the engineering of data, and then of deploying the models
at scale into some compute cluster that cannot go understated, that is a huge factor of success.
And it's hard to believe that details matter so much, we would like to believe that it's
true that there is more and more of a standard formula, as I was saying, like this recipe
that works for everything.
But then when you zoom into each of these projects, then you realize the devil is indeed
in the details, and then the teams have to work kind of together towards these goals.
So engineering of data and obviously clusters and large scale is very important.
And then one that is often not, maybe nowadays it is more clear is benchmark progress, right?
So we're talking here about multiple months of, you know, tens of researchers and people
that are trying to organize the research and so on working together, and you don't know
that you can get there.
I mean, this is the beauty, like if you're not risking to trying to do something that
feels impossible, you're not going to get there, but you need the way to measure progress.
So the benchmarks that you build are critical.
I've seen this beautifully pay out in many projects.
I mean, maybe the one I've seen it more consistently, which means we established the metric, actually
the community did, and then we leveraged that massively is AlphaFold.
This is a project where the data, the metrics were all there, and all it took was, and it's
easier said than done, an amazing team working not to try to find some incremental improvement
and publish, which is one way to do research that is valid, but aim very high and work
literally for years to iterate over that process and working for years with the team.
I mean, it is tricky that also happened to happen partly during a pandemic and so on.
So I think my meta learning from all this is the teams are critical to the success,
and then if now going to the machine learning, the part that's surprising is, so we like
architectures like neural networks, and I would say this was a very rapidly evolving
field until the transformer came, so attention might indeed be all you need, which is the
title, also a good title, although in hindsight is good.
I don't think at the time I thought this is a great title for a paper, but that architecture
is proving that the dream of modeling sequences of any bytes, there is something there that
will stick, and I think these advance in architectures, in kind of how neural networks are architecture
to do what they do.
It's been hard to find one that has been so stable and relatively has changed very little
since it was invented five or so years ago.
So that is a surprise that keeps recurring to other projects.
Try to, on a philosophical or technical level, introspect what is the magic of attention?
What is attention?
It's attention in people that study cognition, so human attention.
I think there's giant wars over what attention means, how it works in the human mind.
So what, there's very simple looks at what attention is in neural network from the days
of attention is all you need, but do you think there's a general principle that's really
powerful here?
Yeah.
So a distinction between transformers and LSTMs, which were what came before, and there
was a transitional period where you could use both.
In fact, when we talked about Alphastat, we used transformers and LSTMs, so it was still
the beginning of transformers.
They were very powerful, but LSTMs were still also very powerful sequence models.
So the power of the transformer is that it has built in what we call an inductive bias
of attention that makes the model, when you think of a sequence of integers, like we discussed
this before.
This is a sequence of words.
When you have to do very hard tasks over these words, this could be we're going to translate
a whole paragraph, or we're going to predict the next paragraph given 10 paragraphs before.
There's some loose intuition from how we do it as a human that is very nicely mimicked
and replicated structurally speaking in the transformer, which is this idea of you're
looking for something, so you're sort of when you just read a piece of text, now you're
thinking what comes next.
You might want to relook at the text or look it from scratch, I mean, readily is because
there's no recurrence.
You're just thinking what comes next, and it's almost hypothesis driven.
So if I'm thinking the next word that I'll write is cat or dog, the way the transformer
works almost philosophically is it has these two hypotheses, is it going to be cat or is
it going to be dog?
And then it says, okay, if it's cat, I'm going to look for certain words, not necessarily
cat, although cat is an obvious word you would look in the past to see whether it makes more
sense to output cat or dog.
And then it does some very deep computation over the words and beyond, so it combines
the words, but it has the query, as we call it, that is cat, and then similarly for dog.
And so it's a very computational way to think about, look, if I'm thinking deeply about text,
I need to go back to look at all of the text, attend over it, but it's not just attention,
but it's guiding the attention, and that was the key insight from an earlier paper is not
how far away is it?
I mean, how far away is it is important, what did I just write about?
That's critical, but what you wrote about 10 pages ago might also be critical.
So you're looking not positionally, but content wise, right?
And you transformers have this beautiful way to query for certain content and pull it out
in a compressed way, so then you can make a more informed decision.
I mean, that's one way to explain transformers.
But I think it's a very powerful inductive bias.
There might be some details that might change over time, but I think that is what makes
transformers so much more powerful than the recurrent networks that were more recency
bias based, which obviously works in some tasks, but it has major flaws.
Platform itself has flaws, and I think the main one, the main challenges, these prompts
that we just were talking about, they can be a thousand words long.
But if I'm teaching you Starcraft, I mean, I'll have to show you videos, I'll have to
point you to whole Wikipedia articles about the game, we'll have to interact probably
as you play your last mi questions.
