title

Overview Artificial Intelligence Course | Stanford CS221: Learn AI (Autumn 2019)

description

For more information about Stanfordâ€™s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3meRgaI
Topics: Overview of course, Optimization
Percy Liang, Associate Professor & Dorsa Sadigh, Assistant Professor - Stanford University
http://onlinehub.stanford.edu/
Associate Professor Percy Liang
Associate Professor of Computer Science and Statistics (courtesy)
https://profiles.stanford.edu/percy-liang
Assistant Professor Dorsa Sadigh
Assistant Professor in the Computer Science Department & Electrical Engineering Department
https://profiles.stanford.edu/dorsa-sadigh
To follow along with the course schedule and syllabus, visit:
https://stanford-cs221.github.io/autumn2019/#schedule
#artificialintelligencecourse
0:00 Introduction
3:30 Why AI?
15:10 AI as Agents
18:20 AI Tools
20:39 Biases
23:28 Summary
34:08 PacMan
43:11 Perquisites, Homework, Exams

detail

{'title': 'Overview Artificial Intelligence Course | Stanford CS221: Learn AI (Autumn 2019)', 'heatmap': [{'end': 5233.802, 'start': 5193.347, 'weight': 1}], 'summary': "The stanford cs221 ai course, led by percy and dorsa, covers ai evolution, fairness, modeling, logic systems, optimization techniques, and dynamic programming. it includes discussions on ai's societal impact, bias, problem-solving paradigms, and regression in ml, emphasizing collaboration and practical applications.", 'chapters': [{'end': 211.08, 'segs': [{'end': 60.375, 'src': 'embed', 'start': 6.297, 'weight': 3, 'content': [{'end': 6.757, 'text': 'All right.', 'start': 6.297, 'duration': 0.46}, {'end': 8.538, 'text': "Let's get started.", 'start': 7.558, 'duration': 0.98}, {'end': 13.942, 'text': "Please try to have a seat if you can find a seat, and let's get the show on the road.", 'start': 9.799, 'duration': 4.143}, {'end': 16.743, 'text': 'So welcome everyone to CS 221.', 'start': 14.502, 'duration': 2.241}, {'end': 18.024, 'text': 'This is Artificial Intelligence.', 'start': 16.743, 'duration': 1.281}, {'end': 20.826, 'text': "Um, and if you're new to Stanford, welcome to Stanford.", 'start': 18.624, 'duration': 2.202}, {'end': 23.127, 'text': "Um, so first let's do some introduction.", 'start': 21.246, 'duration': 1.881}, {'end': 24.008, 'text': "So I'm Percy.", 'start': 23.207, 'duration': 0.801}, {'end': 28.13, 'text': "I'm gonna be one of your instructors, um, teaching this class with Dorsa over there.", 'start': 24.048, 'duration': 4.082}, {'end': 30.832, 'text': 'So if Dorsa wants to say hi, stand up.', 'start': 28.31, 'duration': 2.522}, {'end': 43.24, 'text': "So we're going to be trading off throughout the quarter.", 'start': 35.357, 'duration': 7.883}, {'end': 46.702, 'text': 'And we also have a wonderful teaching team.', 'start': 43.861, 'duration': 2.841}, {'end': 47.982, 'text': 'So these are your CAs.', 'start': 46.822, 'duration': 1.16}, {'end': 55.085, 'text': "So if all the CAs could stand up and I'll give you each person an opportunity to say three words about what you're interested in.", 'start': 48.082, 'duration': 7.003}, {'end': 60.375, 'text': "So let's start with the head CA.", 'start': 57.552, 'duration': 2.823}], 'summary': 'Introduction to cs 221, artificial intelligence class at stanford, led by instructors percy and dorsa, and a team of cas.', 'duration': 54.078, 'max_score': 6.297, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/J8Eh7RqggsU/pics/J8Eh7RqggsU6297.jpg'}, {'end': 211.08, 'src': 'embed', 'start': 107.502, 'weight': 0, 'content': [{'end': 118.047, 'text': "Hi everyone, I'm a first-year master's student and I'm interested in machine learning and differential privacy.", 'start': 107.502, 'duration': 10.545}, {'end': 124.091, 'text': "Hi, I'm a first-year student and I'm interested in reinforcement learning and AI education.", 'start': 118.768, 'duration': 5.323}, {'end': 143.258, 'text': "Everyone, I'm also a coach and I'm interested in the back.", 'start': 139.676, 'duration': 3.582}, {'end': 163.843, 'text': "Well, um, well, they're all on the slide.", 'start': 161.141, 'duration': 2.702}, {'end': 165.724, 'text': 'Um, okay.', 'start': 164.263, 'duration': 1.461}, {'end': 169.226, 'text': 'So, uh, as you can see, we kind of have a very diverse team.', 'start': 165.804, 'duration': 3.422}, {'end': 174.83, 'text': "And so when you're thinking about kind of final projects later in the quarter, you can tap into this kind of incredible resource.", 'start': 169.346, 'duration': 5.484}, {'end': 177.151, 'text': 'Um, so three quick announcements.', 'start': 175.77, 'duration': 1.381}, {'end': 185.313, 'text': "Um, So there's going to be a section every week which will cover both review topics and also advanced topics.", 'start': 177.571, 'duration': 7.742}, {'end': 187.434, 'text': "So this Thursday, there's going to be an overview.", 'start': 185.373, 'duration': 2.061}, {'end': 192.595, 'text': "If you're kind of rusty on Python or rusty on probability, come to this and we'll get you up to speed.", 'start': 187.994, 'duration': 4.601}, {'end': 195.715, 'text': 'Homework, the first homework is out.', 'start': 193.975, 'duration': 1.74}, {'end': 196.816, 'text': "It's posted on the website.", 'start': 195.775, 'duration': 1.041}, {'end': 199.156, 'text': "It's due next Tuesday at 11 PM.", 'start': 196.936, 'duration': 2.22}, {'end': 200.336, 'text': 'So remember the time.', 'start': 199.376, 'duration': 0.96}, {'end': 200.916, 'text': 'That matters.', 'start': 200.416, 'duration': 0.5}, {'end': 203.537, 'text': 'All submissions will be done on Gradescope.', 'start': 201.657, 'duration': 1.88}, {'end': 206.898, 'text': "There's going to be a Gradescope code that will be posted on Piazza.", 'start': 203.757, 'duration': 3.141}, {'end': 207.598, 'text': 'So look out for that.', 'start': 206.918, 'duration': 0.68}, {'end': 211.08, 'text': 'later Okay.', 'start': 208.658, 'duration': 2.422}], 'summary': "First-year master's students interested in machine learning, reinforcement learning, and ai education. weekly section for review and advanced topics. first homework due next tuesday at 11 pm.", 'duration': 103.578, 'max_score': 107.502, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/J8Eh7RqggsU/pics/J8Eh7RqggsU107502.jpg'}], 'start': 6.297, 'title': 'Introduction to ai course at stanford', 'summary': "Introduces the ai course at stanford, led by percy and dorsa, with a collaborative teaching approach. it also discusses student interests in nlp, machine learning, and reinforcement learning, including first and second-year master's students. additionally, it covers course announcements and details of the first homework.", 'chapters': [{'end': 60.375, 'start': 6.297, 'title': 'Cs 221 introduction', 'summary': 'Introduces the artificial intelligence course at stanford university, with percy and dorsa as instructors, and a team of teaching assistants, emphasizing a collaborative approach to teaching.', 'duration': 54.078, 'highlights': ['Percy and Dorsa are the instructors for CS 221, an Artificial Intelligence course at Stanford University. The chapter introduces Percy and Dorsa as the instructors for the CS 221 Artificial Intelligence course at Stanford University.', 'The teaching team includes a group of teaching assistants who will collaborate with the instructors. The chapter mentions a team of teaching assistants who will collaborate with the instructors for the course.', 'The chapter emphasizes a collaborative approach to teaching with the instructors and teaching assistants working together. The chapter emphasizes a collaborative approach to teaching, with the instructors and teaching assistants working together.']}, {'end': 169.226, 'start': 60.655, 'title': 'Student interests in nlp, machine learning, and reinforcement learning', 'summary': "Discusses the diverse interests of students, including natural language processing, machine learning, and reinforcement learning, with a mix of first-year and second-year master's students.", 'duration': 108.571, 'highlights': ['The students expressed a wide range of interests, including natural language processing, machine learning, and reinforcement learning, showcasing a diverse team.', "There is a mix of second-year and first-year master's students, each with specific interests, such as data mining, differential privacy, and AI education.", 'The chapter highlights the variety of interests among the students, with an emphasis on machine learning and natural language processing.']}, {'end': 211.08, 'start': 169.346, 'title': 'Course announcements and first homework', 'summary': 'Covers course announcements including weekly review and advanced topics, the first homework details, and the submission process.', 'duration': 41.734, 'highlights': ['The first homework is out, posted on the website, and due next Tuesday at 11 PM, with all submissions to be done on Gradescope.', 'A section every week will cover both review topics and advanced topics, with an upcoming overview this Thursday for those needing help with Python or probability.', 'There will be a Gradescope code posted on Piazza for submissions.']}], 'duration': 204.783, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/J8Eh7RqggsU/pics/J8Eh7RqggsU6297.jpg', 'highlights': ['The first homework is out, posted on the website, and due next Tuesday at 11 PM, with all submissions to be done on Gradescope.', 'The students expressed a wide range of interests, including natural language processing, machine learning, and reinforcement learning, showcasing a diverse team.', "There is a mix of second-year and first-year master's students, each with specific interests, such as data mining, differential privacy, and AI education.", 'The chapter introduces Percy and Dorsa as the instructors for the CS 221 Artificial Intelligence course at Stanford University.', 'The teaching team includes a group of teaching assistants who will collaborate with the instructors.', 'A section every week will cover both review topics and advanced topics, with an upcoming overview this Thursday for those needing help with Python or probability.']