What Happens When Digital Superintelligence Arrives?
数字超级智能到来时会发生什么？
Dr. Fei-Fei Li & Eric Schmidt at FII9
李飞飞博士和埃里克·施密特在FII9
Moderated by Peter Diamandis
彼得·戴曼迪斯主持
On October 28, 2025, at the FII9 in Riyadh, Dr. Fei-Fei Li and Eric Schmidt discussed the arrival of digital superintelligence, warning it could elevate or destabilize humanity within 3–4 years; they advocated for human-centered AI, ethical governance, and global collaboration to ensure equitable prosperity, with Li emphasizing co-creative partnerships and Schmidt highlighting risks of concentrated power and energy demands.

2025年10月28日，在利雅得举行的FII9会议上，李飞飞博士和埃里克·施密特讨论了数字超级智能的到来，警告它可能在3-4年内提升或颠覆人类；他们倡导以人为本的人工智能、道德治理和全球合作，以确保公平繁荣。李飞飞强调了合作共创的伙伴关系，施密特则强调了权力集中和能源需求的风险。
Introduction
Peter Diamandis:
Welcome everybody. Welcome to a conversation about your future, the future of your companies, your nations, and your kids. We're going to be discussing "superintelligence". What does that mean, and what happens when it arrives? We've been talking about AI, AGI, now perhaps "digital superintelligence or ASI".
彼得·迪亚曼迪斯：
欢迎各位。欢迎来到关于你们未来、你们公司未来、你们国家未来以及你们孩子未来的对话。我们将讨论"超级智能"。这意味着什么，当它到来时会发生什么？我们一直在谈论AI、AGI，现在也许是"数字超级智能或ASI"。
Defining Superintelligence
Peter:
I want to start with the obvious question, and it's one that I don't think anybody has a perfect answer for, but what does superintelligence mean and when is it likely to be here? Eric, we've talked about this. What are your thoughts?
彼得：
我想从一个显而易见的问题开始，这是一个我认为没有人有完美答案的问题，但超级智能意味着什么，它什么时候可能到来？埃里克，我们讨论过这个问题。你有什么想法？
Eric Schmidt:
Thank you Peter, and thanks to everybody for being here, and obviously thanks to Fei, our very close colleague. The generally accepted definition of general intelligence is human level of intelligence (AGI). Human intelligence you can understand because we're all human. You have ideas, you have friends, you think about things, you're creative.

"Superintelligence is defined as intelligence equal to the sum of everyone, or even better than all humans."
There is a belief in our industry that we will get to superintelligence. We don't know exactly how long. There's a group of people who I call the "San Francisco consensus" because they're all living in San Francisco. Maybe it's the weather or the drugs or something, but they all basically think that "it's within 3 to 4 years". I personally think it'll be longer than that, but fundamentally their argument is that there are "compounding effects" that we're seeing now which will race us to this much faster than people think.
埃里克·施密特：
谢谢彼得，感谢大家的到来，显然也要感谢李飞飞博士，我们非常亲密的同事。一般公认的通用智能定义是人类水平的智能（AGI）。人类智能你可以理解，因为我们都是人类。你有想法，你有朋友，你思考事物，你有创造力。

"超级智能被定义为等于所有人总和的智能，甚至比所有人类都要好。"
我们这个行业有一个信念，就是我们会达到超级智能。我们不知道确切需要多长时间。有一群人我称之为"旧金山共识"，因为他们都住在旧金山。也许是天气或药物之类的原因，但他们基本上都认为"这将在3到4年内实现"。我个人认为会需要更长时间，但从根本上说，他们的论点是我们现在看到的"复合效应"将使我们比人们想象的更快地达到这一目标。
Peter:
And Fei, I don't think anybody expected the performance that AI has given us so far. The scaling laws have given us capabilities that are extraordinary. You're the CEO of a new company, the founder of World Labs. You've been at Stanford working on this. How do you think about superintelligence? Do you discuss superintelligence at all in your work?