The context required for us to achieve me being a good teacher to you on the game as
you would want to do it with a model, I think goes well beyond the current capabilities.
So the question is, how do we benchmark this, and then how do we change the structure of
the architectures?
I think there's ideas on both sides, but we'll have to see empirically what ends up
working.
And as you talked about, some of the ideas could be keeping the constraint of that length
in place, but then forming like hierarchical representations to where you can start being
much clever in how you use those thousand tokens.
Indeed.
Yeah, that's really interesting, but it also is possible that this attentional mechanism
where you basically, you don't have a recency bias, but you look more generally, you make
it learnable, the mechanism in which way you look back into the past, you make that learnable.
It's also possible we're at the very beginning of that because that you might become smarter
and smarter in the way you query the past.
So recent past and distant past and maybe very, very distant past.
So almost like the attention mechanism will have to improve and evolve as good as the
tokenization mechanism where so you can represent long-term memory somehow.
Yes.
I mean, hierarchies are very, I mean, it's a very nice word that sounds appealing.
There's lots of work adding hierarchy to the memories.
In practice, it does seem like we keep coming back to the main formula or main architecture.
That sometimes tells us something, there's such a sentence that a friend of mine told
me like, whether it wants to work or not.
So transformer was clearly an idea that wanted to work and then I think there's some principles
we believe will be needed, but finding the exact details, details matter so much, right?
That's going to be tricky.
I love the idea that there's like you as a human being, you want some ideas to work
and then there's the model that wants some ideas to work and you get to have a conversation
to see which more likely the model will win in the end because it's the one, you don't
have to do any work.
The model is the one that has to do the work, so you should listen to the model.
And I really love this idea that you talked about the humans in this picture.
If I could just briefly ask, one is you're saying the benchmarks about the modular humans
working on this, the benchmarks providing a sturdy ground of a wish to do these things
that seem impossible, they give you, in the darkest of times, give you hope because little
signs of improvement, you could, yes, like you're not, somehow you're not lost if you're
have metrics to measure your improvement.
And then there's other aspect you said elsewhere and here today, like titles matter.
I wonder how much humans matter in the evolution of all of this, meaning individual humans.
Something about their interactions, something about their ideas, how much they change the
direction of all of this, like if you change the humans in this picture, like is it that
the model is sitting there and it wants some idea to work or is it the humans, or maybe
the model is providing you 20 ideas that could work.
And depending on the humans you pick, they're going to be able to hear some of those ideas.
In all the, because you're now directing all of deep learning at DeepMind, you get to interact
with a lot of projects, a lot of brilliant researchers.
How much variability is created by the humans in all of this?
Yeah.
I mean, I do believe humans matter a lot at the very least at the time scale of years
on when things are happening and what's the sequencing of it, right?
So you get to interact with people that, I mean, you mentioned this.
Some people really want some idea to work and they'll persist.
And then some other people might be more practical, like I don't care what idea works.
I care about cracking protein folding.
And these, at least these two kind of seem opposite sides.
We need both and we've clearly had both historically and that made certain things happen earlier
or later.
So definitely humans involved in all of these endeavor have had, I would say, years of change
or of ordering how things have happened, which breakthroughs came before which other breakthroughs
and so on.
So certainly that does happen.
And so one other maybe one other axis of distinction is what I called and this is most commonly
used in reinforcement learning is the exploration exploitation trade off as well.
It's not exactly what I meant, although quite related.
So when you start trying to help others, right, like you become a bit more of a mentor to
a large group of people, be it a project or the deep learning team or something, or even
in the community when you interact with people in conferences and so on.
You're identifying quickly, right?
Some things that are explorative or exploitative and it's tempting to try to guide people.
Obviously, I mean, that's what makes like our experience.
We bring it and we try to shape things sometimes wrongly.
And there's many times that I've been wrong in the past.
That's great.
But it would be wrong to dismiss any sort of of the research styles that I'm observing.
And I often get asked, well, you're in industry, right?
So we do have access to large compute scale and so on.
So there's certain kinds of research.
I almost feel like we need to do responsibly and so on.
But it is almost we have the particle accelerator here.
So to speak in physics, so we need to use it.
We need to answer the questions that we should be answering right now for the scientific
progress.
But then at the same time, I look at many advances, including attention, which was discovered
in Montreal initially because of lack of compute, right?
So we were working on sequence to sequence with my friends over at Google Brain at the
time and we were using, I think, 8 GPUs, which was somehow a lot at the time.
And then I think Montreal was a bit more limited in the scale.
But then they discovered this content-based attention concept that then has obviously triggered
things like Transformer.
Not everything obviously starts Transformer.
