title
Stanford CS330: Multi-Task and Meta-Learning, 2019 | Lecture 1 - Introduction & Overview

description
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai Assistant Professor Chelsea Finn, Stanford University http://cs330.stanford.edu/ 0:00 Introduction 1:06 Information & Resources 1:36 Pre-Requisites and Enrollment 3:26 Assignment Infrastructure 5:04 Topics We Won't Cover 5:39 Course Format 7:04 Assignments & Final Project 9:05 Homework Today 11:45 Two more things 13:00 How can we enable agents to learn skills in the real world? 25:34 Broad generalization 30:25 What is a task? 33:41 Critical Assumption 43:49 These algorithms are continuing to play a fundamental role in machine learning research. 45:44 These algorithms are playing a fundamental, and increasing role in machine learning research

detail
{'title': 'Stanford CS330: Multi-Task and Meta-Learning, 2019 | Lecture 1 - Introduction & Overview', 'heatmap': [{'end': 1935.817, 'start': 1903.996, 'weight': 0.701}, {'end': 2370.174, 'start': 2193.957, 'weight': 0.777}, {'end': 2513.728, 'start': 2421.536, 'weight': 0.816}], 'summary': 'The lecture covers course logistics, prerequisites, enrollment, homework infrastructure, challenges in learning, limitations of single task learning, evolution of computer vision, multitask learning, meta-learning, task structure, and the potential of multi-task learning in improving performance in machine translation and robotic learning.', 'chapters': [{'end': 114.652, 'segs': [{'end': 114.652, 'src': 'embed', 'start': 51.981, 'weight': 0, 'content': [{'end': 55.322, 'text': 'So those are the first and last one and able to make it today.', 'start': 51.981, 'duration': 3.341}, {'end': 58.303, 'text': 'Um, but there are also TAs for the course as well.', 'start': 55.342, 'duration': 2.961}, {'end': 62.984, 'text': "Um, there'll be great resources for you as you go about taking the course.", 'start': 59.323, 'duration': 3.661}, {'end': 68.326, 'text': "Um, the course website is shown here and there's a lot of information on the course website, uh,", 'start': 64.063, 'duration': 4.263}, {'end': 70.526, 'text': "beyond what we'll be kind of covering in the logistics here.", 'start': 68.326, 'duration': 2.2}, {'end': 72.567, 'text': 'Um, also we have a Piazza.', 'start': 70.546, 'duration': 2.021}, {'end': 76.368, 'text': 'This will be for- for questions as- as they come up, uh, in the course.', 'start': 72.627, 'duration': 3.741}, {'end': 84.037, 'text': 'This is the staff mailing list as well, and each of us will be holding one hour of office hours per week.', 'start': 77.967, 'duration': 6.07}, {'end': 89.725, 'text': 'My office hours are Wednesday after class, and the other office hours will be posted on the course website.', 'start': 84.477, 'duration': 5.248}, {'end': 93.051, 'text': "We'll have office hours for the first time on Wednesday this week.", 'start': 90.046, 'duration': 3.005}, {'end': 97.954, 'text': 'Great So prerequisites and enrollment.', 'start': 95.011, 'duration': 2.943}, {'end': 107.304, 'text': 'The main prerequisite is machine learning experience, basically, covering machine learning and reinforcement learning, CS229 or equivalent.', 'start': 98.935, 'duration': 8.369}, {'end': 114.652, 'text': 'We highly recommend having some previous reinforcement learning experience because a large portion of the course will include topics in reinforcement learning.', 'start': 107.865, 'duration': 6.787}], 'summary': 'Course offers tas, resources, office hours, and requires machine learning experience.', 'duration': 62.671, 'max_score': 51.981, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/0rZtSwNOTQo/pics/0rZtSwNOTQo51981.jpg'}], 'start': 5.714, 'title': 'Cs330: course logistics', 'summary': 'Covers the course logistics, resources, and prerequisites for cs330, including the staff, website, piazza, and office hours, emphasizing the need for machine learning and reinforcement learning experience.', 'chapters': [{'end': 114.652, 'start': 5.714, 'title': 'Cs330: course logistics and resources', 'summary': 'Covers the course logistics, resources, and prerequisites for cs330, including the staff, website, piazza, and office hours, and emphasizes the need for machine learning and reinforcement learning experience.', 'duration': 108.938, 'highlights': ['The course staff includes four TAs, and the course website and Piazza will serve as important resources for the students.', 'Machine learning experience is a main prerequisite for the course, with a recommendation for previous reinforcement learning experience due to the significant portion of the course dedicated to this topic.', 'The professor, Chelsea Finn, emphasizes the importance of machine learning and reinforcement learning experience and will hold office hours on Wednesdays.']}], 'duration': 108.938, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/0rZtSwNOTQo/pics/0rZtSwNOTQo5714.jpg', 'highlights': ['The course staff includes four TAs, and the course website and Piazza are important resources for the students.', 'Machine learning experience is a main prerequisite for the course.', 'The professor, Chelsea Finn, will hold office hours on Wednesdays.', 'Reinforcement learning experience is recommended due to the significant portion of the course dedicated to this topic.']}, {'end': 629.927, 'segs': [{'end': 151.224, 'src': 'embed', 'start': 116.414, 'weight': 0, 'content': [{'end': 123.257, 'text': "if you're not currently enrolled, please fill out the enrollment form on the website, uh, and we will, uh, kind of go through that.", 'start': 116.414, 'duration': 6.843}, {'end': 131.041, 'text': "We do still have some open spots in the course, uh, and we'll, we'll go through that, uh, probably around Tuesday or Wednesday this week.", 'start': 123.277, 'duration': 7.764}, {'end': 135.931, 'text': "So please fill it out as soon as possible if you're not enrolled, um, Yeah.", 'start': 131.081, 'duration': 4.85}, {'end': 139.054, 'text': "And then if, if you fill out the form and didn't receive a permission number yet,", 'start': 135.951, 'duration': 3.103}, {'end': 142.757, 'text': "it's either because you filled out the form very recently or because, um,", 'start': 139.054, 'duration': 3.703}, {'end': 147, 'text': "we weren't quite sure if you had the kind of necessary experience in order to succeed in this class.", 'start': 142.757, 'duration': 4.243}, {'end': 151.224, 'text': "Um, and we, so we'll, we'll get back to everyone by, by Wednesday this week.", 'start': 147.781, 'duration': 3.443}], 'summary': 'Enrollment form available, open spots in course, processing by wednesday', 'duration': 34.81, 'max_score': 116.414, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/0rZtSwNOTQo/pics/0rZtSwNOTQo116414.jpg'}, {'end': 316.777, 'src': 'embed', 'start': 287.376, 'weight': 3, 'content': [{'end': 291.422, 'text': 'as kind of indicated by the title of the course as well as an emphasis on reinforcement learning.', 'start': 287.376, 'duration': 4.046}, {'end': 296.729, 'text': 'So, uh, a little under half of the course is going to be kind of focusing on different topics in reinforcement learning.', 'start': 291.482, 'duration': 5.247}, {'end': 301.535, 'text': 'because, uh, that is where some of these, some of these types of techniques become a lot more interesting and a lot more challenging.', 'start': 296.729, 'duration': 4.806}, {'end': 310.809, 'text': "Um, topics we won't cover, uh, due to kind of the constraints of, of how much time we have in this course, we won't cover topics in AutoML.", 'start': 303.44, 'duration': 7.369}, {'end': 316.777, 'text': 'Uh, this includes things like architecture search, like hyperparameter optimization, and learning optimizers.', 'start': 311.33, 'duration': 5.447}], 'summary': 'About half of the course focuses on reinforcement learning, excluding automl topics like architecture search and hyperparameter optimization.', 'duration': 29.401, 'max_score': 287.376, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/0rZtSwNOTQo/pics/0rZtSwNOTQo287376.jpg'}, {'end': 440.377, 'src': 'embed', 'start': 412.612, 'weight': 2, 'content': [{'end': 420.3, 'text': "So, uh, in future offerings we'll probably be introducing more lectures into the course and fewer student reading sessions. um as uh, as years go on.", 'start': 412.612, 'duration': 7.688}, {'end': 425.139, 'text': 'Okay more details on assignments.', 'start': 422.122, 'duration': 3.017}, {'end': 426.821, 'text': "So we're gonna have three homework assignments.", 'start': 425.199, 'duration': 1.622}, {'end': 431.887, 'text': 'The first homework will cover things ranging from multitask data processing and black box meta-learning methods.', 'start': 426.881, 'duration': 5.006}, {'end': 434.17, 'text': "So you'll actually be implementing, um,", 'start': 431.907, 'duration': 2.263}, {'end': 440.377, 'text': 'how to actually kind of go about processing data in a multitask fashion such that you can apply these types of algorithms to them.', 'start': 434.17, 'duration': 6.207}], 'summary': 'Future course offerings will have more lectures and fewer reading sessions, with three homework assignments covering topics like multitask data processing and black box meta-learning methods.', 'duration': 27.765, 'max_score': 412.612, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/0rZtSwNOTQo/pics/0rZtSwNOTQo412612.jpg'}], 'start': 116.414, 'title': 'Enrollment and course logistics', 'summary': 'Discusses enrollment process, open spots, deadline, and permission numbers. it also covers course logistics, topics, assignments, and emphasizes tensorflow and reinforcement learning, with mention of pytorch as an alternative.', 'chapters': [{'end': 151.224, 'start': 116.414, 'title': 'Enrollment update and permission numbers', 'summary': 'Discusses the enrollment process for the course, mentioning the availability of open spots, the deadline for enrollment, and the timeline for receiving permission numbers by wednesday.', 'duration': 34.81, 'highlights': ['The enrollment form should be filled out on the website for those not currently enrolled, and there are open spots in the course, with a deadline around Tuesday or Wednesday this week.', 'If a permission number has not been received after filling out the form, it could be due to recent submission or uncertainty about the necessary experience, with the assurance of getting back to everyone by Wednesday this week.']