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LIVErtising 2018 09 Gr_AI_t Expectations

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Keynote 09 of my Master course in digital marketing communication at IHECS, Brussels

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LIVErtising 2018 09 Gr_AI_t Expectations

  1. 1. LIVErtising.netS 2017-18 1 11 Issues in Marketing Communication:
 LIVErtising 18.09 GrAIt Expectations
  2. 2. LIVErtising.net 2
  3. 3. LIVErtising.net 2
  4. 4. LIVErtising.net 3
  5. 5. LIVErtising.net 4
  6. 6. LIVErtising.net 5
  7. 7. LIVErtising.net 5
  8. 8. LIVErtising.net 6
  9. 9. LIVErtising.net 7
  10. 10. LIVErtising.net 8 AI across industries
  11. 11. LIVErtising.net 8 AI across industries FInance TELECOM HEALTH CARE TRANSPORTATION AUTOMOTIVE RETAIL ENERGY & RESOURCES CONSTRUCTION
  12. 12. LIVErtising.net 9 https://youtu.be/IiDAYktbLfo
  13. 13. LIVErtising.net 10
  14. 14. LIVErtising.net 11
  15. 15. Machine Learning is a core, transformative way by which we’re rethinking how we’re doing everything. We are thoughtfully applying it across all our products, be it search, ads, YouTube, or Play. And we’re in early days, but you will see us - in a systematic way - apply Machine Learning in all these areas. Sundar Pichai, Google CEO, 2016
  16. 16. Machine Learning is a core, transformative way by which we’re rethinking how we’re doing everything. We are thoughtfully applying it across all our products, be it search, ads, YouTube, or Play. And we’re in early days, but you will see us - in a systematic way - apply Machine Learning in all these areas. Sundar Pichai, Google CEO, 2016
  17. 17. LIVErtising.net AI marketing services 14
  18. 18. LIVErtising.net AI marketing services 14
  19. 19. LIVErtising.net AI marketing services 14
  20. 20. LIVErtising.net AI marketing services 14
  21. 21. LIVErtising.net AI marketing services 14
  22. 22. LIVErtising.net AI marketing services 14 Email Search Voice assistants E-tail Programmatic Social listening Predictive mktg PAYMENT
  23. 23. LIVErtising.net 15 https://seomelbourne.com/seo-content-writing/rankbrain-analysing-page-relevance/
  24. 24. LIVErtising.net 16
  25. 25. LIVErtising.net 16 One very effective way to leverage Google Analytics data to target users who are most likely to convert when they come back to a site is to use “Smart Lists”. In the words of Google, “a Smart List is a remarketing audience that Google Analytics generates to maximize your conversions.”
  26. 26. LIVErtising.net 16 The list is generated in Analytics by using many elements, including conversion data, location, device, browser, referrer, session duration, and page depth, in order to identify users who are most likely to convert in subsequent sessions. Machine learning at its best, based on actual relevant data together with Google’s powerful technology. https://www.morevisibility.com/blogs/sem/automate-remarketing-list-optimization-with-smart-lists.html One very effective way to leverage Google Analytics data to target users who are most likely to convert when they come back to a site is to use “Smart Lists”. In the words of Google, “a Smart List is a remarketing audience that Google Analytics generates to maximize your conversions.”
  27. 27. LIVErtising.netIHECS2017-1817 https://event.webcasts.com/starthere.jsp?ei=1191003&tp_key=ae49ec3867&sti=aw_blast1w
  28. 28. LIVErtising.net 18
  29. 29. LIVErtising.net 18 2.What Is AI?
  30. 30. LIVErtising.net 19 A I
  31. 31. AI (or Machine Intelligence) is the area of computer science that allows the simulation of human intelligence by machines. • perception • learning • reasoning • problem solving • self-correction and • communication.