There's always a history that is important to recognize because then you can make sure
that then those who might feel now, well, we don't have so much compute, you need to
then help them optimize that kind of research that might actually produce amazing change.
Sometimes it's not as short-term as some of these advancements or perhaps it's a different
time scale.
But the people and the diversity of the field is quite critical that we maintain it.
And at times, especially mixed a bit with hype or other things, it's a bit tricky to
be observing maybe too much of the same thinking across the board.
But the humans definitely are critical and I can think of quite a few personal examples
where also someone told me something that had a huge effect on to some idea.
And then that's why I'm saying at least in terms of ears, probably some things do happen.
Yeah, it's fascinating.
It's also fascinating how constraints somehow are essential for innovation.
And the other thing you mentioned about engineering, I have a sneaking suspicion.
Maybe I over, my love is with engineering.
So I have a sneaking suspicion that all the genius, a large percentage of the genius is
in the tiny details of engineering.
So I think we like to think our genius, the genius is in the big ideas.
I have a sneaking suspicion that because I've seen the genius of details, of engineering
details make the night and day difference, and I wonder if those kind of have a ripple
effect over time.
So that too, so that's taken the engineering perspective that sometimes that quiet innovation
at the level of an individual engineer, or maybe at the small scale of a few engineers
can make all the difference that scales.
Because we're working on computers that are scaled across large groups, that one engineering
decision can lead to ripple effects.
Yes.
It's interesting to think about.
Yeah, I mean, engineering, there's also kind of a historical, it might be a bit random,
because if you think of the history of how especially deep learning and neural networks
took off.
It feels like a bit random, because GPUs happen to be there at the right time for a different
purpose, which was to play video games.
So even the engineering that goes into the hardware, and it might have a time frame might
be very different.
I mean, the GPUs evolved throughout many years where we didn't even were looking at that.
So even at that level, that revolution, so to speak, the ripples are like, we'll see
when they stop.
But in terms of thinking of why is this happening, there's, I think that when I try to categorize
it in sort of things that might not be so obvious, I mean, clearly there's a hardware
revolution, we are surfing thanks to that.
Data centers as well.
I mean, data centers are like, I mean, at Google, for instance, obviously they're serving
Google, but there's also now thanks to that and to have built such amazing data centers,
we can train these models.
Software is an important one.
I think if I look at the state of how I had to implement things to implement my ideas,
how I discarded ideas because they were too hard to implement, yeah, clearly the times
have changed and thankfully we are in a much better software position as well.
And then, I mean, obviously there's research that happens at scale and more people enter
the field, that's great to see, but it's almost enabled by these other things.
And last but not least is also data, right, curating data sets, labeling data sets, these
benchmarks we think about, maybe we'll want to have all the benchmarks in one system,
but it's still very valuable that someone put the thought and the time and the vision
to build certain benchmarks we've seen progress thanks to, but we're going to repurpose the
benchmarks.
That's the beauty of Atari is like, we solved it in a way, but we use it in Gato, it was
critical and I'm sure there's still a lot more to do thanks to that amazing benchmark
that someone took the time to put, even though at the time maybe, oh, you have to think what's
the next iteration of architecture, that's what maybe the field recognizes, but we need
to, that's another thing, we need to balance in terms of humans behind, we need to recognize
all these aspects because they're all critical.
And we tend to think of the genius, the scientist and so on, but I'm glad you're, I know you
have a strong engineering background.
But also I'm a lover of data and the pushback on the engineering comment ultimately could
be the creatives of benchmarks who have the most impact.
Andre Capati, who you mentioned, has recently been talking a lot of trash about ImageNet,
which he has the right to do because of how critical he is about, how essential he is
to the development and the success of deep learning around ImageNet, and you're saying
that that's actually, that benchmark is holding back the field because, I mean, especially
in his context on Tesla autopilot, that's looking at real-world behavior of a system.
It's, there's something fundamentally missing about ImageNet that doesn't capture the real
worldness of things, that we need to have data-sized benchmarks that have the unpredictability,
the edge cases, the whatever the heck it is that makes the real world so difficult to
operate in, we need to have benchmarks with that.
But just to think about the impact of ImageNet as a benchmark, and that really puts a lot
of emphasis on the importance of a benchmark, both sort of internally a deep mind and as
a community.
So one is coming in from within, like, how do I create a benchmark for me to mark and
make progress, and how do I make benchmark for the community to mark and push progress.
You have this amazing paper you co-authored, a survey paper called, Emergent Abilities
of Large Language Models.
It has, again, the philosophy here that I'd love to ask you about.
What's the intuition about the phenomenon of emergence in neural networks, transform
as language models, is there a magic threshold beyond which we start to see certain performance,
and is that different from task to task?
