PUZZLING                      SPEAKER: Jeff Jonas                               Chief Scientist                            ...
Puzzling                              How Context Accumulates                           Jeff Jonas, IBM Distinguished Engi...
FIRETuesday, November 27, 12
FIRETuesday, November 27, 12
You can’t squeeze knowledge out of a pixel.Tuesday, November 27, 12
State of the Union: “Pixel Analytics”        Observation                            Consumer          Space               ...
State of the Union: “Pixel Analytics”        Observation                            Consumer          Space               ...
State of the Union: “Pixel Analytics”                           Red Puzzle Piece Analytics        Observation             ...
State of the Union: “Pixel Analytics”                           Red Puzzle Piece Analytics                            Gree...
State of the Union: “Pixel Analytics”                           Red Puzzle Piece Analytics                            Gree...
Without context … quality predictions are hard to come by.Tuesday, November 27, 12
Context      definition              Better understanding something by              taking into account the things around ...
First … The Data Must Find the Data         Observation                         Consumer           Space                  ...
First … The Data Must Find the Data                           Context Accumulation         Observation                    ...
First … The Data Must Find the Data                                                               Relevance Detection     ...
Big Data                           Pile of ____   In ContextTuesday, November 27, 12
Big Data [in context]. New Physics.Tuesday, November 27, 12
Big Data [in context]. New Physics.                   §More data: better the predictions                     – Lower fals...
Big Data [in context]. New Physics.                   §More data: better the predictions                     – Lower fals...
Puzzling                Vegas                Artwork provided by Hadley                House Licensing, Minneapolis       ...
Puzzling                   270 pieces                Vegas                Artwork provided by Hadley                      ...
Puzzling                   270 pieces                Vegas                Artwork provided by Hadley                      ...
Puzzling                   270 pieces                Vegas                Artwork provided by Hadley                      ...
Puzzling                   270 pieces                Vegas                Artwork provided by Hadley                      ...
Puzzling                   270 pieces                Vegas                Artwork provided by Hadley                      ...
Puzzling                   270 pieces                Vegas                Artwork provided by Hadley                      ...
Puzzling                   270 pieces                Vegas                Artwork provided by Hadley                      ...
Puzzling                   270 pieces                Vegas                Artwork provided by Hadley                      ...
Tuesday, November 27, 12
Tuesday, November 27, 12
Tuesday, November 27, 12
Tuesday, November 27, 12
Tuesday, November 27, 12
Tuesday, November 27, 12
Tuesday, November 27, 12
Tuesday, November 27, 12
Tuesday, November 27, 12
Incremental Context – Incremental DiscoveryTuesday, November 27, 12
Incremental Context – Incremental Discovery      6:40pm               STARTTuesday, November 27, 12
Incremental Context – Incremental Discovery      6:40pm               START      22min                “Hey, this one is a ...
Incremental Context – Incremental Discovery      6:40pm               START      22min                “Hey, this one is a ...
Incremental Context – Incremental Discovery      6:40pm               START      22min                “Hey, this one is a ...
Incremental Context – Incremental Discovery      6:40pm               START      22min                “Hey, this one is a ...
Tuesday, November 27, 12
150 pieces                                              50%          Down Home Music          © Kay Lamb Shannon, Artist. ...
Incremental Context – Incremental DiscoveryTuesday, November 27, 12
Incremental Context – Incremental Discovery      47min                “We should take the sky and grass off the table.”Tue...
Incremental Context – Incremental Discovery      47min                “We should take the sky and grass off the table.”   ...
Incremental Context – Incremental Discovery      47min                “We should take the sky and grass off the table.”   ...
Incremental Context – Incremental Discovery      47min                “We should take the sky and grass off the table.”   ...
Incremental Context – Incremental Discovery      47min                “We should take the sky and grass off the table.”   ...
