The following is a conversation with Ian Goodfellow.

He's the author of the popular textbook on deep learning, simply titled, Deep Learning.

He coined the term of generative adversarial networks, otherwise known as GANs, and with his 2014 paper is responsible for launching the incredible growth of research and innovation in this subfield of deep learning.

He got his BS and MS at Stanford, his PhD at University of Montreal with Yoshua Bengio and Aaron Kervil.

He held several research positions including at OpenAI, Google Brain, and now at Apple as the Director of Machine Learning.

This recording happened while Ian was still at Google Brain, But we don't talk about anything specific to Google or any other organization.

This conversation is part of the Artificial Intelligence Podcast.

If you enjoy it, subscribe on YouTube, iTunes, or simply connect with me on Twitter at Lex Friedman, spelled F-R-I-D.

And now, here's my conversation with Ian Goodfellow.

You open your popular deep learning book with a Russian doll type diagram that shows deep learning as a subset of representation learning, which in turn is a subset of machine learning, and finally a subset of AI.

So this kind of implies that there may be limits to deep learning in the context of AI.

So what do you think is the current limits of deep learning, and are those limits something that we can overcome with time?

Yeah, I think 1 of the biggest limitations of deep learning is that right now it requires really a lot of data, especially labeled data.

There are some unsupervised and semi-supervised learning algorithms that can reduce the amount of labeled data you need, but they still require a lot of unlabeled data.

Reinforcement learning algorithms, they don't need labels, but they need really a lot of experiences.

As human beings, we don't learn to play Pong by failing at Pong 2000000 times.

So just getting the generalization ability better is 1 of the most important bottlenecks in the capability of the technology today.

And then I guess I'd also say deep learning is like a component of a bigger system.

So far, nobody is really proposing to have only what you'd call deep learning as the entire ingredient of intelligence.

You use deep learning as sub-modules of other systems like AlphaGo has a deep learning model that estimates the value function.

Most reinforcement learning algorithms have a deep learning module that estimates which action to take next, but you might have other components.

So you're basically building a function estimator.

Do you think it's possible, you said nobody's kind of been thinking about this so far, but do you think neural networks could be made to reason in the way symbolic systems did in the 80s and 90s to do more, create more like programs as opposed to functions?

Yeah, I think we already see that a little bit.

I already kind of think of neural nets as a kind of program.

I think of deep learning as basically learning programs that have more than 1 step.

So if you draw a flow chart or if you draw a TensorFlow graph describing your machine learning model, I think of the depth of that graph as describing the number of steps that run in sequence, and then the width of that graph is the number of steps that run in parallel.

Now it's been long enough that we've had deep learning working that it's a little bit silly to even discuss shallow learning anymore.

But back when I first got involved in AI, when we used machine learning, we were usually learning things like support vector machines.

You could have a lot of input features to the model, and you could multiply each feature by a different weight.

All those multiplications were done in parallel to each other.

I think what we got with deep learning was really the ability to have steps of a program that run in sequence.

And I think that we've actually started to see that what's important with deep learning is more the fact that we have a multi-step program, rather than the fact that we've learned a representation.

If you look at things like ResNets, for example, they take 1 particular kind of representation and they update it several times.

Back when deep learning first really took off in the academic world in 2006, when Jeff Hinton showed that you could train deep belief networks, Everybody who was interested in the idea thought of it as each layer learns a different level of abstraction.

That the first layer trained on images learns something like edges, and the second layer learns corners, and eventually you get these kind of grandmother-style units that recognize specific objects.

Today I think most people think of it more as a computer program where as you add more layers you can do more updates before you output your final number.

But I don't think anybody believes that layer 150 of the ResNet is a grandmother cell and layer 100 is contours or something like that.

OK, so you're not thinking of it as a singular representation that keeps building.

You think of it as a program, sort of almost like a state.

Representation is a state of understanding.

Yeah, I think of it as a program that makes several updates and arrives at better and better understandings, but it's not replacing the representation at each step, it's refining it.

And in some sense, that's a little bit like reasoning.

It's not reasoning in the form of deduction, but it's reasoning in the form of taking a thought and refining it and refining it carefully until it's good enough to use.

So do you think, and I hope you don't mind, we'll jump philosophical every once in a while, do you think of cognition, human cognition, or even consciousness as simply a result of this kind of sequential representation learning, do you think that can emerge?

Consciousness, it's really hard to even define what we mean by that.

I guess there's, consciousness is often defined as things like having self-awareness, and that's relatively easy to turn into something actionable for a computer scientist to reason about.

People also define consciousness in terms of having qualitative states of experience, like qualia.

There's all these philosophical problems like, could you imagine a zombie who does all the same information processing as a human, but doesn't really have the qualitative experiences that we have, that sort of thing, I have no idea how to formalize or turn it into a scientific question.

