真假视频难辨之后，Runway 的世界模型将走向何处？Cristóbal Valenzuela 谈 Continuum

简要描述Brief Description

你再也无法分辨什么是真实的了。接下来会发生什么——Cristóbal Valenzuela，RunwayYou Can't Tell What's Real Anymore. Here's What Comes Next - Cristóbal Valenzuela， Runway

目录Table of Contents

• 引言
  Intro
• Runway 的首部 AI 电影（2019 年）
  Runway's first AI film (2019)
• 当今最先进的视频技术
  State-of-the-art video today
• 将视频视为下一帧预测
  Video as next-frame prediction
• 视频模型学到了什么：物理规律与物体恒存性
  What video models learn: physics and object permanence
• 从视频到仿真
  From video to simulation
• 让世界模型具备实时性和交互性
  Making world models real-time and interactive
• 机器人与世界交互模型
  Robotics and world interface models
• 向前外推：连续统
  Extrapolating forward: Continuum
• 面向编码智能体的视觉思维
  Visual thinking for coding agents
• 实时、响应式体验
  Live, reactive experiences
• 一切都将被模拟。
  Everything will be simulated

简介Intro

大家好。很高兴来到这里，真的非常激动。感谢大家留下来听最后一场演讲。欢迎来到我的演讲，今天我们要聊聊《黑客帝国》，以及我们如何身处其中。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.

嗯，你知道吗？我在 Runway 工作快十年了。其实我是大约十年前在纽约加入这里的。我想跟你们聊聊我们在创办公司、在纽约建立这个研究实验室的过程中积累的一些经验，以及我们认为视频和像素生成领域的许多工作未来会如何发展。所以，我不想夸大其词，不过我开玩笑说，这可能是一个模拟，也可能不是。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.

Runway 的首部 AI 电影（2019 年）Runway's first AI film (2019)

嗯，所以为了让大家有个概念，我来讲讲这段历史。这就像是在看史前古代遗迹。呃，这是我们在 2019 年制作的第一部 AI 影片，名叫《橙子》。你看得很清楚，一颗橙子从树上掉下来，然后穿过世界各地。这是用我们当时的视频模型和图像模型制作的，它们生成的像素只有 128×128，我们再强行把画面放大到这样的分辨率。现在我觉得这就是艺术。看它就像是在看一部 20 世纪初的电影，你知道吗？它很美，背后也有很多历史。但这是 2019 年的作品。呃，我们在 2018 年，也就是 2017 年创办了公司。所以当时我们想弄清楚这东西有没有发展潜力。呃，我们试着说服自己，只要把它做大，效果自然就会提升，最终能做到接近这样的水平。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. 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.

当今最先进的视频技术State-of-the-art video today

这就是当今最先进的视频技术。这些全都是生成出来的。这些角色都不是真实存在的，那些地点也都不是真实存在的。我敢肯定，你们大概已经知道了，嗯，到现在为止。你们可能已经在网上、在 Twitter 上看过这些了。嗯，这些模型真的非常厉害。最有意思的是，每当我看到这样的东西时，嗯，我仍然相信，再过几年，它看起来就会像那个橙子一样。嗯，所以我想带大家了解一下我们的思路：不仅是我们现在在 Runway 做的事，还有未来几年要做的事，以及推动这些发展的研究。嗯，我们是一家同时处于艺术与科学交汇处的公司。呃，我们深深扎根于艺术和创意社群，也有一些世界顶尖的研究人员在研究扩散模型和大型视频模型。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. 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.

嗯，我喜欢这种组合，因为它让我们能够，你知道，兼得两者之长。呃，我们是一支由科学家和艺术家组成的团队，正在思考如何扩大视频模型和世界模型的规模。嗯，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,

将视频视为下一帧预测Video as next-frame prediction

不过，从某种意义上说，这家公司的核心理念早在 120、130 年前就已经被电影和活动影像发现了。也就是说，如果你想想视频，也许你还记得高中学过的内容：视频是一种视觉错觉。视频就是运动的错觉。只要把画面足够快地串联起来，就能产生运动，就能营造出运动的错觉，也就创造出了活动影像。Runway 做的事情也非常类似。我们正在把这个理念真正提升到一个新高度：如何以足够快的速度、足够大的规模来预测下一帧，从而希望能做到的不只是制作漂亮、精美、有创意的视频？这一切当然都由 Scaling Laws（缩放定律）驱动。我想我们相信缩放定律不仅适用于语言模型，也适用于视频。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 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.

