title
Andrew Ng and Chris Manning Discuss Natural Language Processing

description
Recently, Andrew Ng sat down with Professor Christopher Manning to chat about his journey from studying linguistics to becoming a leading NLP computer scientist. They discuss the past, present, and potential future of computational linguistics. If you would like to get started in AI, the new Coursera Machine Learning Specialization developed by Stanford Online and DeepLearning.AI is the perfect place to start. Learn more 👉 https://stanford.io/3HEj56q

detail
{'title': 'Andrew Ng and Chris Manning Discuss Natural Language Processing', 'heatmap': [], 'summary': "Andrew ng and chris manning discuss the transition from linguistics to nlp research, nlp evolution impacting search engines and e-commerce, deep learning's rise in nlp, language prediction models incorporating world knowledge, and the optimistic outlook on developing advanced nlp models.", 'chapters': [{'end': 216.452, 'segs': [{'end': 41.221, 'src': 'embed', 'start': 7.677, 'weight': 0, 'content': [{'end': 12.28, 'text': "Hi, I'm delighted to be here with my old friend and collaborator, Professor Chris Manning.", 'start': 7.677, 'duration': 4.603}, {'end': 15.922, 'text': 'Chris has a very long and impressive bio, but just briefly.', 'start': 12.84, 'duration': 3.082}, {'end': 21.365, 'text': 'he is professor of computer science at Stanford University and also the director of the Stanford AI Lab.', 'start': 15.922, 'duration': 5.443}, {'end': 28.449, 'text': 'And he also has a distinction of being the most highly cited researcher in NLP, or natural language processing.', 'start': 21.905, 'duration': 6.544}, {'end': 30.79, 'text': 'So really good to be here with you, Chris.', 'start': 28.869, 'duration': 1.921}, {'end': 32.391, 'text': 'Good to get a chance to chat, Andrew.', 'start': 31.03, 'duration': 1.361}, {'end': 41.221, 'text': "So we've known each other, collaborated for many years, and one interesting part of your background I always thought was that,", 'start': 33.658, 'duration': 7.563}], 'summary': 'Prof. chris manning, director of stanford ai lab, is highly cited in nlp.', 'duration': 33.544, 'max_score': 7.677, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6w0Po83ZmjA/pics/6w0Po83ZmjA7677.jpg'}, {'end': 78.18, 'src': 'embed', 'start': 48.065, 'weight': 2, 'content': [{'end': 55.028, 'text': 'Your PhD, if I remember correctly, was in linguistics, and you were studying the syntax of language.', 'start': 48.065, 'duration': 6.963}, {'end': 60.813, 'text': 'So how did you go from studying syntax to being an NLP researcher? Oh, sorry.', 'start': 55.568, 'duration': 5.245}, {'end': 62.394, 'text': 'I can certainly tell you about that.', 'start': 60.853, 'duration': 1.541}, {'end': 67.036, 'text': "But I should also point out that I'm still actually a professor of linguistics as well.", 'start': 62.414, 'duration': 4.622}, {'end': 68.837, 'text': 'I have a joint appointment at Stanford.', 'start': 67.076, 'duration': 1.761}, {'end': 78.18, 'text': 'And once in a blue moon, not very often, I do actually still teach some real linguistics as well as computer-involved natural language processing.', 'start': 69.897, 'duration': 8.283}], 'summary': 'Phd in linguistics, now a professor at stanford, also involved in nlp research', 'duration': 30.115, 'max_score': 48.065, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6w0Po83ZmjA/pics/6w0Po83ZmjA48065.jpg'}, {'end': 126.031, 'src': 'embed', 'start': 100.322, 'weight': 4, 'content': [{'end': 108.873, 'text': 'equally led me to think about ideas that we now very much think about as machine learning or computational ideas.', 'start': 100.322, 'duration': 8.551}, {'end': 118.805, 'text': 'So two of the central ideas in human language how do little children acquire human language and adults?', 'start': 108.953, 'duration': 9.852}, {'end': 122.828, 'text': "well, we're just talking to each other now and we pretty much understand each other,", 'start': 118.805, 'duration': 4.023}, {'end': 126.031, 'text': "and you know that's actually an amazing thing how we managed to do that.", 'start': 122.828, 'duration': 3.203}], 'summary': 'Exploring machine learning and human language acquisition.', 'duration': 25.709, 'max_score': 100.322, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6w0Po83ZmjA/pics/6w0Po83ZmjA100322.jpg'}, {'end': 178.749, 'src': 'embed', 'start': 148.262, 'weight': 3, 'content': [{'end': 150.343, 'text': 'We would have learned a totally different language.', 'start': 148.262, 'duration': 2.081}, {'end': 155.807, 'text': "So it's amazing to think how humans do that, and now maybe machines learn language too.", 'start': 150.403, 'duration': 5.404}, {'end': 160.349, 'text': 'So just tell us more about your journey.', 'start': 156.407, 'duration': 3.942}, {'end': 167.357, 'text': "So you had a Ph.D. in linguistics, and then how did you enter? Yeah, so there's some stuff before that as well.", 'start': 160.409, 'duration': 6.948}, {'end': 173.644, 'text': 'So, I mean, you know, when I was an undergrad, well, officially I actually did three majors.', 'start': 167.757, 'duration': 5.887}, {'end': 174.645, 'text': 'This was in Australia.', 'start': 173.684, 'duration': 0.961}, {'end': 178.749, 'text': 'One in math, one in computer science, and one in linguistics.', 'start': 174.785, 'duration': 3.964}], 'summary': 'Speaker pursued three majors in math, computer science, and linguistics in australia.', 'duration': 30.487, 'max_score': 148.262, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6w0Po83ZmjA/pics/6w0Po83ZmjA148262.jpg'}], 'start': 7.677, 'title': 'Professor chris manning and nlp research', 'summary': "Introduces professor chris manning, director of the stanford ai lab and highly cited nlp researcher, and discusses the speaker's transition from linguistics to nlp research driven by interest in human languages and acquisition, machine learning, and computational ideas.", 'chapters': [{'end': 48.004, 'start': 7.677, 'title': 'Conversation with professor chris manning', 'summary': 'Introduces professor chris manning, a distinguished researcher in machine learning and nlp, who is the director of the stanford ai lab and the most highly cited researcher in nlp.', 'duration': 40.327, 'highlights': ['Professor Chris Manning is the most highly cited researcher in NLP, or natural language processing.', 'Chris Manning is a professor of computer science at Stanford University and the director of the Stanford AI Lab.', 'Chris Manning is a distinguished researcher in machine learning and NLP.']