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人工智慧與物聯網的創新與服務模式

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這是在台灣人工智慧學校的經驗心得分享,會後有些學員來交流互動,希望對大家能有些幫助,加速人工智慧創新、在國際 AI 創新競爭中,走出自己的路。

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人工智慧與物聯網的創新與服務模式

  1. 1. AI chaocraig@gmail.com https://www.slideshare.net/chaocraig/
  2. 2. Cray u u u u u u u u AMI BD u u u BBS
  3. 3. AI AI AI Q & A Agenda
  4. 4. AI AI AI AI
  5. 5. 1. AI
  6. 6. Src: Fortune, 2016/09
  7. 7. /
  8. 8. First Principle Thinking Reasoning from first principles, to break down complicated problems into basic elements and then reassemble them from the ground up. First introduced by Aristotle more than 2,000 years ago Tesla Modal 3 is a rapid (modular/system) innovation but not a disruptive innovation?! How about SpaceX? AI…??
  9. 9. First Principle Thinking
  10. 10. Knowledge Building & Decision Making: Know-what, Know-why, Know-how Objective? Scientific? Feasible? Know-how (Feasible?) Prescriptive Know-Why (Scientific?) Normative Know-What (Objective?) Descriptive Prescriptive D-M: How decisions could be made better? Descriptive D-M: How decisions are made? Normative D-M: How decisions should be made? Entrepreneurship Harvard DBA Harvard Business Review BFCGED
  11. 11. LR( )
  12. 12. LM( ) vs. LR( )
  13. 13. XàY
  14. 14. User-based Recommendation
  15. 15. Netflix Recommendation Challenge 2006~2009 (Netflix )
  16. 16. User-based vs. Item-based Recommendation
  17. 17. Clustering/Categorization Recommendation
  18. 18. Matrix = Associations Rose Navy Olive Alice 0 +4 0 Bob 0 0 +2 Carol -1 0 -2 Dave +3 0 0 Things are associated Like people to colors Associations have strengths Like preferences and dislikes Can quantify associations Alice loves navy = +4, Carol dislikes olive = -2 We don’t know all associations Many implicit zeroes Source: Sean Owen(2012), Cloudera
  19. 19. In Terms of Few Features Can explain associations by appealing to underlying features in common (e.g. “blue-ness”) Relatively few (one “blue-ness”, but many shades) (Alice) (Blue) (Navy) Source: Sean Owen(2012), Cloudera
  20. 20. Losing Information is Helpful When k (= features) is small, information is lost Factorization is approximate (Alice appears to like blue-ish periwinkle too) (Alice) (Blue) (Navy) (Periwinkle) Source: Sean Owen(2012), Cloudera
  21. 21. Eigen value, Eigen Vector & Factor Analysis eigen- is adopted from the German word eigen for "proper", "characteristic". In the 18th century Euler studied the rotational motion of a rigid body and discovered the importance of the principal axes. Lagrange realized that the principal axes are the eigenvectors of the inertia matrix. square matrix: A column vector: v
  22. 22. Src: AmpCamp, 2015
  23. 23. ALS Algorithm • Optimizing X, Y simultaneously is non-convex,hard • If X or Y are fixed, system of linear equations:convex,easy • Initialize Y with random values • Solve for X • Fix X, solve for Y • Repeat (“Alternating”) X YT
  24. 24. A m = n S k k• T’ n m •Σ Singular Value Decomposition Eigen vector
  25. 25. Sample FM Matrix
  26. 26. FM with SGD
  27. 27. Context-aware Matrix Factorization
  28. 28. Richness
  29. 29. vs.
  30. 30. - - -
  31. 31. -
  32. 32. World, Model & Theory Credit: John F. Sowa generalized statements, proven scientifically with evidence Simplified representation, helpful tool to understand specific phenomena NormativePrescriptiveDescriptive
  33. 33. Model?!
  34. 34. 2. AI
  35. 35. Knowledge Building & Decision Making: Know-what, Know-why, Know-how Objective? Scientific? Feasible? Know-how (Feasible?) Prescriptive Know-Why (Scientific?) Normative Know-What (Objective?) Descriptive Prescriptive D-M: How decisions could be made better? Descriptive D-M: How decisions are made? Normative D-M: How decisions should be made? Entrepreneurship Harvard DBA Harvard Business Review BFCGED
  36. 36. Data Science is the ART tuning data into Action Segments Reports For Human (Explanatory) Models Data-driven Actions Intelligence Effectiveness
  37. 37. - -
  38. 38. 3. AI
  39. 39. A new, louder Echo Dot $49.99 Echo Auto $24.99 Echo Sub $129.99 Fire TV Recast $229.99 A slicker Echo Show $229 A new Ring security camera $179 The speaker-less Echo Input $34.99 Alexa microwave $59.99 An Alexa Clock that visualizes your timers $29.99
  40. 40. Amazon Echo
  41. 41. Amazon Echo Dot vs. Google Home Mini $29 vs. £49 15,000+ skills
  42. 42. PC Mobile Things
  43. 43. Tesla + Uber + X = ?
  44. 44. Paradigm Shift(Reverse) Move u Data à program Value u Things à Product Service à Personal Service u Value/revenue shift u What if phone price is near its cost or free?
  45. 45. Box Moving Things Users For example: Camera
  46. 46. IoT Service Users CloudThings IoT Service For example: Home Surveillance Low service feeHigh price
  47. 47. AIoT Service Users Data Cloud Training/ Inference Things AIoT Service For example: Smart Home Surveillance
  48. 48. AIoT Service(Reverse) Users Data Cloud Training/ Inference Things AIoT Service Low price Main revenue stream
  49. 49. Users Data Cloud Training/ Inference Things AIoT Service Low price - - A - - - AIoT Service(Paradigm Shift)
  50. 50. Users Data Cloud Training/ Inference Things AIoT Service Low price - - A - - - AIoT Service(Paradigm Shift) Prescriptive Nomative Descriptive
  51. 51. Business Model Canvas
  52. 52. Traditional Publishing Industry
  53. 53. Long Tail of user-generated niche content
  54. 54. Google Multi-sided Business Model
  55. 55. Why Vertical Integration? Microsoft Surface Book
  56. 56. Why Vertical Integration?
  57. 57. Why Merge?
  58. 58. Amazon vs. XiaoMi Competitive pricing hardwares Mi store vs Amazon market Mi UI vs Alexa 1000- products 1500+ products
  59. 59. Knowledge Building & Decision Making: Know-what, Know-why, Know-how Objective? Scientific? Feasible? Know-how (Feasible?) Prescriptive Know-Why (Scientific?) Normative Know-What (Objective?) Descriptive Prescriptive D-M: How decisions could be made better? Descriptive D-M: How decisions are made? Normative D-M: How decisions should be made? Entrepreneurship Harvard DBA Harvard Business Review BFCGED - A -.
  60. 60. IoT Internet of Things Internet of Transformation
  61. 61. AIoT AI - Internet of Things AI - Internet of Transformation
  62. 62. Artificial Power Artificial Intelligence AIR/AIoT Artificial Power à Artificial Intelligence à
  63. 63. Thanks!Any questions? chaocraig@gmail.com https://www.slideshare.net/chaocraig/

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