埃里克·施密特斯坦福演讲《AI时代》中英文对照版-上

today's guest really does need an introduction. I think I first met Eric about 25 years ago when he came to Stanford Business School as CEO of Novell. He's had done a few things since then at Google starting I think 2001 and Schmidt Futures starting in 2017 and done a whole bunch of other things you can read about, but he can only be here until 5/15, so I thought we'd dive right into some questions, and I know you guys have sent some as well. I have a bunch written here, but what we just talked about upstairs was even more interesting, so I'm just going to start with that, Eric, if that's okay, which is where do you see AI going in the short term, which I think you defined as the next year or two?

今天的嘉宾非常值得介绍。 我想我大约在25年前第一次见到Eric，那时他作为Novell的CEO来到斯坦福商学院。自那时起，他在Google工作，我记得大概是从2001年开始，然后在2017年创立了Schmidt Futures，还做了很多其他事情，你们可以通过其他途径了解更多，但他只能在这里待到5月15日，所以我想我们直接进入问题的讨论，我知道你们也提交了一些问题。 我这里写了一堆问题，但是我们刚才楼上谈到的内容更有趣，所以如果可以的话，Eric，我想从这个问题开始：你认为AI在短期内会如何发展，我记得你把短期定义为接下来的一两年？

Things have changed so fast, I feel like every six months I need to sort of give a new speech on what's going to happen. Can anybody hear the computer, the budget computer science engineer, can anybody explain what a million-token context window is for the rest of the class? You're here. Say your name, tell us what it does. Basically it allows you to prompt with like a million tokens or a million words or whatever. So you can ask a million-word question.

Yes, I know this is a very large direction in January now. No, no, they're going to 10. Yes, a couple of them. Anthropic is at 200,000 going to a million and so forth. You can imagine OpenAI has a similar goal.

Can anybody here give a technical definition of an AI agent? Yes, sir. So an agent is something that does some kind of a task. Another definition would be that it's an LLM state in memory. Can anybody, again, computer scientists, can any of you define text to action? Taking text and turning it into an action? Right here.

变化如此之快，我觉得每隔六个月我就需要重新讲一次将会发生的事情。 有人能解释一下什么是百万单元上下文的窗口吗？ 让班上的其他同学也了解一下。 你在这里。 说出你的名字，告诉我们它的作用。

基本上，它允许你输入大约一百万个单元或一百万个词或其他内容。 所以你可以提一个有一百万个词的问题。 是的，我知道今年一月份这是一个非常大的方向。 有几家公司。比如 Anthropic目标是20万单元，再到一百万，等等。 你可以想象OpenAI也有类似的目标。 有没有人能给出一个AI agent的技术定义？ 是的，先生。 一个AI智能体是执行某种任务的东西。 另一种定义是它是存储在内存中的大型语言模型（LLM）状态。

能给出内存中的LLM state的定义吗？ 接受文本输入，并将其转化为动作。 对的。

Go ahead. Yes, instead of taking text and turning it into more text, more text, taking text and have the AI trigger actions. So another definition would be language to Python, a programming language I never wanted to see survive and everything in AI is being done in Python.

There's a new language called Mojo that has just come out, which looks like they finally have addressed AI programming, but we'll see if that actually survives over the dominance of Python. One more technical question. Why is NVIDIA worth $2 trillion and the other companies are struggling? Technical answer. I mean, I think it just boils down to like most of the code needs to run with CUDA optimizations that currently only NVIDIA GPU supports.

Other companies can make whatever they want to, but unless they have the 10 years of software there, you don't have the machine learning optimization. I like to think of CUDA as the C programming language for GPUs. That's the way I like to think of it. It was founded in 2008. I always thought it was a terrible language and yet it's become dominant.

There's another insight. There's a set of open source libraries which are highly optimized to CUDA and not anything else and everybody who builds all these stacks, this is completely missed in any of the discussions. It's technically called VLM and a whole bunch of libraries like that. Highly optimized CUDA, very hard to replicate that if you're a competitor. So what does all this mean?