事实确实如此。我们现在确实在大幅增加用于训练你们已经见过的那些模型的算力。我认为，随着时间推移，观察模型规模不断扩大的有趣之处在于，涌现出的特性，以及随之出现的智能——当你开始把电影在120年前摸索出的这个非常简单的想法，也就是预测下一帧，真正提升到一个新高度时，就会出现这些，对吧？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?

视频模型学到了什么：物理规律与物体恒存性What video models learn: physics and object permanence

所以，如果我拿一张这样的图像——它并不真实，图里的两个人也不是真人——然后不断根据前几帧预测下一帧的样子，只要速度够快，网络规模也足够大，我就能生成运动。我能制造出运动的错觉。我能生成看起来如此真实的画面，以至于我们人类都难以分辨。我看着那段视频——我做这件事已经有 10 年了——也没法告诉你那到底是不是真的。嗯，我们还对此进行了测量。我们确实尝试过量化人们能否分辨真实视频和生成视频，我认为这个图灵测试我们已经通过了。人们分辨不出来。不过，更有意思的是，预测下一帧时，实际上学到的远不止是生成一帧帧漂亮的画面。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. 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.

你在学习物理，学习物体恒存性，学习重力，学习遮挡，学习运动，学习现实世界中物体的运作方式，而这一切都只通过视频、图像，以及通过下一帧预测来完成。所以，如果你看这样一个复杂的场景，嗯，一个好的视频模型要想理解如何把它生成一段连贯的视频，就需要自然而然地理解这些物体都是什么、它们彼此之间的关系如何、骆驼可能会怎样站在那样一个球上、容器里的水看起来是什么样子。如果我把这一切结合起来，并且做得足够快，希望就能得到一段看起来真实的连贯视频。所以，我们用来评估视频模型的许多基准，并不只是看创意性——创意性基本上就是指你喜不喜欢它——它们实际上是基于物理、几何、物体在现实世界中的行为方式，以及这些物体应该如何表现，还有模拟器或视频世界中的表现。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. 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.

从视频到仿真From video to simulation

所以，你采用下一帧预测这个思路，并把它做得足够快，当然就能得到一些看起来更像视频应用或游戏应用中我们所期待的东西。再说一次，这就只是视频，对吧？然后你把这个思路再提升一个层次。如果我们开始运用下一帧预测这个思路，但把它放到穿行于环境的情境中，或者放到一个看起来像游戏的环境里，会发生什么？你实际上就能开始接近物理引擎、仿真系统了。但这个仿真系统并没有任何物理定律——我们没有描述物体应该如何运动，模型只是凭借规模本身就知道该怎么做。这就变得有点有趣了，因为你只能想到去玩这些世界。这不是《GTA》，而是在一个不存在的环境中，有人玩着另一个人，还有一个不存在的软件。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.

这全都是模拟中的模拟，真的越来越有后设感了，对吧？所以，我说“欢迎来到我的矩阵模拟”可不是在开玩笑。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.

让世界模型实现实时交互Making world models real-time and interactive

嗯，但对我来说最有意思的是，你们之前看到的一切都不是实时生成的。也就是说，那些视频需要几分钟或几秒钟才能制作出来。嗯，我们现在看到的很多东西，在思考未来三四年视频模型和世界模型的发展时，关键是要深入思考：当你开始不只是扩大模型规模，让它们变得大得多、涌现出更多属性，同时还让它们具备交互性时，会发生什么，对吧？比如，沿用下一帧预测这个思路：给模型一帧，让它预测下一帧，再下一帧，再下一帧；但随后我可以在帧预测过程中的任意时刻采取行动，这意味着我可以模拟那个世界中任何可能的分歧或行动。因此，在这个世界里，我可以播放视频，也可以触碰图像的每个部分、每个方面，让它们变得可交互，从而影响那个世界所产生的后果。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.

