Turing++ Questions: A Test for the Science of (Human) Intelligence
AbstractIt is becoming increasingly clear that there is an infinite number of definitions of intelligence. Machines that are intelligent in different narrow ways have been built since the 50s. We are entering now a golden age for the engineering of intelligence and the development of many different kinds of intelligent machines. At the same time there is a widespread interest among scientists in understanding a specific and well defined form of intelligence, that is human intelligence. For this reason we propose a stronger version of the original Turing test. In particular, we describe here an open-ended set of Turing++ Questions that we are developing at the Center for Brains, Minds and Machines at MIT — that is questions about an image. Questions may range from what is there to who is there, what is this person doing, what is this girl thinking about this boy and so on. The plural in questions is to emphasize that there are many different intelligent abilities in humans that have to be characterized, and possibly replicated in a machine, from basic visual recognition of objects, to the identification of faces, to gauge emotions, to social intelligence, to language and much more. The term Turing++ is to emphasize that our goal is understanding human intelligence at all Marr’s levels — from the level of the computations to the level of the underlying circuits. Answers to the Turing++ Questions should thus be given in terms of models that match human behavior and human physiology — the mind and the brain. These requirements are thus well beyond the original Turing test. A whole scientific field that we call the science of (human) intelligence is required to make progress in answering our Turing++ Questions. It is connected to neuroscience and to the engineering of intelligence but also separate from both of them.
How to Cite
Poggio, T., & Meyers, E. (2016). Turing++ Questions: A Test for the Science of (Human) Intelligence. AI Magazine, 37(1), 73-77. https://doi.org/10.1609/aimag.v37i1.2641
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