Hyperparameter Tuning

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.

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.

A theoretical level of AI that surpasses human cognitive abilities across all domains, including creativity and social intelligence.

The process of reducing the numerical precision of a model's weights and activations from floating-point to lower-bit representations, decreasing memory usage and inference latency.

A form of logic that handles approximate reasoning, allowing variables to have degrees of truth rather than strict binary true/false values.

Querying a language model to perform a task it was not explicitly trained on, without providing any examples in the prompt.

A technique where a smaller, simpler model is trained to replicate the behaviour of a larger, more complex model.

The ability of large language models to learn new tasks from examples provided within the input prompt without parameter updates.

The ability of an AI system to maintain performance under varying conditions, adversarial attacks, or noisy input data.

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

Machine Learning

A subset of AI that enables systems to automatically learn and improve from experience without being explicitly programmed.