彼得：
而李飞飞博士，我认为没有人预料到AI迄今为止给我们带来的表现。扩展定律给了我们非凡的能力。你是一家新公司的首席执行官，World Labs的创始人。你一直在斯坦福大学研究这个问题。你如何看待超级智能？你在工作中会讨论超级智能吗？
Dr. Fei-Fei Li:
That's a great question, Peter. When Alan Turing dared humanity with the question of "can we create thinking machines," he was thinking about the fundamental question of intelligence. So the birth of AI is about intelligence, about the profound general ability of what intelligence means. From that point of view, AI is already born as a field that tries to push the boundary of what intelligence means.
李飞飞博士：
这是个很好的问题，彼得。当艾伦·图灵向人类提出"我们能创造会思考的机器吗"这个问题时，他思考的是智能的根本问题。所以AI的诞生就是关于智能，关于智能意味着什么的深刻普遍能力。从这个角度来看，AI已经作为一个试图突破智能边界的领域诞生了。
Now, fast forward to 75 years after Alan Turing, this phrase "superintelligence" is pretty hot in Silicon Valley. I do agree with Eric that the colloquial definition is the capability of AI and computers that's better than any human. But I do think we need to be a little careful.
现在，快进到艾伦·图灵75年之后，"超级智能"这个词在硅谷非常热门。我同意埃里克的观点，通俗的定义是AI和计算机的能力比任何人类都要好。但我认为我们需要小心一点。
Current AI Capabilities: Already Super-Human in Some Ways
Fei-Fei:
First of all, some part of today's AI is already better than any human. For example, AI's ability of speaking many different languages, translating between dozens and dozens of languages—pretty much no human can do that. Or AI's ability to calculate things really fast. AI's ability to know from chemistry to biology to sports, the vast amount of knowledge. So "it's already super to human in many ways".
But it remains a question: "Can AI ever be Newton? Can AI ever be Einstein? Can AI ever be Picasso?"
I actually don't know. For example, we have all the celestial data of the movement of the stars that we observe today. Give that data to any AI algorithm. "It will not be able to deduce Newtonian law of motion." That ability that humans have—"it's the combination of creativity, abstraction". I do not see today's AI or tomorrow's AI being able to do that yet.
李飞飞博士：
首先，今天的AI在某些方面已经比任何人类都要好。例如，AI说多种不同语言的能力，在数十种语言之间翻译——几乎没有人类能做到这一点。或者AI快速计算事物的能力。AI了解从化学到生物学到体育的大量知识。所以"它在许多方面已经超越了人类"。
但仍然存在一个问题："AI能成为牛顿吗？AI能成为爱因斯坦吗？AI能成为毕加索吗？"
我实际上不知道。例如，我们拥有今天观察到的所有天体运动的数据。把这些数据给任何AI算法。"它将无法推导出牛顿运动定律。"人类拥有的那种能力——"这是创造力和抽象能力的结合"。我看不到今天或明天的AI能够做到这一点。
The Challenge of True Breakthrough Discoveries
Eric:
One of the common examples that 李飞飞博士 got right is to think about if you had all of the knowledge in a computer that existed in 1902, could you invent relativity? Basically the physics of today, and the answer today is no.
If you look at what is called "test time compute", where the systems are doing reasoning, they can't take the reasoning that they learned and feed it back into themselves very quickly, whereas if you're a mathematician, you prove something, you can base your next proof on that. It's hard for the systems today, although there are approximations.
埃里克：
李飞飞博士说对的一个常见例子是，如果你在计算机中拥有1902年存在的所有知识，你能发明相对论吗？基本上是今天的物理学，今天的答案是否定的。
如果你看看所谓的"测试时间计算"，系统在进行推理时，它们无法快速地将学到的推理反馈给自己，而如果你是数学家，你证明了一些东西，你可以在此基础上进行下一个证明。对于今天的系统来说这很困难，尽管有一些近似方法。
We don't know where the boundaries are. The example that I'd like to use is let's imagine that we can get computers that can solve everything that we normally can do as humans except for these amazing set of creativities. How do really creative people do it? The best examples are that "they are experts in one area, they see another area, and they have an intuition that the same mechanism will solve a problem of a completely different area". That's an example of something we have to learn how to do with AI.
