AutoML

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.

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.

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.

A documentation framework that provides standardised information about an AI model's intended use, performance characteristics, limitations, and ethical considerations.

A table used to evaluate classification model performance by comparing predicted classifications against actual classifications.

A measure of machine intelligence proposed by Alan Turing, where a machine is deemed intelligent if it can exhibit conversation indistinguishable from a human.

An initial instruction set provided to a language model that defines its persona, constraints, output format, and behavioural guidelines for a given session or application.

The interdisciplinary field dedicated to making AI systems safe, robust, and beneficial while minimizing risks of unintended consequences.

Middleware that manages routing, fallback, load balancing, and model selection across multiple AI providers to optimise cost, latency, and output quality.

The ability of large language models to learn new tasks from examples provided within the input prompt without parameter updates.

The capability of AI agents to invoke external tools, APIs, databases, and software applications to accomplish tasks beyond the model's intrinsic knowledge and abilities.

Machine Learning

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

Automated Machine Learning

The end-to-end automation of the machine learning pipeline including feature engineering, model selection, hyperparameter tuning, and deployment, making ML accessible to non-experts.