Episode 6: Sam Altman - YouTube

My guest today is Sam Altman. He of course is the CEO of OpenAI. 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. A little while after I recorded this episode, I was completely taken by surprise when at least briefly he was let go as the CEO of OpenAI. A lot happened in the days after the firing, including a show of support from nearly all of OpenAI's employees and Sam is back.

今天我的特别嘉宾是Sam Altman。他当然是OpenAI的首席执行官。他在科技行业做了很长时间的企业家和领导者，包括领导Y Combinator，资助了Reddit、Dropbox、Airbnb等出色的项目。在录制这一集之后不久，我完全被惊讶地得知，他被解除了OpenAI的CEO职务，这一事件引发了一系列的反应，包括OpenAI几乎所有员工的支持，现在Sam又回来了。

So before you hear the conversation that we had, let's check in with Sam and see how he's doing. Whoop, whoop, whoop. Hey, Sam. How are you? Oh man, it's been so crazy. I'm all right. It's a very exciting time. How's the team doing? I think a lot of people have remarked on the fact that the team has never felt more productive or more of a mistake or better. So I guess that's like the silver lining of all of this. In some sense, this was like a real moment of growing up for us. We are very motivated to become a better and sort of to become a company ready for the challenges in front of us.

那么，在你听到我们的谈话之前，让我们先来看看山姆的情况。嗷嗷，嗷嗷，嗷嗷。嘿，山姆。你好吗？哦，天啊，一切都太疯狂了。我还好。这是一个非常令人兴奋的时刻。团队怎么样？我想很多人都注意到我们的团队在这个时候变得更加高效、更不容易犯错或更好了。所以我想这可能就是这一切的积极一面。在某种意义上，这对我们来说是一个真正的成长时刻。我们非常有动力，希望成为一个更好的公司，以迎接未来的挑战。

Fantastic. So we won't be discussing that situation and the conversation. However, you will hear about Sam's commitment to build a safe and responsible AI. I hope you enjoy the conversation. Welcome to Unconfused Me, I'm Bill Gates. Today we're gonna focus mostly on AI because it's such an exciting thing and people are also concerned. Welcome, Sam. Thank you so much for having me.

太棒了。所以我们不会讨论那种情况和对话。不过，你将听到有关Sam致力于构建安全和负责任的人工智能的信息。希望你能享受这次对话。欢迎来到《不再困惑》，我是比尔·盖茨。今天我们主要专注于人工智能，因为这是一件令人兴奋的事情，人们也很关注。欢迎，Sam。非常感谢邀请我。

You know, I was privileged to see your work as it evolved and I was very skeptical, like I did not expect JPT to get so good. And I'm still, it blows my mind and we don't really understand the encoding that we know the numbers, we can watch it multiply, but the idea of where is Shakespearean encoded? Do you think we'll gain an understanding of the representation? Oh, 100%. Okay, you know, trying to do this in a human brain is very hard. You could say it's a similar problem which is there, these neurons, they're connected, the connections are like moving and we're not gonna like slice up your brain and watch how it's evolving, but this we can perfectly x-ray and there has been some very good work on interpretability and I think there will be more over time.

你知道吗，我有幸看到你的作品是如何发展的，一开始我非常怀疑，我没有预料到JPT会变得如此优秀。现在我还是很震惊，我们并不真正理解编码，尽管我们知道数字，我们可以看到它们增加，但莎士比亚的编码在哪里呢？你认为我们会获得对这种表示的理解吗？哦，百分之百。嗯，你知道，在人类大脑中做这件事是非常困难的。你可以说它是一个类似的问题，即这些神经元，它们是相互连接的，连接就像是在移动，我们不会去切开你的大脑然后观察它是如何演变的，但这种方法我们可以完美地进行X光检查，已经有一些非常好的解释性工作，我认为随着时间的推移会有更多的进展。

So yeah, I think we will be able to understand these networks, but our current understanding is low. The little bits we do understand have, as you'd expect, been very helpful in improving these things. So we're all motivated to really understand the scientific curiosity aside, but the scale of these is so fast and it is, you know, we could also say where in your brain is Shakespeare encoded and how does that represent it? Yeah, we don't know. We don't really know. But it somehow feels like even less satisfying to say we don't know yet in these like masses of numbers that we're supposed to be able to perfectly x-ray and watch and do any tests we want on.

所以，我认为我们将能够理解这些网络，但我们目前的理解水平很低。我们已经理解的一点点内容如你所料，在改善这些事物方面非常有帮助。因此，我们都受到了极大的动力，不仅仅是因为科学好奇心，而且这些东西的规模是如此之快，你知道的，我们也可以说莎士比亚的思想在你的大脑中的具体位置，并且这如何代表它呢？是的，我们不知道。我们真的不知道。但在这种我们应该能够完美地像X射线一样观察并进行任何我们想要的测试的大量数字中，说我们还不知道感觉上更加不尽人意。

Yeah, I'm pretty sure within the next five years we'll understand it. And in terms of both training efficiency and accuracy, that understanding would let us do far better than we're able to do today, 100%. You know, you see this in a lot of the history of technology where someone makes an empirical discovery. They have no idea what's going on, but it clearly works. And then as the scientific understanding deepens, they can make it so much better. Yeah, no, that in physics, biology, it's sometimes just messing around and it's like, whoa, how does this actually come together?

是的，我非常确定在接下来的五年内我们会理解它。从培训效率和准确性的角度来看，这种理解让我们比今天能做到的更好，100%确定。你知道，在科技发展史上经常发生这种情况，有人做出实证发现，他们完全不知道发生了什么，但显然有效。随着科学理解的加深，他们可以做得更好。对，在物理学、生物学中，有时候只是胡乱尝试，然后突然发现，这到底是怎么结合在一起的呢？

In our case, we had, you know, someone that was, the guy that built GPT-1 sort of did it off by himself and saw this and it was somewhat impressive. But, you know, no deep understanding of how it worked or why it worked. And then it was, we got these scaling laws where we could predict how much better it was going to be. That was why when we told you we could do that demo, we were pretty confident it was gonna work. We hadn't trained the model, but we were pretty confident.

