Fireside Chat w/ Clement Delangue - YouTube
发布时间 2023-03-29 00:00:00 来源
中英文字稿 
Hello. Thanks for coming out tonight. It's a pack house. I think we had something like a thousand people who wanted to attend. People are very excited to see Clam. There's ever-growing enthusiasm for AI. Thanks so much for making it.
大家好，感谢今晚的到来。现在场内已经人满为患了。我想大约有一千人想要来参加。人们对于观看Clam的表演非常兴奋。对于人工智能的热情与日俱增。非常感谢大家的到来。
I'd also like to quickly thank Edwin Lee, Ali Pavilion, Emily, for the Strype AV event, security, food, and catering team. Thank you so much for putting on this event tonight and hosting everybody.
我还想快速感谢Edwin Lee、阿里亭、Emily和Stripe AV活动中的安保、餐食和宴席团队。非常感谢你们今晚举办这个活动并招待所有人。
We're going to be talking about Clam's background in origins, and so I'll keep the intro really brief, which is, you know, Clam is the CEO and co-founder of Hugging Face, which is really one of the main pieces of infrastructure that everybody uses in the AI industry. He's been working on AI for about 15 years now, and he's been in France for about 10 years. And so welcome, and thank you so much for joining us today. Thanks for having me. Excited to be able to chat.
我们今天将会谈论Clam在起源方面的背景，因此我简要介绍一下，你知道，Clam是Hugging Face的CEO和共同创始人，这是人工智能行业中每个人都使用的主要基础设施之一。他已经从事人工智能工作约15年了，他在法国已经待了约10年。欢迎你，非常感谢你今天加入我们。谢谢你的邀请。很高兴能聊天。
Okay. And so, Caduce tells a lot about the origins of Hugging Face, and how you started working on it. What it was originally, how it morphed into what it is today, and how you got started? Yeah, absolutely. As you said, I've been working on AI for quite a while before it was as sexy as hot, and as popular as mainstream as today. And I think that's what gathered our co-founders with three co-founders for Hugging Face around this idea that it's becoming kind of like a new paradigm to build technology, and we were really excited about it.
好的。所以，Caduce 描述了关于 Hugging Face 的起源以及您如何开始开发它的很多信息。它最初是什么样的，如何逐渐演变成为今天的样子，以及您是如何开始的？是的，绝对是。就像您所说的，在 AI 成为热门和主流的今天之前，我一直在从事 AI 方面的工作。我想这就是我们的三位联合创始人聚集在一起，围绕着这个理念开始创立 Hugging Face 的原因，因为它正在成为一种新范式来构建技术，我们对此非常兴奋。
When we started the company, we wanted to work on something that was scientifically challenging because that's the background of one of our co-founders, Thomas. But at the same time, something fun.
当我们成立公司时，我们想要从科学上挑战自己，因为我们的联合创始人之一——托马斯就有这个背景。但同时，我们也希望从中获得乐趣。
And so, we actually started by building an AI Tamaguchi, something like a chat GPT, but really focused on fun and entertainment. At the time, you know, there was Siri Alexa, but without it was pretty, pretty boring to focus only on my productivity answers. And we actually did that for almost three years. We raised the first pre-seed on this idea. Some users really liked it, actually. They changed a couple of billion messages with it, but kind of like organically. And I can tell the story later, we pivoted from that to what we are right now, which is the most used open platform for AI.
因此，我们实际上开始建造一种AI顽皮鬼，类似于聊天GPT，但真正专注于趣味和娱乐。当时，你知道，有Siri、Alexa，但如果只关注我的生产力答案，那真的相当无聊。我们实际上做了这个项目近三年。我们为这个想法筹集了第一个原始种子资金。实际上，有一些用户真的很喜欢它。他们和它交换了数十亿条信息，但有点像自然而然。我可以稍后讲述这个故事，我们从那个项目转向了我们现在的方向，也就是最常用的AI开放平台。
What got you interested in AI to begin with? I mean, you started 15 years ago working in the area. And I feel like AI has gone through different ways of popularity, right? We had Alex Net, sparked a lot of interest. There's a CNN and Arden world. Did you start even before that, or when did you first get interested? Yes, at the time, we weren't even calling it AI or machine learning. The first startup I worked for was a company called MootStux. And we were doing machine learning for computer vision. And we were building a device. So we were building a technology to help you point your phone at an object and recognize it.
你最开始是因为什么而对AI感兴趣的呢？我的意思是，你在15年前就开始从事这个领域。我感觉AI的热度经历了不同的阶段，对吧？比如Alex Net引起了很多兴趣，还有CNN和Arden世界。你是在那个之前开始的，还是什么时候开始感兴趣的？是的，当时我们甚至没有称之为AI或者机器学习。我第一个工作的创业公司叫做MootStux，我们主要做计算机视觉的机器学习。我们正在建造一种设备，这个技术可以帮助你将手机对准一个物体并识别它。
And even at the time, it was kind of like mind-blowing what you were able to do with it. I remember, I think, for me, the realization of how AI could really unlock new capabilities is when I met the founders of this startup, I was working at eBay at the time. And they told me, oh, you acquired this company called Red Laser. That is doing barcode recognition for you to recognize objects and then kind of like put up the eBay page. It told me, you guys suck. You should use machine learning. So if recognizing the barcode, you can actually recognize the objects itself. It's like, you're crazy. It's impossible. You can't do that with traditional software. You can't do that with code. There are too many objects. It's possibilities are just too broad to do that.
