title
David Silver: AlphaGo, AlphaZero, and Deep Reinforcement Learning | Lex Fridman Podcast #86

description
David Silver leads the reinforcement learning research group at DeepMind and was lead researcher on AlphaGo, AlphaZero and co-lead on AlphaStar, and MuZero and lot of important work in reinforcement learning. Support this podcast by signing up with these sponsors: - MasterClass: https://masterclass.com/lex - Cash App - use code "LexPodcast" and download: - Cash App (App Store): https://apple.co/2sPrUHe - Cash App (Google Play): https://bit.ly/2MlvP5w EPISODE LINKS: Reinforcement learning (book): https://amzn.to/2Jwp5zG PODCAST INFO: Podcast website: https://lexfridman.com/podcast Apple Podcasts: https://apple.co/2lwqZIr Spotify: https://spoti.fi/2nEwCF8 RSS: https://lexfridman.com/feed/podcast/ Full episodes playlist: https://www.youtube.com/playlist?list=PLrAXtmErZgOdP_8GztsuKi9nrraNbKKp4 Clips playlist: https://www.youtube.com/playlist?list=PLrAXtmErZgOeciFP3CBCIEElOJeitOr41 OUTLINE: 0:00 - Introduction 4:09 - First program 11:11 - AlphaGo 21:42 - Rule of the game of Go 25:37 - Reinforcement learning: personal journey 30:15 - What is reinforcement learning? 43:51 - AlphaGo (continued) 53:40 - Supervised learning and self play in AlphaGo 1:06:12 - Lee Sedol retirement from Go play 1:08:57 - Garry Kasparov 1:14:10 - Alpha Zero and self play 1:31:29 - Creativity in AlphaZero 1:35:21 - AlphaZero applications 1:37:59 - Reward functions 1:40:51 - Meaning of life CONNECT: - Subscribe to this YouTube channel - Twitter: https://twitter.com/lexfridman - LinkedIn: https://www.linkedin.com/in/lexfridman - Facebook: https://www.facebook.com/LexFridmanPage - Instagram: https://www.instagram.com/lexfridman - Medium: https://medium.com/@lexfridman - Support on Patreon: https://www.patreon.com/lexfridman

detail
{'title': 'David Silver: AlphaGo, AlphaZero, and Deep Reinforcement Learning | Lex Fridman Podcast #86', 'heatmap': [{'end': 1885.059, 'start': 1815.106, 'weight': 0.782}, {'end': 2144.012, 'start': 2072.445, 'weight': 0.849}, {'end': 3566.181, 'start': 3498.349, 'weight': 1}], 'summary': "David silver discusses reinforcement learning, alphazero's evolution, and the impact of ai advancements, including alphago's mastery in games like go, chess, and shogi, culminating in muzero's evolution and the exploration of defining ultimate goals for intelligent systems.", 'chapters': [{'end': 248.449, 'segs': [{'end': 31.154, 'src': 'embed', 'start': 0.049, 'weight': 3, 'content': [{'end': 2.653, 'text': 'The following is a conversation with David Silver,', 'start': 0.049, 'duration': 2.604}, {'end': 9.002, 'text': 'who leads the reinforcement learning research group A Deep Mind and was the lead researcher on AlphaGo,', 'start': 2.653, 'duration': 6.349}, {'end': 16.172, 'text': 'AlphaZero and co-led the AlphaStar and MuZero efforts and a lot of important work in reinforcement learning in general.', 'start': 9.002, 'duration': 7.17}, {'end': 23.632, 'text': 'I believe AlphaZero is one of the most important accomplishments in the history of artificial intelligence.', 'start': 17.27, 'duration': 6.362}, {'end': 31.154, 'text': 'And David is one of the key humans who brought AlphaZero to life together with a lot of other great researchers at DeepMind.', 'start': 24.232, 'duration': 6.922}], 'summary': 'David silver led the research on alphago, alphazero, alphastar, and muzero at deepmind, which are significant accomplishments in ai.', 'duration': 31.105, 'max_score': 0.049, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/uPUEq8d73JI/pics/uPUEq8d73JI49.jpg'}, {'end': 86.77, 'src': 'embed', 'start': 47.005, 'weight': 1, 'content': [{'end': 52.755, 'text': "For everyone feeling the medical, psychological, and financial burden of this crisis, I'm sending love your way.", 'start': 47.005, 'duration': 5.75}, {'end': 54.037, 'text': 'Stay strong.', 'start': 53.456, 'duration': 0.581}, {'end': 55.42, 'text': "We're in this together.", 'start': 54.057, 'duration': 1.363}, {'end': 56.662, 'text': "We'll beat this thing.", 'start': 55.44, 'duration': 1.222}, {'end': 60.05, 'text': 'This is the Artificial Intelligence Podcast.', 'start': 57.728, 'duration': 2.322}, {'end': 68.076, 'text': 'If you enjoy it, subscribe on YouTube, review it with five stars on Apple Podcasts, support on Patreon or simply connect with me on Twitter.', 'start': 60.37, 'duration': 7.706}, {'end': 71.419, 'text': 'at Lex Friedman spelled F-R-I-D-M-A-N.', 'start': 68.076, 'duration': 3.343}, {'end': 77.804, 'text': "As usual, I'll do a few minutes of ads now and never any ads in the middle that can break the flow of the conversation.", 'start': 72.139, 'duration': 5.665}, {'end': 81.487, 'text': "I hope that works for you and doesn't hurt the listening experience.", 'start': 78.464, 'duration': 3.023}, {'end': 86.77, 'text': 'Quick summary of the ads, two sponsors, Masterclass and Cash App.', 'start': 82.608, 'duration': 4.162}], 'summary': 'Sending love to those affected by crisis. two sponsors: masterclass and cash app.', 'duration': 39.765, 'max_score': 47.005, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/uPUEq8d73JI/pics/uPUEq8d73JI47005.jpg'}, {'end': 137.97, 'src': 'embed', 'start': 113.84, 'weight': 0, 'content': [{'end': 120.943, 'text': 'Since Cash App allows you to buy Bitcoin, let me mention that cryptocurrency in the context of the history of money is fascinating.', 'start': 113.84, 'duration': 7.103}, {'end': 124.764, 'text': 'I recommend Ascent of Money as a great book on this history.', 'start': 121.443, 'duration': 3.321}, {'end': 129.466, 'text': 'Debits and credits on ledgers started around 30,000 years ago.', 'start': 125.345, 'duration': 4.121}, {'end': 132.428, 'text': 'The US dollar created over 200 years ago.', 'start': 129.485, 'duration': 2.943}, {'end': 137.97, 'text': 'And Bitcoin, the first decentralized cryptocurrency, released just over 10 years ago.', 'start': 132.888, 'duration': 5.082}], 'summary': 'Cryptocurrency history spans 30,000 years, with bitcoin released 10 years ago.', 'duration': 24.13, 'max_score': 113.84, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/uPUEq8d73JI/pics/uPUEq8d73JI113840.jpg'}, {'end': 256.41, 'src': 'embed', 'start': 226.251, 'weight': 4, 'content': [{'end': 229.655, 'text': 'Pick three courses you want to complete, watch each of them all the way through.', 'start': 226.251, 'duration': 3.404}, {'end': 234.179, 'text': "It's not that long, but it's an experience that will stick with you for a long time, I promise.", 'start': 229.675, 'duration': 4.504}, {'end': 236.361, 'text': "It's easily worth the money.", 'start': 235.22, 'duration': 1.141}, {'end': 238.542, 'text': 'You can watch it on basically any device.', 'start': 236.741, 'duration': 1.801}, {'end': 244.486, 'text': 'Once again, sign up on masterclass.com to get a discount and to support this podcast.', 'start': 239.203, 'duration': 5.283}, {'end': 248.449, 'text': "And now, here's my conversation with David Silver.", 'start': 245.607, 'duration': 2.842}, {'end': 256.41, 'text': 'What was the first program you ever written? And what programming language? Do you remember? I remember very clearly, yeah.', 'start': 249.724, 'duration': 6.686}], 'summary': 'Recommend watching three courses on masterclass.com, easily worth the money, accessible on any device.', 'duration': 30.159, 'max_score': 226.251, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/uPUEq8d73JI/pics/uPUEq8d73JI226251.jpg'}], 'start': 0.049, 'title': "David silver's leadership and ai podcast ads", 'summary': "Discusses david silver's leadership in reinforcement learning and the development of alphazero at deepmind, along with the ai podcast's sponsorship from masterclass and cash app, offering a discount on subscription and promoting robotics and stem education.", 'chapters': [{'end': 46.424, 'start': 0.049, 'title': 'David silver: reinforcement learning and alphazero', 'summary': "Discusses david silver's leadership in reinforcement learning at deepmind, his key role in the development of alphazero, and his contributions to the field of artificial intelligence.", 'duration': 46.375, 'highlights': ['David Silver led the reinforcement learning research group at DeepMind and was the lead researcher on AlphaGo, AlphaZero, AlphaStar, and MuZero.', 'AlphaZero is considered one of the most important accomplishments in the history of artificial intelligence.', 'The conversation with David Silver was recorded before the outbreak of the pandemic.']}, {'end': 248.449, 'start': 47.005, 'title': 'Ai podcast ads: masterclass & cash app', 'summary': 'Discusses the ai podcast, including sponsorship from masterclass and cash app, offering a discount on subscription and encouraging support through using a specific code, with mention of the history of money, bitcoin, and the potential of cryptocurrency, while also promoting first for robotics and stem education, and the range of courses available on masterclass.', 'duration': 201.444, 'highlights': ['Sponsorship from Masterclass and Cash App, offering a discount on subscription and encouraging support through using a specific code The podcast is sponsored by Masterclass and Cash App, offering a discount on subscription and encouraging support through using a specific code, with mention of a limited time offer of getting another all-access pass to share with a friend.', 'Mention of the history of money, Bitcoin, and the potential of cryptocurrency The podcast discusses the history of money, including the start of debits and credits on ledgers around 30,000 years ago, the creation of the US dollar over 200 years ago, and the release of Bitcoin just over 10 years ago, highlighting the potential of cryptocurrency to redefine the nature of money.', 'Promotion of FIRST for robotics and STEM education The podcast promotes FIRST, an organization that helps advance robotics and STEM education for young people around the world, while mentioning that Cash App will donate $10 to FIRST when the specific code is used.', 'Range of courses available on Masterclass, including space exploration, scientific thinking, game design, and conservation Masterclass offers a range of courses including space exploration, scientific thinking, game design, conservation, chess, poker, and many others, with the encouragement to pick three courses to complete and the promise that the experience will stick with the listener for a long time.']