What do we mean by "Smarter?"

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What do we mean by Smarter? The presentation argues that the "smartness" of "smart systems" is not just a product of technology, but that systems can be smart by engaging people and providing a means of integrating their knowledge and expertise. Provides an array of examples, and a close look at Cyclopath, a geowiki that supports the finding of bike-friendly routes around a city.

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What do we mean by "Smarter?"

  1. 1. What do we mean by Smarter ? Tapping the Social Intelligence of Cities and Regions Talk delivered to the Smarter Planet Advisory Board, October 19, 2010 Thomas Erickson [email_address] Social Computing Group IBM T. J. Watson Research Center
  2. 2. Introduction <ul><li>What do we mean by a “smarter” planet? </li></ul>Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center.
  3. 3. Introduction <ul><li>Two views of ‘smartness’ </li></ul><ul><li>‘ Smartness’ = technology </li></ul><ul><ul><li>Sensors and meters to collect data </li></ul></ul><ul><ul><li>Aggregate and analyze the data </li></ul></ul><ul><ul><li>And use that as input to dashboards, visualizations and control systems </li></ul></ul><ul><ul><li>People treated as passive and compliant </li></ul></ul>Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center.
  4. 4. Introduction <ul><li>Two views of ‘smartness’ </li></ul><ul><li>‘Smartness’ = technology </li></ul><ul><ul><li>Sensors and meters to collect data </li></ul></ul><ul><ul><li>Aggregate and analyze the data </li></ul></ul><ul><ul><li>And use that as input to dashboards, visualizations and control systems </li></ul></ul><ul><ul><li>People treated as passive and compliant </li></ul></ul><ul><li>‘Smartness’ = people </li></ul><ul><ul><li>People can gather data </li></ul></ul><ul><ul><li>People can analyze data </li></ul></ul><ul><ul><li>People can act on data </li></ul></ul><ul><ul><li>and they do this in ways that are qualitatively different from what digital systems do </li></ul></ul>Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center.
  5. 5. Introduction <ul><li>Two views of ‘smartness’ </li></ul><ul><li>‘Smartness’ = technology </li></ul><ul><ul><li>Sensors and meters to collect data </li></ul></ul><ul><ul><li>Aggregate and analyze the data </li></ul></ul><ul><ul><li>And use that as input to dashboards, visualizations and control systems </li></ul></ul><ul><ul><li>People treated as passive and compliant </li></ul></ul><ul><li>‘Smartness’ = people </li></ul><ul><ul><li>People can gather data </li></ul></ul><ul><ul><li>People can analyze data </li></ul></ul><ul><ul><li>People can act on data </li></ul></ul><ul><ul><li>and they do this in ways that are qualitatively different from what digital systems do </li></ul></ul><ul><ul><ul><li>Both/And NOT either/or </li></ul></ul></ul>Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center.
  6. 6. Social Intelligence <ul><li>Using the perceptual, cognitive and enactive abilities of large numbers of people to achieve purposeful ends </li></ul>Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center.
  7. 7. Social Intelligence <ul><li>Social intelligence is the use of the perceptual, cognitive and enactive abilities of large numbers of people to achieve purposeful ends </li></ul><ul><li>Examples </li></ul><ul><ul><li>grassroots crisis response (also see Usahidi) </li></ul></ul>Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center.
  8. 8. Social Intelligence <ul><li>Social intelligence is the use of the perceptual, cognitive and enactive abilities of large numbers of people to achieve purposeful ends </li></ul><ul><li>Examples </li></ul><ul><ul><li>grassroots crisis response </li></ul></ul><ul><ul><li>von Ahn’s ESP game (also see “Games with a Purpose) </li></ul></ul>Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center.
  9. 9. Social Intelligence <ul><li>Social intelligence is the use of the perceptual, cognitive and enactive abilities of large numbers of people to achieve purposeful ends </li></ul><ul><li>Examples </li></ul><ul><ul><li>grassroots crisis response </li></ul></ul><ul><ul><li>von Ahn’s ESP game </li></ul></ul><ul><ul><li>Wikipedia </li></ul></ul>Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center.
