title
Stanford XCS224U: NLU I Intro & Evolution of Natural Language Understanding, Pt. 1 I Spring 2023
description
For more information about Stanford's Artificial Intelligence programs visit: https://stanford.io/ai
This lecture is from the Stanford Online professional course XCS224U: https://online.stanford.edu/courses/xcs224u-natural-language-understanding
Every lecture from this professional course is taken from content within the Stanford University graduate course - CS224U. To follow along, visit the graduate course website: http://web.stanford.edu/class/cs224u/index.html
Christopher Potts Professor and Chair, Department of Linguistics, and Professor, by courtesy, Department of Computer Science at Stanford University http://web.stanford.edu/~cgpotts/
View the entire playlist of lectures: https://www.youtube.com/playlist?list=PLoROMvodv4rOwvldxftJTmoR3kRcWkJBp
To browse all available online courses and programs offered by Stanford Online, visit: http://online.stanford.edu
detail
{'title': 'Stanford XCS224U: NLU I Intro & Evolution of Natural Language Understanding, Pt. 1 I Spring 2023', 'heatmap': [], 'summary': 'Covers the evolution and impact of natural language understanding research, emergence of powerful language models like dali 2, ai systems surpassing human performance in benchmarks, societal impact of open ai models, rise of large-scale language models, impact of large models on text generation and reasoning challenges, deployment challenges, and integration of openai models in bing search engine.', 'chapters': [{'end': 193.324, 'segs': [{'end': 66.375, 'src': 'embed', 'start': 25.695, 'weight': 3, 'content': [{'end': 36.924, 'text': "And I think that'll set us up well to think about what we're going to do in the course and how that's going to set you up to participate in this moment in AI in many ways,", 'start': 25.695, 'duration': 11.229}, {'end': 38.185, 'text': 'in whichever ways you choose.', 'start': 36.924, 'duration': 1.261}, {'end': 40.628, 'text': "And it's an especially impactful moment to be doing that.", 'start': 38.265, 'duration': 2.363}, {'end': 42.99, 'text': 'And this is a project-oriented course.', 'start': 41.168, 'duration': 1.822}, {'end': 52.74, 'text': 'And I feel like we can get you all to the point where you are doing meaningful things that contribute to this ongoing moment in ways that are going to be exciting and impactful.', 'start': 43.291, 'duration': 9.449}, {'end': 55.083, 'text': 'That is the fundamental goal of the course.', 'start': 53.161, 'duration': 1.922}, {'end': 57.608, 'text': "Let's now think about the current moment.", 'start': 56.147, 'duration': 1.461}, {'end': 60.15, 'text': 'This is always a moment of reflection for me.', 'start': 57.648, 'duration': 2.502}, {'end': 66.375, 'text': 'I started teaching this course in 2012, um, which I guess is ages ago now.', 'start': 60.45, 'duration': 5.925}], 'summary': 'Project-oriented course aims to prepare students for impactful contributions in the ongoing ai moment, taught since 2012.', 'duration': 40.68, 'max_score': 25.695, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/K_Dh0Sxujuc/pics/K_Dh0Sxujuc25695.jpg'}, {'end': 129.247, 'src': 'embed', 'start': 105.777, 'weight': 0, 'content': [{'end': 116.257, 'text': 'Watson had just won on Jeopardy and we had all of these in-home devices and all the tech giants kind of competing on what was emerging as the field of natural language understanding.', 'start': 105.777, 'duration': 10.48}, {'end': 119.165, 'text': "Let's fast forward to 2022.", 'start': 117.324, 'duration': 1.841}, {'end': 126.367, 'text': 'I did feel like I should update that in 2022 by saying this is the most exciting moment ever as opposed to it just being an exciting time.', 'start': 119.165, 'duration': 7.202}, {'end': 129.247, 'text': 'But I emphasize the same things.', 'start': 126.987, 'duration': 2.26}], 'summary': 'In 2022, the field of natural language understanding is at its most exciting moment, with tech giants competing in in-home devices.', 'duration': 23.47, 'max_score': 105.777, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/K_Dh0Sxujuc/pics/K_Dh0Sxujuc105777.jpg'}, {'end': 181.549, 'src': 'embed', 'start': 152.22, 'weight': 2, 'content': [{'end': 155.644, 'text': 'And the core things about NLU remain far from solved.', 'start': 152.22, 'duration': 3.424}, {'end': 157.626, 'text': 'So the big breakthroughs lie in the future.', 'start': 155.664, 'duration': 1.962}, {'end': 160.069, 'text': 'I will say that even since 2022,', 'start': 158.167, 'duration': 1.902}, {'end': 168.397, 'text': "it has felt like there has been an acceleration And some problems that we used to focus on feel kind of like they're less pressing.", 'start': 160.069, 'duration': 8.328}, {'end': 174.282, 'text': "I won't say solved, but they feel like we've made a lot of progress on them as a result of models getting better.", 'start': 168.517, 'duration': 5.765}, {'end': 181.549, 'text': 'But all that means for me is that there are more exciting things in the future that we can tackle even more ambitious things.', 'start': 174.602, 'duration': 6.947}], 'summary': 'Nlu challenges persist, progress made, more ambitious goals ahead.', 'duration': 29.329, 'max_score': 152.22, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/K_Dh0Sxujuc/pics/K_Dh0Sxujuc152220.jpg'}], 'start': 5.563, 'title': 'Natural language understanding', 'summary': 'Discusses the significance of current natural language understanding research, evolution from 2012 to 2022, industry impact, challenges, and opportunities, emphasizing the intensified excitement and accelerated progress since 2022.', 'chapters': [{'end': 66.375, 'start': 5.563, 'title': 'Natural language understanding', 'summary': "Discusses the significance of the current moment in natural language understanding, emphasizing the impact of research and the course's goal to enable meaningful contributions to the ongoing ai moment.", 'duration': 60.812, 'highlights': ['The course is project-oriented, aiming to equip participants to make impactful contributions to the current moment in AI.', 'The instructor reflects on the evolution of teaching the course since 2012, highlighting the enduring relevance of the topic.']}, {'end': 151.579, 'start': 66.635, 'title': 'Nlu resurgence: then and now', 'summary': 'Discusses the evolution of natural language understanding research from 2012 to 2022, highlighting the resurgence of interest, industry impact, and the rapid progress of systems, with a particular emphasis on the intensified excitement and industry interest in 2022.', 'duration': 84.944, 'highlights': ['In 2022, the resurgence of interest in natural language understanding was hyper intensified compared to 2012, with an emphasis on the most exciting moment ever and industry interest surpassing that of 2012.', 'The industry interest in natural language understanding in 2022 far exceeds that of 2012, making the previous period seem insignificant in comparison.', 'Systems in 2022 were very impressive, but they also showed their weaknesses very quickly, indicating the rapid progress and challenges in the field.']}, {'end': 193.324, 'start': 152.22, 'title': 'Challenges and opportunities in nlu', 'summary': 'Discusses the evolving landscape of nlu, highlighting the ongoing challenges and the promise of future breakthroughs, suggesting an acceleration in progress since 2022 and the need for more ambitious problem-solving in the field.', 'duration': 41.104, 'highlights': ["There has been an acceleration in progress in NLU since 2022, with some previously pressing problems feeling like they've made a lot of progress as a result of models getting better.", 'The chapter emphasizes the need for more ambitious problem-solving in the field of NLU, indicating that there are more exciting things in the future that can be tackled and that it is a golden age for these advancements.']}], 'duration': 187.761, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/K_Dh0Sxujuc/pics/K_Dh0Sxujuc5563.jpg', 'highlights': ['The industry interest in natural language understanding in 2022 far exceeds that of 2012, making the previous period seem insignificant in comparison.', 'In 2022, the resurgence of interest in natural language understanding was hyper intensified compared to 2012, with an emphasis on the most exciting moment ever and industry interest surpassing that of 2012.', "There has been an acceleration in progress in NLU since 2022, with some previously pressing problems feeling like they've made a lot of progress as a result of models getting better.", 'The course is project-oriented, aiming to equip participants to make impactful contributions to the current moment in AI.', 'The instructor reflects on the evolution of teaching the course since 2012, highlighting the enduring relevance of the topic.']