Tuesday, November 27, 12
How Context AccumulatesTuesday, November 27, 12
How Context Accumulates      § With each new observation one of three assertions are made:            1) Un-associated; 2...
How Context Accumulates      § With each new observation one of three assertions are made:            1) Un-associated; 2...
How Context Accumulates      § With each new observation one of three assertions are made:            1) Un-associated; 2...
How Context Accumulates      § With each new observation one of three assertions are made:            1) Un-associated; 2...
How Context Accumulates      § With each new observation one of three assertions are made:            1) Un-associated; 2...
Puzzling Experiment #2                           “Testing the Fast Last Puzzle Piece”Tuesday, November 27, 12
Tuesday, November 27, 12
Tuesday, November 27, 12
Sensemaking on Streams      § Each person gets one piece per round, no collaborating      § Only work the piece         ...
Tuesday, November 27, 12
Tuesday, November 27, 12
Deep Reflection and Consolidation      § Every 10 rounds (40 pieces) you can re-consider what is already known and       ...
Tuesday, November 27, 12
Tuesday, November 27, 12
Tuesday, November 27, 12
Tuesday, November 27, 12
Noteworthy EventsTuesday, November 27, 12
Noteworthy Events      § @ 1.3% (4 pieces) new insight: “It’s Las Vegas and Sahara Hotel!”Tuesday, November 27, 12
Noteworthy Events      § @ 1.3% (4 pieces) new insight: “It’s Las Vegas and Sahara Hotel!”      § @ 4% (12 pieces) the f...
Noteworthy Events      § @ 1.3% (4 pieces) new insight: “It’s Las Vegas and Sahara Hotel!”      § @ 4% (12 pieces) the f...
Noteworthy Events      § @ 1.3% (4 pieces) new insight: “It’s Las Vegas and Sahara Hotel!”      § @ 4% (12 pieces) the f...
Noteworthy Events      § @ 1.3% (4 pieces) new insight: “It’s Las Vegas and Sahara Hotel!”      § @ 4% (12 pieces) the f...
Tuesday, November 27, 12
Compute Effort as Observation Space GrowsTuesday, November 27, 12
Compute Effort as Observation Space Grows                                     57->55                                      ...
Compute Effort as Observation Space Grows                                     57->55                                      ...
More Data, Less ComputeTuesday, November 27, 12
Lessons LearnedTuesday, November 27, 12
Lessons Learned               The last piece was almost as fast as the first.Tuesday, November 27, 12
Lessons Learned               The last piece was almost as fast as the first.          Deep reflection (batch-based patter...
Puzzling Experiment #3                                  “Adults”Tuesday, November 27, 12
SIBOS Conference 2011Tuesday, November 27, 12
SIBOS Conference 2011                           § 100 executives, 10 teamsTuesday, November 27, 12
SIBOS Conference 2011                           § 100 executives, 10 teams                           § 10 puzzles, 10 sm...
SIBOS Conference 2011                           § 100 executives, 10 teams                           § 10 puzzles, 10 sm...
SIBOS Conference 2011                           § 100 executives, 10 teams                           § 10 puzzles, 10 sm...
SIBOS Conference 2011                           § 100 executives, 10 teams                           § 10 puzzles, 10 sm...
SIBOS Conference 2011                           § 100 executives, 10 teams                           § 10 puzzles, 10 sm...
Puzzling Experiment #4                            “Not an Experiment”Tuesday, November 27, 12
Tuesday, November 27, 12
Tuesday, November 27, 12
Tuesday, November 27, 12
Tuesday, November 27, 12
Tuesday, November 27, 12
Tuesday, November 27, 12
Tuesday, November 27, 12
Tuesday, November 27, 12
Tuesday, November 27, 12
Tuesday, November 27, 12
Puzzling Project #4: CommentaryTuesday, November 27, 12
Puzzling Project #4: Commentary      § Despite having only 100 pieces and eight collaborating eyeballs:             – We ...
