Meta 的 AI 之旅：从 Llama 4 到 Muse Spark 及未来

差不多正好一年前，你开始在 Meta 领导 AI 团队。我想或许你可以为我们介绍一下背景。我想知道，你认为 Meta 今天作为一家 AI 公司与一年前相比处于什么位置？具体来说，今天 Meta 作为 AI 公司的声誉与一年前你刚来时相比如何？

It was almost exactly a year ago that you started at Meta leading the AI team. I thought maybe you could set the scene for us. I'm wondering if you can let us know where you think Meta is as an AI company today versus where it was a year ago. Specifically, what's the reputation of Meta as an AI company today versus a year ago when you got there?

是的，我认为对 Meta 和 AI 来说，这是非常激动人心的一年。大约一年前，甚至一年多前，Meta 发布了 Llama 4。虽然那是一个令人兴奋的发布，但它并没有完全达到 Meta 继续构建其目标产品和体验所需的轨迹。所以，自从我加入并创立 Meta 超级智能实验室以来，过去一年我们一直在努力工作。我们经历了一个完整的过程，为我们的模型构建新的 Scaling（规模扩张）阶梯，开发新的基础设施和新的研究，以支持一系列新的模型家族。今年四月，我们发布了这项工作的首批成果：新的 Spark 模型和 Meta AI 的更新。反响非常积极，甚至比我们内部预期的还要好。我们看到 Meta AI 的使用量大幅增长，它在许多应用商店中排名靠前。今天我们正在开发更大的模型，并对我们将向世界展示的成果感到兴奋。我们正处于一个非常激动人心且快速的轨道上，我们很高兴能继续向世界展示我们的成果。过去一年，AI 行业变得非常火热和竞争激烈，我们对此非常重视，但我们真的非常兴奋。

Yeah, I think it's been a very exciting year for Meta and AI. About a year ago, a little more than a year ago, Meta released Llama 4. While it was an exciting release, it wasn't quite on the trajectory that Meta needed to continue building the products and experiences it seeks to build. So we've been hard at work over the past year since I joined and since starting Meta Superintelligence Labs. We've been undergoing an entire process of building a new scaling ladder for our models, developing new sets of infrastructure and new research to power a new series and family of models. Back in April, we released the very first fruits of that labor: the new Spark models and an update to Meta AI. The reception was incredibly positive, even better than we expected internally. We saw incredible gains in usage of Meta AI; it was at the top of many app stores. We're working on our even larger models today and are excited about what we'll be able to demonstrate to the world. We're on a very exciting and fast trajectory, and we're excited to continue showing the world what we produce. The AI industry has gotten very hot and competitive in the past year, and we take that very seriously, but we're really excited.

显然，你们的目标和 OpenAI、Anthropic 等其他公司一样在投入。你觉得你们处于同一梯队吗？Meta 现在是否与 OpenAI、Anthropic 并列，还是你觉得仍有差距？因为我认为一年前确实是那种看法。

Obviously, the goal you're spending in the same way that OpenAI, Anthropic, others are spending. Do you feel like you guys are in that same tier? Is Meta, you know, is it OpenAI, Anthropic, Meta at this point, or do you feel like there's still a bit of a gap? Because I'd say certainly a year ago that was the perception.

是的，我们发布的新 Spark 模型并不处于领先前沿模型的梯队。但我们认为这是轨迹上一个非常令人兴奋的数据点，我们预计即将发布的模型将与世界领先的模型相当有竞争力。

Yeah, the new Spark model that we released is not at the tier of the leading frontier models. But we believe it's a very exciting data point on the trajectory, and we expect the upcoming models we release to be quite competitive with the leading models in the world.

你把新 Spark 称为开胃菜，一个开胃菜模型。顺着这个比喻，主菜模型什么时候来？它会达到你们最终想要的那个梯队吗？

You called new Spark an appetizer, an appetizer model. To build that metaphor out, when does the entree model get here? Will it be at that tier that you guys ultimately want to be at?

是的，我们正在烹饪中。我们很高兴在它准备好时向世界展示。我们在训练过程中看到了非常令人兴奋和有希望的结果。总的来说，我们整个研究工作都围绕可预测的 Scaling（规模扩张）展开。现代 AI 热潮背后的核心信念是，随着你扩展这些模型，你会获得令人难以置信的结果和可预测的能力提升。新 Spark 对我们来说是那个 Scaling（规模扩张）阶梯上的一个早期数据点。我们接下来发布的模型将在 Scaling（规模扩张）曲线上达到更高的点，我们非常兴奋地向世界展示我们将能产出的成果。

Yeah, we are in the process of cooking it. We're excited to show it to the world when it's ready. We're seeing very exciting and promising results in the process of training it right now. Overall, we built the entire research effort around predictable scaling. The central belief behind the modern AI boom is that as you scale these models, you will get incredible results and predictable levels of increased capability. New Spark was an early data point on that scaling ladder for us. The next models we release will be an even greater point on the scaling curves, and we are really excited to show the world what we'll be able to produce.

