I can’t just say “one must bootstrap AI”, how to do it must also be explained. Naturally, I will use my own experience in CAIA’s development, started in 1985.
I shall quickly go through the first step, where I defined a language and knowledge for translating itself into C programs. This is well known by those who write a compiler in the language of this compiler. It was interesting to define a new language rather than using an existing language for two reasons:
1. This language must change over time, so that it becomes more and more declarative. At the beginning, in order to facilitate the compilation, it has many procedural aspects. They are gradually replaced by more declarative possibilities. Declarativity is essential because it is easier for the system to create declarative rather than procedural knowledge; it is also easier to study its own knowledge when it is given in a declarative formalism. Very important elements in this language are sets and bags, which do not imply an order to be followed, contrarily to the lists. Expertises are sets of rules, and rules have sets of clauses.
2. It is important that CAIA and myself could thoroughly examine any module of CAIA when it is executed. An unrestricted access to the present state of CAIA and its knowledge by CAIA itself is essential if we want to give it a kind of consciousness. It is easier to have something that suits us when the system is specially built for this purpose. Black boxes are great foes of intelligence: they restrict consciousness since one does not know what happens when one executes them. There are still two main black boxes for CAIA: the operating system and the C compiler.
Using its knowledge, CAIA translates all its knowledge, either given in the initial formalism or in more declarative formalisms that I later introduced. Since thirty years, CAIA does not contain a single line of C that I have written. All in all, there are 500,000 lines of C, and 13,500 rules. Many rules have not be created by myself, but from rules that create rules.
AI essential goal is to create a general problem solving system; all the human activities are, in fact, problems that we have to solve. The most important problem for AI researchers is to realize a system that could solve every problem, including this last one. It is foolish to begin with the most difficult problem, it is better to consider simpler problems, then to extend this domain gradually. This is one of the main directions of a bootstrap, the other one being to improve the performances.
It is important to choose the initial domain well. CAIA started to solve problems defined by a set of constraints. Firstly, this domain is interesting because many problems may be defined in that way; but it is even more interesting because some of these problems may be used by the solver itself. Since a long time, I have added two such problems:
1. To find symmetries from the study of the formulation of a problem can be stated as a constraint satisfaction problem. This is useful because it reduces the size of the search space, it facilitates the search for new constraints, and it enables to find a decomposition of the search space so that proofs will be easier in each area. I will explain this last point in a future blog, I completely underestimated it: it was a pleasant surprise.
2. For experimenting with a system, it needs many problems. Often, there are not enough problems in the literature, or I have to give them myself; moreover, there is a lack of very, very difficult problems, which are far too hard for human beings. However, finding new problems is a problem that can be defined with constraints; therefore, many CAIA’s problems have been found by CAIA itself.
In the next blog, we will see how a first step has been made to extend this initial domain. When CAIA will be able to solve any problem, one direction of the bootstrap will be completed; however, it will also be necessary to solve these problems more and more efficiently.