ML Fundamentals
Mixture of Experts (MoE)
An AI architecture where different "expert" sub-networks specialize in different types of inputs, and a router picks the right expert.
Definition
A neural network architecture that routes different inputs to different specialized sub-networks (experts). Only a subset of experts activate for each input, improving efficiency. Used in Mixtral and GPT-4.
Why it matters
Enables building larger, more capable models without proportionally increasing compute costs.
Related terms in ML Fundamentals
Activation Functions
The "switch" inside a neural network that decides whether a neuron should fire, allowing the AI to learn complex non-linear patterns.
Active Learning
A technique where the AI asks humans to label only the most confusing examples, saving time and money on data labeling.
Anomaly Detection
Finding the "weird" stuff in a dataset, like a credit card charge in a foreign country or a broken machine part.
Artificial General Intelligence (AGI)
A hypothetical "super-AI" that can learn and do any intellectual task a human can do, not just one specific thing.
From vocabulary to outcomes
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