}, {'end': 216.452, 'start': 48.065, 'title': 'From linguistics to nlp research', 'summary': "Discusses the speaker's transition from studying syntax in linguistics to becoming an nlp researcher, rooted in their interest in human languages and acquisition, leading them to delve into machine learning and computational ideas.", 'duration': 168.387, 'highlights': ["The speaker's interest in human languages and acquisition led them to consider machine learning and computational ideas, as they pondered on how people understand and acquire languages, and the processing involved in human language comprehension.", "The speaker's journey from linguistics to NLP research was influenced by their early interest in human languages and acquisition, leading them to explore machine learning even before grad school.", 'The speaker pursued three majors in math, computer science, and linguistics during their undergraduate studies, reflecting their diverse academic background and multidisciplinary interests.', "The speaker's academic background includes a Ph.D. in linguistics and a joint appointment at Stanford as a professor of linguistics, showcasing their expertise in the field.", "The speaker's academic journey involved a diverse range of studies, including math, computer science, and linguistics, demonstrating their multidisciplinary approach and expertise across various domains."]}], 'duration': 208.775, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6w0Po83ZmjA/pics/6w0Po83ZmjA7677.jpg', 'highlights': ['Professor Chris Manning is the most highly cited researcher in NLP, or natural language processing.', 'Chris Manning is a professor of computer science at Stanford University and the director of the Stanford AI Lab.', "The speaker's academic background includes a Ph.D. in linguistics and a joint appointment at Stanford as a professor of linguistics, showcasing their expertise in the field.", "The speaker's academic journey involved a diverse range of studies, including math, computer science, and linguistics, demonstrating their multidisciplinary approach and expertise across various domains.", "The speaker's interest in human languages and acquisition led them to consider machine learning and computational ideas, as they pondered on how people understand and acquire languages, and the processing involved in human language comprehension."]}, {'end': 696.449, 'segs': [{'end': 341.223, 'src': 'embed', 'start': 314.028, 'weight': 1, 'content': [{'end': 320.712, 'text': 'It sounds like your career was initially more linguistics and, with the rise of data and machine learning and empirical methods,', 'start': 314.028, 'duration': 6.684}, {'end': 325.034, 'text': 'it shifted toward NLP and machine learning and NLP.', 'start': 320.712, 'duration': 4.322}, {'end': 327.736, 'text': 'Yeah, I mean it absolutely certainly shifted,', 'start': 325.414, 'duration': 2.322}, {'end': 335.14, 'text': "and I've certainly sort of shifted much more to doing both natural language processing and machine learning models.", 'start': 327.736, 'duration': 7.404}, {'end': 341.223, 'text': "You know, to some extent, the balance has varied, but I've sort of been with that as a while.", 'start': 335.66, 'duration': 5.563}], 'summary': "The speaker's career shifted from linguistics to focus on nlp and machine learning, with a balance between the two.", 'duration': 27.195, 'max_score': 314.028, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6w0Po83ZmjA/pics/6w0Po83ZmjA314028.jpg'}, {'end': 569.064, 'src': 'embed', 'start': 545.436, 'weight': 2, 'content': [{'end': 556.039, 'text': "even when we're going to an online shopping website or a movie website and typing in what we want and doing a search on a much smaller website than the big search engines.", 'start': 545.436, 'duration': 10.603}, {'end': 562.961, 'text': "that also increasingly uses sophisticated NLP algorithms and it's also creating quite a lot of value.", 'start': 556.039, 'duration': 6.922}, {'end': 567.382, 'text': "Maybe to you it's not the real NLP, but it still seems very valuable.", 'start': 563.181, 'duration': 4.201}, {'end': 568.022, 'text': 'I agree.', 'start': 567.782, 'duration': 0.24}, {'end': 569.064, 'text': "It's very valuable.", 'start': 568.083, 'duration': 0.981}], 'summary': 'Sophisticated nlp algorithms on smaller websites create value.', 'duration': 23.628, 'max_score': 545.436, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6w0Po83ZmjA/pics/6w0Po83ZmjA545436.jpg'}, {'end': 615.934, 'src': 'embed', 'start': 591.087, 'weight': 0, 'content': [{'end': 600.23, 'text': 'NLP has gone through a major shift from all of the rule-based techniques that you alluded to just now to using really machine learning much more pervasively.', 'start': 591.087, 'duration': 9.143}, {'end': 610.894, 'text': 'And so you were one of the people leading parts of that charge and seeing every step of the way of creating some of the steps as it happened.', 'start': 601.651, 'duration': 9.243}, {'end': 615.934, 'text': 'Can you say a bit about that process and what you saw? Sure, absolutely.', 'start': 611.034, 'duration': 4.9}], 'summary': 'Nlp has shifted from rule-based techniques to pervasive use of machine learning, leading to significant advancements.', 'duration': 24.847, 'max_score': 591.087, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6w0Po83ZmjA/pics/6w0Po83ZmjA591087.jpg'}], 'start': 217.073, 'title': 'Nlp evolution & impact', 'summary': "Discusses the speaker's career evolution from linguistics to nlp & ml, emphasizing the shift driven by empirical methods and digital language material. it also outlines nlp evolution from keyword matching to advanced algorithms, impacting search engines and e-commerce.", 'chapters': [{'end': 498.168, 'start': 217.073, 'title': 'Nlp & machine learning evolution', 'summary': "Discusses the evolution of the speaker's career from linguistics to natural language processing and machine learning, emphasizing the shift driven by the rise of empirical methods and the abundance of digital human language material in the early 90s.", 'duration': 281.095, 'highlights': ["The shift in the speaker's career from linguistics to NLP and machine learning was driven by the rise of data and empirical methods, as well as the availability of digital human language material in the early 90s. The speaker's career initially revolved around linguistics but shifted towards natural language processing (NLP) and machine learning due to the increasing availability of digital human language material and the rise of empirical methods.", 'The early 90s marked the beginning of a significant change in natural language processing, with the availability of millions of words of human language material digitally, paving the way for exciting new possibilities. In the early 90s, there was a notable shift in natural language processing as there began to be an abundance of digital human language material, such as legal materials, newspaper articles, and parliamentary handsaws, providing access to millions of words of human language.', 'NLP encompasses tasks like machine translation, question answering, generating advertising copy, and summarization, playing a crucial role in various applications, including web search. Natural language processing (NLP) includes various applications such as machine translation, question answering, generating advertising copy, summarization, and web search, making it an integral part of many daily activities.']