--- author: AI Engineer date: '2026-11-03' guest: '' layout: post.njk source: https://www.youtube.com/watch?v=q4ITwvlFplQ speaker: AI Engineer tags: - video-generation - world-model - scaling-law - simulation - visual-thinking title: Runway 创始人 Cristóbal Valenzuela：从视频生成到世界模型与 Continuum 操作系统 summary: Runway 创始人 Cristóbal Valenzuela 回顾了公司从 2019 年低分辨率模型到如今前沿视频生成的演进，阐述下一帧预测如何学习真实世界物理规律并扩展为世界模型与交互式模拟系统。他重点展示了早期研究成果 Continuum——一种基于实时视频模型构建的操作界面，能将任意内容转化为可交互的“传送门”，并支持代码 Agent 的视觉思考与动态场景生成。 insight: '' draft: true series: '' category: ai-application area: tech-engineering project: [] people: - Cristóbal Valenzuela companies_orgs: - Runway products_models: - Continuum media_books: [] status: evergreen --- [music] >> 大家好。非常非常高兴来到这里。感谢大家留下来听最后一场演讲。欢迎来到我关于“黑客帝国”（The Matrix）以及我们正如何置身其中的演讲。
Original English [music] >> Hey guys. Very very excited to be here. Thank you for staying for the last talk. Welcome to my talk about the Matrix and how we're living through it.
>> [笑声]
Original English >> [laughter]
>> 嗯……那么，你们知道吗？我已经为 Runway 工作了将近十年。实际上大概 10 年前始于这里的纽约。 我想带大家回顾一下我们在建立公司、在纽约建立这个研究实验室的过程中所获得的一些经验。以及我们认为我们在视频、像素生成领域所做的许多工作究竟在向何处推进？因此，我并不是夸大其词，我开玩笑说这可能是一场模拟，也可能不是。
Original English >> Um So, you know what? I've been working on Runway for almost a decade now. Started here in New York, actually, maybe around 10 years ago. And I want to walk you through some of the learnings we've seen through, you know, building the company, building this research lab here in New York. And where do we see a lot of the work that we do in video, in pixel generation, really moving forward? And so, I don't kind of exaggerate and I joke that this is might be a simulation or not.
### 从 2019 年的《橙子》到当今最前沿的视频生成 >> 嗯，为了提供一个视角，我先带大家看一段历史。这就像在看史前的古老遗迹。 呃，这是我们在 2019 年制作的第一部 AI 电影。它叫做《橙子》（The Orange），因为正如大家非常清楚地看到的，它是一个从树上掉下来、在世界不同地方走动的橙子。
Original English Um and and so, for maybe for perspective, I'll walk you through a piece of history. This is like watching prehistoric ancient ruins. Uh this is our first AI film that was done in 2019. It's called the orange because as you can tell very clearly, it's an orange that falls from the tree and walks through different parts of the world.
>> 嗯，这是……这是我们当时拥有的一个视频模型和图像模型生成的，它在以 128 像素乘以 128 像素的规格生成像素，然后我们强行上采样到这种分辨率。 我现在把它看作一种艺术。这就像在看一部 1900 年代的电影，你知道吗？它很美，背后承载着很多历史。但这是 2019 年。呃，我们在 2018 年、2017 年创立了这家公司。所以，我们当时试图弄清楚这是否有任何发展潜力。 呃，我们试图说服自己，如果对其进行扩展（scale），它自然会改善到我们能够达到接近这种水平的程度。
Original English Um this is done this is a video model and image model that we had back then that was creating pixels in 128 pixels by 128 pixels that we then forcedly up sample to to this kind of resolution. I think of this as art now. This is this is like looking at a film from the 1900s, you know? It's it's beautiful. There 's a lot of history behind it. But this is 2019. Uh we started the company in 2018, 2017. And so, we were trying to figure out if this had any lags. Uh we're trying to convince ourselves that if you scale this, it will naturally improve to the point where we can get somewhere close to this.
