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Purpose: Provides a forum for researching potential applications of Machine and Deep Learning in library science, including discussions, publications, and outreach to the wider Library community. Its goal is to educate librarians on uses of the complex techniques of machine learning and to provide a space for critically thinking both about new applications, and about the ethical and social impact of these technologies , as the field rapidly expands in the coming decade.

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Artificial intelligence relate topics at ALA Annual

  • 1.  Artificial intelligence relate topics at ALA Annual

    Posted Jun 23, 2023 03:39 PM

    For anyone attending ALA Annual, here are some presentations and posters relating to artificial intelligence:

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    Saturday, June 24, 2023
    2:30 PM – 3:30 PM CT
    Core Top Ten Technology Trends: Libraries Take On ChatGPT
    Location: Hilton Chicago, Continental B


    Presenter:  Thomas A. Ferren, CMP – Core
    Presenter:  Fernando Aragon – Gwinnett County Public Library
    Presenter:  Trevor Watkins (he/him/his) – George Mason University Libraries
    Presenter:  Jonathan McMichael – Arizona State University
    Presenter:  Hannah Byrd Little, MLIS (she/her/hers) – The Webb School    

    Our panel of experts will be discussing ChatGPT in particular and AI in general as they examine how these emerging technologies impact the LIS field. Panelists represent a wide range of libraries, including academic, public and school library professionals. Join this session to learn background information on what ChatGPT is and does, and what it could mean for your library in the future.


    Learning Objectives:
        Attendees will understand the basic premise and inherent risks and rewards of ChatGPT and similar AI technology.
        Attendees will discuss the emerging technology's ability to address challenges specific to libraries.
        Attendees will reflect on the ethical challenges that arise from ChatGPT.


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    Sunday, June 25, 2023
    9:30 AM – 11:00 AM CT
    What can ChatGPT do for you? (Poster 60)
    Location: McCormick Place, W190a


    Poster Presenter:  Vincci Kwong, n/a – Indiana University South Bend


    ChatGPT is an AI technology that utilizes natural language processing to create human-like conversations with a chatbot. In addition to answering basic factual questions, ChatGPT can teach classes, design an effective marketing campaign, and forecast a better investment mix. Since November 2022, ChatGPT has been a hot topic in the library field, information sciences, education, finance, health care, publishing, and more.


    This poster will provide an overview of ChatGPT and how different industries use it. It then explores how the other uses of ChatGPT can be applied to different types of libraries and library functions. For example, how ChatGPT can be used for library instruction, marketing, customer services etc. In addition, the poster will also discuss limitations and concerns regarding the use of ChatGPT. Furthermore, as technology changes constantly, an update for the latest trends will be provided. Lastly, this poster will provide AI alternatives to ChatGPT for attendees to consider and explore.


    The presenter hopes this poster will provide new ideas for librarians to consider and/or to incorporate ChatGPT for the services or initiatives at their libraries. Other than the poster, the presenter will interact with attendees through a poll regarding the use or potential use of ChatGPT at their libraries.


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    Sunday, June 25, 2023
    1:30 PM – 3:00 PM CT
    Can OpenAI be Taught to Catalog? (Poster 39)
    Location: McCormick Place, W190a


    Poster Presenter:  Nerissa Lindsey, MLIS (she/her/hers) – San Diego State University
    Poster Presenter:  Greta Heng, MLIS – San Diego State University
    Author:  Kate Holvoet, MLIS (she/her/hers) – San Diego State University


    OpenAI learning models like ChatGPT and GPT-4 have exploded onto the learning and library scenes challenging expectations for what our future work will look like. If Artificial Intelligence (AI) can produce workable programming code such as Python, can it be taught to produce structured metadata in formats like MARC 21 or Dublin Core? If so, it has the potential to help libraries improve discovery of open access resources which often have poor metadata quality due to a limited cataloging workforce.


    With this issue in mind San Diego State University Library researchers experimented with open access resources available to OpenAI learning models to see if they could be taught to produce good quality structured metadata. What standards do they do well? When they produce an error, can updates to the query fix the issue for all subsequent queries? Additionally, how well do OpenAI learning models handle applying subject headings?


    This poster will share the results and discuss the findings as well as thoughts on the impact learning models might have for the cataloging profession.

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    Wilhelmina Randtke
    Head of Libraries Systems and Technologies
    Georgia Southern University
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