}, {'end': 696.449, 'start': 498.188, 'title': 'Evolution of nlp and its impact', 'summary': 'Outlines the evolution of natural language processing from traditional keyword matching to sophisticated nlp algorithms, its impact on search engines, e-commerce websites, and the transition from rule-based techniques to pervasive machine learning.', 'duration': 198.261, 'highlights': ['The shift in NLP from rule-based techniques to pervasive machine learning NLP has transitioned from rule-based techniques to using machine learning more pervasively over the last couple of decades, as mentioned by the interviewee.', 'Impact of NLP on search engines and e-commerce websites NLP algorithms have significantly impacted search engines and e-commerce websites, enabling sophisticated language understanding and matching goods to user descriptions, presenting considerable value.', 'Challenges in e-commerce website search E-commerce websites face difficult challenges in matching user descriptions to available products, highlighting the complexity of NLP applications in this domain.']}], 'duration': 479.376, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6w0Po83ZmjA/pics/6w0Po83ZmjA217073.jpg', 'highlights': ['NLP evolution from rule-based techniques to pervasive machine learning over the last couple of decades.', "The shift in the speaker's career from linguistics to NLP and machine learning was driven by the rise of data and empirical methods.", "NLP's impact on search engines and e-commerce websites, enabling sophisticated language understanding and matching goods to user descriptions."]}, {'end': 1169.797, 'segs': [{'end': 724.785, 'src': 'embed', 'start': 696.829, 'weight': 4, 'content': [{'end': 707.856, 'text': 'And so it was only when lots of digital text and speech started to become available that it really seemed like there was this different way that instead,', 'start': 696.829, 'duration': 11.027}, {'end': 715.12, 'text': 'we could start calculating statistics over human language material and building machine learning models.', 'start': 707.856, 'duration': 7.264}, {'end': 718.582, 'text': 'And so that was the first thing that I got into.', 'start': 715.5, 'duration': 3.082}, {'end': 724.785, 'text': 'in the sort of mid to late 1990s.', 'start': 720.743, 'duration': 4.042}], 'summary': 'In the mid to late 1990s, the speaker began working on calculating statistics over human language material and building machine learning models.', 'duration': 27.956, 'max_score': 696.829, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6w0Po83ZmjA/pics/6w0Po83ZmjA696829.jpg'}, {'end': 777.481, 'src': 'embed', 'start': 751.6, 'weight': 3, 'content': [{'end': 760.989, 'text': "And that's roughly when the new interest in deep learning using large artificial neural networks started to take off.", 'start': 751.6, 'duration': 9.389}, {'end': 762.67, 'text': 'For my interest in that.', 'start': 761.609, 'duration': 1.061}, {'end': 771.416, 'text': 'I really have you to thank, Andrew, because at this stage Andrew is still full time at Stanford and he was in the office next door to me.', 'start': 762.67, 'duration': 8.746}, {'end': 777.481, 'text': 'And he was really excited about the new things that were happening in the area of deep learning.', 'start': 771.637, 'duration': 5.844}], 'summary': "Interest in deep learning with large neural networks surged, thanks to andrew's excitement at stanford.", 'duration': 25.881, 'max_score': 751.6, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6w0Po83ZmjA/pics/6w0Po83ZmjA751600.jpg'}, {'end': 904.922, 'src': 'embed', 'start': 875.724, 'weight': 2, 'content': [{'end': 884.772, 'text': 'But I sort of feel like the neural network period, which started effectively about 2010, itself divides in two.', 'start': 875.724, 'duration': 9.048}, {'end': 896.039, 'text': "Because for the first period, let's basically say it's till 2018, We showed a lot of success at building neural networks for all sorts of tasks.", 'start': 884.792, 'duration': 11.247}, {'end': 904.922, 'text': 'We built them for syntactic parsing and sentiment analysis and what else did, question answering.', 'start': 896.159, 'duration': 8.763}], 'summary': 'Neural network period divides in two: 2010-2018, showed success in building networks for various tasks.', 'duration': 29.198, 'max_score': 875.724, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6w0Po83ZmjA/pics/6w0Po83ZmjA875724.jpg'}, {'end': 953.988, 'src': 'embed', 'start': 927.29, 'weight': 0, 'content': [{'end': 938.935, 'text': 'So I think, in looking back now, in some sense the bigger change came around 2018, because that was when the idea of well,', 'start': 927.29, 'duration': 11.645}, {'end': 947.702, 'text': 'we could just start with a large amount of human language material and build large self-supervised models.', 'start': 938.935, 'duration': 8.767}, {'end': 953.988, 'text': 'So that was models then like BERT and GPT and successor models to that.', 'start': 947.762, 'duration': 6.226}], 'summary': 'In 2018, the shift towards large self-supervised models like bert and gpt began.', 'duration': 26.698, 'max_score': 927.29, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6w0Po83ZmjA/pics/6w0Po83ZmjA927290.jpg'}, {'end': 1169.797, 'src': 'embed', 'start': 1140.688, 'weight': 1, 'content': [{'end': 1146.921, 'text': 'And then 2018, maybe this other infection point, what happened after that? Yeah.', 'start': 1140.688, 'duration': 6.233}, {'end': 1155.707, 'text': 'So, I mean, in 2018, that was the point in which, well, sort of really two things happened.', 'start': 1147.321, 'duration': 8.386}, {'end': 1166.935, 'text': 'One thing is that people, well, really in 2017, had developed this new neural architecture, which was much more scalable onto modern parallel GPUs.', 'start': 1156.167, 'duration': 10.768}, {'end': 1169.797, 'text': 'And so that was the transformer architecture.', 'start': 1166.975, 'duration': 2.822}], 'summary': 'In 2018, a new neural architecture, the transformer, was developed to be more scalable on modern gpus.', 'duration': 29.109, 'max_score': 1140.688, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6w0Po83ZmjA/pics/6w0Po83ZmjA1140688.jpg'}], 'start': 696.829, 'title': 'Evolution of nlp and deep learning', 'summary': 'Discusses the evolution of natural language processing, from statistical approaches in the 1990s to the emergence of deep learning around 2010, and the rise of neural network ideas from 2010, the shift in approach around 2018 with the development of large self-supervised models like bert and gpt, and the impact of self-supervised learning in acquiring knowledge of human language.', 'chapters': [{'end': 824.434, 'start': 696.829, 'title': 'Evolution of natural language processing', 'summary': 'Discusses the evolution of natural language processing, starting from statistical approaches in the 1990s to the emergence of deep learning around 2010, influenced by the growing availability of digital text and speech data.', 'duration': 127.605, 'highlights': ['The emergence of deep learning was influenced by the growing availability of digital text and speech data, leading to a shift from