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What are machines learning? How might that impact design?

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Machines have picked up astonishing skills: they beat us at chess and Go, they have learnt to pilot a drone or how to create oil paintings. These impressive feats are pushing the boundaries, they show us what is possible. But machines have picked up skills that are less noteworthy, but way more useful: they have learnt to understand what we say, they can figure out which series we might want to binge on next or how to write compelling news articles.

Machines have learnt many things that are finding their ways into digital products. In this talk, I will give a bird’s-eye view of these developments — and I’ll dare to make some predictions about how these might impact the way we design products.

I gave this talk in Zürich on the 31st of October 2019.

Published in: Design
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What are machines learning? How might that impact design?

  1. 1. Andreas Wolters Last Thursday Talk (UX 🇨🇭 ) 31.10.2019 What are MACHINES LEARNING?
  2. 2. And how might that IMPACT DESIGN? What are MACHINES LEARNING?
  3. 3. Machines are learning to COMMUNICATE NATURALLY Machines are learning to MAKE THINGS Machines are learning to FIND THINGS FOR US Machines are learning to GENERATE ALTERNATIVES Machines are learning to REPRESENT NEEDS Machines are learning to PRESENT THINGS 5
  4. 4. Machines are learning to COMMUNICATE NATURALLY 6
  5. 5. Machines are learning to COMMUNICATE NATURALLY Google Duplex 7
  6. 6. We’re moving away from “computer speak” to natural communication. Machines are learning to COMMUNICATE NATURALLY 8
  7. 7. MACHINES ARE LEARNING TO FIND THINGS FOR US 9
  8. 8. 80% of content on Netflix comes from recommendations.(source) Machines are learning to FIND THINGS FOR US 10
  9. 9. 35% of revenue on Amazon comes from its recommendation engine. (source) Machines are learning to FIND THINGS FOR US 11
  10. 10. We all know Discover Weekly — Spotify uses three information types to create it. (source) • “You two guys are similar, I should present you each other’s songs” (collaborative filtering) • “What does the web say about this song?” (Natural Language Processing) • “This new song.. which other song is it similar to?” (raw audio models) Machines are learning to FIND THINGS FOR US 12
  11. 11. 40% of companies report using an algorithmic approach to personalisation. (source) Machines are learning to FIND THINGS FOR US 13
  12. 12. Machines are learning to REPRESENT NEEDS 14
  13. 13. There are some attempts at automatically segmenting visits into relevant clusters (think personas!). (source) Machines are learning to REPRESENT USER NEEDS 15
  14. 14. Machines are learning to MAKE THINGS 16
  15. 15. Machines are learning to MAKE THINGS Mario Klingemann — My Artificial Muse 17 Porn & GAN?
  16. 16. Machines are learning to MAKE THINGS 18 This person doesn’t exist.
  17. 17. Machines are learning to MAKE THINGS That really happened on Amazon. 19
  18. 18. Machines are learning to MAKE THINGS LA Times - Quakebot 20
  19. 19. Machines are learning to MAKE THINGS "I forced a bot to watch over 1,000 hours of Batman movies and then asked it to write a Batman movie of its own." 21 “ALFRED, Batman’s loyal batler, carries a tray of goth ham.” "I have punched a penguin into prison.”
  20. 20. Machines are learning to GENERATE ALTERNATIVES 22
  21. 21. Design is process of generating, evaluating, abandoning and developing alternatives. Machines are learning to GENERATE ALTERNATIVES 23
  22. 22. Machines are learning to GENERATE ALTERNATIVES A.I. for Kartell by Starck, powered by Autodesk 24
  23. 23. Machines are learning to GENERATE ALTERNATIVES But (better) personalised fashion is on its way. 25
