title
The Mastermind Behind GPT-4 and the Future of AI | Ilya Sutskever

description
In this podcast episode, Ilya Sutskever, the co-founder and chief scientist at OpenAI, discusses his vision for the future of artificial intelligence (AI), including large language models like GPT-4. Sutskever starts by explaining the importance of AI research and how OpenAI is working to advance the field. He shares his views on the ethical considerations of AI development and the potential impact of AI on society. The conversation then moves on to large language models and their capabilities. Sutskever talks about the challenges of developing GPT-4 and the limitations of current models. He discusses the potential for large language models to generate a text that is indistinguishable from human writing and how this technology could be used in the future. Sutskever also shares his views on AI-aided democracy and how AI could help solve global problems such as climate change and poverty. He emphasises the importance of building AI systems that are transparent, ethical, and aligned with human values. Throughout the conversation, Sutskever provides insights into the current state of AI research, the challenges facing the field, and his vision for the future of AI. This podcast episode is a must-listen for anyone interested in the intersection of AI, language, and society. Timestamps: 00:04 Introduction of Craig Smith and Ilya Sutskever. 01:00 Sutskever's AI and consciousness interests. 02:30 Sutskever's start in machine learning with Hinton. 03:45 Realization about training large neural networks. 06:33 Convolutional neural network breakthroughs and imagenet. 08:36 Predicting the next thing for unsupervised learning. 10:24 Development of GPT-3 and scaling in deep learning. 11:42 Specific scaling in deep learning and potential discovery. 13:01 Small changes can have big impact. 13:46 Limits of large language models and lack of understanding. 14:32 Difficulty in discussing limits of language models. 15:13 Statistical regularities lead to better understanding of world. 16:33 Limitations of language models and hope for reinforcement learning. 17:52 Teaching neural nets through interaction with humans. 21:44 Multimodal understanding not necessary for language models. 25:28 Autoregressive transformers and high-dimensional distributions. 26:02 Autoregressive transformers work well on images. 27:09 Pixels represented like a string of text. 29:40 Large generative models learn compressed representations of real-world processes. 31:31 Human teachers needed to guide reinforcement learning process. 35:10 Opportunity to teach AI models more skills with less data. 39:57 Desirable to have democratic process for providing information. 41:15 Impossible to understand everything in complicated situations. Craig Smith Twitter: https://twitter.com/craigss Eye on A.I. Twitter: https://twitter.com/EyeOn_AI

detail
{'title': 'The Mastermind Behind GPT-4 and the Future of AI | Ilya Sutskever', 'heatmap': [{'end': 1629.759, 'start': 1596.292, 'weight': 1}], 'summary': "Ilya sutskever's pivotal role in developing gpt-3 and chatgpt, and his influence on deep learning through alexnet in 2012 are discussed. the evolution of ai from 2003 to gpt-3, limitations of large language models, challenges in predicting high dimensional vectors, and the societal and educational impact of ai are explored.", 'chapters': [{'end': 70.941, 'segs': [{'end': 70.941, 'src': 'embed', 'start': 4.855, 'weight': 0, 'content': [{'end': 8.248, 'text': "I'm Craig Smith and this is Eye on AI.", 'start': 4.855, 'duration': 3.393}, {'end': 24.829, 'text': 'This week I talked to Ilya Sutskever,', 'start': 21.806, 'duration': 3.023}, {'end': 37.644, 'text': 'a co-founder and chief scientist of OpenAI and one of the primary minds behind the large language model GPT-3 and its public progeny, ChatGPT,', 'start': 24.829, 'duration': 12.815}, {'end': 42.069, 'text': "which I don't think it's an exaggeration to say is changing the world.", 'start': 37.644, 'duration': 4.425}, {'end': 46.377, 'text': "This isn't the first time Ilya has changed the world.", 'start': 43.393, 'duration': 2.984}, {'end': 53.886, 'text': 'Jeff Hinton has said, he was the main impetus for AlexNet, the convolutional neural network,', 'start': 46.897, 'duration': 6.989}, {'end': 61.375, 'text': 'whose dramatic performance stunned the scientific community in 2012 and set off the deep learning revolution.', 'start': 53.886, 'duration': 7.489}, {'end': 70.941, 'text': 'As is often the case in these conversations, they assume a lot of knowledge on the part of listeners.', 'start': 64.453, 'duration': 6.488}], 'summary': 'Ilya sutskever, co-founder of openai, has made significant contributions to ai, including gpt-3 and alexnet.', 'duration': 66.086, 'max_score': 4.855, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SjhIlw3Iffs/pics/SjhIlw3Iffs4855.jpg'}], 'start': 4.855, 'title': "Ilya sutskever's contributions", 'summary': "Delves into ilya sutskever's pivotal role in developing gpt-3 and chatgpt, along with his significant influence on the deep learning revolution through alexnet in 2012.", 'chapters': [{'end': 70.941, 'start': 4.855, 'title': 'Ilya sutskever: the mind behind gpt-3 and chatgpt', 'summary': "Explores ilya sutskever's pivotal role in creating gpt-3 and chatgpt, as well as his previous influence on the deep learning revolution with alexnet in 2012.", 'duration': 66.086, 'highlights': ['Ilya Sutskever co-founded OpenAI and played a key role in developing GPT-3 and ChatGPT, which are transforming the world.', 'Ilya Sutskever was a driving force behind AlexNet, the convolutional neural network that revolutionized deep learning in 2012, according to Jeff Hinton.', "Ilya Sutskever's significant contributions to AI have had a profound impact on the scientific community and technological advancements."]}], 'duration': 66.086, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SjhIlw3Iffs/pics/SjhIlw3Iffs4855.jpg', 'highlights': ['Ilya Sutskever co-founded OpenAI and played a key role in developing GPT-3 and ChatGPT, which are transforming the world.', 'Ilya Sutskever was a driving force behind AlexNet, the convolutional neural network that revolutionized deep learning in 2012, according to Jeff Hinton.', "Ilya Sutskever's significant contributions to AI have had a profound impact on the scientific community and technological advancements."]