在我们的情况下，我们有一个人，你知道的，就是那个建造GPT-1的家伙，他自己动手做了一下，看到这个有点厉害。但是，你知道的，对它是如何工作或为什么能工作的深入理解。然后，我们得到了这些规模化定律，我们可以预测它会变得更好。这就是为什么当我们告诉你我们可以做那个演示时，我们相当有信心它会成功。虽然我们还没有训练模型，但我们相当有信心。

And that has led us to a bunch of attempts and better and better scientific understanding of what's going on, but it really came from a place of empirical result first. You know, when you look at the next two years, what do you think some of the key milestones will be? Multi-modality will definitely be important. We started- Which means speech and speech out. Speech and speech out images, eventually video, clearly people really want that. We launched images and audio and it had a much stronger response than we expected.

这导致我们进行了许多尝试，对发生的事情有了越来越好的科学理解，但实际上这一切起初都是由经验结果推动的。你知道，当你看着接下来的两年，你认为一些关键的里程碑会是什么？多模态肯定很重要。我们开始就是- 这意味着语音输入和输出。语音输入和输出的图像，最终是视频，显然人们非常希望这样。我们推出了图像和音频，收到的反应比我们预期的要强烈得多。

We'll be able to push that much further, but maybe the most important areas of progress will be around reason and ability. Right now, GPT-4 can reason in only extremely limited ways and also reliability. You know, if you ask GPT-4 most questions 10,000 times, one of those 10,000 is probably pretty good, but it doesn't always know which one. And you'd like to get the best response of 10,000 each time. So that'll be, that increase in reliability will be important. Customizability and personalization will also be very important. People want very different things out of GPT-4, different styles, different sets of assumptions will make all that possible. And then also, the ability to have it use your own data. So the ability to know about you, your email, your calendar, how you like appointments booked, connected to other outside data sources, all of that. Those will be some of the most important areas of improvement.

我们将能够取得更大进展，但也许最重要的进步领域将围绕着推理和能力展开。目前，GPT-4只能以极其有限的方式进行推理，并且可靠性有待提高。你知道，如果你问GPT-4大多数问题10,000次，这10,000次中可能有一个答案还不错，但它并不总是知道哪个才是最好的。你希望每次得到10,000个答案中最好的一个。所以，可靠性的提高将是重要的。可定制性和个性化也将非常重要。人们希望从GPT-4中得到非常不同的东西，不同的风格，不同的假设集会让一切成为可能。此外，让它能够使用你自己的数据也将很重要。它能够了解你，你的电子邮件，你的日历，你喜欢如何安排约会，连接到其他外部数据源，所有这些。这些将是一些最重要的改进领域。

In the basic algorithm right now, just feed forward, multiply, so to generate every new word, it's essentially doing the same thing. I'll be interested if you ever get to the point where, you know, like solving a complex math equation where you might have to, you know, apply transformations and arbitrary number of times, that the control logic for the reasoning may have to be quite a bit more complex than just what we do today. At a minimum, it seems like we need some sort of adaptive compute. Right now, we spend, you know, the same amount of compute on each token, the dumb one, or like figuring out some complicated math. Yeah, when we say do the Riemann hypothesis, that deserves a lot of compute. The same compute as saying the. Right.

目前的基本算法中，只是前馈、相乘，所以生成每个新词，本质上都是在做同样的事情。如果你能够达到一种程度，例如解决一个复杂的数学方程，可能需要应用多次变换，那么推理的控制逻辑可能会比我们今天所做的更加复杂。至少看起来，我们似乎需要一些自适应计算。目前，我们在每个标记上花费的计算量相同，无论是愚蠢的标记，还是解决一些复杂的数学问题。是的，当我们说需解决黎曼猜想时，那就需要大量的计算。与说“the”一样的计算量。

So at a minimum, we've got to get that to work. We may need much more sophisticated things beyond it. You and I were both part of a Senate education session. And I was pleased that I think about 30 senators came to that and, you know, helping them get up to speed, you know, since it's such a big change age. And I don't think, you know, we could ever say we did too much to draw the politicians in. And yet, you know, when they say, oh, you know, we blew it on social media, you know, we should do better. You know, social media, we still, that is an outstanding challenge that there are very negative elements to that in terms of polarization. And, you know, even now, I'm not sure how we deal with that.

所以，至少我们得让它起作用。我们可能需要更复杂的事物。你我都参加了一次参议院教育会议。我很高兴，约有30名参议员参加了，你知道，帮助他们跟上步伐，因为这是一个巨大的变革时代。我认为，我们永远不能说我们为吸引政治家而付出过多的努力。然而，当他们说：“哦，你知道，我们在社交媒体上搞砸了，我们应该做得更好。”你知道，在社交媒体方面，我们仍然面临着突出挑战，尤其是在极化方面存在非常负面的元素。即使现在，我也不确定我们该如何应对这一问题。

I don't understand why the government was not able to be more effective around social media, but it seems worth trying to understand as a case study for what they're going to go through now with AI. No, it's a good, good case study. And when you talk about the regulation, is it clear to you what sort of regulations would be constructed? I think we're starting to figure that out. It would be very easy to put way too much regulation on this space. And, you know, you can look at lots of examples of where that's happened before. But also, if we are right, and we may turn out not to be, but if we are right, and this technology goes as far as we think it's going to go, it will impact society, geopolitical balance of power, so many things that for these still hypothetical, but future, extraordinarily powerful systems, not like GPT-4, but something with 100,000 or a million times the compute power of that, we have been socializing the idea of a global regulatory body that looks at those super powerful systems, because they do have such global impact.