Puzzling Project #4: Commentary      § Despite having only 100 pieces and eight collaborating eyeballs:             – We ...
Experiment #4: Notes to SelfTuesday, November 27, 12
Experiment #4: Notes to Self      § Excessive ambiguity drives computational cost way upTuesday, November 27, 12
Experiment #4: Notes to Self      § Excessive ambiguity drives computational cost way upTuesday, November 27, 12
Experiment #4: Notes to Self      § Excessive ambiguity drives computational cost way up      § Some drunk people get un...
My RecommendationsTuesday, November 27, 12
My Recommendations      § Context Accumulation            – Investments in general purpose information fusion will often ...
My Recommendations      § Context Accumulation            – Investments in general purpose information fusion will often ...
My Recommendations      § Context Accumulation            – Investments in general purpose information fusion will often ...
Related Blog Posts            Puzzling: How Observations Are Accumulated Into Context            Algorithms At Dead-End: C...
Puzzling                               How Context Accumulates                           Jeff Jonas, IBM Distinguished Eng...
Puzzling                Vegas                           Neuschwanstein Beauty           Down Home Music                Cot...
Tuesday, November 27, 12
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PUZZLING- HOW CONTEXT ACCUMULATES from Structure:Data 2012

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PUZZLING- HOW CONTEXT ACCUMULATES from Structure:Data 2012

  1. 1. PUZZLING SPEAKER: Jeff Jonas Chief Scientist Entity Analytics IBMTuesday, November 27, 12
  2. 2. Puzzling How Context Accumulates Jeff Jonas, IBM Distinguished Engineer Chief Scientist, IBM Entity Analytics Email: jeffjonas@us.ibm.com Blog: www.jeffjonas.typepad.com Twitter: http://www.twitter.com/jeffjonasTuesday, November 27, 12
  3. 3. FIRETuesday, November 27, 12
  4. 4. FIRETuesday, November 27, 12
  5. 5. You can’t squeeze knowledge out of a pixel.Tuesday, November 27, 12
  6. 6. State of the Union: “Pixel Analytics” Observation Consumer Space (An analyst, a system, the sensor itself, etc.)Tuesday, November 27, 12
  7. 7. State of the Union: “Pixel Analytics” Observation Consumer Space (An analyst, a system, the sensor itself, etc.)Tuesday, November 27, 12
  8. 8. State of the Union: “Pixel Analytics” Red Puzzle Piece Analytics Observation Consumer Space (An analyst, a system, the sensor itself, etc.)Tuesday, November 27, 12
  9. 9. State of the Union: “Pixel Analytics” Red Puzzle Piece Analytics Green Puzzle Piece Analytics Observation Consumer Space (An analyst, a system, the sensor itself, etc.)Tuesday, November 27, 12
  10. 10. State of the Union: “Pixel Analytics” Red Puzzle Piece Analytics Green Puzzle Piece Analytics Blue Puzzle Piece Analytics Observation Consumer Space (An analyst, a system, the sensor itself, etc.)Tuesday, November 27, 12
  11. 11. Without context … quality predictions are hard to come by.Tuesday, November 27, 12
  12. 12. Context definition Better understanding something by taking into account the things around it.Tuesday, November 27, 12
  13. 13. First … The Data Must Find the Data Observation Consumer Space (An analyst, a system, the sensor itself, etc.)Tuesday, November 27, 12
  14. 14. First … The Data Must Find the Data Context Accumulation Observation Persistent Consumer Space Context (An analyst, a system, the sensor itself, etc.)Tuesday, November 27, 12
