“From Eliza to Siri and beyond: Promise and challenges of intelligent, language-controlled assistants/chatbots.“

1. CREATIVE CONSTRUCTION FROM ELIZA TO SIRI AND BEYOND: Promise and Challenges of Intelligent, Language-controlled Assistants (Chatbots) Alexander Braun (@almarrone) November 3rd, 2016

2. CHATBOT ARMS RACE

3. 3 CHATBOTS: UNANIMOUSLY THE NEW USER INTERFACE October 2011: Siri

4. 4 CHATBOTS: UNANIMOUSLY THE NEW USER INTERFACE November 2014: Amazon Echo

5. 5 CHATBOTS: UNANIMOUSLY THE NEW USER INTERFACE October 2015: Facebook M

6. 6 CHATBOTS: UNANIMOUSLY THE NEW USER INTERFACE December 2015: Microso? Cortana

7. 7 CHATBOTS: UNANIMOUSLY THE NEW USER INTERFACE May 2016: Google Assistant

8. 8 CHATBOTS: UNANIMOUSLY THE NEW USER INTERFACE July 2016: IBM Watson ConversaKon

9. 9 CHATBOT REVOLUTION? The sky is the limit

13. 13 CHATBOT REVOLUTION?

14. 14 CHATBOT REVOLUTION? Maybe...

21. 03.11.16 21 CLIPPY OR SAMANTHA?

24. HISTORY AND MOTIVATION

25. 25 CHATBOTS NOT REALLY NEW... Published in 2003 hPps://www.amazon.com/Chatbots-KundenkommunikaKon- Xpert-press-German-Alexander/dp/3642624111/

26. 26 CHATBOTS NOT REALLY NEW... 1966: ELIZA by Joseph Weizenbaum

27. 27 MOTIVATION: REDUCE FRICTION, IMPROVE USABILITY From command line to GUI to natural language interface + no app-switching

28. 28 MOTIVATION: IN SEARCH FOR A NEW RUNTIME Breaking the app-install threshold

29. CHALLENGES TODAY

30. 30 BOT TYPES Bot depends on database with finite information coded and curated by humans. Rules-based Bots (-> IVR?) Passing on message to customer service agent when stuck. Curated Bots (-> Ticketing system?)

31. 31 PROBLEMS TODAY: DESIGN FLAWS Identifying common commands

32. 32 PROBLEMS TODAY: DESIGN FLAWS Executing identified commands

33. 33 PROBLEMS TODAY: DESIGN FLAWS Personality getting in the way

34. 34 PROBLEMS TODAY: MAINTENANCE Keeping database up-to-date

35. 35 PROBLEMS TODAY: MAINTENANCE Keeping database up-to-date

36. 36 PROBLEMS TODAY: COMMON SENSE Ambiguity User: Siri, call me an ambulance. Siri: Okay, from now on I’ll call you “an ambulance.” I want to listen to Led Zeppelin. Play Stairway to Heaven. I’d like to listen to reggae. I like No woman no cry.

37. 37 PROBLEMS TODAY: COMMON SENSE Ambiguity “The city councilmen refused the demonstrators a permit because they feared violence.” Who does ‘they’ refer to?

38. 38 PROBLEMS TODAY: COMMON SENSE Ambiguity In shopping, if I say, ‘I want to get a case for my guitar; it should be strong.’ So does ‘it’ refer to the case or the guitar?

39. 39 PROBLEMS TODAY: COMMON SENSE Ambiguity: Chatbots barely better than chance

40. 40 TEACHING COMPUTERS COMMON SENSE •  OpenCyc (http://opencyc.org/) •  Microsoft Concept Graph (https://concept.research.microsoft.com/)

41. 41 STATUS: MORE, NOT LESS FRICTION As opaque as a DOS prompt No UI -> completely transparent to the user Vision Voice looks like the ultimate unlimited, general purpose UI, but actually only works if you can narrow the domain. Reality today

42. 42 STATUS: MORE, NOT LESS FRICTION Where Siri went wrong •  Expectations: Can answer anything – unlimited, general purpose UI. Siri vs. Alexa

43. 43 STATUS: MORE, NOT LESS FRICTION Where Siri went wrong •  Expectations: Can answer anything – unlimited, general purpose UI. •  Reality: Limited to certain, not clearly defined domains. Siri vs. Alexa

44. 44 STATUS: MORE, NOT LESS FRICTION Where Siri went wrong •  Expectations: Can answer anything – unlimited, general purpose UI. •  Reality: Limited to certain, not clearly defined domains. •  Problem: User has to find out via trial and error. Siri vs. Alexa

45. 45 STATUS: MORE, NOT LESS FRICTION Where Siri went wrong •  Expectations: Can answer anything – unlimited, general purpose UI. •  Reality: Limited to certain, not clearly defined domains. •  Problem: User has to find out via trial and error. •  Result: Once users find out, they stop exploring. Assistant gets better, user stays ignorant. Huge promise shrunk to just making voice calls and sending messages to contacts, maybe getting the weather. Siri vs. Alexa

46. 46 STATUS: MORE, NOT LESS FRICTION Alexa‘s learnings – and challenges •  Expectations: Set to narrow scope of domains. Siri vs. Alexa

47. 47 STATUS: MORE, NOT LESS FRICTION Alexa‘s learnings – and challenges •  Expectations: Set to narrow scope of domains. •  Result: Less opaque – less disappointment. Siri vs. Alexa