  10. 10. Social Intelligence <ul><li>Social intelligence is the use of the perceptual, cognitive and enactive abilities of large numbers of people to achieve purposeful ends </li></ul><ul><li>Examples </li></ul><ul><ul><li>grassroots crisis response </li></ul></ul><ul><ul><li>von Ahn’s ESP game </li></ul></ul><ul><ul><li>Wikipedia </li></ul></ul><ul><ul><ul><li>None of these examples – or anything like them – existed ten years ago </li></ul></ul></ul>Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center.
  11. 11. Social Intelligence in Cities & Regions <ul><li>Social intelligence is particularly suited to being applied to make cities and regions smarter. </li></ul>
  12. 12. Social Intelligence in Cities & Regions <ul><li>Social Intelligence is especially appropriate for cities and regions </li></ul><ul><ul><li>That’s where the people are </li></ul></ul><ul><ul><li>Inhabitants develop a deep knowledge of the places they live, work and socialize in </li></ul></ul><ul><ul><li>Inhabitants have a practical motivation for participating: it impacts their daily lives </li></ul></ul><ul><ul><li>Inhabitants identify with places , and have networks of family and friends </li></ul></ul>Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center.
  13. 13. Social Intelligence in Cities & Regions <ul><li>But, as yet, socially intelligent systems for cities are in their infancy </li></ul><ul><ul><li>Perhaps simply a matter of critical mass </li></ul></ul><ul><ul><li>Let’s see what’s out there… </li></ul></ul>Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center.
  14. 14. Social Intelligence in Cities & Regions: Taxonomy <ul><li>Urban Systems: Informing* </li></ul><ul><ul><li>Making information accessible </li></ul></ul><ul><ul><ul><li>Crime maps </li></ul></ul></ul><ul><ul><ul><li>Maps of the urban forest </li></ul></ul></ul><ul><ul><ul><li>Tourist information </li></ul></ul></ul>Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center. * Not social intelligence
  15. 15. Social Intelligence in Cities & Regions: Taxonomy <ul><li>Urban Systems: Transacting </li></ul><ul><ul><li>Supporting 2-way private interactions </li></ul></ul><ul><ul><ul><li>Crime tip solicitation </li></ul></ul></ul><ul><ul><ul><li>Expense report analysis </li></ul></ul></ul><ul><ul><ul><li>Mass surveillance </li></ul></ul></ul>Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center.
  16. 16. Social Intelligence in Cities & Regions: Taxonomy <ul><li>Urban Systems: Sharing </li></ul><ul><ul><li>Public sharing of knowledge </li></ul></ul><ul><ul><ul><li>Reporting potholes and other street problems </li></ul></ul></ul><ul><ul><ul><li>GreenWatch: wearable pollution monitoring sensors </li></ul></ul></ul><ul><ul><ul><li>FourSquare: checking into and registering tips about places </li></ul></ul></ul>Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center.
  17. 17. Social Intelligence in Cities & Regions: Taxonomy <ul><li>Urban Systems: Co-producing </li></ul><ul><ul><li>People work closely together to produce a coherent product </li></ul></ul><ul><ul><ul><li>Community Wikis </li></ul></ul></ul><ul><ul><ul><li>Cyclopath: a user-editable map for finding bicycle-friendly routes </li></ul></ul></ul>Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center.
  18. 18. Social Intelligence in Cities & Regions: Example <ul><li>Problem: Finding bike-friendly routes around the Twin Cities </li></ul><ul><ul><li>Good bike routes differ from good driving routes </li></ul></ul>Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center.
  19. 19. Social Intelligence in Cities & Regions: Example <ul><li>Problem: Finding bike-friendly routes around the Twin Cities </li></ul><ul><ul><li>Good bike routes differ from good driving routes </li></ul></ul>1. Start out in opposite direction to avoid busy main street 2. Take side street that has lights at two busy crossings 5. Although greenway continues in right direction, take Park Ave due to bike lane 3. Enter greenway bike path via “intersection 4. This section of bike path goes through beautiful community gardens Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center.
