Building Smart AI: How Deep Learning Can Get You into Deep Trouble

Recent advances in deep learning have fueled tremendous excitement about the potential for artificial intelligence to solve countless problems. But there are some perils and pitfalls endemic to these new techniques, particularly because they ignore two essential components of the scientific method: (1) understanding the how; and (2) explaining the why. Dr. Michael Housman offers up a two specific examples from his own career as a data scientist to show how a naive application of deep learning algorithms can lead data scientists to the wrong conclusion and offers up some guidance for avoiding these mistakes.