Monthly Archives: May 2015


 For the present time, AI systems are worse than ourselves for some applications, and I believed that the reason was that they have not an associative memory as good as ours; its efficient use is essential for our intuition. In a fraction of a second, it gives us an answer, or allows us to remove an ambiguity. We have already considered this problem in the preceding blog. AI systems rarely use a large corpus of English texts effectively, and don’t do it quickly.

Therefore, I was impressed by the results achieved by Watson, an AI system developed by an IBM team. It can play a Jeopardy! competition, a television game very popular in the States. Several competitors must find as fast as possible the person or the object corresponding to a clue, which is an English sentence in any domain. Watson must first understand the sentence, then find candidate answers in a huge amount of English texts, and finally choose the best match with the clue. All this has to be done in a short time, a few seconds.

Let us consider an example from the match that showed Watson’s superiority. For the clue It’s a poor workman who blames these.“, Watson was the first to find the good answer: tools.

The difficulty comes for three main factors: questions come from a broad domain, the answer must be given with high confidence, and it must be done very fast. Watson does not use Internet, but it has a great deal of English knowledge, including several encyclopedias, among them the full text of Wikipedia; the whole takes four terabytes of storage.

Watson competed against two top champions: Brad Rutter, a 20-time champion, and Ken Jennings, the best player in Jeopardy! history, famous for winning 74 games in a row. Watson won, and won big: $77.147, when the other two only won $21.600 and $24.000.

As Watson includes many modules, I will briefly speak of those that seem the most important for an AI point of view.

In the first step, Watson analyses the clue and extracts keywords and sentence fragments used for finding possible answers from the breadth of its knowledge. This mechanism is a little like our intuition: it is using many heuristics. Its main advantage is that it is very fast. Unfortunately, for us, this often leads to mistakes, because we often merely keep the first response, for lack of time, laziness, or unawareness of the knowledge leading to the result.

For Watson, this step usually gives several possible candidates. Then, far better than us, it will spend a lot of time (that means a few seconds for a computer!) for choosing the most reliable one. It ranks the candidates, and it chooses the first one, provided that it has a sufficient level of confidence. If no one is satisfactory enough, it does not answer.

Many methods enable it to measure its confidence in a particular result. If a result appears several times, Watson will be much more confident in this result. It also tries to evaluate the reliability of a candidate. If the clue is Chile shares its longest land border with this country, it is easy to remove China, which does not share borders with Chile. Two serious candidates are Argentina and Bolivia. The media often speak of the border dispute between Chile and Bolivia; this will tend to favor Bolivia. On the other hand, some results give the lengths of the borders. As these lengths are very different, and these results are rather reliable, Argentina will finally be chosen by the geographic module. However, even when a candidate has most of the wanted characteristics, it will not be chosen if one reliable result forbids it.

Watson may analyze several results and compare their reliability because it is aware of the information used for finding a particular answer. This is a huge advantage for artificial cognition, usually we do not know why our intuition gives a particular result.

Watson correctly answered several difficult clues, for instance clock for Even a broken one of these on your wall is right twice a day.“, and escalator for This clause in a union contract says that wages will rise or fall depending on a standard such as cost of living.

Nevertheless, as human beings, Watson sometimes makes mistakes. The audience had a good laugh when it answered Toronto to the following clue on US towns: Its largest airport is named for a World War II hero; its second largest for a World War II battle.

Realizing a system playing Jeopardy! has shown the possibility of using a huge memory efficiently. Now, the authors of this system want to adapt their methods to the resolution of another problem: medical diagnosis. In that domain, it is also important to give the right answer by reasoning over an unstructured natural language content; moreover, it will have much more useful consequences.