Martin Wattenberg: ML Visualization and Interpretability
The Gradient: Perspectives on AI
Data Visualization and Neural Networks
The guest discusses their background in web publishing and financial journalism, and how it sparked their interest in data visualization. They also talk about their experience with neural networks and the importance of understanding people's mental models when designing visualizations.
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.
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Outline:
* (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) Outro
Links:
* Professor Wattenberg’s homepage and Twitter
* Harvard SEAS application info — Professor Wattenberg is recruiting students!
* Research
* Earlier work
* 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
* TCAV
* Other ML papers:
* The System Model and the User Model: Exploring AI Dashboard Design (recent speculative essay)
* Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task
* Visualizing and Measuring the Geometry of BERT
* Artwork
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