>> 这就是当今最前沿的视频。这全是生成的。这些角色都不是真实的。那些地点也全都不是真实的。 我相信你们——你们可能在座的人现在都知道这个，嗯，你们可能已经在 Twitter 上看到过了。 嗯，这些模型简直好得不可思议。而且最有趣的是，当我看到这样的成果时，嗯，我依然相信，只要再过短短几年，它看起来就会像当年的《橙子》一样。 嗯，所以我想带大家了解一下，不仅是我们今天在 Runway 正在思考什么，还有在接下来的几年里，以及正在塑造这一切的研究。
Original English This is state-of-the-art video today. This is all generated. None of these characters are real. None of those locations are real. I'm sure you You've probably here you know this um, by now. you've probably have seen this online on Twitter. Um, the models are just incredibly good. And the most interesting thing is that when I look at stuff like this, um, I still believe this will look like the orange in just a couple more years. Um, and so I want to walk you through, you know, how we're thinking not only through what we're doing at Runway today, but also in the next couple of years and the research that's shaping that.
### 下一帧预测与世界模型的涌现属性 >> 嗯，我们是一家处于艺术与科学交汇点的公司。呃，我们深深植根于艺术和创意社区，同时我们拥有世界上一些顶尖的研究人员在致力于扩散模型和大型视频模型的研究。嗯，我喜欢这种结合，因为它让我们能够汇聚两者的精华。 呃，我们是一个由科学家和艺术家组成的团队，在思考视频扩展和世界模型的问题。 嗯，但在某种程度上，公司的核心理念早在 120、130 年前就已经被电影和动态影像所发现了—— 那就是，如果你思考视频，也许你们在高中时还记得这个，呃，视频是一种视错觉。视频是运动的错觉。如果你以足够快的速度拼接帧，你就会创造出运动。你创造出了运动的错觉，创造出了动态画面。 所以在 Runway，我们也在做非常类似的事情。我们真正将这个理念推向了下一个高度：我们如何以足够快、足够规模化的方式进行**下一帧预测**（next frame prediction），从而希望能够做出超越单纯漂亮且优美的创意视频的事情。
Original English Um, we are a company that's at the intersection of both both art and science. Uh, we have uh, deep roots in artistic and creative communities and we have some of the best researchers in the world working on diffusion models and large video models. Um, and I like that combination because it allows us to, you know, bring the best of both worlds. Uh, we're we're a team of scientists and artists thinking about the problem of scaling video and world models. Um, but in a way that the core idea of the company was already discovered by cinema and by moving pictures 120, 130 years ago, which is, if you think about video, and maybe you remember this from, uh, from high school, video is an optical illusion. Video is the illusion of movement. That if you do if you stitch frames fast enough, you will create movement. You will create the illusion of movement. You create motion pictures. And so at Runway we're doing kind of something very similar. We're taking that idea really to the next level. How do we do next frame prediction fast enough, scale enough, at in in in in such a way that we can hopefully do more than just pretty pretty and nice creative videos. And so
>> 所有这一切，所有这些，呃，当然都是由**扩展定律**（scaling laws）驱动的。我想我们坚信扩展定律不仅适用于语言模型，同样也适用于视频。事实正是如此。我们现在正在大幅提升投入到你们已经见过的那些模型背后的算力规模。 而且我认为，看到模型随着时间推移不断扩展，最有趣的一点就是出现的涌现属性，即当你开始采用电影在 120 年前发现的这个非常简单的想法——也就是下一帧预测，并真正将下一帧预测推向下一个层次时，所显现出的智能。 对吧？所以，如果我拿出一张像这样的图像，这不是真实的，这两个人不是真实的，而我只是根据前面的帧不断预测下一帧的样子；如果我做得足够快，如果我用一个足够大的网络去做，我就能得到运动。我就会得到运动的错觉。我就会得到对我们来说看起来如此逼真、以至于我们人类无法分辨的东西。 我看着那个视频，我已经做这行 10 年了，我也无法告诉你那究竟是真是假。
Original English all of this all of this uh, is powered by, of course, scaling laws. I think we believe in scaling laws uh, not only apply for for language models, but also for for video. And this has been true. We're now really ramping up the amount of compute that we put behind the the models that you already have seen. And and I think the the interesting thing about, you know, seeing the model scale over time is the emerging properties that appear, the intelligence that appears when you start taking this very simple idea that cinema found itself uh, figuring out 120 years ago, um, which is next frame prediction, and taking next frame prediction really to the next level, right? So, if I take an image like this, which is not real, those two people are not real, and I just keep on predicting what the next frame will look like based on the previous frames, and if I do it fast enough, and if I do it with a network that's big enough, I'll get movement. I'll get the illusion of movement. I'll get something that looks so real to us that we we humans can distinguish. I I look at that video and it's I've been doing this for 10 years. I can't tell you if that's real or not.
