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Key AI-Related Terms

Before you explore more about AI, here are some key terms that you need to understand:

Deep Learning

A type of machine learning process that uses neural networks to process data. Deep learning involves programming machines to recognise complex patterns in pictures, text, sounds and other data to produce insights and predictions. These predictions are measured by performance criteria selected for specific applications. The auto-captioning features available for YouTube and Panopto videos is an example of this. Natural language processing – such as that used by ChatGPT is another example of deep learning. (AWS, n.d.

Generative AI

A type of artificial intelligence that often uses neural networks to recognise patterns in what it has been taught and the questions asked of it in order to create responses most likely to satisfy the request made to it. The output can take many forms – text, audio, still image, video.

GPT (Generative, Pre-trained Transformer)

An AI model that modifies an existing model based on layered neural networks to identify patterns and probabilities in both the requests made to it and the data upon which it has been trained in order to generate content as a response. This content can take the form of text, visuals, audio, etc. 

Large Language Model

An informal term, an LLM is a large neural network programmed to predict the patterns and relationships of words, phrases and sentences in a particular language based on a large amount of data to which it has access.

Machine Learning

A branch of Artificial Intelligence and computer science that focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy. (IBM, n.d.)

For more information, view this overview video on YouTube from IBM "What is Machine Learning?" (Length: 08:22)

Natural Language Processing

The ability to recognise complex patterns in inputs provided in non-programming, naturally written or spoken language and be able to generate an output in that same language.

Virtual assistants such as Siri and Alexa and various chatbots up to and including ChatGPT and its ilk are examples of these.

Neural Networks

Neural networks are modelled on the human brain, in which nodes (artificial neurons) are activated and then connect to others based on a weighting (likelihood of correctness) assigned as a result of training and statistical probability (IBM, n.d.).  


Models that are fed large datasets and then are either

Supervised - where the output has a pre-defined relationship to the output. If you've ever interacted with an "Ask X" chatbot on a government or sales site and received what reads like a pre-written response, chances are that you've dealt with a Pre-Trained, Supervised AI.

Unsupervised - where the model learns the underlying structure and patterns of the input and then creates an output based on calculations of the context and likely response being sought. 


A transformer is a type of neural-network-based machine learning typically used in natural language processing that learns the context and relationships of data by tracking the relationships in a sequence of data. In other words, it derives context based on whole sentences of input rather than analysing individual words.

We as humans do this when encountering new words or words that can have multiple meanings. For instance, if someone says they are going to run to the office and a second person says they are going to run for the office of mayor, we know that the first person means they are going to their place of work and the second means the person is going to attempt to get elected to a local government position.