}], 'duration': 248.4, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/uPUEq8d73JI/pics/uPUEq8d73JI49.jpg', 'highlights': ['David Silver led the reinforcement learning research group at DeepMind and was the lead researcher on AlphaGo, AlphaZero, AlphaStar, and MuZero.', 'AlphaZero is considered one of the most important accomplishments in the history of artificial intelligence.', 'The podcast discusses the history of money, including the start of debits and credits on ledgers around 30,000 years ago, the creation of the US dollar over 200 years ago, and the release of Bitcoin just over 10 years ago, highlighting the potential of cryptocurrency to redefine the nature of money.', 'Masterclass offers a range of courses including space exploration, scientific thinking, game design, conservation, chess, poker, and many others, with the encouragement to pick three courses to complete and the promise that the experience will stick with the listener for a long time.', 'The podcast promotes FIRST, an organization that helps advance robotics and STEM education for young people around the world, while mentioning that Cash App will donate $10 to FIRST when the specific code is used.']}, {'end': 770.305, 'segs': [{'end': 292.728, 'src': 'embed', 'start': 269.982, 'weight': 2, 'content': [{'end': 279.432, 'text': 'So I think first program ever was writing my name out in different colors and getting it to loop and repeat that.', 'start': 269.982, 'duration': 9.45}, {'end': 283.056, 'text': 'And there was something magical about that, which just led to more and more.', 'start': 279.632, 'duration': 3.424}, {'end': 286.082, 'text': 'How did you think about computers back then?', 'start': 284.5, 'duration': 1.582}, {'end': 292.728, 'text': "Like the magical aspect of it that you can write a program and there's this thing that you just gave birth to.", 'start': 286.342, 'duration': 6.386}], 'summary': 'First program: writing name in colors, magical aspect of computers.', 'duration': 22.746, 'max_score': 269.982, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/uPUEq8d73JI/pics/uPUEq8d73JI269982.jpg'}, {'end': 527.947, 'src': 'embed', 'start': 504.037, 'weight': 0, 'content': [{'end': 513.582, 'text': 'but something which is able to take actions in a way which makes things interesting and challenging for the human player.', 'start': 504.037, 'duration': 9.545}, {'end': 525.386, 'text': 'And at that time I was able to build these handcrafted agents which, in certain limited cases, could do things which were able to do better than me,', 'start': 515.082, 'duration': 10.304}, {'end': 527.947, 'text': 'but mostly in these Twitch-like scenarios,', 'start': 525.386, 'duration': 2.561}], 'summary': 'Handcrafted agents outperformed human in certain cases in twitch-like scenarios', 'duration': 23.91, 'max_score': 504.037, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/uPUEq8d73JI/pics/uPUEq8d73JI504037.jpg'}, {'end': 659.071, 'src': 'embed', 'start': 626.837, 'weight': 1, 'content': [{'end': 629.018, 'text': 'This was pre deep learning revolution.', 'start': 626.837, 'duration': 2.181}, {'end': 638.742, 'text': 'But it was a principled self-learning system based on a lot of the principles which which people are still using in deep reinforcement learning.', 'start': 630.598, 'duration': 8.144}, {'end': 640.923, 'text': 'How did I feel?', 'start': 640.263, 'duration': 0.66}, {'end': 659.071, 'text': 'I, I think I found it immensely satisfying that a system which was able to learn from first principles for itself was able to reach the point that it was understanding this domain better than I could and able to outwit me.', 'start': 640.943, 'duration': 18.128}], 'summary': 'Pre deep learning system achieved self-learning, understanding domain better than human.', 'duration': 32.234, 'max_score': 626.837, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/uPUEq8d73JI/pics/uPUEq8d73JI626837.jpg'}], 'start': 249.724, 'title': 'Early passion for computers, ai, and achieving world-class go play', 'summary': "Covers the speaker's early experience with computers and ai, including writing their first program at the age of seven, the fascination with limitless possibilities, and achieving world-class go play through ai development, culminating in a conversation with garry kasparov and murray campbell.", 'chapters': [{'end': 450.746, 'start': 249.724, 'title': 'Early passion for computers and ai', 'summary': "Discusses the speaker's early experience with computers, including writing their first program at the age of seven, the magical aspect of programming, and the fascination with limitless possibilities. it also covers the speaker's exposure to artificial intelligence at an early age and their later pursuit of ai during their university studies.", 'duration': 201.022, 'highlights': ["The speaker's first program was writing their name in different colors and creating a loop to repeat it, igniting their passion for programming at the age of seven. At the age of seven, the speaker wrote their first program, creating a loop to repeat their name in different colors, sparking their passion for programming.", "The speaker's fascination with the limitless possibilities of computers and the ability to create anything, similar to their experience with Lego, instilled a deep interest in programming. The speaker's fascination with the limitless possibilities of computers, akin to their experience with Lego, instilled a deep interest in programming and exploration of its potential.", "The speaker's exposure to artificial intelligence at an early age, influenced by their father's pursuit of a master's degree in AI, and their later academic exploration of the goals of computer science, particularly in the realm of AI, during their university studies. The speaker was exposed to artificial intelligence at an early age through their father's pursuit of a master's degree in AI and later delved into the goals of computer science, particularly in the realm of AI, during their university studies."]}, {'end': 770.305, 'start': 451.847, 'title': 'Ai in games and the achievement of world-class go play', 'summary': 'Discusses the evolution of ai in games, the development of a go program using reinforcement learning, and the achievement of world-class go play, culminating in a conversation with garry kasparov and murray campbell.', 'duration': 318.458, 'highlights': ['The development of a Go program using reinforcement learning The speaker built a Go program using reinforcement learning, enabling it to learn helpful patterns and beat the speaker.', 'Evolution of AI in games and the realization of the need for real AI The speaker worked in the games industry, realizing the short-term fixes and the need for real AI, leading to the pursuit of a PhD to study intelligence.', 'Conversation with Garry Kasparov and Murray Campbell The speaker had a conversation with Garry Kasparov and Murray Campbell, marking a significant moment in AI history.']}], 'duration': 520.581, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/uPUEq8d73JI/pics/uPUEq8d73JI249724.jpg', 'highlights': ['The speaker had a conversation with Garry Kasparov and Murray Campbell, marking a significant moment in AI history.', 'The speaker built a Go program using reinforcement learning, enabling it to learn helpful patterns and beat the speaker.', "The speaker's fascination with the limitless possibilities of computers, akin to their experience with Lego, instilled a deep interest in programming and exploration of its potential.", "The speaker was exposed to artificial intelligence at an early age through their father's pursuit of a master's degree in AI and later delved into the goals of computer science, particularly in the realm of AI, during their university studies.", 'At the age of seven, the speaker wrote their first program, creating a loop to repeat their name in different colors, sparking their passion for programming.']