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An AI toolkit for libraries Cover

An AI toolkit for libraries

By: Michael Upshall  
Open Access
|Nov 2022

References

  1. 1Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, 3rd edition (Pearson, 2016).
  2. 2Ray Kurzweil, “A Wager on the Turing Test: Why I Think I Will Win,” Kurzweil, Kurzweilai.net, April 9, 2002, https://www.kurzweilai.net/a-wager-on-the-turing-test-why-i-think-i-will-win (accessed 23 September 2022).
  3. 3Rory Cellan-Jones, “Stephen Hawking Warns Artificial Intelligence Could End Mankind,” BBC News, December 2, 2014, sec Technology, https://www.bbc.com/news/technology-30290540 (accessed 23 September 2022).
  4. 4A. M. Turing, “Intelligent Machinery, A Heretical Theory*,” Philosophia Mathematica 4, no. 3 (September 1, 1996): 25660, DOI: 10.1093/philmat/4.3.256 (accessed 23 September 2022).
  5. 5Nicholas Carr, The Shallows: How the Internet Is Changing the Way We Think, Read and Remember, Main-Re-issue edition (London: Atlantic Books, 2020).
  6. 6Elon Musk, “Elon Musk: ‘With Artificial Intelligence We Are Summoning the Demon’,” Washington Post, October 24, 2014, https://www.washingtonpost.com/news/innovations/wp/2014/10/24/elon-musk-with-artificial-intelligence-we-are-summoning-the-demon/ (accessed 23 September 2022).
  7. 7Kate Crawford, Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence (New Haven: Yale University Press, 2021). DOI: 10.12987/9780300252392
  8. 8Cathy O’Neil, Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, 01 edition (Penguin, 2016).
  9. 9Erik J. Larson, The Myth of Artificial Intelligence: Why Computers Can’t Think the Way We Do (Cambridge, Massachusetts: Belknap Press, 2021). DOI: 10.4159/9780674259935
  10. 10Yann LeCun, Corinna Cortes, and Christopher J C Burges, “The MNIST Database,” THE MNIST DATABASE, http://yann.lecun.com/exdb/mnist/ (accessed 23 September 2022).
  11. 11Natalia Domagala and Hannah Spiro, “Engaging with the Public about Algorithmic Transparency in the Public Sector,” Centre for Data Ethics and Innovation Blog, June 21, 2021, https://cdei.blog.gov.uk/2021/06/21/engaging-with-the-public-about-algorithmic-transparency-in-the-public-sector/ (accessed 23 September 2022.
  12. 12Domagala and Spiro, “Engaging with the Public.”
  13. 13European Parliament. Directorate General for Parliamentary Research Services. A Governance Framework for Algorithmic Accountability and Transparency (LU: Publications Office, 2019), https://data.europa.eu/doi/10.2861/59990 (accessed 23 September 2022).
  14. 14Rob Merrick, “Fears of Another A-Level-Style Fiasco as Scrutiny of Policies Made by Computer Are Ditched Following Brexit,” The Independent, February 10, 2022.
  15. 15Pedro Domingos, The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World, 1st edition (London: Penguin, 2017).
  16. 16“Artificial General Intelligence,” in Wikipedia (Retrieved 9 May 2022), https://en.wikipedia.org/w/index.php?title=Artificial_general_intelligence&oldid=1086964857 (accessed 23 September 2022).
  17. 17“What Is Supervised Learning?,” IBM Cloud Learn Hub, August 19, 2020, https://www.ibm.com/cloud/learn/supervised-learning (accessed 23 September 2022).
  18. 18Pandu Nayak, “Understanding Searches Better than Ever Before,” Google (blog), October 25, 2019, https://blog.google/products/search/search-language-understanding-bert/ (accessed 23 September 2022).
  19. 19Motahhare Eslami et al., “‘I Always Assumed That I Wasn’t Really That Close to [Her]’: Reasoning about Invisible Algorithms in News Feeds,” in Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, CHI ’15 (New York, NY, USA: Association for Computing Machinery, 2015), 15362, DOI: 10.1145/2702123.2702556 (accessed 23 September 2022).
  20. 20Mary Shultz, “Comparing Test Searches in PubMed and Google Scholar,” Journal of the Medical Library Association: JMLA 95, no. 4 (October 2007): 44245, DOI: 10.3163/1536-5050.95.4.442 (accessed 23 September 2022).
  21. 21G. A. Miller, “The Magical Number Seven plus or Minus Two: Some Limits on Our Capacity for Processing Information,” Psychological Review 63, no. 2 (March 1956): 8197, https://pubmed.ncbi.nlm.nih.gov/13310704/ DOI: 10.1037/h0043158 (accessed 27 September 2022).
  22. 22Larson, The Myth of Artificial Intelligence.
  23. 23Duri Long and Brian Magerko, “What Is AI Literacy? Competencies and Design Considerations,” in Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (CHI ’20: CHI Conference on Human Factors in Computing Systems, Honolulu HI USA: ACM, 2020), 116, DOI: 10.1145/3313831.3376727 (accessed 23 September 2022).
