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Episode 265: Where ZK and ML intersect with Yi Sun and Daniel Kang
Zero Knowledge
Adapted Nonlinearity in Machine Learning Models?
One of the problems with large machine learning models is well there's two problems. So for example the weights of GPT-3 there's 175 billion of them and computing to that is very very expensive. There's actually a lot of computation that happens internally within the model and representing that within ZK is also very expensive. The basic problem is that the proof systems that are really good for matrix multiplication aren't good at nonlinearities and vice versa. We're exploring how to mitigate in terms of the performance implications of that.
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