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What the Flash Crash & Black Boxes can teach us about the Search #searchlove @kelvinnewman

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May 6th, 2010 the Dow Jones Industrial Average plunged about 1000 points only to recover those losses within minutes – this was the Flash Crash. No catastrophes or physical events caused this swing, it was the black boxes of stock market algorithms. Black boxes a lot like Google’s. How do we prepare for the future when even Google doesn’t know how its algorithm works?

Published in: Business

What the Flash Crash & Black Boxes can teach us about the Search #searchlove @kelvinnewman

  1. 1. W H AT T H E F L A S H C R A S H & B L A C K B O X E S C A N T E A C H U S A B O U T T H E F U T U R E O F S E A R C H . @ K E LV I N N E W M A N
  2. 2. W H AT ’ S Y O U R J O B ? @ K E LV I N N E W M A N
  3. 3. Understanding Customers Understanding who pays the bills Understanding Google @ K E LV I N N E W M A N
  4. 4. Ability Time @ K E LV I N N E W M A N
  5. 5. Pop!Tech “Practice isn't the thing you do once you're good. It's the thing you do that makes you good.” ― Malcolm Gladwell, Outliers: The Story of Success
  6. 6. On some Malcolm Gladwell, David Bowie meets Kanye shit, This is dedication, A life lived for art is never a life wasted, Ten thousand hours felt like ten thousand hands, Ten thousands hands, they carry me. ― Macklemore - Ten Thousand Hours floshe24 @ K E LV I N N E W M A N
  7. 7. Understanding Customers Understanding who pays the bills Understanding Google A+ A+ D- @ K E LV I N N E W M A N
  8. 8. Ability Time @ K E LV I N N E W M A N
  9. 9. @ K E LV I N N E W M A N I thought maybe it’s the Dunning-Kruger effect. Adam Sundana
  10. 10. @ K E LV I N N E W M A NAdam Sundana The Dunning–Kruger effect is a cognitive bias manifesting in unskilled individuals suffering from illusory superiority, mistakenly rating their ability much higher than is accurate. This bias is attributed to a metacognitive inability of the unskilled to recognize their ineptitude. Conversely, people with true ability tend to underestimate their relative competence based on the erroneous or exaggerated claims made by unskilled people.
  11. 11. but it was more than that @ K E LV I N N E W M A NEva Rinaldi
  12. 12. T H I N G S I T H O U G H T W E R E T R U E ; N O L O N G E R W E R E @ K E LV I N N E W M A N
  13. 13. T H I N G S I S A I D W O R K E D N O L O N G E R W O R K E D @ K E LV I N N E W M A N
  14. 14. A N D T H AT 3 5 , 0 0 0 W O R D E B O O K I W R O T E A B O U T L I N K B U I L D I N G
  15. 15. this presentation isn't about one weird trick to get better rankings ... @ K E LV I N N E W M A N
  16. 16. it’s about me dealing with the realisation that I didn’t understand Google, quite as much, as I thought I did.
