When requirements grow, programs grow.

The main problem with computing is when developers make something. There is needed new solutions almost immediately. Those new solutions make the application interesting. And many times. Those new solutions mean new abilities for applications. New skills require new libraries. And that thing cumulates the size of the program. 

The new libraries require more space and more effective computing. The thing is that AI is going to more detailed program code. When we think about the first AI "chatbots" the simple programs that asked people some questions, and then the program gave some pre-programmed answers, we must realize that those programs looked intelligent. 

One example of those programs is a program that asks: 

Are you a boy or a girl?

A: Boy

What's your name"?

A: John

How old are you?

A: 23

Where do you live?

Bristol

Then the program said: 

Please to meet you: 

You are a boy, whose name is John. You are 23 years old, and you live in Bristol. And I'm your servant. 

Those things are quite easy to program by using C, Java, or C++. But that program puts answers to the right points in the programmed text. And the form of the text doesn't require us to notice things like "she" or "he".  That simple code is an example of pseudo-intelligence. One "else" command makes it possible to create an answer that if the answer is something else, the computer answers that "you must answer "boy" or "girl". That makes those programs look intelligent. If the programmer has time, it's possible to add all British first names and all the world's city names to the list of allowed worlds. And that gives an appearance of intelligence. 

The thing is that when we think about the modern AI that can control things like cars, we must realize that in the middle is a large language model, LLM. The LLM itself does nothing. But it transfers the data to the part of the code. That controls the self-driving cars. That LLM stays at a tolerable size. But the libraries that the system needs grow. That makes it hard to control data in the system. 

The network-based architecture means that the system can have multiple, limited AIs or LLMs that are connected into the entirety. That thing makes the system more flexible. Because the system has many separate operating cores. It allows the developers to create those cells as modules. Every module is an independently operating part of the structure. 

Above: Mesh network. In network-based systems, every computer runs its own LLM and modules connected to it. So every computer involves certain action series. And, each computer is responsible for its part of the system's skills. 

The structure might look like some kind of mesh topology. That means that there is a central LLM. That is called the specialized LLM to make the mission. The system operates like a mesh topology. When as an example a robot car drives a robot to the shop it sends a mission to the next operator. 

That operator is the robot that goes into the shop. The mesh model means that the system must not bother the central actor all the time. That means that if the central processor or LLM is busy or the system is out of connection the sub-system can continue its mission without the central actor's support or control.  

When a user gives orders to the central LLM. That can transfer the mission to the sub-LLM that has data to complete the mission. The networked structure makes it possible that one mission doesn't keep the entire system busy. When one module operates with some mission. Other parts of the system can perform other duties. 

In modern neural networks, the middle of the system is the LLM. The LLM is connected with sub-LLM systems. The system looks like a neural network. When an owner calls, the car to the door. The central LLM gives a mission to the LLM that controls the car. The car's AI system can interact with surveillance cameras and the surveillance system tells if there are some dangers in that area. 

Or if there are no surveillance cameras the owner can send a drone that looks if there is something behind corners. The drone can hang over the vehicle and send the image to the computer where the car's corners are going. The car requires a 3D camera system and radar. That it can drive safely. 

And then it requires large-size databases that tell what the car should do in all situations that it can face on the road. There are lots of things that the system must avoid. When we think about the models of the complex systems we must create the systems using the cell- or neural-based networked architecture. For these reasons, I told you before.