The Curious Case of Commonsense Intelligence

By Yejin Choi. 2022

Abstract Commonsense intelligence is a long-standing puzzle in AI. Despite considerable advances in deep learning, AI continues to be narrow and brittle due to its lack of common sense. Why is common sense so trivial for humans but so hard for machines? In this essay, I map the twists and turns in recent research adventures toward commonsense AI. As we will see, the latest advances on common sense are riddled with new, potentially counterintuitive perspectives and questions. In particular, I discuss the significance of language for modeling intuitive reasoning, the fundamental limitations of logic formalisms despite their intellectual appeal, the case for on-the-fly generative reasoning through language, the continuum between knowledge and reasoning, and the blend between symbolic and neural knowledge representations.

Subfields: Knowledge Graphs & Representation

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In Knowledge Graphs & Representation

1. Ronan Le Bras
2. Yejin Choi
3. Maarten Sap
4. Daniel Fried

Citations in the corpus

• Neural Theory-of-Mind? On the Limits of Social Intelligence in Large LMs (2022, background): “Why do LLMs need Theory of Mind? Social intelligence, Theory of Mind, and commonsense reasoning have been a longstanding but elusive goal of artificial intelligence for decades (Gunning, 2018; Choi, 2022).”