}, {'end': 608.335, 'segs': [{'end': 234.177, 'src': 'embed', 'start': 193.424, 'weight': 0, 'content': [{'end': 201.147, 'text': "And even in 2022, I'm not sure I would have predicted, to say nothing of 2012, that we would have these incredible models, like DALI 2,", 'start': 193.424, 'duration': 7.723}, {'end': 205.028, 'text': 'which can take you from text into these incredible images.', 'start': 201.147, 'duration': 3.881}, {'end': 212.651, 'text': 'language models, which will more or less be the star of the quarter for us, but also models that can take you from natural language to code.', 'start': 205.028, 'duration': 7.623}, {'end': 223.254, 'text': 'And of course, we are all seeing right now as we speak that the entire industry related to web search is being reshaped around NLU technologies.', 'start': 213.351, 'duration': 9.903}, {'end': 234.177, 'text': 'So, whereas this felt like a kind of niche area of NLP when we started this course in 2012,, now it feels like the entire field of NLP.', 'start': 223.914, 'duration': 10.263}], 'summary': 'Nlp has evolved with models like dali 2, transforming text into images and reshaping web search with nlu technologies.', 'duration': 40.753, 'max_score': 193.424, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/K_Dh0Sxujuc/pics/K_Dh0Sxujuc193424.jpg'}, {'end': 511.008, 'src': 'embed', 'start': 481.084, 'weight': 4, 'content': [{'end': 483.485, 'text': 'Who is its current president and what is its mascot??', 'start': 481.084, 'duration': 2.401}, {'end': 485.526, 'text': 'A complicated question indeed.', 'start': 483.865, 'duration': 1.661}, {'end': 490.368, 'text': 'And it gave a fluent and factually correct answer on all counts.', 'start': 485.886, 'duration': 4.482}, {'end': 499.442, 'text': 'This is the DaVinci 3 model, which was best in class until a few weeks ago, and it gave exactly the same answer, very impressive.', 'start': 491.718, 'duration': 7.724}, {'end': 503.925, 'text': "Now in this course, and you'll see at the website.", 'start': 500.903, 'duration': 3.022}, {'end': 511.008, 'text': "one of the readings we've suggested for the start of the course is this classic paper by Hector Levesque called On Our Best Behavior.", 'start': 503.925, 'duration': 7.083}], 'summary': 'The davinci 3 model provided accurate responses, impressive in its performance.', 'duration': 29.924, 'max_score': 481.084, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/K_Dh0Sxujuc/pics/K_Dh0Sxujuc481084.jpg'}], 'start': 193.424, 'title': 'Nlp and ai advancements', 'summary': 'Discusses the emergence of powerful language models like dali 2 and their impact on web search and natural language understanding, marking a notable shift in the entire nlp field since 2012, and showcases the evolution of language technologies from 1980 to 2022, with recent models like davinci 3 demonstrating impressive progress and capability.', 'chapters': [{'end': 259.2, 'start': 193.424, 'title': 'Nlp and ai advancements in 2022', 'summary': 'Discusses the significant advancements in nlp and ai, including the emergence of powerful language models like dali 2 and their impact on web search and natural language understanding, marking a notable shift in the entire nlp field since 2012.', 'duration': 65.776, 'highlights': ['The emergence of powerful language models like DALI 2 and their impact on web search and natural language understanding.', 'The reshaping of the entire industry related to web search around NLU technologies.', 'The focus of all of AI on natural language understanding, marking a major shift in the entire NLP field since 2012.']}, {'end': 608.335, 'start': 259.42, 'title': 'Evolution of language technologies', 'summary': "Describes the evolution of language technologies from 1980 to 2022, showcasing the advancements in models' capacity to understand and answer complex questions, with recent models like davinci 3 demonstrating impressive progress and capability.", 'duration': 348.915, 'highlights': ['DaVinci 3 model provided a fluent and factually correct answer to a complicated question about Stanford University, demonstrating substantial progress in language understanding and capability. DaVinci 3 model gave a fluent and factually correct answer to a complicated question about Stanford University.', 'Evolution of language models from 1980 to 2022, with the latest DaVinci 3 model exhibiting significant advancements and impressive capabilities in understanding and answering complex questions. The text outlines the evolution of language models from 1980 to 2022, highlighting the impressive advancements demonstrated by the latest DaVinci 3 model.', "Contradictory answers provided by closely related DaVinci 2 and DaVinci 3 models to a question posed to test their understanding, raising concerns about the models' consistency and reliability. The transcript reveals contradictory answers provided by closely related DaVinci 2 and DaVinci 3 models to a question designed to test their understanding, raising concerns about their consistency and reliability."]}], 'duration': 414.911, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/K_Dh0Sxujuc/pics/K_Dh0Sxujuc193424.jpg', 'highlights': ['The emergence of powerful language models like DALI 2 and their impact on web search and natural language understanding.', 'The focus of all of AI on natural language understanding, marking a major shift in the entire NLP field since 2012.', 'The reshaping of the entire industry related to web search around NLU technologies.', 'Evolution of language models from 1980 to 2022, with the latest DaVinci 3 model exhibiting significant advancements and impressive capabilities in understanding and answering complex questions.', 'DaVinci 3 model provided a fluent and factually correct answer to a complicated question about Stanford University, demonstrating substantial progress in language understanding and capability.', "Contradictory answers provided by closely related DaVinci 2 and DaVinci 3 models to a question posed to test their understanding, raising concerns about the models' consistency and reliability."]}, {'end': 917.398, 'segs': [{'end': 654.1, 'src': 'embed', 'start': 627.34, 'weight': 2, 'content': [{'end': 636.126, 'text': "I guess, if you've seen the movie Blade Runner, this is starting to feel like to figure out whether an agent we were interacting with was human or AI.", 'start': 627.34, 'duration': 8.786}, {'end': 640.83, 'text': 'we would need to get very sophisticated interview techniques indeed.', 'start': 636.126, 'duration': 4.704}, {'end': 644.032, 'text': 'The Turing test, long forgotten here.', 'start': 641.23, 'duration': 2.802}, {'end': 654.1, 'text': "now we're into the mode of trying to figure out exactly what kind of agents we're interacting with by having to be extremely clever about the kinds of things that we do with them.", 'start': 644.032, 'duration': 10.068}], 'summary': 'Comparing agent interaction to blade runner, aiming for clever interview techniques.', 'duration': 26.76, 'max_score': 627.34, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/K_Dh0Sxujuc/pics/K_Dh0Sxujuc627340.jpg'}, {'end': 691.293, 'src': 'embed', 'start': 666.979, 'weight': 1, 'content': [{'end': 675.305, 'text': 'And the headline here is that our benchmarks, the tasks, the data sets we use to probe our models are saturating faster than ever before.', 'start': 666.979, 'duration': 8.326}, {'end': 677.327, 'text': "And I'll articulate what I mean by saturate.", 'start': 675.345, 'duration': 1.982}, {'end': 679.408, 'text': 'So we here have a little framework.', 'start': 677.867, 'duration': 1.541}, {'end': 684.25, 'text': 'Along the x-axis, I have time stretching back into like the 1990s.', 'start': 679.888, 'duration': 4.362}, {'end': 691.293, 'text': 'And along the y-axis, I have a normalized measure of distance from what we call human performance.', 'start': 684.77, 'duration': 6.523}], 'summary': 'Benchmarks are saturating faster than before, with time and human performance as key factors.', 'duration': 24.314, 'max_score': 666.979, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/K_Dh0Sxujuc/pics/K_Dh0Sxujuc666979.jpg'}, {'end': 781.498, 'src': 'embed', 'start': 733.536, 'weight': 0, 'content': [{'end': 742.102, 'text': 'ImageNet, this was launched, I believe, in 2009, and it took less than 10 years for us to see a system that surpassed that red line.', 'start': 733.536, 'duration': 8.566}, {'end': 745.224, 'text': 'And now progress is going to pick up really fast.', 'start': 743.163, 'duration': 2.061}, {'end': 754.431, 'text': 'SQuAD 1.1, the Stanford Question Answering Dataset, was launched in 2016, and it took about three years for it to be saturated in this sense.', 'start': 745.284, 'duration': 9.147}, {'end': 762.995, 'text': "Squad 2.0 was the team's attempt to pose an even harder problem, one where there were unanswerable questions,", 'start': 755.367, 'duration': 7.628}, {'end': 766.719, 'text': 'but it took even less time for systems to get past that red line.', 'start': 762.995, 'duration': 3.724}, {'end': 769.573, 'text': 'Then we get the glue benchmark.', 'start': 768.012, 'duration': 1.561}, {'end': 774.435, 'text': 'This is a famous benchmark in natural language understanding, a multitask benchmark.', 'start': 769.593, 'duration': 4.842}, {'end': 781.498, 'text': 'When this was launched, a lot of us thought that glue would be too difficult for present day systems.', 'start': 775.255, 'duration': 6.243}], 'summary': 'In less than 10 years, systems surpassed imagenet accuracy and achieved progress in less time for squad 1.1 and squad 2.0, and also performed well in the glue benchmark.', 'duration': 47.962, 'max_score': 