Why Computer Vision Is a Hard Problem for AI
Computer scientist Alexei Efros suffers from poor eyesight, but this has hardly been a professional setback. It's helped him understand how computers can learn to see. At the Berkeley Artificial Intelligence Research Lab (BAIR), Efros combines massive online data sets with machine learning algorithms to understand, model and re-create the visual world. His work is used in iPhones, Adobe Photoshop, self-driving car technology, and robotics. In 2016, the Association for Computing Machinery awarded him its Prize in Computing for his work creating realistic synthetic images, calling him an “image alchemist.” In this video, Efros talks about the challenges and changing paradigms of computer vision for AI.
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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