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UX for Artificial Intelligence / UXcamp Europe '17 / Berlin / Jan Korsanke

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/ My talk from the UXcamp Europe in Berlin. Please enjoy and feel free and don't hesitate to contact me if you have questions or want to talk about UX and AI

What is artificial intelligence, how do we create collaboration and what’s gonna happen in the future?

Published in: Design
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UX for Artificial Intelligence / UXcamp Europe '17 / Berlin / Jan Korsanke

  1. 1. UX FOR ARTIFICIAL INTELLIGENCE UXcamp Europe, Berlin, 03.June, 2017 Image: Retronaut
  2. 2. Image: Retronaut
  3. 3. _BLANC Image: DMITRI KESSEL/GETTY IMAGES
  4. 4. _BLANC Image:Wikipedia
  5. 5. _BLANC Image: Orion Pictures
  6. 6. Image: 20th Century Fox
  7. 7. _BLANC Image:AP
  8. 8. _BLANC Image: Photo: Ben Hider/Getty Images
  9. 9. _BLANC Image:AP Photo/Lee Jin-ma
  10. 10. _BLANC Image:Tim Kaulen/Carnegie Mellon University
  11. 11. Worries about AI are understandable.
  12. 12. But it’s not humans vs. the machines VS
  13. 13. It’s about creating an awesome together.
  14. 14. THE NEXT 45 MINUTES… WHAT IS ARTIFICIAL INTELLIGENCE, HOW DO WE CREATE COLLABORATION AND WHAT’S GONNA HAPPEN IN THE FUTURE?
  15. 15. HI! I’M JAN! SENIOR UX DUDE EXB RESEARCH & DEVELOPMENT @JANKORSANKE
  16. 16. INTRODUCTION TO AI Image: LONDON EXPRESS/GETTY IMAGES
  17. 17. ARTIFICIAL INTELLIGENCE?
  18. 18. Intelligent behaviour in an autonomous agent — THIS is AI. BEEP BEEP
  19. 19. MACHINE LEARNING DEEP LEARNING NEURAL NETWORKS NATURAL LANGUAGE PROCESSING
  20. 20. Machine Learning is a part of AI. It’s the ability for an algorithm to learn from prior data in order to produce a behaviour. ML is teaching machines to make decisions in situations they have never seen.
  21. 21. Deep Learning is a branch of ML. Stands for a class of optimisation methods of artificial neural networks. AI Machine Learning Deep Learning The goal is, to enrich and improve the many layers of those networks.
  22. 22. A neural network is a collection of connected simple units called artificial neurons. Artificial neural networks find patterns in raw data by combining multiple layers of artificial neurons.
  23. 23. Natural Language Processing is the understanding, processing and reproduction of natural language. NLU is a huge priority and challenge in AI research. Because human communication is not straightforward, but the key to radical progress.
  24. 24. CATEGORIES OF AI
  25. 25. Artificial Narrow Intelligence Artificial General Intelligence Artificial Superintelligence
  26. 26. Narrow Intelligence AI of today can do specific tasks: (driving a car, translation or Netflix binge).
  27. 27. Narrow Intelligence General Intelligence When a machine can do things in a way that is indistinguishable from human behaviour. AI of today can do specific tasks: (driving a car, translation or Netflix binge).
  28. 28. Narrow Intelligence General Intelligence Superartificial Intelligence “An intellect that is much smarter than the best human brains in practically every field: creativity, general wisdom and social skills.” Nick Bostrom When a machine can do things in a way that is indistinguishable from human behaviour. AI of today can do specific tasks: (driving a car, translation or Netflix binge).
  29. 29. _BLANC Machine intelligence is the last invention that humanity will ever need to make. Nick Bostrom “ Image:TED
  30. 30. _BLANC Image: Amazon Image:TED
  31. 31. WHY NOW?
