Computational principles of intelligence: learning and reasoning with neural networks

TL;DR

This paper proposes a novel framework for intelligence based on principles of generative representations, iterative learning, and causal reasoning, aiming to unify problem solving, interpretability, and brain modeling.

Contribution

It introduces a new theoretical framework integrating generative, motivated, and causal principles to advance understanding of intelligence and problem solving.

Findings

Framework offers interpretability and continuous learning.

Supports common sense and problem solving.

Aligns with brain information processing models.

Abstract

Despite significant achievements and current interest in machine learning and artificial intelligence, the quest for a theory of intelligence, allowing general and efficient problem solving, has done little progress. This work tries to contribute in this direction by proposing a novel framework of intelligence based on three principles. First, the generative and mirroring nature of learned representations of inputs. Second, a grounded, intrinsically motivated and iterative process for learning, problem solving and imagination. Third, an ad hoc tuning of the reasoning mechanism over causal compositional representations using inhibition rules. Together, those principles create a systems approach offering interpretability, continuous learning, common sense and more. This framework is being developed from the following perspectives: as a general problem solving method, as a human oriented tool and finally, as model of information processing in the brain.