WEBVTT 00:00.000 --> 00:03.760 The following is a conversation with Ian Goodfellow. 00:03.760 --> 00:06.360 He's the author of the popular textbook on deep learning 00:06.360 --> 00:08.880 simply titled Deep Learning. 00:08.880 --> 00:12.320 He coined the term of generative adversarial networks, 00:12.320 --> 00:14.560 otherwise known as GANs. 00:14.560 --> 00:18.160 And with his 2014 paper is responsible 00:18.160 --> 00:20.440 for launching the incredible growth 00:20.440 --> 00:22.120 of research and innovation 00:22.120 --> 00:24.720 in this subfield of deep learning. 00:24.720 --> 00:27.520 He got his BS and MS at Stanford, 00:27.520 --> 00:30.120 his PhD at University of Montreal 00:30.120 --> 00:33.200 with Yoshua Benjo and Aaron Kervel. 00:33.200 --> 00:35.240 He held several research positions, 00:35.240 --> 00:37.560 including at OpenAI, Google Brain, 00:37.560 --> 00:41.560 and now at Apple as the director of machine learning. 00:41.560 --> 00:45.400 This recording happened while Ian was still a Google Brain, 00:45.400 --> 00:48.520 but we don't talk about anything specific to Google 00:48.520 --> 00:50.760 or any other organization. 00:50.760 --> 00:52.480 This conversation is part 00:52.480 --> 00:54.520 of the artificial intelligence podcast. 00:54.520 --> 00:56.680 If you enjoy it, subscribe on YouTube, 00:56.680 --> 00:59.600 iTunes, or simply connect with me on Twitter 00:59.600 --> 01:03.000 at Lex Freedman, spelled F R I D. 01:03.000 --> 01:07.080 And now here's my conversation with Ian Goodfellow. 01:08.240 --> 01:11.000 You open your popular deep learning book 01:11.000 --> 01:13.600 with a Russian doll type diagram 01:13.600 --> 01:15.880 that shows deep learning is a subset 01:15.880 --> 01:17.160 of representation learning, 01:17.160 --> 01:19.960 which in turn is a subset of machine learning 01:19.960 --> 01:22.520 and finally a subset of AI. 01:22.520 --> 01:25.280 So this kind of implies that there may be limits 01:25.280 --> 01:27.720 to deep learning in the context of AI. 01:27.720 --> 01:31.560 So what do you think is the current limits of deep learning 01:31.560 --> 01:33.120 and are those limits something 01:33.120 --> 01:35.760 that we can overcome with time? 01:35.760 --> 01:37.720 Yeah, I think one of the biggest limitations 01:37.720 --> 01:39.320 of deep learning is that right now 01:39.320 --> 01:42.920 it requires really a lot of data, especially labeled data. 01:43.960 --> 01:45.480 There are some unsupervised 01:45.480 --> 01:47.160 and semi supervised learning algorithms 01:47.160 --> 01:49.480 that can reduce the amount of labeled data you need, 01:49.480 --> 01:52.200 but they still require a lot of unlabeled data. 01:52.200 --> 01:54.200 Reinforcement learning algorithms, they don't need labels, 01:54.200 --> 01:56.280 but they need really a lot of experiences. 01:57.280 --> 01:58.920 As human beings, we don't learn to play a pong 01:58.920 --> 02:01.520 by failing at pong two million times. 02:02.720 --> 02:05.880 So just getting the generalization ability better 02:05.880 --> 02:08.040 is one of the most important bottlenecks 02:08.040 --> 02:10.520 in the capability of the technology today. 02:10.520 --> 02:12.360 And then I guess I'd also say deep learning 02:12.360 --> 02:15.620 is like a component of a bigger system. 02:16.600 --> 02:19.040 So far, nobody is really proposing to have 02:20.600 --> 02:22.000 only what you'd call deep learning 02:22.000 --> 02:25.520 as the entire ingredient of intelligence. 02:25.520 --> 02:29.860 You use deep learning as sub modules of other systems, 02:29.860 --> 02:32.320 like AlphaGo has a deep learning model 02:32.320 --> 02:34.160 that estimates the value function. 02:35.200 --> 02:36.600 Most reinforcement learning algorithms 02:36.600 --> 02:37.880 have a deep learning module 02:37.880 --> 02:40.320 that estimates which action to take next, 02:40.320 --> 02:42.480 but you might have other components. 02:42.480 --> 02:46.120 So you're basically building a function estimator. 02:46.120 --> 02:48.600 Do you think it's possible? 02:48.600 --> 02:51.000 You said nobody's kind of been thinking about this so far, 02:51.000 --> 02:54.320 but do you think neural networks could be made to reason 02:54.320 --> 02:57.720 in the way symbolic systems did in the 80s and 90s 02:57.720 --> 03:00.160 to do more, create more like programs 03:00.160 --> 03:01.440 as opposed to functions? 03:01.440 --> 03:03.920 Yeah, I think we already see that a little bit. 03:04.880 --> 03:08.860 I already kind of think of neural nets as a kind of program. 03:08.860 --> 03:12.920 I think of deep learning as basically learning programs 03:12.920 --> 03:15.280 that have more than one step. 03:15.280 --> 03:16.960 So if you draw a flow chart 03:16.960 --> 03:19.540 or if you draw a TensorFlow graph 03:19.540 --> 03:21.880 describing your machine learning model, 03:21.880 --> 03:23.520 I think of the depth of that graph 03:23.520 --> 03:25.880 as describing the number of steps that run in sequence 03:25.880 --> 03:27.640 and then the width of that graph 03:27.640 --> 03:30.120 as the number of steps that run in parallel. 03:30.120 --> 03:31.680 Now it's been long enough 03:31.680 --> 03:32.880 that we've had deep learning working 03:32.880 --> 03:33.880 that it's a little bit silly 03:33.880 --> 03:35.740 to even discuss shallow learning anymore, 03:35.740 --> 03:38.880 but back when I first got involved in AI, 03:38.880 --> 03:40.080 when we used machine learning, 03:40.080 --> 03:41.280 we were usually learning things 03:41.280 --> 03:43.680 like support vector machines. 03:43.680 --> 03:45.640 You could have a lot of input features to the model 03:45.640 --> 03:48.120 and you could multiply each feature by a different weight. 03:48.120 --> 03:51.240 All those multiplications were done in parallel to each other 03:51.240 --> 03:52.720 and there wasn't a lot done in series. 03:52.720 --> 03:54.360 I think what we got with deep learning 03:54.360 --> 03:58.400 was really the ability to have steps of a program 03:58.400 --> 04:00.320 that run in sequence. 04:00.320 --> 04:03.200 And I think that we've actually started to see 04:03.200 --> 04:05.040 that what's important with deep learning 04:05.040 --> 04:08.000 is more the fact that we have a multi step program 04:08.000 --> 04:10.800 rather than the fact that we've learned a representation. 04:10.800 --> 04:15.120 If you look at things like Resnuts, for example, 04:15.120 --> 04:18.660 they take one particular kind of representation 04:18.660 --> 04:21.040 and they update it several times. 04:21.040 --> 04:23.560 Back when deep learning first really took off 04:23.560 --> 04:25.760 in the academic world in 2006, 04:25.760 --> 04:28.400 when Jeff Hinton showed that you could train 04:28.400 --> 04:30.160 deep belief networks, 04:30.160 --> 04:31.960 everybody who was interested in the idea 04:31.960 --> 04:33.560 thought of it as each layer 04:33.560 --> 04:35.960 learns a different level of abstraction, 04:35.960 --> 04:37.840 that the first layer trained on images 04:37.840 --> 04:38.960 learns something like edges 04:38.960 --> 04:40.420 and the second layer learns corners 04:40.420 --> 04:43.320 and eventually you get these kind of grandmother cell units 04:43.320 --> 04:45.920 that recognize specific objects. 04:45.920 --> 04:48.560 Today, I think most people think of it more 04:48.560 --> 04:52.000 as a computer program where as you add more layers, 04:52.000 --> 04:55.120 you can do more updates before you output your final number. 04:55.120 --> 04:57.160 But I don't think anybody believes that 04:57.160 --> 05:02.040 layer 150 of the Resnet is a grandmother cell 05:02.040 --> 05:05.080 and layer 100 is contours or something like that. 05:06.040 --> 05:08.160 Okay, so you're not thinking of it 05:08.160 --> 05:11.520 as a singular representation that keeps building. 05:11.520 --> 05:15.960 You think of it as a program sort of almost like a state. 05:15.960 --> 05:18.600 The representation is a state of understanding. 05:18.600 --> 05:21.520 Yeah, I think of it as a program that makes several updates 05:21.520 --> 05:23.840 and arrives at better and better understandings, 05:23.840 --> 05:27.500 but it's not replacing the representation at each step. 05:27.500 --> 05:29.160 It's refining it. 05:29.160 --> 05:31.660 And in some sense, that's a little bit like reasoning. 05:31.660 --> 05:33.560 It's not reasoning in the form of deduction, 05:33.560 --> 05:36.960 but it's reasoning in the form of taking a thought 05:36.960 --> 05:39.440 and refining it and refining it carefully 05:39.440 --> 05:41.240 until it's good enough to use. 05:41.240 --> 05:43.560 So do you think, and I hope you don't mind, 05:43.560 --> 05:46.040 we'll jump philosophical every once in a while. 05:46.040 --> 05:50.480 Do you think of, you know, cognition, human cognition, 05:50.480 --> 05:53.520 or even consciousness as simply a result 05:53.520 --> 05:58.120 of this kind of sequential representation learning? 05:58.120 --> 06:00.440 Do you think that can emerge? 06:00.440 --> 06:02.440 Cognition, yes, I think so. 06:02.440 --> 06:05.160 Consciousness, it's really hard to even define 06:05.160 --> 06:06.400 what we mean by that. 06:07.400 --> 06:09.840 I guess there's, consciousness is often defined 06:09.840 --> 06:12.120 as things like having self awareness, 06:12.120 --> 06:15.200 and that's relatively easy to turn it 06:15.200 --> 06:17.200 to something actionable for a computer scientist 06:17.200 --> 06:18.400 to reason about. 06:18.400 --> 06:20.080 People also define consciousness in terms 06:20.080 --> 06:24.000 of having qualitative states of experience, like qualia. 06:24.000 --> 06:25.280 There's all these philosophical problems, 06:25.280 --> 06:27.880 like could you imagine a zombie 06:27.880 --> 06:30.760 who does all the same information processing as a human, 06:30.760 --> 06:33.500 but doesn't really have the qualitative experiences 06:33.500 --> 06:34.720 that we have? 06:34.720 --> 06:37.580 That sort of thing, I have no idea how to formalize 06:37.580 --> 06:39.960 or turn it into a scientific question. 06:39.960 --> 06:41.600 I don't know how you could run an experiment 06:41.600 --> 06:44.880 to tell whether a person is a zombie or not. 06:44.880 --> 06:46.680 And similarly, I don't know how you could run 06:46.680 --> 06:49.680 an experiment to tell whether an advanced AI system 06:49.680 --> 06:53.080 had become conscious in the sense of qualia or not. 06:53.080 --> 06:54.600 But in the more practical sense, 06:54.600 --> 06:56.320 like almost like self attention, 06:56.320 --> 06:58.920 you think consciousness and cognition can, 06:58.920 --> 07:03.240 in an impressive way, emerge from current types 07:03.240 --> 07:05.600 of architectures that we think of as determining. 07:05.600 --> 07:07.920 Or if you think of consciousness 07:07.920 --> 07:12.160 in terms of self awareness and just making plans 07:12.160 --> 07:15.120 based on the fact that the agent itself 07:15.120 --> 07:18.000 exists in the world, reinforcement learning algorithms 07:18.000 --> 07:20.840 are already more or less forced to model 07:20.840 --> 07:23.040 the agent's effect on the environment. 07:23.040 --> 07:26.340 So that more limited version of consciousness 07:26.340 --> 07:30.560 is already something that we get limited versions 07:30.560 --> 07:32.960 of with reinforcement learning algorithms 07:32.960 --> 07:34.640 if they're trained well. 07:34.640 --> 07:37.440 But you say limited. 07:37.440 --> 07:39.920 So the big question really is how you jump 07:39.920 --> 07:42.120 from limited to human level, right? 07:42.120 --> 07:44.640 And whether it's possible, 07:46.840 --> 07:49.000 even just building common sense reasoning 07:49.000 --> 07:50.520 seems to be exceptionally difficult. 07:50.520 --> 07:52.480 So if we scale things up, 07:52.480 --> 07:55.000 if we get much better on supervised learning, 07:55.000 --> 07:56.600 if we get better at labeling, 07:56.600 --> 08:00.640 if we get bigger datasets, more compute, 08:00.640 --> 08:03.880 do you think we'll start to see really impressive things 08:03.880 --> 08:08.760 that go from limited to something echoes 08:08.760 --> 08:10.320 of human level cognition? 08:10.320 --> 08:11.200 I think so, yeah. 08:11.200 --> 08:13.360 I'm optimistic about what can happen 08:13.360 --> 08:16.440 just with more computation and more data. 08:16.440 --> 08:20.120 I do think it'll be important to get the right kind of data. 08:20.120 --> 08:23.160 Today, most of the machine learning systems we train 08:23.160 --> 08:27.560 are mostly trained on one type of data for each model. 