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.

翻译：本文呼吁对符号间人工智能这一新兴领域展开研究，符号间人工智能是指将具有内在意义/含义的符号人工智能与整体产生意义/效果（尽管其单个构件缺乏意义）的亚符号人工智能相结合。符号人工智能的典型形式包括逻辑推理、博弈论与规划问题。亚符号人工智能的典型形式涵盖（非）监督学习与强化学习。符号间人工智能通过融合符号人工智能具有组合性符号意义的世界与亚符号人工智能具有累积性意义的世界，借助符号人工智能原理辅助的亚符号人工智能技术，在符号人工智能的认知体系间建立联结，从而实现两个领域认知的巅峰融合。例如，符号间人工智能可以始于运用符号人工智能理解动态系统，继而通过亚符号人工智能学习系统控制，最终回归符号人工智能以安全地将习得的亚符号控制器应用于动态系统。符号间人工智能通过整合符号与亚符号人工智能以提升整体效能的方式，恰如人类意识与潜意识思维相结合能产生超越单一思维模式的认知效能。本文综述了符号间人工智能范式已取得的部分成果，并指出通过推进该领域研究将可能实现更多突破。