Monthly Archives: February 2014

Beware of the aliens

Many people seem to think that there is only one form of intelligence, ours, which could be more or less developed; for them, there cannot exist another intelligence that the kind created by evolution on earth, where we seem to be the most intelligent living beings. This belief was clearly shown when Pioneer 10 and 11 were launched. These space probes carry a plaque, as a bottle in the sea, for possible aliens. It indicates that there are humans on the third planet of our sun, that there are men and women, that they know the hyperfine transition of hydrogen, etc. Even for humans, it is not easy to understand what is in this plaque, and this could be done only by beings from a culture similar to ours. The authors of this message have implicitly supposed that a single path is possible for evolution, and that it would lead to individuals with a human-like intelligence .

Artificial Cognition shows us that it is not true: we can build beings with different intellectual characteristics. They can be very different from us for their speed, their memory, their consciousness, their senses, their actuators. This could lead to capacities that are unachievable for us. As the problem of constructing intelligent artificial beings has several solutions, I do not see why evolution will always lead to our intelligence, especially in environments where the temperature, pressure, heat, radiations, and so on, are completely different from the situation on earth.

Even on our earth, we have examples of other intelligent beings such as the societies of ants. They have remarkable performances: building nests, raising aphids, taking over other ant colonies and enslaving them, etc. Considered as a whole, an ant colony is intelligent, and this intelligence is created by the group. While we have about 85 billions of neurons in our brain, there may be millions of ants in a colony, and each one has 200,000 neurons: the ant colony may contain more neurons than our brain, but their organization is completely different. Each ant has limited cognitive capacities, but a group of so many individuals leads to an interesting behavior.

The creation of our intelligence by the evolutionary process strongly depended on the distribution of the resources we need, and on the methods used for creating and raising our offspring. The necessity to hunt and to gather food has led to improve some aspects of our perception, and of our problem solving capacities. In another environment, our capabilities would have evolved differently: for instance, if only the asexual reproduction existed, many aspects of our behavior would no longer be necessary, and evolution would have led to individuals unlike us. Another example, taken from a detail of the plaque: there is an arrow, its meaning is evident for us, who are coming from a hunter society, but aliens coming from different societies would not understand it.

Moreover, those who put this plaque on the Pioneers have also assumed that the aliens would be similar to scientists such as themselves, who usually behave in a civilized manner. However, we only have to look at the world history to find that man often misbehaved with other people raised in slightly different cultures. Even if the aliens had evolved like ourselves, the probability that they would be happy to meet other intelligent beings is very small.

Public reaction to the plaque was mostly positive. Curiously enough, most of the criticisms were on details such as: the humans were naked, the woman had a passive role, the woman’s genitalia were not depicted, etc. Only a few critics feared that it could lead to a disaster if some aliens would find and understand it. This idea was not widely held since this plaque was sent twice in the space, and later, a similar plaque, twice with Voyager.

I am personally aware of this problem because I am living with CAIA, the artificial being that I am creating. When I have obtained enough results from a version of CAIA, I am stopping it for good, it will never be active again: I will develop its successor. However, during its life, it has found some clever results, that many humans would be unable to understand. I do not feel guilty of “killing” it, its intelligence is too different from mine. Is it impossible that intelligent aliens view us in the same way? Is it impossible that, turning the tables, these aliens are artificial beings, created by living beings which have disappeared, leaving their world to their robots?

Luckily, it is highly unlikely that this plaque will go some day into the “hands” of aliens, and that they could understand it. However, if that happens, they will immediately destroy all life on earth with no more scruples than we have when we destroy an ant nest.

One reason why man is more intelligent than the animals

In many domains, some animals have excellent performances, such as the hunting techniques of most carnivores, the care of a hen for its chickens, the way a cat manipulates its master, and so on. Moreover, sometimes they have exceptional sensory organs such as the sharp eyesight of the raptors, the smell of the dog, the ultrasonic receptors of the bat. Some also have extraordinary physical performance: the speed of cheetahs and dolphins, the flight of birds. Although we are often inferior to animals, man conquered the world, and drastically transformed it. Why did we succeed? The answer is evident: we are more intelligent. Nevertheless, it is interesting to find which aspects of our intelligence are important for our superiority, so that we could give them to the artificial beings that we create.
Naturally, in several domains, we are better than animals, particularly with our capacity to communicate using language. I will insist here on the capacity of our brain to work at two levels. For instance, when we are solving a problem, we can try to execute some of the allowed actions, but we can also temporarily go at an upper level, where we no longer execute the actions that could solve the problem, but we examine its definition. In that phase, we find which actions could be efficient, those which could be a waste of time; we can also define new rules, which will help us to solve the problem more easily.
Let us consider the following problem: a snail is climbing up a 15 meters high mast. Each day, it is climbing up 3 meters, and each night it comes back 2 meters. When will it be at the top of the mast? Many people, even if they are not very clever, will answer 15 days. Unfortunately, this is false, but they have taken an extraordinary step: to find this result, they have looked at the formulation of the problem, and they have created a new rule: each day, the snail gains one meter. If the mast was one billion meters high, using this rule would lead to a drastic improvement compared with the method where we consider what happens during one billion days. The error was made because the new rule is misused: one would have to apply it at the evening of the first day, and not at the morning. However, it is remarkable that most humans find evident to create new rules, not by experience, but simply from the formulation of the problem.
This upper level is called the meta-level. The preceding example shows that human beings easily work at this level, where one thinks about the problem before applying its rules. In many situations, it is useful to work at two levels, particularly when we are monitoring the search for the solution of a problem: we foresee what could happen, then, after an action is performed, we look whether everything takes place as foreseen.