  32. 32. AI (or Machine Intelligence) is the area of computer science that allows the simulation of human intelligence by machines. • perception • learning • reasoning • problem solving • self-correction and • communication. This includes: •perception •learning •reasoning •problem solving •self-correction and •communication
  33. 33. AI (or Machine Intelligence) is the area of computer science that allows the simulation of human intelligence by machines. • perception • learning • reasoning • problem solving • self-correction and • communication. This includes: •perception •learning •reasoning •problem solving •self-correction and •communication
  34. 34. LIVErtising.net 21 Different meanings of AI 01 02 03
  35. 35. LIVErtising.net 21 Different meanings of AI Scientific discipline01 02 03
  36. 36. LIVErtising.net 21 Different meanings of AI Scientific discipline01 02 03 Artificial capacity
  37. 37. LIVErtising.net 21 Different meanings of AI Scientific discipline01 02 03 Artificial capacity Agents
  38. 38. LIVErtising.net 22 It must be stressed that the demarcation between AI and software in general is not sharp. Nick Bostrom, Superintellingence, p19
  39. 39. LIVErtising.net 23 What horizon does AI have? ANJ 2018 2040 +30
  40. 40. LIVErtising.net 23 What horizon does AI have? ANJ ANI2018 2040 +30
  41. 41. LIVErtising.net 23 What horizon does AI have? ANJ ANI Agi2018 2040 +30
  42. 42. LIVErtising.net 23 What horizon does AI have? ANJ ANI Agi Asi2018 2040 +30
  43. 43. LIVErtising.net 24 The basic AI fields 01 02 03 04 MOVE, WALK, RUN LOOK & SEE Extract / Generate Meaning IDENTIFY PATTERNS, GROUP OBJECTS SPEAK LISTEN WRITE-READ 05
  44. 44. LIVErtising.net 24 The basic AI fields Physical AI 01 02 03 04 MOVE, WALK, RUN LOOK & SEE Extract / Generate Meaning IDENTIFY PATTERNS, GROUP OBJECTS SPEAK LISTEN WRITE-READ 05
  45. 45. LIVErtising.net 24 The basic AI fields Physical AI Computer Vision 01 02 03 04 MOVE, WALK, RUN LOOK & SEE Extract / Generate Meaning IDENTIFY PATTERNS, GROUP OBJECTS SPEAK LISTEN WRITE-READ 05
  46. 46. LIVErtising.net 24 The basic AI fields Physical AI Computer Vision Speech rec & Synt 01 02 03 04 MOVE, WALK, RUN LOOK & SEE Extract / Generate Meaning IDENTIFY PATTERNS, GROUP OBJECTS SPEAK LISTEN WRITE-READ 05
  47. 47. LIVErtising.net 24 The basic AI fields Physical AI Computer Vision Speech rec & Synt NLP 01 02 03 04 MOVE, WALK, RUN LOOK & SEE Extract / Generate Meaning IDENTIFY PATTERNS, GROUP OBJECTS SPEAK LISTEN WRITE-READ 05
  48. 48. LIVErtising.net 24 The basic AI fields Physical AI Computer Vision Speech rec & Synt NLP Heuristic Classification 01 02 03 04 MOVE, WALK, RUN LOOK & SEE Extract / Generate Meaning IDENTIFY PATTERNS, GROUP OBJECTS SPEAK LISTEN WRITE-READ 05
  49. 49. LIVErtising.net 25
  50. 50. LIVErtising.net 25 wHY 3.Now?
