AI Feature Store

AI Bias

Systematic errors in AI outputs that arise from biased training data, flawed assumptions, or prejudicial algorithm design.

Causal Inference

The process of determining cause-and-effect relationships from data, going beyond correlation to establish causation.

Federated Learning

A machine learning approach where models are trained across decentralised devices without sharing raw data, preserving privacy.

AI Inference

The process of using a trained AI model to make predictions or decisions on new, unseen data.

AI Training

The process of teaching an AI model to recognise patterns by exposing it to large datasets and adjusting its parameters.

Hyperparameter Tuning

The process of optimising the external configuration settings of a machine learning model that are not learned during training.

AutoML

Automated machine learning that automates the end-to-end process of applying machine learning to real-world problems.

Reinforcement Learning from Human Feedback

A training paradigm where AI models are refined using human preference signals, aligning model outputs with human values and quality expectations through reward modelling.

Direct Preference Optimisation

A simplified alternative to RLHF that directly optimises language model policies using preference data without requiring a separate reward model.

Model Merging

Techniques for combining the weights and capabilities of multiple fine-tuned models into a single model without additional training, creating versatile multi-capability systems.

Google's custom-designed application-specific integrated circuit for accelerating machine learning workloads.

A method of problem-solving that represents all possible states of a system and searches for a path from initial to goal state.

AI systems designed and trained for a specific task or narrow range of tasks, such as image recognition or language translation.

A technique where a language model is given a small number of examples within the prompt to guide its response pattern.

A metric for evaluating the quality of machine-generated text by comparing it to reference translations or texts.

A degradation phenomenon where AI models trained on AI-generated data progressively lose diversity and accuracy, converging toward a narrow distribution of outputs.

A hypothetical form of AI that possesses the ability to understand, learn, and apply knowledge across any intellectual task a human can perform.

AI designed to handle specific tasks without possessing self-awareness, consciousness, or true understanding of the task domain.

Machine Learning

A subset of AI that enables systems to automatically learn and improve from experience without being explicitly programmed.