}, {'end': 1361.503, 'segs': [{'end': 261.176, 'src': 'embed', 'start': 229.512, 'weight': 0, 'content': [{'end': 232.394, 'text': "And indeed we've seen a lot of success stories, right?", 'start': 229.512, 'duration': 2.882}, {'end': 240.34, 'text': 'uh, AIs that can play Jeopardy or play Go Dota 2, even poker, all these kind of games at superhuman level performance.', 'start': 232.814, 'duration': 7.526}, {'end': 249.087, 'text': 'It can also, you know, read documents and answer questions, do speech recognition, uh, face recognition, um, even kind of medical imaging.', 'start': 240.66, 'duration': 8.427}, {'end': 254.471, 'text': 'And all these tasks are, uh, you read about how successful these, uh, technologies have been.', 'start': 249.407, 'duration': 5.064}, {'end': 261.176, 'text': "Um. and then, if you take a look at outside the kind of the technical circles, there's a lot of people, um in policy,", 'start': 255.231, 'duration': 5.945}], 'summary': 'Ai has shown superhuman performance in games like jeopardy, go, dota 2, and poker, as well as in tasks like document reading, speech recognition, and medical imaging.', 'duration': 31.664, 'max_score': 229.512, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/J8Eh7RqggsU/pics/J8Eh7RqggsU229512.jpg'}, {'end': 313.4, 'src': 'embed', 'start': 289.058, 'weight': 1, 'content': [{'end': 296.656, 'text': 'Um, but how do we get here? And to do that, I wanna take a step back to the summer of 1956.', 'start': 289.058, 'duration': 7.598}, {'end': 298.116, 'text': 'So the place was Dartmouth College.', 'start': 296.656, 'duration': 1.46}, {'end': 304.398, 'text': 'John McCarthy, who was then at MIT, and then after that he founded the Stanford AI Lab,', 'start': 298.496, 'duration': 5.902}, {'end': 313.4, 'text': 'organized a workshop at Dartmouth College with some of the best and brightest minds of the time Marvin Minsky, Claude Shannon, and so on.', 'start': 304.398, 'duration': 9.002}], 'summary': 'In 1956, john mccarthy organized an ai workshop at dartmouth college with leading minds like marvin minsky and claude shannon.', 'duration': 24.342, 'max_score': 289.058, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/J8Eh7RqggsU/pics/J8Eh7RqggsU289058.jpg'}, {'end': 362.7, 'src': 'embed', 'start': 337.71, 'weight': 2, 'content': [{'end': 345.595, 'text': 'Um, there were programs that could play checkers or prove theorems, and sometimes even better than what, um, the human proof would look like.', 'start': 337.71, 'duration': 7.885}, {'end': 349.557, 'text': 'Um, and there was a lot of, optimism.', 'start': 346.376, 'duration': 3.181}, {'end': 351.037, 'text': 'People are really really excited,', 'start': 349.617, 'duration': 1.42}, {'end': 356.679, 'text': 'and you can see these quotes by all these excited people who proclaimed that AI would be solved in a matter of years.', 'start': 351.037, 'duration': 5.642}, {'end': 359.799, 'text': "But we know that didn't really happen.", 'start': 357.719, 'duration': 2.08}, {'end': 362.7, 'text': "And there's this kind of folklore example.", 'start': 359.819, 'duration': 2.881}], 'summary': 'Ai programs showed promise, but high hopes for quick progress were not realized.', 'duration': 24.99, 'max_score': 337.71, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/J8Eh7RqggsU/pics/J8Eh7RqggsU337710.jpg'}, {'end': 726.042, 'src': 'embed', 'start': 702.797, 'weight': 3, 'content': [{'end': 709.663, 'text': 'for a generic algorithm that could train these multi-layer neural networks, because single-layer, remember, was insufficient to do a lot of things.', 'start': 702.797, 'duration': 6.866}, {'end': 714.168, 'text': 'Um. and then one of the kind of the early success stories was Yann LeCun.', 'start': 710.644, 'duration': 3.524}, {'end': 720.816, 'text': 'in 1989, uh applied a convolutional neural network and was able to recognize hand digit- written digits.', 'start': 714.168, 'duration': 6.648}, {'end': 726.042, 'text': 'And this actually got, you know, deployed by the USPS and it was reading kind of zip codes.', 'start': 721.176, 'duration': 4.866}], 'summary': "In 1989, yann lecun's convolutional neural network recognized hand-written digits, leading to usps deployment for reading zip codes.", 'duration': 23.245, 'max_score': 702.797, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/J8Eh7RqggsU/pics/J8Eh7RqggsU702797.jpg'}, {'end': 788.249, 'src': 'embed', 'start': 763.057, 'weight': 4, 'content': [{'end': 769.58, 'text': "Um, you know, the name AI has always been associated with the time John McCarthy logical tradition, that's kind of where it started.", 'start': 763.057, 'duration': 6.523}, {'end': 775.603, 'text': "But um, as you can see that there's also kind of this neuroscience inspired tradition of AI,", 'start': 770.001, 'duration': 5.602}, {'end': 782.167, 'text': 'and the two were kind of really had some deep philosophical differences and over the decades fought with each other kind of quite a bit.', 'start': 775.603, 'duration': 6.564}, {'end': 788.249, 'text': 'But I want to pause for a moment and really think about maybe there are actually deeper connections here.', 'start': 783.086, 'duration': 5.163}], 'summary': 'Ai has two traditions, one logical and one neuroscience-inspired, with deep philosophical differences.', 'duration': 25.192, 'max_score': 763.057, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/J8Eh7RqggsU/pics/J8Eh7RqggsU763057.jpg'}, {'end': 877.106, 'src': 'embed', 'start': 850.467, 'weight': 5, 'content': [{'end': 854.669, 'text': 'statistics or games came from kind of economics optimizations.', 'start': 850.467, 'duration': 4.202}, {'end': 857.59, 'text': 'gradient descent came from um, was you know, in the 50s?', 'start': 854.669, 'duration': 2.921}, {'end': 862.914, 'text': 'completely unrelated to AI, but these techniques kind of develop in a different context.', 'start': 858.63, 'duration': 4.284}, {'end': 867.337, 'text': "And so AI is kind of like, you know, it's kind of like a New York City.", 'start': 863.634, 'duration': 3.703}, {'end': 875.544, 'text': "It's, it's like a melting pot where a lot of the, these techniques get kind of unified and apply to kind of interesting problems.", 'start': 867.477, 'duration': 8.067}, {'end': 877.106, 'text': "And that's what makes it, I think,", 'start': 875.844, 'duration': 1.262}], 'summary': 'Ai techniques unify from various fields for interesting problems.', 'duration': 26.639, 'max_score': 850.467, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/J8Eh7RqggsU/pics/J8Eh7RqggsU850467.jpg'}, {'end': 1025.983, 'src': 'embed', 'start': 1002.967, 'weight': 6, 'content': [{'end': 1011.213, 'text': "We're born with basically nothing, none of these capabilities, but we're born with the capacity and potential to acquire these over time,", 'start': 1002.967, 'duration': 8.246}, {'end': 1012.094, 'text': 'through experience.', 'start': 1011.213, 'duration': 0.881}, {'end': 1017.538, 'text': 'And learning, it seems to be kind of this critical ingredient which drives a lot of the success in AI today.', 'start': 1012.694, 'duration': 4.844}, {'end': 1020.4, 'text': 'But also with, um you know, human intelligence.', 'start': 1017.598, 'duration': 2.802}, {'end': 1025.983, 'text': "it's clear that learning plays such a central role in getting us to the level that we are operating at.", 'start': 1020.4, 'duration': 5.583}], 'summary': 'Learning is critical for success in ai and human intelligence.', 'duration': 23.016, 'max_score': 1002.967, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/J8Eh7RqggsU/pics/J8Eh7RqggsU1002967.jpg'}, {'end': 1118.317, 'src': 'embed', 'start': 1093.075, 'weight': 7, 'content': [{'end': 1099.098, 'text': 'how you can build systems with this level of capability in- that humans have.', 'start': 1093.075, 'duration': 6.023}, {'end': 1103.863, 'text': 'So the other view is, you know, AI tools.', 'start': 1101.641, 'duration': 2.222}, {'end': 1110.47, 'text': "Basically, you would say, okay, well, you know, it's kind of cool to think about how we can, uh, you know, recreate intelligence.", 'start': 1104.204, 'duration': 6.266}, {'end': 1116.575, 'text': "But, you know, we don't really care about making more, um, things like humans.", 'start': 1111.03, 'duration': 5.545}, {'end': 1118.317, 'text': 'We already have a way of, you know, doing that.', 'start': 1116.595, 'duration': 1.722}], 'summary': 'Discussion on building ai systems with human-level capability.', 'duration': 25.242, 'max_score': 1093.075, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/J8Eh7RqggsU/pics/J8Eh7RqggsU1093075.jpg'}, {'end': 1319.088, 'src': 'embed', 'start': 1289.467, 'weight': 8, 'content': [{'end': 1293.329, 'text': 'What could possibly go wrong? Right?, Um and so- so certainly people.', 'start': 1289.467, 'duration': 3.862}, {'end': 1299.973, 'text': 'a lot of people have been thinking about, um, how these biases are kind of creeping up as an open, active area of research.', 'start': 1293.329, 'duration': 6.644}, {'end': 1306.037, 'text': 'Um, something a little bit more kind of sensitive, is, you know, asking?', 'start': 1301.154, 'duration': 4.883}, {'end': 1313.642, 'text': 'well, these systems are being deployed to all these- all these people, whether they kind of want it or- want it or not.', 'start': 1306.037, 'duration': 7.605}, {'end': 1319.088, 'text': "Um, and this- this actually touches on, you know, people's, uh, you know, livelihoods.", 'start': 1314.362, 'duration': 4.726}], 'summary': 'Researching biases in deployed systems impacting livelihoods.', 'duration': 29.621, 'max_score': 1289.467, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/J8Eh7RqggsU/pics/J8Eh7RqggsU1289467.jpg'}], 'start': 211.12, 'title': "Ai's evolution and impact", 'summary': "Discusses ai's evolution from the 1956 dartmouth workshop to present, including its increasing relevance, successful applications in gaming and document analysis, and its potential transformative effects on society and work. it also covers the history of ai, the impact of expert systems, the development of neural networks, and the resurgence of ai with deep learning. additionally, it explores the differences between human intelligence and ai, emphasizing learning processes, diverse capabilities, societal impacts, and potential implications on people's lives.", 'chapters': [{'end': 380.209, 'start': 211.12, 'title': 'Ai evolution: from 1956 to today', 'summary': 'Discusses the evolution of ai from the 1956 dartmouth workshop to the present, highlighting the increasing relevance and impact of ai, including successful applications in gaming, document analysis, and its potential transformative effects on society and work.', 'duration': 169.089, 'highlights': ["AI's increasing relevance and impact, including successful applications in gaming, document analysis, and its potential transformative effects on society and work.", 'The 1956 Dartmouth workshop organized by John McCarthy and other prominent figures, with the ambitious goal of simulating every aspect of learning and intelligence in machines.', "The initial optimism and excitement around AI's potential, with high expectations of solving AI within a matter of years, but the subsequent realization that progress was not as rapid as anticipated."]