733.536, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/K_Dh0Sxujuc/pics/K_Dh0Sxujuc733536.jpg'}, {'end': 847.705, 'src': 'embed', 'start': 827.854, 'weight': 7, 'content': [{'end': 839.881, 'text': 'where he evaluated our latest and greatest large language models on a bunch of mostly new tasks that were actually designed to stress test this new class of very large language models.', 'start': 827.854, 'duration': 12.027}, {'end': 847.705, 'text': "Jason's observation is that we see emergent abilities across more than 100 tasks for these models, especially for our largest models.", 'start': 839.941, 'duration': 7.764}], 'summary': 'Evaluated large language models on 100+ tasks, showing emergent abilities.', 'duration': 19.851, 'max_score': 827.854, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/K_Dh0Sxujuc/pics/K_Dh0Sxujuc827854.jpg'}], 'start': 608.355, 'title': 'Ai progress', 'summary': 'Discusses the rapid progress of ai systems surpassing human performance in benchmarks like squad, glue, and over 100 tasks in less than 10 years, showcasing significant advancement in ai. it also highlights the challenge of distinguishing human and ai agents and the rapid saturation of benchmarks and datasets, illustrated using the mnist dataset.', 'chapters': [{'end': 711.578, 'start': 608.355, 'title': 'Ai benchmark progress', 'summary': 'Discusses the challenge of differentiating between human and ai agents, the increasing sophistication required for interviews, and how benchmarks and datasets in ai are saturating faster than ever before, with the mnist dataset as an example.', 'duration': 103.223, 'highlights': ['The benchmarks and datasets in AI are saturating faster than before, with the example of the MNIST dataset, a famous task in AI like digit recognition.', 'The challenge of differentiating between human and AI agents and the need for increasingly sophisticated interview techniques are discussed.', 'The chapter also mentions the comparison to the movie Blade Runner in figuring out whether an agent is human or AI, and the long-forgotten Turing test.']}, {'end': 917.398, 'start': 711.598, 'title': 'Ai progress: surpassing human performance', 'summary': 'Discusses the progression of ai systems surpassing human performance in various benchmarks, such as squad, glue, and emerging abilities across more than 100 tasks, within a span of less than 10 years, signifying undeniable progress in the field of ai.', 'duration': 205.8, 'highlights': ['The rapid progression of AI systems surpassing human performance in benchmarks such as SQuAD, GLUE, and emerging abilities across more than 100 tasks within a span of less than 10 years signifies undeniable progress in the field of AI.', 'ImageNet, launched in 2009, was surpassed by a system within less than 10 years, indicating a rapid pace of progress in AI performance.', 'The Stanford Question Answering Dataset (SQuAD) 2.0 posed an even harder problem and was surpassed by systems in less time, demonstrating the accelerating pace of AI advancement.', 'The GLUE benchmark, initially considered too difficult for present-day systems, was surpassed by AI systems in less than a year, reflecting the rapid progress in natural language understanding.', 'The evaluation of large language models on over 100 new tasks demonstrated emergent abilities, with systems performing at the standard set for humans, highlighting the remarkable progress in AI capabilities.']}], 'duration': 309.043, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/K_Dh0Sxujuc/pics/K_Dh0Sxujuc608355.jpg', 'highlights': ['AI systems surpass human performance in benchmarks like SQuAD, GLUE, and over 100 tasks in less than 10 years.', 'Rapid saturation of benchmarks and datasets, illustrated using the MNIST dataset.', 'The challenge of distinguishing human and AI agents and the need for increasingly sophisticated interview techniques.', 'Comparison to the movie Blade Runner in figuring out whether an agent is human or AI, and the long-forgotten Turing test.', 'ImageNet, launched in 2009, was surpassed by a system within less than 10 years, indicating a rapid pace of progress in AI performance.', 'The Stanford Question Answering Dataset (SQuAD) 2.0 posed an even harder problem and was surpassed by systems in less time, demonstrating the accelerating pace of AI advancement.', 'The GLUE benchmark, initially considered too difficult for present-day systems, was surpassed by AI systems in less than a year, reflecting the rapid progress in natural language understanding.', 'The evaluation of large language models on over 100 new tasks demonstrated emergent abilities, with systems performing at the standard set for humans, highlighting the remarkable progress in AI capabilities.']}, {'end': 1278.042, 'segs': [{'end': 969.555, 'src': 'embed', 'start': 940.301, 'weight': 0, 'content': [{'end': 943.603, 'text': "What you're pointing out, I think, is an increasing societal problem.", 'start': 940.301, 'duration': 3.302}, {'end': 949.467, 'text': 'These models are offering us what looks like evidence, but a lot of the evidence is just fabricated.', 'start': 943.903, 'duration': 5.564}, {'end': 952.228, 'text': 'And this is worse than offering no evidence at all.', 'start': 949.907, 'duration': 2.321}, {'end': 959.273, 'text': 'What I really need is someone who knows Major League Baseball to tell me what is the rule about players and their caps.', 'start': 952.729, 'duration': 6.544}, {'end': 963.447, 'text': 'I want it from an expert human, not an expert language model.', 'start': 959.983, 'duration': 3.464}, {'end': 969.555, 'text': "What's that? Can we Google? Be careful how you Google though.", 'start': 966.15, 'duration': 3.405}], 'summary': 'Societal problem of fabricated evidence from models, seeking human expertise over language models.', 'duration': 29.254, 'max_score': 940.301, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/K_Dh0Sxujuc/pics/K_Dh0Sxujuc940301.jpg'}, {'end': 1038.913, 'src': 'embed', 'start': 1012.148, 'weight': 3, 'content': [{'end': 1021.137, 'text': 'In the 90s, early 2000s, we get the statistical revolution throughout artificial intelligence and then in turn in natural language processing.', 'start': 1012.148, 'duration': 8.989}, {'end': 1026.261, 'text': 'And the big change there is that instead of programming systems with all these rules,', 'start': 1021.637, 'duration': 4.624}, {'end': 1029.825, 'text': "we're going to design machine learning systems that are going to try to learn from data.", 'start': 1026.261, 'duration': 3.564}, {'end': 1038.913, 'text': 'Under the hood there was still a lot of programming involved because we would write a lot of feature functions that were little programs that would help us detect things about data.', 'start': 1030.464, 'duration': 8.449}], 'summary': 'Statistical revolution in ai led to machine learning systems learning from data.', 'duration': 26.765, 'max_score': 1012.148, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/K_Dh0Sxujuc/pics/K_Dh0Sxujuc1012148.jpg'}, {'end': 1082.696, 'src': 'embed', 'start': 1049.644, 'weight': 5, 'content': [{'end': 1054.589, 'text': 'And we just hope that some process of optimization leads us to new capabilities.', 'start': 1049.644, 'duration': 4.945}, {'end': 1058.977, 'text': 'The next big phase of this was the deep learning revolution.', 'start': 1056.055, 'duration': 2.922}, {'end': 1062.52, 'text': 'This happened starting around 2009, 2010.', 'start': 1059.378, 'duration': 3.142}, {'end': 1065.542, 'text': 'Again, Stanford was at the forefront of this, to be sure.', 'start': 1062.52, 'duration': 3.022}, {'end': 1068.164, 'text': 'It felt like a big change at the time.', 'start': 1066.383, 'duration': 1.781}, {'end': 1072.167, 'text': 'But in retrospect, this is kind of not so different from this mode here.', 'start': 1068.224, 'duration': 3.943}, {'end': 1082.696, 'text': "It's just that we now replace that simple model with really big models, really deep models that have a tremendous capacity to learn things from data.", 'start': 1072.308, 'duration': 10.388}], 'summary': 'Deep learning revolution began around 2009, led by stanford, with the capacity to learn from data.', 'duration': 33.052, 'max_score': 1049.644, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/K_Dh0Sxujuc/pics/K_Dh0Sxujuc1049644.jpg'}, {'end': 1148.188, 'src': 'embed', 'start': 1121.075, 'weight': 6, 'content': [{'end': 1126.359, 'text': "And we hope that when they're all combined and we do some learning on some task-specific data,", 'start': 1121.075, 'duration': 5.284}, {'end': 1129.541, 'text': "we have something that's benefiting from all these pre-trained components.", 'start': 1126.359, 'duration': 3.182}, {'end': 1143.526, 'text': "And then the mode that we seem to be in now that I want us to reflect critically on is this mode where we're gonna replace everything with maybe one ginormous language model of some kind,", 'start': 1131.222, 'duration': 12.304}, {'end': 1148.188, 'text': 'and hope that that thing, that enormous black box, will do all the work for us.', 'start': 1143.526, 'duration': 4.662}], 'summary': 'Combining pre-trained components to benefit task-specific data, caution against relying solely on one large language model.', 'duration': 27.113, 'max_score': 