The core premise of machine learning is that your model should be differentiable. To generate a zero knowledge proof, you have to somehow transform your computation to one where every variable is an integer or modulo. There's some challenge to doing that. But then I always think about the fact that like NVIDIA moved everything to 8-bit and you know like it's not really as smooth as you think it is.
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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