I don't know how you could run an experiment to tell whether a person is a zombie or not.

And similarly, I don't know how you could run an experiment to tell whether an advanced AI system had become conscious in the sense of qualia or not.

the more practical sense, like almost like self-attention, you think consciousness and cognition can in an impressive way emerge from current types of architectures that we think of as

Yeah, yeah, or if you think of consciousness in terms of self-awareness and just making plans based on the fact that the agent itself exists in the world, reinforcement learning algorithms are already more or less forced to model the agent's effect on the environment.

So that more limited version of consciousness is already something that we get limited versions of with reinforcement learning algorithms if they're trained well.

So the big question really is how you jump from limited to human level.

And whether it's possible, you know, Even just building common sense reasoning seems to be exceptionally difficult.

So if we scale things up, if we get much better on supervised learning, if we get better at labeling, if we get bigger data sets, more compute, Do you think we'll start to see really impressive things that go from limited to, you know, something echoes of human level cognition?

I'm optimistic about what can happen just with more computation and more data.

I do think it'll be important to get the right kind of data.

Today, most of the machine learning systems we train are mostly trained on 1 type of data for each model.

But the human brain, we get all of our different senses and we have many different experiences like riding a bike, driving a car, talking to people, reading.

I think when you get that kind of integrated data set, working with a machine learning model that can actually close the loop and interact, We may find that algorithms not so different from what we have today, learn really interesting things when you scale them up a lot and train them on a large amount of multimodal data.

So multimodal is really interesting, but within, like you're working adversarial examples, so selecting within modal, within 1 mode of data, selecting better what are the difficult cases from which you're most useful to learn from.

Oh yeah, like could we get a whole lot of mileage out of designing a model that's resistant to adversarial examples or something like that?

My thinking on that has evolved a lot over the last few years.

When I first started to really invest in studying adversarial examples, I was thinking of it mostly as adversarial examples reveal a big problem with machine learning, and we would like to close the gap between how machine learning models respond to adversarial examples and how humans respond.

After studying the problem more, I still think that adversarial examples are important.

I think of them now more of as a security liability than as an issue that necessarily shows there's something uniquely wrong with machine learning as opposed to humans.

Also, do you see them as a tool to improve the performance of the system?

Not on the security side, but literally just accuracy.

I do see them as a kind of tool on that side, but maybe not quite as much as I used to think.

We've started to find that there's a trade-off between accuracy on adversarial examples and accuracy on clean examples.

Back in 2014, when I did the first adversarially trained classifier that showed resistance to some kinds of adversarial examples.

It also got better at the clean data on MNIST.

And that's something we've replicated several times on MNIST, that when we train against weak adversarial examples, MNIST classifiers get more accurate.

So far, that hasn't really held up on other data sets and hasn't held up when we train against stronger adversaries.

It seems like when you confront a really strong adversary, you tend to have to give something up.

But it's such a compelling idea because it feels like that's how us humans learn,

We try to think of what would we screw up and then we make sure we fix that.

It's also in a lot of branches of engineering, you do a worst case analysis and make sure that your system will work in the worst case.

And then that guarantees that it'll work in all of the messy average cases that happen when you go out into a really randomized world.

Yeah, with driving with autonomous vehicles, there seems to be a desire to just look for, think adversarially, try to figure out how to mess up the system.

And if you can be robust to all those difficult cases, then you can, it's a hand wavy, empirical way to show your system is safe.

Today most adversarial example research isn't really focused on a particular use case, but there are a lot of different use cases where you'd like to make sure that the adversary can't interfere with the operation of your system.

Like in finance, if you have an algorithm making trades for you, people go to a lot of an effort to obfuscate their algorithm.

That's both to protect their IP, because you don't want to research and develop a profitable trading algorithm, then have somebody else capture the gains.

But it's at least partly because you don't want people to make adversarial examples that fool your algorithm into making bad trades.

Or I guess 1 area that's been popular in the academic literature is speech recognition.

If you use speech recognition to hear an audio waveform and then turn that into a command that a phone executes for you, you don't want a malicious adversary to be able to produce audio that gets interpreted as malicious commands, especially if a human in the room doesn't realize that something like that is happening.

In speech recognition, has there been much success in being able to create adversarial examples that fool the system?

I guess the first work that I'm aware of is a paper called Hidden Voice Commands that came out in 2016, I believe.

And they were able to show that they could make sounds that are not understandable by a human, but are recognized as the target phrase that the attacker wants the phone to recognize it as.

Since then, things have gotten a little bit better on the attacker side and worse on the defender side.

It's become possible to make sounds that sound like normal speech, but are actually interpreted as a different sentence than the human hears.

The level of perceptibility of the adversarial perturbation is still kind of high.

When you listen to the recording, it sounds like there's some noise in the background, just like rustling sounds.