Is that us humans just being poetic and romantic, or is there literally some level of which
we start to see breakthrough performance?
Yeah.
I mean, this is a property that we start seeing in systems that actually tend to be, so in
machine learning, traditionally, again, going to benchmarks, I mean, if you have some input
output, like that is just a single input and a single output, you generally, when you train
these systems, you see reasonably smooth curves when you analyze how much the data set size
affects the performance, or how the model size affects the performance, or how long
you train the system for affects the performance.
So if we think of ImageNet, the train curves look fairly smooth and predictable in a way,
and I would say that's probably because of the, it's kind of a one hop reasoning task.
It's like, here is an input, and you think for a few milliseconds, or 100 milliseconds,
300 as a human, and then you tell me, yeah, there's an alpaca in this image.
So in language, we are seeing benchmarks that require more pondering and more thought in
a way, right?
This is just kind of you need to look for some subtleties that involves inputs that you
might think of, or even if the input is a sentence describing a mathematical problem,
there is a bit more processing required as a human and more introspection.
So I think how these benchmarks work means that there is actually a threshold, just going
back to how transformers work in this way of querying for the right questions to get
the right answers.
That might mean that performance becomes random until the right question is asked by the querying
system of a transformer or of a language model, like a transformer.
And then only then you might start seeing performance going from random to non-random.
And this is more empirical, there's no formalism or theory behind this yet, although it might
be quite important, but we're seeing these phase transitions of random performance until
some, let's say, scale of a model, and then it goes beyond that.
And it might be that you need to fit a few low order bits of thought before you can make
progress on the whole task.
And if you could measure, actually, those breakdown of the task, maybe you would see
more smooth, like, yeah, once you get this and this and this and this and this, then
you start making progress in the task.
But it's somehow a bit annoying because then it means that certain questions we might ask
about architectures possibly can only be done at certain scale.
And one thing that, conversely, I've seen great progress on in the last couple years
is this notion of science of deep learning and science of scale in particular.
So on the negative is that there are some benchmarks for which progress might need to
be measured at minimum at certain scale until you see then what details of the model matter
to make that performance better, right?
So that's a bit of a con.
But what we've also seen is that you can sort of empirically analyze behavior of models
at scales that are smaller, right?
So let's say, to put an example, we had this chinchilla paper that revised the so-called
scaling laws of models.
And that whole study is done at a reasonably small scale, right?
Maybe hundreds of millions up to one billion parameters.
And then the cool thing is that you create some loss, right?
Some loss that some trends, right?
You extract trends from data that you see, OK, like, it looks like the amount of data
required to train now a 10x larger model would be this.
And these loss so far, these extrapolations have helped us save compute and just get to
a better place in terms of the science of how should we run these models at scale, how
much data, how much depth, and all sorts of questions we start asking extrapolating from
a small scale.
But then these emergence is sadly that not everything can be extrapolated from scale
depending on the benchmark.
And maybe the harder benchmarks are not so good for extracting these loss.
But we have a variety of benchmarks at least.
So I wonder to which degree the threshold, the phase shift scale is a function of the
benchmark.
So some of that, some of the science of scale might be engineering benchmarks where that
threshold is low, sort of taking a main benchmark and reducing it somehow where the essential
difficulty is left, but the immersion, the scale at which the emergence happens is lower
just for the science aspect of it versus the actual real world aspect.
Yeah, so luckily we have quite a few benchmarks, some of which are simpler or maybe they're
more like, I think people might call these systems one versus systems two style.
So I think what we're not seeing, luckily, is that extrapolations from maybe slightly
more smooth or simpler benchmarks are translating to the harder ones.
But that is not to say that this extrapolation will hit its limits.
And when it does, then how much we scale or how we scale will sadly be a bit suboptimal
until we find better loss, right?
And these laws, again, are very empirical laws.
They're not like physical laws of models, although I wish there would be better theory
about these things as well.
But so far, I would say empirical theory, as I call it, is way ahead than actual theory
of machine learning.
Let me ask you almost for fun.
So this is not Orial as a deep mind person or anything to do with deep mind or Google,
just as a human being.
And looking at these news of a Google engineer who claimed that I guess the Lambda language
model was sentient or had the, and you still need to look into the details of this, but
sort of making an official report and the claim that he believes there's evidence that
this system has achieved sentience.
And I think this is a really interesting case on a human level and a psychological level
on a technical machine learning level of how language models transform our world and also
just philosophical level of the role of AI systems in a human world.
So what did you, what do you find interesting?
What's your take on all of this as a machine learning engineer and a researcher and also
as a human being?
Yeah.
I mean, a few reactions, quite a few actually.
Have you ever briefly thought, is this thing sentient?
Right.
So never.
Absolutely never.
Like even with like Alpha Star, wait a minute.
