How to teach AI?

Morphing neural networks are very fast tools to drive advanced AI-based systems. Those complicated neural networks can involve thousands or even millions of microchips. That allows them to combine data from memory and sensors with extreme accuracy and speed. Teaching AI to operate in a real environment is a complicated process. And the thing is that the morphing neural networks allow the network to drive multiple missions at the same time. 

How to teach AI? Computer memory and microchips are interesting tools. They are very accurate, and that sometimes makes AI training very complicated. If we want to make an AI that recognizes humans, we are in trouble. If we want to make an AI that recognizes certain people like some famous actor, like Tom Cruise, we can make that thing quite easily. We must just have images that are from all angles. Or we must ask that person to put their head into some certain position. Then the system can compile pixels that the CCD camera inputs into the system with images that are in the system memories. In the first case, the neural network can give fast recognition if all the CCD pixels can give an individual data input to the neural network. The system compiles all images that are in the computer's memory and then the system can say, that the person is Tom Cruise.

 If the system can compile all images that are taken from around the faces from different angles. That system makes recognition very fast. But then we face the problem: we know that all people are not Tom Cruises. We must start to globalize face and body images to computers so that they can tell that they see humans. So we must take one step back when we want to recognize that an object is human. 

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When the computer turns a certain person's image to match with species. Or globalize that image with humans as a species the system must remove accuracy.  That means it must remove pixels or replace them with grey pixels and then it can compile that silhouette with a silhouette that is stored in its memory. 

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Normally we recognize persons in certain series. At first, we see characters and then we recognize that character is human, and then after a couple of steps, we recognize that person. But then we must make the AI that recognizes humans and their gender. That means we take a couple of steps back from the individual to global things. We must realize that there must be some common things, the lowest common denominator that we must find in people, is that it recognizes humans as a species. That thing is called fuzzy logic. In precise logic, we must put every person's image on this planet to AI. 

That system gives the personal data of every person that it sees. But that kind of thing makes the system heavy and slow. Precise logic is sometimes easy to cheat. Simply changing glasses is sometimes enough to cheat the systems that use precise logic. There are systems. That must not completely see the match to make an alarm. In those systems certain percentage of the matching pixels causes alarm. There is the possibility that when the computer recognizes only humans it takes images of humans, and then it removes details. When it removes pixels the system combines the image with silhouettes. That is stored in its memories. 

https://www.quantamagazine.org/how-can-ai-id-a-cat-an-illustrated-guide-20250430/