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

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{'title': 'Ian Goodfellow: Generative Adversarial Networks (GANs) | Lex Fridman Podcast #19', 'heatmap': [{'end': 2148.157, 'start': 2094.321, 'weight': 1}], 'summary': 'Ian goodfellow, author of deep learning textbook and director of machine learning at apple, discusses the limits of deep learning, emergence of consciousness in ai, gans evolution, applications in data augmentation, and the future of ai, including automl and security challenges.', 'chapters': [{'end': 50.009, 'segs': [{'end': 50.009, 'src': 'embed', 'start': 0.029, 'weight': 0, 'content': [{'end': 3.091, 'text': 'The following is a conversation with Ian Goodfellow.', 'start': 0.029, 'duration': 3.062}, {'end': 8.315, 'text': "He's the author of the popular textbook on deep learning, simply titled Deep Learning.", 'start': 3.772, 'duration': 4.543}, {'end': 14.739, 'text': 'He coined the term of generative adversarial networks, otherwise known as GANs,', 'start': 8.995, 'duration': 5.744}, {'end': 23.785, 'text': 'and with his 2014 paper is responsible for launching the incredible growth of research and innovation in this subfield of deep learning.', 'start': 14.739, 'duration': 9.046}, {'end': 32.573, 'text': 'He got his BS and MS at Stanford, his PhD at University of Montreal with Yoshua Bengio and Aaron Kerrville.', 'start': 24.766, 'duration': 7.807}, {'end': 40.6, 'text': 'He held several research positions including at OpenAI, Google Brain, and now at Apple as the Director of Machine Learning.', 'start': 33.353, 'duration': 7.247}, {'end': 44.864, 'text': 'This recording happened while Ian was still at Google Brain.', 'start': 41.621, 'duration': 3.243}, {'end': 50.009, 'text': "But we don't talk about anything specific to Google or any other organization.", 'start': 45.384, 'duration': 4.625}], 'summary': 'Ian goodfellow, author of deep learning, coined gans, led growth in deep learning, with diverse research positions.', 'duration': 49.98, 'max_score': 0.029, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Z6rxFNMGdn0/pics/Z6rxFNMGdn029.jpg'}], 'start': 0.029, 'title': 'Ian goodfellow', 'summary': 'Features ian goodfellow, the influential author of deep learning textbook, who coined the term gans, and holds degrees from stanford and the university of montreal. he has held research positions at openai, google brain, and is now the director of machine learning at apple.', 'chapters': [{'end': 50.009, 'start': 0.029, 'title': 'Ian goodfellow: deep learning innovator', 'summary': 'Features ian goodfellow, the author of the influential deep learning textbook, who coined the term gans and catalyzed significant growth in deep learning research. goodfellow earned degrees from stanford and the university of montreal, held research positions at openai, google brain, and is now the director of machine learning at apple.', 'duration': 49.98, 'highlights': ["Ian Goodfellow authored the impactful textbook 'Deep Learning' and introduced the concept of generative adversarial networks (GANs), spurring remarkable growth in the subfield of deep learning.", "Goodfellow's academic background includes earning BS and MS degrees from Stanford and a PhD from the University of Montreal under the guidance of Yoshua Bengio and Aaron Kerrville.", 'He held research positions at prestigious organizations such as OpenAI, Google Brain, and is currently the Director of Machine Learning at Apple.']}], 'duration': 49.98, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Z6rxFNMGdn0/pics/Z6rxFNMGdn029.jpg', 'highlights': ["Ian Goodfellow authored the impactful textbook 'Deep Learning' and introduced the concept of generative adversarial networks (GANs), spurring remarkable growth in the subfield of deep learning.", "Goodfellow's academic background includes earning BS and MS degrees from Stanford and a PhD from the University of Montreal under the guidance of Yoshua Bengio and Aaron Kerrville.", 'He held research positions at prestigious organizations such as OpenAI, Google Brain, and is currently the Director of Machine Learning at Apple.']}, {'end': 304.802, 'segs': [{'end': 135.328, 'src': 'embed', 'start': 95.785, 'weight': 0, 'content': [{'end': 102.65, 'text': 'Yeah, I think one of the biggest limitations of deep learning is that right now, it requires really a lot of data, especially labeled data.', 'start': 95.785, 'duration': 6.865}, {'end': 109.494, 'text': 'There are some unsupervised and semi-supervised learning algorithms that can reduce the amount of labeled data you need,', 'start': 103.991, 'duration': 5.503}, {'end': 111.336, 'text': 'but they still require a lot of unlabeled data.', 'start': 109.494, 'duration': 1.842}, {'end': 115.899, 'text': "Reinforcement learning algorithms, they don't need labels, but they need really a lot of experiences.", 'start': 112.256, 'duration': 3.643}, {'end': 121.202, 'text': "As human beings, we don't learn to play Pong by failing at Pong two million times.", 'start': 117.36, 'duration': 3.842}, {'end': 129.746, 'text': 'Just getting the generalization ability better is one of the most important bottlenecks in the capability of the technology today.', 'start': 122.943, 'duration': 6.803}, {'end': 135.328, 'text': "Then I guess I'd also say deep learning is like a component of a bigger system.", 'start': 130.686, 'duration': 4.642}], 'summary': 'Deep learning requires a lot of labeled and unlabeled data, hindering generalization ability and technological capability.', 'duration': 39.543, 'max_score': 95.785, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Z6rxFNMGdn0/pics/Z6rxFNMGdn095785.jpg'}, {'end': 194.333, 'src': 'embed', 'start': 162.491, 'weight': 4, 'content': [{'end': 165.195, 'text': "You're basically building a function estimator.", 'start': 162.491, 'duration': 2.704}, {'end': 168.58, 'text': "Do you think it's possible?", 'start': 166.277, 'duration': 2.303}, {'end': 171.004, 'text': "you said nobody's kind of been thinking about this so far,", 'start': 168.58, 'duration': 2.424}, {'end': 178.836, 'text': 'but do you think neural networks could be made to reason in the way symbolic systems did in the 80s and 90s, to do more,', 'start': 171.004, 'duration': 7.832}, {'end': 181.06, 'text': 'create more like programs as opposed to functions?', 'start': 178.836, 'duration': 2.224}, {'end': 183.709, 'text': 'Yeah, I think we already see that a little bit.', 'start': 181.468, 'duration': 2.241}, {'end': 188.391, 'text': 'I already kind of think of neural nets as a kind of program.', 'start': 184.909, 'duration': 3.482}, {'end': 194.333, 'text': 'I think of deep learning as basically learning programs that have more than one step.', 'start': 188.911, 'duration': 5.422}], 'summary': 'Exploring the potential for neural networks to reason like symbolic systems, creating programs with more than one step.', 'duration': 31.842, 'max_score': 162.491, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Z6rxFNMGdn0/pics/Z6rxFNMGdn0162491.jpg'}, {'end': 261.63, 'src': 'embed', 'start': 240.492, 'weight': 2, 'content': [{'end': 250.901, 'text': "and i think that we've actually started to see that what's important with deep learning is more the fact that we have a multi-step program rather than the fact that we've learned a representation.", 'start': 240.492, 'duration': 10.409}, {'end': 261.63, 'text': 'if you look at things like res nuts, for example, they take one particular kind of representation and they update it several times Back.', 'start': 250.901, 'duration': 10.729}], 'summary': 'Deep learning emphasizes multi-step programs over single representations.', 'duration': 21.138, 'max_score': 240.492, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Z6rxFNMGdn0/pics/Z6rxFNMGdn0240492.jpg'}], 'start': 50.789, 'title': 'Deep learning in ai', 'summary': 'Discusses the current limits of deep learning, emphasizing the need for large labeled data and the potential of unsupervised and semi-supervised learning algorithms. it also covers the transition from shallow learning to multi-step program-based deep learning models and the evolution of neural networks to reason and create programs.', 'chapters': [{'end': 161.85, 'start': 50.789, 'title': 'Limits of deep learning in ai', 'summary': 'Discusses the current limits of deep learning, emphasizing the requirement for large amounts of labeled data and the need to improve generalization ability, with a focus on the potential of unsupervised and semi-supervised learning algorithms to reduce labeled data requirements.', 'duration': 111.061, 'highlights': ['Deep learning requires a lot of labeled data, with some unsupervised and semi-supervised learning algorithms able to reduce the amount of labeled data needed.', 'Reinforcement learning algorithms do not need labels but require a lot of experiences to function effectively.', 'Improving generalization ability is a critical bottleneck in the capability of current technology, with the need to enhance the ability to learn from fewer examples.', 'Deep learning is a component of a larger system, with proposals to integrate it as a sub-module within other systems rather than being the sole ingredient of intelligence.']}, {'end': 304.802, 'start': 162.491, 'title': 'The evolution of deep learning', 'summary': 'Discusses the transition from shallow learning to deep learning, highlighting the shift towards multi-step program-based deep learning models and the diminishing focus on learning representations, as well as the evolution of neural networks to reason and create programs. it also touches upon the concept of deep learning as a sequence of steps and the change in perception regarding the function of layers in deep neural networks.', 'duration': 142.311, 'highlights': ['Deep learning as multi-step program-based models The discussion emphasizes the shift towards regarding deep learning as multi-step program-based models rather than focusing on learning representations.', 'Transition from shallow learning to deep learning It highlights the transition from shallow learning to deep learning and the diminishing focus on shallow learning techniques.', 'Evolution of neural networks to reason and create programs It discusses the potential of neural networks to reason and create programs, indicating the evolution in the capabilities of neural networks.', 'Perception change regarding the function of layers in deep neural networks The change in perception regarding the function of layers in deep neural networks is mentioned, indicating a shift in the understanding of the role of layers in deep learning.', 'Concept of deep learning as a sequence of steps The concept of deep learning as a sequence of steps is highlighted, indicating a different perspective on the functioning of deep learning models.']