>> 嗯，我们测量过这一点。我们实际上衡量过、试图量化人们是否能区分真实视频和生成视频，我认为那个图灵测试我们已经通过了。呃，人们已经无法分辨了。 但是，这里更有趣的事情是，当你预测下一帧时，实际上发生的是你学到的远不止漂亮的画面。 你正在学习物理学，你正在学习物体恒存性，你正在学习重力，你正在学习遮挡，你正在学习运动，你正在学习物体在现实世界中如何运作，而你仅仅通过视频、通过图像、通过下一帧预测就完成了所有这些。 所以，如果你看一个像这样复杂的事物，嗯，一个好的视频模型为了理解如何把它做成一段连贯一致的视频，它自然需要理解： - 所有这些物体究竟是什么； - 它们各自如何相互关联； - 一只骆驼可能会如何站在那样的球上； - 容器里的水看起来是什么样。 如果我把所有这些结合在一起，并且做得足够快，我有望得到一段在我们看来逼真连贯的视频。
Original English Um and we measure that. We've actually measured tried to quantify um if people can distinguish between real and generated videos, and I think that that Turing test we already passed. Uh people are not able to distinguish. But, the more interesting thing is here is that really what's happening as you predict next frames is you're learning a lot more than just pretty frames. You're learning about physics, you're learning about object permanence, you're learning about gravity, you're learning about occlusion, you're learning about movements, you're learning about how the objects work in the real world, and you're doing that all through just video, through images, through through next frame prediction. So, if you look at a complex thing like this, um a good video model for it to understand how to make this a consistent video will need to naturally understand uh what all these objects are, how each of them relate to one another, how a camel might probably uh stand on a ball like that, what water in a container looks like, and if I do all of that together, and I do it fast enough, I'll get, hopefully, a consistent video that looks real to us.
### 从物理模拟到交互式世界界面模型 >> 所以，我们用来评估视频模型的很多基准测试不仅是创意层面的——创意层面的意思基本上就是你喜不喜欢它——而且实际上是基于物理、基于几何、基于物体在真实世界中如何表现以及这些物体应该如何表现，呃，还有模拟器或视频世界。 因此，你采用这种下一帧预测的理念，并以足够快的速度执行它，当然现在你就能得到某种看起来更像我们对视频应用或游戏应用所期望的东西。再次强调，这仅仅是视频，对吧？ 而你把这个想法带到下一个层次。如果我们开始运用下一帧预测的理念，但把它放在环境背景中，或者某种看起来类似于游戏的东西中，会发生什么？你实际上可以开始越来越接近一个物理引擎、一个模拟系统。 但是这个模拟系统并没有物理定律——我们没有去描述物体的行为应该是什么样，该模型纯粹凭借规模的力量就知道该怎么做。这开始变得非常有趣，因为你可以考虑去玩这些世界。 这不是 GTA，这是某人在一个根本不存在的环境和软件中扮演另一个人。这完全是对模拟的模拟，这变得非常“元”（meta），对吧？ 所以，当我说“欢迎来到我的黑客帝国模拟”时，我可不是在开玩笑。
Original English So, a lot of the uh benchmarks that we use to evaluate our video models are not only creative, it's creative basically means do you like it or do you don't, are actually based on uh on physics, on geometries, on how objects behave in the real world, and how those objects should behave, uh and also simulator or video worlds. So, you take that idea of next frame prediction, and you do it fast enough, and you get, of course, now something that looks more like what we should expect from a video application or a game application. Again, this is just just video, right? And you take you take that idea to the next level. What happens if we start this idea of next frame prediction, but do it in the context of through the environment or something that looks similar to a game, you can start actually getting closer to a physics engine, a simulation system. But the simulation system has no physics laws that we didn't describe what the behavior of the object should be, the model just knows how to do that by pure force of of of scale. And this kind of gets gets kind of interesting because you can only think about playing worlds. This is not GTA, this is someone playing someone else in a environment and a software that doesn't exist. It's all simulation of a simulation, which which gets really meta, right? So, I'm not kidding when I said welcome to my matrix simulation.