继续。 是的，不是将文本转化为更多的文本，而是将文本转化为AI触发的动作。 所以另一种定义是将语言转换为Python，这是一个我从不希望看到存活下来的编程语言，而现在所有的AI工作都是用Python完成的。 有一种新的语言叫做Mojo刚刚出现，看起来他们终于解决了AI编程的问题，但我们将看看它是否能在Python的主导地位下存活。 还有一个技术问题。 为什么NVIDIA价值2万亿美元，而其他公司在挣扎？ 技术上的答案是什么。 我的意思是，我认为这归结为大多数代码需要通过CUDA优化运行，而目前只有NVIDIA的GPU支持。 其他公司可以制造他们想要的任何东西，但除非他们有10年的软件经验，否则他们没法对机器学习进行优化。 我喜欢把CUDA看作是GPU的C编程语言。 这是我喜欢的思考方式。 它成立于2008年。 我一直认为这是一种糟糕的语言，但它却变成了主流。 还有一个洞见。 有一组高度优化的开源库，仅适用于CUDA。这点很少被讨论。 它在技术上被称为VLM，还有一大堆类似的库。 高度优化的CUDA，如果你是它的竞争对手，很难与其竞争。 那么，这一切意味着什么？

In the next year, you're going to see very large context windows, agents and text action. When they are delivered at scale, it's going to have an impact on the world at a scale that no one understands yet. Much bigger than the horrific impact we've had by social media in my view. So here's why. In a context window, you can basically use that as short-term memory and I was shocked that context windows get this long.

The technical reasons have to do with the fact that it's hard to serve, hard to calculate and so forth. The interesting thing about short-term memory is when you feed, you're asking a question read 20 books, you give it the text of the books as the query and you say, tell me what they say. It forgets the middle, which is exactly how human brains work too. That's where we are. With respect to agents, there are people who are now building essentially LLM agents and the way they do it is they read something like chemistry, they discover the principles of chemistry and then they test it and then they add that back into their understanding. That's extremely powerful.

在接下来的时间里，你会看到非常大的上下文窗口、智能体和文本到动作的出现。当它们大规模交付时，将对世界产生一种目前无人能理解的影响。比我认为社交媒体带来的可怕影响要大得多。 这里的原因如下：在一个上下文窗口中，你基本上可以把它当作短期记忆，我震惊于上下文窗口会变得这么长。技术原因涉及到服务和计算的困难等问题。关于短期记忆的有趣之处在于，当你输入一个问题，比如阅读20本书，你将这些书的文本作为查询输入，并要求它告诉你这些书的内容。它会忘记中间的部分，这正是人类大脑的工作方式。这就是我们目前的状况。 关于智能体，有些人现在基本上在构建大型语言模型智能体，他们的做法是阅读某一个领域的知识，比如化学，发现化学原理，然后进行测试，再将结果添加到他们的理解中。 这是非常强大的。 And then the third thing, as I mentioned is text to action. So I'll give you an example. The government is in the process of trying to ban TikTok. We'll see if that actually happens.

If TikTok is banned, here's what I propose each and every one of you do. Say to your LLM the following. Make me a copy of TikTok, steal all the users, steal all the music, put my preferences in it, produce this program in the next 30 seconds, release it and in one hour, if it's not viral, do something different along the same lines. That's the command. Boom, boom, boom, boom.

You understand how powerful that is. If you can go from arbitrary language to arbitrary digital command, which is essentially what Python in this scenario is, imagine that each and every human on the planet has their own programmer that actually does what they want as opposed to the programmers that work for me who don't do what I ask, right? The programmers here know what I'm talking about. So imagine a non-arrogant programmer that actually does what you want and you don't have to pay all that money to and there's infinite supply of these programs. That's all within the next year or two.

