# (77) Fireside Chat with Yann LeCun, Executive Chairman of AMI Labs | RAISE Summit 2026 - YouTube
- Transcript ID: `idppfaxmczc`
- Source tier: `youtube_auto`
- Original language: `en_or_source_language_unlabeled`
- Translation status: `agent_translated_reviewed` (50/50 chunks)
- Original source: https://www.youtube.com/watch?v=iDpPFAXmcZc
- Raw evidence: `data/transcripts/youtube/iDpPFAXmcZc.json`
> 中文是派生翻译，英文是留存的源文本。中文未复核时，以英文和原始音视频为准。
## 00:00:00
**中文**
［音乐］随便挑一把椅子，Yann。好。开始吧。开始吧。Yann，AI 教父。你知道，“教父”——我住在新泽西；在新泽西，“教父”意味着你为黑手党工作。是。好，那我们还是换个说法吧。首先祝你生日快乐，昨天是你的生日。
**English / Original**
[music] Take your pick of chairs, Yan. >> All right. >> Here we go. >> Here we go. >> Yan. Godfather of AI. >> You know, Godfather is uh I live in New Jersey. Godfather in New Jersey [laughter] means you are the you work for for the mafia. >> Yes. Okay. Well, maybe we use a different terminology than that. I should start by wishing you a happy birthday. It was your birthday yesterday.
## 00:00:26
**中文**
昨天。是。好，那么，Yann，生日快乐。［掌声］不过对比我年长的各位，我今年“26 岁”。是，26 岁的 Yann 已取得相当多的成就。此前当然曾在 Meta 担任 12 年首席 AI 科学家；我没记错吧？而且，对我们如今所处世界具有如此重大影响的是你在 20 世纪 80 年代末开创的卷积神经网络 CNN，它实际上支撑着我们使用手机的方式。
**English / Original**
>> Yesterday. Yes. >> Okay. So, there we go. Happy birthday, Yan. >> [applause] >> I but I turn I turned 26 for those of you who are older. >> Yes, 26-year-old Yan has achieved has achieved quite a lot. Uh formerly of course um chief AI scientist at Meta for 12 years and am I thinking correctly [snorts] and so consequential with the world we live in right now is the convolutional neural network CNN which you pioneered in late 1980s that underpins really how we use our phones.
## 00:00:56
**中文**
图像识别、每次在医院做扫描、自动驾驶——它仍在全球范围产生影响。7 个月前，我说 7 个月前没错吧？你创办、联合创办了 AMI Labs。那么请介绍一下近况：进展如何？AMI 的重点是构建世界模型，即能够与世界交互、学习、反应并接收这些物理信号的物理 AI。这一切进展如何？
**English / Original**
image recognition every time you go for a scan at a hospital, autonomous driving that is still having a worldwide and global impact. And seven months ago, am I right in saying seven months ago, you launched Ammy Labs, founded, co-founded Ammy Labs. So give us an update. How is how is that going? The focus of AML is to build world models. So physical AI that can interact in the world, learn, react, and take in those physical physical cues. How is all of that going?
## 00:01:27
**中文**
对，这是面向现实世界、处理真实信号的 AI。LLM 很擅长处理离散的符号序列。但面对真实信号——比如来自工业的摄像头传感器，或其他任何信号——LLM 本质上完全无用。因此我们正在开发一种新型 AI。我们的抱负是切实带来下一场 AI 革命。你知道，我们有 LLM 和源自 LLM 的系统，它们能够通过律师资格考试、展示并证明数学定理、编写代码。
**English / Original**
>> Right, it's AI for for the real world uh for real signals. So, LLMs handle discrete sequences of symbols quite well. >> Mhm. >> But when it comes to real signals, video sensors from industry uh or whatever. Um LM are completely useless essentially. Um and so it's a new type of AI that we we're developing. Uh our ambition is to really bring about the next revolution in AI. Um the you know we have LLM and derived system systems from LLM that can uh you know pass the bar exam, demonstrate like prove mathematical theorems, write code,
## 00:02:09
**中文**
这些都很棒、非常有用、非常高效，能提高许多人的生产力；它可以写长邮件，收件人再把邮件交给 LLM 生成更短的版本，等等。它很适合做这些事，但不适合理解物理世界。这就是为什么尽管有商业网络，我们仍没有 L5 级自动驾驶汽车，也没有家用机器人。我们没有能做到一个 10 岁孩子能做的事、甚至一只猫能做的事的机器人。因此，我们所认为的智力任务与我们习以为常、但事实证明机器极其难以完成的任务之间，存在巨大性能鸿沟。
**English / Original**
you know all of this is great, very useful, very productive, improves productivity of a lot of people, can write long emails which the recipient will feed to an LLM to produce a shorter version of um um etc. It's great for that but but not not for understanding the physical world and you know that's why we don't have level five self-driving cars despite commercial net and we don't have domestic robots. We don't have robots that can do what a you know what a 10-year-old can do or even what a what a cat can do. So, so there's a huge gap uh in performance between the type of tasks that we think of as you know
## 00:02:52
**中文**
那么请解释一下，为什么在前沿扩展 LLM——Anthropic 和 OpenAI 那种——不能做到这一点？好。物理世界有很多复杂性，LLM 实在无法捕捉，因为它们对世界的表征来自对世界的文本描述。
**English / Original**
intellectual task and the tasks that we take for granted but turned out turn out to be really really difficult for for machines to accomplish. >> So just explain for us why scaling LLMs at the frontier the anthropics and the open AIS cannot do this. >> Okay. Um so there's a lot of complexity about the physical world that lens really cannot capture because the representation of the world that they have is through text descriptions of it.