}, {'end': 959.58, 'start': 381.031, 'title': 'History of ai', 'summary': 'Covers the history of ai, including the first ai winter, the limitations of early ai research, the impact of expert systems, the development of neural networks, and the resurgence of ai with deep learning and its societal implications.', 'duration': 578.549, 'highlights': ['The first AI winter occurred due to limited compute power and reliance on exponential search, leading to a period of reduced AI research and funding. The first AI winter was caused by the lack of compute power and the reliance on exponential search, resulting in decreased AI research and funding.', 'Expert systems had a real impact on industries by encoding expert knowledge, but the deterministic rules were not rich enough to capture all nuances of the world, leading to the collapse of the field and the second AI winter. Expert systems had a significant impact on industries by encoding expert knowledge, but their deterministic rules were not sufficient to capture all nuances, leading to the collapse of the field and the second AI winter.', "The development of artificial neural networks, particularly with the rediscovery of the backpropagation algorithm in the 80s, led to a renewed interest in AI and the eventual rise of deep learning, with notable milestones such as Yann LeCun's application of convolutional neural networks in 1989 and the transformation of the computer vision community by AlexNet in 2012. The development of artificial neural networks, including the rediscovery of the backpropagation algorithm in the 80s, sparked renewed interest in AI and led to the rise of deep learning, with milestones such as Yann LeCun's application of convolutional neural networks in 1989 and the transformation of the computer vision community by AlexNet in 2012.", 'The chapter delves into the philosophical differences and overlap between the logical tradition and the neuroscience-inspired tradition of AI, emphasizing the synergy between logic and neural networks and their implications for AI advancements. The chapter explores the philosophical differences and synergy between the logical tradition and the neuroscience-inspired tradition of AI, highlighting their implications for AI advancements.', 'AI draws from various fields, such as statistics and economics, leading to the unification and application of diverse techniques to solve complex problems, akin to a melting pot where unique combinations of existing techniques open up new avenues for AI. AI draws from various fields, such as statistics and economics, leading to the unification and application of diverse techniques to solve complex problems, akin to a melting pot where unique combinations of existing techniques open up new avenues for AI.']}, {'end': 1361.503, 'start': 960.26, 'title': 'Ai and human intelligence', 'summary': "Explores the differences between human intelligence and ai, emphasizing the learning process, diverse capabilities, and societal impacts, while highlighting the challenges and biases in ai systems and their potential implications on people's lives.", 'duration': 401.243, 'highlights': ['The learning process is a critical ingredient driving success in AI and human intelligence. Learning is emphasized as a critical ingredient in both AI and human intelligence, enabling the acquisition of diverse capabilities over time through experience.', 'Challenges in creating AI systems with human-level capabilities and diverse learning like humans possess are highlighted. The difficulty in developing AI systems with capabilities and learning diversity similar to humans, as well as the grand challenge of replicating human-level capability, is emphasized.', "Biases in AI systems, such as societal biases encoded in language models and discriminatory predictions, are discussed, including the potential impact on people's lives. The discussion of biases in AI systems, including societal biases encoded in language models and discriminatory predictions, highlights their potential impact on people's lives and the importance of addressing these biases as an active area of research."]}], 'duration': 1150.383, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/J8Eh7RqggsU/pics/J8Eh7RqggsU211120.jpg', 'highlights': ["AI's increasing relevance and impact, including successful applications in gaming, document analysis, and its potential transformative effects on society and work.", 'The 1956 Dartmouth workshop organized by John McCarthy and other prominent figures, with the ambitious goal of simulating every aspect of learning and intelligence in machines.', "The initial optimism and excitement around AI's potential, with high expectations of solving AI within a matter of years, but the subsequent realization that progress was not as rapid as anticipated.", "The development of artificial neural networks, including the rediscovery of the backpropagation algorithm in the 80s, sparked renewed interest in AI and led to the rise of deep learning, with milestones such as Yann LeCun's application of convolutional neural networks in 1989 and the transformation of the computer vision community by AlexNet in 2012.", 'The chapter explores the philosophical differences and synergy between the logical tradition and the neuroscience-inspired tradition of AI, highlighting their implications for AI advancements.', 'AI draws from various fields, such as statistics and economics, leading to the unification and application of diverse techniques to solve complex problems, akin to a melting pot where unique combinations of existing techniques open up new avenues for AI.', 'Learning is emphasized as a critical ingredient in both AI and human intelligence, enabling the acquisition of diverse capabilities over time through experience.', 'The difficulty in developing AI systems with capabilities and learning diversity similar to humans, as well as the grand challenge of replicating human-level capability, is emphasized.', "The discussion of biases in AI systems, including societal biases encoded in language models and discriminatory predictions, highlights their potential impact on people's lives and the importance of addressing these biases as an active area of research."]}, {'end': 1941.246, 'segs': [{'end': 1462.944, 'src': 'embed', 'start': 1413.568, 'weight': 0, 'content': [{'end': 1421.233, 'text': "Um, we're trying to really kind of dream and think about how do you get these capabilities,", 'start': 1413.568, 'duration': 7.665}, {'end': 1428.678, 'text': 'like learning from very few examples that humans have into you know machines and maybe opening up a kind of uh.', 'start': 1421.233, 'duration': 7.445}, {'end': 1430.9, 'text': 'a different set of technical capabilities.', 'start': 1428.678, 'duration': 2.222}, {'end': 1438.525, 'text': 'But at the same time, uh, we really need to be thinking about how these AI systems are affecting the real world.', 'start': 1431.52, 'duration': 7.005}, {'end': 1444.15, 'text': 'and things like security and biases and fairness all kind of show up.', 'start': 1439.566, 'duration': 4.584}, {'end': 1453.997, 'text': "It's also interesting to note that you know a lot of the challenges in deployment of AI system don't really have necessarily to do with, um, you know,", 'start': 1444.75, 'duration': 9.247}, {'end': 1454.577, 'text': 'humans at all.', 'start': 1453.997, 'duration': 0.58}, {'end': 1462.944, 'text': "I mean humans are incredibly biased, but that doesn't mean we want to build systems, kind of in our um,", 'start': 1454.597, 'duration': 8.347}], 'summary': 'Exploring ai capabilities from few examples, while addressing real-world impact, biases, and fairness.', 'duration': 49.376, 'max_score': 1413.568, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/J8Eh7RqggsU/pics/J8Eh7RqggsU1413568.jpg'}, {'end': 1583.726, 'src': 'embed', 'start': 1551.141, 'weight': 3, 'content': [{'end': 1556.184, 'text': 'Um, so this is a paradigm that I call the, um, modeling inference and learning paradigm.', 'start': 1551.141, 'duration': 5.043}, {'end': 1564.97, 'text': "Um, so the idea here is that there's three pillars which I'll explain in a bit.", 'start': 1557.045, 'duration': 7.925}, {'end': 1573.642, 'text': 'And we can focus on each one of these things kind of in turn.', 'start': 1570.061, 'duration': 3.581}, {'end': 1576.063, 'text': 'So the first pillar is modeling.', 'start': 1574.603, 'duration': 1.46}, {'end': 1583.726, 'text': 'So what is modeling? The modeling is taking the real world, which is really complicated, and building a model out of it.', 'start': 1577.004, 'duration': 6.722}], 'summary': 'A paradigm called modeling inference and learning involves three pillars: modeling, inference, and learning.', 'duration': 32.585, 'max_score': 1551.141, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/J8Eh7RqggsU/pics/J8Eh7RqggsU1551141.jpg'}, {'end': 1865.164, 'src': 'embed', 'start': 1837.587, 'weight': 4, 'content': [{'end': 1840.928, 'text': 'Rather, having, you know, a million lines of code which is unmanageable,', 'start': 1837.587, 'duration': 3.341}, {'end': 1848.349, 'text': 'you have a lot of data which is um collected in kind of a more natural way and a smaller amount of code that can operate on this data.', 'start': 1840.928, 