Direct Preference Optimisation

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.

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.

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.

Computing systems that simulate human thought processes using self-learning algorithms, data mining, pattern recognition, and natural language processing.

A technique combining information retrieval with text generation, allowing AI to access external knowledge before generating responses.

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

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

An approach to AI that uses human-readable symbols and rules to represent problems and derive solutions through logical reasoning.

The simulation of human intelligence processes by computer systems, including learning, reasoning, and self-correction.

A statistical approach to AI that uses Bayes' theorem to update probability estimates as new evidence becomes available.

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

Language Model

A probabilistic model that assigns probabilities to sequences of words, enabling prediction of the next word in a sequence.

RLHF

Reinforcement Learning from Human Feedback — a technique for aligning language models with human preferences through reward modelling.