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?
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.
今天的客人确实需要介绍一下。
我想我第一次见到埃里克是在大约25年前，当时他来到斯坦福商学院担任Novell的首席执行官。
从那时起，他从2001年开始在谷歌做了一些事情，从2017年开始在施密特期货公司做了一些事情，并做了很多你可以阅读的其他事情，但他只能在这里直到5点15分，所以我认为我们应该深入研究一些问题，我知道你们也发了一些。
我在这里写了一堆，但我们刚才在楼上谈论的内容更有趣，所以我要从这个开始，埃里克，如果可以的话，这就是你认为人工智能在短期内会走向何方，我认为你将其定义为未来一两年？
事情变化得如此之快，我觉得每六个月我都需要就将要发生的事情发表一次新的演讲。
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.
谁能听到计算机的声音，预算计算机科学工程师，谁能向课堂上的其他人解释什么是百万个令牌的上下文窗口？
你在这里。
说出你的名字，告诉我们它的作用。
基本上，它允许您使用一百万个令牌或一百万个单词或其他任何东西进行提示。
所以你可以问一个百万字的问题。
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.
是的，我知道这是一月份的一个非常大的方向。
不，不，他们要到 10 个。
是的，他们中的几个。
人类学是 200,000 到 100 万，依此类推。
你可以想象OpenAI也有类似的目标。
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?
在座的任何人都可以给出人工智能代理的技术定义吗？
是的,先生。
因此，代理是执行某种任务的东西。
另一个定义是它是内存中的 LLM 状态。
再说一遍，计算机科学家，你们中的任何一个人能定义文本到动作吗？
Taking text and turning it into an action?
Right here.
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.
获取文本并将其转化为动作？
就在这儿。
继续。
是的，不是获取文本并将其转换为更多文本，而是获取文本并让 AI 触发操作。
因此，另一个定义是 Python 的语言，我从来不想看到这种编程语言生存下来，AI 中的所有内容都是用 Python 完成的。
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.
有一种叫做Mojo的新语言刚刚问世，看起来他们终于解决了AI编程问题，但我们将看看它是否真的能超越Python的主导地位。
还有一个技术问题。
为什么英伟达的市值是2万亿美元，而其他公司却在苦苦挣扎？
技术答案。
我的意思是，我认为这可以归结为大多数代码需要使用 CUDA 优化运行，而目前只有 NVIDIA GPU 支持。
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.
其他公司可以做任何他们想做的事，但除非他们拥有 10 年的软件经验，否则你就没有机器学习优化。
我喜欢将 CUDA 视为 GPU 的 C 编程语言。
这就是我喜欢的想法。
它成立于2008年。
我一直认为这是一种可怕的语言，但它已经成为主导。
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?
还有另一个见解。
有一组开源库，它们针对 CUDA 进行了高度优化，而不是其他任何东西，每个构建所有这些堆栈的人，这在任何讨论中都完全被遗漏了。
从技术上讲，它被称为 VLM 和一大堆类似的库。
高度优化的 CUDA，如果您是竞争对手，则很难复制。
那么这一切意味着什么呢？
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.
在接下来的一年里，你将看到非常大的上下文窗口、代理和文本操作。
当它们大规模交付时，它将以没有人理解的规模对世界产生影响。
在我看来，这比我们通过社交媒体产生的可怕影响要大得多。
所以这就是原因。
在上下文窗口中，您基本上可以将其用作短期记忆，我对上下文窗口会变得如此之长感到震惊。
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.
技术原因与以下事实有关：难以服务、难以计算等等。
关于短期记忆的有趣之处在于，当你喂食时，你问一个问题，读了20本书，你把书中的文本作为查询，然后你说，告诉我他们说什么。
它忘记了中间部分，而这正是人类大脑的工作方式。
这就是我们所处的位置。
关于智能体，有些人现在基本上正在构建LLM智能体，他们这样做的方式是他们阅读化学之类的东西，他们发现化学原理，然后他们测试它，然后他们将其重新添加到他们的理解中。
That's extremely powerful.
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.
这非常强大。
然后第三件事，正如我提到的，是文本到行动。
所以我给你举个例子。
政府正在试图禁止TikTok。
我们将看看这是否真的发生。
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.
如果 TikTok 被禁止，我建议你们每个人都这样做。
对你的法学硕士说以下几句话。
给我复制 TikTok，偷走所有用户，偷走所有音乐，把我的偏好放进去，在接下来的 30 秒内制作这个程序，发布它，然后在一小时内，如果它不是病毒式传播的，按照同样的思路做一些不同的事情。
这就是命令。
砰，砰砰砰，砰。
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.
你明白这是多么强大。
如果你可以从任意语言转向任意的数字命令，这基本上就是这个场景中的Python，想象一下，地球上的每一个人都有自己的程序员，他们实际上做他们想做的事，而不是为我工作的程序员，他们不做我要求的事情，对吧？
这里的程序员知道我在说什么。
所以想象一下，一个不傲慢的程序员实际上做了你想做的事，你不必支付所有的钱，而且这些程序的供应是无限的。
这一切都在未来一两年内完成。
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，我非常确信这三件事的结合将在下一波浪潮中发生。
所以你问还会发生什么。
每六个月我振荡一次。
所以我们处于一个，这是一个偶数奇数的振荡。
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.
因此，目前，前沿模型之间的差距，它们现在只有三个，我将反驳他们是谁，以及其他所有人，在我看来，差距越来越大。
六个月前，我确信差距正在越来越小。
所以我在小公司里投入了很多钱。
现在我不太确定。
我正在和大公司交谈，大公司告诉我他们需要100亿、200亿、500亿、1000亿。
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.
星际之门是1000亿，对吧？
这非常非常困难。
我和山姆谈过奥特曼是我的密友。
他认为这将需要大约3000亿美元，甚至更多。
我向他指出，我已经计算了所需的能量。
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?
然后，我本着充分披露的精神，周五去了白宫，告诉他们我们需要与加拿大成为最好的朋友，因为加拿大有非常好的人，帮助发明了人工智能，还有很多水电。
因为我们作为一个国家没有足够的力量来做到这一点。
另一种选择是让阿拉伯人资助它。
我个人喜欢阿拉伯人。
我在那里花了很多时间，对吧？
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?
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.
但他们不会遵守我们的国家安全规则。
而加拿大和美国
是我们都同意的三巨头的一部分。
因此，这些价值1000亿美元、3000亿美元的数据中心，电力开始成为稀缺资源。
好吧，顺便说一句，如果你按照这个推理思路，我为什么要讨论 CUDA 和 Nvidia？
如果3000亿美元全部流向英伟达，你就知道在股市上该做什么了。
好。
这不是股票推荐。
我没有执照。
嗯，这是一部分，所以我们需要更多的芯片，但英特尔从美国获得了很多钱。
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.
政府，AMD，他们正试图在韩国建立晶圆厂。
如果您的计算机中有英特尔计算机，则请举手，您的任何计算设备中都有英特尔芯片。
好。
垄断就这么多。
嗯，这就是重点。
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.
他们曾经确实拥有垄断地位。
绝对。
英伟达现在拥有垄断地位。
那么，像CUDA这样的进入壁垒，是不是有其他的东西，所以我和Percy聊天，前几天Percy Landy，他在TPU和Nvidia芯片之间切换，这取决于他能获得什么来训练模型。
那是因为他别无选择。
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.
如果他有无限的钱，他会，今天他会从 Nvidia 中选择 B200 架构，因为它会更快。
我不是说，我的意思是，有竞争是件好事。
我与 AMD 和 Lisa Sue 进行了深入的交谈。
他们已经构建了一个东西，它将从您描述的这个 CUDA 架构转化为他们自己的架构，称为 Rockum。
它还没有完全起作用。
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.
他们正在努力。
你在 Google 工作了很长时间，他们发明了变压器架构。
彼得，彼得。
这都是彼得的错。
感谢那边的聪明人，比如彼得和杰夫·迪恩以及所有人。
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.
但现在看起来不像是，他们已经失去了开放人工智能的主动权，甚至最后一个排行榜，我看到了 Anthropix。
克劳德位居榜首。
我问了Sundar这个问题，他并没有给我一个非常尖锐的答案。
也许，也许你对那里发生的事情有一个更清晰或更客观的解释。
本着全面披露的精神，我不再是 Google 的员工。
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.
谷歌认为，工作与生活的平衡、早点回家和在家工作比赢得胜利更重要。
而创业公司，创业公司之所以能成功，是因为人们工作得像地狱一样。
我很抱歉这么直言不讳，但事实是，如果你们都离开大学，去找一家公司，你们就不会让人们在家工作，每周只来一天。
如果你想与谷歌早期的其他创业公司竞争，Microsoft就是这样。
完全。
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.
但现在看来，在我的行业中，我猜，我们的行业有着悠久的历史，公司以真正创新的方式获胜，真正主导了一个领域，而不是将其作为下一个过渡。
所以我们有很好的记录。
我认为事实是创始人是特别的。
创始人需要负责。
创始人很难合作。
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.
他们努力推动人们。
尽管我们不喜欢埃隆的个人行为，但看看他从人们那里得到了什么。
我和他一起吃晚饭，他正在飞翔。
我当时在蒙大拿州。
他当晚10点乘飞机，准备在午夜与 x.ai 会面。
I was in Taiwan, different country, 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.
我在台湾，不同的国家，不同的文化。
他们说这是台积电，我对他们印象深刻。
他们有一条规则，从优秀的物理学家中出来的初生博士必须在工厂的地下室工作。
现在，你能想象让美国物理学家这样做吗？
博士，极不可能。
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.
不同的职业道德。
这里的问题是，我对工作如此苛刻的原因是，这些系统具有网络效应。
所以时间很重要。
在大多数企业中，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.
可口可乐和百事可乐仍然存在，可口可乐和百事可乐之间的战斗将继续进行，一切都是冰河。
当我与电信公司打交道时，典型的电信协议需要 18 个月才能签署。
没有理由花 18 个月的时间做任何事情。
完成它。
我们正处于一个最大增长、最大收益的时期。
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.
而且它也需要疯狂的想法。
就像Microsoft做OpenAI交易时一样，我认为这是我听过的最愚蠢的想法。
基本上将您的 AI 领导权外包给 OpenAI 和 Sam 和他的团队。
我的意思是，这太疯狂了。
在Microsoft或其他任何地方，没有人会这样做。
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.
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?
然而，今天，他们正在成为最有价值的公司。
他们肯定是苹果的正面交锋。
苹果没有一个好的人工智能解决方案，看起来他们让它起作用了。
是的,先生。
在国家安全或地缘政治利益方面，您认为人工智能将如何发挥作用或与中国竞争？
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.
所以我是一个人工智能委员会的主席，我非常仔细地研究了这一点，你可以阅读它。
大约有752页，我总结一下，我们领先了，我们需要保持领先，我们需要很多钱来做到这一点。
我们的客户是参议院和众议院。
随之而来的是《芯片法案》和许多其他类似的东西。
一个粗略的情况是，如果你假设前沿模型向前发展，并且有一些开源模型，那么很可能只有极少数公司可以玩这个游戏。
Countries, excuse me.
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.
各国，对不起。
那些国家是什么，他们是谁？
拥有大量资金和大量人才、强大的教育体系和获胜意愿的国家。
美国就是其中之一。
中国是另一个。
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.
还有多少其他的？
还有其他的吗？
我不知道。
或。
但可以肯定的是，在你们有生之年，美国和中国之间争夺知识霸权的战斗将成为一场大战。
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.
因此，美国政府基本上禁止了英伟达芯片，尽管他们不被允许说这是他们正在做的事情，但他们实际上在中国这样做了。
他们有大约 10 年的芯片优势。
就 sub-DUV（即 sub-five Danometer 芯片）而言，我们有大约 10 年的芯片优势。
因此，一个例子是，今天我们比中国领先几年。
我的猜测是，我们会比中国领先几年，而中国人对此非常生气。
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.
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?
这就像对此感到非常沮丧。
所以这是一件大事。
这是特朗普政府做出的决定，并由拜登政府推动。
你是否发现今天的国会政府正在听取你的建议？
你认为它会带来如此规模的投资吗？
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.
显然，芯片起作用了，但除此之外，还要建立一个庞大的人工智能系统吗？
所以如你所知，我领导着一个非正式的、临时的、非法律的团体。
这与非法行为不同。
没错。
只是为了清楚。
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.
