A New Approach to Understanding How Machines Think →January 22, 2019 • #
This is an interesting interview with Been Kim from Google Brain on developing systems for seeing how trained machines make decisions. One of the major challenges with neural network-based based deep learning systems is that the decision chain used by the AI is a black box to humans. It’s difficult (or impossible) for even the creators to figure out what factors influenced a decision, and how the AI “weighted” the inputs. What Kim is developing is a “translation” framework for giving operators better insight into the decision chain of AI:
Kim and her colleagues at Google Brain recently developed a system called “Testing with Concept Activation Vectors” (TCAV), which she describes as a “translator for humans” that allows a user to ask a black box AI how much a specific, high-level concept has played into its reasoning. For example, if a machine-learning system has been trained to identify zebras in images, a person could use TCAV to determine how much weight the system gives to the concept of “stripes” when making a decision.
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