Intersymbolic AI: Interlinking Symbolic AI and Subsymbolic AI

TL;DR

This paper advocates for Intersymbolic AI, a new field combining symbolic and subsymbolic AI to leverage their complementary strengths for more effective AI systems.

Contribution

It introduces the concept of Intersymbolic AI, outlining its potential to unify symbolic and subsymbolic approaches and reviewing initial successful contributions.

Findings

Intersymbolic AI enables seamless integration of symbolic and subsymbolic methods.

Combining both AI types enhances system effectiveness beyond individual approaches.

Survey of successful Intersymbolic AI applications.

Abstract

This perspective piece calls for the study of the new field of Intersymbolic AI, by which we mean the combination of symbolic AI, whose building blocks have inherent significance/meaning, with subsymbolic AI, whose entirety creates significance/effect despite the fact that individual building blocks escape meaning. Canonical kinds of symbolic AI are logic, games and planning. Canonical kinds of subsymbolic AI are (un)supervised machine and reinforcement learning. Intersymbolic AI interlinks the worlds of symbolic AI with its compositional symbolic significance and meaning and of subsymbolic AI with its summative significance or effect to enable culminations of insights from both worlds by going between and across symbolic AI insights with subsymbolic AI techniques that are being helped by symbolic AI principles. For example, Intersymbolic AI may start with symbolic AI to understand a dynamic system, continue with subsymbolic AI to learn its control, and end with symbolic AI to safely use the outcome of the learned subsymbolic AI controller in the dynamic system. The way Intersymbolic AI combines both symbolic and subsymbolic AI to increase the effectiveness of AI compared to either kind of AI alone is likened to the way that the combination of both conscious and subconscious thought increases the effectiveness of human thought compared to either kind of thought alone. Some successful contributions to the Intersymbolic AI paradigm are surveyed here but many more are considered possible by advancing Intersymbolic AI.