I would assume most QA (question answering) models blow Watson out of the water. A lot has been done since then. See: https://aclweb.org/aclwiki/Question_Answering_(State_of_the_...

(I've done work in QA and have played at building Jeopardy style QA models)

Watson (Jeopardy Watson, not the IBM branding exercise Watson is now) has much weaker text understanding models, but has much much better optimisations for the incremental style of data release that you see in Jeopardy (ie, you get more and more data the longer you listen). IBM did a lot of work optimising when to answer as well as trying to get the correct answer.

The closest analogy that is regularly studied in modern QA research is "Quizbowl"-style datasets, but these tend to be much smaller than the SQUAD datasets that most modern neural network QA systems are built against.

Sure, but Jeopardy is all about AQ (Answer-Questioning) :)

Probably better than this: https://twitter.com/jeopardygoat