Artificial Cognition Architectures and Systemic Implementations

The text analyzes the nature of artificial intelligence not as a copy of the human mind, but as a set of diverse cognitive architectures. The author contrasts the symbolic approach, based on explicit rules and representation manipulation, with connectionism, which relies on distributed knowledge representations emerging through statistical learning. It discusses the evolution from expert systems, through deep neural networks, to transformers and the attention mechanism. The analysis points to a transition from rigid ontologies to flexible generative models, emphasizing differences in how information is generalized, remembered, and planned depending on the adopted system architecture.