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May 31, 2023 · 25m · mad

A Conversation with Chris Wiggins - Author of "How Data Happened"

Chris Wiggins · 19m spoken Matt Turck · 3m spoken
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gold bands on the timeline = statements, start to end. Hover to read, click to jump. CC turns on captions

In this Data Driven NYC session hosted by Matt Turck, Columbia professor and New York Times Chief Data Scientist Chris Wiggins discusses his book 'How Data Happened', tracing the historical evolution of data and statistics into modern AI while exploring its practical, ethical, and technical applications at The New York Times.

How this conversation actually went

Every chapter scored 0–10 on four independent dynamics. Hover any point for the reasoning behind the score. Matt holds 14.3% of the talking time here. How this is scored →

Matt as informed peer 1.9 Guest teaching 3.0 Guest disagreement 1.3 Matt pushing back 0.4
05100:0010:0020:000:11–2:45 · Matt as informed peer 1/10 Welcome and Overview of 'How Data Happened' Matt introduces Chris and his new book but stumbles over Chris's exact title, prompting a quick correction. Chris warmly sets the stage for the book's scope and origin.2:45–5:42 · Matt as informed peer 2/10 The Origins of Statistics as Statecraft Chris playfully rejects Matt's premise about where the book begins, clarifying that it opens in the classroom rather than the 19th century. He then checks whether Matt actually read the venture capital chapter, to which Matt defends his thorough reading.5:42–9:17 · Matt as informed peer 4/10 Data's Mathematical Baptism and Industrial Applications Matt demonstrates clear familiarity with the book's contents by prompting Chris on Francis Galton, Darwin, and the dark historical ties between statistics and eugenics. Chris expands on the Guinness IPO and Victorian statistics history with rich examples.9:17–13:47 · Matt as informed peer 2/10 World War II, Bletchley Park, and Early Computing Matt asks open roadmap questions while Chris delivers an insightful historical breakdown of Bletchley Park, Bell Labs, and early computing. Chris explains how data science, rather than just physics, was central to winning World War II.13:47–16:42 · Matt as informed peer 2/10 The Deep Learning Revolution and Modern AI Matt guides the narrative toward modern AI history by highlighting the pivotal year 2012. Chris provides educational context on the ImageNet competition, deep learning, and neural networks.16:42–21:44 · Matt as informed peer 2/10 Data Science Applications at The New York Times Matt moves the conversation to Chris's role as Chief Data Scientist at The New York Times, asking about LLM applications and tech infrastructure. Chris politely declines to comment on unreleased LLM strategy while sharing technical details about GCP and SQL stacks.21:44–25:39 · Matt as informed peer 0/10 Audience Q&A: Intellectual Property and Algorithmic Bias Audience members ask detailed questions regarding intellectual property rights and algorithmic bias. Chris fields the questions thoughtfully using regulatory frameworks and applied ethics history while Matt moderates.0:11–2:45 · Guest teaching 2/10 Welcome and Overview of 'How Data Happened' Matt introduces Chris and his new book but stumbles over Chris's exact title, prompting a quick correction. Chris warmly sets the stage for the book's scope and origin.2:45–5:42 · Guest teaching 4/10 The Origins of Statistics as Statecraft Chris playfully rejects Matt's premise about where the book begins, clarifying that it opens in the classroom rather than the 19th century. He then checks whether Matt actually read the venture capital chapter, to which Matt defends his thorough reading.5:42–9:17 · Guest teaching 3/10 Data's Mathematical Baptism and Industrial Applications Matt demonstrates clear familiarity with the book's contents by prompting Chris on Francis Galton, Darwin, and the dark historical ties between statistics and eugenics. Chris expands on the Guinness IPO and Victorian statistics history with rich examples.9:17–13:47 · Guest teaching 4/10 World War II, Bletchley Park, and Early Computing Matt asks open roadmap questions while Chris delivers an insightful historical breakdown of Bletchley Park, Bell Labs, and early computing. Chris explains how data science, rather than just physics, was central to winning World War II.13:47–16:42 · Guest teaching 3/10 The Deep Learning Revolution and Modern AI Matt guides the narrative toward modern AI history by highlighting the pivotal year 2012. Chris provides educational context on the ImageNet competition, deep learning, and neural networks.16:42–21:44 · Guest teaching 2/10 Data Science Applications at The New York Times Matt moves the conversation to Chris's role as Chief Data Scientist at