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| # AI & ML in Libraries Literacies {.unnumbered} | ||
| >Contributed by: Nora McGregor, [ORCID iD](https://orcid.org/0000-0001-6560-5586)<br> | ||
| >Original published date: 04/06/2024<br> | ||
| >Last modified: See Github page history | ||
| > | ||
| >Suggested Citation: Nora McGregor, “AI & ML in Libraries Literacies,” *Digital Scholarship & Data Science Essentials for Library Professionals* (2024), [DOI link tbd] | ||
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| ## Introduction: AI & ML terms demystified | ||
| --- | ||
| title: "AI & Machine Learning in Libraries" | ||
| date: 2024-06-04 | ||
| date-modified: 2025-02-10 | ||
| author: | ||
| - name: Nora McGregor | ||
| id: jc | ||
| orcid: 0000-0001-6560-5586 | ||
| email: [email protected] | ||
| affiliation: | ||
| - name: British Library | ||
| city: London | ||
| country: UK | ||
| url: https://www.bl.uk | ||
| abstract: > | ||
| A gentle introduction to AI & Machine Learning demystifying concepts and technologies through the examples of practical applications in library work today. | ||
| keywords: | ||
| - TBD | ||
| - TBD | ||
| license: "CC BY" | ||
| citation: | ||
| container-title: Digital Scholarship & Data Science Essentials | ||
| volume: 1 | ||
| issue: 1 | ||
| doi: TBD | ||
| --- | ||
| ## Introduction | ||
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| AI is mentioned absolutely everywhere these days, first it was just in movies, the news, but now it’s cropping up in our library meetings and strategies and funding calls, but what does it really mean, particularly in a library context? Let’s try to get to the bottom of this! | ||
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| Sometimes folks may speak of or refer to AI as systems and machines that actually have true intelligence, and though today's AI systems are shockingly convincing in how well they perform, what we’re seeing today are just very advanced machine learning algorithms and models performing specific and discrete functions extremely well! We’re a long way off (if ever) from machines having sentience (or, **Artificial General Intelligence (AGI)**/**Strong AI**) so don’t worry! | ||
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| You might also sometimes hear people talk about **Traditional AI** vs **Generative AI**. **Traditional AI** refers to using machine learning based systems for doing tasks like classifying data (e.g., assigning labels to images, automatically transcribing handwritten texts, or identifying genre of digitised texts). This is the type of AI we make a whole lot of use of in the library world. **Generative AI** on the other hand refers broadly to systems whose primary function is to generate new content (e.g., conversation, books, art). This is where conversation generating AI systems like ChatGPT (Generative Pre-trained Transformer) fall under for example and we’re only just now exploring the potential applications for these new powerful Generative AI systems in library work. | ||
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| Generative artificial intelligence (generative AI, GenAI,[1] or GAI) is artificial intelligence capable of generating text, images, videos, or other data using generative models,[2] often in response to prompts.[3][4] Generative AI models learn the patterns and structure of their input training data and then generate new data that has similar characteristics.[5][6] | ||
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| Improvements in transformer-based deep neural networks, particularly large language models (LLMs), enabled an AI boom of generative AI systems in the early 2020s. These include chatbots such as ChatGPT, Copilot, Gemini and LLaMA, text-to-image artificial intelligence image generation systems such as Stable Diffusion, Midjourney and DALL-E, and text-to-video AI generators such as Sora.[7][8][9][10] Companies such as OpenAI, Anthropic, Microsoft, Google, and Baidu as well as numerous smaller firms have developed generative AI models.[3][11][12] | ||
| You might also sometimes hear people talk about **Traditional AI** vs **Generative AI**. **Traditional AI** refers to using machine learning based systems for doing tasks like classifying data (e.g., assigning labels to images, automatically transcribing handwritten texts, or identifying genre of digitised texts). This is the type of AI we make a whole lot of use of in the library world. **Generative AI** on the other hand refers broadly to systems whose primary function is to generate new content (e.g., conversation, books, art), often in response to text or image prompts. This is where conversation generating AI systems like ChatGPT (Generative Pre-trained Transformer) fall under for example and we’re only just now exploring the potential applications for these new powerful Generative AI systems in library work. | ||
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| Whenever AI is being discussed you may often hear the term **Machine Learning (ML)** mentioned, and sometimes they’re used interchangeably which can be confusing! | ||
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| There are of course untold numbers of lists out there with resources for learning more about AI & Machine Learning but I think this particular guide is exceptionally useful in its coverage and topics selected, particularly as they are quite specifically for Librarians: [Add'tl Reading for Librarians & Faculty - Using AI Tools in Your Research - Research Guides at Northwestern University](https://libguides.northwestern.edu/ai-tools-research/librarians) | ||
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| ## Taking the next step | ||
| ## Finding Communities of Practice | ||
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| The [AI4Lam group](https://sites.google.com/view/ai4lam) is an excellent, engaged and welcoming international organisation dedicated to all things AI in Libraries, Archives and Museums. It’s free for anyone to join and is a great first step for anyone interested in learning more about this topic! | ||
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