Monthly Archives: October 2018

Thinking, Fast and Slow

 At least two winners of Nobel prize in Economics have made important contributions to the understanding of human intelligence. Naturally, Herbert Simon is one of them; a little later, Daniel Kahneman has written Thinking, Fast and Slow where he shows the coexistence of two systems in our brain.

System 1 is fast, and it operates automatically without a voluntary control: it jumps to the result. Unfortunately, in return for this immediate response, its answer may be wrong. Moreover, we cannot justify our solution. We have already seen an example of a result found by our system 1: the question was about a beverage and an animal. We had often used a beverage linked to this animal, hence the mistake. System 1 is very efficient in the situations where there are many examples, and where one can evaluate the results accurately. In that case, all the conditions are met so that the learning goes well.

When the situation is not favorable to learning, we do not see our mistakes, and we often accept them, although they contradict the laws of logic. The author describes an experiment where Linda is a thirty years-old single woman, outspoken and very bright. She majored in philosophy, was deeply concerned with issues of discrimination, and participated in antinuclear demonstrations. Then, he asks which alternative is the more probable:

Linda is a bank teller.

Linda is a bank teller and is active in the feminist movement.

More than 85% of the students chose the second option, although this choice is obviously contrary to the mathematical laws. Many students are convinced that their answer is the good one: when, very angry, the author told his students that they had violated a logical rule, one of them shouted “So what!” Anyway, we can understand the answer of those students: from the description of Linda, their system 1 immediately tells them that she is a feminist. Naturally, they chose the alternative that mentions she is a feminist.

The fast system often gives excellent results. However, one must have seen a huge number of examples before competency can be obtained. Therefore, an expert may have excellent performances. We are using system 1 when we are driving a car, when we are playing speed chess, when a man watches a woman (and vice versa), etc. When we have a result, we are convinced that it is right, although we cannot explain it. We often call this mechanism “intuition”.

With the slow system 2, introspective consciousness allow us to know a very little part of what happens in our brain, and to use it. The tasks where we must keep some intermediary results in our working memory are also performed with system 2. For instance, we are using it for doing products in our head, such as 47×28. When the fast system is in a situation where it cannot give an answer, because it is a new situation, or when it knows that it is not good for some kind of problem, it turns on the slow system. Unfortunately, it does not always start its colleague, even when it would be necessary.

It is interesting to compare the operation of our brain with an AI system. Neural networks have similarities with the fast system. They have resulted in several recent successes of AI, such as self driving-cars and playing Go. Many examples are necessary for defining a network, and they cannot explain their solution. They are particularly powerful for applications where perception is important, which are also those where we are using system 1.

However, AI systems have also to solve problems where the preceding methods cannot be used because there are not enough examples with a correct evaluation. A method widely used in AI is to develop a tree, which can be done when a finite set of possible actions is known. Far better than us, AI, using fast computers, can develop huge trees, where many positions are considered. Then, one has only to choose sequences of actions that surely lead to a solution. For us, it is a slow method, performed by system 2. However, it should be supplemented by a fast method, which has to evaluate the value of the leaves. For game playing programs, one uses an evaluation function, which has the characteristics of system 1: fast and no explanation. System 1, used by both humans and artificial systems, has been improved: it knows that some situations are correctly evaluated, and that some are not. In that way, when the tree is too large, one can stop the generation of the tree only at correctly evaluated positions. A very important improvement of system 1 would be that, like in this example, it gives a value, but also a value of this value (from totally accurate to very dubious). Unfortunately, humans often inaccurately assess this meta-value; a chapter of the book is about the illusion of validity.

AI systems may also have more possibilities: for instance, they can analyze the formulation of the problem, and build modules that can solve it. I did it fifty years ago for a General Game Playing Program. I had also implemented in a learning system an Explanation Based Learning module that, firstly generated an explanation of what happened in a game. In that way, it was possible to generalize from only one example, and to apply an analogous method in new positions. In both cases, a huge number of situations is no longer necessary, as it is with system 1. We humans are using system 2 for such activities. For us, system 1 and system 2 cooperate, in the same way that weak AI and strong AI must also cooperate.