  24. 24. Machines are learning to GENERATE ALTERNATIVES Nutella designs 7’000’000 unique labels. 26
  25. 25. Machines are learning to GENERATE ALTERNATIVES Neill D.F. Campbell & Jan Kautz - Learning a Manifold of Fonts. 27
  26. 26. Machines are learning to PRESENT THINGS 28
  27. 27. Netflix produces a number of artworks for each title. It then shows you the one that corresponds with why you’d like this movie.(source) Machines are learning to PRESENT THINGS 29 … because you’re into romantic movies. … because you like movies with Robin Williams.
  28. 28. The Grid promised to “AI websites that design themselves” — and failed. (source) Machines are learning to PRESENT THINGS 30
  29. 29. Machines are learning to PRESENT THINGS UI Bot 31
  30. 30. Flipboard Duplo: a designer sets the rules, the engine generates around 6’000 layouts and chooses the optimal one for each piece. Four stages here: creating layouts, selecting layouts, refining layouts and displaying layouts. (source) Machines are learning to PRESENT THINGS 32
  31. 31. Alibaba built Luban, which creates marketing materials for the products that are sold on their platform — 8’000 banners per second, 6 million banners for 200,000 enterprises already created. (source) Machines are learning to PRESENT THINGS 33
  32. 32. Machines are learning to COMMUNICATE NATURALLY Machines are learning to MAKE THINGS Machines are learning to FIND THINGS FOR US Machines are learning to GENERATE ALTERNATIVES Machines are learning to REPRESENT NEEDS Machines are learning to PRESENT THINGS 34
  33. 33. How will that IMPACT DESIGN?
  34. 34. Design will be AUGMENTED & AUTOMATED We need HUMAN-CENTRED AI We will be MOVING RESEARCH INTO THE PRODUCT WE WILL DESIGN BEHAVIOURS not just visuals
  35. 35. X Designers will be AUGMENTED Design will be AUTOMATED 37
  36. 36. How should designers collaborate with machines?
  37. 37. X AUTOMATION The technology by which a process or procedure is performed with minimal human assistance. 40 AUGMENTATION Technological innovations which are used to optimise the work being carried out.
  38. 38. What’s the difference?
  39. 39. Augmentation means automating small steps in the design process.
  40. 40. Airbnb built a tool that converts rough scribbles into a React prototype. (source) augmentation and automation AIRBNB’S REACT-SKETCHAPP 43
  41. 41. So we’ll automate the whole design process?
  42. 42. augmentation and automation DESIGN PROCESS 45 PROBLEM SOLUTIONS D iscover D eliver D evelop D efine 1 2 3 4 Generative design Preparing UI assets (Netflix) Styling & style transfer (UIbot) Typography
  43. 43. augmentation and automation DESIGN PROCESS 46 PROBLEM SOLUTIONS D iscover D eliver D evelop D efine 1 2 3 4 Augmentation? Augmentation & Automation? Neither?
  44. 44. Augmentation and automation lives on a spectrum, not as an either/or. We can chose how far we want to control the horse. 🏇 augmentation and automation HOW DO WE COLLABORATE WITH HORSES? 47
  45. 45. augmentation and automation DEPENDENT ON TIME & RESOURCES? 48 Low priority project? Automation Augmentation High priority project?
  46. 46. We need HUMAN-CENTRED AI
  47. 47. AI isn’t a problem to solve.
  48. 48. AI helps solve problems.
  49. 49. human-centred AI DESIGN PROCESS 52 PROBLEM SOLUTIONS D iscover D eliver D evelop D efine 1 2 3 4
  50. 50. Designers will need to find the intersection of user needs & AI strengths.
  51. 51. human-centred AI WHAT ARE “AI STRENGTHS”? WHAT CAN AI DO? 54 RECOMMENDATION “You might like this song you’ve never heard before.” PREDICTION “Buy that flight now, it’ll get more expensive soon.” CLASSIFICATION “This image definitely shows a tiger.” CLUSTERING “These groups behave differently from each other.” GENERATING “I can give you another 12’000 designs for a chair.”
