How Intelligence is Understood Today

Abstract

The article is devoted to understanding the ways of explaining intellectual abilities in the light of new developments in the field of artificial intelligence and discoveries related to the study of complex adaptive animal behavior based on the reward system. The paper reviews the latest advances in the development of biologically plausible learning algorithms, the purpose of which is to explain the large amount of accumulated data from the field of neuroscience. Within the framework of this approach, reinforcement learning algorithms are proposed as the basis for any kind of cognitive activity. Understanding intelligence as a set of flexible adaptive abilities to achieve a goal provides a new conceptual framework for explaining how the brain works at a functional level. The formation of forecasts for the future, the construction of time steps and the existence of an internal assessment system in such systems is psychologically and biologically plausible and can potentially become a new milestone in the study of intelligence.