Neural programming creates software that can predict unknowns and adapt to new data. It uses artificial neurons grouped into networks to perform complex tasks and has two steps: entering fundamental information and training. The advantage is that it can adapt to imperfect inputs, like the human brain.
Neural programming is used to create software that mimics basic brain functions. It is a key component of artificial intelligence (AI) and creates software that can predict unknowns, such as weather and stock market trends, as well as games where the computer opponent gets better as he gains experience. The advantage of neural programming over traditional programming is that its software can learn and adapt to new data.
Typically, neural programming uses a computer architecture called neural processing, which uses artificial neurons or nodes grouped into networks to perform complex tasks. Each artificial neuron is activated by a certain numerical 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 the data has a value of -1, execute a function. If the data value is 0, do something else.
Neural programming is a two-step process. The first step is to enter the fundamental information and rules that a software application needs to understand the data it will receive. This software is usually programmed with bias bits, giving more credence to certain types of information. For example, stock market software neural programming will include basic stock market functions, such as the premise that greater demand for a stock increases its value. It will also include some biases, such as how software should pay close attention to trends in its quarterly income reports.
The second step in neural programming is called training. The data is used to teach the software certain trends and possibilities; in general, the more data the software captures, the better it becomes at creating accurate output. For example, the data could teach the computer that when a certain industry has strong second-quarter earnings, it generally means its fourth quarter is slow. Stock values are tied to earnings reports, so the software may eventually predict that sector’s stock will decline after fourth-quarter reports come out when the sector had a strong second quarter. The software output could possibly advise a trader to sell before the fourth quarter earnings reports come out.
Typically, the advantage of neural programming is that software doesn’t need perfect information to work. Unlike traditional programming, which shuts down when errors occur, neural programming can adapt to imperfect inputs by using past information to solve the problem. This is how the human brain works too, although it is much more complex. For example, 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, gait, or voice—trigger recognition. Neural programmers continue to perfect software that will not only mimic the brain, but in some cases be faster and even more accurate.
Protect your devices with Threat Protection by NordVPN