其中包括所有通常的嫌疑人。
而过去一年的通常嫌疑人提出了成为拜登政府人工智能法案的推理基础，这是历史上最长的总统指令。
你说的是特别竞争研究项目吗？
不，这是行政办公室的实际行为。
他们正忙于实施细节。
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.
到目前为止，他们做对了。
因此，举个例子，我们去年的辩论之一是，你如何发现一个已经学会了它但你不知道该问什么的系统中的危险？
换句话说，这是一个核心问题。
它学到了一些不好的东西，但它不能告诉你它学到了什么，你不知道该问它什么。
而且威胁如此之多。
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.
就像它学会了如何以某种你不知道如何问它的新方式混合化学反应。
因此，人们正在努力解决这个问题。
但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 到 26 次翻牌，从技术上讲，这是一个计算的衡量标准，超过这个阈值，你必须向政府报告你正在这样做。
这是规则的一部分。
欧盟只是为了确保他们与众不同，做到了 10 到 25 个。
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 OpenEye has had to train, partly because of the power consumption.
There was no one place where they did.
但这一切都已经足够接近了。
我认为所有这些区别都消失了，因为技术现在会，这个技术术语被称为联合训练，基本上你可以把各个部分合并在一起。
因此，我们可能无法保护人们免受这些新事物的侵害。
嗯，有传言说，这就是OpenEye必须训练的方式，部分原因是功耗。
他们没有一个地方这样做。
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.
好吧，让我们谈谈一场正在进行的真正战争。
我知道你非常参与的事情是乌克兰战争，特别是，我不知道你是否可以谈论白鹳和你的目标是让 500,000 美元、500 美元的无人机摧毁 500 万美元的坦克。
这如何改变战争？
我为国防部长工作了七年，试图改变我们管理军队的方式。
我不是特别喜欢军队，但它非常昂贵，我想看看我是否可以提供帮助。
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.
我认为在我看来，我在很大程度上失败了。
他们给了我一枚奖牌，所以他们必须给失败或其他什么的奖牌获得者。
但我的自我批评是，什么都没有真正改变，美国的制度不会带来真正的创新。
因此，看着俄罗斯人用坦克摧毁带有小老太太和孩子的公寓楼，我简直发疯了。
所以我决定和你的朋友塞巴斯蒂安·特伦（Sebastian Thrun）一起在一家公司工作，他是这里的前教员，还有一大群斯坦福大学的人。
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.
这个想法基本上是做两件事。
在这些本质上是机器人的战争中，以复杂而强大的方式使用人工智能，第二个是降低机器人的成本。
现在你坐在那里，你走了，为什么像我这样的好自由主义者会这样做？
答案是，军队的整个理论就是坦克、大炮和迫击炮，我们可以消灭所有这些，我们可以使至少通过陆路入侵一个国家的惩罚基本上是不可能的。
它应该消除那种陆战。
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.
嗯，这是一个关系问题是，它是否给防守而不是进攻带来了更多的优势？
你甚至能做出这种区分吗？
因为我去年一直在做这件事，所以我学到了很多关于战争的知识，我真的不想知道。
关于战争，需要了解的一件事是，进攻总是有优势，因为你总是可以压倒防御系统。
因此，作为国防战略，你最好有一个非常强大的进攻，如果你需要的话，你可以使用它。
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.
我和其他人正在构建的系统将做到这一点。
由于该系统的运作方式，我现在是一名有执照的军火商、计算机科学家、商人和军火商。
这是一个进步吗？
我不知道。
我不建议在你们的小组中这样做。
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.
我坚持使用人工智能。
由于法律的运作方式，我们是私下进行的，然后在政府的支持下，这一切都是合法的。
它直接进入乌克兰，然后他们打仗。
如果不深入到所有细节，事情就很糟糕了。
我认为，如果在 5 月或 6 月，如果俄罗斯人像他们预期的那样建立起来，乌克兰将失去一整块领土，并开始失去整个国家的过程。
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.
所以情况相当可怕。
如果有人认识玛乔丽·泰勒·格林（Marjorie Taylor Greene），我鼓励你把她从你的联系人列表中删除，因为她就是那个，一个人正在阻止提供数十亿美元的资金来拯救一个重要的民主国家。
我想换个话题来回答一点哲学问题。
所以你、亨利·基辛格和丹·赫滕莱克去年写了一篇关于知识的本质以及它是如何演变的文章。
前几天晚上我也就这个问题进行了讨论。
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.
因此，在历史的大部分时间里，人类对宇宙有一种神秘的理解，然后是科学革命和启蒙运动。
在您的文章中，您认为现在这些模型变得如此复杂和难以理解，以至于我们并不真正知道它们中发生了什么。
我引用理查德·费曼（Richard Feynman）的一句话。
他说：“我无法创造的东西，我不理解。前几天我看到了这句话。
但现在人们正在创造他们可以创造的东西，但他们并不真正理解它们内在的东西。
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.
知识的本质是否在某种程度上发生了变化？
我们是否必须开始只是接受这些模型的词，而他们却无法向我们解释？
我想做的类比是针对青少年的。
如果你有一个青少年，你知道他们是人类，但你无法完全弄清楚他们在想什么。
但不知何故，我们在社会中设法适应了青少年的存在，他们最终摆脱了它。
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?
我只是认真的。
因此，我们可能会拥有无法完全描述的知识系统，但我们了解它们的边界。
我们了解他们能做的局限性。
这可能是我们能得到的最好的结果。
你认为我们会理解这些限制吗？
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.
我们会做得很好。
我所在的小组每周都会开会，他们的共识是，最终你做这种所谓的对抗性人工智能的方式是，实际上会有一些公司你会雇佣并付钱来破坏你的人工智能系统。
就像红队一样。
因此，你将拥有整个公司和整个人工智能系统行业，而不是他们今天所做的人类红队，他们的工作是打破现有的人工智能系统并找到它们的弱点，尤其是他们所拥有的我们无法弄清楚的知识。
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.
All right, let's take some questions from the student.
There's one right there in the back.
Just say your name.
对于斯坦福大学的你来说，这也是一个很棒的项目，因为如果你有一个研究生，他必须弄清楚如何攻击这些大型模型之一，并理解它的作用，那就是培养下一代的伟大技能。
所以对我来说，两人一起旅行是有道理的。
好了，让我们来回答一下学生的一些问题。
后面有一个。
只需说出您的名字。
Earlier you mentioned, and this is related to this comment right now, getting AI that actually does what you want.
You just mentioned adversarial AI, and I'm wondering if you can elaborate on that more.
So it seems to be, besides obviously computer language reasons to get more performant models, but getting them to do what you want to do seems partly unanswered in my view.
Well, you have to assume that the current hallucination problems become less as the technology gets better and so forth.
I'm not suggesting it goes away.
你之前提到过，这与现在的这个评论有关，让人工智能真正做你想做的事。
你刚才提到了对抗性人工智能，我想知道你是否可以更详细地阐述一下。
因此，在我看来，除了明显的计算机语言原因之外，还可以获得更高性能的模型，但让它们做你想做的事似乎部分没有得到解答。
好吧，你必须假设，随着技术的进步，当前的幻觉问题会减少，等等。
我并不是说它消失了。
And then you also have to assume that there are tests for efficacy.
So there has to be a way of knowing that the things exceeded.
So in the example that I gave of the TikTok competitor, and by the way, I was not arguing that you should illegally steal everybody's music.
What you would do if you're a Silicon Valley entrepreneur, which hopefully all of you will be, is if it took off, then you'd hire a whole bunch of lawyers to go clean the mess up, right?
But if nobody uses your product, it doesn't matter that you stole all the content.
然后，您还必须假设有功效测试。
因此，必须有一种方法可以知道事情超过了。
因此，在我举的 TikTok 竞争对手的例子中，顺便说一句，我并不是说你应该非法窃取每个人的音乐。
如果你是硅谷的企业家，希望你们所有人都会这样做，如果它成功了，那么你会雇佣一大群律师来清理烂摊子，对吧？
但是，如果没有人使用您的产品，那么您窃取所有内容并不重要。
And do not quote me.
Right.
Right.
You're on camera.
Yeah, that's right.
并且不要引用我的话。
右。
右。
你在镜头前。
是的，没错。
But you see my point.
In other words, Silicon Valley will run these tests and clean up the mess.
And that's typically how those things are done.
So my own view is that you'll see more and more performative systems with even better tests and eventually adversarial tests, and that will keep it within a box.
The technical term is called chain of thought reasoning.
但你明白我的意思。
换句话说，硅谷将进行这些测试并清理烂摊子。
这些事情通常是这样完成的。
因此，我个人的观点是，你会看到越来越多的高性能系统，这些系统具有更好的测试，最终是对抗性测试，这将将其保持在一个盒子内。
这个专业术语称为思维链推理。
And people believe that in the next few years, you'll be able to generate a thousand steps of chain of thought reasoning, right?
Do this, do this.
It's like building recipes, right?
That the recipes, you can run the recipe and you can actually test that it produced the correct outcome.
And that's how the system will work.
人们相信，在接下来的几年里，你将能够产生一千步的思维链推理，对吧？
做这个，做这个。
这就像建立食谱，对吧？
这些配方，你可以运行配方，你实际上可以测试它是否产生了正确的结果。
这就是系统的运作方式。
Yes, sir?
[inaudible] In general, you seem super positive about the potential for AI's problems.
I'm curious, like, what do you think is going to drive that?
Is it just more compute?
Is it more data?
是的,先生？
[听不清]总的来说，你似乎对人工智能的潜在问题非常乐观。
我很好奇，比如，你认为什么会推动这一点？
只是更多的计算吗？
是更多的数据吗？
Is it fundamental or actual shifts?
Yes.
Do you agree?
The amounts of money being thrown around are mind-boggling.
And I've chosen, I essentially invest in everything because I can't figure out who's going to win.
是根本性的转变还是实际的转变？
是的。
你同意吗？
被扔来扔去的钱数额令人难以置信。
我选择了，我基本上投资于所有事情，因为我无法弄清楚谁会赢。
And the amounts of money that are following me are so large.
I think some of it is because the early money has been made and the big money people who don't know what they're doing have to have an AI component.
And everything is now an AI investment, so they can't tell the difference.
I define AI as learning systems, systems that actually learn.
So I think that's one of them.
跟着我的钱数是如此之大。
我认为部分原因是因为早期的钱已经赚到了，那些不知道自己在做什么的大钱人必须有一个人工智能组件。
现在一切都是人工智能的投资，所以他们无法分辨出其中的区别。
我将人工智能定义为学习系统，即真正学习的系统。
所以我认为这是其中之一。
The second is that there are very sophisticated new algorithms that are sort of post-transformers.
My friend, my collaborator, for a long time has invented a new non-transformer architecture.
There's a group that I'm funding in Paris that has claims to have done the same thing.
There's enormous invention there, a lot of things at Stanford.
And the final thing is that there is a belief in the market that the invention of intelligence has infinite return.
第二个是有非常复杂的新算法，它们有点像后转换器。
我的朋友，我的合作者，长期以来一直发明了一种新的非变压器架构。
我在巴黎资助的一个团体声称也做了同样的事情。
那里有巨大的发明，斯坦福大学有很多东西。
最后，市场上有一种信念，即智能的发明具有无限的回报。
So let's say you put $50 billion of capital into a company, you have to make an awful lot of money from intelligence to pay that back.
So it's probably the case that we'll go through some huge investment bubble, and then it'll sort itself out.
That's always been true in the past, and it's likely to be true here.
And what you said earlier was you think that the leaders are pulling away from the rest.
Right now.
因此，假设你向一家公司投入了500亿美元的资本，你必须从知识产权中赚到很多钱才能偿还这笔钱。
因此，我们可能会经历一些巨大的投资泡沫，然后它会自行解决。
这在过去一直都是正确的，在这里很可能是正确的。
你刚才说的是，你认为领导人正在远离其他人。
马上。
And the question is roughly the following.
There's a company called Mistral in France.
They've done a really good job.
And I'm obviously an investor.
They have produced their second version.
问题大致如下。
法国有一家叫Mistral的公司。
他们做得很好。
我显然是一名投资者。
他们已经制作了第二个版本。
Their third model is likely to be closed because it's so expensive, they need revenue, and they can't give their model away.
So this open source versus closed source debate in our industry is huge.
And my entire career was based on people being willing to share software in open source.
Everything about me is open source.
Much of Google's underpinnings were open source.
