So we need to look deeper. Controversially, we've been taught that these materials will run out. We don't lack ore body deposits. We lack information of where they lie. So if you had a crystal ball, you'd just look into it and start digging out the rocks that are the best and generate the least waste. But we don't have a crystal ball. So the thing that we should do is make predictions of where these materials lie.所以我们需要向更深处探索。一直以来，存在一种争议性的说法：这些矿产资源会枯竭。但实际上，我们并不缺少矿体，我们缺少的是关于它们分布位置的信息。如果你有一个水晶球，只要看一眼，就能直接去挖掘那些品质最好、废料最少的矿石。但现实是我们没有水晶球，所以我们必须依靠预测，推断这些矿产究竟分布在哪里。

My colleagues and I at KoBold are doing what the industry has neglected to do. We aim to predict everything, quantify what we don't know and collect information efficiently. So we're all going to try that right now. I want you to predict 1,000 meters below your feet what the concentration of copper is right where you're sitting. I want you to predict how hard it is, how fractured it is, what's its density? We aim to predict all these things and more. We're developing machine learning technologies that help us predict all of this and rigorously quantify our uncertainties in these predictions. So what does this look like in practice?我和在 KoBold 的同事们正在做这个行业长期忽视的事情。我们的目标是尽可能预测一切，将未知进行量化，并高效地收集信息。现在我想让你们也来尝试一下：试着预测你脚下 1000 米深处的铜浓度是多少？它的硬度如何？裂隙程度怎样？密度又是多少？我们希望能够预测所有这些，甚至更多。为此，我们正在开发机器学习技术，帮助我们完成这些预测，并严格地量化预测中的不确定性。那么，这在实际操作中会是什么样子呢？

When we're exploring for mines, we often fly aircraft thousands of kilometers across the Earth to try collect information such as the Earth's magnetism, its gravitational field, that tells us something about the rocks beneath. But there's a problem. For everything that we're looking at, there are going to be an infinite number of possibilities. And that's because we're building three-dimensional models to fit two-dimensional data. So if a body was smaller and closer to the surface or larger and further away, the measurement would be the same. So this body will also fit the data. And will this one, and this one, and many more.当我们进行矿产勘探时，通常会驾驶飞机在地球上飞行数千公里，以收集数据，例如地球的磁场和引力场信息，这些数据能告诉我们地表下岩石的一些特征。但这里有一个问题：我们观察到的每一个现象，都可能对应无数种可能的解释。这是因为我们用二维数据去构建三维模型。举例来说，如果一个矿体比较小但更接近地表，或者比较大但埋得更深，它们的测量结果可能完全一样。所以，这个矿体可以匹配数据，而另一个也可以，再一个也行，还有更多。