而且它确实有效。当然，正如你所想象的那样，它在各种不同的使用场景中都非常有用。And it works. And of course, this is incredibly useful for a variety of different use cases, as you can imagine.

机器人与世界交互模型Robotics and world interface models

我们现在在 Runway 里看到了一整个机器人实验室。所以，如果你来我们在纽约的办公室，会发现其中一半看起来像好莱坞片场，另一半则是一群机器人在办公室里走来走去、四处移动。这样一来，我们就能打造这些模型，或者开始让这些模型成为自主系统和具身系统的“大脑”，让它们能够行走并理解这个世界。嗯，不过我觉得还有几个更有意思的用例，它们超越了两个显而易见的方向：视频和机器人技术。那就是，当你把屏幕上的任何事物、任何像素都看作视频模型时，会发生什么。嗯，这是个很好的例子。这是一个视频模型。你现在看到了什么？首先，这是一张图像。这张图像可能是真的，也可能不是真的，但我可以实时运行我们的一种视频模型，把它变成一个交互式应用。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.

比如说，我想把车灯打开。也许我想把车转个方向。嗯，我只要点击操作就行了。我还可以再把它转个方向。也许打开引擎盖，看看车里面是什么样的。现在，你刚才看到的这一切都没有实体的 3D 表示。这里没有 Unity，没有 Unreal，也没有 Blender。它只是一个视频模型。视频模型经过训练，能够理解界面中的操作和点击。我们称之为世界动作模型。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.

嗯，抱歉，嗯，一个世界接口模型。至于世界接口模型，我想你们现在应该明白了。它既是一个接口，也是一个世界模型。世界接口模型。嗯，它能让你做出这样的酷炫效果。这里还有另一个应用，另一个例子，展示你可以如何设想实时使用这样的模型。嗯，我可以拿一张图像作为表示，移动里面的物体，并预期它们会像我真的身处那个房间时那样移动。再提醒一下，这里没有 3D 表示。这个环境之前可能根本没有被建模过。它只是一张图像，而这个模型理解物体在空间中移动时的样子，因为这个非常简单的想法：只要规模足够大，下一帧预测就能成为一个世界模型或仿真引擎。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.

向前外推：连续统Extrapolating forward: Continuum

嗯，那么趁我还有 8 分钟，我想向大家展示这个想法如何被推向另一个极端。我们思考的不只是：如果你能控制学校应用程序和学校界面，会发生什么？我们还会继续推演：如果不断把这个想法延伸下去，又会发生什么？顺便说一句，我认为，推演也许是如今在 AI 和公司建设领域最重要的技能之一。接下来 6 个月、12 个月、18 个月会发生什么，可能是最值得尝试预测和理解的事情之一。因此，我们在 Runway 经常会问自己这个问题：会发生什么？如果我们不断扩大模型规模，不断加入更多数据，不断让它们变成更好的模拟系统，会发生什么？如果我们把它们做成能够预测屏幕上任何内容的通用模拟系统呢？真的，是屏幕上的任何内容。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.

所以，我们一直在研究一个称为 continuum 的构想。continuum 的理念是将实时视频模型或世界模型真正提升到一个新的层次。这是一个操作系统。这里的想法是，在不久的将来某个时候，你打开电脑，屏幕上呈现给你的内容可能是由视频模型生成的。接下来我会具体展示这个想法可能会如何塑造……这仍然是非常早期的研究，但我想向你展示这个构想：将视频模型融入你的操作系统，融入你的日常工作。这里有一个例子。我当时在研究旧金山，以及旧金山的雾。我想进一步了解雾何时以及如何形成。这是结构化数据，实际上是一个网站。你可以从互联网上获取这类内容，而 continuum 的理念是，我可以选中这个网站上的任意一部分内容，并将其变成一个入口。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. 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.

我们称之为一个门户。这个门户是一个实时视频。它完全可以交互，而且完全基于你在阅读时所体验的内容。我可能不会把所有文字都读完，对我来说文字太多了。所以我只想看一个视频，而且可以制作一个由你在那里看到的文字所呈现的视频。这些门户可以是 3D 插图，也可以是你正在观看或互动的任何内容。右侧可以显示你之前体验过的所有可视化内容和门户，以及你一直在尝试学习的任何内容。基本上，它就像一段记忆。也就是说，它是你以前见过的想象或视觉感受的存储盘。所以，我当时在研究电池，因此可以回到之前关于电池的那个门户。而且由于它们是实时交互式应用，我可以在任何地方点击它们。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.