But those rustling sounds are actually the adversarial perturbation that makes the phone hear a completely different sentence.

Peter Norvig mentioned that you're writing the deep learning chapter for the fourth edition of the Artificial Intelligence, the Modern Approach book.

So how do you even begin summarizing the field of deep learning in a chapter?

Well, In my case, I waited like a year before I actually wrote anything.

Even having written a full-length textbook before, it's still pretty intimidating to try to start writing just 1 chapter that covers everything.

1 thing that helped me make that plan was actually the experience of having written the full book before, and then watching how the field changed after the book came out.

I've realized there's a lot of topics that were maybe extraneous in the first book, and just seeing what stood the test of a few years of being published and what seems a little bit less important to have included now helped me pare down the topics I wanted to cover for the book.

It's also really nice now that the field is kind of stabilized to the point where some core ideas from the 1980s are still used today.

When I first started studying machine learning, almost everything from the 1980s had been rejected, and now some of it has come back.

So that stuff that's really stood the test of time is what I focused on putting into the book.

There's also, I guess, 2 different philosophies about how you might write a book.

1 philosophy is you try to write a reference that covers everything.

The other philosophy is you try to provide a high-level summary that gives people the language to understand a field and tells them what the most important concepts are.

The first deep learning book that I wrote with Joshua and Aaron was somewhere between the 2 philosophies, that it's trying to be both a reference and an introductory guide.

Writing this chapter for Russell and Norvig's book, I was able to focus more on just a concise introduction of the key concepts and the language you need to read about them more.

In a lot of cases, I actually just wrote paragraphs that said, here's a rapidly evolving area that you should pay attention to.

It's pointless to try to tell you what the latest and best version of a learn-to-learn model is.

I can point you to a paper that's recent right now, but there isn't a whole lot of a reason to delve into exactly what's going on with the latest learning-to-learn approach or the latest module produced by a learning-to-learn algorithm.

You should know that learning-to-learn is a thing and that it may very well be the source of the latest and greatest convolutional net or recurrent net module that you would want to use in your latest project.

But there isn't a lot of point in trying to summarize exactly which architecture and which learning approach got to which level of performance.

So you maybe focused more on the basics of the methodology, so from back propagation to feed forward to recurrent neural networks, convolutional, that kind of thing?

So if I were to ask you, I remember I took algorithms and data structures algorithms course.

I remember the professor asked, what is an algorithm?

And yelled at everybody in a good way that nobody was answering it correctly.

Everybody knew what the algorithm, it was a graduate course.

Everybody knew what an algorithm was, but they weren't able to answer it well.

So let me ask you, in that same spirit, what is deep learning?

I would say deep learning is any kind of machine learning that involves learning parameters of more than 1 consecutive step.

So that would mean shallow learning is things where you learn a lot of operations that happen in parallel.

You might have a system that makes multiple steps, Like you might have hand-designed feature extractors.

Deep learning is anything where you have multiple operations in sequence.

And that includes the things that are really popular today, like convolutional networks and recurrent networks.

But it also includes some of the things that have died out, like Bolton machines, where we weren't using back propagation.

Today, I hear a lot of people define deep learning as gradient descent applied to these differentiable functions.

And I think that's a legitimate usage of the term.

It's just different from the way that I use the term myself.

So what's an example of deep learning that is not gradient-descendant differentiable functions.

In your, I mean not specifically perhaps, but more even looking into the future, what's your thought about that space of approaches?

Yeah, so I tend to think of machine learning algorithms as decomposed into really 3 different pieces.

There's the model, which can be something like a neural net or a Boltzmann machine or a recurrent model.

That basically just describes how do you take data and how do you take parameters?

What function do you use to make a prediction given the data and the parameters.

Another piece of the learning algorithm is the optimization algorithm.

Not every algorithm can be really described in terms of optimization, but what's the algorithm for updating the parameters or updating whatever the state of the network is?

How do you actually represent the world as it comes into your machine learning system?

I think of deep learning as telling us something about what does the model look like.

And basically to qualify as deep, I say that it just has to have multiple layers.

That can be multiple steps in a feed-forward differentiable computation.

That can be multiple layers in a graphical model.

There's a lot of ways that you could satisfy me that something has multiple steps that are each parameterized separately.

I think of gradient descent as being all about that other piece, the how do you actually update the parameters piece.

So you could imagine having a deep model like a convolutional net and training it with something like evolution or a genetic algorithm, and I would say that still qualifies as deep learning.

And then in terms of models that aren't necessarily differentiable, I guess Boltzmann machines are probably the main example of something where you can't really take a derivative and use that for the learning process, but you can still argue that the model has many steps of processing that it applies when you run inference in the model.

So Jeff Hinton suggests that we need to throw away backprop, backpropagation and start all over.

What could an alternative direction of training neural networks look like?

