Machine Common Sense Concept Paper

By D. Gunning. 2018

48 citations recorded by the source corpus.

This paper summarizes some of the technical background, research ideas, and possible development strategies for achieving machine common sense. Machine common sense has long been a critical-but-missing component of Artificial Intelligence (AI). Recent advances in machine learning have resulted in new AI capabilities, but in all of these applications, machine reasoning is narrow and highly specialized. Developers must carefully train or program systems for every situation. General commonsense reasoning remains elusive. The absence of common sense prevents intelligent systems from understanding their world, behaving reasonably in unforeseen situations, communicating naturally with people, and learning from new experiences. Its absence is perhaps the most significant barrier between the narrowly focused AI applications we have today and the more general, human-like AI systems we would like to build in the future. Machine common sense remains a broad, potentially unbounded problem in AI. There are a wide range of strategies that could be employed to make progress on this difficult challenge. This paper discusses two diverse strategies for focusing development on two different machine commonsense services: (1) a service that learns from experience, like a child, to construct computational models that mimic the core domains of child cognition for objects (intuitive physics), agents (intentional actors), and places (spatial navigation); and (2) service that learns from reading the Web, like a research librarian, to construct a commonsense knowledge repository capable of answering natural language and image-based questions about commonsense phenomena.

Subfields: Artificial Intelligence, Knowledge Graphs & Representation, Cognitive Science & Psychology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Knowledge Graphs & Representation

In Cognitive Science & Psychology

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).”
A Systematic Investigation of Commonsense Understanding in Large Language Models (2021, background): “Despite the importance of commonsense understanding in NLP systems (McCarthy et al., 1960; Gunning, 2018; Liu and Singh, 2004), whether commonsense knowledge can be acquired by large language models and through the language modeling objective remains an open question.”