我们不知道边界在哪里。我想用的例子是，让我们想象一下，我们可以让计算机解决我们人类通常能做的所有事情，除了这些惊人的创造力。真正有创造力的人是如何做到的？最好的例子是"他们是一个领域的专家，他们看到另一个领域，并有直觉认为同样的机制可以解决一个完全不同领域的问题"。这是我们必须学习如何用AI做到的一个例子。

An alternative would be to simply do it in brute force using reinforcement learning. The problem is that combinatorially the cost of that is insane, and we're already running out of electricity and so forth. So I think that "to get to real superintelligence, we probably need another algorithmic breakthrough".

另一种选择是简单地使用强化学习以蛮力方式来做。问题是组合成本是疯狂的，而且我们已经耗尽了电力等等。所以我认为"要达到真正的超级智能，我们可能需要另一个算法突破"。
1
Peter:
We need another what?
彼得：
我们需要另一个什么？
2
Eric:
"Algorithmic breakthrough." Another way of dealing with this. The technical term is called "non-stationarity of objectives".
埃里克：
"算法突破。"处理这个问题的另一种方式。技术术语称为"目标的非平稳性"。
What's happening is the systems are trained against objectives. But to do this kind of creativity that 李飞飞博士 is talking about, you need to be able to change the objectives as you're doing them.
正在发生的是系统针对目标进行训练。但要做到李飞飞博士所说的这种创造力，你需要能够在执行过程中改变目标。
Intelligence in Everyone's Pocket: A New Reality
Peter:
We've seen this past year, I think GPT 5 Pro reached an "IQ of like 148", which is extraordinary. And of course, there is no ceiling on this. "The ability for every human on the planet to have an Einstein level—not in the creativity side, but intelligence side—in their pocket changes the game for 8 billion humans." And now with Starlink and with $50 smartphones, it's possible that every single person on the planet has this kind of capability.
Add to that humanoid robots. Add to that a whole slew of other exponential technologies. And the commentary is we're heading towards a "post-scarcity society". Do you believe in that vision, Fei?
彼得：
我们在过去一年看到，我认为GPT 5 Pro达到了"约148的智商"，这是非凡的。当然，这没有上限。"地球上每个人都能在口袋里拥有爱因斯坦级别——不是在创造力方面，而是在智力方面——这改变了80亿人类的游戏规则。"现在有了星链和50美元的智能手机，地球上每个人都有可能拥有这种能力。
再加上人形机器人。再加上一大堆其他的指数技术。评论是我们正在走向"后稀缺社会"。你相信这个愿景吗，李飞飞博士？
Fei-Fei:
I do think we have to be a little careful. I know that we are combining some of the hottest words from Silicon Valley: AI, superintelligence, humanoid robots and all that. To be honest, I think robotics has a long way to go. I think we have to be a little bit careful with the projection of robotics. The ability, the dexterity of human level manipulation is... we have to wait a lot longer to get it.
So, are we entering post-scarcity? I don't know. I'm not as bullish as a typical Silicon Valley person because I think we're entering... I absolutely believe "AI will be augmenting human capabilities in incredibly profound ways". But I think we will continue to see that "the collaboration between humans and AI will be the most productive and fruitful way of doing things".
李飞飞博士：
我确实认为我们必须小心一点。我知道我们正在结合硅谷最热门的一些词汇：AI、超级智能、人形机器人等等。说实话，我认为机器人技术还有很长的路要走。我认为我们必须对机器人技术的预测保持谨慎。人类水平操作的能力、灵巧性……我们需要等待更长的时间才能实现它。
那么，我们正在进入后稀缺时代吗？我不知道。我不像典型的硅谷人那样乐观，因为我认为我们正在进入……我绝对相信"AI将以令人难以置信的深刻方式增强人类能力"。但我认为我们将继续看到"人类和AI之间的协作将是做事情最有成效和最富有成果的方式"。
Economic Impact: $15 Trillion by 2030
Peter:
"AI is going to generate as much as $15 trillion in economic value by 2030". The idea that we're "shifting the foundation of national wealth from capital and labor to computational intelligence". So what's that implication, Eric, for the global economy? How are we going to see redistribution, if you would, of wealth or of capabilities? Are we going to see a leveling of the field between nation states, or are we going to see runaway winners?
