AI Glossary

Artificial intelligence that is as capable as a human at any intellectual task

Artificial intelligence that surpasses the capabilities of the human mind.

A class of microprocessor designed to accelerate AI applications.

Software that can perform certain tasks independently and proactively without the need for human intervention

The task of ensuring that the goals of an AI system are in line with human values.

Assumptions made by an AI model about the data.

A large-scale AI language model developed by OpenAI that generates human-like text.

A computer program designed to simulate human conversation through text or voice interactions.

The computational resources (like CPU or GPU time) used in training or running AI models.

A type of deep learning model that processes data with a grid-like topology

The process of increasing the amount and diversity of data used for training a model by adding slightly modified copies of existing data.

A subfield of machine learning that focuses on training neural networks with many layers

A phenomenon in machine learning in which model performance improves with increased complexity

The representation of data in a new form

A type of machine learning model that does not require hand-engineered features.

An application of artificial intelligence technologies that provides solutions to complex problems within a specific domain.

A subfield of AI focused on creating transparent models that provide clear and understandable explanations of their decisions.

The process of taking a pre-trained machine learning model and adapting it for a slightly different task or specific domain.

Large AI models trained on broad data

A type of machine learning model used to generate new data similar to some existing data.

A specialised hardware unit designed to speed up specific computing tasks such as machine learning computations.

A rule-of-thumb or an educated guess used to make decisions in lieu of a strict algorithm.

Settings or configurations that govern the overall behavior of machine learning models but are not learned from the data.

The process of replacing missing data with estimated values.

The process of using a trained machine learning model to make predictions on new data.