AI Glossary
The definitive dictionary for AI, Machine Learning, and Governance terminology. From Flash Attention to RAG — look up any term.
N
Named Entity Recognition
The NLP task of identifying and classifying named entities in text into predefined categories such as person names, organizations, locations, dates, monetary values, and more.
Narrow AI
AI systems designed and trained for a specific task or narrow set of tasks. All current AI systems are narrow AI — they excel in their domain but cannot generalize outside it.
Natural Language Generation
The AI capability of producing human-readable text from structured data, internal representations, or prompts. NLG is the output side of language AI — turning machine understanding into human words.
Natural Language Inference
The NLP task of determining the logical relationship between two sentences — whether one entails, contradicts, or is neutral with respect to the other.
Natural Language Processing
The branch of AI that deals with the interaction between computers and human language. NLP enables machines to read, understand, generate, and make sense of human language in a useful way.
Natural Language Understanding
The ability of an AI system to comprehend the meaning, intent, and context of human language, going beyond surface-level word matching to grasp semantics, pragmatics, and implied meaning.
Neural Architecture Search
An automated technique for finding optimal neural network architectures by searching through a vast space of possible designs. NAS automates architecture decisions that normally require expert intuition.
Neural Network
A computing system inspired by the biological neural networks in the human brain. It consists of interconnected nodes (neurons) organized in layers that process information and learn to recognize patterns.
Neuro-Symbolic AI
Approaches that combine neural networks (pattern recognition, learning from data) with symbolic AI (logical reasoning, knowledge representation) to get the strengths of both.
No-Code AI
AI platforms that allow users to build, train, and deploy machine learning models without writing any code, using visual interfaces and drag-and-drop tools.
Noise
Random variation or errors in data that do not represent true underlying patterns. In deep learning, noise can also refer to the random input used in generative models.