  17. 17. & I T W I L L C H A N G E T H E WAY Y O U T H I N K A B O U T H O W S E A R C H E N G I N E S W O R K @ K E LV I N N E W M A N
  18. 18. Which is important… Red Touch Media @ K E LV I N N E W M A N
  19. 19. B E C A U S E , W E N E E D T O T H I N K M O R E ( A B O U T H O W W E T H I N K . ) @ K E LV I N N E W M A N
  20. 20. T H E P R O P @ K E LV I N N E W M A N
  21. 21. three stories @ K E LV I N N E W M A N
  22. 22. three lessons @ K E LV I N N E W M A N
  23. 23. three things to do… @ K E LV I N N E W M A N
  24. 24. Story One @ K E LV I N N E W M A N
  25. 25. the first story takes place at the University of Sussex @ K E LV I N N E W M A N
  26. 26. S O M E G R E AT P E O P L E H AV E S T U D I E D T H E R E . @ K E LV I N N E W M A N
  27. 27. I A N M C E WA N N O V E L I S T A N D A U T H O R O F O S C A R W I N N I N G A T O N E M E N T @ K E LV I N N E W M A N
  28. 28. V I R G I N I A WA D E T H R E E T I M E G R A N D - S L A M W I N N E R
  29. 29. M I C H A E L B U E R K L E G E N D A RY B R O A D C A S T E R
  30. 30. K E LV I N N E W M A N B L O K E W H O U S E D T O D J A T T H E S T U D E N T U N I O N @ K E LV I N N E W M A N
  31. 31. B U T O U R S T O RY I S N ’ T A B O U T T H E M @ K E LV I N N E W M A N
  32. 32. A N D H O W T H E Y M E T T H E PA R T N E R S O F T H E I R D R E A M S @ K E LV I N N E W M A N
  33. 33. H A D A W E S O M E K I D S @ K E LV I N N E W M A N
  34. 34. A N D I N E X P L I C A B LY P L AY E D S W I N G - B A L L O N T H E S TA G E W H E R E A B B A W O N E U R O V I S I O N @ K E LV I N N E W M A N
  35. 35. I T ’ S A B O U T @ K E LV I N N E W M A N
  36. 36. D R . A D R I A N T H O M P S O N E V O L U T I O N A RY & A D A P T I V E S Y S T E M S G R O U P @ K E LV I N N E W M A N
  37. 37. U N I V E R S I T Y O F S U S S E X , 1 9 9 6 D E PA R T M E N T O F I N F O R M A T I C S
  38. 38. H E A S K E D C O U L D A C I R C U I T “ E V O LV E ” T O S O LV E A P R O B L E M @ K E LV I N N E W M A N
  39. 39. S U R V I VA L O F T H E F I T T E S T A P P L I E D T O C I R C U I T S T H E E X P E R I M E N T
  40. 40. S U R V I VA L O F T H E F I T T E S T @ K E LV I N N E W M A N
  41. 41. S U R V I VA L O F T H E F I T T E S T @ K E LV I N N E W M A N
  42. 42. S U R V I VA L O F T H E F I T T E S T @ K E LV I N N E W M A N
  43. 43. T H E F I T T E S T W I N O U T AT T H E E X P E N S E O F T H E I R R I VA L S B E C A U S E T H E Y S U C C E E D I N A D A P T I N G T H E M S E LV E S T O T H E I R E N V I R O N M E N T C H A R L E S D A R W I N www.CGPGrey.com @ K E LV I N N E W M A N
  44. 44. P R I M O R D I A L D ATA S O U P O F R A N D O M O N E S A N D Z E R O S
  45. 45. T H E F I R S T H U N D R E D G E N E R AT I O N S W E R E L I T T L E B E T T E R T H A N R A N D O M G U E S S E S JD Hancock @ K E LV I N N E W M A N
  46. 46. By generation #220 there were some encouraging signs. Generation #1400 had a fifty percent success rate. Just after #4000 it worked. @ K E LV I N N E W M A N
  47. 47. S O M E T H I N G S T R A N G E H A P P E N E D I T W O R K E D B U T JD Hancock @ K E LV I N N E W M A N
  48. 48. The circuit solved the problem using thirty-seven logic gates. Five logic cells were connected in ways which wouldn't allow them to influence how the circuit worked. But if you removed them the chip stopped working. @ K E LV I N N E W M A N
  49. 49. When machines teach themselves; they solve problems in ways we can't understand or reverse engineer.
