We have just celebrated the centenary of two achievements of the aviator Adolphe Pégoud: the first parachute jump from a plane, and the invention of aerobatics. Curiously enough, both are strongly connected; it is interesting to understand how the first achievement led to the second one.
Parachute jumps were already made from balloons, but never from a plane. Pégoud thought that it could be useful for leaving a broken-down plane. The other pilots thought that he was crazy to try such a dangerous and pointless experiment. Moreover, as most planes had room for only the pilot, the plane would be lost after the pilot left the plane. Everybody, including Pégoud, did not think much about the future of the plane, but  they believed that it would immediately crash when the pilot would have jumped. Pégoud had chosen an old plane which was expendable. While he was coming down under his parachute, Pégoud looked at his plane, and he was very surprised by its behavior. Instead of immediately crashing, it made many curious maneuvers, for instance, flying upside down, or looping the loop, and this did not lead to a disaster: it carried on with another aerobatics. Pégoud immediately understood the interest of this experience: if the plane could do these figures without a pilot, it could do them with a pilot. Therefore, after being solidly tied up to his plane, he imitated his preceding plane, and he was the first human to fly upside down; a little later, he was looping the loop.

Pégoud realized that a plane could “discover” new maneuvers when it was left autonomous, and he was able to take advantage of this capacity. We, AI researchers, must also imitate Pégoud, leaving our systems free to make choices, to fly around, in situations where we have not given them specific directives. Then, we analyze what it has done, and we possibly find new ideas that we will include in our future systems.

Personally, I have had such an experiment, and it gave me the idea about the importance of a direction of research that I am trying to develop since I am working in AI. In 1960, I began working on a thesis in AI. My goal was to realize a program that had some of the capacities of a mathematician: it received the formulation of a theory (axioms and derivation rules), and it had to find and prove theorems in this theory. Although it could try to prove a conjecture that I gave it, it usually started its work without knowing any theorem of this theory. As Pégoud’s plane, it had no master, it was free to choose which deductions it would try: I had no idea of the directions that it would take.

One day, as I was analyzing the results found by the second version of this system, I was surprised to see that it had found a proof of a theorem different from the proof that I knew, which was given in the logic manuals. It happened that, for proving theorem TA, it did not use another theorem TB, whereas TB was essential for the usual proof; the new proof was shorter and simpler. I tried to understand the reasons behind this success; I discovered that the system, left to itself, had behaved as if it had proven and used a meta-theorem (or theorem on the theory) that allowed to find the proof without using theorem TB: the system bypassed it. After this experiment, as Pégoud, I took over the controls, and I realized a third version of my system, which systematically imitated what I had observed: the system was now able to prove meta-theorems in addition to theorems. It could study the formulation of the theory, and not only using the derivation rules with the already found theorems. This new version had much better results: it proved more theorems, the demonstrations were more elegant, and they were found more quickly.

Since that experiment “à la Pégoud”, I am convinced that an AI system must be able to work at the meta-level if we want it to be both effective and general. In doing so, it can fly over the problems, and discover shortcuts or new methods: it is easier to find the exit of a labyrinth when one is above it.

Such discoveries are possible only when we let our systems free to choose what they will do. If we want to bootstrap AI, we have to be helped by new ideas coming from observations on their behavior, while we are parachuting down after leaving them alone.

Many scientists in Cognitive Science study the cognition of living beings, human or animal ones. However, some capacities of artificial beings are completely different from those of the living ones. Therefore, a new science, Artificial Cognition, will have the task to examine the behavior of artificial beings.

For Cognitive Science, living beings exist; we want to understand the reasons behind their behavior, and their capacities in various situations. We want to know how they memorize, remember, solve problems, take decisions, and so on. We observe them, we use medical imaging to detect what parts of a brain are active when the subject perform a task. One also devises ingenuous experiments that will show how the subject manages to solve a problem cleverly chosen. Naturally, we only study behaviors that exist for at least one kind of living beings.

The situation is different for Artificial Cognition: the goal is to build artificial beings rather than to observe them. Normally, we write computer programs, or we give knowledge to existing systems, and we try to obtain an interesting behavior. For that we usually utilize ordinary computers, but we can also build specialized machines, and this will be probably more frequently the case in the future. Living beings depend on mechanisms created by evolution, which uses mainly a remarkable element, the neuron. They may have extraordinary capacities for adaptation: we can learn to build houses, to write books, to grow plants, etc. Unfortunately, we have also limitations: we cannot increase the size of our working memory to more than about 7 elements; we can only use auxiliary memories such that a paper sheet. They are useful, but not as efficient as our internal memory. We can no more increase the possibilities of our consciousness, many mechanisms of our brain will always be hidden when we are thinking. This is a very serious restriction: consciousness is essential for learning, for monitoring our actions, for finding the reason of our mistakes, and so on.

