A preconference presentation given by Bohyun Kim, CTO and Associate Professor, University of Rhode Island Libraries, at the NISO Plus Conference at Baltimore, MD on February 23, 2020.

1. The Potential and
Challenges ofToday’s AI
Bohyun Kim
CTO & Associate Professor,
University of Rhode Island Libraries
NISO Plus Conference, Baltimore MD, Feb. 23, 2020

2. Today’s participants are from…
• Publishers
• Libraries
• Library/Information systems vendors
• Professional associations
• Consulting service firms
• Funders
• Other places..?
2

3. https://futureoflife.org/background/benefits-risks-of-artificial-intelligence/?cn-reloaded=1
3

4. Q1.WhatWord First Comes toYour Mind
WhenYou Hear “AI” &Why?
4

5. Warm-up Qs
•Q2.Which aspect of AI are you most excited
about?
•Q3.Which aspect of AI are you most concerned
about?
5
*AddYour Ideas to Google Doc*
https://bit.ly/37Hu22l

6. Q4.When AI is adopted everywhere,
what will the world look like?
Q5. How would AI affect your work and life?
Q6.What kind of world would people be living in?
6

7. https://www.cnn.com/2019/12/02/tech/china-facial-recognition-mobile-intl-hnk-scli/index.html
Surveillance
7

8. https://www.theverge.com/2019/7/17/20697540/boston-dynamics-robots-commercial-real-world-business-spot-on-sale
Convenience
8

9. Benevolent AI
9

10. Malicious AI
10

11. Today – Part I
I. AI: Overview
a) What CanToday’s AI Do?
b) How Does AIWork?
c) AI Applications that Use Deep Learning
d) AI for Information Profession/Industry
e) Group-discussion & Sharing
f) Q/As & Comments
Break (10:45 - 11 AM)
11

12. Today – Part II
II. AI & Society
a) Algorithmic Bias/ Opacity
b) Data-ism
c) Human-in-the Loop & Automation’s Last Mile
d) Learning in the Age of AI
e) AI & Ethics
Q/A & Wrap-Up (Noon)
12

13. I. AI: Overview
13

14. What is the Purpose of AI?
14

15. (a)What CanToday’s AI Do ?
15

16. https://deepmind.com/blog/article/alphago-zero-starting-scratch
Reinforcement Learning
16

17. https://www.nytimes.com/2019/02/05/busines
s/media/artificial-intelligence-journalism-
robots.html
AITools for NLG
• Heliograf,
Washington Post
• Wibbitz,
USAToday
• Cyborg,
Bloomberg News
17

18. ComputerVision
real-time translation and object identification from the
camera screen
18

19. More Examples of AI Applications
• Siri
• Alexa
• Tesla
• Amazon
• Netflix
• Pandora
• Nest
19

20. https://www.forbes.com/si
tes/gilpress/2019/07/15/is-
ai-going-to-be-a-jobs-
killer-new-reports-about-
the-future-of-
work/#17fd2057afb2 20

21. (b) How Does AIWork?
21

22. 22

23. Symbolic AI is Rule-Based. (50’s – 80’s)
https://medium.com/@sunilpnwr/expert-systems-42715a5a5b14
23

24. Machine Learning is Data-Driven.
Diagrams from Francois Chollet and J. J. Allaire, Deep Learning with R, 1st edition (Shelter Island, NY:
Manning Publications, 2018).
24

25. https://blogs.nvidia.com/blog/2016/07/29/whats-difference-artificial-intelligence-machine-learning-deep-learning-ai/
Deep Learning Uses Hidden Layers.
25

26. https://www.amchkg.com/shileiblog/2017.1.4deep-learning 26

27. https://www.nytimes.com/2012/06/26/technology/in-a-big-network-of-computers-evidence-of-machine-
learning.html
• +1 billion
parameters
• 16,000 CPU cores
• 10 million
unlabeledYouTube
videos
27

28. (c) AI Applications that Use Deep Learning
28

29. • 9-layer neural
network
• +120 million weights
• 4 million images
https://research.fb.com/publications/deepface-closing-the-gap-
to-human-level-performance-in-face-verification/
https://www.technologyreview.com/s/525586/facebook-creates-
software-that-matches-faces-almost-as-well-as-you-do/ 29

30. https://www.nature.com/news
/deep-learning-boosts-
google-translate-tool-1.20696
30

31. https://www.blog.google/pr
oducts/assistant/lost-
translation-try-interpreter-
mode-google-assistant/
31