  51. 51. LIVErtising.net 26 A very brief historical perspective of AI AI Prehistory Alan TURING Isaac ASIMOV Dartmouth Conference “AI winters” Deep Blue IBM Watson ALPHA GO 1945-50 1956 1974-80 1987 - 93 1997 2011 2016
  52. 52. LIVErtising.net 26 A very brief historical perspective of AI AI Prehistory Alan TURING Isaac ASIMOV Dartmouth Conference “AI winters” Deep Blue IBM Watson ALPHA GO 1945-50 1956 1974-80 1987 - 93 1997 2011 2016
  53. 53. LIVErtising.net 26 A very brief historical perspective of AI AI Prehistory Alan TURING Isaac ASIMOV Dartmouth Conference “AI winters” Deep Blue IBM Watson ALPHA GO 1945-50 1956 1974-80 1987 - 93 1997 2011 2016
  54. 54. LIVErtising.net 26 A very brief historical perspective of AI AI Prehistory Alan TURING Isaac ASIMOV Dartmouth Conference “AI winters” Deep Blue IBM Watson ALPHA GO 1945-50 1956 1974-80 1987 - 93 1997 2011 2016
  55. 55. LIVErtising.net 26 A very brief historical perspective of AI AI Prehistory Alan TURING Isaac ASIMOV Dartmouth Conference “AI winters” Deep Blue IBM Watson ALPHA GO 1945-50 1956 1974-80 1987 - 93 1997 2011 2016
  56. 56. LIVErtising.net 26 A very brief historical perspective of AI AI Prehistory Alan TURING Isaac ASIMOV Dartmouth Conference “AI winters” Deep Blue IBM Watson ALPHA GO 1945-50 1956 1974-80 1987 - 93 1997 2011 2016
  57. 57. LIVErtising.net 27 Why now? 01 02 03 04
  58. 58. LIVErtising.net 27 Why now? Big data access 01 02 03 04
  59. 59. LIVErtising.net 27 Why now? Big data access Computing power 01 02 03 04
  60. 60. LIVErtising.net 27 Why now? ALGORITHMS Big data access Computing power 01 02 03 04
  61. 61. LIVErtising.net 27 Why now? ALGORITHMS Big data access Computing power APIs 01 02 03 04
  62. 62. LIVErtising.net 27 Why now? ALGORITHMS Big data access Computing power APIs 01 02 03 04
  63. 63. LIVErtising.net 27 Why now? ALGORITHMS Big data access Computing power APIs 01 02 03 04
  64. 64. LIVErtising.net 27 Why now? ALGORITHMS Big data access Computing power APIs 01 02 03 04
  65. 65. LIVErtising.net 27 Why now? ALGORITHMS Big data access Computing power APIs 01 02 03 04
  66. 66. LIVErtising.net 27 Why now? ALGORITHMS Big data access Computing power APIs 01 02 03 04
  67. 67. LIVErtising.net 28 An ALGORITHM is a set of instructions that tells an agent what to do. ALGORITHMS
  68. 68. LIVErtising.net 28 An ALGORITHM is a set of instructions that tells an agent what to do. ALGORITHMS
  69. 69. LIVErtising.net 28 An ALGORITHM is a set of instructions that tells an agent what to do. ALGORITHMS (…) it’s a procedure that helps solve a problem, without each time inventing a solution (ABITEBOUL & DOWEK, 2017, 11)
  70. 70. LIVErtising.net 29 (…) it’s a procedure that helps solve a problem, without each time inventing a solution (ABITEBOUL & DOWEK, 2017, 11)
  71. 71. LIVErtising.net 29 (…) it’s a procedure that helps solve a problem, without each time inventing a solution (ABITEBOUL & DOWEK, 2017, 11)
  72. 72. LIVErtising.net 30 Algorithm to convert Fahrenheit into Celsius: 1. Read Fahrenheit temperature 2. Calculate and write Celcius temperature 3. Stop
  73. 73. LIVErtising.net 30 Algorithm to convert Fahrenheit into Celsius: 1. Read Fahrenheit temperature 2. Calculate and write Celcius temperature 3. Stop
  74. 74. LIVErtising.net 31 1.1 1.2 Algorithm to convert Fahrenheit into Celsius: 1. Read Fahrenheit temperature and write it to memory as “F” 2.Calculate and write Celcius temperature: 1. Subtract 32 from F and multiply by 5/9 2. Write the result to memory as C 3.Stop
  75. 75. LIVErtising.netIHECS2017-1832 He introduced the theory that information is a measurable element and defined the basic unit, which would later be called a “bit”.
