Explore the role of associative memories in Transformer models through this 39-minute lecture by Alberto Bietti from the Flatiron Institute. Delve into the internal mechanisms of large language models and their ability to store vast amounts of knowledge from training data. Examine theoretical results on gradient-based learning of weight matrices as associative memories and the impact of over-parameterization on storage capacity. Gain insights into how Transformers adapt to new information in context or prompts through analysis of toy tasks for reasoning and factual recall. Enhance your understanding of Transformers as a computational model and their implications for reliable AI systems.