  15. 15. First … The Data Must Find the Data Relevance Detection Context Accumulation Observation Persistent Consumer Space Context (An analyst, a system, the sensor itself, etc.)Tuesday, November 27, 12
  16. 16. Big Data Pile of ____ In ContextTuesday, November 27, 12
  17. 17. Big Data [in context]. New Physics.Tuesday, November 27, 12
  18. 18. Big Data [in context]. New Physics. §More data: better the predictions – Lower false positives – Lower false negatives §More data: bad data good – Suddenly glad your data is not perfectTuesday, November 27, 12
  19. 19. Big Data [in context]. New Physics. §More data: better the predictions – Lower false positives – Lower false negatives §More data: bad data good – Suddenly glad your data is not perfect §More data: less computeTuesday, November 27, 12
  20. 20. Puzzling Vegas Artwork provided by Hadley House Licensing, Minneapolis © 2011 Giesla Hoelscher All Rights Reserved © 2011 Ravensburger USA, Inc.Tuesday, November 27, 12
  21. 21. Puzzling 270 pieces Vegas Artwork provided by Hadley 90% House Licensing, Minneapolis © 2011 Giesla Hoelscher All Rights Reserved © 2011 Ravensburger USA, Inc.Tuesday, November 27, 12
  22. 22. Puzzling 270 pieces Vegas Artwork provided by Hadley Neuschwanstein Beauty © 2009 Photo Copyright Robert 90% House Licensing, Minneapolis © 2011 Giesla Hoelscher Cushman Hayes © 2009 Ravensburger USA, Inc. All Rights Reserved © 2011 Ravensburger USA, Inc.Tuesday, November 27, 12
  23. 23. Puzzling 270 pieces Vegas Artwork provided by Hadley 200 pieces Neuschwanstein Beauty © 2009 Photo Copyright Robert 90% House Licensing, Minneapolis © 2011 Giesla Hoelscher 66% Cushman Hayes © 2009 Ravensburger USA, Inc. All Rights Reserved © 2011 Ravensburger USA, Inc.Tuesday, November 27, 12
  24. 24. Puzzling 270 pieces Vegas Artwork provided by Hadley 200 pieces Neuschwanstein Beauty © 2009 Photo Copyright Robert Down Home Music © Kay Lamb Shannon, Artist 90% House Licensing, Minneapolis © 2011 Giesla Hoelscher 66% Cushman Hayes © 2009 Ravensburger USA, Inc. Licensed by Cypress Fine Art Licensing All Rights Reserved © 2011 Ravensburger USA Inc. © 2011 Ravensburger USA, Inc.Tuesday, November 27, 12
  25. 25. Puzzling 270 pieces Vegas Artwork provided by Hadley 200 pieces Neuschwanstein Beauty © 2009 Photo Copyright Robert 150 pieces Down Home Music © Kay Lamb Shannon, Artist 90% House Licensing, Minneapolis © 2011 Giesla Hoelscher 66% Cushman Hayes © 2009 Ravensburger USA, Inc. 50% Licensed by Cypress Fine Art Licensing All Rights Reserved © 2011 Ravensburger USA Inc. © 2011 Ravensburger USA, Inc.Tuesday, November 27, 12
  26. 26. Puzzling 270 pieces Vegas Artwork provided by Hadley 200 pieces Neuschwanstein Beauty © 2009 Photo Copyright Robert 150 pieces Down Home Music © Kay Lamb Shannon, Artist Cottage Garden © 2010 Royce B. McClure, Artist 90% House Licensing, Minneapolis © 2011 Giesla Hoelscher 66% Cushman Hayes © 2009 Ravensburger USA, Inc. 50% Licensed by Cypress Fine Art Licensing All Rights Reserved © 2010 Ravensburger USA, Inc. All Rights Reserved © 2011 Ravensburger USA Inc. © 2011 Ravensburger USA, Inc.Tuesday, November 27, 12
  27. 27. Puzzling 270 pieces Vegas Artwork provided by Hadley 200 pieces Neuschwanstein Beauty © 2009 Photo Copyright Robert 150 pieces Down Home Music © Kay Lamb Shannon, Artist 6 pieces Cottage Garden © 2010 Royce B. McClure, Artist 90% 66% 50% 2% House Licensing, Minneapolis Cushman Hayes Licensed by Cypress Fine Art All Rights Reserved © 2011 Giesla Hoelscher © 2009 Ravensburger USA, Inc. Licensing © 2010 Ravensburger USA, Inc. All Rights Reserved © 2011 Ravensburger USA Inc. © 2011 Ravensburger USA, Inc.Tuesday, November 27, 12
