0
SPEAKER_01
Welcome to the Artificial Intelligence Podcast.
0
SPEAKER_01
My name is Lex Friedman.
0
SPEAKER_01
I m a research scientist at MIT.
0
SPEAKER_01
If you enjoy this podcast, please rate it on iTunes or your podcast provider of choice, or simply connect with me on Twitter and other social networks at LexFriedman, spelled F-R-I-D.
0
SPEAKER_01
Today is a conversation with Yoshua Bengio.
0
SPEAKER_01
Along with Jeff Hinton and Yann LeCun, he s considered one of the three people most responsible for the advancement of deep learning during the 1990s and the 2000s and now.
0
SPEAKER_01
Cited 139,000 times, he has been integral to some of the biggest breakthroughs in AI over the past three decades.
0
SPEAKER_01
What difference between biological neural networks and artificial neural networks is most mysterious, captivating, and profound for you?
0
SPEAKER_00
First of all, there s so much we don t know about biological neural networks, and that s very mysterious and captivating because maybe it holds the key to improving artificial neural networks.
0
SPEAKER_00
One of the things I studied
0
SPEAKER_00
recently, something that we don t know how biological neural networks do, but would be really useful for artificial ones, is the ability to do credit assignment through very long time spans.
0
SPEAKER_00
There are things that
0
SPEAKER_00
we can, in principle, do with artificial neural nets, but it s not very convenient and it s not biologically plausible.
0
SPEAKER_00
And this mismatch, I think, this kind of mismatch may be an interesting thing to study to, A, understand better how brains might do these things, because we don t have good corresponding theories with artificial neural nets, and B,
0
SPEAKER_00
maybe provide new ideas that we could explore about things that brain do differently and that we could incorporate in artificial neural nets.
0
SPEAKER_01
So let s break credit assignment up a little bit.
0
SPEAKER_01
It s a beautifully technical term, but it could incorporate so many things.
0
SPEAKER_01
So is it more on the RNN memory side, thinking like that, or is it something about knowledge, building up common sense knowledge over time, or is it more in the reinforcement learning sense that you re picking up rewards over time for a particular, to achieve a certain kind of goal?
0
SPEAKER_00
I was thinking more about the first two meanings, whereby we store all kinds of memories, episodic memories in our brain, which we can access later in order to help us both infer causes of things that we are observing now,
0
SPEAKER_00
and assign credit to decisions or interpretations we came up with a while ago when those memories were stored.
0
SPEAKER_00
And then we can change the way we would have reacted or interpreted things in the past.
0
SPEAKER_00
And now that s credit assignment used for learning.
0
SPEAKER_01
So in which way do you think artificial neural networks, the current LSTM, the current architectures are not able to capture the, presumably you re thinking of very long term.
0
SPEAKER_00
Yes, so current, the current nets are doing a fairly good jobs for sequences with dozens or say hundreds of timestamps.
0
SPEAKER_00
And then it gets sort of harder and harder, and depending on what you have to remember and so on, as you consider longer durations.
0
SPEAKER_00
Whereas humans seem to be able to do credit assignment through essentially arbitrary times, like I could remember something I did last year, and then now because I see some new evidence, I m gonna change my mind about the way I was thinking last year, and hopefully not do the same mistake again.
0
SPEAKER_01
I think a big part of that is probably forgetting.
0
SPEAKER_01
You re only remembering the really important things, so it s very efficient forgetting.
0
SPEAKER_00
Yes, so there s a selection of what we remember, and I think there are really cool connection to higher level cognition here regarding consciousness, deciding, and emotions, so deciding what comes to consciousness and what gets stored in memory, which are not trivial either.
0
SPEAKER_01
So you ve been at the forefront there all along, showing some of the amazing things that neural networks, deep neural networks can do in the field of artificial intelligence, just broadly, in all kinds of applications.
0
SPEAKER_01
But we can talk about that forever, but what, in your view, because we re thinking towards the future, is the weakest aspect of the way deep neural networks represent the world?
0
SPEAKER_01
What is, in your view, is missing?
0
SPEAKER_00
So, current state-of-the-art neural nets trained on large quantities of images or texts
0
SPEAKER_00
have some level of understanding of what explains those data sets, but it s very basic, it s very low level, and it s not nearly as robust and abstract and general as our understanding.
0
SPEAKER_00
Okay, so that doesn t tell us how to fix things, but I think it encourages us to think about how we can maybe train our neural nets differently so that they would focus, for example, on causal explanation, something that we don t do currently with neural net training.