}, {'end': 1652.798, 'segs': [{'end': 1151.91, 'src': 'embed', 'start': 1113.214, 'weight': 0, 'content': [{'end': 1118.495, 'text': "And how else can a machine hope to understand what's going on except through learning?", 'start': 1113.214, 'duration': 5.281}, {'end': 1120.376, 'text': "If you're not learning, what else are you doing??", 'start': 1119.056, 'duration': 1.32}, {'end': 1122.057, 'text': "Well, you're putting all the knowledge into the system.", 'start': 1120.396, 'duration': 1.661}, {'end': 1133.22, 'text': 'And that just feels like something which decades of AI have told us is maybe not a dead end, but certainly has a ceiling to the capabilities.', 'start': 1122.577, 'duration': 10.643}, {'end': 1139.823, 'text': "It's known as the knowledge acquisition bottleneck that the more you try to put into something, the more brittle the system becomes.", 'start': 1133.26, 'duration': 6.563}, {'end': 1142.724, 'text': 'And so you just have to have learning.', 'start': 1140.403, 'duration': 2.321}, {'end': 1143.524, 'text': 'You have to have learning.', 'start': 1142.744, 'duration': 0.78}, {'end': 1151.91, 'text': "That's the only way you're going to be able to get a system which has sufficient knowledge in it, millions and millions of pieces of knowledge,", 'start': 1143.564, 'duration': 8.346}], 'summary': 'Learning is essential for a machine to gain sufficient knowledge, overcoming the knowledge acquisition bottleneck.', 'duration': 38.696, 'max_score': 1113.214, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/uPUEq8d73JI/pics/uPUEq8d73JI1113214.jpg'}, {'end': 1247.86, 'src': 'embed', 'start': 1221.513, 'weight': 1, 'content': [{'end': 1228.676, 'text': 'end game theory, combinatorial game theory and combined with more principled search-based methods,', 'start': 1221.513, 'duration': 7.163}, {'end': 1235.959, 'text': "which we're trying to solve for particular subparts of the game, like life and death, connecting groups together.", 'start': 1228.676, 'duration': 7.283}, {'end': 1240.441, 'text': 'All these amazing subproblems that just emerged in the game of Go.', 'start': 1236.879, 'duration': 3.562}, {'end': 1247.86, 'text': 'there were different pieces all put together into this collage which together would try and play against a human.', 'start': 1240.441, 'duration': 7.419}], 'summary': 'Combining game theory and search methods to solve subparts of go game.', 'duration': 26.347, 'max_score': 1221.513, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/uPUEq8d73JI/pics/uPUEq8d73JI1221513.jpg'}, {'end': 1309.993, 'src': 'embed', 'start': 1277.062, 'weight': 6, 'content': [{'end': 1279.223, 'text': 'just from the outcome, like, you know, learn for itself.', 'start': 1277.062, 'duration': 2.161}, {'end': 1287.67, 'text': 'If you try something, did that help or did it not help? And only through that procedure can you arrive at knowledge which is verified.', 'start': 1279.304, 'duration': 8.366}, {'end': 1293.034, 'text': 'The system has to verify it for itself, not relying on any other third party to say this is right or this is wrong.', 'start': 1287.95, 'duration': 5.084}, {'end': 1298.799, 'text': 'And so that principle was already, you know, very important in those days.', 'start': 1293.635, 'duration': 5.164}, {'end': 1302.202, 'text': 'Unfortunately, we were missing some important pieces back then.', 'start': 1299.399, 'duration': 2.803}, {'end': 1309.993, 'text': "So before we dive into maybe discussing the beauty of reinforcement learning, let's take a step back.", 'start': 1303.331, 'duration': 6.662}], 'summary': 'Learning through verification and self-reliance, emphasizing the importance of reinforcement learning.', 'duration': 32.931, 'max_score': 1277.062, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/uPUEq8d73JI/pics/uPUEq8d73JI1277062.jpg'}], 'start': 770.766, 'title': 'Revolutionizing ai through computergo and reinforcement learning', 'summary': "Covers the evolution of ai with computergo, emphasizing the shift towards incorporating learning and human intuition. it also explores the significance of reinforcement learning in tackling go's enormous search space and its impact on ai progress.", 'chapters': [{'end': 1298.799, 'start': 770.766, 'title': 'Revolutionizing ai with computergo', 'summary': 'Delves into the early days of computergo, highlighting the immense complexity of the game, the failure of classical ai methods, and the necessity of incorporating learning and human intuition, ultimately leading to a transformative approach to ai.', 'duration': 528.033, 'highlights': ['The failure of classical AI methods in tackling the complexity of Go is underscored, with the strongest Go program in the world being defeated by a nine-year-old child, and the realization that a different approach was needed for Go than for other AI domains. Failure of classical AI methods in Go, defeat of strongest Go program by a child, need for a different approach in Go compared to other AI domains', 'The pivotal role of human intuition and the challenge of incorporating it into computers is emphasized, with the intuitive judgment being a key reason why Go was so hard for AI, and the need for something akin to human intuition to solve many problems in AI. Pivotal role of human intuition in Go, challenge of incorporating it into computers, need for human intuition in solving AI problems', 'The necessity of learning in surpassing human levels of performance is discussed, with the recognition that learning is crucial for systems to progress beyond human levels and the limitations of knowledge acquisition without learning. Necessity of learning in surpassing human performance, limitations of knowledge acquisition without learning', "The shift towards a principled approach where the system can learn for itself from the outcome is highlighted, emphasizing the importance of the system verifying knowledge for itself and the brittle nature of previous assembly-based systems. Shift towards a principled approach, system's verification of knowledge, brittleness of previous assembly-based systems"]}, {'end': 1652.798, 'start': 1299.399, 'title': 'The beauty of go and the introduction of reinforcement learning', 'summary': "Discusses the simplicity and complexity of the game of go, its deep history and cultural significance, and the challenges of using traditional heuristic search methods in go due to its enormous search space of around 10 to the 170 positions. it also highlights the entry of reinforcement learning into the speaker's research life and its significance in the pursuit of progress in ai.", 'duration': 353.399, 'highlights': ['The game of Go has remarkably simple rules, played on a 19 by 19 grid, and has an immense knowledge base built up by human players over thousands of years, studied deeply and played by something like 50 million players across the world. The simplicity and complexity of the game of Go, along with its deep history and cultural significance, are emphasized.', "There's around 10 to the 170 positions in the game of Go, making traditional heuristic search methods ineffective and reinforcement learning pivotal in making progress in AI. The enormous search space of around 10 to the 170 positions in the game of Go and the ineffectiveness of traditional heuristic search methods are highlighted.", "The entry of reinforcement learning into the speaker's research life and its significance in the pursuit of progress in AI. The significance of reinforcement learning in the pursuit of progress in AI and its entry into the speaker's research life are emphasized."]}], 'duration': 882.032, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/uPUEq8d73JI/pics/uPUEq8d73JI770766.jpg', 'highlights': ['Failure of classical AI methods in Go, defeat of strongest Go program by a child, need for a different approach in Go compared to other AI domains', 'Pivotal role of human intuition in Go, challenge of incorporating it into computers, need for human intuition in solving AI problems', 'Necessity of learning in surpassing human performance, limitations of knowledge acquisition without learning', "Shift towards a principled approach, system's verification of knowledge, brittleness of previous assembly-based systems", 'The simplicity and complexity of the game of Go, along with its deep history and cultural significance, are emphasized', 'The enormous search space of around 10 to the 170 positions in the game of Go and the ineffectiveness of traditional heuristic search methods are highlighted', "The significance of reinforcement learning in the pursuit of progress in AI and its entry into the speaker's research life are emphasized"]}, {'end': 2614.367, 'segs': [{'end': 1738.476, 'src': 'embed', 'start': 1711.421, 'weight': 4, 'content': [{'end': 1716.927, 'text': 'And if we ever create a human-level intelligence system, it would be at the core of that kind of system.', 'start': 1711.421, 'duration': 5.506}, {'end': 1721.709, 'text': "Let me say it this way, that I think it's helpful to separate out the problem from the solution.", 'start': 1717.527, 'duration': 4.182}, {'end': 1726.051, 'text': 'So I see the problem of intelligence.', 'start': 1722.449, 'duration': 3.602}, {'end': 1735.775, 'text': 'I would say it can be formalized as the reinforcement learning problem and that that formalization is enough to capture most, if not all,', 'start': 1726.051, 'duration': 9.724}, {'end': 1738.476, 'text': 'of the things that we mean by intelligence,', 'start': 1735.775, 'duration': 2.701}], 'summary': 'Problem of intelligence can be formalized as the reinforcement learning problem, capturing most aspects of intelligence.', 'duration': 27.055, 'max_score': 1711.421, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/uPUEq8d73JI/pics/uPUEq8d73JI1711421.jpg'}, {'end': 1885.059, 'src': 'heatmap', 'start': 1815.106, 'weight': 0.782, 'content': [{'end': 1821.887, 'text': "If it's okay, can we take a step back and kind of ask the basic question of what is to you, reinforcement learning??", 'start': 1815.106, 'duration': 6.781}, {'end': 1835.45, 'text': 'So, reinforcement learning is the study and the science and the problem of intelligence in the form of an agent that interacts with an environment.', 'start': 1822.647, 'duration': 12.803}, {'end': 1840.031, 'text': "So the problem you're trying to solve is represented by some environment, like the world in which that agent is situated.", 'start': 1835.53, 'duration': 4.501}, {'end': 1844.412, 'text': 'And the goal of RL is clear, that the agent gets to take actions.', 'start': 1840.791, 'duration': 3.621}, {'end': 1851.173, 'text': 'Those actions have some effect on the environment, and the environment gives back an observation to the agent, saying you know, this is what you see,', 