Learning from failure to tackle hard problems (blog.ml.cmu.edu)

The most important clue to solving a difficult problem is knowing that somebody else has already solved it.

How does this compare to just reducing the likelihood of negative samples?

> The [goal] of machine learning research is to [do better than humans at] theorem proving, algorithmic problem solving, and drug discovery.

Naively, one of those things is not like the others.

When I run into things like this, I just stop reading. My assumption is that a keyword is being thrown in for grant purposes. Who knows what other aspects of reality have been subordinated to politics by the writer.

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That idea feels really relevant to me as a future research direction(not an expert). Could maybe someone explain what I am missing here? Why does this idea not get more attention?! Is it not new? And if so, could one state why it is not commonly employed?