We've been told time and time again that we need to understand data in context: it's an ethical imperative. Not every language gets an LLM; not every population fully understands a technology that's deployed in their community with or without everyone's consent; and certainly we're told that we will make better, safer conclusions with our data if we understand the context. John DeNero looks at things differently: instead of an ethical imperative for understanding data in context, John talks about a structural one. For example, accurately translating language necessitates understanding the context. It's almost as if he read a bunch of French critical theory, thought about deconstruction, and realized that a structural imperative has an ethical valence as well—and vice versa. It's not a paradox, it's deconstruction.

This interview covers John's work as a professor of data science and computer science, his experience as a senior research scientist at Google Translate, thoughts on AI and language, and keeping up with the slang of today's youth.

John DeNero is the Faculty Director of Data Science Undergraduate Studies (DSUS) and Associate Teaching Professor in the UC Berkeley EECS department. He is the co-founder and Chief Scientist at lilt.

John's website

Google Scholar

A Class-Based Agreement Model for Generating Accurately Inflected Translations

This episode is dedicated to MukhammadAziz Umurzokov and Ella Cook, the two Brown University students who passed away on December 13th, 2025.

In language-centric fields we privilege the speaker. Linguistics looks at spoken or signed utterances; linguistic anthropology does as well. But Michael Berman looks at listening, which for him is a process wherein you limit or shift your language practices so as to avoid being generated as a certain type of person (often within a hierarchical relationship). That’s listening. It's about avoiding (or not) taxonomy, stereotypes, perception, and it necessitates an understanding of the power that our ears have. This episode cannot be reduced to a few thematic elements: Michael and I discuss listening, semiotics, C.S. Peirce, suffering and compassion, critiques of linguistics and other sciences, the implicit economic models undergirding scholarship, and his fieldwork in Japan—among other things. I’m struck by how much ground we cover, and yet we make a limited number of rhetorical and analytic moves. Whether we’re talking about what constitutes listening, language ideology, religion, etc.—we’re always taking the minuscule and making it representative (or symptomatic) of something bigger. Maybe that’s a paranoid reading, but I think it’s useful in the context of our conversation. What appears as an individual assessment of language is in fact a societally-engineered and collectively-upheld assessment. What appears as a certain niche orientation to data turns out to be symptomatic of widespread abuses of scientific frameworks. And, as Michael will remind us, the creation of categories and production of knowledge has effects. So let’s pay attention.

This episode took inspiration from the questions that Jonathan Rosa asked in his episode on Tomayto Tomahto a year ago. Before listening to Michael, I encourage listening to Jonathan’s episode if you haven’t already.

Michael Berman

C.S. Peirce

Jonathan Rosa’s episode

Toward a linguistic anthropological approach to listening: An ear with power and the policing of “active listening” volunteers in Japan

Religion overcoming religions: Suffering, secularism, and the training of interfaith chaplains in Japan

Forms of the Affects

“Why The Problem Isn’t Single-Parent Families”

Why Is There So Much Fraud in Academia?

This episode was written, edited, and produced by Talia Sherman. All artwork by Maja Mishevska.

We’re in a paradoxical time for dictionaries, claims Stefan Fatsis. On the one hand, we’re bombarded by words and ways to understand them in this lexically intense, linguistically charged political and cultural moment. On the other hand, the dictionary is struggling. Merriam-Webster—fighting to keep up with AI, machine learning software, and the explosion of voices vying for authority over what words mean—must evolve or compromise on the care put into defining words. But Merriam-Webster isn’t unique, and neither is language, for that matter, in its position within a (political) economy. Competition is healthy.

Throughout NYT-bestselling author Stefan Fatsis’ book, Unabridged: The Thrill of (and Threat to) the Modern Dictionary, readers learn about lexical histories, Merriam-Webster’s backstory, word-enthusiast subcultures, and the importance of a dictionary's measured, apolitical approach to language. As Stefan says, “the demand for life or death information—objective, solid, reality based information that a dictionary like Merriam Webster provides—is critical to the functioning of democracy in a civil society.” So there you have it: the thrill and threat to the modern dictionary. It’s a paradox, hopefully an escapable one.

Stefan Fatsis Website (https://www.bystefanfatsis.com/)

Unabridged - Grove Atlantic Site (https://groveatlantic.com/book/unabridged/)

Is This the End of the Dictionary? - Atlantic OpEd (https://www.theatlantic.com/magazine/archive/2025/10/dictionary-survival-language-evolution/683976/)

American Dialect Society Selects rawdog as 2024 Word of the Year (https://americandialect.org/wp-content/uploads/2025/01/2024-Word-of-the-Year-PRESS-RELEASE.pdf)

Encyclopedia Britannica and Merriam-Webster sue Perplexity AI for copyright and trademark infringement (https://www.theverge.com/news/777344/perplexity-lawsuit-encyclopedia-britannica-merriam-webster)

True Color by Kory Stamper (https://www.penguinrandomhouse.com/books/555914/true-color-by-kory-stamper/)

Here’s why “fuck” is in the dictionary (https://qz.com/973992/a-lexicographer-explains-why-dictionaries-contain-words-like-fuck)

Lindsay Rose Russell (https://english.illinois.edu/directory/profile/russellr)

Peter Sokolowski (https://aceseditors.org/peter-sokolowski)

Ben Zimmer’s episode on Tomayto Tomahto (https://open.spotify.com/episode/1VlBEyUPyhfzRmAoZe5lV2?si=3639e28fc3564c22)

Nicole Holliday’s episode on Tomayto Tomahto