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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