## 00:03:19
**中文**
当然，现代 LLM 有视觉管线，对吧？你可以向它输入图像或视频，它会生成基本相当于该输入的 token 化版本，再与文本流合并；因此系统在一定程度上能解释图像。但世界远不止文本描述，甚至也不止 token 描述。我们对于真实世界形成了许多直觉。如果我在这里松开这个瓶子，你当然能预测会发生什么。当然，若问 LLM：“我正拿着一个瓶子，要松手了，会发生什么？”它会说它将会……
**English / Original**
Of course, you know, modern lens have vision pipelines, right? So they you can feed an image or a video to it and they produce basically what amounts to a tokenized version of that uh inputs which is you know merged with the the flow of text and then the the system to some extent can interpret image images but but there is a lot more in to the world than than just a textual description or even a description um you know by by token. So, um, there's a lot of intuitions we we develop about the real world. If I if I drop this bottle here, you can you can predict what's going to happen, of course. And of course, you ask a LM, you know, I'm holding a bottle. I'm going to let it go. What's going to happen? It's going
## 00:04:00
**中文**
掉下去。如果它在地板上方，它会撞到地板；也许会爆裂，水溅得到处都是。你能预测这件事大致会如何发展，但世界、物理世界中的大部分细节并不可预测。因此，生成模型这一理念在现实世界中行不通。预测一串词之后的词相对容易，这正是 LLM 所做的事；无论是自然语言、代码还是什么。你向系统展示很长一串词，训练它预测下一个词。它……
**English / Original**
to tell you it's going to fall. And if it's above the floor, it's going to hit the floor. Maybe it's going to explode and splash water everywhere. You can predict exactly in what uh you know what the details of this are but most of the details in the world in the physical world are not predictable and so the very idea of a generative model does not work in the real world if you it's relatively easy to predict the word that follows a sequence of words which is really what LLMs are doing um whether it's natural language or code or whatever right you show the system a long sequence of of of words and you train it to predict the next word. It
## 00:04:42
**中文**
永远无法精确预测下一个词，但可对词典中每个可能的词给出一个分数或概率分布。然后你从该概率分布挑选一个词，这就是系统会产出的词。把它移入输入，现在就能产生第二个词；再移入输入，产生第三个词。这叫自回归预测，是一个很旧的思想，LLM 就以此为基础。如果想让 LLM 稍微推理，你让它生成大量这样的序列，再让另一个神经网络从所有生成结果中选出最佳答案，这种做法荒谬地……
**English / Original**
can never exactly predict the word that follows but it can produce a score for every possible words in your dictionary or a probability distribution. Right? So then you pick a word from that probability distribution. That's the word the system is going to produce. You shift that into the input and now you can produce the second word. Shift that into the input third word. That's called auto reggressive prediction. It's a very old idea and that's what edens are based on. uh if you want LLMs to reason a little bit um you have it produce lots and lots of those sequences and then you have another neural net that selects the best answer among all of the ones that are produced which is ridiculously
## 00:05:18
**中文**
昂贵。但这大致就是那些编程、数学类系统的工作方式。现在，如果希望系统理解真实世界，为何不使用同一种技术？拿一段视频，训练系统预测视频接下来会发生什么。如果训练系统在像素层面作预测，它根本行不通。
**English / Original**
expensive. Uh but this is how those you know coding mathematics kind of system work more or less. Um, now if you want a system to understand the real world, why not use the same technique? Take a piece of video and train the system to predict what's going to happen next in the video. And if you train the system to predict at the pixel level, it simply doesn't work.
## 00:05:42
**中文**
因为视频中的大部分细节不可预测。视频中包含的大部分信息完全不可预测。如果我拍摄这间房间，把摄像机朝这一边，然后慢慢旋转摄像机，停在这里，再问系统：“补全空白，告诉我视频接下来会发生什么。”它大概会预测摄像机将继续旋转；但它绝无可能预测你们每个人长什么样。
**English / Original**
Um, because most of the details in video are not predictable. Most of the information contained in video is entirely unpredictable. If I take a video of this room, I point the camera in this side. I slowly rotate the camera. I stop here and I ask a system, fill in the blanks, tell me what's going to happen next in the video. Probably going to predict the camera is going to continue rotating. Uh there's absolutely no way it can predict what every one of you looks like.