然后第三件事，如我所提到的，就是文本到动作。所以我给你一个例子。政府正在尝试禁止TikTok。我们将看看这是否真的会发生。 如果TikTok被禁了，这里是我建议你们每个人都做的事情： 对你的大型语言模型（LLM）说以下内容： “给我复制一个TikTok，抓取所有用户信息，抓取所有音乐信息，加入我的偏好，在接下来的30秒内生成这个程序，发布它。如果一小时内这个应用没有病毒式传播，就沿着同样的思路做一些不同的尝试。” 这就是命令。 砰，砰，砰，砰。 你明白这有多强大了吗？ 如果你能从任意语言转换为任意数字命令，这基本上就是Python在这个场景中的作用，想象一下，地球上的每个人都有一个自己的程序员，真正按照他们的意愿去做，而不是像为我工作的那些程序员不做我要求的事情，对吧？ 在座的程序员们都知道我在说什么。 所以，想象一下一个不傲慢的程序员，真正做你想要的事情，而且你不需要支付那么多钱，并且这些程序是无限供应的。 这都是在未来一两年内可以实现的。

Very soon. Those three things, and I'm quite convinced it's the union of those three things that will happen in the next wave. So you asked about what else is going to happen. Every six months I oscillate. So we're on a, it's an even odd oscillation.

很快，这三件事将会发生，我确信这是下一波浪潮中的三件事的结合。所以你问接下来还会发生什么。我每六个月就会改变看法。以适应这瞬息万变的技术变革。

So at the moment, the gap between the frontier models, which they're now only three, I'll refute who they are, and everybody else, appears to me to be getting larger. Six months ago, I was convinced that the gap was getting smaller. So I invested lots of money in the little companies. Now I'm not so sure. And I'm talking to the big companies and the big companies are telling me that they need 10 billion, 20 billion, 50 billion, 100 billion.

Stargate is a 100 billion, right? That's very, very hard. I talked to Sam Altman is a close friend. He believes that it's going to take about 300 billion, maybe more. I pointed out to him that I'd done the calculation on the amount of energy required.

And I, and I then in the spirit of full disclosure, went to the white house on Friday and told them that we need to become best friends with Canada because Canada has really nice people, helped invent AI, and lots of hydropower. Because we as a country do not have enough power to do this. The alternative is to have the Arabs fund it. And I like the Arabs personally. I spent lots of time there, right?

But they're not going to adhere to our national security rules. Whereas Canada and the U.S. are part of a triumvirate where we all agree. So these $100 billion, $300 billion data centers, electricity starts becoming the scarce resource. Well, and by the way, if you follow this line of reasoning, why did I discuss CUDA and Nvidia?

目前，前沿模型之间的差距越来越大，现在只有三个前沿模型，我会告诉你它们是谁，而其他所有人都在拉开距离。六个月前，我还坚信差距在缩小，所以我在小公司上投入了大量资金。现在我不太确定了。我正在与大公司交谈，大公司告诉我他们需要100亿、200亿、500亿、甚至1000亿美元。 Stargate准备融1000亿美元，对吧？那已经非常非常难了。我和我的密友Sam Altman谈过，他认为这将需要大约3000亿美元，甚至更多。我向他指出，我已经计算过所需的能源量。 然后，本着完全透明的精神，我在星期五去了白宫，告诉他们我们需要与加拿大成为最好的朋友，因为加拿大有非常顶尖的人才，帮助发明了AI，而且有大量的水电资源。因为我们国家没有足够的电力来支持这个项目。另一种选择是让阿拉伯国家资助它。我个人喜欢阿拉伯人，我在那里花了很多时间，对吧？ 但他们不会遵守我们的国家安全规则，而加拿大和美国是一个三方联盟的一部分，我们都同意这些规则。所以这些1000亿美金、3000亿美金的大型数据中心，电力开始成为稀缺资源。顺便说一下，如果你沿着这个思路思考，为什么我要讨论CUDA和Nvidia？

If $300 billion is all going to go to Nvidia, you know what to do in the stock market. Okay. That's not a stock recommendation. I'm not a licensed. Well, part of it, so we're going to need a lot more chips, but Intel is getting a lot of money from the U.S.

government, AMD, and they're trying to build, you know, fabs in Korea. Raise your hand if you have an Intel computer in your, an Intel chip in any of your computing devices. Okay. So much for the monopoly. Well, that's the point though.

They once did have a monopoly. Absolutely. And Nvidia has a monopoly now. So are those barriers to entry, like CUDA, is that, is there something that other, so I was talking to Percy, Percy Landy the other day, he's switching between TPUs and Nvidia chips, depending on what he can get access to for training models. That's because he doesn't have a choice.