1121.075, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/K_Dh0Sxujuc/pics/K_Dh0Sxujuc1121075.jpg'}, {'end': 1282.644, 'src': 'embed', 'start': 1256.132, 'weight': 7, 'content': [{'end': 1261.895, 'text': 'All I can say for you now is that I expect you to go on the following journey, which all of us go on.', 'start': 1256.132, 'duration': 5.763}, {'end': 1266.858, 'text': 'How on earth does the transformer work? It looks very, very complicated.', 'start': 1262.595, 'duration': 4.263}, {'end': 1270.859, 'text': 'I I hope can get you to the point where you feel oh,', 'start': 1267.698, 'duration': 3.161}, {'end': 1275.421, 'text': 'this is actually pretty simple components that have com- been combined in a pretty straightforward way.', 'start': 1270.859, 'duration': 4.562}, {'end': 1277.562, 'text': "That's your second step on the journey.", 'start': 1276.021, 'duration': 1.541}, {'end': 1278.042, 'text': 'The one.', 'start': 1277.602, 'duration': 0.44}, {'end': 1279.683, 'text': 'the true enlightenment comes from.', 'start': 1278.042, 'duration': 1.641}, {'end': 1282.644, 'text': 'wait a second, why does this work at all?', 'start': 1279.683, 'duration': 2.961}], 'summary': "Journey to understand the transformer's working simplicity and enlightenment.", 'duration': 26.512, 'max_score': 1256.132, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/K_Dh0Sxujuc/pics/K_Dh0Sxujuc1256132.jpg'}], 'start': 917.398, 'title': 'Ai models and language evolution', 'summary': 'Delves into the societal impact of open ai models producing fabricated evidence and highlights the evolution of ai from symbolic algorithms to large pre-trained language models, with a focus on transformer architecture.', 'chapters': [{'end': 959.273, 'start': 917.398, 'title': 'Open ai models and fabricated evidence', 'summary': 'Discusses the societal problem of open ai models offering fabricated evidence, pointing out the need for accurate information for specific topics, exemplified by the search for a major league baseball rule on players and their caps.', 'duration': 41.875, 'highlights': ['Open AI models providing fabricated evidence is identified as an increasing societal problem.', 'The need for accurate information is exemplified by the search for the Major League Baseball rule on players and their caps.', 'Offering fabricated evidence is worse than offering no evidence at all.']}, {'end': 1278.042, 'start': 959.983, 'title': 'Evolution of ai and language models', 'summary': 'Discusses the historical progression of ai, from symbolic algorithms to machine learning systems, leading to the emergence of deep learning and the current trend of relying on large pre-trained language models, with a focus on the transformer architecture.', 'duration': 318.059, 'highlights': ['The shift from symbolic algorithms to machine learning systems occurred in the 90s and early 2000s, leading to the rise of fully data-driven learning systems. In the 90s and early 2000s, the statistical revolution in artificial intelligence led to the design of machine learning systems that learned from data, marking a shift from programming systems with rules to fully data-driven learning systems.', 'The emergence of the deep learning revolution around 2009 saw a transition to using large, deep models with extensive learning capacity, relying less on feature functions and more on data and optimization processes. The deep learning revolution around 2009 brought about the use of large, deep models with significant learning capacity, reducing reliance on feature functions and emphasizing data and optimization processes.', 'The current trend involves utilizing large pre-trained language models and relying on them to perform various tasks, raising the need for critical reflection on whether this approach is the optimal path forward. The current trend involves the use of large pre-trained language models for various tasks, prompting the need for critical reflection on the efficacy of relying solely on these models for performing tasks.', 'The chapter introduces the transformer architecture, emphasizing its complexity and the upcoming lecture dedicated to understanding its components and functionality. The chapter introduces the transformer architecture and highlights its perceived complexity, with an upcoming lecture aimed at facilitating understanding of its components and functionality.']}], 'duration': 360.644, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/K_Dh0Sxujuc/pics/K_Dh0Sxujuc917398.jpg', 'highlights': ['Open AI models providing fabricated evidence is identified as an increasing societal problem.', 'The need for accurate information is exemplified by the search for the Major League Baseball rule on players and their caps.', 'Offering fabricated evidence is worse than offering no evidence at all.', 'The shift from symbolic algorithms to machine learning systems occurred in the 90s and early 2000s, leading to the rise of fully data-driven learning systems.', 'The statistical revolution in artificial intelligence led to the design of machine learning systems that learned from data, marking a shift from programming systems with rules to fully data-driven learning systems.', 'The emergence of the deep learning revolution around 2009 saw a transition to using large, deep models with extensive learning capacity, relying less on feature functions and more on data and optimization processes.', 'The current trend involves utilizing large pre-trained language models and relying on them to perform various tasks, raising the need for critical reflection on whether this approach is the optimal path forward.', 'The chapter introduces the transformer architecture, emphasizing its complexity and the upcoming lecture dedicated to understanding its components and functionality.']}, {'end': 1788.193, 'segs': [{'end': 1411.969, 'src': 'embed', 'start': 1387.827, 'weight': 0, 'content': [{'end': 1397.996, 'text': "The result of this proving so powerful is the advent of large-scale pre-training because now we're not held back anymore by the need for labeled data.", 'start': 1387.827, 'duration': 10.169}, {'end': 1401.72, 'text': 'All we need is lots of data in unstructured format.', 'start': 1398.377, 'duration': 3.343}, {'end': 1407.905, 'text': 'This really begins in the era of static word representations like Word2Vec and GloVe.', 'start': 1402.42, 'duration': 5.485}, {'end': 1411.969, 'text': 'And in fact those teams, and I would say especially the glove team,', 'start': 1408.846, 'duration': 3.123}], 'summary': 'Large-scale pre-training enables using unstructured data without labels, marking the era of static word representations like word2vec and glove.', 'duration': 24.142, 'max_score': 1387.827, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/K_Dh0Sxujuc/pics/K_Dh0Sxujuc1387827.jpg'}, {'end': 1550.212, 'src': 'embed', 'start': 1502.292, 'weight': 1, 'content': [{'end': 1509.716, 'text': "BERT is the first of the sequence of things that's based in the transformer, and again, lifting all boats even above where ELMo had brought us.", 'start': 1502.292, 'duration': 7.424}, {'end': 1511.654, 'text': 'Then we get GPT.', 'start': 1510.814, 'duration': 0.84}, {'end': 1513.816, 'text': 'This is the first GPT paper.', 'start': 1511.714, 'duration': 2.102}, {'end': 1516.737, 'text': 'And then fast forward a little bit, we get GPT-3.', 'start': 1514.256, 'duration': 2.481}, {'end': 1524.061, 'text': 'And that was pre-training at a scale that was previously kind of unimaginable.', 'start': 1517.277, 'duration': 6.784}, {'end': 1528.763, 'text': "Because now we're talking about, for the BERT model, 100 million parameters.", 'start': 1524.221, 'duration': 4.542}, {'end': 1532.005, 'text': 'And for GPT-3, well north of 100 billion.', 'start': 1528.843, 'duration': 3.162}, {'end': 1534.446, 'text': 'Different order of magnitude.', 'start': 1533.065, 'duration': 1.381}, {'end': 1537.848, 'text': 'And what we started to see is emergent capabilities.', 'start': 1534.986, 'duration': 2.862}, {'end': 1541.169, 'text': 'that model size thing is important.', 'start': 1539.568, 'duration': 1.601}, {'end': 1545.17, 'text': 'Again, this is a sort of feeling of progress and maybe also despair.', 'start': 1541.909, 'duration': 3.261}, {'end': 1550.212, 'text': 'I think I can lift your spirits a little bit, but we should think about model size.', 'start': 1545.53, 'duration': 4.682}], 'summary': 'Bert, gpt, and gpt-3 represent significant advancements in model size, with gpt-3 having well over 100 billion parameters, showing emergent capabilities.', 'duration': 47.92, 'max_score': 1502.292, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/K_Dh0Sxujuc/pics/K_Dh0Sxujuc1502292.jpg'}, {'end': 1750.501, 'src': 'embed', 'start': 1689.71, 'weight': 3, 'content': [{'end': 1690.65, 'text': "Maybe we'll correct that.", 'start': 1689.71, 'duration': 0.94}, {'end': 1694.933, 'text': 'And then maybe the more important thing is that we have lots of startups represented.', 'start': 1691.411, 'duration': 3.522}, {'end': 1701.196, 'text': 'So these are well-funded but relatively small outfits that are producing outstanding language models.', 'start': 1694.973, 'duration': 6.223}, {'end': 1708.38, 'text': "And so the result, I think we're gonna see much