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Taken together, all of those little circles paint us a picture of how the neural network is representing language, and they give us the scratch pad of the neural network.
总结起来，所有这些小圆圈为我们描述了神经网络表示语言的方法，展现了神经网络的草稿本。
OK, great.
太好了。
Now we have two scratch pads, one from the brain and one from AI.
现在我们有了两张草稿纸，一张来自大脑，一张来自人工智能。
And we want to know: Is AI doing something like what the brain is doing?
我们想知道的是：AI做的事情是否和大脑类似？
How can we test that?
我们要怎么判断呢？
Here's what researchers have come up with.
这是研究人员想出的办法。
We're going to train a new model.
我们要训练一个新的模型。
That new model is going to look at neural network scratch pad for a particular word and try to predict the brain scratch pad for the same word.
新的模型将检查神经网络对某个单词的“草稿”，并试图预测同一个单词的大脑草稿。
We can do it, by the way, around two.
顺便一提，这个过程也可以反过来。
So let's train a new model.
我们来训练一个新的模型，
It’s going to look at the neural network scratch pad for a particular word and try to predict the brain scratchpad.
它会检查神经网络对特定单词的草稿，并预测大脑的草稿。
If the brain and AI are doing nothing alike, have nothing in common, we won't be able to do this prediction task.
如果大脑和AI所做的事情没有任何相似之处，没有任何共同之处，这项预测任务将无法完成。
It won't be possible to predict one from the other.
两者中的任何一个都无法预测另一个。
So we've reached a fork in the road and you can probably tell I'm about to tell you one of two things.
现在我们到了一个岔路口，答案只会是以下两者之一：
I’m going to tell you AI is amazing, or I'm going to tell you AI is an imposter.
要么AI是非常惊人的；要么AI只是一个冒牌货。
Researchers like me love to remind you that AI is nothing like the brain.
像我这样的研究人员特别喜欢说，人工智能与大脑完全不同。
And that is true.
这是事实。
But could it also be the AI and the brain share something in common?
但AI和大脑有没有相似点呢？
So we’ve done this scratch pad prediction task, and it turns out, 75 percent of the time the predicted neural network scratchpad for a particular word is more similar to the true neural network scratchpad for that word than it is to the neural network scratch pad for some other randomly chosen word -- 75 percent is much better than chance.
我们进行了这项预测草稿的任务，结果发现，有 75% 的概率针对某一特定词语的神经网络草稿的预测结果会更类似针对这一词语的真实大脑神经网络草稿，而不是更接近于针对其他随机词语的大脑神经网络草稿。75% 要远高于随机水平。
What about for more complicated things, not just words, but sentences, even stories?
那么对于更复杂的事物，不只是单词，还有句子，甚至故事呢？
Again, this scratch pad prediction task works.
这个草稿预测任务得到了同样的结果。
We’re able to predict the neural network scratch pad from the brain and vice versa.
我们可以从大脑图像预测神经网络，反过来也可以。
Amazing.
太有意思了。
So does that mean that neural networks and AI understand language just like we do?
那么，这是否意味着神经网络和人工智能可以像我们人类一样理解语言呢？
Well, truthfully, no.
说实话，并不是。
Though these scratch pad prediction tasks show above-chance accuracy, the underlying correlations are still pretty weak.
尽管这些草稿预测任务表现出高于随机的准确率，两者底层的相关性仍然非常弱。
And though neural networks are inspired by the brain, they don't have the same kind of structure and complexity that we see in the brain.
尽管神经网络的灵感来自于大脑，它们并不具备大脑呈现的结构和复杂性。
Neural networks also don't exist in the world.
神经网络也不存在于真实世界中。
A neural network has never opened a door or seen a sunset, heard a baby cry.
从来没有一个神经网络打开过门，看到过日落，听到过婴儿的哭声。
Can a neural network that doesn't actually exist in the world, hasn't really experienced the world, really understand language about the world?
一个并不真实存在于世界上、没有真正体验过世界的神经网络，能真正理解描述世界的语言吗？
Still, these scratch pad prediction experiments have held up -- multiple brain imaging experiments, multiple neural networks.
尽管如此，这些草稿预测实验仍然站得住脚——多个大脑成像结果，多个神经网络模型。
We've also found that as the neural networks get more accurate, they also start to use their scratch pad in a way that becomes more brain-like.
我们还发现，随着神经网络变得更加准确，它们也以一种更像大脑的方式使用着草稿纸。
And it's not just language.
这不仅仅是语言方面。
We've seen similar results in navigation and vision.
我们在导航任务和视觉任务上也看到了相似的结果。
So AI is not doing exactly what the brain is doing, but it's not completely random either.
人工智能所做的并不完全和大脑相同，但也不是完全随机。
So from where I sit, if we want to know if AI really understands language like we do, we need to get inside of the Chinese room.
从我的角度看来，如果我们想真正知道AI能否像我们这样理解语言，
We need to know what the AI is doing, and we need to be able to compare that to what people are doing when they understand language.
我们需要进入那个“中文房间”，需要知道AI到底在做什么，需要能将AI的行为与人类理解语言的行为比较。
AI is moving so fast.
人工智能发展得太快了。
Today, I'm asking you, does AI understand language that might seem like a silly question in ten years.
今天我还在问大家，人工智能能否理解语言，可能十年以后，这个问题就会看起来很“傻”。
Or ten months.
也可能十个月。
But one thing will remain true.
但有一件事不会变化。
We are meaning-making humans, and we are going to continue to look for meaning and interpret the world around us.
我们是创造意义的人类。我们将继续寻找意义，解释我们周围的世界。
And we will need to remember that if we only look at the input and output of AI, it's very easy to be fooled.
我们需要记住，如果我们只看AI的输入和输出，我们就容易被骗到。
We need to get inside of the metaphorical room of AI in order to see what's happening.
我们需要真正深入人工智能里的那个“房间”，看到真正在发生的事情。
It's what's inside the counts.
房间里发生了什么才是最重要的。
Thank you.
谢谢。
