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未来的AI设备会是什么样子？
What does the AI device of the future look like?
让我们问问正在研发其核心芯片的首席执行官。
Let's ask the CEO building the chips that will power it.
接下来，我们将与克里斯蒂亚诺·奥曼一起探讨这个问题。
That's coming up with Cristiano Oman right after this.
本集由高通公司赞助播出。
This episode is brought to you by Qualcomm.
高通正在将智能计算带入每一个角落。
Qualcomm is bringing intelligent computing everywhere.
在每一个技术转折点上，高通都一直是值得信赖的合作伙伴，帮助世界应对最重要的挑战。
At every technological inflection point, Qualcomm has been a trusted partner helping the world tackle its most important challenges.
高通领先的AI技术、高性能低功耗计算以及无与伦比的连接解决方案，有能力构建新生态系统、变革产业，并改善我们体验世界的方式。
Qualcomm's leading edge AI, high performance, low power computing, and unrivaled connectivity solutions have the power to build new ecosystems, transform industries, and improve the way we all experience the world.
AI最有价值的应用是否在工业领域？
Can AI's most valuable use be in the industrial setting?
在参观了IFS在纽约市举办的Industrial X发布会，并与IFS首席执行官马克·穆菲特交谈后，我越来越深入地思考这个问题。
I've been thinking about this question more and more after visiting IFS' Industrial X unleashed event in New York City and getting a chance to speak with IFS CEO, Mark Muffett.
为了给出一个清晰的例子，穆菲特告诉我，IFS正在派遣波士顿动力公司的Spot机器人进行巡检，将数据传回IFS的神经中枢，再借助大型语言模型，为需要处理的区域指派合适的技术人员。
To give a clear example, Muffett told me that IFS is sending Boston Dynamics spot robots out for inspection, bringing that data back to the IFS nerve center, which then with the assistance of large language models, can assign the right technician to examine areas that need attending.
这是技术领域一个令人着迷的前沿方向，我很感谢IFS的合作伙伴让我看到了这一点。
It's a fascinating frontier of the technology, and I'm thankful to my partners at IFS for opening my eyes to it.
如需了解更多，请访问ifs.com。
To learn more, go to ifs.com.
网址是ifs.com。
That's ifs.com.
欢迎收听《大型科技》播客，这是一档致力于对科技世界及其更广泛领域进行冷静而深入对话的节目。
Welcome to Big Technology Podcast, a show for cool headed and nuanced conversation of the tech world and beyond.
我们现在位于达沃斯的高通展台，今天为大家准备了一场精彩的节目。
We are here at Davos at the Qualcomm space, and we have a great show for you today.
我们将讨论AI设备的未来。
We're gonna talk about the future of the AI device.
我们将讨论什么是AI电脑，以及是否有人会想要它。
We're gonna talk about what an AI PC is and whether anybody's gonna want it.
我们将讨论数据中心建设、机器人和工业AI。
We're gonna talk about the data center build out robotics and industrial AI.
今天和我们一起讨论的完美嘉宾是高通首席执行官克里斯蒂安·阿蒙。
And here to do it with us is the perfect guest, Qualcomm CEO Cristiano Aman.
克里斯蒂安，很高兴见到你。
Cristiano, great to see you.
我也很高兴见到你。
Great to see you too.
非常高兴能和你进行这次对话。
Very happy having this conversation with you.
当然。
Definitely.
现在正是我们进行这次对话的绝佳时机，因为关于AI设备的讨论正从理论走向现实，而高通可能正处于这一中心。
It's we're it is a perfect time for us to have this conversation because talk of an AI device is going from theoretical to concrete and Qualcomm might be at the center of it.
所以让我为我们的观众简单介绍一下高通公司，如果你还不熟悉的话。
So let me give for our audience, if you're new to Qualcomm, a little bit of a introduction to the company.
一家价值1700亿美元的公司。
A $170,000,000,000 company.
所以它非常庞大。
So it's very big.
它是骁龙芯片的设计者，这款芯片广泛应用于手机，尤其是高端安卓设备，也用于电脑、汽车，以及越来越多的可穿戴设备。
It's the designer of the Snapdragon Snapdragon chip, which is in mobile phones, notably high end Androids, also PCs, autos, and increasingly wearables.
还有龙翼芯片，我们待会儿会谈到，它主要用于机器人等工业应用场景。
There's also the Dragon Wing chip, which we're gonna talk about, which is in industrial use cases like robotics.
而你最近也开始进入AI数据中心服务器领域，用于AI推理。
And you just got into AI data center building servers for AI inference.
因此，你确实是AI故事的核心——无论是在可穿戴设备还是在数据中心。
So a chip designer really at the center of the AI story, whether whether it comes to wearables or in the data center.
我喜欢这个说法。
I like that.
好的。
Okay.
非常好。
Very good.
我觉得这是对高通公司一个很好的介绍。
I think that's a great introduction of Qualcomm.
也许我再补充一点。
Maybe I'll just add one thing to it.
我认为，高通是一家非常独特的半导体公司。
I think, you know, Qualcomm is a very unique semiconductor company.
尤其是在当今环境下，连接性、计算能力和AI处理都至关重要，而高通是为数不多的将这三者全部整合在一家公司内的企业。
Think especially in in today's environment when connectivity is important, computing is important, AI processing is important, one of the few companies that had all of it in in under a a single, roof.
我们可能是少数几家能覆盖从耳机的五瓦到数据中心的五百瓦这样广泛功耗范围的半导体公司之一。
And we're probably one of the few semiconductor companies that go from five watts to your earbud now to 500 watts when you think about a data center.
对于这家公司来说，现在是一个令人兴奋的时刻，对于整个科技行业而言也是如此，因为AI正在渗透到每一个领域。
And, it's exciting time for the company, also exciting time for technology since AI is going into everything.
而为智能手机设计芯片使你处于一个非常独特的地位，因为当我们所有人开始想象AI设备会是什么样子时，显然，AI背后的计算能力至关重要，而你正处在能够实现这一点的位置上。
And designing the chip for the smartphone has put you in a very interesting position because as we all start to imagine what an AI device is going to look like, obviously, when it comes to AI, the compute underneath is really important and you're in position to do it.
最近你谈到过，你认为AI设备的市场机会有多大，我们接下来会探讨这种设备的形态。
And recently you've talked about how your belief is that the market opportunity for an AI device and we're going to get into what the form factor is going to look like.
但这个市场机会将达到一百亿台设备，规模将超过智能手机市场。
But the market opportunity is 10,000,000,000 devices which would make it bigger than the smartphone market.
你是如何得出这个数字的？
How do you get to that number?
这很有趣。
So it's it's it's interesting.
要理解这个数字，关键在于看到智能手机是如何在不同代际中演变的。
And I think for you to for you to get at that number, it's actually important to see, how the smartphone had, you know, evolved over different generations.
我认为你有几点需要考虑。
And And I think you have a couple of things.
首先是手机的演进。
You have the evolution of phones.
你还有计算能力的演进。
You have the evolution of compute.
然后是人工智能如何改变未来的计算方式。
And then how AI changes that going forward.
也许我会带大家稍微回顾一下这段历程，来谈谈它。
And maybe I'll take us a little bit into that journey just to talk about it.
其中一个最大的变化，我不必追溯到2G时代，但当我们在蜂窝网络中引入宽带并意识到可以实现宽带速度时，我们发现宽带的另一端需要一台计算机。
One of the biggest change I won't go all the way back to two gs but one of the biggest change that happened in the phone industry when when we develop broadband into cellular and we said we can have a broadband speeds, we realized that on the other side of the broadband, need a computer.
所以你的手机必须变成一台计算机，你需要开发一台能放在手掌中的计算机，这就是智能手机。
So your phone need to become a computer and you need to develop a computer that will fit in the palm of your hand and that's that's the smartphone.
正是这款改变了计算机命运的智能手机，对吧？
That's the smartphone that changes computer forever, right?
因为它是我们不可或缺的设备，我们始终随身携带，它一直是我们数字生活的中心。
Because it's our inseparable device, we carry with us all the time and it is doing, it's been at the center of our digital life.
现在，随着你持续进步，我认为在当前的智能手机领域，每年的销量已达12亿台。
Now as you keep advancing I think in you know in smartphones right now we are in the billion every single year is 1,200,000,000 phones are purchased.
这是全球第一大消费电子产品，人人都有一部。
It's the number one consumer electronics and everybody has one.
但当你开始思考AI正在发生的变化，尤其是当计算机借助AI开始理解我们时，你就不再只是考虑随身携带的计算机，而是开始考虑可穿戴的计算机——因为如果智能代理要对你有用，它们必须时刻陪伴在你身边。
But when you start thinking about what's happening with AI and especially as computers using AI now understand us, then you're starting to go into not only the computer that you carry but also the computer that you wear especially because if agents are going to be useful for you they're going to be with you all the time.
于是，你从随身携带手机，扩展到佩戴眼镜、戒指、手环、手表等各种设备。
And then you start to go from carrying a phone to also having a glass or a ring or a bracelet, a watch, and all those things.
但这些设备彻底改变了可穿戴设备的本质。
But they changed the nature of what they of wearable used to be.
过去谈到可穿戴设备和技术时，它们的设计只是为了扩展手机的功能。
Wearable was when you talk about wearables and technology, was designed to just extend your phone functionality.
比如，智能手表会告诉你时间，还会把传感器数据传回手机，并向你推送手机的通知。
Like, for example, yes, you have a smartwatch, will tell you the time, but also give you now your sensors back to the phone and give a notification from the phone to you.
但这一切都将改变。
But that's all going to change.
这一切的核心在于连接模型、连接智能代理。
It's all about connecting to a model, connecting to an agent.
随着这些设备发生变化，我们所有人都将开始佩戴它们，这时你就需要考虑庞大的数量了。
As those things change and we all going to start wearing those things, then you start to think about big numbers.
你知道，如果每个人都最终戴上手表、戒指或眼镜，而这些设备又不连接智能代理，那么其规模将与手机相当。
You know, if you have everybody has end up getting a a watch, a ring on on a glass that's not connected to an agent, then you're talking about order of magnitude as big as the phone.
