The core innovation in AI is not the large language models themselves, but the underlying vectorization technology that encodes language, images, and videos into high-dimensional spaces. These embeddings capture complex relationships that are not explicitly taught, representing a significant leap beyond rule-based AI. While current efforts focus on optimizing LLMs as interpreters for these vector spaces, the true potential lies in improving these semantic structures to accelerate AI development. AI
IMPACT Focusing on vectorization and semantic structures could unlock new capabilities beyond current LLM applications.
RANK_REASON The item is a discussion post on Reddit about the perceived core innovation in AI, rather than a primary announcement or research paper.
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