32. https://www.theguardian.com/
technology/2018/may/08/googl
e-duplex-assistant-phone-calls-
robot-human 32

33. https://techcrunch.com/2018/08/20/nyu-and-facebook-team-up-to-supercharge-mri-scans-with-ai/ 33

34. 34
https://firedrop.ai/

35. Questions?
35

36. (d) AI for Information Profession/ Industry
36

37. (i) Abstracting & Indexing
37

38. 38

39. (ii) Information Discovery / Retrieval
& Research Insight
39

40. https://quartolio.com/
40

41. https://yewno.com/edu/concept/102e6bf8445dff7c922e1cc4e997ebf3 41

42. https://mitlibraries-hamlet.mit.edu/similar_to/16605/
42

43. (iii) Feature Detection & Content Extraction
43

44. https://2018.code4lib.org/talks/deep-learning-and-historical-collections
Also see : Eric Phetteplace, Bohyun Kim, and Ashley Blewer, “Reflections on Code4Lib 2018,” ACRLTechConnect (blog),
March 12, 2018, https://acrl.ala.org/techconnect/post/reflections-on-code4lib-2018/. 44

45. 45

46. (iv)Voice User Interface (VUI) & Chatbots
46

47. https://www.eventscribe.com/2018/ALA-Midwinter/fsPopup.asp?Mode=presInfo&PresentationID=348820
47

48. https://books.google.com/talktobooks/
https://research.google.com/semanticexperiences/about.html
+100,000 books
48

49. https://books.google.com/talktobooks/query?q=what%20is%20the%20best%20place%2
0to%20go%20on%20earth 49

50. 50
https://books.google.com/talktobooks/query?q=why%20is%20there%20a%20wage%20difference%20b
etween%20men%20and%20women

51. JessamynWest, “TILT #55 - ‘Hey Google AREWomen Smarter than Men...?,’” TinyLetter (blog), accessed June 11, 2018,
http://tinyletter.com/jessamyn/letters/tilt-55-hey-google-are-women-smarter-than-men. 51

52. Q7. How can libraries / content providers /
information system vendors make the content
and the metadata easier
for AI- powered tools
to ingest, process, and evaluate?
52
*AddYour Ideas to Google Doc*
https://bit.ly/37Hu22lGroup Discussion Q 7-Q11

53. Q8. How will AI and machine learning affect people’s
information-seeking activities?
Q9.-Q11.What are some of the ways in which libraries / content
providers / information system vendors can utilize AI techniques
or AI-powered services/ products in order to
• make their content more accessible, discoverable, and
analyzable,
• Make their services more effective and user-friendly,
• and make their operations more efficient?
53
*AddYour Ideas to Google Doc*
https://bit.ly/37Hu22l
Group Discussion Q 7-Q11
[15 MIN]

54. 54
* Break! ( Now - 11 AM) *

55. II. AI & Society
55

56. (a) Algorithmic Bias / Opacity
56

57. https://www.technologyreview.com/s/604
087/the-dark-secret-at-the-heart-of-ai/
57

58. https://www.wired.com/2017/04/courts-using-ai-sentence-criminals-must-stop-now/
58

59. https://www.aclu.org/blog/privacy-technology/pitfalls-artificial-intelligence-decisionmaking-highlighted-idaho-aclu-case 59

60. http://explainableai.com/
60

61. 61

62. 62
https://www.nytimes.com/2018/02/09/technology/facial-recognition-race-artificial-intelligence.html
https://www.independent.co.uk/life-style/gadgets-and-tech/news/self-driving-car-crash-racial-bias-black-people-study-
a8810031.html
https://www.nytimes.com/2019/05/14/us/facial-recognition-ban-san-francisco.html

63. https://www.microsoft.com/en-us/research/group/fate/
63

64. https://blackinai.github.io/
64

65. https://gizmodo.com/google-employees-resign-in-protest-against-pentagon-con-1825729300 65

66. https://www.blog.google/topics/ai/ai-principles/ 66

67. Questions / Comments?
67

68. (b) Data-ism
68

69. https://www.nytimes.com/2013/02/05/opinion/brooks-the-philosophy-of-data.html.
69

70. Data-ism is a belief that
• everything that can be measured should be
measured;
• data is a transparent and reliable lens that allows
us to filter out emotionalism and ideology;
• data will help us do remarkable things - like
foretell the future.”
–David Brooks, “The Philosophy of Data,”The NewYorkTimes, February 4, 2013,
https://www.nytimes.com/2013/02/05/opinion/brooks-the-philosophy-of-data.html.
70

