Synthetic data can be useful in domains like chess or arithmetic where the state space can be filled out computationally. However, it is challenging to replicate human creativity through synthetic data generation in ambiguous areas like language. There is uncertainty about the effectiveness of synthetic data in enhancing generalized language tasks, as language is not an axiomatic system. The speaker suggests that when faced with a lack of data, creating more data or exploring novel methods may be more beneficial than relying solely on synthetic data. The speaker also highlights the cyclic nature of progress in computer science, suggesting that there might be limits to the effectiveness of current approaches, leading to the need for new solutions and challenges to tackle.

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