彼得：
预测是"到2030年，AI将产生高达15万亿美元的经济价值"。这个想法是我们正在"将国家财富的基础从资本和劳动力转移到计算智能"。那么埃里克，这对全球经济有什么影响？我们将如何看到财富或能力的重新分配？我们会看到国家之间的平等竞争，还是会看到失控的赢家？
Eric:
In your abundance hypothesis, which we've talked a lot about, there may be a flaw in the argument because part of the abundance argument is that it's abundance for everyone. But there's plenty of evidence that "these technologies have network effects which concentrate to a small number of winners".
So you could, for example, imagine a small number of countries getting all those benefits in those countries. You could imagine a small number of firms and people getting those benefits. Those are public policy questions.
Wealth Creation
There's no question the wealth will be created because "the wealth comes from efficiency". And every company that has implemented AI has seen huge gains.
Saudi Arabia Example
Think about here we are in Saudi Arabia. You have all of this oil distribution, all the oil networks, all the losses. AI can easily improve that by 10%, 20%. Those are huge numbers for this country.
Multiple Sectors
If you look in biology and medicine and drug discovery: much faster drug approval cycles, much lower cost trials. Look at materials: much more efficient and easier to build materials.
"The companies that adopt AI quickly get a disproportionate return". The question is: are those gains uniform, which would be our hope, or in my view more likely largely centered around "early adopters, network effects, well-run countries, and perhaps capital".
埃里克：
在你的富足假设中，我们已经谈论了很多，论点中可能存在一个缺陷，因为富足论点的一部分是它对每个人都是富足的。但有大量证据表明"这些技术具有网络效应，会集中到少数赢家身上"。
所以你可以想象，例如，少数国家在这些国家获得所有这些好处。你可以想象少数公司和人获得这些好处。这些是公共政策问题。
毫无疑问，财富将被创造，因为"财富来自效率"。每家实施AI的公司都看到了巨大的收益。想想我们在沙特阿拉伯。你拥有所有这些石油分销、所有的石油网络、所有的损失。AI可以轻松地将其改善10%、20%。对于这个国家来说，这些是巨大的数字。
如果你看看生物学、医学和药物发现：药物批准周期要快得多，试验成本要低得多。看看材料：更高效、更容易制造材料。"快速采用AI的公司获得了不成比例的回报"。问题是：这些收益是均匀的，这是我们的希望，还是在我看来更可能主要集中在"早期采用者、网络效应、管理良好的国家，也许还有资本"周围。
Democratization vs. Shared Prosperity
Peter:
But you could imagine still that we're going to see autonomous cars in which the ownership—"being in an autonomous vehicle is four times cheaper than owning a car". We can see "AI giving us the best physicians, the best health care for free in the same way that Google gave us access to information for free". We will see a "massive demonetization" in so much of our world. I think that will be available to anyone with a smartphone and a decent bandwidth connectivity.
Is that still not what you think will happen? Do you think there's a reason, something that would stop that level of distribution of those services which we spend a lot of our money on today?
彼得：
但你仍然可以想象，我们将看到自动驾驶汽车，其中拥有权——"乘坐自动驾驶汽车比拥有汽车便宜四倍"。我们可以看到"AI以谷歌向我们免费提供信息访问的方式，免费为我们提供最好的医生、最好的医疗保健"。我们将在我们世界的很多方面看到"大规模去货币化"。我认为这对任何拥有智能手机和适当带宽连接的人都是可用的。
这仍然不是你认为会发生的事情吗？你认为有什么原因会阻止我们今天花很多钱购买的这些服务的分发水平吗？

Fei-Fei:
I do think AI democratizes that. I totally agree with you. I think whether it's healthcare or transportation or knowledge, "AI will democratize massively". But I agree with Eric that "this increased global productivity does not necessarily translate to shared prosperity. Shared prosperity is a deeper social problem. It involves policy. It involves geopolitics. It involves distribution, and that's a different problem from the capability of the technology".

