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The Future of Smart Disclosure

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This is the original keynote file for my talk at the Smart Disclosure Summit in Washington DC on March 30, 2012. I will upload a PDF with notes separately.

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The Future of Smart Disclosure

  1. Some Context for Thinking About The Future of Smart Disclosure Tim O’Reilly O’Reilly Media Smart Disclosure Summit March 30, 2012
  2. “The skill of writing is to create a context in which other people can think.” -Edwin Schlossberg
  3. What technology trends tell us about where smart disclosure will ultimately take us
  4. Moore’s Law First 10 Years 150.0 112.5 75.0 Gordon Moore 37.5 0 Jan 1, 1970 Jun 25, 1971 Dec 16, 1972 Jun 9, 1974 Dec 1, 1975 May 24, 1977 Nov 15, 1978 May 8, 1980
  5. Moore’s Law with Gov Drag 150.0 112.5 Clay Johnson Society Gov 75.0 37.5 0 Jan 1, 1970 Jun 25, 1971 Dec 16, 1972 Jun 9, 1974 Dec 1, 1975 May 24, 1977 Nov 15, 1978 May 8, 1980
  6. Gov Vs. Moore 2011 300000000 225000000 150000000 75000000 0 Jan 1, 1970 Dec 16, 1972 Dec 1, 1975 Nov 15, 1978 Oct 30, 1981 Oct 14, 1984 Sep 29, 1987 Sep 13, 1990 Aug 28, 1993 Aug 12, 1996 Jul 28, 1999 Jul 12, 2002 Jun 26, 2005 Jun 10, 2008 May 26, 2
  7.  kayak.com
  8. Government data driving the mapping revolution  USGS and other survey maps  Street maps  Address databases  ...
  9. GPS: A 21st century platform launched in 1973  Massive investment for uncertain return  Policy decisions can have enormous impact  Marketplaces take time to develop, and go in unexpected directions
  10. Lesson 1 Government is a platform
  11. MapQuest - the counterexample
  12. Lesson 2 It’s still really early. Choice engines haven’t yet had their “Google Maps moment”
  13.  Siri
  14. Lesson 3 Seek out commercial partners, don’t just wait for them to come to you
  15.  Health datapalooza
  16. Lesson 4 Keep data formats simple
  17. Lesson 5 Open data policies matter, because private parties will try to hoard data and claim it as their own
  18. A device that knows where I am better than I do, a knowing assistant telling me where to go and how to get there.
  19. An Internet Operating System that Controls Access to Data An application that depends on cooperating cloud data services operating in real time: - Location - Search - Speech recognition - Live Traffic - Imagery
  20. Lesson 6 Real-time data will become the norm. Plan for that future!
  21. “Would you be willing to cross the street with information that was five minutes old?” -Jeff Jonas
  22. Returning to maps... It’s easy to take the blue dot for granted, but it’s a wonder of real-time data coordination
  23.  Combining data from multiple sources is critical – GPS – Cell tower triangulation – WiFi signals – But that’s only the beginning of the sensor revolution
  24.  Uses the accelerometer to note when you’re walking, running, driving, or stationary  Wakes up the location sensors every time you’re stationary for a while  Logs the location and the length of time you were there  Private, encrypted data store on your phone  A platform enabling private, high quality location and movement data for location and “quantified self” fitness apps  Completely automatic (except to correct locations if wrong) and “always on”
  25. Lesson 7 Getting privacy rules right is going to be a matter of thoughtful tradeoffs
  26. The Google Autonomous Vehicle
  27. 2005: Seven Miles in Seven Hours
  28. 2011: Hundreds of thousands of miles in ordinary traffic
  29. Artificial Intelligence “the science and engineering of making intelligent machines” -John McCarthy, 1956
  30. But it isn’t just better AI “We don’t have better algorithms. We just have more data.” - Peter Norvig, Chief Scientist, Google
  31. Human-Computer Symbiosis “The hope is that, in not too many years, human brains and computing machines will be coupled together very tightly, and that the resulting partnership will think as no human brain has ever thought and process data in a way not approached by the information-handling machines we know today.” – Licklider, J.C.R., "Man-Computer Symbiosis", IRE Transactions on Human Factors in Electronics, vol. HFE-1, 4-11, Mar 1960. Eprint
  32. Lesson 8: Look at intent and outcomes, not just acquisition of data
  33. It is precisely because of the overlap between computers and human activity that all this magic becomes possible
  34. Lesson 9: Beyond Smart Disclosure: Feedback Loops and “Algorithmic Regulation”
  35.  credit card fraud detection as an ec
  36.  image of google search for “Smart Disclosure”  if good ads here, this could work for both search quality and ad stuff
  37. “Half the money I spend on advertising is wasted; the trouble is I don't know which half.” - John Wanamaker (1838-1922)
  38. “Only 1% of healthcare spend now goes to diagnosis. We need to shift from the idea that you do diagnosis at the start, followed by treatment, to a cycle of diagnosis, treatment, diagnosis...as we explore what works.” -Pascale Witz, GE Medical Diagnostics
  39. for profit colleges
  40. Lesson 10: Bad actors will always try to game the system.
  41. Lesson 11: The secret of algorithmic data systems is to focus on real time measurement of outcomes
  42. “The legitimate object of government is to do for the people what needs to be done, but which they cannot, by individual effort, do at all, or do so well, for themselves.” -Abraham Lincoln
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This is the original keynote file for my talk at the Smart Disclosure Summit in Washington DC on March 30, 2012. I will upload a PDF with notes separately.

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