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.

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

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

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

The practice of designing and optimising input prompts to elicit desired outputs from large language models.

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

A hypothetical form of AI that possesses the ability to understand, learn, and apply knowledge across any intellectual task a human can perform.

AI systems designed and trained for a specific task or narrow range of tasks, such as image recognition or language translation.

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

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.