I had only been following the story of IBM’s Watson taking on and besting two long standing Jeopardy champions peripherally. It just didn’t strike me as much more than a distraction in the field of general or strong machine intelligence.
Today, Mike Loukides at O’Reilly Radar had a thoughtful piece that has me re-considering. He seems to also be uninterested in Watson’s ability to come up with a single correct response. However, in digging into the processing behind arriving at that datum, he suggests some interesting possibilities.
The next level down in Watson’s analysis is even more interesting. The confidence level assigned to each answer comes from how well the answer matched various sources of information. Possible answers are scored against a number of data sources; these scores are weighted and combined to form the final confidence rating. If exposed to the human users, the scoring process completely changes the kind of relationship we can have with machines. An answer is one thing; a series of alternate answers is something more; but when you’re looking at the reasons behind the answers, you’re finally getting at the heart of intelligence. I’m not going to talk about the Turing Test. But I am suggesting that, when you have the reasons for the alternative answers in hand, you’re suddenly looking at the possibility of a meaningful conversation between human and machine.
Read the rest of the article, he gives some fairly compelling examples and practical applications. I can already see some aspects beyond what he considers, such as helping to map out authority and trust, interactively, for various information sources, a task that is often just too imposing for the casual reader browsing around.
Watson and the future of machine learning, O’Reilly Radar