Having worked with the "original" Watson, I saw first hand how the system stumbled upon a particularly stupid but hard problem as it tried to scale.<p>In 2014, I saw a demo of the original Discovery Advisor, which was at the time the closest commercial equivalent to the "Jeopardy system." This demo took in Wikipedia as a corpus, and a question was asked: "what country produced the greatest amount of wheat in 2012?" The system returned a list of countries as answers, so it wasn't quite nonsensical, but it was clear the answers were incorrect. The answers were countries like "England," "Norway," or "Zimbabwe." This system also returned passages from Wikipedia as supporting evidence, but the passages weren't about wheat production. Instead, they were about quotes that contained the word wheat... such as "let's cut the wheat from the chaff."<p>So of course, some smart-alec in the room Googles the same question, and this was before Google had the ability to return factual answers to factual questions, so instead we got a list of web results. The top result, interestingly, was a Wikipedia article titled "Wheat Production by Country." Opening that article presented a table that clearly showed that China produced the greatest amount of wheat in 2012.<p>Unfortunately, that Watson system at the time didn't read information from tables. I'm not sure if it does now, but I do know that reading data from tables in a manner that can be easily integrated and scaled within a broader semantic processing system is quite difficult. I'm not as focused on the space as I once was, so I'm not sure if the problem has been well solved yet. If not, I'd say it's a worthy area to invest in a solution.