I don't know that back propagation is gonna go away entirely.

Most of the time when we decide that a machine learning algorithm isn't on the critical path to research for improving AI, the algorithm doesn't die, it just becomes used for some specialized set of things.

A lot of algorithms like logistic regression don't seem that exciting to AI researchers who are working on things like speech recognition or autonomous cars today, but there's still a lot of use for logistic regression and things like analyzing really noisy data in medicine and finance, or making really rapid predictions in really time-limited contexts.

So I think backpropagation and gradient descent are around to stay, but they may not end up being everything that we need to get to real human level or superhuman AI.

Are you optimistic about us discovering, you know, backpropagation has been around for a few decades.

So are you optimistic about us as a community being able to discover something better?

I think we likely will find something that works better.

You could imagine things like having stacks of models where some of the lower level models predict parameters of the higher level models.

And so at the top level, you're not learning in terms of literally calculating gradients, but just predicting how different values will perform.

You can kind of see that already in some areas like Bayesian optimization, where you have a Gaussian process that predicts how well different parameter values will perform.

We already use those kinds of algorithms for things like hyperparameter optimization, and in general we know a lot of things other than backprop that work really well for specific problems.

The main thing we haven't found is a way of taking 1 of these other non-backprop-based algorithms and having it really advance the state of the art on an AI-level problem.

But I wouldn't be surprised if eventually we find that some of these algorithms that even the ones that already exist, not even necessarily a new 1, we might find some way of customizing 1 of these algorithms to do something really interesting at the level of cognition or the level of...

I think 1 system that we really don't have working quite right yet is short-term memory.

We have things like LSTMs, they're called long short-term memory.

They still don't do quite what a human does with short-term memory.

Gradient descent to learn a specific fact has to do multiple steps on that fact.

If I tell you, the meeting today is at 3 p.m.

I don't need to say over and over again, it's at 3 p.m., it's at 3 p.m., it's at 3 p.m., it's at 3 p.m.

You just hear it once and you remember it.

There's been some work on things like self-attention and attention-like mechanisms, like the neural Turing machine that can write to memory cells and update themselves with facts like that right away.

But I don't think we've really nailed it yet.

And that's 1 area where I'd imagine that new optimization algorithms or different ways of applying existing optimization algorithms could give us a way of just lightning fast updating the state of a machine learning system to contain a specific fact like that without needing to

have it presented over and over and over again.

So some of the success of symbolic systems in the 80s is they were able to assemble these kinds of facts better.

But there's a lot of expert input required and it's very limited in that sense.

Do you ever look back to that as something that we'll have to return to eventually, sort of dust off the book from the shelf and think about how we build knowledge, representation, knowledge-based-

And like first order logic and entailment and things like that.

In my particular line of work, which has mostly been machine learning security and also generative modeling, I haven't usually found myself moving in that direction.

For generative models, I could see a little bit of it could be useful if you had something like a differentiable knowledge base or some other kind of knowledge base where it's possible for some of our fuzzier machine learning algorithms to interact with a knowledge base.

I mean, neural network is kind of like that.

It's a differentiable knowledge base of sorts.

If we had a really easy way of giving feedback to machine learning models, that would clearly help a lot with generative models.

And so you could imagine 1 way of getting there would be get a lot better at natural language processing.

But another way of getting there would be take some kind of knowledge base and figure out a way for it to actually interact with a neural network.

Being able to have a chat with a neural network.

LARS RASMUSSEN So like 1 thing in generative models we see a lot today is you'll get things like faces that are not symmetrical, like people that have 2 eyes that are different colors.

And I mean, there are people with eyes that are different colors in real life, but not nearly as many of them as you tend to see in the machine learning generated data.

So if you had either a knowledge base that could contain the fact, people's faces are generally approximately symmetric, and eye color is especially likely to be the same on both sides.

Being able to just inject that hint into the machine learning model without it having to discover that itself after studying a lot of data would be a really useful feature.

I could see a lot of ways of getting there without bringing back some of the 1980s technology, but I also see some ways that you could imagine extending the 1980s technology to play nice with neural nets and have it help get there.

So you talked about the story of you coming up with the idea of GANs at a bar with some friends.

You were arguing that this GANs would work, generative adversarial networks, and the others didn't think so.

Then you went home at midnight, coded it up, and it worked.

So if I was a friend of yours at the bar, I would also have doubts.

It's a really nice idea, but I'm very skeptical that it would work.

What was the basis of your intuition why it should work?

I don't wanna be someone who goes around promoting alcohol for the purposes of science.

In this case, I do actually think that drinking helped a little bit.

When your inhibitions are lowered, you're more willing to try out things that you wouldn't try out otherwise.

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Ian Goodfellow: Generative Adversarial Networks (GANs) | Lex Fridman Podcast #19