Why does AI fall into the infinitely continuing loop?
Infinite loops, or infinitely continuing loops, mean that the system is stuck operating with the same problem without a reasonable solution.
All our neurons operate as pairs. When the first neuron sends a message to the receiver. The receiver acknowledges the message. Those receiver neurons send the message. That the message is received. Sometimes something causes a situation. The transmitting neuron sends an acknowledgment back to the receiver. And then. Those neurons start to play a ping-pong, using those neurotransmitters. That means those neurons can fall into a situation where they just surround the same dataset in the form. Called infinity loop.
But no problem, the outside neurons come and remove the loop. That releases those neurons to operate on a new problem. The outside system must only recognize the infinite loop, and that is quite an easy thing to do. The control system, “judge,” must just see. The system under the “judge’s” supervision gives the same answer repeatedly. If the answer is the same multiple times, the supervisor sees that those data processor units, like neurons, can be released to operate on another problem.
The reason why our brains would not fall into a thinking loop is this. We have so many neurons. Our neurons watch each other. And if there is a situation in which some neuron group starts to operate on the same problem repeatedly, and data starts to surround that neuron group, the outside neuron comes and releases those neurons. That means. Outside neuron destroys neurotransmitters that carry the surrounding information. And releases those neurons.
The upper image introduces the algorithm. As you see, data travels in a circle. But sometimes the algorithm makes mistakes. The mistake can happen when the algorithm uses the wrong dataset. Or sometimes the algorithm simply returns the last mission solution to its beginning point. The system should only send a mark that it's not busy. But sometimes the router will send something else to the return point of the algorithm. When data travels in a computer.
One wrong value causes a scandal. And in binary computers. It is not accepted. If the value is more than 1. Binary processors can operate only in states one and zero. The stuck gate causes value 2. The system is stuck. The problem is that the system cannot null itself. And in infinite loops form when data surrounds the system. And it cannot null itself. Without stopping, those algorithms cannot take on the new mission.
Have you ever tried to make an infinite loop in your mind? The infinite loop, or infinitely continuing loop, is the case where thoughts surround in a circle. The infinite continuum is the case where we think, “I had a dream, that I had a dream...”. This means the list of those internal spaces can continue forever. But the fact is this. Our brains cannot make an infinite loop. Or an infinite circle. Things like pi (3,14...the ratio of a circle's circumference to its diameter) are not infinite loops. They are infinite continuums.
Outside systems can deny the infinite loops. When the system operates to solve a problem. The outsider judge system. Checks the answers. If the main system always gives the same answers, the system has fallen into an infinite loop. And the outside system orders stuck systems to dismantle the loop. And reboot the system for the next mission.
Brains can create situations that we might think of as a “virtual infinite circle”, but we are never stuck in that thing. And the reason why the AI can be stuck in those processes is this. The algorithms are like circles. But the second thing is that. The AI operates over the binary computer platforms. The AI is an algorithm group that requires the giant computer centers. There are billions of microchips in that system. But there is one weakness. When the AI or large language model, LLM, starts to solve the problem, it has a certain data handling capacity in use.
Or, the system reserved a certain number of microprocessors for use in that problem. But if the system cannot solve the problem, the AI calls more data handling units to operate on the process. If there are no limits for that process, the system can use its entire capacity. For one problem. That is the thing. That causes the infinite loop. The computer makes its calculations. And then. It makes an error detection. Calculating the same calculations backwards. Another way is to make the error detection. Using two different lines or computers.
If both computers have the same solutions, that means (probably) there are no errors. There is a possibility. There is a common error that causes a false answer in both computers. But the last one is faster. Than the case. Where the system detects errors. By calculating all calculations backward. After that, the system can introduce a solution. But sometimes, Something causes situations that the system cannot detect the errors as it should.
The infinite loop forms in the case that there is no outside actor. Or the system uses its entire processor capacity to solve some problem. In that case, the system has no resources to end the task if the processors are starting to play pin-pong with the solution. That keeps those processors busy, and they have no time to null that process.
There is a need for an outside microprocessor. That gives an order to stop the action. If the entire system is not reserved. There is a system. That denies the main system from falling into infinite loops.