statistical approaches to probabilistic approaches in artificial intelligence and machine learning. Emergence of deep learning, influence of digital text and speech data, shift from statistical to probabilistic approaches', "Andrew's excitement about deep learning and neural networks played a crucial role in sparking early interest in these areas, impacting the speaker's involvement in exploring neural networks. Andrew's influence on deep learning interest, impact on the speaker's involvement in neural networks", "The speaker's early exposure to neural networks during the Stanford days, including taking Dave Rumelhart's class, laid the foundation for his eventual involvement in neural network research. Early exposure to neural networks at Stanford, influence on future research involvement"]}, {'end': 1169.797, 'start': 824.474, 'title': 'Evolution of deep learning in nlp', 'summary': 'Discusses the evolution of deep learning and nlp, highlighting the rise of neural network ideas from 2010, the shift in approach around 2018 with the development of large self-supervised models like bert and gpt, and the impact of self-supervised learning in acquiring knowledge of human language.', 'duration': 345.323, 'highlights': ['The development of large self-supervised models like BERT and GPT around 2018 marked a significant shift in the approach to NLP, allowing models to acquire extensive knowledge of human languages from word prediction over massive amounts of text. Development of large self-supervised models such as BERT and GPT, acquiring extensive knowledge of human languages, impact of word prediction over massive amounts of text', 'The rise of neural network ideas, particularly the period from around 2010 to 2018, demonstrated significant success in building neural networks for various tasks such as syntactic parsing, sentiment analysis, and question answering. Success in building neural networks for tasks like syntactic parsing, sentiment analysis, and question answering', 'The development of the transformer architecture in 2017 allowed for a more scalable neural architecture onto modern parallel GPUs, marking a pivotal moment in the evolution of NLP and deep learning. Development of the transformer architecture, scalability onto modern parallel GPUs']}], 'duration': 472.968, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6w0Po83ZmjA/pics/6w0Po83ZmjA696829.jpg', 'highlights': ['The development of large self-supervised models like BERT and GPT around 2018 marked a significant shift in the approach to NLP, allowing models to acquire extensive knowledge of human languages from word prediction over massive amounts of text.', 'The development of the transformer architecture in 2017 allowed for a more scalable neural architecture onto modern parallel GPUs, marking a pivotal moment in the evolution of NLP and deep learning.', 'The rise of neural network ideas, particularly the period from around 2010 to 2018, demonstrated significant success in building neural networks for various tasks such as syntactic parsing, sentiment analysis, and question answering.', "Andrew's excitement about deep learning and neural networks played a crucial role in sparking early interest in these areas, impacting the speaker's involvement in exploring neural networks.", 'The emergence of deep learning was influenced by the growing availability of digital text and speech data, leading to a shift from statistical approaches to probabilistic approaches in artificial intelligence and machine learning.']}, {'end': 1716.868, 'segs': [{'end': 1301.971, 'src': 'embed', 'start': 1275.149, 'weight': 0, 'content': [{'end': 1279.734, 'text': "And therefore this turns into what's sometimes referred to as an AI complete task, right?", 'start': 1275.149, 'duration': 4.585}, {'end': 1281.275, 'text': 'That you really need.', 'start': 1279.794, 'duration': 1.481}, {'end': 1286.881, 'text': "there's nothing that can't actually be useful in answering this.", 'start': 1281.275, 'duration': 5.606}, {'end': 1288.162, 'text': 'what word comes next?', 'start': 1286.881, 'duration': 1.281}, {'end': 1288.863, 'text': 'sense, right?', 'start': 1288.162, 'duration': 0.701}, {'end': 1299.353, 'text': 'You can be in the World Cup semifinals, the teams are, and you need to know something about soccer to be giving the right answer.', 'start': 1288.923, 'duration': 10.43}, {'end': 1300.69, 'text': 'AI complete?', 'start': 1300.029, 'duration': 0.661}, {'end': 1301.971, 'text': 'is this funny concept right?', 'start': 1300.69, 'duration': 1.281}], 'summary': 'Discussion on ai complete tasks and their practical relevance in real-world scenarios.', 'duration': 26.822, 'max_score': 1275.149, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6w0Po83ZmjA/pics/6w0Po83ZmjA1275149.jpg'}, {'end': 1411.304, 'src': 'embed', 'start': 1388.592, 'weight': 3, 'content': [{'end': 1396.576, 'text': 'But actually, just about all of this stuff we think about, we talk about, we write about it in language.', 'start': 1388.592, 'duration': 7.984}, {'end': 1400.798, 'text': 'We can describe the positions of things relative to each other in language.', 'start': 1396.936, 'duration': 3.862}, {'end': 1406.721, 'text': 'So a surprising amount of other parts of the world are seen in reflection in language.', 'start': 1401.139, 'duration': 5.582}, {'end': 1411.304, 'text': "And therefore, you're learning about all of them too when you learn about language use.", 'start': 1407.062, 'duration': 4.242}], 'summary': 'Language is crucial for describing and understanding the world, reflecting a surprising amount of its aspects.', 'duration': 22.712, 'max_score': 1388.592, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6w0Po83ZmjA/pics/6w0Po83ZmjA1388592.jpg'}, {'end': 1477.103, 'src': 'embed', 'start': 1434.572, 'weight': 1, 'content': [{'end': 1441.856, 'text': 'And so with this trend in NLP, the large language models has been very exciting for the last several years.', 'start': 1434.572, 'duration': 7.284}, {'end': 1446.444, 'text': 'where? what are your thoughts on where all this will go?', 'start': 1442.862, 'duration': 3.582}, {'end': 1453.789, 'text': "well, i mean yeah, so it's just been amazingly successful and exciting, right that?", 'start': 1446.444, 'duration': 7.345}, {'end': 1457.091, 'text': "so we haven't really explained all the details, right?", 'start': 1453.789, 'duration': 3.302}, {'end': 1460.553, 'text': "so there's a first stage of learning these large language models.", 'start': 1457.091, 'duration': 3.462}, {'end': 1469.318, 'text': 'um, where the task is just to predict the next word, and you do that billions of times over a very large piece of text.', 'start': 1460.553, 'duration': 8.765}, {'end': 1477.103, 'text': 'and behold, you get this large neural network which is just a really useful artifact for all sorts of natural language processing tasks.', 'start': 1469.318, 'duration': 7.785}], 'summary': 'Exciting trend in nlp with large language models, successful and useful for natural language processing tasks.', 'duration': 42.531, 'max_score': 