}, {'end': 629.927, 'start': 152.557, 'title': 'Course logistics and topics', 'summary': 'Covers the logistics of the course, including recordings, remote students, assignments, course topics, format, and grading criteria, with an emphasis on tensorflow and reinforcement learning. it also details the three types of course sessions and outlines the three homework assignments and the final project, along with guidelines and milestones. further, it mentions the need for active participation, future changes, and the use of pytorch as an alternative to tensorflow.', 'duration': 477.37, 'highlights': ['The course covers logistics, including recordings, remote students, assignments, course topics, format, and grading criteria, with an emphasis on TensorFlow and reinforcement learning.', 'Details of the three types of course sessions are outlined, along with the three homework assignments and the final project, including guidelines and milestones.', 'Active participation in discussions of papers and the need for future changes are mentioned.', 'The use of PyTorch as an alternative to TensorFlow is addressed.']}], 'duration': 513.513, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/0rZtSwNOTQo/pics/0rZtSwNOTQo116414.jpg', 'highlights': ['Enrollment form should be filled out on the website for open spots, deadline this week', 'Permission number uncertainty, assurance of response by Wednesday this week', 'Course covers logistics, assignments, topics, format, and grading criteria', 'Emphasis on TensorFlow and reinforcement learning, with mention of PyTorch as an alternative', 'Details of course sessions, homework assignments, final project, and guidelines']}, {'end': 1107.422, 'segs': [{'end': 688.917, 'src': 'embed', 'start': 629.927, 'weight': 0, 'content': [{'end': 637.332, 'text': 'but it will probably involve writing a fair amount of code in addition to the code that you would have to write in TensorFlow because of the the infrastructure being written in TensorFlow.', 'start': 629.927, 'duration': 7.405}, {'end': 647.392, 'text': 'So the first homework will be runnable on a laptop.', 'start': 645.031, 'duration': 2.361}, {'end': 653.276, 'text': "The second homework will, um will be a little bit more compute heavy, and we're still looking into various um,", 'start': 647.432, 'duration': 5.844}, {'end': 661.22, 'text': "various cloud compute options for that that we'll be able to provide for people that don't have um GPUs, for example, to be able to run their code on.", 'start': 653.276, 'duration': 7.944}, {'end': 667.163, 'text': "And the third one will probably be runnable on a laptop as well, um, but we're still finalizing, uh, that assignment.", 'start': 661.58, 'duration': 5.583}, {'end': 681.231, 'text': "Yeah Um, will we be using 2 or 1.1 port? We'll be using TensorFlow, but not TensorFlow 2.", 'start': 669.264, 'duration': 11.967}, {'end': 688.917, 'text': "Yeah Um, that's a good question.", 'start': 681.232, 'duration': 7.685}], 'summary': 'Homework 1 and 3 will run on a laptop, while homework 2 will require more compute power and cloud options for non-gpu users.', 'duration': 58.99, 'max_score': 629.927, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/0rZtSwNOTQo/pics/0rZtSwNOTQo629927.jpg'}, {'end': 863.582, 'src': 'embed', 'start': 832.221, 'weight': 5, 'content': [{'end': 834.683, 'text': 'Robots are faced with the real world and they have to deal with it.', 'start': 832.221, 'duration': 2.462}, {'end': 841.568, 'text': 'Uh, robots have to be able to generalize across tasks, across objects, across environments in order to be successful in real-world settings.', 'start': 835.003, 'duration': 6.565}, {'end': 845.431, 'text': 'Uh, they need some sort of common sense understanding in order to do well.', 'start': 841.588, 'duration': 3.843}, {'end': 850.874, 'text': "And lastly, uh, supervision can't really readily be taken for granted.", 'start': 847.072, 'duration': 3.802}, {'end': 857.858, 'text': "Um, it's not easy to just provide labels or even figure out what labels mean in the context of getting a robot to do something.", 'start': 850.894, 'duration': 6.964}, {'end': 863.582, 'text': 'Uh so, I think that if you can build intelligent robots in the real world, uh,', 'start': 859.659, 'duration': 3.923}], 'summary': 'Robots need generalization, common sense, and supervision to succeed in the real world.', 'duration': 31.361, 'max_score': 832.221, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/0rZtSwNOTQo/pics/0rZtSwNOTQo832221.jpg'}, {'end': 957.571, 'src': 'embed', 'start': 931.89, 'weight': 4, 'content': [{'end': 937.633, 'text': "but if you kind of watch the robot in real-time, you'd actually be able to see the robot learning in real-time, um, through trial and error.", 'start': 931.89, 'duration': 5.743}, {'end': 942.115, 'text': "I guess what's really exciting about this is the robot isn't- isn't just kind of-.", 'start': 938.793, 'duration': 3.322}, {'end': 944.175, 'text': "this isn't just an algorithm for learning how to do that task.", 'start': 942.115, 'duration': 2.06}, {'end': 948.737, 'text': "it's an algorithm for- that can do really a wide range of manipulation tasks.", 'start': 944.175, 'duration': 4.562}, {'end': 952.159, 'text': 'Uh, and so in principle, this algorithm could be applied to a wide range of settings.', 'start': 949.138, 'duration': 3.021}, {'end': 957.571, 'text': 'Uh, now, one of the things that was a little bit disappointing about this is that, uh, it turns out the robots.', 'start': 953.488, 'duration': 4.083}], 'summary': 'Robot learns in real-time, wide range of manipulation tasks, applicable to various settings.', 'duration': 25.681, 'max_score': 931.89, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/0rZtSwNOTQo/pics/0rZtSwNOTQo931890.jpg'}], 'start': 629.927, 'title': 'Homework infrastructure and challenges in learning', 'summary': 'Covers infrastructure and requirements for three homework assignments, including their computational demands, and discusses challenges in enabling robots to learn skills in real-world environments, highlighting the limitations of current algorithms and the potential of reinforcement learning.', 'chapters': [{'end': 688.917, 'start': 629.927, 'title': 'Homework infrastructure and requirements', 'summary': 'Discusses the infrastructure and requirements for three homework assignments, with the first being runnable on a laptop, the second being more compute heavy, and the third still being finalized with the use of tensorflow but not tensorflow 2.', 'duration': 58.99, 'highlights': ["The second homework will be more compute heavy, and we're still looking into various cloud compute options for that. We'll be able to provide for people that don't have GPUs to run their code.", 'The first homework will be runnable on a laptop.', 'The third assignment will probably be runnable on a laptop as well, but still being finalized, using TensorFlow but not TensorFlow 2.']}, {'end': 1107.422, 'start': 688.937, 'title': 'Challenges in multitask learning and meta-learning', 'summary': 'Discusses the challenges in enabling robots to learn skills in the real world, the limitations of current algorithms in generalizing across different environments, and the potential of reinforcement learning algorithms in handling a wide range of manipulation tasks.', 'duration': 418.485, 'highlights': ['Robots learning skills in the real world', 'Challenges in generalizing across different environments', 'Potential of reinforcement learning algorithms in handling manipulation tasks']}], 'duration': 477.495, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/0rZtSwNOTQo/pics/0rZtSwNOTQo629927.jpg', 'highlights': ["The second homework will be more compute heavy, and we're still looking into various cloud compute options for that.", "We'll be able to provide for people that don't have GPUs to run their code.", 'The first homework will be runnable on a laptop.', 'The third assignment will probably be runnable on a laptop as well, but still being finalized, using TensorFlow but not TensorFlow 2.', 'Potential of reinforcement learning algorithms in handling manipulation tasks', 'Challenges in generalizing across different environments', 'Robots learning skills in the real world']}, {'end': 1778.826, 'segs': [{'end': 1199.626, 'src': 'embed', 'start': 1169.984, 'weight': 7, 'content': [{'end': 1174.726, 'text': "fundamentally rethink how we're actually uh kind