71. Data-ist Dogma
• We must expand and facilitate the great data flow as a
new mandate over the right of humans to own data and
to restrict its movement.
• Human experiences are only valuable to the degree that
they produce data that can contribute to data flow.
• As a result, epistemologically, socially, and politically,
humans are no longer the source of meaning,
knowledge, or authority.
Yuval Noah Harari, Homo Deus: A Brief History ofTomorrow, (NewYork, NY: Harper, 2017), p.389.
71

72. Techno-Utopianism ⊃
Data-ism / Dataist Dogma
72

73. 73
https://www.gizmodo.com.au/2019/06/robots-are-not-coming-for-your-jobmanagement-is/

74. 74
http://www.infotoday.com/OnlineSearcher/Articles/Technolog
y-and-Power/The-Peril-of-Dataism-135012.shtml

75. (c) Human-in-the-Loop & GhostWork
at the Automation’s Last Mile
75

76. Humans & AI Systems
• Human in the loop
• Human on the loop
• Human off the loop
76

77. https://www.ibm.com/watson/
Delegation of High-
Level Decisions
https://en.th-wildau.de/university/central-facilities/university-library/ifla-wlic-preconference-satellite-meeting/reports-photos/
77
The Role of Human in the Loop

78. https://www.theguardian.com/technology/201
8/jul/06/artificial-intelligence-ai-humans-bots-
tech-companies
78
Automation’s Last Mile

79. 79
Madhumita Murgia, “AI’s New Workforce:The
Data-Labelling Industry Spreads Globally,”
FinancialTimes, July 23, 2019,
https://www.ft.com/content/56dde36c-aa40-
11e9-984c-fac8325aaa04.

80. (d) Learning in the Age of AI
80

81. https://www.technologyreview.com/s/613502/deep-learning-could-reveal-why-the-world-works-the-way-it-does/81

82. 82
AI in Education

83. Most Significant Question
AI Poses to Our Information Profession
•Not so much “how to utilize AI techniques to our
field?”
•What kind of learning and research we should
support and how, in the new era of AI?
•How can we add efforts to ensure that the right kind
of learning and research take place in the new A-
driven learning and research environment?
83

84. 84NewTemple University’s Charles Library Building https://library.temple.edu/explore-charles
What would learners need in the environment
filled with ubiquitous AI tools and systems ?

85. Given the fast-approaching AI age, ask:
•How are we different from intelligent machines?
•How should our learning be different from that
of a data-processing AI algorithm?
* Really important questions facing the information profession*
85

86. What Education is Really About
“A live educator offers more than the content of a course.
Human interaction and presence are important
components of effective pedagogy. Moreover, a teacher
sets an example by embodying the ideals of learning and
critical thinking. Possessed by a spirit of inquiry, the teacher
enacts the process of learning for students to mimic.The
act of mimesis itself matters: one human learning by
watching another, observing the subtle details, establishing
rapport, and connecting to history.”
Douglas Rushkoff, Team Human, 1 edition (NewYork:W.W. Norton & Company, 2019), 45, 48.
86

87. Can we keep what makes learning special
in the age of AI?
87

88. (e) AI & Ethics
88

89. TheTrolley Problem
https://pixel.nymag.com/imgs/daily/selectall/2016/08/09/09-trolley.w710.h473.jpg
89

90. What Is a MachineTo Do?
90

91. https://www.nbcnew
s.com/tech/tech-
news/self-driving-
uber-car-hit-killed-
woman-did-not-
recognize-n1079281 91

92. Ethics for Data Science & AI
• It’s popular for people nowadays to invoke ethics as if it may solve all
the problems.
But… !
• Ethics isn’t there to give us answers.
• Ethics is there to show how complicated the Qs are in the first place.
92

93. What is “intelligence”?
93

94. https://en.wikipedia.org/wiki/Turing_test#/media/File:Turing_test_diagram.png
TuringTest
94

95. Image from MaxTegmark, Life 3.0: Being Human in the Age of Artificial Intelligence (NewYork: Knopf, 2017), p.53.
Figure 2.2: Illustration of Hans Moravec’s “Landscape of Human Competence” 95

96. 96
Mind and intelligence as a
software for download and upload
Abuela in a birthday body

97. Questions / Comments?
https://www.alastore.ala.org/ltr56_2 97

98. ThankYou!
Bohyun Kim
@bohyunkim [Twitter]
http://bohyunkim.net/blog [Blog]
CTO & Associate Professor
University of Rhode Island Libraries
Slides: https://www.slideshare.net/bohyunkim
98