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How Deep Learning Changes the Design Process #NEXT17

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+ Updated version for NEXT Conference Hamburg +
https://nextconf.eu/event/how-deep-learning-is-changing-the-design-process/

Deep learning is a new and exciting subfield of machine learning which attempts to sidestep the whole feature design process. This session explains how it derives from AI, why it quietly became a part of user experience and how it also changes the actual design workflow. The talk highlights a range of use cases and doesn’t forget to illustrate why user experience design for artificial intelligence matters the other way around.

Published in: Design
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How Deep Learning Changes the Design Process #NEXT17

  1. 1. ALEXANDER MEINHARDT @KUTTIN3DGE KRUNCHTIME.ORG
  2. 2. © Lawrence Lek HOW DEEP LEARNING IS CHANGING THE DESIGN PROCESS
  3. 3. WHAT IS DEEP LEARNING? © Lawrence Lek© Lawrence Lek
  4. 4. Artificial Intelligence Machine Learning Deep Learning
  5. 5. Artificial Intelligence Machine Learning Deep Learning Early artificial intelligence stirs exitement Machine learning begins to flourish. Deep learning breaktroughs drive AI boom. 1950’s 1950’s 1970’s 1980’s 1990’s 2000’s 2010’s
  6. 6. THE TERMS ARE OFTEN USED INTERCHANGEABLY – YET THEY AREN’T THE SAME
  7. 7. Machine Learning Machine learning is the ability to learn without being explicitly programmed. It helps programs to learn from their own mistakes and improve their performance over time.
  8. 8. Machine Learning Data mining Image recognition Facebook newsfeed Netflix suggestions Big Data analysis Amazon suggestions Predictive analytics
  9. 9. Machine Learning Notifications Predictive Keyboards Spam/Quality Control Systems Copywriting Design Patterns Gesture Recognition Content Actions/ Feedback Magic Numbers Search Related Content Character Recognition
  10. 10. Machine Learning Notifications Predictive Keyboards Spam/Quality Control Systems Copywriting Design Patterns Gesture Recognition Content Actions/ Feedback Magic Numbers Search Related Content Character Recognition
  11. 11. Artificial IntelligenceAI landscape © Narrative Science
  12. 12. © Narrative Science
  13. 13. Artificial IntelligenceAI landscape © cloud-nqb.com
  14. 14. Artificial IntelligenceAI landscape © Lux Research
  15. 15. © Matthew Mayo Machine Learning Artifical Intelligence Data Mining Data Science Deep Learning Big Data
  16. 16. Deep Learning Deep Learning is based on Artificial Neural Networks. These are systems that are designed to process information in ways that are similar to the ways biological brains work. It uses a certain set of Machine Learning algorithms that run in multiple layers.
  17. 17. Artificial IntelligenceMachine Learning vs Deep Learning © Rasmus Rothe
  18. 18. AI HAS BY NOW SUCCEEDED IN DOING ESSENTIALLY EVERYTHING THAT REQUIRES ‘THINKING’ Donald Knuth, Stanford University
  19. 19. BUT HAS FAILED TO DO MOST OF WHAT PEOPLE DO ‘WITHOUT THINKING’ Donald Knuth, Stanford University
  20. 20. Possible Desirable Profitable A user experience powered by a system that learns © Fabien Girardin
  21. 21. TensorFlow / Lite Caffe2Go Machine Learning Kit
  22. 22. © Lawrence Lek WHAT CAN BE DONE TODAY? © Lawrence Lek
  23. 23. © Frank Chen
  24. 24. © Frank Chen
  25. 25. © Frank Chen
  26. 26. © Frank Chen
  27. 27. © Frank Chen