  20. 20. Social Intelligence in Cities & Regions: Example <ul><li>Problem: Finding bike-friendly routes around the Twin Cities </li></ul><ul><ul><li>Good bike routes differ from good driving routes </li></ul></ul><ul><ul><li>Much of the information that makes this a good route isn’t on regular maps </li></ul></ul>1. Start out in opposite direction to avoid busy main street 2. Take side street that has lights at two busy crossings 5. Although greenway continues in right direction, take Park Ave due to bike lane 3. Enter greenway bike path via “intersection 4. This section of bike path goes through beautiful community gardens Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center.
  21. 21. Social Intelligence in Cities & Regions: Cyclopath <ul><li>Solution: Cyclopath </li></ul><ul><ul><li>A user-editable map (a geowiki) </li></ul></ul><ul><ul><li>with ‘official’ data (e.g., USGS, MNDoT) </li></ul></ul><ul><ul><li>and user-entered data </li></ul></ul><ul><ul><li>Notes: </li></ul></ul><ul><ul><ul><li>This is not by IBM, although it has a few small connections </li></ul></ul></ul><ul><ul><ul><li>Cyclopath is open source, and a product of the GroupLens lab at the University of Minnesota </li></ul></ul></ul>Terveen, Priedhorsky, et al. ~ GroupLens Lab, University of Minnesota Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center.
  22. 22. Social Intelligence in Cities & Regions: Cyclopath <ul><li>The User Interface </li></ul><ul><ul><li>Map and map key </li></ul></ul>Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center. Terveen, Priedhorsky, et al. ~ GroupLens Lab, University of Minnesota
  23. 23. Social Intelligence in Cities & Regions: Cyclopath <ul><li>The User Interface </li></ul><ul><ul><li>Map and map key </li></ul></ul><ul><ul><li>Map controls </li></ul></ul><ul><ul><ul><li>edit, zoom, pan </li></ul></ul></ul>Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center. Terveen, Priedhorsky, et al. ~ GroupLens Lab, University of Minnesota
  24. 24. Social Intelligence in Cities & Regions: Cyclopath <ul><li>The User Interface </li></ul><ul><ul><li>Map and map key </li></ul></ul><ul><ul><li>Map controls </li></ul></ul><ul><ul><li>Control panels </li></ul></ul><ul><ul><ul><li>request routes, adjust view, revert changes, etc. </li></ul></ul></ul>Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center. Terveen, Priedhorsky, et al. ~ GroupLens Lab, University of Minnesota
  25. 25. Social Intelligence in Cities & Regions: Cyclopath <ul><li>Map elements </li></ul><ul><ul><li>Blocks (street) </li></ul></ul><ul><ul><li>Points </li></ul></ul>Block Point Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center. Terveen, Priedhorsky, et al. ~ GroupLens Lab, University of Minnesota
  26. 26. Social Intelligence in Cities & Regions: Cyclopath <ul><li>Map elements </li></ul><ul><ul><li>Blocks (street) </li></ul></ul><ul><ul><li>Points </li></ul></ul><ul><ul><li>Tags ( points ) </li></ul></ul><ul><ul><li>Notes ( points ) </li></ul></ul>Tags Notes for this point Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center. Terveen, Priedhorsky, et al. ~ GroupLens Lab, University of Minnesota
  27. 27. Social Intelligence in Cities & Regions: Cyclopath <ul><li>Map elements </li></ul><ul><ul><li>Blocks (street) </li></ul></ul><ul><ul><li>Points </li></ul></ul><ul><ul><li>Tags (points, blocks ) </li></ul></ul><ul><ul><li>Notes (points, blocks ) </li></ul></ul>Tags for this block Notes Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center. Terveen, Priedhorsky, et al. ~ GroupLens Lab, University of Minnesota
