AI Glossary

Pictures and video that are deliberately altered to generate misinformation and disinformation. Advances in generative AI have lowered the barrier for the production of deepfakes.

An algorithm that iteratively denoises an artificially corrupted signal in order to generate new, high-quality outputs. In recent years it has been at the forefront of image generation and protein design.

A type of AI focused on creating new content, such as text, music, images, video, and animations, based on existing data and stimulated by prompts from the human user.

A semiconductor processing unit that enables a large number calculations to be computed in parallel. Historically this was required for rendering computer graphics. Since 2012 GPUs have adapted for training DL models, which also require a large number of parallel calculations.

Large language models, such as ChatGPT, are unable to identify if the phrases they generate make sense or are accurate. This can sometimes lead to inaccurate results, also known as ‘hallucination’ effects, where large language models generate plausible sounding but inaccurate text. Hallucinations can also result from biases in training datasets or the model’s lack of access to up-to-date information

The use of AI to create new images based on certain inputs or prompts. Examples include generating art or realistic photos from textual descriptions.

A model trained on vast amounts of (often) textual data to predict the next word in a self-supervised manner. The term “LLM” is used to designate multi-billion parameter LMs, but this is a moving definition.

A subset of AI that often uses statistical techniques to give machines the ability to “learn” from data without being explicitly given the instructions for how to do so. This process is known as “training” a “model” using a learning “algorithm” that progressively improves model performance on a specific task.

Data that describes or provides information about other data.

A representation of a system learned from data. In AI, a model is trained on datasets to perform specific tasks, such as recognising images or generating text.