  32. 32. _BLANC AI WINTER Image: WALLACE G. LEVISON/THE LIFE PICTURE COLLECTION/GETTY IMAGES
  33. 33. Smarter and better algorithms Much more data More computing power
  34. 34. WHAT AI CAN AND CAN’T DO RIGHT NOW
  35. 35. _BLANC Image: oneyard.com/
  36. 36. Image: Science Picture Co Collection Mix: Subjects Getty Images/
  37. 37. Image: wikipedia.org
  38. 38. _BLANC Image:AP Photo/Lee Jin-ma
  39. 39. Image: Beltz & Gelberg
  40. 40. !AI has by now succeeded in doing essentially everything that requires ‘thinking’, but has failed to do most of what people and animals do ‘without thinking.’ Donald Knuth
  41. 41. _BLANC Image: Giphy
  42. 42. Surprisingly, despite AI’s breadth of impact, the types of it being deployed are still extremely limited.
  43. 43. AI work requires carefully choosing an input and a response and providing the necessary data to help the AI understand the input to response relationship. Choosing those things creatively has already revolutionized many industries. It is poised to revolutionize many more.
  44. 44. UX FOR AI Image: BETTMANN/CORBIS
  45. 45. It’s about creating an awesome together.
  46. 46. WHAT DOES IT MEAN?
  47. 47. AI algorithms should make people’s jobs simpler, easier, and more productive. Status quo: most AI software tools are developed by software engineers.
  48. 48. Image: http://imgur.com/ // mistaspeedy
  49. 49. The large and at the same time easy UX challenge: Create tools, which can be populated with knowledge without someone with a PhD in Machine Learning having to be in the room.
  50. 50. Look back: First commercial software Image: Rexhep-bunjaku/Wikimedia Commons
  51. 51. User Experience will probably mark out the winners from the losers in the race to commercial success for AI software firms. Image: Rexhep-bunjaku/Wikimedia Commons
  52. 52. DISCLAIMER: Image: LESLIE JONES COLLECTION/BOSTON PUBLIC LIBRARY
  53. 53. _BLANC DISCLAIMER: All design- and usability-paradigms are of course relevant for AI tools too. Image: LESLIE JONES COLLECTION/BOSTON PUBLIC LIBRARY
  54. 54. USE CASES
  55. 55. As artificial intelligence algorithms infiltrate the enterprise, organizational learning matters as much as machine learning. - AI - - FRAGILE - Image: focusmovers.com/
  56. 56. Prefer smart algorithms over well thought use cases is the biggest mistake in many recent company AI initiatives. Image: wikipedia.org
  57. 57. The hunt for better results is gonna shift from training the algorithms to improve the use cases. Image: No source
  58. 58. Aipoly Vision Image:AipolyVision
  59. 59. Image:AipolyVision
  60. 60. ?Or your only doing it, because everybody does it? Please ask yourself the question, if AI is beneficial for your case.
  61. 61. QUESTION AND ANSWER
  62. 62. Our history with computers Our expectations of interaction with computers has developed in the age of search.
  63. 63. Image: Google
  64. 64. Question Answer
  65. 65. Question Answer
  66. 66. tion An We’ve learned that search is good for some types of query but bad for others.
  67. 67. tion An But, what if the system asks you a question in return?
  68. 68. Image: Giphy
  69. 69. Question Answer In the future, questions will be solved by returning questions… …and the path to the correct answers includes a series of answers I have to give first. Question Answer Question Answer Question Answer Question
  70. 70. There will be a shift in expectation: some computers don’t serve ‘dumb answers’ but ‘smart questions’. So what are the rules and etiquette of computers asking us questions and conversing with us?
  71. 71. In particular how users form a view about whether the time investment in this kind of interaction is likely to yield a positive reward. What has learned about the usage and acceptance of Wizards or decision trees will help here.