  28. 28. Social Intelligence in Cities & Regions: Cyclopath <ul><li>Map elements </li></ul><ul><ul><li>Blocks (street) </li></ul></ul><ul><ul><li>Points </li></ul></ul><ul><ul><li>Tags (points, blocks) </li></ul></ul><ul><ul><li>Notes (points, blocks) </li></ul></ul><ul><ul><li>Ratings ( blocks only ) </li></ul></ul><ul><ul><ul><li>personal (private) </li></ul></ul></ul><ul><ul><ul><li>estimated (from others) </li></ul></ul></ul><ul><ul><ul><li>computed (from MN DoT data) </li></ul></ul></ul>Rating for this block Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center. Terveen, Priedhorsky, et al. ~ GroupLens Lab, University of Minnesota
  29. 29. Social Intelligence in Cities & Regions: Cyclopath <ul><li>Map elements </li></ul><ul><ul><li>Blocks (street) </li></ul></ul><ul><ul><li>Points </li></ul></ul><ul><ul><li>Tags (points, blocks) </li></ul></ul><ul><ul><li>Notes (points, blocks) </li></ul></ul><ul><ul><li>Ratings (blocks only) </li></ul></ul><ul><ul><li>Intersections </li></ul></ul><ul><ul><ul><li>How streets connect (or not) </li></ul></ul></ul>Intersection Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center. Terveen, Priedhorsky, et al. ~ GroupLens Lab, University of Minnesota
  30. 30. Social Intelligence in Cities & Regions: Cyclopath <ul><li>Map elements </li></ul><ul><ul><li>Blocks (street) </li></ul></ul><ul><ul><li>Points </li></ul></ul><ul><ul><li>Tags (points, blocks) </li></ul></ul><ul><ul><li>Notes (points, blocks) </li></ul></ul><ul><ul><li>Ratings (blocks only) </li></ul></ul><ul><ul><li>Intersections </li></ul></ul><ul><ul><ul><li>How streets connect (or not) </li></ul></ul></ul><ul><ul><ul><li>Important for computing routes – data often missing or inaccurate for bikes </li></ul></ul></ul>Intersections? Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center. Terveen, Priedhorsky, et al. ~ GroupLens Lab, University of Minnesota
  31. 31. Social Intelligence in Cities & Regions: Cyclopath <ul><li>Map elements </li></ul><ul><ul><li>Blocks (street) </li></ul></ul><ul><ul><li>Points </li></ul></ul><ul><ul><li>Tags (points, blocks) </li></ul></ul><ul><ul><li>Notes (points, blocks) </li></ul></ul><ul><ul><li>Ratings (blocks only) </li></ul></ul><ul><ul><li>Intersections </li></ul></ul><ul><ul><li>Regions (not shown) </li></ul></ul><ul><ul><ul><li>Public (neighborhoods) </li></ul></ul></ul><ul><ul><ul><li>Private (watch regions) </li></ul></ul></ul>Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center. Terveen, Priedhorsky, et al. ~ GroupLens Lab, University of Minnesota
  32. 32. Social Intelligence in Cities & Regions: Cyclopath <ul><li>Editing </li></ul><ul><ul><li>Users need to edit data because </li></ul></ul><ul><ul><ul><li>it might be missing </li></ul></ul></ul><ul><ul><ul><li>it might be wrong </li></ul></ul></ul><ul><ul><ul><li>it might be misaligned </li></ul></ul></ul><ul><ul><ul><li>and users have a deep qualitative knowledge of places the is rarely found in official data sets </li></ul></ul></ul>Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center. Terveen, Priedhorsky, et al. ~ GroupLens Lab, University of Minnesota
  33. 33. Social Intelligence in Cities & Regions: Cyclopath <ul><li>Editing example </li></ul><ul><ul><li>Here’s a street I added. I gave it a name, a type, and a bikeability rating </li></ul></ul>Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center. Terveen, Priedhorsky, et al. ~ GroupLens Lab, University of Minnesota
  34. 34. Social Intelligence in Cities & Regions: Cyclopath <ul><li>Editing example </li></ul><ul><ul><li>Here’s a street I added. I gave it a name, a type, and a bikeability rating </li></ul></ul><ul><ul><li>And I can set a “watch region” so I can see if anyone changes it </li></ul></ul>Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center. Terveen, Priedhorsky, et al. ~ GroupLens Lab, University of Minnesota