'duration': 7.421}, {'end': 1851.93, 'text': 'And this paradigm has really been, um, a terribly powerful.', 'start': 1848.629, 'duration': 3.301}, {'end': 1858.399, 'text': 'One thing to think about in terms of machine learning is that it requires a leap of faith.', 'start': 1852.964, 'duration': 5.435}, {'end': 1865.164, 'text': 'Right? So you can go through the mechanics of, you know, down- downloading some machine learning code and you train a model.', 'start': 1859.42, 'duration': 5.744}], 'summary': 'Using a smaller amount of code to manage a natural collection of data can be a powerful paradigm in machine learning.', 'duration': 27.577, 'max_score': 1837.587, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/J8Eh7RqggsU/pics/J8Eh7RqggsU1837587.jpg'}, {'end': 1944.189, 'src': 'embed', 'start': 1919.647, 'weight': 5, 'content': [{'end': 1926.65, 'text': 'Um, and so reflex models are these, um, are models which just require a fixed set of computations.', 'start': 1919.647, 'duration': 7.003}, {'end': 1935.339, 'text': 'So examples like our linear classifiers, deep neural networks, um, and most of these models are the ones that people in machine learning, um, use.', 'start': 1927.35, 'duration': 7.989}, {'end': 1939.204, 'text': 'Models is almost synonymous with, um, reflex, um, in, you know, machine learning.', 'start': 1935.419, 'duration': 3.785}, {'end': 1941.246, 'text': "An important thing that there's no feed for it.", 'start': 1939.584, 'duration': 1.662}, {'end': 1944.189, 'text': "It's just like you get your input, bam, bam, bam, and here's your output.", 'start': 1941.346, 'duration': 2.843}], 'summary': 'Reflex models, like linear classifiers and neural networks, are key in machine learning as they require fixed computations and provide immediate output.', 'duration': 24.542, 'max_score': 1919.647, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/J8Eh7RqggsU/pics/J8Eh7RqggsU1919647.jpg'}], 'start': 1364.444, 'title': "Ai's fairness and problem-solving paradigms", 'summary': 'Discusses challenges in ai fairness, societal impact, and bias, emphasizing the need for addressing biases and security. it also introduces problem-solving paradigm for modeling, inference, and learning, highlighting the importance of structuring problems, efficient computation, and the role of machine learning and reflex models.', 'chapters': [{'end': 1528.764, 'start': 1364.444, 'title': 'Ai, fairness, and real-world impact', 'summary': 'Discusses the challenges of fairness and equality in ai, the societal impact of ai systems, and the complexity of solving real-world problems with ai, highlighting the need to address biases and security issues.', 'duration': 164.32, 'highlights': ['The challenges of fairness and equality in AI Different notions of fairness and equality in AI can be mathematically incompatible, posing a significant challenge in achieving consensus and addressing biases.', 'Societal impact of AI systems AI systems have real-world implications, including biases, fairness, and security concerns, highlighting the need to consider the broader impact of AI on society.', 'Complexity of solving real-world problems with AI Addressing complex real-world problems using AI, such as self-driving cars and disease diagnosis, presents a considerable gap between conceptualization and practical implementation, requiring innovative approaches.']}, {'end': 1941.246, 'start': 1529.165, 'title': 'Problem-solving paradigm for modelling, inference, and learning', 'summary': 'Introduces the modeling, inference, and learning paradigm, emphasizing the importance of structuring problems, simplifying information for modeling, efficient computation for inference, and parameter fitting through learning, while highlighting the role of machine learning and reflex models in problem-solving.', 'duration': 412.081, 'highlights': ['The chapter introduces the modeling, inference, and learning paradigm, emphasizing the importance of structuring problems, simplifying information for modeling, efficient computation for inference, and parameter fitting through learning. It explains the paradigm of modeling, inference, and learning, highlighting the significance of structuring problems and simplifying information for modeling, efficient computation for inference, and parameter fitting through learning.', 'Machine learning is highlighted as a central tenet in problem-solving, allowing the complexity to shift from code to data, and emphasizing the leap of faith required for generalization. Machine learning is emphasized as a key element, shifting complexity from code to data, requiring a leap of faith for generalization.', 'Reflex models are discussed as simplistic models that rely on fixed computations, including linear classifiers and deep neural networks, commonly used in machine learning. Reflex models, such as linear classifiers and deep neural networks, are highlighted as simplistic models used in machine learning.']}], 'duration': 576.802, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/J8Eh7RqggsU/pics/J8Eh7RqggsU1364444.jpg', 'highlights': ['Different notions of fairness and equality in AI pose a significant challenge in achieving consensus and addressing biases.', 'AI systems have real-world implications, including biases, fairness, and security concerns, highlighting the need to consider the broader impact of AI on society.', 'Addressing complex real-world problems using AI presents a considerable gap between conceptualization and practical implementation, requiring innovative approaches.', 'The chapter introduces the modeling, inference, and learning paradigm, emphasizing the importance of structuring problems, simplifying information for modeling, efficient computation for inference, and parameter fitting through learning.', 'Machine learning is emphasized as a key element, shifting complexity from code to data, requiring a leap of faith for generalization.', 'Reflex models, such as linear classifiers and deep neural networks, are highlighted as simplistic models used in machine learning.']}, {'end': 2503.905, 'segs': [{'end': 1994.51, 'src': 'embed', 'start': 1962.294, 'weight': 1, 'content': [{'end': 1965.195, 'text': 'Um, but for the rest of us, um, I have no idea.', 'start': 1962.294, 'duration': 2.901}, {'end': 1966.776, 'text': "I don't even know wait, who's moving again?", 'start': 1965.555, 'duration': 1.221}, {'end': 1974.079, 'text': 'Um so so, in these kind of situations, we need something perhaps a little bit more powerful than a reflex.', 'start': 1967.236, 'duration': 6.843}, {'end': 1977.621, 'text': 'We need agents that can kind of plan and think, um, ahead.', 'start': 1974.119, 'duration': 3.502}, {'end': 1984.825, 'text': 'So the idea behind state-based models is that we model the world as a set of states which capture any given situation, like, uh,', 'start': 1978.281, 'duration': 6.544}, {'end': 1987.266, 'text': 'a position in a in a game.', 'start': 1984.825, 'duration': 2.441}, {'end': 1994.51, 'text': 'And actions that take us between states which correspond to things that, um, you can do in the, in this game.', 'start': 1988.287, 'duration': 6.223}], 'summary': 'State-based models enable planning and thinking ahead in situations, like a position in a game, by capturing states and actions.', 'duration': 32.216, 'max_score': 1962.294, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/J8Eh7RqggsU/pics/J8Eh7RqggsU1962294.jpg'}, {'end': 2028.601, 'src': 'embed', 'start': 2004.218, 'weight': 0, 'content': [{'end': 2011.324, 'text': 'uh, you might think of um planning such as gen- you know generation um in natural language, or generating an image.', 'start': 2004.218, 'duration': 7.106}, {'end': 2014.386, 'text': 'um, you know, are, uh, can be cast in this way as well.', 'start': 2011.324, 'duration': 3.062}, {'end': 2020.413, 'text': "So there's three types of state-based models, each of which we'll cover in, um, you know, weeks of time.", 'start': 2015.627, 'duration': 4.786}, {'end': 2026.379, 'text': "So search problems are the classic, uh, you control everything so you're just trying to fi- find the optimal path.", 'start': 2020.513, 'duration': 5.866}, {'end': 2028.601, 'text': "There are cases where there's randomness.", 'start': 2026.839, 'duration': 1.762}], 'summary': 'Three state-based models for planning, covering search problems and cases with randomness.', 'duration': 24.383, 'max_score': 2004.218, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/J8Eh7RqggsU/pics/J8Eh7RqggsU2004218.jpg'}, {'end': 2118.544, 'src': 'embed', 'start': 2087.036, 'weight': 5, 'content': [{'end': 2091.201, 'text': 'Um, but some problems are not really most naturally cast as state-based models.', 'start': 2087.036, 'duration': 4.165}, {'end': 2094.284, 'text': 'For example, you know how many of you have played Sudoku or have played it before?', 'start': 2091.221, 'duration': 3.063}, {'end': 2102.451, 'text': 'So the goal of Sudoku is to fill in these uh um blanks with numbers so that, um, every row,', 'start': 2094.705, 'duration': 7.746}, {'end': 2104.833, 'text': 'column and three-by-three sub-block has digits one through nine.', 'start': 2102.451, 'duration': 2.382}, {'end': 2105.774, 'text': "so it's a bunch of constraints.", 'start': 2104.833, 'duration': 0.941}, {'end': 2112.459, 'text': "Um, and there's no kind of sense in which you have to do it in a certain order, right?", 'start': 2106.574, 'duration': 5.885}, {'end': 2118.544, 'text': 'Whereas the- the- the order in how you move in- in chess or something is, you know, pretty important.', 'start': 2112.499, 'duration': 6.045}], 'summary': 'Sudoku presents constraints with no specific order, unlike chess.', 'duration': 31.508, 'max_score': 2087.036, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/J8Eh7RqggsU/pics/J8Eh7RqggsU2087036.jpg'}, {'end': 2232.005, 'src': 'embed', 'start': 2207.41, 'weight': 4, 'content': [{'end': 2215.494, 'text': 'But it turns out that, um, you can formulate it in a kind of more natural way, as a variable-based model, which allows you to, uh,', 'start': 2207.41, 'duration': 8.084}, {'end': 2218.135, 'text': 'take advantage of some kind of more efficient algorithms to solve it.', 'start': 