1434.572, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6w0Po83ZmjA/pics/6w0Po83ZmjA1434572.jpg'}, {'end': 1540.71, 'src': 'embed', 'start': 1515.998, 'weight': 4, 'content': [{'end': 1521.659, 'text': 'because it meant that the model had enormous knowledge of language and it could generalize very quickly.', 'start': 1515.998, 'duration': 5.661}, {'end': 1530.423, 'text': 'So, unlike the sort of the standard old days of of supervised learning, where it was kind of well.', 'start': 1521.779, 'duration': 8.644}, {'end': 1536.427, 'text': 'if you give me 10, 000 labeled examples, I might be able to produce a halfway decent model for you.', 'start': 1530.423, 'duration': 6.004}, {'end': 1540.71, 'text': "But if you give me 50, 000 labeled examples, it'll be a lot better.", 'start': 1536.688, 'duration': 4.022}], 'summary': "Model's language knowledge allows quick generalization, improving with more labeled examples.", 'duration': 24.712, 'max_score': 1515.998, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6w0Po83ZmjA/pics/6w0Po83ZmjA1515998.jpg'}, {'end': 1692.802, 'src': 'embed', 'start': 1663.73, 'weight': 2, 'content': [{'end': 1668.438, 'text': 'Just tell the NLP system, the large language model what you want and it seems to magically do it.', 'start': 1663.73, 'duration': 4.708}, {'end': 1676.329, 'text': "I'm curious do you think prompt engineering is the path of the future where, when I write these prompts,", 'start': 1669.159, 'duration': 7.17}, {'end': 1680.212, 'text': "I sometimes find it works miraculously and sometimes it's frustrating?", 'start': 1676.329, 'duration': 3.883}, {'end': 1686.637, 'text': 'The process of rewording my instructions to tweak the wording to get it just right to generate the result I want.', 'start': 1680.733, 'duration': 5.904}, {'end': 1692.802, 'text': "So do you think problem engineering is the wave of the future or do you think there's an intermediate hack?", 'start': 1686.838, 'duration': 5.964}], 'summary': 'Prompt engineering: the future of nlp? aims for precise result.', 'duration': 29.072, 'max_score': 1663.73, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6w0Po83ZmjA/pics/6w0Po83ZmjA1663730.jpg'}], 'start': 1170.898, 'title': 'Language models and nlp trends', 'summary': 'Discusses how language prediction models incorporate world knowledge, making it an ai-complete task, and examines the impact of large language models in nlp, highlighting the potential for minimal supervised data and prompt engineering to improve results.', 'chapters': [{'end': 1432.771, 'start': 1170.898, 'title': 'Language understanding and world knowledge', 'summary': 'Discusses how language prediction models learn not only the structure and meaning of sentences but also facts about the world, making it an ai-complete task, and explores the universality of language in reflecting other aspects of the world.', 'duration': 261.873, 'highlights': ['Language prediction models learn to understand the structure and meaning of sentences and know facts about the world to predict the next word, making it an AI-complete task. The models not only predict the next word based on preceding words but also need to understand the entire sentence and have world knowledge to give accurate predictions.', 'Language reflects a surprising amount of other parts of the world, and learning about language use involves learning about various aspects of the world. Language is a reflection of various aspects of the world, and learning about language involves understanding different facets, even if some real-world activities cannot be perfectly described in language.', 'Predicting the next word involves understanding the whole sentence and knowing facts about the world, which challenges the concept of AI completeness and the universality of language. The task of predicting the next word requires understanding the entire sentence and having world knowledge, challenging the concept of AI completeness and highlighting the universal nature of language in reflecting other aspects of the world.']}, {'end': 1716.868, 'start': 1434.572, 'title': 'Trends in nlp and large language models', 'summary': 'Discusses the rise of large language models in nlp, their impact on natural language processing tasks, and the potential for minimal supervised data and prompt engineering to achieve better results, shaping the future of nlp.', 'duration': 282.296, 'highlights': ['The rise and success of large language models in NLP, which has revolutionized natural language processing tasks.', 'The impact of large language models in reducing the need for extensive supervised data, as they can achieve great results with minimal labeled examples.', 'The potential of prompt engineering in shaping the future of NLP, with the ability to instruct the large language model to perform specific tasks, indicating a shift in the NLP paradigm.']}], 'duration': 545.97, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6w0Po83ZmjA/pics/6w0Po83ZmjA1170898.jpg', 'highlights': ['Language prediction models need to understand the entire sentence and have world knowledge to give accurate predictions, making it an AI-complete task.', 'The rise and success of large language models in NLP has revolutionized natural language processing tasks.', 'The potential of prompt engineering in shaping the future of NLP, indicating a shift in the NLP paradigm.', 'Language reflects a surprising amount of other parts of the world, and learning about language involves understanding different facets.', 'The impact of large language models in reducing the need for extensive supervised data, achieving great results with minimal labeled examples.', 'Predicting the next word challenges the concept of AI completeness and highlights the universal nature of language in reflecting other aspects of the world.']}, {'end': 2156.039, 'segs': [{'end': 1820.829, 'src': 'embed', 'start': 1793.147, 'weight': 1, 'content': [{'end': 1805.917, 'text': "But the basic idea that we're moving into this age where actually human language will be able to be used as an instruction language to tell your computer what to do.", 'start': 1793.147, 'duration': 12.77}, {'end': 1817.487, 'text': 'So, instead of having to use menus and radio buttons and things like that, or writing Python code instead of either of those things,', 'start': 1806.078, 'duration': 11.409}, {'end': 1820.829, 'text': "that you'll be able to say what you want and the computer will do it.", 'start': 1817.487, 'duration': 3.342}], 'summary': 'Human language will soon be used as an instruction language for computers, replacing menus, radio buttons, and python code.', 'duration': 27.682, 'max_score': 1793.147, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6w0Po83ZmjA/pics/6w0Po83ZmjA1793147.jpg'}, {'end': 1902.523, 'src': 'embed', 'start': 1843.628, 'weight': 2, 'content': [{'end': 1852.89, 'text': 'But in the last couple of decades the trend has been to rely less on rule-based engineering and more on machine learning, on data,', 'start': 1843.628, 'duration': 9.262}, {'end': 1853.871, 'text': 