08:27.560 --> 08:31.380 But the human brain, we get all of our different senses 08:31.380 --> 08:33.880 and we have many different experiences 08:33.880 --> 08:36.320 like riding a bike, driving a car, 08:36.320 --> 08:37.940 talking to people, reading. 08:39.160 --> 08:42.440 I think when we get that kind of integrated dataset 08:42.440 --> 08:44.400 working with a machine learning model 08:44.400 --> 08:47.640 that can actually close the loop and interact, 08:47.640 --> 08:50.480 we may find that algorithms not so different 08:50.480 --> 08:51.840 from what we have today, 08:51.840 --> 08:53.240 learn really interesting things 08:53.240 --> 08:54.400 when you scale them up a lot 08:54.400 --> 08:58.240 and train them on a large amount of multimodal data. 08:58.240 --> 08:59.640 So multimodal is really interesting, 08:59.640 --> 09:04.000 but within, like you're working adversarial examples. 09:04.000 --> 09:09.000 So selecting within model, within one mode of data, 09:11.120 --> 09:13.800 selecting better at what are the difficult cases 09:13.800 --> 09:16.120 from which you're most useful to learn from. 09:16.120 --> 09:18.880 Oh, yeah, like could we get a whole lot of mileage 09:18.880 --> 09:22.280 out of designing a model that's resistant 09:22.280 --> 09:24.080 to adversarial examples or something like that? 09:24.080 --> 09:26.280 Right, that's the question. 09:26.280 --> 09:27.760 My thinking on that has evolved a lot 09:27.760 --> 09:28.920 over the last few years. 09:28.920 --> 09:31.280 When I first started to really invest 09:31.280 --> 09:32.760 in studying adversarial examples, 09:32.760 --> 09:36.320 I was thinking of it mostly as adversarial examples 09:36.320 --> 09:39.000 reveal a big problem with machine learning. 09:39.000 --> 09:41.160 And we would like to close the gap 09:41.160 --> 09:44.160 between how machine learning models respond 09:44.160 --> 09:46.560 to adversarial examples and how humans respond. 09:47.640 --> 09:49.160 After studying the problem more, 09:49.160 --> 09:51.960 I still think that adversarial examples are important. 09:51.960 --> 09:55.440 I think of them now more of as a security liability 09:55.440 --> 09:57.800 than as an issue that necessarily shows 09:57.800 --> 09:59.880 there's something uniquely wrong 09:59.880 --> 10:02.800 with machine learning as opposed to humans. 10:02.800 --> 10:04.600 Also, do you see them as a tool 10:04.600 --> 10:06.480 to improve the performance of the system? 10:06.480 --> 10:10.760 Not on the security side, but literally just accuracy. 10:10.760 --> 10:13.480 I do see them as a kind of tool on that side, 10:13.480 --> 10:16.640 but maybe not quite as much as I used to think. 10:16.640 --> 10:18.520 We've started to find that there's a trade off 10:18.520 --> 10:21.680 between accuracy on adversarial examples 10:21.680 --> 10:24.360 and accuracy on clean examples. 10:24.360 --> 10:28.320 Back in 2014, when I did the first adversarily trained 10:28.320 --> 10:30.840 classifier that showed resistance 10:30.840 --> 10:33.040 to some kinds of adversarial examples, 10:33.040 --> 10:36.040 it also got better at the clean data on MNIST. 10:36.040 --> 10:37.720 And that's something we've replicated several times 10:37.720 --> 10:39.640 on MNIST, that when we train 10:39.640 --> 10:41.480 against weak adversarial examples, 10:41.480 --> 10:43.880 MNIST classifiers get more accurate. 10:43.880 --> 10:47.080 So far that hasn't really held up on other data sets 10:47.080 --> 10:48.880 and hasn't held up when we train 10:48.880 --> 10:50.720 against stronger adversaries. 10:50.720 --> 10:53.160 It seems like when you confront 10:53.160 --> 10:55.720 a really strong adversary, 10:55.720 --> 10:58.080 you tend to have to give something up. 10:58.080 --> 11:00.520 Interesting, but it's such a compelling idea 11:00.520 --> 11:04.800 because it feels like that's how us humans learn 11:04.800 --> 11:06.320 to do the difficult cases. 11:06.320 --> 11:08.760 We try to think of what would we screw up 11:08.760 --> 11:11.000 and then we make sure we fix that. 11:11.000 --> 11:13.680 It's also in a lot of branches of engineering, 11:13.680 --> 11:15.800 you do a worst case analysis 11:15.800 --> 11:18.720 and make sure that your system will work in the worst case. 11:18.720 --> 11:20.400 And then that guarantees that it'll work 11:20.400 --> 11:24.360 in all of the messy average cases that happen 11:24.360 --> 11:27.440 when you go out into a really randomized world. 11:27.440 --> 11:29.560 Yeah, with driving with autonomous vehicles, 11:29.560 --> 11:31.840 there seems to be a desire to just look 11:31.840 --> 11:34.880 for think adversarially, 11:34.880 --> 11:36.920 try to figure out how to mess up the system. 11:36.920 --> 11:40.640 And if you can be robust to all those difficult cases, 11:40.640 --> 11:43.600 then you can, it's a hand wavy empirical way 11:43.600 --> 11:45.800 to show your system is safe. 11:45.800 --> 11:47.000 Yeah, yeah. 11:47.000 --> 11:49.120 Today, most adversarial example research 11:49.120 --> 11:51.640 isn't really focused on a particular use case, 11:51.640 --> 11:54.000 but there are a lot of different use cases 11:54.000 --> 11:55.080 where you'd like to make sure 11:55.080 --> 11:57.720 that the adversary can't interfere 11:57.720 --> 12:00.200 with the operation of your system. 12:00.200 --> 12:01.040 Like in finance, 12:01.040 --> 12:03.320 if you have an algorithm making trades for you, 12:03.320 --> 12:04.640 people go to a lot of an effort 12:04.640 --> 12:06.680 to obfuscate their algorithm. 12:06.680 --> 12:08.080 That's both to protect their IP 12:08.080 --> 12:10.880 because you don't want to research 12:10.880 --> 12:13.560 and develop a profitable trading algorithm 12:13.560 --> 12:16.120 then have somebody else capture the gains. 12:16.120 --> 12:17.160 But it's at least partly 12:17.160 --> 12:19.000 because you don't want people to make adversarial 12:19.000 --> 12:21.240 examples that fool your algorithm 12:21.240 --> 12:22.560 into making bad trades. 12:24.360 --> 12:26.560 Or I guess one area that's been popular 12:26.560 --> 12:30.160 in the academic literature is speech recognition. 12:30.160 --> 12:34.400 If you use speech recognition to hear an audio waveform 12:34.400 --> 12:37.680 and then turn that into a command 12:37.680 --> 12:39.640 that a phone executes for you, 12:39.640 --> 12:41.840 you don't want a malicious adversary 12:41.840 --> 12:43.600 to be able to produce audio 12:43.600 --> 12:46.280 that gets interpreted as malicious commands, 12:46.280 --> 12:47.800 especially if a human in the room 12:47.800 --> 12:50.320 doesn't realize that something like that is happening. 12:50.320 --> 12:52.000 In speech recognition, 12:52.000 --> 12:53.920 has there been much success 12:53.920 --> 12:58.440 in being able to create adversarial examples 12:58.440 --> 12:59.760 that fool the system? 12:59.760 --> 13:00.880 Yeah, actually. 13:00.880 --> 13:02.440 I guess the first work that I'm aware of 13:02.440 --> 13:05.120 is a paper called Hidden Voice Commands 13:05.120 --> 13:08.480 that came out in 2016, I believe. 13:08.480 --> 13:09.560 And they were able to show 13:09.560 --> 13:11.920 that they could make sounds 13:11.920 --> 13:14.960 that are not understandable by a human 13:14.960 --> 13:18.400 but are recognized as the target phrase 13:18.400 --> 13:21.360 that the attacker wants the phone to recognize it as. 13:21.360 --> 13:24.040 Since then, things have gotten a little bit better 13:24.040 --> 13:27.600 on the attacker side when worse on the defender side. 13:28.680 --> 13:33.360 It's become possible to make sounds 13:33.360 --> 13:35.600 that sound like normal speech 13:35.600 --> 13:39.000 but are actually interpreted as a different sentence 13:39.000 --> 13:40.720 than the human hears. 13:40.720 --> 13:42.720 The level of perceptibility 13:42.720 --> 13:45.360 of the adversarial perturbation is still kind of high. 13:46.600 --> 13:48.160 When you listen to the recording, 13:48.160 --> 13:51.040 it sounds like there's some noise in the background, 13:51.040 --> 13:52.960 just like rustling sounds. 13:52.960 --> 13:54.360 But those rustling sounds are actually 13:54.360 --> 13:55.560 the adversarial perturbation 13:55.560 --> 13:58.040 that makes the phone hear a completely different sentence. 13:58.040 --> 14:00.120 Yeah, that's so fascinating. 14:00.120 --> 14:01.640 Peter Norvig mentioned that you're writing 14:01.640 --> 14:04.280 the deep learning chapter for the fourth edition 14:04.280 --> 14:05.840 of the Artificial Intelligence, 14:05.840 --> 14:07.320 the Modern Approach Book. 14:07.320 --> 14:10.680 So how do you even begin summarizing 14:10.680 --> 14:12.960 the field of deep learning in a chapter? 14:12.960 --> 14:16.840 Well, in my case, I waited like a year 14:16.840 --> 14:19.080 before I actually wrote anything. 14:19.080 --> 14:20.280 Is it? 14:20.280 --> 14:22.600 Even having written a full length textbook before, 14:22.600 --> 14:25.560 it's still pretty intimidating 14:25.560 --> 14:27.800 to try to start writing just one chapter 14:27.800 --> 14:29.040 that covers everything. 14:31.080 --> 14:33.160 One thing that helped me make that plan 14:33.160 --> 14:34.280 was actually the experience 14:34.280 --> 14:36.680 of having written the full book before 14:36.680 --> 14:39.080 and then watching how the field changed 14:39.080 --> 14:40.920 after the book came out. 14:40.920 --> 14:42.280 I realized there's a lot of topics 14:42.280 --> 14:44.960 that were maybe extraneous in the first book 14:44.960 --> 14:47.560 and just seeing what stood the test 14:47.560 --> 14:49.360 of a few years of being published 14:49.360 --> 14:52.160 and what seems a little bit less important 14:52.160 --> 14:53.760 to have included now helped me pare down 14:53.760 --> 14:55.920 the topics I wanted to cover for the book. 14:56.840 --> 14:59.560 It's also really nice now that the field 14:59.560 --> 15:00.920 is kind of stabilized to the point 15:00.920 --> 15:04.720 where some core ideas from the 1980s are still used today. 15:04.720 --> 15:06.640 When I first started studying machine learning, 15:06.640 --> 15:09.520 almost everything from the 1980s had been rejected 15:09.520 --> 15:11.320 and now some of it has come back. 15:11.320 --> 15:13.440 So that stuff that's really stood the test of time 15:13.440 --> 15:15.880 is what I focused on putting into the book. 15:16.880 --> 15:21.240 There's also, I guess, two different philosophies 15:21.240 --> 15:23.120 about how you might write a book. 15:23.120 --> 15:24.760 One philosophy is you try to write a reference 15:24.760 --> 15:26.160 that covers everything. 15:26.160 --> 15:27.960 The other philosophy is you try to provide 15:27.960 --> 15:30.320 a high level summary that gives people 15:30.320 --> 15:32.360 the language to understand a field 15:32.360 --> 15:34.920 and tells them what the most important concepts are. 15:34.920 --> 15:37.080 The first deep learning book that I wrote 15:37.080 --> 15:39.240 with Joshua and Aaron was somewhere 15:39.240 --> 15:41.240 between the two philosophies, 15:41.240 --> 15:43.640 that it's trying to be both a reference 15:43.640 --> 15:45.760 and an introductory guide. 15:45.760 --> 15:48.920 Writing this chapter for Russell and Norvig's book, 15:48.920 --> 15:52.800 I was able to focus more on just a concise introduction 15:52.800 --> 15:54.240 of the key concepts and the language 15:54.240 --> 15:56.000 you need to read about them more. 15:56.000 --> 15:57.560 In a lot of cases, I actually just wrote paragraphs 15:57.560 --> 16:00.080 that said, here's a rapidly evolving area 16:00.080 --> 16:02.400 that you should pay attention to. 16:02.400 --> 16:04.760 It's pointless to try to tell you what the latest 16:04.760 --> 16:09.760 and best version of a learn to learn model is. 16:11.440 --> 16:13.640 I can point you to a paper that's recent right now, 16:13.640 --> 16:16.880 but there isn't a whole lot of a reason to delve 16:16.880 --> 16:20.440 into exactly what's going on with the latest 16:20.440 --> 16:22.960 learning to learn approach or the latest module 16:22.960 --> 16:24.960 produced by a learning to learn algorithm. 16:24.960 --> 16:26.760 You should know that learning to learn is a thing 16:26.760 --> 16:29.480 and that it may very well be the source 16:29.480 --> 16:32.200 of the latest and greatest convolutional net 16:32.200 --> 16:34.520 or recurrent net module that you would want to use 16:34.520 --> 16:36.040 in your latest project. 16:36.040 --> 16:38.200 But there isn't a lot of point in trying to summarize 16:38.200 --> 16:42.280 exactly which architecture and which learning approach 16:42.280 --> 16:44.040 got to which level of performance. 