AI Glossary
The definitive dictionary for AI, Machine Learning, and Governance terminology. From Flash Attention to RAG — look up any term.
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Image Classification
A computer vision task that assigns a category label to an entire image. The model determines what the main subject of the image is from a predefined set of categories.
Image Segmentation
A computer vision task that assigns a label to every pixel in an image, dividing it into meaningful regions. It identifies not just what objects are present but their exact shapes and boundaries.
Impact Assessment
A systematic evaluation of the potential effects an AI system may have on individuals, groups, and society. Impact assessments consider both positive outcomes and potential harms.
In-Context Learning
An LLM's ability to learn new tasks from examples or instructions provided within the prompt, without any weight updates or fine-tuning. The model adapts its behavior based on the context given.
Incident Response for AI
Procedures for identifying, containing, and resolving failures or harmful behaviors in deployed AI systems. AI incident response adapts traditional IT incident management for AI-specific challenges.
Inference
The process of using a trained model to make predictions on new, previously unseen data. Inference is what happens when an AI model is deployed and actively serving results to users.
Inference Optimization
Techniques for making AI model inference faster, cheaper, and more efficient. This includes quantization, batching, caching, speculative decoding, and hardware optimization.
Information Extraction
The task of automatically extracting structured information (entities, relationships, events) from unstructured text documents.
Instruction Dataset
A curated collection of instruction-response pairs used to train or fine-tune models to follow human instructions. The quality and diversity of this dataset directly shapes model behavior.
Instruction Following
An LLM's ability to accurately understand and execute user instructions, including complex multi-step directives with specific constraints on format, tone, length, and content.
Instruction Hierarchy
A framework for prioritizing different levels of instructions when they conflict — system prompts typically override user prompts, which override context from retrieved documents.
Instruction Tuning
A fine-tuning approach where a model is trained on a dataset of instruction-response pairs, teaching it to follow human instructions accurately. This transforms a text-completion model into a helpful assistant.
Instructor Embedding
An embedding approach where you provide instructions that describe the task alongside the text, producing task-specific embeddings from a single model.
Interpretability
The degree to which a human can understand the internal mechanisms and reasoning process of a machine learning model. More interpretable models allow deeper inspection of how they work.