}, {'end': 787.874, 'segs': [{'end': 98.241, 'src': 'embed', 'start': 70.941, 'weight': 3, 'content': [{'end': 77.007, 'text': "primarily because I don't want to waste the limited time I have to speak to people like Ilya,", 'start': 70.941, 'duration': 6.066}, {'end': 87.297, 'text': 'explaining concepts or people or events that can easily be Googled or Binged, I should say, where the ChatGPT can explain for you.', 'start': 77.007, 'duration': 10.29}, {'end': 94.02, 'text': 'The conversation with Ilya follows a conversation with Jan LeCun in a previous episode.', 'start': 88.138, 'duration': 5.882}, {'end': 98.241, 'text': "so if you haven't listened to that episode, I encourage you to do so.", 'start': 94.02, 'duration': 4.221}], 'summary': 'Avoiding wasting time by explaining easily searchable topics, promoting listening to previous episodes.', 'duration': 27.3, 'max_score': 70.941, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SjhIlw3Iffs/pics/SjhIlw3Iffs70941.jpg'}, {'end': 406.349, 'src': 'embed', 'start': 371.95, 'weight': 2, 'content': [{'end': 387.054, 'text': 'So, in a nutshell, I had the realization that if you train a large neural network on a large sorry, large and deep,', 'start': 371.95, 'duration': 15.104}, {'end': 389.115, 'text': 'because back then the deep part was still new', 'start': 387.054, 'duration': 2.061}, {'end': 400.704, 'text': 'If you train a large and a deep neural network on a big enough data set that specifies some complicated tasks that people do, such as vision,', 'start': 389.135, 'duration': 11.569}, {'end': 406.349, 'text': 'but also others, and you just train that neural network, then you will succeed necessarily.', 'start': 400.704, 'duration': 5.645}], 'summary': 'Training large and deep neural network on big data leads to success.', 'duration': 34.399, 'max_score': 371.95, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SjhIlw3Iffs/pics/SjhIlw3Iffs371950.jpg'}, {'end': 626.273, 'src': 'embed', 'start': 599.542, 'weight': 0, 'content': [{'end': 608.092, 'text': 'It was clear to me, to us, that transformers address the limitations of recurrent neural networks of learning long-term dependencies.', 'start': 599.542, 'duration': 8.55}, {'end': 613.358, 'text': "It's a technical thing, but it was like we switched to transformers right away.", 'start': 608.773, 'duration': 4.585}, {'end': 618.725, 'text': 'And so the very nascent GPT effort continued then.', 'start': 614.139, 'duration': 4.586}, {'end': 626.273, 'text': 'And then like with the transformer, it started to work better and you make it bigger.', 'start': 620.788, 'duration': 5.485}], 'summary': 'Transformers address limitations of recurrent neural networks for learning long-term dependencies, leading to improved performance.', 'duration': 26.731, 'max_score': 599.542, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SjhIlw3Iffs/pics/SjhIlw3Iffs599542.jpg'}, {'end': 742.001, 'src': 'embed', 'start': 712.861, 'weight': 1, 'content': [{'end': 728.014, 'text': 'The great breakthrough of deep learning is that it provides us with the first ever way of productively using scale and getting something out of it in return.', 'start': 712.861, 'duration': 15.153}, {'end': 734.935, 'text': 'Like before, that like, what would people use large computer clusters for?', 'start': 729.571, 'duration': 5.364}, {'end': 739.759, 'text': 'I guess they would do it for weather simulations or physics simulations or something.', 'start': 735.316, 'duration': 4.443}, {'end': 740.64, 'text': "but that's about it.", 'start': 739.759, 'duration': 0.881}, {'end': 742.001, 'text': 'Maybe movie making.', 'start': 741.16, 'duration': 0.841}], 'summary': 'Deep learning enables productive use of large computer clusters for scale, with applications in weather simulations, physics simulations, and movie making.', 'duration': 29.14, 'max_score': 712.861, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SjhIlw3Iffs/pics/SjhIlw3Iffs712861.jpg'}], 'start': 70.941, 'title': 'Maximizing limited speaking time and evolution of ai & gpt-3', 'summary': 'Emphasizes not wasting limited speaking time and encourages exploring previous episodes. it also discusses the evolution of ai from 2003 to the development of transformers and gpt-3, highlighting the significance of scale in deep learning.', 'chapters': [{'end': 129.211, 'start': 70.941, 'title': 'Maximizing limited speaking time', 'summary': 'Discusses the importance of not wasting limited speaking time by explaining easily accessible concepts or events, and encourages listeners to explore previous episodes. it features a conversation with ilya, following a previous episode with jan lecun.', 'duration': 58.27, 'highlights': ['The importance of not wasting limited speaking time by explaining easily accessible concepts or events.', 'Encouragement for listeners to explore previous episodes.', 'Featuring a conversation with Ilya, following a previous episode with Jan LeCun.']}, {'end': 787.874, 'start': 129.211, 'title': 'Evolution of ai & gpt-3', 'summary': 'Discusses the evolution of ai, starting from the early interest in ai and machine learning in 2003, the breakthrough in convolutional neural networks, to the development of transformers and gpt-3, emphasizing the significance of scale in deep learning.', 'duration': 658.663, 'highlights': ["The realization that training a large and deep neural network on a big enough data set for complicated tasks would necessarily lead to success, based on the logic that the human brain can solve these tasks quickly, was a pivotal moment in the evolution of AI. Significance of training large and deep neural networks, logic based on human brain's ability to solve tasks quickly.", 'The transition from recurrent neural networks to transformers addressed the limitations of learning long-term dependencies and significantly contributed to the development of GPT-3. Transformation from recurrent neural networks to transformers, impact on the development of GPT-3.', 'The discussion highlights the significance of scale in deep learning, emphasizing the breakthrough of deep learning as the first-ever way of productively using scale and getting something in return. Significance of scale in deep learning, breakthrough in using scale productively.']