2215.494, 'duration': 2.641}, {'end': 2224.7, 'text': "Right It's- think about these models as kind of different, um, analogy as like a programming language.", 'start': 2218.835, 'duration': 5.865}, {'end': 2226.981, 'text': 'So, yes, you could write everything in.', 'start': 2224.74, 'duration': 2.241}, {'end': 2232.005, 'text': 'you know C++, but sometimes writing in you know Python, or- or SQL.', 'start': 2226.981, 'duration': 5.024}], 'summary': 'Formulate variable-based model for more efficient algorithms, similar to using different programming languages.', 'duration': 24.595, 'max_score': 2207.41, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/J8Eh7RqggsU/pics/J8Eh7RqggsU2207410.jpg'}], 'start': 1941.346, 'title': 'Ai modeling', 'summary': 'Discusses state-based and variable-based models in ai, covering different types and applications, including pac-man strategy, constraint satisfaction problems, bayesian networks, and logic-based modeling, highlighting the importance of efficient algorithms and the use of various models.', 'chapters': [{'end': 2045.194, 'start': 1941.346, 'title': 'State-based models in problem solving', 'summary': 'Discusses the need for agents that can plan and think ahead in problem-solving scenarios, and introduces state-based models as a powerful approach to model the world, covering different types of state-based models and their applications in various domains.', 'duration': 103.848, 'highlights': ['Introduces state-based models as a powerful approach to model the world, covering different types of state-based models and their applications in various domains. State-based models are introduced as a powerful approach to model the world, covering different types and applications such as robotics, motion planning, navigation, generation in natural language, and image generation.', 'Discusses the need for agents that can plan and think ahead in problem-solving scenarios. The need for agents that can plan and think ahead in problem-solving scenarios is highlighted, emphasizing the limitations of reflex-based approaches in certain situations.', 'Explains the three types of state-based models: search problems, cases with randomness, and adversarial games. The chapter explains the three types of state-based models: search problems, cases with randomness, and adversarial games, each presenting different challenges and considerations in problem-solving scenarios.']}, {'end': 2503.905, 'start': 2045.254, 'title': 'Understanding ai modeling', 'summary': 'Covers the application of state-based and variable-based models in ai, discussing pac-man strategy, constraint satisfaction problems, bayesian networks, and logic-based modeling with a focus on natural language processing and reasoning, emphasizing the importance of efficient algorithms and the use of various models for different scenarios.', 'duration': 458.651, 'highlights': ['The chapter covers the application of state-based and variable-based models in AI, discussing Pac-Man strategy, constraint satisfaction problems, Bayesian networks, and logic-based modeling with a focus on natural language processing and reasoning. The lecture covers various AI models including state-based and variable-based models, discussing applications such as Pac-Man strategy, constraint satisfaction problems, Bayesian networks, and logic-based modeling with a focus on natural language processing and reasoning.', 'Emphasizing the importance of efficient algorithms and the use of various models for different scenarios. The importance of efficient algorithms and the use of different models for distinct scenarios is emphasized throughout the lecture.', 'The chapter discusses the formulation of Sudoku as a variable-based model to take advantage of more efficient algorithms for solving it. The lecture discusses the formulation of Sudoku as a variable-based model to take advantage of more efficient algorithms for solving it, rather than a state-based model.']}], 'duration': 562.559, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/J8Eh7RqggsU/pics/J8Eh7RqggsU1941346.jpg', 'highlights': ['State-based models are a powerful approach to model the world, covering different types and applications such as robotics, motion planning, navigation, generation in natural language, and image generation.', 'The need for agents that can plan and think ahead in problem-solving scenarios is highlighted, emphasizing the limitations of reflex-based approaches in certain situations.', 'The chapter explains the three types of state-based models: search problems, cases with randomness, and adversarial games, each presenting different challenges and considerations in problem-solving scenarios.', 'The lecture covers various AI models including state-based and variable-based models, discussing applications such as Pac-Man strategy, constraint satisfaction problems, Bayesian networks, and logic-based modeling with a focus on natural language processing and reasoning.', 'The importance of efficient algorithms and the use of different models for distinct scenarios is emphasized throughout the lecture.', 'The lecture discusses the formulation of Sudoku as a variable-based model to take advantage of more efficient algorithms for solving it, rather than a state-based model.']}, {'end': 3022.615, 'segs': [{'end': 2572.457, 'src': 'embed', 'start': 2544.861, 'weight': 0, 'content': [{'end': 2547.603, 'text': 'Of course, this is, uh, based on, you know, logic systems.', 'start': 2544.861, 'duration': 2.742}, {'end': 2555.449, 'text': 'Um, so it is brittle, but this is kind of just a proof of concepts to give you a taste of what I mean when I, uh, say logic.', 'start': 2548.124, 'duration': 7.325}, {'end': 2562.203, 'text': 'So, uh, so these systems need to be able to digest these heterogeneous information and reason deeply with that information.', 'start': 2556.27, 'duration': 5.933}, {'end': 2566.072, 'text': "And we'll see kind of how, um, logic systems can, you know, do that.", 'start': 2562.243, 'duration': 3.829}, {'end': 2572.457, 'text': 'Okay So that completes the tour of the topics of this class.', 'start': 2567.475, 'duration': 4.982}], 'summary': 'Logic systems process heterogeneous information to reason deeply.', 'duration': 27.596, 'max_score': 2544.861, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/J8Eh7RqggsU/pics/J8Eh7RqggsU2544861.jpg'}, {'end': 2614.121, 'src': 'embed', 'start': 2591.331, 'weight': 2, 'content': [{'end': 2602.836, 'text': "Okay So what are we trying to do in this course? Um, so, uh, prerequisites, um, there's programming, um, discrete math, and, uh, probability.", 'start': 2591.331, 'duration': 11.505}, {'end': 2609.319, 'text': 'So you need to be able to code, you need to be able to um, do some math and, uh, some kind of basic proofs right?', 'start': 2603.016, 'duration': 6.303}, {'end': 2614.121, 'text': 'So these are the classes that are um required or at least recommended.', 'start': 2609.339, 'duration': 4.782}], 'summary': 'Course requires programming, discrete math, and probability skills for coding and basic proofs.', 'duration': 22.79, 'max_score': 2591.331, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/J8Eh7RqggsU/pics/J8Eh7RqggsU2591331.jpg'}, {'end': 2695.103, 'src': 'embed', 'start': 2670.69, 'weight': 3, 'content': [{'end': 2678.553, 'text': 'Each homework is a mix of write-written and programming problems centered on a particular application covering one particular type of model essentially.', 'start': 2670.69, 'duration': 7.863}, {'end': 2681.875, 'text': "Um, like I mentioned before, there's a competition for extra credit.", 'start': 2679.353, 'duration': 2.522}, {'end': 2685.597, 'text': "There's also some extra credit problems in the, in the, in the homeworks.", 'start': 2681.915, 'duration': 3.682}, {'end': 2691.501, 'text': "Um, and when you submit code, um, we're gonna run, we have an autograder that runs.", 'start': 2686.318, 'duration': 5.183}, {'end': 2695.103, 'text': "It's gonna run on all the test cases, but you get a feedback only a subset.", 'start': 2691.861, 'duration': 3.242}], 'summary': 'Homeworks include mix of written and programming problems, with extra credit competition and autograder for feedback.', 'duration': 24.413, 'max_score': 2670.69, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/J8Eh7RqggsU/pics/J8Eh7RqggsU2670690.jpg'}, {'end': 2847.611, 'src': 'embed', 'start': 2820.937, 'weight': 5, 'content': [{'end': 2826.421, 'text': 'um, we will hopefully give you a lot of structure in terms of saying okay, how do you define your task?', 'start': 2820.937, 'duration': 5.484}, {'end': 2830.823, 'text': "How do you implement different um baselines and oracles, which I'll explain later.", 'start': 2826.461, 'duration': 4.362}, {'end': 2831.744, 'text': 'How do you evaluate??', 'start': 2830.883, 'duration': 0.861}, {'end': 2833.925, 'text': "How do you, um, analyze what you've done?", 'start': 2831.804, 'duration': 2.121}, {'end': 2841.229, 'text': 'Um, and each of you will- each project group will be assigned a CA mentor, um, to help you, uh, through the process.', 'start': 2834.526, 'duration': 6.703}, {'end': 2847.611, 'text': "And you're always welcome to come to my office hours or Dorsa's or any of the CAs to get additional um help,", 'start': 2841.569, 'duration': 6.042}], 'summary': 'Structure provided for task definition, baseline implementation, and evaluation; ca mentors assigned for guidance.', 'duration': 26.674, 'max_score': 2820.937, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/J8Eh7RqggsU/pics/J8Eh7RqggsU2820937.jpg'}, {'end': 2916.523, 'src': 'embed', 'start': 2887.662, 'weight': 6, 'content': [{'end': 2889.143, 'text': 'So all the details are on the course website.', 'start': 2887.662, 'duration': 1.481}, {'end': 2894.667, 'text': "Okay So one last thing, and it's really important, and that's the honor code.", 'start': 2890.144, 'duration': 4.523}, {'end': 2900.091, 'text': "Okay, So, especially if you're um, you know you've probably heard this if you've been at Stanford.", 'start': 2895.087, 'duration': 5.004}, {'end': 2903.573, 'text': "if you haven't, then I want to really kind of make this clear.", 'start': 2900.091, 'duration': 3.482}, {'end': 