即使在当时，人们也感到惊叹于AI可以实现的功能。我记得当我在eBay工作时，遇到了这个初创公司的创始人，我意识到AI真正能够释放出新的能力。他们告诉我，你们收购了一家名为Red Laser的公司，可以通过条形码识别来识别物体，然后放在eBay页面上。他们告诉我，你们的做法很糟糕。你们应该使用机器学习。所以如果可以识别条形码，实际上可以直接识别物体本身。但我认为这是疯狂的，因为这是不可能完成的任务。传统的软件和代码无法完成这个任务，因为物品太多，可能性太广泛了。
And they were actually managing to do that with some form of machine learning at the time. So that's when I realized, wow, you can do so many new things with this new technology. And that probably led me to where I am today.
当时，他们实际上正在使用某种机器学习技术来做到这一点。这时我意识到，哇，你可以用这项新技术做很多新的事情。这可能导致了我今天的处境。
That's cool. So you then started HuggingFace. You're going to do like AI Tamagotchi. And I think it's funny how you used to say AI and people would sneer at you and they'd be like, no, no, no, it's machine learning. And so I feel like the Lingo has shifted back to AI again, given what some of these systems can do. And then what made you decide to move in a very different direction of what HuggingFace is?
太酷了！你掀起了HuggingFace公司，计划创造AI版的Tamagotchi。过去说“AI”时，人们总是嗤之以鼻，说那只是机器学习，现在有了这些新系统，术语又回归到了AI了。那么，是什么促使你决定让HuggingFace朝着一个截然不同的方向发展呢？
Yes, it was very organic. With one of these founding moments, it's a good thing that we had striped because I think it's Pat Kudisan who talked first about the importance of not just founding a company but having founding moments that changed the trajectory of your company. And for us that happened thanks to Thomas Wolf, one of our co-founders. I think it was like Friday night. I've seen this thing called Bert that was released by Google. But it kind of sucks because it's on TensorFlow. I think I'm going to spend the weekend porting that into PyTorch. And we're like, yeah, you do you. You know, have fun. Have fun during your weekends.
是的，这非常有机会。有了这些创始时刻之一，我们有条纹是一件好事，因为我认为帕特·库迪桑首先谈到了不仅创办公司的重要性，而且有一个能够改变公司发展轨迹的创始时刻。对我们来说，这要归功于我们的其中一位共同创始人托马斯·沃尔夫。我想那是星期五晚上。我看到了谷歌发布的名为Bert的东西。但它有一些问题，因为它基于TensorFlow。我想在PyTorch中花费周末进行移植。我们说：是的，你做你的事情。你知道，享受周末的快乐。
And on Monday it came back and it's like, okay, I'm going to release it. And it released it on GitHub, tweeted about it. And we got like a thousand likes, which for us at the time we were like, nobody's like French, French, nobody's. We're like, what's happening? Why are people liking this very specific, very niche, very kind of like technical tweet about PyTorch port of Bert? Like, oh, there's something there.
星期一，这个人回来了，他决定发布这个东西。他把它放在GitHub上，并在Twitter上发布了相关信息。我们得到了大约一千个赞，对于当时的我们来说，这是如此惊人，因为我们认为没有人关注法国的这个领域。我们想知道，为什么人们会喜欢这个关于PyTorch的Bert端口的技术Tweet，这很具体、很独特的内容？我们明白了，原来这个东西是有市场的。
So we kept kind of like exploring that. We, you know, joined them, started to add other models to the GitHub repository. And the community came together. People started to fix bugs for us in the repository. We're like, why are people doing that? They started adding models. They started, for example, the first GPT. They added the next models that were released. And really fast, we ended up with like one of the most popular GitHub repository for AI. And that's kind of like what transition does from this first idea to where we are now.
所以我们一直在探索这个想法。我们加入了他们，开始向GitHub存储库添加其他模型。社区也加入进来了。人们开始在存储库中为我们修复错误。我们想，为什么人们要这样做呢？他们开始添加模型。例如，第一个GPT就是由他们添加的。接下来也添加了其他的模型。很快，我们就拥有了一个最受欢迎的AI GitHub存储库。这就是Transition从第一个想法到现在的转变。
Great. And could you describe for people who, I'm sure most people know, but could you describe for people what? How can you face us today and how it's used and the importance of the product and the platform and the ecosystem?
太好了。但是你能否为大家描述一下，虽然大多数人都知道，但你能否描述一下这个产品、平台和生态系统的使用方法以及其重要性，以便今天能够面对我们？
Yes. Now we look at to be the most used open platform for AI. You can think of it as, as mentioned before, some sort of a GitHub for AI. So the same way GitHub is this platform where companies host code, collaborate on code, share code, test code. We're the same way, but for machine learning artifacts. So there's been more than a million repositories that have been hosted on the hugging face platform with models.
是的。现在我们看向成为最常用的人工智能开放平台。你可以把它想象成前面提到的一种类似于GitHub的人工智能平台。就像GitHub是一个平台，公司可以在上面托管代码，协作编写代码，分享代码和测试代码一样，我们也是如此，但是面向的是机器学习工件。在hugging face平台上，已经托管了超过一百万个存储库，其中包括模型。
Most of them open source. So maybe you've heard of stable diffusion, T5 Bert, originally, obviously, Bloom. For example, Whisper for audio data sets. There's over 20,000 open data sets that you can use on the platform. And demos over 100,000 demos are hosted on the platform. And more than 15,000 companies are using the platform to bring AI into their features, into their products or into their workflows.
