Explore AIstify's latest reporting, research, and expert analysis tagged with "artificial intelligence", collected in one continuously updated archive.
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.
AI alignment is the practice of directing AI systems toward intended goals and human values while keeping their behavior safe and controllable.
Multimodal AI is artificial intelligence that understands or generates multiple data types, including text, images, audio, video, and sensor input.
OpenAI has launched GPT-5.5, a new flagship model designed for coding, computer use, knowledge work, and scientific research, with stronger performance, lower token usage, and broader real-world autonomy than GPT-5.4.
Model quantization lowers the numerical precision of AI weights or calculations to reduce memory, inference time, energy use, and deployment cost.
Model drift is the decline or change in AI performance that occurs when real-world data and relationships move away from training conditions.
A world model is an AI representation that predicts how an environment may change, helping agents simulate outcomes before choosing an action.
A loss function measures model error during training and gives an optimization algorithm the objective used to adjust AI model parameters.