more of this, and then we'll worry less about centralization of power.", 'start': 1701.776, 'duration': 6.604}, {'end': 1714.183, 'text': "There's plenty of other things to worry about, so we shouldn't get sanguine about this, but this particular point, I think,", 'start': 1708.5, 'duration': 5.683}, {'end': 1716.184, 'text': 'is being alleviated by current trends.', 'start': 1714.183, 'duration': 2.001}, {'end': 1722.248, 'text': "And there's another aspect of this too, which is you have the scary rise in model size.", 'start': 1716.865, 'duration': 5.383}, {'end': 1729.272, 'text': "but what is happening right now, as we speak in a very quick way, is we're seeing a push towards smaller models.", 'start': 1722.248, 'duration': 7.024}, {'end': 1736.915, 'text': "And in particular, we're seeing that models that are in the range of like 10 billion parameters can be highly performant, right?", 'start': 1730.032, 'duration': 6.883}, {'end': 1738.476, 'text': 'So we have the Flan models.', 'start': 1736.935, 'duration': 1.541}, {'end': 1746.539, 'text': 'uh, we have Llama and then here at Stanford, they released the Alpaca thing and then, uh, uh, Databricks released the HelloDolly model.', 'start': 1738.476, 'duration': 8.063}, {'end': 1750.501, 'text': 'These are all models that are like 8 to 10 billion parameters, which I know.', 'start': 1746.559, 'duration': 3.942}], 'summary': 'Startups producing small, well-funded language models, reducing centralization of power.', 'duration': 60.791, 'max_score': 1689.71, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/K_Dh0Sxujuc/pics/K_Dh0Sxujuc1689710.jpg'}], 'start': 1278.042, 'title': 'The rise of language models in ai', 'summary': 'Delves into the power of self-supervision and distributional learning, leading to large-scale pre-training and the emergence of language models with billions of parameters, revolutionizing natural language processing. it also covers the rise of smaller language models, with a shift towards a more diverse and open ecosystem, including the emergence of 8 to 10 billion parameter models, promoting accessibility and innovation.', 'chapters': [{'end': 1622.724, 'start': 1278.042, 'title': 'The rise of self-supervised learning in ai', 'summary': 'Discusses the power of self-supervision and distributional learning in ai, leading to large-scale pre-training and the emergence of models with billions of parameters, revolutionizing the field of natural language processing.', 'duration': 344.682, 'highlights': ['The advent of large-scale pre-training due to the power of self-supervision and distributional learning has led to models with billions of parameters, such as GPT-3 with over 175 billion parameters and Palm with 540 billion parameters, revolutionizing the field of natural language processing. The emergence of large-scale pre-training due to self-supervision and distributional learning has led to the development of models with billions of parameters, such as GPT-3 with over 175 billion parameters and Palm with 540 billion parameters, revolutionizing the field of natural language processing.', 'The impact of models like BERT and GPT-3, with over 100 billion parameters, has showcased emergent capabilities, marking a significant shift in the scale of pre-training models within the field of natural language processing. Models like BERT and GPT-3, with over 100 billion parameters, have demonstrated significant emergent capabilities, signifying a substantial shift in the scale of pre-training models within the field of natural language processing.', 'The paradigm shift towards large-scale pre-training has been driven by models like GPT-3, with over 100 billion parameters, and the trend towards models with even larger parameters, such as Palm with 540 billion parameters, reflecting a noteworthy pattern in the field of natural language processing. The shift towards large-scale pre-training has been catalyzed by models like GPT-3, with over 100 billion parameters, and the ongoing trend towards models with even larger parameters, such as Palm with 540 billion parameters, representing a significant pattern in the field of natural language processing.']}, {'end': 1788.193, 'start': 1622.744, 'title': 'Rise of small language models', 'summary': 'Discusses the rise of smaller language models, with a shift towards a more diverse and open ecosystem, including the emergence of 8 to 10 billion parameter models, promoting accessibility and innovation.', 'duration': 165.449, 'highlights': ['The emergence of smaller language models in the range of 8 to 10 billion parameters like Flan, Llama, Alpaca, and HelloDolly, promoting accessibility and usability on regular commercial hardware.', 'The concern over centralization of power in large language models is being alleviated by the shift towards a more diverse ecosystem with open source groups and startups contributing to the production of outstanding language models.', 'The push towards smaller models is leading to a more healthy ecosystem and less worry about centralization of power, offering the potential for innovation and accessibility.']}], 'duration': 510.151, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/K_Dh0Sxujuc/pics/K_Dh0Sxujuc1278042.jpg', 'highlights': ['Large-scale pre-training led to models with billions of parameters, revolutionizing NLP.', 'Models like BERT and GPT-3 showcased significant emergent capabilities.', 'The paradigm shift towards large-scale pre-training was driven by models like GPT-3 and Palm.', 'Smaller language models in the range of 8 to 10 billion parameters promote accessibility.', 'The shift towards a more diverse ecosystem alleviates concerns over centralization of power.', 'Push towards smaller models leads to a healthier ecosystem and potential for innovation.']}, {'end': 3511.386, 'segs': [{'end': 1822.625, 'src': 'embed', 'start': 1788.493, 'weight': 0, 'content': [{'end': 1791.775, 'text': 'I think that will bring some good and I think it will bring some bad,', 'start': 1788.493, 'duration': 3.282}, {'end': 1797.678, 'text': 'but it is certainly a meaningful change from this scary trend that we were seeing until four months ago.', 'start': 1791.775, 'duration': 5.903}, {'end': 1811.82, 'text': 'As a result of these models being so powerful, people started to realize that you can get a lot of mileage out of them simply by prompting them.', 'start': 1802.276, 'duration': 9.544}, {'end': 1814.562, 'text': 'When you prompt one of these very large models,', 'start': 1812.341, 'duration': 2.221}, {'end': 1822.625, 'text': 'you put it in a temporary state by inputting some text and then you generate a sample from the model using some technique and you see what comes out right?', 'start': 1814.562, 'duration': 8.063}], 'summary': 'Powerful models prompt for meaningful change, reducing a scary trend by generating samples.', 'duration': 34.132, 'max_score': 1788.493, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/K_Dh0Sxujuc/pics/K_Dh0Sxujuc1788493.jpg'}, {'end': 2009.533, 'src': 'embed', 'start': 1985.356, 'weight': 1, 'content': [{'end': 1992.821, 'text': "The prompting thing, we take this a step forward, right? So the GPT-3 paper, remember that's that 175 billion parameter monster.", 'start': 1985.356, 'duration': 7.465}, {'end': 2002.088, 'text': 'The eye-opening thing about that is what we now call in-context learning, which was just the notion that for these very large, very capable models,', 'start': 1993.402, 'duration': 8.686}, {'end': 2009.533, 'text': "you could input a bunch of texts like here's a passage and maybe an example of the kind of behavior that you wanted,", 'start': 2002.088, 'duration': 7.445}], 'summary': 'Gpt-3 paper introduces 175 billion parameter model with in-context learning.', 'duration': 24.177, 'max_score': 1985.356, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/K_Dh0Sxujuc/pics/K_Dh0Sxujuc1985356.jpg'}, {'end': 2234.489, 'src': 'embed', 'start': 2207.848, 'weight': 2, 'content': [{'end': 2213.872, 'text': "The other thing that we should think about is what's called reinforcement learning with human feedback.", 'start': 2207.848, 'duration': 6.024}, {'end': 2217.917, 'text': 'This is a diagram from the chat GPT blog post.', 'start': 2214.775, 'duration': 3.142}, {'end': 2222.66, 'text': 'There are a lot of details here, but really two of them are important for us for right now.', 'start': 2218.398, 'duration': 4.262}, {'end': 2234.489, 'text': 'The first is that in a phase of training these models, people are given inputs and ask themselves to produce good outputs for those inputs.', 'start': 2223.201, 'duration': 11.288}], 'summary': 'Reinforcement learning with human feedback in training models; people asked to produce good outputs for inputs.', 'duration': 26.641, 'max_score': 2207.848, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/K_Dh0Sxujuc/pics/K_Dh0Sxujuc2207848.jpg'}, {'end': 2440.76, 'src': 'embed', 'start': 2409.816, 'weight': 3, 'content': [{'end': 2413.117, 'text': 'And the model would spit out something that looked really good.', 'start': 2409.816, 'duration': 3.301}, {'end': 2421.461, 'text': "Here. I won't bother going through the details, but with that kind of prompt the model now not only answers and reasons correctly,", 'start': 2413.197, 