它们大部分都是开源的。也许你听说过稳定扩散、T5 Bert、原始的、显然的、布卢姆等。例如，针对音频数据集的Whisper。在该平台上，有超过20,000个开放数据集可供使用。并且该平台托管了超过100,000个演示文稿。超过15,000家公司正在使用该平台将人工智能引入其功能、产品或工作流程。
Yeah. Some of the most popular questions on Dory or through the air table form that people asked were around the future directions. Because given the centrality of where hugging faces, there's so many directions that could go and everything from bespoke B2B hosting to tooling to other types of products or activities.
是的。Dory或空气表单上最受欢迎的问题之一是关于未来方向的。因为鉴于Hugging Faces的核心位置，将来有许多可能的方向，从定制的B2B托管到工具和其他类型的产品或活动都有可能。
What are some of the major directions that you fix are pursuing currently in terms of product?
目前就产品而言，您所追求的主要方向是哪些？
I would say there are two main directions that we're following right now. One is like we're seeing that AI is turning from complex these niche techniques solving some problems to the default paradigm to build all tech. And for us that means going from text that is really used on the platform right now. That is also really really used and text to image to expand to every single domain right. So for example, last week we started to see the first open source text to video models right. We started to starting to see in the platform a lot of time series models right like to do financial prediction to do like your ETA when you order you over. We also starting to see more and more biology chemistry models.
我认为我们正在追随两个主要方向。一是我们看到人工智能从解决一些特定问题的复杂技术转变为构建所有技术的默认范式。对我们来说，这意味着从目前在平台上真正使用的文本开始。这也意味着文本转图像将扩展到每个领域。例如，上周我们开始看到首个开源文本到视频模型。我们也开始在平台上看到了很多时间序列模型，如金融预测和点餐配送时间预测等。我们也开始看到越来越多的生物化学模型。
So kind of like making sure that we support this broadening use cases for AI is one. So second one is making it easier for everyone to build AI including software engineers. Historically our platform has been more like designed for machine learning engineers and people who are really kind of like training models were optimizing models assessing models.
这就像确保我们支持AI广泛的使用案例。第二项是让每个人都能更轻松地构建AI，包括软件工程师。历史上，我们的平台更多地是针对机器学习工程师和那些真正培训、优化、评估模型的人所设计的。
What we're seeing now especially with the AI APIs is that everyone wants to do AI right even complex software engineers product managers infrastructure engineers. So a big focus of ours and some of the things that we've released in the past few weeks and I will keep releasing is kind of like reducing the barrier to entry to using our platform. Because ultimately we think every single company or every single team should be able to use open source to train their own models right.
我们目前看到的特别是人工智能API，大家都想正确地使用AI，即使是复杂的软件工程师，产品经理，基础设施工程师。因此，我们的重点是降低使用我们平台的门槛，并且我们在过去几周发布了一些东西，并将继续发布，以此来降低入门难度。因为我们认为最终每个公司或每个团队都应该能够使用开源来训练自己的模型。
Everyone is is talking today about you know chat GPT about GPT4 but I think in a few months or in a few years every single company is going to build their own GPT4 and they're going to train their own GPT4. The same way today if you think of it every company has their own code repository right and there's as many code repositories as companies.
今天每个人都在谈论GPT聊天机器人以及GPT4，但我认为在几个月或几年内，每个公司都将建立自己的GPT4，并训练自己的GPT4。就像今天每个公司都有自己的代码库一样，不同的公司有着不同数量的代码库。
We think tomorrow every single company is going to have their own models their own machine learning capabilities not really outsource it to someone else but really have these capabilities that will allow them to differentiate themselves to cater to their specific audience or their company. So if you could speak audience or is this specific use cases.
我们认为，明天每个公司都将拥有自己的模型和自己的机器学习能力，不在外包给其他人，而是真正拥有自己的能力，这将使它们能够区别于其他公司，迎合其特定的受众或公司需求。因此，如果您能够针对特定的受众或使用情况进行讲解，则会更好理解。
You know it's interesting because when you talk about the future one thing that I'm really struck and buy is you know if I look back over the course of my career there have been multiple or a small number of very large paradigm shifts or platform shifts right so there was the Internet was was obviously a huge transition in terms of bringing everybody online.
你知道这很有意思，因为当你谈到未来的时候，有一件事情真正令我震撼和深思，就是如果我回顾一下我职业生涯的过程，有过很多次或者只有很少次的非常重大的范式转移或者平台的转移，比如互联网显然是一个将每个人都带到线上的巨大转变。
You know then a few years later we ended up with mobile and cloud so suddenly you could host anything anywhere and simultaneously people could access any product from anywhere in the world. Crypto I feel was almost like a side branch that went down the financial services route but didn't become a true platform at least not yet in terms of compute.
你知道几年后我们拥有了移动和云技术，突然之间你可以在任何地方托管任何东西，同时人们可以从世界任何地方访问任何产品。我觉得加密货币几乎像一个分支，进入了金融服务领域，但至少在计算方面它还没有成为真正的平台。
And then now we have AI and it feels like with each platform shift you have three or four things to change right the input and output of how you program a system shifts in some ways or at least the types of data you deal with user accessibility new I shifts right how do you actually interface with something for mobile was different from the desktop. And then the size and magnitude of the implications that shift are massive right and so if we view a I as a new platform how do you view or how do you mentioned everybody will have their own forms dpd4 it seems like the nature of programming itself may change at some point.
现在我们拥有人工智能(AI)，每次平台转换时似乎都要改变三、四个方面：输入和输出的编程系统方式会以某种方式改变，或者至少你处理的数据类型、用户可访问性、新的接口方式等方面会发生变化。这种转变对于整个系统的规模和影响都是巨大的。因此，如果我们将AI视为一种新的平台，那么如何看待或提及这种变化，每个人都将有自己不同的方式，程序本身的性质也可能会在某些时候发生变化。
And we can put aside the whole question around do we also create a digital species and maybe we talk about that at the end. But how does hacking face in the player role in terms of this massive transition of platforms. And then the next thing is we really like and which capacity analogy of like software 1.0 right which is the way and the methodology that we've been building technology with for the past 15 years.
