The morphing neural networks are coming.
A morphing neural network is a network that can remove or connect parts to it. One version of the morphing network is the network of workstations. And when the system requires some software users can install that software into one workstation. And then that workstation scales that program over entire networks. Morphing neural networks can be drone swarms, they can be networks of computers. Or those neural networks can be groups of living neurons.
Those neural networks are interesting because they can connected with surveillance tools like sensors. Then those systems can collect data from large areas and analyze and interconnect data. That system is collected from different types of sensors. And that makes those morphing neural networks interesting.
When we think about nanotechnology and living neurons we can make a system that can scale new skills to the brain faster than ever before. In that system, the lab-grown neurons will equipped with extremely small microchips where computers can keep contact. The laboratory crew can inject those neurons into the receiver's brain tissue.
Then that system will transmit the required information to the brain. By using those microchipped neurons. When the transmitter sends impulses to nanotechnical microchips the neuron sends that information through the brain. And that allows to transfer of new skills to the brain. In non-organic networks computers and other kinds of computing tools replace those living neurons.
In regular computers, the morphing architecture makes the system more resistant to damage. If there is one part in that system that not working right, the morphing network can remove that actor. Then the system can take a new workstation to replace that workstation. The learning system makes it possible for the system to stay updated autonomously.
When research teams work with AI-controlled morphing neural networks, they create the systems with ultimate error-handling capacity. The AI-based control systems can observe that the entirety works as it should. The AI-based control systems can make the system more advanced and more intelligent than nobody expected. The term intelligence means the ability to answer questions and respond to problems. That the system faces.
In things like fusion tests the morphing AI can follow things like how clean fusion material the system uses. Then it needs other values like temperature and power of magnetic fields. And then that system can adjust those values. When the AI finds a way to extend the time of fusion the system can store those variables in its memories. Then it can find the best possible mixture for making a fusion reaction that lasts as long as possible.
The morphing neural networks can collect data from all fusion laboratories on Earth. Then the system can compare the results of those tests. The idea is the same as when telescopes collect data about things like supernovas. In real life, those telescopes search the sky to find supernovas. That system searches the supernovas from multiple stars at the same time.
And because data collection is wide-ranging this kind of search makes sure. Those sensors see supernovas. If those sensors follow one star, that star can detonate after billions of years. But if sensors follow multiple stars like star clusters there is a much bigger possibility to see supernova in one night. The morphing neural network can collect data that different types of telescopes send. Same way morphing neural networks can collect information from fusion test sites and compare that information with other sources.
https://scitechdaily.com/wonderfully-weird-how-hafnia-is-paving-the-way-for-neuromorphic-computing/
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