Scaling laws in AI refer to the correlation between a model's performance and its size, including parameters, data, and computation. Primarily used by AI researchers and developers, these laws enable comprehensive understanding of AI efficiency and effectiveness. Optimizing such laws significantly benefits industries relying on AI, promoting seamless automation, superior data analysis, and improved decision-making.
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