  50. 50. Story Two @ K E LV I N N E W M A N
  51. 51. Mike McCarthy A former marketer for mortgage company Quinn Dombrowski @ K E LV I N N E W M A N
  52. 52. Southern California. 2:45 PM, 6th of May 2010. Jayseaka @ K E LV I N N E W M A N
  53. 53. Mike tried to sell shares he inherited, that should have been worth about $45k but actually sold for closer to $28k @ K E LV I N N E W M A N
  54. 54. @ K E LV I N N E W M A N
  55. 55. Moments before each share was selling for $60. Minutes later they’d returned to that price @ K E LV I N N E W M A N
  56. 56. T H E L O W E S T P R I C E I N S E V E N Y E A R S . @ K E LV I N N E W M A N
  57. 57. it quickly spread across the whole market @ K E LV I N N E W M A N
  58. 58. B I G G E S T O N E D AY D R O P I N T H E D O W J O N E S I N D U S T R I A L AV E R A G E ’ S 1 1 8 Y E A R H I S T O RY @ K E LV I N N E W M A N
  59. 59. The index fell by thousand points or 9% Twenty minutes later it had bounced back six hundred points. @ K E LV I N N E W M A N
  60. 60. T H I R T Y O F T H E U S ’ S B I G G E S T C O M PA N I E S H A D I N S TA N T LY L O S T 9 % O F T H E I R VA L U E @ K E LV I N N E W M A N
  61. 61. JD Hancock @ K E LV I N N E W M A N
  62. 62. W H Y D I D I T H A P P E N ? @ K E LV I N N E W M A N
  63. 63. FAT F I N G E R S T H E T H E O R I E S JD Hancock @ K E LV I N N E W M A N
  64. 64. JD Hancock T H E G L I T C H T H E T H E O R I E S @ K E LV I N N E W M A N
  65. 65. T H E I M PA C T O F H I G H F R E Q U E N C Y T R A D E R S T H E T H E O R I E S JD Hancock @ K E LV I N N E W M A N
  66. 66. JD Hancock I T M AY H AV E B E E N A L L O F T H E S E R E A S O N S , O R N O N E O F T H E M . T H E S Y S T E M I S T O O C O M P L E X T O U N D E R S TA N D B U T T H E A L G O B E H AV E D A S T H E Y S H O U L D T H E R E A L I T Y @ K E LV I N N E W M A N
  67. 67. Algorithms can react in peculiar ways when they come into contact with other algorithms, and the real world
  68. 68. Story Three @ K E LV I N N E W M A N
  69. 69. D R . I A N M A L C O L M M A T H E M A T I C I A N A T T H E U N I V E R S I T Y O F T E X A S A T A U S T I N Jamie Henderson @ K E LV I N N E W M A N
  70. 70. Isla Nublar, Costa Rica @ K E LV I N N E W M A N
  71. 71. @ K E LV I N N E W M A N
  72. 72. N O T R E A L LY D R . I A N M A L C O L M Jamie Henderson
  73. 73. http://brandonbird.com/ D R . I A N M A L C O L M A C T U A L LY F I C T I O N A L C H A R A C T E R I N J U R A S S I C PA R K @ K E LV I N N E W M A N
  74. 74. J U R A S S I C PA R K D O E S A N E X C E L L E N T J O B O F E X P L A I N I N G C H A O S T H E O RY. @ K E LV I N N E W M A N
  75. 75. “ W H E N T H E P R E S E N T D E T E R M I N E S T H E F U T U R E , B U T T H E A P P R O X I M AT E P R E S E N T D O E S N O T A P P R O X I M AT E LY D E T E R M I N E T H E F U T U R E . ” @ K E LV I N N E W M A N Edward Norton Lorenz
  76. 76. P H Y S I C S I S G O O D AT D E S C R I B I N G C E R TA I N K I N D S O F B E H AV I O U R L I N E A R E Q U A T I O N S Niki Odolphie @ K E LV I N N E W M A N
  77. 77. 55Laney69 @ K E LV I N N E W M A N E V E N I F Y O U K N E W E V E RY T H I N G A B O U T H O W T H E W E AT H E R W O R K E D Y O U S T I L L C O U L D N ’ T P R E D I C T I T N O N - L I N E A R E Q U A T I O N S
  78. 78. The weather is s complex & chaotic system @ K E LV I N N E W M A N
  79. 79. @ K E LV I N N E W M A N
  80. 80. C O M P L E X ≠ C O M P L I C AT E D @ K E LV I N N E W M A N
  81. 81. D I F F E R E N C E B E T W E E N S I M P L E , C O M P L I C AT E D A N D C O M P L E X S Y S T E M S Simple Systems Complicated Systems Complex Systems Like Following A Recipe For A Meal Like Sending A Rocket To The Moon Like Being A Parent To A Child The Relationship Between Cause And Effect Is Obvious The Relationship Between Cause And Effect Requires Expert Knowledge The Relationship Between Cause And Effect Is Hard To Determine @ K E LV I N N E W M A N