The New York Times, asking about LLM applications and tech infrastructure. Chris politely declines to comment on unreleased LLM strategy while sharing technical details about GCP and SQL stacks.21:44–25:39 · Guest teaching 3/10 Audience Q&A: Intellectual Property and Algorithmic Bias Audience members ask detailed questions regarding intellectual property rights and algorithmic bias. Chris fields the questions thoughtfully using regulatory frameworks and applied ethics history while Matt moderates.0:11–2:45 · Guest disagreement 1/10 Welcome and Overview of 'How Data Happened' Matt introduces Chris and his new book but stumbles over Chris's exact title, prompting a quick correction. Chris warmly sets the stage for the book's scope and origin.2:45–5:42 · Guest disagreement 3/10 The Origins of Statistics as Statecraft Chris playfully rejects Matt's premise about where the book begins, clarifying that it opens in the classroom rather than the 19th century. He then checks whether Matt actually read the venture capital chapter, to which Matt defends his thorough reading.5:42–9:17 · Guest disagreement 1/10 Data's Mathematical Baptism and Industrial Applications Matt demonstrates clear familiarity with the book's contents by prompting Chris on Francis Galton, Darwin, and the dark historical ties between statistics and eugenics. Chris expands on the Guinness IPO and Victorian statistics history with rich examples.9:17–13:47 · Guest disagreement 1/10 World War II, Bletchley Park, and Early Computing Matt asks open roadmap questions while Chris delivers an insightful historical breakdown of Bletchley Park, Bell Labs, and early computing. Chris explains how data science, rather than just physics, was central to winning World War II.13:47–16:42 · Guest disagreement 0/10 The Deep Learning Revolution and Modern AI Matt guides the narrative toward modern AI history by highlighting the pivotal year 2012. Chris provides educational context on the ImageNet competition, deep learning, and neural networks.16:42–21:44 · Guest disagreement 2/10 Data Science Applications at The New York Times Matt moves the conversation to Chris's role as Chief Data Scientist at The New York Times, asking about LLM applications and tech infrastructure. Chris politely declines to comment on unreleased LLM strategy while sharing technical details about GCP and SQL stacks.21:44–25:39 · Guest disagreement 1/10 Audience Q&A: Intellectual Property and Algorithmic Bias Audience members ask detailed questions regarding intellectual property rights and algorithmic bias. Chris fields the questions thoughtfully using regulatory frameworks and applied ethics history while Matt moderates.0:11–2:45 · Matt pushing back 0/10 Welcome and Overview of 'How Data Happened' Matt introduces Chris and his new book but stumbles over Chris's exact title, prompting a quick correction. Chris warmly sets the stage for the book's scope and origin.2:45–5:42 · Matt pushing back 1/10 The Origins of Statistics as Statecraft Chris playfully rejects Matt's premise about where the book begins, clarifying that it opens in the classroom rather than the 19th century. He then checks whether Matt actually read the venture capital chapter, to which Matt defends his thorough reading.5:42–9:17 · Matt pushing back 1/10 Data's Mathematical Baptism and Industrial Applications Matt demonstrates clear familiarity with the book's contents by prompting Chris on Francis Galton, Darwin, and the dark historical ties between statistics and eugenics. Chris expands on the Guinness IPO and Victorian statistics history with rich examples.9:17–13:47 · Matt pushing back 0/10 World War II, Bletchley Park, and Early Computing Matt asks open roadmap questions while Chris delivers an insightful historical breakdown of Bletchley Park, Bell Labs, and early computing. Chris explains how data science, rather than just physics, was central to winning World War II.13:47–16:42 · Matt pushing back 0/10 The Deep Learning Revolution and Modern AI Matt guides the narrative toward modern AI history by highlighting the pivotal year 2012. Chris provides educational context on the ImageNet competition, deep learning, and neural networks.16:42–21:44 · Matt pushing back 1/10 Data Science Applications at The New York Times Matt moves the conversation to Chris's role as Chief Data Scientist at The New York Times, asking about LLM applications and tech infrastructure. Chris politely declines to comment on unreleased LLM strategy while sharing technical details about GCP and SQL stacks.21:44–25:39 · Matt pushing back 0/10 Audience Q&A: Intellectual Property and Algorithmic Bias Audience members ask detailed questions regarding intellectual property rights and algorithmic bias. Chris fields the questions thoughtfully using regulatory frameworks and applied ethics history while Matt moderates.

