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
- 5. AI AI AI AI
- 6. 1. AI
- 9. /
- 10. 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…??
- 12. 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
- 16. LR( )
- 17. LM( ) vs. LR( )
- 18. XàY
- 20. Netflix Recommendation Challenge 2006~2009 (Netflix )
- 21. User-based vs. Item-based Recommendation
- 23. 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
- 24. 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
- 25. 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
- 26. 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
- 28. 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
- 29. A m = n S k k• T’ n m •Σ Singular Value Decomposition Eigen vector
- 30. Sample FM Matrix
- 31. FM with SGD
- 33. Richness
- 34. vs.
- 35. - - -
- 36. -
- 38. World, Model & Theory Credit: John F. Sowa generalized statements, proven scientifically with evidence Simplified representation, helpful tool to understand specific phenomena NormativePrescriptiveDescriptive
- 39. Model?!
- 40. 2. AI
- 46. 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
- 47. Data Science is the ART tuning data into Action Segments Reports For Human (Explanatory) Models Data-driven Actions Intelligence Effectiveness
- 48. - -
- 50. 3. AI
- 51. 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
- 52. Amazon Echo
- 53. Amazon Echo Dot vs. Google Home Mini $29 vs. £49 15,000+ skills
- 54. PC Mobile Things
- 55. Tesla + Uber + X = ?
- 56. 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?
- 57. Box Moving Things Users For example: Camera
- 58. IoT Service Users CloudThings IoT Service For example: Home Surveillance Low service feeHigh price
- 59. AIoT Service Users Data Cloud Training/ Inference Things AIoT Service For example: Smart Home Surveillance
- 60. AIoT Service(Reverse) Users Data Cloud Training/ Inference Things AIoT Service Low price Main revenue stream
- 61. Users Data Cloud Training/ Inference Things AIoT Service Low price - - A - - - AIoT Service(Paradigm Shift)
- 62. Users Data Cloud Training/ Inference Things AIoT Service Low price - - A - - - AIoT Service(Paradigm Shift) Prescriptive Nomative Descriptive
- 65. Long Tail of user-generated niche content
- 67. Google Multi-sided Business Model
- 69. Why Vertical Integration? Microsoft Surface Book
- 71. Why Merge?
- 72. Amazon vs. XiaoMi Competitive pricing hardwares Mi store vs Amazon market Mi UI vs Alexa 1000- products 1500+ products
- 74. 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 -.
- 75. IoT Internet of Things Internet of Transformation
- 76. AIoT AI - Internet of Things AI - Internet of Transformation
- 77. Artificial Power Artificial Intelligence AIR/AIoT Artificial Power à Artificial Intelligence à