Zachary Lipton

Zachary Lipton, an assistant professor at Carnegie Mellon University and AI expert, dives into the evolving landscape of machine learning and deep learning. He discusses how NLP is dominating AI, highlights breakthroughs like DeepMind's AlphaFold for protein folding, and critiques the current peer-review system. Lipton emphasizes the significance of fairness and causal insights in AI, addressing challenges in incorporating ethical considerations. He reflects on the need for innovation amidst established techniques, revealing exciting opportunities for 2022 and beyond.

* Have suggestions for future podcast guests (or other feedback)? Let us know here!* Want to write with us? Send a pitch using this form :)In episode 45 of The Gradient Podcast, Daniel Bashir speaks to Zachary Lipton. Zachary is an Assistant Professor of Machine Learning and Operations Research at Carnegie Mellon University, where he directs the Approximately Correct Machine Intelligence Lab. He holds a joint appointment between CMU’s ML Department and Tepper School of Business, and holds courtesy appointments at the Heinz School of Public Policy and the Software and Societal Systems Department. His research spans core ML methods and theory, applications in healthcare and natural language processing, and critical concerns about algorithms and their impacts.Subscribe to The Gradient Podcast:  Apple Podcasts  | Spotify | Pocket Casts | RSSFollow The Gradient on TwitterOutline:* (00:00) Intro* (2:30) From jazz music to AI* (4:40) “fix it in post” we had some technical issues :)* (4:50) spicy takes, music and tech* (7:30) Zack’s plan to get into grad school* (9:45) selection bias in who gets faculty positions* (12:20) The slow development of Zack’s wide range of research interests, Zack’s strengths coming into ML research* (22:00) How Zack got attention early in his PhD* (27:00) Should PhD students meander?* (30:30) Faults in the QA model literature* (35:00) Troubling Trends, antecedents in other fields* (39:40) Pretraining LMs on nonsense words, new paper!* The new paper (9/29)* (47:25) what “BERT learns linguistic structure” misses* (56:00) making causal claims in ML* (1:05:40) domain-adversarial networks don’t solve distribution shift, underspecified problems* (1:09:10) the benefits of floating between communities* (1:14:30) advice on finding inspiration and learning* (1:16:00) “fairness” and ML solutionism* (1:21:10) epistemic questions, how we make determinations of fairness* (1:29:00) Zack’s drives and motivationsLinks:* Zachary’s Homepage* Papers* DL Foundations, Distribution Shift, Generalization* Does Pretraining for Summarization Require Knowledge Transfer?* How Much Reading Does Reading Comprehension Require?* Learning Robust Global Representations by Penalizing Local Predictive Power* Detecting and Correcting for Label Shift with Black Box Predictors* RATT* Explanation/Interpretability/Fairness* The Mythos of Model Interpretability* Evaluating Explanations* Does mitigating ML’s impact disparity require treatment disparity?* Algorithmic Fairness from a Non-ideal Perspective* Broader perspectives/critiques* Troubling Trends in Machine Learning Scholarship* When Curation Becomes Creation Get full access to The Gradient at thegradientpub.substack.com/subscribe

Zachary Lipton, Chief Technology & Science Officer at Abridge and Associate Professor at Carnegie Mellon, dives into the transformative power of AI in healthcare. He discusses how effective AI must start with meaningful conversations rather than mere documentation. Lipton reveals the complexities of customizing AI for medical environments, the negative impact of burnout due to clerical work, and innovations like digital scribes. He emphasizes building trust in AI and the balance between personalizing care and maintaining accuracy in medical documentation.