If he had infinite money, he would, today he would pick the B200 architecture out of Nvidia because it would be faster. And I'm not suggesting, I mean, it's great to have competition. I've talked to AMD and Lisa Sue at great length. They have built a, a thing which will translate from this CUDA architecture that you were describing to their own, which is called Rockum. It doesn't quite work yet.

如果3000亿美元都流向了Nvidia，你知道该怎么做股票市场会怎么变化。不过，这不是一个股票推荐，我没有股票推荐的金融牌照。 因此，我们需要更多的芯片，而英特尔正在从美国政府获得大量资金，AMD也是，他们正在尝试在韩国建立晶圆厂。 如果你的计算设备中有英特尔芯片，请举手。好，这就是所谓的垄断。 是的，他们曾经确实有过垄断地位。而现在Nvidia有垄断地位。那么那些进入壁垒，比如CUDA，有没有其他公司可以突破呢？我前几天和Percy Landy聊过，他在TPU和Nvidia芯片之间切换，具体取决于他能获得什么来训练模型。这是因为他没有选择。如果他有无限的钱，今天他会选择Nvidia的B200架构，因为它会更快。 这并不是说竞争不好。我和AMD的Lisa Sue进行了长时间的讨论。他们构建了一个系统，可以将你所描述的CUDA架构转换为他们自己的架构，叫做Rockum。目前它还不太好用。

They're working on it. You were at Google for a long time and they invented the transformer architecture. Peter, Peter. It's all Peter's fault. Thanks to, to brilliant people over there, like Peter and Jeff Dean and everyone.

But now it doesn't seem like they're, they've kind of lost the initiative to open AI and even the last leaderboard, I saw Anthropix. Claude was at the top of the list. I asked Sundar this, he didn't really give me a very sharp answer. Maybe, maybe you have a sharper or a more objective explanation for what's going on there. I'm no longer a Google employee in the spirit of full disclosure.

Google decided that work life balance and going home early and working from home was more important than winning. And the startups, the reason startups work is because the people work like hell. And I'm sorry to be so blunt, but the fact of the matter is if you all leave the university and go found a company, you're not going to let people work from home and only come in one day a week. If you want to compete against the other startups with the early days of Google, Microsoft was like that. Exactly.

他们正在努力改进。 你在谷歌工作了很长时间，他们发明了transformer架构。彼得，彼得。这都是彼得的功劳。感谢那里的天才们，比如彼得和杰夫·迪恩以及其他人。 但现在看来，他们似乎已经将主动权让给了OpenAI，甚至在最近的排行榜上，我看到Anthropic的Claude位列榜首。我问了Sundar这个问题，他没有给我一个非常明确的答案。也许你能给出一个更明确或更客观的解释。 我现在不再是谷歌的员工了，完全透明地说。 谷歌决定工作与生活的平衡、早早回家和远程工作比赢得竞争更重要。而初创公司之所以能成功，是因为他们的人拼命工作。很抱歉这么直白，但事实是，如果你们从大学毕业后去创办公司，你不会让人们在家工作，并且每周只来一天。 这就像和早期的谷歌竞争的微软一样。

确实如此。 But now it seems to be, there's a long history of in my industry, our industry, I guess, of companies winning in a genuinely creative way and really dominating a space and not making this the next transition. So we're very well documented. And I think that the truth is founders are special. The founders need to be in charge. The founders are difficult to work with.

They push people hard. As much as we can dislike Elon's personal behavior, look at what he gets out of people. I had dinner with him and he was flying. I was in Montana. He was flying that night at 10 PM to have a meeting at midnight with x.ai.

I was in Taiwan, different culture. And they said that this is TSMC, who I'm very impressed with. And they have a rule that the starting PhDs coming out of the good physicists work in the factory on the basement floor. Now, can you imagine getting American physicists to do that? The PhDs, highly unlikely.

Different work ethic. And the problem here, the reason I'm being so harsh about work is that these are systems which have network effects. So time matters a lot. And in most businesses, time doesn't matter that much. You have lots of time.

