Using machine learning to solve hard math problems in practice (AWM colloquium)

Abstract: AI is taking off and we could say we are living in “the AI Era”. Progress in AI today is based on mathematics and statistics under the covers of machine learning models. This talk will explain recent work on AI4Crypto, where we train AI models to attack Post Quantum Cryptography (PQC) schemes based on lattices. I will use this work as a case study in training ML models to solve hard math problems in practice. Our AI4Crypto project has developed AI models capable of recovering secrets in post-quantum cryptosystems (PQC). The standardized PQC systems were designed to be secure against a quantum computer, but are not necessarily safe against advanced AI!

Understanding the concrete security of these standardized PQC schemes is important for the future of e-commerce and internet security. So instead of saying that we are living in a “Post-Quantum” era, we should say that we are living in a “Post-AI” era!

number theory

Audience: researchers in the topic

UCSD number theory seminar

Series comments: Most talks are preceded by a pre-talk for graduate students and postdocs. The pre-talks start 40 minutes prior to the posted time (usually at 1:20pm Pacific) and last about 30 minutes.