'sometimes lots of data.', 'start': 1852.89, 'duration': 0.981}, {'end': 1857.087, 'text': 'look into the future.', 'start': 1855.327, 'duration': 1.76}, {'end': 1864.549, 'text': 'where do you think that mix of hand-coded constraints or other constraints, explicit constraints versus,', 'start': 1857.087, 'duration': 7.462}, {'end': 1869.71, 'text': "let's get a neural network and throw lots of data at it where do you think that balance will fall?", 'start': 1864.549, 'duration': 5.161}, {'end': 1881.033, 'text': "I think that there's no doubt that using learning from data is the way forward and what we're going to continue to do.", 'start': 1871.051, 'duration': 9.982}, {'end': 1890.777, 'text': "But I think there's still a space for models that have more structure, more inductive bias,", 'start': 1881.673, 'duration': 9.104}, {'end': 1895.58, 'text': 'that have some kind of basis of exploiting the nature of language.', 'start': 1890.777, 'duration': 4.803}, {'end': 1902.523, 'text': "So in recent years, the model that's been enormously successful is the transformer neural network.", 'start': 1895.64, 'duration': 6.883}], 'summary': 'Shift from rule-based to data-driven approach in language models, with space for structured models like transformer neural networks.', 'duration': 58.895, 'max_score': 1843.628, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6w0Po83ZmjA/pics/6w0Po83ZmjA1843628.jpg'}, {'end': 1960.336, 'src': 'embed', 'start': 1927.072, 'weight': 0, 'content': [{'end': 1935.299, 'text': "But it's been incredibly successful in the domain where you have humongous, humongous amounts of data right?", 'start': 1927.072, 'duration': 8.227}, {'end': 1943.506, 'text': 'So that these transformer models for these large language models are now being trained on tens of billions of words of text.', 'start': 1935.339, 'duration': 8.167}, {'end': 1960.336, 'text': 'When I started off in statistical natural language processing and some of the traditional linguists used to complain about the fact that I was collecting statistics from 30 million words of news wire and building a predictive model,', 'start': 1944.226, 'duration': 16.11}], 'summary': 'Transformer models now trained on tens of billions of words, a significant increase from previous 30 million words.', 'duration': 33.264, 'max_score': 1927.072, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6w0Po83ZmjA/pics/6w0Po83ZmjA1927072.jpg'}, {'end': 2115.176, 'src': 'embed', 'start': 2088.321, 'weight': 3, 'content': [{'end': 2098.726, 'text': "I doubt it will be traditional I don't think it'll be by people explicitly putting traditional linguistic rules into the system.", 'start': 2088.321, 'duration': 10.405}, {'end': 2100.447, 'text': "I don't think that's the way forward.", 'start': 2098.807, 'duration': 1.64}, {'end': 2110.113, 'text': "On the other hand, I think what we're starting to see is models like these transformer models,", 'start': 2100.988, 'duration': 9.125}, {'end': 2115.176, 'text': 'are actually discovering the structure of language themselves, right?', 'start': 2110.613, 'duration': 4.563}], 'summary': 'Transformer models are discovering language structure, not traditional rules.', 'duration': 26.855, 'max_score': 2088.321, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6w0Po83ZmjA/pics/6w0Po83ZmjA2088321.jpg'}], 'start': 1717.488, 'title': 'The future of nlp technology', 'summary': 'Discusses the potential for human language to be used for computers, with an optimistic outlook on developing advanced natural language processing models. it also highlights the success of transformer models in language processing, emphasizing the exponential increase in training data and the potential for models to discover language structure on their own.', 'chapters': [{'end': 1926.732, 'start': 1717.488, 'title': 'Future of nlp technology', 'summary': 'Discusses the future of nlp technology, emphasizing the potential for human language to be used as an instruction language for computers, with an optimistic outlook on the development of more advanced and user-friendly natural language processing models.', 'duration': 209.244, 'highlights': ['The potential for human language to be used as an instruction language for computers, eliminating the need for traditional input methods, is highlighted as a transformative and promising aspect of NLP technology.', 'The trend in the development of NLP technology has been to rely less on rule-based engineering and more on machine learning, particularly the use of transformer neural networks, which have been incredibly successful in processing language data.', 'The balance between hand-coded constraints and machine learning with extensive data is a topic of discussion, with the speaker emphasizing the importance of models that have more structure and inductive bias alongside the utilization of learning from data.', 'The speaker expresses optimism towards the future development of NLP technology, anticipating a progression towards models that do not require specific wording and can understand human language more naturally, akin to human-to-human communication.']}, {'end': 2156.039, 'start': 1927.072, 'title': 'Transformer models and language learning', 'summary': 'Discusses the success of transformer models in language processing, highlighting the exponential increase in data used for training, the contrast between human learning and machine learning efficiency, and the potential for models to discover language structure on their own.', 'duration': 228.967, 'highlights': ['Transformer models are now being trained on tens of billions of words of text, showing a significant increase in data usage compared to traditional models. The modern transformers are using at least two orders of magnitude more data, with the potential for scaling up to three orders of magnitude, indicating the effective scaling up strategy in language processing.', 'Human learning is more efficient in extracting information from limited data compared to current machine learning algorithms, which require significantly more data to learn. Human learning is structured towards the structure of the world, allowing it to learn more quickly from less data, while current machine learning algorithms are much less efficient in data usage and require more data than a child.', 'Transformer models are capable of discovering the structure of language on their own, including fundamental facts about different languages, without being explicitly programmed with this knowledge. Transformer models are able to learn and understand language structure, such as subject-verb-object order in different languages, showing the potential for models to autonomously discover language features and context.']