Glossary · M

Terms starting with M

13 of 250 glossary terms. Each entry links to its own page with why it matters and where it fits.

Definitions here are written for engineers, not grammarians: what the term means, why it matters when it comes up, and where it sits in the curriculum — most term pages name the lessons that lean on the concept, so you can jump from a definition straight to the teaching. Use the letter bar below to move through the alphabet, or press the slash key to search the whole corpus directly.

Every entry carries three things besides the definition: the plain aliases it travels under (listed as “also”), a category tag for which stretch of the curriculum it belongs to, and the cross-references that place it next to its neighbors — the terms you would meet in the same lesson. A term earns a page here only when a lesson in the curriculum actually uses it, so the corpus stays the shape of the material instead of the shape of a dictionary.

One honest way to use this page: read down the list of entries below and note every term you could not define in a sentence if a colleague asked. That list — not the whole letter — is your revision list, and each entry links to the reading that closes the gap.

  • MCP (Model Context Protocol)

    Agents & tools

    An open JSON-RPC protocol for a host to connect to servers that expose tools, resources, prompts, and extensions through defined request, result, discovery, and transport contracts. In revision 2026-07-28, every request carries its protocol version and client capabilities instead of relying on an initialization handshake or protocol session.

  • Maximum Marginal Relevance (MMR)

    Retrieval & generation

    A selection rule that balances relevance to the query with novelty relative to items already selected.

  • Membership Inference

    Security & governance

    An attack that estimates whether a particular record or example was included in a model's training data by observing model outputs or other accessible signals.

  • Mixed Precision

    Math & training

    A numerical strategy that uses different data types for different operations, often lower precision for many matrix operations and higher precision for values that need more range or stability.

  • MoE (Mixture of Experts)

    Models & inference

    An architecture with multiple expert subnetworks and a learned router that selects a subset for each input unit, often each token. Sparse activation can increase total parameter capacity without using every expert on every forward pass.

  • Modality

    Multimodal systems

    A form of information with its own structure and acquisition process, such as text, image, audio, video, depth, or sensor measurements.

  • Modality Alignment

    Multimodal systems

    Learning or establishing correspondences between representations from different modalities so semantically or temporally related items can be matched.

  • Model Card

    Evaluation & safety

    A structured report describing a model's intended uses, evaluation conditions, performance characteristics, limitations, and relevant ethical or safety considerations.

  • Model Router

    AI-native development

    A component that selects a model or provider for a request using requirements such as capability, latency, cost, context size, policy, and current availability.

  • Model Serving

    Infrastructure & serving

    The runtime and API layer that loads versioned model artifacts, accepts inference requests, schedules execution, manages resources, and returns results under an operational contract.

  • Multi Round-Trip Request (MRTR)

    also: MRTR

    Agents & tools

    An MCP request pattern in which an operation returns `resultType: input_required` with one or more `inputRequests`, then the client retries the original method with `inputResponses` and the exact returned `requestState`.

  • Multimodal Fusion

    Multimodal systems

    Combining evidence or learned representations from more than one modality to produce a joint representation, prediction, or generated output.

  • Multimodal Model

    Multimodal systems

    A model that learns from, relates, or generates more than one modality through representation, alignment, fusion, translation, or coordinated prediction.