On the contrary, in Artificial Cognition, we are not restricted to the neuron, we can build the mechanisms that we have defined. This possibility does not exist in the usual Cognitive Science: nature has built the beings that we want to study. In Artificial Cognition, we put ourselves in the place of evolution, which worked during billions of years on zillions of subjects. It succeeded in creating living beings, often remarkably adapted to their background. However, nobody is particularly well adapted to the artificial environments that man created, such as solving mathematical problems, playing chess, managing a large company, etc. As we have invented many of these activities, we have chosen them so that we can have reasonable performance in these domains, using capacities that evolution gave us for different goals such as hunting game for food. At the start, when on tries to build a new system, we are inspired by our methods, such as they have been discovered by Cognitive Science scientists. In doing so, we are using only a small part of the possibilities of Artificial Cognition, we must also utilize all the possibilities of computers, even those that we cannot have. Artificial beings will have much better performances than us when they use not only all of our methods, but also many other methods that we cannot use. We are unable to use many useful methods because we have not enough neurons, because they are not wired in the necessary way; it may be also simply because our neurons have intrinsic limitations, for instance, they do not allow to load new knowledge in our brain easily. Perhaps, there are capacities that evolution did not give us either because they were not useful for our ancestors, or because there are jumps that evolution cannot make.

The methodology and the potentiality of the usual Cognitive Science and of Artificial Cognition are very different. We are not limited to the existing beings, but it is very difficult to build new beings. However, there is a strong tie between these two sciences: building an artificial being is defining a model. If it behaves as living beings do, this model will give an excellent description of the mechanisms that Cognitive Science wants to find. On the other hand, when we want to build an artificial being, the first thing to do is always to start with the implementation of the methods that are used by living beings. Nevertheless, we have to progress from this starting point, and we will arrive perhaps some day to build artificial beings that will be able to achieve tasks extremely difficult for us. For instance, we will see artificial beings capable of building other artificial beings more effective than themselves.

Usually, being clever and working a lot are qualities, they are very much desirable for a researcher. This is true for most domains, but not in AI.

Indeed, an AI researcher naturally wants to have results as good as possible. Unfortunately, in the present state of AI, it is difficult to build systems that know how to modify the formulation of the problems they must solve, or to find new knowledge in order to solve them efficiently. It is easier for the AI researcher to do this work himself so that his system will have better results when it uses the improvements that he has made to the formulation of the problem, or to the specific knowledge that he has found with great efforts. For evaluating the quality of an AI system, we must not uniquely take account of its performances, but mainly of the quantity of artificial versus human intelligence that allowed these excellent results.

Let us consider chess programs. They have a remarkable level: 29 of them have an Elo rating higher than 2872, Elo of the world champion, Magnus Carlsen. The authors of these programs have made an extraordinary work, and I admire them very much. However, I admire much less their programs, which are unable to play another game than chess. Intelligence is not a Dirac delta function, extremely successful on a very small domain, and unable to have any other activity. These programs do not even know that they are playing chess: chess rules are programmed, and the system cannot examine them for understanding why it has played a weak move. Clever humans have written the combinatorial programs that generated all the winning positions when there are at most seven pieces on the board. Other clever humans have written the evaluation functions that evaluate the interest of a position. They have also written the combinatorial program developing a huge tree which is used for choosing the move that will be played during the game. Good players, often grandmasters, have worked out the data bases including the best opening sequences of moves.

If the goal is only to have a high-performance program, they are right: I would try the same approach if it was my goal. However, if the goal is to create an intelligent system, the system must be very different: it has to think of the quality of its moves, using the game rules, to find methods for selecting the best moves, to write programs taking into account its analyses, and so on. Even if its performances are not as good as those of the present programs, this system would be interesting because it would be more general and, for me, more intelligent.

Our systems have to become more autonomous, to find new methods rather than only executing a method that we have discovered. If so, they have a potential for improving themselves. For the future development of AI, it is better to devise systems which are perhaps not as good as the systems that we entirely build, but which are able to find by themselves some of the methods used in our best systems. In that way, more efficient systems than our present realizations will perhaps emerge with the passing years.

We, AI researchers, behave as the parents who are doing their children’s homework. They get excellent marks, but the essential goal is missed: the children must become able to solve problems alone.