}], 'duration': 438.551, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6w0Po83ZmjA/pics/6w0Po83ZmjA1717488.jpg', 'highlights': ['Transformer models are now being trained on tens of billions of words of text, showing a significant increase in data usage compared to traditional models.', 'The potential for human language to be used as an instruction language for computers, eliminating the need for traditional input methods, is highlighted as a transformative and promising aspect of NLP technology.', 'The trend in the development of NLP technology has been to rely less on rule-based engineering and more on machine learning, particularly the use of transformer neural networks, which have been incredibly successful in processing language data.', 'Transformer models are capable of discovering the structure of language on their own, including fundamental facts about different languages, without being explicitly programmed with this knowledge.', 'The balance between hand-coded constraints and machine learning with extensive data is a topic of discussion, with the speaker emphasizing the importance of models that have more structure and inductive bias alongside the utilization of learning from data.']}, {'end': 2838.057, 'segs': [{'end': 2183.222, 'src': 'embed', 'start': 2156.519, 'weight': 0, 'content': [{'end': 2164.828, 'text': "part of what they're learning is the same kind of structure that linguists have laid out as the sort of structure of different human languages.", 'start': 2156.519, 'duration': 8.309}, {'end': 2172.436, 'text': "So it's as if, over many decades, linguists discovered certain things and, by training on billions of words,", 'start': 2164.848, 'duration': 7.588}, {'end': 2176.02, 'text': 'transformers are discovering the same things that linguists discovered in human languages.', 'start': 2172.436, 'duration': 3.584}, {'end': 2176.7, 'text': 'Yeah, absolutely.', 'start': 2176.04, 'duration': 0.66}, {'end': 2177.101, 'text': "That's cool.", 'start': 2176.76, 'duration': 0.341}, {'end': 2183.222, 'text': 'So all this is really exciting progress in NLP driven by machine learning and by other things.', 'start': 2178.576, 'duration': 4.646}], 'summary': 'Transformers in nlp mimic linguistic structures, driving exciting progress in machine learning.', 'duration': 26.703, 'max_score': 2156.519, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6w0Po83ZmjA/pics/6w0Po83ZmjA2156519.jpg'}, {'end': 2223.862, 'src': 'embed', 'start': 2199.517, 'weight': 1, 'content': [{'end': 2212.555, 'text': "I think there's just no doubt at all that we're still in the early stages of seeing the impact of this new approach, where, effectively, software,", 'start': 2199.517, 'duration': 13.038}, {'end': 2221.52, 'text': 'computer science is being reinvented on the basis of much more use of machine learning and the various other things that come away from that.', 'start': 2212.555, 'duration': 8.965}, {'end': 2223.862, 'text': 'And then more generally across industries.', 'start': 2221.58, 'duration': 2.282}], 'summary': 'Early stages of impact of new approach in software and computer science with more use of machine learning.', 'duration': 24.345, 'max_score': 2199.517, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6w0Po83ZmjA/pics/6w0Po83ZmjA2199517.jpg'}, {'end': 2309.182, 'src': 'embed', 'start': 2276.589, 'weight': 3, 'content': [{'end': 2279.53, 'text': "including the transformer that we've talked about a lot today.", 'start': 2276.589, 'duration': 2.941}, {'end': 2281.291, 'text': 'You definitely should know about transformers.', 'start': 2279.59, 'duration': 1.701}, {'end': 2288.574, 'text': "And indeed they're increasingly being used in every other part of machine learning as well for vision bioinformatics.", 'start': 2281.611, 'duration': 6.963}, {'end': 2290.995, 'text': 'even robotics is now using transformers.', 'start': 2288.574, 'duration': 2.421}, {'end': 2299.558, 'text': "But beyond that, I think it's also useful to learn something about human language and the nature of the problems that it involves.", 'start': 2291.955, 'duration': 7.603}, {'end': 2309.182, 'text': "Because I mean, even though people aren't directly going to be encoding rules of human language into their computing system,", 'start': 2299.999, 'duration': 9.183}], 'summary': 'Transformers are increasingly used in machine learning for vision, bioinformatics, and robotics, as well as human language problems.', 'duration': 32.593, 'max_score': 2276.589, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6w0Po83ZmjA/pics/6w0Po83ZmjA2276589.jpg'}, {'end': 2353.35, 'src': 'embed', 'start': 2319.006, 'weight': 4, 'content': [{'end': 2320.807, 'text': "that's still a useful skill to have.", 'start': 2319.006, 'duration': 1.801}, {'end': 2325.92, 'text': 'then in terms of learning the foundations, learning about these concepts.', 'start': 2322.219, 'duration': 3.701}, {'end': 2338.003, 'text': 'um, you had entered ai from a linguistic background and we now see people from you know all walks of life wanting to to start doing work in ai.', 'start': 2325.92, 'duration': 12.083}, {'end': 2346.105, 'text': 'um, what are your thoughts on the preparation one should have, or any any thoughts on how to start from something other than computer science or ai?', 'start': 2338.003, 'duration': 8.102}, {'end': 2353.35, 'text': 'so there are lots of places you can come from and vector across in different ways.', 'start': 2346.105, 'duration': 7.245}], 'summary': 'Diverse backgrounds entering ai, need for foundational learning and preparation beyond computer science.', 'duration': 34.344, 'max_score': 2319.006, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6w0Po83ZmjA/pics/6w0Po83ZmjA2319006.jpg'}, {'end': 2461.786, 'src': 'embed', 'start': 2392.956, 'weight': 2, 'content': [{'end': 2396.238, 'text': "You don't actually need to understand a lot of highly technical stuff.", 'start': 2392.956, 'duration': 3.282}, {'end': 2404.342, 'text': 'You need to have some kind of high level conception about what is the idea of machine learning, and how do I go about training a model,', 'start': 2396.258, 'duration': 8.084}, {'end': 2408.904, 'text': "and what should I look at in the numbers that are being printed out to see if it's working right?", 'start': 2404.342, 'duration': 4.562}, {'end': 2414.107, 'text': "But you don't actually have to have a higher degree to be able to build these models.", 'start': 2409.245, 'duration': 4.862}, {'end': 2422.296, 'text': "we're seeing is lots of high school students are getting into doing this, because it's actually something that,", 'start': 2415.988, 'duration': 6.308}, {'end': 2428.044, 'text': 'if you have some basic computer skills and a bit of programming, you can pick up and do.', 'start': 2422.296, 'duration': 5.748}, {'end': 2439.452, 'text': "It's just way more accessible than lots of stuff that preceded it, whether in AI or outside of AI and other areas like operating systems or security.", 'start': 2428.424, 'duration': 11.028}, {'end': 2446.734, 'text': "But if you want to get to a deeper level than that and actually want to understand more of what's going on,", 'start': 2439.832, 'duration': 6.902}, {'end': 2452.016, 'text': "I think you can't really get there if you don't have a certain mathematics foundation.", 'start': 2446.734, 'duration': 5.282}, {'end': 2461.786, 'text': 'Like at the end of the day, that