0
SPEAKER_00
Also, one thing I ll talk about in my talk this afternoon is instead of learning separately from images and videos on one hand and from text on the other hand, we need to do a better job of jointly learning about language and about the world to which it refers so that both sides can help each other.
0
SPEAKER_00
We need to have good world models in our neural nets for them to really understand sentences which talk about what s going on in the world.
0
SPEAKER_00
And I think we need language input to help provide clues about what high level concepts like semantic concepts should be represented at the top levels of these neural nets.
0
SPEAKER_00
In fact, there is evidence that the purely unsupervised learning of representations doesn t give rise to high level representations that are as powerful as the ones we re getting from supervised learning.
0
SPEAKER_00
And so the clues we re getting just with the labels, not even sentences, is already very powerful.
0
SPEAKER_01
Do you think that s an architecture challenge, or is it a data set challenge?
0
SPEAKER_01
Neither.
0
SPEAKER_01
I m tempted to just end it there, but can you elaborate slightly?
0
SPEAKER_00
Ah.
0
SPEAKER_00
Of course, data sets and architectures are something you want to always play with, but I think the crucial thing is more the training objectives, the training frameworks.
0
SPEAKER_00
For example, going from passive observation of data to more
0
SPEAKER_00
active agents which learn by intervening in the world, the relationships between causes and effects, the sort of objective functions which could be important to allow the highest level explanations to rise from the learning,
0
SPEAKER_00
which I don t think we have now, the kinds of objective functions which could be used to reward exploration, the right kind of exploration.
0
SPEAKER_00
So these kinds of questions are neither in the data set nor in the architecture, but more in how we learn under what objectives and so on.
0
SPEAKER_01
Yeah, I ve heard you mention in several contexts the idea of the way children learn, they interact with objects in the world, and it seems fascinating because in some sense, except with some cases in reinforcement learning, that idea
0
SPEAKER_01
is not part of the learning process in artificial neural networks.
0
SPEAKER_01
It s almost like, do you envision something like an objective function saying, you know what, if you poke this object in this kind of way, it would be really helpful for me to further learn.
0
SPEAKER_00
Sort of almost guiding some aspect of learning.
0
SPEAKER_00
So I was talking to Rebecca Sachs just an hour ago,
0
SPEAKER_00
And she was talking about lots and lots of evidence from infants seem to clearly pick what interests them in a directed way.
0
SPEAKER_00
And so they re not passive learners.
0
SPEAKER_00
They focus their attention on aspects of the world which are most interesting, surprising in a non-trivial way that makes them change
0
SPEAKER_00
their theories of the world.
0
SPEAKER_01
So, that s a fascinating view of the future progress, but on a more maybe boring question, do you think going deeper and larger, so do you think just increasing the size of the things that have been increasing a lot in the past few years will also make significant progress?
0
SPEAKER_01
Some of the representational issues that you mentioned, they re kind of shallow in some sense.
0
SPEAKER_00
Understanding prior.
0
SPEAKER_00
In the sense of abstraction.
0
SPEAKER_01
In the sense of abstraction.
0
SPEAKER_00
They re not getting some, like you said.
0
SPEAKER_00
I don t think that having more depth in the network in the sense of instead of 100 layers, we have 10,000 is going to solve our problem.
0
SPEAKER_00
You don t think so?
0
No.
0
SPEAKER_01
Is that obvious to you?
0
SPEAKER_00
Yes.
0
SPEAKER_00
What is clear to me is that engineers and companies and labs and grad students will continue to tune architectures and explore all kinds of tweaks to make the current state of the art ever slightly better.
0
SPEAKER_00
But I don t think that s going to be nearly enough.
0
SPEAKER_00
I think we need some fairly drastic changes in the way that we are considering learning.
0
SPEAKER_00
to achieve the goal that these learners actually understand in a deep way the environment in which they are, you know, observing and acting.
0
SPEAKER_01
But I guess I was trying to ask a question that s more interesting than just more layers.
0
SPEAKER_01
It s basically, once you figure out a way to learn through interacting, how many parameters does it take to store that information?
0
SPEAKER_01
So I think our brain is quite bigger than most neural networks.
0
SPEAKER_00
Right, right.
0
SPEAKER_00
Oh, I see what you mean.
0
SPEAKER_00
Oh, I m with you there.
0
SPEAKER_00
So I agree that in order to build neural nets with the kind of broad knowledge of the world that typical adult humans have, probably the kind of competing power we have now is going to be insufficient.
