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Explore the intersection of deep learning and pure mathematics in this thought-provoking lecture by Geordie Williamson from the University of Sydney. Delve into the potential impact of deep neural networks on mathematical research, examining instances where this technology has proven useful and led to intriguing developments in pure mathematics. Gain insights into a pure mathematician's perspective on interacting with deep learning, and contemplate the future role of machine learning in mathematical proofs. Recorded at IPAM's Machine Assisted Proofs Workshop, this 52-minute talk offers a unique opportunity to consider the evolving relationship between artificial intelligence and traditional mathematical approaches.