我认为这才是令人兴奋的地方。
I think that's what exciting.
这就是我们看待移动行业未来的方式。
That's how we think about the future of the of the mobile industry.
但这里有个问题。
But here's the question.
问题是，为什么这些东西必须是可穿戴的？
The question is why does it need to be wearable?
我在年底之前和萨姆·阿尔特曼聊过，现在OpenAI打算推出一系列设备，但传言称这些设备将是手机大小、没有屏幕的。
I was speaking with Sam Altman right before the end of the year and now OpenAI is gonna build a family of devices, but the rumor had been that it's going to be a smartphone sized device, no screen.
它只会倾听你，然后向你推送关于你生活的通知。
And it just listens to you and then it will push you notifications about your life.
我当时就想，为什么不能直接做个手机应用呢？
And I was like, well, why can't it just be an app on the phone?
为什么非得是可穿戴设备呢？
Why does it have to be a wearable?
好吧。
Okay.
它不一定非得长那样。
It doesn't have to look.
我们正在和他们合作。
We're working with them.
很遗憾，我不能告诉你它是什么。
Unfortunately, I cannot tell you what it is.
你会看到的，而且会非常令人兴奋。
You will see and it's going be exciting.
但这里我们需要换种思路来思考这个问题，对吧？
But here's, we need to be thinking about this a little bit different, right?
可穿戴设备是其中之一。
Wearable is one of the things.
它还会更多。
It's going to be more.
所以我先来回答你的问题。
So I'll start first answering your question.
整个个人AI设备类别，人类早就决定好要佩戴什么了，对吧？
This whole category of personal AI devices is humans already decided what they're going to wear a long time ago, right?
所以我认为你和我不太可能会戴一个大头盔。
So I don't think we you and I are gonna be wearing like a big helmet.
我觉得我们可以戴眼镜。
I think we can wear glasses.
我们可以戴首饰。
We can wear jewelry.
人类基本上已经决定了要佩戴什么，而我们的工作就是让电子产品变得更紧凑，将强大的计算能力融入小型设备中——这源自我们的手机技术，你可以把电子元件融入所有这些物品中，再加上连接智能代理的功能，将会非常有用。
We so humans kind of decide what they're gonna wear, and you can put you can you can make, you know, that's our job to make electronics very dense and a lot of computing power in small form factors come from our phone DNA and you can put electronics in all of this plus connectivity connect to an agent is going to be very useful.
但你可以在桌上放一个设备。
But you could have something in your desk.
它可能就在你床边的某个地方。
It could be it could have, you know, something in you know, next to your bed.
你可以连接到不同设备中的智能代理。
You can connect to agents in different devices.
我认为我们会看到，一切都会以某种方式变得智能。
And I think what we'll see, everything will become smart in one way.
因为最关键的根本点在于，现在的计算机已经能够理解我们所看到的、所说的、所写的，这略微改变了人机交互方式，也由此改变了计算机的整个定义。
Because see, the biggest fundamental thing is this now the computers understand what we see, what we say, what we write, and that changes a little bit the human computer interface and with that changes the whole you know, definition of what the computer is.
可穿戴设备对我们来说是最合乎逻辑的，因为我们考虑的是移动性和随身携带的设备，但你也可以在桌上放一些设备。
So wearable is the most logical thing to us because we're thinking about mobility and things you're going to carry with you, But you could have things in your desk.
看待这个问题的方式是，想想那些正处于技术过渡中的设备。
See, the way to think about this is let's think about devices that get caught in the transition of technology.
比如，你现在面前就有一台笔记本电脑。
For example, you're now you have a laptop right in front of you.
对吧？
Right?
我可以打赌，你现在用的这款笔记本是高通芯片的，我敢说它支持触屏功能。
And I can bet you right now, and I see it's Qualcomm powered, I can bet you that the laptop has the ability for you to touch the screen.
但你可能并不经常触屏。
But you probably don't touch that often.
你更常使用键盘。
You use the keyboard.
这个用户界面原本就是为键盘设计的。
That's was designed for the user interface was designed for this.
你触摸你的手机。
You touch your phone.
当你从口袋里拿出手机时，你会触碰应用。
Now the phone, when you pull your phone out of your pocket, you're going to be touching, going to apps.
用手指对着手机比划来拍照，并不自然。
It's not very natural for you to point in the phone like this to try to record images.
眼镜就是这样。
Glasses are.
你的头一动，摄像头就会跟着你动，也许你可以跟手机对话。
Your head moves, camera move of you, maybe you can talk to the phone.
也许手机就在这里，你在拿起它之前就对它说话。
Maybe the phone is here and you talk to it before you pick it up.
因此，你的桌上还会出现其他你能够与之对话的设备。
I so therefore, there's gonna be other things that gonna be in your desk that you're gonna talk to.
所以我们还不知道这些设备会如何发展，但我认为回到你的问题，可穿戴设备很自然地会成为我们佩戴和随身携带的东西。
So we don't know how those things are going to pan out, but I think going back to your question, wearables is logical that wearables are going to be things that we'd be wearing and carrying around.
但能帮我们具体描述一下这种体验会是什么样的吗？
But help us flush out a little bit about what this experience will be like.
是的。
Yes.
我的意思是，我们还没到那一步。
I mean, we're not there yet.
不。
No.
我们经历过很多次的中断和重启。
And we've had many stops and starts.
谷歌眼镜就是一个例子。
Google glass was an example.
人们很早就开始在头上佩戴计算设备了。
People were wearing computing on their heads a long time ago.
现在看来，技术终于发展到可能真正有用的地步了。
Now it seems like the technology is actually getting there to the point where maybe it will be useful.
也许它能理解我们的使用情境。
Maybe it can make sense of our context.
所以，克里斯蒂亚诺，当你想到把芯片装进眼镜，或者其他一些不同形式的设备，人们会使用它们并获得某种体验时。
So, Cristiano, when you think about, alright, I'm gonna put chips in glasses and maybe some other different formats, and people will use them and have x experience.
这种体验是什么？
What is that experience?
是的。
Yes.
我们来谈谈体验。
Let's talk about the experience.
我要把这次对话分一下。
And I'm gonna, break this conversation.
我会谈谈体验，也会谈谈背后的技术。
I'll talk about the experience and I'm gonna talk about the technology goes, you know, behind it.
想想现在的眼镜是怎么运作的。
So think about how glasses are performing today.
比如，你有Meta Ray-Ban眼镜。
You know, you have, for example, the Meta Ray Ban glasses.
我认为今年谷歌生态系统内还会推出其他类型的眼镜。
I think there's gonna be other glasses coming within, the Google ecosystem, this year.
那么，现在的眼镜都在做些什么？
And what are the glasses doing today?
比如，你有摄像头，可以看见你看到的东西，能够理解图像并为图像添加注释；你还有麦克风和扬声器，可能有显示屏，也可能没有。
Like, you have cameras, so you see what you see, you can understand the image, it can annotate the image, and you have a microphone, it has a speaker, may or may not have display.
你知道，即使没有显示屏，也有使用场景，比如Meta Ray-Ban眼镜。
You know, you have use cases even without the display, like you have the METARY Bang glasses.
这种体验是什么样的？
What the experience look like?
首先，为了让这些设备普及，它们必须具备极低的使用门槛，并且体验必须实用。
You're gonna be, first of all, for those things to get scale, they have to have very low friction and the experience has to be useful.
否则，这就只是个噱头。
Otherwise, it's like a gimmick.
你不会去用它的。
You're not gonna use it.
所以体验会是这样的。
So the experience is gonna be like this.
我正在跟你说话，然后假设我在观众中看到一个人，我直接问：这个人是谁？眼镜会告诉我：我不知道，让我查一下。
I am I'm talking to you and then let's say I I see somebody in the audience and I just said who is this person and the glass will tell me I don't know let me check.
我上网查了一下，这个人是这位，这个名字是……我说，哦，原来如此，是的。
I check on the web it is this person here is this is this person name I said oh okay Yeah.
你知道，你之前见过她。
You know, you met her before.
这位人士曾经给你发过一封邮件。
There was an email that was sent to you from this person.
它必须像你身边随时有个朋友一样。
It has to be something like you have like you have your friend with you all the time.
你走在街上，我说，这是什么？
You're walking on the street and I said, what is this?
这就是它的样子。
This is what it is.
或者甚至当你开始一天的工作时，你的助手会主动来找你，说：嘿，我注意到你现在好像有空。
Or even something like you go into your day and your agent is going to come to you and say, You know, I noticed that right now you seem to be free.
我可以和你聊聊你的日程安排吗？
Can I talk about your agenda?
我们需要解决一个冲突。
There's a conflict we need to resolve.
这些例子展示了这种体验将会是什么样子。
Those are examples of how this experience is going to be.
它将是一个能够理解你的上下文、感知你周围环境、你所见所言，并实时做出反应的代理。
It's going be this agent that has the ability to understand your context, understand what is around you, around what you see, what you say, and react in real time.
有趣的是，我们还没有到达那一步，但你已经看到了变革的萌芽。
And what is interesting is we're not there yet, but you see the beginnings of the change.
我喜欢做类比。
And I like to do parallels.
所以我要跟你讲一个与智能手机的类比。
So I'm gonna go tell you the parallel with the smartphone.
当智能手机刚出现时，比如你第一次看到iPhone或Android手机时，可能——我不太确定数字对不对——大概只有10个应用。
When the first time the smartphone arrived, like when you when you saw the iPhone, you saw the Android, maybe, I don't know, I may get this number wrong, but maybe like there was 10 apps.
然后你会说，好吧。
And you say, okay.
这些就是那十个新应用。
Those are the 10 new apps.
当时你根本无法想象，未来会有数十万甚至更多的应用。
You couldn't at the time imagine that you're gonna have probably hundreds of thousands of apps.
如果你现在看看自己的手机，就会发现你已经有了大量的应用。
And if you probably look at your phone right now, you know, you have a ton of apps.
