Expertise

Topics:  Machine Learning, Machine Intelligence, Natural Language Processing (NLP), Deep Learning

Zachary Lipton is the Chief Technology Officer and Chief Scientist at Abridge, where he oversees the builder organization responsible for all of product development and AI research. He is also the Raj Reddy Associate Professor of Machine Learning at Carnegie Mellon University, where he directs the Approximately Correct Machine Intelligence (ACMI) lab, whose research focuses include the theoretical and engineering foundations of robust and adaptive machine learning algorithms, applications to both prediction and decision-making problems in clinical medicine, natural language processing, and the impact of machine learning systems on society. He is the founder of the Approximately Correct blog (approximatelycorrect.com) and a co-author of Dive Into Deep Learning, an interactive open-source book drafted entirely through Jupyter notebooks that has reached millions of readers.

Media Experience

For Shiv Rao, practicing doctor and founder of $2.75 billion AI startup Abridge, innovation is an art form  — Fortune
This is a feature on Shiv Rao (CMU alumnus) and Founder/CEO of Abridge, which uses AI to turn doctor-patient conversations into clinical notes in real-time. Zack Lipton (School of Computer Science) is Abridge's CTO who put aside a life as a professional jazz saxophonist to join Rao.

OpenAI shakeup has rocked Silicon Valley, leaving some techies concerned about future of AI  — CNBC
“I imagine Microsoft might ask for a board seat next time they decide to plow $15 billion into a startup,” said Zachary Lipton, a Carnegie Mellon University professor of machine learning and operations research.

What’s the Future for A.I.?  — The New York Times
“This will affect tasks that are more repetitive, more formulaic, more generic,” said Zachary Lipton, a professor at Carnegie Mellon who specializes in artificial intelligence and its impact on society.

What’s wrong with “explainable A.I.”  — Fortune
“Everyone who is serious in the field knows that most of today’s explainable A.I. is nonsense,” Zachary Lipton, a computer science professor at Carnegie Mellon University, recently told me. Lipton says he has had many radiologists reach out to him for help after their hospitals deployed a supposedly explainable A.I. system for interpreting medical imagery whose explanations don’t make sense—or, at the very least, are irrelevant to what a radiologist really wants to know about a medical image.

Is AI overhyped? Researchers weigh in on technology's promise and problems  — CBC News
But Zachary Lipton, an assistant professor at Carnegie Mellon University's machine learning department and school of business, worries that machine learning's success at making predictions "can blind people to the fact that not every problem is a prediction problem."