How to solve AI problem with a computer?

 

The main problem with today's computer which includes the software as well is the architecture was designed to solve existing problems. This is an advantage for problems from the domain of mathematics and database but it is at the time a bottleneck in the domain of Artificial Intelligence.

Let me give an example. Today's computers are great for storing table with numbers on the hard drive. The reason is that modern computers have an endless amount of hard drive storage and they have operating systems and database program to manage the information. The same technology failed for handling robotics problems. Even very powerful databases which are running on a supercomputer can't control a simple maze robot which has only 2 sensors and 2 motors.

The problem with AI problems is not how to solve these issues, but to formulate first the problem. AI has nothing to do with databases and surprisingly it has nothing to do with algorithms. The consequence is, that it is not possible to use database programs, programming languages are operating systems to address the issues. Unfortunately, the amount of remaining techniques is little or even zero so the question is how to research AI problem at all.

What we can say for sure is, that Artificial Intelligence has mostly to do with the grounding problem. Grounding means how to convert the real world into a computer representation. Some terms in the context of grounding are: cost maps, notation, motion capture, semantic representation.

None of these terms is located in the classical computer science but they are dedicated AI terms. The shared assumption is, that the world is converted into a data structure, and a cost map or motion capture will help in this task. 

The term "Cost map" is very complicated to explain because it bridges the reality with computer science. A cost map can be realized as a data structure and needs some sort of algorithm to process it, and at the same time, it has to do with a robot which is moving in a maze and with obstacle collisions. The same two perspective situation is available for motion capture which is also a computer science topic and something which has to do with the reality.

The symbol grounding problem is an intermediate layer which combines computer science with reality perception. It is a trick how to explain to a computer what the objective is. From a computer science perspective, a cost map or a mocap recording is a trivial task. In both cases it is stored in a integer array which is not very big. Normal database problems are more complicated to realized than a csv file for mocap recording. On the same hand, a mocap recording can be called an advanced technology because it is very new. It makes sense to summarize these efforts under the term Grounding problem. Grounding means usually to convert the reality into computer science. It is about storing costs and other heuristics in a data structure.

The reason why the subject has to do with Artificial Intelligence is because after a domain was grounded it is surprisingly trivial to solve it with a computer. Every computer built in the last 40 years is able to search in a cost map for the lowest costs and the underlying algorithm is the same what is known as A* which is some sort of mainstream algorithm in game programming.