大数据不等于科学规律( 二 )_我们从天文学史获得的重要教训是

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如今，大数据和机器学习为许多科学问题提供了新的解决方法。而天文学史为我们提供了一个有趣的角度去审视如何运用数据引导科学，这或许是一个很好的警示。

Big data and machine learning are powering new approaches to many scienti?c questions. But the history of astronomy o?ers an interesting perspective on how data informs science—and perhaps a cautionary tale.

早期的巴比伦天文学家采用了今天我们称之为纯“大数据”或者“模式识别”的方法。他们积累了数个世纪的太阳、月球和行星运动及日月食的观测数据，从中找出了不同的循环周期。只需假设这些周期会继续下去，他们就能为种植、灌溉和收割的时间提供合理指导，制定出可靠的占星术，并提前预测月食发生的时间。

Early Babylonian astronomers took what today we'd call a pure "big data" or "pattern recognition" approach. They accumulated observations of solar, lunar and planetary motion and eclipses for many centuries and identi?ed various cycles that had repeated many times. Simply by assuming that those cycles would continue, they were able to give good advice for planting, irrigation and harvest times, to cast credible horoscopes and to predict in advance when lunar eclipses would occur.