Human-centered Machine Learning: a Machine-in-the-loop Approach | by Chenhao Tan | Medium

In 1950, Alan Turing asked the question: “can machines think?” This question has inspired excellent research in the area of artificial intelligence and machine learning. Today, machine learning shows great promise in a wide range of applications, from autonomous driving cars, to face recognition, to medical diagnosis, to bail decisions, where judges decide the fate of arrestees. As machine learning becomes more and more integrated in daily life, applying a human-centered perspective to these tools becomes necessary. Implicit in Turing’s question are two assumptions related to humans that have profoundly impacted the development of machine learning. First, when reading his question, it is understood that when he uses the verb “think,”he is referring to a distinctly human activity — he is not asking whether a machine can think like a sponge can think. The phrasing of the question implies that the emulation of human thought and performance is the target. Second, despite clever human desig

In 1950, Alan Turing asked the question: “can machines think?” This question has inspired excellent research in the area of artificial intelligence and machine learning. Today, machine learning shows great promise in a wide range of applications, from autonomous driving cars, to face recognition, to medical diagnosis, to bail decisions, where judges decide the fate of arrestees. As machine learning becomes more and more integrated in daily life, applying a human-centered perspective to these tools becomes necessary. Implicit in Turing’s question are two assumptions related to humans that have