  35. 35. Social Intelligence in Cities & Regions: Cyclopath <ul><li>Editing example </li></ul><ul><ul><li>Here’s a street I added. I gave it a name, a type, and a bikeability rating </li></ul></ul><ul><ul><li>And I can set a “watch region” so I can see if anyone changes it </li></ul></ul><ul><ul><li>Later on, someone else added the tag “unpaved” </li></ul></ul>Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center. Terveen, Priedhorsky, et al. ~ GroupLens Lab, University of Minnesota
  36. 36. Social Intelligence in Cities & Regions: Cyclopath <ul><li>Editing example </li></ul><ul><ul><li>Here’s a street I added. I gave it a name, a type, and a bikeability rating </li></ul></ul><ul><ul><li>And I can set a “watch region” so I can see if anyone changes it </li></ul></ul><ul><ul><li>Later on, someone else added the tag “unpaved” </li></ul></ul><ul><ul><li>Later I added a note </li></ul></ul>Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center. Terveen, Priedhorsky, et al. ~ GroupLens Lab, University of Minnesota
  37. 37. Social Intelligence in Cities & Regions: Cyclopath <ul><li>Computing routes </li></ul><ul><ul><li>Now we can use all this data to compute bike-friendly routes </li></ul></ul>Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center. Terveen, Priedhorsky, et al. ~ GroupLens Lab, University of Minnesota
  38. 38. Social Intelligence in Cities & Regions: Cyclopath <ul><li>Computing routes </li></ul><ul><ul><li>Now we can use all this data to compute bike-friendly routes </li></ul></ul><ul><ul><ul><li>Enter From and To </li></ul></ul></ul>Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center. Terveen, Priedhorsky, et al. ~ GroupLens Lab, University of Minnesota
  39. 39. Social Intelligence in Cities & Regions: Cyclopath <ul><li>Computing routes </li></ul><ul><ul><li>Now we can use all this data to compute bike-friendly routes </li></ul></ul><ul><ul><ul><li>Enter From and To </li></ul></ul></ul><ul><ul><ul><li>Decide whether to minimize distance or favor bikeability </li></ul></ul></ul>Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center. Terveen, Priedhorsky, et al. ~ GroupLens Lab, University of Minnesota
  40. 40. Social Intelligence in Cities & Regions: Cyclopath <ul><li>Computing routes </li></ul><ul><ul><li>Now we can use all this data to compute bike-friendly routes </li></ul></ul><ul><ul><ul><li>Enter From and To </li></ul></ul></ul><ul><ul><ul><li>Decide whether to minimize distance or favor bikeability </li></ul></ul></ul><ul><ul><ul><li>And select tags to avoid, bonus or penalize when computing route </li></ul></ul></ul>Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center. Terveen, Priedhorsky, et al. ~ GroupLens Lab, University of Minnesota
  41. 41. Social Intelligence in Cities & Regions: Cyclopath <ul><li>Computing routes </li></ul><ul><ul><li>Now we can use all this data to compute bike-friendly routes </li></ul></ul><ul><ul><ul><li>Enter From and To </li></ul></ul></ul><ul><ul><ul><li>Decide whether to minimize distance or favor bikeability </li></ul></ul></ul><ul><ul><ul><li>And select tags to avoid, bonus or penalize when computing route </li></ul></ul></ul><ul><ul><li>Notice that much of this data is user entered: point names, tags, bikeability </li></ul></ul>Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center. Terveen, Priedhorsky, et al. ~ GroupLens Lab, University of Minnesota
  42. 42. Social Intelligence in Cities & Regions: Cyclopath <ul><li>Computing routes </li></ul><ul><ul><li>The route </li></ul></ul>Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center. Terveen, Priedhorsky, et al. ~ GroupLens Lab, University of Minnesota
  43. 43. Social Intelligence in Cities & Regions: Cyclopath <ul><li>Computing routes </li></ul><ul><ul><li>The route </li></ul></ul><ul><ul><ul><li>Can be color-coded according to various dimensions (e.g., hills, bikeability) </li></ul></ul></ul>Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center. Terveen, Priedhorsky, et al. ~ GroupLens Lab, University of Minnesota