}], 'duration': 716.933, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SjhIlw3Iffs/pics/SjhIlw3Iffs70941.jpg', 'highlights': ['The transition from recurrent neural networks to transformers addressed the limitations of learning long-term dependencies and significantly contributed to the development of GPT-3. Transformation from recurrent neural networks to transformers, impact on the development of GPT-3.', 'The discussion highlights the significance of scale in deep learning, emphasizing the breakthrough of deep learning as the first-ever way of productively using scale and getting something in return. Significance of scale in deep learning, breakthrough in using scale productively.', "The realization that training a large and deep neural network on a big enough data set for complicated tasks would necessarily lead to success, based on the logic that the human brain can solve these tasks quickly, was a pivotal moment in the evolution of AI. Significance of training large and deep neural networks, logic based on human brain's ability to solve tasks quickly.", 'The importance of not wasting limited speaking time by explaining easily accessible concepts or events. Encouragement for listeners to explore previous episodes. Featuring a conversation with Ilya, following a previous episode with Jan LeCun.']}, {'end': 1503.593, 'segs': [{'end': 821.956, 'src': 'embed', 'start': 790.015, 'weight': 0, 'content': [{'end': 801.005, 'text': "The limitation of large language models as they exist is their knowledge is contained in the language that they're trained on.", 'start': 790.015, 'duration': 10.99}, {'end': 806.549, 'text': 'And most human knowledge, I think everyone agrees is non-linguistic.', 'start': 801.025, 'duration': 5.524}, {'end': 814.151, 'text': "I'm not sure Noam Chomsky agrees, but There's a problem in large language models.", 'start': 806.569, 'duration': 7.582}, {'end': 821.956, 'text': 'As I understand it, their objective is to satisfy the statistical consistency of the prompt.', 'start': 814.311, 'duration': 7.645}], 'summary': 'Limitation of large language models: knowledge primarily linguistic, problem in satisfying statistical consistency of the prompt.', 'duration': 31.941, 'max_score': 790.015, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SjhIlw3Iffs/pics/SjhIlw3Iffs790015.jpg'}, {'end': 986.325, 'src': 'embed', 'start': 959.849, 'weight': 2, 'content': [{'end': 966.733, 'text': 'Yet to predict, you eventually need to understand the true underlying process that produced the data.', 'start': 959.849, 'duration': 6.884}, {'end': 975.557, 'text': 'To predict the data well, to compress it well, you need to understand more and more about the world that produced the data.', 'start': 967.373, 'duration': 8.184}, {'end': 986.325, 'text': 'as our generative models become extraordinarily good, they will have, I claim, a shocking degree of understanding,', 'start': 977.452, 'duration': 8.873}], 'summary': 'To predict and compress data well, understanding the underlying process is crucial; generative models aim for a high level of understanding.', 'duration': 26.476, 'max_score': 959.849, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SjhIlw3Iffs/pics/SjhIlw3Iffs959849.jpg'}, {'end': 1148.847, 'src': 'embed', 'start': 1122.629, 'weight': 3, 'content': [{'end': 1130.816, 'text': 'as good as one would hope, or as, or rather as good as they could be, which is why, for example, for a system like chat,', 'start': 1122.629, 'duration': 8.187}, {'end': 1137.001, 'text': 'gpt is a language model that has an additional reinforcement learning training process.', 'start': 1130.816, 'duration': 6.185}, {'end': 1139.783, 'text': 'we call it reinforcement, learning from human feedback.', 'start': 1137.001, 'duration': 2.782}, {'end': 1148.847, 'text': 'but the thing to understand about that process is this We can say that the pre-training process, when you just train a language model,', 'start': 1139.783, 'duration': 9.064}], 'summary': 'Gpt has additional reinforcement learning for training, making it as good as it could be for systems like chat.', 'duration': 26.218, 'max_score': 1122.629, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SjhIlw3Iffs/pics/SjhIlw3Iffs1122629.jpg'}, {'end': 1363.576, 'src': 'embed', 'start': 1336.089, 'weight': 4, 'content': [{'end': 1346.372, 'text': "The first claim is that it is desirable for a system to have multimodal understanding where it doesn't just know about the world from text.", 'start': 1336.089, 'duration': 10.283}, {'end': 1358.655, 'text': 'And my comment on that will be that indeed multimodal understanding is desirable because you learn more about the world.', 'start': 1347.932, 'duration': 10.723}, {'end': 1361.296, 'text': 'You learn more about people.', 'start': 1359.435, 'duration': 1.861}, {'end': 1363.576, 'text': 'You learn more about their condition.', 'start': 1361.736, 'duration': 1.84}], 'summary': 'Multimodal understanding improves knowledge about the world, people, and conditions.', 'duration': 27.487, 'max_score': 1336.089, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SjhIlw3Iffs/pics/SjhIlw3Iffs1336089.jpg'}], 'start': 790.015, 'title': 'Limitations and challenges of language models', 'summary': 'Highlights the evolving limitations of large language models in understanding non-linguistic human knowledge, the potential for generative models to gain a deeper understanding of the world through text projection, and the desirability of multimodal understanding in language models, emphasizing statistical regularities in prediction and compression.', 'chapters': [{'end': 1039.124, 'start': 790.015, 'title': 'Limitations of large language models', 'summary': 'Highlights the limitations of large language models in understanding non-linguistic human knowledge, the evolving