Watch a 56-minute research seminar from Harvard CMSA's New Technologies in Mathematics series featuring Meta AI researcher Timo Schick exploring how language models can be trained to autonomously utilize external tools. Learn about Toolformer, an innovative model that demonstrates the ability to independently determine when and how to call APIs, select appropriate arguments, and incorporate returned results to enhance token prediction. Discover how this approach significantly improves zero-shot performance across various tasks while maintaining core language modeling capabilities, addressing common limitations like arithmetic operations and factual lookups that typically challenge large language models. Gain insights into how this self-teaching methodology helps bridge the paradoxical gap between language models' sophisticated reasoning abilities and their struggles with basic functionalities.