我们可以先不谈关于我们是否也创造了数字物种的整个问题，也许我们可以在最后讨论这个问题。但是，在这个平台大转型的过程中，黑客在游戏玩家的角色中扮演着什么样的角色呢？接下来我们真正喜欢的是软件1.0的能力类比，这是过去15年我们一直以来构建技术的方式和方法。
And now AI is software 2.0 right it's a new methodology it's a new way of building building all technology it's a new paradigm the new default to build all technology. And if you think of that you know you need for this new paradigm better tools more adapted tools to do that. And you need better communities you need ways for teams to collaborate and for the whole ecosystem to collaborate. And that's that's what we're kind of like trying to provide like a new tooling a new collaborative platform to build AI better.
现在，人工智能是2.0软件，这是一种新的方法，一种构建所有技术的新方式，一种新的范例来构建所有技术。如果你思考一下这一点，你会发现你需要更好的工具，更适应的工具来实现这个新的范例。你需要更好的社区，需要团队合作的方式，整个生态系统合作的方式。这就是我们努力提供的东西，一种新的工具，一种新的协作平台，来更好地构建人工智能。
We also trying to build the future that we excited about I think a lot of people are kind of scared about AI right now and the potential and and the risks associated to it. And the way we think about things if you can build a future where everyone is able to understand AI and build AI.
我们正在尝试建立一个让我们感到兴奋的未来。我认为现在许多人对于人工智能的潜在风险感到害怕。但是，我们的思路是，如果能够建立一个让每个人都能理解并能够构建人工智能的未来，那么我们就能够避免风险。
You remove a lot of these risks because you involve more people so you reduce for example the poverty of very biased systems. You give the tools for regulators to actually put in place safeguards. And you give companies capabilities to align the systems that they use and provide to their users and customers with their values right which is what you want ultimately you know you want strive to be able to say you know this is our values. So this is how we building AI in the line with these values so that's also something important that we're trying trying to do.
通过涉及更多的人，您可以减少许多这些风险，从而减少非常偏见系统的贫困。您给监管者提供工具，实际上制定保障措施。您还可以为公司提供能力，使他们使用和向用户提供的系统与其价值观保持一致，这也是您最终想要的，您想要努力实现的是，您知道这是我们的价值观。这是我们如何按照这些价值观构建人工智能，这也是我们正在尝试做的重要事情。
We say sometimes that our mission is to democratize good good machine learning. And we're working really really hard on that because we think it's it's important for for the world. Yeah, it feels like I'm hugging faces always been very consistent in terms of wanting to have ethical AI or ways to participate that are strong and alignment.
有时我们说我们的使命是使机器学习更加民主化。我们正在非常努力地工作，因为我们认为这对世界很重要。一直以来，Hugging Face始终希望拥有道德上合乎标准的人工智能或强有力且一致的参与方式。
I've a number of companies like for example on Thropic has this approach of like constitutional AI right where they basically say we almost provide a constitution as we train the model for how which had govern the activities or actions of the model that results. What are the approaches that you think work best and what do you hope that people are doing more of relative to alignment.
我有很多像Thropic这样的公司，其做法是基于AI的宪法权力，基本上我们会提供一个宪法来训练模型，以规定模型的活动或结果的行为。你认为哪些方法是最有效的，相对于实现对齐，你希望人们能更多地做些什么？
Alignment is this kind of like complicated terms because it means different things to different people. It can be taken from like the ethical standpoint in terms of like alignment between values and systems. A lot of people use it today as more kind of like accuracy improvement to be honest when they can like do some alignment work they actually make the models more accurate thanks to reinforcement learning with human feedback.
"对齐"是一个复杂的术语，因为不同的人对它的意思有不同的理解。可以从伦理的角度来看，如价值观和系统之间的对齐。今天很多人把它视为提高准确性的手段，通过人类反馈的强化学习实际上可以使模型更精确。
So it's kind of like hard to to to debate around that. I think in in general in my opinion you can't control improve and align a system that you don't understand. So the main thing that we're trying to push at taking face is more transparency in terms of like how the systems are built. What data they're trained on. What are the limitations. What are the biases. And I think if you create more transparency around that you can't like almost create a system that is more ethical at core. So that's kind of like the biggest thing that we're focusing on.
所以这有点难以辩论。在我看来，你不能控制、改进和协调你不了解的系统。所以我们在TakeFace试图推动的主要事情是更多的透明度，例如系统的建立方式、训练数据、局限性和偏见。我认为如果你在这些方面创造更多的透明度，你几乎可以创造一个更为道德的系统核心。这是我们着重关注的最大的事情。
What is your biggest concern in terms of how open source AI could be misused or abused. There's a lot of kind of like things that can be dangerous with with AI. However, it's distributed right through API's or open source. The biggest thing is is dual use right. When you want to kind of like use it in a way that is not the right way that model builders defines. And so one thing that we've been experimenting with which is super super early and probably not a solution to everything is creating new forms of licenses for models.
你认为开源人工智能被误用或滥用的最大顾虑是什么？人工智能有很多种危险性，特别是它被通过API或开源方式分发时。最大的问题在于双重用途。当你想以不符合建模者定义的方式使用它时。因此，我们一直在尝试一些超级早期的东西，可能并不是解决所有问题的办法，即创建新的模型许可证形式。
So we've been we've been supporting something called rail and open rail which is responsible AI license which is supposed to be an open license for everyone to be able to use the model. But that defines uses that are prevented from the model authors. As a way to create kind of like legal challenges for people to use it the wrong way. But that's that's kind of like one one approach that that we've taken to try to mitigate some of the dual use of of AI in general.
