IBM's A Smarter Planet blog covers IBM research and products, including Watson, Jeopardy, quantum computing and its annual 5 in 5 predictions.
HN links it as the primary source for Watson details, such as its Prolog use and low confidence on the airport clue, and for IBM predictions.
David Ferrucci, the manager of the Watson project at IBM, on why he thinks Watson got the Final Jeopardy question wrong:
"First, the category names on Jeopardy! are tricky. The answers often do not exactly fit the category. Watson, in his training phase, learned that categories only weakly suggest the kind of answer that is expected, and, therefore, the machine downgrades their significance. The way the language was parsed provided an advantage for the humans and a disadvantage for Watson, as well. “What US city” wasn’t in the question. If it had been, Watson would have given US cities much more weight as it searched for the answer. Adding to the confusion for Watson, there are cities named Toronto in the United States and the Toronto in Canada has an American League baseball team. It probably picked up those facts from the written material it has digested. Also, the machine didn’t find much evidence to connect either city’s airport to World War II. (Chicago was a very close second on Watson’s list of possible answers.) So this is just one of those situations that’s a snap for a reasonably knowledgeable human but a true brain teaser for the machine."
http://asmarterplanet.com/blog/2011/02/watson-on-jeopardy-day-two-the-confusion-over-an-airport-clue.html
> The natural language processing is its biggest weakness.
There were several documentaries about Watson after it won Jeopardy, and this weakness was quite evident to those paying attention.
Watson never really understood Alex's Jeopardy "answers" at all. This was quite obvious in "final Jeopardy". Watson's response clearly showed its limitations.
Here's how it went down:[1]
The category was US Cities, and the answer
was: “Its largest airport was named for a
World War II hero; its second largest,
for a World War II battle.”
The two human contestants wrote
“What is Chicago?” for its O’Hare and Midway,
but Watson’s response was a lame
“What is Toronto???”
Basically Watson's Jeopardy responses were a very refined equivalent of the Google "I'm feeling lucky" button.
I have to believe that the situation gets a lot lot trickier in a "medical context".
Still, couldn't it be possible to harness Watson as a super-smart assistant to humans? But there's probably not enough money to be made in that.
[1] http://asmarterplanet.com/blog/2011/02/watson-on-jeopardy-day-two-the-confusion-over-an-airport-clue.html
Watson was not confident in that answer - only 30% (http://asmarterplanet.com/blog/2011/02/watson-on-jeopardy-day-two-the-confusion-over-an-airport-clue.html). Had that been a normal question, it wouldn't have buzzed in. It only answered because Final Jeopardy is the only time when not answering and answering incorrectly have the same penalty.
I think it says something about this "prediction" that most of the text on the IBM page about it (http://asmarterplanet.com/blog/2011/12/the-next-5-in-5-mind-reading-is-no-longer-science-fiction.html) is:
Vote for this as the coolest IBM 5 in 5 prediction by clicking the “Like” button below.
Join the Twitter conversation at #IBM5in5
Here's some more discussion about using word analysis to predict psychosis:
http://asmarterplanet.com/blog/2015/08/word-analysis-proves-effective-pre-psychosis-diagnosis.html
And here's an Atlantic article: http://www.theatlantic.com/technology/archive/2015/08/speech-analysis-schizophrenia-algorithm/402265/
Can someone change this to link to the actual IBM blog entry [1] instead of the CNET fluff piece?
[1] http://asmarterplanet.com/blog/2011/12/the-next-5-in-5-our-forecast-of-five-innovations-that-will-alter-the-landscape-within-five-years.html