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

  • 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

  • TensorFlow.js

  • Visualizing translation

  • 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

  • The Shape of Song

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