“Elon Musk doesn’t really deserve to have a voice in the public discourse about machine learning. He’s not an expert…”
Professor Zachary Lipton is an Assistant Professor in the Tepper School of Business at Carnegie Mellon University, with an appointment in the Machine Learning Department. He recently completed four years of PhD studies at UC San Diego’s Artificial Intelligence Group.
His research interests are eclectic, spanning both methods, applications, and social impacts of machine learning (ML), there exist a few notable clusters. He is especially interested in modeling temporal dynamics and sequential structure in healthcare data, e.g., Learning to Diagnose. Additionally, he works on critical questions related to how we use ML in the wild, yielding The Mythos of Model Interpretability, and more recent work on the desirability and reconcilability of various statistical interpretations of fairness.
He is a native of New Rochelle, New York, attended Columbia University as an undergraduate, and is a jazz saxophonist.
Terrance Jackson: What is the difference between artificial intelligence, machine learning, and deep learning?
Zachary Lipton: From the crazy way these topics are covered in the media, it can be hard to tell the meanings of the various terms. Often they are compared to each other, e.g. what deep learning can do vs what machine learning can do. The most faithful, simple way to put it is that they have a subset relationship. AI was a field long before people were interested in machine learning. It encompasses the study of how to do, with machines, all things that we think requires something like human intelligence. Of course that makes it a bit of a moving target. Once we know how to do something well, such as playing chess, then we sometimes don’t subsequently view it as a critical piece of AI.