0
SPEAKER_00
So the good news is there are hardware companies building neural net chips and so it s going to get better.
0
SPEAKER_00
However, the good news in a way, which is also a bad news, is that even our state-of-the-art deep learning methods fail to learn models that understand even very simple environments like some grid worlds that we have built.
0
SPEAKER_00
Even these fairly simple environments, I mean, of course, if you train them with enough examples, eventually they get it.
0
SPEAKER_00
But it s just like instead of what humans might need just dozens of examples, these things will need millions for very, very, very simple tasks.
0
SPEAKER_00
And so I think there s an opportunity for academics who don t have the kind of computing power that say Google has to do really important and exciting research to advance the state of the art in training frameworks, learning models, agent learning in even simple environments that are synthetic, that seem trivial, but yet current machine learning fails on.
0
SPEAKER_01
We talked about priors.
0
SPEAKER_01
and common sense knowledge.
0
SPEAKER_01
It seems like we humans take a lot of knowledge for granted.
0
SPEAKER_01
So what s your view of these priors of forming this broad view of the world, this accumulation of information, and how we can teach neural networks or learning systems to pick that knowledge up?
0
SPEAKER_01
So knowledge, you know, for a while, the artificial intelligence was maybe in the 80s, like, there s a time where knowledge representation, knowledge acquisition, expert systems, I mean, the symbolic AI was a view, was an interesting problem set to solve, and it was kind of put on hold a little bit, it seems like.
0
SPEAKER_01
Because it doesn t work.
0
SPEAKER_01
It doesn t work, that s right.
0
SPEAKER_01
That s right.
0
SPEAKER_01
But the goals of that- Remain important.
0
SPEAKER_01
Yes, remain important.
0
SPEAKER_01
And how do you think those goals can be addressed?
0
SPEAKER_00
Right, so first of all, I believe that one reason why the classical expert systems approach failed is because a lot of the knowledge we have, so you talked about common sense, intuition,
0
SPEAKER_00
There s a lot of knowledge like this, which is not consciously accessible.
0
SPEAKER_00
There are lots of decisions we re taking that we can t really explain, even if sometimes we make up a story.
0
SPEAKER_00
And that knowledge is also necessary for machines to take good decisions.
0
SPEAKER_00
And that knowledge is hard to codify in expert systems, rule-based systems, and classical AI formalism.
0
SPEAKER_00
And there are other issues, of course, with the old AI, like not really good ways of handling uncertainty.
0
SPEAKER_00
I would say something more subtle, which we understand better now, but I think still isn t enough in the minds of people.
0
SPEAKER_00
There s something really powerful that comes from distributed representations, the thing that really makes neural nets work so well.
0
SPEAKER_00
And it s hard to replicate that kind of power in a symbolic world.
0
SPEAKER_00
The knowledge in expert systems and so on is nicely decomposed into like a bunch of rules.
0
SPEAKER_00
Whereas if you think about a neural net, it s the opposite.
0
SPEAKER_00
You have this big blob of parameters which work intensely together to represent everything the network knows.
0
SPEAKER_00
And it s not sufficiently factorized.
0
SPEAKER_00
And so I think this is one of the weaknesses of current neural nets.
0
SPEAKER_00
that we have to take lessons from classical AI in order to bring in another kind of compositionality, which is common in language, for example, and in these rules, but that isn t so native to neural nets.
0
SPEAKER_01
And on that line of thinking, disentangled representations.
0
SPEAKER_01
Yes.
0
SPEAKER_00
So let me connect with disentangled representations, if you might, if you don t mind.
0
SPEAKER_00
So for many years, I ve thought, and I still believe, that it s really important that we come up with learning algorithms, either unsupervised or supervised, or reinforcement, whatever, that build representations in which the important factors, hopefully causal factors, are nicely separated and easy to pick up from the representation.
0
SPEAKER_00
So that s the idea of disentangled representations.
0
SPEAKER_00
It says, transform the data into a space where everything becomes easy.
0
SPEAKER_00
We can maybe just learn with linear models about the things we care about.
0
SPEAKER_00
And I still think this is important, but I think this is missing out on a very important ingredient, which classical AI systems can remind us of.
0
SPEAKER_00
So let s say we have these disentangled representations.
0
SPEAKER_00
You still need to learn about the relationships between the variables, those high-level semantic variables.
0
SPEAKER_00
They re not going to be independent.
0
SPEAKER_00
I mean, this is like too much of an assumption.