李飞飞博士：
我确实认为AI使之民主化。我完全同意你的观点。我认为无论是医疗保健、交通还是知识，"AI将大规模民主化"。但我同意埃里克的观点，"这种全球生产力的提高不一定会转化为共同繁荣。共同繁荣是一个更深层次的社会问题。它涉及政策。它涉及地缘政治。它涉及分配，这是一个与技术能力不同的问题"。
National Strategies: Who Wins in the AI Race?
Peter:
So what's your advice to the country leaders that are here that are seeing ASI as a future for someone else and not for themselves? What should they be doing? I mean this is the speed at which it's deploying. They don't have a lot of time to make critical decisions.
彼得：
那么，你对那些认为ASI是别人的未来而不是自己的未来的国家领导人有什么建议？他们应该做什么？我的意思是这是它部署的速度。他们没有太多时间做出关键决策。
Eric:
Well, it's worth describing where we are now. In the United States, because of the depth of our capital markets and because of the extraordinary chips that are available in the Taiwanese manufacturers, TSMC in particular, America has this huge lead in building these what are called "hyperscalers".
"If there's going to be superintelligence, it's going to come from those efforts." That's a big deal. If there is superintelligence, imagine a company like Google inventing this, for example. I am obviously biased. And "what's the value of being able to solve every problem that humans can't solve? It's infinite."
埃里克：
嗯，值得描述一下我们现在的处境。在美国，由于我们资本市场的深度，以及台湾制造商，特别是台积电提供的非凡芯片，美国在构建这些所谓的"超大规模计算机"方面拥有巨大的领先优势。
"如果会有超级智能，它将来自这些努力。"这是一件大事。如果有超级智能，想象一下例如像谷歌这样的公司发明了这个。我显然有偏见。而"能够解决人类无法解决的所有问题的价值是什么？是无限的。"
Peter: Sure.
彼得：当然。
Eric:
So that's the goal, right? Saudi has done a good job of partnering with America. The hyperscalers will be located here and in the UAE. That's a good strategy. So that's a good example of how you partner. "You figure out which side you're on. Hopefully it's the United States. And you work with the US firms."
埃里克：
所以这就是目标，对吗？沙特在与美国合作方面做得很好。超大规模计算机将设在这里和阿联酋。这是一个好策略。所以这是如何合作的一个很好的例子。"你要弄清楚你站在哪一边。希望是美国。你与美国公司合作。"
Fei-Fei:
I do think "countries should all invest in their own human capital, invest in partnerships, and invest in their own technological stack as well as the business ecosystem". This is, as Eric said, it depends on the strength and particularity of the different countries, but I think "not investing in AI would be macroscopically the wrong thing to do".
李飞飞博士：
我确实认为"各国都应该投资于自己的人力资本，投资于伙伴关系，并投资于自己的技术堆栈以及商业生态系统"。正如埃里克所说，这取决于不同国家的实力和特殊性，但我认为"不投资AI在宏观上将是错误的事情"。
The Data Center Question: Should Every Country Build One?
Peter:
So under the thesis that that investment involves building out data centers in your nation. Do you think every country should be building out a data center that it has sovereign AI running on?
彼得：
所以在投资涉及在你的国家建设数据中心的论点下。你认为每个国家都应该建设一个运行主权AI的数据中心吗？
Fei-Fei:
Every country is a very sweeping statement. I do think it depends. Obviously for a region like this, absolutely, where oil, energy is cheaper and such an important region in the world. But if we're talking about smaller countries, I don't know if every single country can afford to build data centers. But there are other areas of investment, right?
李飞飞博士：
每个国家是一个非常笼统的说法。我认为这取决于具体情况。显然对于像这样的地区，绝对需要，那里的石油、能源更便宜，在世界上是如此重要的地区。但如果我们谈论的是较小的国家，我不知道每个国家是否都能负担得起建设数据中心。但还有其他投资领域，对吧？
Eric:
Let me give you an example. Let's pick Europe. It's easy to pick on Europe. Energy costs are high, right? Financing costs are not low. So the odds of Europe being able to build very large data centers is extremely low. But they can partner with countries where they can do it. France, for example, did a partnership with Abu Dhabi. So there are examples of that.
