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Neural processing originally referred to the way the brain works, but the term is more typically used to describe a computer architecture that mimics that biological function. In computers, neural processing gives software the ability to adapt to changing situations and to improve its function as more information becomes available. Neural processing is used in software to do tasks such as recognize a human face, predict the weather, analyze speech patterns, and learn new strategies in games.
The human brain is composed of approximately 100 billion neurons. These neurons are nerve cells that individually serve a simple function of processing and transmitting information. When the nerve cells transmit and process in clusters, called a neural network, the results are complex – such as creating and storing memory, processing language, and reacting to sudden movement.
Artificial neural processing mimics this process at a simpler level. A small processing unit, called a neuron or node, performs a simple task of processing and transmitting data. As the simple processing units combine basic information through connectors, the information and processing becomes more complex. Unlike traditional computer processors, which need a human programmer to input new information, neural processors can learn on their own once they are programmed.
For example, a neural processor can improve at checkers. Just like a human brain, the computer learns that certain moves by an opponent are made to create traps. Basic programming might allow the computer to fall for the trap the first time. The more often a certain trap appears, however, the greater attention the computer pays to that data and begins to react accordingly.
Neural programmers call the increasing attention that the computer pays to certain outcomes "weight." Traditional processing would provide the computer only with the basic rules of the game and a limited number of strategies. Neural processing, by gathering data and paying greater attention to more important information, learns better strategies as time goes on.
The power of neural processing is in its flexibility. In the brain, information is presented as an electrochemical impulse – a small jolt or a chemical signal. In artificial neural processing, the information is presented as a numeric value. That value determines whether the artificial neuron goes active or stays dormant, and it also determines where it sends its signal. If a certain checker is moved to a certain square, for instance, the neural network reads that information as numeric data. That data is compared against a growing amount of information, which in turn creates an action or output.
@Charred - The problem with conscious processing in my opinion is that we don’t fully understand how the brain works.
Are the brain and the mind the same thing? Do neural networks truly replicate the genius of the human mind or are they just whittled down approximations?
I think we have many years, and many breakthroughs to go, before we can truly create a thinking machine. At best, all we can produce in the meantime is an impressive simulation.
Neural network programming is one of the most fascinating fields in computer science. I have read up on it, and while I can’t claim to be an expert, I do understand the basic concepts.
Where I think it holds the most promise is in the field of artificial intelligence, especially with computer games. Games can use neural networks to learn and get better over time.
The article uses the example of checkers and of course we have computers that can play chess and defeat world champions. But the usefulness in games is more than trivial.
I believe that it has military applications as well. The military is often engaged in war games and exercises like that, and artificial intelligence could prove to be very useful in helping to conduct – and win – such operations.