Mixture of Experts (MoE)
An AI architecture where multiple specialized sub-networks (experts) handle different parts of a task. A gating network routes each input to the most relevant experts, achieving high performance with fewer active parameters per query.
Why it matters
Mixture of Experts lets a model be huge yet fast, because only the relevant sub-networks activate for any given input rather than the entire model. This is how some of the newest frontier models deliver strong performance without a proportional jump in cost per response. Recognizing the term helps you understand why certain large models feel surprisingly efficient and why "total size" alone doesn't tell the whole story.
In practice
Think of a hospital where a receptionist routes each patient to the right specialist instead of every doctor examining everyone. In an MoE model, a gating network sends a coding question to the parts trained on code and a poetry request elsewhere. You never see this happen, but it's why a model with hundreds of billions of parameters can still reply quickly and at reasonable cost.
Related terms
AI Dubbing
Automated translation and re-voicing of audio/video content into other languages while preserving the original speaker's voice characteristics, timing, and emotional delivery.
AI Orchestration
Coordinating multiple AI models, tools, and data sources in a unified pipeline. An orchestration layer manages prompt routing, context passing, error handling, and output aggregation across different AI services.
API (Application Programming Interface)
A way for software applications to communicate with each other. AI APIs let developers integrate AI capabilities into their own applications programmatically.
Attention Mechanism
A technique that allows AI models to focus on the most relevant parts of input data when generating output. In language models, attention determines which words in a sentence are most important for understanding each other word.
Diffusion Model
A type of generative AI that creates images by gradually removing noise from a random starting point. Powers tools like Stable Diffusion, DALL-E, and Midjourney. Works by learning the reverse of a noise-adding process.
Distillation
A technique where a smaller 'student' model learns to replicate the behavior of a larger 'teacher' model. Produces compact models that retain most of the teacher's capability while being faster, cheaper, and deployable on smaller devices.