他们的第三个模型可能会被关闭，因为它太贵了，他们需要收入，而且他们不能放弃他们的模型。
因此，在我们这个行业中，这种开源与闭源的争论是巨大的。
我的整个职业生涯都是基于人们愿意分享开源软件。
关于我的一切都是开源的。
谷歌的大部分基础都是开源的。
Everything I've done technically.
And yet, it may be that the capital costs, which are so immense, fundamentally changes how software is built.
You and I were talking.
My own view of software programmers is that software programmers' productivity will at least double.
There are three or four software companies that are trying to do that.
我在技术上所做的一切。
然而，如此巨大的资本成本可能会从根本上改变软件的构建方式。
你和我在说话。
我自己对软件程序员的看法是，软件程序员的生产力至少会翻一番。
有三四家软件公司正在尝试这样做。
I've invested in all of them in the spirit.
And they're all trying to make software programmers more productive.
The most interesting one that I just met with is called Augment.
And I always think of an individual programmer.
And they said, that's not our target.
我已经在精神上对他们所有人进行了投资。
他们都在努力提高软件程序员的工作效率。
我刚遇到的最有趣的一个叫做Augment。
我总是想到一个单独的程序员。
他们说，这不是我们的目标。
Our target are these 100 person software programming teams on millions of lines of code where nobody knows what's going on.
Well, that's a really good AI thing.
Will they make money?
I hope so.
So a lot of questions here.
我们的目标是这些 100 人的软件编程团队，他们拥有数百万行代码，没有人知道发生了什么。
嗯，这是一件非常好的事情。
他们会赚钱吗？
希望如此。
所以这里有很多问题。
Hi.
So at the very beginning you mentioned that there's the combination of the context window expansion.
The agents and the text to action is going to have unimaginable impacts.
First of all, why is the combination important?
And second of all, I know that you're not like a crystal ball and you can't necessarily tell the future.
你好。
所以在一开始，你就提到了上下文窗口扩展的组合。
代理人和行动文本将产生难以想象的影响。
首先，为什么这种组合很重要？
其次，我知道你不像一个水晶球，你不一定能预知未来。
But why do you think it's beyond anything that we could imagine?
I think largely because the context window allows you to solve the problem of recency.
The current models take a year to train roughly 18 months, six months of preparation, six months of training, six months of fine tuning.
So they're always out of date.
Context window, you can feed what happened.
但为什么你认为这超出了我们所能想象的任何事情？
我认为很大程度上是因为上下文窗口允许您解决新近度问题。
目前的模型需要一年的时间来训练大约18个月，六个月的准备，六个月的训练，六个月的微调。
所以它们总是过时的。
上下文窗口，您可以提供发生的事情。
You can ask it questions about the Hamas Israel war in a context.
That's very powerful.
It becomes current like Google.
In the case of agents, I'll give you an example.
I set up a foundation which is funding a nonprofit.
你可以在特定的背景下向它询问有关哈马斯以色列战争的问题。
这非常强大。
它像谷歌一样变得最新。
就代理而言，我举个例子。
我成立了一个基金会，为一个非营利组织提供资金。
I don't know if there's chemists in the room.
I don't really understand chemistry.
There's a tool called ChemCrow, C-R-O-W, which was an LLM-based system that learned chemistry.
And what they do is they run it to generate chemistry hypotheses about proteins and they have a lab which runs the tests overnight and then it learns.
That's a huge acceleration, accelerant in chemistry, material science and so forth.
我不知道房间里有没有化学家。
我真的不懂化学。
有一种工具叫做ChemCrow，C-R-O-W，这是一个基于LLM的学习化学的系统。
他们所做的是运行它来产生关于蛋白质的化学假设，他们有一个实验室，在一夜之间运行测试，然后学习。
这是一个巨大的加速，在化学、材料科学等领域都是促进剂。
So that's an agent model.
And I think the text to action can be understood by just having a lot of cheap programmers, right?
And I don't think we understand what happens, and this is again your area of expertise, what happens when everyone has their own programmer.
And I'm not talking about turning on and off the lights.
I imagine, another example, for some reason you don't like Google.
所以这是一个代理模型。
而且我认为可以通过有很多便宜的程序员来理解行动的文本，对吧？
我认为我们不明白会发生什么，这又是你的专业领域，当每个人都有自己的程序员时会发生什么。
我不是在谈论开灯和关灯。
我想，另一个例子，出于某种原因，你不喜欢谷歌。
So you say, "Build me a Google competitor." Yeah, you personally, you don't build me a Google competitor.
"Search the web.
Build a UI.
Make a good copy.
Add generative AI in an interesting way.
所以你说，“让我成为谷歌的竞争对手。是的，你个人，你不会让我成为谷歌的竞争对手。
“搜索网络。
构建 UI。
做一个好的副本。
以一种有趣的方式添加生成式 AI。
Do it in 30 seconds and see if it works." Right?
So a lot of people believe that the incumbents, including Google, are vulnerable to this kind of an attack.
Now, we'll see.
There were a bunch of questions who were sent over by Slatter.
I want to get some of them were upvoted.
在 30 秒内完成，看看它是否有效。右？
因此，很多人认为，包括谷歌在内的现有公司很容易受到这种攻击。
现在，我们拭目以待。
有一堆问题都是斯莱特送过来的。
我想让他们中的一些人被点赞。
So here's one.
We talked a little bit of this last year.
How can we stop AI from influencing public opinion, misinformation, especially during the upcoming election?
What are the short and long-term solutions for them?
Most of the misinformation in this upcoming election and globally will be on social media.
所以这里有一个。
我们去年谈过一点这个问题。
我们如何阻止人工智能影响公众舆论、错误信息，尤其是在即将到来的选举期间？
他们的短期和长期解决方案是什么？
在即将到来的选举和全球范围内，大多数错误信息都将出现在社交媒体上。
And the social media companies are not organized well enough to police it.
If you look at TikTok, for example, there are lots of accusations that TikTok is favoring one kind of misinformation over another.
And there are many people who claim without proof, that I'm aware of, that the Chinese are forcing them to do it.
I think we have a mess here.
And the country's going to have to learn critical thinking.
而且社交媒体公司的组织不够好，无法对其进行监管。
例如，如果你看看 TikTok，就会有很多指责 TikTok 偏爱一种错误信息而不是另一种错误信息。
据我所知，有很多人在没有证据的情况下声称，中国人正在强迫他们这样做。
我认为我们这里一团糟。
这个国家将不得不学习批判性思维。
That may be an impossible challenge for the U.S.
But the fact that somebody told you something does not mean that it's true.
Could it go too far the other way?
That there's things that really are true and nobody believes anymore.
You get some people call it a "pestimological crisis" that now, you know, Elon says, "No, I never did that.
对美国来说，这可能是一个不可能的挑战。
但是，有人告诉你一些事情并不意味着它是真的。
它会不会走得太远了？
有些事情真的是真的，没有人再相信了。
有些人称之为“悲观危机”，现在，你知道，埃隆说，“不，我从来没有那样做过。
Prove it." Oh, let's use Donald Trump.
Look, I think we have a trust problem in our society.
Democracies can fail.
And I think that the greatest threat to democracy is misinformation because we're going to get really good at it.
When I managed YouTube, the biggest problems we had on YouTube were that people would upload false videos and people would die as a result.
证明这一点。哦，让我们用唐纳德·特朗普。
听着，我认为我们的社会存在信任问题。
民主国家可能会失败。
我认为对民主的最大威胁是错误信息，因为我们要真正擅长它。
当我管理YouTube时，我们在YouTube上遇到的最大问题是人们会上传虚假视频，结果人们会死亡。
And we had a no-death policy.
Shocking.
And it was just horrendous to try to address this.
And this is before generative A.I.
I don't have a good answer.
我们有一个不死的政策。
噩。
试图解决这个问题真是太可怕了。
这是在生成式人工智能之前。
我没有一个好的答案。
One technical is not an answer, but one thing that seems like it could mitigate that I understand why it's more widely used is public key authentication.
That when Joe Biden speaks, why isn't it digitally signed like SSL is?
Or that celebrities or public figures or others, couldn't they have a public key?
Yeah, it's a form of public key and then some form of certainty of knowing how the system When I send my credit card to Amazon, I know it's Amazon.
I wrote a paper and published it with Jonathan Haidt, who's the one working on the anxiety generation stuff.
一个技术不是答案，但有一件事似乎可以缓解，我理解为什么它被更广泛地使用是公钥身份验证。
当乔·拜登（Joe Biden）讲话时，为什么它不像SSL那样进行数字签名？
或者说，名人、公众人物或其他人，难道他们就没有公钥吗？
是的，这是一种公钥的形式，然后是某种形式的确定性，知道系统如何当我将信用卡发送到亚马逊时，我知道它是亚马逊。
我写了一篇论文，并与乔纳森·海特（Jonathan Haidt）一起发表，他是研究焦虑一代的人。
It had exactly zero impact.
And he's a very good communicator.
I probably am not.
So my conclusion was that the system is not organized to do what you said.
You had a paper advocating what we did?
它的影响完全为零。
他是一个非常好的沟通者。
我可能不是。
所以我的结论是，这个系统没有组织起来做你说的。
你有一篇论文宣传我们所做的？
Advocating your proposal.
Okay, my proposal.
No, what you said.
Yeah, right.
And my conclusion is the CEOs in general are maximizing revenue.
倡导您的提案。
好的，我的建议。
不，你说的是什么。
是的，对。
我的结论是，CEO们总体上都在最大化收入。
To maximize revenue, you maximize engagement.
To maximize engagement, you maximize outrage.
The algorithms choose outrage because that generates more revenue.
Therefore, there's a bias to favor crazy stuff.
And on all sides, I'm not making a partisan statement here.
为了最大限度地提高收入，您可以最大限度地提高参与度。
为了最大限度地提高参与度，你就要最大限度地提高愤怒。
算法之所以选择愤怒，是因为这会产生更多的收入。
因此，有一种偏爱疯狂事物的偏见。
从各方面来看，我在这里不是在发表党派声明。
That's a problem.
That's got to get addressed in a democracy.
And my solution to TikTok, we talked about this earlier privately, is there was when I was a boy, there was something called the equal time rule, because TikTok is really not social media.
It's really television, right?
There's a programmer making you the numbers by the way are 90 minutes a day, 200 TikTok videos per TikTok user in the United States.
这是一个问题。
这必须在民主制度中得到解决。
而我对 TikTok 的解决方案，我们之前私下里谈过这个问题，就是在我还是个孩子的时候，有一种叫做平等时间规则的东西，因为 TikTok 真的不是社交媒体。
这真的是电视，对吧？
顺便说一句，有一个程序员给你的数字是每天 90 分钟，美国每个 TikTok 用户 200 个 TikTok 视频。
It's a lot, right?
So and the government is not going to do the equal time rule, but it's the right thing to do.
Some form of balance that is required.
All right, let's take some more questions.
Two quick questions.
很多，对吧？
因此，政府不会执行平等时间规则，但这是正确的做法。
需要某种形式的平衡。
好了，让我们再问一些问题。
两个简短的问题。
One, economic impact of LMs.
Slower, like, market impacts.
Slower.
You originally anticipated CHEG and a couple of service people.
And then two, do you think academia deserves or should get AI subsidies?
第一，LMs的经济影响。
较慢的，就像市场影响一样。
慢。
你原本期待的是CHEG和几个服务人员。
第二，你认为学术界是否应该或应该获得人工智能补贴？
Or do you think they should just partner with big players out there?
I pushed really, really hard on getting data centers for universities.
If I were a faculty member in the computer science department here, I would be beyond upset that I can't build the algorithms with my graduate students that will do the kind of PhD research.
And I'm forced to work with these.
And the companies have not, in my view, been generous enough with respect to that.
或者你认为他们应该与那里的大公司合作？
我非常非常努力地为大学建立数据中心。
如果我是这里计算机科学系的一名教员，我会感到非常沮丧，因为我不能和我的研究生一起构建能够进行博士研究的算法。
我被迫与这些人一起工作。
在我看来，这些公司在这方面还不够慷慨。
The faculty members that I talk with, many of whom you know, spend lots of time waiting for their credits from Google Cloud.
That's terrible.
This is an explosion we want America to win.
We want American universities.
There's lots of reasons to think that the right thing to do is to get it to them.
