Category: Artificial Intelligence/Robots

Search Results Ranking Using Machine-Learning Algorithms: "Best Match: New Relevance Search for PubMed"

Nicolas Fiorini et al. have published "Best Match: New Relevance Search for PubMed" in PLOS Biology.

Here's an excerpt:

PubMed is a free search engine for biomedical literature accessed by millions of users from around the world each day. With the rapid growth of biomedical literature—about two articles are added every minute on average—finding and retrieving the most relevant papers for a given query is increasingly challenging. We present Best Match, a new relevance search algorithm for PubMed that leverages the intelligence of our users and cutting-edge machine-learning technology as an alternative to the traditional date sort order. The Best Match algorithm is trained with past user searches with dozens of relevance-ranking signals (factors), the most important being the past usage of an article, publication date, relevance score, and type of article. This new algorithm demonstrates state-of-the-art retrieval performance in benchmarking experiments as well as an improved user experience in real-world testing (over 20% increase in user click-through rate). Since its deployment in June 2017, we have observed a significant increase (60%) in PubMed searches with relevance sort order: it now assists millions of PubMed searches each week. In this work, we hope to increase the awareness and transparency of this new relevance sort option for PubMed users, enabling them to retrieve information more effectively.

Academic Library as Scholarly Publisher Bibliography | Digital Curation and Digital Preservation Works | Open Access Works | Digital Scholarship | Digital Scholarship Sitemap

"Smart Machines and Human Expertise: Challenges for Higher Education"

Diana Oblinger has published "Smart Machines and Human Expertise: Challenges for Higher Education" in EDUCAUSE Review.

Here's an excerpt:

Changes brought about by AI and robots are taking place in the professions faster than they are in higher education. Without a close connection to business and industry, higher education will be challenged to anticipate the changes in our disciplines and professions. Even if higher education is a keen observer of changes, can programs adjust quickly enough?

Academic Library as Scholarly Publisher Bibliography | Digital Curation and Digital Preservation Works | Open Access Works | Digital Scholarship | Digital Scholarship Sitemap

AI Copyright?: "Authors and Machines"

Jane C. Ginsburg and Luke Ali Budiardjo have self-archived "Authors and Machines."

Here's an excerpt:

Today, developments in computer science have created a new form of machine—the "artificially intelligent" system apparently endowed with "computational creativity"—that introduces challenging variations on the perennial question of what makes one an "author" in copyright law: Is the creator of a generative program automatically the author of the works her process begets, even if she cannot anticipate the contents of those works?

Academic Library as Scholarly Publisher Bibliography | Digital Curation and Digital Preservation Works | Open Access Works | Digital Scholarship | Digital Scholarship Sitemap

Horizon Report 2018 Higher Education Edition

EDUCAUSE has released the Horizon Report 2018 Higher Education Edition.

Here's an excerpt:

The Horizon Report highlights six trends, six challenges, and six developments relating to educational technology and practices that are likely to enter mainstream use within their focus sectors over the next five years (2018–22).

Academic Library as Scholarly Publisher Bibliography | Digital Curation and Digital Preservation Works | Open Access Works | Digital Scholarship | Digital Scholarship Sitemap

"Machine Learning and the Library or: How I Learned to Stop Worrying and Love My Robot Overlords"

Charlie Harper has published "Machine Learning and the Library or: How I Learned to Stop Worrying and Love My Robot Overlords" in he Code4Lib Journal.

Here's an excerpt:

Machine learning algorithms and technologies are becoming a regular part of daily life – including life in the libraries. Through this article, I hope to:

* To introduce the reader to the basic terminology and concepts of machine learning
* To make the reader consider the potential ethical and privacy issues that libraries will face as machine learning permeates society
* To demonstrate hypothetical possibilities for applying machine learning to circulation and collections data using TensorFlow/Keras and open datasets

For a look at earlier AI activity in libraries, see: “Artificial Intelligence in Libraries in the Late 1980's and Early 1990's.”

