AI能根据阶段性成果推断出最终结果吗

Can AI Predict What Will Happen?It’s not the only role of science—and in the sections that follow we’ll explore others.But historically what’s often been viewed as a defining feature of successful science is:can it predict what will happen?So now we can ask:does AI give us a dramatically better way to do this?这并不是科学的唯一作用，在接下来的部分中我们将探索其他作用。但从历史上看，通常被视为成功科学的一个决定性特征是：它能预测将会发生什么吗？所以现在我们可以问：人工智能是否为我们提供了一种更好的方法来做到这一点？In the simplest case we basically want to use AI to do inductive inference.We feed in the results of a bunch of measurements,then ask the AI to predict the results of measurements we haven’t yet done.At this level,we’re treating the AI as a black box; it doesn’t matter what’s happening inside; all we care about is whether the AI gives us the right answer.We might think that somehow we can set up the AI up so that it “isn’t making any assumptions”—and is just “following the data”.But it’s inevitable that there’ll be some underlying structure in the AI,that makes it ultimately assume some kind of model for the data.在最简单的情况下，我们基本上想用人工智能来做归纳推理。我们输入一系列测量结果，然后要求人工智能预测我们尚未完成的测量结果。在这个层面上，我们将人工智能视为一个黑匣子；里面发生什么并不重要；我们关心的只是人工智能是否给出了正确的答案。我们可能会认为，我们可以通过某种方式设置人工智能，使其“不做出任何假设”，而只是“遵循数据”。但人工智能中不可避免地会存在一些底层结构，这使得它最终假设某种数据模型。

It’s a ubiquitous story in machine learning.One trains a neural net to successfully predict,classify,or whatever.But if one “looks inside” it’s very hard to tell what’s going on.Here’s the final result of applying an image identification neural network:这是机器学习中无处不在的故事。人们训练神经网络来成功地进行预测、分类或进行其他操作。但如果一个人“向内看”，就很难说出到底发生了什么。这是应用图像识别神经网络的最终结果：And here are the “intermediate thoughts” generated after going through about half the layers in the network:以下是经过网络中大约一半层后产生的“中间想法”：Maybe something here is a “definitive signature of catness”.But it’s not part of our current scientific lexicon—so we can’t usefully use it to develop a “scientific narrative” that explains how the image should be interpreted.也许这里的东西是“猫性的最终标志”。但它不是我们当前科学词典的一部分，因此我们无法有效地使用它来开发解释图像应如何解释的“科学叙述”。