Machine learning image classifiers use context clues to help understand the contents of a photo, room, or any type of space. For example, if they manage to identify a dining-room table with a high degree of confidence, that can help resolve ambiguity about other objects nearby, identifying them as chairs. Kind of a “where there is smoke there must be fire” ideology. This interesting topic was brought to us by Boing Boing in their article, “There’s a literal elephant in machine learning’s room.”

The downside of this powerful approach is that it means machine learning classifiers can be fairly easy confused. The presence of the unexpected item throws the classifiers into dire confusion. In addition to struggling to identify the odd and out of place item, they also struggle with everything else in the scene.

Most cognitive processes are contextual in the sense that they depend on the environment, or context, inside which they are carried on. Even concentrating on the issue of contextuality in reasoning, many different notions of context can be found in artificial intelligence.

Melody K. Smith

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