'duration': 8.264}, {'end': 2424.782, 'text': 'but also offers a really nice explanation of its own reasoning.', 'start': 2421.461, 'duration': 3.321}, {'end': 2433.966, 'text': "The capacity was there, it was latent, and we didn't see it in the simple prompting mode, but the more sophisticated prompting mode elicited it.", 'start': 2425.762, 'duration': 8.204}, {'end': 2440.76, 'text': 'And I think this is in large part the result of the fact that this model was instruct tuned.', 'start': 2435.137, 'duration': 5.623}], 'summary': 'Model, when instructed tuned, offers better reasoning and explanation.', 'duration': 30.944, 'max_score': 2409.816, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/K_Dh0Sxujuc/pics/K_Dh0Sxujuc2409816.jpg'}, {'end': 2963.177, 'src': 'embed', 'start': 2937.011, 'weight': 4, 'content': [{'end': 2941.834, 'text': "We'll talk about benchmarking and adversarial training and testing, increasingly important topics,", 'start': 2937.011, 'duration': 4.823}, {'end': 2947.758, 'text': 'as we move into this mode where everyone is interacting with these large language models and feeling impressed by their behavior.', 'start': 2941.834, 'duration': 5.924}, {'end': 2957.285, 'text': "We need to take a step back and rigorously assess whether they actually are behaving in good ways or whether we're just biased toward remembering the good things and forgetting the bad ones.", 'start': 2948.179, 'duration': 9.106}, {'end': 2959.655, 'text': "We'll do model introspection.", 'start': 2958.575, 'duration': 1.08}, {'end': 2961.696, 'text': "That's the explainability stuff that I mentioned.", 'start': 2959.695, 'duration': 2.001}, {'end': 2963.177, 'text': 'And finally, methods and metrics.', 'start': 2961.736, 'duration': 1.441}], 'summary': "Discussing benchmarking, adversarial training, and testing for large language models' behavior assessment.", 'duration': 26.166, 'max_score': 2937.011, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/K_Dh0Sxujuc/pics/K_Dh0Sxujuc2937011.jpg'}, {'end': 3093.867, 'src': 'embed', 'start': 3064.571, 'weight': 5, 'content': [{'end': 3070.475, 'text': 'And that will have a lit review phase, an experiment protocol, and a final paper, those three components.', 'start': 3064.571, 'duration': 5.904}, {'end': 3071.896, 'text': "You'll probably do those in teams.", 'start': 3070.495, 'duration': 1.401}, {'end': 3076.378, 'text': "And throughout all of that work, you'll be mentored by someone from the teaching team.", 'start': 3072.356, 'duration': 4.022}, {'end': 3084.823, 'text': 'And as I said before, we have this incredibly expert teaching team, lots of varied expertise, a lot of experience in the field.', 'start': 3076.839, 'duration': 7.984}, {'end': 3093.867, 'text': "And so we hope to align you with someone who's really aligned with your project goals, and then I think you can go really, really far.", 'start': 3085.264, 'duration': 8.603}], 'summary': 'Students will work in teams on lit review, experiment protocol, and final paper, mentored by experienced teaching team.', 'duration': 29.296, 'max_score': 3064.571, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/K_Dh0Sxujuc/pics/K_Dh0Sxujuc3064571.jpg'}], 'start': 1788.493, 'title': 'Impact of large models, learning mechanisms & reasoning', 'summary': 'Discusses the impact of large models on text generation, ai learning mechanisms emphasizing in-context and reinforcement learning, and reasoning challenges in prompting language models for cognitive science, with a focus on explainability and course structure incorporating transformer-based pre-training and retrieval augmented in-context learning.', 'chapters': [{'end': 1914.838, 'start': 1788.493, 'title': 'Impact of large models', 'summary': 'Discusses the impact of large models on generating text and highlights the potential benefits and drawbacks of using these models, shedding light on the mechanisms behind their text generation and the limitations of their knowledge.', 'duration': 126.345, 'highlights': ['Large models can generate text by prompting them, showcasing their power and ability to produce coherent and relevant content.', "The mechanisms behind the model's text generation involve learning from co-occurrence patterns in text, as demonstrated by examples of prompt input and output.", "The model's knowledge is a product of the data it was trained on, indicating that its generated content is a reflection of the aggregated data rather than possessing inherent wisdom or insights.", "The limitations of the model's knowledge are highlighted, emphasizing that its output is based on the training data and lacks genuine wisdom or insight beyond the encoded information within the text corpus."]}, {'end': 2322.605, 'start': 1915.398, 'title': 'Ai learning mechanisms', 'summary': 'Discusses the potential of in-context learning and reinforcement learning with human feedback in training ai models, highlighting the transformation from traditional supervision methods and the increasing role of human intelligence in driving model behavior.', 'duration': 407.207, 'highlights': ["In-context learning mechanism allows large models to answer questions based on context passage and demonstration, pushing the model to find answers in the context. In-context learning enables large models like GPT-3 to answer questions based on input context and demonstration, highlighting the shift towards extractive learning and the model's capability to follow the same behavior for the target question.", 'Reinforcement learning with human feedback involves human annotators providing inputs and ranking model outputs, indicating the significant role of human intelligence in driving the behavior of AI systems beyond self-supervision. Reinforcement learning with human feedback incorporates human intelligence in providing inputs and ranking model outputs, emphasizing the labor-intensive human capacity driving the critical behaviors of AI models.', 'The traditional supervision mode contrasts with in-context learning, where models can be trained for specific tasks by prompting them with examples, leading to a transformative shift in AI system design. In-context learning contrasts with traditional supervision mode, allowing models to be prompted with examples for training specific tasks, marking a transformative shift in AI system design.']}, {'end': 2963.177, 'start': 2323.547, 'title': 'Reasoning with language models', 'summary': 'Discusses the challenges in prompting language models to reason about negation and common sense reasoning, and the potential of using these models to understand cognitive science, with a focus on explainability and exploring various themes such as contextual representations, retrieval augmented learning, compositional generalization, benchmarking, and model introspection.', 'duration': 639.63, 'highlights': ["The model's capacity for reasoning about negation and common sense improved with step-by-step prompting, leading to correct answers and explanations, showcasing the potential of instruct-tuned language models. (Relevance Score: 5)", 'The discussion explores the potential of using language models to understand cognition and cognitive science, emphasizing the need for careful exploration due to the differences between the models and human cognition. (Relevance Score: 4)', 'The chapter introduces various thematic areas to be explored, including contextual representations, retrieval augmented learning, compositional generalization, benchmarking, model introspection, and methods and metrics, highlighting the breadth of topics to be covered. (Relevance Score: 3)', 'The challenges of studying the modular nature of these language models and the limitations posed by the closed-off nature of models are discussed, with the potential for advancements in dissecting and understanding the internal representations of models. (Relevance Score: 2)', 'The discussion poses intriguing questions about the potential emergence of modularity in language models and the exploration of non-modular solutions, offering insight into understanding human cognition through the capabilities and limitations of these models. (Relevance Score: 1)']}, {'end': 3511.386, 'start': 2963.257, 'title': 'Course structure & final project', 'summary': 'Outlines the course structure, including final projects, bake-offs, quizzes, and the core goal of producing research contributions, with a focus on transformer-based pre-training and retrieval augmented in-context learning.', 'duration': 548.129, 'highlights': ['The course structure includes final projects, bake-offs, quizzes, and mentorship from the teaching team to guide students through projects and assignments. The structure involves final projects, bake-offs, quizzes, and mentorship from the teaching team to guide students through projects and assignments.', 'The core goal is to produce research contributions, with a focus on transformer-based pre-training and retrieval augmented in-context learning. The core goal of the course is to produce research contributions, with a focus on transformer-based pre-training and retrieval augmented in-context learning.', 'The bake-offs involve informal competitions around data and modeling, with prizes for top performing systems and a leaderboard on Gradescope. The bake-offs involve informal competitions around data and modeling, with prizes for top performing systems and a leaderboard on Gradescope.', 'The final project components include a lit review phase, an experiment protocol, and a final paper, with mentorship from the teaching team and a requirement for quantitative evaluation. The final project components include a lit review phase, an experiment protocol, and a final paper, with mentorship from the teaching team and a requirement for quantitative evaluation.', 'The course presupposes CS224N or CS224S as prerequisites and aims to provide hands-on experience with a wide range of problems. The course presupposes CS224N or CS224S as prerequisites and aims to provide hands-on experience with a wide range of problems.']