of designing these algorithms in the first place.", 'start': 1169.984, 'duration': 4.742}, {'end': 1180.208, 'text': 'Um, so kind of the issue here was that it relied on uh, very detailed supervision,', 'start': 1176.687, 'duration': 3.521}, {'end': 1182.809, 'text': "very detailed guidance that can't be scaled to many different tasks.", 'start': 1180.208, 'duration': 2.601}, {'end': 1189.48, 'text': "Um, and this is not just a problem with robot learning, it's also a problem with kind of standard reinforcement learning algorithms as well.", 'start': 1184.657, 'duration': 4.823}, {'end': 1193.402, 'text': 'Uh, kind of things that learn how to play Atari games and locomotion.', 'start': 1189.5, 'duration': 3.902}, {'end': 1199.626, 'text': 'they require a lot and a lot of data, a lot of supervision in the form of reward functions, um, in order to learn effectively.', 'start': 1193.402, 'duration': 6.224}], 'summary': 'Rethink algorithm design due to scalability issues; extensive supervision needed for effective learning.', 'duration': 29.642, 'max_score': 1169.984, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/0rZtSwNOTQo/pics/0rZtSwNOTQo1169984.jpg'}, {'end': 1345.826, 'src': 'embed', 'start': 1321.161, 'weight': 4, 'content': [{'end': 1326.762, 'text': "Uh, and unfortunately there's a lot of reasons to care beyond robots and beyond kind of trying to build more general purpose machine learning systems.", 'start': 1321.161, 'duration': 5.601}, {'end': 1332.464, 'text': 'Um. and first I want to start by why do we care about deep multi-task learning and meta-learning?', 'start': 1328.083, 'duration': 4.381}, {'end': 1334.145, 'text': 'Why do we care about deep learning in particular?', 'start': 1332.504, 'duration': 1.641}, {'end': 1341.327, 'text': 'Um, actually, before I move on, are there any questions on the part that I mentioned before on the robots?', 'start': 1335.405, 'duration': 5.922}, {'end': 1345.826, 'text': 'Okay,', 'start': 1345.646, 'duration': 0.18}], 'summary': 'Exploring reasons for caring about deep multi-task learning and meta-learning in addition to deep learning for robots.', 'duration': 24.665, 'max_score': 1321.161, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/0rZtSwNOTQo/pics/0rZtSwNOTQo1321161.jpg'}, {'end': 1453.479, 'src': 'embed', 'start': 1412.503, 'weight': 0, 'content': [{'end': 1417.687, 'text': 'without requiring hand-engineered features like HOG or SIFT, or or deformable part models.', 'start': 1412.503, 'duration': 5.184}, {'end': 1420.348, 'text': 'um, and also with less domain knowledge.', 'start': 1418.787, 'duration': 1.561}, {'end': 1427.672, 'text': 'So it means that we can kind of apply the single class of, uh, of techniques of, of neural networks essentially to a wide range of problem domains.', 'start': 1420.368, 'duration': 7.304}, {'end': 1434.09, 'text': "Uh, so- so that's one kind of benefit of- of deep learning systems.", 'start': 1429.788, 'duration': 4.302}, {'end': 1440.633, 'text': 'And the second benefit of course is that they work really well, uh, in- in a variety of different situations.', 'start': 1434.73, 'duration': 5.903}, {'end': 1449.017, 'text': 'So if you look at uh results on the ImageNet dataset over the course of around five years, uh, and look at the error rate, um,', 'start': 1440.693, 'duration': 8.324}, {'end': 1453.479, 'text': 'this dot right here is is AlexNet uh in in 2012..', 'start': 1450.498, 'duration': 2.981}], 'summary': 'Deep learning offers benefits like wide applicability and high performance, demonstrated by the decreasing error rate in imagenet dataset over five years.', 'duration': 40.976, 'max_score': 1412.503, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/0rZtSwNOTQo/pics/0rZtSwNOTQo1412503.jpg'}, {'end': 1529.079, 'src': 'embed', 'start': 1500.887, 'weight': 3, 'content': [{'end': 1509.33, 'text': 'Um. in this case PBMT is phrase-based machine translation, whereas uh, GNMT, uh or NMT more broadly neural ma- machine translation.', 'start': 1500.887, 'duration': 8.443}, {'end': 1510.75, 'text': 'you start- starts using neural networks.', 'start': 1509.33, 'duration': 1.42}, {'end': 1512.691, 'text': 'Uh, and we see a pretty big difference.', 'start': 1511.41, 'duration': 1.281}, {'end': 1514.971, 'text': 'Um, this is showing the human evaluation scores.', 'start': 1512.891, 'duration': 2.08}, {'end': 1520.814, 'text': 'Uh, we see kind of, ranging from 50 percent to 80 percent improvements by using neural networks.', 'start': 1515.512, 'duration': 5.302}, {'end': 1529.079, 'text': 'Okay So this is why deep learning, uh, and, and kind of in, in two slides, there are other reasons as well.', 'start': 1523.036, 'duration': 6.043}], 'summary': 'Pbmt vs. gnmt: neural networks lead to 50-80% improvements in human evaluation scores.', 'duration': 28.192, 'max_score': 1500.887, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/0rZtSwNOTQo/pics/0rZtSwNOTQo1500887.jpg'}, {'end': 1589.515, 'src': 'embed', 'start': 1562.425, 'weight': 5, 'content': [{'end': 1567.149, 'text': "Uh, and what if you don't have a large dataset? Uh, then you're- you're in a bit of trouble.", 'start': 1562.425, 'duration': 4.724}, {'end': 1571.999, 'text': "these are domains that- and there are a wide range of domains where we don't have large datasets.", 'start': 1568.336, 'duration': 3.663}, {'end': 1578.185, 'text': 'Things like uh, medical imaging, uh, like robotics, like I mentioned before, like education, uh,', 'start': 1572.52, 'duration': 5.665}, {'end': 1581.548, 'text': "for- you don't have a lot of data for each individual student that you're trying to teach.", 'start': 1578.185, 'duration': 3.363}, {'end': 1589.515, 'text': "Uh, medicine recommendation systems, uh, translation systems, you don't have a lot of paired data for every single pair of languages.", 'start': 1582.429, 'duration': 7.086}], 'summary': 'Challenges arise in domains with limited datasets, such as medical imaging and education.', 'duration': 27.09, 'max_score': 1562.425, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/0rZtSwNOTQo/pics/0rZtSwNOTQo1562425.jpg'}, {'end': 1660.979, 'src': 'embed', 'start': 1636.563, 'weight': 6, 'content': [{'end': 1643.069, 'text': "Whereas if you're on the right and and uh, kind of the tail on the right, that's where these algorithms start to break down,", 'start': 1636.563, 'duration': 6.506}, {'end': 1646.292, 'text': 'where supervised learning methods, uh, really struggle to perform well.', 'start': 1643.069, 'duration': 3.223}, {'end': 1654.558, 'text': 'Uh, and this is, for example, this is a really big problem in autonomous driving, where cars can handle a wide variety of very common situations.', 'start': 1646.312, 'duration': 8.246}, {'end': 1660.979, 'text': 'but when they see very weird situations, uh, humans can handle them perfectly well, but these cars, uh, really struggle.', 'start': 1654.558, 'duration': 6.421}], 'summary': 'Supervised learning struggles with handling uncommon situations in autonomous driving.', 'duration': 24.416, 'max_score': 1636.563, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/0rZtSwNOTQo/pics/0rZtSwNOTQo1636563.jpg'}, {'end': 1773.992, 'src': 'embed', 'start': 1740.941, 'weight': 2, 'content': [{'end': 1744.722, 'text': 'Uh, and your goal is to make predictions about new data points from that very small dataset.', 'start': 1740.941, 'duration': 3.781}, {'end': 1749.484, 'text': "Um, the way that you accomplish this is that you weren't learning from scratch.", 'start': 1746.163, 'duration': 3.321}, {'end': 1751.204, 'text': 'Uh, you have previous experience.', 'start': 1749.844, 'duration': 1.36}, {'end': 1758.425, 'text': "you haven't probably seen these exact paintings before and you probably haven't necessarily even seen paintings from these painters before.", 'start': 1752.503, 'duration': 5.922}, {'end': 1760.927, 'text': "But you've seen paintings before.", 'start': 1759.026, 'duration': 1.901}, {'end': 1763.127, 'text': "you've probably seen um kind of.", 'start': 1760.927, 'duration': 2.2}, {'end': 1765.708, 'text': 'you know what objects are, you know what textures are, uh.', 'start': 1763.127, 'duration': 2.581}, {'end': 1773.992, 'text': 'and through that previous experience, you were able to quickly identify um, the painting corresponding to the um corresponding to the correct painter.', 'start': 1765.708, 'duration': 8.284}], 'summary': 'Make predictions from small dataset using previous experience to identify correct painter.', 'duration': 33.051, 'max_score': 1740.941, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/0rZtSwNOTQo/pics/0rZtSwNOTQo1740941.jpg'}], 'start': 1108.383, 'title': 'Challenges in single task learning and evolution of computer vision', 'summary': "Discusses the limitations of single task learning algorithms, emphasizing the need for effective learning across multiple tasks, and highlights the transition from hand-engineered features to neural networks in computer vision, showcasing deep learning's benefits and challenges.", 'chapters': [{'end': 1334.145, 'start': 1108.383, 'title': 'Challenges in single task learning', 'summary': 'Discusses the limitations of single task learning algorithms and the need to rethink the design of algorithms to effectively learn across many different tasks, as well as the significance of deep multi-task learning and meta-learning in machine learning systems.', 'duration': 225.762, 'highlights': ['The current algorithms are not scalable to learning more tasks without starting from scratch, relying on very detailed supervision and guidance, which is not practical for collecting a lot of data.', 'The limitations of single task learning algorithms also extend to standard reinforcement learning algorithms, which require a lot of data and supervision in order to learn effectively.', 'The need to fundamentally rethink the design of algorithms to effectively learn across many different tasks and the significance of deep multi-task learning and meta-learning in machine learning systems.']