Memristor-based technology can solve AI’s energy problem.

“A new brain-inspired nanoelectronic device offers a glimpse into a future where artificial intelligence hardware consumes far less energy while becoming more adaptable. Credit: Shutterstock” (ScitechDaily, Tiny Brain-Inspired Device Could Solve AI’s Biggest Energy Problem)

The memristor is a resistor. That remember. Its position when electricity is cut. Wikipedia determines memristor like this: 

“A memristor (a portmanteau of memory resistor) is a non-linear two-terminal electrical component relating electric charge and magnetic flux linkage. It was described and named in 1971 by Leon Chua, completing a theoretical quartet of fundamental electrical components which also comprises the resistor, capacitor, and inductor” (Wikipedia, Memristor)

The memristor is the tool. That can answer the AI’s energy problem. The memristor can remember its position. So. That means the position can act as binary memory or even quantum memory in modern computers. Even if one memristor has positions 1 and 0, that means those memristors can act as a hard disk. And in those cases. The memristor group can act as brain cells for the computer. The memristor uses less energy than standard components, and it can be the answer to AI’s energy problem. 

This technology allows the creation of brain-mimicking systems. 

The ability to use memristors as mass memory makes it possible to decrease the need for electricity. These kinds of systems can also help to keep the system’s temperatures low. And it can improve the system’s effectiveness. When we think. About. The use of the DNA-controlled nanomachines as artificial neurons, the memristors in those machines’ manipulators, can revolutionize computer technology. In those axons. The memristor determines if the value in each axon is 1 or 0. In human brains, each axon. Or their ion channels have a value of 0 or 1. 

The thing that makes human brains so effective is this. There are so many neurons. And the second thing is their morphing neural structure. In that morphing neural network, the system can separate a certain number of neurons into different entities. So that means the brain can share missions between neuron groups. 

Those neuron groups, or neuron clusters, can work with different problems. This is why we can walk, talk, and think. At the same time. But when brains see some bigger problems. It can combine those clusters to work. For one purpose. If the system, or its creator, uses  nanomachines as artificial neurons, it can simply create a thing that mimics the brain. 

https://scitechdaily.com/tiny-brain-inspired-device-could-solve-ais-biggest-energy-problem/

https://en.wikipedia.org/wiki/Memristor