我不明白为什么政府在社交媒体上不够有效，但值得尝试理解，作为他们现在将要面对的AI的案例研究。是的，这是一个很好的案例研究。当谈到监管时，你清楚地知道会制定什么样的规定吗？我认为我们正在开始弄清楚。对这个领域过度监管是非常容易的。你知道，我们可以看很多之前发生过的例子。但是，如果我们是正确的，也许我们最终可能不正确，但如果我们是正确的，这项技术将影响社会、地缘政治平衡等诸多方面。为了这些仍然是假设，但未来将会成为非常强大系统的事物，不像GPT-4，而是具有100,000或一百万倍计算能力的东西，我们一直在社会化的思想是设立一个全球监管机构来审视那些超级强大的系统，因为它们具有如此巨大的全球影响。

And one model we talk about is something like the IAEA. So for nuclear energy, we decided the same thing. This needs a global agency of some sort because of the potential for global impact. I think that could make sense. There'll be a lot of shorter term issues of what are these models allowed to say and not say, how do we think about copyright? Different countries are going to think about those differently and that's fine. You know, some people think, okay, if there are models that are so powerful, we're scared of them, you know, the reason nuclear regulation works globally is basically everyone, at least on the civilian side, you know, wants to share safety practices and it has been fantastic. When you get over into the weapon side of nuclear, you know, you don't have that same thing. And so if the key is to stop the entire world from doing something dangerous, you'd almost want global government, which, you know, today for many issues like climate, terrorism, you know, we see that it's hard for us to cooperate and people even invoke sort of US-China competition to say why any notion of slowing down would be inappropriate.

我们谈论的一个模式类似于国际原子能机构（IAEA）。因此，对于核能，我们也做出了相同的决定。由于可能造成全球性影响，这需要某种全球性机构。我认为这是合理的。会有很多短期问题，比如这些模式可以说和不能说什么，我们如何考虑版权？不同的国家会以不同方式思考这些问题，这是可以接受的。有些人认为，如果有些模式非常强大，我们会感到害怕。你知道，核能规范在全球范围内基本上是有效的，至少在民用领域，每个人都希望分享安全实践，这一直效果很好。当涉及到核武器方面时，情况就不同了。因此，如果关键是阻止全世界从事危险行为，你几乎希望有全球政府。你知道，对于诸如气候、恐怖主义之类的许多问题，我们看到很难进行合作，人们甚至提及美中竞争来解释为什么任何减缓的想法都不合适。

So isn't it gonna be hard to, any idea of slowing down or going slow enough to be careful will be hard to enforce? Yeah, I think if it comes across as asking for a slowdown, that'll be really hard. If it is instead says, okay, any, do what you want, but any compute cluster above a certain extremely high power threshold and given the cost here, we're talking maybe five in the world, something like that, any cluster like that has got to submit to the equivalent of international weapons inspectors and the model there has to be made available for safety audit, pass some tests during training and before deployment. That feels possible to me. I wasn't that sure before, but I did a big trip around the world this year, talked ahead of state in many of the countries that would need to participate in this and there was almost universal support for it. So I think that's not gonna save us from everything. There are still gonna be things that are gonna go wrong with much smaller scale systems. In some cases, probably pretty badly wrong, but I think that can help us with the biggest tier of risks. I do think AI in the best case can help us with some hard for sure, hard problems, including polarization because potentially that breaks democracy and that would be a super bad thing. Right now, I guess we're looking a lot of productivity improvement from AI, which that's overwhelmingly a very good thing. Which areas are you most excited about?

那么，要不要减速或者慢下来以便小心谨慎地操作，这会不会很困难？是的，我认为如果是要求减速的话，会非常困难。如果改为说，好吧，你可以按照自己的意愿行事，但是任何一个超过极高功率阈值的计算集群，考虑到成本，全球可能只有五个左右，任何类似的集群都必须接受类似于国际武器检查员的检查，并且该模型必须公开供安全审计，通过培训和部署前进行一些测试。我觉得这是可行的。之前我并不太确定，但今年我周游了世界，和许多需要参与其中的国家的国家元首进行了对话，几乎所有人都对此表示支持。我认为这不能解决所有问题。仍然会有一些规模较小的系统会出问题。在一些情况下，可能会出现相当严重的问题，但我认为这可以帮助我们处理最大的风险层次。我认为在最好的情况下，人工智能可以帮助我们解决一些确实非常棘手的问题，包括极化，因为潜在地这可能会破坏民主，那将是非常糟糕的事情。现在，我猜我们正在期待从人工智能中获得大量的生产力改进，这绝对是一件非常好的事情。你最感兴趣的领域是哪些？

Yeah, so first of all, I always think it's worth remembering that we're just sort of on this long continuous curve. So like right now we have AI systems that can do tasks. They certainly can't do jobs, but they can do tasks and there's productivity gain there. Eventually they'll be able to do more things that we think of like a job today. And we'll of course find new jobs and better jobs. And I totally believe that if you give people way more powerful tools, it's not just they can work a little faster, they can do qualitatively different things. And so right now maybe we can speed up a programmer 3x. It's about what we see. I mean, that's one of the categories that we're most excited about. It's working super well.

是的，首先，我始终认为值得记住的是，我们只是在这条漫长的连续曲线上。就像现在我们有能够完成任务的人工智能系统。它们当然不能完成工作，但可以完成任务，这里面有生产力的增益。最终它们将能够做更多我们今天认为是工作的事情。我们当然会发现新的工作和更好的工作。我完全相信，如果给人们更强大的工具，不仅仅是他们可以工作更快，他们可以做出质的不同的东西。所以现在也许我们可以把程序员的速度提高3倍。这是我们最为兴奋的类别之一。这个领域发展得非常好。

But if you make a programmer three times more effective, it's not just that they can write, they can do three times more stuff. It's that they can, at that high level of abstraction, using more of their brain power, they can now think of totally different things. And it's like, you know, going from punch cards to higher level languages didn't just let us program a little faster, let us do these qualitatively new things. And we're really seeing that. And so as we look at these next steps of things that can do a more complete task, you can like imagine a little agent that you can say, go write this whole program for me. I'll ask you a few questions along the way, but it won't just be writing that few functions at a time. That'll enable a bunch of new stuff.