>> 嗯……但对我来说最有趣的事情是，你们之前看到的一切都不是实时的。意味着制作那些视频花了几分钟或几秒钟。 嗯，当我们思考未来三四年的视频模型和世界模型时，我们现在看到的很多东西，是在深入思考：当你不仅扩展模型、让它们变得庞大得多、具有多得多的涌现属性，而且开始让它们**具备交互性**时，会发生什么？对吧？ 同样运用下一帧预测的想法：你拿出一帧，预测下一帧、再下一帧、再下一帧、再下一帧，但我可以在那个帧预测过程中的任何时间点采取行动，这意味着我可以在那个世界中模拟任何可能的分歧或行动。 所以在这样的世界里，我可以播放视频，但我也可以去触碰那张图像的任何部分、任何方面，并使其具备交互性，从而影响那个世界带来的后果。而且它确实奏效了。
Original English Um But the most interesting thing for me is that everything you saw before was not real-time. Meaning that it took a couple minutes or seconds for those videos to be made. Um a lot of what we're seeing right now, when we think about, you know, the next three, four years of video models and world models, is to deeply think about what happens when you start not only scaling the models and making them much much much bigger with much more emerging properties, but also what happens when you start making them interactive, right? So, take the same idea of next frame prediction, you take a frame, you predict the next frame, the next, the next, the next, but then I can act at any point in time in that frame prediction, which means I can simulate any possible divergence or action in that world. So, in this world, I could play the video, but I can also come and touch every part any aspect of that image and make it interactive to affect the consequences of of that world. And it works.
>> 当然，正如你们可以想象的，这对各种不同的用例都极其有用。我们现在在 Runway 设立了一个完整的机器人实验室。因此，如果你来到我们纽约的办公室，一半看起来像好莱坞制片厂，另一半则是一群机器人在办公室里穿行走动。 这样我们就能够让这些模型、或者开始让这些模型成为自主系统和具身系统在世界上行走和推理的大脑。 嗯，但还有几个更有趣的用例，我认为超越了视频和机器人这两个显而易见的方向，那就是：当你把屏幕上的任何东西、任何像素都看作一个视频模型时，会发生什么？ 嗯，这里有一个很好的例子。这是一个视频模型。你现在看到了什么？首先，这是一张图像。那张图像可能是真实的，也可能不是，但我可以实时运行我们的一个视频模型，并使其成为一个交互式应用程序。 比方说，我想打开车灯；也许我想把车转过来；嗯，我只需点击它就行。我可以再转过来，也许打开引擎盖看看车内部有什么。 现在，你刚才在这里看到的一切，都没有物理的 3D 表色。没有 Unity，没有 Unreal，没有 Blender。 这仅仅是一个视频模型。一个经过训练、能够理解界面中的动作和点击的视频模型。我们称之为“世界动作模型”——
Original English And of course, this is incredibly useful for a variety of different use cases, as you can imagine. We're now seeing an entire robotics lab in Runway. So, if you come to our office in New York, half of it is what looks like a Hollywood studio. Half of it is a bunch of robots walking through and moving through the office. And so we're able to make these models or start making these models the brains of how autonomous and embodiment systems walk and reason about the world. Um but there's a couple of more interesting use cases that I think go beyond the two obvious one, which is video and robotics, which is what happens when you think about anything anything any pixel on a screen as a video model. Um here's a good example. This is a video model. What do you see right now? Well, this is an image first of all. That image could be real or not, but I could run one of our video models in real time and make this an interactive application. So, let's say I want to, you know, turn the lights of the car. Maybe I want to like turn the car around. Um I can just click through it. I can turn it around again. Maybe open the hood and see what's inside the car. Now, everything you just saw here has no physical 3D representation. There's no Unity. There's no Unreal. There's no Blender. It's just a video model. A video model has been trained to understand actions and clicks in an interface. We call this a world action model.
