The new processor technology makes AI and morphing networks more intelligent.

    The new processor technology makes AI and morphing networks more intelligent. 

The power of the computers determines the power of the AI. 

The most powerful morphing network that can run the AI is a quantum computer network where networked quantum computers are operating as an entirety. Quantum computers operate through binary systems. Binary- or gate systems deliver information into quantum systems. And those binary computers that pre-process information for qubits can also networked as regular computers. 

Google's Deep Mind AI can make better weather forecasts than single supercomputers. The fact is that the supercomputers do nothing without operating systems and software. AI is software that power connected with the platform's or hardware's ability to run complicated software. 

The AI that interconnects multiple regular PCs to one entirety can be more powerful than systems or algorithms that are running on single supercomputers. But the fact is that the AI algorithm can interconnect supercomputers as the network-based entirety, which increases their power. In the morphing networks, the AI can have multiple layers, where it operates. 

The thing that makes AI-controlled morphing networks powerful is that the AI can interconnect quantum computers into one entirety. However, the AI-driven morphing network can operate as a virtual quantum computer. In that model, the AI drives parts of the problem into the binary computers. In a very complicated series, the binary system must know only some values from that series. 

Then it can share a mission to each member of the network by cutting the series by using those values as milestones. Then the computers can handle the calculation parts at the same time. But that requires. The AI has the values. The AI can be used to cut that calculation. To make it lighter for the network. 

*****************************************************''

Intelligent technology can extend battery lifetime in laptops and mobile systems. 

In laptops, the intelligent operating system and the intelligent kernel can shut down a couple of cores from multicore processors. In that model, the variable microchips use only the power that they require for solving problems. The problem is how to make the microchip know that it needs more power and more cores. The value that the system can use is the time. If the solution runs over a certain time the system calls more cores for working with that problem. 

If the problem is hard, the system can connect more cores to the operation. The variable architecture could increase the use of the battery. But that thing requires an intelligent kernel that can communicate with AI-based algorithms. When problems are not complicated the AI can disconnect some cores in multicore processors. Or share other works for those other cores. 

**********************************************************

There is no limit to the morphing network's size. The morphing network can be interconnected computers. Or it can be multiple nano-size microprocessors in multicore processors. 

While operating with regular problems the network-based AI can interconnect for example the computers in one classroom in their entirety. If the system's power is not enough. The AI can ask for assistance from other computers. So the AI extends its network and calculating capacity. In that process, the AI sends virtual sockets to other systems. And when the mission is done the AI can remove itself from the system's memory. 

And that allows it to call more calculation power for operation. Then the AI can also ask help from higher-class networks like supercomputers. It can send problems to supercomputer networks. And if it gets access the AI can copy itself and its program code to the supercomputers and network them. The fact is that AI can network all computers on Earth to one entirety. If those computers are connected to the net. 

Complicated algorithms require complicated and powerful machines. And that's why R&D teams work with photonic- and quantum processors. This is the reason why neurologists are hardly working with brains and learning process. Heavy and complicated algorithms require high-power computers and new operational models. If the AI-driven robot operates on streets its sensors drive very large data mass to the computer. And those mobile systems have limited operating abilities. 

The main problem with robots and computers is that complicated code requires powerful microchips. Powerful microchips require lots of electricity. That limits the battery's operational time. The answer for that problem can be the intelligent operating system that turns microprocessors into the power-saving mode. In multicore processor. The system can shut down a couple of its cores. If the system doesn't require all its power. 

https://www.space.com/google-deepmind-ai-weather-forecasts-artificial-intelligence