Google’s TurboQuant paper claims significant efficiency gains for AI model quantization but is criticized for undercrediting prior work and using skewed benchmarks. This practice, where big labs rebrand iterative improvements as groundbreaking through selective citations and favorable comparisons, raises concerns about credit allocation, reproducibility, and the signal-to-noise ratio in ML research.
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Ali NematiWritten by Ali
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