你基本上在很短的时间内从重建团队、重建实验室到推出新 Spark 模型。从开胃菜到下一阶段的最大障碍是什么？是资源吗？你们投入了大量资金。仅仅是时间问题？还是人才？什么能让你们的模型达到前沿水平？

You went from basically rebuilding the team, rebuilding the lab to this new Spark model in a very short amount of time. What is the biggest barrier from getting to that appetizer to the next level? Is it resources? You guys are spending a ton of money. Is it just simply time? Is it talent? What's going to bring your models to that frontier?

关键在于继续扩展数据、投入模型的算力，以及继续扩展研究。因此，要持续推动基础研究突破，以继续推进模型的进步，并构建支持这一切的基础设施。从很多方面来说，今年是所有实验室大幅扩展其模型的一年，而我们正以更快的轨迹这样做，因为过去一年我们一直在做所有这些工作。我们需要构建基础设施，扩展数据，扩展算力，训练这些大模型，并向世界展示它们。

It's about continuing to scale the data, the compute going into the models, as well as continuing to scale research. So continue to drive advances in underlying research breakthroughs to continue driving forward the progress in the models and building infrastructure to support all of this. This is in many ways a year where all the labs are dramatically scaling up their models, and we are on a much faster trajectory to do so because we've been doing all this work over the past year. We need to build the infrastructure, scale the data, scale the compute, train these large models, and show them to the world.

我想谈谈模型策略。在你加入 Meta 之前，一切都是开源的。这绝对是总体策略。但 Muse Spark 模型不是开源的。我记得你在之前的采访中说过，在你们测试时，开源感觉不安全。你能深入解释一下你的意思以及你们是如何做出这个决定的吗？

I want to talk about model strategy a little bit. Before you got to Meta, everything was open source. That was definitely the overarching strategy. The Muse Spark model is not open source. I believe I heard you on a prior interview basically say that as you guys were testing it, it didn't feel safe to open source. Can you go deeper on what you mean by that and how you made that decision?

作为 Meta 超级智能实验室的一部分，我们更新了高级 AI Scaling（规模扩张）框架，这是我们如何看待开发这些非常强大的模型时面临的风险，以及我们希望在早期测试中如何处理这些风险。我们在准备报告中发布了训练 Muse Spark 过程中看到的很多内容。我们看到的一些情况是，它在早期训练中确实触发了一些高风险领域，特别是生物风险，而且许多风险都升高了。这是整个行业在过去一年随着模型大幅改进而看到的情况。我们当然不是唯一看到这些风险随着模型扩展和研究前沿推进而出现的人。我们看到这些风险被触发，并意识到当我们在产品中发布像新 Spark 这样的模型时，我们有很多方法可以减轻其中一些风险，并确保我们能够以安全和负责任的方式发布它。当你开源一个模型时，要这样做就困难得多，因为人们可以在我们可能不完全理解的各种环境中使用该模型。

One of the things we did as part of Meta Superintelligence Labs is we updated our advanced AI scaling framework, which is our view of the risks we see in developing these very powerful models and how we want to handle those risks as we see them in early testing. We published a lot of what we saw in the process of training Muse Spark in our preparedness report. Some of the things we saw is that it actually triggered some high-risk areas in the course of early training, particularly around bio risk, but also a number of the risks were elevated. This is something the entire industry has seen as the models have improved dramatically over the past year. We certainly aren't the only ones to see a host of these risks show up as we scaled up the models and pushed the frontier of research. We saw these risks triggered and realized that when we launch a model like New Spark in a product, we have a lot of ways to mitigate some of these risks and ensure we can launch it in a safe and responsible way. It's much harder to do that when you open source a model, because people can use that model in all sorts of contexts that we may not have full understanding of.

所以，我们目前正在开发我们认为适合且安全开源、同时尽可能保持性能能力的模型。

So, we're in the process right now of developing models that we believe are fit and safe to be open source while still maintaining as much of the performance capabilities as possible.