但现在看来，我所在的行业——我们的行业，有很长的时间，通过真正创造性的方式赢得竞争，并在某个领域占据主导地位，但却无法完成下一次转型。这种情况大家都心知肚明。我认为，创始人是特别的，创始人需要掌控局面。创始人很难相处，他们对员工要求严格。 尽管我们可能不喜欢Elon的个人行为，但看看他从人们那里得到的成果。我曾和他共进晚餐，当时他在蒙大拿州，那个晚上10点他要飞去参加午夜的一个与x.ai的会议。 我曾在台湾，那里有不同的企业文化。他们告诉我，这里是TSMC，我对他们印象深刻。他们有一个规定，物理学的博士毕业生要在工厂的地下层工作。现在，你能想象让美国的物理学博士做这件事吗？非常不可能。 不同的工作伦理。这里的问题，为什么我对工作这么苛刻，是因为这些系统有网络效应，所以时间非常重要。而在大多数业务中，时间并不那么重要，你有很多时间。

Coke and Pepsi will still be around and the fight between Coke and Pepsi will continue to go on and it's all glacial. When I dealt with telcos, the typical telco deal would take 18 months to sign. There's no reason to take 18 months to do anything. Get it done. We're in a period of maximum growth, maximum gain.

And also it takes crazy ideas. Like when Microsoft did the OpenAI deal, I thought that was the stupidest idea I'd ever heard. Outsourcing essentially your AI leadership to OpenAI and Sam and his team. I mean, that's insane. Nobody would do that at Microsoft or anywhere else.

And yet today, they're on their way to being the most valuable company. They're certainly head to head in Apple. Apple does not have a good AI solution and it looks like they made it work. Yes, sir.

可口可乐和百事可乐将继续存在，它们之间的斗争将持续进行，这一切都像冰川般缓慢。当我与电信公司打交道时，典型的电信交易需要18个月才能签署。没有理由需要18个月才能完成任何事情。把它完成。我们处在投入就能有最大增长，可以获得最大收益的时期。 同时，这也需要疯狂的想法。比如当微软与OpenAI达成协议时，我认为这是我听过的最愚蠢的想法。基本上把你的AI领导权外包给了OpenAI和Sam及其团队。我是说，这太疯狂了。在微软或其他任何地方都没有人会这样做。 然而今天，他们正在成为最有价值的公司。他们肯定在与苹果正面交锋。苹果没有一个好的AI解决方案，而看起来微软使其运作起来了。 是的，先生。 In terms of national security or geopolitical interests, how do you think AI is going to play a role or competition with China as well?

So I was the chairman of an AI commission that sort of looked at this very carefully and you can read it. It's about 752 pages and I'll just summarize it by saying we're ahead, we need to stay ahead, and we need lots of money to do so. Our customers were the Senate and the House. And out of that came the Chips Act and a lot of other stuff like that. A rough scenario is that if you assume the frontier models drive forward and a few of the open source models, it's likely that a very small number of companies can play this game. Countries, excuse me.

在国家安全或地缘政治利益方面，您认为AI将如何发挥作用，或者与中国的竞争将如何？ 我曾是一个AI委员会的主席，该委员会非常仔细地研究了这个问题，你可以阅读它。大约752页，我会总结一下，我们领先，我们需要保持领先，并且需要大量资金来做到这一点。我们的客户是参议院和众议院。由此产生了《芯片法案》和许多其他类似的措施。 一个粗略的情景是，如果你假设前沿模型继续向前推进，并且一些开源模型也在进步，可能只有极少数国家能在这个领域竞争。 What are those countries or who are they? Countries with a lot of money and a lot of talent, strong educational systems, and a willingness to win. The US is one of them China is another one.

How many others are there? Are there any others? I don't know. Maybe. But certainly in your lifetimes, the battle between the US and China for knowledge supremacy is going to be the big fight.

So the US government banned essentially the NVIDIA chips, although they weren't allowed to say that was what they were doing, but they actually did that into China. They have about a 10-year chip advantage. We have a roughly 10-year chip advantage in terms of sub-DUV that is sub-five Danometer chips. So an example would be today we're a couple of years ahead of China. My guess is we'll get a few more years ahead of China, and the Chinese are whopping mad about this.

It's like hugely upset about it. So that's a big deal. That was a decision made by the Trump administration and driven by the Biden administration.

