ML Fundamentals
Emergent Capabilities
Surprising abilities that appear in large AI models that were never explicitly trained for, they just emerge at scale.
Definition
Capabilities that arise unexpectedly in large models trained at sufficient scale, which were not present in smaller versions. Examples include in-context learning, chain-of-thought reasoning, and tool use.
Why it matters
One of the most fascinating phenomena in modern AI, and a key reason why scaling continues to produce breakthroughs.
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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