Discussion of article "Brute force approach to pattern search"

A neural network is essentially also a kind of brute force. But its algorithms are very different from simple brute force algorithms. I will not provide the details of specific neural network architectures and their elements, but will try to provide a general description. I think, if we stick to a certain architecture, we limit the capabilities of our algorithm in advance. A fixed architecture is an irreparable limitation. A neural network is a kind of architecture of a possible strategy in our case. As a result, the configuration of a neural network always corresponds to a certain file with a network map. This always points to a collection of certain units. It is like with a 3D printer: Set item parameters and the printer will produce it. Thus, a neural network is a general code that does not make sense without a map. This is like taking any advanced programming language and simply creating an empty project without utilizing all its capabilities. As a result, the empty template does nothing. The same is with the neural network. Unlike brute force, a neural network can provide almost unlimited variability in strategies, any number of criteria and higher efficiency. The only disadvantage of this approach is that the efficiency greatly depends on the code quality. An increasing system complexity may lead to increased resource intensiveness of the program. As a result, our strategy is converted into a network map, which is its equivalent. The same is done in the brute force approach, but here we work with a simple sequence of some numbers. This sequence is much simpler than a network map, it is easier to compute, but it also has a limit in terms of efficiency. The below scheme displays the above explanation.