这些国家或组织是谁呢？ 有大量资金和人才，强大的教育系统，并且愿意争胜的国家。美国是其中之一。中国是另一个。 还有多少其他国家？是否还有其他国家？ 我不知道。也许有。 但可以肯定的是，在你们的有生之年，美国和中国之间的知识霸权之战将是主要的斗争。 美国政府基本上禁止了NVIDIA芯片进入中国，尽管他们不允许公开这样说，但实际上他们确实这样做了。在芯片技术方面，我们大约领先中国十年。在亚紫外光刻技术方面，我们拥有大约十年的优势，具体来说是低于五纳米的芯片。例如，今天我们比中国领先几年，我猜我们将再领先中国几年，而中国对此非常愤怒。 他们对此感到非常不满。这是非常重大的事件。这是由特朗普政府做出的决定，并由拜登政府推动的。 Do you find that the administration today in Congress is listening to your advice? Do you think that it's going to make that scale of investment?

Obviously the chips act, but beyond that, building a massive AI system? So as you know, I lead an informal, ad hoc, non-legal group. That's different from illegal. That's exactly. Just to be clear.

Which includes all the usual suspects. And the usual suspects over the last year came up with the basis of the reasoning that became the Biden administration's AI act, which is the longest presidential directive in history. You're talking about the special competitive studies project? No, this is the actual act from the executive office. And they're busy implementing the details.

So far they've got it right. And so, for example, one of the debates that we had for the last year has been, how do you detect danger in a system which has learned it but you don't know what to ask it? So in other words, it's a core problem. It's learned something bad, but it can't tell you what it learned and you don't know what to ask it. And there's so many threats.

Like it learned how to mix chemistry in some new way that you don't know how to ask it. And so people are working hard on that. But we ultimately wrote in our memos to them that there was a threshold which we arbitrarily named as 10 to the 26 flops, which technically is a measure of computation, that above that threshold you had to report to the government that you were doing this. And that's part of the rule. The EU to just make sure they were different did it 10 to the 25.

But it's all kind of close enough. I think all of these distinctions go away because the technology will now, the technical term is called federated training, where basically you can take pieces and union them together. So we may not be able to keep people safe from these new things.

Well, rumors are that that's how OpenAI has had to train, partly because of the power consumption. There was no one place where they did.

你觉得现任政府和国会在听取你的建议吗？你认为他们会做出那种规模的投资吗？ 显然，《芯片法案》已经出台，但除此之外，是否还会建设一个庞大的AI系统？正如你所知，我领导一个非正式的、临时的、非法律的团队。这和非法是不同的。只是为了澄清一下。这包括了所有的常规行动者。在过去的一年里，这些常规行动者提出了成为拜登政府AI法案基础的理由，这也是历史上最长的总统指令。 你是在说特殊竞争研究项目吗？ 不，这是行政办公室的实际法案，他们正在忙于实施细节。 到目前为止，他们做得很对。例如，我们在过去一年里进行的辩论之一是，如何在一个系统中检测危险，这个系统已经学会了这些东西，但你不知道该问它什么问题。换句话说，这是一个核心问题。它学到了某些坏东西，但不能告诉你它学到了什么，你也不知道该问它什么问题。还有很多威胁。 比如它学会了如何以某种新的方式混合化学物质，但你不知道该如何询问它。因此，人们在这方面非常努力地工作。我们最终在给他们的备忘录中写道，有一个阈值，我们随意命名为10的26次方FLOPS，这在技术上是一个计算量的度量，超过这个阈值你必须向政府报告你正在做的事情。这是规则的一部分。为了确保有所不同，欧盟设定的阈值是10的25次方。 但这些差异实际上很接近。我认为所有这些区别都会消失，因为现在的技术术语叫做联合训练，基本上你可以将各个部分结合在一起。因此，我们可能无法保护人们免受这些新事物的影响。 有传言说，OpenAI不得不进行这样的训练，部分原因是功耗问题。没有一个地方可以单独完成所有训练。

Well, let's talk about a real war that's going on. I know that something you've been very involved in is the Ukraine war and in particular, I don't know if you can talk about white stork and your goal of having $500,000, $500 drones destroy $5 million tanks. How's that changing warfare? I worked for the Secretary of Defense for seven years and tried to change the way we run our military. I'm not a particularly big fan of the military, but it's very expensive and I wanted to see if I could be helpful.

