Computational principles of intelligence: learning and reasoning with neural networks
This paper attempts to contribute to a general theory of intelligence, which is a foundational problem for the entire field of AI.
This paper proposes a novel framework for intelligence based on three principles: generative and mirroring learned representations, a grounded and intrinsically motivated learning process, and ad hoc tuning of reasoning over causal compositional representations using inhibition rules. These principles aim to create a system with interpretability, continuous learning, and common sense.
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.