The Roles of Symbols in Neural-based AI: They are Not What You Think!

TL;DR

This paper redefines symbols as external communication tools and internal self-communication mechanisms in intelligent agents, highlighting their role in efficient knowledge transfer, reasoning, and learning in neural systems.

Contribution

It introduces a novel neuro-symbolic hypothesis and architecture that integrates subsymbolic representations with symbolic concepts for improved AI reasoning.

Findings

Symbols facilitate knowledge transfer and reasoning in neural systems.

Neuroscience insights support symbolic representation in AI.

Proposed architecture combines subsymbolic and symbolic processes.

Abstract

We propose that symbols are first and foremost external communication tools used between intelligent agents that allow knowledge to be transferred in a more efficient and effective manner than having to experience the world directly. But, they are also used internally within an agent through a form of self-communication to help formulate, describe and justify subsymbolic patterns of neural activity that truly implement thinking. Symbols, and our languages that make use of them, not only allow us to explain our thinking to others and ourselves, but also provide beneficial constraints (inductive bias) on learning about the world. In this paper we present relevant insights from neuroscience and cognitive science, about how the human brain represents symbols and the concepts they refer to, and how today's artificial neural networks can do the same. We then present a novel neuro-symbolic hypothesis and a plausible architecture for intelligent agents that combines subsymbolic representations for symbols and concepts for learning and reasoning. Our hypothesis and associated architecture imply that symbols will remain critical to the future of intelligent systems NOT because they are the fundamental building blocks of thought, but because they are characterizations of subsymbolic processes that constitute thought.