你的手机之所以变得越来越好，是因为应用商店不断推出新的应用。
So your phone got better over time because all of a sudden, a new app became available in the app store.
我认为，智能代理的发展也会是这样。
And I think that's how it's gonna be with those agents.
最终，智能代理会与其他服务集成，你就会开始看到它的变化。
Eventually, the agent gets integrated with some other service, and you started to see it.
比如，我们在印度有一个客户正在研发智能眼镜。
For example, we have a customer of us in India that is doing smart glasses.
他们已经将其与数字支付系统整合了。
They integrated with the digital payment system.
所以现在你可以扫描一个二维码，说‘支付这个’，它就会自动支付。
So now you can look at a QR code and say pay this and it will pay.
你可以从‘翻译这个’、‘给我解释一下这个’、‘支付这个’，到收到账单后说‘我收到了这张账单，请从我的支票账户扣款，完成后通知我’，你还可以拍张照片发给我，因为我想要保留一份副本。
And so you go from translate this, explain this to me, pay this, you can get a bill and you say I got this bill, please pay this bill, get out of my checkings account, notify me when it's done, and you may take a picture and email to me because I want to keep a copy of it.
这些将成为你与这些计算机交互的方式，这就是体验的未来模样。
Those are going to be how you're going to interact with those computers and that's what the experience is going to be look like.
有没有可能存在一种情况，我们与计算机靠得太近了？你有没有想过，有时候独处的时光其实很美好，而现在智能代理却总在你有片刻空闲时出现。
Is there a world where we get too close to computers where you think about sometimes that free time is really nice And now the agents being like, you know, he has a moment.
我会主动去帮他解决一个冲突，或者帮他理解这个人是谁，而不是让他自己走过去问‘我们以前见过吗？’
I'm gonna go and help him resolve a conflict or I'll help him understand who this person is as opposed to them, you know, him going up and asking who the, you know, have we met before?
最终，人类和计算机会不会靠得太近了？
Does does does there eventually come a point where humanity and computers come too close together?
这是个好问题。
That's a good question.
我认为我也不知道这个问题的答案，但我觉得，像所有事情一样，最终决定权在你手上。
I and I think I don't know the answer to the question, but I think like everything, it's going to be for you to decide.
看。
Look.
你知道的，我们中有一些人，但不是所有人。
It's you know, there are some of us, not all of us.
有时候你只是把手机放下，就会是这样。
Sometimes you just put your phone down, and it's gonna be like that.
你必须自己决定什么时候该断开连接。
You're just gonna have to decide when it's time to to to disconnect.
但我感觉这会有点不同，因为现在我们与计算机协作会更容易，计算机也会更容易与我们协作。
But I I feel it's gonna it's gonna be a little bit different because now, we are going to it's gonna be easier to work, with, computers, and the computers are gonna be, easier to work with us.
我想用你问我这个问题来讲个有趣的事。
And I'm gonna use this question that you asked me to tell something funny.
我当时不是学计算机科学的，正在和高通的一位客户讨论 exactly 这件事，关于智能眼镜、摄像头，以及现在摄像头能看见你所看到的并标注图像。
I wasn't, in CS, and, I was having a conversation with a customer, Qualcomm, about this exactly this thing, about the smart glasses and the camera and the fact that now the camera see what you see and can annotate the image.
然后有人说，你知道吗，有时候你其实想忘记一些事情？
And then somebody said, you know, what if sometimes there are things that you want to forget?
然后答案是：你可能会，但AI不会忘记。
And then the answer was, you may, but the AI won't forget.
你知道，这些将会是有趣的事情，关于技术，我想我们会看到人类如何使用它，以及它们将如何发展。
It's, you know, those are going be interesting things like with technology, I think how humans are going to use it and how those are going to be developed, we're going to see it.
这个对话的自然延伸是，随着AI变得更强大，人类与AI越来越接近，会有人希望说：让我们融合在一起。
The natural extension of this conversation is, as AI becomes more powerful, and humanity comes closer to AI, there's going to be people that are going to want to say, let's just bring us together.
埃隆·马斯克曾谈到他创立神经链接公司（Neuralink）的原因，他说最终AI会变得比人类更强大，我们最好与之融合，否则它们会毁灭我们。
Elon Musk has talked about how the reason for building Neuralink, his brain computer interface company is, he said eventually AI is gonna get more powerful than humans and we better merge with them or they're gonna destroy us.
所以我想问你，你会和AI融合吗？
So I want to just ask you, would you merge with AI?
不会。
No.
但你看，在我们刚才的对话中，当我们谈到技术过多时，你讲的都是非常面向消费者的角度。
But look, in the in the conversation that we just had, we're talking very consumer centric when you said about, you know, too much technology.
但当你从消费者领域转向企业领域时，就更容易理解了。
But it's easier to also understand when you move from the consumer to the enterprise.
如果你认真思考一下，现在实际上已经出现了一些应用场景，尤其是在工业领域，当某个操作员、炼油工或其他工作人员面对设备时，突然有一个智能代理在你身边，你走到某个设备前问：‘我该怎么操作这个？’
If you actually think about the fact that if you have the ability to learn everything in real time like we're actually seeing some use cases right now especially for industrial when when you have somebody that is as an operator of of an equipment or of a refiner or everything and then all of a sudden you have this agent with you that you get to a particular equipment and you say, how do I operate this?
它会告诉你：‘这就是操作方法。’
And they will say, here's how you're gonna operate it.
你这么做，再那么做。
You do this, you do that.
因此，能够实时获取知识，我认为这为普及知识和学习带来了极其巨大的机遇。
So the ability for you to have access to knowledge in real time, I think there's incredibly, incredible opportunity to actually democratize knowledge and learning.
这也是AI在增强人类能力方面的一个关联点。
Think so that's another thing about the connection between, you know, AI in in in terms of augmenting human capabilities.
我们之所以这么说，是因为我们从手机上看到了这一点。
We we can say that because we saw that with phones.
是的。
Right.
通过手机，许多国家得以接入互联网，从而实现了数字化的普及。
With phones is how many nations got access to the Internet and became, you know, access to digital through the phone.
你知道，以前并没有电脑。
You know, there wasn't to a computer.
我认为让人们连接起来并能访问互联网，这具有极大的赋能作用。
And I think it was it's incredibly empowering to have people to be connected and ability Internet.
我认为，个人AI设备可能会带来同样的变化。
I think maybe that's gonna be the same thing with those personal AI devices.
好的。
Okay.
我马上会换个话题，但我问你是否要与AI融合时，你很快就说‘不’。
I'm gonna move off this in a second, but I asked if you'd merge with AI and you said no very quickly.
是的。
Yes.
为什么你这么直接地拒绝？
Why the reflexive no?
哦，因为你看，这是不一样的。
Oh, because look, it's it's different.
我觉得，你知道，这挺有趣的。
I think, you know, it's I think it's fun.
我觉得人们喜欢那些关于科幻的故事。
I think people like to have those stories about science fiction.
我有一个非常明确的信念。
I'm I have a very clear belief.
我觉得人类是存在的。
I think there's humans.
有人性。
There's humanity.
人工智能是我们创造的。
AI is our creation.
它是基于我们所做的事情进行训练的。
It's trained on the stuff that we do.
我觉得如果我们仔细观察很多这些模型的话。
I think if we look a lot of those models.
所以它本质上是一种旨在增强人类能力的工具，但不会剥夺我们的人性。
So it's really a tool designed to augment, but it won't take away our humanity.
好的。
Okay.
关于设备形态，简单说一下。
Very quickly on form factor.
你多次提到了眼镜。
You've mentioned glasses a number of times.
但你没提到耳机。
You didn't mention earbuds.
你知道，当思考这场竞争的发展趋势时，不同公司正在对不同的设备形态下注，尤其是当你关注像我们在这个科技播客中常讨论的那些科技巨头时。
And, you know, when you think about the way that this competition is shaping up, you have different companies making different bets on different form factors, especially when you look at the tech giants, big technology as we like to cover here on the big technology podcast.
Meta 正在大力押注 AI 驱动的眼镜。
You have Meta making a big bet on AI powered glasses.
正如你提到的，谷歌，我认为他们也会做出一个非常大的投入。
Google, as you mentioned, I think we're gonna see a very big bet from them.
谷歌眼镜第二代，也许他们会取个新名字。
Google Glass part two, maybe they'll have a new name.
苹果公司可能要到2027年才会推出他们的眼镜产品。
Apple, it might be 2027 until we see a pair of glasses for them.
也许他们的重点会放在AirPods上，看看AI如何已经通过AirPods实现翻译等功能，但这些功能仍需改进，也许他们会通过与谷歌的合作来实现。
Maybe their big bet is gonna be the the AirPods and how AI you already is delivered in the AirPods with things like translate and I mean series inside there but still has some work to do and maybe they'll do it with their Google partnership.
你为什么觉得眼镜比耳塞更好？
Why why do you think glasses over earbuds?
我觉得不能说哪个更好，我认为，那些真正研发个人AI设备的公司，我们有幸与他们合作，因此我们拥有更广泛的视野。
Look I won't say one over the other we we have the benefit I think I've been I will assume the majority of the companies that are actually building personal AI devices we have I think the benefit of working with them.
我们有相当广泛的洞察力。
We have a pretty broad visibility.
举个例子，现在有些公司正在设计带有摄像头的耳塞。
Like I give an example, there are some companies right now they're designing an earbud with a camera.
带摄像头的耳塞。
An earbud with a camera.
带摄像头。
With a camera.
因为如果你把它戴在耳朵里，同时又有摄像头，你就能看到前方的东西。
Because if you put in your ear and you have a camera, you can see in front of you.
所以它能提供一些额外的上下文，而不仅仅是一个扬声器和麦克风。
So it can provide some context in addition to having a just a speaker and a microphone.
我想这又回到了我们一开始讨论的问题。
I think it go back to the how started this conversation.
人类会经常佩戴哪些东西呢？
What are the things that humans are going to wear and wear most of the time?