48. 48 STATUS: MORE, NOT LESS FRICTION Alexa‘s learnings – and challenges •  Expectations: Set to narrow scope of domains. •  Result: Less opaque – less disappointment. •  Problem: Efficiency/Effectiveness... 1.  "Alexa, add sponges to my shopping list.“ 2.  Open Alexa app. 3.  Select "Shopping and To-Do Lists" from the sandwich menu. 4.  Select "Sponges" from the list. 5.  Select "Search Amazon for sponges" from pop-up list. 6.  Wait for iOS to open the Amazon app (separate app from the Alexa app). 7.  Scroll down and select the brand I like. 8.  Check out. Siri vs. Alexa

49. 49 STATUS: MORE, NOT LESS FRICTION Efficiency and discoverability

50. WHERE BOTS WORK

51. 51 USAGE SCENARIOS Architecture: Thread-centric UI paradigm

52. 52 USAGE SCENARIOS Architecture: Horizontal AI with API access to other services

53. 53 USAGE SCENARIOS Where bots work •  Quick interactions: The fewer number of required back-and-forth

54. 54 USAGE SCENARIOS Where bots work •  Quick interactions: The fewer number of required back-and-forth •  Simple interactions: Not many options to choose from (shoe shopping...)

55. 55 USAGE SCENARIOS Where bots work •  Quick interactions: The fewer number of required back-and-forth •  Simple interactions: Not many options to choose from (shoe shopping...) •  Context available to bot: Reducing load on user by pulling relevant cues and context to make interations efficient

56. 56 USAGE SCENARIOS Where bots work •  Quick interactions: The fewer number of required back-and-forth •  Simple interactions: Not many options to choose from (shoe shopping...) •  Context available to bot: Reducing load on user by pulling relevant cues and context to make interations efficient •  Users maintain control: Clear permissions – not just „on“ and „blocked“

57. 57 USAGE SCENARIOS Where bots work •  Quick interactions: The fewer number of required back-and-forth •  Simple interactions: Not many options to choose from (shoe shopping...) •  Context available to bot: Reducing load on user by pulling relevant cues and context to make interations efficient •  Users maintain control: Clear permissions – not just „on“ and „blocked“ •  Users don‘t have investments in existing apps: New or temporary interactions

58. THANK YOU! Phone: +49 (0)30 / 4004 1922 www.creativeconstruction.de ALEXANDER BRAUN @almarrone | alexander@creativeconstruction.de

59. APPENDIX

60. 60 BOT PLATFORMS •  Facebook Bot Engine (https://wit.ai/) •  API.ai: Web-base bot framework (https://docs.api.ai/) •  Microsoft Bot Framework (https://dev.botframework.com/) •  Viv (http://viv.ai/) -> acquired by Samsung

61. 61 LINKS & THANKS http://allthingsd.com/20111011/the-iphone-finds-its-voice/ http://thenextweb.com/insider/2014/11/06/amazon-introduces-echo-voice-controlled-assistant-home/ https://www.wired.com/2015/08/facebook-launches-m-new-kind-virtual-assistant/ https://www.engadget.com/2015/12/09/microsoft-officially-launches-cortana-on-iphone-and-android/ http://mashable.com/2016/05/18/google-assistant/ https://www.ibm.com/watson/developercloud/conversation.html http://ben-evans.com/benedictevans/2016/3/30/chat-bots-conversation-and-ai-as-an-interface https://techcrunch.com/2015/09/29/forget-apps-now-the-bots-take-over/ http://venturebeat.com/2016/08/02/why-chatbots-are-replacing-apps/ http://venturebeat.com/2016/05/01/the-200-billion-dollar-chatbot-disruption/ http://venturebeat.com/2016/10/23/ai-agents-like-alexa-siri-and-m-will-create-the-first-trillion-dollar-company/ http://s2.quickmeme.com/img/c3/c3305497a908aed0e1792f5d6be8bb85b8878081952e9874ad45fe22692451d0.jpg http://www.recode.net/2016/10/12/13251618/mossberg-apple-siri-digital-assistant-dumb https://www.theguardian.com/technology/2016/apr/14/facebook-messenger-spammy-chatbot-must-improve-and-fast https://www.theguardian.com/technology/2016/apr/29/please-facebook-dont-make-me-speak-to-your-awful-chatbots https://www.technologyreview.com/s/601897/tougher-turing-test-exposes-chatbots-stupidity/ https://www.technologyreview.com/s/601329/is-the-chatbot-trend-one-big-misunderstanding/ http://www.geekwire.com/2016/long-journey-facebook-messenger-boss-says-chatbots-got-really-overhyped/ http://venturebeat.com/2016/10/07/lets-be-honest-chatbots-kind-of-suck-for-now/ https://cdn.theatlantic.com/assets/media/img/mt/2015/06/image043/lead_960.jpg?1435096373 https://www.wired.com/2014/01/siri-her-reaction/ https://www.amazon.com/Chatbots-Kundenkommunikation-Xpert-press-German-Alexander/dp/3642624111/ https://www.cs.umd.edu/hcil/muiseum/weizenbaum/joseph_pic.jpg https://knowledgeillusion.files.wordpress.com/2012/04/4966605.gif https://publib.boulder.ibm.com/infocenter/toolsctr/v1r0/topic/uxspi/artwork/cli_commands.jpg http://www.conceptdraw.com/How-To-Guide/picture/OSX10.10YosemiteApps.png http://pocketnow.com/wp-content/uploads/2011/10/TellMe1.jpg http://blogs.aspect.com/bot-framework-s-comparison/ http://commonsensereasoning.org/winograd.html http://opencyc.org/ https://concept.research.microsoft.com/ http://ben-evans.com/benedictevans/2016/10/10/echo-interfaces-and-friction https://medium.com/@aeisenberger/messaging-bots-and-slack-a52c894a4b2d#.ot0103mti

“From Eliza to Siri and beyond: Promise and challenges of intelligent, language-controlled assistants/chatbots.“

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“From Eliza to Siri and beyond: Promise and challenges of intelligent, language-controlled assistants/chatbots.“