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The network-based solutions can help to research AI.



When we are thinking the AI as a tool. That analyzes things there should be something that confirms the solution. The confirmation of results can be made by using multiple data handling tools. Those data processing tools can form the network of the multiple independently operating units. That works with the same problem. When data processing units are making solutions. They send that thing to the central processing unit. 

And then if there are no errors or differences that solution would have no mistakes. But there is the possibility that some data processing units would get a different solution than the main group. And the system should inform the user that there is some kind of anomaly in the solutions. The information that the user gets. 


The model of modern AI is that there are two layers of AI. 


1) The AI-based operating system can independently determine the retake of the code of the linear AI-based software. The AI-based operating system can choose the algorithm that is in use in each case.

If the AI would pick up things like stones there is an error level that makes the operation acceptable. The AI solution tells that 12 stones must pick and put in a bag. And then the operating system would make the software run 12 times for collecting enough stones. 

2) The AI-based solution that controls things like robot hands or data collection. The AI-based software is the solution that is running on the AI-based operating system. The solution would see that if the tool that it uses is not fine. That means that the system might first choose the shovel. 

But if the stones are too heavy. The software might ask to use another tool like a forked stick hand. In that case, the operating system just turns responsibility for the action to another hand. Or it can call another robot to the place. 

The learning system measures the weight of the stone. And then it makes the query which machine has enough powerful tools for picking up those stones. The controlling AI can search that can the system make its job. And if there are not strong enough robots it can ask permission to call assistance from other companies. The system can tell that "there are about 100 kg stones that must put the kevlar bag". And then the system can ask the machine that can make that thing. 


The operators must determine the error level. In this case, there must be a certain part of the processing units that gets a certain value. 


Might be an example like that 2/3 processing units have this kind of solution. But because 1/3 has a different solution. That means there is possible that there is a mistake. And then the user can use the solution but the system can retake that operation. The thing is that the neural networks allow control and observe the operations of the AI from outside it. And that thing can use to create more effective and powerful code. 

The outside system would observe the functions of the main system. And that information can contain data like. Is there some kind of problems with memory handling? Or are there some unnecessary loops? And that thing makes it possible to create more effective code. The thing is that the system that drives the program code of the AI might also use the AI. That means the system can independently retake the part of the code if the operation is not successful. And that thing is making those systems extremely powerful. 


The fuzzy logic is making the AI effective. 


When we are thinking about the case that the AI should calculate things like lorries. There are two ways to make that thing. The AI can use certain logic that makes it slow. A certain logic means that the AI has images of every lorry in the markets and then the system would take the image of every car. And then the AI can compare the images that it takes with images stored in the database. 

That thing takes time. Another way is to take images by benefiting the CCD camera. Or the CCD camera's electro-optical element. When the system takes an image it can compile it with the matrix images pixel by pixel. In that case, there is a port image of the typical representers of every type of vehicle. What the system can face on the road. 

The system compiles the image by using the port image. When a certain number of pixels are matching the system would recognize that the vehicle is a lorry. If the system would need deeper analysis, that thing can send an image forward of the data line to confirm the vehicle's type and owner. In the case that we want to make a car that drives automatically, we must remember that the bus that comes from the stop has the right to come first from the bus stop. 

In the same way, the emergency vehicles must go first if they are at the emergency drive. The AI can have images of this kind of vehicle that need special attention and actions. So when the CCD camera makes the match with those things it can slow down the vehicle. The fuzzy logic means that there is a parameter like 80% of the data must match for some case. And that thing allows the AI to make something. 

If the system uses precise logic even the different text or color in the object can make the system react the wrong way. The fuzzy-logic means that the system cannot always get the precise same data from nature as from a controlled environment. The cars that the system calculates can be dirty. Or they can have something like ski boxes on the roof. And the system must separate and sort them by using images that the CCD camera takes from traffic. That case gives information about what type of vehicles are traveling on the road each time. 


Image:https://scitechdaily.com/images/Artistic-Artificial-Intelligence-Concept-1536x1152.jpg


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