nature of these limitations, and the potential for generative models to gain a deeper understanding of the world through text projection, while emphasizing the importance of statistical regularities in prediction and compression.', 'duration': 249.109, 'highlights': ['Large language models lack an underlying understanding of reality, as demonstrated by the disconnect between their generated content and the actual world, raising the question of addressing this limitation in future research.', 'The evolving nature of the limitations of language models is emphasized, with a caution against confidently defining their limitations, as they may significantly change in the future.', 'The significance of learning statistical regularities in language models is highlighted, suggesting that it enables a deeper understanding of the world and its subtleties, projecting a potential for generative models to gain a shocking degree of understanding through text expression and projection.']}, {'end': 1503.593, 'start': 1039.124, 'title': 'Challenges and potential of language models', 'summary': 'Discusses the limitations of language models, particularly neural networks like chat gpt, in producing accurate outputs, the potential of reinforcement learning from human feedback to address these limitations, and the desirability of multimodal understanding in language models.', 'duration': 464.469, 'highlights': ['Reinforcement learning from human feedback can improve the output quality of language models like chat GPT. The process of reinforcement learning from human feedback can quickly teach language models to produce good outputs by providing feedback whenever the output is inappropriate or does not make sense.', 'The limitations of neural networks like chat GPT include a tendency to hallucinate and produce inaccurate outputs. Neural networks, including chat GPT, have a propensity for making up information and hallucinating, which limits their usefulness.', "The desirability of multimodal understanding in language models for better comprehension of tasks and people's needs. Multimodal understanding is desirable as it allows language models to learn more about the world, people, and their conditions, ultimately leading to better task comprehension and understanding of people's needs."]}], 'duration': 713.578, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SjhIlw3Iffs/pics/SjhIlw3Iffs790015.jpg', 'highlights': ['Large language models lack an underlying understanding of reality, raising the question of addressing this limitation in future research.', 'The evolving nature of the limitations of language models is emphasized, cautioning against confidently defining their limitations as they may significantly change in the future.', 'Learning statistical regularities in language models enables a deeper understanding of the world and its subtleties, projecting a potential for generative models to gain a shocking degree of understanding through text expression and projection.', 'Reinforcement learning from human feedback can improve the output quality of language models like chat GPT, quickly teaching them to produce good outputs by providing feedback whenever the output is inappropriate or does not make sense.', "The desirability of multimodal understanding in language models for better comprehension of tasks and people's needs, allowing them to learn more about the world, people, and their conditions, ultimately leading to better task comprehension and understanding of people's needs."]}, {'end': 1812.527, 'segs': [{'end': 1553.681, 'src': 'embed', 'start': 1505.174, 'weight': 0, 'content': [{'end': 1517.211, 'text': 'So the proposal in the paper makes a claim that One of the big challenges is predicting high dimensional vectors which have uncertainty about them.', 'start': 1505.174, 'duration': 12.037}, {'end': 1518.272, 'text': 'So, for example,', 'start': 1517.631, 'duration': 0.641}, {'end': 1526.537, 'text': "predicting an image like the paper makes a very strong claim there that it's a major challenge and we need to use a particular approach to address that.", 'start': 1518.272, 'duration': 8.265}, {'end': 1529.879, 'text': 'But one thing which I found surprising,', 'start': 1527.898, 'duration': 1.981}, {'end': 1537.724, 'text': 'or at least unacknowledged in the paper is that the current autoregressive transformers already have that property.', 'start': 1529.879, 'duration': 7.845}, {'end': 1540.651, 'text': "I'll give you two examples.", 'start': 1539.71, 'duration': 0.941}, {'end': 1545.314, 'text': 'One is given one page in a book, predict the next page in a book.', 'start': 1541.291, 'duration': 4.023}, {'end': 1548.097, 'text': 'There could be so many possible pages that follow.', 'start': 1546.015, 'duration': 2.082}, {'end': 1551.379, 'text': "It's a very complicated high dimensional space and we deal with it just fine.", 'start': 1548.157, 'duration': 3.222}, {'end': 1553.681, 'text': 'The same applies to images.', 'start': 1552.34, 'duration': 1.341}], 'summary': 'Challenges in predicting high dimensional vectors with uncertainty, but autoregressive transformers already address this.', 'duration': 48.507, 'max_score': 1505.174, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SjhIlw3Iffs/pics/SjhIlw3Iffs1505174.jpg'}, {'end': 1623.316, 'src': 'embed', 'start': 1596.292, 'weight': 2, 'content': [{'end': 1601.295, 'text': "well, the current approaches can't deal with predicting high dimensional distributions.", 'start': 1596.292, 'duration': 5.003}, {'end': 1602.596, 'text': 'I think they definitely can.', 'start': 1601.356, 'duration': 1.24}, {'end': 1604.638, 'text': 'So maybe this is another point that I would make.', 'start': 1603.057, 'duration': 1.581}, {'end': 1615.004, 'text': "And then what you're talking about converting pixels into vectors, it's essentially turning everything into language.", 'start': 1605.258, 'duration': 9.746}, {'end': 1621.335, 'text': 'A vector is Like a string of text, right? Define language though.', 'start': 1615.024, 'duration': 6.311}, {'end': 1623.316, 'text': 'You turn it into a sequence.', 'start': 1621.876, 'duration': 1.44}], 'summary': 'Challenges