我们一直在支持名为“铁路”和“开放铁路”的事物，这是一种负责人工智能许可证，旨在为每个人提供开放式许可证，以便能够使用该模型。但是这定义了某些使用方式，防止了模型作者的使用。这是一种创造法律挑战的方法，以防止人们使用方式不当。但这是我们采取的一种方法，尝试减轻人工智能在一般情况下的双重使用问题。
I guess as you look at the world of open source versus close source. One of the things that's really been happening is when before many of the different industrial research labs, the Googles and the open eyes of the world would publish a model they'd actually also publish the architecture of the model they publish paper that goes in depth in terms of how the thing works. You know the original transformer paper was recently explicit. And now they're starting to curtail the amount of information that's coming out with each incremental model.
我觉得你看待开源和闭源的世界时，会发现一些变化。以前，很多独立研究实验室、谷歌和其他开源界的公司会发布他们的模型以及这些模型的架构，并发表详细深入的论文来阐述这个模型是如何工作的。你知道最初的Transformer论文最近也是非常详尽的。但现在，他们开始限制每种增量模型所发布的信息量。
Do you think that puts open source at a disadvantage or how do you think about the future particularly on the large language model side because when I look at the image gen models they tend to be reasonably inexpensive to train they tend to be more open source heavy. And it really seems to be more along the lines of the foundation models where this could become an issue because of the ones that need massive scalability and compute.
你认为这会使开源处于劣势吗？你认为未来特别是在大型语言模型方面的发展如何？因为当我看到图像生成模型时，它们倾向于训练成本较低，也更加重视开源。而这似乎更符合基础模型的发展方向，其中需要大规模可扩展性和计算能力，可能会成为一个问题。
Are you concerned about the lack of publishing that's starting to happen and how do you think about the delta between open and close source models for big foundation models. Yeah, it's definitely a challenge. I think it's good to remember that we got where we are today thanks to open science and open source right everything every system that. That is around today is built stands on on the shoulders of of giants right if there wasn't research papers for for birds for for transformers for 45 for GPT. Maybe we would be like 50 years away from where we are today I think that's what created this massive positive loop that made the progress of AI I think faster than anything you've seen before.
你是否担心出版越来越少，并且如何看待大型基础模型中开源和闭源模型之间的差距？这确实是一个挑战。我认为值得记住的是，我们今天到达的位置都要归功于开放的科学和开源的模型。所有现在存在的系统都是在伟大的巨人的肩膀上构建而成的。如果没有这些鸟、变压器、45、GPT等的研究论文，我们可能还需要50年才能达到今天的进展水平。我认为这就是创造了这种巨大的积极循环，使得AI的进步比以前任何时候都要快。
So we're going to start doing that it's going to slow down right it's going to take more time and we'll just kind of like move slower as as a field. But I think one thing that we're seeing is that you know life a board vacuum I think that's that's the proverb right so I think if some companies and some organizations just start to do less open research or less open source. What we think is that other organizations will take over and actually reap the benefit of it so for example we sing a lot of collective decentralized collective there's like a Luther AI that announced a non profit few few weeks ago you have organizations like Alenei in in Seattle you have organizations like stability AI runway ML you have academia that is coming back in the picture right the original stable diffusion was built in a German university in a group a research group called com this using stand for it doing more and more in open source and for open research.
所以我们要开始这样做，这会减缓速度，需要更多时间，我们会更慢地前进。但我认为我们看到的一件事是生命不能存在真空，我认为这是谚语。因此，如果一些公司和组织开始做更少的开放研究或开放源代码，我们认为其他组织将接管并获得其利益。例如，我们看到了许多集体去中心化的组织，比如不久前成立的一个名为Luther AI的非营利组织，还有位于西雅图的Alenei组织，以及如稳定性AI、runway ML这样的组织。学术界也重新回到画面中来了，最初的稳定扩散是在德国一所大学一个研究小组(叫com) 中建立的，这个组越来越多地在开源和开放研究方面做出贡献。
So I think ultimately that's that's what we're going to see why we're going to see like different sets of organizations taking taking over and can't like contributing to open research and open source because at the end of the day it's not going to go anywhere right I mean if you look at traditional software there's always open source and close source right. And open science is not going to go anywhere because the goal of most scientists is actually to contribute to the society and not just to do something to make the company money so I think that's what's going to happen maybe like the types of companies that are doing open research and open source are going to evolve but I'm not too scared about it.
我认为最终我们将会看到不同类型的组织参与开放研究和开源，这是为什么呢？因为最终开放科学是不会消失的，就像传统软件有开源和闭源一样。大多数科学家的目标是为社会做贡献，而不仅仅是为公司赚钱，所以我认为这将会发生，也许做开放研究和开源的公司的类型会发生变化，但我并不担心这个问题。
One kind of like proof of that is that the number of models and open source models number of open source data sets number of like open demos on hugging faces been actually accelerating for for the past past few months and you're right in pointing out that we're a little bit biased on text right that's when I were where appropriate to read ahead of open source right large language models.
