CAIA, my colleague

 Since 1985, I am working on the realization of CAIA, Chercheur Artificiel en Intelligence Artifice (an Artificial Artificial Intelligence Researcher). My goal is to bootstrap AI: the system in its present state must help me for the following steps of its development. Advancing in a bootstrap is an art, we must first give the system the capacities that will be the most useful for the next steps. If we are on the right path, we are continuously saying to ourselves: If I had completed what I am doing, it would be much easier to do it.

To be successful, an AI system needs a huge amount of knowledge. We have seen that it is easier to find, give, and modify knowledge given is a declarative form. Therefore, the first step was to define a language L more declarative than programming languages. Then, I wrote, in language L, the knowledge for translating any bit of knowledge in L into the programming language C. Naturally, programs were necessary for the beginning, and I wrote several C programs for initiating this process. I have quickly removed them, after using them only once: they wrote programs doing the same task as themselves, they were generated from the knowledge, given in L, that indicates how to make this translation. For the present time, the 12,600 rules that make up an important part of CAIA's knowledge, are translated by themselves into 470,000 lines of C. Since more than 25 years, CAIA does not include a single line of C that I have written myself: CAIA  has relieved me of the task of writing programs.

This was only the first step: the final goal is that CAIA does everything. In order to progress, I am choosing a module that I have created, and I add knowledge to CAIA so that it could also create this module; when it is done, I remove my original module. One could think that this process would never stop because I have added a new module for removing an old one! Luckily, some modules can work on themselves: reflexivity is the key of a bootstrap. With this method, I am never short of ideas; I am only short of time for implementing these ideas.

Once CAIA could write its programs, I chose to apply it to the domain of combinatorial problems, such as Crypt-Arithmetic problems, Sudoku, the Knight's tour, the eight Queens puzzle, the Magic Squares or Cubes, etc. In the whole, I have defined 180 families of problems, some of them including more than one hundred problems. Why do I choose this domain? Because many problems can be formulated in this way, and particularly two families of problems that could help CAIA and myself to solve problems; this is especially interesting for a bootstrap.

The first problem is to find symmetries in the formulation of a problem. This is useful because one adds new constraints, so the solutions are easier to find. In that way, one no longer gives a lot of uninteresting solutions because they can be easily found from the preceding solutions: in a magic square, it is a waste of time to give the solutions that can be obtained from an already found solution by horizontal, vertical or central symmetries.  Moreover, when a new propriety, such as a new constraint, has been found, one can immediately deduce all the symmetrical properties. There are several kinds of symmetries, and I have defined as a combinatorial problem the search of the commonest symmetry, the formulation of a particular problem being the data of this problem. Usually, the human researcher finds the symmetries of each new problem, and CAIA has relieved of this task.

The second problem creates new problems. For experimenting a system, one must give it problems to solve, and there is not always a wealth of material on this subject. Moreover, some problems have a lot of data; this takes time, and leads to errors. Finally, one finds problems built to be solved by humans, while more difficult problems are necessary for testing the system. Therefore, for some families of problems, I have defined the problem to find new problems in this family such that they likely will be interesting. This is another task that CAIA relieved me of: it defined the problems and stores their data.

In this bootstrap, CAIA and myself are working together. It happens that the activities where we are successful are different: for the present time, it works faster, and it makes fewer mistakes, while I am more creative. It is unlikely that I can increase my speed, while I hope that its creativity will raise: its involvement will continue to increase. I owe CAIA a lot: without CAIA, I never could have realized CAIA.

3 thoughts on “CAIA, my colleague

  1. Hi,

    I react to the extensive use of the word “problem” in this post. It is just like if colleagues were seen as problem solvers, i.e., something that, given a precise problem instance will find a solution to this problem. For bringing computers beyond that point, it may be good not treating them this way.

    Thanks for sharing your thought and experience. If I a doing research in AI, this is thanks to you!

  2. I believe that human beings are constantly solving problems: choosing the good word, finding a restaurant, or how to go to the airport, defining a computer program, writing a post on a blog, choosing the date of a vacation, etc. All these activities are problems, and we have to find a solution. In some cases, we learned to build stereotyped solutions, but learning that was also a problem!

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