}], 'duration': 254.013, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Z6rxFNMGdn0/pics/Z6rxFNMGdn050789.jpg', 'highlights': ['Deep learning requires a lot of labeled data, with some unsupervised and semi-supervised learning algorithms able to reduce the amount of labeled data needed.', 'Improving generalization ability is a critical bottleneck in the capability of current technology, with the need to enhance the ability to learn from fewer examples.', 'Deep learning as multi-step program-based models The discussion emphasizes the shift towards regarding deep learning as multi-step program-based models rather than focusing on learning representations.', 'Transition from shallow learning to deep learning It highlights the transition from shallow learning to deep learning and the diminishing focus on shallow learning techniques.', 'Evolution of neural networks to reason and create programs It discusses the potential of neural networks to reason and create programs, indicating the evolution in the capabilities of neural networks.']}, {'end': 832.521, 'segs': [{'end': 399.924, 'src': 'embed', 'start': 372.216, 'weight': 1, 'content': [{'end': 377.739, 'text': "and that's relatively easy to turn into something actionable for a computer scientist to reason about.", 'start': 372.216, 'duration': 5.523}, {'end': 383.445, 'text': 'People also define consciousness in terms of having qualitative states of experience, like qualia.', 'start': 378.419, 'duration': 5.026}, {'end': 393.856, 'text': "There's all these philosophical problems like could you imagine a zombie who does all the same information processing as a human but doesn't really have the qualitative experiences that we have?", 'start': 383.965, 'duration': 9.891}, {'end': 399.924, 'text': 'That sort of thing I have no idea how to formalize or turn it into a scientific question.', 'start': 394.878, 'duration': 5.046}], 'summary': 'Defining consciousness and its challenges for computer scientists and philosophers.', 'duration': 27.708, 'max_score': 372.216, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Z6rxFNMGdn0/pics/Z6rxFNMGdn0372216.jpg'}, {'end': 517.659, 'src': 'embed', 'start': 488.404, 'weight': 0, 'content': [{'end': 490.185, 'text': 'echoes of human level cognition?', 'start': 488.404, 'duration': 1.781}, {'end': 491.125, 'text': 'I think so, yeah.', 'start': 490.345, 'duration': 0.78}, {'end': 495.347, 'text': "I'm optimistic about what can happen just with more computation and more data.", 'start': 491.245, 'duration': 4.102}, {'end': 499.029, 'text': "I do think it'll be important to get the right kind of data.", 'start': 495.367, 'duration': 3.662}, {'end': 506.512, 'text': 'Today, most of the machine learning systems we train are mostly trained on one type of data for each model.', 'start': 500.149, 'duration': 6.363}, {'end': 509.474, 'text': 'But the human brain.', 'start': 507.573, 'duration': 1.901}, {'end': 517.659, 'text': 'we get all of our different senses and we have many different experiences, like riding a bike, driving a car, talking to people, reading.', 'start': 509.474, 'duration': 8.185}], 'summary': 'Optimistic about achieving human-level cognition with more computation and diverse data.', 'duration': 29.255, 'max_score': 488.404, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Z6rxFNMGdn0/pics/Z6rxFNMGdn0488404.jpg'}, {'end': 615.796, 'src': 'embed', 'start': 569.947, 'weight': 2, 'content': [{'end': 572.768, 'text': 'When I first started to really invest in studying adversarial examples,', 'start': 569.947, 'duration': 2.821}, {'end': 579.111, 'text': 'I was thinking of it mostly as adversarial examples reveal a big problem with machine learning,', 'start': 572.768, 'duration': 6.343}, {'end': 586.314, 'text': 'and we would like to close the gap between how machine learning models respond to adversarial examples and how humans respond.', 'start': 579.111, 'duration': 7.203}, {'end': 591.236, 'text': 'After studying the problem more, I still think that adversarial examples are important.', 'start': 587.735, 'duration': 3.501}, {'end': 602.202, 'text': "I think of them now more of as a security liability than as an issue that necessarily shows there's something uniquely wrong with machine learning as opposed to humans.", 'start': 591.977, 'duration': 10.225}, {'end': 610.15, 'text': 'Also, do you see them as a tool to improve the performance of the system? Not on the security side, but literally just accuracy.', 'start': 602.842, 'duration': 7.308}, {'end': 615.796, 'text': 'I do see them as a kind of tool on that side, but maybe not quite as much as I used to think.', 'start': 610.811, 'duration': 4.985}], 'summary': "Adversarial examples reveal machine learning's security liability and potential to improve system accuracy.", 'duration': 45.849, 'max_score': 569.947, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Z6rxFNMGdn0/pics/Z6rxFNMGdn0569947.jpg'}, {'end': 726.628, 'src': 'embed', 'start': 701.489, 'weight': 4, 'content': [{'end': 706.671, 'text': "it's a hand wavy empirical way to show your system is, uh, Yeah.", 'start': 701.489, 'duration': 5.182}, {'end': 711.656, 'text': "Today most adversarial example research isn't really focused on a particular use case,", 'start': 707.032, 'duration': 4.624}, {'end': 719.022, 'text': "but there are a lot of different use cases where you'd like to make sure that the adversary can't interfere with the operation of your system.", 'start': 711.656, 'duration': 7.366}, {'end': 722.806, 'text': 'Like in finance, if you have an algorithm making trades for you.', 'start': 720.283, 'duration': 2.523}, {'end': 726.628, 'text': 'people go to a lot of an effort to obfuscate their algorithm.', 'start': 723.346, 'duration': 3.282}], 'summary': 'Adversarial example research focuses on preventing interference in various use cases like finance.', 'duration': 25.139, 'max_score': 701.489, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Z6rxFNMGdn0/pics/Z6rxFNMGdn0701489.jpg'}, {'end': 807.289, 'src': 'embed', 'start': 788.542, 'weight': 5, 'content': [{'end': 800.607, 'text': 'And they were able to show that they could make sounds that are not understandable by a human but are recognized as the target phrase that the attacker wants the phone to recognize it as.', 'start': 788.542, 'duration': 12.065}, {'end': 807.289, 'text': 'Since then, things have gotten a little bit better on the attacker side and worse on the defender side.', 'start': 801.547, 'duration': 5.742}], 'summary': 'Attackers can produce sounds recognized by phones as target phrases, leading to a decline in defense.', 'duration': 18.747, 'max_score': 788.542, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Z6rxFNMGdn0/pics/Z6rxFNMGdn0788542.jpg'}], 'start': 306.162, 'title': 'Emergence of consciousness and adversarial examples in ai', 'summary': 'Discusses the emergence of consciousness and cognition in ai, challenges in defining consciousness, potential advancements in human-level cognition, and the impact of adversarial examples on system accuracy and security across domains such as autonomous vehicles, finance, and speech recognition.', 'chapters': [{'end': 657.169, 'start': 306.162, 'title': 'Emergence of consciousness in ai', 'summary': 'Discusses the emergence of consciousness and cognition in ai, the challenges in defining and formalizing consciousness, the potential for impressive advancements in human-level cognition through increased computation and diverse multimodal data, and the evolving perspective on adversarial examples as a security liability and their impact on system accuracy.', 'duration': 351.007, 'highlights': ['The potential for impressive advancements in human-level cognition through increased computation and diverse multimodal data The speaker is optimistic about the potential for achieving human-level cognition through increased computation and diverse multimodal data.', 'Challenges in defining and formalizing consciousness The difficulty in defining consciousness is mentioned, especially in terms of qualitative experiences and the philosophical problem of formalizing it or turning it into a scientific question.', "Evolving perspective on adversarial examples as a security liability and their impact on system accuracy The speaker's evolving perspective on adversarial examples is discussed, highlighting the shift from viewing them as a problem with machine learning to seeing them as a security liability and their trade-off between accuracy on clean and adversarial examples."]}, {'end': 832.521, 'start': 658.089, 'title': 'Adversarial examples in engineering and ai', 'summary': 'Discusses the concept of adversarial examples in engineering and ai, highlighting their impact on various domains such as autonomous vehicles, finance, and speech recognition, with specific examples and the evolving capabilities of attackers to create adversarial examples.', 'duration': 174.432, 'highlights': ['Adversarial examples are a compelling idea in engineering and AI, demonstrating how humans learn by considering difficult cases and ensuring systems work in worst-case scenarios. Demonstrates the human learning process and the application of worst-case analysis in engineering to ensure system robustness.', 'Adversarial example research spans across various use cases such as finance, where algorithms making trades are protected to prevent adversarial interference. Illustrates the importance of protecting trading algorithms in finance from adversarial examples.', 'In speech recognition, attackers have been able to create sounds that are recognized as target phrases by the system, indicating the evolving capabilities of attackers in creating adversarial examples. Highlights the success of attackers in creating adversarial examples in speech recognition systems.']