  44. 44. Social Intelligence in Cities & Regions: Cyclopath <ul><li>Computing routes </li></ul><ul><ul><li>The route </li></ul></ul><ul><ul><ul><li>Can be color-coded according to various dimensions (e.g., hills, bikeability) </li></ul></ul></ul><ul><ul><ul><li>Has a cue sheet </li></ul></ul></ul>Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center. Terveen, Priedhorsky, et al. ~ GroupLens Lab, University of Minnesota
  45. 45. Social Intelligence in Cities & Regions: Cyclopath <ul><li>Computing routes </li></ul><ul><ul><li>The route </li></ul></ul><ul><ul><ul><li>Can be color-coded according to various dimensions (e.g., hills, bikeability) </li></ul></ul></ul><ul><ul><ul><li>Has a cue sheet </li></ul></ul></ul><ul><ul><ul><li>Feedback can be provided </li></ul></ul></ul>Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center. Terveen, Priedhorsky, et al. ~ GroupLens Lab, University of Minnesota
  46. 46. Social Intelligence in Cities & Regions: Cyclopath <ul><li>Computing routes </li></ul><ul><ul><li>The route </li></ul></ul><ul><ul><ul><li>Notice: my route starts out in the “wrong” direction – but that’s good because it avoids busy streets </li></ul></ul></ul>Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center. Terveen, Priedhorsky, et al. ~ GroupLens Lab, University of Minnesota
  47. 47. Social Intelligence in Cities & Regions: Cyclopath <ul><li>Computing routes </li></ul><ul><ul><li>The route </li></ul></ul><ul><ul><ul><li>Notice: my route starts out in the “wrong” direction – but that’s good because it avoids busy streets </li></ul></ul></ul><ul><ul><ul><li>And it has the other advantages I mentioned earlier </li></ul></ul></ul>Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center. Terveen, Priedhorsky, et al. ~ GroupLens Lab, University of Minnesota
  48. 48. Social Intelligence in Cities & Regions: Cyclopath <ul><li>Computing routes </li></ul><ul><ul><li>The route </li></ul></ul><ul><ul><ul><li>Notice: my route starts out in the “wrong” direction – but that’s good because it avoids busy streets </li></ul></ul></ul><ul><ul><ul><li>And it has the other advantages I mentioned earlier </li></ul></ul></ul><ul><ul><ul><li>And the route is also half a mile shorter than that offered by Google Maps’ new bike routing feature </li></ul></ul></ul>Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center. Terveen, Priedhorsky, et al. ~ GroupLens Lab, University of Minnesota
  49. 49. Social Intelligence in Cities & Regions: Cyclopath <ul><li>Does it really work? </li></ul><ul><ul><li>Will people really use it? </li></ul></ul><ul><ul><li>Will people go to the trouble of adding data? </li></ul></ul><ul><ul><li>Will the added data make a difference? </li></ul></ul>Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center. Terveen, Priedhorsky, et al. ~ GroupLens Lab, University of Minnesota
  50. 50. Social Intelligence in Cities & Regions: Cyclopath <ul><li>Does it really work? </li></ul><ul><ul><li>It’s used! (in season) </li></ul></ul><ul><ul><ul><li>1,500 registered users in all </li></ul></ul></ul><ul><ul><ul><li>daily: 15-30 registered logins; 150 unregistered </li></ul></ul></ul><ul><ul><ul><li>150 route requests per day </li></ul></ul></ul>Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center. Terveen, Priedhorsky, et al. ~ GroupLens Lab, University of Minnesota
  51. 51. Social Intelligence in Cities & Regions: Cyclopath <ul><li>Does it really work? </li></ul><ul><ul><li>Edits matter </li></ul></ul><ul><ul><ul><li>~10,000 edits by 400+ users </li></ul></ul></ul><ul><ul><ul><li>When routes requested during first 2 weeks were recomputed 9 months later (i.e. with 9 months of user-added data), the new routes were about 1K shorter (14.8  13.8L) </li></ul></ul></ul>Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center. Terveen, Priedhorsky, et al. ~ GroupLens Lab, University of Minnesota For example: indicating “connectivity” between Como Ave and the Intercampus Transitway allowed computation of a new route that is .6 K shorter than the old route
