Artificial Intelligence & Large Language Models: Oxford Lecture — #35
Manifold
Three Perspectives for Deep Learning and Neural Nets
Deep neural network architecture gives us a general framework for learning approximations to high dimensional functions from data. If this kind of theoretical heuristic turns out to be true, it sort of has long-term consequences about the nature of intelligence in the universe. In other machine learning work that I've done applied to genomics, we showed that there's a very special kind of phase transition that occurs when you give certain kind of learning algorithms sufficient genomic data.
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