Hard tasks may be easier than easy tasks

What we are easily doing may be very difficult for an AI system. Conversely, it can easily perform tasks that are very difficult for human beings. The reason is that, during millions of years, evolution made us very expert for some activities such as perception, but has not adapted us to many activities that are not useful for feeding or breeding. Seeing where a soccer ball isĀ  is not evident for an artificial player when this ball has not been specially colored so that it clearly appears in the background. Our ancestors, hunters or gatherers, needed excellent perceptual abilities for surviving; we are not even better than animals in that domain, evolution considerably increased the potential of all living beings. On the contrary, living beings are not so good for reasoning: most animals cannot make simple deductions. Among the best ones, apes are able to see that it is necessary to move a box under bananas fastened to the ceiling so that they could take them; this is one of the most difficult problems that they can solve. Man is incomparably more successful than apes for reasoning; however, we have severe limitations. Many people do not find easily whether there are more animals that are not ducks, or more animals that are not birds. We overestimate our performances in this domain because we can only compare ourselves with the animals. Moreover, we try to avoid as much as possible problems that we have great difficulties to solve.
The Spring 2012 issue of the AI magazine describes a competition for chess-playing robots. Since several years, some programs are very strong, better than the best human players: they accept to play against programs with human friendly restrictions, for instance, the program can only use a part of the endings database, which now contain all the winning positions up to seven pieces. In this competition, the difficulty was not to find a good move, but to perceive the board seen by a camera, and to play effectively this move. The system does not receive the last move via a keyboard, and it does not display its own move on a monitor. It only sees a real chess position with the usual figures of the chess pieces. Afterwards, once it has found its move, it really plays it, first removing a possible captured enemy man, then taking its own piece, and moving it to its new square.
In this experiment, the robot is not very skillful. For evaluating the competitors, points were awarded for making a move in less than 5 minutes, points were subtracted for knocking over a piece, or failing to place a piece fully within its arrival square. To keep the game short, only 10 moves were played by each side. Moreover, some participants were helped by defining an environment facilitating the perception of the scene: one robot (not the winner) was playing with blue pieces against yellow ones, on a white and green chessboard. Although the participants have realized exceptionally well designed robots, the perceptual and motor limitations of the robots, compared with the remarkable quality of chess programs, clearly show the difference of the results amongĀ  applications common to man and machine. For an artificial being, it is easier to find a winning move against the human world chess champion than to play this move on the chessboard physically!

Naturally, we must carry on with the realization of applications using our perceptual and motor abilities: such robots can be very helpful, and in some cases they can completely replace us for dangerous or boring applications. For instance, robots that can drive a car will be interesting when they will become skillful enough. However, implementing an AI system that solves a problem, which is extraordinary difficult for us, could be no more difficult to realize than building a system solving another problem that every man, and in some cases even animals, can solve easily.
Although a mathematician has recently proven that using mathematics could help finding a bride, this is an exceptional situation: it is unlikely that outstanding mathematicians have better rates of survival and reproduction than other humans. Our capacities in this domain are only a by-product of capacities that has been developed for other goals. The best mathematicians are very good in a relative scale, compared to other human beings, but they may be very low in an absolute scale, when one considers all the entities that could some day perform mathematical activities. Perhaps, they are only one-eyed among blinds, maybe very far from what could be done by artificial beings. I remember how chess players were laughing in the 1970s when they were looking at the moves played by the best programs at this time. They have completely changed their opinion on this point, even the grandmasters now use programs for preparing their matches.
The results obtained in AI show that AI research is extremely difficult for humans. For this reason, I am developing CAIA, an artificial Artificial Intelligence researcher. This is certainly a very difficult task, but evolution has not built us for being good AI researchers. Therefore, this problem is perhaps not so difficult that we could believe at first sight. When I have managed to improve CAIA so that it takes over from me for some particular task, it always performs better than myself.

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