'start': 1845.652, 'duration': 5.521}, {'end': 1851.554, 'text': 'or sense.', 'start': 1851.173, 'duration': 0.381}, {'end': 1857.675, 'text': "And one special thing which it gives back is called the reward signal, how well it's doing in the environment.", 'start': 1852.914, 'duration': 4.761}, {'end': 1865.977, 'text': 'And the reinforcement learning problem is to simply take actions over time so as to maximize that reward signal.', 'start': 1858.175, 'duration': 7.802}, {'end': 1869.761, 'text': 'So a couple of basic questions.', 'start': 1867.418, 'duration': 2.343}, {'end': 1873.726, 'text': 'What types of RL approaches are there?', 'start': 1871.223, 'duration': 2.503}, {'end': 1885.059, 'text': "So I don't know if there's a nice brief in-words way to paint a picture of sort of value-based, model-based, policy-based reinforcement learning.", 'start': 1873.946, 'duration': 11.113}], 'summary': 'Reinforcement learning is the study of agent-environment interaction to maximize reward signal, with types including value-based, model-based, and policy-based approaches.', 'duration': 69.953, 'max_score': 1815.106, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/uPUEq8d73JI/pics/uPUEq8d73JI1815106.jpg'}, {'end': 2144.012, 'src': 'heatmap', 'start': 2072.445, 'weight': 0.849, 'content': [{'end': 2079.827, 'text': "So learning is required because it's the only way to achieve good performance in any sufficiently large and complex environment.", 'start': 2072.445, 'duration': 7.382}, {'end': 2081.907, 'text': "So that's the first step.", 'start': 2080.547, 'duration': 1.36}, {'end': 2085.328, 'text': 'And so that step gives commonality to all of the other pieces.', 'start': 2082.286, 'duration': 3.042}, {'end': 2090.123, 'text': 'because now you might ask well, what should you be learning? What does learning even mean?', 'start': 2085.328, 'duration': 4.795}, {'end': 2097.345, 'text': "In this sense, learning might mean well, you're trying to update the parameters of some system,", 'start': 2090.443, 'duration': 6.902}, {'end': 2100.226, 'text': 'which is then the thing that actually picks the actions.', 'start': 2097.345, 'duration': 2.881}, {'end': 2103.426, 'text': 'And those parameters could be representing anything.', 'start': 2101.426, 'duration': 2}, {'end': 2107.467, 'text': 'They could be parameterizing a value function or a model or a policy.', 'start': 2103.466, 'duration': 4.001}, {'end': 2113.589, 'text': "And so, in that sense, there's a lot of commonality in that whatever is being represented, there is the thing which is being learned,", 'start': 2108.588, 'duration': 5.001}, {'end': 2116.73, 'text': "and it's being learned with the ultimate goal of maximizing rewards.", 'start': 2113.589, 'duration': 3.141}, {'end': 2123.124, 'text': 'But the way in which you decompose the problem is really what gives the semantics to the whole system.', 'start': 2118.61, 'duration': 4.514}, {'end': 2128.562, 'text': 'Like. are you trying to learn something to predict well, like a value, function or a model??', 'start': 2123.144, 'duration': 5.418}, {'end': 2130.763, 'text': 'Are you learning something to perform well, like a policy??', 'start': 2128.582, 'duration': 2.181}, {'end': 2135.906, 'text': 'And the form of that objective is kind of giving the semantics to the system.', 'start': 2131.764, 'duration': 4.142}, {'end': 2140.269, 'text': 'And so it really is, at the next level down, a fundamental choice.', 'start': 2136.367, 'duration': 3.902}, {'end': 2144.012, 'text': 'And we have to make those fundamental choices as system designers,', 'start': 2140.309, 'duration': 3.703}], 'summary': 'Learning is crucial for good performance in complex environments, with the ultimate goal of maximizing rewards.', 'duration': 71.567, 'max_score': 2072.445, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/uPUEq8d73JI/pics/uPUEq8d73JI2072445.jpg'}, {'end': 2342.068, 'src': 'embed', 'start': 2291.396, 'weight': 1, 'content': [{'end': 2299.698, 'text': 'do you have good intuition about why it works at all and works as well as it does, i think.', 'start': 2291.396, 'duration': 8.302}, {'end': 2302.599, 'text': 'let me take two parts to that question.', 'start': 2299.698, 'duration': 2.901}, {'end': 2310.804, 'text': "i think it's not surprising to me that the idea of reinforcement learning works,", 'start': 2302.599, 'duration': 8.205}, {'end': 2317.163, 'text': "because in some sense I feel it's the only thing which can ultimately.", 'start': 2310.804, 'duration': 6.359}, {'end': 2326.785, 'text': 'And so I feel we have to address it and there must be success as possible, because we have examples of intelligence and it must, at some level,', 'start': 2317.483, 'duration': 9.302}, {'end': 2337.587, 'text': 'be possible to acquire experience and use that experience to do better in a way which is meaningful to environments of the complexity that humans can deal with.', 'start': 2326.785, 'duration': 10.802}, {'end': 2338.267, 'text': 'It must be.', 'start': 2337.807, 'duration': 0.46}, {'end': 2342.068, 'text': 'Am I surprised that our current systems can do as well as they can do?', 'start': 2339.587, 'duration': 2.481}], 'summary': 'Reinforcement learning works due to its adaptability and success in handling complex environments.', 'duration': 50.672, 'max_score': 2291.396, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/uPUEq8d73JI/pics/uPUEq8d73JI2291396.jpg'}, {'end': 2554.046, 'src': 'embed', 'start': 2525.45, 'weight': 0, 'content': [{'end': 2528.851, 'text': 'what will they think of the algorithms that we developed back now?', 'start': 2525.45, 'duration': 3.401}, {'end': 2536.013, 'text': 'Will it be looking back at these days and thinking that?', 'start': 2528.951, 'duration': 7.062}, {'end': 2542.684, 'text': 'Will we look back and feel that these algorithms were naive first steps, or will they still be the fundamental ideas which are used even in 100,000,', 'start': 2536.013, 'duration': 6.671}, {'end': 2542.834, 'text': '10,000 years??', 'start': 2542.684, 'duration': 0.15}, {'end': 2554.046, 'text': "Yeah, and they'll watch back to this conversation with a smile, maybe a little bit of a laugh.", 'start': 2546.235, 'duration': 7.811}], 'summary': 'Reflecting on the longevity of developed algorithms, with a touch of humor.', 'duration': 28.596, 'max_score': 2525.45, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/uPUEq8d73JI/pics/uPUEq8d73JI2525450.jpg'}], 'start': 1652.818, 'title': 'Reinforcement learning', 'summary': 'Discusses the significance and overview of reinforcement learning, emphasizing its core role in defining intelligence, solving ai, and the surprising properties of neural networks, with a call for open-mindedness and humility in the field.', 'chapters': [{'end': 1814.365, 'start': 1652.818, 'title': 'Reinforcement learning as core of intelligence', 'summary': 'Discusses the significance of reinforcement learning as the core of intelligence, emphasizing its potential to define intelligence and its role in solving ai, with a call for open-mindedness and humility in the early stages of the field.', 'duration': 161.547, 'highlights': ['Reinforcement learning is seen as the path to progress in computer go and is considered as the key to defining intelligence and solving the problem of AI.', 'The speaker believes that the problem of intelligence can be formalized as the reinforcement learning problem, capturing most, if not all, aspects of intelligence.', 'The chapter emphasizes the importance of remaining open-minded and modest in the early days of the reinforcement learning field, acknowledging the potential for better ideas and many amazing discoveries ahead.', 'The speaker suggests that any system solving AI in the future would have reinforcement learning within it, highlighting the importance of solution methods in addressing the reinforcement learning problem.', 'The speaker believes that the field of reinforcement learning is in its early days and anticipates many amazing discoveries ahead, emphasizing the need to remain modest and open-minded about the best approaches to solving any problem.']}, {'end': 2032.364, 'start': 1815.106, 'title': 'Reinforcement learning overview', 'summary': 'Provides an overview of reinforcement learning, emphasizing the interaction of an agent with its environment, the role of reward signals, and the decomposition of the solution method into value functions, policies, and models.', 'duration': 217.258, 'highlights': ['The goal of reinforcement learning is to maximize the reward signal by taking actions that affect the environment and receive feedback in the form of observations and rewards.', 'Reinforcement learning approaches involve value-based, model-based, and policy-based methods, which contribute to decomposing the complex problem into manageable components.', 'The fundamental building blocks in reinforcement learning include the representation of value functions, policies, and models, which form the basis for different branches of RL.']