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

1. Chris Heilmann (codepo8), PNWPHP conference, Seattle, September 2017

2. Machine Learning,
Deep Learning,
Artificial Intelligence,
Big Data…

6. We live in a
post-data leak
world. We
have been
recorded and
categorised

7. Lots of companies
are using this data.

8. Some in amazing
ways, others in
shady ones.

9. There should not be
only a few that have
access and do
something with it.

10. Let’s democratise
intelligent machines
and human
interfaces.

11. How do we remember
and learn?

13. Repetition

14. Repetition
Comparison

15. Repetition
Comparison
Explanation

16. Repetition
Comparison
Explanation
Association

18. complex and
erratic leaps

19. we are terrible
at repetition
bored

24. That the
returned data is
fit for human
consumption

26. Where do I find a nice
restaurant around here that is
open tomorrow around lunch
time?

27. nice
restaurant here that is
open tomorrow around lunch
time?
Search term

28. nice
restaurant here that is
open tomorrow around lunch
time?
What is nice?
Search term

29. nice
restaurant here
tomorrow around lunch
time?
What is nice?
Search term
Location

30. nice
restaurant here
tomorrow around lunch
time
What is nice?
Search term
Location
Calculated time frame

31. Where nice
restaurant here
tomorrow around lunch
time
What is nice?
Search term
Location
Calculated time frame
Calculated result

32. Our current hype around
Artificial Intelligence is driven
by Sci-Fi concepts.

33. The age-old dream of a
ubiquitous, all-knowing
computer butler.

34. One that understands all
human communication quirks
and also has a delicious sense
of sarcasm.

35. In essence: a human, that isn’t
human but appears to be.

36. Which is how we set ourselves
up for failure.

37. Ubiquitous computing easily
becomes a nuisance when it
records without giving us the
right answers.

38. It is very easy to create a
creepy, annoying chat bot. We
are not forgiving with them as
we are with humans.

39. Artificial Intelligence
is most effective when it
enhances in the background.

40. Repetition
Comparison
Explanation
Association

41. Repetition
Comparison
Explanation
Association

42. What you can use AI for in
your interfaces right now:
Visual Recognition
Voice Recognition
Natural Language Processing
Emotion Recognition
Entity Recognition

43. Visual recognition

44. Visual recognition (positives)
 Automated tagging and
clustering of images
 Accessibility benefit of
automated alternative content
 Biometric login
 Automated “art direction”
 Automated moderation

45. Visual recognition (negatives)
 Privacy issues
 Wrong and possibly offensive
automated labeling
 False moderation and failed
moderation
 Connection / Upload latency
 Insufficiently trained models

46. Voice recognition

47. Voice recognition (positives)
 Visual impairment or no screen
 Hands-free interaction (phone,
car, headsets…)
 Faster than typing, more natural
 Magical “Star Trek” factor

48. Voice recognition (negatives)
 Intrusive as hell
 Disappointing error handling
 Language and accent issues
 Low sound quality or loud
surrounding
 Latency in recognition

49. Natural Language Processing

50. 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

51. 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

52. Emotion recognition

53. 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

54. 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

55. Entity recognition

56. Entity recognition (positives)
 Automated tagging and cross
referencing
 Opportunity to add third party
content (Wikipedia is the classic)
 Intelligent auto-complete

57. Entity recognition (negatives)
 False recognition
 Language differences
 Lack of value of automated
content

60. You can REST easy…

61. You can REST easy…

62. You can REST easy…

63. https://en-gb.wordpress.org/plugins/cloudinary-image-management-and-manipulation-in-the-cloud-cdn/

64. Everything counts in large amounts

66. Repetition
Comparison
Explanation
Association

67. 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

68. Custom Vision Service
https://azure.microsoft.com/en-us/services/cognitive-services/custom-vision-service/

69. https://azure.microsoft.com/en-us/services/cognitive-services/custom-speech-service/
Custom Speech Service (CRIS)

70. Intelligent systems will not go
away. If anything, they’ll be
part of any platform soon.

71. We can own part of this and
create human interfaces for all.

72. Or we can hope that others
use this power only for good.

73. I – for one, don’t consider this
a good bet. I’d rather play and
be part of this revolution.
And so can you.

74. 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