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A parameter is a learned numerical value, such as a neural network weight, that determines how an AI model transforms input into output.
Optimization finds model parameters or decisions that best satisfy an objective while respecting practical resource and safety constraints.
Bias is a systematic distortion in AI results that can produce unfair or inaccurate outcomes for particular groups, situations, or data patterns.
Optical character recognition (OCR) is an AI technology that converts text in images, scans, photos, and PDFs into searchable machine-readable data.
Clarity applies contextual understanding to market intelligence, enabling auditable, high-quality AI wealth advisory for financial institutions.
Explainable AI uses methods that help people understand, evaluate, and challenge how an AI system reaches a decision or prediction.
One-shot learning is an AI approach that recognizes a new task or category from one example by using knowledge learned during previous training.
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Normalization transforms data or neural network activations into a consistent scale or distribution to support stable and efficient AI model training.
Negative prompting tells a generative AI model what to avoid in an image or other output, helping steer style, content, and unwanted artifacts.
Named entity recognition (NER) is an NLP task that finds and classifies people, places, organizations, dates, products, and other entities in text.
Model Context Protocol (MCP) is an open standard that connects AI applications with external tools, data sources, and reusable context.
An embedding is a numerical representation that places related words, images, documents, or other items close together in a mathematical space.
A mixture of experts (MoE) is an AI architecture that routes each input to selected subnetworks, increasing capacity without activating every parameter.