in predicting high-dimensional distributions, but potential for improvement in converting pixels to vectors.', 'duration': 27.024, 'max_score': 1596.292, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SjhIlw3Iffs/pics/SjhIlw3Iffs1596292.jpg'}, {'end': 1629.759, 'src': 'heatmap', 'start': 1596.292, 'weight': 1, 'content': [{'end': 1601.295, 'text': "well, the current approaches can't deal with predicting high dimensional distributions.", 'start': 1596.292, 'duration': 5.003}, {'end': 1602.596, 'text': 'I think they definitely can.', 'start': 1601.356, 'duration': 1.24}, {'end': 1604.638, 'text': 'So maybe this is another point that I would make.', 'start': 1603.057, 'duration': 1.581}, {'end': 1615.004, 'text': "And then what you're talking about converting pixels into vectors, it's essentially turning everything into language.", 'start': 1605.258, 'duration': 9.746}, {'end': 1621.335, 'text': 'A vector is Like a string of text, right? Define language though.', 'start': 1615.024, 'duration': 6.311}, {'end': 1623.316, 'text': 'You turn it into a sequence.', 'start': 1621.876, 'duration': 1.44}, {'end': 1629.759, 'text': 'Yeah A sequence of what? Like you could argue that even for a human life is a sequence of bits.', 'start': 1624.237, 'duration': 5.522}], 'summary': 'Current approaches struggle with high-dimensional distribution prediction, but there is potential for improvement in converting pixels into vectors and representing data as language.', 'duration': 33.467, 'max_score': 1596.292, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SjhIlw3Iffs/pics/SjhIlw3Iffs1596292.jpg'}, {'end': 1757.708, 'src': 'embed', 'start': 1649.288, 'weight': 3, 'content': [{'end': 1658.45, 'text': "It matters as in like you can get a 10 X efficiency gain, which is huge in practice, but conceptually I claim it doesn't matter.", 'start': 1649.288, 'duration': 9.162}, {'end': 1674.074, 'text': 'On this idea of having an army of human trainers that are working with chat GPT or a large language model.', 'start': 1659.75, 'duration': 14.324}, {'end': 1680.096, 'text': 'to guide it in effect with reinforcement learning.', 'start': 1675.953, 'duration': 4.143}, {'end': 1695.068, 'text': "But just intuitively, that doesn't sound like an efficient way of teaching a model about the underlying reality of its language.", 'start': 1681.457, 'duration': 13.611}, {'end': 1699.231, 'text': "Isn't there a way of automating that?", 'start': 1696.689, 'duration': 2.542}, {'end': 1719.911, 'text': "And to Jens Credit, I think that's what he's talking about is coming up with an algorithmic means of teaching a model the underlying reality,", 'start': 1701.112, 'duration': 18.799}, {'end': 1722.792, 'text': 'without a human having to intervene.', 'start': 1719.911, 'duration': 2.881}, {'end': 1725.513, 'text': 'Yeah So I have two comments on that.', 'start': 1723.592, 'duration': 1.921}, {'end': 1726.953, 'text': 'I think.', 'start': 1726.633, 'duration': 0.32}, {'end': 1733.352, 'text': 'So the first place, so I have a different view on the question.', 'start': 1728.928, 'duration': 4.424}, {'end': 1735.834, 'text': "So I wouldn't agree with the phrasing of the question.", 'start': 1733.372, 'duration': 2.462}, {'end': 1745.082, 'text': 'I claim that our pre-trained models already know everything they need to know about the underlying reality.', 'start': 1737.495, 'duration': 7.587}, {'end': 1757.708, 'text': 'They already have this knowledge of language and also a great deal of knowledge about the processes that exist in the world that produce this language.', 'start': 1746.377, 'duration': 11.331}], 'summary': 'Debating the efficiency gain of human trainers vs. automating model teaching.', 'duration': 108.42, 'max_score': 1649.288, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SjhIlw3Iffs/pics/SjhIlw3Iffs1649288.jpg'}], 'start': 1505.174, 'title': 'Challenges in predicting high dimensional vectors and teaching language models', 'summary': 'Discusses challenges in predicting high dimensional vectors, emphasizing the capability of autoregressive transformers, and efficiency of human trainers working with language models, citing examples and highlighting the knowledge pre-trained models possess.', 'chapters': [{'end': 1648.528, 'start': 1505.174, 'title': 'Challenges in predicting high dimensional vectors', 'summary': "Discusses the challenges in predicting high dimensional vectors with uncertainty, arguing that autoregressive transformers already have the capability, citing examples such as predicting pages in a book and generating images with openai's igpt.", 'duration': 143.354, 'highlights': ["Autoregressive transformers already have the capability to predict high dimensional vectors, as demonstrated by their successful use in predicting pages in a book and generating images with OpenAI's IGPT. The current autoregressive transformers have the capability to predict high dimensional vectors, as shown by their successful use in predicting pages in a book and generating images with OpenAI's IGPT.", "The claim in the paper about the inability of current approaches to deal with predicting high dimensional distributions is challenged with examples of successful image generation techniques using autoregressive transformers. The paper's claim about the inability of current approaches to predict high dimensional distributions is challenged with examples of successful image generation techniques using autoregressive transformers, such as OpenAI's IGPT and Google's Party.", 'Discussion on converting pixels into vectors and the concept of turning everything into language, highlighting the argument that on some level, the distinction between various approaches is immaterial. The discussion on converting pixels into vectors and turning everything into language, while highlighting the argument that the distinction between various approaches is immaterial on some level, as everything could be seen as a sequence of bits.']