Welcome to the AI LibGuide. This guide aims to inform and support your use of AI at university. Before you use Generative AI as part of your studies, make sure you have read the university guidance to ensure you are using AI in an appropriate manner.
This guide will be updated regularly so make sure you check back for the latest updates.
Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think, learn, and make decisions. These systems use algorithms and data to perform tasks that typically require human intelligence, such as problem-solving, language understanding, and decision-making.
Examples of AI in Everyday Life:
Personal Assistants: Virtual assistants like Siri, Alexa, and Google Assistant use natural language processing to understand and respond to your queries.
Recommendations: AI powers recommendations on platforms like Netflix, Spotify, and Amazon by analysing your preferences and behaviour.
Healthcare: AI is used in fitness apps, wearable devices, and virtual health assistants to monitor health and provide insights.
Transportation: Navigation apps like Google Maps and ride-hailing services like Uber rely on AI for real-time traffic analysis and route optimization.
Social Media: AI algorithms help curate content, detect inappropriate posts, and recommend connections or pages.
Finance: Fraud detection systems, automated customer support, and budgeting tools are driven by AI.
Smart Homes: Devices like thermostats, security cameras, and smart lights use AI to adapt to your habits and preferences.
Customer Service: Chatbots and automated email systems use AI to provide quick and accurate responses to customer inquiries.
Note. Image generated using the prompt “An image to represent AI,” by Deep AI image generator. Deep AI, 2024. https://deepai.org/machine-learning-model/text2img
OpenAI.(2023).ChatGPT (November 2024 version) [Large language model]. https://chatgpt.com/
Computer systems designed to perform tasks that usually require human intelligence such as learning, problem-solving and decision making.
A type of AI that generates text, pictures or data in response to user prompts. It uses machine learning to create new data with similar characteristics to the data it was trained on.
Note. Image generated using the prompt “An image to represent generative AI,” by Deep AI image generator. Deep AI, 2024. https://deepai.org/machine-learning- model/text2img
An advanced AI system trained on vast amounts of text, designed to understand and generate human language. ChatGPT and Copilot are examples of LLMs.
A type of AI that allows a system to learn and improve from examples without all its instructions being explicitly programmed.
Note. Image generated using the prompt “Machine learning,” by Deep AI image generator. Deep AI, 2024. https://deepai.org/machine-learning-model/text2img
A branch of AI that involves teaching computers to understand, interpret, and generate human language in a way that is both meaningful and useful.
The name given to plausible sounding but inaccurate results generated by LLMs. These occur as LLMs are unable to identify if the phrases they generate make sense or are accurate.
Note. Image generated using the prompt “AI hallucinations” by Deep AI image generator. Deep AI, 2024. https://deepai.org/machine-learning-model/text2img
Bias in GenAI is where the output produced can be unfair, prejudiced or perpetuates stereotypes. Bias is often a result of training data that contains limited or prejudiced data.
The practice of designing, developing, and deploying AI with certain values, such as being trustworthy, ethical, transparent, explainable, fair, robust and upholding privacy rights.
A specific input or instruction given to a generative model to generate output.
A technique used to develop or refine the output from GenAI software. Prompt engineering techniques can include providing extra information or context in your prompt or giving the software further commands to refine the output.
Training data refers to the set of examples or information used to teach a machine learning model how to perform a specific task.
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