Beyond the Turing test: Applying Bloom’s taxonomy to machine learning

From the abstract for Jim Parker and Shannon Jaeger’s Learning in Artificial Intelligence: Does Bloom’s Taxonomy Apply?:

From the early days of science and philosophy, humans have wondered exactly what it is that makes us intelligent beings. Plato felt that a good education should include instruction in music, gymnastics, and dialectic, and pondered and how we learned the things we did. Humans also been interested in building machines that mimic their abilities. The original motivation of building mechanical machines was to free us from mundane tasks and to allow fewer people to do particular difficult jobs. Today we build electronic machines that not only do our chores and to assist us physically, but also to help us intellectually.

A side affect of building intelligent, electronic machines is that we come to know how humans work a little bit better, and we also learn to better appreciate our amazing ability to learn. There is a danger that we may take the results of artificial intelligence too seriously, and to extend them in ways that are not appropriate. The issues surrounding the mechanisms of artificial intelligence and machine learning may or may not apply to the way that people learn. They are simply the way that we imagine we may learn, a model that needs to be tested against reality. On the other hand, if it makes a computer appear intelligent then it is sufficient, then it is sufficient for our purposes whether it is the way people do it or not. The difference is in intent: are we attempting to automate some difficult process, or are we trying to determine how the human brain acquires new information?

— Joe

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