NLP
Word Embeddings
Representing words as lists of numbers where similar words have similar numbers, "king" and "queen" are close together.
Definition
Dense vector representations of words in a continuous vector space where semantically similar words are mapped to nearby points. Early methods include Word2Vec and GloVe; modern approaches use contextual embeddings.
Why it matters
The foundation that made NLP work well, the idea that meaning can be captured as geometry in vector space.
Related terms in NLP
BERT
Google's breakthrough AI model that reads sentences in both directions at once to understand context better.
Chain of Thought (CoT)
Asking an AI to "show its work" and think step-by-step, which makes it much better at solving math and logic problems.
Context Window
The maximum amount of text an AI can read and consider at one time, like how many pages of notes it can hold in its head.
Conversational AI
AI that can have natural back-and-forth conversations with humans, chatbots, voice assistants, and customer service bots.
From vocabulary to outcomes
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