}], 'duration': 526.359, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Z6rxFNMGdn0/pics/Z6rxFNMGdn0306162.jpg', 'highlights': ['The potential for impressive advancements in human-level cognition through increased computation and diverse multimodal data', 'Challenges in defining and formalizing consciousness', 'Evolving perspective on adversarial examples as a security liability and their impact on system accuracy', 'Adversarial examples are a compelling idea in engineering and AI, demonstrating how humans learn by considering difficult cases and ensuring systems work in worst-case scenarios', 'Adversarial example research spans across various use cases such as finance, where algorithms making trades are protected to prevent adversarial interference', 'In speech recognition, attackers have been able to create sounds that are recognized as target phrases by the system, indicating the evolving capabilities of attackers in creating adversarial examples']}, {'end': 1649.1, 'segs': [{'end': 922.233, 'src': 'embed', 'start': 896.932, 'weight': 1, 'content': [{'end': 903.858, 'text': "It's also really nice now that the field is kind of stabilized to the point where some core ideas from the 1980s are still used today.", 'start': 896.932, 'duration': 6.926}, {'end': 910.864, 'text': 'When I first started studying machine learning, almost everything from the 1980s had been rejected, and now some of it has come back.', 'start': 904.819, 'duration': 6.045}, {'end': 915.609, 'text': "So that stuff that's really stood the test of time is what I focused on putting into the book.", 'start': 911.405, 'duration': 4.204}, {'end': 922.233, 'text': "There's also, I guess, two different philosophies about how you might write a book.", 'start': 917.11, 'duration': 5.123}], 'summary': 'Core ideas from the 1980s are now used in machine learning, signaling stability and longevity in the field.', 'duration': 25.301, 'max_score': 896.932, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Z6rxFNMGdn0/pics/Z6rxFNMGdn0896932.jpg'}, {'end': 961.258, 'src': 'embed', 'start': 935.361, 'weight': 0, 'content': [{'end': 944.768, 'text': "The first deep learning book that I wrote with Joshua and Aaron was somewhere between the two philosophies that it's trying to be both a reference and an introductory guide.", 'start': 935.361, 'duration': 9.407}, {'end': 954.235, 'text': "Writing this chapter for Russell Norvig's book, I was able to focus more on just a concise introduction of the key concepts and the language.", 'start': 945.929, 'duration': 8.306}, {'end': 955.436, 'text': 'you need to read about them more.', 'start': 954.235, 'duration': 1.201}, {'end': 961.258, 'text': "In a lot of cases, I actually just wrote paragraphs that said, here's a rapidly evolving area that you should pay attention to.", 'start': 956.016, 'duration': 5.242}], 'summary': 'The first deep learning book aimed to be both a reference and an introductory guide, focusing on concise introduction and indicating rapidly evolving areas.', 'duration': 25.897, 'max_score': 935.361, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Z6rxFNMGdn0/pics/Z6rxFNMGdn0935361.jpg'}, {'end': 1072.518, 'src': 'embed', 'start': 1039.801, 'weight': 2, 'content': [{'end': 1043.324, 'text': 'So let me ask you in that same spirit what is deep learning?', 'start': 1039.801, 'duration': 3.523}, {'end': 1055.685, 'text': 'I would say deep learning is any kind of machine learning that involves learning parameters of more than one consecutive step.', 'start': 1044.597, 'duration': 11.088}, {'end': 1062.969, 'text': 'So, I mean, shallow learning is things where you learn a lot of operations that happen in parallel.', 'start': 1057.344, 'duration': 5.625}, {'end': 1072.518, 'text': 'You might have a system that makes multiple steps, like you might have hand-designed feature extractors, but really only one step is learned.', 'start': 1063.77, 'duration': 8.748}], 'summary': 'Deep learning involves learning parameters of more than one consecutive step.', 'duration': 32.717, 'max_score': 1039.801, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Z6rxFNMGdn0/pics/Z6rxFNMGdn01039800.jpg'}, {'end': 1155.919, 'src': 'embed', 'start': 1130.241, 'weight': 3, 'content': [{'end': 1136.006, 'text': "There's the model, which can be something like a neural net or a Bolton machine or a recurrent model.", 'start': 1130.241, 'duration': 5.765}, {'end': 1141.291, 'text': 'And that basically just describes how do you take data and how do you take parameters,', 'start': 1136.647, 'duration': 4.644}, {'end': 1146.176, 'text': 'and what function do you use to make a prediction given the data and the parameters?', 'start': 1141.291, 'duration': 4.885}, {'end': 1152.718, 'text': 'Another piece of the learning algorithm is the optimization algorithm.', 'start': 1147.376, 'duration': 5.342}, {'end': 1155.919, 'text': 'Not every algorithm can be really described in terms of optimization,', 'start': 1153.178, 'duration': 2.741}], 'summary': 'Transcript discusses models and optimization algorithms in machine learning.', 'duration': 25.678, 'max_score': 1130.241, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Z6rxFNMGdn0/pics/Z6rxFNMGdn01130241.jpg'}, {'end': 1329.65, 'src': 'embed', 'start': 1302.421, 'weight': 4, 'content': [{'end': 1309.645, 'text': 'Are you optimistic about us discovering, you know, backpropagation has been around for a few decades.', 'start': 1302.421, 'duration': 7.224}, {'end': 1317.986, 'text': 'So are you optimistic about us as a community being able to discover something better? Yeah, I am.', 'start': 1310.346, 'duration': 7.64}, {'end': 1321.227, 'text': 'I think we likely will find something that works better.', 'start': 1318.486, 'duration': 2.741}, {'end': 1329.65, 'text': 'You could imagine things like having stacks of models where some of the lower level models predict parameters of the higher level models.', 'start': 1321.887, 'duration': 7.763}], 'summary': 'Community optimistic about discovering better model than backpropagation.', 'duration': 27.229, 'max_score': 1302.421, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Z6rxFNMGdn0/pics/Z6rxFNMGdn01302421.jpg'}, {'end': 1466.324, 'src': 'embed', 'start': 1440.968, 'weight': 5, 'content': [{'end': 1448.838, 'text': 'new optimization algorithms or different ways of applying existing optimization algorithms could give us a way of just lightning fast,', 'start': 1440.968, 'duration': 7.87}, {'end': 1454.144, 'text': 'updating the state of a machine learning system to contain a specific fact like that,', 'start': 1448.838, 'duration': 5.306}, {'end': 1456.287, 'text': 'without needing to have it presented over and over and over again.', 'start': 1454.144, 'duration': 2.143}, {'end': 1466.324, 'text': 'So some of the success of symbolic systems in the 80s is they were able to assemble these kinds of facts better.', 'start': 1457.057, 'duration': 9.267}], 'summary': 'New optimization algorithms can update machine learning systems lightning fast, assembling facts like symbolic systems in the 80s.', 'duration': 25.356, 'max_score': 1440.968, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Z6rxFNMGdn0/pics/Z6rxFNMGdn01440968.jpg'}, {'end': 1615.283, 'src': 'embed', 'start': 1588.419, 'weight': 6, 'content': [{'end': 1592.22, 'text': 'I could see a lot of ways of getting there without bringing back some of the 1980s technology,', 'start': 1588.419, 'duration': 3.801}, {'end': 1599.243, 'text': 'but I also see some ways that you could imagine extending the 1980s technology to play nice with neural nets and have it help get there.', 'start': 1592.22, 'duration': 7.023}, {'end': 1606.215, 'text': 'Awesome So you talked about the story of you coming up with the idea of GANs at a bar with some friends.', 'start': 1600.109, 'duration': 6.106}, {'end': 1614.122, 'text': "You were arguing that this, you know, GANs would work, generative adversarial networks, and the others didn't think so.", 'start': 1607.216, 'duration': 6.906}, {'end': 1615.283, 'text': 'Then you went home.', 'start': 1614.662, 'duration': 0.621}], 'summary': 'Discussion on 1980s technology integration with neural nets and the inception of gans at a bar.', 'duration': 26.864, 'max_score': 1588.419, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Z6rxFNMGdn0/pics/Z6rxFNMGdn01588419.jpg'}], 'start': 832.962, 'title': 'Writing deep learning chapter', 'summary': "Discusses the process of writing the deep learning chapter, emphasizing the challenge of summarizing the evolving field, the shift in field's stability, and providing an in-depth exploration of deep learning encompassing its definition, differentiable functions, non-backpropagation models, potential of alternative training methods, and future direction of machine learning algorithms.", 'chapters': [{'end': 1039.561, 'start': 832.962, 'title': 'Deep learning in ai: writing the chapter', 'summary': "Discusses the process of writing the deep learning chapter for the fourth edition of the artificial intelligence and modern approach book, emphasizing the challenge of summarizing the evolving field and the focus on core concepts and language. it also highlights the shift in the field's stability and the balance between providing a reference and a high-level summary for readers.", 'duration': 206.599, 'highlights': ['The challenge of summarizing the evolving field of deep learning for a chapter, emphasizing the need to focus on core concepts and language. The speaker discusses the challenge of summarizing the entire field of deep learning in just one chapter, highlighting the need to focus on core concepts and language to provide readers with an understanding of the most important concepts.', "The shift in the field's stability, with core ideas from the 1980s still being used today and the speaker's focus on including concepts that have stood the test of time in the book. The speaker emphasizes the stability of the field of deep learning, noting that core ideas from the 1980s are still relevant today. This stability influenced the decision to focus on including concepts that have stood the test of time in the book.", "The balance between providing a reference and a high-level summary for readers, with the speaker's approach being to offer a concise introduction of key concepts and language. The speaker discusses the two philosophies of writing a book and explains the approach taken, aiming to provide a concise introduction of key concepts and language, striking a balance between providing a reference and a high-level summary for readers."]