介绍： If you haven't stayed up three nights being anxious about AI, if you haven't had an existential crisis about it, you probably haven't really experienced AI.
如果你没有因为人工智能而焦虑到一连三晚睡不着觉，如果你没有因为它而产生存在主义危机，那你可能还没有真正体验过人工智能。

It is a weird thing.
它很奇怪。

...

介绍： If you haven't stayed up three nights being anxious about AI, if you haven't had an existential crisis about it, you probably haven't really experienced AI.
如果你没有因为人工智能而焦虑到一连三晚睡不着觉，如果你没有因为它而产生存在主义危机，那你可能还没有真正体验过人工智能。

It is a weird thing.
它很奇怪。

What's it mean to be human? What's it mean to think? What will I do for a living? What will my kids do?
作为人类意味着什么？思考意味着什么？我将以什么为生？我的孩子会做什么？

What does it mean that it's better than me at some of this stuff? Is this real or is it an illusion?
在某些方面比我强意味着什么？它是真实的还是一种幻觉？

Nobody actually knows where AI is heading right now and how good it's going to get.
事实上，没有人确切知道人工智能现在的发展方向，以及它会变得有多好。

But, we shouldn't feel like we don't have control over how AI is used.
但是，我们不应该觉得我们无法控制人工智能的使用方式。

As managers and leaders, you get to make these choices about how to deploy these systems to increase human flourishing.
作为管理者和领导者，你需要做出选择，决定如何部署这些系统，让人类更加繁荣。

As individuals, we get to decide how to be the human who uses these systems well. AI is here to stay.
作为个人，我们可以决定如何成为能很好地使用这些系统的人。人工智能会继续存在。

That is something that you get to make a decision about how you want to handle, and to learn to work with, and learn to thrive with, rather than to just be scared of.
这是你可以决定如何处理的事情，要学会与之合作，学会与之共同成长，而不仅仅是害怕它。

I'm Ethan Mollick, a professor at the Wharton School of the University of Pennsylvania where I study innovation, entrepreneurship, and artificial intelligence.
我是伊桑·莫利克，宾夕法尼亚大学沃顿商学院的教授，我在学校研究创新、创业和人工智能。

I'm the author of the book Co-Intelligence: Living and Working with AI.
我是《共同智慧：与人工智能共同生活和工作》一书的作者。

Artificial intelligence is about prediction. Basically, AI is a very fancy autocomplete.
人工智能就是预测。基本上，人工智能是一种非常高级的自动完成。

For a long time, that was about numerical prediction and doing sort of complex algorithms of math so that Netflix could recommend a show for you to watch, Amazon could figure out where to site its next warehouse, or Tesla could figure out how to use data to make sure its cars were driving automatically.
很久以来，它都与数值预测以及进行某种复杂的数学算法有关，从而让Netflix给你推荐节目观看，让亚马逊弄清楚在哪里建立下一个仓库，或者让特斯拉知道如何利用数据来确保其汽车自动驾驶。

The thing that these systems were bad at predicting was the next word in a sentence.
这些系统不擅长预测的是句子中的下一个单词。

So if your sentence ended with the word "filed," it didn't know whether you were filing your taxes or filing your nails.
如果你的句子以“filed”这个词结尾，它不知道你是在报税还是在修剪指甲。

What happened that was different was the innovation of the large language model.
不同之处在于大型语言模型的创新。

In 2017, a breakthrough paper called "Attention is All You Need" outlined a new kind of AI called the "transforming with attention mechanism" that basically let the AI pay attention to not just the final word in the sentence, but the entire context of the sentence, the paragraph, the page, and so on.
2017年，一篇名为《注意力是你所需要的一切》的突破性论文概述了一种名为“带有注意力机制的转换”的新型人工智能，它不仅让人工智能关注句子中的最后一个单词，而且还关注句子、段落、页面等整个上下文。

Large language models work by taking huge amounts of information, like all the data on the internet.
大型语言模型的工作原理是获取大量信息，例如互联网上的所有数据。

There's a lot of Harry Potter fan fiction, for example, because that's what the internet contains.
比如，有很多《哈利·波特》的同人小说，这些内容就包含在互联网上。

And based on all of this data, the AI goes through a process called pre-training.
并且基于这些数据，人工智能会经历一个称为预训练的过程。

And this is that really expensive part that only a few companies in the world can do.
这部分才是真正昂贵的部分，世界上只有少数几家公司能承担得起。

And during that time, the AI learns the relationships between words or parts of words called tokens.
在这段时间里，人工智能学习了单词或部分单词（称为符号）之间的关系。

So it learns that "kiwi" and "strawberry" are closely related, but that "hawk" and "potato" are not closely related.
它学到了“猕猴桃”和“草莓”有密切的关系，但“鹰”和“土豆”没有密切的关系。

It learns across thousands of dimensions in a multidimensional space we can't understand. That lets it do predictions.
它在一个我们无法理解的多维空间中学习了数千个维度。这让它拥有了预测的能力。