这表明有一个证据表明，过去几个月里模型数量、开源模型数量、开源数据集数量、以及在Hugging Faces上的开源演示数量都在加速增长。你说得对，我们在文本领域有点偏见，因为在大型语言模型方面，提前阅读开源文档是适当的。
But if you like a look at audio you know can't like the best things are like whisper for example thanks to open AI that that is open source if you look at text to image stable diffusion is huge and probably like bigger than any poverty system if you look at biology chemistry time series also open source is very powerful. So I think it's always some sort of a cycle right sometimes perpetuary gets ahead thanks to some some companies are doing a really good job that company is doing an amazing job for example right now but sometimes open source catch catches up sometimes it's going to be a head sometimes it's going to be a little bit later that's kind of like the little bit of like normal technology cycle I would say.
如果你对音频外观感兴趣，你知道最好的东西就像是耳语，例如感谢开源AI，这是开放的资源。如果你看一下文本到图像稳定扩散，它非常强大，可能比任何贫困系统都要大。如果你看生物化学时间序列，也是开源的，非常强大。所以我认为总是存在某种循环，有时候是由于某些公司做得非常好而让专有技术处于领先地位，比如现在某个公司正在做出色的工作，但有时开源技术会赶上，有时会领先一些，有时会稍晚一些，这就是一种正常的技术周期。
Yeah I think that's true if you look at passive technology cycles it looks like often the really successful large open source approaches that are offsetting commercial efforts tend to actually have a large commercial backer who wants to offset the activities of others it's almost like strategic counter positioning so for example in the 90s the biggest sponsor of Linux was IBM because they were trying to counter Microsoft and then if you look at a variety of you know open source.
我认为这是对的，如果你看看被动技术周期，通常成功的大型开源方法会抵消商业努力，它们往往会有一个强大的商业后台支持，希望抵消其他人的活动，这几乎像是战略对立的位置。例如，在90年代，Linux最大的赞助商是IBM，因为他们试图对抗微软，如果你看看各种开源。
You know mobile browsers webkit you know is backed by either apple or Google depending on the branch. Who do you think or do you think somebody will emerge in terms of one of the becoming one of the major sponsors of open source like is does Amazon do it to offset Google and the relationship between Microsoft and open AI in the cloud is it in video as a oracle as it a conglomeration of multiple parties or do you think a government or somebody else may enter being in this case.
Yeah I think there are a lot of big tech companies that have like kind of like good alignments with like open science and open source you mentioned some of them like Amazon has been really good backer of open source and video has been a very good support Microsoft has been supporting open source a lot too. So yeah I think some of some of these going to come from from there i'm also excited about more governments involvement in kind of like democratizing access to compute which has been kind of like one challenge for large language models.
你知道移动浏览器的Webkit吧，它的支撑者是苹果或谷歌，取决于分支。你认为谁会成为开源主要赞助商之一的人呢？是亚马逊，为了抵消谷歌之间的关系，还是微软和云开源AI之间的关系？是Oracle，还是多个方组合起来？或者您认为政府或其他人可能进入这种情况。
是的，我认为许多大型科技公司与开放科学和开源有良好的关系，你提到了一些像亚马逊一样一直支持开源的公司，Video一直是很好的支持者，微软也一直在支持开源。所以，我认为有些人会来自这些人，我也对更多的政府参与到普及计算的工作中感到兴奋，这一直是大型语言模型所面临的挑战之一。
So when when we trained with the big science group a model called bloom which at the time when we released it was the largest language models language model that was open source. We got support for from a French supercomputer called john Z so I'm excited to see that more because I think if you look at how. Public policy and and kind of like governments can have positive impact I think providing compute to universities or like independent organizations nonprofits for in order to avoid concentration of power and create more transparency is a very obvious way where they can have an impact and the positive impact on society.
当我们与大型科学团队一起训练一个被称为"Bloom"的模型时，那时我们发布的是一种开源且最大的语言模型。我们得到了一个名为约翰·Z的法国超级计算机的支持。我很高兴看到这个情况出现的更多，因为我认为，如果你看一下公共政策和政府如何产生积极影响，我认为为大学或独立组织的非营利机构提供计算能力，以避免权力集中并创造更多的透明度，这是一种非常明显的方式，他们可以对社会产生积极的影响。
So I'm also excited about that's about disability for public organizations to support more open source and open research in AI. Yeah it makes a lot of sense I guess if you look at the types of open source there's going to be models of various sizes and do your point on the large language models if you assume. You know the rumor of the public estimates are you know GPT three took 10 million dollars at the time although I guess now it would be seven million dollars to train and then GPT four say was 50 to 100 if you're to do from scratch and then maybe GPT five is 200 million dollars and GPT six is half a billion or whatever it is you keep scaling up cost and so you need these sort of large sponsors to at least be at the cutting edge of all times but then one model behind maybe dramatically cheaper and so it's interesting to ask how that world evolves relative to government.
我对公共机构为支持更开放的人工智能开源和开放研究的残障问题感到兴奋。如果你看看各种开源的类型，我觉得它很有意义，因为会有各种大小的模型，符合你关于大型语言模型的观点。你知道公众对于GPT三是大约1000万美元，在那个时间，但现在可能是700万美元的估计。GPT四是50到100亿美元，如果从头开始做，然后GPT五可能是2亿美元，GPT六是5亿美元或者是其他的数量，成本不断增加，所以你需要这些大赞助商至少时刻保持在前沿，但是一个模型会比后面的模型性价比更高，所以有趣的是，相对于政府，这个世界是如何发展的。
To government intervention or corporate intervention or other things in terms of sponsoring these models. We've the caveats that we've seen that some scaling is is good. We don't really know if that's the scaling that helps the current emerging of the year to be honest and that that's one of the challenge of the lack of transparency that's that's happening right now.
