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Who is Doing the Work? Designing for AI across modes of interaction.

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The foundational question for designing AI systems is: Who is doing the work? The human with help from the AI? Or the AI with help from the human? Once designers understand and become familiar with the differences of these modes of interaction, they can more confidently design modern systems that take advantage of AI APIs. Noessel literally wrote the book on Agentive Tech last year, and is working on the follow-up book about Assistant Tech. In this workshop participants gather into design pairs. Then short lectures explain the core concepts and start the creative ideas flowing, followed by integration exercises where participants put these ideas into action. Each team refines ideas across the day, and integrating these modes into a cohesive whole. Time allowing, teams are asked to volunteer to present the key ideas from their designs.

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Who is Doing the Work? Designing for AI across modes of interaction.

  1. 1. W H O I S D O I N G T H E W O R K ? D E S I G N I N G A C R O S S M O D E S O F I N T E R A C T I O N W I T H A N A I I N T E R A C T I O N 1 9 @chrisnoessel @thebenremington W E D N E S D AY 6 F E B 2 0 1 9 D I S C I P L I N E I N F L U X
  2. 2. @ C H R I S N O E S S E L • 20+ years interaction design • Interaction Design Institute Ivrea • Microsoft Futures Prototyping Group • scifiinterfaces.com, Make It So: Interaction Design Lessons from Science Fiction (Rosenfeld Media 2012) • About Face 4th Edition (2016) • Designing Agentive Technology:
 AI That Works for People (Rosenfeld Media 2017) • Senior Lead Designer,
 Watson Customer Experience, IBM
  3. 3. @ T H E B E N R E M I N G T O N • Seattlite 1994–1996 • Multiple Career Rabbit Holes • Urban Design at UC Berkeley • General Assembly 2013 • Interaction Designer, Epsilon
  4. 4. S H A R I N G W O R K W I T H A N A I
  5. 5. C AV E AT 1 / 3
  6. 6. C AV E AT: W I T T G E N S T E I N ’ S L A D D E R You build on a useful lie To get to the nuanced truth
  7. 7. T H E “ L I E ” I S … Structuring the human-AI relationship is as simple as HABA-MABA Humans are Better At / Machines are Better At
  8. 8. T H E M A P I S N O T T H E T E R R I T O RY
  9. 9. C AV E AT 2 / 3
  10. 10. T H I S I S M O S T LY A B O U T W H AT Y O U R P R O D U C T D O E S , 
 L E S S A B O U T T H E I N T E R FA C E O R M I C R O I N T E R A C T I O N S
  11. 11. C AV E AT 3 / 3
  12. 12. Katerina Kamprani “The Uncomfortable”
  13. 13. T H I S I S A L M O S T C E RTA I N LY W R O N G http://bit.ly/DoingTheWorkFeedback
  14. 14. W H I C H A I ?
  15. 15. A I H A S F L AV O R S
  16. 16. Icon by Guilhem from the Noun Project What if our software could learn from its mistakes, and let users correct it when it’s wrong? How could it improve over time to help them? Machine learning Icons by Ryan Beck from the Noun Project Could we save their persona time and effort by predicting their needs? How would we surface options? How could we not bother them? Predictive technologies Icon by Aybige Aya from the Noun Project If the environment could see and understand things were happening, what would it look for? How might it use that to help personas? Computer vision
  17. 17. Icon by Guilhem from the Noun Project What if our software could learn from its mistakes, and let users correct it when it’s wrong? How could it improve over time to help them? Machine learning Icons by Ryan Beck from the Noun Project Could we save their persona time and effort by predicting their needs? How would we surface options? How could we not bother them? Predictive technologies Icon by Aybige Aya from the Noun P If the environment could s and understand things w happening, what would it for? How might it use tha help personas? Computer vision
  18. 18. A M O D E L O F U S E
  19. 19. image: GetNarrative
  20. 20. T H E H U M A N D O E S T H E W O R K
  21. 21. T H E H U M A N D O E S T H E W O R K
  22. 22. T H E H U M A N D O E S T H E W O R K
  23. 23. T H E A I D O E S T H E W O R K
  24. 24. T H E A I D O E S T H E W O R K
  25. 25. T H E Y S H A R E T H E W O R K
  26. 26. H U M A N G E T S H E L P
  27. 27. H U M A N G E T S H E L P
  28. 28. H U M A N G E T S H E L P ?
  29. 29. H U M A N G E T S H E L P
  30. 30. H U M A N G E T S H E L P
  31. 31. A G E N T I V E I S T H E N E W M O D E O F U S E
  32. 32. A G E N T I V E I S T H E N E W M O D E O F U S E
  33. 33. A G E N T I V E I S T H E N E W M O D E O F U S E
  34. 34. A M O D E L B A S E D O N W H O I S D O I N G T H E W O R K
  35. 35. D E S I G N I N G F O R A S S I S TA N T
  36. 36. The Education of Achilles Giovanni Battista Cipriani c.1776 A G O O D A S S I S TA N T H E L P S . 
 A G R E AT A S S I S TA N T H E L P S Y O U G E T B E T T E R .
  37. 37. tput inputsee think do
  38. 38. W O R K I N G I N S C E N A R I O S A scenario is a story that tells of… 1. a user 2. in a situation 3. encountering a problem 4. going through a number of see-think-do steps 5. to solve the problem
  39. 39. B R E A K
  40. 40. D E S I G N I N G F O R A N A G E N T
  41. 41. C A M E R A S W I T H O U T P H O T O G R A P H E R S
  42. 42. image: Roomba
  43. 43. VA C U U M S W I T H O U T VA C U U M - E R S image: Roomba
  44. 44. image: Google
  45. 45. C A R S W I T H O U T D R I V E R S image: Google
  46. 46. D O E S T H I N G S 
 O N U S E R S ’ B E H A L F A G E N T I V E T E C H
  47. 47. image: Scarecrow / Paul Souders image: ShotSpotter A S B I G A S Y O U C A N T H I N K image: Tertill
  48. 48. image: Scarecrow / Paul Soudersimage: Avy
  49. 49. B I T. LY / B O W T I E D I A G R A M
  50. 50. H A N D O F F A N D TA K E B A C K
  51. 51. Human initiates | AI initiates
  52. 52. R E H E A R S E & P R E S E N T
  53. 53. Y O U R E H E A R S E • Teams have 3 minutes each to present their designs to the room. (Random selection: Names in a hat!) • Pick your favorite or most surprising designs. • Rehearse as many times as you can. Get it under 03:00. • Don’t want to present? Use this time to help us improve: http://bit.ly/ DoingTheWorkFeedback G E T I T D O W N T O 3 M I N U T E S
  54. 54. Y O U P R E S E N T • The prior team times the current team. • The current team draws the next team. • Since we don’t have enough time for everyone to present, please tweet a description to me, tag @AgentiveTech. A S M A N Y A S T I M E A L L O W S
  55. 55. Congratulations.
  56. 56. U X A B S O R B S A I RIP Stephen Hillenburg
  57. 57. U X A B S O R B S A I
  58. 58. U X A B S O R B S A I
  59. 59. Q U E S T I O N S & 
 A N S W E R S bit.ly/agentivebook Follow @agentivetech, @chrisnoessel     http://bit.ly/DoingTheWorkFeedback

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