NEURAL NETWORKS VERSUS CONVENTIONAL COMPUTERS

Abstract

The first artificial neuron was developed in 1943 by neurophysiologist Warren McCulloch and logician Walter Pitts. However, the limited technology available at that time restricted its practical application. Neural networks, with their remarkable ability to extract meaning from complex and imprecise data, are now widely used to identify patterns and detect trends that may be too intricate for humans or traditional computer techniques to recognize. A trained neural network can be regarded as an “expert” within the specific category of information it has been exposed to during training. In contrast, conventional computers operate using an algorithmic approach, whereby the system follows a predetermined set of instructions to solve a problem. If the exact steps required are not known, the computer cannot execute the task. Neural networks, on the other hand, process information in a manner similar to the human brain. They consist of a large number of highly interconnected processing units (neurons) that work in parallel to solve problems. Unlike conventional programming, neural networks learn by example and cannot be explicitly programmed for a particular task. Importantly, neural networks and conventional computers are not competitors but complements. While algorithmic approaches are more suitable for tasks such as arithmetic operations, neural networks excel in tasks that require pattern recognition or adaptive learning. In many applications, the most effective systems combine both approaches, with conventional computers supervising neural networks to achieve maximum efficiency.