关于资助这些模型的政府干预或企业干预或其他事情，我们需要注意一些限制条件。我们看到一些扩展是有好处的，但我们真的不知道那是否有助于当前年度的新兴问题，这是缺乏透明度带来的挑战之一。
Actually, I really just a question what do you think are the bases for the emerging behavior and what do you think is the biggest driver for scale going forward as a compute is a data is algorithms is it something else.
实际上，我只是想问你认为新兴行为的基础是什么，你认为推动计算、数据和算法规模增长的最大推动因素是什么，还是还有其他因素？
I think we're starting to realize and have like a better consensus is in the science community that data and not only the quantity of data but the quality of data is starting to matter more than just blindly scaling the content.
我认为我们开始意识到，并在科学界形成更好的共识，即数据的重要性不仅在于数据的数量，而更在于数据的质量，而不是盲目地扩大内容的规模。
But I think also something that can't like is important to remember is that training a very good large model today is still very much an art. It's not just a simple recipe of saying like you have good data, you have a lot of compute, you're going to get a good model.
我认为还有一件不能被忽略的事情，那就是训练一个非常好的大型模型现在仍然非常依赖技巧和经验。这并不仅仅是一个简单的公式，比如你有好的数据、有大量计算资源，你就能得到一个好的模型。
It's very much still like very difficult, very hard to understand technical and very it's almost like alchemy. But very small number of people really managed to do today right and maybe it's like 20 people in the world today maybe it's 50 people in the world today it's very very small number I think people sometimes don't don't realize that.
这仍然非常困难，很难理解技术，几乎像炼金术一样。但是现在真正能够做到的人非常少，也许全球只有20个人或50个人，非常非常少。我认为有时人们不意识到这一点。
And so I think there's a lot of progress also to be made on understanding the techniques to get to a good model almost independently of computing and data.
所以我认为，在理解如何获得一个良好的模型的技术上，独立于计算和数据，还有很多进展可以取得。
Do you think it's a small number of people? It's a it's a billion dollar question right if it was easy to to know I think everyone would would be doing it. I think I think interesting me it's a mix of technical skills, science skills and kind of like almost products management skills which are kind of like unique.
你认为这是一小部分人吗？这是一个价值十亿美元的问题，如果知道答案很容易，我想每个人都会做这件事。我认为有趣的是，这需要相当多的技术技能、科学技能和产品管理技能，这些技能都是独特的。
That's yeah like it's not just a matter of like you know doing the right training but it's kind of like knowing how much more training you want to do it's a matter of kind of like understanding when you want to really things when you want to keep doing kind of like optimizations before launching your training run.
这就像不仅仅是做正确的训练的问题，而是了解你想要做多少更多的训练，同时也是了解在启动训练之前你想要什么时候真正地完成事情，什么时候想要继续进行优化的问题。
When you want to kind of like start the big six months three months training run or where where you should kind of like keep experimenting. So yeah it's a mix it's a mix of all of that which makes it super hard but super fun at the same time right if it was was too easy with it wasn't me fun.
当你想要开始为期六个月或三个月的大型训练跑或者在哪里你应该保持尝试新的方法。所以，这是一个混合体，这使得它非常困难，但同样也非常有趣。如果太容易了，那就不好玩了。
But hopefully it gets it gets easier and it gets more democratized so that everyone can kind of like take advantage of that with the benefits of that learn from that and then as we said before be like better systems for for each organization.
希望未来能变得更简单且更民主化，这样每个人都可以从中获益、学到东西，之后利用它们成为更好的组织系统，为每个组织都创造更好的机制。
Where do you think are the most exciting areas of AI research right now or where do you wish more people are working. I'm super excited about you know I mean it's fun to do like text right and I'm just here for short periods of time so I went to a couple of like Akkistan and there are some some really cool stuff.
你认为当前最令人兴奋的AI研究领域在哪里，或者你希望更多的人能够从事哪方面的研究？我对于文本方面的研究非常感兴趣，而且我只会在这里短暂停留，所以我去了一些地方，比如Akkistan，那里真的有一些非常酷的东西。
But I think I think it's interesting and important to work on you know more technically challenging problems right now especially in the other domains like I'm super excited about biology. How do you apply AI to biology how do you apply AI to chemistry to kind of like both can't like have positive impact in the world but also to differentiate yourself and can't like build more technically challenging stack for for AI.
我认为目前特别是在其他领域，例如生物学，更加重要且有趣的是研究技术上更具挑战性的问题。我非常兴奋，想知道如何将人工智能应用于生物学和化学，既能够对世界产生积极的影响，又能够区别开来并为AI构建更多技术上具有挑战性的堆栈。
So these are some of the things I'm excited about right now.
这些是我现在感到兴奋的一些事情。
And then how do you think about that I feel like there's two views of the world and maybe neither is fully correct in terms of general purpose models versus niche models right so some people are making the argument which is you just keep scaling up models you get you make them more and more general eventually they can do anything.
你认为这个问题怎么样呢？我感觉世界上存在两种观点，也许两种观点都未能完全正确地描述通用模型和细分模型之间的区别。有些人认为，只要一直扩展模型的规模，让它们更加通用，最终就能做任何事情。
And the other side of it we were saying well just do the focus small model that is targeted to the specific thing that you're trying to do with the data set that you're trying to do can be highly perform and you don't need to wait for the big generalization. Where do you think will be in three or four years.
另一方面，我们正在说的是，只需使用针对您所处理的数据集的特定事物进行聚焦的小型模型，便可以高效执行，无需等待大规模普遍性。您认为我们三到四年后将处于什么地步？
表达意思：我们正在讨论的是，可以使用聚焦于特定事物的小型数据模型进行高效处理，而不必等待大规模普遍性。请问您认为在未来三到四年中，我们将处于什么样的位置？
Yeah that's a good that's a good question I've tried to stop doing predictions in AI because it's too hard these days like I say something in three months three months later it goes completely as a way around and I look like a fool so I won't I won't do too many predictions.