  28. 28. Image recognition error rate
  29. 29. COMPUTER VISION USE CASES
  30. 30. Google Lens Can an algorithm see what you see and help you take action based on this information?
  31. 31. Google Lens
  32. 32. Google Lens
  33. 33. Google Search
  34. 34. Blippr
  35. 35. Photoeditor SDK Can an algorithm extract foreground content from its background?
  36. 36. Photoeditor SDK
  37. 37. Photoeditor SDK
  38. 38. Adobe - Deep Image Matting
  39. 39. Adobe - Deep Image Matting
  40. 40. Adobe - Deep Image Matting
  41. 41. Adobe - Deep Image Matting
  42. 42. Google Research Can an algorithm remove watermarks from stock photos?
  43. 43. Google Research
  44. 44. Google Research
  45. 45. Google Research
  46. 46. Google Research Can an algorithm understand what makes good photography?
  47. 47. Google Research
  48. 48. Google Research
  49. 49. Google Research
  50. 50. Google Research
  51. 51. EyeEm - Vision Can an algorithm learn and suggest aesthetics in a way an art director would?
  52. 52. EyeEm - VisionEyeEm - Vision
  53. 53. Neural Storyteller Can an algorithm make up poetry from a given image?
  54. 54. Neural Storyteller
  55. 55. Neural Storyteller
  56. 56. Neural Storyteller
  57. 57. Rapto Can an algorithm make up lyrics from any given object?
  58. 58. HOW DEEP LEARNING CHANGES THE DESIGN PROCESS Rapto
  59. 59. FACE RECOGNITION USE CASES
  60. 60. Google Allo Can an algorithm create an avatar for you on the fly?
  61. 61. Google Allo
  62. 62. Google Allo
  63. 63. Adobe Sensei Can an algorithm optimize a selfie on the fly?
  64. 64. Adobe Selfie App
  65. 65. Adobe Sensei Content intelligence Face Aware Editing Visual Stock Search Auto Lip Sync Semantic Segmentation Face-Aware Liquify
  66. 66. Apple - Machine Learning Kit
  67. 67. Apple - Live Photo Loops
  68. 68. Apple - Portrait Lightting
  69. 69. DRAWING RECOGNITION USE CASES
  70. 70. Apple Notes Can an algorithm understand what you were writing?
  71. 71. Apple Notes
  72. 72. Apple - Natural Language API
  73. 73. Google Quick Draw Can an algorithm understand what you were drawing?
  74. 74. Google Quick Draw
  75. 75. Google Quick Draw
  76. 76. Google Quick Draw
  77. 77. Google AutoDraw Can an algorithm even help to teach you drawing?
  78. 78. Google AutoDraw
  79. 79. Google AutoDraw
  80. 80. pix2pix Can an algorithm turn your drawings into compositions?
  81. 81. pix2pix
  82. 82. pix2pix
  83. 83. pix2pix
  84. 84. pix2pix Philli p Leoni e Tobi
  85. 85. Drawing music Can an algorithm turn your drawings into music?
  86. 86. Drawing music
  87. 87. IT DOESN’T STOP HERE
  88. 88. Lyrebird Can an algorithm create realistic voice overs from a given person?
  89. 89. Lyrebird
  90. 90. University of Washington Can an algorithm create realistic video from audio files alone?
  91. 91. University of Washington
  92. 92. pix2code Can an algorithm program a user interface from any given layout?
  93. 93. pix2code
  94. 94. pix2code
  95. 95. Style Transfer Can an algorithm re-create complex aesthetics and even fine art?
  96. 96. Style Transfer
  97. 97. Style Transfer
  98. 98. Style Transfer
  99. 99. Style Transfer
  100. 100. Style Transfer
  101. 101. Style Transfer
  102. 102. Style Transfer
  103. 103. Adobe Research
  104. 104. ING / Microsoft
  105. 105. ING / Microsoft
  106. 106. ING / Microsoft
  107. 107. Creative Adversarial Networks Can an algorithm invent new styles on its own?
  108. 108. Creative Adversarial Networks