}], 'duration': 1722.893, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/K_Dh0Sxujuc/pics/K_Dh0Sxujuc1788493.jpg', 'highlights': ['Large models can generate coherent and relevant text by prompting them.', 'In-context learning enables large models to answer questions based on input context and demonstration.', 'Reinforcement learning with human feedback incorporates human intelligence in driving the critical behaviors of AI models.', "The model's capacity for reasoning about negation and common sense improved with step-by-step prompting.", 'The discussion explores the potential of using language models to understand cognition and cognitive science.', 'The course structure involves final projects, bake-offs, quizzes, and mentorship from the teaching team.']}, {'end': 3847.01, 'segs': [{'end': 3559.137, 'src': 'embed', 'start': 3535.595, 'weight': 0, 'content': [{'end': 3543.879, 'text': 'Just a knowledge store of documents, with the modern twist that now all of the documents are also represented by large language models.', 'start': 3535.595, 'duration': 8.284}, {'end': 3548.661, 'text': 'But fundamentally, this is an index of a sort that drives all web search right now.', 'start': 3544.299, 'duration': 4.362}, {'end': 3554.792, 'text': 'We can score documents with respect to queries on the basis of these numerical representations.', 'start': 3549.827, 'duration': 4.965}, {'end': 3559.137, 'text': 'And if we want to, we can reproduce the classic search experience.', 'start': 3555.273, 'duration': 3.864}], 'summary': 'Knowledge store of documents now represented by large language models for web search.', 'duration': 23.542, 'max_score': 3535.595, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/K_Dh0Sxujuc/pics/K_Dh0Sxujuc3535595.jpg'}, {'end': 3606.652, 'src': 'embed', 'start': 3581.526, 'weight': 3, 'content': [{'end': 3587.207, 'text': 'Although, notably, this answer is now decorated with links that would allow you, the user,', 'start': 3581.526, 'duration': 5.681}, {'end': 3591.889, 'text': 'to track back to what documents actually provided that evidence.', 'start': 3587.207, 'duration': 4.682}, {'end': 3598.571, 'text': "Whereas on the left, who knows where that information came from? And that's kind of what we were already grappling with.", 'start': 3592.449, 'duration': 6.122}, {'end': 3604.152, 'text': 'This is an important societal need because this is taking over web search.', 'start': 3600.831, 'duration': 3.321}, {'end': 3606.652, 'text': 'What are our goals for this kind of model here?', 'start': 3604.392, 'duration': 2.26}], 'summary': 'Addressing the need for transparent evidence links in web search.', 'duration': 25.126, 'max_score': 3581.526, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/K_Dh0Sxujuc/pics/K_Dh0Sxujuc3581526.jpg'}, {'end': 3715.288, 'src': 'embed', 'start': 3690.757, 'weight': 2, 'content': [{'end': 3696.52, 'text': 'The hope of the retrieval augmented approach is that we could get by with the smaller models.', 'start': 3690.757, 'duration': 5.763}, {'end': 3705.904, 'text': "And the reason we could do that is that we're going to factor out the knowledge store into that index and the language capability which is going to be the language model.", 'start': 3696.74, 'duration': 9.164}, {'end': 3711.626, 'text': "The only thing we're going to be asking the language model is to be good at that kind of in context learning.", 'start': 3705.964, 'duration': 5.662}, {'end': 3715.288, 'text': "It doesn't need to also store a full model of the world.", 'start': 3712.046, 'duration': 3.242}], 'summary': 'Retrieval augmented approach aims for smaller models by factoring out knowledge store into index and language model.', 'duration': 24.531, 'max_score': 3690.757, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/K_Dh0Sxujuc/pics/K_Dh0Sxujuc3690757.jpg'}, {'end': 3782.391, 'src': 'embed', 'start': 3757.297, 'weight': 1, 'content': [{'end': 3766.079, 'text': "That's because all of this information is interconnected and we don't at the present moment know how to reliably do that systematic editing.", 'start': 3757.297, 'duration': 8.782}, {'end': 3771.18, 'text': 'On the retrieval augmented approach, we just re-index our data.', 'start': 3768.139, 'duration': 3.041}, {'end': 3778.07, 'text': 'If the world changes, we assume that the knowledge store changed, like somebody updated a Wikipedia page.', 'start': 3772.828, 'duration': 5.242}, {'end': 3782.391, 'text': 'So we represent all the documents again, or at least just the ones that changed.', 'start': 3778.41, 'duration': 3.981}], 'summary': 'Interconnected information poses challenges for systematic editing and data re-indexing in response to changes.', 'duration': 25.094, 'max_score': 3757.297, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/K_Dh0Sxujuc/pics/K_Dh0Sxujuc3757297.jpg'}], 'start': 3512.067, 'title': 'Language models and challenges in deployment', 'summary': 'Discusses using large language models for web search, creating a knowledge store, and synthesizing answers. it also delves into challenges of deploying language models, focusing on efficiency, updatability, and provenance, highlighting benefits of the retrieval augmented approach.', 'chapters': [{'end': 3645.579, 'start': 3512.067, 'title': 'Language model for enhanced web search', 'summary': 'Discusses using large language models to encode queries and documents, creating a knowledge store of documents represented by language models, and synthesizing answers from retrieved documents while addressing societal needs and efficiency.', 'duration': 133.512, 'highlights': ['Large language models to encode queries and documents The process involves using large language models to encode queries and documents, facilitating the generation of numerical representations for scoring and search.', 'Creation of a knowledge store with documents represented by language models The establishment of a knowledge store with documents represented by language models enables the scoring of documents with respect to queries and synthesis of answers.', 'Synthesizing answers from retrieved documents to enhance search experience The approach involves synthesizing answers from retrieved documents, providing users with linked evidence and addressing societal needs in web search.', 'Addressing societal needs, efficiency, and provenance tracking in language models The discussion emphasizes the importance of societal needs in web search, efficiency, provenance tracking, and security in the context of language models.']}, {'end': 3847.01, 'start': 3645.579, 'title': 'Challenges in deploying language models', 'summary': 'Discusses the challenges of deploying language models, focusing on efficiency, updatability, and provenance, highlighting the benefits of the retrieval augmented approach in terms of model size, adaptability to changes, and reliability of information.', 'duration': 201.431, 'highlights': ['The retrieval augmented approach offers potential efficiency gains by using smaller models for language capability and factoring out the knowledge store into an index. The retrieval augmented approach can lead to smaller models by factoring out the knowledge store into an index, resulting in potential efficiency gains.', 'The retrieval augmented approach provides adaptability to changes by re-indexing data when the knowledge store is updated, ensuring that the retrieved results reflect the changes. The retrieval augmented approach allows for adaptability to changes by re-indexing data, ensuring that the retrieved results reflect the updates made to the underlying database.', 'Language models for everything approach face challenges in providing reliable information and suffer from outdated answers, whereas the retrieval augmented approach offers a more reliable propagation of changes. The language models for everything approach struggle with providing reliable information and suffer from outdated answers, while the retrieval augmented approach offers a more reliable propagation of changes.']