The difference between AI and the human brain is that AI always works with all or nothing principle. For the binary computers, everything is black and white. And if the AI-controlled binary system will not stop trying some solution, even if it sees that thing, as impossible. But, the problem is that. The AI makes only things. That is programmed in it.
To see that something is impossible. The AI requires an algorithm that allows it to see that it cannot make something. Without those parameters, the pocket-size robot tries to move the car, until its battery is empty, if it gets ordered. The AI requires a stop code that activates if a certain number of pullings don't give suitable solutions.
The main problem with binary systems is that they require two other systems to make fuzzy or uncertain solutions. The binary system can handle only one object per time. And that means the binary system requires another system, that makes a deeper analysis of the situation that it detects. In that model, the first reaction that the AI makes is the reflex. Then the system will react to the situation by selecting a certain database.
So when the AI sees something it always reacts to that thing with all its power. The human brain analyzes the situation and then makes decisions. This is one of the biggest differences between AI and the human brain. If an AI-controlled robot will get the order to transfer something, AI will move the thing but when it presses its fingers it will make that with all its power. There might be levels of how strong the AI must press its fingers.
But all those levels like using 5, 6,7... etc. kilogram force are independent solutions. There could be the main database there are orders on how to react if the hand cannot move the object. If the hand cannot move an object by using 5 kg pressure it selects another strength. So the system requires an algorithm that makes it touch the object with the necessary force. The forces that robots use programs in certain tables in databases.
When a robot must turn the steering wheel it will turn it so much as it turns. The human brain will use fuzzy logic in that process. And that makes fuzzy logic more useful in everyday life than precise logic. But in computer memory, fuzz logic is made by using multiple databases. The system follows how the car follows the road. And then, it selects the database that fits best for the situation.
So there is a limited number of degrees in how a robot turns the car. The steering system requires lots of databases so that it can turn the wheel in the right way to the right direction. The linear computing model means that when the AI faces a situation where coded orders for some situation match with some database the AI selects that solution.
And that means the AI will not compare that possibility with other possibilities. In a linear model, there is only one possibility, that the AI selects. The human brain uses the circular model or model of internal circles where the brain compares the solution that it selects with other solutions. The most out solution is the thing that the outsider observer sees. But there are internal layers that the brain modifies. In that model, those circles are choosing information from each other. And that thing makes the human brain more versatile than computers.
https://bigthink.com/neuropsych/not-how-your-brain-works/
https://www.chitkara.edu.in/blogs/what-is-artificial-intelligence-and-future-scope/
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