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Chris Wiggins

Associate Professor, Columbia University · 3 appearances on the record.

computed by AI from the episodes · how this works → · full disclaimer →

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Chris Wiggins is an Associate Professor of Applied Mathematics at Columbia University. He previously served as Chief Data Scientist at The New York Times, where he led machine learning and data science initiatives.

20statements → 8claims → 6claims resolved → 50%fully supported → 4/5average certainty → 2.05/5average debate potential → 4.2/5argument clarity · the sources → 1said about them ↓

3 supported 2 partly supported 1 contradicted 2 not checkable as stated how the 8 claims stand · each chip opens the sources

8 assertions · 2 opinions · 8 insights · 2 disclosures · every statement was checked. The predictions and assertions are the 8 claims: statements the public record can support or contradict. 6 are resolved, and 2 name no date, number or outcome precise enough to check. Everything else (opinions, insights, what ifs, disclosures) can never be settled by the record, so it carries no assessment.

The record, in short

What the tape says about how Chris argues and how the claims held up. Everything they said, and everything said about them, is in the tabs below.

Their most notable supported claim

Assertion Supported
Wiggins: 'Statistics' entered English to mean statecraft, not math or data
“Statistics entered the English language to mean statecraft. It had nothing to do with math, and it certainly had nothing to do with data.”
Chris Wiggins May 31, 2023 ▶ 4:42 A Conversation with Chris Wiggins - Author of "How Data Happened"

Their most notable contradicted claim

Assertion Contradicted
Wiggins: Early US computing was funded by intelligence for data processing
“That story had its own mirror on the other side of the Atlantic in Bell Labs, and how Bell Labs played a crucial role in scaling up code breaking as a computational problem that pairs Bell Labs with the nascent intelligence community, which goes on to fund IBM…”
Chris Wiggins May 31, 2023 ▶ 10:34 A Conversation with Chris Wiggins - Author of "How Data Happened"

Expressed certainty vs assessment result

none yet certainty 1
none yet certainty 2
none yet certainty 3
70% certainty 4
50% certainty 5

weighted support: a fully supported claim counts one, a partly supported claim counts half. Each filled bar is clickable and opens exactly those claims; "none yet" means nothing said at that certainty level has resolved yet

Argument clarity: do they answer the question? how? →

4.2 / 5 directness 4.3 · coherence 4.3 · precision 4.1 · compression 3.6

redirected or did not address 2 of 9 assessed questions (22%). Watch them ▸

This is a score against a rubric. It is not a rank. Every host question → answer exchange is scored with names hidden on directness, coherence, precision and compression, 1–5 each, on meaning alone: disfluencies are ignored, and only raw unedited episodes count. This is the score that measures thought. Every scored exchange, scores shown → · The rubric and its checks →

How they sound: speaking style how? →

256 words/min while actually speaking · 18.6 um and uh per 1k words

Measured by listening to the audio itself: 10,918 words across 3 episodes of raw-level tape, transcribed verbatim with every um and uh kept, each one attributed only where the alignment onto our timed stream is unambiguous. These are measurements of speaking style. We do not rank them: across this corpus, fluency and argument quality are nearly uncorrelated (ρ≈0.2), and smooth talking does not signal clear thinking. How it's measured →

Everything Chris Wiggins said on the MAD Podcast that made the record, most notable first. Filter by type, assessment or year in the ledger →