0
SPEAKER_00
They re going to have some interesting relationships that allow to predict things in the future, to explain what happened in the past.
0
SPEAKER_00
The kind of knowledge about those relationships in a classical AI system is encoded in the rules.
0
SPEAKER_00
Like a rule is just like a little piece of knowledge that says, oh, I have these two, three, four variables that are linked in this interesting way.
0
SPEAKER_00
Then I can say something about one or two of them given a couple of others, right?
0
SPEAKER_00
In addition to disentangling the elements of the representation, which are like the variables in a rule-based system, you also need to disentangle
0
SPEAKER_00
the mechanisms that relate those variables to each other.
0
SPEAKER_00
So like the rules.
0
SPEAKER_00
So the rules are neatly separated.
0
SPEAKER_00
Each rule is living on its own.
0
SPEAKER_00
And when I change a rule because I m learning, it doesn t need to break other rules.
0
SPEAKER_00
Whereas current neural nets, for example, are very sensitive to what s called catastrophic forgetting, where after I ve learned some things and then I learn new things, they can destroy the old things that I
1
SPEAKER_00
had learned.
1
SPEAKER_00
If the knowledge was better factorized and separated, disentangled, then you would avoid a lot of that.
1
SPEAKER_00
Now you can t do this in the sensory domain, but
1
SPEAKER_00
My idea is that when you project the data in the right semantic space, it becomes possible to now represent this extra knowledge beyond the transformation from input to representations, which is how representations act on each other and predict the future and so on.
1
SPEAKER_00
in a way that can be neatly disentangled.
1
SPEAKER_00
So now it s the rules that are disentangled from each other and not just the variables that are disentangled from each other.
1
SPEAKER_01
And you draw a distinction between semantic space and pixel.
1
SPEAKER_01
Does there need to be an architectural difference?
1
SPEAKER_00
Well, yeah.
1
SPEAKER_00
So there s the sensory space like pixels, where everything is entangled.
1
SPEAKER_00
the information, like the variables are completely interdependent in very complicated ways.
1
SPEAKER_00
And also computation, like it s not just variables, it s also how they are related to each other is all intertwined.
1
SPEAKER_00
But I m hypothesizing that in the right high-level representation space, both
1
SPEAKER_00
the variables and how they relate to each other can be disentangled, and that will provide a lot of generalization power.
1
SPEAKER_00
Generalization power.
1
SPEAKER_01
Yes.
1
SPEAKER_01
Distribution of the test set is assumed to be the same as the distribution of the training set.
1
SPEAKER_00
Right.
1
SPEAKER_00
This is where current machine learning is too weak.
1
SPEAKER_00
It doesn t tell us anything.
1
SPEAKER_00
It s not able to tell us anything about how our neural nets, say, are going to generalize to a new distribution.
1
SPEAKER_00
And people may think, well, but there s nothing we can say if we don t know what the new distribution will be.
1
SPEAKER_00
The truth is, humans are able to generalize new distributions.
1
SPEAKER_00
How are we able to do that?
1
SPEAKER_00
Because there is something, these new distributions, even though they could look very different from the twin distributions,
1
SPEAKER_00
They have things in common.
1
SPEAKER_00
So let me give you a concrete example.
1
SPEAKER_00
You read a science fiction novel, the science fiction novel, maybe, you know, brings you in some other planet where things look very different on the surface, but it s still the same laws of physics.
1
SPEAKER_00
Right.
1
SPEAKER_00
And so you can read the book and you understand what s going on.
1
SPEAKER_00
So the distribution is very different, but because you can transport a lot of the knowledge you had from earth about the underlying cause and effect relationships and physical mechanisms and all that, and maybe even social interactions, you can now make sense of what is going on on this planet where like visually, for example, things are totally different.
1
SPEAKER_01
Taking that analogy further and distorting it, let s enter a science fiction world of, say, Space Odyssey 2001 with Hal.
1
SPEAKER_01
Or maybe, which is probably one of my favorite AI movies.
1
SPEAKER_01
Me too.
1
SPEAKER_01
And then there s another one that a lot of people love that may be a little bit outside of the AI community is Ex Machina.
1
SPEAKER_01
I don t know if you ve seen it.
1
SPEAKER_01
By the way, what are your views on that movie?
1
SPEAKER_01
Are you able to enjoy it?
1
SPEAKER_01
So there are things I like and things I hate.
1
SPEAKER_01
You could talk about that in the context of a question I want to ask, which is, there s quite a large community of people from different backgrounds, often outside of AI, who are concerned about existential threat of artificial intelligence.