  72. 72. Usern den erfolgreichen und einfachen Umgang mit Algorithmen,Trainingsmodellen, Dateneingabe, Darstellung, etc. ermöglichen. If customers can sense or ‘smell’ a waste of time they will bail out.The earliest moments of interaction with AI are likely to be critical, and they probably need deliberate design. Image: http://www.roadpickle.com/spam-museum-of-austin-mn/ - Steve Johnson
  73. 73. COMPLEXITY
  74. 74. !The large amount of data and the complexity in visualisation is way to big to pass it unfiltered to the user.
  75. 75. Word2Vec word Quelle: Label by: Image:Tensorflow
  76. 76. Image:Tensorflow
  77. 77. Image:Tensorflow
  78. 78. Image: www.nytimes.com/2016/12/14/magazine/the-great-ai-awakening.html
  79. 79. WORLD KI Image: Google
  80. 80. AI is already part of so many applications, we just don’t know it. Image: Google
  81. 81. Similar to good design, AI should be decent, but still support the user in a best possible way. Image: Google
  82. 82. The large amount of data and the complexity in visualisation is way to big to pass it unfiltered to the user. Our job is, to enable the user to browse and analyse large amounts of data. Easy isn’t it?
  83. 83. STATUS, NOTIFICATIONS AND ONBOARDING
  84. 84. When is it done? What is happening? How do I start?
  85. 85. Image:Tensorflow
  86. 86. Image: Giphy
  87. 87. Challenge: Some calculations take forever Image:A. R. MOORE/NATIONAL GEOGRAPHIC CREATIVE/CORBIS
  88. 88. Images: Slack;WhatsApp; Facebook
  89. 89. Image: Google
  90. 90. Image: Jeff Patton - User story mapping
  91. 91. P.S.: I miss you! Image: Jeff Patton - User story mapping
  92. 92. Estimation of processing time Status indicator, process steps Good onboarding, no empty state, a simple case
  93. 93. BIAS
  94. 94. This sounds very inappropriate. man : computer programmer woman : homemaker Not for an AI.
  95. 95. Image: MITTech Review
  96. 96. ? In 2013, researchers at Google set loose a neural network on a corpus of 3 million words taken from Google News texts. The neural net’s goal was to look for patterns in the way words appear next to each other.
  97. 97. Words with similar meanings occupied similar parts of this vector space. They could represent these patterns using vectors in a vector space with some 300 dimensions. The relationships between words could be captured by simple vector algebra: “man : king :: woman : queen.” “sister : woman :: brother : man,”
  98. 98. This data set is called Word2vec and is hugely powerful. Researchers use it to better understand everything from machine translation to intelligent Web searching. Image: http://mylearning9.com/?p=4
  99. 99. Image: http://flatironschurch.com/fi-messages/so-far-so-good-far-good-hope/
  100. 100. There is a problem with this database: It is obviously sexist. Image: http://flatironschurch.com/fi-messages/so-far-so-good-far-good-hope/
  101. 101. Examples: Paris : France ::Tokyo : x x = Japan father : doctor :: mother : x x = nurse man : computer programmer :: woman : x x = homemaker SAY WHAAAAAAT
  102. 102. Survey, whether these analogies are appropriate or inappropriate. Removed the warp with “hard de-biasing”-process. Searching the vector space for word pairs that produce a similar vector to “she: he”. Correct the warp, but preserve the overall structure of the space. Image: Retronaut / Mashable
  103. 103. Computer programmer CV That has important applications - One example: web search Computer programmer #3 #1 #2 #4
  104. 104. The AI community itself has a homemade problem with this kind of challenge, because most of them are still white, young and male. Image: R.H. Fowler
  105. 105. TRUST
  106. 106. Image: Flickr/Pilots Of Swiss
  107. 107. Image: http://www.wikiwand.com/it/Otto_Disc
  108. 108. Image: Google
  109. 109. We generally perceive other people to be reasonably competent drivers. Mostly. We understand why people behave the way they do on an intuitive level, and feel like we can predict how they will behave. We don’t have this empathy for current AI.
