Why Computer Vision Is a Hard Problem for AI
126,881
Published 2023-10-24
00:00 Why vision is a hard problem
1:18 History of computer vision
2:01 Alexei's scientific superpower
3:14 The role of large-scale data
3:37 Computer vision in the Berkeley Artificial Intelligence Lab
4:15 The drawbacks of supervised learning
4:57 Self-supervised learning
5:33 Test-time training
7:08 The future of computer vision
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All Comments (21)
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I love that with 120.000 citations, he is regarding the grad students and the next generation of scientists as his biggest achievement.
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It's great that there are professors out there that value their students as their greatest achievement!
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As a computer scientist working in Computer Vision tasks (and other AI applications) for medical imaging processing, this video made me smile :)
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Wonderful video! I love everything this channel has made!
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I love how at 8:08 one of the students' phone falls out of their pocket and everyone turns and looks at it
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my favorite topic in CS
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Thank you for the insights and this very well produced video!
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Love this channel
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Wonderful! Looking forward to the future!
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thank you for explanation!
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Love the short video!❤
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Thank you👍
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All very interesting. I wonder if we are limiting computer vision by only considering human vision. Each other organism has vision selected to make the organism successful, and its not like ours. I wonder if there is something we can learn from this diversity of purpose for visual systems in all organisms. Alexei Efros has touched on this diversity of purpose with his own experience of vision.
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so amazing.😍😍🤩🤩.good luck.
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I had an idea when I was working on my thesis that if we have transformer for vision and a new embedding system that treat the visual data like human we can have a model that will understand the images of the universe that is beyond the computer ability of human brains such as the cosmic microwave background. But it’s an idea only😢
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Nice informative video.
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what about use analogue computing in the futur for AI ?
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Man.. I wish you were my CS professor. 👍
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Cool!!!❤❤
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This is a very good interview. I am glad to see that it's validating my intuition, about the fact that models should continuously learn instead to being frozen, and then retrained from scratch. One of the biggest difficulties to improve the current techniques is reducing models size. I don't know how much data a real brain can store, but given the miniaturization of current chips, I suspect we are wasting resouces. Anecdote: I have bad eyesight as well. 😂