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.

Architectures that enable AI agents to store, retrieve, and reason over information from past interactions, providing continuity and personalisation across conversations.

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

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

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

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

A formal representation of knowledge as a set of concepts, categories, and relationships within a specific domain.

A form of logical inference that seeks the simplest and most likely explanation for a set of observations.

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

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.