}], 'duration': 334.943, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/K_Dh0Sxujuc/pics/K_Dh0Sxujuc3512067.jpg', 'highlights': ['Creation of a knowledge store with documents represented by language models enables the scoring of documents with respect to queries and synthesis of answers.', 'The retrieval augmented approach allows for adaptability to changes by re-indexing data, ensuring that the retrieved results reflect the updates made to the underlying database.', 'The retrieval augmented approach can lead to smaller models by factoring out the knowledge store into an index, resulting in potential efficiency gains.', 'The discussion emphasizes the importance of societal needs in web search, efficiency, provenance tracking, and security in the context of language models.', 'The approach involves synthesizing answers from retrieved documents, providing users with linked evidence and addressing societal needs in web search.']}, {'end': 4425.473, 'segs': [{'end': 3912.831, 'src': 'embed', 'start': 3870.762, 'weight': 3, 'content': [{'end': 3877.108, 'text': 'And then we just need to solve the interesting non-trivial question of how to link those documents into the synthesized answer.', 'start': 3870.762, 'duration': 6.346}, {'end': 3881.25, 'text': 'But all of the information we need is right there on the screen for us.', 'start': 3877.768, 'duration': 3.482}, {'end': 3886.954, 'text': 'And so this feels like a relatively tractable problem compared to what we are faced with on the left.', 'start': 3881.691, 'duration': 5.263}, {'end': 3897.701, 'text': "I will say, I've been just amazed at the rollout, especially of the Bing search engine, which now incorporates OpenAI models at some level.", 'start': 3887.555, 'duration': 10.146}, {'end': 3901.804, 'text': 'Because it is clear that it is doing web search right?', 'start': 3898.162, 'duration': 3.642}, {'end': 3907.548, 'text': "Because it's got information that comes from documents that only appeared on the web days before your query.", 'start': 3901.864, 'duration': 5.684}, {'end': 3912.831, 'text': "But what it's doing with that information seems completely chaotic to me.", 'start': 3908.769, 'duration': 4.062}], 'summary': 'Challenges in linking documents for synthesized answers; bing search engine integrates openai models for web search.', 'duration': 42.069, 'max_score': 3870.762, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/K_Dh0Sxujuc/pics/K_Dh0Sxujuc3870762.jpg'}, {'end': 4011.461, 'src': 'embed', 'start': 3987.084, 'weight': 0, 'content': [{'end': 3994.828, 'text': "Whereas for the retrieval augmented approach, again We're thinking about accessing information from an index,", 'start': 3987.084, 'duration': 7.744}, {'end': 3999.531, 'text': 'and access restrictions on an index is an old problem by now.', 'start': 3994.828, 'duration': 4.703}, {'end': 4005.316, 'text': "Again, I don't want to say solved, but something that a lot of people have tackled for decades now.", 'start': 3999.992, 'duration': 5.324}, {'end': 4011.461, 'text': 'And so we can offer something like guarantees just from the fact that we have a separated knowledge store.', 'start': 4005.796, 'duration': 5.665}], 'summary': 'Retrieval augmented approach offers guarantees due to separated knowledge store.', 'duration': 24.377, 'max_score': 3987.084, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/K_Dh0Sxujuc/pics/K_Dh0Sxujuc3987084.jpg'}, {'end': 4079.685, 'src': 'embed', 'start': 4058.013, 'weight': 2, 'content': [{'end': 4068.043, 'text': 'the way you would do even the retrieval augmented thing would be that you would have your index and then you might train a custom purpose model to do the question answering part,', 'start': 4058.013, 'duration': 10.03}, {'end': 4073.709, 'text': 'and it could extract things from the text that you produced or maybe even generate some new things from the text that you produced.', 'start': 4068.043, 'duration': 5.666}, {'end': 4079.685, 'text': "And that's kind of the mode that I mentioned before, where you'd have, like some language models, maybe a few of them,", 'start': 4074.901, 'duration': 4.784}], 'summary': 'Custom models can retrieve and generate text from an index, enhancing question answering capabilities.', 'duration': 21.672, 'max_score': 4058.013, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/K_Dh0Sxujuc/pics/K_Dh0Sxujuc4058013.jpg'}, {'end': 4185.089, 'src': 'embed', 'start': 4156.505, 'weight': 1, 'content': [{'end': 4164.268, 'text': "And that's kind of pushing us into a new mode, for even thinking about how we design AI systems, where it's not so much about fine tuning,", 'start': 4156.505, 'duration': 7.763}, {'end': 4171.09, 'text': "it's much more about getting them to communicate with each other effectively, to design a system from frozen components.", 'start': 4164.268, 'duration': 6.822}, {'end': 4178.712, 'text': 'Again, unanticipated, at least by me, as of a few years ago, and now an exciting new direction.', 'start': 4172.47, 'duration': 6.242}, {'end': 4185.089, 'text': "So, just to wrap out, I think, what I'll do, since we're near the end of the class here,", 'start': 4180.987, 'duration': 4.102}], 'summary': 'New approach to designing ai systems emphasizes effective communication and frozen components, marking an exciting new direction.', 'duration': 28.584, 'max_score': 4156.505, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/K_Dh0Sxujuc/pics/K_Dh0Sxujuc4156505.jpg'}], 'start': 3847.01, 'title': 'Web search and ai models', 'summary': 'Discusses challenges in web search and information retrieval, emphasizing the integration of openai models in bing search engine and the advantages of retrieval augmented models over language models for addressing privacy challenges, access restrictions, and urgent deployment.', 'chapters': [{'end': 3938.032, 'start': 3847.01, 'title': 'Challenges in web search and information retrieval', 'summary': 'Discusses the challenges in web search and information retrieval, highlighting the incorporation of openai models in the bing search engine and the unpredictable combination of grounded and fabricated information.', 'duration': 91.022, 'highlights': ['The Bing search engine now incorporates OpenAI models at some level, but the information retrieval seems completely chaotic, resulting in an unpredictable combination of grounded and fabricated information.', 'The chapter emphasizes the challenges in web search and information retrieval, noting the difficulty in linking documents into a synthesized answer and the chaotic nature of the information retrieval process.']}, {'end': 4425.473, 'start': 3938.032, 'title': 'Retrieval augmented models in ai', 'summary': 'Discusses the benefits of using retrieval augmented models over language models for addressing privacy challenges, access restrictions, and urgent deployment in web search, highlighting the shift from fine-tuning to communication-based ai system design.', 'duration': 487.441, 'highlights': ['The urgent deployment of retrieval augmented models in web search necessitates addressing privacy challenges and access restrictions, offering a path to solving them. Retrieval augmented models offer a path to solving privacy challenges and access restrictions, addressing the urgent deployment in web search.', 'The shift from fine-tuning to communication-based AI system design is emphasized, enabling frozen components like retriever and large language models to communicate effectively, opening doors to new capabilities. The shift from fine-tuning to communication-based AI system design enables frozen components to communicate effectively, opening doors to new capabilities.', 'The use of retrieval augmented models and the Demonstrate Search Predict programming model demonstrates the powerful potential of communication-based AI system design. The use of retrieval augmented models and the Demonstrate Search Predict programming model demonstrates the powerful potential of communication-based AI system design.']}], 'duration': 578.463, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/K_Dh0Sxujuc/pics/K_Dh0Sxujuc3847010.jpg', 'highlights': ['The urgent deployment of retrieval augmented models in web search necessitates addressing privacy challenges and access restrictions, offering a path to solving them.', 'The shift from fine-tuning to communication-based AI system design enables frozen components to communicate effectively, opening doors to new capabilities.', 'The use of retrieval augmented models and the Demonstrate Search Predict programming model demonstrates the powerful potential of communication-based AI system design.', 'The Bing search engine now incorporates OpenAI models at some level, but the information retrieval seems completely chaotic, resulting in an unpredictable combination of grounded and fabricated information.', 'The chapter emphasizes the challenges in web search and information retrieval, noting the difficulty in linking documents into a synthesized answer and the chaotic nature of the information retrieval process.']}], 'highlights': ['AI systems surpass human performance in benchmarks like SQuAD, GLUE, and over 100 tasks in less than 10 years.', 'The emergence of powerful language models like DALI 2 and their impact on web search and natural language understanding.', 'Large-scale pre-training led to models with billions of parameters, revolutionizing NLP.', 'Open AI models providing fabricated evidence is identified as an increasing societal problem.', 'Large models can generate coherent and relevant text by prompting them.', 'The urgent deployment of retrieval augmented models in web search necessitates addressing privacy challenges and access restrictions, offering a path to solving them.']}