  82. 82. Even with billions at stake, the world’s greatest scientists cannot predict the outcomes of a complex and chaotic system with precision
  83. 83. S O W H AT ’ S T H I S G O T T O D O W I T H S E A R C H ? @ K E LV I N N E W M A N
  84. 84. H E D O E S N ’ T K N O W H O W G O O G L E W O R K S Steve Jurvetson @ K E LV I N N E W M A N
  85. 85. H E C A N ’ T K N O W H O W G O O G L E W O R K S storyspinn @ K E LV I N N E W M A N
  86. 86. A N D H E H A S N ’ T G O T A B L O O D Y C L U E . @ K E LV I N N E W M A N
  87. 87. G O O G L E ’ S A L G O I S P E R F E C T E X A M P L E O F A C O M P L E X & C H A O T I C S Y S T E M Les Chatfield
  88. 88. A R E A M A C H I N E L E A R N I N G C O M PA N Y @ K E LV I N N E W M A N
  89. 89. http://research.google.com/pubs/ ArtificialIntelligenceandMachineLearning.html i
  90. 90. $400 Million on AI Company @ K E LV I N N E W M A N
  91. 91. When machines teach themselves; they solve problems in ways we can't understand or reverse engineer. JD Hancock @ K E LV I N N E W M A N
  92. 92. Algorithms can react in peculiar ways when they come into contact with other algorithms, and the real world JD Hancock @ K E LV I N N E W M A N
  93. 93. When Google release an update or change to their algorithm they can never fully anticipate how it will work in real world nor how the real world will react to that change. @ K E LV I N N E W M A N
  94. 94. Even with billions at stake, the world’s greatest scientists cannot predict the outcomes of a complex and chaotic system with precision Noah @ K E LV I N N E W M A N
  95. 95. In a complex and chaotic systems the relationship between cause and effect is very hard to determine @ K E LV I N N E W M A N
  96. 96. I used to think the search algorithms work like clocks Arjan Richter @ K E LV I N N E W M A N
  97. 97. they’re more like clouds jojo nicdao @ K E LV I N N E W M A N
  98. 98. Or actually closer to how the climate works. Evil Twin21401 @ K E LV I N N E W M A N
  99. 99. I’m not saying small scale search experiment are a waste of time. @ K E LV I N N E W M A N
  100. 100. Well actually I am. @ K E LV I N N E W M A N
  101. 101. “You don’t need a weatherman to know which way the wind blows” Alberto Cabello @ K E LV I N N E W M A N
  102. 102. “You don’t need a weatherman to know which way the wind blows” goblinbox @ K E LV I N N E W M A N there’s a difference between interesting and useful
  103. 103. three things to do… @ K E LV I N N E W M A N
  104. 104. or actually three books to read @ K E LV I N N E W M A N
  105. 105. @ K E LV I N N E W M A N
  106. 106. @ K E LV I N N E W M A N
  107. 107. @ K E LV I N N E W M A N
  108. 108. A N D F I N A L LY, T H I N K A B O U T H O W Y O U T H I N K A B O U T G O O G L E @ K E LV I N N E W M A N
  109. 109. B E C A U S E , W E N E E D T O T H I N K M O R E ( A B O U T H O W W E T H I N K . ) @ K E LV I N N E W M A N
  110. 110. B U T W H A T A B O U T T H E B O X @ K E LV I N N E W M A N
  111. 111. E S S E N T I A L LY I T ’ S A M A C G U F F I N @ K E LV I N N E W M A N
  112. 112. W H A T I S A M A C G U F F I N ? @ K E LV I N N E W M A N
  113. 113. @ K E LV I N N E W M A N
  114. 114. @ K E LV I N N E W M A N
  115. 115. @ K E LV I N N E W M A N
  116. 116. T H E O B J E C T T H AT D R I V E S T H E S T O RY F O R WA R D A N D I S O F V I TA L I M P O R TA N T T O B O T H T H E H E R O E S A N D V I L L A I N S E V E N I F T H E S P E C I F I C S O F T H E O B J E C T I T S E L F R E M A I N O B S C U R E O R A R E U N I M P O R TA N T. @ K E LV I N N E W M A N
  117. 117. Our box contains all of the Google Algorithm on a USB key, would you open it? @ K E LV I N N E W M A N
  118. 118. W H AT T H E F L A S H C R A S H & B L A C K B O X E S C A N T E A C H U S A B O U T T H E F U T U R E O F S E A R C H . @ K E LV I N N E W M A N

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