By Peter Fingar

Actually, to use common sense, the title of this article should be in the words of renowned architect, Frank Lloyd Wright, “There is nothing more uncommon than common sense.” Hmm? Is that common sense, or commonsense, or common-sense. It takes some real common sense to know the difference.

In artificial intelligence research, commonsense knowledge is the collection of facts and information that an ordinary person is expected to know. The commonsense knowledge problem is the ongoing project in the field of knowledge representation (a sub-field of artificial intelligence) to create a commonsense knowledge base: a database containing all the general knowledge that most people possess. The database must be represented in a way that it is available to artificial intelligence programs that use natural language or make inferences about the ordinary world. Such a database is a type of ontology of which the most general are called upper ontologies.

The commonsense problem is considered to be among the hardest in all of AI research because the breadth and detail of commonsense knowledge is enormous. Any task that requires commonsense knowledge is considered AI-complete: to be done as well as a human being does it, it requires the machine to appear as intelligent as a human being. These tasks include machine translation, object recognition, text mining and many nuanced decisions. To do these tasks perfectly, the machine simply has to know what the text is talking about or what objects it may be looking at, and this is impossible in general, unless the machine is familiar with all the same concepts that an ordinary person is familiar with.

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