}, {'end': 1649.1, 'start': 1039.801, 'title': 'Defining deep learning', 'summary': 'Provides an in-depth exploration of deep learning, encompassing its definition, differentiable functions, non-backpropagation models, the potential of alternative training methods, and the future direction of machine learning algorithms.', 'duration': 609.299, 'highlights': ['Deep learning encompasses machine learning involving learning parameters of more than one consecutive step. Deep learning involves learning parameters of multiple operations in sequence, distinguishing it from shallow learning where operations happen in parallel.', "Deep learning includes models like neural nets, Bolton machines, and recurrent models, emphasizing the significance of the model, optimization algorithm, and data set in machine learning algorithms. Deep learning includes various models, optimization algorithms, and data set representations, crucial for understanding the model's structure and training process.", 'The potential for discovering better training methods beyond backpropagation and gradient descent is optimistic, possibly involving stacks of models and customized algorithms. Optimism exists for finding improved training methods, such as using stacks of models and customizing existing algorithms, potentially advancing AI capabilities.', 'The challenge of short-term memory in machine learning and the potential for new optimization algorithms to facilitate lightning-fast updates in a machine learning system are highlighted. Challenges in short-term memory and the potential for new optimization algorithms to enable rapid updates in machine learning systems are discussed, indicating opportunities for advancement.', 'The discussion of integrating 1980s technology with neural networks for generative models and the potential of differentiable knowledge bases to interact with machine learning models is explored. The potential integration of 1980s technology with neural networks and the interaction of differentiable knowledge bases with machine learning models are considered for enhancing generative models.']}], 'duration': 816.138, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Z6rxFNMGdn0/pics/Z6rxFNMGdn0832962.jpg', 'highlights': ['The challenge of summarizing the evolving field of deep learning for a chapter, emphasizing the need to focus on core concepts and language.', "The shift in the field's stability, with core ideas from the 1980s still being used today and the speaker's focus on including concepts that have stood the test of time in the book.", 'Deep learning encompasses machine learning involving learning parameters of more than one consecutive step.', 'Deep learning includes models like neural nets, Bolton machines, and recurrent models, emphasizing the significance of the model, optimization algorithm, and data set in machine learning algorithms.', 'The potential for discovering better training methods beyond backpropagation and gradient descent is optimistic, possibly involving stacks of models and customized algorithms.', 'The challenge of short-term memory in machine learning and the potential for new optimization algorithms to facilitate lightning-fast updates in a machine learning system are highlighted.', 'The discussion of integrating 1980s technology with neural networks for generative models and the potential of differentiable knowledge bases to interact with machine learning models is explored.']}, {'end': 2329.842, 'segs': [{'end': 1688.318, 'src': 'embed', 'start': 1649.761, 'weight': 0, 'content': [{'end': 1657.008, 'text': "So I have noticed in general that I'm less prone to shooting down some of my ideas when I have had a little bit to drink.", 'start': 1649.761, 'duration': 7.247}, {'end': 1663.706, 'text': "I think if I had had that idea at lunchtime, I probably would have thought it's hard enough to train one neural net.", 'start': 1658.004, 'duration': 5.702}, {'end': 1667.187, 'text': "you can't train a second neural net in the inner loop of the outer neural net.", 'start': 1663.706, 'duration': 3.481}, {'end': 1673.549, 'text': "That was basically my friend's objection, was that trying to train two neural nets at the same time would be too hard.", 'start': 1668.087, 'duration': 5.462}, {'end': 1678.472, 'text': 'so it was more about the training process, unless so my skepticism would be.', 'start': 1674.289, 'duration': 4.183}, {'end': 1686.337, 'text': "you know, i'm sure you could train it, but, uh, the thing would converge to, would not be able to generate anything reasonable, any,", 'start': 1678.472, 'duration': 7.865}, {'end': 1688.318, 'text': 'any kind of reasonable realism.', 'start': 1686.337, 'duration': 1.981}], 'summary': 'Having a little bit to drink makes me less prone to shooting down ideas, as opposed to being skeptical while sober.', 'duration': 38.557, 'max_score': 1649.761, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Z6rxFNMGdn0/pics/Z6rxFNMGdn01649761.jpg'}, {'end': 1900.833, 'src': 'embed', 'start': 1875.621, 'weight': 3, 'content': [{'end': 1881.664, 'text': 'There are some kinds of GANs, like FlowGAN, that can do both, but mostly GANs are about generating samples,', 'start': 1875.621, 'duration': 6.043}, {'end': 1883.945, 'text': 'generating new photos of cats that look realistic.', 'start': 1881.664, 'duration': 2.281}, {'end': 1888.807, 'text': 'And they do that completely from scratch.', 'start': 1885.466, 'duration': 3.341}, {'end': 1891.729, 'text': "It's analogous to human imagination.", 'start': 1889.448, 'duration': 2.281}, {'end': 1900.833, 'text': "When a GAN creates a new image of a cat, It's using a neural network to produce a cat that has not existed before.", 'start': 1892.229, 'duration': 8.604}], 'summary': 'Gans like flowgan generate realistic new cat photos using neural networks.', 'duration': 25.212, 'max_score': 1875.621, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Z6rxFNMGdn0/pics/Z6rxFNMGdn01875621.jpg'}, {'end': 1951.269, 'src': 'embed', 'start': 1922.086, 'weight': 4, 'content': [{'end': 1927.09, 'text': "What's specific to GANs is that we have a two-player game in the game theoretic sense.", 'start': 1922.086, 'duration': 5.004}, {'end': 1933.176, 'text': 'And as the players in this game compete, one of them becomes able to generate realistic data.', 'start': 1928.111, 'duration': 5.065}, {'end': 1935.478, 'text': 'The first player is called the generator.', 'start': 1934.036, 'duration': 1.442}, {'end': 1939.982, 'text': 'It produces output data, such as just images, for example.', 'start': 1936.198, 'duration': 3.784}, {'end': 1944.205, 'text': "And at the start of the learning process, it'll just produce completely random images.", 'start': 1940.782, 'duration': 3.423}, {'end': 1946.727, 'text': 'The other player is called the discriminator.', 'start': 1945.146, 'duration': 1.581}, {'end': 1951.269, 'text': "The discriminator takes images as input and guesses whether they're real or fake.", 'start': 1947.407, 'duration': 3.862}], 'summary': 'Gans use a two-player game to generate realistic data like images.', 'duration': 29.183, 'max_score': 1922.086, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Z6rxFNMGdn0/pics/Z6rxFNMGdn01922086.jpg'}, {'end': 2148.157, 'src': 'heatmap', 'start': 2094.321, 'weight': 1, 'content': [{'end': 2101.825, 'text': "That still doesn't really explain why, when you produce samples that are new, why do you get compelling images rather than just garbage?", 'start': 2094.321, 'duration': 7.504}, {'end': 2103.106, 'text': "that's different from the training set.", 'start': 2101.825, 'duration': 1.281}, {'end': 2106.968, 'text': "And I don't think we really have a good answer for that,", 'start': 2103.886, 'duration': 3.082}, {'end': 2114.612, 'text': 'especially if you think about how many possible images are out there and how few images the generative model sees during training.', 'start': 2106.968, 'duration': 7.644}, {'end': 2119.875, 'text': 'It seems just unreasonable that generative models create new images as well as they do.', 'start': 2115.572, 'duration': 4.303}, {'end': 2124.678, 'text': "especially considering that we're basically training them to memorize rather than generalize.", 'start': 2121.015, 'duration': 3.663}, {'end': 2130.263, 'text': "I think part of the answer is there's a paper called Deep Image Prior,", 'start': 2126.24, 'duration': 4.023}, {'end': 2134.907, 'text': "where they show that you can take a convolutional net and you don't even need to learn the parameters of it at all.", 'start': 2130.263, 'duration': 4.644}, {'end': 2136.368, 'text': 'You just use the model architecture.', 'start': 2134.987, 'duration': 1.381}, {'end': 2140.191, 'text': "and it's already useful for things like inpainting images.", 'start': 2137.689, 'duration': 2.502}, {'end': 2148.157, 'text': 'I think that shows us that the convolutional network architecture captures something really important about the structure of images,', 'start': 2141.112, 'duration': 7.045}], 'summary': 'Generative models create compelling new images despite limited training data, challenging the expectation of memorization over generalization.', 'duration': 53.836, 'max_score': 2094.321, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Z6rxFNMGdn0/pics/Z6rxFNMGdn02094321.jpg'}, {'end': 2271.339, 'src': 'embed', 'start': 2243.17, 'weight': 5, 'content': [{'end': 2249.174, 'text': 'you have a model that tells you how much probability it assigns to a particular example,', 'start': 2243.17, 'duration': 6.004}, {'end': 2252.596, 'text': 'and you just maximize the probability assigned to all the training examples.', 'start': 2249.174, 'duration': 3.422}, {'end': 2271.339, 'text': "It turns out that it's hard to design a model that can create really complicated images or really complicated audio waveforms and still have it be possible to estimate the likelihood function from a computational point of view?", 'start': 2253.756, 'duration': 17.583}], 'summary': 'Maximize probability for training examples; challenging to estimate likelihood function computationally for complex images and audio waveforms.', 'duration': 28.169, 'max_score': 2243.17, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Z6rxFNMGdn0/pics/Z6rxFNMGdn02243170.jpg'}], 'start': 1649.761, 'title': "Alcohol's impact on idea evaluation and understanding generative adversarial networks", 'summary': 'Delves into the impact of alcohol on idea evaluation, revealing increased openness to complex ideas, and explores the concept of generative adversarial networks (gans), including their challenges, dynamics, and advantages over likelihood-based generative models in creating realistic images.', 'chapters': [{'end': 1688.318, 'start': 1649.761, 'title': "Alcohol's impact on idea evaluation", 'summary': 'Discusses the impact of alcohol on idea evaluation, revealing that the individual feels less inhibited after drinking, leading to more openness to exploring complex ideas, such as training two neural networks simultaneously.', 'duration': 38.557, 'highlights': ['The individual states feeling less prone to shooting down ideas after having a drink. The individual mentions being less critical of their ideas after consuming alcohol.', "The friend's objection was that training two neural nets at the same time would be too hard. The friend expressed skepticism about the difficulty of training two neural networks concurrently.", 'Expressed skepticism about the convergence and generation capabilities of the neural nets when trained simultaneously. The individual expressed doubt about the ability of the neural nets to generate reasonable results when trained concurrently.']