2909.718, 'text': 'So I encourage you all to kind of collaborate, discuss together,', 'start': 2904.214, 'duration': 5.504}, {'end': 2916.523, 'text': 'but when you- when it comes to actually the homeworks you have to write up your homework and you know, code it independently.', 'start': 2909.718, 'duration': 6.805}], 'summary': 'Emphasizing the importance of independent work for homework assignments and collaboration for discussions.', 'duration': 28.861, 'max_score': 2887.662, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/J8Eh7RqggsU/pics/J8Eh7RqggsU2887662.jpg'}], 'start': 2504.125, 'title': 'Logic systems and course logistics in ml', 'summary': "Delves into logic systems' ability to reason deeply in ml, showcasing potential for open-ended tasks, and covers course prerequisites, objectives, and logistics, emphasizing collaboration and adherence to the honor code.", 'chapters': [{'end': 2566.072, 'start': 2504.125, 'title': 'Logic systems and reasoning in ml', 'summary': 'Discusses how logic systems can reason deeply with diverse and rich experiences, contrasting with typical ml systems, and demonstrates the capacity to understand and act based on one statement, showcasing the potential for open-ended tasks.', 'duration': 61.947, 'highlights': ['Logic systems can reason deeply with diverse and rich experiences, showcasing a different approach from typical ML systems.', 'The system can understand and act based on one statement, demonstrating its capacity for open-ended tasks.', "Typical ML systems require millions of examples for one task, contrasting with the system's ability to understand based on one statement."]}, {'end': 3022.615, 'start': 2567.475, 'title': 'Course logistics and expectations', 'summary': 'Covers the course prerequisites, objectives, and logistics, including details about homeworks, exams, and the project, emphasizing the importance of collaboration and adherence to the honor code while providing guidance on group formation and project milestones.', 'duration': 455.14, 'highlights': ['The course prerequisites include programming, discrete math, and probability, with an emphasis on math and programming as core elements for doing interesting things in AI. The course requires proficiency in programming, discrete math, and probability, highlighting the importance of math and programming as core elements for engaging in AI.', 'The coursework includes eight homeworks, a competition for extra credit, and an autograder that runs on all test cases but provides feedback on only a subset, akin to a train set and a test set in machine learning. The coursework consists of eight homeworks, which include a competition for extra credit and use an autograder that runs on all test cases but provides feedback on only a subset, similar to a train set and a test set in machine learning.', 'The exam assesses the ability to apply learned knowledge to solve new problems and differs from the homework in terms of problem-solving nature, being written and closed-book with the allowance of a one-page notes reference. The exam evaluates the application of learned knowledge to solve new problems, differing from the homework in terms of problem-solving nature, format, and materials permitted.', 'The project, conducted in groups of three, involves several milestones, including a proposal, progress report, poster session, and final report, and offers significant freedom and structure, with each group assigned a CA mentor for assistance. The project, undertaken in groups of three, encompasses various milestones and provides significant freedom and structure, with each group assigned a CA mentor and encouraged to seek additional assistance when needed.', 'The honor code emphasizes the importance of independent work on homeworks and prohibits code sharing, copying from GitHub, and posting assignments on GitHub, with the use of Moss to detect code duplication, and encourages collaboration and discussion while ensuring individual coding efforts. The honor code stresses the significance of independent work on homeworks, prohibits code sharing and posting assignments on GitHub while allowing collaboration and discussion, with the use of Moss to detect code duplication and ensure adherence to the rules.']}], 'duration': 518.49, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/J8Eh7RqggsU/pics/J8Eh7RqggsU2504125.jpg', 'highlights': ['Logic systems can reason deeply with diverse and rich experiences, showcasing a different approach from typical ML systems.', 'The system can understand and act based on one statement, demonstrating its capacity for open-ended tasks.', 'The course prerequisites include programming, discrete math, and probability, with an emphasis on math and programming as core elements for doing interesting things in AI.', 'The coursework consists of eight homeworks, which include a competition for extra credit and use an autograder that runs on all test cases but provides feedback on only a subset, similar to a train set and a test set in machine learning.', 'The exam evaluates the application of learned knowledge to solve new problems, differing from the homework in terms of problem-solving nature, format, and materials permitted.', 'The project, undertaken in groups of three, encompasses various milestones and provides significant freedom and structure, with each group assigned a CA mentor and encouraged to seek additional assistance when needed.', 'The honor code stresses the significance of independent work on homeworks, prohibits code sharing and posting assignments on GitHub while allowing collaboration and discussion, with the use of Moss to detect code duplication and ensure adherence to the rules.']}, {'end': 3635.354, 'segs': [{'end': 3111.237, 'src': 'embed', 'start': 3082.374, 'weight': 0, 'content': [{'end': 3086.697, 'text': "Okay So what is optimization? There's two flavors of optimization that we care about.", 'start': 3082.374, 'duration': 4.323}, {'end': 3091.861, 'text': "There's, uh, discrete optimization where you're trying to find the best, uh, discrete object.", 'start': 3087.218, 'duration': 4.643}, {'end': 3099.147, 'text': "For example, you're trying to find the best, uh, path or some- the path P that minimizes the cost of that path.", 'start': 3091.901, 'duration': 7.246}, {'end': 3106.373, 'text': "Um, we're going to talk about one algorithmic tool, um based on dynamic programming,", 'start': 3100.207, 'duration': 6.166}, {'end': 3111.237, 'text': 'which is a very powerful way of solving these um complex optimization problems.', 'start': 3106.373, 'duration': 4.864}], 'summary': 'Optimization involves discrete and dynamic programming for solving complex problems.', 'duration': 28.863, 'max_score': 3082.374, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/J8Eh7RqggsU/pics/J8Eh7RqggsU3082374.jpg'}, {'end': 3446.643, 'src': 'embed', 'start': 3409.394, 'weight': 2, 'content': [{'end': 3416.417, 'text': 'But this just introduces a lot of, um, you know, ways of doing it which all kind of result in the same answer.', 'start': 3409.394, 'duration': 7.023}, {'end': 3424.32, 'text': "So why don't we just start more systematically at one end and then just proceed and try to chisel off the problem?", 'start': 3417.077, 'duration': 7.243}, {'end': 3425.861, 'text': "um kind of, let's say, from the end?", 'start': 3424.32, 'duration': 1.541}, {'end': 3433.451, 'text': 'Okay So, um, start at the end.', 'start': 3427.466, 'duration': 5.985}, {'end': 3443.84, 'text': 'Okay So, so now we have this, um, problem.', 'start': 3437.575, 'duration': 6.265}, {'end': 3446.643, 'text': "I'm gonna draw a problem in a little box here.", 'start': 3443.86, 'duration': 2.783}], 'summary': 'Introducing various approaches to solve a problem, aiming for a systematic and progressive method.', 'duration': 37.249, 'max_score': 3409.394, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/J8Eh7RqggsU/pics/J8Eh7RqggsU3409394.jpg'}, {'end': 3644.801, 'src': 'embed', 'start': 3612.938, 'weight': 1, 'content': [{'end': 3613.338, 'text': 'Yeah, yeah.', 'start': 3612.938, 'duration': 0.4}, {'end': 3613.758, 'text': "That's it.", 'start': 3613.378, 'duration': 0.38}, {'end': 3614.499, 'text': 'Yeah Yeah.', 'start': 3613.878, 'duration': 0.621}, {'end': 3615.76, 'text': 'Yeah Great idea.', 'start': 3614.759, 'duration': 1.001}, {'end': 3617.141, 'text': "Let's do dynamic programming.", 'start': 3615.86, 'duration': 1.281}, {'end': 3621.344, 'text': "Um, so that's what I'm kinda trying to build up from, uh, build up to.", 'start': 3617.501, 'duration': 3.843}, {'end': 3626.567, 'text': 'Okay, So, um, So, if you look at this,', 'start': 3621.804, 'duration': 4.763}, {'end': 3633.613, 'text': 'so dynamic programming is a kind of general technique that essentially allows you to express a more complicated problem than a simpler problem.', 'start': 3626.567, 'duration': 7.046}, {'end': 3635.354, 'text': "So let's start with this problem.", 'start': 3634.213, 'duration': 1.141}, {'end': 3641.398, 'text': 'If we start at the end, um, if the two match, then well, we can just immediately uh, you know,', 'start': 3635.414, 'duration': 5.984}, {'end': 3644.801, 'text': "delete these two and that's- it's gonna be the same right?", 'start': 3641.398, 'duration': 3.403}], 'summary': 'Discussing dynamic programming for problem-solving.', 'duration': 31.863, 'max_score': 3612.938, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/J8Eh7RqggsU/pics/J8Eh7RqggsU3612938.jpg'}], 'start': 3023.155, 'title': 'Optimization techniques in inference and learning', 'summary': 'Discusses the importance of optimization in inference and learning, highlighting discrete and continuous optimization, and emphasizing the use of dynamic programming. it also explains gradient descent for continuous optimization and its effectiveness, using examples and introducing the concept of dynamic programming.', 'chapters': [{'end': 3153.452, 'start': 3023.155, 'title': 'Optimization techniques in inference and learning', 'summary': 'Discusses the importance of optimization in inference and learning, highlighting the two flavors of optimization: discrete and continuous, and emphasizing the use of dynamic programming for solving complex optimization problems.', 'duration': 130.297, 'highlights': ['The chapter emphasizes the importance of working in groups of three for increased productivity and mentions that solo projects are not expected to cover one-third of the work.', 'The instructor delves into technical details and focuses on the inference and learning components of the course, particularly discussing optimization through the lens of discrete and continuous optimization.', 'The chapter explains the concept of optimization, covering its two flavors: discrete optimization, where dynamic programming is highlighted as a powerful way of solving complex problems, and continuous optimization, which involves finding the best vector of real numbers that minimizes some objective function.']