M (54 terms)

Machine Learning A subfield of AI where models learn patterns from data to make predictions or decisions, improving with experience ra... Intermediate MAP Estimation Bayesian parameter estimation using the mode of the posterior distribution. Intermediate Marginalization Eliminating variables by integrating over them. Advanced Market Design Designing efficient marketplaces. Advanced Market Impact Effect of trades on prices. Advanced Market Microstructure Mechanics of price formation. Intermediate Markov Decision Process Formal framework for sequential decision-making under uncertainty. Intermediate Masked Language Model Predicts masked tokens in a sequence, enabling bidirectional context; often used for embeddings rather than generation. Intermediate Materials Discovery AI discovering new compounds/materials. Advanced Maximum Likelihood Estimation Estimating parameters by maximizing likelihood of observed data. Intermediate Mean Squared Error Average of squared residuals; common regression objective. Intermediate Mechanism Design Designing systems where rational agents behave as desired. Advanced Medical Imaging AI AI applied to X-rays, CT, MRI, ultrasound, pathology slides. Intermediate Memory Mechanisms for retaining context across turns/sessions: scratchpads, vector memories, structured stores. Intermediate Memory Augmentation Extending agents with long-term memory stores. Intermediate Mental Simulation Imagined future trajectories. Frontier Mesa-Optimizer Learned subsystem that optimizes its own objective. Advanced Message Passing Neural Network GNN framework where nodes iteratively exchange and aggregate messages from neighbors. Intermediate Meta-Cognition Awareness and regulation of internal processes. Frontier Meta-Learning Methods that learn training procedures or initializations so models can adapt quickly to new tasks with little data. Intermediate Mixed-Motive Game Combination of cooperation and competition. Advanced Mixture of Experts Routes inputs to subsets of parameters for scalable capacity. Intermediate MLOps Practices for operationalizing ML: versioning, CI/CD, monitoring, retraining, and reliable production management. Intermediate Mode Collapse Generator produces limited variety of outputs. Advanced Model A parameterized mapping from inputs to outputs; includes architecture + learned parameters. Intermediate Model Card Standardized documentation describing intended use, performance, limitations, data, and ethical considerations. Intermediate Model Disclosure Requirement to reveal AI usage in legal decisions. Intermediate Model Documentation Required descriptions of model behavior and limits. Intermediate Model Governance Policies and practices for approving, monitoring, auditing, and documenting models in production. Intermediate Model Inventory Central catalog of deployed and experimental models. Intermediate Model Inversion Inferring sensitive features of training data. Intermediate Model Moat Competitive advantage from proprietary models/data. Intermediate Model Orchestration Coordinating models, tools, and logic. Intermediate Model Predictive Control Optimizes future actions using a model of dynamics. Intermediate Model Registry Central system to store model versions, metadata, approvals, and deployment state. Intermediate Model Release Control Restricting distribution of powerful models. Intermediate Model Risk Risk of incorrect financial models. Intermediate Model Risk Management Framework for identifying, measuring, and mitigating model risks. Intermediate Model Stealing Reconstructing a model or its capabilities via API queries or leaked artifacts. Intermediate Model Tiering Classifying models by impact level. Intermediate Model Watermarking Embedding signals to prove model ownership. Intermediate Model-Based RL RL using learned or known environment models. Advanced Model-Free RL RL without explicit dynamics model. Advanced Momentum Uses an exponential moving average of gradients to speed convergence and reduce oscillation. Intermediate Monitoring Observing model inputs/outputs, latency, cost, and quality over time to catch regressions and drift. Intermediate Monte Carlo Estimation Approximating expectations via random sampling. Advanced Morphological Computation Physical form contributes to computation. Advanced Motion Planning Computing collision-free trajectories. Advanced Multi-Agent System Multiple agents interacting cooperatively or competitively. Intermediate Multi-Head Attention Allows model to attend to information from different subspaces simultaneously. Intermediate Multimodal Fusion Combining signals from multiple modalities. Intermediate Multimodal Model Models that process or generate multiple modalities, enabling vision-language tasks, speech, video understanding, etc. Intermediate Multitask Learning Training one model on multiple tasks simultaneously to improve generalization through shared structure. Intermediate Mutual Information Quantifies shared information between random variables. Intermediate