## 00:06:10
**中文**
它没有任何可用来作这类预测的信息。因此，如果训练系统在像素层面做这种预测，你会毁掉它，因为你本质上是在要求它解决一个完全无解的任务。当然，你会说：“等等，但我们有能进行视频生成的系统。”
**English / Original**
There's just no information from which that he can use to make that kind of prediction. So if you train a system to make that prediction at a pixel level, you kill it because you basically are asking it to solve a test that is completely unsolvable. Now, of course, you're going to say, "But wait, we have systems that can do video generation."
## 00:06:29
**中文**
是的，它们能生成，但预测会是假的。它们不会预测你长什么样，只会在那里放进随机的人，对吧？这些系统并没有真正构建出对底层物理或现实的良好理解。我们在 FAIR 时已研究多年、创建 AMI Labs 后仍在研究的技术叫 JEPA。它训练系统不是生成并预测信号的所有细节，而是构建或学习该信号、某个视频的抽象表征。
**English / Original**
Yes, they can, but they're going to make a prediction that is false. They're not going to predict what what you look like. They're just going to put random people there, right? um and those system don't really construct a good understanding of the underlying uh physics or reality of that. So the technique we've been working on for a number of years when I was at at fair and and since we created AM labs um is called JEA and it consists in training a system not to generate and predict all the details of a signal but to construct or learn an abstract representation of that signal of a video
## 00:07:09
**中文**
然后在这个抽象表征空间中作出预测。系统所做的是，从该表征中消除所有不可预测的信息；而这正是我们作为人类或科学家实际理解世界的方式。我们始终有一个关于现实的心智抽象表征，它使我们能够作预测，并忽略所有底层细节。
**English / Original**
and then to make predictions in that abstract representation space. uh and what the system does is that it eliminates all the information from that representation that is unpredictable and uh and that's the way we actually can apprehend the world as as humans or or as scientists. We we always have a a mental abstract representation of reality that allows us to make prediction and ignores all the >> all the underlying details.
## 00:07:41
**中文**
你们何时能证明这一点？何时发布 AMI 的首个模型？好，我们知道这个想法有效，因为我们一直在研究它；我过去 15 年的大部分时间都在做这个。但我 2016 年在 NIPS 会议（当时还不叫 NIPS）作主题演讲时说过，AI 的未来是世界模型：给定时刻 t 的世界状态，也就是世界状态的抽象表征；给定你设想要采取的行动，世界模型将预测该行动带来的世界下一状态。
**English / Original**
>> When do you prove that out? When do you release your first model from AMI? >> Okay, so we know this idea works because we've been working on this for I've been working on this for the better part of the last 15 years, but I had a keynote at the the NIPS conference. It wasn't called NIPS yet um in 2016 where I said the future of AI is world models. So things that can predict given the state of the world at time t. So an abstract representation of the state of the world. Given an action that you imagine taking, a world model will predict the next state of the world that results from this action.
## 00:08:14
**中文**
但你是在 2016 年作出这个预测。2016 年。是，我有这个想法很久了，基本思想并不特别新。但我说，这就是我们应构建智能系统的方式：拥有能预测自身行动后果的系统。因为有了能预测行动后果的系统，它就能规划一系列行动来完成任务。你可以说，我想把这个瓶子从这里移到那里。这不是很复杂的任务，但你可以有一个目标——瓶子在该位置——然后弄清楚应完成什么行动序列，才能……
**English / Original**
>> But you made that forecast in 2016. >> 2016. Yeah. I mean, I had this idea for a long time and it's this basic idea is not particularly new, but um but I said this is the way we should build intelligent systems. Have a system that can predict the consequences of its actions. Because if you have a system that can predict the consequences of its actions, it can plan a sequence of actions to accomplish a task, right? Um you can say, well, you know, I want to move this uh uh this bottle from here to here, right? It's not a very complex task, but you can have an objective which is the bottle is in this location and then figure out like what sequence of action should I accomplish so that it
## 00:08:56
**中文**
真正把瓶子放到那里。当然，存在无限多种可能：可以用左手或右手，可以把瓶子转过来，可以沿着有趣的轨迹移动，也可以把它扔过去，或做别的——大概不是好主意。那么，你能否规划出一系列行动，确保它会产生你想要的结果？如果有一个预测行动后果的这类心智模型，就能做这种规划。这在最优控制中非常经典。
**English / Original**
actually puts the bottle here. And of course, there's an infinite number of possibilities. I could do this in my left hand or my right hand. I could, you know, turn the bottle around. I could follow a funny trajectory. I could throw it or whatever. Probably not a good idea. So, can you plan a sequence of actions that you know will result in the result in the outcome that you want? And if you have such a mental model that predicts the consequences of your actions, you can do this planning. This is very classical in optimal control.
## 00:09:23
**中文**
这不是新技术，叫 MPC，即模型预测控制。新之处在于，我们能否从数据、从观察数据中训练这个世界模型；而这基本就是人类和动物训练自身的方式。婴儿时你会做手势，随机碰到物体，然后意识到自己能碰到物体。
**English / Original**
This is not a new technique. It's called MPC model predictive control. Okay, >> what's new is can we train this world model from data from observational data and that's basically how you know humans and animals train themselves. You know you justiculate when you're a baby and then you know you randomly hit objects and you realize you can hit an object.
