YouSaid · the spoken record

Ian Goodfellow

lines on the record: 86
first: 2019-04-18
most recent: 2019-04-18
sittings or episodes: 1
sources: podcast

Every line below is reproduced as it was said and linked to the record it came from. Nothing here is summarised or generated. Directory · Search · Corrections

“Would have more or less the same problem as the negative phase in the Bolton machine. That trying to train the discriminator in the inner loop, you just couldn't get it to keep up with the generator and the outer loop. And that would prevent it from converging to anything useful.”
2019-04-18 · Lex Fridman Podcast · Ian Goodfellow: Generative Adversarial Networks (GANs) · IDENTIFIED FROM THE TRANSCRIPT · source
“One of them is called the positive phase where you load data into the model and tell the model to make the data more likely. The other one is called the negative phase where you draw samples from the model and tell the model to make those samples less likely. In a deep Bolton machine, it's not trivial to generate a sample. You have to actually run an iterative process that gets better and better samples coming closer and closer to the distribution the model represents. So during the training process, you're always running these two systems at the same time. One that's updating the parameters of the model and another one that's trying to generate samples from the model. And they worked really well in things like MNIST, but a lot of us in the lab, including me, had tried to get deep bolts and machines to scale past MNIST to things like generating color photos. And we just couldn't get the two processes to stay synchronized. So when I had the idea for GANS, a lot of people thought that the discrimination”
2019-04-18 · Lex Fridman Podcast · Ian Goodfellow: Generative Adversarial Networks (GANs) · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, so part of what all of us were thinking about when we had this conversation was deep bolt-in machines, which a lot of us in the lab, including me, were a big fan of deep bolts and machines at the time. They involved two separate processes running at the same time.”
2019-04-18 · Lex Fridman Podcast · Ian Goodfellow: Generative Adversarial Networks (GANs) · IDENTIFIED FROM THE TRANSCRIPT · source
“I don't want to 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. So I have noticed in general that I'm less prone to shooting down some of my own ideas when I have had a little bit to drink. I think if I had had that idea at lunchtime, I probably would have thought it's hard enough to train one neural nut. You can't train a second neural nut in the inner loop of the. Outer neural net. That was basically my friend's objection was that trying to train two neural nets at the same time would be too hard.”
2019-04-18 · Lex Fridman Podcast · Ian Goodfellow: Generative Adversarial Networks (GANs) · IDENTIFIED FROM THE TRANSCRIPT · source
“So, like one thing in generative models, we see a lot today is you'll get things like faces that are not symmetrical, like people that have two 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.”
2019-04-18 · Lex Fridman Podcast · Ian Goodfellow: Generative Adversarial Networks (GANs) · IDENTIFIED FROM THE TRANSCRIPT · source
“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 one 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.”
2019-04-18 · Lex Fridman Podcast · Ian Goodfellow: Generative Adversarial Networks (GANs) · IDENTIFIED FROM THE TRANSCRIPT · source
“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 the knowledge base.”
2019-04-18 · Lex Fridman Podcast · Ian Goodfellow: Generative Adversarial Networks (GANs) · IDENTIFIED FROM THE TRANSCRIPT · source
“You to do a gradient step on each one. 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. That's one area where I'd imagine that new optimization algorithms are 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.”
2019-04-18 · Lex Fridman Podcast · Ian Goodfellow: Generative Adversarial Networks (GANs) · IDENTIFIED FROM THE TRANSCRIPT · source
“AI level problem. But I wouldn't be surprised if eventually we find that some of these algorithms, even the ones that already exist, not even necessarily new one, we might find some way of customizing one of these algorithms to do something really interesting at the level of cognition or the level of, I think one system that we really don't have working quite right yet is like 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. 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.”
2019-04-18 · Lex Fridman Podcast · Ian Goodfellow: Generative Adversarial Networks (GANs) · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, I am. 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 one of these other non-BAC prop based algorithms and having it really advance this.”
2019-04-18 · Lex Fridman Podcast · Ian Goodfellow: Generative Adversarial Networks (GANs) · IDENTIFIED FROM THE TRANSCRIPT · source
“I don't know that back propagation is going to 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.”
2019-04-18 · Lex Fridman Podcast · Ian Goodfellow: Generative Adversarial Networks (GANs) · IDENTIFIED FROM THE TRANSCRIPT · source
“Basically, to qualify as deep, I say that it just has to have multiple layers. That can be multiple steps in a feedforward 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 if gradient descent is being all about that other piece, how do you actually update the parameters piece? So you can 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's still qualifies as deep learning. And then in terms of models that aren't necessarily differentiable, I guess Bolton 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 information.”
2019-04-18 · Lex Fridman Podcast · Ian Goodfellow: Generative Adversarial Networks (GANs) · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, so I tend to think of machine learning algorithms as decomposed into really three different pieces. There's the model, which can be something like a neural net or a Bolton machine or a recurrent model. And that basically just describes how do you take data and how do you take parameters and 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 or 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? And then the last part is the data set. Like how do you actually represent the world as it comes into your machine learning system? So I think of deep learning as telling us something about what does the model look like.”
2019-04-18 · Lex Fridman Podcast · Ian Goodfellow: Generative Adversarial Networks (GANs) · IDENTIFIED FROM THE TRANSCRIPT · source
“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.”