1
SPEAKER_01
And you ve seen this community develop over time, you ve seen, you have a perspective.
1
SPEAKER_01
So what do you think is the best way to talk about AI safety, to think about it, to have discourse about it within AI community and outside, and grounded in the fact that Ex Machina is one of the main sources of information for the general public about AI?
1
SPEAKER_00
So I think you re putting it right.
1
SPEAKER_00
There s a big difference between
1
SPEAKER_00
the sort of discussion we ought to have within the AI community and the sort of discussion that really matter in the general public.
1
SPEAKER_00
So I think that the picture of Terminator and, you know, AI loose and killing people and super intelligence that s going to destroy us, whatever we try, isn t really so useful for the public discussion because
1
SPEAKER_00
For the public discussion, the things I believe really matter are the short term and medium term, very likely negative impacts of AI on society, whether it s from security, like, you know, Big Brother scenarios with face recognition or killer robots, or the impact on the job market, or concentration of power and discrimination, all kinds of
1
SPEAKER_00
social issues, which could actually, some of them could really threaten democracy, for example.
1
SPEAKER_01
Just to clarify, when you said killer robots, you mean autonomous weapon systems.
1
SPEAKER_00
Yes, I don t mean, no, that s right.
1
SPEAKER_00
So I think these short and medium term concerns should be important parts of the public debate.
1
SPEAKER_00
Now, existential risk, for me, is a very unlikely consideration,
1
SPEAKER_00
still worth academic investigation in the same way that you could say, should we study what could happen if a meteorite, you know, came to earth and destroyed it?
1
SPEAKER_00
So I think it s very unlikely that this is going to happen in or happen in a reasonable future.
1
SPEAKER_00
It s, it s very,
1
SPEAKER_00
The sort of scenario of an AI getting loose goes against my understanding of at least current machine learning and current neural nets and so on.
1
SPEAKER_00
It s not plausible to me.
1
SPEAKER_00
But of course, I don t have a crystal ball and who knows what AI will be in 50 years from now.
1
SPEAKER_00
So I think it is worth that scientists study those problems.
1
SPEAKER_00
It s just not a pressing question as far as I m concerned.
1
SPEAKER_01
So, before I continue down that line, I have a few questions there, but what do you like and not like about Ex Machina as a movie?
1
SPEAKER_01
Because I actually watched it for the second time and enjoyed it.
1
SPEAKER_01
I hated it the first time, and I enjoyed it quite a bit more the second time when I sort of learned to accept certain pieces of it, see it as a concept movie.
1
SPEAKER_01
What was your experience?
1
SPEAKER_01
What were your thoughts?
1
SPEAKER_00
So the negative is the picture it paints of science is totally wrong.
1
SPEAKER_00
Science in general and AI in particular.
1
SPEAKER_00
Science is not happening in some hidden place by some really smart guy.
1
SPEAKER_00
One person.
1
SPEAKER_00
One person.
1
SPEAKER_00
This is totally unrealistic.
1
SPEAKER_00
This is not how it happens.
1
SPEAKER_00
Even a team of people in some isolated place will not make it.
1
SPEAKER_00
Science moves by small steps thanks to the collaboration and community of a large number of people interacting.
1
SPEAKER_00
And all the scientists who are expert in their field kind of know what is going on, even in the industrial labs.
1
SPEAKER_00
It s information flows and leaks and so on.
1
SPEAKER_00
And the spirit of it is very different from the way science is painted in this movie.
1
SPEAKER_01
Yeah, let me ask on that point.
1
SPEAKER_01
It s been the case to this point that kind of even if the research happens inside Google or Facebook, inside companies, it still kind of comes out, ideas come out.
1
SPEAKER_01
Do you think that will always be the case with AI?
1
SPEAKER_01
Is it possible to bottle ideas to the point where there s a set of breakthroughs that go completely undiscovered by the general research community?
1
SPEAKER_01
Do you think that s even possible?
1
SPEAKER_00
It s possible, but it s unlikely.
1
SPEAKER_00
Unlikely.
1
SPEAKER_00
It s not how it is done now.
1
SPEAKER_00
It s not how I can foresee it in the foreseeable future.
1
SPEAKER_00
But of course I don t have a crystal ball and so who knows?
1
SPEAKER_00
This is science fiction after all.
1
SPEAKER_00
But usually science- I think it s ominous that the lights went off during that discussion.