16:44.040 --> 16:49.040 So you maybe focus more on the basics of the methodology. 16:49.240 --> 16:52.480 So from back propagation to feed forward 16:52.480 --> 16:55.160 to recurrent networks, convolutional, that kind of thing. 16:55.160 --> 16:56.480 Yeah, yeah. 16:56.480 --> 17:00.320 So if I were to ask you, I remember I took algorithms 17:00.320 --> 17:03.720 and data structures algorithms, of course. 17:03.720 --> 17:08.120 I remember the professor asked, what is an algorithm? 17:09.200 --> 17:12.200 And he yelled at everybody in a good way 17:12.200 --> 17:14.040 that nobody was answering it correctly. 17:14.040 --> 17:16.360 Everybody knew what the algorithm, it was graduate course. 17:16.360 --> 17:18.120 Everybody knew what an algorithm was, 17:18.120 --> 17:19.800 but they weren't able to answer it well. 17:19.800 --> 17:22.360 So let me ask you, in that same spirit, 17:22.360 --> 17:23.580 what is deep learning? 17:24.520 --> 17:29.520 I would say deep learning is any kind of machine learning 17:29.520 --> 17:34.520 that involves learning parameters of more than one 17:34.720 --> 17:36.020 consecutive step. 17:37.280 --> 17:40.760 So that, I mean, shallow learning is things where 17:40.760 --> 17:43.760 you learn a lot of operations that happen in parallel. 17:43.760 --> 17:46.720 You might have a system that makes multiple steps, 17:46.720 --> 17:51.000 like you might have hand designed feature extractors, 17:51.000 --> 17:52.600 but really only one step is learned. 17:52.600 --> 17:55.440 Deep learning is anything where you have multiple 17:55.440 --> 17:56.880 operations in sequence. 17:56.880 --> 17:59.400 And that includes the things that are really popular 17:59.400 --> 18:01.280 today, like convolutional networks 18:01.280 --> 18:04.640 and recurrent networks, but it also includes some 18:04.640 --> 18:08.280 of the things that have died out, like Bolton machines, 18:08.280 --> 18:10.880 where we weren't using back propagation. 18:11.960 --> 18:14.240 Today, I hear a lot of people define deep learning 18:14.240 --> 18:19.240 as gradient descent applied to these differentiable 18:20.400 --> 18:24.240 functions, and I think that's a legitimate usage 18:24.240 --> 18:25.920 of the term, it's just different from the way 18:25.920 --> 18:27.800 that I use the term myself. 18:27.800 --> 18:32.360 So what's an example of deep learning that is not 18:32.360 --> 18:34.720 gradient descent and differentiable functions? 18:34.720 --> 18:37.400 In your, I mean, not specifically perhaps, 18:37.400 --> 18:39.760 but more even looking into the future. 18:39.760 --> 18:44.300 What's your thought about that space of approaches? 18:44.300 --> 18:46.340 Yeah, so I tend to think of machine learning algorithms 18:46.340 --> 18:50.200 as decomposed into really three different pieces. 18:50.200 --> 18:53.000 There's the model, which can be something like a neural net 18:53.000 --> 18:56.600 or a Bolton machine or a recurrent model. 18:56.600 --> 18:59.520 And that basically just describes how do you take data 18:59.520 --> 19:03.480 and how do you take parameters and what function do you use 19:03.480 --> 19:07.320 to make a prediction given the data and the parameters? 19:07.320 --> 19:09.920 Another piece of the learning algorithm is 19:09.920 --> 19:13.880 the optimization algorithm, or not every algorithm 19:13.880 --> 19:15.920 can be really described in terms of optimization, 19:15.920 --> 19:18.880 but what's the algorithm for updating the parameters 19:18.880 --> 19:21.680 or updating whatever the state of the network is? 19:22.600 --> 19:26.280 And then the last part is the data set, 19:26.280 --> 19:29.200 like how do you actually represent the world 19:29.200 --> 19:32.120 as it comes into your machine learning system? 19:33.160 --> 19:35.800 So I think of deep learning as telling us something 19:35.800 --> 19:39.040 about what does the model look like? 19:39.040 --> 19:41.240 And basically to qualify as deep, 19:41.240 --> 19:44.560 I say that it just has to have multiple layers. 19:44.560 --> 19:47.360 That can be multiple steps in a feed forward 19:47.360 --> 19:49.240 differentiable computation. 19:49.240 --> 19:52.040 That can be multiple layers in a graphical model. 19:52.040 --> 19:53.560 There's a lot of ways that you could satisfy me 19:53.560 --> 19:56.160 that something has multiple steps 19:56.160 --> 19:58.920 that are each parameterized separately. 19:58.920 --> 20:00.640 I think of gradient descent as being all about 20:00.640 --> 20:01.560 that other piece, 20:01.560 --> 20:04.240 the how do you actually update the parameters piece? 20:04.240 --> 20:05.960 So you could imagine having a deep model 20:05.960 --> 20:08.680 like a convolutional net and training it with something 20:08.680 --> 20:11.280 like evolution or a genetic algorithm. 20:11.280 --> 20:14.640 And I would say that still qualifies as deep learning. 20:14.640 --> 20:16.040 And then in terms of models 20:16.040 --> 20:18.760 that aren't necessarily differentiable, 20:18.760 --> 20:22.480 I guess Bolton machines are probably the main example 20:22.480 --> 20:25.560 of something where you can't really take a derivative 20:25.560 --> 20:28.000 and use that for the learning process. 20:28.000 --> 20:32.320 But you can still argue that the model has many steps 20:32.320 --> 20:33.760 of processing that it applies 20:33.760 --> 20:35.800 when you run inference in the model. 20:35.800 --> 20:38.960 So it's the steps of processing that's key. 20:38.960 --> 20:41.320 So Jeff Hinton suggests that we need to throw away 20:41.320 --> 20:44.960 back propagation and start all over. 20:44.960 --> 20:46.520 What do you think about that? 20:46.520 --> 20:48.600 What could an alternative direction 20:48.600 --> 20:51.000 of training neural networks look like? 20:51.000 --> 20:52.880 I don't know that back propagation 20:52.880 --> 20:54.680 is going to go away entirely. 20:54.680 --> 20:57.120 Most of the time when we decide 20:57.120 --> 20:59.200 that a machine learning algorithm 20:59.200 --> 21:03.440 isn't on the critical path to research for improving AI, 21:03.440 --> 21:04.640 the algorithm doesn't die, 21:04.640 --> 21:07.760 it just becomes used for some specialized set of things. 21:08.760 --> 21:11.160 A lot of algorithms like logistic regression 21:11.160 --> 21:14.000 don't seem that exciting to AI researchers 21:14.000 --> 21:16.760 who are working on things like speech recognition 21:16.760 --> 21:18.400 or autonomous cars today, 21:18.400 --> 21:21.080 but there's still a lot of use for logistic regression 21:21.080 --> 21:23.960 and things like analyzing really noisy data 21:23.960 --> 21:25.640 in medicine and finance 21:25.640 --> 21:28.720 or making really rapid predictions 21:28.720 --> 21:30.680 in really time limited contexts. 21:30.680 --> 21:33.440 So I think back propagation and gradient descent 21:33.440 --> 21:34.520 are around to stay, 21:34.520 --> 21:38.760 but they may not end up being everything 21:38.760 --> 21:40.840 that we need to get to real human level 21:40.840 --> 21:42.360 or super human AI. 21:42.360 --> 21:44.680 Are you optimistic about us discovering? 21:44.680 --> 21:49.680 You know, back propagation has been around for a few decades. 21:50.240 --> 21:54.080 So are you optimistic about us as a community 21:54.080 --> 21:56.800 being able to discover something better? 21:56.800 --> 21:57.640 Yeah, I am. 21:57.640 --> 22:01.840 I think we likely will find something that works better. 22:01.840 --> 22:05.520 You could imagine things like having stacks of models 22:05.520 --> 22:08.720 where some of the lower level models predict parameters 22:08.720 --> 22:10.200 of the higher level models. 22:10.200 --> 22:12.160 And so at the top level, 22:12.160 --> 22:13.480 you're not learning in terms of literally 22:13.480 --> 22:15.800 calculating gradients, but just predicting 22:15.800 --> 22:17.680 how different values will perform. 22:17.680 --> 22:19.560 You can kind of see that already in some areas 22:19.560 --> 22:21.400 like Bayesian optimization, 22:21.400 --> 22:22.960 where you have a Gaussian process 22:22.960 --> 22:24.800 that predicts how well different parameter values 22:24.800 --> 22:25.880 will perform. 22:25.880 --> 22:27.680 We already use those kinds of algorithms 22:27.680 --> 22:30.240 for things like hyper parameter optimization. 22:30.240 --> 22:31.640 And in general, we know a lot of things 22:31.640 --> 22:33.240 other than back prop that work really well 22:33.240 --> 22:35.000 for specific problems. 22:35.000 --> 22:38.240 The main thing we haven't found is a way of taking one 22:38.240 --> 22:41.160 of these other non back prop based algorithms 22:41.160 --> 22:43.520 and having it really advance the state of the art 22:43.520 --> 22:46.160 on an AI level problem. 22:46.160 --> 22:47.120 Right. 22:47.120 --> 22:49.600 But I wouldn't be surprised if eventually we find 22:49.600 --> 22:51.560 that some of these algorithms that, 22:51.560 --> 22:52.760 even the ones that already exist, 22:52.760 --> 22:54.200 not even necessarily a new one, 22:54.200 --> 22:59.200 we might find some way of customizing one of these algorithms 22:59.200 --> 23:00.560 to do something really interesting 23:00.560 --> 23:05.240 at the level of cognition or the level of, 23:06.400 --> 23:08.680 I think one system that we really don't have working 23:08.680 --> 23:12.920 quite right yet is like short term memory. 23:12.920 --> 23:14.480 We have things like LSTMs, 23:14.480 --> 23:17.000 they're called long short term memory. 23:17.000 --> 23:20.000 They still don't do quite what a human does 23:20.000 --> 23:21.720 with short term memory. 23:22.840 --> 23:26.920 Like gradient descent to learn a specific fact 23:26.920 --> 23:29.360 has to do multiple steps on that fact. 23:29.360 --> 23:34.120 Like if I tell you, the meeting today is at 3pm, 23:34.120 --> 23:35.440 I don't need to say over and over again. 23:35.440 --> 23:38.640 It's at 3pm, it's at 3pm, it's at 3pm, it's at 3pm. 23:38.640 --> 23:40.400 For you to do a gradient step on each one, 23:40.400 --> 23:43.160 you just hear it once and you remember it. 23:43.160 --> 23:46.920 There's been some work on things like self attention 23:46.920 --> 23:50.400 and attention like mechanisms like the neural Turing machine 23:50.400 --> 23:53.160 that can write to memory cells and update themselves 23:53.160 --> 23:54.880 with facts like that right away. 23:54.880 --> 23:56.880 But I don't think we've really nailed it yet. 23:56.880 --> 24:01.880 And that's one area where I'd imagine that new optimization 24:02.080 --> 24:04.240 algorithms or different ways of applying existing 24:04.240 --> 24:07.280 optimization algorithms could give us a way 24:07.280 --> 24:10.120 of just lightning fast updating the state 24:10.120 --> 24:12.400 of a machine learning system to contain 24:12.400 --> 24:14.920 a specific fact like that without needing to have it 24:14.920 --> 24:17.000 presented over and over and over again. 24:17.000 --> 24:21.440 So some of the success of symbolic systems in the 80s 24:21.440 --> 24:26.200 is they were able to assemble these kinds of facts better. 24:26.200 --> 24:29.080 But there's a lot of expert input required 24:29.080 --> 24:31.120 and it's very limited in that sense. 24:31.120 --> 24:34.720 Do you ever look back to that as something 24:34.720 --> 24:36.560 that we'll have to return to eventually 24:36.560 --> 24:38.440 sort of dust off the book from the shelf 24:38.440 --> 24:42.400 and think about how we build knowledge, representation, 24:42.400 --> 24:43.240 knowledge. 24:43.240 --> 24:44.840 Like will we have to use graph searches? 24:44.840 --> 24:45.800 Graph searches, right. 24:45.800 --> 24:47.720 And like first order logic and entailment 24:47.720 --> 24:48.560 and things like that. 24:48.560 --> 24:49.560 That kind of thing, yeah, exactly. 24:49.560 --> 24:51.200 In my particular line of work, 24:51.200 --> 24:54.560 which has mostly been machine learning security 24:54.560 --> 24:56.720 and also generative modeling, 24:56.720 --> 25:00.560 I haven't usually found myself moving in that direction. 25:00.560 --> 25:03.520 For generative models, I could see a little bit of, 25:03.520 --> 25:06.520 it could be useful if you had something like a, 25:06.520 --> 25:09.680 a differentiable knowledge base 25:09.680 --> 25:11.000 or some other kind of knowledge base 25:11.000 --> 25:13.840 where it's possible for some of our fuzzier 25:13.840 --> 25:16.880 machine learning algorithms to interact with a knowledge base. 25:16.880 --> 25:19.040 I mean, your network is kind of like that. 25:19.040 --> 25:21.440 It's a differentiable knowledge base of sorts. 