但是如果你让一个程序员的效率提高三倍，不仅仅是他们可以写代码，他们也可以做三倍的事情。因为在这种高度抽象的情况下，利用更多的大脑能力，他们现在可以想到完全不同的事情。就好像，你知道的，从穿孔卡转向更高级别的编程语言不仅让我们编程速度更快，还让我们做出质的不同的东西。我们正在真正看到这一点。因此，当我们看着能完成更完整任务的下一步发展时，你可以想象一个小代理，你可以说，帮我写整个程序。在此过程中我会问你一些问题，但这不只是一次写几个函数。这将开启一些全新的可能性。

And then again, it'll do even more complex stuff. Someday, maybe there's an AI where you can say, you know, go start and run this company for me. And then someday there's maybe an AI where you can say, like, go discover new physics. And it's the stuff that we're seeing now is very exciting and wonderful. But I think it's worth always put in context of this technology that, at least for the next five or 10 years will be on a very steep improvement curve. These are the stupidest the models will ever be. But coding is probably the area, the single area, from a productivity gain we're most excited about today. Massively deployed and, you know, at scale usage at this point, healthcare and education are two things that are coming up that curve that we're very excited about too. But the thing that is a little daunting is, unlike previous technology improvements, this one could improve very rapidly. And there's kind of no upper bound. I mean, the idea that it achieves human levels on a lot of areas of work, you know, even if it's not doing unique science, it, you know, can do support calls and sales calls.

再说，它还会做更复杂的事情。也许，总有一天会有一种人工智能，你可以告诉它去开始并经营这家公司。也许，总有一天会有一种人工智能，你可以告诉它去发现新的物理学。我们现在看到的东西非常令人兴奋和美妙。但我认为，值得一直放在这种技术的背景下，至少在接下来的五到十年里，将会有一个非常陡峭的提高曲线。这些可能是模型中最愚蠢的时刻。但编码可能是从生产力增益方面我们今天最为激动的领域。当前已被大规模部署和规模使用的领域，医疗保健和教育是我们也非常激动的两个领域。但令人有些畏惧的是，与以往的技术改进不同，这次的提高可能会非常迅速。而且没有上限。我是说，它达到在很多领域的人类水平的想法，即使它并不做独特的科学，它也能够处理支持电话和销售电话。

I guess, you and I do have some concern, along with this good thing, that it'll force us to adapt faster than we've had to ever before. That's the scary part. It's not that we have to adapt. It's not that humanity is not super adaptable. We've been through these massive technological shifts and a massive percentage of the jobs that people do can change over a couple of generations. And over a couple of generations, we seem to absorb that just fine. We've seen that with the great technological revolutions of the past. Each technological revolution has gotten faster and this will be the fastest by far. And that's the part that I find potentially a little scary, is just the speed with which society is going to have to adapt and that the labor market will change. One aspect of AI is robotics or blue collar jobs when you get sort of hands and feet that are at human level capability. And, you know, the incredible chat GPT breakthrough has kind of gotten us focused on the white collar thing, which is super appropriate.

我猜，你和我确实有一些担忧，随着这件好事一起，会迫使我们比以往任何时候都更快地适应。那是可怕的部分。不是说我们必须适应。也不是说人类不适应能力强。我们已经经历过巨大的技术变革，人们所做的大部分工作在几代人的时间内都可能会改变。而在几代人的时间内，我们似乎都能够很好地吸收这一切。我们已经看到了过去巨大的技术革命。每次技术革命都会变得更快，而这次将是迄今为止最快的。这就是我觉得有点可怕的部分，就是社会必须适应的速度以及劳动力市场将会发生变化。人工智能的一个方面是机器人技术，或者说蓝领工作，当你得到类似人类水平能力的手和脚时。而且，你知道，令人难以置信的GPT突破将我们聚焦在白领方面，这是非常恰当的。

But I do worry people are losing the focus on the blue collar piece. So how do you see robotics? Super excited for that. We started robots too early. And so we had to put that project on hold. It was hard for the wrong reasons. It wasn't helping us make progress with the difficult parts of the ML research. And, you know, we were like dealing with bad simulators and breaking tendons and things like that. And also we realized more and more over time that what we really first needed was intelligence and cognition. And then we could figure out how to adapt it to physicality. And it was easier to start with that the way we've built these language models. But we have always planned to come back to it. We've started investing a little bit in robotics companies.

但我担心人们失去了对蓝领工人的关注。那么您如何看待机器人技术呢？对此我感到非常兴奋。我们之前启动了机器人项目，但因为时间过早，不得不暂时搁置。这让我们感到困难的原因不在于技术，而是它并没有帮助我们在机器学习研究的困难部分取得进展。我们不得不处理一些糟糕的仿真器以及损坏的肌腱等问题。随着时间的推移，我们越来越意识到我们最初需要的是智能和认知能力，然后我们才能想办法适应物理世界。就像我们构建这些语言模型一样，这种方法更容易。但我们始终计划回到机器人领域。我们已经开始在机器人公司进行一些投资。

I think on the physical hardware side, there's finally, for the first time that I've ever seen, really exciting new platforms being built there. And at some point, we will be able to use our models as you are saying with their language understanding and future video understanding to say, all right, like, let's do amazing things with a robot. But if the hardware guys, you know, who've done a good job on legs, actually get the arms, hands, fingers, piece, and then we couple it, you know, and it's not ridiculously expensive, that could change the job market for a lot of the blue color type work pretty rapidly.

我认为从硬件的角度来看，我从未见过的第一次，确实有一些非常令人兴奋的新平台正在建立。在某个时候，我们将能够像您所说的那样利用我们的模型及其语言理解和未来视频理解能力，来做出惊人的机器人。但是，如果硬件方面的人员，你知道，那些在腿部表现不错的人，真的能够装备上手臂、手、手指等部件，然后我们把它们结合起来，你懂的，而且不会过于昂贵，这可能会相当快速地改变许多蓝领工作的就业市场。

Certainly the prediction, like the consensus prediction, if we rewind seven or 10 years, was that the impact was going to be blue color work first, white color work second, creativity, maybe never, but certainly last, because that was magic and human. Obviously, it's gone exactly the direction. And I think there's like a lot of interesting takeaways about why that happened. You know, creative work actually, the hallucinations of the GPT models is a feature, not a bug, it lets you discover some new things. Whereas if you're, you know, having a robot move, having machinery around, you better be really precise with that.

