AI Bias

Causal Inference

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

AI Feature Store

A centralised platform for storing, managing, and serving machine learning features consistently across training and inference.

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.

An inference strategy that starts with a goal and works backward through rules to determine what facts must be true.

A graphical plot illustrating the diagnostic ability of a binary classifier as its discrimination threshold is varied.

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

A theoretical form of AI that would have consciousness, self-awareness, and the ability to truly understand rather than simulate understanding.

A technique where a smaller, simpler model is trained to replicate the behaviour of a larger, more complex model.

The practice of making AI systems' operations, data usage, and decision processes openly visible to stakeholders.

A specialised processor designed to accelerate neural network computations in edge devices and mobile platforms.

A computational approach where problems are defined as a set of variables, domains, and constraints that must all be simultaneously satisfied.