Realizing artificial systems more intelligent than ourselves is possible

In 2006, we commemorated the 50th anniversary of AI, but I was not very happy: was it the time to celebrate? This is not evident when we compare the quiet development of our domain with the prodigious changes made during the same period in many other areas, for instance, the huge growth of power and applications of computers. The applications and the performances of AI have not improved with the same speed; most of its successes came from the increase in computer speed that enables us to use successfully combinatorial methods in domains where it would have been hopeless 50 years ago. Why is there such a slow progress?
This does not mean that nothing has been made, on the contrary, many interesting results have been found. Moreover, some useful systems have been implemented, for instance, several successful game playing programs for Chess, Checkers, Backgammon, Scrabble, etc.; a recent spectacular success was obtained for Jeopardy! For many games, the best programs are at least as good, and sometimes better, than the best human players. Many interesting theoretical results have also been proven, useful methods have been discovered. However, AI has not yet changed the lives of human beings, although we are trying to create artificial beings with a superior intelligence, quality essential in most of our activities.

One reason of the slowness of our advance is that AI is a tougher domain than it was thought 57 years ago. Realizing systems at least as intelligent as human beings is one of the most difficult tasks ever undertaken by humanity. I am convinced that systems much more intelligent than ourselves are possible; however, I am not convinced that human beings are intelligent enough to realize alone such systems.

Another reason is that the usual way of doing research is wrong for AI: it does not favor the directions of research that must be developed if we want to realize really intelligent artificial systems. Particularly, the importance given for the number of publications is excessive: this does not encourage researchers to realize the large systems that will have the knowledge enabling them to perform efficiently in many domains. It is difficult to make many publications on such systems because we waste a lot of time on practical problems writing programs for our computers, and debugging them.
It is easier to write a lot of papers on theoretical domains; therefore, papers in AI are too often mathematical papers. For instance, in the 105 pages of the August 2013 issue of the journal Artificial Intelligence, 85 theorems, lemmas, corollaries, and propositions were proven, none of them by an AI system! These papers are sometimes useful, but we must also do experiments with programs using a large amount of knowledge. While realizing and experimenting such programs, one often finds new ideas: we are always overseeing important aspects, and the computer shows our weaknesses mercilessly. We need its collaboration.

However, we must not be too pessimistic. Man needs a help for developing AI research. Then, who can help us? The only candidates are AI systems themselves. Therefore, it is essential to bootstrap AI: AI can help us to improve AI. Bootstrapping is paradoxical, how a system can help to build itself? In reality, a version of a system helps to build a better version of itself. Our civilization is the product of a bootstrap: we could not make the present computers if we had no computers! We should focus our efforts on the realization of AI systems whose main goal is to help AI researchers to devise more successful and more ambitious AI systems. This collaboration is a fruitful one since, for the present time, human beings and AI systems are not good in the same activities.

Therefore, I am working since 1985 on a system called CAIA (in French: Chercheur Artificiel en Intelligence Artificielle) whose goal is to create an Artificial Artificial Intelligence Researcher. I will explain later why I have chosen this direction, how I am progressing in this bootstrap, and what are the difficulties met with this approach.

If we do not try to overcome these difficulties, we will still be almost at the same state for the centenary of AI.

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