眼镜，我相信眼镜是最自然的选择，也许是因为我从13岁就开始戴眼镜了，所以我已经习惯了。
Glasses, I am a believer that glasses is the most natural and maybe because I wear glasses since I was 13 so I'm you know I it doesn't I'm used to them.
但当你转头时，你的摄像头也会跟着转动，它离你的眼睛很近，你应该好好想想这一点。
But when you turn your head, you know, your camera goes with you, it's close to your eyes, you should be thinking about this.
这让我意识到，当你问可穿戴设备的问题时，我本该立刻想到这一点，因为这是回答这个问题最简单的方式。
This is a I should have thought about that when you asked the question about wearables because that's the most simple way to answer that question.
如果AI能够理解我们所看到的、所说的，那么它就会更贴近我们的感官。
If the AI understands what we see, what we say, here, it's going to be closer to our senses.
而眼镜，能捕捉到一切。
And glasses, it captures everything.
它更靠近你的嘴巴，也更靠近你的耳朵。
It's closer to your mouth, closer to your ear.
但耳机也是同样的道理。
But earbud, it's the same thing.
只是缺少了视觉功能，所以有些人会在耳机上加装摄像头。
It's just missing the vision and that's why some people putting a camera on an earbud.
但如果你的耳机连接到一个IP地址，你就能连接到一个智能代理，并与之对话。
But if you just have an earbud connected to an IP address, you can connect to an agent and you can have a conversation with the agent.
那密码呢？
What about PIN?
也是同样的情况。
Same thing.
这是另一种在设备上加装摄像头的方式。
It's another way to put a camera on it.
还有吊坠、珠宝这类形式。
There's pendants, the jewelry.
所以我们拭目以待吧。
So it's we'll see.
但我认为你会看到人们尝试各种不同的形态。
But I think you're gonna see people experiment with the form factors.
我认为眼镜很可能是这些设备的主要形态。
Think glasses is likely gonna be the primary way that those devices are going to be built.
那么假设眼镜胜出了。
So let's say glasses is the winner.
你觉得外观设计重要吗？
Do you think that style matters?
我给你一个二选一的问题。
Let me give you a binary here.
我宁愿要款式更时尚但助手最差的眼镜，还是款式不那么时尚但助手超强的眼镜？
I have the more stylish glasses with the worst assistant or the less stylish glasses with an amazing assistant.
哪个会赢？
Which wins?
这是个很好的问题。
This is a great question.
这是个很好的问题，因为我们将看到行业中的另一个趋势：当你开始考虑可穿戴设备时，就会出现时尚与科技的融合。
This is a great question because we're going to see another thing happening in the industry which is when you start thinking about wearables then you're going to have the mix of fashion and technology.
我其实想在这里做一个预测。
And I actually think I'm going to make a prediction here.
我不想对任何其他公司不敬，但我认为横向模式将胜过垂直模式。
I don't want to be offensive to any other company but I think that's where horizontal model is going to win versus vertical model.
我说这个的原因是，地球上几乎不可能每个人都使用完全相同的眼镜。
And the reason I'm saying that is because it's very unlikely that everybody on earth is going to use the same exact glasses.
人们想要不同的形态、不同的颜色，这各不相同。
People want different form factors, they want different colors, it is different.
尤其是你佩戴的东西，情况都一样。
It's the same especially things that you wear.
因此，我认为会出现不同的品牌。
As a result, I think you're going to have different brands.
这会有点有趣的动态，因为你戴的是雷朋，还是说你戴的是Meta？
There are going to be it will be a little bit of an interesting dynamic because is that is that a Ray Ban or is, you know, is a Ray Ban that you're wearing or it's a meta?
如果是这家消费电子公司生产的雷朋，那它属于消费电子品牌，还是雷朋？
If it is a Ray Ban made by that consumer electronics company, is there a consumer electronics brand or is Ray Ban?
我们拭目以待。
We'll see.
但我认为时尚与科技将会融合，并且会有很多选择。
But I think you're going to have the combination of fashion and technology and there's going to be choices.
不同年龄层的人会拥有不同的品牌。
Different brands for different people from different age groups and etc.
所以我认为我们会看到非常多的多样性。
So I think that we're going to see a lot of diversity.
这与手机领域非常不同，因为在手机领域，大多数人使用的手机都差不多。
Very unlike the phone space when you know most people will carry a similar phone.
我觉得这会不一样。
I think that's gonna be different.
我来回答自己的问题。
I'm gonna answer my own question.
我宁愿要一个好助手配上难看的眼镜，也不要好看的眼镜配上糟糕的助手。
I'll take the better assistant and the ugly glasses over the nice glasses and the bad assistant.
是的，最好的东西可能是最棒的、最成功的智能眼镜会搭配最好的助手。
Yeah, I the best thing is maybe the the the most nice maybe the most successful glass is gonna pair with the best assistant.
最终，你会觉得我们能达到那个状态。
Eventually, you would think we get there.
是的，我也这么想。
Yeah, think so.
为AI设备的竞争给我们设个障碍吧。
Handicap the AI device race for us.
我们有很多公司正在投入这一领域。
We have many companies that are running at this.
我们有Meta，它长期以来一直押注元宇宙，如今已真正转向了智能眼镜领域。
We have Meta that's been making this multi year metaverse bet which has really transformed into the smart glasses bet.
我们有谷歌，所有迹象都表明如此。
We have Google which all indications are.
我的意思是，你看看他们最近的《思考游戏》纪录片，他们只是把手机对准各种东西，然后问：这是什么？
I mean, you look at their recent thinking game documentary, they're just like pointing their phone at at things and saying, is this?
这就像是你需要一副眼镜。
It's like you need glasses.
你还有OpenAI。
You have open AI.
你正在与OpenAI合作这个项目。
You're working with OpenAI on this project.
这将是一系列分布在不同场景中的设备。
Family of devices that are gonna be in a bunch of different places.
苹果显然也必须被视为这里的实力玩家。
And Apple obviously has to be considered a power player here as well.
谁会赢？
Who wins?
听我说，我会从互联网的早期阶段来回答这个问题，对吧？
Look, I'll answer this question by going into the beginning of the internet, right?
奥特克不是最终胜出的社交媒体，最终是Facebook，后来是Instagram。
So Orkut wasn't the social media that won, end up being Facebook and then later Instagram.
我认为MapQuest也不是主要的地图服务，最终是谷歌地图。
I think MapQuest wasn't the main map, you know, eventually it was Google Maps.
现在下定论还为时过早。
So it's early to call.
我认为你看到了这些公司。
I think you see all those companies.
我认为它们拥有庞大的生态系统。
I think they have big ecosystem.
他们正在投资自己的生态系统。
They're investing on their ecosystem.
我们拭目以待会发生什么。
We'll see what happens.
不过，我会试着给你一点答案。
However, I'm gonna try to give you a little bit of an answer.
我觉得……我有这样一个观点，这可能需要更长时间来讨论，但归根结底，边缘计算的赢家将是人工智能竞赛的赢家。
I I think the the I have this view, and this is maybe a longer conversation that we're gonna have time for, but I think at the end of the day, the winner of the edge is gonna be the winner of the AI race.
我这么说的原因是，尤其是对于所有个人化的内容，边缘计算拥有真实的上下文。
And the reason I say that is because, the especially for for everything that is personal, the edge has real context.
你知道，你可以
You know, you can
意思是你的手机、你的设备，你使用的那些设备
Meaning your phone, your device the devices that you use
与人类所处的位置相对。
as Where opposed the humans are.
对。
Right.
人类不会敲开数据中心的门说：给我一点AI。
The humans don't knock on the data center and say, give me some AI.
他们会通过那边的其他设备来体验它。
They're gonna experience it to some other devices over there.
而实际情况是，如果你看看模型是如何被训练的，模型是基于互联网上可用的信息进行训练的。
And what happened is, if you look how models got trained, models got trained on the information available on the internet.
但当你展望未来，当模型融入了物理AI，能够理解我们的世界、理解你的上下文、理解你时，它将比那些仅基于互联网数据训练的通用模型对你更有用。
But when you fast forward to a model that is when you add physical AI, understanding our world, understanding your context, understanding you, that's going to be a lot more useful for you than a generic model that got trained on data available on the internet.
因此，谁能获得这些数据，谁就处于非常强大的位置。
So whoever had access to that data is in a very, very strong position.
所以，那些已经拥有各种设备广泛布局的公司具有优势。
So it's companies that have, you know, presence in all of those different devices already.
我认为他们有优势。
I think they have an advantage.
我不会与他们对赌。
I will not bet against them.
好的。
All right.
但让我再深入一点和你探讨，因为我们已经见过这些公司了。
But then let me let me take this a level deeper than with you because we have seen those companies.
我就直接点名了。
I'll just name them.
亚马逊、苹果、谷歌、Meta。
Amazon, Apple, Google, Meta.
它们都曾试图打造这种具有上下文感知能力的个人助手。
They've all tried to build this contextually aware personal assistant.
我们听过关于Alexa Plus、Apple Intelligence、Meta各种聊天伙伴以及谷歌Gemini的演示。
We've heard presentations about Alexa Plus and Apple intelligence and the meta, all the different buddies you can have in the meta properties, Google obviously with Gemini.
但尽管它们拥有如此多的数据，我们仍然没有真正拥有一个能实现其承诺的助手。
But even though they have all this data, we still don't really have an assistant that's capable of doing what they've promised.
我的意思是，苹果可能是最引人注目且最有希望推出这种情境感知助手的公司，它能帮你确认航班时间，并提醒你：该去机场了。
I mean, Apple might be the most notable and promising this contextually aware assistant that will help you figure out when your flight is and tell you, all right, time to get to the airport.
但他们至今还没有做到。
They haven't done that yet.
是什么阻碍了这些公司呢？
What is holding these companies back?
是硬件问题，还是人工智能问题？
Is it a hardware problem, a AI problem?
瓶颈在哪里？
Where is the bottleneck?
我认为这是多种因素的综合结果。
I think it's a combination of things.