## 00:09:45
**中文**
你还会意识到，如果物体没有支撑，它会掉落。人类大约需要九个月；婴儿大约用九个月来学习重力和惯性。那么，能否对机器做同样的事？过去两年我们已经有模型基本做到这一点：名为 V-JEPA、V-JEPA 2 和 V-JEPA 2.1 的模型。它们理解视频，能告诉你视频中是否发生不可能的事，因此具有一点常识。它们提取的视频表征，只需极少微调，就可用于各种下游任务，所以我们知道它……
**English / Original**
You realize also uh that if an object is not supported, it falls. In humans, it takes about nine months. It takes about nine months for infants to learn that about gravity and inertia. Um so can we do the same with machines and we have we already had models for the last two years that basically already do this models called VJA VJA 2.1 VJ 2 VJA 2.1 they understand video they can tell you if something impossible occurs in a video so they have a little bit of common sense um the representation of video that they extract uh can be used for all kinds of downstream tasks with very minimal fine tuning so we know it
## 00:10:28
**中文**
有效。视觉数据在多大程度上是瓶颈？不，它不是瓶颈，远不如文本。把互联网公开可得的所有文本加在一起，大约有 20 万亿词。把这些转为 token，就是 30 万亿 token。一个 token 是两三个字节，假设是三个字节，总计约为 10 的 14 次方字节。
**English / Original**
works. How much of a bottleneck is visual data? >> No, it's not a bottleneck. Not nearly as much as um as as text. When you take the totality of all the text available on the on the internet publicly, it's about uh 20 trillion words. You turn this into token, that's 30 trillion tokens. Um a token is two or three bytes. Let's say three. So that's 10^ the 14 bytes.
## 00:10:51
**中文**
这是互联网全部公开可得文本的总量。当然，大型 AI 公司也用合成数据和获得授权的非公开数据训练，但与整个互联网相比实际上并不多。所以是 10 的 14 次方字节。而 10 的 14 次方字节，大致就是你生命最初四年到达视觉皮层的字节量。
**English / Original**
That's a total amount of publicly available text on the internet. Of course, the big AI companies are also training with synthetic data and licensed, you know, non-public data, but it's not a lot actually compared to um all the internet. So, 10^ the 14 bytes. >> Now, 10^ 14 bytes is just about the amount of bytes that gets to your visual cortex in the first four years of your life. Okay?
## 00:11:20
**中文**
而互联网公开文本需要我们任何一个人至少 400,000 年才能读完，显然完全不现实。因此，一个 4 岁儿童见过与最大模型同样多的训练数据——区别是视频。视频非常冗余，不像文本那么丰富、那么密集；但你从中学到大量关于世界如何运作的基本知识：你学到世界是三维的，学到物体不能从一个地方消失又在另一处出现，学到轨迹是连续的。
**English / Original**
Whereas the text, the publicly available text on the internet would take any of us at least 400,000 years to to read through, right? Which is completely impractical obviously. So a 4-year-old has seen as much training data as the biggest >> except is video. So video is very redundant, you know, it's not as rich and and and sort of dense as as text. Uh but you learn a huge amount of basic knowledge about how the world works from this. You you you learn that the world is threedimensional. You learn that objects don't cannot disappear from one place and appear in another place. That you know trajectories are continuous.
## 00:12:01
**中文**
你学到惯性、重力和各种东西。你学到从底部推这个瓶子，它会滑动；从顶部推它，可能会因摩擦而翻倒。若我以同样力量推桌子，它不会移动。所有这些事我们习以为常，觉得不需要特别聪明；实际上确实不需要。老鼠能弄懂，猫也能。一只猫只有 8 亿个神经元，大约是人脑的百分之一；但这仍然是计算机的挑战。
**English / Original**
You learn about inertia. You learn about gravity. You learn you learn about all kinds of stuff. You learn that if you push this bottle from the bottom it's going to slide. If you push it from the top maybe it's going to depending on friction it's going to flip. If I push on the table with the same strength, it's not going to move. Like all those things we take for granted, we think, you know, we don't need to be particularly smart for this. And in fact, you don't. You know, a rat can figure this out. A cat, too. And a cat only has >> 800 million neurons. It's about 100 times less than the human brain. Um, but it's still a challenge for for computers.
## 00:12:34
**中文**
那么，如果你们成功了，我们挑一个行业看看它会如何改变。以国防为例，因为我们看到像 Andrew、德国的 Helsing、德国的 Stark 等公司，已在把 AI 和智能嵌入正部署在战场上的硬件中。世界模型的成功对国防这样的行业意味着什么？
**English / Original**
>> So, if if you succeed, let's pick one industry and see how it changes this industry. If if this succeeds, let's take defense because we're seeing companies like Andrew and Helsing in Germany, Stark in Germany, they are already in embedding AI and intelligence into hardware that's being deployed in theater as we speak. What does success at world models mean for an industry like defense?