与我交谈的教职员工，其中许多人你认识，他们花了很多时间等待 Google Cloud 的学分。
太可怕了。
这是一场我们希望美国赢得的爆炸。
我们想要美国的大学。
有很多理由认为正确的做法是将其传达给他们。
So I'm working hard on that.
And your first question was labor market impact.
I'll defer to the real expert here.
As your amateur economist taught by Eric, I fundamentally believe that the college education high skills task will be fine because people will work with these systems.
I think the systems is no different from any other technology wave.
所以我正在努力解决这个问题。
你的第一个问题是对劳动力市场的影响。
我将在这里听从真正的专家。
正如埃里克（Eric）教授的业余经济学家一样，我从根本上相信，大学教育的高技能任务会很好，因为人们会使用这些系统。
我认为这些系统与任何其他技术浪潮没有什么不同。
The dangerous jobs and the jobs which require very little human judgment will get replaced.
We've got about five minutes left.
So let's go really quick with some quick.
I'll let you pick them, Eric.
Yes, ma'am.
危险的工作和几乎不需要人为判断的工作将被取代。
我们还有大约五分钟的时间。
因此，让我们快速地进行一些快速的操作。
我会让你选的，埃里克。
是的，女士。
Hi.
I'm really curious about the text to action and its impact on, for example, computer science education.
I'm wondering what you have thoughts on how CS education should transform to meet the age.
Well, I'm assuming that computer scientists as a group in undergraduate school will always have a programmer buddy with them.
So when you learn your first for loop and so forth and so on, you'll have a tool that will be your natural partner.
你好。
我真的很好奇从文本到行动及其对计算机科学教育的影响。
我想知道你对计算机科学教育应该如何转型以适应时代有什么看法。
好吧，我假设计算机科学家作为一个群体，在本科学校里总是会有一个程序员伙伴。
因此，当你学习了你的第一个for循环等等时，你就会有一个工具，它将成为你的自然伙伴。
And that's how the teaching will go on.
That the professor, he or she will talk about the concepts, but you'll engage with it that way.
And that's my guess.
Yes, ma'am, behind you.
Yeah.
这就是教学将如何继续进行的方式。
教授，他或她会谈论这些概念，但你会以这种方式参与其中。
这是我的猜测。
是的，马，在你身后。
是的。
You're talking more about the non-transformer architectures that you're excited about.
I think one that's been talked about is like state models, but then now a longer context class.
I'm more so curious what you're seeing in this case.
I don't understand the math well enough.
I'm really pleased that we have produced jobs for mathematicians because the math here is so complicated, but basically they are different ways of doing gradient descent, matrix multiply, faster and better.
你说的更多的是你感兴趣的非变压器架构。
我认为已经讨论过的一个类似于状态模型，但现在是一个更长的上下文类。
我更好奇你在这种情况下看到了什么。
我对数学的理解不够好。
我真的很高兴我们为数学家创造了就业机会，因为这里的数学非常复杂，但基本上它们是进行梯度下降、矩阵乘法、更快和更好的不同方式。
And transformers, as you know, is a sort of systematic way of multiplying at the same time.
That's the way I think about it.
And it's similar to that, but different math.
Let's see, over here.
Yes, sir.
如你所知，变压器是一种系统性的同时乘法方式。
我就是这么想的。
它与此相似，但数学方法不同。
让我们看看，在这里。
是的,先生。
Go ahead.
You mentioned in your paper on natural security that you have China and the U.S.
and the help of modern architectures today.
The next 10 and the next cluster down are all other U.S.
allies or teed up nicely through the U.S.
继续。
您在关于自然安全的论文中提到，您有中国和美国。
以及当今现代建筑的帮助。
接下来的 10 个和下一个集群是所有其他美国。
盟友或通过美国很好地配合。
allies.
I'm curious what your take is on those 10 and the middle that aren't formally allies.
How likely are they to get on board with securing our security deadline and what would hold them back from wanting to get on board?
The most interesting country is India because the top AI people come from India to the U.S.
and we should let India keep some of its top talent.
盟友。
我很好奇你对这 10 个不是正式盟友的中间人有什么看法。
他们有多大可能同意确保我们的安全期限，是什么会阻止他们想要加入？
最有趣的国家是印度，因为顶尖的人工智能人才来自印度到美国。
我们应该让印度留住一些顶尖人才。
Not all of them, but some of them.
And they don't have the kind of training facilities and programs that we so richly have here.
To me, India is the big swing state in that regard.
China's lost.
It's not going to come back.
不是全部，而是其中的一部分。
他们没有我们这里如此丰富的培训设施和项目。
对我来说，印度是这方面的大摇摆国家。
中国输了。
它不会再回来了。
They're not going to change the regime as much as people wish them to do.
Japan and Korea are clearly in our camp.
Taiwan is a fantastic country whose software is terrible, so that's not going to work.
Amazing hardware.
And in the rest of the world, there are not a lot of other good choices that are big.
他们不会像人们希望他们那样改变政权。
日本和韩国显然属于我们的阵营。
台湾是一个了不起的国家，它的软件很糟糕，所以这是行不通的。
惊人的硬件。
而在世界其他地方，没有很多其他好的选择。
Europe is screwed up because of Brussels.
It's not a new fact.
I spent 10 years fighting them.
And I worked really hard to get them to fix the EU act and they still have all the restrictions that make it very difficult to do our kind of research in Europe.
My French friends have spent all their time battling Brussels and Macron, who's a personal friend, is fighting hard for this.
欧洲因为布鲁塞尔而搞砸了。
这不是一个新事实。
我花了 10 年时间与他们战斗。
我非常努力地让他们修改欧盟的法案，但他们仍然有所有的限制，这使得我们在欧洲进行此类研究变得非常困难。
我的法国朋友把所有的时间都花在与布鲁塞尔的斗争上，而马克龙是我的私人朋友，正在为此而努力奋斗。
And so France, I think, has a chance.
I don't see Germany coming and the rest is not big enough.
Go ahead.
Yes, ma'am.
So I learned you're an engineer by training, like you call the compiler.
所以我认为法国有机会。
我不认为德国会来，其余的还不够大。
继续。
是的，马。
所以我了解到你是一名受过训练的工程师，就像你所说的编译器一样。
Given the capabilities that you envision these models having, should we still spend time learning to code?
Because ultimately, it's the old thing of why do you study English if you can speak English?
You get better at it.
You really do need to understand how these systems work, and I feel very strongly.
Yes, sir.
鉴于您设想的这些模型具有的功能，我们是否仍应花时间学习编码？
因为归根结底，这是一件老话，如果你会说英语，你为什么要学习英语？
你会做得更好。
你确实需要了解这些系统是如何工作的，我对此有非常强烈的感觉。
是的,先生。
Yeah.
I'm curious if you've explored the distributed setting and I'm asking because, sure, like making a large cluster is difficult, but MacBooks are powerful.
There's a lot of small machines across the world.
So do you think like folding at home or a similar idea works for training?
It does not.
是的。
我很好奇你是否探索过分布式设置，我问是因为，当然，像制作一个大集群很困难，但 MacBook 功能强大。
世界上有很多小型机器。
那么，你认为像在家折叠或类似的想法对训练有用吗？
事实并非如此。
Yeah, we've looked very hard at this.
So the way the algorithms work is you have a very large matrix and you have essentially a multiplication function.
So think of it as going back and forth and back and forth.
And these systems are completely limited by the speed of memory to CPU or GPU.
And in fact, the next iteration of Nvidia chips has combined all those functions into one chip.
是的，我们已经非常认真地研究了这个问题。
所以算法的工作方式是你有一个非常大的矩阵，你基本上有一个乘法函数。
所以可以把它想象成来回走动，来回走动。
而这些系统完全受到内存速度对CPU或GPU的限制。
事实上，Nvidia芯片的下一次迭代已将所有这些功能结合到一个芯片中。
The chips are now so big that they glue them all together.
And in fact, the package is so sensitive that the package is put together in a clean room as well as the chip itself.
So the answer looks like supercomputers and speed of light, especially memory interconnect, really dominate it.
So I think unlikely for a while.
Is there a way to segment the LLM?
现在的芯片太大了，以至于它们把它们都粘在一起。
事实上，封装非常敏感，以至于封装和芯片本身都是在洁净室中组装在一起的。
因此，答案看起来超级计算机和光速，尤其是内存互连，确实占据了主导地位。
所以我认为暂时不太可能。
有没有办法对 LLM 进行细分？
So Jeff Dean, last year when he spoke here, talked about having these different parts of it that you would train separately and then kind of federate.
In order to do that, you'd have to have 10 million such things and then the way you would ask the questions would be too slow.
He's talking about eight or 10 or 12 supercomputers.
Yeah, yeah.
So not at the level of MacBooks.
所以杰夫·迪恩（Jeff Dean）去年在这里发言时，谈到了让这些不同的部分分开训练，然后进行某种程度的联合。
为了做到这一点，你必须有1000万个这样的东西，然后你提出问题的方式就会太慢了。
他说的是8台、10台或12台超级计算机。
是的。
所以没有达到MacBook的水平。
Not at his level.
Yeah.
Let's see, in the back.
Yes, way back.
So I know after GQQ was released in the New York Times to open it up for using their works for training, where do you think that's going to go and what that means for data processing?
没有达到他的水平。
是的。
让我们看看，在后面。
是的，很久以前。
所以我知道，在《纽约时报》发布GQQ后，开放使用他们的作品进行培训，你认为这将走向何方，这对数据处理意味着什么？
I used to do a lot of work on the music licensing stuff.
What I learned was that in the 60s, there was a series of lawsuits that resulted in an agreement where you get a stipulated royalty whenever your song is played.
Even they don't even know who you are.
It's just paid into a bank.
And my guess is it'll be the same thing.
我曾经在音乐许可方面做了很多工作。
我了解到的是，在 60 年代，有一系列的诉讼导致了一项协议，每当播放您的歌曲时，您都会获得规定的版税。
甚至他们甚至不知道你是谁。
它只是支付到银行。
我的猜测是这将是一回事。
There'll be lots of lawsuits and there'll be some kind of stipulated agreement, which will just say you have to pay X percent of whatever revenue you have in order to use ASCAP BMI.
ASCAP BMI.
Look them up.
It's along.
It will seem very old to you, but I think that's how it will alternate.
会有很多诉讼，会有一些规定的协议，只是说你必须支付你所拥有的任何收入的X%才能使用ASCAP BMI。
ASCAP 体重指数。
查找它们。
它一直都在。
对你来说，这似乎很古老，但我认为这就是它交替的方式。
Yes, sir.
Yeah, it seems like there's a few players that are dominating AI, right?
And they'll continue to dominate.
And they seem to overlap with the large companies that all the antitrust regulation is kind of focused on.
How do you see those two trends kind of...
是的,先生。
是的，似乎有一些玩家在主导人工智能，对吧？
他们将继续占据主导地位。
它们似乎与所有反垄断法规所关注的大公司重叠。
您如何看待这两种趋势......
Yeah, do you see regulators breaking up these companies and how will that affect the...
Yeah.
So in my career, I helped Microsoft get broken up and it wasn't broken up.
And I fought for Google to not be broken up and it's not been broken up.
So it sure looks to me like the trend is not to be broken up.
是的，你是否看到监管机构拆分这些公司，这将如何影响......
是的。
所以在我的职业生涯中，我帮助Microsoft分拆了，它并没有分拆。
我为谷歌不被分拆而奋斗，它没有被分拆。
因此，在我看来，这种趋势肯定不会被打破。
As long as the companies avoid being John D.
Rockefeller the senior.
And I studied this.
Look it up.
That's how antitrust law came.
只要公司避免成为 John D。
老洛克菲勒。
我研究了这一点。
查一查。
反托拉斯法就是这样诞生的。
I don't think the governments will act...
The reason you're seeing these large companies dominate is who has the capital to build these data centers, right?
So my friend Reed and my friend Mustapha...
He's coming next week, two weeks from now.
Have Reed talk to you about the decision that they made to take inflection and essentially piece part it into Microsoft.
我不认为政府会采取行动......
你看到这些大公司占据主导地位的原因是谁有资本来建造这些数据中心，对吧？
所以我的朋友里德和我的朋友穆斯塔法......
他下周就要来了，两周后。
让 Reed 和你谈谈他们做出的决定，即采取 intrient 并将其基本上部分化为 Microsoft。
Basically, they decided they couldn't raise the tens of billions of dollars.
Is that number public that you mentioned earlier?
No.
Have Reed give you the number.
Maybe Reed can say it.