Artificial Intelligence in Libraries in the Late 1980’s and Early 1990’s

In the late 1980's and early 1990's, academic libraries were creating prototype and operational expert systems using expert system shells and logic programming languages, such as Prolog.

A snapshot of this activity in ARL libraries is:

Expert Systems in ARL Libraries, SPEC Kit 174. Bailey, Charles W., Jr., and Judy E. Myers. Washington, DC: Association of Research Libraries, 1991.

In-depth treatments include:

Alberico, Ralph, and Mary Micco. Expert Systems For Reference and Information Retrieval. Westport: Meckler, 1990.

Aluri, Rao., and Donald E. Riggs, eds. Expert Systems in Libraries. Norwood, N.J.: Ablex, 1990.

You can get a sense of the AI activities in research libraries during this period by reading articles about the University of Houston Libraries' grant-funded Intelligent Reference Information System Project, which was prototyped in expert system shells and completed in Prolog. The Prolog code was freely distributed to over 500 libraries and other institutions (on floppy disk!).

Bailey, Charles W., Jr., and Robin N. Downes. "Intelligent Reference Information System (IRIS)," In 101 Success Stories of Information Technology in Higher Education: The Joe Wyatt Challenge," ed. Judith V. Boettcher, 402-407. New York: McGraw-Hill, 1993.

Bailey, Charles W., Jr. "The Intelligent Reference Information System Project: A Merger of CD-ROM LAN and Expert System Technologies." Information Technology and Libraries 11 (September 1992): 237-244.

Bailey, Charles W., Jr., and Thomas C. Wilson. "The Intelligent Reference Information System CD-ROM Network." In Library LANs: Case Studies in Practice and Application, ed. Marshall Breeding, 157-171. Westport, CT: Meckler, 1992.

Bailey, Charles W., Jr. "Building Knowledge-Based Systems for Public Use: The Intelligent Reference Systems Project at the University of Houston Libraries." In Convergence: Proceedings of the Second National Conference of the Library and Information Technology Association, October 2-6, 1988, ed. Michael Gorman, 190-194. Chicago: American Library Association., 1990.

Bailey, Charles W., Jr., and Kathleen Gunning. "The Intelligent Reference Information System." CD-ROM Librarian 5 (September 1990): 10-19.

Bailey, Charles W., Jr., Jeff Fadell, Judy E. Myers, and Thomas C. Wilson. "The Index Expert System: A Knowledge-Based System to Assist Users in Index Selection." Reference Services Review 17, no. 4 (1989): 19-28.

For an example of contemporaneous thinking about AI potentials among librarians, see:

Bailey, Charles W., Jr. "Intelligent Library Systems: Artificial Intelligence Technology and Library Automation Systems." In Advances in Library Automation and Networking, vol. 4, ed. Joe A. Hewitt, 1-23. Greenwich, CT: JAI Press, 1991.

There has been very little activity in this area since the turn of the 21st century, but here's an example:

Ma, Wei. "A Database Selection Expert System Based on Reference Librarian's Database Selection Strategy: A Usability and Empirical Evaluation." Journal of the American Society for Information Science & Technology, 53 no. 7 (2002): 567-580.

Academic Library as Scholarly Publisher Bibliography | Digital Curation and Digital Preservation Works | Open Access Works | Digital Scholarship | Digital Scholarship Sitemap

The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation

Twenty-six authors from 14 institutions have released The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation.

Here's an excerpt:

This report surveys the landscape of potential security threats from malicious uses of artificial intelligence technologies, and proposes ways to better forecast, prevent, and mitigate these threats. We analyze, but do not conclusively resolve, the question of what the long-term equilibrium between attackers and defenders will be. We focus instead on what sorts of attacks we are likely to see soon if adequate defenses are not developed

Research Data Curation Bibliography, Version 8 | Digital Curation and Digital Preservation Works | Open Access Works | Digital Scholarship | Digital Scholarship Sitemap