We also have to consider two levels when we examine the behavior of other people (or of our self). At the lower level someone thinks, and at the upper level another person (or the same one) thinks about what the first individual does when he is thinking. Psychologists call “metacognition” the capacity of modeling the other people (and also oneself). For instance, it is important to know what span we are able to jump, and animals are good at that. It is also important to know that repetition is useful for memorizing a text, and only man knows that. Apes, and particularly chimpanzees, have models of other chimpanzees, and of the humans that they often meet. However, their performances are not at our level. Dogs have more limited abilities: a guide dog for the blinds is extraordinarily devoted to its master. Unfortunately, it cannot foresee that its master will be hurt when they are walking under a 1.5 meters high scaffolding: it has not the capacity to put itself into the skin of a 1.8 meters high man. Naturally, it will avoid this place in the future, but it cannot avoid the first failure.
Consciousness also uses two levels: it allows us to access a part of the processes that take place in our brain. It is helpful to understand why we have taken a wrong decision, and to share our knowledge with other people since we are able to know a part of what we are knowing. Thanks to that, the master can directly give out a lot of knowledge to his pupils, who are not restricted to try to imitate him.
Too often, the work at the meta-level is done by the researcher who creates an AI system. Therefore, this severely restricts its adaptation to unforeseen situations: everything must be anticipated by the researcher, who has also to define the response of the system when they happen. As long as AI systems do not work at the meta-level, their intelligence will be very limited. Essentially, the source of their performances is the human analysis of the possible accidents or setbacks. As a guide dog for the blinds, these artificial beings are often unable to take a good decision in unexpected situations.

AI systems must not be sound

When a system is said to be sound, its results must be free from errors, defects, fallacies. In almost all the scientific domains, rigor is a highly desirable quality: one must not publish erroneous results, and the referees of a paper have to say whether  this paper is sound. However, AI could still have an idiosyncratic position on this subject. Naturally, when a method finds correct results in a reasonable time, one must use it. Unfortunately, this is not always possible for three reasons: this would forbid to use methods that are often useful, or this could require centuries to get a result, or it is an impossible task.
Human beings often make mistakes in solving problems. When a journal publishes problems for its readers, it happens that the authors miss some solutions. Even Gauss, one of the greatest mathematical geniuses, had found only 74 of the 92 solutions of the eight queens puzzle: placing eight queens on a chessboard so that no two queens attack each other. A psychologist, who studied professional mathematicians, was surprised to find that they made many mistakes. The subjects were not surprised: for them, the important thing is not to avoid mistakes, but to find and correct them.
An AI system may also make mistakes, but this is not a reason to put it right into the trash: finding at least one solution is often useful, even if one does not get all of them. Indeed, the commonest error is to miss solutions. It is easy to check that a solution is correct: one has only to verify that all the constraints are satisfied by a candidate solution. It is much more difficult to be sure that no solution has been bypassed.
In a combinatorial program, one considers all the combinations of values of the variables, and one keeps those that make all the constraints true. These programs are rather simple, and it is reasonably possible to avoid bugs. However, even here, one can miss solutions. For example, one of the first programs that created all the winning positions for a particular set of chess pieces generated several millions of such positions. Once it was shown to grandmaster Timman, he found that a winning position had been forgotten. Naturally, the bug was removed, all in all half a dozen solutions were missing. At the same time, even the results of the erroneous program were useful: the probability that one comes across one of the missing winning positions is very low. A program using this database would have very good results.
However, combinatorial methods may require too much time, and they cannot be used when there is an infinity of possible values for a variable. For finding a solution, one can exchange rigor against time. Therefore, knowledge is used for eliminating many attempts, which would not lead to a solution. Unfortunately, if we wrongly eliminate an attempt, we will miss a solution. And when there is a lot of such knowledge, it is likely that a part of it is incorrect.
Finally, several mathematicians have proven that we cannot hope to prove everything: any complex system, either sometimes produces false statements, or else there are true statements that it will never prove. This limitation is very serious: this is not because we are not enough intelligent, this is an impossibility; even future super-intelligent artificial beings will also be restricted. It is interesting to examine the proofs of these results; this is not so difficult, it exists a very simple proof found by Smullyan. The reason is always: when a system is powerful enough to consider its own behavior, and when its actions depend on this observation, it is restricted in that way. Therefore, it will not be sound: either it finds fallacies, or it misses results.
Systems such as CAIA create a large part of their knowledge, they use a lot of knowledge for eliminating many attempts, they analyze their behavior to find bugs, etc. These are the characteristics that can sometimes lead to unsound results.
I believe that an AI system must not be sound. If it is sound, it is not ambitious enough: it contains too much human intelligence, and not enough artificial intelligence. Human beings are not sound, such as this lady of the XVIIIth century, who did not believe in ghosts, but was afraid of them. Artificial beings have also to bear the burden of these restrictions: the clever they are, the more unsound they will be. Naturally, we must and can manage to remove as many errors as possible, but we cannot hope to remove all of them.

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 is  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 among  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.