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Data: Past, Present, and Future (Lecture 1, Spring 2018)

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Slides from Lecture 1 of "Data: Past, Present, and Future",
Jan 17 2018.
New class on how data is impacting our professional, political, and personal realities. Taught by Profs Matt Jones and Chris Wiggins

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Data: Past, Present, and Future (Lecture 1, Spring 2018)

  1. 1. Data Past Present Future Wiggins + Jones SEAS + A&S Lecture 1: Jan 17 2018
  2. 2. how did this end up in my news feed? - math - hardware - system - funding - market - regulation - data this was not possible 20 years ago. - why? - what did people do instead?
  3. 3. Session replay scripts
  4. 4. “In all cultures and all periods of recorded human history, people share the belief that the face alone suffices to reveal innate traits of a person.”
  5. 5. We’ve been here before
  6. 6. We’ve been here before
  7. 7. We’ve been here before
  8. 8. We’ve been here before Insert quote from Blaise here
  9. 9. Statistical sciences always political Dream of sciences of social difference Central to development of Statistics And the Data sciences
  10. 10. Florence Nightingale & Data Visualization “Experience has shown that without special information and skilful application of the resources of science in preserving health, the drain on our home population must exhaust our means. The introduction, therefore, of a proper sanitary system into the British army is of essential importance to the public interests.”
  11. 11. Florence Nightingale & Data Visualization “Upon the British race alone the integrity of that empire at this moment appears to depend. The conquering race must retain possession.”
  12. 12. Every week Scientific and mathematical development Technologies and engineering Driving forces: money, prestige, resources, Imperial competition Power, ethics, and data intensive knowledge
  13. 13. Tech story: three chronological stages Data and Math Data and Engineering Data and Technology
  14. 14. Data technologies Census and government survey Information processing machines and digital computers Always on network infrastructure
  15. 15. Power How should social and political order be organized on basis of science and engineering? How do technologies transform the social and political order? How do technologies augment and diminish democratic orders? Autocratic ones?
  16. 16. Power and politics* New technologies mean new capabilities These capabilities are first available to those in power (cf., “The future is already here — it's just not very evenly distributed.” --Gibson) How does this distribution of capability reorder power? How are data-empowered algorithms an example of this dynamic - of capability, and - of reinforcing or distributing power? * politics here meaning the dynamics of power, not to be confused with “voting”
  17. 17. Arc of class
  18. 18. Qualitative statistics “Vulgar” statistics Mathematical statistics Computation+ Crypto “intelligence” AI winter AI renaissance Machine learning 1770s 1830s 1900 WWII 1960s 1980s 1990s You are here. data 1770s-present: capabilities Regression + Hypothesis testing
  19. 19. Qualitative statistics “Vulgar” statistics Mathematical statistics Computation+ Crypto “intelligence” AI winter AI renaissance Machine learning 1770s 1830s 1900 WWII 1960s 1980s 1990s You are here. data 1770s-present: capabilities Regression + Hypothesis testing huge breakpoint
  20. 20. Qualitative statistics “Vulgar” statistics Mathematical statistics Computation+ Crypto “intelligence” AI winter AI renaissance Machine learning 1770s 1830s 1900 WWII 1960s 1980s 1990s You are here. (statecraft) (policy+eugenics) (math-vs-science) (data @ war) (SIGINT) (data wars (for funding)) (surveillance economy) (defense+advertising) Regression + Hypothesis testing data 1770s-present: capabilities & intents
  21. 21. Qualitative statistics “Vulgar” statistics Mathematical statistics Computation+ Crypto “intelligence” AI winter AI renaissance Machine learning 1770s 1830s 1900 WWII 1960s 1980s 1990s You are here. .gov .edu .mil .ai .com Regression + Hypothesis testing data 1770s-present: capabilities & intents
  22. 22. Qualitative statistics “Vulgar” statistics Mathematical statistics Computation+ Crypto “intelligence” AI winter AI renaissance Machine learning 1770s 1830s 1900 WWII 1960s 1980s 1990s You are here. Regression + Hypothesis testing data 1770s-present: capabilities & intents persistent observation: dynamics of capabilities implies dynamics of power
  23. 23. Qualitative statistics “Vulgar” statistics Mathematical statistics Computation+ Crypto “intelligence” AI winter AI renaissance Machine learning 1770s 1830s 1900 WWII 1960s 1980s 1990s You are here. Regression + Hypothesis testing data 1770s-present: capabilities & intents persistent observation: dynamics of capabilities implies dynamics of power, i.e., is political
  24. 24. Qualitative statistics “Vulgar” statistics Mathematical statistics Computation+ Crypto “intelligence” AI winter AI renaissance Machine learning 1770s 1830s 1900 WWII 1960s 1980s 1990s You are here. Regression + Hypothesis testing data 1770s-present: capabilities & intents (also, how did this end up in my news feed?)
  25. 25. How we know what the state of the state is
  26. 26. Regression, correlation and eugenics
  27. 27. Experimental design, hypothesis test, decision theory To play this game with the greatest chance of success, the experimenter cannot afford to exclude the possibility of any possible arrangement of soil fertilities, and his best strategy is to equalize the chance that any treatment shall fall on any plot by determining it by chance himself. -R.A. Fisher
  28. 28. World War 2: Turing and statistical cryptography
  29. 29. AI and its many winters
  30. 30. Pattern Recognition to . . .
  31. 31. … Machine Learning
  32. 32. Research ethics (and the lack thereof)
  33. 33. Research ethics (and the lack thereof)
  34. 34. Silicon Valley and the Attention Economy
  35. 35. De-anonymization
  36. 36. Weekly structure Monday Lecture and discussion Expectation arrive having done the week’s readings Wednesday Laboratory Expectation arrive with laptop ready to collaborate
  37. 37. No prerequisites
  38. 38. Two tracks more technical background track (60%) ● pursue a semester long project culminating in a 15pp paper and any associated code ● ● complete 3 problem sets ● ● short final presentation on paper more humanistic background track (60%) ● write a 10 pp paper on a topic of their choice ● ● complete 5 problem sets, these problem sets will involve both computational work and writing work ● ● short final presentation on paper
  39. 39. Jupyter notebooks: code without any coding INSTALLING PYTHON + JUPYTER USING JUPYTER NOTEBOOKS
  40. 40. Required work Postings on readings in slack Problem sets (extensions of lab work, done in Jupyter) Final paper Your presence
  41. 41. Hardest part of class: Registration CC + Barnard + GS History-APAM 2901 Call number: 72327 SEAS Applied Math 82901 Call Number: 72327 A+S and Consortium Grad: History GR6998 section #10 Call number: 86347

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