Word Embedding
A representation of words as dense numerical vectors in a continuous space, where semantically similar words are positioned closer together, enabling mathematical operations on language.
Word embeddings revolutionized NLP when Word2Vec was introduced by Google in 2013, demonstrating that vector arithmetic could capture semantic relationships (e.g., 'king' minus 'man' plus 'woman' equals 'queen'). GloVe (2014) and fastText (2016) further refined the approach. Modern contextual embeddings from BERT and GPT produce different vectors for the same word depending on context, resolving ambiguity. Word embeddings are the bridge between human language and the numerical representations that neural networks process.
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Related Terms
Artificial General Intelligence (AGI)
A hypothetical form of AI that can understand, learn, and apply knowledge across any intellectual task at or above human level, rather than being specialized for specific tasks.
AI Alignment
The research field focused on ensuring AI systems behave in accordance with human values and intentions, particularly as systems become more capable.
Fine-Tuning
The process of further training a pre-trained AI model on a specific, smaller dataset to specialize it for a particular task or domain, requiring far less compute than training from scratch.
Foundation Model
A large AI model trained on broad data that can be adapted to a wide range of downstream tasks — examples include GPT-4, Claude, Gemini, and Llama.
Machine Learning
A subset of AI where systems learn patterns from data rather than being explicitly programmed, improving their performance on tasks through experience without human-written rules.
Model Training
The computationally intensive process of teaching an AI model by feeding it data and adjusting its parameters to minimize errors, often requiring thousands of GPUs running for weeks or months.
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