  76. 76. LIVErtising.net 33
  77. 77. LIVErtising.net 33 Replace a by b Do c then d While e, repeat f If g then h
  78. 78. LIVErtising.net 33 1. Attribution 2. Sequence 3. Loop 4. Test Replace a by b Do c then d While e, repeat f If g then h
  79. 79. LIVErtising.netIHECS2017-1834
  80. 80. LIVErtising.netIHECS2017-1834
  81. 81. LIVErtising.netIHECS2017-1834
  82. 82. LIVErtising.net 35
  83. 83. LIVErtising.net 36
  84. 84. LIVErtising.net 36 Symbolic approach / Deterministic Programming
  85. 85. LIVErtising.net 36 Symbolic approach / Deterministic Programming
  86. 86. LIVErtising.net 36 Symbolic approach / Deterministic Programming 1. Space Complexity 2. Time Complexity 3. Human Complexity
  87. 87. LIVErtising.net 37
  88. 88. LIVErtising.net 38
  89. 89. LIVErtising.net 38
  90. 90. LIVErtising.net 38
  91. 91. LIVErtising.net 39 4.machine LEARNING
  92. 92. LIVErtising.net 40
  93. 93. LIVErtising.net 40
  94. 94. LIVErtising.net 41 https://a360-wp-uploads.s3.amazonaws.com/wp-content/uploads/hearingr/2018/03/Figure1-3.jpg
  95. 95. LIVErtising.net 41 https://a360-wp-uploads.s3.amazonaws.com/wp-content/uploads/hearingr/2018/03/Figure1-3.jpg
  96. 96. LIVErtising.net 41 https://a360-wp-uploads.s3.amazonaws.com/wp-content/uploads/hearingr/2018/03/Figure1-3.jpg
  97. 97. AI is the replication of human intelligence in computers Radu Raicea
  98. 98. AI is the replication of human intelligence in computers Radu Raicea Machine Learning refers to the ability of a machine to learn using large data sets instead of hard coded rules Radu Raicea
  99. 99. AI is the replication of human intelligence in computers Radu Raicea Machine Learning refers to the ability of a machine to learn using large data sets instead of hard coded rules Radu Raicea Deep learning are algorithms arranged in layers to mimic the brain’s learning patters Daniel Ramsay
  100. 100. LIVErtising.net 43 Machine learning / Deep Learning Symbolic approach / Deterministic Programming
  101. 101. LIVErtising.net 43 Machine learning / Deep Learning Symbolic approach / Deterministic Programming
  102. 102. LIVErtising.net 44
  103. 103. LIVErtising.net 44
  104. 104. LIVErtising.net 44
  105. 105. LIVErtising.net 44
  106. 106. LIVErtising.net 45 https://medium.freecodecamp.org/want-to-know-how-deep-learning-works-heres-a-quick-guide-for-everyone-1aedeca88076
  107. 107. LIVErtising.net 46 https://medium.freecodecamp.org/want-to-know-how-deep-learning-works-heres-a-quick-guide-for-everyone-1aedeca88076
  108. 108. LIVErtising.net 47
  109. 109. LIVErtising.netIHECS2017-1848
  110. 110. LIVErtising.net 49 https://www.mathworks.com/content/dam/mathworks/tag-team/Objects/i/88174_92991v00_machine_learning_section1_ebook.pdf
  111. 111. LIVErtising.net 50 https://www.mathworks.com/content/dam/mathworks/tag-team/Objects/i/88174_92991v00_machine_learning_section1_ebook.pdf
  112. 112. LIVErtising.net 50 https://www.mathworks.com/content/dam/mathworks/tag-team/Objects/i/88174_92991v00_machine_learning_section1_ebook.pdf
  113. 113. LIVErtising.net 50 https://www.mathworks.com/content/dam/mathworks/tag-team/Objects/i/88174_92991v00_machine_learning_section1_ebook.pdf