当然，预测就像共识预测一样，如果我们回放七年或十年前，预测是蓝领工作将首先受影响，白领工作其次，创造力可能永远不会被影响，但肯定会是最后一个受影响的，因为那是魔法和人类。显然，事情确实发展成了这个方向。我认为，关于为什么会这样发生，有很多有趣的启示可以得到。你知道，创造性工作实际上，GPT模型产生的幻觉不是缺陷，而是一种特征，它让你发现一些新事物。而如果你是在让机器人移动，周围有机械设备，你最好要非常精确。

And I think this is just a case of, you've got to follow where technology goes and you have preconceptions, but sometimes the science doesn't want to go that way. So what applications on your phone do you use the most? Slack. Really? Yeah. I wish I could say chat GPT. No, it's okay. Even more than like email. Way more than email. The only thing that I was thinking possibly was messages, but like I messages, but yeah, more than that. And so like inside opening eye, there's a lot of coordination going on. What about you? It's Outlook. I'm, you know, this old style email guy, either out of the browser, because of course a lot of my news is coming through the browser.

我认为这只是一个情况，你必须跟随技术发展的方向，你可能会有一些先入为主的观念，但有时科学并不总是朝着那个方向发展。那么你手机上最常用的应用是什么？Slack。真的吗？是的。我希望我能说是聊天GPT。不，没关系。比电子邮件还要多。我唯一可能想到的是信息，但就是信息，但是，比信息还要多。在 Inside Opening Eye 里，有很多协调工作。你呢？是 Outlook。我是那种老派的电子邮件人，不管是在浏览器内还是在浏览器外，因为当然很多我的消息都是通过浏览器传送的。

I didn't quite count the browsers in app. It's not possible I use it more, but I still don't, I still have that Slack. I'm like, we're, I'm on Slack all day. Incredible. Well, we've got a turntable here and I have Sam, like I have for other guests to bring one of his favorite records. So what have we got? So I brought the new four seasons of Vivaldi recomposed by Max Richter. I like music with no words for working. And this, it had like the old comfort of Vivaldi and like pieces I knew really well, but enough new notes that it was just a totally different experience. And there's these like pieces of music that you like form these strong emotional attachments to because you were like listening them a lot in a key period of your, of your life. This was something that I listened to a lot lot while we were starting opening eye. I think it's like very beautiful music. It's like soaring and optimistic and just like perfect for me for working. And I thought the new version is just super great.

我并没有准确地数一数应用中的浏览器。虽然我没有更多地使用它，但我仍然没有，我仍然在使用Slack。我就像，我们，我整天都在Slack上。不可思议。好吧，我们这里有一台唱片机，我请了Sam，就像我邀请其他客人一样，带了他最喜欢的唱片之一。那我们选了什么？所以我带来了马克斯·里希特重新创作的新四季。我喜欢没有歌词的音乐来工作。这张唱片既有维瓦尔第的老旧舒适感，又有一些我非常熟悉的曲目，但足够新奇，让它成为一种全新的体验。有些音乐会让你对其产生强烈的情感依恋，因为你在生活的一个关键时期经常听它们。这是我们开始开发“eye”时我经常听的音乐。我觉得这是非常美丽的音乐。它起伏动人、乐观向上，对我来说工作时简直完美。我觉得这个新版本很棒。

Is it performed by an orchestra? It is the Chineche orchestra. Oh, fantastic. Should I play it? Yeah, let's. OK. This is like the intro to the song we're going for. MUSIC Do you wear headphones? I do. And do your colleagues give you a hard time about listening to classical music? I don't even know what I listen to because I do care phones. But it's very hard for me to work in silence. Like I can do it, but it's not my natural song. Yeah, no, it's fascinating. Songs with words, I agree. I would find that distracting. But this is more of a moon type thing. Yeah, and I put it, I have it quiet. Like I can't listen to a lot of music either. But it's somehow just always what I've done.

这是由管弦乐队演奏的吗？是的，是Chineche管弦乐队。哇，太棒了。我应该播放吗？是的，让我们来播放。好的，这是我们要演奏的歌曲的引子。音乐开始了。你戴耳机吗？是的。你的同事会因为你听古典音乐而取笑你吗？我根本不知道我听了什么，因为我戴耳机。但是对我来说在沉默中工作很难。我可以做到，但这不是我的自然状态。是的，没有马上词的歌曲，我同意。我会觉得分心。但这就是比较像月亮的那一种事情。是的，我也把声音调得很低。我也不能听很多音乐。但不知怎么的，我一直都这样做。

No, it's fantastic. Thanks for bringing it. You know, now with AI, to me, if you do get to the incredible capability, you know, AGI, AGI, plus, I guess I, you know, there's three things I worry about. One is that a bad guy is in control of the system. And so if we have good guys who have equally powerful systems, that hopefully minimizes that problem. There's the chance of the system taking control. And for some reasons, I'm less concerned about that. I'm glad other people are. The one that sort of befuddles me is human purpose.

不，这太棒了。谢谢你带来这个。你知道，现在有了人工智能，对我来说，如果你真的达到了令人难以置信的能力，你知道，AGI，AGI，再加上，我猜我，你知道，有三件事让我担心。一是坏人控制系统。所以如果我们有同样强大的系统的好人，希望能最大程度地减少这个问题。系统接管的可能性。但因为某些原因，我对此并不太担忧。我很高兴其他人有这样的担忧。让我困扰的是人类的目的。

I get a lot of excitement that, hey, I'm good at working on malaria and malaria eradication and getting smart people and applying resources to that. When the machine says to me, Bill, go play pickleball. I've got malaria eradication. You're just a slow thinker. Then, you know, it is a philosophically confusing thing. And how you organize society, yes, we're going to improve education, but education to do what if you get to this extreme, which we still have a big uncertainty.