25:21.440 --> 25:22.280 Yeah. 25:22.280 --> 25:27.280 But if we had a really easy way of giving feedback 25:27.600 --> 25:29.240 to machine learning models, 25:29.240 --> 25:32.400 that would clearly help a lot with, with generative models. 25:32.400 --> 25:34.680 And so you could imagine one way of getting there would be, 25:34.680 --> 25:36.720 get a lot better at natural language processing. 25:36.720 --> 25:38.920 But another way of getting there would be, 25:38.920 --> 25:40.280 take some kind of knowledge base 25:40.280 --> 25:42.800 and figure out a way for it to actually interact 25:42.800 --> 25:44.080 with a neural network. 25:44.080 --> 25:46.080 Being able to have a chat with a neural network. 25:46.080 --> 25:47.920 Yeah. 25:47.920 --> 25:50.920 So like one thing in generative models we see a lot today is, 25:50.920 --> 25:54.480 you'll get things like faces that are not symmetrical. 25:54.480 --> 25:56.800 Like, like people that have two eyes 25:56.800 --> 25:58.200 that are different colors. 25:58.200 --> 25:59.560 And I mean, there are people with eyes 25:59.560 --> 26:00.840 that are different colors in real life, 26:00.840 --> 26:03.480 but not nearly as many of them as you tend to see 26:03.480 --> 26:06.120 in the machine learning generated data. 26:06.120 --> 26:08.120 So if you had either a knowledge base 26:08.120 --> 26:10.200 that could contain the fact, 26:10.200 --> 26:13.360 people's faces are generally approximately symmetric 26:13.360 --> 26:15.920 and eye color is especially likely 26:15.920 --> 26:17.920 to be the same on both sides. 26:17.920 --> 26:20.160 Being able to just inject that hint 26:20.160 --> 26:22.000 into the machine learning model 26:22.000 --> 26:23.800 without having to discover that itself 26:23.800 --> 26:25.760 after studying a lot of data 26:25.760 --> 26:28.360 would be a really useful feature. 26:28.360 --> 26:30.120 I could see a lot of ways of getting there 26:30.120 --> 26:32.200 without bringing back some of the 1980s technology, 26:32.200 --> 26:35.160 but I also see some ways that you could imagine 26:35.160 --> 26:38.240 extending the 1980s technology to play nice with neural nets 26:38.240 --> 26:40.040 and have it help get there. 26:40.040 --> 26:40.880 Awesome. 26:40.880 --> 26:44.360 So you talked about the story of you coming up 26:44.360 --> 26:47.040 with the idea of GANs at a bar with some friends. 26:47.040 --> 26:50.400 You were arguing that this, you know, 26:50.400 --> 26:53.080 GANs would work generative adversarial networks 26:53.080 --> 26:54.680 and the others didn't think so. 26:54.680 --> 26:58.400 Then you went home at midnight, coded up and it worked. 26:58.400 --> 27:01.320 So if I was a friend of yours at the bar, 27:01.320 --> 27:02.720 I would also have doubts. 27:02.720 --> 27:03.880 It's a really nice idea, 27:03.880 --> 27:06.800 but I'm very skeptical that it would work. 27:06.800 --> 27:09.280 What was the basis of their skepticism? 27:09.280 --> 27:13.200 What was the basis of your intuition why it should work? 27:14.360 --> 27:16.840 I don't wanna be someone who goes around promoting alcohol 27:16.840 --> 27:18.280 for the purposes of science, 27:18.280 --> 27:21.040 but in this case, I do actually think 27:21.040 --> 27:23.080 that drinking helped a little bit. 27:23.080 --> 27:25.360 When your inhibitions are lowered, 27:25.360 --> 27:27.400 you're more willing to try out things 27:27.400 --> 27:29.640 that you wouldn't try out otherwise. 27:29.640 --> 27:32.480 So I have noticed in general 27:32.480 --> 27:34.560 that I'm less prone to shooting down some of my own ideas 27:34.560 --> 27:37.960 when I have had a little bit to drink. 27:37.960 --> 27:40.800 I think if I had had that idea at lunchtime, 27:40.800 --> 27:42.280 I probably would have thought it. 27:42.280 --> 27:43.720 It's hard enough to train one neural net. 27:43.720 --> 27:44.880 You can't train a second neural net 27:44.880 --> 27:48.080 in the inner loop of the outer neural net. 27:48.080 --> 27:49.800 That was basically my friend's objection 27:49.800 --> 27:52.720 was that trying to train two neural nets at the same time 27:52.720 --> 27:54.280 would be too hard. 27:54.280 --> 27:56.120 So it was more about the training process 27:56.120 --> 28:01.120 unless, so my skepticism would be, I'm sure you could train it 28:01.160 --> 28:03.200 but the thing would converge to 28:03.200 --> 28:05.840 would not be able to generate anything reasonable 28:05.840 --> 28:08.240 and any kind of reasonable realism. 28:08.240 --> 28:11.360 Yeah, so part of what all of us were thinking about 28:11.360 --> 28:15.280 when we had this conversation was deep Bolton machines, 28:15.280 --> 28:17.000 which a lot of us in the lab, including me, 28:17.000 --> 28:19.480 were a big fan of deep Bolton machines at the time. 28:20.640 --> 28:24.240 They involved two separate processes running at the same time. 28:24.240 --> 28:27.400 One of them is called the positive phase 28:27.400 --> 28:30.440 where you load data into the model 28:30.440 --> 28:32.920 and tell the model to make the data more likely. 28:32.920 --> 28:34.480 The other one is called the negative phase 28:34.480 --> 28:36.280 where you draw samples from the model 28:36.280 --> 28:38.880 and tell the model to make those samples less likely. 28:40.480 --> 28:42.400 In a deep Bolton machine, it's not trivial 28:42.400 --> 28:43.320 to generate a sample. 28:43.320 --> 28:46.280 You have to actually run an iterative process 28:46.280 --> 28:48.520 that gets better and better samples 28:48.520 --> 28:50.720 coming closer and closer to the distribution 28:50.720 --> 28:52.120 the model represents. 28:52.120 --> 28:53.240 So during the training process, 28:53.240 --> 28:56.560 you're always running these two systems at the same time. 28:56.560 --> 28:58.360 One that's updating the parameters of the model 28:58.360 --> 28:59.880 and another one that's trying to generate samples 28:59.880 --> 29:01.120 from the model. 29:01.120 --> 29:03.720 And they worked really well on things like MNIST, 29:03.720 --> 29:05.200 but a lot of us in the lab, including me, 29:05.200 --> 29:08.840 had tried to get deep Bolton machines to scale past MNIST 29:08.840 --> 29:11.320 to things like generating color photos, 29:11.320 --> 29:13.480 and we just couldn't get the two processes 29:13.480 --> 29:15.360 to stay synchronized. 29:16.720 --> 29:18.120 So when I had the idea for GANs, 29:18.120 --> 29:19.720 a lot of people thought that the discriminator 29:19.720 --> 29:21.960 would have more or less the same problem 29:21.960 --> 29:25.360 as the negative phase in the Bolton machine, 29:25.360 --> 29:27.840 that trying to train the discriminator in the inner loop, 29:27.840 --> 29:29.960 you just couldn't get it to keep up 29:29.960 --> 29:31.560 with the generator in the outer loop. 29:31.560 --> 29:33.360 And that would prevent it from 29:33.360 --> 29:35.240 converging to anything useful. 29:35.240 --> 29:36.880 Yeah, I share that intuition. 29:36.880 --> 29:37.720 Yeah. 29:39.560 --> 29:42.000 But turns out to not be the case. 29:42.000 --> 29:43.800 A lot of the time with machine learning algorithms, 29:43.800 --> 29:45.200 it's really hard to predict ahead of time 29:45.200 --> 29:46.960 how well they'll actually perform. 29:46.960 --> 29:48.160 You have to just run the experiment 29:48.160 --> 29:49.200 and see what happens. 29:49.200 --> 29:53.480 And I would say I still today don't have like one factor 29:53.480 --> 29:54.840 I can put my finger on and say, 29:54.840 --> 29:58.360 this is why GANs worked for photo generation 29:58.360 --> 30:00.240 and deep Bolton machines don't. 30:02.000 --> 30:04.560 There are a lot of theory papers showing that 30:04.560 --> 30:06.400 under some theoretical settings, 30:06.400 --> 30:09.640 the GAN algorithm does actually converge. 30:10.720 --> 30:14.200 But those settings are restricted enough 30:14.200 --> 30:17.560 that they don't necessarily explain the whole picture 30:17.560 --> 30:20.760 in terms of all the results that we see in practice. 30:20.760 --> 30:22.360 So taking a step back, 30:22.360 --> 30:24.880 can you, in the same way as we talked about deep learning, 30:24.880 --> 30:28.440 can you tell me what generative adversarial networks are? 30:29.480 --> 30:31.400 Yeah, so generative adversarial networks 30:31.400 --> 30:34.000 are a particular kind of generative model. 30:34.000 --> 30:36.320 A generative model is a machine learning model 30:36.320 --> 30:38.880 that can train on some set of data. 30:38.880 --> 30:41.280 Like say you have a collection of photos of cats 30:41.280 --> 30:44.040 and you want to generate more photos of cats, 30:44.040 --> 30:47.120 or you want to estimate a probability distribution 30:47.120 --> 30:49.840 over cats so you can ask how likely it is 30:49.840 --> 30:51.840 that some new image is a photo of a cat. 30:52.920 --> 30:55.840 GANs are one way of doing this. 30:55.840 --> 30:59.200 Some generative models are good at creating new data. 30:59.200 --> 31:00.840 Other generative models are good 31:00.840 --> 31:02.600 at estimating that density function 31:02.600 --> 31:06.600 and telling you how likely particular pieces of data are 31:06.600 --> 31:09.760 to come from the same distribution as the training data. 31:09.760 --> 31:12.440 GANs are more focused on generating samples 31:12.440 --> 31:15.640 rather than estimating the density function. 31:15.640 --> 31:17.720 There are some kinds of GANs, like flow GAN, 31:17.720 --> 31:18.560 that can do both, 31:18.560 --> 31:21.680 but mostly GANs are about generating samples, 31:21.680 --> 31:24.240 generating new photos of cats that look realistic. 31:25.240 --> 31:29.360 And they do that completely from scratch. 31:29.360 --> 31:32.240 It's analogous to human imagination 31:32.240 --> 31:34.760 when a GAN creates a new image of a cat. 31:34.760 --> 31:39.320 It's using a neural network to produce a cat 31:39.320 --> 31:41.040 that has not existed before. 31:41.040 --> 31:44.560 It isn't doing something like compositing photos together. 31:44.560 --> 31:47.080 You're not literally taking the eye off of one cat 31:47.080 --> 31:49.000 and the ear off of another cat. 31:49.000 --> 31:51.320 It's more of this digestive process 31:51.320 --> 31:53.920 where the neural net trains in a lot of data 31:53.920 --> 31:55.560 and comes up with some representation 31:55.560 --> 31:57.360 of the probability distribution 31:57.360 --> 31:59.760 and generates entirely new cats. 31:59.760 --> 32:00.880 There are a lot of different ways 32:00.880 --> 32:01.960 of building a generative model. 32:01.960 --> 32:05.640 What's specific to GANs is that we have a two player game 32:05.640 --> 32:08.080 in the game theoretic sense. 32:08.080 --> 32:10.280 And as the players in this game compete, 32:10.280 --> 32:13.920 one of them becomes able to generate realistic data. 32:13.920 --> 32:16.120 The first player is called the generator. 32:16.120 --> 32:20.640 It produces output data, such as just images, for example. 32:20.640 --> 32:22.400 And at the start of the learning process, 32:22.400 --> 32:25.120 it'll just produce completely random images. 32:25.120 --> 32:27.360 The other player is called the discriminator. 32:27.360 --> 32:29.680 The discriminator takes images as input 32:29.680 --> 32:31.560 and guesses whether they're real or fake. 32:32.480 --> 32:34.200 You train it both on real data, 32:34.200 --> 32:36.120 so photos that come from your training set, 32:36.120 --> 32:37.840 actual photos of cats. 32:37.840 --> 32:39.880 And you try to say that those are real. 32:39.880 --> 32:41.920 You also train it on images 32:41.920 --> 32:43.840 that come from the generator network. 32:43.840 --> 32:46.720 And you train it to say that those are fake. 32:46.720 --> 32:49.200 As the two players compete in this game, 32:49.200 --> 32:50.920 the discriminator tries to become better 32:50.920 --> 32:53.280 at recognizing whether images are real or fake. 32:53.280 --> 32:54.760 And the generator becomes better 32:54.760 --> 32:56.960 at fooling the discriminator into thinking 32:56.960 --> 32:59.560 that its outputs are real. 33:00.760 --> 33:03.560 And you can analyze this through the language of game theory 33:03.560 --> 33:06.920 and find that there's a Nash equilibrium 33:06.920 --> 33:08.600 where the generator has captured 33:08.600 --> 33:10.800 the correct probability distribution. 33:10.800 --> 33:12.160 So in the cat example, 33:12.160 --> 33:14.560 it makes perfectly realistic cat photos. 