基本上，他们认为他们无法筹集到数百亿美元。
你之前提到的这个数字是公开的吗？
不。
让里德给你号码。
也许里德可以说。
I know you got to go.
I don't want to hold you back.
I want to leave with...
Shall we do one?
This gentleman.
我知道你得走了。
我不想拖累你。
我想带着...
我们来做一个好吗？
这位绅士。
I also have a question for you.
One more.
Yeah, go ahead.
Thank you so much.
I was wondering where all of this is going to lead countries who are non-participants in development of frontier models and access to compute, for example.
我还有一个问题要问你。
再来一个。
是的，去吧。
非常感谢。
我当时在想，所有这些将导致那些不参与前沿模型开发和计算获取的国家走向何方。
The rich get richer and the poor do the best they can.
They'll have to...
The fact of the matter is this is a rich country's game, right?
Huge capital, lots of technically strong people, strong government support, right?
There are two examples.
富人越来越富，穷人尽其所能。
他们必须...
事实是，这是一个富裕国家的游戏，对吧？
巨大的资本，很多技术强大的人才，强大的政府支持，对吧？
有两个例子。
There are lots of other countries that have all sorts of problems.
They don't have those resources.
They'll have to find a partner.
They'll have to join with somebody else, something like that.
I want to leave it...
还有很多其他国家也有各种各样的问题。
他们没有这些资源。
他们必须找到一个合作伙伴。
他们必须与其他人一起加入，类似这样的事情。
我想离开它...
Because I think the last time we met you, you were at a hackathon at AGI House and I know you spend a lot of time helping young people as they create a lot of wealth, and you spoke very passionately about wanting to do that.
Do you have any advice for folks here as they're building their...
They're writing their business plans for this class or policy proposals or research proposals at this stage of the careers going forward?
Well, I teach a class in the business school on this, so you should come to my class.
I am struck by the speed with which you can build demonstrations of new ideas.
因为我想我们上次见到你的时候，你正在AGI House参加一个黑客马拉松，我知道你花了很多时间帮助年轻人创造很多财富，你非常热情地谈到了想要这样做。
你对这里的人们有什么建议吗，因为他们正在建设他们的...
他们正在为这门课写商业计划书，还是在职业生涯的这个阶段写政策建议或研究建议？
好吧，我在商学院教一门关于这个的课程，所以你应该来我的课。
我对你能建立新想法的演示的速度感到震惊。
So, in one of the hackathons I did, the winning team, the command was, "Fly the drone between two towers," and it was given a virtual drone space.
And it figured out how to fly the drone, what the word between meant, generated the code in Python, and flew the drone in the simulator through the tower.
It would have taken a week or two from good professional programmers to do that.
I'm telling you that the ability to prototype quickly...
Part of the problem with being an entrepreneur is everything happens faster.
所以，在我做的一个黑客马拉松中，获胜的团队，命令是，“让无人机在两座塔之间飞行”，然后它被赋予了一个虚拟的无人机空间。
它弄清楚了如何驾驶无人机，单词 between 是什么意思，用 Python 生成了代码，并在模拟器中将无人机飞过塔楼。
优秀的专业程序员需要一两个星期的时间才能做到这一点。
我告诉你，快速原型制作的能力......
成为企业家的部分问题在于一切都发生得更快。
Well, now, if you can't get your prototype built in a day using these various tools, you need to think about that, right?
Because that's who your competitor is doing.
So, I guess my biggest advice is when you start thinking about a company, it's fine to write a business plan.
In fact, you should ask the computer to write your business plan for you, as long as it's legal.
No, no.
好吧，现在，如果你不能使用这些不同的工具在一天内构建你的原型，你需要考虑一下，对吧？
因为这就是你的竞争对手正在做的事情。
所以，我想我最大的建议是，当你开始考虑一家公司时，写一份商业计划书是可以的。
事实上，只要是合法的，你就应该让电脑为你写出你的商业计划。
不 不。
Actually, I should talk about that after you leave this.
But I think it's very important to prototype your idea using these tools as quickly as you can, because you can be sure there's another person doing exactly that same thing in another company, in another university, in a place that you've never been.
All right.
Well, thanks very much, Aaron.
Thank you all.
其实，我应该在你离开这里后再谈这个问题。
但我认为尽快使用这些工具来构建你的想法原型非常重要，因为你可以确定在另一家公司、另一所大学、一个你从未去过的地方，有另一个人在做同样的事情。
好吧。
嗯，非常感谢，亚伦。
谢谢大家。
I'm going to rush off.
Thank you.
So, actually, let me pick up on that very last point, because I don't think I talked about in the first class about using LLMs, which is welcome in this class for the assignments, but it has to get to your full disclosure.
So, when you use them, whether it's for the weekly assignments or for the final project or whatever, just like you would if you asked your friendly uncle or a classmate or anybody else to give you advice, you should do that, or if you have notes that you include in there.
So, what I thought I'd do is I want to talk a little bit about AIs as a GPT and what that means in terms of business and implications.
我要赶紧走。
谢谢。
所以，实际上，让我谈谈最后一点，因为我认为我在第一堂课上没有谈到使用LLMs，这在本课的作业中是受欢迎的，但它必须得到你的充分披露。
所以，当你使用它们时，无论是用于每周作业，还是用于最终项目或其他任何事情，就像你要求友好的叔叔、同学或其他任何人给你建议一样，你应该这样做，或者如果你有笔记，你要包含在那里。
所以，我想我要做的是我想谈谈人工智能作为 GPT 的问题，以及它在商业和影响方面意味着什么。
But before we do that, I just want to see if there are any questions you want to pick up on things that Eric brought up that I'll try and channel some of his thoughts, and we can talk about the things that came up, and then we can move on.
Yeah, go ahead.
One of the questions I want to ask is in relation to regulation, if the goal is to maintain supremacy, how do you create the right incentives so that everyone, allies and non-allies, are motivated to follow it?
You mean among companies that are competing with each other?
Companies are in countries, the U.S.
但在我们这样做之前，我只是想看看你是否想就埃里克提出的事情提出任何问题，我会试着引导他的一些想法，我们可以谈论出现的事情，然后我们可以继续前进。
是的，去吧。
我想问的一个问题是，与监管有关，如果目标是保持至高无上的地位，那么你如何创造正确的激励措施，使每个人，无论是盟友还是非盟友，都有动力去遵循它？
你是说在相互竞争的公司之间？
公司位于美国等国家。
and the EU, and it doesn't just become sort of a hamper or obstruct kind of development for the ones that choose to follow the regulations?
It's super tricky.
There's a book, Co-Opetition, that Mary Nailbough wrote about this, because there are definitely places where regulation can help companies and help an industry survive.
So regulation doesn't necessarily slow things.
I mean, standards are a good example, and having that clarified can make it easier for companies to compete.
还有欧盟，它不会成为那些选择遵守法规的人的阻碍或阻碍发展吗？
这非常棘手。
玛丽·奈尔布（Mary Nailbough）写了一本名为《竞合》的书，因为肯定有一些地方的监管可以帮助公司并帮助一个行业生存。
因此，监管并不一定会减慢事情的发生。
我的意思是，标准就是一个很好的例子，澄清这一点可以使公司更容易竞争。
So I've talked to a lot of the executives of these companies, and there are places where they wish there were some common standards, and sometimes there's a bit of a race to the bottom as well on some of the dangerous things.
One of the other reasons that the folks at Google say that they didn't move as fast is they felt like these LMs could be misused or dangerous, but their hand was sort of forced.
I was talking to some folks at one of the other big companies, and they said, "We weren't going to release this feature, but now competitors are doing it, so we're going to have to release it as well." So this is where regulation, there might be some interest in coordinating on regulation, but it's also, obviously, the more obvious thing is that it is used to hinder competition, and a lot of people, for instance, think that the reasons that some of the big companies are very opposed to some of the open source and making things more widely open source is they want to slow down competitors.
So there's both of those things going on.
Yeah.
因此，我与这些公司的很多高管进行了交谈，有些地方他们希望有一些共同的标准，有时在一些危险的事情上也会有一些竞争。
谷歌的人说他们没有那么快的另一个原因是，他们觉得这些LM可能被滥用或危险，但他们的手有点被迫。
我和其他一家大公司的一些人交谈，他们说，“我们本来不打算发布这个功能，但现在竞争对手正在这样做，所以我们也必须发布它。因此，这就是监管的地方，可能对协调监管有一些兴趣，但显然，更明显的是，它被用来阻碍竞争，例如，很多人认为一些大公司非常反对一些开源并使事情更广泛开源的原因是他们想减慢竞争对手的速度。
所以这两件事都在发生。
是的。
Quick question over there.
I just want to follow up on a comment about, should we still learn to code?
Should we still study English?
Are those going to be useful?
And Eric's replied, yes, like college-educated, high-skilled jobs or tasks are still going to be safe, but everything else that's going to require image editing might not be.
那边快速提问。
我只是想跟进一条评论，我们是否还应该学习编码？
我们还应该学习英语吗？
这些会有用吗？
埃里克回答说，是的，就像受过大学教育的高技能工作或任务仍然是安全的，但其他需要图像编辑的东西可能不是。
That's kind of like an interesting one.
Maybe we'll talk some more about that in a few minutes, but it is interesting to think about where the AI systems just replace what people are doing versus they complement them.
And in coding right now, it appears that they're not actually that helpful for the really best coders.
They're very helpful for moderately good coders.
But if you don't know anything at all about coding, they're not helpful either.
这有点像一个有趣的问题。
也许我们会在几分钟内更多地讨论这个问题，但有趣的是，人工智能系统在哪里取代了人们正在做的事情，而不是补充了人们正在做的事情。
在现在的编码中，它们似乎对真正最好的编码人员实际上并没有那么有帮助。
它们对于中等优秀的编码人员非常有帮助。
但是，如果您对编码一无所知，那么它们也没有帮助。
So it's kind of an inverted you.
And you can see why that would be the case, that if you don't even understand the code that they generate right now is often buggy or it isn't exactly right.
So if you can't even interpret and understand what's going on, you can't use it very effectively.
And for now, the very best coders, it appears that the code that is generated isn't at that level, so you get that U shape.
But that means if you don't know any code, you do need to have some in order for it to be useful.
所以这有点像是倒置的你。
你可以看到为什么会这样，如果你甚至不理解他们现在生成的代码，那么它们现在往往有问题，或者并不完全正确。
因此，如果你甚至不能解释和理解正在发生的事情，你就不能非常有效地使用它。
就目前而言，最好的编码人员，似乎生成的代码不在那个级别，所以你得到了那个U形。
但这意味着如果你不懂任何代码，你确实需要有一些代码才能让它有用。
And I think that's true for a lot of applications right now, that you have to have some basic understanding in order to get the most of it.
I think it's an interesting open question if that's always going to be the case.
I put up at the last class very briefly this slide that had level 0 through 5 autonomous cars.
And one of the things that actually we can talk about now is I'm trying to sort through is what if you took that paradigm and you applied it to all tasks in the economy?
Like how many would they go through?
我认为现在很多应用程序都是如此，你必须有一些基本的理解才能充分利用它。
我认为这是一个有趣的开放性问题，如果情况总是如此的话。
在最后一堂课上，我非常简短地介绍了这张幻灯片，它有 0 到 5 级的自动驾驶汽车。
实际上，我们现在可以谈论的一件事是，我试图梳理的是，如果你采用这种范式并将其应用于经济中的所有任务，会怎样？
他们会经历多少次？
So with autonomous cars, we aren't really at level 5 very much although I don't know how many of you guys have ridden in a Waymo, one of the Waymo cars.
So that one seems pretty good, although Sebastian Thrun, who I rode in it with, says it's just incredibly expensive right now.
They probably lose $50 to $100.
He doesn't know he's not there.
He started the program, but he's not there anymore.
因此，对于自动驾驶汽车，我们并没有真正达到5级，尽管我不知道你们中有多少人乘坐过Waymo，Waymo汽车之一。
所以这个看起来还不错，尽管和我一起骑车的塞巴斯蒂安·特伦（Sebastian Thrun）说它现在非常昂贵。
他们可能会损失 50 到 100 美元。
他不知道他不在那里。
他开始了这个项目，但他已经不在那里了。
But just all of the costs of running it, it's not practical.
Maybe it'll get down the curve.
Lidar will get cheaper, but we have a lot of sort of autonomous cars at level 2, 3, even 4, arguably, where humans are still involved.