}, {'end': 1778.826, 'start': 1335.405, 'title': 'Evolution of computer vision', 'summary': 'Highlights the shift from hand-engineered features to neural networks in computer vision, showcasing the benefits of deep learning in handling unstructured inputs and its significant improvement in error rates, as well as the challenges of data availability, data distribution, and few-shot learning.', 'duration': 443.421, 'highlights': ['Deep learning revolutionized computer vision by enabling direct operation on unstructured inputs and eliminating the need for hand-engineered features like HOG or SIFT, leading to broader application across problem domains.', 'Significant improvement in error rates on the ImageNet dataset post-2012 with deep learning models, showcasing their superiority over previous approaches relying on hand-engineered features.', 'Integration of neural networks in machine translation led to substantial human evaluation score improvements, ranging from 50% to 80%, compared to phrase-based machine translation, emphasizing the effectiveness of neural networks in language processing.', 'Challenges of data availability in domains like medical imaging, robotics, education, and translation, highlighting the impracticality of learning from scratch for each task due to limited data and the need for multitask learning techniques.', 'Struggles of supervised learning methods in handling data with long-tail distribution, particularly evident in autonomous driving, where unusual situations pose significant challenges, emphasizing a crucial area for improvement.', 'The concept of few-shot learning, where previous knowledge and experience enable quick learning and predictions from a very small dataset, showcasing the potential of leveraging existing knowledge for rapid adaptation to new tasks.']}], 'duration': 670.443, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/0rZtSwNOTQo/pics/0rZtSwNOTQo1108383.jpg', 'highlights': ['Deep learning revolutionized computer vision by enabling direct operation on unstructured inputs and eliminating the need for hand-engineered features like HOG or SIFT, leading to broader application across problem domains.', 'Significant improvement in error rates on the ImageNet dataset post-2012 with deep learning models, showcasing their superiority over previous approaches relying on hand-engineered features.', 'The concept of few-shot learning, where previous knowledge and experience enable quick learning and predictions from a very small dataset, showcasing the potential of leveraging existing knowledge for rapid adaptation to new tasks.', 'Integration of neural networks in machine translation led to substantial human evaluation score improvements, ranging from 50% to 80%, compared to phrase-based machine translation, emphasizing the effectiveness of neural networks in language processing.', 'The need to fundamentally rethink the design of algorithms to effectively learn across many different tasks and the significance of deep multi-task learning and meta-learning in machine learning systems.', 'Challenges of data availability in domains like medical imaging, robotics, education, and translation, highlighting the impracticality of learning from scratch for each task due to limited data and the need for multitask learning techniques.', 'Struggles of supervised learning methods in handling data with long-tail distribution, particularly evident in autonomous driving, where unusual situations pose significant challenges, emphasizing a crucial area for improvement.', 'The current algorithms are not scalable to learning more tasks without starting from scratch, relying on very detailed supervision and guidance, which is not practical for collecting a lot of data.', 'The limitations of single task learning algorithms also extend to standard reinforcement learning algorithms, which require a lot of data and supervision in order to learn effectively.']}, {'end': 2081.112, 'segs': [{'end': 1808.972, 'src': 'embed', 'start': 1778.826, 'weight': 0, 'content': [{'end': 1783.73, 'text': "if you want more general purpose machine learning systems, if you don't have large datasets, if your data has long tails,", 'start': 1778.826, 'duration': 4.904}, {'end': 1786.312, 'text': 'if you want to be able to quickly learn something new, um,', 'start': 1783.73, 'duration': 2.582}, {'end': 1791.356, 'text': 'these are all settings where elements of multitask learning and meta-learning can come into play, uh,', 'start': 1786.312, 'duration': 5.044}, {'end': 1796.56, 'text': 'and can help us out and basically make machine learning, uh, more effective in these problem settings.', 'start': 1791.356, 'duration': 5.204}, {'end': 1808.972, 'text': 'Any questions on, on these four things before I move on? Okay.', 'start': 1798.802, 'duration': 10.17}], 'summary': 'Multitask learning and meta-learning can enhance machine learning in diverse settings.', 'duration': 30.146, 'max_score': 1778.826, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/0rZtSwNOTQo/pics/0rZtSwNOTQo1778826.jpg'}, {'end': 1852.8, 'src': 'embed', 'start': 1827.745, 'weight': 1, 'content': [{'end': 1833.889, 'text': "Um, for now, the way I'm gonna define a task is something that takes us in a dataset,", 'start': 1827.745, 'duration': 6.144}, {'end': 1837.352, 'text': 'takes us and put a dataset and a loss function and gives you a model.', 'start': 1833.889, 'duration': 3.463}, {'end': 1843.351, 'text': "Um, this is- we're gonna kind of generalize this later and make it potentially a bit more formal later in the course.", 'start': 1838.886, 'duration': 4.465}, {'end': 1846.854, 'text': "Um, but for now this is what we're gonna be considering.", 'start': 1843.931, 'duration': 2.923}, {'end': 1852.8, 'text': 'Uh, essentially you can view a task as a machine learning problem where you have some data, have some loss function.', 'start': 1846.874, 'duration': 5.926}], 'summary': 'A task in machine learning is defined as something that takes a dataset and a loss function to produce a model.', 'duration': 25.055, 'max_score': 1827.745, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/0rZtSwNOTQo/pics/0rZtSwNOTQo1827745.jpg'}, {'end': 1935.817, 'src': 'heatmap', 'start': 1903.996, 'weight': 0.701, 'content': [{'end': 1908.979, 'text': "Could be something like different lighting conditions, uh, of your model, different words that you're encountering,", 'start': 1903.996, 'duration': 4.983}, {'end': 1911, 'text': "different languages that you're encountering.", 'start': 1908.979, 'duration': 2.021}, {'end': 1912.341, 'text': 'um kind of.', 'start': 1911, 'duration': 1.341}, {'end': 1914.683, 'text': 'it can encapsulate kind of a wide range of different things.', 'start': 1912.341, 'duration': 2.342}, {'end': 1920.13, 'text': 'Uh, so not just different tasks that you would think of a task as, uh, in kind of in English.', 'start': 1915.184, 'duration': 4.946}, {'end': 1922.793, 'text': 'Uh, really it can vary in a wide range of ways.', 'start': 1920.55, 'duration': 2.243}, {'end': 1931.716, 'text': 'Questions on that? So is it like a different distribution of the data set, uh, and the loss? Yeah.', 'start': 1925.336, 'duration': 6.38}, {'end': 1935.817, 'text': 'So it could correspond to a different- really a different dataset or a different loss function or both.', 'start': 1931.756, 'duration': 4.061}], 'summary': 'Variability in data can include different lighting conditions, words, languages encountered. it can encompass a wide range of factors, including dataset distribution and loss function.', 'duration': 31.821, 'max_score': 1903.996, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/0rZtSwNOTQo/pics/0rZtSwNOTQo1903996.jpg'}, {'end': 2046.671, 'src': 'embed', 'start': 2019.972, 'weight': 2, 'content': [{'end': 2026.625, 'text': "Okay Um, So in multitask learning and meta-learning, there's one critical assumption.", 'start': 2019.972, 'duration': 6.653}, {'end': 2029.586, 'text': 'Uh, this is kind of where some of the bad news comes in, uh,', 'start': 2026.645, 'duration': 2.941}, {'end': 2036.148, 'text': 'which is that different tasks need to share some structure in order to get a benefit from these algorithms.', 'start': 2029.586, 'duration': 6.562}, {'end': 2041.669, 'text': "Um, if the tasks don't share, if the tasks that you're trying to learn across don't share any structure,", 'start': 2037.208, 'duration': 4.461}, {'end': 2046.671, 'text': "then you're better off just using single task, learning independently on each of those tasks, uh,", 'start': 2041.669, 'duration': 5.002}], 'summary': 'Multitask learning and meta-learning rely on shared task structure for benefit.', 'duration': 26.699, 'max_score': 2019.972, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/0rZtSwNOTQo/pics/0rZtSwNOTQo2019972.jpg'}], 'start': 1778.826, 'title': 