我有很多兴奋，因为我擅长研究疟疾和疟疾根除，并且能够吸引聪明人才并投入资源。当机器告诉我，比尔，去打壁球吧。我在进行疟疾根除。你只是个思维慢的人。那时，你知道，这是一个在哲学上令人困惑的事情。关于如何组织社会，是的，我们将改善教育，但教育是为了做什么，如果你达到了这个极端，我们仍然有很大的不确定性。

But for the first time, the chance that might come in the next 20 years is not zero. There's a lot of psychologically difficult parts of working on the technology, but this is for me the most difficult. Because I also. Yeah, you have a lot of satisfaction from that. And it's like, in some real sense, this might be like the last hard thing I ever do. Well, our minds are so organized around scarcity, scarcity of teachers and doctors and good ideas that partly I do wonder if a generation that grows up without that scarcity will find the philosophical notion of how to organize society and what to do. Maybe they'll come up with a solution and I'm afraid my mind is so shaped around scarcity, I mean, to have a hard time thinking of it.

但是，这是第一次，在未来20年可能会有一丝机会。 在技术发展过程中有很多心理上困难的部分，但对我来说，这是最困难的。 因为我也。 是的，你会从中获得很多满足感。 从某种实质上讲，这可能是我所做的最后一件困难的事情。 嗯，我们的思维方法是围绕着稀缺展开的，稀缺的老师、医生和好的想法，我有时会思考，一代人如果在没有那种稀缺的情况下长大，会对如何组织社会和应该做什么的哲学观念产生什么样的想法。也许他们会提出一种解决方案，我害怕我的思维是如此受到稀缺的影响，即便如此，我也难以去思考。

That's what I tell myself to and what I truly believe, that although we are giving something up here in some sense, we are gonna have things that are smarter than us. If we can get into this world of post-scarcity, we will find new things to do. They'll feel very different. Maybe instead of solving malaria, you're deciding which galaxy you'd like and what you're gonna do with it. I'm confident we're never gonna run out of problems and we're never gonna run out of different ways to find fulfillment and do things for each other and sort of understand how we play our human games for other humans in this way that's gonna remain really important. It's gonna be different for sure, but I think the only way out is through, we just have to go do this thing. It's gonna happen. This is like now an unstoppable technological course.

这就是我告诉自己的，也是我真心相信的，虽然在某种程度上我们放弃了一些东西，但我们将会得到比我们聪明的东西。如果我们能进入后稀缺世界，我们将找到新的事情去做。它们会感觉非常不同。也许你不再是在解决疟疾，而是在决定哪个星系是你喜欢的，你要怎么处理它。我相信我们永远不会遇到问题耗尽，我们永远不会缺少取得满足感、为彼此做事以及理解我们如何为其他人玩我们的人类游戏的不同方式。这样的方式将会始终非常重要。当然，它将会有所不同，但我认为唯一的出路就是前进，我们必须去做这件事。它将会发生。这就像是一种无法阻止的技术进程。

The value is too great and I'm pretty confident, very confident, we'll make it work, but it does feel like it's gonna all be so different. The way to apply this to certain current problems, like getting kids a tutor and helping to motivate them or discover drugs for Alzheimer's. I think it's pretty clear how to do that. Whether AI can help us go to war less, be less polarized, you'd think it should drive intelligence and not being polarized kind of is common sense and not having more as common sense, but I do think that's a lot of people would be skeptical. So I'd love to have people working on the hardest human problems, like whether we get along with each other.

这个价值太重要了，我非常有信心，非常自信，我们会让它成功，但是感觉一切都会变得很不同。将这种方法应用到某些当前问题上，比如给孩子找一个补习老师，帮助激励他们或者找到治疗阿尔茨海默病的药物。我觉得如何做到这一点是相当清楚的。无论人工智能是否能帮助我们少打仗，减少极端化，你会认为它应该推动智慧，而不是极端化，这会引起共识，而不是更多的共识，但是我认为很多人会持怀疑态度。所以我希望有人在解决最困难的人类问题上努力，比如我们是否能相互理解。

You know, I think that would be extremely positive if we thought the AI could contribute to humans getting along with each other. I believe that it will surprise us on the upside. The technology will surprise us with how much it can do. We've gotta find out and see, but I'm very optimistic and I agree with you, what a contribution would that be? In terms of equity, technology is often expensive, like a PC or internet connection and it takes time to come down in cost. I guess the cost of running these AI systems, it looks pretty good that the cost per evaluation is gonna come down a lot. It's come down, enormous amount already. GPT-3, which is the model we've had out the longest and the most time to optimize. In the three years, that's three and a little bit years that's been out.

你知道，我认为如果我们认为AI可以帮助人类相互和谐相处，那将是非常积极的。我相信它会给我们带来惊喜。技术会让我们惊讶于它能做到多少。我们必须研究并看看，但我非常乐观，我赞同你的看法，这将是什么样的贡献呢？就公平性而言，技术通常昂贵，比如个人电脑或互联网连接，而且需要时间降低成本。我想这些AI系统运行的成本，看起来每次评估的成本将会大大降低。已经降低了，已经降低了很多。GPT-3是我们发布时间最长、最有时间优化的模型。在这三年、三年多的时间里已经推出了。

We've been able to bring the cost down by, I think a factor of 40. So for three years time, that's a pretty good start. For 3.5, we've brought it down. I would bet close to 10 at this point, four is newer, so we haven't had as much time to bring the cost down there, but we will continue to bring the cost down. I think we are on the steepest curve of cost reduction at ever of any technology. I know way better than Moore's Law. It's not only that we figure out how to make the models more efficient, but also as we understand the research better, we can get more knowledge, we can get more ability into a smaller model.