}, {'end': 3635.354, 'start': 3154.372, 'title': 'Gradient descent for continuous optimization', 'summary': 'Explains the principle of gradient descent and its effectiveness in solving continuous optimization problems, using the example of computing edit distance between two strings and introduces the concept of dynamic programming.', 'duration': 480.982, 'highlights': ['The chapter introduces the concept of computing edit distance between two strings, illustrating how it is a fundamental problem in AI and provides examples of different edit distances. The problem of computing edit distance between two strings is explained, demonstrating its significance in AI and providing examples of different edit distances for illustrative purposes.', 'The discussion emphasizes the need to simplify the problem and reduce it to a simpler form, leading to the concept of starting at the end and systematically chiseling off the problem. The importance of simplifying the problem and the concept of starting at the end to systematically reduce the problem is emphasized, providing a structured approach to solving the edit distance problem.', 'The concept of dynamic programming is introduced as a general technique to simplify complex problems into simpler subproblems, leading to the solution of the edit distance problem. The concept of dynamic programming is introduced as a technique to simplify complex problems into simpler subproblems, leading to the solution of the edit distance problem and setting the stage for further exploration of dynamic programming.']}], 'duration': 612.199, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/J8Eh7RqggsU/pics/J8Eh7RqggsU3023155.jpg', 'highlights': ['The chapter explains the concept of optimization, covering its two flavors: discrete optimization, where dynamic programming is highlighted as a powerful way of solving complex problems, and continuous optimization, which involves finding the best vector of real numbers that minimizes some objective function.', 'The concept of dynamic programming is introduced as a general technique to simplify complex problems into simpler subproblems, leading to the solution of the edit distance problem.', 'The discussion emphasizes the need to simplify the problem and reduce it to a simpler form, leading to the concept of starting at the end and systematically chiseling off the problem.']}, {'end': 4510.061, 'segs': [{'end': 3959.485, 'src': 'embed', 'start': 3905.494, 'weight': 0, 'content': [{'end': 3909.616, 'text': "that's simple enough that um where s equals t or something.", 'start': 3905.494, 'duration': 4.122}, {'end': 3910.437, 'text': "then you're done.", 'start': 3909.616, 'duration': 0.821}, {'end': 3915.659, 'text': "Um, but then, you know, how- how do I- how do I know? Suppose I've solved this.", 'start': 3911.337, 'duration': 4.322}, {'end': 3920.921, 'text': 'Suppose someone just told you, okay, I know this, um, cost, I know this cost, I know this cost.', 'start': 3915.799, 'duration': 5.122}, {'end': 3927.696, 'text': 'What- what should you do? Yeah, you should take the minimum, right? Like remember, we wanna minimize the edit distance.', 'start': 3921.602, 'duration': 6.094}, {'end': 3929.638, 'text': "So, um, there's three things you can do.", 'start': 3927.776, 'duration': 1.862}, {'end': 3935.843, 'text': 'Each of them has some cost of doing that action, which is, you know, one, every edit is the same cost.', 'start': 3930.418, 'duration': 5.425}, {'end': 3939.346, 'text': "And then there's a cost of, you know, continuing to do whatever you're doing.", 'start': 3936.163, 'duration': 3.183}, {'end': 3941.788, 'text': "And so we're just gonna take the minimum over those.", 'start': 3940.066, 'duration': 1.722}, {'end': 3959.485, 'text': "Yeah Yeah, so I was trying to argue that if you're going to right to left, it's without lots of generality.", 'start': 3943.029, 'duration': 16.456}], 'summary': 'Algorithmic approach to minimize edit distance using minimum costs.', 'duration': 53.991, 'max_score': 3905.494, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/J8Eh7RqggsU/pics/J8Eh7RqggsU3905494.jpg'}, {'end': 4024.287, 'src': 'embed', 'start': 3997.398, 'weight': 2, 'content': [{'end': 4001.94, 'text': "So, um, So I'm going to define a function.", 'start': 3997.398, 'duration': 4.542}, {'end': 4002.981, 'text': 'It takes two strings.', 'start': 4001.98, 'duration': 1.001}, {'end': 4007.562, 'text': "And then I'm going to define a recurrence.", 'start': 4003.701, 'duration': 3.861}, {'end': 4011.523, 'text': "So recurrences is, I guess, one word I haven't really used.", 'start': 4008.382, 'duration': 3.141}, {'end': 4019.406, 'text': 'But this is really the way you should think about dynamic programs and this idea of taking complex problems and breaking it down.', 'start': 4011.583, 'duration': 7.823}, {'end': 4024.287, 'text': "It's going to show up in search problems, MDPs, and games.", 'start': 4019.486, 'duration': 4.801}], 'summary': 'Defining a function with recurrences for dynamic programming, used in search problems, mdps, and games.', 'duration': 26.889, 'max_score': 3997.398, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/J8Eh7RqggsU/pics/J8Eh7RqggsU3997398.jpg'}, {'end': 4441.78, 'src': 'embed', 'start': 4403.934, 'weight': 3, 'content': [{'end': 4404.975, 'text': 'Yeah, you can memo.', 'start': 4403.934, 'duration': 1.041}, {'end': 4409.998, 'text': 'I think I heard the word memoize, which is another way to kind of think about memoize plus.', 'start': 4404.975, 'duration': 5.023}, {'end': 4413.901, 'text': 'um, I guess recurrence is is dynamic programming, I guess.', 'start': 4409.998, 'duration': 3.903}, {'end': 4421.446, 'text': "Um, so I'm gonna show you kind of this, um, way to do it which is pretty un-invasive.", 'start': 4414.742, 'duration': 6.704}, {'end': 4429.292, 'text': 'Um, and generally I recommend people, well, get the slow version working and then try to make it faster.', 'start': 4422.567, 'duration': 6.725}, {'end': 4431.994, 'text': "Don't try to be, you know, too slick at once.", 'start': 4429.332, 'duration': 2.662}, {'end': 4441.78, 'text': "Okay So I'm gonna make this cache, right? And I'm gonna say if mn is in the cache, then I'm gonna return whatever's in the cache.", 'start': 4432.974, 'duration': 8.806}], 'summary': 'Explaining memoization and dynamic programming, recommending starting with slow version and then optimizing.', 'duration': 37.846, 'max_score': 4403.934, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/J8Eh7RqggsU/pics/J8Eh7RqggsU4403934.jpg'}], 'start': 3635.414, 'title': 'Edit distance algorithm and dynamic programming recurrence', 'summary': 'Discusses the edit distance algorithm for transforming strings, considering substitution, insertion, and deletion, aiming to minimize edit distance. it also explains defining a recurrence for computing minimum edit distance, using memoization to avoid exponential time complexity.', 'chapters': [{'end': 3997.358, 'start': 3635.414, 'title': 'Edit distance algorithm', 'summary': 'Discusses the edit distance algorithm for determining the minimum number of operations needed to transform one string into another, considering actions such as substitution, insertion, and deletion, with the goal of minimizing the edit distance.', 'duration': 361.944, 'highlights': ['The chapter explores the concept and applications of the edit distance algorithm, focusing on the minimum number of operations required to transform one string into another. The concept and applications of the edit distance algorithm are explored, emphasizing the minimum number of operations required for string transformation.', 'The algorithm considers actions such as substitution, insertion, and deletion to minimize the edit distance between two strings. The algorithm encompasses actions like substitution, insertion, and deletion to minimize the edit distance between two strings.', 'The discussion delves into the cost associated with each action and emphasizes the goal of minimizing the edit distance by selecting the minimum cost option. The cost associated with each action is discussed, highlighting the objective of minimizing the edit distance by selecting the option with the minimum cost.']}, {'end': 4510.061, 'start': 3997.398, 'title': 'Dynamic programming recurrence', 'summary': 'Explains the concept of defining a recurrence to compute the minimum edit distance between two strings, with consideration of base cases and optimization using memoization to avoid exponential time complexity.', 'duration': 512.663, 'highlights': ['The chapter introduces the concept of defining a recurrence to compute the minimum edit distance between two strings, emphasizing the importance of breaking down complex problems and its relevance in search problems, MDPs, and games.', 'The chapter discusses the computation of base cases, including scenarios where one string is empty, and the handling of matching and non-matching cases, with clear illustration of the costs and reductions involved.', 'The chapter addresses the issue of exponential time complexity in the initial implementation and introduces the concept of memoization as an optimization technique to avoid redundant computations and improve efficiency.']