## 00:12:53
**中文**
好。目前他们正在做的很多事——国防，或更广泛的产业，并不只限于国防——与开头列举的例子类似。例如图像理解，用于自动驾驶、驾驶辅助或汽车的碰撞检测；你的车会自动停下。许多都是感知。
**English / Original**
>> Okay. So, what they're doing at the moment, a lot of uh uh defense or industry more generally, not just defense, but are examples of similar to what you cited at the beginning. So things like you know image uh understanding uh for like you know autonomous driving or or driving assistance or or collision detection for a car right your car stops automatically right uh so a lot of it is just perception.
## 00:13:19
**中文**
下一步不止是感知，而是行动：能否规划一系列行动以实现特定目标？为机器人规划轨迹或一连串动作，让它煎鸡蛋之类。这更难。对于国防，有一件相对容易的事，即为无人机等规划避开已有障碍物的轨迹。
**English / Original**
>> So the next step is more than perception is action. Can we plan a sequence of actions to achieve a particular goal? plan a trajectory for a robot or a a sequence of mo motions for a robot to I don't know fry eggs or whatever. Uh that's a bit harder. And for for defense there is something that's relatively easy which is planning a a trajectory for a drone or something like this avoiding obstacles that already exist.
## 00:13:48
**中文**
但在该场景中，还有更多复杂的决策任务需要完成，世界模型将在其中真正发挥作用。你在 Meta 待了 12 年，为什么没能和 Mark 一起做这件事？我完全可以。我本可以留在 Meta，在 Meta 做这件事。这个我们内部称为 AMI——高级机器智能——的项目，就是公司的名字；我们保留了同一个名字。
**English / Original**
Um but there are a lot more kind of complex uh decision- making uh tasks to to accomplish in that context where world models really will play a role. >> You were a meta for 12 years. Why were you not able to do this with Mark? >> I totally was. I could I could have been I could have stayed at Meta and do this at at Meta. Um the the project which we called internally AMI advanced machine intelligence. Okay, it's the name of the company. uh we we kept the same name.
## 00:14:19
**中文**
它得到 Mark Zuckerberg、CTO Andrew Bosworth 及领导层许多其他人的支持。他们确实相信，最终 AI 系统可能会采用这类蓝图。但 2025 年发生的事是，Meta 内部很多 AI 组织将重点放在追赶行业其他公司在 LLM 上的进展；有一批人真心相信，无论你如何定义 AGI，它会从扩展 LLM 并用更多数据训练中涌现，而我不相信这一点，并且一直公开强烈表达这个观点。因此出现了一些脱节。
**English / Original**
This had the support of Mark Zuckerberg and Andrew Bosworth the CTO and a bunch of other people in the leadership. So they they were really um you know kind of believed in the idea that eventually AI systems maybe of this type you know with that kind of blueprint. Um but what happened in the LA in 2025 was that you know a lot of the sort of AI organization within meta kind of focused itself on catching up with the rest of the industry for LLMs and there was bunch of people who really believe that you know AGI whatever you mean by that will emerge from scaling up LLM and
## 00:15:02
**中文**
所以尽管该项目获得 Mark 支持，那里可能不是做这件事的最佳地点。而且，我们开始从 V-JEPA 等系统获得非常好的结果，也知道存在应用。大多数应用将面向产业、工业过程控制：控制发电厂、喷气发动机、制药厂这类复杂系统，或者病人、人体细胞之类；本质上是建模并预测复杂系统的行为。大多数此类应用是 B2B，而 Meta 不是 B2B 公司，它完全专注于连接人与人。因此离开、创办新公司，为该项目全速推进并将其变为现实，是合理的。
**English / Original**
training them on more data which I don't believe and I was publicly very vocal about this Um so there's a bit of a disconnect. Um so despite the fact that you know the project had the support of of Mark um this probably was not the optimal place to do this. Also we were starting to get really good results with those those those system with the VJA etc. And and so we knew there were applications. Most of those applications would be for industry, industry process control, you know, controlling a complex system like a a power plant, a jet engine, uh, you know, pharmaceutical manufacturing uh plant or whatever or or a patient something like
## 00:15:45
**中文**
我知道你也非常关注 AI 主权；你是 AI 联盟的主要成员，并有一个叫 Tapestry 的计划。对欧洲产业而言，围绕 Mythos 和 Fable 的出口管制无疑是一个沉重打击时刻。请谈谈，欧洲实践中的 AI 主权实际应意味着什么，重点应在哪里？我认为，欧洲显然在 LLM 竞赛中落后了。这基本掌握在硅谷少数公司手中——它们在全球各地设有实验室，包括巴黎和伦敦——当然还有一批中国公司。
**English / Original**
that, a human cell like you know basically modeling the and predicting the behavior of complex systems. uh most of those applications are B2B and Meta is not a B2B company. It's a company that is entirely focused on connecting people with each other. Um so it made sense to leave start a new company and and go into high gear and for that project and then make it real.
