AI key terms

Artificial Intelligence (AI): when machines or software can perform tasks which usually require human intelligence, e.g. problem solving.
Bias: Where an output generated by the AI model leans toward a certain view over another. Bias is often due to the model being trained on a skewed dataset which only reflects certain types of people or a limited range of perspectives. If the dataset contains biases, the model will then replicate those biases.
Chatbot: A software program that is designed to simulate human conversation, often through text interactions.
Completion: The generated output of a generative AI in response to a given prompt.
Deep Learning: A type of machine learning that uses neural networks with usually three or more (i.e. deep) layers to understand complex patterns in datasets.
Generative Artificial Intelligence (Generative AI): A type of AI that can produce text, images, or other media when given prompts.
Generative Adversarial Network: A type of machine learning model called a neural network, specially designed to imitate the structure and function of a human brain. This type of AI is generally more capable of recognising complex patterns in creative data types such as images, sounds, and video.
Generative Pre-trained Transformer (GPT): A family of neural network models that uses the transformer architecture and is trained on large datasets to generate human-like output.
Large Language Model (LLM): A deep learning model that has been trained on large amounts of data and understands and generates text in a human-like fashion.
Machine Learning: A type of AI that focuses on using data and algorithms to mimic how humans learn.
Model: A software program that has been trained to perform specific tasks.
Natural Language Processing (NLP): A field of AI that focuses on how computers can understand, interpret, and generate human language.
Neural Network: A method in artificial intelligence that teaches computers to process data by mimicking how the human brain processes information.
Output: The generated answer of a generative AI's response to a given prompt, also known as 'completion'.
Prompt: The user input to a generative AI system, which guides the AI to generate the desired output. It is often in the form of questions in natural language.
Prompt Engineering: The technique of designing and crafting the prompt to get the best possible output.
Tool (or interface): The software that a user uses.
Training: The process of a model automatically learning patterns based on data.
Transformer: A neural network that learns context and meaning by tracking relationships in sequential data, such as words in sentences.