}, {'end': 2614.367, 'start': 2032.364, 'title': 'Deep reinforcement learning', 'summary': 'Explains the importance of learning in reinforcement learning, the role of deep learning in representing different components of the solution, and the surprising and beautiful properties of neural networks in high dimensions.', 'duration': 582.003, 'highlights': ['Reinforcement learning is required for good performance in any sufficiently large and complex environment, giving commonality to all other pieces. Reinforcement learning is necessary for achieving good performance in complex environments.', 'Deep reinforcement learning utilizes powerful representations offered by neural networks to represent different components of the solution and has no ceiling to its performance. Deep reinforcement learning leverages neural networks to represent different components and has unlimited performance potential.', 'Neural networks can learn incredibly complex representations and continue to perform well in high dimensions, despite the presence of nonlinear surfaces, leading to surprising and beautiful properties. Neural networks can learn complex representations and perform well in high dimensions, exhibiting surprising and beautiful properties.', 'The ability of neural networks to learn in high dimensions is a surprising and beautiful property, challenging low-dimensional intuitions and indicating a truly universal approach. The ability of neural networks to learn in high dimensions challenges low-dimensional intuitions and signifies a universal approach.']}], 'duration': 961.549, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/uPUEq8d73JI/pics/uPUEq8d73JI1652818.jpg', 'highlights': ['Reinforcement learning is seen as the path to progress in computer go and is considered as the key to defining intelligence and solving the problem of AI.', 'The speaker believes that the problem of intelligence can be formalized as the reinforcement learning problem, capturing most, if not all, aspects of intelligence.', 'The goal of reinforcement learning is to maximize the reward signal by taking actions that affect the environment and receive feedback in the form of observations and rewards.', 'Deep reinforcement learning utilizes powerful representations offered by neural networks to represent different components of the solution and has no ceiling to its performance.', 'Neural networks can learn incredibly complex representations and continue to perform well in high dimensions, despite the presence of nonlinear surfaces, leading to surprising and beautiful properties.']}, {'end': 3497.849, 'segs': [{'end': 2697.505, 'src': 'embed', 'start': 2668.804, 'weight': 0, 'content': [{'end': 2676.451, 'text': 'not by humans saying whether that position is good or not, or even humans providing rules as to how you might evaluate it.', 'start': 2668.804, 'duration': 7.647}, {'end': 2690.181, 'text': 'but instead by allowing the system to randomly play out the game until the end multiple times and taking the average of those outcomes as the prediction of what will happen.', 'start': 2677.455, 'duration': 12.726}, {'end': 2697.505, 'text': "So, for example, if you're in the game of Go, the intuition is that you take a position and you get the system to kind of play,", 'start': 2690.642, 'duration': 6.863}], 'summary': 'Ai evaluates game positions by simulating multiple plays to predict outcomes.', 'duration': 28.701, 'max_score': 2668.804, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/uPUEq8d73JI/pics/uPUEq8d73JI2668804.jpg'}, {'end': 2733.758, 'src': 'embed', 'start': 2707.373, 'weight': 4, 'content': [{'end': 2713.14, 'text': 'And if white ends up winning more of those random games than black, then it favors white.', 'start': 2707.373, 'duration': 5.767}, {'end': 2717.765, 'text': 'So that idea was known as Monte Carlo logic.', 'start': 2713.62, 'duration': 4.145}, {'end': 2727.733, 'text': 'search, and a particular form of Monte Carlo search that became very effective and was developed in computer go, first by Remy Coulomb in 2006,,', 'start': 2719.467, 'duration': 8.266}, {'end': 2733.758, 'text': 'and then taken further by others, was something called Monte Carlo tree search,', 'start': 2727.733, 'duration': 6.025}], 'summary': 'Monte carlo logic favors white in random games, leading to monte carlo tree search.', 'duration': 26.385, 'max_score': 2707.373, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/uPUEq8d73JI/pics/uPUEq8d73JI2707373.jpg'}, {'end': 2834.594, 'src': 'embed', 'start': 2788.019, 'weight': 1, 'content': [{'end': 2793.262, 'text': 'MoGo was evaluating purely by random rollouts against itself.', 'start': 2788.019, 'duration': 5.243}, {'end': 2799.759, 'text': "And in a way, it's it's truly remarkable that random play should give you anything at all.", 'start': 2793.882, 'duration': 5.877}, {'end': 2807.105, 'text': "Why, in this perfectly deterministic game that's very precise and involves these very exact sequences?", 'start': 2799.919, 'duration': 7.186}, {'end': 2811.227, 'text': 'why is it that randomization is helpful?', 'start': 2807.105, 'duration': 4.122}, {'end': 2826.523, 'text': "And so the intuition is that randomization captures something about the nature of the search tree from a position that you're understanding the nature of the search tree from that node onwards by using randomization.", 'start': 2812.128, 'duration': 14.395}, {'end': 2828.165, 'text': 'And this was a very powerful idea.', 'start': 2827.004, 'duration': 1.161}, {'end': 2831.431, 'text': "And I've seen this in other spaces.", 'start': 2829.289, 'duration': 2.142}, {'end': 2834.594, 'text': 'I talked to Richard Karp and so on.', 'start': 2831.711, 'duration': 2.883}], 'summary': 'Mogo uses random rollouts to understand search tree nature, a powerful idea in game ai.', 'duration': 46.575, 'max_score': 2788.019, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/uPUEq8d73JI/pics/uPUEq8d73JI2788019.jpg'}], 'start': 2614.367, 'title': 'Evolution of computer go programs', 'summary': "Discusses the revolutionary heuristic search in mogo and its impact, and the evolution of computer go programs from monte carlo logic to alphago's deep learning victory, showcasing human-level play without search.", 'chapters': [{'end': 2668.804, 'start': 2614.367, 'title': 'Revolutionizing heuristic search in computergo', 'summary': 'Discusses the revolution in heuristic search during the development of mogo and its impact on evaluating positions, shaping the future of complex systems.', 'duration': 54.437, 'highlights': ['The revolution in heuristic search during the development of MoGo was a major new development in the context of ComputerGo, shaping the future of complex systems.', 'The idea was essentially that a position could be evaluated or a state in general could be evaluated, impacting the way heuristic search was done.']}, {'end': 3497.849, 'start': 2668.804, 'title': "Alphago's journey: from monte carlo logic to deep learning", 'summary': 'Explores the evolution of computer go playing programs, from monte carlo logic search to the groundbreaking alphago victory over the world champion, demonstrating the power of deep learning in achieving human-level play without search.', 'duration': 829.045, 'highlights': ["AlphaGo's ability to reach human van level, master level, at the full game of go 19 by 19 boards without any search at all. AlphaGo's achievement of reaching human master level at the full game of Go on 19 by 19 boards without any search, showcasing the power of deep learning in playing at the level of the best Monte Carlo tree search systems.", "AlphaGo's victory over the world champion as a watershed moment, with 100 million people watching the match online live. The significance of AlphaGo's victory over the world champion, with 100 million people watching the match online live, marking a watershed moment in the history of AI and computer Go.", "The use of human data as an expedient step to help AlphaGo's development and understanding of the system, serving the purpose of breaking down a hard challenge into pieces easier to understand for researchers. The strategic use of human data in AlphaGo's development to aid in understanding the system, breaking down a complex challenge into more manageable components for researchers."]}], 'duration': 883.482, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/uPUEq8d73JI/pics/uPUEq8d73JI2614367.jpg', 'highlights': ["AlphaGo's victory over the world champion, with 100 million people watching the match online live, marking a watershed moment in the history of AI and computer Go.", 'The revolution in heuristic search during the development of MoGo was a major new development in the context of ComputerGo, shaping the future of complex systems.', "AlphaGo's ability to reach human master level at the full game of Go on 19 by 19 boards without any search, showcasing the power of deep learning in playing at the level of the best Monte Carlo tree search systems.", "The use of human data in AlphaGo's development to aid in understanding the system, breaking down a complex challenge into more manageable components for researchers.", 'The idea that a position could be evaluated or a state in general could be evaluated, impacting the way heuristic search was done.']