33:14.560 --> 33:17.160 And the discriminator is unable to do better 33:17.160 --> 33:18.720 than random guessing, 33:18.720 --> 33:21.800 because all the samples coming from both the data 33:21.800 --> 33:24.000 and the generator look equally likely 33:24.000 --> 33:25.840 to have come from either source. 33:25.840 --> 33:28.320 So do you ever sit back 33:28.320 --> 33:31.280 and does it just blow your mind that this thing works? 33:31.280 --> 33:35.840 So from very, so it's able to estimate the density function 33:35.840 --> 33:38.640 enough to generate realistic images. 33:38.640 --> 33:43.640 I mean, yeah, do you ever sit back and think, 33:43.640 --> 33:46.760 how does this even, this is quite incredible, 33:46.760 --> 33:49.280 especially where against have gone in terms of realism. 33:49.280 --> 33:51.600 Yeah, and not just to flatter my own work, 33:51.600 --> 33:53.840 but generative models, 33:53.840 --> 33:55.400 all of them have this property 33:55.400 --> 33:58.800 that if they really did what we asked them to do, 33:58.800 --> 34:01.040 they would do nothing but memorize the training data. 34:01.040 --> 34:02.920 Right, exactly. 34:02.920 --> 34:05.720 Models that are based on maximizing the likelihood, 34:05.720 --> 34:08.200 the way that you obtain the maximum likelihood 34:08.200 --> 34:09.720 for a specific training set 34:09.720 --> 34:12.440 is you assign all of your probability mass 34:12.440 --> 34:15.120 to the training examples and nowhere else. 34:15.120 --> 34:18.440 For GANs, the game is played using a training set. 34:18.440 --> 34:21.160 So the way that you become unbeatable in the game 34:21.160 --> 34:23.440 is you literally memorize training examples. 34:25.360 --> 34:28.880 One of my former interns wrote a paper, 34:28.880 --> 34:31.040 his name is Vaishnav Nagarajan, 34:31.040 --> 34:33.080 and he showed that it's actually hard 34:33.080 --> 34:36.120 for the generator to memorize the training data, 34:36.120 --> 34:39.160 hard in a statistical learning theory sense, 34:39.160 --> 34:42.200 that you can actually create reasons 34:42.200 --> 34:47.200 for why it would require quite a lot of learning steps 34:48.400 --> 34:52.200 and a lot of observations of different latent variables 34:52.200 --> 34:54.360 before you could memorize the training data. 34:54.360 --> 34:55.680 That still doesn't really explain 34:55.680 --> 34:58.280 why when you produce samples that are new, 34:58.280 --> 34:59.880 why do you get compelling images 34:59.880 --> 35:02.400 rather than just garbage that's different 35:02.400 --> 35:03.800 from the training set. 35:03.800 --> 35:06.960 And I don't think we really have a good answer for that, 35:06.960 --> 35:07.920 especially if you think about 35:07.920 --> 35:10.240 how many possible images are out there 35:10.240 --> 35:15.240 and how few images the generative model sees during training. 35:15.440 --> 35:16.920 It seems just unreasonable 35:16.920 --> 35:19.200 that generative models create new images 35:19.200 --> 35:22.080 as well as they do, especially considering 35:22.080 --> 35:23.760 that we're basically training them to memorize 35:23.760 --> 35:25.000 rather than generalize. 35:26.240 --> 35:28.920 I think part of the answer is there's a paper 35:28.920 --> 35:31.480 called Deep Image Prior where they show 35:31.480 --> 35:33.080 that you can take a convolutional net 35:33.080 --> 35:35.000 and you don't even need to learn the parameters of it at all. 35:35.000 --> 35:37.640 You just use the model architecture. 35:37.640 --> 35:41.080 And it's already useful for things like in painting images. 35:41.080 --> 35:43.760 I think that shows us that the convolutional network 35:43.760 --> 35:45.880 architecture captures something really important 35:45.880 --> 35:47.960 about the structure of images. 35:47.960 --> 35:50.960 And we don't need to actually use learning 35:50.960 --> 35:52.200 to capture all the information 35:52.200 --> 35:54.000 coming out of the convolutional net. 35:55.240 --> 35:58.400 That would imply that it would be much harder 35:58.400 --> 36:01.240 to make generative models in other domains. 36:01.240 --> 36:03.600 So far, we're able to make reasonable speech models 36:03.600 --> 36:04.880 and things like that. 36:04.880 --> 36:07.440 But to be honest, we haven't actually explored 36:07.440 --> 36:09.800 a whole lot of different data sets all that much. 36:09.800 --> 36:13.920 We don't, for example, see a lot of deep learning models 36:13.920 --> 36:18.440 of like biology data sets 36:18.440 --> 36:19.880 where you have lots of microarrays 36:19.880 --> 36:22.240 measuring the amount of different enzymes 36:22.240 --> 36:23.080 and things like that. 36:23.080 --> 36:25.240 So we may find that some of the progress 36:25.240 --> 36:27.360 that we've seen for images and speech turns out 36:27.360 --> 36:30.120 to really rely heavily on the model architecture. 36:30.120 --> 36:32.960 And we were able to do what we did for vision 36:32.960 --> 36:36.080 by trying to reverse engineer the human visual system. 36:37.040 --> 36:39.800 And maybe it'll turn out that we can't just 36:39.800 --> 36:42.560 use that same trick for arbitrary kinds of data. 36:43.480 --> 36:45.920 Right, so there's aspect of the human vision system, 36:45.920 --> 36:49.280 the hardware of it that makes it, 36:49.280 --> 36:51.120 without learning, without cognition, 36:51.120 --> 36:53.640 just makes it really effective at detecting the patterns 36:53.640 --> 36:54.960 we see in the visual world. 36:54.960 --> 36:57.280 Yeah, that's really interesting. 36:57.280 --> 37:02.280 What, in a big quick overview in your view, 37:04.640 --> 37:06.280 what types of GANs are there 37:06.280 --> 37:10.080 and what other generative models besides GANs are there? 37:10.080 --> 37:13.360 Yeah, so it's maybe a little bit easier to start 37:13.360 --> 37:14.640 with what kinds of generative models 37:14.640 --> 37:15.920 are there other than GANs. 37:16.840 --> 37:20.840 So most generative models are likelihood based 37:20.840 --> 37:23.920 where to train them, you have a model 37:23.920 --> 37:27.320 that tells you how much probability it assigns 37:27.320 --> 37:29.080 to a particular example, 37:29.080 --> 37:31.480 and you just maximize the probability assigned 37:31.480 --> 37:33.680 to all the training examples. 37:33.680 --> 37:36.200 It turns out that it's hard to design a model 37:36.200 --> 37:39.200 that can create really complicated images 37:39.200 --> 37:42.280 or really complicated audio waveforms 37:42.280 --> 37:46.200 and still have it be possible to estimate 37:46.200 --> 37:51.200 the likelihood function from a computational point of view. 37:51.200 --> 37:53.200 Most interesting models that you would just write 37:53.200 --> 37:56.200 down intuitively, it turns out that it's almost impossible 37:56.200 --> 37:58.200 to calculate the amount of probability 37:58.200 --> 38:00.200 they assign to a particular point. 38:00.200 --> 38:04.200 So there's a few different schools of generative models 38:04.200 --> 38:06.200 in the likelihood family. 38:06.200 --> 38:09.200 One approach is to very carefully design the model 38:09.200 --> 38:12.200 so that it is computationally tractable 38:12.200 --> 38:15.200 to measure the density it assigns to a particular point. 38:15.200 --> 38:18.200 So there are things like auto regressive models, 38:18.200 --> 38:23.200 like pixel CNN, those basically break down 38:23.200 --> 38:26.200 the probability distribution into a product 38:26.200 --> 38:28.200 over every single feature. 38:28.200 --> 38:32.200 So for an image, you estimate the probability of each pixel 38:32.200 --> 38:35.200 given all of the pixels that came before it. 38:35.200 --> 38:37.200 There's tricks where if you want to measure 38:37.200 --> 38:40.200 the density function, you can actually calculate 38:40.200 --> 38:43.200 the density for all these pixels more or less in parallel. 38:44.200 --> 38:46.200 Generating the image still tends to require you 38:46.200 --> 38:50.200 to go one pixel at a time, and that can be very slow. 38:50.200 --> 38:52.200 But there are, again, tricks for doing this 38:52.200 --> 38:54.200 in a hierarchical pattern where you can keep 38:54.200 --> 38:56.200 the runtime under control. 38:56.200 --> 38:59.200 Are the quality of the images it generates 38:59.200 --> 39:02.200 putting runtime aside pretty good? 39:02.200 --> 39:04.200 They're reasonable, yeah. 39:04.200 --> 39:07.200 I would say a lot of the best results 39:07.200 --> 39:10.200 are from GANs these days, but it can be hard to tell 39:10.200 --> 39:14.200 how much of that is based on who's studying 39:14.200 --> 39:17.200 which type of algorithm, if that makes sense. 39:17.200 --> 39:19.200 The amount of effort invested in it. 39:19.200 --> 39:21.200 Yeah, or the kind of expertise. 39:21.200 --> 39:23.200 So a lot of people who've traditionally been excited 39:23.200 --> 39:25.200 about graphics or art and things like that 39:25.200 --> 39:27.200 have gotten interested in GANs. 39:27.200 --> 39:29.200 And to some extent, it's hard to tell, 39:29.200 --> 39:32.200 are GANs doing better because they have a lot of 39:32.200 --> 39:34.200 graphics and art experts behind them? 39:34.200 --> 39:36.200 Or are GANs doing better because 39:36.200 --> 39:38.200 they're more computationally efficient? 39:38.200 --> 39:40.200 Or are GANs doing better because 39:40.200 --> 39:43.200 they prioritize the realism of samples 39:43.200 --> 39:45.200 over the accuracy of the density function? 39:45.200 --> 39:47.200 I think all of those are potentially 39:47.200 --> 39:51.200 valid explanations, and it's hard to tell. 39:51.200 --> 39:53.200 So can you give a brief history of GANs 39:53.200 --> 39:59.200 from 2014 with Paper 13? 39:59.200 --> 40:01.200 Yeah, so a few highlights. 40:01.200 --> 40:03.200 In the first paper, we just showed that 40:03.200 --> 40:05.200 GANs basically work. 40:05.200 --> 40:07.200 If you look back at the samples we had now, 40:07.200 --> 40:09.200 they look terrible. 40:09.200 --> 40:11.200 On the CFAR 10 data set, you can't even 40:11.200 --> 40:13.200 see the effects in them. 40:13.200 --> 40:15.200 Your paper, sorry, you used CFAR 10? 40:15.200 --> 40:17.200 We used MNIST, which is Little Handwritten Digits. 40:17.200 --> 40:19.200 We used the Toronto Face Database, 40:19.200 --> 40:22.200 which is small grayscale photos of faces. 40:22.200 --> 40:24.200 We did have recognizable faces. 40:24.200 --> 40:26.200 My colleague Bing Xu put together 40:26.200 --> 40:29.200 the first GAN face model for that paper. 40:29.200 --> 40:32.200 We also had the CFAR 10 data set, 40:32.200 --> 40:35.200 which is things like very small 32x32 pixels 40:35.200 --> 40:40.200 of cars and cats and dogs. 40:40.200 --> 40:43.200 For that, we didn't get recognizable objects, 40:43.200 --> 40:46.200 but all the deep learning people back then 40:46.200 --> 40:48.200 were really used to looking at these failed samples 40:48.200 --> 40:50.200 and kind of reading them like tea leaves. 40:50.200 --> 40:53.200 And people who are used to reading the tea leaves 40:53.200 --> 40:56.200 recognize that our tea leaves at least look different. 40:56.200 --> 40:58.200 Maybe not necessarily better, 40:58.200 --> 41:01.200 but there was something unusual about them. 41:01.200 --> 41:03.200 And that got a lot of us excited. 41:03.200 --> 41:06.200 One of the next really big steps was LAPGAN 41:06.200 --> 41:10.200 by Emily Denton and Sumith Chintala at Facebook AI Research, 41:10.200 --> 41:14.200 where they actually got really good high resolution photos 41:14.200 --> 41:16.200 working with GANs for the first time. 41:16.200 --> 41:18.200 They had a complicated system 41:18.200 --> 41:20.200 where they generated the image starting at low res 41:20.200 --> 41:22.200 and then scaling up to high res, 41:22.200 --> 41:24.200 but they were able to get it to work. 41:24.200 --> 41:30.200 And then in 2015, I believe later that same year, 41:30.200 --> 41:35.200 Alec Radford and Sumith Chintala and Luke Metz 41:35.200 --> 41:38.200 published the DC GAN paper, 41:38.200 --> 41:41.200 which it stands for Deep Convolutional GAN. 41:41.200 --> 41:43.200 It's kind of a nonunique name 41:43.200 --> 41:46.200 because these days basically all GANs 41:46.200 --> 41:48.200 and even some before that were deep and convolutional, 41:48.200 --> 41:52.200 but they just kind of picked a name for a really great recipe 41:52.200 --> 41:55.200 where they were able to actually using only one model 41:55.200 --> 41:57.200 instead of a multi step process, 41:57.200 --> 42:01.200 actually generate realistic images of faces and things like that. 42:01.200 --> 42:05.200 That was sort of like the beginning 42:05.200 --> 42:07.200 of the Cambrian explosion of GANs. 42:07.200 --> 42:09.200 Once you had animals that had a backbone, 42:09.200 --> 42:12.200 you suddenly got lots of different versions of fish 42:12.200 --> 42:15.200 and four legged animals and things like that. 42:15.200 --> 42:17.200 So DC GAN became kind of the backbone 42:17.200 --> 42:19.200 for many different models that came out. 42:19.200 --> 42:21.200 Used as a baseline even still. 