}], 'duration': 874.647, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/J8Eh7RqggsU/pics/J8Eh7RqggsU3635414.jpg', 'highlights': ['The algorithm encompasses actions like substitution, insertion, and deletion to minimize the edit distance between two strings.', 'The discussion delves into the cost associated with each action and emphasizes the goal of minimizing the edit distance by selecting the option with the minimum cost.', 'The chapter introduces the concept of defining a recurrence to compute the minimum edit distance between two strings, emphasizing the importance of breaking down complex problems and its relevance in search problems, MDPs, and games.', 'The chapter addresses the issue of exponential time complexity in the initial implementation and introduces the concept of memoization as an optimization technique to avoid redundant computations and improve efficiency.']}, {'end': 5238.145, 'segs': [{'end': 4573.02, 'src': 'embed', 'start': 4538.769, 'weight': 1, 'content': [{'end': 4543.992, 'text': 'trying to formulate it as a recurrence of a complicated problem in terms of smaller problems.', 'start': 4538.769, 'duration': 5.223}, {'end': 4551.036, 'text': 'Um, and like I said before, this is gonna kind of show up, um, um, over and over again in this class.', 'start': 4544.792, 'duration': 6.244}, {'end': 4567.256, 'text': 'Yeah Yeah, so the question is why does this reduce, uh, redundancy? Is that right? Um, so maybe I can do it kind of pictorially.', 'start': 4553.797, 'duration': 13.459}, {'end': 4573.02, 'text': "Um. if you think about, let's say, you have a um a problem here right?", 'start': 4568.077, 'duration': 4.943}], 'summary': 'Formulating complex problems as smaller recurrences, reducing redundancy in class.', 'duration': 34.251, 'max_score': 4538.769, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/J8Eh7RqggsU/pics/J8Eh7RqggsU4538769.jpg'}, {'end': 4629.591, 'src': 'embed', 'start': 4604.277, 'weight': 0, 'content': [{'end': 4610.059, 'text': 'Whereas, um, if you do memoization, you pay in the number of nodes here, which a lot of this is shared.', 'start': 4604.277, 'duration': 5.782}, {'end': 4616.84, 'text': "Like here, um, you know, once you compute this, no matter if you're coming from here or here, you're kind of using the same value.", 'start': 4610.099, 'duration': 6.741}, {'end': 4621.145, 'text': "Okay So let's, let's move on.", 'start': 4619.284, 'duration': 1.861}, {'end': 4629.591, 'text': "So the second problem, um, we're gonna talk about is, uh, has to do with continuous optimization.", 'start': 4622.066, 'duration': 7.525}], 'summary': 'Memoization shares computed values, reducing node count. continuous optimization is the second problem.', 'duration': 25.314, 'max_score': 4604.277, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/J8Eh7RqggsU/pics/J8Eh7RqggsU4604277.jpg'}, {'end': 4750.454, 'src': 'embed', 'start': 4713.226, 'weight': 2, 'content': [{'end': 4715.668, 'text': 'Okay, So, um, how do you do this??', 'start': 4713.226, 'duration': 2.442}, {'end': 4730.137, 'text': "Um, so, there's a principle called least squares, which says well, if you give me a line which is given in this case by a slope w, um,", 'start': 4716.588, 'duration': 13.549}, {'end': 4732.439, 'text': "I'm going to tell you how bad this is.", 'start': 4730.137, 'duration': 2.302}, {'end': 4739.504, 'text': 'And badness is measured by looking at all the training points and looking at these distances.', 'start': 4733.38, 'duration': 6.124}, {'end': 4750.454, 'text': "Right So here I have, um, you know, this particular- a particular, let's say, point, you know, x i.", 'start': 4740.686, 'duration': 9.768}], 'summary': 'Explaining least squares principle for measuring badness in line fitting', 'duration': 37.228, 'max_score': 4713.226, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/J8Eh7RqggsU/pics/J8Eh7RqggsU4713226.jpg'}, {'end': 5012.919, 'src': 'embed', 'start': 4983.269, 'weight': 3, 'content': [{'end': 4988.993, 'text': 'compute the derivative and it says keep on going this way, maybe you overshoot and then you come back and then you know, hopefully, uh,', 'start': 4983.269, 'duration': 5.724}, {'end': 4990.714, 'text': "you'll end up kind of at the minimum.", 'start': 4988.993, 'duration': 1.721}, {'end': 4997.178, 'text': "Okay? So, uh, let's try to see what this looks like in code.", 'start': 4991.774, 'duration': 5.404}, {'end': 5006.977, 'text': 'So gradient descent is, you know, one of the simplest algorithms,', 'start': 5003.635, 'duration': 3.342}, {'end': 5012.919, 'text': 'but it really underlies essentially all the algorithms that you people use in machine learning.', 'start': 5006.977, 'duration': 5.942}], 'summary': 'Gradient descent is a fundamental algorithm in machine learning, computing derivatives to find the minimum.', 'duration': 29.65, 'max_score': 4983.269, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/J8Eh7RqggsU/pics/J8Eh7RqggsU4983269.jpg'}, {'end': 5233.802, 'src': 'heatmap', 'start': 5193.347, 'weight': 1, 'content': [{'end': 5206.611, 'text': 'So iteration, um, print out the function and, um, T value, okay? All right.', 'start': 5193.347, 'duration': 13.264}, {'end': 5209.012, 'text': "So let's compute the gradient.", 'start': 5206.711, 'duration': 2.301}, {'end': 5212.406, 'text': 'And um.', 'start': 5211.185, 'duration': 1.221}, {'end': 5217.45, 'text': 'so you can see that the iteration we first start out with w equals 0, then it moves to 0.3,.', 'start': 5212.406, 'duration': 5.044}, {'end': 5223.674, 'text': "um, and then it moves to 0.7999999, and then it looks like it's converging to 0.8..", 'start': 5217.45, 'duration': 6.224}, {'end': 5229.178, 'text': 'And meanwhile, the function value is going down from 20 to, uh, 7.2, which happens to be the optimal answer.', 'start': 5223.674, 'duration': 5.504}, {'end': 5230.86, 'text': 'So the correct answer here is 0.8.', 'start': 5229.459, 'duration': 1.401}, {'end': 5233.802, 'text': "Okay So that's it.", 'start': 5230.86, 'duration': 2.942}], 'summary': 'Iterative function converges to optimal answer 0.8, reducing function value from 20 to 7.2.', 'duration': 40.455, 'max_score': 5193.347, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/J8Eh7RqggsU/pics/J8Eh7RqggsU5193347.jpg'}], 'start': 4511.782, 'title': 'Dynamic programming and regression in ml', 'summary': 'Discusses dynamic programming, highlighting its reduction of redundancy through memoization. it also introduces regression in machine learning and demonstrates the optimization through gradient descent to achieve a regression for predicting housing prices.', 'chapters': [{'end': 4629.591, 'start': 4511.782, 'title': 'Dynamic programming and memoization', 'summary': 'Discusses dynamic programming, showcasing its reduction of redundancy through memoization, and continuous optimization.', 'duration': 117.809, 'highlights': ['Dynamic programming involves solving a problem by formulating it as a recurrence of smaller problems, showing up repeatedly in the class.', 'Memoization reduces redundancy by sharing computed values, decreasing the computational cost compared to computing from scratch for each path.', 'Continuous optimization is the second problem discussed in the chapter.']}, {'end': 5238.145, 'start': 4630.291, 'title': 'Introduction to regression', 'summary': 'Introduces the concept of regression in machine learning, explaining the process of fitting a line through given data points using the principle of least squares and demonstrating the optimization through gradient descent, ultimately achieving a regression to predict housing prices.', 'duration': 607.854, 'highlights': ['The chapter explains the process of fitting a line through given data points using the principle of least squares, aiming to minimize the error by finding the line that best fits the points.', 'It introduces the principle of gradient descent as an optimization method, demonstrating how to iteratively adjust the slope to minimize the error function and achieve the best fit for the given data points.', 'The chapter demonstrates the application of gradient descent in code, showcasing the iterative process of adjusting the slope to minimize the error function and achieve the optimal solution.']}], 'duration': 726.363, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/J8Eh7RqggsU/pics/J8Eh7RqggsU4511782.jpg', 'highlights': ['Memoization reduces redundancy by sharing computed values, decreasing computational cost.', 'Dynamic programming involves solving a problem by formulating it as a recurrence of smaller problems.', 'The chapter explains the process of fitting a line through given data points using the principle of least squares.', 'It introduces the principle of gradient descent as an optimization method, demonstrating how to iteratively adjust the slope to minimize the error function.', 'Continuous optimization is the second problem discussed in the chapter.']}], 'highlights': ['The stanford cs221 ai course, led by percy and dorsa, covers ai evolution, fairness, modeling, logic systems, optimization techniques, and dynamic programming.', 'The students expressed a wide range of interests, including natural language processing, machine learning, and reinforcement learning, showcasing a diverse team.', "AI's increasing relevance and impact, including successful applications in gaming, document analysis, and its potential transformative effects on society and work.", 'Different notions of fairness and equality in AI pose a significant challenge in achieving consensus and addressing biases.', 'State-based models are a powerful approach to model the world, covering different types and applications such as robotics, motion planning, navigation, generation in natural language, and image generation.', 'Logic systems can reason deeply with diverse and rich experiences, showcasing a different approach from typical ML systems.', 'The concept of dynamic programming is introduced as a general technique to simplify complex problems into simpler subproblems, leading to the solution of the edit distance problem.', 'The algorithm encompasses actions like substitution, insertion, and deletion to minimize the edit distance between two strings.', 'Memoization reduces redundancy by sharing computed values, decreasing computational cost.', 'The chapter explains the process of fitting a line through given data points using the principle of least squares.']}