}, {'end': 2329.842, 'start': 1688.318, 'title': 'Understanding generative adversarial networks', 'summary': 'Discusses the challenges faced in training deep boltzmann machines, the concept of generative adversarial networks (gans) as a type of generative model, the two-player game dynamics in gans, and the limitations of likelihood-based generative models compared to gans in creating realistic images.', 'duration': 641.524, 'highlights': ['Generative adversarial networks (GANs) are a type of generative model used for generating samples, particularly focused on creating new data such as realistic images of cats. Explains GANs as a type of generative model specifically focused on generating samples, particularly emphasizing the creation of realistic images of cats.', 'The training process for GANs involves a two-player game with the generator and discriminator competing, leading to a Nash equilibrium where the generator captures the correct probability distribution to create realistic data. Describes the two-player game dynamics in GANs, where the generator and discriminator compete to reach a Nash equilibrium that enables the generator to create realistic data.', 'Likelihood-based generative models, unlike GANs, face challenges in creating complicated images or audio waveforms due to the computational difficulty in estimating the likelihood function for complex data. Compares likelihood-based generative models to GANs, highlighting the challenges faced by likelihood-based models in creating complex images or audio waveforms due to computational limitations.']}], 'duration': 680.081, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Z6rxFNMGdn0/pics/Z6rxFNMGdn01649761.jpg', 'highlights': ['Alcohol increases openness to ideas, reducing criticism', 'Training two neural nets concurrently is doubted by a friend', 'Doubt expressed about the capability of neural nets to generate reasonable results when trained concurrently', 'Generative adversarial networks (GANs) focus on creating realistic images of cats', 'GANs involve a two-player game with the generator and discriminator competing', 'Likelihood-based generative models face challenges in creating complex images or audio waveforms']}, {'end': 2969.915, 'segs': [{'end': 2417.15, 'src': 'embed', 'start': 2368.822, 'weight': 2, 'content': [{'end': 2374.226, 'text': 'are GANs doing better because they have a lot of graphics and art experts behind them?', 'start': 2368.822, 'duration': 5.404}, {'end': 2378.249, 'text': "Or are GANs doing better because they're more computationally efficient?", 'start': 2374.866, 'duration': 3.383}, {'end': 2385.493, 'text': 'Or are GANs doing better because they prioritize the realism of samples over the accuracy of the density function?', 'start': 2379.069, 'duration': 6.424}, {'end': 2390.437, 'text': "I think all of those are potentially valid explanations and it's hard to tell.", 'start': 2385.693, 'duration': 4.744}, {'end': 2399.961, 'text': 'Can you give a brief history of GANs from 2014, were you paper 13? Yeah.', 'start': 2390.457, 'duration': 9.504}, {'end': 2400.802, 'text': 'A few highlights.', 'start': 2400.081, 'duration': 0.721}, {'end': 2404.323, 'text': 'In the first paper, we just showed that GANs basically work.', 'start': 2401.042, 'duration': 3.281}, {'end': 2407.805, 'text': 'If you look back at the samples we had now, they look terrible.', 'start': 2404.784, 'duration': 3.021}, {'end': 2411.627, 'text': "On the CIFAR-10 dataset, you can't even recognize objects in them.", 'start': 2408.866, 'duration': 2.761}, {'end': 2417.15, 'text': 'Your paper, sorry, you use CIFAR-10? We use MNIST, which is little handwritten digits.', 'start': 2412.287, 'duration': 4.863}], 'summary': "Gans' success attributed to graphics expertise, computational efficiency, and realism prioritization. early samples were poor quality.", 'duration': 48.328, 'max_score': 2368.822, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Z6rxFNMGdn0/pics/Z6rxFNMGdn02368822.jpg'}, {'end': 2662.814, 'src': 'embed', 'start': 2634.173, 'weight': 0, 'content': [{'end': 2642.139, 'text': 'To get down to less than 1% accuracy required around 60,000 examples until maybe about 2014 or so.', 'start': 2634.173, 'duration': 7.966}, {'end': 2651.205, 'text': 'In 2016, with this semi-supervised GAN project, Tim was able to get below 1% error using only 100 labeled examples.', 'start': 2642.859, 'duration': 8.346}, {'end': 2657.77, 'text': 'So that was about a 600x decrease in the amount of labels that he needed.', 'start': 2653.646, 'duration': 4.124}, {'end': 2662.814, 'text': "He's still using more images than that, but he doesn't need to have each of them labeled.", 'start': 2658.13, 'duration': 4.684}], 'summary': 'In 2016, tim achieved <1% error using 100 labeled examples, a 600x decrease from 60,000 examples required in 2014.', 'duration': 28.641, 'max_score': 2634.173, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Z6rxFNMGdn0/pics/Z6rxFNMGdn02634173.jpg'}, {'end': 2708.317, 'src': 'embed', 'start': 2684.611, 'weight': 1, 'content': [{'end': 2693.973, 'text': "Yeah, some researchers at Brain Zurich actually just released a really great paper on semi-supervised GANs, where their goal isn't to classify,", 'start': 2684.611, 'duration': 9.362}, {'end': 2697.674, 'text': "it's to make recognizable objects despite not having a lot of label data.", 'start': 2693.973, 'duration': 3.701}, {'end': 2707.656, 'text': "They were working off of DeepMind's BigGAN project and they showed that they can match the performance of BigGAN using only 10%, I believe,", 'start': 2698.834, 'duration': 8.822}, {'end': 2708.317, 'text': 'of the labels.', 'start': 2707.656, 'duration': 0.661}], 'summary': 'Researchers at brain zurich released a paper on semi-supervised gans, matching biggan performance using only 10% of the labels.', 'duration': 23.706, 'max_score': 2684.611, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Z6rxFNMGdn0/pics/Z6rxFNMGdn02684611.jpg'}, {'end': 2959.713, 'src': 'embed', 'start': 2929.381, 'weight': 3, 'content': [{'end': 2931.883, 'text': "So it's a lot like the real versus fake discriminator in GANs.", 'start': 2929.381, 'duration': 2.502}, {'end': 2938.006, 'text': 'And then the feature extractor you can think of as loosely analogous to the generator in GANs,', 'start': 2932.903, 'duration': 5.103}, {'end': 2948.391, 'text': "except what it's trying to do here is both fool the domain recognizer into not knowing which domain the data came from and also extract features that are good for classification.", 'start': 2938.006, 'duration': 10.385}, {'end': 2959.713, 'text': 'So at the end of the day, in the cases where it works out, you can actually get features that work about the same in both domains.', 'start': 2949.131, 'duration': 10.582}], 'summary': 'Feature extractor aims to fool domain recognizer and extract features good for classification, yielding similar features in both domains.', 'duration': 30.332, 'max_score': 2929.381, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Z6rxFNMGdn0/pics/Z6rxFNMGdn02929381.jpg'}], 'start': 2330.842, 'title': 'Evolution of gans and their applications', 'summary': 'Discusses the evolution of gans from 2014, including their initial performance on different datasets, and their applications in semi-supervised learning, reducing labeled examples for classification, and domain adversarial learning for domain adaptation.', 'chapters': [{'end': 2462.468, 'start': 2330.842, 'title': 'Evolution of gans', 'summary': 'Discusses the evolution of gans, highlighting their quality, effectiveness, and the challenges in assessing their success, and provides a brief history of gans from 2014, including their initial performance on different datasets.', 'duration': 131.626, 'highlights': ['The early success of GANs was based on their ability to produce reasonable images, although with limitations on recognition, and they have attracted a lot of interest from graphics and art experts.', 'The difficulty in determining the reasons behind the success of GANs, whether it is due to expertise, computational efficiency, or prioritization of realism over accuracy, poses a challenge in evaluating their effectiveness.', 'The initial GAN models showed limited success in producing recognizable objects, with variations in performance on datasets such as MNIST, Toronto Face Database, and CIFAR-10, but this uniqueness in the failed samples sparked excitement among deep learning enthusiasts.']}, {'end': 2969.915, 'start': 2463.688, 'title': 'Evolution of gans and their applications', 'summary': 'Discusses the evolution of gans, from lapgan to dcgan, and highlights their use in semi-supervised learning, reducing labeled examples for classification, and domain adversarial learning for domain adaptation.', 'duration': 506.227, 'highlights': ["Tim Solomons' semi-supervised GAN project achieved below 1% error using only 100 labeled examples, a 600x decrease from previous methods.", "Research from Brain Zurich demonstrated the ability to match BigGAN's performance using only 10% of labels, using a clustering algorithm for object grouping.", 'Domain adversarial learning is akin to GANs, involving a feature extractor and domain recognizer to extract features that work well in different domains.']