  52. 52. Social Intelligence in Cities & Regions: Cyclopath <ul><li>An experiment: “Work Hints” </li></ul><ul><ul><li>Is it possible to elicit and focus social intelligence? </li></ul></ul>Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center. Priedhorsky, Masli, Terveen CHI ‘10
  53. 53. Social Intelligence in Cities & Regions: Cyclopath <ul><li>An experiment: “Work Hints” </li></ul><ul><ul><li>Is it possible to elicit and focus social intelligence? </li></ul></ul><ul><ul><li>Try asking people </li></ul></ul>Cyclopath needs your help “… We have created a system which will automatically direct you to areas of the map that need work (more bikeability ratings entered or edits to the geography of the map itself)…” <link to “work hints” window> Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center. Priedhorsky, Masli, Terveen CHI ‘10
  54. 54. Social Intelligence in Cities & Regions: Cyclopath <ul><li>An experiment: “Work Hints” </li></ul><ul><ul><li>Is it possible to elicit and focus social intelligence? </li></ul></ul><ul><ul><li>Try asking people </li></ul></ul><ul><ul><ul><li>Direct a person to an area that needs work </li></ul></ul></ul><ul><ul><ul><li>let them work until they’re ‘done’ </li></ul></ul></ul><ul><ul><ul><li>ask if they want to do another area </li></ul></ul></ul>Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center. Priedhorsky, Masli, Terveen CHI ‘10
  55. 55. Social Intelligence in Cities & Regions: Cyclopath <ul><li>An experiment: “Work Hints” </li></ul><ul><ul><li>Is it possible to elicit and focus social intelligence? </li></ul></ul><ul><ul><li>Try asking people </li></ul></ul><ul><ul><ul><li>Direct a person to an area that needs work </li></ul></ul></ul><ul><ul><ul><li>let them work until they’re ‘done’ </li></ul></ul></ul><ul><ul><ul><li>ask if they want to do another area </li></ul></ul></ul>Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center. Priedhorsky, Masli, Terveen CHI ‘10
  56. 56. Social Intelligence in Cities & Regions: Cyclopath <ul><li>“ Work Hints” results: a surprise </li></ul><ul><ul><ul><li>People did about the same amount of work per trial </li></ul></ul></ul><ul><ul><ul><li>BUT they did three times as many trials: 17.7 vs 5.0 trials </li></ul></ul></ul>Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center. Priedhorsky, Masli, Terveen CHI ‘10
  57. 57. Social Intelligence in Cities & Regions: Cyclopath <ul><li>“ Work Hints” results: in general </li></ul><ul><ul><ul><li>Visually highlighting work opportunities leads to more work </li></ul></ul></ul><ul><ul><ul><li>Users do ‘extra’ work (beyond what is highlighted) </li></ul></ul></ul><ul><ul><ul><li>Taking users to areas they are familiar with leads to more work of certain types </li></ul></ul></ul><ul><ul><ul><li>Issuing a “call to action” and visually highlighting causes a broader range of users to do work (and moreover the “lead workers” are different) </li></ul></ul></ul>Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center.
  58. 58. Social Intelligence in Cities & Regions: Cyclopath <ul><li>Cyclopath Futures </li></ul><ul><ul><li>Cyclopath doesn’t have to be about bicyclists </li></ul></ul><ul><ul><ul><li>skiers (iceWiki) </li></ul></ul></ul><ul><ul><ul><li>walkers </li></ul></ul></ul><ul><ul><ul><li>disabled </li></ul></ul></ul><ul><ul><ul><li>urban tourists </li></ul></ul></ul><ul><ul><ul><li>local history </li></ul></ul></ul><ul><ul><ul><li>garden clubs </li></ul></ul></ul><ul><ul><ul><li> </li></ul></ul></ul>Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center.
