280 | François Chollet on Deep Learning and the Meaning of Intelligence
Sean Carroll's Mindscape: Science, Society, Philosophy, Culture, Arts, and Ideas
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Distance Defines Semantic Similarity
The core principle of large language models (LLMs) and deep learning is that relationships among elements, such as tokens or pixels, can be quantified as distances in a geometric vector space. This mapping allows for measuring semantic similarity, where closely situated points indicate concepts that frequently co-occur. The concept aligns with Hebbian learning, suggesting that just as neurons that activate together become more interconnected, tokens that share context will cluster together spatially in the model. In transformers, this mechanism involves computing distances, such as cosine similarities, to assess the relationships between various tokens, reinforcing the idea that proximity in vector space reflects semantic relationships.
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