A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

Machine Ethics: The study of ethical issues and principles related to AI and autonomous systems, addressing questions of morality, responsibility, and fairness.

Machine Learning (ML): A subset of artificial intelligence (AI) that involves the use of algorithms to enable computers to learn from and make predictions or decisions based on data without explicit programming.

Machine Learning Operations (MLOps): A set of practices and tools that streamline the deployment, monitoring, and management of machine learning models in production.

Machine-to-Machine (M2M): Communication and interaction between AI-powered devices or systems without human intervention, facilitating automation in various industries.

Machine Translation: The use of machine learning and natural language processing techniques to automatically translate text or speech from one language to another.

Machine Vision: An AI technology that allows machines to interpret and understand visual information from the world, often used in image and video analysis.

Managed AI Services: SAAS platforms that offer AI capabilities as a service, including pre-built models, data management, and model training, to simplify AI adoption for businesses.

Medical AI: AI applications in the healthcare industry, including diagnostics, drug discovery, and patient care improvements.

Metadata: Data that provides information about other data, such as the creation date, author, or format of a file; important for AI in data organization and context.

Microservices: A software architecture approach that breaks down applications into small, independently deployable services, often used to create scalable AI applications.

Middleware: Software that acts as a bridge between different software applications, often used to integrate AI solutions into existing systems.

Mobile AI: AI technologies optimized for mobile devices, enabling features like voice assistants, image recognition, and predictive text on smartphones and tablets.

Model Accuracy: A measure of how well a machine learning model’s predictions match the actual outcomes; a critical factor in evaluating AI system performance.

Model Deployment: The process of making a trained machine learning model available for use in production environments, allowing it to make real-time predictions.

Model Explainability: The ability to understand and interpret the decisions and predictions made by AI models, a crucial factor for building trust in AI systems.

Model Training: The process of teaching a machine learning algorithm by providing it with a dataset to learn from, enabling it to make predictions or classifications.

Multi-Agent Systems: AI systems composed of multiple agents (individual AI entities) that work together to achieve a common goal, often used in complex decision-making scenarios.

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