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My guest today is Sam Altman. He, of course, is the CEO of OpenAI.
今天的嘉宾是萨姆·奥尔特曼，OpenAI 的首席执行官。
He’s been an entrepreneur and a leader in the tech industry for a long time, including running Y Combinator, that did amazing things like funding Reddit, Dropbox, Airbnb.
他是一名企业家和科技行业的领导者，运营Y Combinator，该公司做出了许多成就，例如为Reddit、Dropbox、Airbnb出资。
Sam is also involved in companies that could help solve climate like Helion and Oklo, so a broad range of activity.
萨姆还投资了Helion和Oklo等有助于解决气候问题的公司，他的业务范围十分广泛。
Today we’re going to focus mostly on AI, because it’s such an exciting thing, and people are also concerned. Welcome, Sam.
今天我们主要讨论人工智能，这很令人兴奋，大家也都很感兴趣。欢迎你，萨姆。
Thank you so much for having me.
非常感谢您邀请我。
I was privileged to see your work as it evolved, and I was very skeptical.
我很荣幸看到你的研究不断发展，但又对此非常怀疑。
I didn’t expect ChatGPT to get so good. It blows my mind, and we don’t really understand the encoding.
我没想到ChatGPT取得了如此大的成就，这让我大吃一惊，而且我们并不完全理解编码。
We know the numbers, we can watch it multiply, but the idea of where is Shakespearean encoded?
我们知道数字，可以观察数字倍增，但是莎士比亚的思想是在哪里编码的呢？
Do you think we’ll gain an understanding of the representation? A hundred percent.
你觉得人类会理解编码形式吗？百分百会。
Trying to do this in a human brain is very hard.
尝试在人脑中做到这一点非常困难。
You could say it’s a similar problem, which is there are these neurons, they’re connected.
你可以说这是一个类似的问题，就是这些神经元是相连的。
The connections are moving and we’re not going to slice up your brain and watch how it’s evolving, but this we can perfectly x-ray.
这些连接是动态的，我们不会切开大脑观察它是如何演变的，但我们完全可以用X射线检查。
There has been some very good work on interpretability, and I think there will be more over time.
已经有一些非常好的研究解释了这点，我认为随着时间的推移将会有更多相关研究。
I think we will be able to understand these networks, but our current understanding is low.
我认为我们将能够理解这些神经网络，但目前理解还很肤浅。
The little bits we do understand have, as you’d expect, been very helpful in improving these things.
正如你所期望的，我们了解的一点点内容对于改进这些事情大有裨益。
We’re all motivated to really understand them, scientific curiosity aside, but the scale of these is so vast.
抛开求知欲不谈，我们都有动力去真正理解它们，但它们的规模太过宏大。
We also could say, where in your brain is Shakespeare encoded, and how is that represented?
我们还可以说，莎士比亚在你大脑中的哪个位置被编码，以及它是如何表示的？
We don’t know. We don’t really know, but it somehow feels even less satisfying to say we don’t know yet in these masses of numbers, that we’re supposed to be able to perfectly x-ray and watch and do any tests we want to on.
我们不知道。确实，我们不知道，但在某种程度上，如果说我们还不知道大量数字，不知道能够利用X光检查、观看并进行任何测试，这会令人大失所望。