  52. 52. human-centred AI WHAT’S THE PROCESS? 55 1 2 3 4 Find the intersection of user needs & AI strengths Assess automation vs. augmentation. Design the reward function. Translate user needs into data needs.
  53. 53. human-centred AI WHAT’S THE PROCESS? 56 1 2 3 4 Find the intersection of user needs & AI strengths Assess automation vs. augmentation. Design the reward function. Translate user needs into data needs. Mostly a design problem Mostly a data science problem
  54. 54. Designers should support on data needs?
  55. 55. human-centred AI TRAINING DATA NEEDS TO REFLECT USAGE “Racist soap dispenser” 58
  56. 56. Where’s the validation?
  57. 57. human-centred AI WIZARD OF OZ TESTING 60
  58. 58. WE WILL DESIGN BEHAVIOURS not just visuals.
  59. 59. A user’s experience will largely be created by algorithms — through the behaviours they output.
  60. 60. How do we design behaviours?
  61. 61. How do we design behaviours characters?
  62. 62. What should machines be like?
  63. 63. designing behaviours MACHINES SHOULD BE AS SMART AS A PUPPY 69 Josh Clark
  64. 64. designing behaviours MACHINES SHOULD BE AS MEAN AS MICROSOFT 70
  65. 65. Which qualities are important to a good experience?
  66. 66. designing behaviours TRUST 72 Business insider
  67. 67. designing behaviours TRUST IS WAY MORE THAN PRIVACY 73 source “Robot performance and attributes were the largest contributors to the development of trust.” → competence → reliability
  68. 68. designing behaviours TRUST IS IMPORTANT 74 source
  69. 69. Algorithmic data is often shown with full certainty. → Showing how (un-)certain our algorithm is allows users to calibrate their trust. designing behaviours TRANSPARENCY & CERTAINTY 75
  70. 70. designing behaviours BEHAVIOUR SHOULD BE NATURAL 76
  71. 71. The design challenge of the future is to design trustworthy characters with coherent behaviours.
  72. 72. We will be MOVING RESEARCH INTO THE PRODUCT
  73. 73. moving research into the product WHAT HAS HAPPENED WITH NETFLIX? 80 1 Explicit user input “Choose 3 you like” 2 Hypotheses “She might like X.” 3 Validation “She watched X.” “She rated X highly.”
  74. 74. moving research into the product WHAT HAS HAPPENED WITH NETFLIX? 81 1 Explicit user input “Choose 3 you like” 2 Hypotheses “She might like X.” 3 Validation “She watched X.” “She rated X highly.” Explicit “research” Implicit “research”
  75. 75. What do recommendation algorithms know about our users?
  76. 76. moving research into the product AMAZON IS USING RECOMMENDATION DATA 84 source “Several years ago, Amazon suddenly ramped up Basics and began using its massive collection of sales data to launch products that already existed, but at lower prices. If something isn’t an immediate hit, Amazon pulls it and moves on.”
  77. 77. This is limited to finding things right now — more’s possible.
  78. 78. moving research into the product NEVERMIND: A BIO-FEEDBACK VIDEO GAME 86 source It uses webcam (for facial expression analysis) or a heart rate sensor. “The more the player is under stress, the harder the game becomes.”
  79. 79. Machines are learning to COMMUNICATE NATURALLY Machines are learning to MAKE THINGS Machines are learning to FIND THINGS FOR US Machines are learning to GENERATE ALTERNATIVES Machines are learning to REPRESENT NEEDS Machines are learning to PRESENT THINGS 87
  80. 80. Design will be AUGMENTED & AUTOMATED We need HUMAN-CENTRED AI We will be MOVING RESEARCH INTO THE PRODUCT WE WILL DESIGN BEHAVIOURS not just visuals
  81. 81. Machines are learning to WRITE BATMAN! "I forced a bot to watch over 1,000 hours of Batman movies and then asked it to write a Batman movie of its own." 89 “You drink water, I drink anarchy.” “I have never followed a rule. That is my rule. Do you follow? I don’t.” (UN)FORTUNATELY HUMAN
  82. 82. andreas-wolters.com hello@andreas-wolters.com linkedin.com/in/andreaswolters/QUESTIONS?

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