And you see a lot of other tasks like coding.
I just talked about that.
但只是运行它的所有成本，这是不切实际的。
也许它会走下坡路。
激光雷达会变得更便宜，但我们有很多2级、3级甚至4级的自动驾驶汽车，可以说，人类仍然参与其中。
你会看到很多其他任务，比如编码。
我刚才谈到了这一点。
On the other hand, chess, that slide, the slide before it, I talked about what's sometimes called advanced chess or freestyle chess.
When Gary Kasparov, after he lost to Deep Blue in 1998, '97, he started this set of competitions where humans and machines could work together.
And for a long time, when I gave my TED talk, it was true, my TED talk in 2012, 2013, it was true at that time that a human working with a machine could beat Deep Blue or any chess computer.
And so the very best chess playing entities were these combinations.
That's not true anymore.
另一方面，国际象棋，那张幻灯片，之前的幻灯片，我谈到了有时被称为高级国际象棋或自由式国际象棋的东西。
当加里·卡斯帕罗夫（Gary Kasparov）在1998年，97年输给深蓝（Deep Blue）后，他开始了这一系列人类和机器可以一起工作的比赛。
在很长一段时间里，当我做TED演讲时，这是真的，我在2012年、2013年的TED演讲，在那个时候，一个使用机器的人可以打败深蓝或任何国际象棋电脑。
因此，最好的国际象棋游戏实体就是这些组合。
现在已经不是这样了。
AlphaZero and other programs like that, they would get nothing from a human contributing, just be like kind of an annoyance to the chess machine.
So that went through level zero, machines not being able to do anything, through a period where they work together to a period where it's fully autonomous in a span of 20 years or so.
It would be interesting if anybody wants to work on a research project or if any of you guys have thoughts right now, what are the criteria for which kinds of tasks in the economy will be in that middle zone?
Because that middle zone is kind of a nice one for us humans where the machines are helping us, but humans are still indispensable to creating value and that would be, that's a zone where you can have higher productivity, more wealth and performance, but also more likely to have shared prosperity because labor is sort of inherently distributed, whereas technology and capital, as Eric was just saying, potentially could be very concentrated.
Do you have a thought on that?
AlphaZero和其他类似的程序，他们不会从人类的贡献中得到任何东西，就像对国际象棋机器的一种烦恼。
因此，这经历了零级，机器无法做任何事情，经历了一个它们一起工作的时期，直到在20年左右的时间里完全自主的时期。
如果有人想做一个研究项目，或者你们中的任何人现在有想法，那会很有趣，经济中的哪些任务将处于中间区域的标准是什么？
因为这个中间区域对我们人类来说是一个很好的区域，机器正在帮助我们，但人类仍然是创造价值不可或缺的，那就是，在这个区域，你可以拥有更高的生产力、更多的财富和绩效，但也更有可能拥有共享繁荣，因为劳动力是天生分布的。 而正如埃里克刚才所说，技术和资本可能会非常集中。
你对此有什么想法吗？
I was just going to ask kind of a related question.
He was saying also that we have a 10 year like chip manufacturing.
Yeah, I was surprised about that.
And I think what was interesting to me as a labor economist is that it was really like a green flag I've seen in literature and news that, okay, if we're on showing all of this chip manufacturing, isn't that going to create some sort of resurgence in blue collar jobs?
And I wondered if you had any thoughts about intelligent robotic models or human labor.
我只是想问一个相关的问题。
他还说，我们有一个像芯片制造一样的10年。
是的，我对此感到惊讶。
我认为作为一名劳动经济学家，对我来说有趣的是，这真的就像我在文学和新闻中看到的一面绿旗，好吧，如果我们展示所有这些芯片制造，这不会在蓝领工作中创造某种复苏吗？
我想知道你是否对智能机器人模型或人类劳动有任何想法。
Well, I don't think it's going to be much of a, I mean, how many of you guys have visited the chip fab, anybody?
You guys, some, a few of you have.
How many workers were in that fab?
Yeah, I mean, well, okay.
So the answer is zero.
嗯，我认为这不会太大，我的意思是，你们中有多少人参观过芯片厂，有人吗？
你们，你们中的一些人，你们中的一些人。
那个晶圆厂有多少工人？
是的，我的意思是，好吧，好吧。
所以答案是零。
Like the reason they don't let you, they don't let anyone go in because we humans are too like clumsy and dirty and like, you know, we can't, this just, so it's all robotic.
It's sealed inside.
So there is like work to, you know, bring stuff to them, et cetera.
And if a robot like falls over or something goes wrong, they have to put on, you've probably seen these like, they look like space suits, you know, they have to go in and then they kind of maybe adjust something and then they go back out and hope they didn't break anything.
That's, so it's basically lights out.
就像他们不让你进去的原因，他们不让任何人进去，因为我们人类太笨拙和肮脏了，就像，你知道，我们不能，这只是，所以这都是机器人。
它被密封在里面。
所以就像工作一样，你知道，给他们带来东西，等等。
如果像这样的机器人摔倒或出了什么问题，他们必须穿上，你可能已经见过这些，他们看起来像宇航服，你知道，他们必须进去，然后他们可能会调整一些东西，然后他们回到外面，希望他们没有破坏任何东西。
就是这样，所以它基本上是熄灯的。
Yeah, I don't think it's, there are some, there is some like more sophisticated labor required that I don't think it's like a blue collar research.
In fact, one of the reasons that Apple reshored MacBook production to Texas is not because labor is so cheap in Texas or anything.
It's that they don't actually require a whole lot of labor anymore.
So it's a pretty labor, I think.
US manufacturing is surging in terms of output, but in terms of employment, it's not really growing all that much.
是的，我不认为是，有一些，有一些，需要更复杂的劳动力，我认为这不像蓝领研究。
事实上，苹果将MacBook生产转移到德克萨斯州的原因之一并不是因为德克萨斯州的劳动力如此便宜。
而是他们实际上不再需要大量的劳动力。
所以我认为这是一项相当艰巨的工作。
美国制造业在产出方面正在飙升，但就就业而言，它并没有真正增长那么多。
Yeah.
Let's go over here.
Yeah.
Do you think you have an inflation point coming for agents or text action models in the next year?
Oh, yeah.
是的。
让我们到这里过去。
是的。
你认为明年代理或文本行动模型的通货膨胀点是否即将到来？
哦，是的。
No, no.
Well, he said what Eric, I'm hearing similar thing.
Actually, he had a really nice way of putting those three trends.
I've heard about them all separately, but I think it was good to bring them all together.
Earlier today, I was talking to Andrew Eng, and he's like been beating this drum about agents in particular as being sort of the wave of 2024 where Andrew had a nice way of describing it that like, as you guys know, like if you have an LLM, I don't know, write an essay or something like that, it writes it one word at a time and it just goes through in one pass and writes the essay.
不 不。
好吧，他说了什么埃里克，我听到了类似的事情。
实际上，他有一种非常好的方式来表达这三个趋势。
我分别听说过它们，但我认为把它们放在一起是件好事。
今天早些时候，我和 Andrew Eng 交谈，他一直在敲响关于代理人的鼓，尤其是 2024 年的浪潮，安德鲁有一种很好的方式来描述它，就像你们知道的，就像如果你有法学硕士学位，我不知道，写一篇文章或类似的东西， 它一次写一个字，然后一口气通过并写这篇文章。
And it's pretty good.
But imagine if you had to do that, like no backspace, no chance to make an outline first.
You just kind of go through.
The agents now will say, okay, first make an outline.
That's the first step you do when you write an essay.
而且这还不错。
但是想象一下，如果你不得不这样做，就像没有退格键，没有机会先制定大纲一样。
你只是经历了。
代理人现在会说，好吧，先做一个大纲。
这是你写文章时要做的第一步。
And then, you know, fill in each paragraph, then go back and see if the flow is right.
Now go back and check the voice.
Is this the right level for our audience?
Now, you know, and by iterating like that, you can write a much, much better essay or any kind of a task.
This is a real revolution.
然后，你知道，填写每个段落，然后回过头来看看流程是否正确。
现在回去检查声音。
这对我们的观众来说是否合适？
现在，你知道了，通过这样的迭代，你可以写一篇更好的文章或任何类型的任务。
这是一场真正的革命。
There's all sorts of things you can just do much better if you do that.
Then the thing about the context window is also really important.
So I'm just going to quote smart people that I know.
Eric Corvitz, I was on a panel with him at the GSB.
Some of you may have been there.
如果你这样做，你可以做各种各样的事情，做得更好。
那么关于上下文窗口的事情也非常重要。
所以我只想引用我认识的聪明人的话。
埃里克·科维茨（Eric Corvitz），我和他一起在GSB参加了一个小组。
你们中的一些人可能去过那里。
It was last week.
And he had this nice taxonomy.
People were asking about fine-tuning.
I think Susan was asking about fine-tuning.
And he said, well, there's really three ways that you can take a model and have it more customized.
那是上周。
他有一个很好的分类法。
人们在询问微调的问题。
我想苏珊是在问关于微调的问题。
他说，嗯，实际上有三种方法可以让你拿一个模型，让它更加定制。
One is you can fine-tune it, which basically like train it some more.
Another is with larger and larger context windows.
And the third is with RAG or techniques like that that are retrieval augmented generation where it goes and accesses external data.
But these context windows seem to be like remarkably effective now.
I guess, as Eric was saying, we thought it was hard.
一个是你可以微调它，这基本上就像训练它一样。
另一个是具有越来越大的上下文窗口。
第三种是使用 RAG 或类似的技术，这些技术是检索增强生成，它访问并访问外部数据。
但是这些上下文窗口现在看起来非常有效。
我想，正如埃里克所说，我们认为这很难。
Maybe Peter can explain.
But for some reason, we're able to make much, much bigger ones.
And now as you can load a whole book or a whole set of books, you can load all sorts of information in there.
And that can give you all of the context around it.
So that's a pretty big revolution.
也许彼得可以解释。
但出于某种原因，我们能够制造出更大的。
现在，由于您可以加载整本书或整套书籍，因此您可以在其中加载各种信息。
这可以为您提供围绕它的所有背景信息。
所以这是一场相当大的革命。
It opens up a bunch of capabilities that we just didn't have before, including having things much more current, as Eric was saying.
Did you want to follow up on that?
That's a good question.
I mean, there's certainly a lot more capital going in, but that kind of begs the question in the comments, why is all this capital going there as opposed to somewhere else?
And I think if you look at the arc of history, sometimes it looks kind of smooth.
正如 Eric 所说，它开启了我们以前没有的一系列功能，包括让事情更加最新。
你想跟进吗？
这是个好问题。
我的意思是，肯定有更多的资本进入，但这在评论中引出了一个问题，为什么所有这些资本都流向那里而不是其他地方？
我认为，如果你看一下历史的弧线，有时它看起来有点顺畅。
But if you look more closely, there's a lot of jumps.
There are certain big inventions and smaller inventions.
And Andrew Carparthi was saying that he was playing around with physics.
And to really make progress in physics, to be a top physicist, you have to be incredibly smart, study a whole lot.
And maybe if you're lucky, you could make some small incremental contribution, and some people do.
但如果你更仔细地观察，会发现有很多跳跃。
有一些大的发明和较小的发明。
安德鲁·卡帕蒂（Andrew Carparthi）说他在玩弄物理学。
要想在物理学上真正取得进步，要成为一名顶尖的物理学家，你必须非常聪明，学习很多东西。
也许如果你幸运的话，你可以做一些小的增量贡献，有些人会这样做。
But he says that right now in AI machine learning, we seem to be in an era where there's just a lot of low-hanging fruit, that there have been some breakthroughs.
And instead of exhausting the space, like picking all the food off of a tree, it's more like combinatorics.
In second machinery, they talk about building blocks.
When you put two building blocks together, or Lego blocks, you can make more and more.
Right now, we seem to be in an era where there's just a lot of opportunity, and people are recognizing that.
但他表示，目前在人工智能机器学习方面，我们似乎处于一个有很多唾手可得的果实的时代，已经取得了一些突破。
它不是像从树上摘下所有食物那样耗尽空间，而是更像是组合学。
在第二种机械中，他们谈论的是积木。
当你把两个积木或乐高积木放在一起时，你可以制作得越来越多。
现在，我们似乎处于一个充满机会的时代，人们正在认识到这一点。
And discovery, one discovery begets another discovery, begets another opportunity.
And because of that, it attracts the investment.
And more people are involved.
