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Artificial Intelligence, data and analytics



**A Generative AI Primer. By Michael Webb. Jisc. 2 January 2024.

“Publishing an intro to generative AI is a challenge as things are moving so quickly. However, we think things have now settled down enough for us to bring together information in a single place, to create a short primer.” Table of contents

1. Introduction

2. An Introduction to the Generative AI Technology
2.1 ChatGPT
2.2. Microsoft Copilot
2.3. Google Bard
2.4. Other Models
2.5. A summary of key capabilities, limitations, and concerns around ChatGPT and other Large Language Models
2.6 Image Generation
2.7 Beyond Chatbots
3. Impact of Generative AI on Education
3.1 Assessment
3.1.1 Guidance on advice to students
3.1.2 The role of AI detectors
3.2 Use in Learning and Teaching
3.2.1 Examples of use by students
3.2.2 Examples of use by teaching staff
3.2.3 Examples of uses to avoid
3.3 Adapting curriculum to reflect the use of AI in work and society.

4. Regulation

5. Summary

AI update from the Information Training Team by Phil Bradley**
Originally posted to the lis-link@JISCMAIL.AC.UK listserv on 27th November 2023. This is a useful update for librarians covering

* ChatGPT and other chatbots

* AI Search

* Content creation

Artificial Intelligence in academic library strategy in the United Kingdom and the Mainland of China. Yingshen Huang a, Andrew M. Cox b, John Cox C. The Journal of Academic Librarianship Volume 49, Issue 6, November 2023, There is growing recognition of the value of applying Artificial Intelligence (AI) in libraries. This study explores how academic libraries have responded to this opportunity at the level of strategy, what is the status of the application of AI, if any, and what are the different emphases of development comparing the UK and China. The data for the study was strategy documentation from high-ranking universities and their libraries. The sample consisted of the top 25 universities from the United Kingdom and top 25 from the Mainland of China according to the QS world university rankings. Explicit mention of Artificial Intelligence and related technologies is rarely found in strategic plans of universities in the UK but most Chinese universities mention them in their vision statements which focus on the development of new majors and research of the technology. Though several libraries have already implemented applications based on AI or claim to be “smart” or “intelligent” most academic library strategic plans or agendas do not emphasize AI. This is one of the first studies to explore the current status of AI applied in academic libraries as a sector and to compare experiences internationally.

The impact of Generative AI on libraries? Ex Libris [White paper] October 2023 Valid concerns and challenges surround Generative AI, leading to a growing debate on regulation. However, academic libraries cannot overlook its significant potential benefits. With an objective to deliver optimal service to users, libraries are looking at how their trusted vendors and suppliers may use Generative AI to help them achieve this goal.

Artificial Intelligence and Libraries Bibliography
Charles W. Bailey, Jr. Houston: Digital Scholarship, 2023
The Artificial Intelligence and Libraries Bibliography includes over 125 selected English-language articles and books that are useful in understanding how libraries are exploring and adopting modern artificial intelligence (AI) technologies. It covers works from January 2018 through August 2023

Library strategy and Artificial Intelligence . By Dr Andrew M Cox. [Blog] Understanding AI in Education [Jisc] National Centre for AI 5 June 2023 On April 20th 2023 the Information School, University of Sheffield invited five guest speakers from across the library sectors to debate “Artificial Intelligence: Where does it fit into your library strategy?”==== 23 resources to get up to speed on AI in 2023. Selected by the IFLA Artificial Intelligence SIG [Special Interest Group] Version 1 29/12/2022. IFLA

How artificial intelligence might change academic library work: Applying the competencies literature and the theory of the professions
Andrew Cox, Journal of the Association for information Science and Technology [JASIST]
07 March 2022 “The probable impact of artificial intelligence (AI) on work, including professional work, is contested, but it is unlikely to leave them untouched. The purpose of this conceptual paper is to consider the likelihood of the adoption of different approaches to AI in academic libraries.”

Understanding AI.

“To understand where AI should be used and will be most successful, one must understand what AI really is. AI, or machine learning, refers to a broad set of algorithms that can solve a specific set of problems, if trained properly”.

The success of artificial intelligence depends on data. Nick Ismail Information Age [blog] 23 April 2018

“The AI bucket consists of:

  • Big data
  • Analytics
  • Machine learning
  • Natural language processing
  • Data visualisation
  • Decision logic”

Components of AI

“A composite including:
• Big data
• Analytics
• Machine learning
• Natural language processing
• Data visualisation
• Decision logic”

Smith, A. (2016). Big Data Technology, Evolving Knowledge Skills and Emerging Roles. Legal Information Management, 16(4), 219-224.

Common AI Terms

(Taken from:) AI in the UK: ready, willing and able? HOUSE OF LORDS Select Committee on Artificial Intelligence.Report of Session 2017–19 HL Paper 100 16 April 2018.


A series of instructions for performing a calculation or solving a problem, especially with a computer. They form the basis for everything a computer can do, and are therefore a fundamental aspect of all AI systems.

Expert system

A computer system that mimics the decision-making ability of a human expert by following pre-programmed rules, such as ‘if this occurs, then do that’. These systems fuelled much of the earlier excitement surrounding AI in the 1980s, but have since become less fashionable, particularly with the rise of neural networks.

Machine learning

One particular form of AI, which gives computers the ability to learn from and improve with experience, without being explicitly programmed. When provided with sufficient data, a machine learning algorithm can learn to make predictions or solve problems, such as identifying objects in pictures or winning at particular games, for example.

Neural network

Also known as an artificial neural network, this is a type of machine learning loosely inspired by the structure of the human brain. A neural network is composed of simple processing nodes, or ‘artificial neurons’, which are connected to one another in layers. Each node will receive data from several nodes ‘above ’it, and give data to several nodes ‘below’ it. Nodes attach a ‘weight’ to the data they receive, and attribute a value to that data. If the data does not pass a certain threshold, it is not passed on to another node. The weights and thresholds of the nodes are adjusted when the algorithm is trained until similar data input results
in consistent outputs.

Deep learning

A more recent variation of neural networks, which uses many layers of artificial neurons to solve more difficult problems. Its popularity as a technique increased significantly from the mid-2000s onwards, as it is behind much of the wider interest in AI today. It is often used to classify information from images, text or sound

AI in the UK: ready, willing and able? HOUSE OF LORDS Select Committee on Artificial Intelligence
Report of Session 2017–19 HL Paper 100 16 April 2018.

artificial_intelligence.txt · Last modified: 2024/05/29 04:59 by paul