Future AI Paradigms
Rongyao discusses the ongoing scaling of AI and the evolution of learning paradigms, highlighting the shift from supervised learning to pre-training and prompting techniques. He emphasizes the challenges posed by memory consumption in transformer models and the need for breakthroughs in both hardware and architecture to enhance AI capabilities. The conversation also touches on the potential of prompting to solve complex problems, including mathematics, by leveraging the vast knowledge encoded in large language models.In this clip
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