}], 'duration': 639.073, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Z6rxFNMGdn0/pics/Z6rxFNMGdn02330842.jpg', 'highlights': ["Tim Solomons' semi-supervised GAN project achieved below 1% error using only 100 labeled examples, a 600x decrease from previous methods.", "Research from Brain Zurich demonstrated the ability to match BigGAN's performance using only 10% of labels, using a clustering algorithm for object grouping.", 'The early success of GANs was based on their ability to produce reasonable images, although with limitations on recognition, and they have attracted a lot of interest from graphics and art experts.', 'Domain adversarial learning is akin to GANs, involving a feature extractor and domain recognizer to extract features that work well in different domains.', 'The initial GAN models showed limited success in producing recognizable objects, with variations in performance on datasets such as MNIST, Toronto Face Database, and CIFAR-10, but this uniqueness in the failed samples sparked excitement among deep learning enthusiasts.', 'The difficulty in determining the reasons behind the success of GANs, whether it is due to expertise, computational efficiency, or prioritization of realism over accuracy, poses a challenge in evaluating their effectiveness.']}, {'end': 3465.79, 'segs': [{'end': 2997.963, 'src': 'embed', 'start': 2970.975, 'weight': 2, 'content': [{'end': 2974.476, 'text': 'So do you think of GANs being useful in the context of data augmentation??', 'start': 2970.975, 'duration': 3.501}, {'end': 2978.419, 'text': 'Yeah, one thing you could hope for with GANs is,', 'start': 2975.518, 'duration': 2.901}, {'end': 2985.76, 'text': "you could imagine I've got a limited training set and I'd like to make more training data to train something else like a classifier.", 'start': 2978.419, 'duration': 7.341}, {'end': 2997.963, 'text': 'You could train the GAN on the training set and then create more data and then maybe the classifier would perform better on the test set after training on this bigger GAN-generated data set.', 'start': 2987.261, 'duration': 10.702}], 'summary': 'Gans can be used for data augmentation to create more training data for classifiers, improving performance on test sets.', 'duration': 26.988, 'max_score': 2970.975, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Z6rxFNMGdn0/pics/Z6rxFNMGdn02970975.jpg'}, {'end': 3129.451, 'src': 'embed', 'start': 3100.587, 'weight': 0, 'content': [{'end': 3106.392, 'text': "There's a paper from Casey Green's lab that shows how you can train a GAN using differential privacy.", 'start': 3100.587, 'duration': 5.805}, {'end': 3112.317, 'text': 'And then the samples from the GAN still have the same differential privacy guarantees as the parameters of the GAN.', 'start': 3107.093, 'duration': 5.224}, {'end': 3119.243, 'text': 'So you can make fake patient data for other researchers to use and they can do almost anything they want with that data,', 'start': 3112.757, 'duration': 6.486}, {'end': 3121.905, 'text': "because it doesn't come from real people.", 'start': 3119.243, 'duration': 2.662}, {'end': 3129.451, 'text': "And the differential privacy mechanism gives you clear guarantees on how much the original people's data has been protected.", 'start': 3122.085, 'duration': 7.366}], 'summary': 'Training gan with differential privacy protects patient data with clear guarantees.', 'duration': 28.864, 'max_score': 3100.587, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Z6rxFNMGdn0/pics/Z6rxFNMGdn03100587.jpg'}, {'end': 3226.831, 'src': 'embed', 'start': 3198.67, 'weight': 1, 'content': [{'end': 3205.659, 'text': "And you want to make sure that the feature analyzer is not able to guess the value of the sensitive variable that you're trying to keep private.", 'start': 3198.67, 'duration': 6.989}, {'end': 3209.255, 'text': 'I love this approach.', 'start': 3206.733, 'duration': 2.522}, {'end': 3215.761, 'text': "With the feature, you're not able to infer the sensitive variables.", 'start': 3209.275, 'duration': 6.486}, {'end': 3218.864, 'text': "It's quite brilliant and simple actually.", 'start': 3216.402, 'duration': 2.462}, {'end': 3226.831, 'text': 'Another way I think that GANs in particular could be used, for fairness would be to make something like a CycleGAN,', 'start': 3219.605, 'duration': 7.226}], 'summary': 'Feature analyzer prevents guessing sensitive variable values. gans, like cyclegan, could be used for fairness.', 'duration': 28.161, 'max_score': 3198.67, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Z6rxFNMGdn0/pics/Z6rxFNMGdn03198670.jpg'}, {'end': 3359.847, 'src': 'embed', 'start': 3290.042, 'weight': 3, 'content': [{'end': 3300.772, 'text': 'gans are able to generate data and you start to think about deep fakes or being able to sort of maliciously generate data that fakes the identity of other people.', 'start': 3290.042, 'duration': 10.73}, {'end': 3303.114, 'text': 'Is this something of a concern to you?', 'start': 3301.232, 'duration': 1.882}, {'end': 3310.403, 'text': 'Is this something if you look 10, 20 years into the future, is that something that pops up in your work,', 'start': 3303.235, 'duration': 7.168}, {'end': 3312.786, 'text': "in the work of the community that's working on generative models?", 'start': 3310.403, 'duration': 2.383}, {'end': 3317.209, 'text': "I'm a lot less concerned about 20 years from now than the next few years.", 'start': 3313.547, 'duration': 3.662}, {'end': 3325.714, 'text': "I think there will be a kind of bumpy cultural transition as people encounter this idea that there can be very realistic videos and audio that aren't real.", 'start': 3317.449, 'duration': 8.265}, {'end': 3333.278, 'text': "I think 20 years from now, people will mostly understand that you shouldn't believe something is real just because you saw a video of it.", 'start': 3326.294, 'duration': 6.984}, {'end': 3337.1, 'text': "People will expect to see that it's been cryptographically signed.", 'start': 3334.098, 'duration': 3.002}, {'end': 3343.222, 'text': 'or have some other mechanism to make them believe that the content is real.', 'start': 3338.4, 'duration': 4.822}, {'end': 3345.622, 'text': "There's already people working on this.", 'start': 3344.402, 'duration': 1.22}, {'end': 3351.624, 'text': "There's a startup called TruePic that provides a lot of mechanisms for authenticating that an image is real.", 'start': 3345.662, 'duration': 5.962}, {'end': 3359.847, 'text': "They're maybe not quite up to having a state actor try to evade their verification techniques,", 'start': 3352.805, 'duration': 7.042}], 'summary': 'Generative models raise concerns about realistic fake data; authentication mechanisms are being developed.', 'duration': 69.805, 'max_score': 3290.042, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Z6rxFNMGdn0/pics/Z6rxFNMGdn03290042.jpg'}], 'start': 2970.975, 'title': 'Gans in data augmentation and concerns about generative models', 'summary': 'Explores the utility of gans in data augmentation for classifiers, creation of differentially private data, and ensuring fairness in machine learning models. additionally, it delves into concerns about potential misuse of generative models, cultural transitions, and development of authentication mechanisms to verify media authenticity.', 'chapters': [{'end': 3290.042, 'start': 2970.975, 'title': 'Utility of gans in data augmentation', 'summary': 'Discusses the potential of gans in generating training data for classifiers, the use of gans for creating differentially private data, and the application of gans in ensuring fairness in machine learning models by preventing the use of specific sensitive variables.', 'duration': 319.067, 'highlights': ['The potential of GANs in generating training data for classifiers The chapter explores the idea of training GANs on a limited training set to create more data for improving classifier performance on the test set.', "Creating differentially private data using GANs The discussion includes the application of GANs in generating fake patient data with differential privacy guarantees, thereby protecting the original people's data while enabling research use.", 'Ensuring fairness in machine learning models with GANs The chapter explains a method using GANs to create models that are incapable of using specific variables, thereby preventing the inference of sensitive variables internally, and discusses the potential application of CycleGAN for testing equitable treatment across different groups.']}, {'end': 3465.79, 'start': 3290.042, 'title': 'Concerns about future of generative models', 'summary': 'Discusses the concerns regarding the potential misuse of generative models, the cultural transition in accepting realistic but fake content, and the development of authentication mechanisms to verify the authenticity of media, with a particular focus on the challenges and potential solutions in this area.', 'duration': 175.748, 'highlights': ['Authentication mechanisms are being developed to verify the authenticity of media. There is ongoing work on developing mechanisms, like TruePic, to authenticate images, although they may not yet be fully effective against sophisticated attempts to evade verification techniques.', "The cultural transition to accepting realistic but fake content may pose challenges in the near future. The speaker anticipates a 'bumpy cultural transition' as people encounter the idea of very realistic yet fake videos and audio, indicating a concern for the immediate future.", 'There are concerns about the potential misuse of generative models in the next few years. The speaker expresses less concern about the distant future but emphasizes the potential for misuse in the near term, highlighting the urgency of addressing this issue.']