AI Literacy

Why do I need to learn this?

With the rapid development of generative AI, or generative artificial intelligence, since the end of 2022, tools using Large Language Models or generative art have become freely available and offer images and text which students may want to use in their study and their assignments.  You may also find they are increasingly used in the workplace and need to be prepared for that.

It is important to grasp how these AI tools work in order to avoid any possibility of plagiarism.  It is also important to understand whether it is ethical to use them at all for a variety of reasons.

These pages will help you:

  • recognize AI in the world around you and have an appreciation of its impact;
  • recognize different kinds of AIs;
  • understand how AI works at a basic level and how much intelligence AI has;
  • understand AIs’ strengths and weaknesses;
  • better develop your prompt engineering;
  • consider various ethical issues such as hallucinations, bias, accountability, transparency, and reasonable academic use.

See also our Library Guide on Using Generative AI in coursework or research:

 

 

Understanding AI Basics

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• What is AI?

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You may see references to Narrow AI, General AI and Super AI.

Narrow AI (sometimes called ‘weak AI’) is aimed at solving one particular problem – the chatbot offering help with Information Services queries is an example, Amazon uses AI to recommend books (and other items) to you, or perhaps your medical professional is using it to help diagnose certain diseases.

AI is not as new as you might think. In the 1950s Alan Turing proposed a test of machine intelligence called The Imitation Game. (You may have seen a film with that title which looks at some of Turing’s life). Arthur Samuel, a computer scientist, created a computer program to play draughts (or checkers) and it learned to play. The first workshop on the subject was in 1955 which was the first use of the phrase.

The 1960s and 1970s saw developments in the field with programs created for specific purposes and AIs use in robotics (with the first industrial robot starting work on an assembly line in 1961). An AI boom occurred in the 1980s which saw Deep Learning and Expert Systems become more popular - computers learning from mistakes and making independent decisions.

In 1997, Deep Blue beat Gary Kasparov, then the world chess champion, the first computer to do so. In 2002 the first Roomba (vacuum cleaner) appeared and a year later NASA landed two rovers on Mars which explored without direct human oversight. In 2011 Apple released Siri, the first popular virtual assistant.

General AI has become more mainstream recently and aims to be able to respond to any questions or problems put to it. Self-driving cars, Siri or Large Language Models such as ChatGPT or Bard are examples. Arguably, we are not there yet and there are considerable limitations to such technology.

Super AI would be able to surpass human intelligence and as yet only exists in fiction such as HAL from 2001: A Space Odyssey, Skynet from the Terminator films, or Isaac Asimov’s Bicentennial Man.

For more on the subject, search the Library catalogue or Discovery for artificial intelligence or machine learning or generative AI.

Or view some short videos introducing the subject.

 

• The role of data in AI

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• How do AIs learn?

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AI in Practice

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Ethical Considerations

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Developing AI Literacy

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Resources and Further Learning

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Referencing work which uses generative AI

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Test Your Knowledge

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The Future of AI

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