所以你们还是会做开源。但听起来 Llama 不是你们要做的品牌或支柱。所有开源的东西都会是 Muse Spark 或与之相关的吗？

So, you will still do open source. It sounds like Llama though is not the brand or the pillar that you're going to do. Is everything open source going to be Muse Spark or adjacent?

你知道，我们内部关于品牌有激烈的讨论，但目前没什么可分享的，不过是的。

You know, we have exciting debates about branding internally, and nothing to share right now, but yeah.

好的。你暗示过的即将推出的大模型，如果可以的话，给我们一个大致的感觉。显然，每个模型、每家公司可能都以某些方面著称。你是否觉得你们正朝着一个方向前进，让 Meta 的模型以在某些方面领先而闻名？比如，你们希望接下来推出的东西实现什么？

Okay. The big models that are coming that you've hinted at, give us a general sense if you can. Obviously, each model, each company is perhaps known for certain things. Do you feel as though you're moving in a direction where Meta's models are going to be known for best in class at X versus Y? Like, what are you hoping to accomplish with what you guys come out with next?

是的，在 New Spark 中，一些让我们印象深刻的能力领域，即使它比我们最终打算训练的模型小得多，也集中在多模态能力上。所以，它处理图像、视频、音频的能力显然对 Meta 的业务非常重要和关键。此外，它在健康方面的能力也非常令人印象深刻。这让我们非常兴奋，你知道，健康是我们认为至关重要的领域，因为我们把这些模型扩展到全球数十亿人。另外，我们在模型创造能力上看到的许多早期结果，比如 vibe code、创建小游戏或小玩意儿等，都非常强大。所以，我们正在加倍投入这些领域，并继续投资于模型的智能体能力。因此，我们非常期待即将发布的模型成为非常强大的智能体，并具备多模态、健康等许多其他优势。最终，我们真正想为世界构建的是尽可能为全球每个人提供最好的个人智能体。

Yeah, so already in New Spark, some areas where we were really impressed by the capabilities, even those again like a much smaller model than ultimately we intend to train, were around multi-modality capabilities. So, its ability to handle images, video, audio, and that's obviously very important and critical for Meta's business. Also, its capabilities in health were really impressive. And that was very exciting for us, you know, health is an area that we view as really critical as we scale these models out to billions and millions of people all around the world. And then also a lot of the early results we saw in the ability of the model to create, you know, vibe code and create little games or artifacts or whatnot were very powerful. So, we are doubling down on some of these and continuing to invest into the agentic capabilities of the model. So, we're really excited for the upcoming models we release to be very very capable agents paired with a lot of these other strengths around multi-modality, around health and many others. And ultimately, what we're really excited to build for the world is the best personal agents for everybody around the world as much as possible.

我想马上谈谈智能体，因为你们昨天刚发布了关于智能体的消息。你们还有其他正在开发的东西可以聊。但在我们快速转向安全话题之前，我确实想问一个关于中国的问题。你曾公开谈论过中国人工智能的风险和威胁。你们也训练过一些中国的开源模型。我只是想知道，你能给我们一个感觉，你现在如何看待中国，通过人工智能的视角？它是否像我们历史上听到的那样是一种威胁？你觉得情况有变化吗？

I want to get to agents in just a second because you just made some news on agents actually yesterday. You have other stuff in the works that we can talk about. But before we pivot off safety real quick, I did want to ask a question just about China. You've been, you know, you've talked publicly about the risks and the threat of AI coming out of China. You guys have also trained on some Chinese open source models. I'm just wondering, can you give us a sense of like how you view China right now in the, you know, through the lens of AI? Is it a threat in the way that we've heard historically? Do you feel like that's changed?

是的，我认为美国在技术以及人工智能所能创造的经济利益方面保持领先至关重要。我认为这非常关键。如果你看看文明史，技术进步对于国家或文明适应和采纳非常重要，这真正决定了长期历史进程。所以，我认为美国能够在人工智能领域领先非常重要。这也是 Meta 关注的重点，同时确保我们能够为美国的领先做出贡献。

Yeah, I think it is incredibly important for the United States to lead on technology and the economic benefits that can be created from AI. I think this is very very critical. If you look at the history of civilization, technological advances are very important for countries or civilizations to adapt to and be able to adopt, and that really defines the course of history over long arcs. So, I think it's very important that the United States is able to lead on AI. And that's a huge part of our focus at Meta as well as ensuring that we are able to contribute to the United States leading.

你认为我们现在处于什么位置？作为一个国家，美国领先吗？

Where do you think we are right now? As a country, is the US leading?