  114. 114. LIVErtising.net 50 https://www.mathworks.com/content/dam/mathworks/tag-team/Objects/i/88174_92991v00_machine_learning_section1_ebook.pdf
  115. 115. LIVErtising.net 51 Two stages in ML https://insidebigdata.com/2016/06/03/how-to-use-analytics-driven-embedded-systems-to-drive-smart-technology-development/ 2 1
  116. 116. LIVErtising.net 51 Two stages in ML https://insidebigdata.com/2016/06/03/how-to-use-analytics-driven-embedded-systems-to-drive-smart-technology-development/ 2
  117. 117. LIVErtising.net 51 Two stages in ML https://insidebigdata.com/2016/06/03/how-to-use-analytics-driven-embedded-systems-to-drive-smart-technology-development/
  118. 118. LIVErtising.net 52 https://teachablemachine.withgoogle.com/
  119. 119. LIVErtising.net 53
  120. 120. LIVErtising.net 53 5.LIMITS
  121. 121. LIVErtising.net 54 The limits of Machine Learning 01 02 03 04 05
  122. 122. LIVErtising.net 54 The limits of Machine Learning 01 02 03 04 05 Data sets
  123. 123. LIVErtising.net 54 The limits of Machine Learning 01 02 03 04 05 Data sets Data labelling
  124. 124. LIVErtising.net 54 The limits of Machine Learning 01 02 03 04 05 Data sets Data labelling Opacity
  125. 125. LIVErtising.net 54 The limits of Machine Learning 01 02 03 04 05 Data sets Data labelling Opacity Generalization
  126. 126. LIVErtising.net 54 The limits of Machine Learning 01 02 03 04 05 Data sets Data labelling Opacity Generalization Bias generation
  127. 127. LIVErtising.net 55
  128. 128. LIVErtising.net 56
  129. 129. LIVErtising.net 56 6.advert(A)Ising?
  130. 130. LIVErtising.net 57 AI contribution to Marketing Communication
  131. 131. LIVErtising.net 57 AI contribution to Marketing Communication Predicting consumer behaviour
  132. 132. LIVErtising.net 57 AI contribution to Marketing Communication Predicting consumer behaviour Personalizing contents & com
  133. 133. LIVErtising.net 57 AI contribution to Marketing Communication Predicting consumer behaviour Personalizing contents & com Detecting trends
  134. 134. LIVErtising.net 57 AI contribution to Marketing Communication Predicting consumer behaviour Personalizing contents & com Detecting trends Optimizing the Ps
  135. 135. LIVErtising.net 57 AI contribution to Marketing Communication Predicting consumer behaviour Personalizing contents & com Detecting trends Optimizing the Ps Integrating Mar & Com data
  136. 136. LIVErtising.net 57 AI contribution to Marketing Communication Predicting consumer behaviour Personalizing contents & com Detecting trends Optimizing the Ps Integrating Mar & Com data Analytics & Attributing Results
  137. 137. LIVErtising.net 57 AI contribution to Marketing Communication Predicting consumer behaviour Personalizing contents & com Detecting trends Optimizing the Ps Integrating Mar & Com data Analytics & Attributing Results Monitoring the Brand
  138. 138. LIVErtising.net 58 Personalizing contents & com
  139. 139. LIVErtising.net 59 Personalizing contents & com
  140. 140. LIVErtising.net 60 Personalizing contents & com
  141. 141. LIVErtising.net 61 Personalizing contents & com
  142. 142. LIVErtising.netS 2017-18 Personalizing contents & com
  143. 143. LIVErtising.netS 2017-18 Personalizing contents & com DSA
