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Neural programming is used to create software that mimics the brain’s basic functions. It is a key component to artificial intelligence (AI) and creates software that can predict unknowns, such as weather and stock market trends as well as games in which the cyber opponent improves as it gains experience. The advantage of neural programming over traditional programming is its software is able to learn and adapt to new data.
Generally, neural programming employs a computing architecture called neural processing, which uses artificial neurons or nodes that are clustered into networks to perform complex tasks. Each artificial neuron is triggered by a certain numeric value, which determines when and where it will send a signal to the next neuron. A single neuron is programmed with a simple if-then rule for a basic task. If data has a value of -1, then it performs one function. If the data value is 0, it does something else.
Neural programming is a two-step process. The first step is to input fundamental information and rules that a software application needs to understand the data it will receive. This software is usually programmed with bits of bias, giving more credence to certain types of information. For instance, neural programming of stock market software will include the basic functions of stock market trading, such as the premise that greater demand for a stock increases its value. It will also include certain biases, such as how the software should pay close attention to trends in quarterly income reports.
The second step in neural programming is called training. Data are used to teach the software certain trends and possibilities; generally, the more data the software takes in, the better it becomes at creating accurate outputs. For instance, data might teach the computer that when a certain industry has strong second quarter earnings, it generally means its fourth quarter is sluggish. Stock values are tied to earnings reports, so the software could eventually predict that stocks for that industry will go down after fourth quarter reports are in when the industry had a strong second quarter. The software’s output might eventually advise a trader to sell before fourth quarter earnings reports come out.
Typically, the advantage of neural programming is that software does not need perfect information to function. Unlike traditional programming, which shuts down when errors occur, neural programming can adjust to imperfect inputs by using past information to resolve the problem. This is how the human brain works as well, though it is far more complex. For instance, a human might be able to recognize an old friend, even if that friend has gained weight or grown a beard; other aspects of the friend – facial structures, eyes, his manner of walking or voice – trigger the recognition. Neural programmers continue to refine software that will not only mimic the brain, but in some cases be faster and even more accurate.