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.

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