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But in general we might say that the neural net is “picking out certain features” (maybe pointy ears are among them), and using these to determine what the image is of. But are those features ones for which we have names—like “pointy ears”? Mostly not.但总的来说，我们可以说神经网络“挑选出某些特征”，然后使用这些特征来确定图像所代表的是什么。但这些特征是否与我们有名称的特征相似，比如“尖尖的耳朵”？大多数并不是。Are our brains using similar features? Mostly we don’t know. But it’s notable that the first few layers of a neural net like the one we’re showing here seem to pick out aspects of images (like edges of objects) that seem to be similar to ones we know are picked out by the first level of visual processing in brains.我们的大脑是否使用类似的特征？我们大多数情况下并不知道。但值得注意的是，我们在这里展示的网络的前几层似乎会提取出与大脑中视觉处理的第一层所提取的相似的图像特征（如物体边缘）。But let’s say we want a “theory of cat recognition” in neural nets. We can say: “Look, this particular net does it”—and immediately that gives us some sense of “how hard a problem” it is (and, for example, how many neurons or layers might be needed). But at least as of now we don’t have a way to “give a narrative description” of what the network is doing. And maybe that’s because it truly is computationally irreducible, and there’s no general way to find what it does except by explicitly tracing each step. Or maybe it’s just that we haven’t “figured out the science”, and identified the “natural laws” that allow us to summarize what’s going on.但是，假设我们想要一个神经网络中的“猫识别理论”。我们可以说：“看，这个特定的网络能做到这一点”——这立刻让我们对“问题有多难”有了一定的了解（例如，可能需要多少神经元或层数）。但至少到目前为止，我们还没有办法“用叙述性的方式描述”网络在做什么。也许这是因为它在计算上是不可化简的，除非我们显式地追踪每一步。或者也许是因为我们还没有“解释这个科学”，找到了允许我们总结发生的事情的“自然法则”。We’ll encounter the same kinds of issues when we talk about generating language with ChatGPT. And again it’s not clear whether there are ways to “summarize what it’s doing”. But the richness and detail of language (and our experience with it) may allow us to get further than with images.当我们讨论使用 ChatGPT 生成语言时，我们将遇到相同类型的问题。同样，目前还不清楚是否有办法“总结它在做什么”。但是语言的丰富性和细节（以及我们对它的经验）可能使我们在这方面取得更大的进展。“点赞有美意，赞赏是鼓励”
