A World-Self Model Towards Understanding Intelligence

Abstract

The symbolism, connectionism and behaviorism approaches of artificial intelligence have achieved a lot of successes in various tasks, while we still do not have a clear definition of "intelligence" with enough consensus in the community (although there are over 70 different "versions" of definitions). The nature of intelligence is still in darkness. In this work we do not take any of these three traditional approaches, instead we try to identify certain fundamental aspects of the nature of intelligence, and construct a mathematical model to represent and potentially reproduce these fundamental aspects. We first stress the importance of defining the scope of discussion and granularity of investigation. We carefully compare human and artificial intelligence, and qualitatively demonstrate an information abstraction process, which we propose to be the key to connect perception and cognition. We then present the broader idea of "concept", separate the idea of self model out of the world model, and construct a new model called world-self model (WSM). We show the mechanisms of creating and connecting concepts, and the flow of how the WSM receives, processes and outputs information with respect to an arbitrary type of problem to solve. We also consider and discuss the potential computer implementation issues of the proposed theoretical framework, and finally we propose a unified general framework of intelligence based on WSM.

21 pages, 5 figures

References in corpus (7)

• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge
• The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence
• Neurosymbolic AI: The 3rd Wave
• FastMoE: A Fast Mixture-of-Expert Training System
• Faster and Safer Training by Embedding High-Level Knowledge into Deep Reinforcement Learning
• Separating the World and Ego Models for Self-Driving