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What P vs. NP is about
We recently made a Polylog video about the P vs NP problem. As usual, our goal was to present an underrated topic in a broadly understandable way, while being slightly imprecise and leaving out the…
We didn’t opt for this choice since it raises questions like “What kinds of logical connectives are allowed?” or “How do you quickly evaluate formulas with many nested parentheses?”. In the video, we showed how this view makes it clear that if P=NP, hash functions cannot exist because their entire shtick is to be easy to compute but hard to invert. To me, the essence of deep learning has nothing to do with trying to mimick biological systems or something in that sense; it’s the observation that if your circuits are continuous, there’s a clear algorithmic way of inverting/optimizing them using backpropagation.
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