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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
Chris Wiggins Chief Data Scientist at The New York Times and Associate Professor of Applied Mathematics at Columbia University
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
Chris Wiggins
Chief Data Scientist
at The New York Times
and Associate Professor of Applied Mathematics at Columbia University
Fayyad Executive
Director
at the
Institute
for Experiential AI
Usama
Questions & Answers
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