但我比你描述的更乐观。
But I am more optimistic, think than you described.
我觉得我们已经开始看到一些真正体验的雏形了。
I think we're starting to see I think the beginning of some real experiences.
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我认为你需要达到一定的成熟度。
I think you have to get the maturity.
我认为首先AI模型需要变得更加成熟。
I think first of all the AI models need to get more mature.
我认为它们需要变得更强大。
I think they need to get more capable.
我认为即使在AI领域内也发生了许多变化，你开始看到专家混合、思维链推理等不同技术，每种技术都专精于特定任务。
I think you had a lot of changes even within AI, you started to see mix of experts, started to see chain of thought reasoning, so you have different things specialized in specific tasks.
我认为我们才刚刚进入物理AI的初期阶段，这对实现上下文感知至关重要。
I think we're just the beginning of physical AI, which is really important for you to have context.
所以我认为这一定会发生。
So I think this is going to happen.
另一个方面是计算能力。
The other part of it is compute.
你需要大量的高性能计算资源，而这正是我们的用武之地。
You need to have a lot of high performance compute, and this is where we come into the picture.
因为你不能全部依赖云端，因为还有延迟问题。
Because you cannot do everything on the cloud because of also latency.
如果回到你让我描述体验的时候，这对你来说不会有什么用。
It is not gonna be useful for you if I go back to when you asked me to describe the experience.
如果你我和走在街上，我问你：这个人是谁？
If you and I are walking together in the street and I'm gonna say, hey, who's this person?
你回答说：这是某某。
And you say, this is so and so.
你不能说：等一下。
The answer, you can't be say, hold on.
让我想一想。
Let me think it.
我们继续走吧。
Let's keep walking.
我一直在思考这个问题。
I've been thinking about it.
然后那个人就走过去了，你错过了重点。
And then the person went by, you missed the point.
我认为你必须在设备上完成某些特定的操作。
And I think you're gonna have to have certain things you need to do on the device.
它需要足够快。
It needs to be fast.
就像现在所有公司一样，语音转文字功能正开始在本地运行，因为你无法容忍任何延迟。
Like all companies right now, voice to text, they're starting to do locally because you can't you don't tolerate any delay.
所以我们终将实现这一点。
So and we're gonna get there.
是的。
Yeah.
我们刚才在房间里聊到，可能在滑雪场上，眼镜能为你推荐适合你水平的雪道。
We were just talking earlier in in the room here about potentially being on the ski hill and having the glasses point you down the hill that suits your skill set.
但如果你得等上两分钟，你可能还是个初学者，却已经滑到了黑钻道上。
But if you have to wait like two minutes, you might be a bunny hill skier and down the black diamond.
所以你真的希望能够快速工作。
So you really want to be able to work fast.
而且你会弄坏你的眼镜。
And you're break your glasses.
是的。
Yeah.
你的眼镜会是第一个受损的部件。
Your glasses will be the first casualty.
对。
Yes.
所有
All
没错。
right.
我们现在和高通公司的首席执行官克里斯蒂亚诺·奥曼在一起。
We're we're here with Cristiano Oman, CEO of Qualcomm.
在达沃斯的高通展台，我们本周将进行四场对话，非常高兴能来到这里。
Here at Qualcomm Space at Davos, we're gonna be doing four conversations through the week here and thrilled to be here.
休息过后，我们将讨论AIPCs、AI数据中心、AI建设面临的限制以及机器人技术。
On the other side of this break, we're gonna talk about AIPCs, AI data center, the constraints on the AI build out, and robotics.
如果时间允许，我们会在之后立即回来。
If we have time, we'll be back right after this.
问题就在这里。
Here's the problem.
你的数据无处不在地暴露着。
Your data is exposed everywhere.
个人信息分散在数百个网站上，通常未经你的同意。
Personal data is scattered across hundreds of websites, often without your consent.
这意味着数据经纪商会购买和出售你的信息，包括地址、电话号码、电子邮件、社会安全号码、政治观点，这种暴露会带来真实的风险，如身份盗窃、诈骗、跟踪、骚扰、歧视以及更高的保险费率。
This means data brokers buy and sell your information, address, phone number, email, social security number, political views, and that exposure leads to real risks, including identity theft, scams, stalking, harassment, discrimination, and higher insurance rates.
Incogni会追踪并从数据经纪商、目录、人物搜索网站和商业数据库中删除你的个人信息。
Incogni tracks down and removes your personal data from data brokers, directories, people search sites, and commercial databases.
这是它的运作方式。
Here's how it works.
您创建账户并提供少量必要信息，以便定位您的个人资料。
You create your account and share minimal information needed to locate your profiles.
然后您授权 Incogni 代表您联系数据经纪人，Incogni 将自动从数百家经纪人处删除您的数据，并通过定制方式移除。
You then authorize Incogni to contact data brokers on your behalf, and then Incogni removes your data both automatically from hundreds of brokers and via custom removal.
此外，我们还提供30天内全额退款保障。
There's also a 30 money back guarantee.
用 Incogni 重新掌控您的个人数据。
Take back your personal data with Incogni.
前往 incogni.com/bigtechpod，并在结账时使用代码 big tech pod。
Go to incogni.com/bigtechpod and use code big tech pod at checkout.
我们的代码可让您享受年度计划6折优惠。
Our code will get you 60% off annual plans.
快去试试吧。
Go check it out.
你好。
Hi.
我是亚历克斯·坎特罗维茨。
This is Alex Kantrowitz.
我是《大科技》播客的主持人，长期从事报道工作，同时也是CNBC的电视评论员。
I'm the host of big technology podcast, a longtime reporter, and an on air contributor to CNBC.
如果你和我一样，正在试图弄清楚人工智能如何改变商业世界和我们的生活，
And if you're like me, you're trying to figure out how artificial intelligence is changing the business world and our lives.
那么每周在《大科技》节目中，我都会邀请构建AI技术的公司核心人物以及试图影响这一领域的外部人士，探讨这一切将走向何方。
So each week on big technology, I host key actors from the companies building AI tech and outsiders trying to influence it, asking where this is all going.
比如英伟达、微软、亚马逊。
Places like NVIDIA, Microsoft, Amazon.
所以，如果你想在理财、职业选择和饭局对话中显得更明智，就去你常用的播客应用收听《大科技》播客吧。
So if you wanna be smart with your wallet, your career choices, and at dinner parties, listen to Big Technology Podcast in your podcast app of choice.
我们回到《大科技》播客，特别节目正在达沃斯现场为您直播。
And we're back here on Big Technology Podcast, special edition here at Davos.
我们现在在这里直播，周一一起讨论，周二在我们的各个渠道同步播出，继续聊聊人工智能将如何改变设备。
We're here broadcasting, talking together on a Monday going live across our channels on Tuesday and let's keep going here about what how AI will transform devices.
AI电脑这个话题一直让我很感兴趣。
The AI PC is is a subject that has been interesting to me.
很多人谈论，如果电脑内置了人工智能，你就能更高效地工作，它真的能彻底改变你的工作方式。
A lot of note a lot of noise about how if you have AI baked into your computer then you'll be able to be more productive and it can really transform the way that you work.
这只是营销宣传。
That's the marketing.
但实际上，这一承诺的落地进展缓慢，远未达到现实预期。
In reality that rollout, that promise has been slow to meet the reality.
这是戴尔产品负责人在接受The Verge采访时说的话。
This is from the head of product at Dell speaking to The Verge.
他说：‘在过去一年中，尤其是从消费者的角度来看，我们发现他们并不是因为AI而购买产品。’
He says, what we've learned over the course of the year, especially from a consumer's perspective, is they're not buying based on AI.
事实上，我认为AI反而让他们更困惑，而不是帮助他们理解具体能带来什么效果。
And in fact, I think AI probably confuses them more than it helps them understand a specific outcome.
显然，高通公司对AI PC的成功有利益攸关。
Obviously, Qualcomm has a stake in the success of AIPCs.
今天发生了什么？
What is happening today?
未来会怎样？
And where is it going?
这是一个很棒的讨论话题。
It's a it's a great topic of conversation.
听我说，首先，当我们进入PC领域时，我认为推动骁龙PowerPC销售的主要原因是，我们提供了长达数天的电池续航、强大的性能，以及令人兴奋的轻薄设计。
Look, first of all, as as we entered the PC space, I would argue that in that a lot of what's driving the sale of Snapdragon PowerPC is the fact that we deliver multi day battery life, a lot of performance in a very exciting fin and light form factor.
对吧？
Right?
所以我们只是打造了一台更好的PC。
So we just build a better PC.
在消费者层面，我同意这一点，目前你还没看到很多智能代理。
On the consumer side, I would agree with that, that you don't see yet a lot of agents.
而且你知道，我知道人们都希望马上看到这一点。
And and, you know, I I know people want to see this right away.
我真希望它能马上被看到。
I wish it was seen right away.
在消费端，我并不完全反对这个观点，因为微软刚刚为Windows推出了一个智能代理。
I don't necessarily disagree with that on the consumer front because Microsoft just launched an agent for Windows.
它刚刚发布。
It just launched.
所以我认为，随着人们越来越依赖智能代理，大家会越来越多地使用它，你也会看到一些运行在你设备上的应用。
So I think I think it's going to people are going to use it more and more as you're starting to rely on agents and I think you're going to see things that are going to be running on your device.
但我认为这并不是AIPC的核心故事。
But I think that's not the story for AIPC.
这个故事有点不同。
The story is a little bit different.
我们看到AIPC的发展，以及我们实际上能够在笔记本电脑上运行高性能推理的能力。
What we see happening with AIPC and the fact that we actually have the ability to run significant high performance inference on a laptop.
我们看到的是另一种情况。
What we're seeing is something else.
我们现在看到的是，你的电脑上有很多很多应用程序和服务都在进行大量的云端计算。
What we're seeing is right now you have many, many, many applications and services on your PC that are doing a lot of cloud computation.