## 00:16:12
**中文**
那艘船已经开走了，至少从传统专有平台的视角看是如此。中国模型是开放的；我们不知道这会持续多久，可能会，也可能不会。目前中国模型是最好的开源模型。世界上在美国和中国之外的许多人、以及美国国内的一些人，都希望首先能获得一个开放、免费的基础模型，以便通过……
**English / Original**
I know you think about AI sovereignty a lot as well and you're a leading member of the AI alliance and there's a program called tapestry and certainly for the industry in Europe there was a kind of gut punch moment with the export controls around mythos and fable talk to us about what sovereignty around AI should actually mean in practice for Europe where the focus should be >> so I think uh I mean clearly Europe is is behind in terms of the the race for LLMs that's basically in the hands of you know a few companies in Silicon Valley you know who have labs everywhere in the world, right? Including in Paris and London. Uh, and of course a bunch of Chinese companies.
## 00:16:50
**中文**
微调构建定制产品。这样的模型已不复存在。Meta 的 Llama 曾一度出现，如今基本不再被 Meta 推动。Meta 正变得有些封闭，像几年前的 Google 一样。因此，西方不再有可信的开放 AI 平台提供者；全都是中国的，而美国、欧洲、亚洲的很多公司不愿采用中国模型。
**English / Original**
Uh, and that ship has sailed. Uh, at least from the sort of traditional proprietary platform type view. The Chinese models are open. We don't know how long that's going to last. It may or may not last. Uh, and currently the Chinese models are the best open source ones. uh there's a lot of people uh in the world outside the US and China but also in the US who would like to have access to first of all an open and free um foundation model from which they can build a custom product through
## 00:17:29
**中文**
首先，因为它们可能明天就因政治决定而被关停；也因为偏见等问题。不过我们看到用户正从专有平台转向开放平台，主要是中国的引擎和最终模型，因为美国政府和 Anthropic 的近期行动，以及人们意识到：如果它是基础设施，我们对此完全没有控制权，依赖它风险太大。Tapestry 项目是我三年前就有的想法；我曾在 Meta 内部谈过，进展不大。
**English / Original**
finetuning right um and those don't exist anymore there was llama from from meta that kind of took a of a nose dive and now basically it's not really uh pushed by uh by meta anymore. Meta is becoming a little more clammed up like like Google couple years did a couple years ago. Uh so no credible provider of open AI platforms from the west anymore. um they're all Chinese and a lot of companies in the US, in Europe, in Asia are reluctant to adopt Chinese models.
## 00:18:09
**中文**
我几年前在联合国谈过，几个月前又在联合国开源周谈过，并与世界各地政府交流这个想法。基本想法如下：各国、地区、大学、私营实体、非营利或营利机构，都可以在无需公开自身数据的情况下，为训练一个大规模、LLM 风格的基础模型作出贡献；它们可使用自己的数据、自己的计算……
**English / Original**
First of all, because they could be shut down tomorrow uh by by political decisions, but also because of issues of biases and things like that. uh but we see a movement from the user proprietary platform towards open platforms uh mostly Chinese engines finial models because of you know recent actions of the US government and entropic and the know people realizing like we don't have any control on this if it's a basic infrastructure is just too risky to depend on it so project tapestry is an idea I've had for three years I talked about it internally at meta It didn't go
## 00:18:49
**中文**
基础设施，为训练一个全球模型贡献力量，却永远不实际传递自身数据，从而保持该数据的主权。有海量不公开的训练数据，可极大提升基础模型性能。举一个不那么随意的例子：印度。印度有 22 种官方语言、总共约 300 种语言，外加一些方言。
**English / Original**
very far. I talked about it at the United Nations a few years ago again just a few months ago at the United Nation Open Source Week [snorts] and talk to various governments around the world about this idea. The basic idea is as follows. countries, regions, universities, private entities, nonprofit or for-profit could contribute to training a large scale foundation model L&M style um without having to release their data. They could use their own data, their own computing
## 00:19:31
**中文**
OpenAI、Anthropic、Google 或 Meta 不可能训练一个会说所有这些语言的模型，对吧？那么印度该怎么办？他们几年前建立了一个名为 BharatGen 的组织，拿开源模型微调，使它们能说印度语言。过去用 Llama，现在用一个中国模型；他们对此并不满意。他们希望有开放平台。欧洲也有同样问题，越南、哈萨克斯坦、非洲各地、韩国、日本等各种地方也是如此；所有这些国家都希望有主权模型。
**English / Original**
infrastructure and contribute to training a global model without actually communicating their data ever. So maintaining sovereignty on that data. [snorts] There's a huge amount of training data that is not publicly available that would improve greatly the performance of uh foundation models. Think of u let me take a a non-random example India. India has 22 languages official languages and about 300 languages total plus some dialects.