}, {'end': 1812.527, 'start': 1649.288, 'title': 'Teaching language models', 'summary': 'Discusses the efficiency of human trainers working with language models, the importance of automating model teaching, and the knowledge pre-trained models already possess about language and the real world processes, highlighting the compressed representations of real world processes language models learn.', 'duration': 163.239, 'highlights': ['Pre-trained language models possess knowledge about language and real world processes, including thoughts, feelings, and interactions, represented by compressed neural net processes.', 'The efficiency gain of having human trainers working with language models is 10X, but conceptually it is claimed to not matter.', 'Jens Credit suggests coming up with an algorithmic means of teaching a model the underlying reality without human intervention.']}], 'duration': 307.353, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SjhIlw3Iffs/pics/SjhIlw3Iffs1505174.jpg', 'highlights': ['Autoregressive transformers can predict high dimensional vectors, demonstrated by successful use in predicting pages and generating images.', 'Successful image generation techniques challenge the claim about the inability of current approaches to predict high dimensional distributions.', 'Discussion on converting pixels into vectors and turning everything into language, highlighting the immaterial distinction between various approaches.', 'Pre-trained language models possess knowledge about language and real world processes, including thoughts, feelings, and interactions.', 'Efficiency gain of having human trainers working with language models is 10X, but conceptually it is claimed to not matter.', 'Jens Credit suggests coming up with an algorithmic means of teaching a model the underlying reality without human intervention.']}, {'end': 2576.689, 'segs': [{'end': 1908.02, 'src': 'embed', 'start': 1835.937, 'weight': 0, 'content': [{'end': 1837.318, 'text': "those teachers aren't on their own.", 'start': 1835.937, 'duration': 1.381}, {'end': 1839.779, 'text': 'they are working with our tools together.', 'start': 1837.318, 'duration': 2.461}, {'end': 1841.24, 'text': 'they are very efficient.', 'start': 1839.779, 'duration': 1.461}, {'end': 1845.844, 'text': "it's like the tools are doing the majority of the work.", 'start': 1841.24, 'duration': 4.604}, {'end': 1847.786, 'text': 'but you do need to have.', 'start': 1845.844, 'duration': 1.942}, {'end': 1849.547, 'text': 'you need to have oversight,', 'start': 1847.786, 'duration': 1.761}, {'end': 1855.993, 'text': 'you need to have people reviewing the behavior because you want to have it to eventually to achieve a very high level of reliability.', 'start': 1849.547, 'duration': 6.446}, {'end': 1860.517, 'text': 'but overall,', 'start': 1855.993, 'duration': 4.524}, {'end': 1872.589, 'text': "I'll say that we are at the same time this second step after we take the finished pre-trained model and then we apply the reinforcement learning on it.", 'start': 1860.517, 'duration': 12.072}, {'end': 1878.831, 'text': 'There is indeed a lot of motivation to make it as efficient and as precise as possible,', 'start': 1873.69, 'duration': 5.141}, {'end': 1882.451, 'text': 'so that the resulting language modeling will be as well behaved as possible.', 'start': 1878.831, 'duration': 3.62}, {'end': 1891.873, 'text': 'So yeah, there is these human teachers who are teaching them a model with desired behavior.', 'start': 1882.872, 'duration': 9.001}, {'end': 1900.473, 'text': 'They are also using AI assistance And the manner in which they use AI systems is constantly increasing.', 'start': 1892.273, 'duration': 8.2}, {'end': 1902.675, 'text': 'So their own efficiency keeps increasing.', 'start': 1900.814, 'duration': 1.861}, {'end': 1908.02, 'text': 'So maybe this will be one way to answer this question.', 'start': 1904.016, 'duration': 4.004}], 'summary': 'Ai tools enhance teacher efficiency, requiring oversight for reliable outcomes. reinforcement learning aims for efficient and precise language modeling with human guidance.', 'duration': 72.083, 'max_score': 1835.937, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SjhIlw3Iffs/pics/SjhIlw3Iffs1835937.jpg'}, {'end': 2086.8, 'src': 'embed', 'start': 2055.088, 'weight': 4, 'content': [{'end': 2060.85, 'text': 'You talk about the similarities between the brain and neural nets.', 'start': 2055.088, 'duration': 5.762}, {'end': 2065.952, 'text': "There's a very interesting observation that Jeff Hinton made to me.", 'start': 2060.87, 'duration': 5.082}, {'end': 2077.016, 'text': "I'm sure it's not new to other people, but that large models, or large language models in particular, hold a tremendous amount of data,", 'start': 2065.952, 'duration': 11.064}, {'end': 2086.8, 'text': 'with a modest number of parameters compared to the human brain, which has trillions and trillions of parameters,', 'start': 2077.016, 'duration': 9.784}], 'summary': 'Large language models hold a tremendous amount of data with a modest number of parameters compared to the human brain.', 'duration': 31.712, 'max_score': 2055.088, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SjhIlw3Iffs/pics/SjhIlw3Iffs2055088.jpg'}, {'end': 2178.751, 'src': 'embed', 'start': 2150.888, 'weight': 3, 'content': [{'end': 2153.171, 'text': 'I think it will be possible to learn more from less data.', 'start': 2150.888, 'duration': 2.283}, {'end': 2159.279, 'text': "I think it's just, I think it requires some creative ideas, but I think it is possible.", 'start': 2153.952, 'duration': 5.327}, {'end': 2163.784, 'text': 'And I think learning more from less data will unlock a lot of different possibilities.', 'start': 2159.859, 'duration': 3.925}, {'end': 2172.527, 'text': 'It will allow us to teach our AIs the skills that is missing and to convey to it our desires and preferences,', 'start': 2163.845, 'duration': 8.682}, {'end': 2175.229, 'text': 'exactly how we want it to behave more easily.', 'start': 2172.527, 'duration': 2.702}, {'end': 2178.751, 'text': 'So I would say that faster learning is indeed very nice.', 'start': 2176.029, 'duration': 2.722}], 'summary': 'Learning more from less data can enable faster ai training and improved customization.', 'duration': 27.863, 'max_score': 2150.888, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SjhIlw3Iffs/pics/SjhIlw3Iffs2150888.jpg'}, {'end': 