我认为目前美国是领先的。是的。

I think right now the US is leading. Yes.

好的。

Okay.

而且我认为这是一种情况，我们始终需要跟踪许多其他国家的进展，尤其是中国，要非常深思熟虑，准确了解每个国家正在发生的事情以及这些事情发生的原因，但我认为目前我们领先。

And I think it's one of these situations where it's important for us always to track progress from many other countries, but especially China, be very thoughtful and understand exactly what's happening within each country and what are the reasons those things are happening, but I think right now we're ahead.

什么可能危及这一点？什么可能危及这种领先地位？最有可能阻止这一点的事情是什么？

And what could put that at risk? What could put that lead at risk? What's the most threatening thing to stop that?

这是个好问题。我的意思是，最终我认为我们正处于一个阶段，整个行业通过持续扩展这些模型、应用更多算力、更多数据所取得的研究进展令人难以置信地兴奋。在某种程度上，今天研究的进展和速度几乎是奇迹般的。所以，我认为我们能够保持这种进展速度，能够继续扩展这些模型，这一点很重要。

That's a good question. I mean, ultimately I think we are in a phase where the research advancements industry-wide that we're seeing from continuing to scale these models, apply more compute, apply more data to these models are just incredibly exciting. And in some ways the progress and pace of research today is nearly miraculous. So, I think it's important that we're able to continue this pace of progress, that we're able to continue being able to continue scaling these models.

是的。终于到智能体了。你们昨天，我相信是昨天，宣布了一个商业智能体。所以广告商可以用它与客户互动。我猜最终甚至可以帮助开发广告活动之类的。但你们也在开发消费者智能体。请告诉我你们的愿景，最终智能体将如何触及我们所有人。我想知道，我的智能体会像我的电子邮件地址一样，有一个核心智能体，也许还有一个次要智能体吗？还是会像手机上的应用一样，我生活中的每个任务都有一个智能体？你设想我们作为社会将如何使用它们？

Yeah. Agents now, finally. You guys just yesterday, I believe it was, announced a business agent. So advertisers can use this to interact with customers. I presume eventually help even develop ad campaigns, things like that. But you're also developing a consumer agent. Talk me through your vision for how ultimately agents will reach all of us. Like I guess I'm wondering, am I going to have an agent similar to my email address where I have one core agent and maybe a secondary agent? Or is it going to be like the apps on my phone where I have one agent for every single task in my life? Like what do you envision we're going to be using as a society?

是的，我认为最终可能会介于两者之间。我们真的相信人们可能会有一个、两个或少数几个他们依赖的智能体。也许他们有一个个人智能体，专注于健康、维护人际关系、帮助他们成为更好的父母、与朋友和家人相处得更好。然后也许他们在工作中也使用同一个智能体，特别是如果他们在小企业工作、是创业者或在较小的组织中工作。然后也许在某些情况下，如果你在大型企业或大公司工作，这些智能体会分叉并分离。这与电子邮件或我们日常使用的许多其他关键技术没有太大不同。所以，我们认为最终智能体将成为非常个性化的东西，随着时间的推移，你会发现自己越来越依赖它们，用于越来越多的个人生活和工作生活。这将是一个整个社会共同经历的过程。

Yeah, I think it'll probably land somewhere in between those. I think we really believe that people are probably going to have one, maybe two, maybe a small handful of agents that they rely on. And maybe they have a personal agent that's focused on things like their health and maintaining their personal relationships and helping them be a better parent and be better with their friends and family. And then perhaps they use that same agent in their work lives, especially if they're working in a small business or they're an entrepreneur or working within a smaller organization. And then maybe there are worlds where if you work within a larger enterprise or a larger company, then these become bifurcated and separated. Not too much unlike email, let's say, or plenty of other key technologies that we use on a day-to-day basis. So we think that ultimately agents will be something that become deeply personal and should be things where over time you find yourself being able to rely on them more and more for more and more of your personal life, more and more of your work life. And that'll be a process that all society goes through together.

你觉得 Meta，尤其是——再说一遍，很多问题在你来之前就存在了——但长期以来的隐私问题，人们会愿意信任一个 Meta 智能体来处理你描述的那些个人生活任务吗？

Do you feel that Meta in particular, and again, a lot of this happened before you got there, but long history of privacy-related issues, are people going to be willing to trust a Meta agent with the personal tasks of their life that you're describing?

