Machine Learning (ML)Machine Learning (ML) is a subfield of Artificial Intelligence (AI) focused on building systems that can learn from data, identify patterns, and make decisions with minimal human intervention. Unlike traditional software, which relies on "hard-coded" rules (e.g., if X happens, then do Y), ML uses mathematical algorithms to create a model that improves its performance as it is exposed to more data Read More
Model ChainingModel Chaining is an architectural pattern in which multiple AI models are linked together in a sequence, such that the output of one model serves as the input for the next. This approach allows developers to break down a high-complexity problem into smaller, specialized sub-tasks, each handled by the model best suited for that specific job. Read More
Multi-Agent SystemA Multi-Agent System (MAS) is a computational framework where multiple autonomous or semi-autonomous AI agents interact within a shared environment to achieve specific goals. While a single AI agent is like a talented freelancer, a Multi-Agent System is like a high-functioning corporate department. Read More
Multi-hop ReasoningMulti-hop Reasoning is the cognitive process where an AI system connects multiple, distinct pieces of information often from different documents or data sources to arrive at a conclusion. Read More
Multi-Turn ConversationA Multi-Turn Conversation is an interaction between a human and an AI system that spans multiple back-and-forth exchanges (or "turns") rather than ending after a single prompt and response Read More
Multimodal Language ModelA Multimodal Language Model (MMLM) is an advanced AI system capable of processing, understanding, and generating information across multiple "modalities" or types of data, such as text, images, audio, and video. Read More
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