How Data Happened: A History from the Age of Reason to the Age of AI with Chris Wiggins

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Welcome!

Today’s Events

DistinguishedLecturerSeminar

How Data Happened: A History from the Age of Reason

to the Age of AI

Chris Wiggins, Chief Data Scientist at The New York Times and Associate Professor of Applied Mathematics at Columbia University

FiresideChat

Chris Wiggins and Usama Fayyad, Executive Director at the Institute for Experiential AI

Q&A

DistinguishedLecturerSeminar

How Data Happened: A History from the Age of Reason to the

Age of AI

How Data Happened: a history from the age of reason to the (new) age of AI + = chris wiggins columbia class: data-ppf.github.io book: bit.ly/hdh-book

Act 1: across the two cultures:

Act 1: a dream, inspired (2016)

- confronting the present (and future!)

Act

1: capabilities: functional, rhetorical, critical

structure: a history of “data”, 1770-present

challenge: a history of “data”, 1770-present

challenge: a history of “data”, 1770-present

2017-09-05: cathy o’neil

2018-01-08: safiya noble

2018-01-23: virginia eubanks

2019-01-15: shoshana zuboff

2019-06-17: ruha benjamin

ch 2: Social physics and the l’homme moyen

“vulgar” statistics & resistance to numeration also: “moving targets”, even “statistics”

From A.Q.

from T. Porter

ch. 3 The statistics of the deviant

“intelligence” is a moving mirror : Spearman 1908
“intelligence” is a moving mirror: Spearman 1923

Part II: “Data at War”

Ch 6: Data and computation; labor and gender

Bletchley, Bayes, and Bell Labs

Turing’s view of “can machines think?”
Turing’s view of “can machines think?”

Ch 7: “AI 1.0”: intelligence, without data

Ch 7: “AI 1.0”: intelligence, without data

“I invented the term artificial intelligence...when we were trying to get money for a summer study” - JCM, 1973

moving target: AI 1955 (via HDH)

moving target: AI 1955 (via HDH)

moving target: ML 1984 (via HDH)

Knowledge Acquisition Bottleneck problem facing knowledge “the central engineering today, the bottleneck of knowledge acquisition.”

Ch 8: Volume, Variety, and Velocity

“big data” had to be invented… and sold!

UNIVAC and NSA documents on early networking and databases

Note that *having* data << *understanding* data

the birth of privacy concerns: state & corporate data

1984 congressional testimony on mingling of databases

Ch 9: Machines, Learning

Walks at Bletchley Park

• Learning from experience

• Reasoning using rules (logic, mathematics) plus facts

Walks at Bletchley Park

• Learning from experience

• Reasoning using rules (logic, mathematics) plus facts

intelligence and information

Shannon’s small language model, 1945, “sufficiently close to English for many cryptographic purposes”: stochastic parakeet << parrot

moving target: AI (now, GAI)

Shannon’s small language model, 1945, “sufficiently close to English for many cryptographic purposes”: stochastic parakeet << parrot

moving target: AI (now, GAI)

try it yourself! bit.ly/IEAI-words
moving
try it yourself! bit.ly/IEAI-words
target: AI (now, GAI)

moving target: AI (now, GAI)

bit.ly/IEAI-words
try it yourself!

moving target: AI (now, GAI)

try it
bit.ly/IEAI-words
yourself!

moving target: ML, 1984

moving target: ML, 2011

”Pattern Recognition”

Prediction >> interpretability

Netflix Prize (2009)

Ensemble chaos

early “Mixture of Experts”

Neural Nets Triumph first as “deep learning”

Then rebranded as “AI” ~2015

Ch 11: The Science of Data

Ch 10: The Science of Data

Ch 10: The Science of Data

Ch 10: The Science of Data

Ch 10: The Science of Data

Ch 11: The Battle for Data Ethics

The Battle for Data Ethics

The Battle for Data Ethics

also: “tech fixes”

The Battle for Data Ethics

The Battle for Data Ethics

from Markkula Center

The Battle for Data Ethics

Ch 13 Solutions beyond solutionism

Solutions beyond solutionism

3-player unstable game (adapted from Janeway)

theories of change: Gartner, 1995 tech (inc.

bias; terminators; etc. rational exuberance irrational exuberance
AI)

theories of change: Gartner, 1995 tech (inc.

AI)

- the arc we learned from the students - there is important material that is not being taught (not to future statisticians) (not to future senators and CEOs) - data, truth, and power what do we hope people get out of the book?

How Data Happened: a history from the age of reason to the age of algorithms + = chris wiggins columbia class: data-ppf.github.io book: bit.ly/hdh-book

Fireside Chat

at The New York Times

Fayyad Executive

at the

for Experiential AI

Usama

Questions & Answers

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