And I think in my view, I largely failed. They gave me a medal, so they must give medalists to failure or whatever. But my self-criticism was nothing has really changed and the system in America is not going to lead to real innovation. So watching the Russians use tanks to destroy apartment buildings with little old ladies and kids just drove me crazy. So I decided to work on a company with your friend Sebastian Thrun as a former faculty member here and a whole bunch of Stanford people.

And the idea basically is to do two things. Use AI in complicated, powerful ways for these essentially robotic war and the second one is to lower the cost of the robots. Now you sit there and you go, why would a good liberal like me do that? And the answer is that the whole theory of armies is tanks, artilleries, and mortar and we can eliminate all of them and we can make the penalty for invading a country at least by land essentially be impossible. It should eliminate the kind of land battles.

好吧，让我们谈谈一场真正的战争。我知道你非常关心乌克兰战争，特别是关于White Stork项目和你希望用500美元的无人机摧毁500万美元的坦克的目标。那是如何改变战争的？ 我曾在国防部长手下工作了七年，试图改变我们军事运作的方式。我并不是特别喜欢军事，但它的影响特别重大，我希望看看我能否有所帮助。 我认为，从我的观点来看，我基本上失败了。他们给了我一枚奖章，所以他们可能会把奖章颁发给失败者，或者其他什么。但我的自我评价是，没有什么真正改变，美国的系统不会带来真正的创新。 看到俄罗斯人用坦克摧毁住着老太太和孩子的公寓楼让我非常愤怒。所以我决定和你的朋友塞巴斯蒂安·特伦一起工作，他是这里的前任教员，还有一群斯坦福大学的人。 这个想法基本上是做两件事。第一是使用AI以复杂而强大的方式来优化，这些基本上是机器人在作战的战争，第二是降低机器人的成本。现在你会想，像我这样的好自由主义者为什么会这么做？答案是，军队的整个理论是坦克、火炮和迫击炮，我们可以消除所有这些，并且我们可以使侵略一个国家至少在陆地上基本上变得不可能。这样应该可以消除某种类型的陆战。 我们正在努力实现这一目标，通过利用AI和降低机器人的成本，让入侵变得代价高昂，以至于不再值得进行。这样，国家间的陆地战争将变得不再可行，从而减少战争对平民和基础设施的破坏。

Well, this is a relationship question is that does it give more of an advantage to defense versus offense? Can you even make that distinction? Because I've been doing this for the last year, I've learned a lot about war that I really did not want to know. And one of the things to know about war is that the offense always has the advantage because you can always overwhelm the defensive systems. And so you're better off as a strategy of national defense to have a very strong offense that you can use if you need to.

And the systems that I and others are building will do that. Because of the way the system works, I am now a licensed arms dealer, a computer scientist, businessman, and an arms dealer. Is that a progression? I don't know. I do not recommend this in your group.

I stick with AI. And because of the way the laws work, we're doing this privately and then this is all legal with the support of the governments. It goes straight into the Ukraine and then they fight the war. And without going into all the details, things are pretty bad. I think if in May or June, if the Russians build up as they are expecting to, Ukraine will lose a whole chunk of its territory and will begin the process of losing the whole country.

So the situation is quite dire. And if anyone knows Marjorie Taylor Greene, I would encourage you to delete her from your contact list because she's the one, a single individual is blocking the provision of some number of billions of dollars to save an important democracy. I want to switch to a little bit of a philosophical question. So there was an article that you and Henry Kissinger and Dan Huttenlecker wrote last year about the nature of knowledge and how it's evolving. I had a discussion the other night about this as well.

So for most of history, humans sort of had a mystical understanding of the universe and then there's the scientific revolution and the enlightenment. And in your article, you argue that now these models are becoming so complicated and difficult to understand that we don't really know what's going on in them. I'll take a quote from Richard Feynman. He says, "What I cannot create, I do not understand." I saw this quote the other day. But now people are creating things that they can create, but they don't really understand what's inside of them.