}, {'end': 5039.828, 'segs': [{'end': 3566.181, 'src': 'heatmap', 'start': 3498.349, 'weight': 1, 'content': [{'end': 3503.693, 'text': 'And we knew for sure that there were still imperfections in AlphaGo which were going to be exposed to the whole world watching.', 'start': 3498.349, 'duration': 5.344}, {'end': 3508.075, 'text': 'And so, yeah, it was, I think, a great experience.', 'start': 3504.453, 'duration': 3.622}, {'end': 3514.399, 'text': 'And I feel privileged to have been part of it, privileged to have led that amazing team.', 'start': 3508.175, 'duration': 6.224}, {'end': 3519.342, 'text': 'I feel privileged to have been in a moment of history, like you say.', 'start': 3514.419, 'duration': 4.923}, {'end': 3526.406, 'text': 'but also lucky that you know, in a sense I was insulated from the knowledge of.', 'start': 3520.902, 'duration': 5.504}, {'end': 3534.893, 'text': 'I think it would have been harder to focus on the research if the full kind of reality of what was gonna come to pass had been known to me and the team.', 'start': 3526.406, 'duration': 8.487}, {'end': 3536.374, 'text': 'I think it was.', 'start': 3535.353, 'duration': 1.021}, {'end': 3543.62, 'text': 'you know, we were in our bubble and we were working on research and we were trying to answer the scientific questions, and then, bam, you know,', 'start': 3536.374, 'duration': 7.246}, {'end': 3544.481, 'text': 'the public sees it.', 'start': 3543.62, 'duration': 0.861}, {'end': 3547.283, 'text': 'And I think it was better that way in retrospect.', 'start': 3544.561, 'duration': 2.722}, {'end': 3552.452, 'text': 'Were you confident that I guess what were the chances that you could get the win?', 'start': 3547.529, 'duration': 4.923}, {'end': 3561.958, 'text': "So, just like you said, I'm a little bit more familiar with another accomplishment that we may not even get a chance to talk to.", 'start': 3553.633, 'duration': 8.325}, {'end': 3566.181, 'text': 'I talked to Oriol Villales about Alpha Star, which is another incredible accomplishment.', 'start': 3562.398, 'duration': 3.783}], 'summary': 'Privileged to lead amazing team, insulated from public knowledge. reflecting on successful experience.', 'duration': 67.832, 'max_score': 3498.349, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/uPUEq8d73JI/pics/uPUEq8d73JI3498349.jpg'}, {'end': 4362.556, 'src': 'embed', 'start': 4331.434, 'weight': 0, 'content': [{'end': 4332.896, 'text': 'But it also missed some things.', 'start': 4331.434, 'duration': 1.462}, {'end': 4342.547, 'text': 'So the fact that the evaluation function, the way that the chess position was understood, was created by humans and not by the machine,', 'start': 4333.276, 'duration': 9.271}, {'end': 4348.912, 'text': "is a limitation, which means that There's a ceiling on how well it can do.", 'start': 4342.547, 'duration': 6.365}, {'end': 4353.733, 'text': 'but maybe more importantly, it means that the same idea cannot be applied in other domains,', 'start': 4348.912, 'duration': 4.821}, {'end': 4362.556, 'text': "where we don't have access to the human grandmasters and that ability to encode exactly their knowledge into an evaluation function.", 'start': 4353.733, 'duration': 8.823}], 'summary': 'Limitation in chess ai due to human-created evaluation function, hindering application in other domains.', 'duration': 31.122, 'max_score': 4331.434, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/uPUEq8d73JI/pics/uPUEq8d73JI4331434.jpg'}, {'end': 4532.534, 'src': 'embed', 'start': 4475.559, 'weight': 2, 'content': [{'end': 4484.605, 'text': 'So how big of an intellectual leap was this that self-play could achieve superhuman level performance on its own?', 'start': 4475.559, 'duration': 9.046}, {'end': 4488.347, 'text': 'And maybe could you also say what is self-play?', 'start': 4485.766, 'duration': 2.581}, {'end': 4491.808, 'text': 'We kind of mentioned it a few times, but', 'start': 4488.367, 'duration': 3.441}, {'end': 4494.688, 'text': 'So let me start with self-play.', 'start': 4491.808, 'duration': 2.88}, {'end': 4504.811, 'text': "So the idea of self-play is something which is really about systems learning for themselves, but in the situation where there's more than one agent.", 'start': 4495.388, 'duration': 9.423}, {'end': 4510.532, 'text': "And so if you're in a game and the game is played between two players,", 'start': 4505.811, 'duration': 4.721}, {'end': 4519.468, 'text': 'then self-play is really about understanding that game just by playing games against yourself rather than against any actual real opponent.', 'start': 4510.532, 'duration': 8.936}, {'end': 4532.534, 'text': "And so it's a way to discover strategies without having to actually need to go out and play against any particular human player, for example.", 'start': 4520.088, 'duration': 12.446}], 'summary': 'Self-play enables systems to learn by playing games against themselves, without needing human opponents.', 'duration': 56.975, 'max_score': 4475.559, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/uPUEq8d73JI/pics/uPUEq8d73JI4475559.jpg'}, {'end': 4686.691, 'src': 'embed', 'start': 4659.669, 'weight': 1, 'content': [{'end': 4663.612, 'text': 'And to me, the motivation for that was always that we could then plug it into other domains.', 'start': 4659.669, 'duration': 3.943}, {'end': 4666.615, 'text': 'But we saved that until later.', 'start': 4665.054, 'duration': 1.561}, {'end': 4677.802, 'text': 'In fact, just for fun, I could tell you exactly the moment where the idea for AlphaZero occurred to me,', 'start': 4669.393, 'duration': 8.409}, {'end': 4686.691, 'text': "because I think there's maybe a lesson there for researchers who are too deeply embedded in their research and working 24-7 to try and come up with the next idea.", 'start': 4677.802, 'duration': 8.889}], 'summary': 'The motivation was to plug it into other domains; the idea for alphazero occurred later.', 'duration': 27.022, 'max_score': 4659.669, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/uPUEq8d73JI/pics/uPUEq8d73JI4659669.jpg'}, {'end': 4972.72, 'src': 'embed', 'start': 4947.223, 'weight': 3, 'content': [{'end': 4952.909, 'text': 'And the principle is the same, that if you bestow a system with the ability to correct its own errors,', 'start': 4947.223, 'duration': 5.686}, {'end': 4957.752, 'text': 'then it can take you from random to something slightly better than random,', 'start': 4953.67, 'duration': 4.082}, {'end': 4961.214, 'text': 'because it sees the stupid things that the random is doing and it can correct them.', 'start': 4957.752, 'duration': 3.462}, {'end': 4965.536, 'text': "And then it can take you from that slightly better system and understand well what's that doing wrong?", 'start': 4961.674, 'duration': 3.862}, {'end': 4968.438, 'text': 'And it takes you on to the next level and the next level.', 'start': 4965.996, 'duration': 2.442}, {'end': 4972.72, 'text': 'And this progress can go on indefinitely.', 'start': 4969.558, 'duration': 3.162}], 'summary': 'Empowering a system to correct errors leads to continuous improvement and indefinite progress.', 'duration': 25.497, 'max_score': 4947.223, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/uPUEq8d73JI/pics/uPUEq8d73JI4947223.jpg'}], 'start': 3498.349, 'title': "Alphago's evolution and impact", 'summary': "Explores alphago's imperfections, victories, triumph, and evolution to alphago zero, highlighting its impact on human knowledge and game playing, including alphazero's unprecedented performance of 100 wins to zero over its predecessor.", 'chapters': [{'end': 3543.62, 'start': 3498.349, 'title': "Alphago's imperfections and research bubble", 'summary': 'Discusses the experience of being part of the alphago team, acknowledging imperfections in alphago and feeling privileged to be part of a historic moment, while also reflecting on the challenge of focusing on research amidst the impending exposure of imperfections.', 'duration': 45.271, 'highlights': ['Being part of the AlphaGo team and acknowledging imperfections in AlphaGo', 'Feeling privileged to be part of a historic moment', 'Reflecting on the challenge of focusing on research amidst the impending exposure of imperfections']}, {'end': 3843.471, 'start': 3543.62, 'title': "Alphago's victory and unconventional moves", 'summary': "Explores alphago's victory over lee sedol, including the team's confidence, predictions, and the pivotal moments in the games, such as alphago's audacious invasion and unconventional move, showcasing its creativity and the impact on human knowledge of the game.", 'duration': 299.851, 'highlights': ["The first game was magical because it was the first time that a computer program had defeated a world champion in this grand challenge of Go. The first game marked the historic moment of a computer program defeating a world champion in the game of Go, demonstrating AlphaGo's exceptional capability.", "The second game became famous for a move known as Move 37, played by AlphaGo, which broke all of the conventions of Go and exhibited creativity not anticipated by humans. AlphaGo's Move 37 broke conventional Go rules and showcased its creativity, introducing a new idea not previously known in the game.", "AlphaGo invaded Lee Sedol's territory towards the end of the game, surprising him and demonstrating audacity. AlphaGo's audacious invasion of Lee Sedol's territory surprised him, showcasing its boldness and strategic prowess."]}, {'end': 4450.08, 'start': 3843.471, 'title': "Alphago's triumph and impact", 'summary': "Discusses alphago's victories against human go champions, lee sedol's retirement, and the impact of alphago and ai on game playing and beyond, including magnus carlsen's improvement in performance attributed to alphazero.", 'duration': 606.609, 'highlights': ["AlphaGo's victories against human Go champions, including Lee Sedol and the impact of AI on game playing and beyond, including Magnus Carlsen's improvement in performance attributed to AlphaZero. AlphaGo's victories against human Go champions and the impact of AI on game playing and beyond, including Magnus Carlsen's improvement in performance attributed to AlphaZero.", "Lee Sedol's retirement and the significance of AlphaGo's match for humans and AI, opening new possibilities in the game of Go. Lee Sedol's retirement and the significance of AlphaGo's match for humans and AI, opening new possibilities in the game of Go.", "Garry Kasparov's respect for AlphaGo's achievements, the progress in computer chess with AlphaZero, and Magnus Carlsen's performance improvement attributed to AlphaZero. Garry Kasparov's respect for AlphaGo's achievements, the progress in computer chess with AlphaZero, and Magnus Carlsen's performance improvement attributed to AlphaZero."]