如果你能依赖电脑上可用的计算能力，不仅速度会更快，而且经济模式也完全不同。
And if you could rely on the computing that is available on the PC, not only is it gonna be faster, but it has a completely different economics.
我举个例子。
I'll give an example.
如果你是一家SaaS公司，而目前所有的SaaS公司都正受到AI的威胁。
If you're a SaaS company and all the SaaS companies right now are being threatened by AI.
如果你是一家SaaS公司，你说我要在自己的应用里集成一个智能代理，每次我有这些数据时就运行它，而你一直在为云端的计算机付费，那么如果你改用设备本地的计算资源，你的商业模式将发生巨大变化。
If you're if you're a SaaS company and you say, I have I'm gonna have an agent within my application and every time I'm going to I have this data I'm going to run-in, and you're paying for a computer in the cloud, your economics change dramatically if you actually use the computer into the device.
我再给你一个实际的例子。
I'll give you, like, a practical example.
现在有很多这样的情况。
There's many things now.
你只需要一个按钮。
You just have a button.
你在微软Copilot上就能看到这一点。
You see that on the Microsoft Copilot.
你在许多不同的应用程序中都能看到这一点。
You see that across a number of different applications.
总结一下这个。
Summarize this.
比如你有一堆数据。
Like you have a bunch of data.
你有一份文档的几页内容。
You have several pages of a document.
总结一下这个。
Summarize this.
你可以完全依赖云端，支付云计算成本来运行模型，或者直接在你的电脑上运行那个总结文本的模型。
You can go all the way to the cloud and have a cost of cloud compute to run the model or you can run that model that summarizes on your text in the computer.
这是免费的，因为使用的是你已有的电脑。
That's free because it's the computer that you already have.
因此，我们开始看到企业甚至应用程序对在设备上的AI引擎上运行部分应用产生了浓厚兴趣，这现在已经开始了。
So we are starting to see a lot of interest for enterprises or even applications to start running a portion of the application on the AI engine on the device and that's starting right now.
所以，购买AI PC硬件而不是让Claude Code接管你的电脑，主要原因在于成本。
So the reason to buy AI PC hardware as opposed to like let's say letting Claude Code take over your computer, mostly it's cost.
我觉得你可以看到，我举个例子，还有很多类似的情况，比如游戏。
I think you see you well I just give you one example there's more like gaming for example.
目前许多游戏引擎都在考虑在PC上使用AI。
A lot of the gaming engines right now are thinking about using AI on the PC.
例如，在一个角色扮演游戏中，你可以与一个角色进行对话。
For example you can have on an RPG game, you have a dialogue with a character.
比如通过模型进行对话，游戏玩法就会发生变化。
Like a model, you have a dialogue, the gameplay changes.
这里有成本的例子，也有新用例的例子，还有智能代理的例子。
There's an example of cost, there are example of new use cases, example of agent.
我认为你问题的答案是，首先，你为什么要购买搭载高通芯片的PC？
I think the answer to your question is, first of all, why should you buy a Snapdragon powered PC?
因为从定义上讲，即使你不使用AI功能，它的续航时间也会更长，能持续多天，并且使用体验会像你的手机一样。
Because by definition, even if you're not using AI, it's gonna be a faster multi day battery life, and it's going to feel like your phone.
你可以整天使用笔记本电脑外出，而不需要携带充电器。
You can use your laptop all day without you go places, don't take the charger with you.
第二部分，作为消费者，你为什么要购买一台AI PC？
The second part of it, why should you buy an AI PC as a consumer?
作为消费者，我认为随着时间推移，你会看到越来越多的应用程序加入AI前端，并利用PC上的能力，但这一切对你来说都是透明的。
As a consumer, I think over time you're going to see more and more apps having an AI front end and they're to leverage the capabilities on the PC, but it's going to be transparent to you.
在企业层面，我认为经济模式将会改变，因为许多独立软件开发商和SaaS应用都将需要设备端的计算能力，我认为这将带来重大影响。
On the enterprise, I think the economics are going to change because you know those a lot of the ISVs and SaaS applications are going to require the onboard computing and I think that's going to make a difference.
非常有趣。
Very interesting.
所以软件公司将来会把这个作为硬性要求。
So that'll be a requirement from software companies.
所以高通也进入了数据中心领域，你们正在建设数据中心。
So Qualcomm has also gotten into the data center world and you're building data centers.
显然，你们已经在设备中使用了芯片，正如我们之前讨论的，但现在你们正在为AI推理构建数据中心。
So obviously you have the chips in the devices like we talked about, but now you're working on building data centers for AI inference.
那么我们来谈谈，不如你先给我们讲讲，高通为什么要做这个决定？
So let's talk a little bit about well, actually, why don't you first give us a little bit about why this is a move that Qualcomm is making?
是的。
Yes.
而且你看。
And it was look.
我们一直认为，AI在数据中心的发展趋势是，你已经开始看到大量针对训练的基础设施建设。
We always believe that what's going to happen with AI in a data center, you started to see all this build out for training.
但最终，现在这一点已经很清楚了。
But eventually and now now it's well understood.
当我们开发解决方案时，这就是我们所关注的。
When we start develop our solutions, that's what we put.
最终，推理将取代训练，你只要想一想这一点就明白了。
Eventually, inference is gonna take over training because just think about that for a second.
如果你的公司花费数十亿美元建设用于训练的数据中心，你自然期望获得投资回报。
If your company is spending billions of dollars building a data center for training, you expect to get a return on that investment.
所以当你开始将人工智能投入生产时，你实际上是在进行推理。
So when you start putting AI into production, you're doing inference.
我们一直认为，当进入推理阶段时，不同的AI厂商之间将面临激烈竞争。
And we always believe that when you go to inference, there's going to be a lot of competition between the different AI players.
因此，我认为总拥有成本至关重要，功耗是多少很重要，架构也很关键。
So then I think the total cost of ownership matters, how much power you consume matters, and the architecture matters.
所以，对你问题的第一个回答是：我们意识到，当数据中心开始转向推理时，我们有机会利用自身优势，构建一种高效节能的推理解决方案，将我们在边缘端开发的技术扩展到数据中心。
So first answer to your question is, we realize when the data centers start to transition to inference, we have an opportunity to leverage our assets to build a very power efficient inference solution for the data center, scaling the technology that we developed for the edge.
因为手机中的功耗效率很高，而功耗正是人工智能的瓶颈，你可以利用这一优势，将其应用到数据中心。
Because the power is efficient in the phone and power is such a bottleneck in AI, you can use that advantage and put it in a data center.
这就是我们的逻辑。
That's the that's the logic.
如果你只是看看今天的情况，你会看到人工智能正在迅猛增长，但能源的增长却没有同步。
If you just if you just look at today, you have this, very aggressive ramp, of growth of AI and you don't have the same ramp on energy.
你知道，现有的能源和人工智能之间存在一个差距。
You know the right that there is a gap between available energy and AI.
所以我认为能源将成为一种稀缺资源。
So I think energy is going be scarce resource.
此外，运营一个推理数据中心，这是运营成本中最大的项目之一。
Also to operate an inference data center, that's one of the biggest, you know, items in operating expenses.
然后我认为人们希望采用一种不同的架构，这是我回答的第二部分。
And then I think people wanted to have a different architecture, which is the second part of my answer.
我回答的第二部分是，我们认为数据中心将进入另一个解耦阶段，让我解释一下我的意思。
The second part of my answer is we believe that the data center is going to another process of disaggregation and let me explain what I mean by that.
如果你看看移动行业，如今你的智能手机从半导体角度来看是一个巨大的工程挑战，因为我必须在手机里集成大量的计算能力。
One of the key things that happen in the mobile industry, if you look at your smartphone today, your smartphone, it's a very difficult engineering challenge from a semiconductor standpoint because I have to pack a lot of computing in your smartphone.
它还得能放进口袋里。
It has to fit in your pocket.
它不能变热。
It cannot get hot.
你会摸到你的脸。
You're going to touch your face.
它不能变热。
It cannot get hot.
我不能使用风扇。
I cannot have fans.
我不能在智能手机上使用液冷。
I cannot do liquid cooling on the smartphone.
你的电池必须能撑一整天。
And your battery has to last all day.
否则，它就没用。
Otherwise, it's it's not useful.
它毫无价值。
It's worthless.
是的。
Yes.
因此，为了实现这一点，我们不得不完善计算的解耦——姑且这么描述吧。
So in order to do that, we had to perfect, the disaggregation of the compute for lack of a better way to describe it.
我举个例子。
I'll give an example.
在个人电脑上，一切都是以CPU为中心的。
In the PC, everything was CPU centric.
所以，如果你要解码音乐或视频，就会让CPU全速运行。
So if you're gonna do a decode of music or you do decode of a video, you go and load up the CPU.
但在手机上不能这么做。
You can't do that on the phone.
耗电太多了。
It burns too much power.
因此，你必须为音乐编码专门设计硬件，为拍照时的JPEG编码专门设计硬件，为视频解码专门设计硬件。
So you create a dedicated hardware just for music to code, a dedicated hardware just to do JPEG encode when you take a picture, a dedicated hardware for you to do video decode.
而且所有东西都聚合在一起了。
And everything is aggregated.
我认为你这样做是为了最大化利用电池中可用的能源。
And I think you do that because you want it to maximize the use of the available energy in the battery for you.
这些在手机里都存在吗？
And this all exists in the phone?
手机里确实有这些。
Exists in the phone.
我们称之为异构计算。
It's the most we call heterogeneous compute.
如果你看一下今天的骁龙芯片，它有多个专用于不同任务的引擎。
If you look of a Snapdragon today, it has several engines for different things.
我们并不会把所有事情都交给CPU，甚至不会都交给GPU。
We don't run everything on a CPU or even for that fact on the GPU.
数据中心也在走向这种模式，而我们正开始看到去聚合化的趋势。
Data centers go into that, and we're starting to see, disaggregation.
他们为预处理阶段使用了一种架构。
There's an architecture that they use for pre field.
他们为解码阶段使用了一种架构。
There's an architecture they use for decode.