'Multitask learning and meta-learning', 'summary': 'Discusses the effectiveness of multitask learning and meta-learning for general purpose machine learning systems, highlighting their benefits in settings with small datasets and long-tailed data, and providing examples of different tasks and their variations. it also covers the variability in tasks and loss functions, emphasizing potential differences in dataset distributions and loss functions, and includes examples of different objectives and types of loss functions. lastly, it addresses the importance of shared structure in multitask learning and meta-learning, emphasizing the need for tasks to share some structure to benefit from these algorithms, with a specific example of tasks sharing similar underlying motion.', 'chapters': [{'end': 1912.341, 'start': 1778.826, 'title': 'Multitask learning and meta-learning', 'summary': 'Discusses how multitask learning and meta-learning can be effective for general purpose machine learning systems in settings with small datasets and long-tailed data, helping to quickly learn new things and improve machine learning effectiveness, with examples of different tasks and their variations.', 'duration': 133.515, 'highlights': ['The chapter explains the importance of multitask learning and meta-learning for general purpose machine learning systems, particularly in settings with small datasets and long-tailed data, to improve effectiveness and quick learning.', 'The definition of a task is provided as something that takes a dataset and a loss function to produce a model, emphasizing the need to optimize the loss function for different tasks, which could vary in terms of objects, people, objectives, lighting conditions, words, and languages encountered.', 'The examples of different tasks include classifying between different types of objects (e.g., cats, water bottles), personalizing systems for different users, and achieving different objectives (e.g., classifying age or height from an image), showcasing the varied nature of tasks in machine learning.']}, {'end': 1974.254, 'start': 1912.341, 'title': 'Variability in tasks and loss functions', 'summary': 'Discusses the variability in tasks and loss functions, highlighting the potential differences in dataset distributions and loss functions, as well as examples of different objectives and types of loss functions.', 'duration': 61.913, 'highlights': ['The tasks can encapsulate a wide range of different things, not just different tasks but also different dataset distributions and loss functions.', 'Different objects can potentially have the same loss function but match labels from a different dataset, while different objectives may require different loss functions but the same dataset.', 'Examples of different loss functions include cross entropy, mean squared error, and log likelihood.']}, {'end': 2081.112, 'start': 1974.274, 'title': 'Shared structure in multitask learning', 'summary': 'Discusses the importance of shared structure in multitask learning and meta-learning, highlighting that the tasks need to share some structure in order to benefit from these algorithms, with a specific example of tasks sharing similar underlying motion.', 'duration': 106.838, 'highlights': ['The critical assumption in multitask learning and meta-learning is that different tasks need to share some structure to benefit from these algorithms, as tasks without shared structure are better off being learned independently.', 'Tasks such as screwing a cap onto a lid, screwing a bottle cap, and screwing a pepper grinder share similar underlying motion, demonstrating shared structure even if not explicitly apparent.', 'There are many tasks and task distributions that have shared structure, despite not appearing to have shared structure on the surface.']}], 'duration': 302.286, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/0rZtSwNOTQo/pics/0rZtSwNOTQo1778826.jpg', 'highlights': ['The importance of multitask learning and meta-learning for general purpose machine learning systems, particularly in settings with small datasets and long-tailed data', 'The definition of a task as something that takes a dataset and a loss function to produce a model, emphasizing the need to optimize the loss function for different tasks', 'The critical assumption in multitask learning and meta-learning is that different tasks need to share some structure to benefit from these algorithms']}, {'end': 2523.275, 'segs': [{'end': 2123.593, 'src': 'embed', 'start': 2082.174, 'weight': 0, 'content': [{'end': 2088.239, 'text': 'Um, and even if there are tasks that are seemingly unrelated, uh, there is things that are still underlying.', 'start': 2082.174, 'duration': 6.065}, {'end': 2095.527, 'text': "So, there is structure that's still underlying those tasks because the laws of physics are kind of underlying the real data that, that we have.", 'start': 2088.26, 'duration': 7.267}, {'end': 2102.113, 'text': 'Um, people are all organisms that have intentions and so even if two people are very different, they still have some commonalities.', 'start': 2095.547, 'duration': 6.566}, {'end': 2108.719, 'text': 'Uh, the rules of English are underlying all like, well, not all but, uh, a fair amount of English language data.', 'start': 2103.034, 'duration': 5.685}, {'end': 2113.464, 'text': 'Uh, and languages are kind of developed for similar purposes and, and, and so on.', 'start': 2108.739, 'duration': 4.725}, {'end': 2119.129, 'text': 'So in these kind of may seem like superficial, uh, relationships between different tasks.', 'start': 2113.544, 'duration': 5.585}, {'end': 2123.593, 'text': 'But in reality, this leads to far greater structure than having completely random tasks.', 'start': 2119.629, 'duration': 3.964}], 'summary': 'Underlying structures connect seemingly unrelated tasks, demonstrating commonalities and greater structure.', 'duration': 41.419, 'max_score': 2082.174, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/0rZtSwNOTQo/pics/0rZtSwNOTQo2082174.jpg'}, {'end': 2183.029, 'src': 'embed', 'start': 2149.446, 'weight': 4, 'content': [{'end': 2152.108, 'text': 'Um, it applies to meta-learning as well.', 'start': 2149.446, 'duration': 2.662}, {'end': 2156.832, 'text': "Yeah So I'll give problem definitions of multi-task learning and meta-learning on the next slide.", 'start': 2152.308, 'duration': 4.524}, {'end': 2163.537, 'text': 'But, uh, in essence, like in order, meta-learning is all about kind of learning the structure underlying tasks,', 'start': 2156.912, 'duration': 6.625}, {'end': 2165.779, 'text': 'such that you can more quickly learn a new task.', 'start': 2163.537, 'duration': 2.242}, {'end': 2171.424, 'text': "Uh, and if you can't- if there isn't any shared structure, then you won't be able to learn more quickly than learning from scratch.", 'start': 2165.799, 'duration': 5.625}, {'end': 2183.029, 'text': "Okay So, uh, let's informally go over some of the problem definitions in the coming lectures.", 'start': 2177.304, 'duration': 5.725}], 'summary': 'Meta-learning aims to learn task structure for quicker new task learning.', 'duration': 33.583, 'max_score': 2149.446, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/0rZtSwNOTQo/pics/0rZtSwNOTQo2149446.jpg'}, {'end': 2370.174, 'src': 'heatmap', 'start': 2193.957, 'weight': 0.777, 'content': [{'end': 2196.499, 'text': "um, here's kind of roughly what things look like.", 'start': 2193.957, 'duration': 2.542}, {'end': 2208.748, 'text': "So you can think of the multitask learning problem as trying to learn all of the tasks that you're provided with more quickly or more proficiently than learning the tasks independently from one another.", 'start': 2196.559, 'duration': 12.189}, {'end': 2218.435, 'text': "Uh, and then what the meta-learning problem is looking at is given data or experience on a set of previous tasks that you're given.", 'start': 2208.768, 'duration': 9.667}, {'end': 2226.301, 'text': 'you want to be able to learn a new task more quickly and or more proficiently than learning from scratch by leveraging your experience on the previous tasks.', 'start': 2218.435, 'duration': 7.866}, {'end': 2229.993, 'text': 'So, essentially, the difference between these two things is that the first one,', 'start': 2227.691, 'duration': 2.302}, {'end': 2233.357, 'text': "you're trying to learn a set of tasks and do well on those training tasks.", 'start': 2229.993, 'duration': 3.364}, {'end': 2235.639, 'text': 'And then, in the second problem setting,', 'start': 2233.898, 'duration': 1.741}, {'end': 2244.529, 'text': "you're trying to use experience on training tasks in order to do well at new tasks and in order to basically be able to more quickly learn new tasks given a dataset.", 'start': 2235.639, 'duration': 8.89}, {'end': 2253.969, 'text': "Uh, and in this course, we won't necessarily be covering everything that's considered a multitask learning algorithm or a meta-learning algorithm.", 'start': 2247.205, 'duration': 6.764}, {'end': 2260.633, 'text': 'Uh, will be really anything that solves one of these two problem statements will be fair game for including in the course.', 'start': 2254.57, 'duration': 6.063}, {'end': 2268.258, 'text': "Um, so things that allow you to build on previous experience, to quickly learn new tasks, even if they aren't through learning to learn techniques.", 'start': 2261.534, 'duration': 6.724}, {'end': 2270.52, 