What is "machine learning"?
"Machine Learning" is the process by which a computer answers questions, and if the questions are answered wrong or incorrectly, the computer corrects the mistake, learns from it, slightly modifies the programming and search restrictions, and remembers the correct answer to the exact question in the event it appears again in another place.
How did the IBM team employ that concept in the development of Watson's AI? What advantage did that provide over previous attempts at "intelligence"?
Watson's AI learned throught the process stated above, except every time the computer made a mistake, it came closer and closer to the correct answer through later questions that were answered by searching through thousands of documents and bits of data stored in memory. The advantage was that every search became more and more accurate until the exact answer to every question was given, and although the IBM team never reached that point, they are continuing to get closer and closer.
I've often mentioned the term "Empirical Scepticism". What does that mean? How does that relate to the concept of Machine Learning? How does this relate to your life?
In this particular case, Empirical (scientific) Scepticism is the questioning or doubtfulness in one's knowledge or abilities, so, in relation to the concept of "Machine Learning," the idea is to doubt the credibility in Watson's sources or the search results and specifications. Basically, the idea revolves around modifying the programming to improve the answers that Watson gives. The idea relates to me personally in my second-guessing of myself. Take mathematics for example; I always double-check my answers, make sure that I did everything correctly and within the accepted deviations, according to the directions and the information given and by the rules of the geometrical shape for which the figures are being calculated.