Opinion
Wiggins: Data science, not physics, won World War II
“I grew up as a physicist thinking that, you know, physics really won World War II, but now that I'm a data scientist, I realize that it was actually data science that won World War II, but that story was classified for about 75 years, which is the story of how…”
Chris Wiggins May 31, 2023 ▶ 9:33 A Conversation with Chris Wiggins - Author of "How Data Happened"
Insight
Every publishing company is now a startup searching for a business model
“I like to use Steve Blank's definition of a startup, that a startup is a temporary organization in search of a scalable and repeatable business model. And in that sense, every publisher is now a startup, because the business model of publishing just completely…”
Chris Wiggins Jan 16, 2015 ▶ 6:43 Chris Wiggins, NY Times // Data Science at The New York Times (Hosted by FirstMark Capital)
Opinion
The New Republic and First Look Media failures were total people failures
“Those are both, like, total people failures, right”
Chris Wiggins Jan 16, 2015 ▶ 20:57 Chris Wiggins, NY Times // Data Science at The New York Times (Hosted by FirstMark Capital)
Insight
Wiggins: Data science hiring should prioritize listening skills over domain expertise
“So, I think what you're looking for is not a particularly somebody with a domain background, but somebody who's proven themselves to be a good listener.”
Chris Wiggins Dec 5, 2013 ▶ 35:45 Panel Discussion // Data Driven #16 // May 2013
Insight
Wiggins: Students incorrectly assume that published academic papers are inherently true
“My biggest pain point is, is trying to re-educate students who have read a bad paper, and because it was published, they think it's true.”
Chris Wiggins Dec 5, 2013 ▶ 50:18 Panel Discussion // Data Driven #16 // May 2013
Assertion Supported
Wiggins: 'Statistics' entered English to mean statecraft, not math or data
“Statistics entered the English language to mean statecraft. It had nothing to do with math, and it certainly had nothing to do with data.”
Chris Wiggins May 31, 2023 ▶ 4:42 A Conversation with Chris Wiggins - Author of "How Data Happened"
Insight
Wiggins: Early eugenicists believed data would improve society, not oppress it
“They weren't writing about themselves like we're the baddies and we really want to oppress the crap out of people. They wrote about themselves like we're going to do a solid for society and we're going to make society better with data.”
Chris Wiggins May 31, 2023 ▶ 8:41 A Conversation with Chris Wiggins - Author of "How Data Happened"
Assertion Contradicted
Wiggins: Early US computing was funded by intelligence for data processing
“That story had its own mirror on the other side of the Atlantic in Bell Labs, and how Bell Labs played a crucial role in scaling up code breaking as a computational problem that pairs Bell Labs with the nascent intelligence community, which goes on to fund IBM…”
Chris Wiggins May 31, 2023 ▶ 10:34 A Conversation with Chris Wiggins - Author of "How Data Happened"
Assertion Supported
Wiggins: The creator of 'artificial intelligence' coined the term for funding
“The guy who invented the term is on record as saying I made up the term to get money”
Chris Wiggins May 31, 2023 ▶ 12:17 A Conversation with Chris Wiggins - Author of "How Data Happened"
Assertion Not checkable as stated
Wiggins: Early AI research rejected data in favor of logic
“It's really for the first half of the life of artificial intelligence, people thought it had nothing to do with data whatsoever.”
Chris Wiggins May 31, 2023 ▶ 12:41 A Conversation with Chris Wiggins - Author of "How Data Happened"
Insight
Data science differs from ML through interdisciplinary domain collaboration
“The thing that makes data science different from machine learning is not just getting epsilon better predictive accuracy on learning, you know, cat's faces from pictures. It's this thing where you interact with somebody from a different discipline, and then so…”
Chris Wiggins Jan 16, 2015 ▶ 4:07 Chris Wiggins, NY Times // Data Science at The New York Times (Hosted by FirstMark Capital)
Insight
Tech companies and digital media now operate as church, state, and engineering
“I like to think about the New York Times or any technology company now as church, state, and engineering”
Chris Wiggins Jan 16, 2015 ▶ 9:00 Chris Wiggins, NY Times // Data Science at The New York Times (Hosted by FirstMark Capital)
Insight
Wiggins: Supervised models beat clustering because errors are clear
“Working on, on supervised learning or predictive models to be reassuring because I know if I'm wrong. Whereas, you know, models where I'm clustering, I sort of never know at the end of the day, should I have clustered things a different way?”
Chris Wiggins Jan 16, 2015 ▶ 10:38 Chris Wiggins, NY Times // Data Science at The New York Times (Hosted by FirstMark Capital)
Insight
Data literacy requires critical, rhetorical, and functional skills equally
“Critical literacy, rhetorical literacy, and functional literacy, I think, are all equally important parts of having a data literate society.”
Chris Wiggins Jan 16, 2015 ▶ 27:34 Chris Wiggins, NY Times // Data Science at The New York Times (Hosted by FirstMark Capital)
Assertion Partly supported
Wiggins: Sir Francis Galton coined regression, correlation, and eugenics
“One of the founders of mathematical statistics we look at is Sir Francis Galton. Distant cousin of Charles Darwin, who gives us the word regression, give us the word correlation, and gives us the word eugenics.”
Chris Wiggins May 31, 2023 ▶ 8:17 A Conversation with Chris Wiggins - Author of "How Data Happened"
Assertion Not checkable as stated
Wiggins: The New York Times data science team has about 22 people
“So the data science team is about a 22 person team that develops and deploys machine learning. For newsroom and business problems.”
Chris Wiggins May 31, 2023 ▶ 17:14 A Conversation with Chris Wiggins - Author of "How Data Happened"
Assertion Partly supported
US print advertising spend fell about 50% from 2008 to 2012
“Print advertising spend in the United States lost about 50% of its value in like four years, 2008 to 2012.”
Chris Wiggins Jan 16, 2015 ▶ 7:06 Chris Wiggins, NY Times // Data Science at The New York Times (Hosted by FirstMark Capital)
Disclosure
NYT data science prefers buying or using open source over building custom
“In terms of build or buy, if we, if there's something out there we can buy, we'll buy it. And if there's an open source alternative, then we'll definitely use that because most of the group know open source.”
Chris Wiggins Jan 16, 2015 ▶ 19:42 Chris Wiggins, NY Times // Data Science at The New York Times (Hosted by FirstMark Capital)
Assertion Supported
Wiggins: Guinness was the hottest IPO of the late 19th century
“The, that chapter opens up with the hottest IPO in the late 19th century, which was Guinness. So Guinness, the beer company, IPO'd in late 1800, and like literally people were breaking the doors down to try to get on that, get in on that IPO.”
Chris Wiggins May 31, 2023 ▶ 6:03 A Conversation with Chris Wiggins - Author of "How Data Happened"
Disclosure
Wiggins: NYT data science stack relies on SQL, scikit-learn, and Go
“So the data stack is, in my team, the data stack is SQL and scikit, and occasionally Go”
Chris Wiggins May 31, 2023 ▶ 20:55 A Conversation with Chris Wiggins - Author of "How Data Happened"

The other half of the tape: Chris Wiggins's own voice is left out of every number here. Other people bring the name up 1 time in 1 episode on the MAD Podcast. every mention, with the transcript →

Who brings them up most Neil Capel 1

Every mention by year

tap a year for its mentions
0011112013episodesmentions
0112013episodes it came up in
000.50.5112013episodesmentions per episode
2013 1 mention in 1 episode

Appearances (3)

EpisodeDateSpeaking time
A Conversation with Chris Wiggins - Author of "How Data Happened" May 31, 2023 19m
Chris Wiggins, NY Times // Data Science at The New York Times (Hosted by FirstMark Capital Jan 16, 2015 21m
Panel Discussion // Data Driven #16 // May 2013 Dec 5, 2013 11m
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