A model design that splits a neural network into specialized sub-networks and activates only a few of them for each input.
In a standard model, every parameter takes part in processing every token. A mixture of experts model instead contains many expert sub-networks plus a router that decides which experts handle each piece of input. Only a small fraction of the model's total parameters is active at any moment.
This design lets developers build models with very large total capacity while keeping the computing cost per request closer to that of a much smaller model. Several well-known large language models use this approach. For a business using such a model through an API, the effect is mostly invisible, appearing as faster or cheaper responses. The trade-offs include higher memory requirements, since all experts must be loaded, and more complex training.
An AI model trained on vast amounts of text to understand and generate human-like language, code, and other content.
Neural NetworkA machine learning model made of layers of connected nodes, loosely inspired by the brain, that learns patterns by adjusting connection weights.
TransformerA neural network architecture, introduced in 2017, that uses attention mechanisms and underpins most modern large language models.
A plain-English map of the state AI laws in effect or enacted as of October 2026, from Colorado's rewritten AI Act to chatbot, hiring and deepfake rules.
A buyer's checklist for AI meeting assistants covering recording consent laws, accuracy testing, integrations, data retention, security and cost.
A worked AI ROI formula, the baseline metrics to capture before a pilot starts, and the hidden costs that make AI business cases look better than they are.