2284.385, 'src': 'embed', 'start': 2250.657, 'weight': 7, 'content': [{'end': 2255.68, 'text': 'But if you get something very useful, something very valuable, something that you can solve, a lot of problems that we have,', 'start': 2250.657, 'duration': 5.023}, {'end': 2259.822, 'text': 'which we really want solved, then the cost can be justified.', 'start': 2255.68, 'duration': 4.142}, {'end': 2265.405, 'text': 'But in terms of the processors, faster processors, yeah, any day.', 'start': 2260.963, 'duration': 4.442}, {'end': 2270.928, 'text': 'Are you involved at all in the hardware question?', 'start': 2268.387, 'duration': 2.541}, {'end': 2275.531, 'text': 'Do you work with Cerebrus, for example, the wafer scale?', 'start': 2270.948, 'duration': 4.583}, {'end': 2284.385, 'text': 'chips no, all our hardware comes from azure and gpus they provide.', 'start': 2276.914, 'duration': 7.471}], 'summary': 'Valuable solutions justify costs, prefer faster processors, use hardware from azure and gpus.', 'duration': 33.728, 'max_score': 2250.657, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SjhIlw3Iffs/pics/SjhIlw3Iffs2250657.jpg'}, {'end': 2413.291, 'src': 'embed', 'start': 2364.781, 'weight': 5, 'content': [{'end': 2374.582, 'text': "it's unpredictable exactly how governments will use this technology as a source of getting advice of various kinds.", 'start': 2364.781, 'duration': 9.801}, {'end': 2378.926, 'text': 'I think that to the question of democracy,', 'start': 2375.443, 'duration': 3.483}, {'end': 2388.015, 'text': "one thing which I think could happen in the future is that because you have these neural nets and they're going to be so pervasive and they're going to be so impactful in society,", 'start': 2378.926, 'duration': 9.089}, {'end': 2394.622, 'text': "we will find that it is desirable to have some kind of a democratic process where this, let's say,", 'start': 2388.015, 'duration': 6.607}, {'end': 2403.707, 'text': "the citizens of a country provide some information to the neural net about how they'd like things to be, how they'd like it to behave,", 'start': 2394.622, 'duration': 9.085}, {'end': 2405.488, 'text': 'or something along these lines.', 'start': 2403.707, 'duration': 1.781}, {'end': 2406.848, 'text': 'I could imagine that happening.', 'start': 2405.488, 'duration': 1.36}, {'end': 2413.291, 'text': 'that can be a very like a high bandwidth form of democracy, perhaps,', 'start': 2406.848, 'duration': 6.443}], 'summary': "Neural nets could enable a high-bandwidth form of democracy for citizens to provide input on how they'd like things to be.", 'duration': 48.51, 'max_score': 2364.781, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SjhIlw3Iffs/pics/SjhIlw3Iffs2364781.jpg'}, {'end': 2470.911, 'src': 'embed', 'start': 2442.141, 'weight': 6, 'content': [{'end': 2456.106, 'text': 'Do you think AI systems will eventually be large enough that they can understand a situation and analyze all of the variables.', 'start': 2442.141, 'duration': 13.965}, {'end': 2462.308, 'text': 'But you would need a model that does more than absorb language, I would think.', 'start': 2457.766, 'duration': 4.542}, {'end': 2464.249, 'text': 'What does it mean to analyze all the variables?', 'start': 2462.468, 'duration': 1.781}, {'end': 2469.991, 'text': 'Eventually, there will be a choice you need to make where you say these variables seem really important.', 'start': 2464.909, 'duration': 5.082}, {'end': 2470.911, 'text': 'I want to go deep.', 'start': 2470.031, 'duration': 0.88}], 'summary': 'Ai systems will need to analyze all variables to make important decisions.', 'duration': 28.77, 'max_score': 2442.141, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SjhIlw3Iffs/pics/SjhIlw3Iffs2442141.jpg'}], 'start': 1813.967, 'title': 'Ai in society and education', 'summary': 'Delves into the use of reinforcement learning in ai teaching, emphasizing collaboration between human teachers and ai systems. it also explores challenges in ai research, focusing on learning from less data and the potential societal impact of ai.', 'chapters': [{'end': 1996.572, 'start': 1813.967, 'title': 'Reinforcement learning in ai teaching', 'summary': 'Discusses the use of reinforcement learning in ai teaching, emphasizing the collaboration between human teachers and ai systems to improve efficiency and precision in language modeling.', 'duration': 182.605, 'highlights': ['The collaboration between human teachers and AI systems is emphasized to improve efficiency and precision in language modeling. The chapter discusses the efficient collaboration between human teachers and AI systems to improve language modeling.', 'Reinforcement learning is applied to pre-trained models to achieve efficient and precise language modeling. The chapter emphasizes the application of reinforcement learning to pre-trained models for efficient and precise language modeling.', 'The need for oversight and review of AI behavior to achieve a high level of reliability is highlighted. The chapter discusses the importance of oversight and review of AI behavior to ensure a high level of reliability.']}, {'end': 2250.056, 'start': 1997.212, 'title': 'Ai research: learning from less data', 'summary': 'Discusses the challenges in ai research, focusing on making models more reliable, learning faster from less data, and the need for faster processors to scale further.', 'duration': 252.844, 'highlights': ['The current structure of the technology requires a lot of data, especially early in training, but there are opportunities to learn more from less data, unlocking different possibilities.', 'Faster learning and making models more reliable are crucial in AI research, as it will allow teaching AIs missing skills and conveying preferences more easily.', "The observation that large language models hold a tremendous amount of data with a modest number of parameters compared to the human brain raises questions about what's missing in large models to handle more parameters and data."]