  109. 109. Fontjoy Can an algorithm generate aesthetic combinations?
  110. 110. Fontjoy
  111. 111. Fontjoy Can an algorithm generate aesthetic combinations?
  112. 112. Logo Experiment Can an algorithm generate logos?
  113. 113. Logo Experiment Rob Peart tried to generate Death Metal logos from Neural Networks ;)
  114. 114. Darkthrone Logo Experiment
  115. 115. Waking The Cadaver Logo Experiment
  116. 116. From the sunset, forest and grief Logo Experiment
  117. 117. Logo Experiment
  118. 118. Logo Experiment
  119. 119. Logo Experiment
  120. 120. Logo Experiment
  121. 121. Can an algorithm generate logos? Logo Experiment
  122. 122. NVIDIA - Iray Can an algorithm squash production times?
  123. 123. NVIDIA - Iray
  124. 124. NVIDIA - Iray
  125. 125. Autodesk - DreamCatcher Can an algorithm generate products that solve complex problems?
  126. 126. Autodesk - DreamCatcher
  127. 127. Autodesk - DreamCatcher The three structural elements shown are all designed to carry the same structural loads and forces.
  128. 128. Autodesk - DreamCatcher A Lightweight bike stem generated by an algorithm
  129. 129. Autodesk - DreamCatcher This is a car frame that is designed by a generative algorithm
  130. 130. Autodesk - DreamCatcher A a lightweight load-bearing engine block
  131. 131. Objectifier Can an algorithm let objects respond to human gestures?
  132. 132. Objectifier
  133. 133. Objectifier
  134. 134. Objectifier
  135. 135. WHAT IS NEXT? © Lawrence Lek© Lawrence Lek
  136. 136. Artificial Narrow Intelligence Artificial General Intelligence Artificial Superintelligence
  137. 137. Open AI - UNIVERSE
  138. 138. WHAT ARE CHALLENGES FOR UX? © Lawrence Lek
  139. 139. MACHINE LEARNING WON’T REACH ITS POTENTIAL IF IT DOESN’T DEVELOP IN TANDEM WITH USER EXPERIENCE DESIGN Caroline Sinders, Fast Company
  140. 140. Develop relevant use cases
  141. 141. Spotify © Fabien Girardin
  142. 142. Spotify © Fabien Girardin
  143. 143. MACHINE LEARNING WILL CHANGE CUSTOMER PERSONAS FOREVER Andre Smith, Digitalist Magazine / SAP
  144. 144. Re-think customer personas
  145. 145. Create intuitive AI interfaces
  146. 146. Make tons of data manageable
  147. 147. Use data to be super-relevant or be silent
  148. 148. Let users tell about poor information
  149. 149. • Filter Bubble - Tweak algorithms to be less accurate • Profile Detox - Let an open door to reshape profiles • Human Computation - Enlist humans to give more diversity © Fabien Girardin Design for discovery
  150. 150. EVERY TIME I CHECK MY PHONE, I’M PLAYING THE SLOT MACHINE Tristan Harris, a former Google product manager
  151. 151. Design for engagement responsibly
  152. 152. Empathy is not (yet) available
  153. 153. Questions are the new answers © Jan Korsanke
  154. 154. Illustrate for transparency
  155. 155. Seamful design
  156. 156. Machine bias: AI can lead to discrimination
  157. 157. ULTIMATELY, DESIGNERS MUST ACT AS A BULWARK AGAINST IRRESPONSIBLE, UNETHICAL USE OF AI Katharine Schwab, Fast Company
  158. 158. Satya Nadella, Microsoft CEO • AI must be designed to assist humanity • AI must be transparent • AI must maximize efficiencies without destroying the dignity of people • AI must be designed for intelligent privacy • AI must have algorithmic accountability • AI must guard against bias
  159. 159. LET’S MAKE DIGITAL SUCK LESS
  160. 160. @kuttin3dge hotline@krunchtime.org krunchtime.org THANK YOU

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