The problem with AI is that it cannot flex

Artificial intelligence is smart. But the cooperation of AI and others is not so good as it should be. The problem with artificial intelligence is that it doesn´t flex with regulations. One way to demonstrate this thing is to think of the driver who is always following the law. 

If there is a "stop" sign at the crossroads that thing shows that another car has the right of way. But the problem is in cases where the AI-controlled vehicle has the right to first. What if another vehicle is near the crossing point and doesn't slow or stop? If the AI cannot flex. That means it would cause an impact. 

The problem with the programming of the AI is what kind of actions involve its programming. The traffic regulations are the thing that requires that everybody follow them. But what if somebody doesn't care about rules. And doesn't care about the "stop" sign? 

When artificial intelligence operates it doesn't use imagination. So it predicts that also all other people are following the rules. But the situation is different in real life. In real life, we are facing situations where we must flex. But the question is: how to tell the machine that it should flex in some situations?

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Fuzzy logic

The idea of fuzzy logic is that it makes machines like a robot working smoothly and more flexible. The system has the main action like the walker has the right to go before the vehicle. But then there are sub-determinators like the distance and speed of the oncoming vehicle when the robot should let the vehicle go before it. 

In the airfields, fuzzy logic used computers might have determinators what makes them lead the aircraft to the runway. Normally the system just follows the row and uses runways simultaneously. But in the cases of emergency, that system can let the aircraft pass the row. 

That kind of thing can make aviation safer. Even if the computers are not leading the aircraft to ground independently they can show the necessary information for air traffic controllers. That data can involve things like information on the free runway. And the system can send the rescue crew to that runway.

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We all know that the walker should go first in all cases when the road is needed to cross. But the fact is that if the walking robot is about one meter ahead of the car and it doesn't have regulations what is the safe distance of the vehicle when the robot can cross the road. It walks just under the vehicle. 

The answer to that problem is called fuzzy logic. The fuzzy logic means that the system has no strict orders for that kind of thing. The parameter for crossing the road goes that the walker must always let go before the vehicle. But then the programming of the robot has the values like distances and speed of the incoming vehicle. Those determine the exceptions for the main rule. 

Those exceptions are making the AI-driven robot smart and flexible. The thing is that the system as the rules what to do in certain cases. But then some sub-determinators are adjusting the actions. In cases like the vehicle is near the robot. 

https://visionsoftheaiandfuture.blogspot.com/

The fixed "logical trees" are so-called pseudo-intelligent.

A logical tree means the computer program, which asks fixed questions. And the answers are fixed too. And the user can give those answers by using radio buttons in the form. That kind of program might seem very intelligent. But the fact is that they are the only series of answers and questions. 

Those programs require fixed answers. And if somebody answers something else that causes errors. So if there is no match for an answer in the database, the program can say that "allowed answers are "A" "B" or "C". 

But that is required only when the user writes that answer from the keyboard. If the programmer uses a form where is the radio buttons there are no errors in the program line. 

That kind of form is well-known from many exams. In tests, this kind of program is routing the answer to the "error" database or "right" database. And the points are calculated from the answers that are stored in those databases. 

We can make the logical chains in for the computer or their programs. Those logical chains are the chains of questions and pre-ordered answers that are taking us somewhere. Those questions and answers can make by using radio buttons in the forms. So that kind of thing doesn't require complex programs and even the student can make that thing. 

The questions and answers are fixed in an entirety that looks like a tree. The question can have alternatives A, B, and C. which are alike crossroads. And each of them will route the user to a certain route. And after all, there is the end of the route. In that fixed model the user uses the fixed choices and the limit of that tree is the time of its maker. 

So if the end of the chain of the question is "Cogito Ergo Sum" or the computer congrats user and says: "you are like René Descartes" that might make the user feel impressed. But if the user would stay and follow the other users of that tree, that user might see that every other user will get the same answer.  Does that make the impression? Is the computer more intelligent than humans in the case where there are chains of questions and answers in its memory?

Why chess is used as a tool for making learning artificial intelligence programs? The reason is that chess is limited and the rules of the game are clear enough that the learning programs are easy to make. 

And is the computer more intelligent than humans if it plays chess better than humans? Maybe the computer can play chess better than humans. But that kind of program cannot do anything else. And humans are more multifunction than some chess computers. 

That thing is one of the biggest questions in computing. Making the chess program is not so difficult as many people expect. The system includes multiple databases, where are the movements of every single piece of the game. And maybe those databases are connected by using some famous chess games as the matrix. The movements of the winner are just stored in those databases. 

Breaking that game is an "easy thing" if the person knows what games are stored in those databases. And that gives the possibility to predict the movements of that computer. But if there are many chess games stored in the database the computer can choose another matrix or database if the first one is ineffective. 

A learning system that benefits the cumulation of the information is harder to make but it's more flexible. In that model, the computer uses the matrix game and records the movements of the opponent. The opponent is winning or losing and the computer analyzes which is better it or the opponent. Then it stores the opponent's game in its memory, and it can build series of the game matrixes.