Terence Tao on Nostr: I am increasingly of the opinion that the most productive near-term adoptions of AI ...

I am increasingly of the opinion that the most productive near-term adoptions of AI in mathematics will primarily come not from applying the most powerful models to the most challenging problems (although we will see a few isolated examples of progress along those lines, especially when large amounts of computational resources and expert attention are applied), but from using medium-powered tools to accelerate and scale up more mundane and time-consuming, but still essential, research tasks, using the accumulated human experience with (and understanding of) such tasks to guide, verify, and safely incorporate the AI output into one's workflows. In such use cases, the output of the AI tool could also have been produced (with increased expenditure of time and attention) by a human expert - but this is actually a feature rather than a bug, as it allows for the AI output to be readily and reliably assessed, confirmed, and converted to a format that such experts are already comfortable working with.

An example of such a mundane task is literature review: locating relevant prior literature on a given problem. If the problem already has a commonly agreed upon name, as well a well-established community of researchers working on it, then existing web search and bibliographic search tools are already more than adequate to find both past and current literature on the problem: in particular, the citation graph between the literature will be dense enough that one can start with one key paper in the subject and perform both forward and backward citation searches to obtain a reasonably complete picture of the current state of knowledge on the problem. (1/4)