  28. 28. Puzzling 270 pieces Vegas Artwork provided by Hadley 200 pieces Neuschwanstein Beauty © 2009 Photo Copyright Robert 150 pieces Down Home Music © Kay Lamb Shannon, Artist 6 pieces Cottage Garden © 2010 Royce B. McClure, Artist 90% 66% 50% 2% House Licensing, Minneapolis Cushman Hayes Licensed by Cypress Fine Art All Rights Reserved © 2011 Giesla Hoelscher © 2009 Ravensburger USA, Inc. Licensing © 2010 Ravensburger USA, Inc. All Rights Reserved © 2011 Ravensburger USA Inc. © 2011 Ravensburger USA, Inc. 30 pieces 10% (duplicates)Tuesday, November 27, 12
  29. 29. Tuesday, November 27, 12
  30. 30. Tuesday, November 27, 12
  31. 31. Tuesday, November 27, 12
  32. 32. Tuesday, November 27, 12
  33. 33. Tuesday, November 27, 12
  34. 34. Tuesday, November 27, 12
  35. 35. Tuesday, November 27, 12
  36. 36. Tuesday, November 27, 12
  37. 37. Tuesday, November 27, 12
  38. 38. Incremental Context – Incremental DiscoveryTuesday, November 27, 12
  39. 39. Incremental Context – Incremental Discovery 6:40pm STARTTuesday, November 27, 12
  40. 40. Incremental Context – Incremental Discovery 6:40pm START 22min “Hey, this one is a duplicate!”Tuesday, November 27, 12
  41. 41. Incremental Context – Incremental Discovery 6:40pm START 22min “Hey, this one is a duplicate!” 35min “I think some pieces are missing.”Tuesday, November 27, 12
  42. 42. Incremental Context – Incremental Discovery 6:40pm START 22min “Hey, this one is a duplicate!” 35min “I think some pieces are missing.” 37min “Looks like a bunch of hillbillies on a porch.”Tuesday, November 27, 12
  43. 43. Incremental Context – Incremental Discovery 6:40pm START 22min “Hey, this one is a duplicate!” 35min “I think some pieces are missing.” 37min “Looks like a bunch of hillbillies on a porch.” 44min “Hillbillies, playing guitars, sitting on a porch, near a barber sign … and a banjo!”Tuesday, November 27, 12
  44. 44. Tuesday, November 27, 12
  45. 45. 150 pieces 50% Down Home Music © Kay Lamb Shannon, Artist. Licensed by Cypress Fine Art Licensing © 2011 Ravensburger USA Inc.Tuesday, November 27, 12
  46. 46. Incremental Context – Incremental DiscoveryTuesday, November 27, 12
  47. 47. Incremental Context – Incremental Discovery 47min “We should take the sky and grass off the table.”Tuesday, November 27, 12
  48. 48. Incremental Context – Incremental Discovery 47min “We should take the sky and grass off the table.” 2hr “Let’s switch sides, and see if we can make sense of this from different perspectives.”Tuesday, November 27, 12
  49. 49. Incremental Context – Incremental Discovery 47min “We should take the sky and grass off the table.” 2hr “Let’s switch sides, and see if we can make sense of this from different perspectives.” 2hr10m “Wait, there are three … no, four puzzles.”Tuesday, November 27, 12
  50. 50. Incremental Context – Incremental Discovery 47min “We should take the sky and grass off the table.” 2hr “Let’s switch sides, and see if we can make sense of this from different perspectives.” 2hr10m “Wait, there are three … no, four puzzles.” 2hr17m “We need a bigger table.”Tuesday, November 27, 12
  51. 51. Incremental Context – Incremental Discovery 47min “We should take the sky and grass off the table.” 2hr “Let’s switch sides, and see if we can make sense of this from different perspectives.” 2hr10m “Wait, there are three … no, four puzzles.” 2hr17m “We need a bigger table.” 2hr18m “I think you threw in a few random pieces.”Tuesday, November 27, 12