>> [嗤笑] >> 嗯，抱歉，呃，是**世界界面模型**（world interface model）。 而世界界面模型，嗯，我觉得大家现在都理解了。它是一个界面，同时也是一个世界模型。世界界面模型。 嗯，它能让你做非常酷的事情，就像这样。 这里有另一个应用，另一个关于你如何考虑实时使用这种模型的例子。 嗯，我可以获取一张图像、一个表征，移动物体，并期望它们像我真正身处那个房间里一样移动。再次提醒大家，这里没有任何 3D 表征，这个环境之前可能根本没有被建模过。它只是一张图像，以及一个理解物体在空间中移动是什么样子的模型——这是因为一个非常简单的理念：充分扩展后的下一帧预测可以变成一个世界模型或模拟引擎。
Original English >> [snorts] >> Um sorry, um a world interface model. And the world interface model, well, I I feel like you guys understand now. It's It's It's an interface and it's a world model. World interface model. Um allows you to very cool stuff like this. And so here's another application another um example of of how you could think about using a model like this in real time. Um I can take an image a representation, move objects around, and expect them to move as I was if you if as I was actually in that room. Again, remember there's no 3D representation of this. There This environment might not actually be have having modeled before. It's just an image and the model that understands how objects look moving in a space because of this very simple idea that next frame prediction scale well enough can become a world model or a simulation engine.
### Continuum：基于视频模型的操作系统与“传送门” >> 嗯……那么利用我剩下的 8 分钟时间，我想向大家展示这个被推向另一个极致的理念。 我们不仅在思考如果你能控制这些很酷的应用程序和很酷的界面会发生什么，而且在思考如果你不断进行外推（extrapolating）会怎样。顺便说一句，我认为“外推”可能是如今在 AI 领域、在创办公司过程中最重要的技能之一。在接下来的 6 个月、12 个月、18 个月里会发生什么，可能是最需要尝试去预测和理解的事情之一。 因此我们在 Runway 经常问自己这个问题：会发生什么？如果我们不断扩展模型会发生什么？如果我们继续加入更多数据、如果我们不断让它们成为更好的模拟系统，会发生什么？ 如果我们让它们成为能够预测屏幕上任何内容的通用模拟系统呢？字面意义上屏幕上的任何内容。 所以我们一直在研究一个我们称之为 **Continuum** 的想法。 Continuum 就是将实时视频模型或世界模型的理念真正推向下一个层级。这是一个操作系统。这里的理念是，在不久的将来的某个时刻，你可能会打开电脑，而你在屏幕上看到的流式传输内容可能全部来自一个视频模型。
Original English Um so with the 8 minutes I have left, I want to show you this idea taken to another extreme. We're we're thinking not only about what happens if you can control the school applications and the school interfaces, but what happens if you keep extrapolating and I think by the way extrapolating may be one of the most important skills these days in in AI in in company building. What happens over the next 6 months, 12 months, 18 months may be one of the most important things to to try to predict and understand. And so we ask ourselves that question a lot at Runway. What happens? What will happen if we keep scaling the models? If we keep adding more data, if we keep making them better simulation systems. If we make them universal simulation systems that can predict anything on a screen. Literally anything on a screen. So we've been working on this idea that we call continuum. And continuum is the the idea of real-time video models or world models taken really to the next the next degree. This is an operating system. The idea here is that you might open your computer at some point in the near future and what you see streaming to your screen may be coming from a video model.
