  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.