}, {'end': 5039.828, 'start': 4450.76, 'title': 'Alphago zero: self-play and learning', 'summary': 'Discusses the evolution from alphago to alphago zero, highlighting the concept of self-play, removing reliance on human expert games for pre-training, and the ability of self-play to achieve superhuman level performance, leading to the development of a system that can learn for itself all the knowledge required to play games such as go, resulting in alphazero outperforming its predecessor by 100 games to zero.', 'duration': 589.068, 'highlights': ["AlphaZero's ability to achieve superhuman level performance through self-play AlphaZero's achievement of outperforming its predecessor by 100 games to zero showcases the significant leap in performance through self-play, demonstrating its ability to learn for itself all the knowledge required to play games such as Go.", 'Concept of self-play and its role in systems learning for themselves The concept of self-play is about systems learning for themselves through playing games against themselves, allowing them to discover strategies without needing to play against human opponents, thereby achieving a more general and less brittle system.', "Removing reliance on human expert games for pre-training in AlphaGo Zero AlphaGo Zero's removal of reliance on human expert games for pre-training represents a profound step, indicating a shift towards systems that can learn for themselves, without the need for human-derived knowledge."]}], 'duration': 1541.479, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/uPUEq8d73JI/pics/uPUEq8d73JI3498349.jpg', 'highlights': ["AlphaZero's achievement of outperforming its predecessor by 100 games to zero showcases the significant leap in performance through self-play, demonstrating its ability to learn for itself all the knowledge required to play games such as Go.", 'The concept of self-play is about systems learning for themselves through playing games against themselves, allowing them to discover strategies without needing to play against human opponents, thereby achieving a more general and less brittle system.', "AlphaGo's victories against human Go champions and the impact of AI on game playing and beyond, including Magnus Carlsen's improvement in performance attributed to AlphaZero.", "AlphaGo invaded Lee Sedol's territory towards the end of the game, surprising him and demonstrating audacity. AlphaGo's audacious invasion of Lee Sedol's territory surprised him, showcasing its boldness and strategic prowess.", "The first game marked the historic moment of a computer program defeating a world champion in the game of Go, demonstrating AlphaGo's exceptional capability."]}, {'end': 5846.517, 'segs': [{'end': 5565.927, 'src': 'embed', 'start': 5539.767, 'weight': 3, 'content': [{'end': 5551.662, 'text': "And so in that sense, the process of reinforcement learning or the self-play approach that was used by AlphaZero is it's the essence of creativity.", 'start': 5539.767, 'duration': 11.895}, {'end': 5558.104, 'text': "It's really saying at every stage, you're playing according to your current norms and you try something.", 'start': 5552.182, 'duration': 5.922}, {'end': 5562.926, 'text': "And if it works out, you say, hey, here's something great.", 'start': 5558.684, 'duration': 4.242}, {'end': 5564.327, 'text': "I'm gonna start using that.", 'start': 5563.406, 'duration': 0.921}, {'end': 5565.927, 'text': 'And then that process.', 'start': 5564.987, 'duration': 0.94}], 'summary': 'Reinforcement learning in alphazero fosters creativity by adapting and incorporating successful strategies.', 'duration': 26.16, 'max_score': 5539.767, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/uPUEq8d73JI/pics/uPUEq8d73JI5539767.jpg'}, {'end': 5610.054, 'src': 'embed', 'start': 5584.176, 'weight': 1, 'content': [{'end': 5592.001, 'text': 'or I can start to sacrifice stones or give up on pieces or play shoulder hits on the fifth line or whatever it is.', 'start': 5584.176, 'duration': 7.825}, {'end': 5596.524, 'text': "The system's discovering things like this for itself continually, repeatedly, all the time.", 'start': 5592.461, 'duration': 4.063}, {'end': 5605.771, 'text': 'And so it should come as no surprise to us then when, if you leave these systems going, that they discover things that are not known to humans,', 'start': 5597.144, 'duration': 8.627}, {'end': 5610.054, 'text': 'that to the human norms are considered creative.', 'start': 5605.771, 'duration': 4.283}], 'summary': 'Ai system continuously discovers unknown creative strategies.', 'duration': 25.878, 'max_score': 5584.176, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/uPUEq8d73JI/pics/uPUEq8d73JI5584176.jpg'}, {'end': 5719.673, 'src': 'embed', 'start': 5692.652, 'weight': 2, 'content': [{'end': 5704.001, 'text': 'Even just the first to me maybe just makes me feel good as a human being that a self-playing mechanism that knows nothing about us humans discovers patterns that we humans do.', 'start': 5692.652, 'duration': 11.349}, {'end': 5710.005, 'text': "It's like an affirmation that we're doing okay as humans.", 'start': 5704.661, 'duration': 5.344}, {'end': 5715.829, 'text': "In this domain, in other domains, we figured out it's like the Churchill quote about democracy.", 'start': 5710.626, 'duration': 5.203}, {'end': 5719.673, 'text': "It's the It sucks, but it's the best song we've tried.", 'start': 5715.889, 'duration': 3.784}], 'summary': 'Ai discovering human patterns affirms our progress in understanding and creating.', 'duration': 27.021, 'max_score': 5692.652, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/uPUEq8d73JI/pics/uPUEq8d73JI5692652.jpg'}, {'end': 5797.896, 'src': 'embed', 'start': 5773.461, 'weight': 0, 'content': [{'end': 5780.564, 'text': 'and have a real impact in the real world, like autonomous vehicles, for example, which seems like a very far-out dream at this point.', 'start': 5773.461, 'duration': 7.103}, {'end': 5790.449, 'text': 'So I absolutely do hope and imagine that we will get to the point where ideas just like these are used in all kinds of different domains.', 'start': 5781.245, 'duration': 9.204}, {'end': 5797.896, 'text': 'In fact, one of the most satisfying things as a researcher is when you start to see other people use your algorithms in unexpected ways.', 'start': 5791.189, 'duration': 6.707}], 'summary': 'Researcher hopes to see widespread use of algorithms in various domains, impacting real world like autonomous vehicles.', 'duration': 24.435, 'max_score': 5773.461, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/uPUEq8d73JI/pics/uPUEq8d73JI5773461.jpg'}], 'start': 5039.828, 'title': 'Ai advancements and impact of alphazero', 'summary': "Delves into the evolution of ai through alphago, alphago zero, and alphazero, showcasing their mastery in games like go, chess, and shogi, and their evolution to muzero. it also discusses alphazero's creativity, impact, and potential applications in real-world problems.", 'chapters': [{'end': 5488.492, 'start': 5039.828, 'title': 'Alphazero: mastering games and ai', 'summary': 'Discusses the advancements in ai through the examples of alphago, alphago zero, and alphazero, showcasing their ability to excel in games like go, chess, and shogi, as well as their evolution to muzero, which demonstrated the capability to learn and plan in uncertain and complex environments.', 'duration': 448.664, 'highlights': ["MuZero's ability to reach superhuman performance in Go, Chess, and Shogi without explicit rules, solely through trial and error learning, showcasing its capability to understand and plan in uncertain environments. Superhuman performance in Go, Chess, and Shogi.", "AlphaZero's success in beating the world's strongest computer chess program and achieving superhuman performance in Japanese chess without the need for modifications, demonstrating its capability to excel in diverse games. Beating world's strongest computer chess program and achieving superhuman performance in Japanese chess.", 'The prediction that an algorithm like Alpha Zero, given greater computational resources, would consistently beat previous systems 100 games to zero, indicating the potential for continual improvement and dominance in gaming. Consistent victory of 100 games to zero against previous systems.']}, {'end': 5846.517, 'start': 5489.805, 'title': "Alphazero's creativity and impact", 'summary': "Discusses how alphazero's self-play approach led to the discovery of new patterns and strategies, demonstrating creativity, and its potential to be applied in various domains, including real-world problems, such as chemical synthesis and quantum computation.", 'duration': 356.712, 'highlights': ["AlphaZero's self-play approach led to the discovery of new patterns and strategies, demonstrating creativity. AlphaZero's self-play approach allowed it to continually discover new ideas and patterns, such as joseki in the game of Go, and even discard traditional patterns in favor of its own, influencing human players' strategies.", "Potential application of AlphaZero's self-play mechanism in various domains, including real-world problems such as chemical synthesis and quantum computation. The speaker envisions the self-play mechanism being applied in domains beyond games, highlighting its potential impact on real-world applications, such as chemical synthesis and quantum computation, as evidenced by recent Nature papers utilizing AlphaZero's algorithms for significant societal problems.", "Impact of AlphaZero's algorithms in unexpected domains. The speaker finds satisfaction in seeing other researchers apply AlphaZero's algorithms in unexpected ways, such as the improved chemical synthesis pathways and understanding quantum computation, demonstrating the broad potential impact of AlphaZero's innovations."]