>> 所以，接下来我将向大家具体展示这个想法会如何成型。这仍然是非常早期的研究，但我想向大家展示这种将视频模型结合为你操作系统的一部分、你日常工作一部分的想法。 举个例子：我当时正在研究旧金山以及旧金山的雾。我想了解更多关于它在何时以及如何发生的信息。这是结构化数据。这其实是一个网站，这是你从互联网上获取的内容。 而现在 Continuum 拥有这样一个构想：我可以选择该网站中的任何一部分内容，并将其变成一个**传送门**（portal）。我们称之为传送门。 传送门是一个实时视频。它是完全可交互的，并且完全基于你正在体验和阅读的内容。我可能不会通读所有那些文字，文字对我来说太多了。所以我只想获取一段视频，我就可以生成一段由大家在那儿看到的文本所表征的视频。 这些传送门可以是 3D 插图，也可以是你正在观看或与之交互的任何内容。在右侧，你可以保留之前体验过的所有可视化内容和传送门，保留你一直试图学习的任何东西。 基本上它是一个记忆库。它是你之前看过的想象或视觉感知的“硬盘”。 在这里我当时正在研究电池，所以我可以回到我之前关于电池的传送门。因为它们是实时交互式应用，我可以在任何地方点击它们，可以拖动它们，可以开始提问，而视频和内容本身将会根据我的交互以及我操作它的方式发生改变。
Original English So I'll show you next specific how this idea could could shape. This is still very early research, but I want to show you this idea of combining video models as part of your operating system, as part of your daily work. So here's an example. I was researching, you know, San Francisco and the fog in San Francisco. I wanted to learn more about when and how it happens. This is structural data. This is actually a website. This is something you can take from from the internet and now continuum has this idea that I can select any part of content in that website and make it a portal. We call this a portal. And the portal is a real-time video. It 's fully interactive and fully based on the content you're experiencing reading through. I probably won't read through all of that text. It's too much text for me. So I just want to get a video and I can make a video that's represented by by the text you see there. These portals can be 3D illustrations. It can be any content that you're watching or interacting with. On the right you can have all the visualizations and portals you've you've experienced before, anything you've been trying to learn. Basically it's a it's a memory. So, it's the disk of the imaginations or the visual sensations you've seen before. So, here I was researching about batteries, and so I can go back to the portal I had around batteries before. And since they're real-time interactive applications, I can click them anywhere. I can move them around. I can start asking questions and the videos and and the content itself will change based on based on my interactions and the way I'm I'm managing through it.
>> 这里是另一个非常酷的例子，当团队第一次展示给我看的时候，简直让我大为震撼。 因为它是实时视频，所以你可以让它与任何东西完全交互。不仅能通过点击，比如调整应用程序窗口大小也会改变其行为。对吧？所以我可以调整大小。 我在这里正在学习光合作用。如果我调整应用程序的大小，我就会得到不同的体验。而且由于它同样是完全交互式的应用，我可以点击它，在细胞的不同部分或光合作用的运作过程中进行导航，从而对我试图学习和做的事情有更好的理解。 再次说明，对于这一点，我仍然感到有些不确定的事情是：我不知道该怎么称呼它。我不知道这算不算是……它感觉不像普通的可视化，感觉不像视频，感觉也不像游戏。它介于这些事物之间，我认为我们需要共同努力去弄清楚我们究竟该如何称呼这类交互。 我相信大家会听到类似的声音，［嗤笑］另一件事是你可以改变 prompt 的结构，或者从一开始创建该交互的内容。
Original English Here's another very cool example that when I the first time I saw it when the team showed it to me, it was like blew my mind. Since it's a real-time video, you can make it fully interactive with anything. Not only with clicks, but like resizing the applications would also change the behavior. Right? So, I can resize. I'm I'm learning about photosynthesis here. If I resize the application, I get a different experience. And since it's again fully interactive applications, I can click through it, navigate through parts of the cell or how photosynthesis work to get a better understanding of what I'm trying to learn and do. Again, the one thing that still feels to me kind of uncertain about this is I don't know how to call this. I don't know if this is doesn't feel like a visualization. It doesn't feel like a video. It doesn't feel like a game. It's something in between, and I think we need to do the collective work of figuring out how we call these kind of interactions in the first place. And I'm sure [snorts] you hear kind of the other thing is that you can change the structure of the prompt or what's actually creating that interaction in the first place.