And in economics, sometimes when more resources go in, you get diminishing returns, like in, I don't know, in agriculture or in mining.
Other places, there's increasing returns.
而发现，一个发现带来另一个发现，带来另一个机会。
正因为如此，它吸引了投资。
更多的人参与进来。
在经济学中，有时当更多的资源进入时，你会得到递减的回报，就像在农业或采矿业一样，我不知道。
其他地方，回报率越来越高。
And more engineers coming to Silicon Valley makes the existing engineers more valuable, not less valuable.
So we seem to be in an era where that's happening.
And then the flywheel of the additional investment, the additional dollars for training, all of that makes them more and more powerful.
I don't know how long this will continue, but I don't, you know, it just seems that there are some technologies that hit this really fertile period, and there's positive feedback and some help.
We seem to be in one of those right now.
更多的工程师来到硅谷，使现有的工程师更有价值，而不是更有价值。
因此，我们似乎正处于一个正在发生这种情况的时代。
然后是额外投资的飞轮，额外的培训资金，所有这些都使他们变得越来越强大。
我不知道这种情况会持续多久，但我不认为，你知道，似乎有一些技术可以达到这个真正肥沃的时期，并且有积极的反馈和一些帮助。
我们现在似乎处于其中之一。
So people who are trained in getting in the field are making contributions that are often quite significant in a faster time than they might have in some other fields.
Encouraging all of you guys, I think are doing the right thing right now.
Yeah.
Let's take a couple more questions, and then yeah.
Okay, how about over here?
因此，接受过该领域培训的人在比其他一些领域更快地做出的贡献往往非常重要。
鼓励你们所有人，我认为现在正在做正确的事情。
是的。
让我们再问几个问题，然后是的。
好吧，这边怎么样？
So not everyone can sit in a room and have all these discussions and debates around AI.
And so I'd like to get your thoughts on AI literacy for non-technical stakeholders, whether they're policy makers that have to make it in somewhat informed judgment, or the general public like, you know, using tech.
How do you think about explaining technical basics versus discussing abstract implications that don't necessarily have it right in?
Well, that's a hard one.
I have to say there's been a sea change recently in terms of how much people in Congress and elsewhere are paying more attention to this topic.
因此，不是每个人都能坐在一个房间里，围绕人工智能进行所有这些讨论和辩论。
因此，我想听听你对非技术利益相关者的人工智能素养的看法，无论他们是必须做出某种明智判断的政策制定者，还是喜欢使用技术的公众。
你如何看待解释技术基础知识与讨论不一定包含技术基础知识的抽象含义？
嗯，这很难。
我不得不说，最近国会和其他地方的人们更加关注这个话题的程度发生了翻天覆地的变化。
It used to be not something that they were interested in.
Now everyone's trying to understand it a little bit better.
And I think that there are a lot of margins where people can make contributions.
They can make contributions in the technical side.
But if anything, I mean, my bet is that the business and economic side is where the bigger bottleneck is right now.
这曾经不是他们感兴趣的事情。
现在每个人都在试图更好地理解它。
而且我认为人们可以做出贡献的地方有很多利润。
他们可以在技术方面做出贡献。
但如果有什么不同的话，我的意思是，我敢打赌，商业和经济方面是目前更大的瓶颈所在。
That, you know, even if, you know, if you made enormous contribution to the technology side, you still, there's still a gap converting that into something that will change policy.
So understand if you're into political science or politician, understanding what are the implications for democracy and for misinformation and power and concentration.
Those are things that are not well understood at all.
I don't know that a computer scientist is necessarily the right person to try to understand that.
But understanding enough about the technology so you know what might be possible.
你知道，即使，你知道，如果你在技术方面做出了巨大的贡献，你仍然，将其转化为改变政策的东西仍然存在差距。
因此，如果你对政治学或政治家感兴趣，就要明白这对民主、错误信息、权力和集中有什么影响。
这些都是根本没有被很好地理解的事情。
我不知道计算机科学家是否一定是试图理解这一点的合适人选。
但是对这项技术有足够的了解，这样你就知道什么是可能的。
And then thinking through what are the dynamics like Henry Kissinger was doing with Eric Schmidt in his book.
If you're an economist thinking through the labor market implications, the implications for concentration, the implications for inequality and jobs, implications for productivity and what drives productivity.
Those are things that are very ripe right now.
And you could go through lots of different fields where there's, you know, understanding well enough what the technology might be capable of.
But then thinking through the implications.
然后思考一下，就像亨利·基辛格在他的书中对埃里克·施密特所做的那样，动态是什么。
如果你是一名经济学家，正在思考劳动力市场的影响，对集中度的影响，对不平等和就业的影响，对生产力的影响以及驱动生产力的因素。
这些都是现在已经非常成熟的事情。
你可以穿越许多不同的领域，你知道，在这些领域中，你对这项技术可能能做什么有充分的理解。
但随后仔细思考其影响。
That's I think where some of the biggest payoffs are.
I mean, let me give you a little bit more of a concrete example.
And this is something I was going to talk about last week.
Electricity was also a general purpose technology.
And general purpose technologies have this characteristic that they're part of in and of themselves.
我认为这就是一些最大的回报所在。
我的意思是，让我再举一个具体的例子。
这是我上周要谈论的事情。
电力也是一种通用技术。
通用技术具有这种特性，它们本身就是其中的一部分。
But one of the real powers of general purpose technologies, GPTs as I was saying, is that they give complimentary, they ignite complimentary innovations.
So electricity, light bulbs and computers and electric motors and electric motors give you compressors and refrigerators and air conditioning.
You can just kind of have a whole set cascade of additional innovations from this one innovation.
And most of the value comes from these complimentary innovations.
One thing people don't appreciate enough is that some of the most important complimentary innovations are organizational and human capital complementarities.
但正如我所说，通用技术 GPT 的真正力量之一是它们给予互补，它们点燃互补的创新。
因此，电力、灯泡、电脑、电动机和电动机为您提供了压缩机、冰箱和空调。
你可以从这一项创新中获得一整套额外的创新。
大部分价值来自这些互补的创新。
人们不太欣赏的一件事是，一些最重要的互补创新是组织和人力资本的互补性。
So with electricity, when they first introduced electricity into factories, Paul David here at Stanford studied what happened to those factories.
And surprisingly, not much.
The factories when they started electrifying, they were not significantly more productive than the previous factories that were powered by steam engines.
He's like, well, that's kind of weird because this seems like a pretty important technology.
Is it just a fad?
因此，在电力方面，当他们首次将电力引入工厂时，斯坦福大学的保罗·戴维（Paul David）研究了这些工厂的情况。
令人惊讶的是，并不多。
当这些工厂开始电气化时，它们的生产力并不比以前由蒸汽机驱动的工厂高得多。
他说，嗯，这有点奇怪，因为这似乎是一项非常重要的技术。
这只是一种时尚吗？
Obviously not.
The factories before electricity were powered by steam engines.
They typically had a big steam engine in the middle and then crankshafts and pulleys that powered all the equipment.
And it was all distributed.
But you tried to have it as close to the steam engine as possible because if you make the crankshaft too long, it would break the torsion.
显然不是。
在通电之前，工厂由蒸汽机提供动力。
他们通常在中间有一个大蒸汽机，然后是曲轴和滑轮，为所有设备提供动力。
而且它都是分布式的。
但是你试图让它尽可能靠近蒸汽机，因为如果你让曲轴太长，它会破坏扭转。
When they introduced electricity, he found that in factory after factory, they would pull out the steam engine and they would get the biggest electric motor they could find and put it where the steam engine used to be and fire it up.
But you know, it didn't really change production a whole lot.
You can see that that's not a big deal.
So then they started building entirely new factories from scratch in a new location.
What did those look like?
当他们引入电力时，他发现在一个又一个工厂中，他们会拉出蒸汽机，他们会得到他们能找到的最大的电动机，然后把它放在蒸汽机原来的地方并点燃它。
但是你知道，它并没有真正改变生产。
你可以看到这没什么大不了的。
因此，他们开始在新地点从头开始建造全新的工厂。
那些是什么样子的？
Just like the old ones.
They would take the same model.
Some engineer would make a blueprint, you know, maybe take it, make a big X where the steam engine says, no, no, put electric motor here.
And they'd go and build a fresh factory.
Again, not a big improvement in productivity.
就像旧的一样。
他们将采用相同的模型。
一些工程师会画一个蓝图，你知道，也许拿它，做一个大X，蒸汽机说，不，不，把电动机放在这里。
他们会去建一个新的工厂。
同样，生产力没有太大的提高。
It took about 30 years before we started seeing a fundamentally different kind of factory where instead of having the central power source, you know, a big one in the middle, you had distributed power because electric motors, as you guys know, you know, you can make them big, you can make a medium, you can make them really, really small.
You can have them all connected in different ways.
So they started having each piece of equipment have a separate piece of a separate motor instead of one big one.
They called it unit drive instead of group drive.
I went and read the books in Baker Library at Harvard Business School from like 1914.
大约花了 30 年的时间，我们才开始看到一种根本不同的工厂，在那里，我们没有中央电源，你知道，中间有一个大的，你有分布式电力，因为电动机，正如你们知道的，你知道，你可以让它们变大，你可以制造介质，你可以真的制造它们， 真的很小。
您可以以不同的方式将它们全部连接起来。
因此，他们开始让每件设备都有一个单独的独立电机，而不是一个大电机。
他们称其为单元驱动器而不是组驱动器。
从1914年开始，我去哈佛商学院的贝克图书馆读了这些书。
And it was like this whole debate about unit drive versus group drive.
Well, when they started doing that, then they had a new layout of factories where it was typically on a single story where the machinery was not based on how much power it needed, but based on the on something else, the flow of materials.
And you started having these assembly line systems.
That led to a huge improvement in productivity, like a doubling of productivity or tripling in some cases.
So the lesson is not that electricity was a fad or dud and was overhyped.
这就像关于单位驱动与群体驱动的整个辩论。
好吧，当他们开始这样做时，他们就有了一个新的工厂布局，通常在一个单一的楼层上，机器不是基于它需要多少动力，而是基于其他东西，即材料的流动。
你开始拥有这些装配线系统。
这导致了生产力的巨大提高，例如生产力翻了一番或在某些情况下增加了两倍。
因此，教训并不是电力是一种时尚或无用，并且被过度炒作了。
Electricity was a fundamentally valuable technology.
But it wasn't until they had that process innovation, that organizational innovation of rethinking how to do production that you got the big payoff.
There's a lot of stories like that.
I only told you one of them.
We don't want that much time.
电力是一项从根本上说很有价值的技术。
但是，直到他们有了流程创新，重新思考如何进行生产的组织创新，你才得到了巨大的回报。
像这样的故事很多。
我只告诉了你其中一个。
我们不需要那么多时间。
So I tell you the other ones.
But in some of my books and articles, if you look at the steam engine and others, you had similar generational lags decades before people realized that this technology could allow you to do something completely different than you used to do.
I think AI is a bit like that in some ways, that there's going to be a lot of organizational innovations, going to be new business models, new ways of organizing an economy that we hadn't thought of before.
Right now, people are mostly just retrofitting.
I could go through a whole other set of skill changes that are complementary.
所以我告诉你其他的。
但是在我的一些书和文章中，如果你看看蒸汽机和其他东西，你就会发现，在人们意识到这项技术可以让你做一些与以前完全不同的事情之前，你就已经有了类似的代际滞后。
我认为人工智能在某些方面有点像这样，将会有很多组织创新，将会出现新的商业模式，组织经济的新方式，这是我们以前从未想过的。
现在，人们大多只是在改装。
我可以进行一系列其他的技能更改，这些更改是互补的。
I don't know what they all are.
You have to be creative to think about them.
But that's what the gap is.
In the case of early computers, it's literally like 10 times more investment in organizational capital and human capital, if you look at the size of the investments, to the hardware and software.
So that's very big.
我不知道它们都是什么。
你必须有创造力来思考它们。
但这就是差距所在。
就早期计算机而言，如果你看一下投资的规模，那么在硬件和软件方面的投资实际上就像是组织资本和人力资本的10倍。
所以这是非常大的。
That said, I'm open to adjusting my thoughts on this a bit because ChatGPT and some of the other tools, they have been adopted very quickly and they have much more quickly been able to change things, in part because you don't need to learn Python to the same degree.
You can do a lot of things just in English.
And you can get a lot of value just by putting them on top of the existing organization.
So some of it's happening faster.
