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Non-trivial pursuits: Learning machines and forgetful humans

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Keynote at PNWPHP covering Machine Learning and How we should go about using it to build human interfaces.

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Non-trivial pursuits: Learning machines and forgetful humans

  1. 1. Chris Heilmann (codepo8), PNWPHP conference, Seattle, September 2017
  2. 2. Machine Learning, Deep Learning, Artificial Intelligence, Big Data…
  3. 3. We live in a post-data leak world. We have been recorded and categorised
  4. 4. Lots of companies are using this data.
  5. 5. Some in amazing ways, others in shady ones.
  6. 6. There should not be only a few that have access and do something with it.
  7. 7. Let’s democratise intelligent machines and human interfaces.
  8. 8. How do we remember and learn?
  9. 9. Repetition
  10. 10. Repetition Comparison
  11. 11. Repetition Comparison Explanation
  12. 12. Repetition Comparison Explanation Association
  13. 13. complex and erratic leaps
  14. 14. we are terrible at repetition bored
  15. 15. That the returned data is fit for human consumption
  16. 16. Where do I find a nice restaurant around here that is open tomorrow around lunch time?
  17. 17. nice restaurant here that is open tomorrow around lunch time? Search term
  18. 18. nice restaurant here that is open tomorrow around lunch time? What is nice? Search term
  19. 19. nice restaurant here tomorrow around lunch time? What is nice? Search term Location
  20. 20. nice restaurant here tomorrow around lunch time What is nice? Search term Location Calculated time frame
  21. 21. Where nice restaurant here tomorrow around lunch time What is nice? Search term Location Calculated time frame Calculated result
  22. 22. Our current hype around Artificial Intelligence is driven by Sci-Fi concepts.
  23. 23. The age-old dream of a ubiquitous, all-knowing computer butler.
  24. 24. One that understands all human communication quirks and also has a delicious sense of sarcasm.
  25. 25. In essence: a human, that isn’t human but appears to be.
  26. 26. Which is how we set ourselves up for failure.
  27. 27. Ubiquitous computing easily becomes a nuisance when it records without giving us the right answers.
  28. 28. It is very easy to create a creepy, annoying chat bot. We are not forgiving with them as we are with humans.
  29. 29. Artificial Intelligence is most effective when it enhances in the background.
  30. 30. Repetition Comparison Explanation Association
  31. 31. Repetition Comparison Explanation Association
  32. 32. What you can use AI for in your interfaces right now: Visual Recognition Voice Recognition Natural Language Processing Emotion Recognition Entity Recognition
  33. 33. Visual recognition
  34. 34. Visual recognition (positives)  Automated tagging and clustering of images  Accessibility benefit of automated alternative content  Biometric login  Automated “art direction”  Automated moderation
  35. 35. Visual recognition (negatives)  Privacy issues  Wrong and possibly offensive automated labeling  False moderation and failed moderation  Connection / Upload latency  Insufficiently trained models
  36. 36. Voice recognition
  37. 37. Voice recognition (positives)  Visual impairment or no screen  Hands-free interaction (phone, car, headsets…)  Faster than typing, more natural  Magical “Star Trek” factor
  38. 38. Voice recognition (negatives)  Intrusive as hell  Disappointing error handling  Language and accent issues  Low sound quality or loud surrounding  Latency in recognition
  39. 39. Natural Language Processing
  40. 40. NLP (positives)  Allowing humans to ask human questions  Proper translation of content not word-by-word, but by meaning  Conversational interfaces that keep the user engaged
  41. 41. NLP (negatives)  Users are conditioned to think in clicks and to enter keywords – natural language feels out of place  Language differences are still a problem, not all people speak English
  42. 42. Emotion recognition
  43. 43. Emotion recognition (positives)  Feedback channel for product tests  React to the most annoyed customers first  Find happy quotes and customers to promote  Navigate media by emotion
  44. 44. Emotion recognition (negatives) False recognition results in hurtful messaging  Quality issues can result in very wrong results  Voice emotion recognition is still a tough one to crack
  45. 45. Entity recognition
  46. 46. Entity recognition (positives)  Automated tagging and cross referencing  Opportunity to add third party content (Wikipedia is the classic)  Intelligent auto-complete
  47. 47. Entity recognition (negatives)  False recognition  Language differences  Lack of value of automated content
  48. 48. You can REST easy…
  49. 49. You can REST easy…
  50. 50. You can REST easy…
  51. 51. https://en-gb.wordpress.org/plugins/cloudinary-image-management-and-manipulation-in-the-cloud-cdn/
  52. 52. Everything counts in large amounts
  53. 53. Repetition Comparison Explanation Association
  54. 54. Language Understanding Intelligence Service (LUIS) https://docs.microsoft.com/en-us/azure/cognitive-services/LUIS/Home  Brazilian Portuguese  Chinese  Dutch  English  French  French Canadian  German  Italian  Japanese  Korean  Spanish  Spanish Mexican
  55. 55. Custom Vision Service https://azure.microsoft.com/en-us/services/cognitive-services/custom-vision-service/
  56. 56. https://azure.microsoft.com/en-us/services/cognitive-services/custom-speech-service/ Custom Speech Service (CRIS)
  57. 57. Intelligent systems will not go away. If anything, they’ll be part of any platform soon.
  58. 58. We can own part of this and create human interfaces for all.
  59. 59. Or we can hope that others use this power only for good.
  60. 60. I – for one, don’t consider this a good bet. I’d rather play and be part of this revolution. And so can you.
  61. 61. https://www.flickr.com/photos/bcymet/3356449350 https://www.flickr.com/photos/marcja/3583398125 https://www.flickr.com/photos/frogman2212/3970181993 https://www.flickr.com/photos/johnath/7356295658 https://www.flickr.com/photos/doggybytes/4194394234 https://www.flickr.com/photos/monophonicgirl/3985633864 Chris Heilmann @codepo8

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