So I think if you take a global view and you figure out who your partners are, you have a better chance. "The one that I worry a lot about is Africa. And the reason is: how does Africa benefit from this?" So there's obviously some benefit of globalization, better crop yields and so forth. But "without stable governments, strong universities, major industrial structures, which Africa with some exceptions lacks, it's going to lag. It's been lagging for years. How do we get ahead of that? I don't think that problem is solved."
埃里克：
让我给你举个例子。让我们选择欧洲。批评欧洲很容易。能源成本很高，对吧？融资成本也不低。所以欧洲能够建造非常大的数据中心的可能性极低。但它可以与能够做到这一点的国家合作。例如，法国与阿布扎比建立了伙伴关系。所以有这样的例子。
所以我认为，如果你从全球角度出发，弄清楚你的合作伙伴是谁，你就有更好的机会。"我非常担心的一个是非洲。原因是：非洲如何从中受益？"所以显然全球化有一些好处，更好的作物产量等等。但"没有稳定的政府、强大的大学、主要的工业结构，而非洲除了少数例外都缺乏这些，它将落后。它已经落后了多年。我们如何超越这一点？我认为这个问题没有得到解决。"
The Five-Year Horizon: Solving Math, Physics, Chemistry, Biology?
Peter:
We've seen incredible progress with AI today effectively beginning what people call "solving math," that potentially tips physics, chemistry, biology. And we have the potential—my time frame is the next 5 years, others may think longer—to be in a position to "solve everything" where the level of discovery and the level of new product creation, new materials, biological therapeutics and such begins to grow at a super exponential rate. How do you think about that world in five years, Eric?
彼得：
我们看到AI今天取得了令人难以置信的进步，有效地开始了人们所说的"解决数学"，这可能会影响物理学、化学、生物学。我们有潜力——我的时间框架是未来5年，其他人可能认为更长——处于"解决一切"的位置，其中发现水平和新产品创造水平、新材料、生物治疗等开始以超指数速率增长。埃里克，你如何看待五年后的世界？
Eric:
First, I think it's likely to occur, and the reason technically is that all of the large language models are essentially doing "next word prediction". And if you have a "limited vocabulary", which math is, and software is, and also cyber attacks are (I'm sorry to say), you can make progress because "they're scale free. All you have to do is just do more".
So if you do software, you can verify it. You can do more software. If you do math, you can verify it, do more math. "You're not constrained by real reality, physics and biology". So it's likely in the next few years that in math and software, you'll see the greatest gains, and we all understand your point that math is at the basis of everything else.
埃里克：
首先，我认为这很可能发生，技术原因是所有大型语言模型本质上都在做"下一个词预测"。如果你有一个"有限的词汇表"，数学是，软件是，而且网络攻击也是（我很抱歉这样说），你可以取得进展，因为"它们是无规模的。你所要做的就是做更多"。
所以如果你做软件，你可以验证它。你可以做更多的软件。如果你做数学，你可以验证它，做更多的数学。"你不受真实现实、物理和生物学的约束"。所以在未来几年，在数学和软件方面，你可能会看到最大的收益，我们都理解你的观点，即数学是其他一切的基础。
I think there's probably a longer period of time to get the real world right, which is why she founded the company of which I'm an investor. Do you want to talk about that?
我认为可能需要更长的时间才能让现实世界正确，这就是为什么她创立了我投资的公司。你想谈谈这个吗？
Fei-Fei:
Yeah. Well, first of all, I actually want to respectfully disagree.
李飞飞博士：
是的。嗯，首先，我实际上想恭敬地表示不同意。
Peter:
Okay.
彼得：
好的。
Fei-Fei:
"I do not think that we will solve all the problems, fundamental math and physics and chemistry problems in five years."
李飞飞博士：
"我不认为我们将在五年内解决所有问题，基础数学、物理和化学问题。"
Peter:
We're going to take a bet on that one.
Fei-Fei:
Yes. So, FII14.
Peter:
Okay, you got it.
彼得：
我们要为此打个赌。
李飞飞博士：
是的。所以，FII14见。
彼得：
好的，就这样定了。
"Part of humanity's greatest capability is to actually come up with new problems. You know, as Albert Einstein said, most of science is asking the right question", and we will continue to find new questions to ask. And there are so many fundamental questions in our science and math that we haven't answered.