What?
Sadly though, I think, yeah, sadly I have not, yeah, I think the current, any of the current
models, although very useful and very good, yeah, I think we're quite far from that.
And there's kind of a converse side story.
So one of, one of the, my passions is about science in general.
And I think I feel I'm a bit of like a failed scientist.
That's why I came to machine learning because you always feel, and you start seeing this
that machine learning is maybe the science that can help other sciences as we've seen,
right?
Like you, you know, it's such a powerful tool.
So thanks to that angle, right?
That, okay, I love science.
I love, I mean, I love astronomy.
I love biology.
But I'm not an expert and I decided, well, the thing I can do better at is computers.
But having, especially with, when I was a bit more involved in Alpha Fault, learning
a bit about proteins and about biology and about life, the complexity, it feels like
it really is like, I mean, if you start looking at the things that are going on at, you know,
at the atomic level, and also, I mean, there's, there's obviously the, we are maybe inclined
to try to think of neural networks as like the brain, but the complexities and the amount
of magic that it feels when, I mean, I don't, I'm not an expert.
So it naturally feels more magic, but looking at biological systems as opposed to these
computer computational brains just makes me like, wow, this, there's such level of
complexity difference still, right?
Like orders of magnitude complexity that, sure, these weights, I mean, we train them
and they do nice things, but they're not at the level of biological entities, brains,
cells.
It just feels like it's just not possible to achieve the same level of complexity behavior
and my belief when I talk to other beings is certainly shaped by this amazement of biology
that maybe because I know too much, I don't have about machine learning, but I certainly
feel it's very far fetched and far in the future to be calling or to be thinking, well,
this mathematical function that is differentiable is, is, is in fact, sentient and so on.
So there's something on that point that it's very interesting.
So you know enough about machines and enough about biology to know that there's many orders
of magnitude of difference and complexity, but you know how machine learning works.
So the interesting question from human beings that are interacting with a system that don't
know about the underlying complexity and I've seen people and probably including myself
that have fallen in love with things that are quite simple.
Yeah.
I mean, and so maybe the complexity is one part of the picture, but maybe that's not
a necessary, that's not a necessary condition for sentience, for perception or emulation
of sentience.
Right.
So, I mean, I guess the other side of this is that's how I feel personally.
I mean, you asked me about the person, right?
Now it's very interesting to see how other humans feel about things, right?
This is we are like, again, like I'm not as amazed about things that I feel like this
is not as magical as this other thing because of maybe how I got to learn about it and how
I see the curve a bit more smooth because I, you know, like just seen the progress of
language models since Shannon in the fifties and actually looking at that time scale, we're
not that fast progress, right?
I mean, what we were thinking at the time like almost 100 years ago is not that dissimilar
to what we're doing now, but at the same time, yeah, obviously others my experience, right?
The personal experience, I think no one should, you know, I think no one should tell others
how they should feel.
I mean, the feelings are very personal, right?
So how others might feel about the models and so on.
That's one part of the story that is important to understand for me personally as a researcher.
And then when I maybe disagree or I don't understand or see that, yeah, maybe this, this
is not something I think right now is reasonable knowing all that I know, one of the other
things and perhaps partly why it's great to be talking to you and reaching out to the
world about machine learning is, hey, let's make, let's demystify a bit the magic and
try to see a bit more of the math and the fact that literally to create these models,
if we had the right software, it would be 10 lines of code.
And then just a dump of the internet.
So versus like then the complexity of like the creation of humans from their inception,
right?
And also the complexity of evolution of the whole universe to where we are that is feels
orders of magnitude more complex and fascinating to me.
So I think, yeah, maybe part of the only thing I'm thinking about trying to tell you is,
yeah, I think explaining a bit of the magic, there is a bit of magic, it's good to be in
love obviously with what you do at work.
And I'm certainly fascinated and surprised quite often as well.
But I think hopefully as experts in biology, hopefully we will tell me this is not as magic
and I'm happy to learn that through interactions with the larger community, we can also have
a certain level of education that in practice also will matter because I mean, one question
is how you feel about this, but then the other very important is you starting to interact
with these in products and so on, it's good to understand a bit what's going on, what's
not going on, what's safe, what's not safe and so on, right?
Otherwise, the technology will not be used properly for good, which is obviously the
goal of all of us, I hope.
So let me then ask the next question.
Do you think in order to solve intelligence or to do to replace the leg spot that does
interviews as we started this conversation with, do you think the system needs to be
sentient?
Do you think it needs to achieve something like consciousness?
And do you think about what consciousness is in the human mind that could be instructive
for creating AI systems?
Yeah, honestly, I think probably not to the degree of intelligence that there's this brain
that can learn, can be extremely useful, can challenge you, can teach you, conversely,
you can teach it to do things.