因此，我们正在构建我们认为是GPU之后的技术。
So we're building what we believe is post GPU.
当你开始进行推理并需要专用引擎时，我们就在构建这些。
When you started to do inference and you needed dedicated engines, we're building that.
我确实认为，英伟达收购Croc验证了为不同任务使用不同引擎的UDIF理念。
I actually believe that the NVIDIA acquisition of Croc validates the UDIF different engines for different things.
我认为这就是我们正在做的。
And I think that's what we're doing.
我认为这也是我们对数据中心的关注重点。
And I think that's our focus on data center.
好的。
Okay.
我们来谈谈机器人。
Let's talk about robotics.
你相信人形机器人的炒作吗？
Are you buying the hype on humanoid robots?
我很喜欢和你进行这场对话。
I will like like this whole conversation with you.
我一直在做比较，接下来我会用汽车行业来做个类比，以阐明我们的策略。
I've been I've been doing comparisons and, and I'm gonna do a comparison with automotive to kinda outline our strategy.
但让我先给你答案。
But let me give you the answer first.
我相信人形机器人的机会。
I buy I buy the opportunity to humanoid robot.
然而，这个机会将以不同的方式显现，而且其中一些事情需要时间。
However, the opportunity is going to manifest itself different and some of those things going to take time.
例如，直接回答你的问题，一个能待在你家里并完成你所有指令的机器人，训练它需要很长时间。
For example, to get straight to your question, a robot that is going to be with you in your house and it's going to do everything you ask the robot to do, it's going to take time to train that.
这非常困难。
It's very difficult.
远程操作员。
Teleoperators.
这很困难。
It's difficult.
每户人家的情况都不一样。
Every house is not going to be the same.
每个任务也不尽相同。
Every task is not going be the same.
需要大量的训练。
It's going to be a lot of training required.
话虽如此，能够完成某些任务并反复执行的机器人，实际上并不是一个难以解决的问题。
Having said that, a robot that can can do certain tasks and do that task over and over, that's actually not a hard problem to solve.
因此，接下来我要给出我的类比比喻。
So with that, I'm gonna give you my comparison metaphor.
当我们开始为汽车行业构建平台时，我们对自己的汽车业务感到非常自豪，同时也进入了自动驾驶技术栈。
When we start out of when we start building platform for automotive and we're very proud of our automotive business right now, we also got into a stack for autonomous driving.
当你想到自动驾驶，想到像五级自动驾驶出租车那样没有方向盘、你坐到后排小憩的场景时，这需要大量的训练，因为你可以从0%做到95%。
When you think about autonomous driving, when you think about robotaxi like a level five, no steering wheel, you go to the back seat and you take a nap, that requires a lot of training because you can get to zero to 95%.
但如果你想达到99.999%的边缘情况覆盖率，你就必须进行大量的训练。
But if you were get to 99.999% of the corner case, you have to do a lot of training.
然而，如果你采用辅助驾驶，仍然由人类负责接管方向盘，一旦发生状况……
However, if you do assisted driving with the human still responsible to pick up the steering wheel and something happens.
那么你就可以将这种技术应用到从二级到三级甚至更高级别的每一辆车中。
Then you have the ability to put this in every car from level two to plus to plus plus to level three and then all the way to level four.
这是一个巨大的市场机会。
So that's a massive market opportunity.
而这正是我们目前正在做的事情。
And that's what we're doing right now.
你可以将某种形式的辅助驾驶功能推广到每一款车型上。
You can bring some form of assisted driving to every single model.
我对机器人也有同样的感受。
I feel the same way about robotics.
如果你制造一个类人机器人或类人手臂，并利用为人类设计的世界，然后让机器人学习特定任务，我认为这已经正在发生。
If you do a humanoid robot or humanoid arm, and you do anything that it can leverage the world that's being designed for us, And you train the robot on a particular task that I think we're very it's already happening.
从商业角度来看，我认为这个机会非常巨大。
And I I believe the opportunity from a business standpoint is massive.
这就是为什么我们专注于工业机器人，因为你能够训练机器人。
That's why we're really focused on industrial robots because you can train a robot.
例如，你的任务是在夜间去超市把商品放回货架。
For example, your task is going go to the supermarket at night and put the stuff back in the shelf.
这是一个自包含的问题。
That's a self contained problem.
你并不是在训练机器人去做所有事情。
You're not training a robot to do everything.
我认为能够做所有事情的机器人还需要一些时间才能实现。
I think the robot that will do everything is going to take a little bit of time until we get there.
中国举办了一场人形机器人的半程马拉松，精彩片段看起来非常搞笑。
There was a half marathon in China of humanoid robots and the highlights looked really funny.
机器人在起跑线上摔倒，有的机器人整个团队拉着绳子，像甩东西一样把它们甩到赛道边，这些机器人速度很快，跑得也很远，凭借强大的动力，它们甚至冲出了赛道。
Robots falling on their face the starting line and robots taking their whole team holding on to ropes and like flinging them into the side of the course and people went pretty fast and pretty far with that the power the robot had as it sort of crashed out of the course.
但有些机器人还是以相当快的速度完成了比赛。
But some of those robots finished pretty pretty fast.
我不会说它们的速度超过了我半程马拉松的成绩——虽然最终它们一定会超过，但它们的完成成绩已经相当不错了。
I won't say they they beat my half marathon time which eventually they will, but they were respectable in their finish.
这还包括了更换电池所花的时间。
And that included time for battery changes.
有人认为，在中国，中国离生产流程非常近。
And the argument has been that in China, China is so close to the production process.
想想他们的汽车，就在那里。
Think about you know their cars right there.
这是一场电动汽车的热潮，因为他们长期从事电池和电子产品的制造。
This is an electric car boom because they've been building things with batteries and electronics for so long.
谷歌DeepMind的首席执行官德米斯·哈萨比斯最近表示，中国在人工智能模型方面仅比西方最先进的技术落后几个月，但在机器人领域似乎已经领先。
Demis Esabas, the CEO of Google DeepMind recently said that China is only a couple months behind the state of the art Western models but it seems like they're ahead on robotics.
你同意这个观点吗？
Do you do you agree with that argument?
说实话，我认为中国有很多方面都非常了不起。
Look, there's there are many things I think that China it's as is remarkable.
我觉得他们正在做的事情非常出色。
I think what they're doing.
大家都谈论中国的速度。
Think I think there's everybody talks about the China speed.
我们深知这一点，因为我们在中国有众多合作伙伴，使用我们的技术，不仅限于汽车，还包括手机、机器人和工业领域。
We know that I think from having a number of different partners in China using our technology from not only cars but also phones, now robots and industrial.
我认为，靠近一个庞大的工业基地，确实能让你快速原型设计、快速制造、快速试错，这种说法是有道理的。
And I think there is some merit in the argument that you're closer to a very large industrial base and you can prototype fast, you can build things fast, you can fail fast.
我认为这些优势对技术发展非常有帮助。
And I think those things are helpful in developing the technology.
但机器人所需的技术非常广泛。
But the technology that's going be required for robotics, it's very, very broad.
你会涉及到先进半导体，我认为中国公司在这方面与高通等公司有合作。
You go from advanced semiconductors, I think that's one area that the China companies are partner with companies like Qualcomm and others.
我认为，将会有大量生态系统对训练和大量软件至关重要。
You're going to have a lot of ecosystem, I think, that is going be important for training, a lot of software.
但没错，这非常令人着迷。
But yes, this is fascinating.
大家都在竞速，事情进展得很快。
Everybody is on a race and and things are moving fast.
最后，我想谈谈工业AI，我认为在当前的AI讨论中，它获得的关注最少。
Lastly, want to talk about industrial AI, which is something that I think as far as the AI conversation gets probably the least ink.
但它却是当今最有趣的一些进展之一。
But as some of the most interesting stuff that's happening today.
我的意思是，即使在这里，我们也有一个机器人，它只用了几周时间、花费50美元就建成了。
I mean, even here at the space, we have a robot that we're looking at that was built in just a couple of weeks with a $50 Yes.
高通芯片，运行得相当不错。
Qualcomm chip and moving pretty well.
谈谈人工智能在工业领域的应用吧，也许你可以解释一下为什么人们不太关注它。
Talk a little bit about the applications of AI in the industrial space and maybe why you think people aren't paying so much attention to it.
只是因为它不够炫酷，上不了头条。
It's just it's not sexy enough for like the headlines.
你知道，我认为现在大家的关注点都集中在数据中心上。
You know, that's that's I'll say it's probably there's so much attention on data center right now.
这几乎占据了所有讨论的焦点，我认为数据中心的讨论占据了主导地位。
That is it probably takes all of the air, I think, in in the conversation data center.
就连我们提到正在为数据中心开发一些产品，都引起了大量关注。
I'll probably even resonate just the fact that we said we're building some for the data center, it got a lot of attention.
但现实是，人工智能在工业领域的机遇巨大。
But the reality is the industrial opportunity for AI is massive.
这个机遇非常巨大，因为你几乎可以在任何东西上部署人工智能处理，你会发现每一个行业、每一个领域都有大量的应用场景。
It's massive because you can put, you know, AI processing on pretty much everything and you find that every single industry, every single vertical has a massive number of use cases.
这在零售业、仓储业、医疗业、制造业和能源领域都成立。
It's true in retail, it's true in warehousing, it's true in healthcare, it's true in manufacturing, in energy.
我们实际上看到了巨大的需求，尤其是当你能够实时处理来自物理AI、电机、机器等的信号时，这些设备都可以安装传感器。
And we're actually seeing an incredible amount of demand, especially because if you actually have ability to process in real time things that come from physical AI, motors, machines, you know, all of those things you can put sensor.
但举个例子，我们先不谈各种复杂的机器，单说计算机视觉，比如一个摄像头。
But just to give an example, if I we don't get too fancy with different machines, just in computer vision alone, a camera.
你可以在生产线上安装一个摄像头，训练模型来判断传送带上出现的物品是否符合预期，仅靠摄像头就能实现质量控制。
You can put a camera on a manufacturing line and you train the model just to see if what's coming in the conveyor belt against the template, you know, is what you expected you do quality control with, you know, with just a camera.
