It's particularly challenging in areas where as a human, you actually can't evaluate the model output. For example, if the model is just evaluating the classifying images, that's fine for humans to post-process and check. But if you're relying on machine learning to do something a bit superhuman, maybe make some prediction, well, the whole point is that you as a human shouldn't really be overriding the model. And so there you really want the gold standard model not a cheaper approximation.
This week, Anna Rose and Tarun Chitra dive back into the topic of ZK ML with guests Yi Sun, co-founder of Axiom, and Daniel Kang, Assistant Professor of computer science at UIUC. They discuss Yi and Daniel’s previous academic work and what led them to get interested in ZK topics and specifically ZK ML. They then dive into a discussion about 2 recent papers which examine the use of ZK within Machine Learning architectures.
Here are some additional links for this episode:
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