1
SPEAKER_00
So the problem, again, there s a, you know, one thing is the movie and you could imagine all kinds of science fiction.
1
SPEAKER_00
The problem with, for me, maybe similar to the question about existential risk,
1
SPEAKER_00
is that this kind of movie paints such a wrong picture of what is actual, you know, the actual science and how it s going on that it can have unfortunate effects on people s understanding of current science.
1
SPEAKER_00
And so that s kind of sad.
1
SPEAKER_00
There s an important principle in research, which is diversity.
1
SPEAKER_00
So in other words,
1
SPEAKER_00
Research is exploration.
1
SPEAKER_00
Research is exploration in the space of ideas.
1
SPEAKER_00
And different people will focus on different directions.
1
SPEAKER_00
And this is not just good, it s essential.
1
SPEAKER_00
So I m totally fine with people exploring directions that are contrary to mine or look orthogonal to mine.
1
SPEAKER_00
I am more than fine, I think it s important.
1
SPEAKER_00
I and my friends don t claim we have universal truth, especially about what will happen in the future.
1
SPEAKER_00
Now that being said, we have our intuitions and then we act accordingly, according to where we think we can be most useful and where society has the most to gain or to lose.
1
SPEAKER_00
we should have those debates and not end up in a society where there s only one voice and one way of thinking and research money is spread out, so.
1
SPEAKER_01
So disagreement is a sign of good research, good science, so.
1
SPEAKER_01
Yes.
1
SPEAKER_01
The idea of bias in the human sense of bias.
1
SPEAKER_01
Yeah.
1
SPEAKER_01
How do you think about instilling in machine learning something that s aligned with human values in terms of bias?
1
SPEAKER_01
We, intuitively as human beings, have a concept of what bias means, of what a fundamental respect for other human beings means, but how do we instill that into machine learning systems, do you think?
1
SPEAKER_00
So I think there are short-term things that are already happening and then there are long-term things that we need to do.
1
SPEAKER_00
In the short term, there are techniques that have been proposed and I think will continue to be improved and maybe alternatives will come up to take datasets
1
SPEAKER_00
in which we know there is bias, we can measure it.
1
SPEAKER_00
Pretty much any data set where humans are being observed taking decisions will have some sort of bias, discrimination against particular groups and so on.
1
SPEAKER_00
And we can use machine learning techniques to try to build predictors classifiers that are going to be less biased.
1
SPEAKER_00
We can do it, for example, using adversarial methods to make our systems less sensitive to these variables we should not be sensitive to.
1
SPEAKER_00
So these are clear, well-defined ways of trying to address the problem.
1
SPEAKER_00
Maybe they have weaknesses, and more research is needed, and so on.
1
SPEAKER_00
But I think, in fact, they are sufficiently mature that governments should start regulating companies where it matters, say, like insurance companies, so that they use those techniques.
1
SPEAKER_00
Because those techniques will probably reduce the bias.
1
SPEAKER_00
But at a cost, for example, maybe their predictions will be less accurate.
1
SPEAKER_00
And so companies will not do it until you force them.
1
SPEAKER_00
Alright, so this is short term.
1
SPEAKER_00
Long term, I m really interested in thinking of how we can instill moral values into computers.
1
SPEAKER_00
Obviously, this is not something we ll achieve in the next five or 10 years.
1
SPEAKER_00
how can we, you know, there s already work in detecting emotions, for example, in images and sounds and texts, and also studying how different agents interacting
1
SPEAKER_00
in different ways may correspond to patterns of, say, injustice, which could trigger anger.
1
SPEAKER_00
So these are things we can do in the medium term and eventually train computers to model, for example, how humans react emotionally
1
SPEAKER_00
I would say the simplest thing is unfair situations which trigger anger.
1
SPEAKER_00
This is one of the most basic emotions that we share with other animals.
1
SPEAKER_00
I think it s quite feasible within the next few years that we can build systems that can detect these kinds of things to the extent, unfortunately, that they understand enough about the world around us, which is a long time away.
1
SPEAKER_00
But maybe we can initially do this in virtual environments.
1
SPEAKER_00
So you can imagine like a video game.
1
SPEAKER_00
where agents interact in some ways and then some situations trigger an emotion, I think we could train machines to detect those situations and predict that the particular emotion will likely be felt if a human was playing one of the characters.
1
SPEAKER_01
You have shown excitement and done a lot of excellent work with unsupervised learning.
1
SPEAKER_01
But there s been a lot of success on the supervised learning side.