}], 'duration': 494.815, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Z6rxFNMGdn0/pics/Z6rxFNMGdn02970975.jpg', 'highlights': ["Creating differentially private data using GANs The discussion includes the application of GANs in generating fake patient data with differential privacy guarantees, thereby protecting the original people's data while enabling research use.", 'Ensuring fairness in machine learning models with GANs The chapter explains a method using GANs to create models that are incapable of using specific variables, thereby preventing the inference of sensitive variables internally, and discusses the potential application of CycleGAN for testing equitable treatment across different groups.', 'The potential of GANs in generating training data for classifiers The chapter explores the idea of training GANs on a limited training set to create more data for improving classifier performance on the test set.', 'Authentication mechanisms are being developed to verify the authenticity of media. There is ongoing work on developing mechanisms, like TruePic, to authenticate images, although they may not yet be fully effective against sophisticated attempts to evade verification techniques.', "The cultural transition to accepting realistic but fake content may pose challenges in the near future. The speaker anticipates a 'bumpy cultural transition' as people encounter the idea of very realistic yet fake videos and audio, indicating a concern for the immediate future.", 'There are concerns about the potential misuse of generative models in the next few years. The speaker expresses less concern about the distant future but emphasizes the potential for misuse in the near term, highlighting the urgency of addressing this issue.']}, {'end': 3739.452, 'segs': [{'end': 3510.177, 'src': 'embed', 'start': 3484.061, 'weight': 0, 'content': [{'end': 3489.783, 'text': 'Do you think there are still many such groundbreaking ideas in deep learning that could be developed so quickly?', 'start': 3484.061, 'duration': 5.722}, {'end': 3494.43, 'text': 'Yeah, I do think that there are a lot of ideas that can be developed really quickly.', 'start': 3491.048, 'duration': 3.382}, {'end': 3499.652, 'text': 'GANs were probably a little bit of an outlier on the whole one hour time scale.', 'start': 3496.071, 'duration': 3.581}, {'end': 3510.177, 'text': 'But just in terms of low resource ideas, where you do something really different on the algorithm scale and get a big payback.', 'start': 3500.193, 'duration': 9.984}], 'summary': 'Many groundbreaking ideas in deep learning can be developed quickly, with gans being an outlier in terms of time scale.', 'duration': 26.116, 'max_score': 3484.061, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Z6rxFNMGdn0/pics/Z6rxFNMGdn03484061.jpg'}, {'end': 3553.007, 'src': 'embed', 'start': 3521.103, 'weight': 1, 'content': [{'end': 3526.967, 'text': 'If I had the GAN idea today, it would be a lot harder to prove that it was useful than it was back in 2014,', 'start': 3521.103, 'duration': 5.864}, {'end': 3533.25, 'text': 'because I would need to get it running on something like ImageNet or CelebA at high resolution.', 'start': 3526.967, 'duration': 6.283}, {'end': 3535.512, 'text': 'Those take a while to train.', 'start': 3534.511, 'duration': 1.001}, {'end': 3540.135, 'text': "You couldn't train it in an hour and know that it was something really new and exciting.", 'start': 3535.552, 'duration': 4.583}, {'end': 3542.956, 'text': 'Back in 2014, training on MNIST was enough.', 'start': 3541.075, 'duration': 1.881}, {'end': 3553.007, 'text': 'But there are other areas of machine learning where I think a new idea could actually be developed really quickly with low resources.', 'start': 3544.298, 'duration': 8.709}], 'summary': "In 2014, proving gan idea's usefulness quicker; now, high-res datasets need longer training.", 'duration': 31.904, 'max_score': 3521.103, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Z6rxFNMGdn0/pics/Z6rxFNMGdn03521103.jpg'}, {'end': 3640.238, 'src': 'embed', 'start': 3609.862, 'weight': 2, 'content': [{'end': 3613.326, 'text': 'and everybody probably has a different idea of what interpretability means in their head.', 'start': 3609.862, 'duration': 3.464}, {'end': 3618.751, 'text': "If we could define some concept related to interpretability, that's actually measurable.", 'start': 3613.946, 'duration': 4.805}, {'end': 3623.576, 'text': 'that would be a huge leap forward, even without a new algorithm that increases that quantity.', 'start': 3618.751, 'duration': 4.825}, {'end': 3630.925, 'text': 'And also, once we had the definition of differential privacy, it was fast to get the algorithms that guaranteed it.', 'start': 3624.337, 'duration': 6.588}, {'end': 3635.772, 'text': 'So you could imagine, once we have definitions of good concepts and interpretability,', 'start': 3631.406, 'duration': 4.366}, {'end': 3640.238, 'text': 'we might be able to provide the algorithms that have the interpretability guarantees quickly too.', 'start': 3635.772, 'duration': 4.466}], 'summary': 'Defining measurable interpretability concepts can lead to faster algorithm guarantees.', 'duration': 30.376, 'max_score': 3609.862, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Z6rxFNMGdn0/pics/Z6rxFNMGdn03609862.jpg'}, {'end': 3682.597, 'src': 'embed', 'start': 3657.076, 'weight': 3, 'content': [{'end': 3663.78, 'text': 'I think that it definitely takes better environments than we currently have for training agents,', 'start': 3657.076, 'duration': 6.704}, {'end': 3667.503, 'text': 'that we want them to have a really wide diversity of experiences.', 'start': 3663.78, 'duration': 3.723}, {'end': 3671.146, 'text': "I also think it's going to take really a lot of computation.", 'start': 3668.824, 'duration': 2.322}, {'end': 3673.408, 'text': "It's hard to imagine exactly how much.", 'start': 3671.847, 'duration': 1.561}, {'end': 3682.597, 'text': "So you're optimistic about simulation, simulating a variety of environments as the path forward? I think it's a necessary ingredient, yeah.", 'start': 3673.788, 'duration': 8.809}], 'summary': 'Better environments and diverse experiences needed for training agents, requiring a lot of computation. simulation is a necessary ingredient for progress.', 'duration': 25.521, 'max_score': 3657.076, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Z6rxFNMGdn0/pics/Z6rxFNMGdn03657076.jpg'}, {'end': 3726.727, 'src': 'embed', 'start': 3701.653, 'weight': 4, 'content': [{'end': 3708.717, 'text': 'And today we have many different models that can each do one thing, and we tend to train them on one dataset or one RL environment.', 'start': 3701.653, 'duration': 7.064}, {'end': 3716.122, 'text': 'Sometimes there are actually papers about getting one set of parameters to perform well in many different RL environments.', 'start': 3710.218, 'duration': 5.904}, {'end': 3726.727, 'text': "But we don't really have anything like an agent that goes seamlessly from one type of experience to another and really integrates all the different things that it does over the course of its life.", 'start': 3717.023, 'duration': 9.704}], 'summary': 'Current models specialize in one task, lacking seamless integration across experiences.', 'duration': 25.074, 'max_score': 3701.653, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Z6rxFNMGdn0/pics/Z6rxFNMGdn03701653.jpg'}], 'start': 3466.191, 'title': 'Developing deep learning and advancing machine learning', 'summary': 'Highlights the rapid development of deep learning ideas, exemplified by gans being developed within an hour, and the challenges in proving utility due to longer training times. it also explores ripe areas for advancement in machine learning, such as fairness, interpretability, and artificial general intelligence, emphasizing the potential for significant progress through measurable concepts and algorithms.', 'chapters': [{'end': 3542.956, 'start': 3466.191, 'title': 'Rapid development of deep learning ideas', 'summary': 'Discusses the rapid development of deep learning ideas, with a notable example of gans being developed within an hour, and the current challenges of proving the utility of new ideas due to longer training times for high-resolution datasets.', 'duration': 76.765, 'highlights': ['Developing GANs within an hour was a notable example of rapid idea implementation.', 'Challenges in proving the utility of new ideas include longer training times for high-resolution datasets like ImageNet or CelebA.']}, {'end': 3739.452, 'start': 3544.298, 'title': 'Ripe areas for advancements in machine learning', 'summary': 'Discusses the potential for rapid development in machine learning areas like fairness, interpretability, and artificial general intelligence, where defining measurable concepts and creating algorithms based on these definitions could lead to significant advancements.', 'duration': 195.154, 'highlights': ['Defining measurable concepts related to interpretability could lead to significant advancements in the field. Defining a concept related to interpretability that is measurable could have a huge impact on the field, as seen in the case of differential privacy, where a technical definition led to the design of algorithms guaranteeing privacy.', 'Artificial general intelligence may require better training environments and a significant amount of computation. Building a system with human-level intelligence may require diverse training environments and a substantial amount of computation, potentially making simulation a necessary ingredient for progress.', 'Current machine learning models tend to specialize in one area, lacking seamless integration of diverse experiences. Existing machine learning models often focus on specific tasks or environments, lacking the seamless integration of diverse experiences that may be essential for artificial general intelligence.']}], 'duration': 273.261, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Z6rxFNMGdn0/pics/Z6rxFNMGdn03466191.jpg', 'highlights': ['Developing GANs within an hour was a notable example of rapid idea implementation.', 'Challenges in proving the utility of new ideas include longer training times for high-resolution datasets like ImageNet or CelebA.', 'Defining measurable concepts related to interpretability could lead to significant advancements in the field.', 'Artificial general intelligence may require better training environments and a significant amount of computation.', 'Current machine learning models tend to specialize in one area, lacking seamless integration of diverse experiences.']