是的，我认为这是智能体领域一个最重要的社会问题。我们在智能体安全等方面投入了大量创新和技术，确保这些智能体尊重你的隐私，尊重你的边界，并持续以支持这一点的方式设计产品。所以，这绝对是我们非常非常重视、也相当深思熟虑的事情。最终，我们很兴奋能向世界展示我们构建的东西，但我们认为这不仅仅是 Meta 的问题。随着我们构建越来越强大的智能体，这是一个全行业的问题。我认为这在很多方面重新定义了人类与技术的关系，这是我们都必须共同思考和解决的问题。

Yeah, I think this is one of the most important societal questions for agents writ large. I mean, there's an incredible amount of innovation and technology that we've built out on things like agentic safety, ensuring that these agents are respectful to your privacy, ensuring that they're respectful of your boundaries, and continuing to design products in a way that supports that. So, this is definitely something that we're taking very, very seriously and being quite thoughtful about. Ultimately, we're excited to show the world what we've built, but we think this is not even just a Meta problem. This is an industry-wide problem as we build more and more powerful agents. I think it is a redefinition of humans' relationship with technology in many ways, and that's something we're all going to have to think through and work through together.

我们多久能看到 Meta 的消费级智能体？

How soon will we see a Meta consumer agent?

我们正在积极烹饪中。

We are actively cooking it.

烹饪主菜？

Cooking the entree?

是的，没错。

Yeah, yes.

好的。

Okay.

但说真的，这是我们在 Meta AI 内部对新的 Spark 发布感到非常兴奋的事情之一。即使在那时候，我们内部也在烹饪东西，包括更大的模型以及你提到的那些产品，我们甚至比四月推出的东西更兴奋。

But no, this is one of the things that was very exciting for us internally about the new Spark launch in Meta AI. Even when those launched, we were cooking things internally, both the larger models as well as some of these products that you referred to that we're if anything more excited about than what we came out with in April.

你现在是怎么使用智能体的？据报道，你的老板马克·扎克伯格基本上有一个 Zuckbot，或者各种版本的智能体，他把一些 CEO 职责交给它们。现在我们在台上，有没有一个 Alexbot 在帮你做部分工作？

How are you using agents right now? Your boss, Mark Zuckerberg, it's been reported he has like a Zuckbot essentially or various versions of agents that he's tasking some of his CEO duties to. Is there an Alexbot that's doing part of your job right now while we're on stage?

嗯，我当然经常使用智能体来支持和帮助我的工作。我认为在很多方面，作为公司领导者，关键在于你能否尽可能了解公司发生的一切，并帮助支持你的团队，持续更好地执行。所以，我认为智能体可以在很多方面帮助你，但最让我兴奋的智能体用途是在个人生活中。我用一个智能体来帮助我更健康，还用另一个来帮助我与朋友保持联系，确保我维持这些关系。我认为这些用例与完全没有智能体的世界截然不同。这些是我历史上很难保持的事情，无论是健康还是与所有朋友保持联系。有一个智能体来帮助你做好这些事情，是相当变革性的。

Well, I definitely use agents to support and help me in my work a lot. I think that in many ways, being a leader within a company is really about how well you are able to understand everything that's happening at the company to the best you can and help support your team and continue to execute better and better. So, I think there are all sorts of things that agents can do to help you there, but the uses of agents that are probably most exciting to me are the ones where I use them in my personal life. So, I use an agent to help me be healthier, and I use an agent to help me keep in touch with my friends and ensure that I maintain those relationships. I think these are use cases that are very notably different from the world where I didn't use an agent at all. These are things that have been hard historically for me to stay on top of, both my health as well as keeping in touch with all my friends. Having an agent that's there to help support you do a good job with those things has been pretty transformational.

不知怎么我们只剩几分钟了，但我想问一些关于 AI 和社会的更大问题。不过，我想先从 Meta 开始，对吧？你的团队获得了巨额投资。你们在 AI 上投入了数千亿美元。与此同时，上个月底有裁员。所以，有些人，有些说法是，‘嘿，这是为了抵消那个。’作为 AI 负责人，我只是想知道你如何应对这个现实？你在做一个你兴奋的产品，但同时公司在说，‘嘿，这也在导致失业。’你对此有什么感觉？你在内部如何处理？

Somehow we only have a couple minutes left, but I wanted to ask some bigger picture questions about AI and society. But I actually want to start at Meta, right? So, your team is getting an immense investment. You guys are investing hundreds of billions of dollars in AI writ large. At the same time, there were layoffs at the end of last month. So, there are people and some of the framing is, 'Hey, this is to offset that.' As the person in charge of AI, I'm just wondering how do you deal with that reality, right? That you are working on a product that you're excited about, but at the same time, the company is saying, 'Hey, this is costing jobs as well.' Like how does that make you feel? How do you deal with that internally?