42:21.200 --> 42:23.200 Yeah, yeah. 42:23.200 --> 42:26.200 And so from there, I would say some interesting things we've seen 42:26.200 --> 42:30.200 are there's a lot you can say about how just 42:30.200 --> 42:33.200 the quality of standard image generation GANs has increased, 42:33.200 --> 42:36.200 but what's also maybe more interesting on an intellectual level 42:36.200 --> 42:40.200 is how the things you can use GANs for has also changed. 42:40.200 --> 42:44.200 One thing is that you can use them to learn classifiers 42:44.200 --> 42:47.200 without having to have class labels for every example 42:47.200 --> 42:49.200 in your training set. 42:49.200 --> 42:51.200 So that's called semi supervised learning. 42:51.200 --> 42:55.200 My colleague at OpenAI, Tim Solomon, who's at Brain now, 42:55.200 --> 42:57.200 wrote a paper called 42:57.200 --> 42:59.200 Improved Techniques for Training GANs. 42:59.200 --> 43:01.200 I'm a coauthor on this paper, 43:01.200 --> 43:03.200 but I can't claim any credit for this particular part. 43:03.200 --> 43:05.200 One thing he showed on the paper is that 43:05.200 --> 43:09.200 you can take the GAN discriminator and use it as a classifier 43:09.200 --> 43:12.200 that actually tells you this image is a cat, 43:12.200 --> 43:14.200 this image is a dog, this image is a car, 43:14.200 --> 43:16.200 this image is a truck. 43:16.200 --> 43:18.200 And so not just to say whether the image is real or fake, 43:18.200 --> 43:22.200 but if it is real to say specifically what kind of object it is. 43:22.200 --> 43:25.200 And he found that you can train these classifiers 43:25.200 --> 43:28.200 with far fewer labeled examples 43:28.200 --> 43:30.200 than traditional classifiers. 43:30.200 --> 43:33.200 So if you supervise based on also 43:33.200 --> 43:35.200 not just your discrimination ability, 43:35.200 --> 43:37.200 but your ability to classify, 43:37.200 --> 43:40.200 you're going to converge much faster 43:40.200 --> 43:43.200 to being effective at being a discriminator. 43:43.200 --> 43:44.200 Yeah. 43:44.200 --> 43:46.200 So for example, for the MNIST dataset, 43:46.200 --> 43:49.200 you want to look at an image of a handwritten digit 43:49.200 --> 43:53.200 and say whether it's a zero, a one, or two, and so on. 43:53.200 --> 43:57.200 To get down to less than 1% accuracy, 43:57.200 --> 44:00.200 we required around 60,000 examples 44:00.200 --> 44:03.200 until maybe about 2014 or so. 44:03.200 --> 44:07.200 In 2016, with this semi supervised GAN project, 44:07.200 --> 44:10.200 Tim was able to get below 1% error 44:10.200 --> 44:13.200 using only 100 labeled examples. 44:13.200 --> 44:16.200 So that was about a 600x decrease 44:16.200 --> 44:18.200 in the amount of labels that he needed. 44:18.200 --> 44:21.200 He's still using more images than that, 44:21.200 --> 44:23.200 but he doesn't need to have each of them labeled as, 44:23.200 --> 44:25.200 you know, this one's a one, this one's a two, 44:25.200 --> 44:27.200 this one's a zero, and so on. 44:27.200 --> 44:29.200 Then to be able to, for GANs, 44:29.200 --> 44:31.200 to be able to generate recognizable objects, 44:31.200 --> 44:33.200 so objects from a particular class, 44:33.200 --> 44:36.200 you still need labeled data, 44:36.200 --> 44:38.200 because you need to know 44:38.200 --> 44:41.200 what it means to be a particular class cat dog. 44:41.200 --> 44:44.200 How do you think we can move away from that? 44:44.200 --> 44:46.200 Yeah, some researchers at Brain Zurich 44:46.200 --> 44:49.200 actually just released a really great paper 44:49.200 --> 44:51.200 on semi supervised GANs, 44:51.200 --> 44:54.200 where their goal isn't to classify, 44:54.200 --> 44:56.200 to make recognizable objects 44:56.200 --> 44:58.200 despite not having a lot of labeled data. 44:58.200 --> 45:02.200 They were working off of DeepMind's BigGAN project, 45:02.200 --> 45:04.200 and they showed that they can match 45:04.200 --> 45:06.200 the performance of BigGAN 45:06.200 --> 45:10.200 using only 10%, I believe, of the labels. 45:10.200 --> 45:12.200 BigGAN was trained on the ImageNet data set, 45:12.200 --> 45:14.200 which is about 1.2 million images, 45:14.200 --> 45:17.200 and had all of them labeled. 45:17.200 --> 45:19.200 This latest project from Brain Zurich 45:19.200 --> 45:21.200 shows that they're able to get away with 45:21.200 --> 45:25.200 having about 10% of the images labeled. 45:25.200 --> 45:29.200 They do that essentially using a clustering algorithm, 45:29.200 --> 45:32.200 where the discriminator learns to assign 45:32.200 --> 45:34.200 the objects to groups, 45:34.200 --> 45:38.200 and then this understanding that objects can be grouped 45:38.200 --> 45:40.200 into similar types, 45:40.200 --> 45:43.200 helps it to form more realistic ideas 45:43.200 --> 45:45.200 of what should be appearing in the image, 45:45.200 --> 45:47.200 because it knows that every image it creates 45:47.200 --> 45:50.200 has to come from one of these archetypal groups, 45:50.200 --> 45:53.200 rather than just being some arbitrary image. 45:53.200 --> 45:55.200 If you train again with no class labels, 45:55.200 --> 45:57.200 you tend to get things that look sort of like 45:57.200 --> 46:00.200 grass or water or brick or dirt, 46:00.200 --> 46:04.200 but without necessarily a lot going on in them. 46:04.200 --> 46:06.200 I think that's partly because if you look 46:06.200 --> 46:08.200 at a large ImageNet image, 46:08.200 --> 46:11.200 the object doesn't necessarily occupy the whole image, 46:11.200 --> 46:15.200 and so you learn to create realistic sets of pixels, 46:15.200 --> 46:17.200 but you don't necessarily learn 46:17.200 --> 46:19.200 that the object is the star of the show, 46:19.200 --> 46:22.200 and you want it to be in every image you make. 46:22.200 --> 46:25.200 Yeah, I've heard you talk about the horse, 46:25.200 --> 46:27.200 the zebra cycle, gang mapping, 46:27.200 --> 46:30.200 and how it turns out, again, 46:30.200 --> 46:33.200 thought provoking that horses are usually on grass, 46:33.200 --> 46:35.200 and zebras are usually on drier terrain, 46:35.200 --> 46:38.200 so when you're doing that kind of generation, 46:38.200 --> 46:43.200 you're going to end up generating greener horses or whatever. 46:43.200 --> 46:45.200 So those are connected together. 46:45.200 --> 46:46.200 It's not just... 46:46.200 --> 46:47.200 Yeah, yeah. 46:47.200 --> 46:49.200 You're not able to segment, 46:49.200 --> 46:52.200 to be able to generate in a segmental way. 46:52.200 --> 46:55.200 So are there other types of games you come across 46:55.200 --> 47:00.200 in your mind that neural networks can play with each other 47:00.200 --> 47:05.200 to be able to solve problems? 47:05.200 --> 47:09.200 Yeah, the one that I spend most of my time on is in security. 47:09.200 --> 47:13.200 You can model most interactions as a game 47:13.200 --> 47:16.200 where there's attackers trying to break your system 47:16.200 --> 47:19.200 or the defender trying to build a resilient system. 47:19.200 --> 47:22.200 There's also domain adversarial learning, 47:22.200 --> 47:25.200 which is an approach to domain adaptation 47:25.200 --> 47:27.200 that looks really a lot like GANs. 47:27.200 --> 47:31.200 The authors had the idea before the GAN paper came out. 47:31.200 --> 47:33.200 Their paper came out a little bit later, 47:33.200 --> 47:38.200 and they were very nice and cited the GAN paper, 47:38.200 --> 47:41.200 but I know that they actually had the idea before it came out. 47:41.200 --> 47:45.200 Domain adaptation is when you want to train a machine learning model 47:45.200 --> 47:47.200 in one setting called a domain, 47:47.200 --> 47:50.200 and then deploy it in another domain later, 47:50.200 --> 47:52.200 and you would like it to perform well in the new domain, 47:52.200 --> 47:55.200 even though the new domain is different from how it was trained. 47:55.200 --> 47:58.200 So, for example, you might want to train 47:58.200 --> 48:01.200 on a really clean image dataset like ImageNet, 48:01.200 --> 48:03.200 but then deploy on users phones, 48:03.200 --> 48:06.200 where the user is taking pictures in the dark 48:06.200 --> 48:08.200 and pictures while moving quickly 48:08.200 --> 48:10.200 and just pictures that aren't really centered 48:10.200 --> 48:13.200 or composed all that well. 48:13.200 --> 48:16.200 When you take a normal machine learning model, 48:16.200 --> 48:19.200 it often degrades really badly when you move to the new domain 48:19.200 --> 48:22.200 because it looks so different from what the model was trained on. 48:22.200 --> 48:25.200 Domain adaptation algorithms try to smooth out that gap, 48:25.200 --> 48:28.200 and the domain adversarial approach is based on 48:28.200 --> 48:30.200 training a feature extractor, 48:30.200 --> 48:32.200 where the features have the same statistics 48:32.200 --> 48:35.200 regardless of which domain you extracted them on. 48:35.200 --> 48:37.200 So, in the domain adversarial game, 48:37.200 --> 48:39.200 you have one player that's a feature extractor 48:39.200 --> 48:42.200 and another player that's a domain recognizer. 48:42.200 --> 48:44.200 The domain recognizer wants to look at the output 48:44.200 --> 48:47.200 of the feature extractor and guess which of the two domains 48:47.200 --> 48:49.200 the features came from. 48:49.200 --> 48:52.200 So, it's a lot like the real versus fake discriminator in GANs. 48:52.200 --> 48:54.200 And then the feature extractor, 48:54.200 --> 48:57.200 you can think of as loosely analogous to the generator in GANs, 48:57.200 --> 48:59.200 except what it's trying to do here 48:59.200 --> 49:02.200 is both fool the domain recognizer 49:02.200 --> 49:05.200 into not knowing which domain the data came from 49:05.200 --> 49:08.200 and also extract features that are good for classification. 49:08.200 --> 49:13.200 So, at the end of the day, in the cases where it works out, 49:13.200 --> 49:18.200 you can actually get features that work about the same 49:18.200 --> 49:20.200 in both domains. 49:20.200 --> 49:22.200 Sometimes this has a drawback where, 49:22.200 --> 49:24.200 in order to make things work the same in both domains, 49:24.200 --> 49:26.200 it just gets worse at the first one. 49:26.200 --> 49:28.200 But there are a lot of cases where it actually 49:28.200 --> 49:30.200 works out well on both. 49:30.200 --> 49:33.200 So, do you think of GANs being useful in the context 49:33.200 --> 49:35.200 of data augmentation? 49:35.200 --> 49:37.200 Yeah, one thing you could hope for with GANs 49:37.200 --> 49:39.200 is you could imagine, 49:39.200 --> 49:41.200 I've got a limited training set 49:41.200 --> 49:43.200 and I'd like to make more training data 49:43.200 --> 49:46.200 to train something else like a classifier. 49:46.200 --> 49:50.200 You could train the GAN on the training set 49:50.200 --> 49:52.200 and then create more data 49:52.200 --> 49:55.200 and then maybe the classifier would perform better 49:55.200 --> 49:58.200 on the test set after training on this bigger GAN generated data set. 49:58.200 --> 50:00.200 So, that's the simplest version 50:00.200 --> 50:02.200 of something you might hope would work. 50:02.200 --> 50:05.200 I've never heard of that particular approach working, 50:05.200 --> 50:08.200 but I think there's some closely related things 50:08.200 --> 50:11.200 that I think could work in the future 50:11.200 --> 50:13.200 and some that actually already have worked. 50:13.200 --> 50:15.200 So, if we think a little bit about what we'd be hoping for 50:15.200 --> 50:17.200 if we use the GAN to make more training data, 50:17.200 --> 50:20.200 we're hoping that the GAN will generalize 50:20.200 --> 50:23.200 to new examples better than the classifier would have 50:23.200 --> 50:25.200 generalized if it was trained on the same data. 50:25.200 --> 50:27.200 And I don't know of any reason to believe 50:27.200 --> 50:30.200 that the GAN would generalize better than the classifier would. 50:30.200 --> 50:33.200 But what we might hope for is that the GAN 50:33.200 --> 50:37.200 could generalize differently from a specific classifier. 