Cows are thirsty

Several years ago, I was in the subway with one of my children. For keeping him entertained, I asked him a question: “what are the cows drinking?” Knowing me, he did not answer immediately: he sniffed out a trap, it was too easy to answer this question! A passenger, whom I did not know, was surprised by this total ignorance, and he could not refrain from saying: “They are drinking milk, my little child!” This mistake is natural: this is a hoax. It is due to a very useful mechanism, that often enables us to give a correct answer quickly. The question was on a beverage and on cows; as cows are associated with milk, and milk is a beverage, we have the answer immediately.

It is better to avoid systems giving incorrect results. However, if the result is that one gives no answer at all, this is not satisfactory. Many AI researchers have an excellent mathematical training. Therefore they overwhelmingly reject all possibilities of error: if one accepts a contradiction, one can prove everything. Luckily, human beings survive their contradictions.

Methods that can lead to erroneous results may be useful when we do not know a perfect method, or when it is too costly over time. It may be better to have a result quickly rather than waiting centuries for a perfect result. For instance, current Chess programs play very well, better than the world champion, but they certainly do not always play the best move. This was an issue for Edgar Poe, when he was asserting that Maelzel’s Chess Player was not an automaton. He was right: a human chess player was hidden into the machine. Nevertheless, one of his arguments was incorrect: he believed that it was not a machine, because sometimes it lost the game. Sorry, Edgar, a machine may be imperfect. However, we know a perfect method for playing the best chess move: one fully develops the tree of all the possible moves for each player. As the depth of the tree is finite, it can be done in a finite time, and one can find the perfect solution from this tree. Unfortunately, the time necessary for generating it goes beyond anything we can imagine.

Fast mechanisms are used by our unconscious; we call them intuition. Unfortunately, sometimes they are making mistakes. They are also useful for resolving ambiguities in natural language texts. It may be dangerous to use the results found by our intuition: we must cautiously check that they are correct when the consequences could be serious. To do that, our conscious mechanisms verify the results given by our intuition. However, calling them is not automatic: for each result, we must decide whether we will check it.

Intuition is an essential mechanism for human beings, and especially for animals: this is the only way enabling them to take decisions that are not genetically programmed. It always gives results quickly; moreover, we can use it in situations where we do not know other methods. As living beings, artificial beings will have to use similar methods. Nothing prevents us to give them such capabilities. Unfortunately, this is not so easy because we do not know them, since they are unconscious; we have to discover and experiment them in our systems. Another difficulty is that our love of correct methods will reject the bunch of heuristics that makes up intuition.

Fortunately, we have now a clearer idea of the mechanisms called ‘intuition’. For Herbert Simon: “The situation has provided a cue; this cue has given the expert access to information stored in memory, and the information provides the answer. Intuition is nothing more and nothing less than recognition.” I cannot see why AI systems could not use this mechanism.

The horror of error is dangerous: some research directions are neglected because they can lead to dubious results. This favors rigorous mathematical methods in AI, even when their application is limited because they are too time-consuming.

If artificial beings were never making mistakes, that would mean that they would be too restricted. They must not be refrained from using methods that can be very helpful when carefully used. However, we must also give them the possibility to find out when they are wrong: we have to add modules verifying the results. For instance, CAIA checks that all the solutions it finds satisfy all the constraints. Unfortunately, it is possible that it misses solutions, but we have already seen that, sometimes, great mathematicians also miss some of the solutions.

We will progress when our systems believe that cows drink milk, see that this is wrong, then find the right answer, as my embarrassed fellow traveler finally did.