}, {'end': 4096.41, 'segs': [{'end': 3765.887, 'src': 'embed', 'start': 3740.453, 'weight': 0, 'content': [{'end': 3751.942, 'text': "We don't really have an agent that goes from playing a video game to reading the Wall Street Journal to predicting how effective a molecule will be as a drug or something like that.", 'start': 3740.453, 'duration': 11.489}, {'end': 3756.557, 'text': 'What do you think is a good test for intelligence in your view?', 'start': 3753.254, 'duration': 3.303}, {'end': 3765.887, 'text': "It's been a lot of benchmarks, started with Alan Turing, natural conversation being a good benchmark for intelligence.", 'start': 3757.018, 'duration': 8.869}], 'summary': 'Challenges in creating an agent for diverse tasks. benchmark: natural conversation.', 'duration': 25.434, 'max_score': 3740.453, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Z6rxFNMGdn0/pics/Z6rxFNMGdn03740453.jpg'}, {'end': 3818.424, 'src': 'embed', 'start': 3791.407, 'weight': 1, 'content': [{'end': 3801.292, 'text': 'you could just point an agent at the CIFAR-10 problem, and it downloads and extracts the data and trains a model and starts giving you predictions.', 'start': 3791.407, 'duration': 9.885}, {'end': 3810.398, 'text': "I feel like something that doesn't need to have every step of the pipeline assembled for it definitely understands what it's doing.", 'start': 3802.533, 'duration': 7.865}, {'end': 3814.301, 'text': 'Is AutoML moving into that direction, or are you thinking way even bigger?', 'start': 3810.579, 'duration': 3.722}, {'end': 3818.424, 'text': 'AutoML has mostly been moving toward.', 'start': 3814.542, 'duration': 3.882}], 'summary': 'Automl aims for seamless data processing and model training.', 'duration': 27.017, 'max_score': 3791.407, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Z6rxFNMGdn0/pics/Z6rxFNMGdn03791407.jpg'}, {'end': 3942.192, 'src': 'embed', 'start': 3913.845, 'weight': 2, 'content': [{'end': 3916.287, 'text': "So proving that I'm not a robot with today's technology.", 'start': 3913.845, 'duration': 2.442}, {'end': 3918.33, 'text': "Yeah, that's pretty straightforward.", 'start': 3917.108, 'duration': 1.222}, {'end': 3925.458, 'text': "Like my conversation today hasn't veered off into, you know, talking about the stock market or something because of my training data.", 'start': 3918.41, 'duration': 7.048}, {'end': 3931.385, 'text': 'But I guess more generally trying to prove that something is real from the content alone is incredibly hard.', 'start': 3926.019, 'duration': 5.366}, {'end': 3937.489, 'text': "That's one of the main things I've gotten out of my GAN research that You can simulate almost anything.", 'start': 3931.446, 'duration': 6.043}, {'end': 3942.192, 'text': 'And so you have to really step back to a separate channel to prove that something is real.', 'start': 3937.749, 'duration': 4.443}], 'summary': 'Proving authenticity through content is challenging due to simulating capabilities in gan research.', 'duration': 28.347, 'max_score': 3913.845, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Z6rxFNMGdn0/pics/Z6rxFNMGdn03913845.jpg'}, {'end': 3977.029, 'src': 'embed', 'start': 3948.675, 'weight': 3, 'content': [{'end': 3952.137, 'text': "So according to my own research methodology, there's just no way to know at this point.", 'start': 3948.675, 'duration': 3.462}, {'end': 3953.798, 'text': 'So what?', 'start': 3953.018, 'duration': 0.78}, {'end': 3959.121, 'text': "uh, last question, problem stands out for you that you're really excited about challenging in the near future?", 'start': 3953.798, 'duration': 5.323}, {'end': 3962.964, 'text': 'I think resistance to adversarial examples,', 'start': 3960.103, 'duration': 2.861}, {'end': 3968.066, 'text': 'figuring out how to make machine learning secure against an adversary who wants to interfere it and control it.', 'start': 3962.964, 'duration': 5.102}, {'end': 3971.887, 'text': 'that is one of the most important things researchers today could solve.', 'start': 3968.066, 'duration': 3.821}, {'end': 3977.029, 'text': 'In all domains, image, language, driving.', 'start': 3972.267, 'duration': 4.762}], 'summary': 'Challenging resistance to adversarial examples in machine learning is a crucial problem across various domains.', 'duration': 28.354, 'max_score': 3948.675, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Z6rxFNMGdn0/pics/Z6rxFNMGdn03948675.jpg'}, {'end': 4057.268, 'src': 'embed', 'start': 4036.185, 'weight': 4, 'content': [{'end': 4047.707, 'text': "One methodology that I think is not a specific methodology but a category of solutions that I'm excited about today is making dynamic models that change every time they make a prediction.", 'start': 4036.185, 'duration': 11.522}, {'end': 4052.087, 'text': "Right now we tend to train models and then, after they're trained,", 'start': 4048.427, 'duration': 3.66}, {'end': 4057.268, 'text': 'we freeze them and we just use the same rule to classify everything that comes in from then on.', 'start': 4052.087, 'duration': 5.181}], 'summary': 'Exciting dynamic models for predictions, changing every time, unlike frozen models.', 'duration': 21.083, 'max_score': 4036.185, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Z6rxFNMGdn0/pics/Z6rxFNMGdn04036185.jpg'}], 'start': 3740.453, 'title': 'Future of ai, automl, and security challenges', 'summary': 'Delves into potential benchmarks for intelligence in ai, direction of automl, and challenges in proving authenticity of ai-generated content. it also emphasizes the importance of addressing resistance to adversarial examples in ai security and the need for dynamic models to mitigate security vulnerabilities.', 'chapters': [{'end': 3948.515, 'start': 3740.453, 'title': 'Future of ai and automl', 'summary': 'Discusses the potential benchmarks for intelligence in ai, the direction of automl, and the challenges in proving the authenticity of ai-generated content, emphasizing the need for ai systems to demonstrate understanding and autonomy in complex tasks.', 'duration': 208.062, 'highlights': ['Ian Goodfellow emphasizes the need for benchmarks that truly impress, where AI systems can autonomously accomplish tasks without heavy human intervention, such as downloading and processing data for training models.', 'The discussion delves into the direction of AutoML, focusing on whether the system can design architectures effectively, indicating a shift towards AI systems understanding and accomplishing tasks autonomously.', 'The challenge of proving the authenticity of AI-generated content is highlighted, with the difficulty of discerning real from simulated content and the need for innovative approaches, such as blockchain verification.', 'The conversation explores the difficulty in proving the authenticity of AI-generated content, pointing out the ease of simulating almost anything and the necessity of innovative methods for verification, such as blockchain.', 'The topic of benchmarks for intelligence in AI is raised, emphasizing the need for systems to demonstrate understanding and autonomy in tasks, representing a step forward in real AI.']}, {'end': 4096.41, 'start': 3948.675, 'title': 'Ai security challenges and future', 'summary': 'Discusses the importance of addressing resistance to adversarial examples in ai security to ensure machine learning is secure against interference, emphasizing the need for dynamic models that change with each prediction to mitigate security vulnerabilities.', 'duration': 147.735, 'highlights': ['The importance of addressing resistance to adversarial examples in AI security Emphasizes the need to make machine learning secure against adversaries by preventing interference and control, a crucial challenge for researchers today.', 'The significance of dynamic models that change with each prediction Advocates for the development of dynamic models that update predictions to make it harder for adversaries to take control of the system and ensure security against repeated exploitation.', 'The unpredictable business opportunities in AI and its potential impact on future domains Discusses the uncertainty in predicting the future business opportunities in AI and its potential impact on yet-to-be-encountered domains, akin to the unexpected evolution of phone usage over the years.']}], 'duration': 355.957, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Z6rxFNMGdn0/pics/Z6rxFNMGdn03740453.jpg', 'highlights': ['AI systems need benchmarks to autonomously accomplish tasks without heavy human intervention, emphasizing the need for real AI.', "AutoML's direction focuses on designing architectures effectively, indicating a shift towards AI systems understanding and accomplishing tasks autonomously.", 'Challenges in proving the authenticity of AI-generated content are highlighted, emphasizing the difficulty of discerning real from simulated content and the need for innovative verification methods.', 'Addressing resistance to adversarial examples in AI security is crucial, emphasizing the need to make machine learning secure against adversaries.', 'Advocates for the development of dynamic models that update predictions to ensure security against repeated exploitation.']}], 'highlights': ["Ian Goodfellow authored the impactful textbook 'Deep Learning' and introduced the concept of generative adversarial networks (GANs), spurring remarkable growth in the subfield of deep learning.", "Goodfellow's academic background includes earning BS and MS degrees from Stanford and a PhD from the University of Montreal under the guidance of Yoshua Bengio and Aaron Kerrville.", 'He held research positions at prestigious organizations such as OpenAI, Google Brain, and is currently the Director of Machine Learning at Apple.', 'Deep learning requires a lot of labeled data, with some unsupervised and semi-supervised learning algorithms able to reduce the amount of labeled data needed.', 'The potential for impressive advancements in human-level cognition through increased computation and diverse multimodal data.', 'Challenges in defining and formalizing consciousness.', 'The challenge of summarizing the evolving field of deep learning for a chapter, emphasizing the need to focus on core concepts and language.', 'Alcohol increases openness to ideas, reducing criticism.', "Tim Solomons' semi-supervised GAN project achieved below 1% error using only 100 labeled examples, a 600x decrease from previous methods.", "Creating differentially private data using GANs The discussion includes the application of GANs in generating fake patient data with differential privacy guarantees, thereby protecting the original people's data while enabling research use.", 'Developing GANs within an hour was a notable example of rapid idea implementation.', 'AI systems need benchmarks to autonomously accomplish tasks without heavy human intervention, emphasizing the need for real AI.']}