我可以把它们移来移去。我可以开始提问，视频和内容本身也会根据我的互动以及我浏览内容的方式而改变。这里还有另一个非常酷的例子：我第一次看到它时，团队向我展示了它，简直让我惊呆了。由于这是实时视频，你可以让它与任何内容进行完全交互。不仅可以点击，调整应用程序的大小也会改变它的行为。对吧？所以我可以调整大小。我正在这里学习光合作用。如果我调整应用程序的大小，就会获得不同的体验。而且，由于它同样是完全交互式的应用程序，我可以点击其中的内容，在细胞的各个部分之间导航，或者了解光合作用的过程，从而更好地理解我想要学习和完成的内容。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. 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.

再说一次，对我来说，这件事唯一让我觉得仍有些不确定的地方，就是我不知道该怎么称呼它。我不知道这算不算一种可视化。它不像视频，也不像游戏。它介于两者之间，我认为我们需要共同努力，先弄清楚这类交互到底该怎么称呼。而且我相信你也听到了另一点：你可以改变提示词的结构，或者改变最初促成这种交互的实际内容。这里还有另一个例子。我要输入点东西。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 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. Here is another example. I'm going to type something.

面向编码智能体的视觉思维Visual thinking for coding agents

这也是我觉得目前探索得最不充分的方向之一。我们称之为视觉思维。我们已经将 Continuum 接入了一个编程智能体。编程智能体可以借助 Continuum，以可视化的方式思考问题。比如，你让编程智能体为你的自行车创建一个 3D 模型。它不必只通过代码来思考，而是可以启动一个 Continuum 实例，从视觉角度思考这个问题。想想看，你肯定认识一些人，或者这里有很多人，可能都是视觉思维者。所谓视觉思维者，就是先通过视觉来思考问题和构思点子。这能帮助你立足于现实，并构想出一些最想实现的事情。智能体也有点类似，它们具备视觉思维的能力。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.

智能体可以构建出某种想象空间，你可以进入其中并与之互动。所以，不仅智能体可以与它互动，我自己也能看到它。这样一来，我就能以更好的方式引导智能体，而不只是通过文本来引导它。对我们来说，这是一个非常有力量的想法。这是一种全新的范式。我们把这个想法称为“视觉思维”。嗯。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. Um

实时响应式体验Live, reactive experiences

我最后一个例子想说明的是，你可以如何思考将 Continuum 打造成远不止是按照我之前展示的方式来使用或浏览交互式结构内容，而是让它成为完全鲜活、能够响应变化的体验。在这个例子中，我搜索的是火星。Continuum 理解了我想搜索的内容后，还会为此实时推荐专门设计的界面。它为我创建了一个火星前哨站，其中的数据实际上应该准确反映目前火星上的机器人，或者预计到 2030 年会在火星上的机器人。由于这是完全交互式的，我可以与之互动，可以前往不同地点，也可以四处移动，就像在 3D 系统或 3D Continuum 中移动一样。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.

嗯，而且它确实有效。真是太迷人了。嗯Um And it works. Just fascinating. Um

一切都将被模拟。Everything will be simulated

所以，我说一切都会被模拟出来时，嗯，我真正想做的是帮助——不只是帮助我们的团队，而是帮助全世界——发展到这样一个阶段：我们今天看到的很多东西，希望再过几年或几个月听起来都会显而易见。回头看 Continuum 时，它可能会让人觉得和我们刚才给大家看的那个橙子视频非常接近。Continuum，以及我们在视频和 Runway 中围绕模拟所做的很多事情，都还处于非常早期的阶段，但我们正逐渐走到一个真正可行、而且能够大规模应用的阶段。所以，我们真正感到兴奋的是，接下来的 8 到 12 个月里，我们会陆续发布我在这里展示的大部分内容，供大家使用。谢谢大家。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.