But it turns out, unexpectedly, when large language models get big enough, they also do all kinds of other things we didn't expect.
但事实证明，出乎意料的是，当大型语言模型变得足够大时，它们还会做各种我们预想不到的其他事情。

We didn't expect them to be good at medicine, but they were actually quite good and beat doctors under many circumstances.
我们没想到它们擅长医学，但它们在医学方面其实相当出色，在许多情况下连医生都比不过。

We didn't expect them to be good at creativity, but they can generate ideas better than most humans can.
我们没想到它们擅长创造，但它们能比大多数人类产生更好的想法。

And so they're general purpose models. They do many different things.
所以它们是通用模型。它们可以做很多不同的事情。

Interestingly, "GPT" doesn't just stand for the "GPT" in "ChatGPT".
有趣的是，“GPT”并不仅仅代表“ChatGPT”中的“GPT”。

It also stands for "general purpose technology," which is one of these once in a generation technologies, things like steam power or the internet or electrification that change everything they touch.
它还代表着“通用技术”，这是一种一代人只能遇到一次的技术，就像蒸汽动力、互联网或电气化一样，改变了它们所触及的一切。

They alter society. They alter how we work.
它们改变了社会。改变了我们的工作方式。

They alter how we relate to each other in ways that are really hard to anticipate.
它们改变了我们彼此之间的联系方式，这种方式真的很难预测。

So you can't think in certainties. You should think in scenarios.
你不能确定地思考。你应该在设想中思考。

And there's really four scenarios in the future.
而未来实际上有四种设想。

The first is actually, I think, the least likely, which is that the world is static, that this is the best AI you're ever going to use. I think that's unlikely.
第一个，其实也是我认为最不可能的，就是世界是静态的，现在就是你能用到的最好的人工智能。我认为这不太可能。

In fact, whatever AI you're using now is the worst AI you're ever going to use.
事实上，无论你现在使用的是什么AI，它都将是你用过的最差的AI。

Even if the core large language model development stopped right now, there's another ten years of just making it work better with tools and with industry in ways that'll continue to be disruptive.
即便核心的大型语言模型开发现在就停止，还有十年时间，让它在与工具和行业的结合上发挥更好的作用，这将继续带来颠覆性的影响。

So I think that's a dangerous view because it isn't static. It's evolving.
所以我觉得这种想法很危险，因为它不是静止的。而是不断发展的。

So I want to skip actually to the last scenario before covering scenarios two and three.
在介绍场景二和三之前，我想先直接跳到最后一个场景。

So scenario four is AGI, artificial general intelligence.
场景四是AGI，即通用人工智能。

This is the idea that a machine will be smarter than a human in almost all tasks.
这个想法是指机器在几乎所有任务中都将比人类更聪明。

And this is the explicit goal of OpenAI. They want to build AGI.
这也是OpenAI的明确目标。他们想要构建AGI。

And there's a lot of debate about what this means.
至于它到底意味着什么，存在很多争论。

When we have a machine smarter than a human and it can do all humans' jobs, can it create AI smarter than itself?
当我们拥有一台比人类更聪明的机器，并且它可以完成人类的所有工作时，它能否创造出比自己更聪明的人工智能呢？

Then we have artificial superintelligence, ASI, and humans become obsolete overnight.
然后就有了人工超级智能，ASI，人类一夜之间就过时了。

And there's people genuinely worried about this, and I think it's worth spending a little time being slightly worried, too, because other people are.
有人真的很担心这个，我认为也值得花一点时间稍微担心一下，因为其他人都在担心。

But I think that that scenario tends to take agency away from us because it's something that happens to us.
但我认为这种情况往往会剥夺我们的自主性，因为它是发生在我们自己身上的事情。

And I think that it's more important to worry about what I call scenarios two and three, which is continued linear or exponential growth.
并且我认为，更重要的是要担心我所说的第二种和第三种情况，即持续的线性或指数增长。

We don't know how good AI is going to get.
我们不知道人工智能会变得多好。

Right now, the doubling time for capability is about every five to nine months, which is an exceptionally fast doubling time.
目前，人工智能能力翻倍的时间大约是每五到九个月，这个时间非常快。

Moore's Law, which is the rule that's kind of kept the computer world going, doubles the power of computer processing chips every twenty-four to twenty- six months.
摩尔定律是一种使计算机世界得以发展的规则，它每二十四到二十六个月，就能让计算机处理芯片的性能翻倍。

So this is a very fast rate of growth.
这种增长速度非常快。

It's very likely that AIs will continue to improve and get better in the near term, and now is a good time for you to start to figure out how to use AI to support what makes you human or good at things, and what things as AI gets better that you might want to start handing off more to the AI.
在短期内，人工智能很可能会继续改进和变得更好，现在是一个很好的时机，你可以开始思考如何使用人工智能来支持那些使你成为人类或擅长的事情，以及随着人工智能的优化，你可能会想把哪些事情更多地交给人工智能去做。