Why are bigger neural networks better?

We can think that neural networks are like the social networks of humans. A bigger social network means that person gets more information. The large social network is making it possible to get information from many points. And if the social network is heterogeneous there are multiple different types of actors that keep the information versatile. In computers and AI, versatile sensors guarantee that the system can get information even if some sensors are unable to work. In the human world, versatile information guarantees. Those actors can find a solution. Even if one road to a solution is closed.

First, we must describe the word "big". Word "big" can mean a large number of data processors. Or it can mean a large area where the network collects data. When we are thinking of big neural networks as the things like human brains. We must realize that the high number of connections between the high number of data processors called neurons is making human brains the ultimate tool.

But there is one thing that makes a high number of neural connections problematic. And that thing is that the high number of neural connections is making systems slower. The reason for that is simple. Neurons must search for the right connections longer time. The thing that makes human brains so effective is that they can begin the computing process in multiple areas at the same time. And then they are connecting those processes to one entirety.

In the cases of human brains, we can see that 200 billion neurons can make more things than 10 neurons. This makes human brains so powerful tools.

If there are only a few neural connections the system has fewer connections to select. And that makes the system fast. But a large number of connections gives more skills to the system. That allows connecting multiple data bites by using more possibilities. Data that is stored in the neural system is like in a puzzle. Every database in that entirety can reconnect with other pieces. And that thing makes the system more independent than regular systems.

Interconnecting databases means that in one database is an object. That object can be a screwdriver. The system can connect that tool for every single action where a screwdriver is needed. And those actions are a group of databases. If there is a lot of databases where that tool is connected. That means the system must not all the time ask for pieces of advice. That means the system is more independent.

When we are thinking cases. Where robots are connected to one entirety we are talking about the multiple internal neural systems. There can be the macrosystem where the robots are connected with surveillance cameras and other things. But those robots are equipped with their internal neural networks. If the system can see things from longer distances it can make better decisions. It can get a larger data mass for operating.

The large-area neural network that interconnects robots with fixed surveillance systems is effective for this reason. It can predict things better. In the cases that the entire city area is interconnected as a neural network, the system sees if there is a rush in somewhere.

But if the system will get data about traffic from the 100 kilometers distance it has more time to calculate the optimal time for traffic lights. In ideal cases, the system would know where all users of roads are going. And it can create the shortest and the most economic road along with optimal time for leaving home for each person. But there is one problem with the AI-controlled world.

The AI would not realize that we are doing something, because we want to do those things. If we think about the case where is no money and AI controls how many goods every person would get? We are facing one very interesting problem. What if we don't like some food? The AI can see that some food is a very popular thing.

But there is always some person who cannot eat it. And that means the AI would cause a very interesting situation. If it faces the person who has an allergy to things like chocolate. We believe that everybody likes chocolate. But that allergic person would probably like that thing. So that kind of thing is making the AI-driven word absurd.

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Cogito ergo sum. I think; therefore I am (René Descartes, French philosopher)

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