Technology and the built world

Multi-agent reinforcement learning

Multi-agent reinforcement learning (MARL) is the branch of machine learning in which multiple agents learn, through reinforcement learning, to cooperate or compete in a shared environment.

Multi-agent system

A multi-agent system (MAS) is a system of autonomous software or robotic agents that coordinate in a shared environment, a field growing out of distributed artificial intelligence.

Multi-agent systems (LLM)

A multi-agent LLM system is an arrangement in which two or more large language model instances, each given a role prompt and tools, coordinate on a task.

Multi-armed bandit

The multi-armed bandit (MAB), also called the K-armed bandit, is a sequential decision problem in which an agent must balance exploring new actions against exploiting the best known one.

Multi-band transmission, also called band-division multiplexing, is a fiber-optic technique carrying traffic in several spectral bands at once, most commonly C+L, to expand capacity of installed fiber.

Multi-channel memory architecture

Multi-channel memory architecture, including dual-channel, triple channel, and quad-channel memory, is a design where a processor's memory controller accesses multiple memory channels at once, aggregating their bandwidth.

Multi-chip module

A multi-chip module (MCM) is an electronic package that mounts two or more integrated circuit dice on a shared substrate, shortening interconnects and raising circuit density.

Multi-contact locomotion

Multi-contact locomotion is a mode of legged-robot movement, usually by humanoids, that uses hands or the torso along with the feet to climb stairs with a handrail, crawl, and climb ladders.

Multi-core processor

A multi-core processor, also known as a multicore CPU or chip-level multiprocessor, is a single integrated circuit containing two or more cores that share caches and run instruction streams in parallel.

Multi-crew pilot licence

The multi-crew pilot license (MPL) is a commercial pilot license defined by ICAO in 2006 that qualifies a pilot to act as co-pilot in commercial air transport on multi-crew aeroplanes.

Multi-document summarization

Multi-document summarization (MDS) is a natural language processing task that produces one summary from a cluster of related documents, resolving redundancy and merging complementary information.

Multi-factor authentication

Multi-factor authentication (MFA), also called two-factor authentication, is a security method requiring two or more identity proofs, like a password plus a code or biometric, before access is granted.

Multi-fidelity Bayesian optimization

Multi-fidelity Bayesian optimization (MFBO) is a method for optimizing expensive black-box functions when cheaper, less accurate approximations, such as low-fidelity simulations, are available, cutting evaluation costs.

Multi-frequency signaling

Multi-frequency signaling (MF) is an in-band telephone trunk signaling method that carries dialed digits as tone pairs; the Bell System introduced it after World War II.

Multi-function printer

A multi-function printer (MFP), also called an all-in-one printer, combines printing, scanning, copying, and sometimes faxing in one machine, typically a laser or inkjet engine with an integrated scanner.

Multi-functional Satellite Augmentation System

The Multi-functional Satellite Augmentation System (MSAS), originally the MTSAT Satellite-based Augmentation System, is Japan's satellite-based augmentation system, broadcasting GPS correction and integrity data to aircraft from geostationary satellites since 2007.

Multi-head attention

Multi-head attention is a neural network mechanism, introduced in the 2017 paper Attention Is All You Need, that runs several attention operations in parallel and forms the Transformer's attention layer.

Multi-head latent attention

Multi-head latent attention (MLA) is an attention mechanism for transformer language models, introduced by DeepSeek-AI in May 2024, that compresses the KV cache into a small latent vector per token.

Multi-label classification

Multi-label classification is a supervised machine learning task assigning each instance one or more labels at once, applied to text categorization, gene analysis, and search engines.

Multi-level cell

A multi-level cell (MLC) is a NAND flash memory cell that stores two bits per cell across four voltage states, trading higher storage density for slower writes and shorter lifespan.

Multi-link operation

Multi-link operation (MLO) is a defining feature of Wi-Fi 7 (IEEE 802.11be) that lets a device use several radio links at once for higher throughput and lower latency.

Multi-link suspension

Multi-link suspension, also called four-link suspension, is an independent wheel suspension using three or more separate links per wheel, used chiefly at the rear axle.

Multi-material topology optimization

Multi-material topology optimization is a computational design method that determines a structure's layout and which of several materials occupies each region, first formulated by Thomsen in 1993.

Multi-mission Affordable Capacity Effector

The Multi-mission Affordable Capacity Effector (MACE) is a United States Navy program to procure a low-cost, air-launched hypersonic strike missile for carrier-based fighters, with Castelion's Blackbeard as the first candidate.

Multi-mode and convertible tunnel boring shields

A multi-mode or convertible tunnel boring shield is a tunnel boring machine that switches between open, EPB, and slurry modes in a single drive through alternating soft ground and rock.

Multi-objective Bayesian optimization

Multi-objective Bayesian optimization (MOBO) is a sample-efficient method for optimizing several expensive, competing objectives, returning an approximation of the Pareto front rather than a single optimum.

Multi-objective evolutionary algorithm

A multi-objective evolutionary algorithm (MOEA) is an optimization method that returns a Pareto set approximation, a set of mutually nondominated solutions, rather than a single best point.

Multi-objective evolutionary optimization

Multi-objective evolutionary optimization uses population-based evolutionary algorithms to optimize two or more conflicting objectives at once, seeking trade-off solutions that converge to and evenly cover the Pareto front.

Multi-objective optimization

Multi-objective optimization, also known as Pareto optimization, deals with problems having two or more conflicting objectives optimized simultaneously; the result is a set of trade-off Pareto-optimal solutions.