1
SPEAKER_01
And one of the things I m really passionate about is how humans and robots work together.
1
SPEAKER_01
And in the context of supervised learning, that means the process of annotation.
1
SPEAKER_01
Do you think about the problem of annotation put in a more interesting way as humans teaching machines?
1
SPEAKER_00
Yes.
1
SPEAKER_00
I think it s an important subject.
1
SPEAKER_00
Reducing it to annotation may be useful for somebody building a system tomorrow, but longer term, the process of teaching
1
SPEAKER_00
I think it s something that deserves a lot more attention from the machine learning community.
1
SPEAKER_00
So there are people who ve coined the term machine teaching.
1
SPEAKER_00
So what are good strategies for teaching a learning agent?
1
SPEAKER_00
And can we design, train a system that is going to be a good teacher?
1
SPEAKER_00
So in my group, we have a project
1
SPEAKER_00
called a BBI or a BBI game where there is a game or a scenario where there s a learning agent and a teaching agent.
1
SPEAKER_00
Presumably the teaching agent would eventually be a human, but we re not there yet.
1
SPEAKER_00
And the role of the teacher is to use its knowledge of the environment, which it can acquire using whatever way, brute force.
1
SPEAKER_00
to help the learner learn as quickly as possible.
1
SPEAKER_00
So the learner is gonna try to learn by itself, maybe using some exploration and whatever, but the teacher can choose, can have an influence on the interaction with the learner so as to guide the learner, maybe teach it the things that the learner has most trouble with or just add the boundary between what it knows and doesn t know and so on.
1
SPEAKER_00
So there s a tradition of these kind of ideas from other fields, like tutorial systems, for example, and AI.
1
SPEAKER_00
And of course, people in the humanities have been thinking about these questions.
1
SPEAKER_00
But I think it s time that machine learning people look at this, because in the future, we ll have more and more
1
SPEAKER_00
human-machine interaction with a human-in-a-loop, and I think understanding how to make this work better.
1
SPEAKER_01
All the problems around that are very interesting and not sufficiently addressed.
1
SPEAKER_01
You ve done a lot of work with language, too.
1
SPEAKER_01
What aspect of the traditionally formulated Turing test, a test of natural language understanding and generation, in your eyes is the most difficult?
1
SPEAKER_01
of conversation, what in your eyes is the hardest part of conversation to solve for machines?
1
SPEAKER_00
So I would say it s everything having to do with the non-linguistic knowledge, which implicitly you need in order to make sense of sentences.
1
SPEAKER_00
Things like the Winograd schema, so these sentences that are semantically ambiguous.
1
SPEAKER_00
In other words, you need to understand enough about the world in order to really interpret properly those sentences.
1
SPEAKER_00
I think these are interesting challenges for machine learning because they point in the direction of building systems that both understand how the world works and this causal relationships in the world and associate that knowledge with how to express it in language, either for reading or writing.
1
SPEAKER_00
You speak French?
1
SPEAKER_00
Yes, it s my mother tongue.
1
SPEAKER_01
It s one of the romance languages.
1
SPEAKER_01
Do you think passing the Turing test and all the underlying challenges we just mentioned depend on language?
1
SPEAKER_01
Do you think it might be easier in French than it is in English?
1
SPEAKER_01
Or is it independent of language?
1
SPEAKER_00
I think it s independent of language.
1
SPEAKER_00
I would like to build systems that can use the same principles, the same learning mechanisms to learn from human agents whatever their language.
1
SPEAKER_01
Well, certainly us humans can talk more beautifully and smoothly in poetry.
1
SPEAKER_01
So I m Russian originally.
1
SPEAKER_01
I know poetry in Russian is maybe easier to convey complex ideas than it is in English.
1
SPEAKER_01
But maybe I m showing my bias, and some people could say that about French.
1
SPEAKER_01
But of course, the goal, ultimately, is our human brain is able to utilize any kind of those languages to use them as tools to convey meaning.
1
SPEAKER_00
Yeah.
1
SPEAKER_00
Of course, there are differences between languages, and maybe some are slightly better at some things.
1
SPEAKER_00
But in the grand scheme of things, where we re trying to understand how the brain works, and language, and so on, I think these differences are minute.
1
SPEAKER_01
So you ve lived, perhaps, through an AI winter of sorts.
1
SPEAKER_01
Yes.
1
SPEAKER_01
How did you stay warm and continuing your research?
1
SPEAKER_01
With friends.
1
SPEAKER_01
Stay warm with friends.
1
SPEAKER_01
With friends.