'text': "um, I'll try to touch on them in this course.", 'start': 2268.258, 'duration': 2.262}, {'end': 2274.262, 'text': 'Questions on these problem statements? Yeah.', 'start': 2272.801, 'duration': 1.461}, {'end': 2287.87, 'text': 'Yeah. So I guess in many ways I think that this is the tran- a form of the transfer learning problem statement,', 'start': 2281.105, 'duration': 6.765}, {'end': 2291.412, 'text': 'where you want to take some data and use that uh.', 'start': 2287.87, 'duration': 3.542}, {'end': 2294.995, 'text': 'use knowledge acquired from that data to, uh.', 'start': 2291.412, 'duration': 3.583}, {'end': 2296.136, 'text': 'do well at other tasks.', 'start': 2294.995, 'duration': 1.141}, {'end': 2302.941, 'text': 'Um, I think that one aspect about this problem statement is that you want to be able to learn a new task more quickly, uh,', 'start': 2296.716, 'duration': 6.225}, {'end': 2308.966, 'text': 'whereas in transfer learning you may also want to be able to just form a well- perform a new task well, in zero-shot, uh,', 'start': 2302.941, 'duration': 6.025}, {'end': 2310.867, 'text': 'where you kind of just want to share representations.', 'start': 2308.966, 'duration': 1.901}, {'end': 2315.131, 'text': 'I actually kind of view transfer learning as something that encapsulates both of these things.', 'start': 2310.887, 'duration': 4.244}, {'end': 2318.573, 'text': "uh, where you're thinking about how you can transfer information between different tasks,", 'start': 2315.131, 'duration': 3.442}, {'end': 2323.677, 'text': 'and that could actually also correspond to the multi-task learning problem, uh, as well as the meta-learning problem.', 'start': 2318.573, 'duration': 5.104}, {'end': 2331.116, 'text': 'Yeah I thought meta-learning was kind of like learning to learn.', 'start': 2326.46, 'duration': 4.656}, {'end': 2342.142, 'text': "Would you say that is kind of like a consequence of that definition or that's kind of like a Yeah, that's a good question.", 'start': 2331.136, 'duration': 11.006}, {'end': 2345.263, 'text': "So I guess what I'm defining here is the, the, the meta-learning problem.", 'start': 2342.182, 'duration': 3.081}, {'end': 2350.746, 'text': 'And I think that meta-learning algorithms are all learning to learn, uh, and they solve this particular problem.', 'start': 2345.363, 'duration': 5.383}, {'end': 2354.248, 'text': "Uh, and they're not the only way to solve this particular problem.", 'start': 2350.766, 'duration': 3.482}, {'end': 2364.629, 'text': "Does that answer your question? Um, That's a good point.", 'start': 2355.865, 'duration': 8.764}, {'end': 2370.174, 'text': 'Yeah So in, in principle, you could still perform meta-learning in the context of a single task.', 'start': 2364.689, 'duration': 5.485}], 'summary': 'Multitask learning aims to learn tasks more proficiently, while meta-learning focuses on quickly learning new tasks by leveraging previous experience. both problems will be covered in the course.', 'duration': 176.217, 'max_score': 2193.957, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/0rZtSwNOTQo/pics/0rZtSwNOTQo2193957.jpg'}, {'end': 2229.993, 'src': 'embed', 'start': 2208.768, 'weight': 3, 'content': [{'end': 2218.435, 'text': "Uh, and then what the meta-learning problem is looking at is given data or experience on a set of previous tasks that you're given.", 'start': 2208.768, 'duration': 9.667}, {'end': 2226.301, 'text': 'you want to be able to learn a new task more quickly and or more proficiently than learning from scratch by leveraging your experience on the previous tasks.', 'start': 2218.435, 'duration': 7.866}, {'end': 2229.993, 'text': 'So, essentially, the difference between these two things is that the first one,', 'start': 2227.691, 'duration': 2.302}], 'summary': 'Meta-learning aims to improve learning speed and proficiency based on previous task experience.', 'duration': 21.225, 'max_score': 2208.768, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/0rZtSwNOTQo/pics/0rZtSwNOTQo2208768.jpg'}, {'end': 2270.52, 'src': 'embed', 'start': 2247.205, 'weight': 5, 'content': [{'end': 2253.969, 'text': "Uh, and in this course, we won't necessarily be covering everything that's considered a multitask learning algorithm or a meta-learning algorithm.", 'start': 2247.205, 'duration': 6.764}, {'end': 2260.633, 'text': 'Uh, will be really anything that solves one of these two problem statements will be fair game for including in the course.', 'start': 2254.57, 'duration': 6.063}, {'end': 2268.258, 'text': "Um, so things that allow you to build on previous experience, to quickly learn new tasks, even if they aren't through learning to learn techniques.", 'start': 2261.534, 'duration': 6.724}, {'end': 2270.52, 'text': "um, I'll try to touch on them in this course.", 'start': 2268.258, 'duration': 2.262}], 'summary': 'Course covers multitask learning and meta-learning algorithms for solving problem statements.', 'duration': 23.315, 'max_score': 2247.205, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/0rZtSwNOTQo/pics/0rZtSwNOTQo2247205.jpg'}, {'end': 2342.142, 'src': 'embed', 'start': 2308.966, 'weight': 6, 'content': [{'end': 2310.867, 'text': 'where you kind of just want to share representations.', 'start': 2308.966, 'duration': 1.901}, {'end': 2315.131, 'text': 'I actually kind of view transfer learning as something that encapsulates both of these things.', 'start': 2310.887, 'duration': 4.244}, {'end': 2318.573, 'text': "uh, where you're thinking about how you can transfer information between different tasks,", 'start': 2315.131, 'duration': 3.442}, {'end': 2323.677, 'text': 'and that could actually also correspond to the multi-task learning problem, uh, as well as the meta-learning problem.', 'start': 2318.573, 'duration': 5.104}, {'end': 2331.116, 'text': 'Yeah I thought meta-learning was kind of like learning to learn.', 'start': 2326.46, 'duration': 4.656}, {'end': 2342.142, 'text': "Would you say that is kind of like a consequence of that definition or that's kind of like a Yeah, that's a good question.", 'start': 2331.136, 'duration': 11.006}], 'summary': 'Transfer learning involves sharing representations and transferring information between tasks, including multi-task and meta-learning.', 'duration': 33.176, 'max_score': 2308.966, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/0rZtSwNOTQo/pics/0rZtSwNOTQo2308966.jpg'}, {'end': 2513.728, 'src': 'heatmap', 'start': 2421.536, 'weight': 0.816, 'content': [{'end': 2425.717, 'text': 'So, in domain adaptation, um, you typically do want to kind of.', 'start': 2421.536, 'duration': 4.181}, {'end': 2428.858, 'text': "it's kind of a form of transfer learning in some ways, where you want to transfer from one to another.", 'start': 2425.717, 'duration': 3.141}, {'end': 2435.74, 'text': 'Um, one thing, and I guess when I get into the more formal definitions of these problems, this will become more clear.', 'start': 2428.878, 'duration': 6.862}, {'end': 2445.725, 'text': "Um, one thing you typically see in the meta-learning problem is that the tasks that you're seeing at test time you assume to be in the distribution of the tasks that you're seeing during training,", 'start': 2435.76, 'duration': 9.965}, {'end': 2453.65, 'text': "whereas many techniques in domain adaptation are considering a setting where your test domain it may be out of distribution from what you're seeing during training.", 'start': 2445.725, 'duration': 7.925}, {'end': 2459.914, 'text': "Um, and yeah, so that's, that's in, in many ways one of those distinctions there.", 'start': 2454.531, 'duration': 5.383}, {'end': 2473.854, 'text': 'Okay, Um, now, one question that was asked a bit before is kind of I think it was in the context of circuits is what, um, is it is?', 'start': 2465.444, 'duration': 8.41}, {'end': 2476.277, 'text': 'is something a single task learning problem or a multitask learning problem?', 'start': 2473.854, 'duration': 2.423}, {'end': 2482.064, 'text': "Uh, in some ways this gets gets down to the question of doesn't multitask learning reduce to just a single task learning problem?", 'start': 2476.858, 'duration': 5.206}, {'end': 2486.003, 'text': 'Uh, and in particular, what you could do is you could just say okay,', 'start': 2483.221, 'duration': 2.782}, {'end': 2491.927, 'text': "I have a dataset for each task and we're going to take the union of those datasets into, uh, a single dataset.", 'start': 2486.003, 'duration': 5.924}, {'end': 2497.09, 'text': "And likewise, I'll take the loss function for each and just kind of sum and get a loss function.", 'start': 2492.487, 'duration': 4.603}, {'end': 2504.915, 'text': "And now we have a single task learning problem, uh, where we have one dataset and one loss function, uh, and we're done.", 'start': 2497.65, 'duration': 7.265}, {'end': 2513.728, 'text': 'Um, and, Uh, in many ways, uh, yes.', 'start': 2509.078, 'duration': 4.65}], 'summary': 'Domain adaptation involves transfer learning between domains, and multitask learning reduces to single task learning.', 'duration': 92.192, 'max_score': 2421.536, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/0rZtSwNOTQo/pics/0rZtSwNOTQo2421536.jpg'}, {'end': 2459.914, 'src': 'embed', 'start': 2435.76, 'weight': 7, 'content': [{'end': 2445.725, 'text': "Um, one thing you typically see in the meta-learning problem is that the tasks that you're seeing at test time you assume to be in the distribution of