AI Inference

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 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.

The research field focused on ensuring AI systems act in accordance with human values, intentions, and ethical principles.

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

The field of AI dedicated to representing information about the world in a form that computer systems can use for reasoning.

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

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

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

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

An AI algorithm that generates a sequence of actions to achieve a specified goal from an initial state.