Werner Heisenberg’s 1923 PhD defense at the University of Munich nearly ended in failure.
Strong in theory but weak in experimental physics, he struggled with practical questions during his oral exam, alarming the examiners, especially Wilhelm Wien.
His mentor, Arnold Sommerfeld, argued that Heisenberg’s extraordinary theoretical talent outweighed his lack of laboratory skills. He ultimately passed by a narrow margin, making the defense one of the most notorious in physics history.
An interesting part of CVPR was the TC PAMI meeting, followed by 3 proposals on how to improve CVPR:
1) @georgiagkioxari highlighted that award candidates are often industry papers, and proposed an "Academic best paper award"
2) @taiyasaki noted that CVPR best paper award winner did not provide code or weights, and proposed that awards would not be given to closed papers
3) @pjreddie noted that many institutions are funded by defense contracts, and proposed that CVPR would not accept papers by these institutions
Scientific research is fundamental to advancing civilization and helping people globally to solve the most critical problems, from medicine to materials, from brain science to physics, and much beyond. This is only possible when scientists have access to the best tools of the time to conduct scientific research, including having access to AI-based tools.
I’m getting increasingly annoyed by young people complaining that they cannot do AI-related research unless they join big industrial labs… well, here is my reply: academia is supposed to work on ideas that money cannot buy!
I want to offer some unsolicited advice to computer vision researchers jumping into robotics. Don't focus too much on VLMs, VLAs etc. That's fine, but the real action is at the sensorimotor level. Most of the open problems in robotics are in manipulation, which is about hand-object interaction, and contacts and forces are central. Proprioception and tactile sensing are as important as vision. Don't get seduced by cherry-picked demos. You can't do robotics without doing robotics.
We've lost an absolute giant today. RIP Dimitri Bertsekas. His probability and optimization books got me through my masters. Massive loss for the MIT community and the field.
Our internal data shows Claude is accelerating AI development—a possible path to recursive self-improvement, or AI autonomously building a more capable successor.
It’s happening faster than we thought, and the implications deserve greater attention. anthropic.com/institute/recu…
one-slide rebuttal for explicit models of the world by @ShenlongWang in favor of “vision science” as coined by Bill Freeman earlier in the morning.
also, this is the funniest thing I’ve seen today 😄
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