This chapter discusses the idea of training AI language models by emulating human writing and the potential limitations of this approach. They explore a strategy called 'chain of thought prompting' that involves breaking down the problem-solving process into steps and evaluating each step with a third-party model. This technique showed promising results and could potentially preview the capabilities of much larger models. The relevance of math in objectively evaluating the performance of AI systems is also highlighted.
Our 145th episode with a summary and discussion of last week's big AI news, this time around with guest co-hosts Kevin and Gavin from AI For Humans podcast
Check out the AI For Humans episode on which Andrey and Jeremie guest co-host here.
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- (01:52:23) Outro