}], 'duration': 806.689, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/uPUEq8d73JI/pics/uPUEq8d73JI5039828.jpg', 'highlights': ["MuZero's ability to reach superhuman performance in Go, Chess, and Shogi solely through trial and error learning, showcasing its capability to understand and plan in uncertain environments.", "AlphaZero's self-play approach led to the discovery of new patterns and strategies, demonstrating creativity and influencing human players' strategies.", "Potential application of AlphaZero's self-play mechanism in various domains, including real-world problems such as chemical synthesis and quantum computation, highlighting its potential impact on real-world applications.", "AlphaZero's success in beating the world's strongest computer chess program and achieving superhuman performance in Japanese chess without the need for modifications, demonstrating its capability to excel in diverse games.", 'The prediction that an algorithm like Alpha Zero, given greater computational resources, would consistently beat previous systems 100 games to zero, indicating the potential for continual improvement and dominance in gaming.', "Impact of AlphaZero's algorithms in unexpected domains, such as improved chemical synthesis pathways and understanding quantum computation, demonstrating the broad potential impact of AlphaZero's innovations."]}, {'end': 6471.792, 'segs': [{'end': 5950.421, 'src': 'embed', 'start': 5922.767, 'weight': 0, 'content': [{'end': 5928.249, 'text': "So I think when we think about intelligence, it's really important to be clear about the problem of intelligence.", 'start': 5922.767, 'duration': 5.482}, {'end': 5934.692, 'text': "And I think it's clearest to understand that problem in terms of some ultimate goal that we want the system to try and solve for.", 'start': 5928.429, 'duration': 6.263}, {'end': 5940.014, 'text': "And, after all, if we don't understand the ultimate purpose of the system,", 'start': 5935.412, 'duration': 4.602}, {'end': 5943.316, 'text': "do we really even have a clearly defined problem that we're solving at all?", 'start': 5940.014, 'duration': 3.302}, {'end': 5950.421, 'text': 'Now, within that, as with your example for humans,', 'start': 5944.392, 'duration': 6.029}], 'summary': 'Understanding the ultimate goal is crucial for defining the problem of intelligence.', 'duration': 27.654, 'max_score': 5922.767, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/uPUEq8d73JI/pics/uPUEq8d73JI5922767.jpg'}, {'end': 6309.22, 'src': 'embed', 'start': 6285.433, 'weight': 1, 'content': [{'end': 6292.88, 'text': 'which is understanding it at a level by which we can maybe implement it and understand it as AI researchers or computer scientists.', 'start': 6285.433, 'duration': 7.447}, {'end': 6301.01, 'text': 'Or you can understand it at the level of the mechanistic thing which is going on that there are these atoms bouncing around in the brain and they lead to the outcome of that system.', 'start': 6293.4, 'duration': 7.61}, {'end': 6309.22, 'text': "It's not in contradiction with the fact that it's also a decision-making system that's optimizing for some goal and purpose.", 'start': 6301.43, 'duration': 7.79}], 'summary': 'Understanding ai at implementable and mechanistic levels for optimization.', 'duration': 23.787, 'max_score': 6285.433, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/uPUEq8d73JI/pics/uPUEq8d73JI6285433.jpg'}], 'start': 5847.838, 'title': 'Defining intelligence and ultimate goals', 'summary': "Explores the importance of defining the ultimate goal for intelligent systems, potential intrinsic rewards in reinforcement learning, and the necessity of well-defined problems for understanding and implementing intelligence. it also delves into understanding systems at multiple levels, from the universe's purpose to the creation of artificial intelligence, highlighting various goals such as maximizing entropy, reproduction, and the creation of learning systems.", 'chapters': [{'end': 6099.22, 'start': 5847.838, 'title': 'Defining intelligence and ultimate goals', 'summary': 'Explores the importance of defining the ultimate goal for intelligent systems, the potential for intrinsic rewards in reinforcement learning, and the analogy of human sub-goals to the ultimate goal, emphasizing the necessity of a well-defined problem for understanding and implementing intelligence.', 'duration': 251.382, 'highlights': ['The importance of defining the ultimate goal for intelligent systems Emphasizes the necessity of a well-defined problem for understanding and implementing intelligence.', 'Potential for intrinsic rewards in reinforcement learning Discusses the idea of flexibility for discovering intrinsic rewards when the true reward is uncertain.', 'Analogy of human sub-goals to the ultimate goal Compares human sub-goals to the ultimate goal, emphasizing the importance of a measurable and evaluated ultimate goal for intelligent systems.']}, {'end': 6471.792, 'start': 6099.5, 'title': 'Understanding systems and goals', 'summary': "Explores the concept of understanding systems at multiple levels, from the universe's purpose to the creation of artificial intelligence, highlighting the goal of maximizing entropy, evolution's goal of reproduction, and the creation of learning systems.", 'duration': 372.292, 'highlights': ["The purpose of the universe is to maximize entropy, seen as a goal developed by certain people at MIT. The universe's purpose is viewed as maximizing entropy, proposed by certain individuals at MIT.", "Evolution's goal is to reproduce as effectively as possible, leading to the development of brains and intelligences to support this goal. Evolution's goal is efficient reproduction, prompting the development of brains and intelligences to achieve this goal.", "The creation of learning systems in the brain enables the programming of goals to achieve any goal, providing a flexible notion of intelligence. The brain's learning systems enable goal programming, contributing to a flexible notion of intelligence.", 'Building intelligent systems allows for the achievement of goals more effectively than direct human action, leading to the creation of new layers of systems. Building intelligent systems enables more effective goal achievement than direct human action, leading to the creation of new system layers.', 'Machine intelligence can access abilities like intuition and creativity, marking a turning point in history. A turning point is reached where machine intelligence can access abilities like intuition and creativity, previously thought to be exclusive to humans.']}], 'duration': 623.954, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/uPUEq8d73JI/pics/uPUEq8d73JI5847838.jpg', 'highlights': ['The importance of defining the ultimate goal for intelligent systems Emphasizes the necessity of a well-defined problem for understanding and implementing intelligence.', 'Potential for intrinsic rewards in reinforcement learning Discusses the idea of flexibility for discovering intrinsic rewards when the true reward is uncertain.', 'Analogy of human sub-goals to the ultimate goal Compares human sub-goals to the ultimate goal, emphasizing the importance of a measurable and evaluated ultimate goal for intelligent systems.', "The purpose of the universe is to maximize entropy, seen as a goal developed by certain people at MIT. The universe's purpose is viewed as maximizing entropy, proposed by certain individuals at MIT.", "Evolution's goal is to reproduce as effectively as possible, leading to the development of brains and intelligences to support this goal. Evolution's goal is efficient reproduction, prompting the development of brains and intelligences to achieve this goal.", "The creation of learning systems in the brain enables the programming of goals to achieve any goal, providing a flexible notion of intelligence. The brain's learning systems enable goal programming, contributing to a flexible notion of intelligence.", 'Building intelligent systems allows for the achievement of goals more effectively than direct human action, leading to the creation of new layers of systems. Building intelligent systems enables more effective goal achievement than direct human action, leading to the creation of new system layers.', 'Machine intelligence can access abilities like intuition and creativity, marking a turning point in history. A turning point is reached where machine intelligence can access abilities like intuition and creativity, previously thought to be exclusive to humans.']}], 'highlights': ["AlphaZero's achievement of outperforming its predecessor by 100 games to zero showcases the significant leap in performance through self-play, demonstrating its ability to learn for itself all the knowledge required to play games such as Go.", 'The concept of self-play is about systems learning for themselves through playing games against themselves, allowing them to discover strategies without needing to play against human opponents, thereby achieving a more general and less brittle system.', "MuZero's ability to reach superhuman performance in Go, Chess, and Shogi solely through trial and error learning, showcasing its capability to understand and plan in uncertain environments.", 'The importance of defining the ultimate goal for intelligent systems Emphasizes the necessity of a well-defined problem for understanding and implementing intelligence.', 'The podcast discusses the history of money, including the start of debits and credits on ledgers around 30,000 years ago, the creation of the US dollar over 200 years ago, and the release of Bitcoin just over 10 years ago, highlighting the potential of cryptocurrency to redefine the nature of money.']}