David Montoya, Satisfi | Location and Context World 2014
Channel: SiliconANGLE theCUBE
Date: 15/01/2017

David Montoya, Satisfi, Location & Context World with Jeff Frick

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However, that said, it proved a surprisingly amazing show against the reining champions and did in fact win the day.

IBM’s Watson has been put to task before, by Greg Lindsay and he has spoken up on how he managed to beat the machine—3 times. In an article published at Fast Company, Greg goes over what he expected the machine’s primary weaknesses to be and how he managed to exploit them using a carefully crafted trivia stratagem to keep himself in the lead. Although, from the way it reads, just barely:

    My strategy was simply to take Watson’s strengths away from him. Having no idea what those strengths were, however, I had to make several assumptions.

    First, I assumed he’d be impossible to beat on the buzzer, which had never been my strong suit, anyway. Instead, I took a page from The Princess Bride (the book, not the movie), specifically Inigo Montoya’s duel against the Man in Black. As long as Montoya was able to keep the fight on rocky terrain, his defensive prowess awarded him the advantage. Once the Man in Black maneuvered him onto open ground, however, Montoya’s was overwhelmed by his speed. So it would go with Watson, I figured. Binary relationships–countries and their capitals, for instance–would be easy for him to figure out, and he would beat me to the buzz every time. So I had to steer him into categories full of what I called “semantic difficulty”–where the clues’ wordplay would trip him up. I would have to outthink him.

    Second, I would need to find and win the Daily Doubles to deny Watson a coup de grace and to keep pace in what I figured would be a losing war of attrition. (This was based on personal experience–I had rallied from last place to win my first Jeopardy! match only after a Daily Double on the very last clue.)

    Finally, I had to be in the lead heading into Final Jeopardy. If Watson could confidently decide on an answer in only three seconds, I shuddered to think how infallible he would be given all of thirty.

Watson uses a powerful data-mining engine to pull facts and figures out of textual sources. This gives the machine (casually referred to as “him” by most, so I’ll keep doing that) a giant advantage with clues that have direct semantic linkage to facts such a geography, names, places, things, et cetera. However, as Greg correctly assumed, Watson had trouble with cultural context and linguistic semantics that played on allegory, slang, and intracultural jargon. Things that we might take for granted in a conversation about comic books would probably wiff right over Watson’s head because it’s an inference that occurs in the relationship between minds and doesn’t rise directly from the text.