Causal Inference

AI Bias

Systematic errors in AI outputs that arise from biased training data, flawed assumptions, or prejudicial algorithm design.

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.

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.

Specialised hardware designed to speed up AI computations, including GPUs, TPUs, and custom AI chips.

A table used to evaluate classification model performance by comparing predicted classifications against actual classifications.

A documentation framework that provides standardised information about an AI model's intended use, performance characteristics, limitations, and ethical considerations.

Reducing the precision of neural network weights and activations from floating-point to lower-bit representations for efficiency.

Artificial intelligence algorithms processed locally on edge devices rather than in centralised cloud data centres.

The creation of artificially produced datasets that mimic the statistical properties of real-world data, used for training AI models while preserving privacy.

A machine learning approach where models learn to perform tasks from only a small number of labelled examples, often achieved through in-context learning in large language models.

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