50:37.200 --> 50:39.200 So, one thing I think is worth trying 50:39.200 --> 50:41.200 that I haven't personally tried, but someone could try is 50:41.200 --> 50:44.200 what if you trained a whole lot of different generative models 50:44.200 --> 50:46.200 on the same training set, 50:46.200 --> 50:48.200 create samples from all of them 50:48.200 --> 50:50.200 and then train a classifier on that. 50:50.200 --> 50:52.200 Because each of the generative models 50:52.200 --> 50:54.200 might generalize in a slightly different way, 50:54.200 --> 50:56.200 they might capture many different axes of variation 50:56.200 --> 50:58.200 that one individual model wouldn't. 50:58.200 --> 51:01.200 And then the classifier can capture all of those ideas 51:01.200 --> 51:03.200 by training in all of their data. 51:03.200 --> 51:06.200 So, it'd be a little bit like making an ensemble of classifiers. 51:06.200 --> 51:08.200 An ensemble of GANs in a way. 51:08.200 --> 51:10.200 I think that could generalize better. 51:10.200 --> 51:12.200 The other thing that GANs are really good for 51:12.200 --> 51:16.200 is not necessarily generating new data 51:16.200 --> 51:19.200 that's exactly like what you already have, 51:19.200 --> 51:23.200 but by generating new data that has different properties 51:23.200 --> 51:25.200 from the data you already had. 51:25.200 --> 51:27.200 One thing that you can do is you can create 51:27.200 --> 51:29.200 differentially private data. 51:29.200 --> 51:31.200 So, suppose that you have something like medical records 51:31.200 --> 51:34.200 and you don't want to train a classifier on the medical records 51:34.200 --> 51:36.200 and then publish the classifier 51:36.200 --> 51:38.200 because someone might be able to reverse engineer 51:38.200 --> 51:40.200 some of the medical records you trained on. 51:40.200 --> 51:42.200 There's a paper from Casey Green's lab 51:42.200 --> 51:46.200 that shows how you can train again using differential privacy. 51:46.200 --> 51:48.200 And then the samples from the GAN 51:48.200 --> 51:51.200 still have the same differential privacy guarantees 51:51.200 --> 51:53.200 as the parameters of the GAN. 51:53.200 --> 51:55.200 So, you can make fake patient data 51:55.200 --> 51:57.200 for other researchers to use 51:57.200 --> 51:59.200 and they can do almost anything they want with that data 51:59.200 --> 52:02.200 because it doesn't come from real people. 52:02.200 --> 52:04.200 And the differential privacy mechanism 52:04.200 --> 52:07.200 gives you clear guarantees on how much 52:07.200 --> 52:09.200 the original people's data has been protected. 52:09.200 --> 52:11.200 That's really interesting, actually. 52:11.200 --> 52:13.200 I haven't heard you talk about that before. 52:13.200 --> 52:15.200 In terms of fairness, 52:15.200 --> 52:19.200 I've seen from AAAI your talk, 52:19.200 --> 52:21.200 how can adversarial machine learning 52:21.200 --> 52:23.200 help models be more fair 52:23.200 --> 52:25.200 with respect to sensitive variables? 52:25.200 --> 52:28.200 Yeah. So, there's a paper from Emma Storky's lab 52:28.200 --> 52:31.200 about how to learn machine learning models 52:31.200 --> 52:34.200 that are incapable of using specific variables. 52:34.200 --> 52:36.200 So, say, for example, you wanted to make predictions 52:36.200 --> 52:39.200 that are not affected by gender. 52:39.200 --> 52:41.200 It isn't enough to just leave gender 52:41.200 --> 52:43.200 out of the input to the model. 52:43.200 --> 52:45.200 You can often infer gender from a lot of other characteristics. 52:45.200 --> 52:47.200 Like, say that you have the person's name, 52:47.200 --> 52:49.200 but you're not told their gender. 52:49.200 --> 52:53.200 Well, if their name is Ian, they're kind of obviously a man. 52:53.200 --> 52:55.200 So, what you'd like to do is make a machine learning model 52:55.200 --> 52:58.200 that can still take in a lot of different attributes 52:58.200 --> 53:02.200 and make a really accurate informed prediction, 53:02.200 --> 53:05.200 but be confident that it isn't reverse engineering gender 53:05.200 --> 53:08.200 or another sensitive variable internally. 53:08.200 --> 53:10.200 You can do that using something very similar 53:10.200 --> 53:12.200 to the domain adversarial approach, 53:12.200 --> 53:15.200 where you have one player that's a feature extractor 53:15.200 --> 53:18.200 and another player that's a feature analyzer. 53:18.200 --> 53:21.200 And you want to make sure that the feature analyzer 53:21.200 --> 53:24.200 is not able to guess the value of the sensitive variable 53:24.200 --> 53:26.200 that you're trying to keep private. 53:26.200 --> 53:29.200 Right. Yeah, I love this approach. 53:29.200 --> 53:34.200 So, with the feature, you're not able to infer 53:34.200 --> 53:36.200 the sensitive variables. 53:36.200 --> 53:39.200 It's brilliant. It's quite brilliant and simple, actually. 53:39.200 --> 53:42.200 Another way I think that GANs in particular 53:42.200 --> 53:44.200 could be used for fairness would be 53:44.200 --> 53:46.200 to make something like a cycle GAN, 53:46.200 --> 53:49.200 where you can take data from one domain 53:49.200 --> 53:51.200 and convert it into another. 53:51.200 --> 53:54.200 We've seen cycle GAN turning horses into zebras. 53:54.200 --> 53:59.200 We've seen other unsupervised GANs made by Mingyu Liu 53:59.200 --> 54:02.200 doing things like turning day photos into night photos. 54:02.200 --> 54:05.200 I think for fairness, you could imagine 54:05.200 --> 54:08.200 taking records for people in one group 54:08.200 --> 54:11.200 and transforming them into analogous people in another group 54:11.200 --> 54:14.200 and testing to see if they're treated equitably 54:14.200 --> 54:16.200 across those two groups. 54:16.200 --> 54:18.200 There's a lot of things that would be hard to get right 54:18.200 --> 54:21.200 and make sure that the conversion process itself is fair. 54:21.200 --> 54:24.200 And I don't think it's anywhere near something 54:24.200 --> 54:26.200 that we could actually use yet. 54:26.200 --> 54:28.200 But if you could design that conversion process very carefully, 54:28.200 --> 54:30.200 it might give you a way of doing audits 54:30.200 --> 54:33.200 where you say, what if we took people from this group, 54:33.200 --> 54:35.200 converted them into equivalent people in another group? 54:35.200 --> 54:39.200 Does the system actually treat them how it ought to? 54:39.200 --> 54:41.200 That's also really interesting. 54:41.200 --> 54:46.200 You know, in popular press 54:46.200 --> 54:48.200 and in general, in our imagination, 54:48.200 --> 54:51.200 you think, well, GANs are able to generate data 54:51.200 --> 54:54.200 and you start to think about deep fakes 54:54.200 --> 54:57.200 or being able to sort of maliciously generate data 54:57.200 --> 55:00.200 that fakes the identity of other people. 55:00.200 --> 55:03.200 Is this something of a concern to you? 55:03.200 --> 55:06.200 Is this something, if you look 10, 20 years into the future, 55:06.200 --> 55:10.200 is that something that pops up in your work, 55:10.200 --> 55:13.200 in the work of the community that's working on generative models? 55:13.200 --> 55:15.200 I'm a lot less concerned about 20 years from now 55:15.200 --> 55:17.200 than the next few years. 55:17.200 --> 55:20.200 I think there will be a kind of bumpy cultural transition 55:20.200 --> 55:22.200 as people encounter this idea 55:22.200 --> 55:25.200 that there can be very realistic videos and audio that aren't real. 55:25.200 --> 55:27.200 I think 20 years from now, 55:27.200 --> 55:30.200 people will mostly understand that you shouldn't believe 55:30.200 --> 55:33.200 something is real just because you saw a video of it. 55:33.200 --> 55:37.200 People will expect to see that it's been cryptographically signed 55:37.200 --> 55:41.200 or have some other mechanism to make them believe 55:41.200 --> 55:43.200 that the content is real. 55:43.200 --> 55:45.200 There's already people working on this, 55:45.200 --> 55:47.200 like there's a startup called TruePick 55:47.200 --> 55:50.200 that provides a lot of mechanisms for authenticating 55:50.200 --> 55:52.200 that an image is real. 55:52.200 --> 55:55.200 They're maybe not quite up to having a state actor 55:55.200 --> 55:59.200 try to evade their verification techniques, 55:59.200 --> 56:02.200 but it's something that people are already working on 56:02.200 --> 56:04.200 and I think will get right eventually. 56:04.200 --> 56:08.200 So you think authentication will eventually win out? 56:08.200 --> 56:11.200 So being able to authenticate that this is real and this is not? 56:11.200 --> 56:13.200 Yeah. 56:13.200 --> 56:15.200 As opposed to GANs just getting better and better 56:15.200 --> 56:18.200 or generative models being able to get better and better 56:18.200 --> 56:21.200 to where the nature of what is real is normal. 56:21.200 --> 56:25.200 I don't think we'll ever be able to look at the pixels of a photo 56:25.200 --> 56:28.200 and tell you for sure that it's real or not real, 56:28.200 --> 56:32.200 and I think it would actually be somewhat dangerous 56:32.200 --> 56:34.200 to rely on that approach too much. 56:34.200 --> 56:36.200 If you make a really good fake detector 56:36.200 --> 56:38.200 and then someone's able to fool your fake detector 56:38.200 --> 56:41.200 and your fake detector says this image is not fake, 56:41.200 --> 56:43.200 then it's even more credible 56:43.200 --> 56:46.200 than if you've never made a fake detector in the first place. 56:46.200 --> 56:50.200 What I do think we'll get to is systems 56:50.200 --> 56:52.200 that we can kind of use behind the scenes 56:52.200 --> 56:55.200 to make estimates of what's going on 56:55.200 --> 56:59.200 and maybe not use them in court for a definitive analysis. 56:59.200 --> 57:04.200 I also think we will likely get better authentication systems 57:04.200 --> 57:08.200 where, imagine that every phone cryptographically 57:08.200 --> 57:10.200 signs everything that comes out of it. 57:10.200 --> 57:12.200 You wouldn't be able to conclusively tell 57:12.200 --> 57:14.200 that an image was real, 57:14.200 --> 57:18.200 but you would be able to tell somebody who knew 57:18.200 --> 57:21.200 the appropriate private key for this phone 57:21.200 --> 57:24.200 was actually able to sign this image 57:24.200 --> 57:28.200 and upload it to this server at this time stamp. 57:28.200 --> 57:31.200 You could imagine maybe you make phones 57:31.200 --> 57:35.200 that have the private keys hardware embedded in them. 57:35.200 --> 57:37.200 If a state security agency 57:37.200 --> 57:39.200 really wants to infiltrate the company, 57:39.200 --> 57:42.200 they could probably plant a private key of their choice 57:42.200 --> 57:44.200 or break open the chip 57:44.200 --> 57:46.200 and learn the private key or something like that. 57:46.200 --> 57:48.200 But it would make it a lot harder 57:48.200 --> 57:51.200 for an adversary with fewer resources to fake things. 57:51.200 --> 57:53.200 For most of us, it would be okay. 57:53.200 --> 57:58.200 You mentioned the beer and the bar and the new ideas. 57:58.200 --> 58:01.200 You were able to come up with this new idea 58:01.200 --> 58:04.200 pretty quickly and implement it pretty quickly. 58:04.200 --> 58:06.200 Do you think there are still many 58:06.200 --> 58:08.200 such groundbreaking ideas in deep learning 58:08.200 --> 58:10.200 that could be developed so quickly? 58:10.200 --> 58:13.200 Yeah, I do think that there are a lot of ideas 58:13.200 --> 58:15.200 that can be developed really quickly. 58:15.200 --> 58:18.200 GANs were probably a little bit of an outlier 58:18.200 --> 58:20.200 on the whole one hour time scale. 58:20.200 --> 58:24.200 But just in terms of low resource ideas 58:24.200 --> 58:26.200 where you do something really different 58:26.200 --> 58:29.200 on a high scale and get a big payback, 58:29.200 --> 58:32.200 I think it's not as likely that you'll see that 58:32.200 --> 58:35.200 in terms of things like core machine learning technologies 58:35.200 --> 58:37.200 like a better classifier 58:37.200 --> 58:39.200 or a better reinforcement learning algorithm 58:39.200 --> 58:41.200 or a better generative model. 58:41.200 --> 58:43.200 If I had the GAN idea today, 58:43.200 --> 58:45.200 it would be a lot harder to prove that it was useful 58:45.200 --> 58:47.200 than it was back in 2014 58:47.200 --> 58:50.200 because I would need to get it running on something 58:50.200 --> 58:54.200 like ImageNet or Celeb A at high resolution. 58:54.200 --> 58:56.200 Those take a while to train. 