}, {'end': 2576.689, 'start': 2250.657, 'title': 'Future of ai in society', 'summary': 'Discusses the potential impact of ai on society, suggesting the possibility of ai-driven democratic processes and the ability of ai systems to analyze complex situations, leading to potential societal and governmental implications.', 'duration': 326.032, 'highlights': ['AI-driven democratic processes could involve citizens providing information to neural networks to influence decision-making, potentially leading to a high bandwidth form of democracy.', 'AI systems may eventually possess the capability to analyze complex situations and variables, offering significant assistance in various societal and organizational contexts.', 'Faster processors and advanced hardware can justify the cost of AI solutions, potentially enabling the development of more capable models with the ability to solve a wide range of problems.', 'The potential future use of AI technology by governments for advice and decision-making, with the impact on democracy being a topic of concern and speculation.', 'The interviewee acknowledges the unpredictable nature of how governments will utilize AI technology for advice and decision-making, hinting at the need for societal and governmental adaptations to accommodate the pervasive influence of neural networks.']}], 'duration': 762.722, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SjhIlw3Iffs/pics/SjhIlw3Iffs1813967.jpg', 'highlights': ['The collaboration between human teachers and AI systems is emphasized to improve efficiency and precision in language modeling.', 'Reinforcement learning is applied to pre-trained models to achieve efficient and precise language modeling.', 'The need for oversight and review of AI behavior to achieve a high level of reliability is highlighted.', 'Faster learning and making models more reliable are crucial in AI research, as it will allow teaching AIs missing skills and conveying preferences more easily.', "The observation that large language models hold a tremendous amount of data with a modest number of parameters compared to the human brain raises questions about what's missing in large models to handle more parameters and data.", 'AI-driven democratic processes could involve citizens providing information to neural networks to influence decision-making, potentially leading to a high bandwidth form of democracy.', 'AI systems may eventually possess the capability to analyze complex situations and variables, offering significant assistance in various societal and organizational contexts.', 'Faster processors and advanced hardware can justify the cost of AI solutions, potentially enabling the development of more capable models with the ability to solve a wide range of problems.', 'The potential future use of AI technology by governments for advice and decision-making, with the impact on democracy being a topic of concern and speculation.']}], 'highlights': ['Ilya Sutskever co-founded OpenAI and played a key role in developing GPT-3 and ChatGPT, which are transforming the world.', 'Ilya Sutskever was a driving force behind AlexNet, the convolutional neural network that revolutionized deep learning in 2012, according to Jeff Hinton.', "Ilya Sutskever's significant contributions to AI have had a profound impact on the scientific community and technological advancements.", 'The transition from recurrent neural networks to transformers addressed the limitations of learning long-term dependencies and significantly contributed to the development of GPT-3.', 'The realization that training a large and deep neural network on a big enough data set for complicated tasks would necessarily lead to success, based on the logic that the human brain can solve these tasks quickly, was a pivotal moment in the evolution of AI.', 'The importance of not wasting limited speaking time by explaining easily accessible concepts or events. Encouragement for listeners to explore previous episodes. Featuring a conversation with Ilya, following a previous episode with Jan LeCun.', 'Large language models lack an underlying understanding of reality, raising the question of addressing this limitation in future research.', 'Reinforcement learning from human feedback can improve the output quality of language models like chat GPT, quickly teaching them to produce good outputs by providing feedback whenever the output is inappropriate or does not make sense.', "The desirability of multimodal understanding in language models for better comprehension of tasks and people's needs, allowing them to learn more about the world, people, and their conditions, ultimately leading to better task comprehension and understanding of people's needs.", 'Autoregressive transformers can predict high dimensional vectors, demonstrated by successful use in predicting pages and generating images.', 'The collaboration between human teachers and AI systems is emphasized to improve efficiency and precision in language modeling.', 'Reinforcement learning is applied to pre-trained models to achieve efficient and precise language modeling.', 'The need for oversight and review of AI behavior to achieve a high level of reliability is highlighted.', 'Faster learning and making models more reliable are crucial in AI research, as it will allow teaching AIs missing skills and conveying preferences more easily.', "The observation that large language models hold a tremendous amount of data with a modest number of parameters compared to the human brain raises questions about what's missing in large models to handle more parameters and data.", 'AI-driven democratic processes could involve citizens providing information to neural networks to influence decision-making, potentially leading to a high bandwidth form of democracy.', 'AI systems may eventually possess the capability to analyze complex situations and variables, offering significant assistance in various societal and organizational contexts.', 'Faster processors and advanced hardware can justify the cost of AI solutions, potentially enabling the development of more capable models with the ability to solve a wide range of problems.', 'The potential future use of AI technology by governments for advice and decision-making, with the impact on democracy being a topic of concern and speculation.']}