2019-04-18 · Lex Fridman Podcast · Ian Goodfellow: Generative Adversarial Networks (GANs) · IDENTIFIED FROM THE TRANSCRIPT · source
“Would say deep learning is any kind of machine learning that involves learning parameters of more than one consecutive step. So, I 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, but really only one step is learned. 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 backpropagation.”
2019-04-18 · Lex Fridman Podcast · Ian Goodfellow: Generative Adversarial Networks (GANs) · IDENTIFIED FROM THE TRANSCRIPT · source
“I can point you to a paper that's recent right now, but there isn't a whole lot of 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.”
2019-04-18 · Lex Fridman Podcast · Ian Goodfellow: Generative Adversarial Networks (GANs) · IDENTIFIED FROM THE TRANSCRIPT · source
“There's also, I guess, two different philosophies about how you might write a book. One philosophy is you try to write a reference that covers everything. And 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 Yoshua and Aaron was somewhere between the two philosophies, that it's trying to be both a reference and an introductory guide. Writing this chapter for Russell 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.”
2019-04-18 · Lex Fridman Podcast · Ian Goodfellow: Generative Adversarial Networks (GANs) · IDENTIFIED FROM THE TRANSCRIPT · source
“One thing that helped me make that plan was actually the experience of having read 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 time.”
2019-04-18 · Lex Fridman Podcast · Ian Goodfellow: Generative Adversarial Networks (GANs) · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, in my case, I waited like a year before I actually wrote anything. Is it? Even having written a full length textbook before, it's still pretty intimidating to try to start writing just one chapter that covers everything.”
2019-04-18 · Lex Fridman Podcast · Ian Goodfellow: Generative Adversarial Networks (GANs) · IDENTIFIED FROM THE TRANSCRIPT · source
“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 here a completely different sentence.”
2019-04-18 · Lex Fridman Podcast · Ian Goodfellow: Generative Adversarial Networks (GANs) · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, actually, 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.”
2019-04-18 · Lex Fridman Podcast · Ian Goodfellow: Generative Adversarial Networks (GANs) · IDENTIFIED FROM THE TRANSCRIPT · source
“I guess one 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.”
2019-04-18 · Lex Fridman Podcast · Ian Goodfellow: Generative Adversarial Networks (GANs) · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah 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. 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 than 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.”
2019-04-18 · Lex Fridman Podcast · Ian Goodfellow: Generative Adversarial Networks (GANs) · IDENTIFIED FROM THE TRANSCRIPT · source
“We try to think of what would we screw up, and then we make sure we fix that. Yeah. 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.”
2019-04-18 · Lex Fridman Podcast · Ian Goodfellow: Generative Adversarial Networks (GANs) · IDENTIFIED FROM THE TRANSCRIPT · source
“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 adversarily 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 helped 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.”
2019-04-18 · Lex Fridman Podcast · Ian Goodfellow: Generative Adversarial Networks (GANs) · IDENTIFIED FROM THE TRANSCRIPT · source
“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? Right. 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.”
2019-04-18 · Lex Fridman Podcast · Ian Goodfellow: Generative Adversarial Networks (GANs) · IDENTIFIED FROM THE TRANSCRIPT · source
“I think so, yeah. 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 one 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.”
2019-04-18 · Lex Fridman Podcast · Ian Goodfellow: Generative Adversarial Networks (GANs) · IDENTIFIED FROM THE TRANSCRIPT · source
“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.”
2019-04-18 · Lex Fridman Podcast · Ian Goodfellow: Generative Adversarial Networks (GANs) · IDENTIFIED FROM THE TRANSCRIPT · source
“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.”
2019-04-18 · Lex Fridman Podcast · Ian Goodfellow: Generative Adversarial Networks (GANs) · IDENTIFIED FROM THE TRANSCRIPT · source
“Cognition, yes, I think so. Consciousness, it's really hard to even define what we mean by that. I guess 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. And there's all these philosophical problems like, could you imagine?”
2019-04-18 · Lex Fridman Podcast · Ian Goodfellow: Generative Adversarial Networks (GANs) · IDENTIFIED FROM THE TRANSCRIPT · source
“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”
2019-04-18 · Lex Fridman Podcast · Ian Goodfellow: Generative Adversarial Networks (GANs) · IDENTIFIED FROM THE TRANSCRIPT · source
“But I don't think anybody believes that layer 150 of the resNet is a grandmother's cell and layer 100 is contours or something like that”
2019-04-18 · Lex Fridman Podcast · Ian Goodfellow: Generative Adversarial Networks (GANs) · IDENTIFIED FROM THE TRANSCRIPT · source
“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 one 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 cell 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.”
2019-04-18 · Lex Fridman Podcast · Ian Goodfellow: Generative Adversarial Networks (GANs) · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, I think we already see that a little bit. 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 one step. So if you draw a flowchart 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. We 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. There wasn't a lot done in series. I think what we got with deep learning was really the ability to have steps of a program that run in sequence.”
2019-04-18 · Lex Fridman Podcast · Ian Goodfellow: Generative Adversarial Networks (GANs) · IDENTIFIED FROM THE TRANSCRIPT · source
“So, just getting the generalization ability better is one 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 submodules 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.”
2019-04-18 · Lex Fridman Podcast · Ian Goodfellow: Generative Adversarial Networks (GANs) · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, I think one 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 two million times.”
2019-04-18 · Lex Fridman Podcast · Ian Goodfellow: Generative Adversarial Networks (GANs) · IDENTIFIED FROM THE TRANSCRIPT · source