我们已经成功将成本降低了，我想是降低了40倍。因此，三年时间内，这是一个非常好的起点。到3.5时，我们已经将其降低了。我认为目前接近10，四是更新的，所以我们还没有那么多时间将成本降低，但我们将继续努力。我认为我们正在经历任何技术成本降低中最陡峭的曲线。我认为远远超过了摩尔定律。不仅是我们找出了如何让模型更有效率，而且随着我们对研究的理解更深，我们能够将更多的知识、能力纳入到更小的模型中。

So I think we are gonna drive the cost of intelligence down to so close to zero, that it will be just this a foreign after transformation for society. Right now, my basic model of the world is cost of intelligence, cost of energy. Those are the two biggest inputs to quality of life, particularly for poor people, but overall, if you can drive both of those way down at the same time, the amount of stuff you can have, the amount of improvement you can deliver for people, it's quite enormous, and we are on a curve, at least for intelligence, we will really, really deliver on that promise. But even at the current cost, which again, this is the highest it will ever be in much more than we want.

所以我认为我们将把智能成本降至接近零，这对社会来说将是一个巨大的转变。目前，我对世界的基本模型是智能成本和能源成本。这两者是影响生活质量的最大因素，特别是对贫困人口，但总体而言，如果你能同时将这两者成本大幅降低，你可以拥有的东西、为人们带来的改善之处是巨大的。我们正在走在一条曲线上，至少对于智能而言，我们将真正实现这一承诺。但即使在当前成本下，这是远高于我们所希望的。

For 20 bucks a month, you get a lot of GPT-4 access and way more than 20 bucks worth of value. So I think we're already like, we've come down pretty far. And what about the competition? Is that kind of a fun thing that many people are working on this all at once? It's both like annoying and motivating and fun. I'm sure you've felt similarly, but it does push us to be better and do faster and do things faster. We're very confident in our approach. We have a lot of people that I think are skating to where the puck was and we're going to where the puck is going and feels all right.

每月20美元，您将获得大量GPT-4访问权限，价值远远超过20美元。所以我认为我们已经降价相当多了。那么竞争又怎么样呢？许多人同时从事这项工作，这是不是一件有趣的事情？这既令人讨厌又让人有动力，又很有趣。我相信你也有类似的感受，但这确实促使我们变得更好、更快、更有效率。我们对自己的方法非常有信心。我们有很多人认为应该滑向曲棍球在的地方，而我们正在走向曲棍球的未来，感觉不错。

I think people would be surprised at how small open AI is. How many employees do you have? About 500, so we're a little bigger than before. That's why. Okay. But by Google Microsoft, Apple stands. It's tiny. And we have to not only run the research lab, but now we have to run a real business and product, two products.

我认为人们会对OpenAI规模有多小感到惊讶。你们有多少员工？大约500人，所以我们比以前稍微大一点。这就是原因。好的。但与谷歌、微软、苹果相比，我们很小。现在我们不仅要运营研究实验室，而且还要经营一个真正的业务和两个产品。

So that the scaling of all your capacities, including talking to everybody in the world and listening to all those constituencies, that's got to be fascinating for you right now. It's very fascinating. Is it mostly a young company or is it an older company than average? Okay. It's not a bunch of 24-year-old programmers. It's true, my perspective is warped because I'm in my 60s.

现在你能够扩展所有的能力，包括与世界各地所有人交谈和倾听所有利益相关者，这对你来说一定很迷人。这非常迷人。这家公司主要是年轻公司吗，还是比较老的公司？好的。这不是一群24岁的程序员。事实上，我的观点可能有些扭曲，因为我已经60多岁了。

I see you and you're younger, but you're right. It's 40. You have a lot in 40s. 30s, 40s, 50s. Yeah, so it's not the early Apple Microsoft, which we were really kids. Yeah, it's not. And I've reflected on that. I think companies have gotten older in general. And I don't know quite what to make of that. I think it's like somehow a bad sign for society. But I tracked this at YC and the best founders have trended older over time. Yeah, that's fascinating.

我看到你比较年轻，但你说得对。这是40岁。40多岁有很多事情。30岁、40岁、50岁。是的，不像早期的苹果微软，那时我们还是小孩子。是的，没错。我反思过这一点。我觉得公司普遍变老了。但我不太清楚这究竟意味着什么。我觉得这可能是对社会的一种不太好的迹象。但我在 YC 观察到，最好的创始人随着时间推移年龄增长。是的，这很有趣。

And then in our case, it's a little bit older than the average, even still. No, you have got to learn a lot by your whole Y Combinator helping these companies. I guess that was good training. That was super helpful. That was super helpful. Yeah. Including seeing mistakes. Totally.

然后在我们的案例中，比平均水平略旧一点，即便如此。不，你必须通过整个Y Combinator帮助这些公司来学到很多东西。我想那是一个很好的训练。那确实非常有帮助。包括看到错误。完全正确。

OpenAI did a lot of things that are very against the standard YC advice. We took 4 1 1 2 years to launch our first product. We started the company without any idea of what a product would be. We were not talking to users. And I still don't recommend that for most companies. But having learned the rules and seen them at YC made me feel like I understood when and how and why we could break them. And we really did things that were just so different than any other company I've seen.

OpenAI在很多方面都违反了标准的YC建议。我们用了4年零11个月的时间才推出第一个产品。我们开办公司时根本没有想好要做什么产品。我们没有和用户交流。我仍然不建议大多数公司这样做。但是在学习了规则并在YC看到它们之后，我觉得我懂得了何时以及为什么我们可以打破它们。我们真的做了一些与我见过的任何其他公司都不同的事情。

The key was the talent that you assembled and letting them be focused on the big, big problem, not some near term revenue thing. I think Silicon Valley investors would not have supported us at the level we needed. Because we had to spend so much capital on the research before getting to the product.

关键在于你组建的人才，并让他们专注于重大问题，而不是一些短期的收入问题。我认为硅谷的投资者不会以我们所需的水平支持我们。因为在产品推出之前，我们必须在研究上投入大量资本。

We just said, eventually the model will be good enough that we know it's going to be valuable to people. But we were very grateful for the partnership with Microsoft because this kind of way ahead of revenue investing is not something the venture capital industry is good at. No, and then capital costs were reasonably significant, almost at the edge of what venture would ever be comfortable with. Maybe past. Yeah, maybe past.