  52. 52. Tuesday, November 27, 12
  53. 53. How Context AccumulatesTuesday, November 27, 12
  54. 54. How Context Accumulates § With each new observation one of three assertions are made: 1) Un-associated; 2) Placed near like neighbors; or 3) ConnectedTuesday, November 27, 12
  55. 55. How Context Accumulates § With each new observation one of three assertions are made: 1) Un-associated; 2) Placed near like neighbors; or 3) Connected § Must favor the false negativeTuesday, November 27, 12
  56. 56. How Context Accumulates § With each new observation one of three assertions are made: 1) Un-associated; 2) Placed near like neighbors; or 3) Connected § Must favor the false negative § New observations sometimes reverse earlier assertionsTuesday, November 27, 12
  57. 57. How Context Accumulates § With each new observation one of three assertions are made: 1) Un-associated; 2) Placed near like neighbors; or 3) Connected § Must favor the false negative § New observations sometimes reverse earlier assertions § As the working space expands, computational effort increasesTuesday, November 27, 12
  58. 58. How Context Accumulates § With each new observation one of three assertions are made: 1) Un-associated; 2) Placed near like neighbors; or 3) Connected § Must favor the false negative § New observations sometimes reverse earlier assertions § As the working space expands, computational effort increases § Given sufficient observations, there can come a tipping point. Thereafter, confidence improves while computational effort decreases!Tuesday, November 27, 12
  59. 59. Puzzling Experiment #2 “Testing the Fast Last Puzzle Piece”Tuesday, November 27, 12
  60. 60. Tuesday, November 27, 12
  61. 61. Tuesday, November 27, 12
  62. 62. Sensemaking on Streams § Each person gets one piece per round, no collaborating § Only work the piece – Figure out where it goes – If you stumble upon something else worth fixing, fix it – When there is no more to do on that piece, stop and say you are done § If you have new insight, tell me § Each assembler is timed for each pieceTuesday, November 27, 12
  63. 63. Tuesday, November 27, 12
  64. 64. Tuesday, November 27, 12
  65. 65. Deep Reflection and Consolidation § Every 10 rounds (40 pieces) you can re-consider what is already known and collaborate while doing this § Spend as much time as needed, until not much more can be accomplished § Puzzle chunks are counted before and afterTuesday, November 27, 12
  66. 66. Tuesday, November 27, 12
  67. 67. Tuesday, November 27, 12
  68. 68. Tuesday, November 27, 12
  69. 69. Tuesday, November 27, 12
  70. 70. Noteworthy EventsTuesday, November 27, 12
  71. 71. Noteworthy Events § @ 1.3% (4 pieces) new insight: “It’s Las Vegas and Sahara Hotel!”Tuesday, November 27, 12
  72. 72. Noteworthy Events § @ 1.3% (4 pieces) new insight: “It’s Las Vegas and Sahara Hotel!” § @ 4% (12 pieces) the first two pieces connectTuesday, November 27, 12
  73. 73. Noteworthy Events § @ 1.3% (4 pieces) new insight: “It’s Las Vegas and Sahara Hotel!” § @ 4% (12 pieces) the first two pieces connect § @ 37% (112 pieces) a puzzle piece is processed by a “pipeline” in 2.7 seconds – Why? “Never seen anything like it.” § @ 48% (144 pieces) new insight: – “Big welcome Las Vegas sign with everything from the strip around it.”Tuesday, November 27, 12
