Neurocomputers and machine learning.

Neurocomputers or morphing neural networks are tools. That can be more revolutionary than quantum computers. The quantum computer's strength it it must not stop when it gets a new mission. The neural computer must not stop either. There is always a port, where the system can dump data. The number of gates makes human brains so powerful. 

The system can always have a free gate for important data, and because the system makes its operations at multiple points at the same time, the neural computer will not stuck so easily. There are always processors or neurons that can take on the work if some part of the system is busy. 

The neural network is a system, where there are multiple processors. When an intelligent system uses quantum calculation, that means the system cuts the mission into smaller parts. Then it shares those bites with multiple processors. 

This method makes things like CRISPR so powerful. The system can cut the DNA into the bites. Then each of the data handling units where the DNA analyzer cooperates with the AI can handle those DNA bites at the same time. 

What is machine learning? 

Machine learning means that the machine learns like humans. When an operator once teaches something to a machine, the machine can make that thing independently. The classic example of machine learning is this:  the robot car that should carry its owner to work. The owner can drive the car to the workplace first time. 

And then. The robot car can drive that journey automatically. The robot car's operator can teach the route to the car driving that car. Or the driver can use virtual reality. In that case, the city is modeled in the virtual world, and the driver can drive the route in a virtual workspace. Then the system copies the route to the physical system. Or the owner can make the route using the map application. Then the map application transfers the route to the car's computer and the GPS. 

If the neighbor has the same workplace the networked car can ask for the route from the neighbor's car. The neural network can connect entire traffic in city areas. The system can adjust the speed and other things, and that makes traffic fluent. The system means that cars are communicating with other road users. 

There can be the triangular-measurement positioning system for the cases, where the GPS is down. That system can be based on the GPS base stations. In this case, all GPS base stations know their GPS location points. The navigator quantifies the robot's position measuring the directions of the base stations. Base stations can measure the direction of the vehicle. And three base stations can use triangular measurement to locate that point. 

The learning machine can learn complicated things. The idea is that the human operator will make things first. If the human chef wants to teach how to make a cake to the robot. The chef can use the action camera, which documents everything the chef makes. Then that data will driven through the AI, which tells the robot what raw materials the robot must collect. 

The system complies with details about the packages and other things. The robot can use a laser spectrometer to estimate things like eggs fresh enough. The spectrometer measures the rotting gas from those eggs. 

https://www.quantamagazine.org/what-is-machine-learning-20240708/