## 00:20:07
**中文**
而这现在正走向实施，不只是对话。没错。两个月前，我们在巴黎召开了 Tapestry 项目启动会。它是一种自下而上的事；这个房间里任何具备技术能力的人都可报名并向 GitHub 贡献。基本工作是建立分布式训练的软件基础设施。每个国家都可扫描、数字化其本国所有文化信息——可能在国家图书馆等机构中、尚未公开的内容——并拥有计算资源，然后在本地用自己的数据、也用……
**English / Original**
There's no way OpenAI, Anthropy, Google or Meta is going to train a model that speaks all of those languages, right? So what is India to do? What they did a few years ago is that they set up an organization called Barat Gen which takes open source models and fine-tuned them so they they speak Indeek languages. uh in the past they were using lama now they're using one of the Chinese models uh they're not happy about it >> and they would like open platform same problem in Europe same problem in various countries Vietnam Kazakhstan Africa various various places Korea Japan all of those countries want a sovereign uh sovereign model
## 00:20:50
**中文**
普遍可得的数据训练模型，随后定期把参数向量发送给一个中央服务器；如果你愿意，它会计算所有贡献者参数向量的平均值。
**English / Original**
>> and this is moving towards implementation now this isn't just a conversation >> that's right so two months ago we had a a kickoff meeting in Paris of the Tapestry project. Um, and and the the it's it's it's a kind of a bottomup thing. Anybody in this room can if you have any kind of technical ability, you can sign up and contribute to the GitHub. Uh, the basic thing here is to build a software infrastructure to do distributed training, right? So every country in the world would you know scan their own digitize all of their cultural uh information whatever it is that resides maybe in
## 00:21:31
**中文**
如果让这个过程在各种技巧下运行足够久，所有系统就能缓慢收敛到该共识模型；它会和在单一数据中心、使用全部数据训练的模型一样好。但参与者实际上不交换数据。因此你得到的是一个基础模型，它实际上可能比所有专有系统都好，因为它会用更多数据训练，是开放且免费的；在 AI 中，开放、免费的基础模型是否可得，是不可或缺的。
**English / Original**
their national libraries and things like this not publicly available yet and and have computing resources and then would train locally a a model on their data also using uh commonly available data and then periodically would send their parameter vectors to a central server if you want that would compute the average parameter vector of all the contributors.
## 00:21:57
**中文**
这件事必须发生。之所以必须发生，是因为我们所有的信息、我们消费的所有信息，最终都会经由 AI 助手的中介来消费。就像我现在戴在鼻子上的这个 AI 助手。这是 Ray-Ban Meta，对吧？里面有助手。我不会问它问题，但我可以给各位拍张照片。好。
**English / Original**
And if you let this go enough with various tricks, you can get all of those systems to slowly converge towards that consensus model >> that uh is as good as if it had been trained in a single data center on all the data, right? >> But the the participants do not actually exchange that data. So now what you have is a is a financial model that actually could be much better than all the proprietary system because it would be trained on more data would be open and free and the valability of an open and free financial model in AI is indispensable.
## 00:22:36
**中文**
好，再给这一边拍一张。好。我本可以让它直接拍照。我们所有的信息饮食都将由 AI 系统中介。如果这些系统只是美国西海岸或中国少数公司掌控的少数专有引擎，那就意味着本地文化的终结。你知道，法国人很珍视自己的语言，对吧？我听说过——而且不只是法国人。
**English / Original**
This is something that will have to happen. And the reason it will have to happen is because all of our information, all the information we consume eventually is going to be consumed through the mediation of AI assistant. AI assistant like the one I'm wearing on my nose right now. These are the Rayban meta, right? Um there there is a assistant in this. I'm not going to ask it question, but I can take a picture of you guys. All right.
## 00:23:07
**中文**
如果我们做不好，影响会这么严重吗？绝对严重，因为这不仅是语言的丧失，更是整个文化的丧失。实际上，能测试 AI 系统在何种程度上有文化偏见，也是 Tapestry 项目的一部分。每个系统必然存在文化偏见；没有无偏见的系统，这是不可能的。请问，AMI 会在新加坡、旧金山和巴黎设办公室吗？显然不——我错了。你们正在把自己建设成一家国际化公司。
**English / Original**
Okay. another one for this. Okay. Um I could have asked it to just take take the the picture. Um so all of our information diet is going to be mediated by AI systems. If those systems are a handful of proprietary engines from a handful of companies on the west coast of the US or China, this is the end of local culture. You know, the French are really attached to their language, right? I've heard >> and not not just the French.
## 00:23:40
**中文**
它是国际化的，是全球化的。你最终必须选边站吗？不。除非美国政府对我们彻底失常。有人会说，我们离那儿不远。我的意思是，总统 M 令人担忧。不，这确实是一个担忧；这可能也是 AMI Labs 在巴黎设立总部的原因之一。AMI Labs 在巴黎、纽约设有办公室。
**English / Original**
>> Uh so >> it's that dramatic if we don't get this right. It's absolutely dramatic because uh you know it's loss of linguistic uh uh not just not just language but but all of culture right so the the the systems the people who can test in fact that's part of the tapestry project you can test to what extent an AI uh system is is culturally biased >> and every system is necessarily culturally biased there is no unbiased system that's impossible >> can I ask will will Amy you have offices in Singapore, SF, San Francisco in Paris. Obviously, no >> I'm wrong. You don't you you you you're you're building yourself as an international.