  74. 74. Noteworthy Events § @ 1.3% (4 pieces) new insight: “It’s Las Vegas and Sahara Hotel!” § @ 4% (12 pieces) the first two pieces connect § @ 37% (112 pieces) a puzzle piece is processed by a “pipeline” in 2.7 seconds – Why? “Never seen anything like it.” § @ 48% (144 pieces) new insight: – “Big welcome Las Vegas sign with everything from the strip around it.” § @ 65% (196 pieces) the first false positive is detected and correctedTuesday, November 27, 12
  75. 75. Noteworthy Events § @ 1.3% (4 pieces) new insight: “It’s Las Vegas and Sahara Hotel!” § @ 4% (12 pieces) the first two pieces connect § @ 37% (112 pieces) a puzzle piece is processed by a “pipeline” in 2.7 seconds – Why? “Never seen anything like it.” § @ 48% (144 pieces) new insight: – “Big welcome Las Vegas sign with everything from the strip around it.” § @ 65% (196 pieces) the first false positive is detected and corrected § @ 75% (224 pieces) new insight: “It is getting easier.”Tuesday, November 27, 12
  76. 76. Tuesday, November 27, 12
  77. 77. Compute Effort as Observation Space GrowsTuesday, November 27, 12
  78. 78. Compute Effort as Observation Space Grows 57->55 87->61 36->34 54->29 83->72 82->54 55->1Tuesday, November 27, 12
  79. 79. Compute Effort as Observation Space Grows 57->55 87->61 36->34 54->29 83->72 82->54 55->1Tuesday, November 27, 12
  80. 80. More Data, Less ComputeTuesday, November 27, 12
  81. 81. Lessons LearnedTuesday, November 27, 12
  82. 82. Lessons Learned The last piece was almost as fast as the first.Tuesday, November 27, 12
  83. 83. Lessons Learned The last piece was almost as fast as the first. Deep reflection (batch-based pattern discovery) was significantly more important than I had thought.Tuesday, November 27, 12
  84. 84. Puzzling Experiment #3 “Adults”Tuesday, November 27, 12
  85. 85. SIBOS Conference 2011Tuesday, November 27, 12
  86. 86. SIBOS Conference 2011 § 100 executives, 10 teamsTuesday, November 27, 12
  87. 87. SIBOS Conference 2011 § 100 executives, 10 teams § 10 puzzles, 10 small tablesTuesday, November 27, 12
  88. 88. SIBOS Conference 2011 § 100 executives, 10 teams § 10 puzzles, 10 small tables § Duplicate and missing piecesTuesday, November 27, 12
  89. 89. SIBOS Conference 2011 § 100 executives, 10 teams § 10 puzzles, 10 small tables § Duplicate and missing pieces Lessons:Tuesday, November 27, 12
  90. 90. SIBOS Conference 2011 § 100 executives, 10 teams § 10 puzzles, 10 small tables § Duplicate and missing pieces Lessons: 1. They learned federated search bites.Tuesday, November 27, 12
  91. 91. SIBOS Conference 2011 § 100 executives, 10 teams § 10 puzzles, 10 small tables § Duplicate and missing pieces Lessons: 1. They learned federated search bites. 2. I watched as an early bias misdirected their attention … but then over time new observations corrected this bias.Tuesday, November 27, 12
  92. 92. Puzzling Experiment #4 “Not an Experiment”Tuesday, November 27, 12
  93. 93. Tuesday, November 27, 12
  94. 94. Tuesday, November 27, 12
  95. 95. Tuesday, November 27, 12
  96. 96. Tuesday, November 27, 12
  97. 97. Tuesday, November 27, 12
  98. 98. Tuesday, November 27, 12
  99. 99. Tuesday, November 27, 12
  100. 100. Tuesday, November 27, 12
  101. 101. Tuesday, November 27, 12
  102. 102. Tuesday, November 27, 12
  103. 103. Puzzling Project #4: CommentaryTuesday, November 27, 12
  104. 104. Puzzling Project #4: Commentary § Despite having only 100 pieces and eight collaborating eyeballs: – We began to suspect there were missing and random pieces – We had an alarming number of false positives1 – It took significantly more effort/time than expected 1 The primary source being one overly intoxicated pipeline.Tuesday, November 27, 12
