In episode 99 of The Gradient Podcast, Daniel Bashir speaks to Professor Martin Wattenberg.Professor Wattenberg is a professor at Harvard and part-time member of Google Research’s People + AI Research (PAIR) initiative, which he co-founded. His work, with long-time collaborator Fernanda Viégas, focuses on making AI technology broadly accessible and reflective of human values. At Google, Professor Wattenberg, his team, and Professor Viégas have created end-user visualizations for products such as Search, YouTube, and Google Analytics. Note: Professor Wattenberg is recruiting PhD students through Harvard SEAS—info here.Have suggestions for future podcast guests (or other feedback)? Let us know here or reach us at editor@thegradient.pubSubscribe to The Gradient Podcast:  Apple Podcasts  | Spotify | Pocket Casts | RSSFollow The Gradient on TwitterOutline:* (00:00) Intro* (03:30) Prof. Wattenberg’s background* (04:40) Financial journalism at SmartMoney* (05:35) Contact with the academic visualization world, IBM* (07:30) Transition into visualizing ML* (08:25) Skepticism of neural networks in the 1980s* (09:45) Work at IBM* (10:00) Multiple scales in information graphics, organization of information* (13:55) How much information should a graphic display to whom? * (17:00) Progressive disclosure of complexity in interface design* (18:45) Visualization as a rhetorical process* (20:45) Conversation Thumbnails for Large-Scale Discussions* (21:35) Evolution of conversation interfaces—Slack, etc.* (24:20) Path dependence — mutual influences between user behaviors and technology, takeaways for ML interface design* (26:30) Baby Names and Social Data Analysis — patterns of interest in baby names* (29:50) History Flow* (30:05) Why investigate editing dynamics on Wikipedia?* (32:06) Implications of editing patterns for design and governance* (33:25) The value of visualizations in this work, issues with Wikipedia editing* (34:45) Community moderation, bureaucracy* (36:20) Consensus and guidelines* (37:10) “Neutral” point of view as an organizing principle* (38:30) Takeaways* PAIR* (39:15) Tools for model understanding and “understanding” ML systems* (41:10) Intro to PAIR (at Google)* (42:00) Unpacking the word “understanding” and use cases* (43:00) Historical comparisons for AI development* (44:55) The birth of TensorFlow.js* (47:52) Democratization of ML* (48:45) Visualizing translation — uncovering and telling a story behind the findings* (52:10) Shared representations in LLMs and their facility at translation-like tasks* (53:50) TCAV* (55:30) Explainability and trust* (59:10) Writing code with LMs and metaphors for using* More recent research* (1:01:05) The System Model and the User Model: Exploring AI Dashboard Design* (1:10:05) OthelloGPT and world models, causality* (1:14:10) Dashboards and interaction design—interfaces and core capabilities* (1:18:07) Reactions to existing LLM interfaces* (1:21:30) Visualizing and Measuring the Geometry of BERT* (1:26:55) Note/Correction: The “Atlas of Meaning” Prof. Wattenberg mentions is called Context Atlas* (1:28:20) Language model tasks and internal representations/geometry* (1:29:30) LLMs as “next word predictors” — explaining systems to people* (1:31:15) The Shape of Song* (1:31:55) What does music look like? * (1:35:00) Levels of abstraction, emergent complexity in music and language models* (1:37:00) What Prof. Wattenberg hopes to see in ML and interaction design* (1:41:18) OutroLinks:* Professor Wattenberg’s homepage and Twitter* Harvard SEAS application info — Professor Wattenberg is recruiting students!* Research* Earlier work* A Fuzzy Commitment Scheme* Stacked Graphs—Geometry & Aesthetics* A Multi-Scale Model of Perceptual Organization in Information Graphics* Conversation Thumbnails for Large-Scale Discussions* Baby Names and Social Data Analysis* History Flow (paper)* At Harvard and Google / PAIR* Tools for Model Understanding: Facets, SmoothGrad, Attacking discrimination with smarter ML*