你把摄像头放在超市货架上，就能实时监控库存情况。
You put the camera, for example, into looking at a shelf of a supermarket, you now can have the ability to check inventory real time.
你可以实时管理店内库存，并在线销售店内的商品。
You can actually sell online what's in the store with a real time, I think, management inventory.
你也可以在智能停车场的摄像头上看车牌识别。
You can put the same camera on a smart CD and you're reading license plates.
我认为这是一个巨大无比的机会。
And I think it's it's a massive, massive opportunity.
我在达沃斯期间与许多工业公司举行的会议中，有一些就是与这些公司进行的。
Some of the many meetings are actually where I'm having here at Davos is with industrial companies.
他们对工业人工智能非常感兴趣。
They're super interested in industrial AI.
我认为这实际上正在发生。
And I think that's actually happening right now.
好的，还剩五分钟。
Okay, five minutes left.
我有两个问题要问你。
Two questions for you.
我之所以非常高兴能与你对话，原因之一是你能看见未来——因为当某项技术即将大规模生产时，比如有人在开发AI可穿戴设备，第一个找的就是你。
One of the reasons why I'm so happy to be speaking with you is because in a sense you can see the future right because you're the when something is going to be mass produced you're the first call that's being made from let's say someone building an AI wearable.
你与Meta紧密合作，因此对它们预期的需求有很好的了解，因为它们需要你的芯片来构建产品。
You're working closely with Meta so you have a pretty good understanding of the demand that they're anticipating because they need your chips to be able to build things out.
在思考AI基础设施建设以及AI设备发展的同时，也请你展望一下你眼中未来的图景。
Thinking about the AI build out and maybe also the AI device build out and looking into the crystal ball that you have of what the future looks like.
事情会继续以同样的速度发展吗？
Are things going to continue apace?
它们还能保持如此快的步伐吗？
Can they possibly keep moving as fast as they have been?
我觉得这个问题其实很有针对性。
Look, I feel that the and I think that question is really directed.
我认为数据中心方面可能已经发生了变化，而在设备端——也就是起点——我们正看到一个明显的上升趋势，比如智能眼镜每个季度都在持续增长；但更广泛的问题是关于数据中心的速度，我的回答是：
I think what's probably happened on the data center because on the personal on the device side which is the beginning I think we're seeing a big trajectory like for example glasses continue to increase quarter over quarter but I think the broader question is to the speed on the data center and here's my answer.
如果我们回到2000年互联网泡沫破裂的时候，对吧？
If we if we go back to the year 2000 when the.com crash, right?
我当时经历了互联网泡沫的调整。
I have that correction on on the.com.
回到2000年，想想那时我们对互联网的预期是什么。
Go back to year 2000 and you think about what we thought back then what the internet would be.
我可以告诉你，二十五年后的今天，现在已经远远超出了当年人们的想象。
I will tell you that today, twenty five years later, twenty six years now, it is actually way bigger than people thought it would be.
所以，无论他们2000年时怎么想，现在的互联网实际上要大得多。
So whatever they thought is in 2000, the internet will be exactly way bigger right now.
你甚至还能在上面买宠物食品。
And you can still buy pet food on this one.
是的。
Yeah.
然而，这一切并不是在2000年就发生的。
However, it didn't happen all in 2000.
它是逐步发生的。
It happened.
我认为，从长远来看，人工智能的发展规模将超过人们的预期。
I think what's gonna happen is AI right now, in the long run is going to be bigger than people think.
从长远来看，它可能被低估了。
It's probably underhyped for the long run.
现在的问题是，它将多快被部署，会有多普及，以及我们会看到什么。
Now how fast this is going to get deployed and and how pervasive and what we'll see.
我们能在太空中继续建造吗？
Could we continue to build, at the space?
这是有可能的。
It's possible.
这种放缓也是有可能的。
Could could this slowdown is also possible.
我们感到兴奋的是，我认为终于——这更多是针对高通——人们终于意识到边缘计算的机会是巨大的。
What we're excited about it, and I think it's finally, and this is more for Qualcomm, finally people just woke up that the edge opportunity is massive.
我认为之前所有关于数据中心的热议，现在有一部分开始转向关注边缘计算了，我认为我们才刚刚开始踏上这条曲线。
And I think all of this air that was all about data center, some of it is started going to the paying attention to the edge right now and I think we're just the beginning of that curve.
好吧，我终于要问你一个达沃斯问题了。
Okay finally I got to ask you a Davos question.
说吧。
Go ahead.
在这里达沃斯，我们身后就是滑雪坡。
Here at Davos we have the slopes behind us.
这是真实的。
This is this is real.
给那些感到疑惑的人。
For those wondering.
你知道，企业们经历了一段非常有趣的历程。
You know, the corporations have been through this really interesting journey.
曾经有一段时间，他们推崇利益相关者资本主义，关注股东之外的群体。
There's been moments where they've been into what's called stakeholder capitalism, where they think about the group of people beyond the shareholder.
而我认为，我们现在正处在一个更加赤裸裸追求利润的阶段。
And and I think we're kind of in a moment now where there's there's more of a naked pursuit of the bottom line.
我并不是在说高通。
I'm not speaking about Qualcomm.
我只是说，总体来看，企业似乎已经放弃了那种他们关心利润之外其他事物的幻觉。
I'm just saying broadly, seems like corporations are are much more they've they've sort of put away this illusion that they care about much else than than the bottom line.
而且我想知道，我们在这里，在世界经济论坛的户外，这场活动将有48场关于人工智能的对话。
And and I wonder if if, you know we're here at the right outside the World Economic Forum, there's 48 conversations that will happen in this event that will be about AI.
人们会谈论人工智能如何能够治愈癌症，或者为我们提供治愈癌症的最佳机会，并赋能弱势群体。
People will be talking about how AI will be able to cure cancer or get our best chance at curing cancer and empower the disempowered.
所以我很想知道，从你的角度来看，你认为人工智能会成为新的利他主义，或者新的企业利他主义吗？
And so I'm curious, like from your perspective, do you think AI is going to be the new altruism or the new corporate altruism?
这是好事还是坏事？
And is that a good or a bad thing?
这是一个复杂的问题。
It's a complicated question.
听我说，我认为人工智能只是一种技术工具。
Look, I think it's a it's a technology is a tool.
我认为它会像计算机一样，继续发挥作用。
I think it's going like like computers did it and it will continue to do.
它将帮助加速许多事情，比如加速药物研发。
Think will will help accelerate many things, help you know accelerate for example drug discovery as an example.
它将帮助提升许多方面的生产力，正如我之前所说。
It will help you know many things will increase productivity as I said before.
它可能会使教育更加普及。
It's probably going to democratize education.
它将改变我们对教育的看法。
It's going to change how we think about education.
这是一件需要不断变化的事情。
This is something to keep changing.
它将是一种工具。
It's going to be a tool.
我不认为它会像那种彻底改变社会的东西。
I don't think it's going to be like this change this society kind of thing.
我来给你一个非常个人化的回答。
I'll tell you how I'll give you a very personal answer.
当我——这可能会暴露我的年龄，但很糟糕。
When I and this is this is gonna be terrible because it's gonna show my age.
但我大学毕业时，互联网才刚刚起步。
But when I got out of college, right, it's just the beginning of the internet.
不过，我还记得我刚参加第一份工作时，那里有一台传真机，你得跑去传真机那里取前一晚收到的传真，再把要发的传真放进去，而且还有人正在手动打字，发送公司内部备忘录。
Still, I remember going to my first job and there was like a fax machine and you gotta go to the fax machine and you get the faxes that you got overnight and put the other faxes in there and you have somebody still typing, you know, intercompany memos.
我们现在已经不再谈论这些事了。
Like, we don't talk about this anymore.
我认为，当互联网和电子邮件出现时，那是一场革命，而我认为人工智能也将带来类似的革命，就像当年计算机的出现一样，但会更进一步，让我们用计算机做更多事情。
I think when the Internet arrived and email arrived, was a revolution and it was I think the AI is gonna be that kind of revolution, almost like computers, but it's gonna be like us doing things with computer just more.
这就是我的感受。
That's how I feel about it.
好的。
Alright.
嗯，能关注这个领域真是太棒了，因为每次我以为自己跟上了节奏，就会冒出新的东西，我觉得你将会处于这一切的中心，随着即将推出的各种设备，也许当OpenAI发布这一系列产品时，我们可以再聊聊竞争格局。
Well, it is it's been amazing following this space because every every time I think I'm caught up there's something new and I think that you're gonna be right at the center of it with all the devices that are gonna come out and you know maybe when OpenAI does release this family of devices we can talk again about about the state of the competition.
顺便说一下，我们今天现场有很多观众。
By the way we have a great live audience with us.
大家热闹一点，让外面的人也能听到。
Guys make some noise so people can hear it.
感谢克里斯蒂亚诺和高通团队邀请我来到达沃斯你们的场地，非常兴奋能参与这么多关于人工智能现状的精彩对话。
To to Cristiano and the Qualcomm team, thank you for having me here at your space at Davos and very excited to be engaging in a number of really great conversations about the state of AI.
我相信，听完这些对话后，我们的观众会对未来的发展方向有清晰的理解，而这次对话是一个绝佳的开端。
I'm sure that our audience by the end of them will have a really good understanding of where things are going and this was a great way to kick it off.
所以，克里斯蒂亚诺，非常感谢你参加这档节目。
So, Cristiano, thank you so much for coming on the show.
不。
No.
谢谢你。
Thank you.
谢谢你。
Thank you.
和你进行这场对话我真的很开心。
I really had fun having this conversation with you.
谢谢你。
Thank you.
我也是。
Me too.
好了，各位。
Alright, everybody.
谢谢收听，我们下次再见，欢迎收听《大科技》播客。
Thanks for listening, and we'll see you next time on Big Technology Podcast.
谢谢。
Thank you.
非常感谢。
Thank you very much.
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