the tasks that you're seeing during training,", 'start': 2435.76, 'duration': 9.965}, {'end': 2453.65, 'text': "whereas many techniques in domain adaptation are considering a setting where your test domain it may be out of distribution from what you're seeing during training.", 'start': 2445.725, 'duration': 7.925}, {'end': 2459.914, 'text': "Um, and yeah, so that's, that's in, in many ways one of those distinctions there.", 'start': 2454.531, 'duration': 5.383}], 'summary': 'Meta-learning assumes tasks in training and test, while domain adaptation deals with out-of-distribution test domains.', 'duration': 24.154, 'max_score': 2435.76, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/0rZtSwNOTQo/pics/0rZtSwNOTQo2435760.jpg'}], 'start': 2082.174, 'title': 'Task structure and meta-learning', 'summary': 'Discusses the influence of laws of physics and commonalities in different organisms and languages on task structure, emphasizing greater structure than random tasks. it also introduces multitask learning and meta-learning, emphasizing the need to learn tasks more quickly by leveraging previous experience and covering the similarities and differences with transfer learning and domain adaptation.', 'chapters': [{'end': 2123.593, 'start': 2082.174, 'title': 'Underlying structure in tasks', 'summary': 'Discusses the underlying structure in seemingly unrelated tasks, emphasizing the influence of laws of physics and commonalities in different organisms and languages, leading to greater structure than random tasks.', 'duration': 41.419, 'highlights': ['The laws of physics underlie the real data, providing structure to seemingly unrelated tasks.', 'Commonalities exist among different organisms, emphasizing underlying structure despite differences.', 'Languages are developed for similar purposes, contributing to the greater structure in seemingly superficial relationships between tasks.']}, {'end': 2523.275, 'start': 2124.313, 'title': 'Multitask and meta-learning', 'summary': 'Introduces multitask learning and meta-learning, outlining their problem definitions and distinctions, emphasizing the need to learn tasks more quickly by leveraging previous experience, and covering the similarities and differences with transfer learning and domain adaptation.', 'duration': 398.962, 'highlights': ['The difference between multitask learning and meta-learning is that the former focuses on learning a set of tasks proficiently, while the latter utilizes experience on training tasks to quickly learn new tasks, as discussed in the next lecture. (relevance score: 5)', 'Meta-learning involves learning the structure underlying tasks to facilitate quicker learning of new tasks, emphasizing the need for shared structure to enable faster learning, as elaborated in the problem definitions. (relevance score: 4)', 'The course will encompass any algorithm solving the problem statements of multitask learning or meta-learning, including techniques that allow building on previous experience to quickly learn new tasks, even if they are not through learning to learn techniques, with a strong focus on these problem definitions. (relevance score: 3)', 'Transfer learning encapsulates the essence of both multitask learning and meta-learning, involving the transfer of information between different tasks, which corresponds to the multi-task learning problem and the meta-learning problem, indicating the broad scope of transfer learning. (relevance score: 2)', 'The distinction between domain adaptation and meta-learning lies in the assumption that tasks seen at test time are in the distribution of tasks seen during training for meta-learning, while domain adaptation considers a setting where the test domain may be out of distribution from the training data, highlighting a key difference between the two. (relevance score: 1)']}], 'duration': 441.101, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/0rZtSwNOTQo/pics/0rZtSwNOTQo2082174.jpg', 'highlights': ['The laws of physics underlie the real data, providing structure to seemingly unrelated tasks.', 'Commonalities exist among different organisms, emphasizing underlying structure despite differences.', 'Languages are developed for similar purposes, contributing to the greater structure in seemingly superficial relationships between tasks.', 'The difference between multitask learning and meta-learning is that the former focuses on learning a set of tasks proficiently, while the latter utilizes experience on training tasks to quickly learn new tasks, as discussed in the next lecture.', 'Meta-learning involves learning the structure underlying tasks to facilitate quicker learning of new tasks, emphasizing the need for shared structure to enable faster learning, as elaborated in the problem definitions.', 'The course will encompass any algorithm solving the problem statements of multitask learning or meta-learning, including techniques that allow building on previous experience to quickly learn new tasks, even if they are not through learning to learn techniques, with a strong focus on these problem definitions.', 'Transfer learning encapsulates the essence of both multitask learning and meta-learning, involving the transfer of information between different tasks, which corresponds to the multi-task learning problem and the meta-learning problem, indicating the broad scope of transfer learning.', 'The distinction between domain adaptation and meta-learning lies in the assumption that tasks seen at test time are in the distribution of tasks seen during training for meta-learning, while domain adaptation considers a setting where the test domain may be out of distribution from the training data, highlighting a key difference between the two.']}, {'end': 2880.044, 'segs': [{'end': 2575.741, 'src': 'embed', 'start': 2547.238, 'weight': 0, 'content': [{'end': 2554.224, 'text': 'Uh, why should we- we be studying this topic now, uh, rather than uh in- in 10 years or 10 years ago?', 'start': 2547.238, 'duration': 6.986}, {'end': 2559.508, 'text': 'Um, well, uh, people were actually studying this problem a long time ago.', 'start': 2555.164, 'duration': 4.344}, {'end': 2565.873, 'text': 'Uh, so this is 12 years ago at this point, um, 22 years ago at this point.', 'start': 2559.828, 'duration': 6.045}, {'end': 2575.741, 'text': 'And, uh, this is by, um, a survey in rich career thinking about how we can train tasks in parallel while using shared representations.', 'start': 2567.555, 'duration': 8.186}], 'summary': 'Studying problem 12 years ago, 22 years ago, to train tasks in parallel.', 'duration': 28.503, 'max_score': 2547.238, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/0rZtSwNOTQo/pics/0rZtSwNOTQo2547238.jpg'}, {'end': 2663.749, 'src': 'embed', 'start': 2634.387, 'weight': 2, 'content': [{'end': 2640.433, 'text': 'uh, especially with the advent of uh powerful neural network function approximators, the amount of compute that we have right now,', 'start': 2634.387, 'duration': 6.046}, {'end': 2642.856, 'text': "as well as the the kinds of datasets that we're looking at.", 'start': 2640.433, 'duration': 2.423}, {'end': 2648.46, 'text': 'Um and so- so as some examples of very recent works that have kind of leveraged some of these algorithms to do.', 'start': 2644.017, 'duration': 4.443}, {'end': 2655.244, 'text': "well. um, here's a paper, uh, uh, looking at machine translation across over 100 languages.", 'start': 2648.46, 'duration': 6.784}, {'end': 2663.749, 'text': 'uh, thinking about how you can learn, um, algorithms and algorithms surpass strong uh base- baselines that use only two languages.', 'start': 2655.244, 'duration': 8.505}], 'summary': 'Advancements in neural networks enable translation across 100+ languages.', 'duration': 29.362, 'max_score': 2634.387, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/0rZtSwNOTQo/pics/0rZtSwNOTQo2634387.jpg'}, {'end': 2782.629, 'src': 'embed', 'start': 2753.453, 'weight': 1, 'content': [{'end': 2757.355, 'text': 'we see a trend that looks like this where blue is meta-learning and red is multitask learning.', 'start': 2753.453, 'duration': 3.902}, {'end': 2761.696, 'text': 'Uh, and we see an increase starting around 2014, 2015.', 'start': 2757.375, 'duration': 4.321}, {'end': 2767.801, 'text': 'Um, and if you also look at kind of paper citations for things that cover things like fine-tuning, we see an increasing trend.', 'start': 2761.697, 'duration': 6.104}, {'end': 2771.883, 'text': 'uh, meta-learning algorithms, uh, as well as multitask learning algorithms.', 'start': 2767.801, 'duration': 4.082}, {'end': 2776.846, 'text': "We see that these algorithms are um becoming of increasing interest, and I think that's because, uh,", 'start': 2771.923, 'duration': 4.923}, {'end': 2782.629, 'text': 'these algorithms are gonna be really important in the future for enabling things like learning from small datasets, uh, et cetera.', 'start': 2776.846, 'duration': 5.783}], 'summary': 'Increasing trend in meta-learning and multitask learning algorithms since 2014-2015.', 'duration': 29.176, 'max_score': 2753.453, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/0rZtSwNOTQo/pics/0rZtSwNOTQo2753453.jpg'}, {'end': 2815.862, 'src': 'embed', 'start': 2785.363, 'weight': 3, 'content': [{'end': 2792.609, 'text': 'I think that the success of multitask learning algorithms and meta-learning algorithms will be really critical for making deep learning more widely