我们刚刚说过，最终这个模型会足够好，我们知道它对人们是有价值的。但我们非常感激与微软的合作伙伴关系，因为这种超前投资的方式并不是风险投资行业擅长的。不，而且资本成本相当可观，几乎接近风险投资可能会感到舒适的边缘。也许是过去。是的，也许是过去。

And I give you a sought to incredible credit for thinking through how do you take this brilliant AI organization and couple it into the large software company. And it has been very, very synergistic. It's been wonderful, yeah. You really touched on it, though, and this was something I learned from my commentator.

我得要非常赞赏你深思熟虑，如何将这家杰出的人工智能公司与大型软件公司结合起来。而且这种结合效果非常协同。这一点真的非常棒。你所提到的确实是关键，这也正是我从我的评论者那里学到的东西。

We just said, we are going to get the best people in the world at this. We are going to make sure that we're all aligned at where we're going in this AGI mission. But beyond that, we're going to let people do their thing and we're going to realize it's going to go through some twists and turns and take a while. And we had a theory that turned out to be roughly right, but a lot of the tactics along the way turned out to be super wrong.

我们刚刚说，我们将会找到世界上最优秀的人来参与这项任务。我们将确保我们在人工通用智能任务中的目标一致。但除此之外，我们会让人们按照自己的方式去做，我们也意识到这个过程可能会经历一些曲折，需要一段时间。我们有一个大致正确的理论，但沿途的许多策略都被证明是错误的。

And we just tried to follow the science. Yeah, I remember going and seeing the demonstration and thinking, OK, what's the path to revenue on that one? What does that like? And in these frenzied times, you're still holding on to an incredible team. Yeah, great people really want to work with great colleagues. And so there's a deep center of gravity there. And then also people, I mean, this sounds so cliche and every company says it, but people feel the mission so deeply.

我们只是试图遵循科学。是的，我记得去看到示范，然后想，好的，这个项目有收入的路径吗？这个项目会是怎样的呢？在这些疯狂的时期，你仍然坚守着一个不可思议的团队。是的，优秀的人真的希望与优秀的同事一起工作。所以在这里有一个深深的重心。而且人们，我是说这听起来很陈词滥调，每个公司都会这样说，但人们对使命感触很深。

Like everyone wants to be in the room for the creation of AGI. No, it must be exciting. And I can see the energy when you come up and blow me away again with the demos. I'm seeing new people, new ideas. You're continuing to move at a really incredible speed. What's the piece of advice you give most often? Well, I think there's so many different forms of talent. And really in my career, I thought, OK, just pure IQ, like engineering IQ. And of course, you can apply that to financial and sales.

就像每个人都希望参与AGI的创造过程一样。不，这一定很令人兴奋。我能感受到你们展示的Demo再次让我惊叹时所带来的能量。我看到了新面孔，新想法。你们在以非常惊人的速度继续前进。你最经常给出的建议是什么？嗯，我认为才华有很多不同形式。在我的职业生涯中，我认为，好吧，纯粹的智商，比如工程智商。当然，你可以将这种才华应用到金融和销售中。

But that turned out to be so wrong. And building teams where you have the right mix of skills is so important. And so getting people to think for their problem, how do they build that team that has all the different skills? That's probably the one that I think is the most helpful. I mean, yes, telling kids math, science is cool. If you like it. But it's that talent mix that really surprised me. What about you? What advice do you give? I think it's something about how people, most people are sort of miscalibrated on risk.

但最终证明是错误的。在建立团队时，拥有合适技能组合是非常重要的。因此，让人们思考他们的问题，如何建立具有各种技能的团队？这可能是我认为最有帮助的。我的意思是，告诉孩子们数学、科学是很酷的。如果你喜欢的话。但那种天赋组合真的让我感到惊讶。你呢？你有什么建议？我认为大多数人对风险的认识都有点偏差。

And they're afraid to leave the soft, cushy job behind to go do the thing they really want to do when, in fact, if they don't do that, they look back at them at their lives, like, man, I never went to go start this company when I'm a starter. I never tried to go be an AI researcher. I think that's sort of much riskier. And related to that, being clear about what you want to do and asking people for what you want, I think goes a surprising in the wrong way. And so I think a lot of people get trapped in spending their time and not the way they want to do.

而他们害怕放弃舒适的工作去做他们真正想做的事情，事实上，如果他们不这样做，当他们回顾自己的生活时，会感到遗憾，我从未去创办这家公司，我从未尝试成为一名人工智能研究者。我认为这样做风险更大。与此相关的是，明确自己想做的事情，并向他人要求你想要的东西，我认为这样做会出奇地行不通。所以我认为很多人陷入了花费时间在他们不想做的事情上的困境中。

And probably the most frequent advice I give is to try to fix that some way or other. Yeah, if you can get people to end a job where they feel they have a purpose, it's more fun. And sometimes that's how they can have gigantic impact. That's for sure. Well, thanks for coming. It was a fantastic conversation. And in the years ahead, I'm sure we'll get to talk a lot more as we try to shape the AI in the best way possible. Thanks a lot for having me. I really enjoyed it.

也许我最经常给出的建议是尽量想办法解决这个问题。是的，如果你让人们做一份他们觉得有使命感的工作，这会更有趣。有时这样他们才能产生巨大的影响。这是肯定的。嗯，谢谢你的到来。这是一次很棒的对话。在未来的岁月里，我敢肯定我们会越来越多地交流，努力将人工智能塑造得更好。非常感谢邀请我。我真的很享受这次交流。

Unconfused Me is a production of the Gates Notes. Special thanks to my guest today, Sam Altman. Can you remind me what your first computer was? A Mac LCSU. Oh, nice choice. It was a good one. I still have it. Still works.

《Unconfused Me》是盖茨笔记的制作。特别感谢今天的嘉宾山姆·奥尔特曼。你能提醒我你的第一台电脑是什么吗？Mac LCSU。哦，不错的选择。这是一台好电脑。我现在还留着它。它还在运行。