"人类最大能力的一部分实际上是提出新问题。你知道，正如阿尔伯特·爱因斯坦所说，科学的大部分是提出正确的问题"，我们将继续找到新的问题来问。在我们的科学和数学中有这么多我们还没有回答的基本问题。
World Labs: Building Large World Models
Peter:
李飞飞博士, your new company World Labs is creating extraordinarily persistent, photorealistic worlds. Are you expecting that we are going to be spending a lot more of our time in virtual worlds? I mean my 14-year-old boys right now are spending way too much time in their virtual gaming worlds. But is this what we're going to do in 10, 20 years in a post ASI world where we don't have to work as much, we have a lot more free time, our robots maybe by then are serving us? Are we going to live in the virtual worlds?
彼得：
李飞飞博士，你的新公司World Labs正在创造非常持久的、逼真的世界。你是否期望我们将花更多时间在虚拟世界中？我的意思是我14岁的儿子们现在在他们的虚拟游戏世界中花费了太多时间。但这是我们在10年、20年后在后ASI世界中要做的事情吗，在那里我们不必工作那么多，我们有更多的空闲时间，我们的机器人可能到那时正在为我们服务？我们会生活在虚拟世界中吗？
李飞飞博士:
Great question. So what we are doing is building "large world models". That's a problem that's after large language models. Humans have the ability to have the kind of "spatial intelligence" that we can understand the physical 3D world. We can imagine any kind of 3D worlds and be able to reason and interact with it. So we do not yet, up till what our company has been doing, we do not have such a world model. So "World Labs, the company I co-founded and I'm CEO of, has just created the first large world model."
The future I see—I actually agree with you that "we will be spending more time in the multiverse of the virtual worlds". It doesn't mean that the reality, the real world, this world, this physical world is gone. It's just "so much of our productivity, our entertainment, our communication, our education are going to be a hybrid of virtual and physical world".
李飞飞博士：
好问题。所以我们正在做的是构建"大型世界模型"。这是在大型语言模型之后的一个问题。人类有能力拥有那种"空间智能"，我们可以理解物理3D世界。我们可以想象任何类型的3D世界，并能够推理和与之互动。所以直到我们公司所做的，我们还没有这样的世界模型。所以"World Labs，我共同创立并担任首席执行官的公司，刚刚创建了第一个大型世界模型。"
我看到的未来——我实际上同意你的观点，"我们将在虚拟世界的多元宇宙中花费更多时间"。这并不意味着现实、真实世界、这个物理世界消失了。只是"我们的生产力、娱乐、交流、教育的大部分都将是虚拟和物理世界的混合体"。

Think about in medicine. How we conduct surgery is very much going to be a "hybrid world of augmented reality, virtual reality as well as physical reality". And we can do that in every single sector. So humanity using these large world models are going to "enter the infinite universe".
想想医学。我们如何进行手术将非常像"增强现实、虚拟现实以及物理现实的混合世界"。我们可以在每个部门都这样做。因此，使用这些大型世界模型的人类将"进入无限宇宙"。
Peter:
And I had a chance to see your model backstage. It's amazing. If you haven't yet, go check out 李飞飞博士's World Labs. The technology she's building is going to be world changing.
彼得：
我有机会在后台看到你的模型。太棒了。如果你还没有，去看看李飞飞博士的World Labs。她正在构建的技术将改变世界。
The Ultimate Question: What Will Humans Do?
Peter:
My last question here is about human capital. "Superintelligence has been called 'the last invention humanity will ever make'" as it could automate eventually every process. We'll see if it automates discovery. We'll see how much of creation it automates. But in a world where the best strategy, science and economic decisions are being made by machines at some point, "what is the ultimate irreplaceable function of human intellect and leadership? What are humans innately going to be left with in 10, 20 years?"
