New Advances in Artificial Intelligence and Machine Learning
72,631
Publicado 2023-12-15
This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company
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0:00 Overview
1:04 Image Classification
3:14 The Importance of Training Data
7:19 Generative Images
8:22 Image Captioning
9:18 DALL-E 2
11:08 History of Deep Dream
14:15 Text Generation and NLP
16:24 ChatGPT and LLMs
18:55 What is ML good at?
20:36 Reinforcement Learning and Atari
24:45 Chaos and Weather
26:45 Outro
Todos los comentarios (21)
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Just found out you’re my brothers PHD advisor. Been watching your videos for differential equations all semester. Hope you’re giving sam a sufficiently hard time!
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Hi Steve, thanks for continuing your wonderful creation. As an electrical engineering student I used a lot in your videos regarding DSP (theory and in Matlab) and control. I would just want to add that I would love to see more videos in these topics. I believe a lot of student would be glad. Thanks in advance❤
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Good job, Professor Brunton! I am in love with your video series. One thing that stands out about these videos is the duration; perfect timing, and commensurate with the topics discussed.
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It's good to see you back, Dr. I like these videos where you step back and give a broad picture & the consequences of machine learning. I thought memGPT was a major breakthrough in AI, paving the way for entry into education, legal & medical areas, at least in teaching.
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Great video, Professor Brunton! Using data-driven methods, along with helping us understand complex mathematics, also provides a computationally better solution. Considering the case of complicated simulations like crash simulations or fluid processing in a large industry in multiple stages. I would be glad to know your view on this. I'm really looking forward to more fantastic and insightful lectures. Thanks for everything ❤
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This is exactly the video I was looking for! Puts everything in perspective
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I think multi-modality and the Transformer architecture are 2 of the most significant developments in the past ten years. The topics covered here were the ones most people have heard about. There are too many topics to cover, but a few less well known examples would be chip design, policy development, protein folding, and superresolution.
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thank you dear for these information, we hop there will be more of these kind of video which enhance and help new comer to this technology will be inspire by it... Thank you again
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Hi Steve, awesome lecture as always. What do you think of implicit memory in neural nets? Do you think a subconscious is a apt analogy? Also, do you think an AI can be built with a qubit architecture with 20 years? Keep up the fantastic work at making these complicated topics accessible for us nonexperts. Good luck to us all.
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This is a very awesome summary.
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Termites, amazing ahahahah. Steve, been following you for years and now we cite you in our Msc thesis! I'm a student in TU Delft Aerodynamics and lot of people are working with sparse regression models and data assimilation/machine learning for fluid phoenomema!❤
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Great video!
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PINNs are the state of the for the last question, very very nice video.
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thank you for this overview! pretty clear explanation. the question i'm curios about is it possible to get rid off all analytical descriptions of physics in favor of learning net models. any chance a general AI would be able to solve any kind of physical world problem?
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As an engineer using Machine Learning for time series prediction and control systems I would be very interested in your survey of engineering capabilities. It seems even on control systems there is a huge emphasis on image processing and little support elsewhere.
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Hey Steve do you think that a fractalized image could be used to speed up training of neural nets?
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great summary of magic.
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Thanks Steve, you see awesome..
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How did you project your slides? Really nice aesthetic 🎉
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Thank you so much..