I'm not sure it's necessary, personally speaking, but if consciousness or any other biological
or evolutionary lesson can be repurposed to then influence our next set of algorithms,
that is a great way to actually make progress, right?
And the same way I try to explain transformers a bit how it feels we operate when we look
at text specifically, these insights are very important, right?
So there's a distinction between details of how the brain might be doing computation.
I think my understanding is, sure, there's neurons and there's some resemblance to neural
networks, but we don't quite understand enough of the brain in detail, right, to be able
to replicate it.
But then if you zoom out a bit, how we then, our thought process, how memory works, maybe
even how evolution got us here, what's exploration, exploitation, like how these things happen,
I think these clearly can inform algorithmic level research.
And I've seen some examples of these being quite useful to then guide the research, even
it might be for the wrong reasons, right?
So I think biology and what we know about ourselves can help a whole lot to build essentially
what we call AGI, this general, the real ghetto, right, the last step of the chain, hopefully.
But consciousness in particular, I don't myself at least think too hard about how to add that
to the system.
But maybe my understanding is also very personal about what it means, right?
I think this, even that in itself is a long debate that I know people have often and maybe
I should learn more about this.
Yeah, and I personally, I noticed the magic often on a personal level, especially with
physical systems like robots.
I have a lot of legged robots now in Austin that I play with.
And even when you program them, when they do things you didn't expect, there's an immediate
anthropomorphization.
And you notice the magic and you start to think about things like sentience that has
to do more with effective communication and less with any of these kind of dramatic things.
It seems like a useful part of communication.
Having the perception of consciousness seems like useful for us humans.
We treat each other more seriously.
We are able to do a nearest neighbor shoving of that entity into your memory correctly,
all that kind of stuff seems useful, at least to fake it, even if you never make it.
So maybe like, yeah, mirroring the question.
And since you talked to a few people, then you do think that we'll need to figure something
out in order to achieve intelligence in a grander sense of the world.
Yeah, I personally believe yes, but I don't even think it'll be like a separate island
we'll have to travel to.
I think it will emerge quite naturally.
Okay.
So it's easier than for us, then thank you.
But the reason I think it's important to think about is you will start, I believe, like with
this Google engineer, you will start seeing this a lot more, especially when you have
AI systems that are actually interacting with human beings that don't have an engineering
background.
And we have to prepare for that because there will be, I do believe there will be a civil
rights movement for robots as silly as it is to say, there's going to be a large number
of people that realize there's these intelligent entities with whom I have a deep relationship
and I don't want to lose them.
They've come to be a part of my life and they mean a lot.
They have a name, they have a story, they have a memory and we start to ask questions
about ourselves.
Well, this thing sure seems like it's capable of suffering because it tells all these stories
of suffering.
It doesn't want to die and all those kinds of things and we have to start to ask ourselves
questions.
There's a difference between a human being and this thing.
So when you engineer, I believe from an engineering perspective, from like a deep mind or anybody
that builds systems, there might be laws in the future where you're not allowed to engineer
systems with displays of sentience unless they're explicitly designed to be that, unless
it's a pet.
So if you have a system that's just doing customer support, you're legally not allowed
to display sentience.
We'll start to ask ourselves that question and then that's going to be part of the software
engineering process.
Which features do we have and one of them is communications of sentience?
But it's important to start thinking about that stuff, especially how much it captivates
public attention.
Yeah, absolutely.
It's definitely a topic that is important we think about and I think in a way, I always
say not every movie is equally on point with certain things, but certainly science fiction
in this sense at least has prepared society to start thinking about certain topics that
even if it's too early to talk about as long as we are reasonable, it's certainly going
to prepare us for both the research to come and how to, I mean, there's many important
challenges and topics that come with building an intelligent system, many of which you just
mentioned.
So I think we're never going to be fully ready unless we talk about this and we start also,
as I said, just kind of expanding the people we talk to, to not include only our own researchers
and so on, in fact, places like DeepMind but elsewhere, there's more interdisciplinary
groups forming up to start asking and really working with us on these questions.
Because obviously, this is not initially what your passion is when you do your PhD, but
certainly it is coming, right?
So it's fascinating kind of, it's the thing that brings me to one of my passions that
is learning.
In this sense, this is kind of a new area that as a learning system myself, I want to
keep exploring and I think it's great to see parts of the debate and even I seen a level
of maturity in the conferences that deal with AI.
If you look five years ago to now, just the amount of workshops and so on has changed
so much.
It is impressive to see how much topics of safety ethics and so on come to the surface,
which is great.
And if you were too early, clearly, it's fine.
I mean, it's a big field and there's lots of people with lots of interests that will
do progress or make progress.