speaking balance: gold is Matt, purple is the guest (3 minute bins)

0:00 · Matt 50.6% · guest 49.4%0:00 · Matt 50.6% · guest 49.4%3:00 · Matt 4% · guest 96%3:00 · Matt 4% · guest 96%6:00 · Matt 9.6% · guest 90.4%6:00 · Matt 9.6% · guest 90.4%9:00 · Matt 7.5% · guest 92.5%9:00 · Matt 7.5% · guest 92.5%12:00 · Matt 17.5% · guest 82.5%12:00 · Matt 17.5% · guest 82.5%15:00 · Matt 14.4% · guest 85.6%15:00 · Matt 14.4% · guest 85.6%18:00 · Matt 16.6% · guest 83.4%18:00 · Matt 16.6% · guest 83.4%21:00 · Matt 2.2% · guest 97.8%21:00 · Matt 2.2% · guest 97.8%24:00 · Matt 3.5% · guest 96.5%24:00 · Matt 3.5% · guest 96.5%
Sharpest disagreement ▶ 3:01 Playful premise rejection on book starting point

Chris directly challenges Matt's framing of the book's timeline with 'Yes. But no.', correcting Matt's historical assumption by clarifying that the book actually starts in a modern classroom setting.

 Hardest push from Matt ▶ 4:30 Host insists on having read the book completely

When Chris teasingly questions if Matt managed to reach the chapter on venture capital, Matt immediately pushes back to defend his preparation, asserting 'Of course, I read it all.'

 Biggest teaching moment ▶ 3:01 Guest corrects premise and quizzes host on text

Chris gently corrects Matt's factual assumption about the book's opening structure and tests Matt's familiarity with the venture capital chapter.

 Matt holds his own ▶ 7:23 Host demonstrates knowledge of Galton and eugenics

Matt displays clear reading knowledge and subject awareness by introducing Francis Galton, his relation to Darwin, and the dark history of statistics being applied to eugenics before the guest expands on it.
the scores for every segment, with the reasoning behind each
ChapterTopicMatt as informed peerGuest teachingGuest disagreementMatt pushing backWhy
Welcome and Overview of 'How Data Happened' 1210 Matt introduces Chris and his new book but stumbles over Chris's exact title, prompting a quick correction. Chris warmly sets the stage for the book's scope and origin.
The Origins of Statistics as Statecraft 2431 Chris playfully rejects Matt's premise about where the book begins, clarifying that it opens in the classroom rather than the 19th century. He then checks whether Matt actually read the venture capital chapter, to which Matt defends his thorough reading.
Data's Mathematical Baptism and Industrial Applications 4311 Matt demonstrates clear familiarity with the book's contents by prompting Chris on Francis Galton, Darwin, and the dark historical ties between statistics and eugenics. Chris expands on the Guinness IPO and Victorian statistics history with rich examples.
World War II, Bletchley Park, and Early Computing 2410 Matt asks open roadmap questions while Chris delivers an insightful historical breakdown of Bletchley Park, Bell Labs, and early computing. Chris explains how data science, rather than just physics, was central to winning World War II.
The Deep Learning Revolution and Modern AI 2300 Matt guides the narrative toward modern AI history by highlighting the pivotal year 2012. Chris provides educational context on the ImageNet competition, deep learning, and neural networks.
Data Science Applications at The New York Times 2221 Matt moves the conversation to Chris's role as Chief Data Scientist at The New York Times, asking about LLM applications and tech infrastructure. Chris politely declines to comment on unreleased LLM strategy while sharing technical details about GCP and SQL stacks.
Audience Q&A: Intellectual Property and Algorithmic Bias 0310 Audience members ask detailed questions regarding intellectual property rights and algorithmic bias. Chris fields the questions thoughtfully using regulatory frameworks and applied ethics history while Matt moderates.

Statements from this episode (10)

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
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
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
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
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
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
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
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
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
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
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