### AI 智能体的“视觉思考”与实时动态世界 >> 这里是另一个例子。我要输入一些内容。 我觉得这是目前为止最缺乏探索的领域之一。我们称之为**视觉思考**（visual thinking）。 我们把 Continuum 连接到了一个代码智能体（coding agent）上。代码智能体能做的事情是，它可以利用 Continuum 来对一个问题进行可视化思考。 想象一下，你让你的代码智能体为你的自行车创建一个 3D 模型。该模型不仅能尝试单纯通过代码来思考，还可以启动一个版本的 Continuum 来在视觉上思考这个问题。 如果大家想过某人……我相信你们有朋友，或者在场的很多人也许就是视觉型思考者。什么是视觉型思考者？那就是首先通过视觉来思考问题和想法的人。这有助于你锚定并构想出你想做的一些最好的事情。这在某种程度上非常相似。 智能体具备进行视觉思考的能力。智能体可以构建出一种你可以接入并与之交互的想象。因此，不仅智能体可以与之交互，我自己也能看到它，而且我能以比单纯仅通过文本要好得多的方式来引导该智能体。对我们而言，这是一个非常强大的想法。这是一种全新的范式，我们称这个概念为“视觉思考”。
Original English Here is another example. I'm going to type something. And this is one of the ones that I feel has been the most underexplored yet. This is we call this visual thinking. So, we've connected Continuum to a coding agent. And what the coding agent can do is it can use Continuum to visualize through a problem. So, imagine you ask your coding agent to create a 3D model for your bike. And instead of the model trying to just think through that via code, the the can spin a version of Continuum to think through the problem visually. So, if you ever think about someone or I'm sure you have friends or maybe some a lot of people here maybe visual thinkers. What a visual thinker is is it thinks through problems and through ideas visually first. And that helps you ground and project some of the best things you want to do. And this is somehow very similar. Agents have the ability to do visual thinking. Agents can spin up a sort of imagination that you can tap and and interact with. So, not only the agent can interact with it, I can also see it myself. And I can guide the agent in much better ways that I might have able to guide it just via text alone. Which is for us a very powerful idea. It's a completely new paradigm. And we call this this this idea uh visual visual thinking.
>> 嗯，我的最后一个例子是关于如何思考让 Continuum 不仅仅是按照我之前向大家展示的方式使用或浏览交互式结构化内容，而是真正让它们成为完全实时的、具有响应性的活体体验（live experiences）。 在这个例子中，我当时正在搜索火星。嗯，当 Continuum 理解了我试图搜索的意图时，它还会推荐专门为此实时设计的界面。 所以在这里，它为我创建了这个火星前哨基地，它实际上应该准确包含了关于现在实际在火星上的机器人、或者预计可能在 2030 年到达的机器人的数据。 嗯，由于这完全是交互式的，我可以与它互动。我可以去往各个地方，我可以四处走动，方式就像我可能在一个 3D 系统或 3D Continuum 中四处走动一样。 嗯，而且它确实奏效了。简直令人着迷。
Original English Um The last example I have is how you think about taking, you know, Continuum to become much more than just uh using or navigating interactive structural content in in the way I've showed you before, but actually making them fully live experiences that are reactive. So, in this case I was searching for Mars. Um and as as Continuum understands the intention of what I'm trying to search from, it also suggests interfaces that are specifically designed in real time just for that. So, here it created for me this Mars outpost that's actually should be uh accurate with the data about the actual like robots that are in Mars right now uh or the ones that are projected maybe in 2030. Um and since this is fully interactive, I can interact with it. I can go to places. I can move around in the same way that I might move around, you know, in a 3D system or in a 3D Continuum. Um And it works. Just fascinating.
### 未来展望：一切皆可模拟 >> 嗯，所以，当我说“一切都将被模拟”时，嗯，我真的在努力理解、或者帮助不仅是我们的团队、更是全世界达到这样一个阶段：今天我们所看到的很多东西，希望在短短几年或几个月后听起来都会变得显而易见。 我们将回头审视 Continuum，它可能会感觉非常接近我们刚刚展示给大家看的那个《橙子》视频。 Continuum 以及我们在视频和 Runway 中所做的很多关于模拟的事情，目前仍处于非常早期的阶段，但我们正在走向一个它真正能够工作、并且真正能够扩展的节点。 因此，我们真正感到兴奋的是，在接下来的 8 到 12 个月里，我们将把今天在这里展示的大部分内容发布给所有人使用。谢谢大家。 >> [掌声]
Original English Um So, when I say everything will be simulated, um I'm really trying to understand or help not really our team, but the world get to the point where a lot of what we'll see today will sound obvious, hopefully in a few more years or months. We'll look back at Continuum and it will might feel very close to the video of the orange that we just showed you. Continuum and a lot of the things we're doing in in video and in Runway when it comes to simulation are still in very early days, but we're getting to a point where it actually it actually works and it really scales. So, what we're really excited about is that for the next 8 to 12 months we're releasing most of what I showed here for everyone to use. Thank you, guys. >> [applause]