And obviously, I don't believe we're too late.
So in that sense, I think it's great that we're doing this already.
It's better be too early than too late when it comes to super intelligent AI systems.
Let me ask, speaking of sentient AIs, you gave props to your friend, Elias Itzgiver,
for being elected the fellow of the World Society.
So just as a shout out to a fellow researcher and a friend, what's the secret to the genius
of Elias Itzgiver?
And also, do you believe that his tweets of as you have hypothesized and Andre Capati
did as well, are generated by a language model?
Yeah.
So I strongly believe Elias is going to visit in a few weeks, actually.
So I'll ask him in person.
But will he tell you the truth?
Yes, of course.
Okay.
Hopefully.
I mean, ultimately, we all have sharepads and there's friendships that go beyond obviously
institutional institutions and so on.
So hope he tells me the truth.
Well, maybe the AI system is holding him hostage somehow.
Maybe he has some videos about he doesn't want to release.
So maybe it has taken control over him so he can't tell you.
If I see him in person, then he will know.
But I think Elias' personality, just knowing him for a while, everyone in Twitter, I guess,
gets a different persona and I think Elias one does not surprise me.
So I think knowing Elias from before social media and before AI was so prevalent, I recognize
a lot of his character.
So that's something for me that I feel good about a friend that hasn't changed or is still
true to himself, right?
Obviously there is a fact that your field becomes more popular and he is obviously one
of the main figures in the field having done a lot of advancement.
So I think that the tricky bit here is how to balance your true self with the responsibility
that your words carry.
So in this sense, I think, yeah, I appreciate the style and I understand it, but it created
debates on some of his tweets, right?
That maybe it's good we have them early anyways, right?
But yeah, then the reactions are usually polarizing.
I think we're just seeing kind of the reality of social media a bit there as well, reflected
on that particular topic or set of topics he is tweeting about.
Yeah.
I mean, it's funny they speak to this tension.
He was one of the early seminal figures in the field of deep learning.
So there's a responsibility with that, but he's also from having interacted with him
quite a bit.
He's just a brilliant thinker about ideas and which as are you and that there's a tension
between becoming the manager versus like the actual thinking through very novel ideas.
The yeah, the scientist versus the manager and he's one of the great scientists of our
time.
This was quite interesting.
And also people tell me quite silly, which I haven't quite detected yet, but in private,
we'll have to see about that.
Yeah.
Yeah.
I mean, just on the point of, I mean, Ilya has been an inspiration, I mean, quite a few
colleagues I can think shaped the person you are, like Ilya certainly gets probably the
top spot if not close to the top.
And if we go back to the question about people in the field, like how the role would have
changed the field or not, I think Ilya's case is interesting because he really has a deep
belief in the scaling up of neural networks.
There was a talk that is still famous to this day from the sequence to sequence paper where
he was just claiming, just give me supervised data and a large neural network and then you
will solve basically all the problems.
That vision was already there many years ago.
So it's good to see someone who is in this case very deeply into this style of research
and clearly has had a tremendous track record of successes and so on.
The funny bit about that talk is that we rehearsed the talk in a hotel room before and the original
version of that talk would have been even more controversial.
So maybe I'm the only person that has seen the unfiltered version of the talk.
And maybe when the time comes, maybe we should revisit some of the skip slides from the talk
from Ilya.
But I really think the deep belief into some certain style of research pays out.
It is good to be practical sometimes and I actually think Ilya and myself are practical
but it's also good there's some sort of long-term belief and trajectory.
Obviously, there's a bit of lack involved but it might be that that's the right path
then you clearly are ahead and hugely influential to the field as he has been.
Do you agree with that intuition that maybe was written about by Rich Sutton in the bitter
lesson that the biggest lesson that can be read from 70 years of AI research is that
general methods that leverage computation are ultimately the most effective?
Do you think that intuition is ultimately correct?
General methods leverage computation, allowing the scaling of computation to do a lot of
the work.
And so the basic task of us humans is to design methods that are more and more general versus
more and more specific to the tasks at hand.
I certainly think this essentially mimics a bit of the deep learning research almost
like philosophy that on the one hand we want to be data agnostic.
We don't want to pre-process data sets.
We want to see the bytes like the true data as it is and then learn everything on top.
So very much agree with that and I think scaling up feels at the very least again necessary
for building incredible complex systems.
It's possibly not sufficient barring that we need a couple of breakthroughs.
I think Rich Sutton mentioned search being part of the equation of scale and search.
I think search, I've seen it, that's been more mixed in my experience.
So from that lesson in particular, search is a bit more tricky because it is very appealing
to search in domains like Go where you have a clear reward function that you can then
discard some search traces.
But then in some other tasks, it's not very clear how you would do that.