  110. 110. Siri doesn’t make life- changing decisions for you. It’s okay if it isn’t really clear how it comes to its conclusions. Image: Jim Merithew/Cult of Mac
  111. 111. BEEPBEEP But interacting with a system that makes an important decision for you requires much more than a few buttons and a status indicator. Where the magic happens Damn complex input Damn complex output
  112. 112. Trust Empathy HumanTechnology If the purpose of smart systems is to make sophisticated subtle decisions, it is pointless if people can’t trust them to do so.
  113. 113. Image: Google
  114. 114. Image: Google
  115. 115. Image: UBER
  116. 116. Image: UBER
  117. 117. But what if AI seriously can make better decisions? Street signs X-Rays Animals ?Cytology Characters
  118. 118. Image:Youtube: Markus Mathias
  119. 119. Amazon Rekognition
  120. 120. REASONING
  121. 121. Image: IBM Watson
  122. 122. Image: IBM Watson
  123. 123. Image: wikipedia.org
  124. 124. Image: wikipedia.org
  125. 125. Independent which form of reasoning you use, make sure the user is able to understand it. Image: wikipedia.org
  126. 126. Level of detail for the reasoning HighLow Jan (Village Idiot)
  127. 127. SUMMARY AND OUTLOOK Image: IMAGE: STANLEY LEWIS/BIPS/GETTY IMAGES
  128. 128. Think about use cases for the AI Questions will be the new answers Trust in machines is hard work Fit your reasoning on your user Don’t pass the complexity to the user Usable without a PhD in Machine Learning No bias. Seriously.This sucks
  129. 129. WHERE AI CAN HARM
  130. 130. AI has the potential to reflect both the best and the worst of humanity. AI providing conversation and comfort to the lonely AI engaging in racial discrimination.
  131. 131. The biggest harm that AI is likely to do to individuals in the short term is job displacement, as the amount of work we can automate with AI is vastly bigger than before. It’s our turn to make sure we are building a world in which every individual has an opportunity to thrive.
  132. 132. ? What about us? Those that want to stay relevant in their professions will need to focus on skills and capabilities that artificial intelligence has trouble replicating. Understanding, motivating, and interacting with human beings. Megan Beck & Barry Libert
  133. 133. _BLANC Image: Guinness book of records
  134. 134. WHERE AI CAN HARM (Part2)
  135. 135. _BLANC Image: wikipedia.org
  136. 136. _BLANC WHERE AI CAN HARM Image: OpenAI
  137. 137. _BLANC WHERE AI CAN HARM “No set of individuals has control over advanced set of AI” “AI on its own will not develop something bad, it’s just the people using it in a way thats bad” Elon Musk Image: OpenAI
  138. 138. WHEN IS IT GONNA HAPPEN
  139. 139. _BLANC
  140. 140. When will we reach HLMI? 10 % 50 % 90 % TOP 100 2024 2050 2070 COMBINED 2022 2040 2075 Optimistic guess Realistic guess Safe guess
  141. 141. 10 % 50 % 90 % TOP 100 2024 2050 2070 COMBINED 2022 2040 2075 Optimistic guess Realistic guess Safe guess Artificial Superintelligence: approx. 20-30 years later When will we reach HLMI?
  142. 142. BETTER BE PREPARED :)
  143. 143. RETURN TO START
  144. 144. _BLANC Image: Giphy
  145. 145. It’s about creating an awesome together.
  146. 146. THANKYOU!! Image: Retronaut
  147. 147. ANY QUESTIONS? @JANKORSANKE
  148. 148. ICONS: Adrien Coquet Anbileru Adaleru Aneeque Ahmed Arjuazka Artem Kovyazin Baboon designs Creative Stall Eucalyp Fatahillah Hea Poh Lin Hopkins Jake Dunham Kirill Kolchenko Knut M. Synstad Lorie Shaull Oksana Latysheva Pictohaven ProSymbols Ralf Schmitzer Shmidt Sergey Souvik Maity Vinaya Kumar P.V Zidney

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