  59. 59. Social Intelligence in Cities & Regions: Cyclopath <ul><li>Cyclopath Futures </li></ul><ul><ul><li>Cyclopath doesn’t have to be about bicyclists </li></ul></ul><ul><ul><li>Nor does it have to be just for route finding </li></ul></ul><ul><ul><ul><li>‘ what if’ planning </li></ul></ul></ul><ul><ul><ul><li>keeping inventories </li></ul></ul></ul><ul><ul><ul><li>tracking change over time </li></ul></ul></ul><ul><ul><ul><li>visualizing resources and resource use </li></ul></ul></ul><ul><ul><ul><li> </li></ul></ul></ul>Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center.
  60. 60. Closing Remarks <ul><li>Recap and takeaways… </li></ul>Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center.
  61. 61. Closing Remarks <ul><li>Two types of smartness </li></ul><ul><ul><li>Technical AND social (complementary, not exclusive) </li></ul></ul><ul><ul><li>… and IBM needs to pay more attention to the social part… </li></ul></ul>Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center.
  62. 62. Closing Remarks <ul><li>Social intelligence is old; what’s new is that digital systems enable it to take new and powerful forms </li></ul><ul><li>None of these examples – or anything like them – existed ten years ago </li></ul>Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center.
  63. 63. Closing Remarks <ul><li>Cyclopath is a neat example of an important new class of apps that </li></ul><ul><ul><li>combine human-sourced knowledge with digital data to create a common resource </li></ul></ul><ul><ul><li>provide mechanisms for eliciting and focusing human work to enhance the resource </li></ul></ul><ul><ul><li>enable computations that provide resource-based services </li></ul></ul><ul><ul><li>offer the potential of providing a platform for community collaboration </li></ul></ul>Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center.
  64. 64. Closing Remarks <ul><li>Takeaways </li></ul><ul><ul><li>People have rich and nuanced knowledge of their habitats </li></ul></ul><ul><ul><li>People are willing to do work to contribute this knowledge </li></ul></ul><ul><ul><li>Systems can be designed so that they can elicit and focus such work </li></ul></ul><ul><ul><li>If the elicited knowledge is in a form that digital systems can use, the knowledge can be used in computations and services </li></ul></ul>Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center.
  65. 65. Closing Remarks <ul><li>Takeaways </li></ul><ul><ul><li>People have rich and nuanced knowledge of their habitats </li></ul></ul><ul><ul><li>People are willing to do work to contribute this knowledge </li></ul></ul><ul><ul><li>Systems can be designed so that they can elicit and focus such work </li></ul></ul><ul><ul><li>If the elicited knowledge is in a form that digital systems can use, the knowledge can be used in computations and services </li></ul></ul><ul><li>Two conjectures </li></ul><ul><ul><li>People who are collocated in cities and regions offer particularly fertile ground for social intelligence because of their deep knowledge and local motivation </li></ul></ul><ul><ul><li>Smart systems that succeed in getting people to participate – in providing, analyzing and acting on knowledge – are more likely to be seen as acceptable and legitimate </li></ul></ul>Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center.
  66. 66. Closing Remarks <ul><li>Credits and Connections </li></ul><ul><ul><li>The ESP Game is by Luis von Ahn and colleagues at CMU </li></ul></ul><ul><ul><li>The Cyclopath project is by Terveen, Priedhorsky, et al. at the University of Minnesota </li></ul></ul><ul><ul><ul><li>(Priedhorsky started at IBM Research in Cambridge, in September 2010) </li></ul></ul></ul><ul><ul><li>I can be reached at Thomas Erickson/Watson/IBM or [email_address] , and browsed at http://www.visi.com/~snowfall </li></ul></ul>Thomas Erickson, Social Computing Group, IBM T. J. Watson Research Center.

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