Watson, the new Jeopardy Champion

Watson is a supercomputer developed by IBM to be good at question answering. But it is not only good at question answering but also playing at Jeopardy, where sometimes questions are tricky if not total nonsense for humans.

What does it have to do with Artificial Intelligence? Watson is fully autonomous, during a game of Jeopardy it does not need any human intervention to know the question and chose and answer for it.

We could say that Watson is the natural evolution of Deep Blue. Deep Blue is able to play chess which is - strategy aside - easy to implement for a computer. After this success, IBM wanted to aim for another challenge. They first thought about developing a machine able to pass the Turing test but felt that the public would not be that receptive to such an achievement. That's when they thought about the well-know television quiz Jeopardy. It was at that time that Ken Jennings was in his winning streak which still is the longest ever reached. They made the bet to develop Watson, a computer able to win against such a champion.

Therefore, to reach this goal, Watson is able to understand natural language, sentences which are not easy to understand for everyone, find the clues and provide the corresponding answer.

At first, Watson had a basic behavior. It tried to find the key words in the answers given by the Jeopardy host, look through its large database for texts which were related to these words, try to extract possible questions from it with an associated to probability and chose as a final answer the one with the largest probability. If it seems a quite easy task, this was already a challenge because it had to do it in a limited amount of time so that it would try to answer before the humans it would play against. Therefore, parallelization was used to do it as fast as it could.

When this had been done, Watson could barely beat a ten years old. It had not yet reached the level of a Jeopardy champion. There were too many questions it answered wrong, even if it was fast. The problem was often that Watson did not understand the kind of answer it should give. For example, when a month was required Watson could give the noun of a person as answer. That is when machine learning came in. Watson was fed with lots of Jeopardy questions with their right answer. Then an associated algorithm enabled it to give more importance to some words compared to others, understand the kind of answer expected for a given category.

Was it good enough? Not yet. The last critical point was to add some online learning (real time learning during a Jeopardy game). Sometimes, Watson would give the same answer than a candidate had given previously, when the candidate was wrong. Watson had to eliminate this answer from itss computations. Moreover it would help the computer to understand for a given category what kind of answer is expected after knowing the first answers given.

Watson competing against the two greatest Jeopardy champion. Image : IBM

In early 2011, Watson was ready for its well-know exhibition match. Its opponents were Ken Jennings who had the longest unbeaten run at 74 winning appearances and Brad Rutter who had earned the biggest prize pot with a total of $3.25 million. Ironically some considered Rutter and Jennings Jeopardy-winning machines.

By the end of the first of the special exhibition match shows, the score was tied and no one could guess the final outcome. Then Double Jeopardy started. Watson powered through questions, winning even with answers it was far from convinced about, and placing odd bets that came good. By the end of the second episode, it had $25,000 more than its closest opponent, Rutter.

At the end of the third episode, all three correctly answered the last question "William Wilkinson's 'An account of the principalities of Wallachia and Moldavia' inspired this author's most famous novel" with "who is Bram Stoker?" but Jennings appended his response with: "I for one welcome our new computer overlords". He, and Rutter, had lost to Watson.

Now IBM is trying to adapt Watson for a more business-like use. For example, they are trying to transform it into a healthcare assistant for doctors where after describing your symptoms, Watson would find the probable disease you have and propose the medicines you should use.