1
SPEAKER_01
Okay, so it s important to have friends.
2
SPEAKER_01
What have you learned from the experience?
2
SPEAKER_01
Listen to your inner voice.
2
SPEAKER_01
Don t, you know, be trying to just please the crowds and the fashion.
2
SPEAKER_01
And if you have a strong intuition about something that is not contradicted by actual evidence, go for it.
2
SPEAKER_00
I mean, it could be contradicted by people.
2
SPEAKER_00
But not your own instinct of based on everything you ve learned.
2
SPEAKER_01
So of course, you have to adapt your beliefs when your experiments contradict those beliefs.
2
SPEAKER_01
But you have to stick to your beliefs otherwise.
2
SPEAKER_01
It s what allowed me to go through those years.
2
SPEAKER_01
It s what allowed me to persist in directions that took time
2
SPEAKER_01
whatever other people think, took time to mature and bring fruits.
2
SPEAKER_00
So history of AI is marked with these, of course it s marked with technical breakthroughs, but it s also marked with these seminal events that capture the imagination of the community.
2
SPEAKER_00
Most recent, I would say, AlphaGo being the world champion human Go player was one of those moments.
2
SPEAKER_00
What do you think the next such moment might be?
2
SPEAKER_01
Okay, so first of all, I think that these so-called seminal events are overrated.
2
SPEAKER_01
As I said, science really moves by small steps.
2
SPEAKER_01
Now, what happens is
2
SPEAKER_01
you make one more small step and it s like the drop that fills the bucket and then you have drastic consequences because now you re able to do something you were not able to do before.
2
SPEAKER_01
Or now, say, the cost of building some device or solving a problem becomes cheaper than what existed and you have a new market that opens up.
2
SPEAKER_01
So especially in the world of commerce and applications,
2
SPEAKER_01
impact of a small scientific progress could be huge.
2
SPEAKER_01
But in the science itself, I think it s very, very gradual and... Where are these steps being taken now?
2
SPEAKER_01
So there s unsupervised learning?
2
SPEAKER_01
Right, so if I look at one trend that I like in my community, so for example, at Milan, my institute, what are the two hottest topics?
2
SPEAKER_01
GANs and reinforcement learning.
2
SPEAKER_01
Even though in Montreal in particular, reinforcement learning was something pretty much absent just two or three years ago.
2
SPEAKER_01
So there s really a big interest from students and there s a big interest from people like me.
2
SPEAKER_01
So I would say this is something where we re going to see more progress, even though it hasn t yet provided much in terms of actual industrial fallout.
2
SPEAKER_01
Like even though there s AlphaGo, there s no, like Google is not making money on this right now.
2
SPEAKER_01
But I think over the longterm, this is really, really important for many reasons.
2
SPEAKER_01
So in other words, I would say reinforcement learning may be more generally agent learning, because it doesn t have to be with rewards.
2
SPEAKER_01
It could be in all kinds of ways that an agent is learning about its environment.
2
SPEAKER_00
Now, reinforcement learning you re excited about, do you think GANs could provide something, some moment?
2
SPEAKER_01
Well, GANs or other generative models, I believe, will be crucial ingredients in building agents that can understand the world.
2
SPEAKER_01
A lot of the successes in reinforcement learning in the past has been with policy gradient, where you just learn a policy, you don t actually learn a model of the world.
2
SPEAKER_01
But there are lots of issues with that.
2
SPEAKER_01
And we don t know how to do model-based RL right now.
2
SPEAKER_01
But I think this is where we have to go.
2
SPEAKER_01
in order to build models that can generalize faster and better, like to new distributions, that capture, to some extent at least, the underlying causal mechanisms in the world.
2
SPEAKER_00
Last question.
2
SPEAKER_00
What made you fall in love with artificial intelligence?
2
SPEAKER_00
If you look back, what was the first moment in your life when you were fascinated by either the human mind or the artificial mind?
2
SPEAKER_01
You know, when I was an adolescent, I was reading a lot, and then I started reading science fiction.
2
SPEAKER_01
There you go.
2
SPEAKER_01
That s it, that s where I got hooked.
2
SPEAKER_01
And then I had one of the first personal computers, and I got hooked in programming.
2
SPEAKER_01
And so, it just, you know.
2
SPEAKER_00
Start with fiction, and then make it a reality.
2
SPEAKER_00
That s right.
2
SPEAKER_00
Yoshua, thank you so much for talking to me.
2
SPEAKER_00
My pleasure.