model interpretability
Model interpretability is the ability to understand why an AI model produced a result and what internal features or processes influenced it.
AI Terminology
Search clear AI terminology definitions, acronyms, related concepts and the reviewed videos that explain them.
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Clear filtersmodel offloading
AI model offloading moves selected model weights or runtime state from accelerator memory to system memory or storage.
model parallelism
AI model parallelism splits one model's computation or parameters across multiple processors so they work together on the same workload.
model personality
Model personality is the recognizable tone, manner, and interaction style an AI model presents across conversations.
model pool
An AI model pool is a defined set of models eligible to handle a particular task or class of requests.
model portability
AI model portability is the ability to move a model and its workload between compatible hardware, runtimes or inference providers.
model post-training
AI model post-training adapts a pretrained model using additional examples, preferences, rewards or domain data after its initial large-scale training.
model preview
An AI model preview is limited early access to a model before its final availability, terms or behavior are established.
model proxy
An AI model proxy is an intermediary service that receives model requests and forwards them to one or more compatible models or providers.
model ranking
AI model ranking orders candidate models for a task using defined performance, cost, latency or preference signals.
model refusal
Model refusal occurs when an AI model declines all or part of a request because of safety rules, uncertainty, capability limits, or policy.
model scaling
AI model scaling increases resources such as model size, training data or computation to improve an AI model's measured capabilities.
model serving
AI model serving is the infrastructure and software process that makes a trained model available to handle inference requests reliably and efficiently.
model switching
Model switching is the act of moving a workload or user from one AI model to another when needs or model behavior change.
model training
AI model training is the process of adjusting a model's parameters so its outputs better satisfy objectives demonstrated by data, feedback or tasks.
model-based grader
A model-based grader uses an AI model to evaluate, compare or score outputs according to defined criteria.
motion graphics
Motion graphics are animated visual-design elements such as titles, shapes, illustrations and interface components used to communicate or decorate.
multi-head latent attention
MLAMulti-head latent attention compresses attention key and value representations into a lower-dimensional latent form to reduce memory and inference cost.
multi-joint robot coordination
Multi-joint robot coordination controls several joints together so their combined movement produces a stable and accurate physical action.
multi-robot action orchestration
Multi-robot action orchestration coordinates the timing, responsibility and dependencies of actions performed by separate robots in one workflow.