彼得：
我在这里的最后一个问题是关于人力资本。"超级智能被称为'人类将制造的最后一项发明'"，因为它最终可以自动化每一个过程。我们将看到它是否能自动化发现。我们将看到它能自动化多少创造。但在一个最佳策略、科学和经济决策在某个时候由机器做出的世界中，"人类智力和领导力的最终不可替代功能是什么？10年、20年后，人类天生将剩下什么？"
Eric:
Well, in 20 years, we will enjoy watching each other compete in human sports, knowing that the robots can beat us 100% of the time.
埃里克：
嗯，在20年后，我们将享受观看彼此在人类运动中竞争，知道机器人可以100%的时间击败我们。
1
Peter:
But if you go to Formula 1, you're going to want to see a human driver, not an automated car.
彼得：
但如果你去看一级方程式赛车，你会想看到一个人类驾驶员，而不是自动驾驶汽车。
2
Eric:
Yes. So "humans will always be interested in what other humans can do", and we'll have our own contests, and perhaps the supercomputers will have their own contest too.
埃里克：
是的。所以"人类将永远对其他人类能做什么感兴趣"，我们将有自己的比赛，也许超级计算机也会有自己的比赛。
But your reasoning presumes many things. It presumes a breakout of intelligence in computers that's human-like. Unlikely—"probably a different kind of intelligence". It presumes that humans are largely not involved in that process. Highly unlikely. All of the evidence, and 李飞飞博士 said this very well, is going to be "human and computer interaction". Basically, we will all have—going back to what you said about 8 billion people with smartphones with Einstein in their phone—the smart people, of which there's a lot, will use that to make themselves more productive. "The win will be the teaming between a human and their judgment and a supercomputer and what it can think."
但你的推理假设了很多事情。它假设计算机中会出现类似人类的智能突破。不太可能——"可能是一种不同类型的智能"。它假设人类在很大程度上不参与这个过程。非常不可能。所有证据，李飞飞博士说得很好，将是"人类和计算机的互动"。基本上，我们都会有——回到你所说的80亿人拥有智能手机，手机里有爱因斯坦——聪明的人，有很多，会用它来提高自己的生产力。"胜利将是人类及其判断与超级计算机及其能思考的东西之间的团队合作。"
And remember that there is a limit to this craze. "Supercomputers and superintelligence need energy". So perhaps what will happen at some point is the supercomputers will say, "Huh, we need more energy and these humans are not building fusion fast enough. So we'll accelerate it. We'll come up with a new form of energy." Now this is science fiction. But you could imagine at some point "the objective function of the system says, 'What do I need? I need more chips or more energy and I'll design it myself.'" Now, that would be a great moment to see.
记住，这种狂热是有限度的。"超级计算机和超级智能需要能源"。所以也许在某个时刻会发生的是超级计算机会说，"嗯，我们需要更多的能源，这些人类建造聚变的速度不够快。所以我们会加速它。我们会想出一种新的能源形式。"现在这是科幻小说。但你可以想象在某个时刻"系统的目标函数说，'我需要什么？我需要更多的芯片或更多的能源，我会自己设计它。'"现在，那将是一个很好的时刻。
Peter:
I agree.
彼得：
我同意。
Closing: Human Dignity and Agency Must Remain Central
Fei-Fei:
I do want to say it's so important as we talk about AGI and ASI that "the most important thing that we keep in mind is human dignity and human agency. Our world, unless we are going to wipe out this species, which we're not, has to be human-centered. Whether it's automation or collaboration, it needs to put human agency and dignity and human well-being in the center of all this." Whether it's technology, business, product, policy or any of that. "And I think we cannot lose our focus from that."
李飞飞博士：
我确实想说，在我们谈论AGI和ASI时非常重要的是"我们要记住的最重要的事情是人类尊严和人类主体性。我们的世界，除非我们要消灭这个物种，而我们不会，必须以人为中心。无论是自动化还是协作，它都需要将人类主体性、尊严和人类福祉放在所有这一切的中心。"无论是技术、商业、产品、政策或其他任何东西。"我认为我们不能失去对此的关注。"
Thank You
Peter:
Amen. Everybody, ladies and gentlemen, Fei-Fei, Eric Schmidt, thank you all.
End of Discussion
彼得：
阿门。各位，女士们先生们，李飞飞博士、埃里克·施密特，谢谢大家。
讨论结束