Although recently one of our recent works which actually was mostly mimicking or a continuation
and even the team and the people involved were pretty much very intersecting with Alpha
Star was Alpha Code in which we actually saw the bitter lesson how scale of the models
and then a massive amount of search yielded this very interesting result of being able
to have human level code competition.
So I've seen examples of it being literally mapped to search and scale.
I'm not so convinced about the search bit but certainly I'm convinced scale will be
needed so we need general methods.
We need to test them and maybe we need to make sure that we can scale them given the
hardware that we have in practice but then maybe we should also shape how the hardware
looks like based on which methods might be needed to scale.
And that's an interesting contrast of this GPU comment that is we got it for free almost
because games were using this but maybe now if sparsity is required we don't have the
hardware although in theory many people are building different kinds of hardware these
days but there's a bit of this notion of hardware lottery for scale that might actually have
an impact at least on the scale of years on how fast we will make progress to maybe a
version of neural nets or whatever comes next that might enable truly intelligent agents.
Do you think in your lifetime we will build an AGI system that would undeniably be a thing
that achieves human level intelligence and goes far beyond?
I definitely think it's possible that it will go far beyond but I'm definitely convinced
that it will be human level intelligence and I'm hypothesizing about the beyond because
the beyond bit is a bit tricky to define especially when we look at the current formula of starting
from this imitation learning standpoint right so we can certainly imitate humans at language
and beyond so getting at human level through imitation feels very possible.
Going beyond will require reinforcement learning and other things and I think in some areas
that certainly already has paid out I mean go being an example that's my favorite so
far in terms of going beyond human capabilities but in general I'm not sure we can define
reward functions that from a seat of imitating human level intelligence that is general and
then going beyond that bit is not so clear in my lifetime but certainly human level yes
and I mean that in itself is already quite powerful I think so going beyond I think it's
obviously not we're not going to not try that if then we get to superhuman scientist and
discovery and advancing the world but at least human level is also in general is also very
very powerful.
Well especially if human level or slightly beyond is integrated deeply with human society
and there's billions of agents like that do you think there's a singularity moment beyond
which our world will be just very deeply transformed by these kinds of systems because now you're
talking about intelligence systems that are just I mean this is no longer just a going
from horse and buggy to the car it feels like a very different kind of shift in what it
means to be a living entity on earth are you afraid are you excited of this world.
I'm afraid if there's a lot more so I think maybe we'll need to think about if we truly
get there just thinking of limited resources like you know humanity clearly hit some limits
and then there's some balance hopefully that biologically the planet is imposing and we
should actually try to get better at this as we know there's there's quite a few issues
with having too many people coexisting in a resource limited way.
So for digital entities is an interesting question I think such a limit maybe should
exist but maybe it's going to be imposed by energy availability because this also consumes
energy in fact most systems are more inefficient than we are in terms of energy required.
But definitely I think as a society we'll need to just work together to find what would
be reasonable in terms of growth or how we coexist if that is to happen.
I am very excited about obviously the aspects of automation that make people that obviously
don't have access to certain resources or knowledge for them to have those that access
I think those are the applications in a way that I'm most exciting to see and to personally
work towards.
Yeah there's going to be significant improvements in productivity and the quality of life across
the whole population which is very interesting but I'm looking even far beyond us becoming
a multi-planetary species and just as a quick bet last question do you think as humans become
multi-planetary species go outside our solar system all that kind of stuff do you think
there will be more humans or more robots in that future world?
So will humans be the quirky intelligent being of the past or is there something deeply fundamental
to human intelligence that's truly special where we will be part of those other planets
not just AI systems?
I think we're all excited to build AGI to empower or make us more powerful as human
species not to say there might be some hybridization I mean this is obviously speculation but there
are companies also trying to the same way medicine is making us better maybe there are
other things that are yet to happen on that but if the ratio is not at most one to one
I would not be happy so I would hope that we are part of the equation but maybe there's
maybe a one to one ratio feels like possible constructive and so on but it would not be
good to have a misbalance at least from my core beliefs and the why I'm doing what I'm
doing when I go to work and I research what I research.
So this is how I know you're human this is how you've passed the Turing test and you
are one of the special humans Oriol it's a huge honor that you would talk with me and
I hope we get the chance to speak again maybe once before the singularity once after and
see how our view of the world changes thank you again for talking today thank you for
the amazing work you do you're shining example of a researcher and a human being in this
community.
Thanks a lot Lex yeah looking forward to before the singularity certainly.
And maybe after.
Thanks for listening to this conversation with Oriol Vinialis to support this podcast
please check out our sponsors in the description and now let me leave you with some words
from Alan Turing those who can imagine anything can create the impossible thank you for listening
and hope to see you next time.