58:56.200 --> 58:58.200 You couldn't train it in an hour 58:58.200 --> 59:01.200 and know that it was something really new and exciting. 59:01.200 --> 59:04.200 Back in 2014, training on MNIST was enough. 59:04.200 --> 59:07.200 But there are other areas of machine learning 59:07.200 --> 59:11.200 where I think a new idea could actually be developed 59:11.200 --> 59:13.200 really quickly with low resources. 59:13.200 --> 59:15.200 What's your intuition about what areas 59:15.200 --> 59:18.200 of machine learning are ripe for this? 59:18.200 --> 59:23.200 Yeah, so I think fairness and interpretability 59:23.200 --> 59:27.200 are areas where we just really don't have any idea 59:27.200 --> 59:29.200 how anything should be done yet. 59:29.200 --> 59:31.200 Like for interpretability, 59:31.200 --> 59:33.200 I don't think we even have the right definitions. 59:33.200 --> 59:36.200 And even just defining a really useful concept, 59:36.200 --> 59:38.200 you don't even need to run any experiments. 59:38.200 --> 59:40.200 It could have a huge impact on the field. 59:40.200 --> 59:43.200 We've seen that, for example, in differential privacy 59:43.200 --> 59:45.200 that Cynthia Dwork and her collaborators 59:45.200 --> 59:48.200 made this technical definition of privacy 59:48.200 --> 59:50.200 where before a lot of things were really mushy 59:50.200 --> 59:53.200 and with that definition, you could actually design 59:53.200 --> 59:55.200 randomized algorithms for accessing databases 59:55.200 --> 59:59.200 and guarantee that they preserved individual people's privacy 59:59.200 --> 1:00:03.200 in a mathematical quantitative sense. 1:00:03.200 --> 1:00:05.200 Right now, we all talk a lot about 1:00:05.200 --> 1:00:07.200 how interpretable different machine learning algorithms are, 1:00:07.200 --> 1:00:09.200 but it's really just people's opinion. 1:00:09.200 --> 1:00:11.200 And everybody probably has a different idea 1:00:11.200 --> 1:00:13.200 of what interpretability means in their head. 1:00:13.200 --> 1:00:16.200 If we could define some concept related to interpretability 1:00:16.200 --> 1:00:18.200 that's actually measurable, 1:00:18.200 --> 1:00:20.200 that would be a huge leap forward 1:00:20.200 --> 1:00:24.200 even without a new algorithm that increases that quantity. 1:00:24.200 --> 1:00:28.200 And also, once we had the definition of differential privacy, 1:00:28.200 --> 1:00:31.200 it was fast to get the algorithms that guaranteed it. 1:00:31.200 --> 1:00:33.200 So you could imagine once we have definitions 1:00:33.200 --> 1:00:35.200 of good concepts and interpretability, 1:00:35.200 --> 1:00:37.200 we might be able to provide the algorithms 1:00:37.200 --> 1:00:42.200 that have the interpretability guarantees quickly, too. 1:00:42.200 --> 1:00:46.200 What do you think it takes to build a system 1:00:46.200 --> 1:00:48.200 with human level intelligence 1:00:48.200 --> 1:00:51.200 as we quickly venture into the philosophical? 1:00:51.200 --> 1:00:55.200 So artificial general intelligence, what do you think it takes? 1:00:55.200 --> 1:01:01.200 I think that it definitely takes better environments 1:01:01.200 --> 1:01:03.200 than we currently have for training agents, 1:01:03.200 --> 1:01:08.200 that we want them to have a really wide diversity of experiences. 1:01:08.200 --> 1:01:11.200 I also think it's going to take really a lot of computation. 1:01:11.200 --> 1:01:13.200 It's hard to imagine exactly how much. 1:01:13.200 --> 1:01:16.200 So you're optimistic about simulation, 1:01:16.200 --> 1:01:19.200 simulating a variety of environments as the path forward 1:01:19.200 --> 1:01:21.200 as opposed to operating in the real world? 1:01:21.200 --> 1:01:23.200 I think it's a necessary ingredient. 1:01:23.200 --> 1:01:27.200 I don't think that we're going to get to artificial general intelligence 1:01:27.200 --> 1:01:29.200 by training on fixed data sets 1:01:29.200 --> 1:01:32.200 or by thinking really hard about the problem. 1:01:32.200 --> 1:01:36.200 I think that the agent really needs to interact 1:01:36.200 --> 1:01:41.200 and have a variety of experiences within the same lifespan. 1:01:41.200 --> 1:01:45.200 And today we have many different models that can each do one thing, 1:01:45.200 --> 1:01:49.200 and we tend to train them on one dataset or one RL environment. 1:01:49.200 --> 1:01:53.200 Sometimes there are actually papers about getting one set of parameters 1:01:53.200 --> 1:01:56.200 to perform well in many different RL environments, 1:01:56.200 --> 1:01:59.200 but we don't really have anything like an agent 1:01:59.200 --> 1:02:02.200 that goes seamlessly from one type of experience to another 1:02:02.200 --> 1:02:05.200 and really integrates all the different things that it does 1:02:05.200 --> 1:02:07.200 over the course of its life. 1:02:07.200 --> 1:02:10.200 When we do see multiagent environments, 1:02:10.200 --> 1:02:16.200 they tend to be similar environments. 1:02:16.200 --> 1:02:19.200 All of them are playing an action based video game. 1:02:19.200 --> 1:02:24.200 We don't really have an agent that goes from playing a video game 1:02:24.200 --> 1:02:27.200 to reading the Wall Street Journal 1:02:27.200 --> 1:02:32.200 to predicting how effective a molecule will be as a drug or something like that. 1:02:32.200 --> 1:02:36.200 What do you think is a good test for intelligence in your view? 1:02:36.200 --> 1:02:41.200 There's been a lot of benchmarks started with Alan Turing, 1:02:41.200 --> 1:02:46.200 natural conversation being a good benchmark for intelligence. 1:02:46.200 --> 1:02:53.200 What would you and good fellows sit back and be really damn impressed 1:02:53.200 --> 1:02:55.200 if a system was able to accomplish? 1:02:55.200 --> 1:02:59.200 Something that doesn't take a lot of glue from human engineers. 1:02:59.200 --> 1:03:07.200 Imagine that instead of having to go to the CIFAR website and download CIFAR 10 1:03:07.200 --> 1:03:11.200 and then write a Python script to parse it and all that, 1:03:11.200 --> 1:03:16.200 you could just point an agent at the CIFAR 10 problem 1:03:16.200 --> 1:03:20.200 and it downloads and extracts the data and trains a model 1:03:20.200 --> 1:03:22.200 and starts giving you predictions. 1:03:22.200 --> 1:03:28.200 I feel like something that doesn't need to have every step of the pipeline assembled for it 1:03:28.200 --> 1:03:30.200 definitely understands what it's doing. 1:03:30.200 --> 1:03:34.200 Is AutoML moving into that direction or are you thinking way even bigger? 1:03:34.200 --> 1:03:39.200 AutoML has mostly been moving toward once we've built all the glue, 1:03:39.200 --> 1:03:44.200 can the machine learning system design the architecture really well? 1:03:44.200 --> 1:03:49.200 I'm more of saying if something knows how to pre process the data 1:03:49.200 --> 1:03:52.200 so that it successfully accomplishes the task, 1:03:52.200 --> 1:03:56.200 then it would be very hard to argue that it doesn't truly understand the task 1:03:56.200 --> 1:03:58.200 in some fundamental sense. 1:03:58.200 --> 1:04:02.200 I don't necessarily know that that's the philosophical definition of intelligence, 1:04:02.200 --> 1:04:05.200 but that's something that would be really cool to build that would be really useful 1:04:05.200 --> 1:04:09.200 and would impress me and would convince me that we've made a step forward in real AI. 1:04:09.200 --> 1:04:13.200 You give it the URL for Wikipedia 1:04:13.200 --> 1:04:18.200 and then next day expect it to be able to solve CIFAR 10. 1:04:18.200 --> 1:04:22.200 Or you type in a paragraph explaining what you want it to do 1:04:22.200 --> 1:04:28.200 and it figures out what web searches it should run and downloads all the necessary ingredients. 1:04:28.200 --> 1:04:37.200 So you have a very clear, calm way of speaking, no ums, easy to edit. 1:04:37.200 --> 1:04:44.200 I've seen comments for both you and I have been identified as both potentially being robots. 1:04:44.200 --> 1:04:48.200 If you have to prove to the world that you are indeed human, how would you do it? 1:04:48.200 --> 1:04:53.200 I can understand thinking that I'm a robot. 1:04:53.200 --> 1:04:57.200 It's the flip side of the Turing test, I think. 1:04:57.200 --> 1:05:00.200 Yeah, the prove your human test. 1:05:00.200 --> 1:05:08.200 Intellectually, so you have to, is there something that's truly unique in your mind 1:05:08.200 --> 1:05:13.200 as it doesn't go back to just natural language again, just being able to talk the way out of it? 1:05:13.200 --> 1:05:16.200 So proving that I'm not a robot with today's technology, 1:05:16.200 --> 1:05:18.200 that's pretty straightforward. 1:05:18.200 --> 1:05:25.200 My conversation today hasn't veered off into talking about the stock market or something because it's my training data. 1:05:25.200 --> 1:05:31.200 But I guess more generally trying to prove that something is real from the content alone is incredibly hard. 1:05:31.200 --> 1:05:37.200 That's one of the main things I've gotten out of my GAN research, that you can simulate almost anything 1:05:37.200 --> 1:05:42.200 and so you have to really step back to a separate channel to prove that something is real. 1:05:42.200 --> 1:05:48.200 So I guess I should have had myself stamped on a blockchain when I was born or something, but I didn't do that. 1:05:48.200 --> 1:05:52.200 So according to my own research methodology, there's just no way to know at this point. 1:05:52.200 --> 1:05:59.200 So what last question, problem stands out for you that you're really excited about challenging in the near future? 1:05:59.200 --> 1:06:06.200 I think resistance to adversarial examples, figuring out how to make machine learning secure against an adversary 1:06:06.200 --> 1:06:11.200 who wants to interfere it and control it, that is one of the most important things researchers today could solve. 1:06:11.200 --> 1:06:17.200 In all domains, image, language, driving and everything. 1:06:17.200 --> 1:06:22.200 I guess I'm most concerned about domains we haven't really encountered yet. 1:06:22.200 --> 1:06:28.200 Imagine 20 years from now when we're using advanced AIs to do things we haven't even thought of yet. 1:06:28.200 --> 1:06:37.200 If you ask people what are the important problems in security of phones in 2002, 1:06:37.200 --> 1:06:43.200 I don't think we would have anticipated that we're using them for nearly as many things as we're using them for today. 1:06:43.200 --> 1:06:47.200 I think it's going to be like that with AI that you can kind of try to speculate about where it's going, 1:06:47.200 --> 1:06:53.200 but really the business opportunities that end up taking off would be hard to predict ahead of time. 1:06:53.200 --> 1:06:58.200 What you can predict ahead of time is that almost anything you can do with machine learning, 1:06:58.200 --> 1:07:04.200 you would like to make sure that people can't get it to do what they want rather than what you want 1:07:04.200 --> 1:07:08.200 just by showing it a funny QR code or a funny input pattern. 1:07:08.200 --> 1:07:12.200 You think that the set of methodology to do that can be bigger than any one domain? 1:07:12.200 --> 1:07:15.200 I think so, yeah. 1:07:15.200 --> 1:07:20.200 One methodology that I think is not a specific methodology, 1:07:20.200 --> 1:07:25.200 but a category of solutions that I'm excited about today is making dynamic models 1:07:25.200 --> 1:07:28.200 that change every time they make a prediction. 1:07:28.200 --> 1:07:32.200 Right now, we tend to train models and then after they're trained, we freeze them. 1:07:32.200 --> 1:07:37.200 We just use the same rule to classify everything that comes in from then on. 1:07:37.200 --> 1:07:40.200 That's really a sitting duck from a security point of view. 1:07:40.200 --> 1:07:44.200 If you always output the same answer for the same input, 1:07:44.200 --> 1:07:49.200 then people can just run inputs through until they find a mistake that benefits them, 1:07:49.200 --> 1:07:53.200 and then they use the same mistake over and over and over again. 1:07:53.200 --> 1:08:00.200 I think having a model that updates its predictions so that it's harder to predict what you're going to get 1:08:00.200 --> 1:08:04.200 will make it harder for an adversary to really take control of the system 1:08:04.200 --> 1:08:06.200 and make it do what they want it to do. 1:08:06.200 --> 1:08:10.200 Yeah, models that maintain a bit of a sense of mystery about them 1:08:10.200 --> 1:08:12.200 because they always keep changing. 1:08:12.200 --> 1:08:14.200 Ian, thanks so much for talking today. It was awesome. 1:08:14.200 --> 1:08:36.200 Thank you for coming in. It's great to see you.