And in some of the papers that you may have read for the readings here, you know, we had like 15, 20, 30% productivity gains pretty quickly.
也就是说，我愿意稍微调整一下我对此的看法，因为 ChatGPT 和其他一些工具，它们已经被非常迅速地采用，并且能够更快地改变事情，部分原因是你不需要学习 Python 到相同的程度。
你可以用英语做很多事情。
而且，只要将它们置于现有组织的顶部，您就可以获得很多价值。
因此，其中一些发生得更快。
在你可能读过的一些论文中，你知道，我们的生产力很快就提高了15%、20%、30%。
But my suspicion is that it will be even bigger once people figure out these complementary innovations.
And so that's a long way of answering your question about it.
It's not just the technical skills.
It's figuring out all the other stuff, all the ways of rethinking things.
So those of you who are at the business school or in economics, you know, there's a lot of opportunity there to rethink your areas now that you've been given this amazing set of technologies.
但我怀疑的是，一旦人们弄清楚这些互补的创新，它就会变得更大。
因此，要回答您的问题还有很长的路要走。
这不仅仅是技术技能。
它正在弄清楚所有其他的东西，所有重新思考事物的方法。
所以，你们中那些在商学院或经济学工作的人，你知道，现在有很多机会来重新思考你们的领域，因为你们已经获得了这套惊人的技术。
Yeah, question.
It seems like you're expressing more caution than Eric was with regard to the speed of transformation.
Am I correct in saying that?
Well, so I would make a distinction between two things.
I'll defer to him and others on the technology side.
是的，问题。
看起来你在转变速度方面比埃里克更谨慎。
我这么说对吗？
好吧，所以我要区分两件事。
我会听从他和技术方面的其他人的意见。
We're going to hear from several other folks.
And there are people who are equally optimistic as him or even more optimistic on the technology side.
There's also people who are less optimistic.
But technology alone is not enough to create productivity.
So you can have an amazing technology.
我们将听取其他几个人的意见。
而有些人和他一样乐观，甚至在技术方面更乐观。
也有一些人不那么乐观。
但是，仅靠技术还不足以创造生产力。
因此，您可以拥有一项了不起的技术。
And then for various reasons, A, maybe people just don't figure out an effective way to use it.
Another is it may be regulatory things.
I mean, some of my computer science colleagues introduced and developed better radiology systems for reading medical images.
They weren't adopted because of cultural, you know, people just didn't want them.
They didn't want and there are safety reasons.
然后由于各种原因，A，也许人们只是没有找到有效的方法来使用它。
另一个原因是可能是监管方面的问题。
我的意思是，我的一些计算机科学同事介绍并开发了更好的放射学系统来阅读医学图像。
他们不是因为文化原因而被收养的，你知道，人们只是不想要他们。
他们不想要，而且有安全原因。
When I did an analysis of which tasks I could help the most and which professions were most affected, I was surprised that airline pilots was kind of near the top.
But I think that a lot of people would not feel comfortable not having the pilot go down with you.
So they sort of you want to have the human in there.
So there are a lot of different things that might slow it down significantly.
And I think that's something we need to be conscious of.
当我分析哪些任务我能帮助最大，哪些职业受到的影响最大时，我惊讶地发现航空公司的飞行员已经接近顶峰。
但我认为，如果没有飞行员和你一起坠落，很多人会感到不舒服。
所以他们有点你想让人类在那里。
因此，有很多不同的事情可能会显着减慢它的速度。
我认为这是我们需要意识到的事情。
And if we could address those bottlenecks, that would probably do more for productivity than just working on the technology alone.
Yeah, question.
So Eric had an interesting comment on data centers in universities.
I think this is a larger point of like, and I was going to ask him why doesn't he write a check?
People are asking him that question.
如果我们能够解决这些瓶颈，那可能会比仅仅在技术上工作对生产力有更大的帮助。
是的，问题。
因此，埃里克（Eric）对大学的数据中心发表了有趣的评论。
我认为这是一个更大的问题，我打算问他为什么不写支票？
人们在问他这个问题。
Sort of like, what is the role of the university ecosystem?
Obviously, there is this larger I'm sure all of the CS professors here.
So I'll take I mean, I think it'd be great if there were more funding.
I mean, the federal government has something called the national AI resource that is helping a little bit, but it's in like the millions of dollars, tens of millions of dollars, not billions of dollars, let alone hundreds of billions of dollars.
Although Eric did mention to me before class that they're working on something that could be much, much bigger.
有点像，大学生态系统的作用是什么？
显然，我相信这里所有的计算机科学教授都有这个更大的问题。
所以我认为我的意思是，如果有更多的资金，那就太好了。
我的意思是，联邦政府有一种叫做国家人工智能资源的东西，它正在提供帮助，但它大约有数百万美元，数千万美元，而不是数十亿美元，更不用说数千亿美元了。
尽管埃里克在上课前确实向我提到过，他们正在做一些可能要大得多的事情。
He's pushing for something much, much bigger.
I don't know if it'll happen.
That's for training these really large models.
I had a really interesting conversation with Jeff Hinton once.
Jeff Hinton, as you know, is sort of like one of the godfathers of deep learning.
他正在推动更大的目标。
我不知道这是否会发生。
这是为了训练这些非常大的模型。
我曾经和Jeff Hinton有过一次非常有趣的对话。
如你所知，Jeff Hinton有点像深度学习的教父之一。
And I asked him like what kind of hardware he found most useful for doing his work.
And he was sitting at his laptop and kind of just tapped his MacBook.
And it just reminded me there's a whole other set of research that maybe universities have a competitive advantage in, which is not training hundred billion dollar models, but it's innovating new algorithms like whatever comes after Transformers and there's a lot of other ways that people can make contributions.
So maybe there's a little bit of a divisional labor.
我问他，他觉得什么样的硬件对他的工作最有用。
他坐在笔记本电脑前，轻点了一下他的MacBook。
它只是提醒了我，还有一整套其他的研究，也许大学在其中具有竞争优势，它不是训练千亿美元的模型，而是在创新新的算法，就像《变形金刚》之后的任何东西一样，人们可以通过很多其他方式做出贡献。
所以也许有一点分工。
I'm all for and support my colleagues asking for more budgets for GPUs, but that's not always where academics can make the biggest contribution.
Some of it comes from ideas and new ways of different perspective about thinking about things, new approaches.
And that's likely where we have an advantage.
I had dinner with Sendham Melanathon last week.
He just moved from Chicago to MIT.
And he was a researcher.
我完全支持并支持我的同事们要求为 GPU 增加预算，但这并不总是学术界可以做出最大贡献的地方。
其中一些来自不同角度思考事物的想法和新方法。
这可能是我们的优势所在。
上周我和 Sendham Melanathon 共进晚餐。
他刚从芝加哥搬到麻省理工学院。
他是一名研究员。
We're talking about what is the comparative advantage of universities?
And he made the case, you know, patience is one of them, that there are people in universities who are working on very long term projects.
You know, there's people working on fusion.
They've been working on fusion for a long time, not because they're going to get, you know, a lot of money this year or 10 years from now, probably from building a fusion plant or even 20 years.
I don't know how long it is for fusion.
我们谈论的是大学的比较优势是什么？
他提出了一个案例，你知道，耐心就是其中之一，大学里有些人正在从事非常长期的项目。
你知道，有人在研究核聚变。
他们从事核聚变研究已经有很长一段时间了，不是因为他们会在今年或10年后获得很多钱，可能是通过建造核聚变工厂甚至20年。
我不知道融合需要多长时间。
But, you know, it's just something that people are willing to work on even if the timelines are a little further.
It's harder for companies to afford to have those kinds of timelines.
So there's a comparative advantage or divisional labor in terms of what universities might be able to do.
We have just a couple minutes left.
This is kind of fun.
但是，你知道，这只是人们愿意做的事情，即使时间线更远一些。
对于公司来说，承担这样的时间表是更困难的。
因此，就大学可能能够做什么而言，存在着比较优势或分工劳动。
我们只剩下几分钟了。
这很有趣。
So we'll just do one or two more questions.
And then I want to talk a little bit about the projects.
Yeah.
Go ahead.
I'm Kevin.
所以我们再问一两个问题。
然后我想谈谈这些项目。
是的。
继续。
我是凯文。
I was wondering about the emerging capabilities of AI.
It seemed that Eric was leaning more towards the architectural differences and designing better models versus the last class we talked about, Morse law instead.
So I'm wondering how you sort of...
Well, he said all three.
So you guys remember the scaling laws?
我对人工智能的新兴能力感到好奇。
看起来，与我们谈论的上堂课（摩尔斯定律）相比，埃里克似乎更倾向于架构差异并设计出更好的模型。
所以我想知道你是怎么......
嗯，他说了这三个。
所以你们还记得缩放定律吗？
It had like three parts to it.
I think I put the scaling law that Dario and team...
So there's more compute, more data and algorithmic improvements, including more parameters.
And all three of them, I think I heard Eric say all three of them were important.
But not to be dismissed, this last one, like new architectures, all three of them, I think, are being important.
它有三个部分。
我想我把达里奥和团队的缩放定律......
因此，有更多的计算、更多的数据和算法改进，包括更多的参数。
他们三个，我想我听到埃里克说他们三个都很重要。
但不可忽视的是，最后一个，就像新的架构一样，我认为这三个都很重要。
So I think there was another question in there, though, also.
How much closer are we to like an AGI type system?
So Eric doesn't think we're like that close to AGI type systems, although I don't think it's like a sharp definition.
You know, in fact, that was one of the...
I was going to ask him that question, but we ran out of time.
所以我认为这其中还有另一个问题。
我们离喜欢AGI类型的系统还有多远？
所以Eric不认为我们像AGI类型的系统那么接近，尽管我不认为这是一个清晰的定义。
你知道，事实上，那是......
我本来想问他这个问题，但我们的时间不多了。
It would have been good to hear him describe it.
But when I was talking to him, it's just not that sharply defined thing.
In some ways, AGI is already here.
Peter Norvig wrote an article called AGI is already here.
I don't know if it's in the reading packet.
如果能听他描述一下就好了。
但是当我和他交谈时，这并不是那么明确的事情。
在某些方面，AGI已经在这里。
彼得·诺维格（Peter Norvig）写了一篇名为《AGI已经在这里》的文章。
我不知道它是否在阅读包中。
I think if it's not, I'll put it in there.
It's a fun little article with Blaise Iarca.
And a lot of the things that 20 years ago people would have said, this is what AGI is.
That's kind of what LLMs are doing.
Not as well, maybe, but it's sort of solving problems in a more general way.
我想如果不是，我会把它放在那里。
这是一篇有趣的小文章，作者是布莱斯·伊尔卡（Blaise Iarca）。
20年前人们会说的很多事情，这就是AGI是什么。
这就是LLMs正在做的事情。
也许不是那么好，但它以一种更普遍的方式解决问题。
On the other hand, there's obviously many things they do much worse than humans currently.
Ironically, physical tasks are one of the ones that humans have a comparative advantage in right now.
You guys may know Moravec's paradox.
Hans Moravec pointed out that often the kinds of things that a three-year-old or a four-year-old can do, like buttoning a shirt or walking upstairs, are very hard to get a machine to be able to do.
Whereas a lot of things that a lot of PhDs have trouble doing, like solving convex optimization problems, are things that machines are often quite good at.
另一方面，显然他们目前做的很多事情比人类差得多。
具有讽刺意味的是，体力劳动是人类目前具有比较优势的任务之一。
你们可能知道莫拉维克的悖论。
汉斯·莫拉维克（Hans Moravec）指出，通常，一个三岁或四岁的孩子可以做的事情，比如扣衬衫扣子或上楼，很难让机器能够完成。
然而，很多博士都难以做到的事情，比如解决凸优化问题，往往是机器非常擅长的事情。
So it's not quite things that are easy for humans and hard for computers and other things that are hard for humans and easy for computers.
They're not like the same scale.
And next week we have Mira Morati, Chief Technology Officer of OpenAI, briefly the CEO of OpenAI.
So come with your questions for her.
We'll see you.
因此，对人类来说容易，对计算机来说困难的事情，对人类来说并不容易的事情，对计算机来说并不容易的事情并不完全相同。
它们不像相同的规模。
下周，我们邀请到了 OpenAI 的首席技术官 Mira Morati，她短暂地担任了 OpenAI 的首席执行官。
所以带着你的问题来问她。
我们拭目以待。
