The Potential and Challenges of Today's AI
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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.
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The Potential and Challenges of Today's AI
- 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
- 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
- 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
- 14. What is the Purpose of AI? 14
- 15. (a)What CanToday’s AI Do ? 15
- 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
- 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
- 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
- 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
- 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
- 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
- 61. 61
- 68. (b) Data-ism 68
- 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
- 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
- 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
- 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
- 90. What Is a MachineTo Do? 90
- 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
- 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
- 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