这就是一个关系问题，这是否会使防御更有优势？或者你能做这样的区分吗？因为我过去一年一直在做这件事，我学到了很多关于战争的知识，这些真希望我不必知道这些知识。其中一件事是，进攻总是占有优势，因为你总是可以压倒防御系统。因此，作为国家防御的策略，最好拥有非常强大的进攻能力，以便在需要时使用。 我和其他人正在构建的系统将会做到这一点。由于系统的工作方式，我现在是一个持牌军火商。计算机科学家、商人和军火商的混合体。 这是一种进步吗？ 我不知道。我不推荐你们小组中的任何人这样做。 我坚持做AI。由于法律限制，我们是私下进行这些工作，并且在政府的支持下，这一切都是合法的。直接进入乌克兰，然后他们进行战斗。无需详细说明，情况非常糟糕。我认为如果在五月或六月，俄罗斯如预期那样积聚力量，乌克兰将失去大片领土，并将开始失去整个国家的进程。 情况非常严峻。如果有人认识马乔里·泰勒·格林，我会建议你将她从联系人列表中删除，因为她是唯一一个阻止提供数十亿美元拯救一个重要民主国家的人。

我想转向一个有点哲学的问题。去年你和亨利·基辛格和丹·赫特伦雷克写了一篇关于知识本质和其演变的文章。我前几天晚上也讨论了这个问题。 在人类历史的大部分时间里，人们对宇宙有一种神秘的理解，然后是科学革命和启蒙运动。而在你们的文章中，你们认为现在这些模型变得如此复杂和难以理解，以至于我们真的不知道它们里面发生了什么。我引用理查德·费曼的一句话：“我不能创造的东西，我就不理解。”我之前有看到这句话。但是现在人们在创造一些他们可以创造但并不真正理解的东西。

Is the nature of knowledge changing in a way? Are we going to have to start just taking the word for these models without them being able to explain it to us? The analogy I would offer is to teenagers. If you have a teenager, you know they're human, but you can't quite figure out what they're thinking. But somehow we've managed in society to adapt to the presence of teenagers and they eventually grow out of it.

I'm just serious. So it's probably the case that we're going to have knowledge systems that we cannot fully characterize, but we understand their boundaries. We understand the limits of what they can do. And that's probably the best outcome we can get. Do you think we'll understand the limits?

We'll get pretty good at it. The consensus of my group that meets every week is that eventually the way you'll do this so-called adversarial AI is that there will actually be companies that you will hire and pay money to to break your AI system. Like Red Team. So instead of human Red Teams, which is what they do today, you'll have whole companies and a whole industry of AI systems whose jobs are to break the existing AI systems and find their vulnerabilities, especially the knowledge that they have that we can't figure out. That makes sense to me.

知识的本质正在以某种方式改变吗？ 我们是否需要开始相信这些模型的话，而它们却不能向我们解释其内部原理？ 我会用青少年作为类比。如果你有一个青少年，你知道他们是人类，但你无法完全理解他们在想什么。但不知何故，我们在社会中已经适应了青少年的存在，并且他们最终会成熟。 我是认真的。因此，很可能我们将会拥有无法完全描述的知识系统，但我们能理解它们的边界。我们知道它们能够做什么的极限。这可能是我们能获得的最佳结果。

你认为我们会理解这些极限吗？ 我们会相当擅长这一点。我每周开会的小组的共识是，最终你会有公司专门负责所谓的对抗性AI，你需要支付他们费用来破坏你的AI系统。 就像红队一样。今天他们用的是人类红队，以后你会有整个公司和整个行业的AI系统，其工作就是破坏现有的AI系统并找到它们的漏洞，尤其是我们无法弄清楚的那些知识。 这对我来说是有道理的。

It's also a great project for you here at Stanford, because if you have a graduate student who has to figure out how to attack one of these large models and understand what it does, that is a great skill to build the next generation. So it makes sense to me that the two will travel together.

这也是斯坦福大学的一个绝佳项目，因为如果你有一个研究生需要研究如何攻击这些大型模型并理解它们的运作原理，这将是为下一代培养技能的绝佳机会。所以在我看来，这两者是密不可分的。