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I would like to point out a much more exciting modelling process, whereby neural networks extract the underlying boolean logic from simulation outputs: https://
by evrimoztamur 1y ago
I would like to point out a much more exciting modelling process, whereby neural networks extract the underlying boolean logic from simulation outputs: https://google-research.github.io/self-organising-systems/difflogic-ca/?hn https://google-research.github.io/self-organising-systems/di...
I firmly believe that differentiable logic CA is the winner, in particular because it extracts the logic directly, and thus leads to generalize-able programs as opposed to staying stuck in matrix multiplication land.
- max_ 1y agoThis is one of those papers that are so good I would like to keep it secret. I shared it with a friend and he thought its wasn't that useful. That made me happy since I knew my secret maybe safe.
- _dark_matter_ 1y agoI think it's interesting, but I don't see how it's useful. Can you describe what you think is useful about it?
- Legend2440 1y agoIf you look at the learned gates, it does not directly extract the underlying rules of Conway's game of life. It has many more gates than are necessary and they have the same complex, uninterpretable structure you see in a neural network. The training method they're using is the same as used for quantized neural networks. Your 'neurons' being logic gates doesn't mean you're doing logic, it's still statistics.
- awesomeMilou 1y agoTo ruin this for everyone: The underlying optimization that enables these to run as computationally efficient as they do, is patented: https://patents.google.com/patent/WO2023143707A1/en?inventor=Felix+Petersen&after=priority:20200101 https://patents.google.com/patent/WO2023143707A1/en?inventor...