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 thought experiment by John Searle arguing that executing a program cannot give a computer genuine understanding or consciousness.

The branch of ethics examining moral issues surrounding the development, deployment, and impact of artificial intelligence on society.

When an AI model generates plausible-sounding but factually incorrect or fabricated information with high confidence.

The AI capability to make inferences based on everyday knowledge that humans typically take for granted.

The frameworks, policies, and regulations that guide the responsible development and deployment of AI technologies.

The ability of an AI system to maintain performance under varying conditions, adversarial attacks, or noisy input data.

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

A structured representation of real-world entities and the relationships between them, used by AI for reasoning and inference.

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.