  105. 105. Puzzling Project #4: Commentary § Despite having only 100 pieces and eight collaborating eyeballs: – We began to suspect there were missing and random pieces – We had an alarming number of false positives1 – It took significantly more effort/time than expected § Why? Common shapes and that vast purple haze (lots of ambiguity). 1 The primary source being one overly intoxicated pipeline.Tuesday, November 27, 12
  106. 106. Experiment #4: Notes to SelfTuesday, November 27, 12
  107. 107. Experiment #4: Notes to Self § Excessive ambiguity drives computational cost way upTuesday, November 27, 12
  108. 108. Experiment #4: Notes to Self § Excessive ambiguity drives computational cost way upTuesday, November 27, 12
  109. 109. Experiment #4: Notes to Self § Excessive ambiguity drives computational cost way up § Some drunk people get unreasonably optimisticTuesday, November 27, 12
  110. 110. My RecommendationsTuesday, November 27, 12
  111. 111. My Recommendations § Context Accumulation – Investments in general purpose information fusion will often yield greater value than investments in specialized, single-sensor, algorithms (pixel analytics).Tuesday, November 27, 12
  112. 112. My Recommendations § Context Accumulation – Investments in general purpose information fusion will often yield greater value than investments in specialized, single-sensor, algorithms (pixel analytics). § Real-time Analytics Over Big Data – If something can be engineered for real-time, do that. There is a competitive advantage when one can respond intelligently while a transaction is still happening.Tuesday, November 27, 12
  113. 113. My Recommendations § Context Accumulation – Investments in general purpose information fusion will often yield greater value than investments in specialized, single-sensor, algorithms (pixel analytics). § Real-time Analytics Over Big Data – If something can be engineered for real-time, do that. There is a competitive advantage when one can respond intelligently while a transaction is still happening. § Deep Reflection – Do not underestimate the value of periodic deep reflection (pattern discovery). Do this more often. And put this emerging insight to immediate use via feedback loops.Tuesday, November 27, 12
  114. 114. Related Blog Posts Puzzling: How Observations Are Accumulated Into Context Algorithms At Dead-End: Cannot Squeeze Knowledge Out Of A Pixel Data Finds Data Big Data. New Physics. General Purpose Sensemaking Systems and Information Colocation Data Beats Math And Easy to Reach Email: jeffjonas@us.ibm.com Twitter: http://www.twitter.com/jeffjonasTuesday, November 27, 12
  115. 115. Puzzling How Context Accumulates Jeff Jonas, IBM Distinguished Engineer Chief Scientist, IBM Entity Analytics Email: jeffjonas@us.ibm.com Blog: www.jeffjonas.typepad.com Twitter: http://www.twitter.com/jeffjonasTuesday, November 27, 12
  116. 116. Puzzling Vegas Neuschwanstein Beauty Down Home Music Cottage Garden Artwork provided by Hadley © 2009 Photo Copyright Robert © Kay Lamb Shannon, Artist © 2010 Royce B. McClure, Artist House Licensing, Minneapolis Cushman Hayes Licensed by Cypress Fine Art All Rights Reserved © 2011 Giesla Hoelscher © 2009 Ravensburger USA, Inc. Licensing © 2010 Ravensburger USA, Inc. All Rights Reserved © 2011 Ravensburger USA Inc. © 2011 Ravensburger USA, Inc.Tuesday, November 27, 12
  117. 117. Tuesday, November 27, 12

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