  24. 24“Online-Spellcheck.Com,” https://www.online-spellcheck.com/ (accessed 23 September 2022).
  25. 25Jenna Burrell, “How the Machine ‘Thinks’: Understanding Opacity in Machine Learning Algorithms,” Big Data & Society 3, no. 1 (January 6, 2016): 2053951715622512, DOI: 10.1177/2053951715622512 (accessed 23 September 2022).
  26. 26“Cambridge Core, Recommender Links for Book Chapter,” Cambridge Core, https://www.cambridge.org/core/books/abs/python-programming-for-biology/machine-learning/570B575C26034A8CB9A7AF7E17A795AB/ (accessed 23 September 2022).
  27. 27Eugene Garfield, “Citation Indexes for Science,” Science 122, no. 3159 (July 15, 1955): 10811, DOI: 10.1126/science.122.3159.108 (accessed 23 September 2022).
  28. 28J. E. Hirsch, ‘An Index to Quantify an Individual’s Scientific Research Output’, Proceedings of the National Academy of Sciences 102, no. 46 (November, 15 2005): 1656972, DOI: 10.1073/pnas.0507655102 (accessed 23 September 2022).
  29. 29“Scite: See How Research Has Been Cited,” scite.ai, https://scite.ai/ (accessed 23 September 2022).
  30. 30“Scholarcy,” Scholarcy|The long-form article summariser, https://www.scholarcy.com/ (accessed 23 September 2022).
  31. 31“Semantic Scholar|AI-Powered Research Tool,” https://www.semanticscholar.org/ (accessed 23 September 2022).
  32. 32Marco Valenzuela, Vu Ha, and Oren Etzioni, “Identifying Meaningful Citations,” in Workshops at the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015, 6, http://ai2-website.s3.amazonaws.com/publications/ValenzuelaHaMeaningfulCitations.pdf (accessed 27 September 2022).
  33. 33“Scholarcy”.
  34. 34“2021 Trends in Library Analytics,” EBSCO Information Services, Inc., December 13, 2021, https://www.ebsco.com/blogs/ebscopost/2021-trends-library-analytics (accessed 23 September 2022).
  35. 35“William Shakespeare,” Academic Influence, https://academicinfluence.com/people/william-shakespeare-1 (accessed 23 September 2022).
  36. 36“F-Score,” Wikipedia, May 9, 2022, https://en.wikipedia.org/w/index.php?title=F-score&oldid=1086969326 (accessed 23 September 2022).
  37. 37N.B. Harikrishnan, “Confusion Matrix, Accuracy, Precision, Recall, F1 Score,” Analytics Vidhya (blog), December 10, 2019, https://medium.com/analytics-vidhya/confusion-matrix-accuracy-precision-recall-f1-score-ade299cf63cd (accessed 23 September 2022).
  38. 38Yeow Goh et al., “Evaluating Human versus Machine Learning Performance in Classifying Research Abstracts,” Scientometrics 125 (July 18, 2020): 11971212, DOI: 10.1007/s11192-020-03614-2 (accessed 23 September 2022).
  39. 39Karla Bernardi et al., “Gender Disparity in Authorship of Peer-Reviewed Medical Publications,” The American Journal of the Medical Sciences 360, no. 5 (November 2020): 51116, DOI: 10.1016/j.amjms.2019.11.005 (accessed 23 September 2022).
  40. 40Tyler Machado, Molly Callahan, and Eunice Esomonu, “Do Women Publish Less than Men in Scientific Fields?,” News @ Northeastern, March 5, 2020, https://news.northeastern.edu/2020/03/05/do-women-publish-less-than-men-in-scientific-fields-turns-out-scientists-have-been-asking-the-wrong-question/ (accessed 23 September 2022).
  41. 41“Web of Science Reviewer Locator,” Clarivate, https://clarivate.com/products/scientific-and-academic-research/research-publishing-solutions/web-of-science-reviewer-locator/ (accessed 23 September 2022).
  42. 42“Background: Algorithms,” 50 Examples 1.0 Documentation, https://fiftyexamples.readthedocs.io/en/latest/algorithms.html (accessed 23 September 2022).
  43. 43Ralph Ewerth et al., “‘Are Machines Better Than Humans in Image Tagging?’ – A User Study Adds to the Puzzle,” Advances in Information Retrieval, ed. Joemon M Jose et al., Lecture Notes in Computer Science (Cham: Springer International Publishing, 2017), 18698, DOI: 10.1007/978-3-319-56608-5_15 (accessed 23 September 2022).
  44. 44Ron Kohavi, Diane Tang, and Ya Xu, Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing (Cambridge: Cambridge University Press, 2020), DOI: 10.1017/9781108653985 (accessed 23 September 2022).
DOI: https://doi.org/10.1629/uksg.592 | Journal eISSN: 2048-7754
Language: English
Submitted on: Jun 8, 2022
Accepted on: Jul 18, 2022
Published on: Nov 1, 2022
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 3 issues per year

© 2022 Michael Upshall, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.