告别队友是非常困难的。我认为众所周知，这是一个团队要经历的挑战，也是需要承认的事情。我们最终对 AI 的进展和正在构建的产品感到非常兴奋，并很高兴将它们带给世界。但是，是的，我认为经营一家大公司非常复杂，我们正在处理很多这些问题，但我们不会掉以轻心。归根结底，我们对自己正在构建的东西感到兴奋。

It's incredibly difficult to say goodbye to teammates. I think it's very well known that this is a challenging thing to go through as a team and something that is important to acknowledge. We are ultimately really excited about the progress that we're making in AI and the products that we're building, and we're excited to bring those to the world. But, yeah, I think that running a large company is very complex, and we're working through a lot of those issues, but we don't take any of it lightly. At the end of the day, we're excited about what we're building.

我认为失业总体上是 AI 最大的恐惧之一。你对此怎么看？如果 AI 按照每个人的设想发展，我们达到超级智能，有没有一个世界，AI 可以运行并与我们所有人共存，同时我们保持就业？

I think job loss in general is probably one of the biggest fears with AI. What's your view on that? If AI goes the way that everyone envisions and we reach superintelligence, is there a world in which that can operate and live alongside all of us staying employed?

我认为这是我们应该高度关注、密切跟踪并努力理解其影响的事情。我认为有一件事我们很少谈论，但也在发生，而且非常令人兴奋，那就是 AI 正在使世界上创建的企业比以往任何时候都多。我们在数据中看到了这一点。我相信许多公司也在他们的数据中看到了这一点。如今，通过使用 AI 工具创办的新公司比以往任何时候都多，而且这些数字还在增长。我预计随着 AI 工具变得越来越强大，我们会看到更多的企业家，更多的小企业被创办。所以，经济是一台复杂的机器，我们在数据中看到的一件事是，有更多的小企业被创办，更多的企业家，更多的机会给企业家。我们对此感到非常兴奋。我们很高兴能支持世界各地的小企业。

I think it's something that we should pay a lot of attention to and track closely and try to understand what the impacts are. I think one thing that we rarely talk about, but is also happening that's very exciting, is that AI is enabling the creation of more businesses than ever before in the world. We see this in our data. I'm sure many companies see this in their data. There are more new companies being started today through the use of AI tools than ever before, and those numbers are only growing. I expect as AI tools become more and more powerful, we'll see more entrepreneurs, more small businesses being started. So, the economy is a complex machine, and one of the things we see within our data today is that there are more small businesses being started, more entrepreneurs, more opportunity for entrepreneurs. We're really excited about that. We're excited about supporting small businesses throughout the world.

你觉得我们这周看到的特朗普行政令，政府想要在发布前审查一些模型，你对此怎么看？

Is it the kind of thing that you feel we saw the Trump executive order just this week where the administration wants to sort of review some of these models before they're released. I'm curious what you thought of that, first of all.

第二，你认为是否还有其他类型的监管可能有助于防止失业，或者防止人工智能凌驾于人类之上的世界？

But two, is there another type of regulation that you think actually might be helpful to prevent, you know, job loss or prevent a world in which AI is taking priority over the humans?

是的，我认为，首先，这是一项非常重要且强大的技术，所以我认为政府正在深入思考我们应该如何看待这项技术、如何负责任地部署它以及这一切意味着什么，这非常好。所以，总的来说，我认为政府一直积极参与并深思熟虑这个问题，这真的很棒。

Yeah, I think well, I mean, first of all, I think this is a really important and powerful technology, and so I think it's great that the administration is deeply considering how we should be thinking about the technology, how we should think about responsible deployment, and what all that entails. So, broadly speaking, I think it's been really great that this has been an issue that the administration has been really involved on and very thoughtful of.

你不认为这样的审查过程会减缓创新吗？

You don't think it slows innovation to have a review process like that?

嗯，我认为这始终是一种平衡。监管在几乎所有情况下都很难做到恰到好处，但我认为我们从这些模型中看到的是，它们正变得极其强大，因此我们最终如何部署它们是需要深思熟虑的。

Well, I think it's always a balance. Regulation is notoriously difficult to get right in almost all contexts, but I think that what we're seeing from these models is just that they're becoming dramatically more capable, and I think it's important that we're thoughtful about how we deploy them ultimately.

好的。

Okay.