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7 Myths of AI

When it comes to AI and its applications, there are a number of myths being perpetuated by the mainstream media. It's time to dispel these myths because the opportunity to apply AI to your business is real.

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7 Myths of AI

  1. 1. Today’s Presenter R OB IN B OR D OLI @rbordoli Husband, father, Brit, Cambridge & Stanford grad. CEO @CrowdFlower the #HumanInTheLoop platform for #DataScience & #MachineLearning teams making #AI work
  2. 2. Mainstream media AI coverage
  3. 3. Emotionally charged AI stories
  4. 4. AI humor
  5. 5. As a result, there are some Myths about AI being perpetuated
  6. 6. It’s time to dispel these Myths…
  7. 7. …because the opportunity to apply AI to your business is real
  8. 8. The 7 Myths of AI
  9. 9. M Y T H 1 AI is Magic
  10. 10. AI is Data, Math, Patterns & Iteration
  11. 11. TD = Training Data
  12. 12. ML = Machine Learning
  13. 13. Let’s just take a peek inside “Machine Learning Model”
  14. 14. ML = Patterns & Iteration
  15. 15. ML = Patterns & Iteration
  16. 16. ML = Patterns & Iteration
  17. 17. Do I have enough variables? • Jane was on a 14 day vacation.  Need a new variable “Work Day” with TRUE/FALSE values • Jane was sick on day 15.  Need a new variable “Healthy” with TRUE/FALSE values Do I have enough training data? • On day 14 the Outlook was “Thunder & Lightning” • On day 15 the Temperature was 106°F ML = Patterns & Iteration
  18. 18. OK, let’s zoom out from “Machine Learning Model”
  19. 19. ML = Machine Learning
  20. 20. ML = Machine Learning
  21. 21. ML = Machine Learning
  22. 22. 4 HITL = Human-in-the-Loop
  23. 23. M Y T H 2 AI is only for the technology elite
  24. 24. Data Scientists Compute Data Storage Machine Learning in the Cloud
  25. 25. 26,372 Businesses with revenue > $50M
  26. 26. <0.2% Revenue $100k investment to start AI
  27. 27. M Y T H 3 AI is only for billion dollar new problems
  28. 28. AI is for million dollar existing problems T R U T H 3
  29. 29. Categorizing support tickets
  30. 30. TD = Training Data
  31. 31. ML = Machine Learning
  32. 32. ML = Machine Learning
  33. 33. ML = Machine Learning
  34. 34. 4 HITL= Human in the Loop
  35. 35. M Y T H 4 Algorithms are more important than data
  36. 36. Effect of Better Algorithms Classifier Error Rate
  37. 37. T R U T H 4 Algorithms are not more important than the quality and quantity of data
  38. 38. Classifier Error Rate Effect of Data Quantity
  39. 39. Effect of Data Quality Classifier Error Rate
  40. 40. M Y T H 5 Machines > Humans
  41. 41. Understanding the Triumph of AlphaGo PERCEPTION: Machine Defeats Human
  42. 42. T R U T H 5 Machines complement Humans
  43. 43. Machines and Humans have Different Capabilities Find the Eigenvectors Find the Leopard Print Dress
  44. 44. M Y T H 6 AI is Machines replacing Humans
  45. 45. T R U T H 6 AI is Machines augmenting Humans
  46. 46. Machine High Confidence Predictions Human Low Confidence Predictions
  47. 47. M Y T H 7 AI = ML
  48. 48. ML without TD is like a Car without Gas
  49. 49. ML without HITL leads to bad outcomes
  50. 50. T R U T H 7 AI = TD + ML + HITL
  51. 51. The 7 Truths of AI
  52. 52. M Y T H 1 AI is Magic
  53. 53. M Y T H 2 AI is only for the technology elite
  54. 54. M Y T H 3 AI is only for billion dollar new problems
  55. 55. AI is for million dollar existing problems T R U T H 3
  56. 56. M Y T H 4 Algorithms are more important than data
  57. 57. T R U T H 4 Algorithms are not more important than the quality and quantity of data
  58. 58. M Y T H 5 Machines > Humans
  59. 59. T R U T H 5 Machines complement Humans
  60. 60. M Y T H 6 AI is Machines replacing Humans
  61. 61. T R U T H 6 AI is Machines augmenting Humans
  62. 62. M Y T H 7 AI = ML
  63. 63. T R U T H 7 AI = TD + ML + HITL
  64. 64. ai.crowdflower.com

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When it comes to AI and its applications, there are a number of myths being perpetuated by the mainstream media. It's time to dispel these myths because the opportunity to apply AI to your business is real.

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