00:00:00 The video showcases a map of 5,000 recent machine learning papers, revealing topics such as protein sequencing, adversarial attacks, and multi-agent reinforcement learning.
00:05:00 The YouTube video “What’s New In Machine Learning?” introduces various new developments in machine learning, including energy-based predictive representation, human level performance on Atari games, and more.
00:10:00 In this video, the author discusses some of the new developments in machine learning, including zero shot reinforcement learning and successor representations.
00:15:00 The video discusses current machine learning research, including work on intrinsically motivated learning and differential privacy.
00:20:00 This video discusses recent developments in machine learning, including adversarial training, adaptive neuron selection, and label correction.
00:25:00 This video discusses the new features of machine learning, including the Chain of Thought, visual reasoning, and scene reconstruction. It also covers low-compute areas, such as dynamic Valkyrie and point-based representations.
00:30:00 In this video, the author discusses some of the newest advances in machine learning, including ultra realistic singing voices and speed. They also mention some of the more popular applications of machine learning, such as reinforcement learning and graph neural networks.
00:35:00 In this video, different types of machine learning are covered, including adaptive gradients, networks, event-based classification, and meta learning. Surprisingly, binary neural networks are shown to be very efficient in accelerating neural network inference.
00:40:00 In this video, the presenter discusses some of the new developments in machine learning, including online learning and self-supervised learning. They also mention a paper on meta learning.
if I should have posted this as a post, please reply and say so. I’ve no idea how much of the stuff I find interesting is useful to others or how much to broadcast it.
I will use it to get an outline of two ML Safety videos before summarizing them in more detail myself. I will put these summaries in a shortform, and will likely comment on this tool’s performance after watching the videos.
oh summarize.tech is super bad, it only gives you a very general sense, sometimes it nails it but sometimes it’s very wrong and its overconfidence makes it hard to tell which until you watch yourself. sometimes it’s clearly self contradictory, which helps identify where it messed up.
I understand its performance is likely high variance and that it misses the details.
My use with it is in structuring my own summaries. I can follow the video and fill in the missing pieces and correct the initial summary as I go along. I haven’t viewed it as a replacement for a human summarization.
https://atlas.nomic.ai/map/01ff9510-d771-47db-b6a0-2108c9fe8ad1/3ceb455b-7971-4495-bb81-8291dc2d8f37 map of submissions to iclr
“What’s new in machine learning?”—youtube—summary (via summarize.tech):
00:00:00 The video showcases a map of 5,000 recent machine learning papers, revealing topics such as protein sequencing, adversarial attacks, and multi-agent reinforcement learning.
00:05:00 The YouTube video “What’s New In Machine Learning?” introduces various new developments in machine learning, including energy-based predictive representation, human level performance on Atari games, and more.
00:10:00 In this video, the author discusses some of the new developments in machine learning, including zero shot reinforcement learning and successor representations.
00:15:00 The video discusses current machine learning research, including work on intrinsically motivated learning and differential privacy.
00:20:00 This video discusses recent developments in machine learning, including adversarial training, adaptive neuron selection, and label correction.
00:25:00 This video discusses the new features of machine learning, including the Chain of Thought, visual reasoning, and scene reconstruction. It also covers low-compute areas, such as dynamic Valkyrie and point-based representations.
00:30:00 In this video, the author discusses some of the newest advances in machine learning, including ultra realistic singing voices and speed. They also mention some of the more popular applications of machine learning, such as reinforcement learning and graph neural networks.
00:35:00 In this video, different types of machine learning are covered, including adaptive gradients, networks, event-based classification, and meta learning. Surprisingly, binary neural networks are shown to be very efficient in accelerating neural network inference.
00:40:00 In this video, the presenter discusses some of the new developments in machine learning, including online learning and self-supervised learning. They also mention a paper on meta learning.
if I should have posted this as a post, please reply and say so. I’ve no idea how much of the stuff I find interesting is useful to others or how much to broadcast it.
Thank you for bringing my attention to this.
It seems quite useful, hence my strong upvote.
I will use it to get an outline of two ML Safety videos before summarizing them in more detail myself. I will put these summaries in a shortform, and will likely comment on this tool’s performance after watching the videos.
oh summarize.tech is super bad, it only gives you a very general sense, sometimes it nails it but sometimes it’s very wrong and its overconfidence makes it hard to tell which until you watch yourself. sometimes it’s clearly self contradictory, which helps identify where it messed up.
I understand its performance is likely high variance and that it misses the details.
My use with it is in structuring my own summaries. I can follow the video and fill in the missing pieces and correct the initial summary as I go along. I haven’t viewed it as a replacement for a human summarization.