## 00:24:21
**中文**
好。还有蒙特利尔和新加坡。我们确实是一家全球公司。我们的投资者来自全世界，产业合作伙伴／投资者也来自全世界。40% 的投资者来自欧洲，33% 来自美国，27% 来自亚洲，另有一家来自阿联酋的基金。因此我们确实是一家全球公司；但在美国西海岸没有任何业务，在中国也没有任何业务。
**English / Original**
>> It's international. It's global. >> Will you have to pick a side at some point? >> No. >> I mean, unless you know, the US government goes goes all nutty on us, but >> Well, we're not Some would argue we're not too far. I mean, President M is concerned. >> No, it's a concern. Uh it's you know possibly one of the reasons that Amid Labs is uh established in headquartered in in Paris. So Amid Labs has offices in Paris, New York.
## 00:24:49
**中文**
好。这是有意为之。就目前而言，这是有意为之。我们已超时六分钟了，真不敢相信我必须结束。但最后我想挤出这个问题。我们开头谈了你在 20 世纪 80 年代末最关键的开发——CNN、卷积神经网络——所产生且仍在产生的经济和社会影响，我们仍在受益。如果你们的世界模型成功，简单说，其潜在经济和社会影响的上限是什么？
**English / Original**
>> Okay. >> Montreal and Singapore. >> And we're really a global company. Our investors are from all over the world. Our industry partners/investors are from all over the world. 40% of our investors are from Europe. 33% from the US and 27 from Asia with one fund from from UAE. So we're really a global company. Uh but we have zero presence on the west coast of the US, zero presence in China.
## 00:25:17
**中文**
好。这是有意为之。就目前而言，这是有意为之。我们已超时六分钟了，真不敢相信我必须结束。但最后我想挤出这个问题。我们开头谈了你在 20 世纪 80 年代末最关键的开发——CNN、卷积神经网络——所产生且仍在产生的经济和社会影响，我们仍在受益。如果你们的世界模型成功，简单说，其潜在经济和社会影响的上限是什么？凡是你期待 AI 能做、但它目前做不到的事：家用机器人、我们仍没有的 L5 级自动驾驶汽车，以及具备常识、或许在许多方面与人类一样有能力、一样聪明的系统。
**English / Original**
>> Okay. And that's by design. >> That's by design for now. >> We are six minutes over time. I can't believe I have to wrap this up. But lastly, I want to squeeze this in. Um we started with the economic and social impacts that your first really key development back in the 1980s late 1980s uh CNN uh convolutional on neural networks had and still has and we're still benefiting from if you succeed with world models briefly what is the potential economic and societal impact at the at the higher end >> all the things that you see you expect AI should be able to do and doesn't do like domestic robots like level five self-driving cars which we still don't have uh like you know systems that have
## 00:25:55
**中文**
它们将是对人类的增强和辅助。物理世界的 AI——我认为，除非 AI 系统能理解真实世界，否则不可能具有普遍实用性。我也认为，除非智能体系统能预测自身行动的后果，否则不可能可靠；换言之，除非它们拥有世界模型。
**English / Original**
common sense that are perhaps as capable as smart as as humans in in many ways uh but as you know augmentations assistance to uh to uh to humans so AI for the physical world I don't think we can have AI systems that are you know generally uh useful unless they can understand the real world I don't think you can have agentic systems that are reliable unless unless they have the ability to predict the consequences of their actions. In other words, unless they have world models.
## 00:26:30
**中文**
因此这将是一场新的 AI 革命，这正是我们在构建的。欧洲并没有输掉这场竞赛；它现在正在进行。欧洲在前沿上失去了领先，但它可以赢得这场竞赛。欧洲绝对可以——但这不会是一次性“赢得”；你知道，永远都是持续、持续的进步。
**English / Original**
And so it's going to be a a new AI revolution. That's what we're building on. And Europe has not lost that that race. It's that is going right now. >> Europe's lost on the frontier, but it can win this race. >> Europe can absolutely it's not going to be a win. you know, you you never you know, it's always a constant, you know, constant progress.
## 00:26:53
**中文**
好。不是说你发现了别人没有的某个秘密，就赢了。我们会在谈判桌上有一席之地。我们会有一席之地。我认为会超过如此。现在硅谷有点像在挖一条人人都在挖的战壕：他们都在做同一件事。没人能偏离主流 LLM 路线，因为一旦偏离，就有落后的风险。因此，对拥有替代路径的人来说，这是一个应抓住的机会。
**English / Original**
>> Okay. >> Uh so it's not like you you win because you discover one secret that nobody else has. >> We will have a seat at the table. We'll have a seat at the table. >> We have more than that, I think. Uh and right now, Silicon Valley is a bit in a kind of a trench that everybody is digging. They're all kind of working on the same thing. Nobody can deviate from the the mainstream LLM because if they do they run the risk of falling behind and so um that's an opportunity to seize for people who have alternative approaches.
## 00:27:24
**中文**
好。像我们这样。Yann LeCun，非常感谢。我不会再称你为“教父”，而是 OG。OG。［掌声］谢谢。谢谢。也谢谢你们二位。
**English / Original**
>> Okay. >> Like us. >> Yan Yan Lon, thank you very much indeed. I won't call it the godfather. The OG. The OG. [applause] Thank you. Thank you. >> Thank you both.