STEM与日常科技·英语30篇（3）

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量化推理与精度损失

When AI models run on phones or cars, engineers compress numbers from 32-bit floats down to just 8 bits.
This quantization saves memory and speeds up math—but small rounding errors accumulate during complex calculations.
A self-driving car might misjudge a pedestrian’s distance by 20 cm if quantization isn’t carefully calibrated.
Techniques like ‘quantization-aware training’ teach models to expect those tiny errors before deployment.
Audio enhancers using low-bit math sometimes add faint digital hiss—not because they’re broken, but by design trade-off.
Not all tasks suffer equally: recognizing a cat needs less precision than calculating rocket trajectory.
Engineers balance speed, size, and accuracy—like choosing between a sharp pencil sketch and a high-res satellite image.
Understanding this loss helps users trust AI tools without overestimating what ‘smart’ really means in hardware limits.