AI Glossary

A field of AI that focuses on the interaction between computers and human language, enabling machines to understand, interpret, and generate human language as it spoken and written.

A series of algorithms modelled after the human brain, designed to recognize patterns and make decisions. They are used in tasks like image and speech recognition.

Open-source often means the underlying code used to run AI models is freely available for testing, scrutiny and improvement.

Software components or modules that extends the functionality of an LLM system into a wide range of areas, including travel reservations, e-commerce, web browsing and mathematical calculations.

A user input often written in natural language that is used to instruct an LLM to generate something or take action.

The craft of designing and optimising user requests to an LLM or LLM-based chatbot to get the most effective result, often achieved through significant experimentation.

Reinforcement Learning in AI is a type of machine learning where an agent learns to make decisions by taking actions in an environment to maximize cumulative rewards. Through trial and error, the agent improves its performance by receiving feedback in the form of rewards or penalties for its actions.

An area of ML in which software agents learn goal-oriented behavior by trial and error in an environment that provides rewards or penalties in response to their actions (called a “policy”) towards achieving that goal.

A form of unsupervised learning, where manually labeled data is not needed. Raw data is instead modified in an automated way to create artificial labels to learn from. An example of SSL is learning to complete text by masking random words in a sentence and trying to predict the missing ones.

A deep learning model designed for generating high-quality images, contributing to advances in generative art and media.