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This is certainly out of my depth but how would this model 'reduction' improve provability? It would (I assume) reduce instances of non-desirable behaviour. Bu

• ganzuul 7y ago
  To my layman's understanding; by reducing instances of undefined behavior as those instances should come from the noise of the data. Noise looks like structure in very-high dimensional views of data, so you need bounds on that stuff. For autonomous vehicles the 'proof' to the regulator can be the signature of the engineer. - Fortunately some engineers have higher standards. - I think in the future the trainig data will be from simulations that create curriculum learning datasets so that the noise characteristics are perfectly known. The ML algorithms can be written with dependent types, so that you can prove your code does what you think it does. Another challenge is inductive bias, which is a lot like confirmation bias. This bias comes from choosing an ML algo which is sensitive to certain information and blind to others. You need to navigate the set of all possible functions, AKA Hilbert space, to overcome it. Fortunately only a small corner of this space is relevant to our universe and Tensor Networks seem to address this problem. It looks like a lot of work to put these pieces together but at least it looks like the problem is tractable.