deep learning is based on calculus and you need to be optimizing functions.', 'start': 2452.336, 'duration': 9.45}], 'summary': 'Machine learning models are accessible without high-level degrees, appealing to high school students; however, deeper understanding requires a foundation in mathematics, specifically calculus for deep learning optimization.', 'duration': 68.83, 'max_score': 2392.956, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6w0Po83ZmjA/pics/6w0Po83ZmjA2392956.jpg'}], 'start': 2156.519, 'title': 'Progress in nlp and ai foundations', 'summary': 'Covers exciting progress in nlp driven by machine learning, emphasizing its impact and automation opportunities. it also discusses the accessibility of ai, emphasizing diverse backgrounds and the reduced need for calculus in building ai models.', 'chapters': [{'end': 2290.995, 'start': 2156.519, 'title': 'Progress in nlp and machine learning', 'summary': 'Discusses the exciting progress in nlp driven by machine learning, emphasizing the impact of the new approach and the opportunities for automation and interpretation of human language material, while highlighting the importance of foundational knowledge in machine learning and specific models like transformers.', 'duration': 134.476, 'highlights': ["Linguists' structure of human languages is being discovered by transformers through training on billions of words.", 'The impact of the new approach in software and computer science is still in its early stages, creating opportunities for more automation and use of interpretation of human language material.', 'Foundational knowledge of machine learning methods and understanding of building models from data, including the core technical methods and ideas, is crucial for entering the field.', 'Knowing about transformers is essential as they are increasingly being used in various areas of machine learning, such as vision, bioinformatics, and robotics.']}, {'end': 2838.057, 'start': 2291.955, 'title': 'Ai foundations and accessibility', 'summary': 'Discusses the accessibility of ai, emphasizing the diverse backgrounds from which people can enter ai and the accessibility of building ai models with software packages. it emphasizes the importance of mathematics foundation for deeper understanding, while acknowledging the reduced need to understand calculus due to improved libraries and abstractions.', 'duration': 546.102, 'highlights': ['The chapter discusses the accessibility of AI, emphasizing the diverse backgrounds from which people can enter AI. It mentions that people from various backgrounds, such as chemistry, physics, history, and psychology, are venturing into AI and machine learning.', 'It emphasizes the accessibility of building AI models with software packages, making it easier for individuals with basic computer skills and programming knowledge to get involved. The speaker notes that high school students are getting into AI due to its accessibility, and highlights the ease of building AI models with basic computer skills and programming knowledge.', 'It emphasizes the importance of mathematics foundation for deeper understanding, while acknowledging the reduced need to understand calculus due to improved libraries and abstractions. The speaker highlights the necessity of a mathematics foundation for deeper understanding of AI, particularly in deep learning, which is based on calculus. However, it is also mentioned that improved libraries and abstractions, such as TensorFlow and PyTorch, have reduced the need to understand calculus for building deep learning models.']}], 'duration': 681.538, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6w0Po83ZmjA/pics/6w0Po83ZmjA2156519.jpg', 'highlights': ['Transformers are discovering the structure of human languages through training on billions of words.', 'The impact of the new approach in software and computer science is creating opportunities for more automation and use of interpretation of human language material.', 'Foundational knowledge of machine learning methods and understanding of building models from data is crucial for entering the field.', 'Knowing about transformers is essential as they are increasingly being used in various areas of machine learning.', 'The chapter discusses the accessibility of AI, emphasizing the diverse backgrounds from which people can enter AI.', 'It emphasizes the accessibility of building AI models with software packages, making it easier for individuals with basic computer skills and programming knowledge to get involved.', 'High school students are getting into AI due to its accessibility, highlighting the ease of building AI models with basic computer skills and programming knowledge.', 'The importance of mathematics foundation for deeper understanding is emphasized, while acknowledging the reduced need to understand calculus due to improved libraries and abstractions.']}], 'highlights': ['The development of large self-supervised models like BERT and GPT around 2018 marked a significant shift in the approach to NLP, allowing models to acquire extensive knowledge of human languages from word prediction over massive amounts of text.', 'The development of the transformer architecture in 2017 allowed for a more scalable neural architecture onto modern parallel GPUs, marking a pivotal moment in the evolution of NLP and deep learning.', 'The rise of neural network ideas, particularly the period from around 2010 to 2018, demonstrated significant success in building neural networks for various tasks such as syntactic parsing, sentiment analysis, and question answering.', "The speaker's academic journey involved a diverse range of studies, including math, computer science, and linguistics, demonstrating their multidisciplinary approach and expertise across various domains.", "The shift in the speaker's career from linguistics to NLP and machine learning was driven by the rise of data and empirical methods.", 'The potential for human language to be used as an instruction language for computers, eliminating the need for traditional input methods, is highlighted as a transformative and promising aspect of NLP technology.', 'The trend in the development of NLP technology has been to rely less on rule-based engineering and more on machine learning, particularly the use of transformer neural networks, which have been incredibly successful in processing language data.', 'The balance between hand-coded constraints and machine learning with extensive data is a topic of discussion, with the speaker emphasizing the importance of models that have more structure and inductive bias alongside the utilization of learning from data.', "The speaker's academic background includes a Ph.D. in linguistics and a joint appointment at Stanford as a professor of linguistics, showcasing their expertise in the field.", "The speaker's interest in human languages and acquisition led them to consider machine learning and computational ideas, as they pondered on how people understand and acquire languages, and the processing involved in human language comprehension."]}