  144. 144. LIVErtising.net 63
  145. 145. LIVErtising.net 63 Optimizing the Ps
  146. 146. LIVErtising.net 64 Optimizing the Ps
  147. 147. LIVErtising.net 65 Analytics & Attributing Results
  148. 148. LIVErtising.net 65 Analytics & Attributing Results
  149. 149. LIVErtising.net 66 Attribution models
  150. 150. LIVErtising.net 67 AI contribution to Marketing Communication Predicting consumer behaviour Personalizing contents & com Detecting trends Optimizing the Ps Integrating Mar & Com data Analytics & Attributing Results
  151. 151. LIVErtising.net 67 AI contribution to Marketing Communication Predicting consumer behaviour Personalizing contents & com Detecting trends Optimizing the Ps Integrating Mar & Com data Analytics & Attributing Results Monitoring the Brand
  152. 152. LIVErtising.net 68 Monitoring the Brand
  153. 153. LIVErtising.net 68 Monitoring the Brand
  154. 154. LIVErtising.net 69
  155. 155. LIVErtising.netS 2017-18 70 Monitoring the Brand
  156. 156. LIVErtising.net 71
  157. 157. LIVErtising.net 72
  158. 158. LIVErtising.net 73
  159. 159. LIVErtising.net IHECS 2016-17 74
  160. 160. LIVErtising.netIHECS2017-1875 http://www.altimetergroup.com/pdf/reports/Image-Intelligence-Making-Visual-Content-Predictive-Altimeter.pdf
  161. 161. LIVErtising.netIHECS2017-1875 http://www.altimetergroup.com/pdf/reports/Image-Intelligence-Making-Visual-Content-Predictive-Altimeter.pdf
  162. 162. LIVErtising.netIHECS2017-1875 http://www.altimetergroup.com/pdf/reports/Image-Intelligence-Making-Visual-Content-Predictive-Altimeter.pdf
  163. 163. LIVErtising.netIHECS2017-1875 http://www.altimetergroup.com/pdf/reports/Image-Intelligence-Making-Visual-Content-Predictive-Altimeter.pdf
  164. 164. LIVErtising.netIHECS2017-1875 http://www.altimetergroup.com/pdf/reports/Image-Intelligence-Making-Visual-Content-Predictive-Altimeter.pdf
  165. 165. LIVErtising.netS 2017-18 76
  166. 166. LIVErtising.netS 2017-18 76
  167. 167. LIVErtising.netS 2017-18 77 Monitoring the Brand
  168. 168. LIVErtising.netS 2017-18 77 Monitoring the Brand
  169. 169. LIVErtising.netS 2017-18 77 Monitoring the Brand
  170. 170. LIVErtising.netS 2017-18 77 Monitoring the Brand
  171. 171. LIVErtising.netS 2017-18 77 Monitoring the Brand
  172. 172. LIVErtising.netIHECS2017-1878 https://pages.crimsonhexagon.com/rs/284-XQB-702/images/Consumer-Insights-Team-Guide.pdf
  173. 173. LIVErtising.netS 2017-18 79 pre-‘90s promotion interaction SOCIAL connect tivity immersion offline digital ‘90s 2004 2007 2015 2017 community MOBILE & local virtual Based on Rori Duboff, Next Frontiers of Marketing, Slideshare, 2016. Recent evolution of THE MEDIA algo rhithmic singularity
  174. 174. LIVErtising.net 80 Recent evolution of COMMUNICATION MODELS 1 2 3 4
  175. 175. LIVErtising.net 81 7.all Rosy?
  176. 176. LIVErtising.net 82
  177. 177. LIVErtising.net 83
  178. 178. LIVErtising.net 83
  179. 179. LIVErtising.net 83
  180. 180. LIVErtising.net 83
  181. 181. LIVErtising.net 83
  182. 182. LIVErtising.net 84 Succes means always wanting to get more. Happiness means realising that what you have is fabulous.
  183. 183. LIVErtising.net 85

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