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Scholarly use of A.I. tools

How to use generative A.I. tools like ChatGPT, Bing Search, DALL-E 2, and others in academic settings ethically and in accordance with standards of academic integrity. How to reference content created by them or with their assistance.

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Defining the term A.I. and the concept behind it

What is Artificial Intelligence?

History of the term A.I. and the underlying concept

The term "Artificial Intelligence" was coined by computer scientist John McCarthy and his team at the University of Stanford in 1955. The term was mentioned for the first time in A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence. The proposal even provides an initial outline of the A.I. concept:

...the artificial intelligence problem is taken to be that of making a machine behave in ways that would be called intelligent if a human were so behaving.

McCarthy et al, 1955

More recent definitions have continued to appear very similar to the original conceptualization:

Al is best understood as a set of techniques aimed at approximating some aspect of human or animal cognition using machines.

Calo, 2017 

Defining A.I. - Concepts and technologies

Artificial Intelligence – An umbrella term 

Today, the term artificial intelligence is often used as an umbrella term under which various types, concepts or specific technologies may be categorized. 

Please note: The terminology around A.I. is not being used consistently across resources, especially when it comes to A.I. typology. While definitions are mostly consistent in regards to A.I.-related technologies, the interchangeable use of some terms around types of A.I. can lead to confusion. UVic Libraries will vet and include resources here, that might help clarify A.I.-related terminology and that make it more comprehensive.

Types of Artificial Intelligence

Different types of Artificial Intelligence that can be found in the literature include...

  • Weak/Narrow vs. Strong/General A.I. 
    • Weak/Narrow A.I. = A.I. with limited capabilities, even if highly specialized in what it is supposed to do. ChatGPT is still considered a Weak A.I., for example, despite its impressive and diverse capabilities.
    • Strong/General A.I. (AGI) = An Artificial Intelligence, that meets or exceeds human-like cognitive capabilities and is not limited to certain tasks. The development of an AGI is the declared long-term goal of A.I. research and development, but at this point in time, this goal has not yet been achieved. 
  • Multitasking/Multimodal/Generalist A.I. = A.I. that is designed and trained to (or in some cases: taught itself to) do more than one task. The boundaries between uni-tasking A.I. tools and generalist ones can be fluid, making precise definitions and clear language around this topic challenging.

A.I. Technologies

Technological concepts that are summarized under the A.I. umbrella term include

  • Machine learning = Computer systems performing tasks they are not programmed to do, instead they are programmed to learn how to do them
  • Neural networks = Computer systems resembling the human brain 
  • Deep learning = Machine learning by example, where neural networks are using large sets of training data to train themselves, for example how to recognize patterns
  • Large language models (LLMs) = Deep learning applications, utilizing massive text corpora to train themselves how to generate, recognize, summarize, translate, predict almost any kind of text

Video: A.I. explained

Watch this 4 minute video by the Boston Museum of Science for a quick introduction of what A.I. means:

Early History of Generative A.I. - Example

ELIZA - the first chatbot

References and further reading

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This work by The University of Victoria Libraries is licensed under a Creative Commons Attribution 4.0 International License unless otherwise indicated when material has been used from other sources.