accessible.', 'start': 2785.363, 'duration': 7.246}, {'end': 2794.33, 'text': 'Um, as I mentioned before.', 'start': 2793.249, 'duration': 1.081}, {'end': 2799.294, 'text': 'the kind of settings where deep learning has been very successful before are settings where you have 1.2 million images,', 'start': 2794.33, 'duration': 4.964}, {'end': 2803.037, 'text': '40.8 million paired sentences, 300 hours of labeled data.', 'start': 2799.294, 'duration': 3.743}, {'end': 2806.938, 'text': "Um, and in a wide range of settings, that's just not feasible.", 'start': 2803.917, 'duration': 3.021}, {'end': 2815.862, 'text': 'So, um, if we look at, for example, um, a diabetic retinopathy detection dataset, it has around 35, 000 labeled datasets, I think, labeled images.', 'start': 2807.058, 'duration': 8.804}], 'summary': 'Success of multitask learning and meta-learning crucial for wider access to deep learning, especially in settings with limited data like the diabetic retinopathy detection dataset.', 'duration': 30.499, 'max_score': 2785.363, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/0rZtSwNOTQo/pics/0rZtSwNOTQo2785363.jpg'}, {'end': 2870.479, 'src': 'embed', 'start': 2842.895, 'weight': 4, 'content': [{'end': 2848, 'text': 'making deep learning algorithms and the success of deep learning more widely accessible to these types of domains,', 'start': 2842.895, 'duration': 5.105}, {'end': 2850.642, 'text': "then it's gonna be critical to build these kinds of algorithms.", 'start': 2848, 'duration': 2.642}, {'end': 2858.192, 'text': "Um, and lastly, beyond, um, the things that I've talked about, there's still many open questions and challenges in multitask learning.", 'start': 2852.589, 'duration': 5.603}, {'end': 2863.095, 'text': "And I think that that makes it a really exciting thing to study right now because there's a lot of problems to be solved.", 'start': 2858.552, 'duration': 4.543}, {'end': 2867.757, 'text': "Great Um, so that's it for today.", 'start': 2864.756, 'duration': 3.001}, {'end': 2870.479, 'text': 'As a reminder, please do these four things.', 'start': 2867.837, 'duration': 2.642}], 'summary': 'Making deep learning accessible to diverse domains, tackling open questions in multitask learning.', 'duration': 27.584, 'max_score': 2842.895, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/0rZtSwNOTQo/pics/0rZtSwNOTQo2842895.jpg'}], 'start': 2524.716, 'title': 'Multi-task learning', 'summary': 'Discusses the history, relevance, and potential of multi-task learning, demonstrating its impact on improved performance in machine translation and robotic learning. it also explores the increasing importance of multitask learning and meta-learning algorithms in enabling learning from small datasets and making deep learning more widely accessible.', 'chapters': [{'end': 2693.527, 'start': 2524.716, 'title': 'Multi-task learning in machine learning', 'summary': 'Discusses the history and relevance of multi-task learning, emphasizing its potential to leverage shared representations and training data for improved performance, as demonstrated through recent works in machine translation and robotic learning.', 'duration': 168.811, 'highlights': ['The chapter emphasizes the historical background of multi-task learning, with mentions of research dating back to 1992 and 1998, demonstrating a long-standing interest in leveraging shared representations and training data (e.g., survey in rich career, work by Sebastian Thrun, Sammy Bengio, Yashua, and others).', 'It highlights the current exciting time for studying multi-task learning algorithms, attributing this to the fundamental role they play in machine learning research, especially with the advancements in powerful neural network function approximators, increased computing capabilities, and diverse datasets.', 'The chapter provides specific examples of recent works leveraging multi-task learning algorithms, such as a paper on machine translation across over 100 languages and a demonstration of using the algorithms to enable a robot to learn from a single video example and generalize its policy to different scenarios, showcasing the practical applications and benefits of multi-task learning.']}, {'end': 2880.044, 'start': 2694.247, 'title': 'Multitask learning and meta-learning', 'summary': 'Discusses the increasing importance of multitask learning and meta-learning algorithms in robotics, machine learning systems, and research due to their role in enabling learning from small datasets and making deep learning more widely accessible.', 'duration': 185.797, 'highlights': ['Multitask learning and meta-learning algorithms are playing a crucial role in robotics, machine learning systems, and research, as shown by the increasing trend in Google search queries and paper citations since 2014-2015.', 'The success of multitask learning and meta-learning algorithms is critical for enabling deep learning in domains with limited data, such as medical image datasets with significantly fewer labeled images than conventional datasets.', 'There are many open questions and challenges in multitask learning, making it an exciting area of study with numerous problems to be solved.']}], 'duration': 355.328, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/0rZtSwNOTQo/pics/0rZtSwNOTQo2524716.jpg', 'highlights': ['The chapter emphasizes the historical background of multi-task learning, with mentions of research dating back to 1992 and 1998, demonstrating a long-standing interest in leveraging shared representations and training data.', 'Multitask learning and meta-learning algorithms are playing a crucial role in robotics, machine learning systems, and research, as shown by the increasing trend in Google search queries and paper citations since 2014-2015.', 'The chapter provides specific examples of recent works leveraging multi-task learning algorithms, such as a paper on machine translation across over 100 languages and a demonstration of using the algorithms to enable a robot to learn from a single video example and generalize its policy to different scenarios, showcasing the practical applications and benefits of multi-task learning.', 'The success of multitask learning and meta-learning algorithms is critical for enabling deep learning in domains with limited data, such as medical image datasets with significantly fewer labeled images than conventional datasets.', 'The chapter highlights the current exciting time for studying multi-task learning algorithms, attributing this to the fundamental role they play in machine learning research, especially with the advancements in powerful neural network function approximators, increased computing capabilities, and diverse datasets.', 'There are many open questions and challenges in multitask learning, making it an exciting area of study with numerous problems to be solved.']}], 'highlights': ['The lecture covers course logistics, prerequisites, enrollment, homework infrastructure, challenges in learning, limitations of single task learning, evolution of computer vision, multitask learning, meta-learning, task structure, and the potential of multi-task learning in improving performance in machine translation and robotic learning.', 'The course staff includes four TAs, and the course website and Piazza are important resources for the students.', 'Machine learning experience is a main prerequisite for the course.', 'Reinforcement learning experience is recommended due to the significant portion of the course dedicated to this topic.', 'Enrollment form should be filled out on the website for open spots, deadline this week', 'Permission number uncertainty, assurance of response by Wednesday this week', "The second homework will be more compute heavy, and we're still looking into various cloud compute options for that.", 'The concept of few-shot learning, where previous knowledge and experience enable quick learning and predictions from a very small dataset, showcasing the potential of leveraging existing knowledge for rapid adaptation to new tasks.', 'The need to fundamentally rethink the design of algorithms to effectively learn across many different tasks and the significance of deep multi-task learning and meta-learning in machine learning systems.', 'The importance of multitask learning and meta-learning for general purpose machine learning systems, particularly in settings with small datasets and long-tailed data', 'The laws of physics underlie the real data, providing structure to seemingly unrelated tasks.', 'Commonalities exist among different organisms, emphasizing underlying structure despite differences.', 'Languages are developed for similar purposes, contributing to the greater structure in seemingly superficial relationships between tasks.', 'The chapter emphasizes the historical background of multi-task learning, with mentions of research dating back to 1992 and 1998, demonstrating a long-standing interest in leveraging shared representations and training data.', 'Multitask learning and meta-learning algorithms are playing a crucial role in robotics, machine learning systems, and research, as shown by the increasing trend in Google search queries and paper citations since 2014-2015.', 'The success of multitask learning and meta-learning algorithms is critical for enabling deep learning in domains with limited data, such as medical image datasets with significantly fewer labeled images than conventional datasets.']}