嗯，这是一个好问题。我尝试停止在人工智能领域做预测，因为现在太难了。比如我在三个月前说了什么，在三个月后，可能完全变了，我看起来就像个傻瓜。所以我不会做太多的预测。
But I usually try to more like look at the past and data points since chat GP got released companies have uploaded to hugging face over 100,000 models right and I don't think company is like trained models for fun right if they can use something else if they don't need the training they would.
通常情况下，我更喜欢关注过去和数据点。自从Chat GP发布以来，公司已经上传了超过10万个模型到Hugging Face。如果他们不需要培训，而是可以使用其他东西，我不认为公司会无聊地训练模型。
And then interesting other interesting data point is that if you look at all the models on the hugging face hub the most used ones are actually models from 500 million to 5 billion parameters.
另外一个有趣的数据点是，如果你看一下Hugging Face平台上所有的模型，那么使用最多的其实都是由5亿到50亿个参数构成的模型。
And I think the reason why is that when you get kind of like more customized specialized models you get something that is like first like simpler to understand and iterate on you get something that is faster most of the time which sometimes can run like on on device on your phone or like on specific hardware.
我认为这个原因是，当你获得更加定制化和专业化的模型时，你会得到一个更容易理解和迭代的东西，它通常更快，有时可以在设备上或特定的硬件上运行。
Something that is cheaper cheaper to run and actually gets you better accuracy for your specific use case when you when you specialize it sometimes from some implications when you're doing a chatbot for customer supports where customers are asking for.
有些东西在运行成本更低的情况下，如果你专门定制它，可以为你的特定使用情况提供更好的准确性，尤其是在为客户支持创建聊天机器人时有时存在一些影响，因为客户要求各不相同。
And then you know your last in voice you probably don't need a chatbot to be able to tell you about the meaning of life and the weather in San Francisco you know you just need it to be really good at your specific use case. And what we're seeing is that having a more specialized customized smaller model for for that usually is is a better fit. And then you use cases like if you're being for example and you want to do like a general search and giant to to be able to answer all these questions obviously like a large more general model makes makes sense. Ultimately I mean I think that's going to always be all sorts of different models the same way there are all sorts of code based right like you wouldn't like today you don't really say like my code base is better than yours you don't say like stripe code base is better than Facebook code base. I just do different things right they answer different problems to different questions the same for models you know like there's no one model these better than those are you smart like what model makes sense for your for your use case and how how can you can fly optimize it for your specific use case.
然后，你知道你最后的发票，你可能不需要一个聊天机器人来告诉你生命的意义和旧金山的天气，你只需要它真的擅长于你特定的用例。我们看到的是，拥有更专业、定制、较小的模型通常更适合。例如，如果你正在进行一般性搜索，想回答所有这些问题，那么显然采用大型的更普遍的模型是有意义的。最终，我认为会有各种各样的模型，就像有各种各样的基于代码的模型。就像你不会说今天我的代码比你的好，你也不会说Stripe代码库比Facebook代码库好。它们只是不同的，它们解决不同的问题，回答不同的问题。就像模型一样，没有一个模型比其他模型更好，你需要为你的用例考虑哪种模型更合适，以及如何对其进行优化，以适应你特定的使用情况。
The last question I wanted to ask before we open things up to the audience is around business models and business opportunities and I'll leave the confundrum see of data bricks has this really good framework for open source where he says. With open source you first start out with some open source software and just making that work is like hitting a grand slam and baseball and then you put down the baseball bat and you pick up a golf club and you had a hole in one to have a successful business so it's almost like you need two miracles in order to build something amazing and open source sustainable as a company as well as a product. How do you think about monetization of hugging face and what are some of the directions that you all are going for and that for that.
在我们向观众开放问题之前，我想问的最后一个问题是关于商业模式和商业机会的，我会引用Databricks的一个非常好的开源框架来解释这个问题。他说，使用开源软件时，首先需要让软件运行起来，这就像在棒球比赛中打出一个全垒打，但为了建立一个可持续的公司和产品，你需要像挥杆击球一样下一场高尔夫比赛并打出一个“一杆成洞”的壮举。因此，你需要两个奇迹才能建立出令人惊叹且持久的开源公司和产品。你们如何考虑挤压脸和如何寻找商业化的方向呢？
I don't know if I agree with this no because I think open source also gives you like superpowers and things that you couldn't do with with added I know that for us you know like a said we're the kind of like random French founders and if it wasn't for for the community for the contributors for for the people helping us on the open source people sharing their models. I mean we wouldn't be where we are today right so it also creates new new capabilities not only on the challenges. For us the way we've approached it is that you know when you have kind of like an open platform like like hugging face. The way to monetize is always some sort of a premium model or some kind of like version of a premium model. So we have 15,000 companies using us right now and we have 3000 companies paying us to use to use some of our services and usually they pay for additional features like enterprise features right some companies they want security they want user management or they pay for for compute like that. They want to run on faster hardware they want to run the inference on the platform they want to run the training on the platform and like that we found kind of like a good balance where if you were company actually contributing to the community into the ecosystem you're releasing your models in open source.
我不知道我是否同意这个观点，因为我认为开源也会给你带来超能力和你在不开源的情况下做不到的一些事情。我知道对于我们这样的随机的法国创始人，如果不是社区、贡献者和那些分享他们的模型的人帮助我们，我们今天不会到达这个位置。所以这也创造了新的能力，不仅仅是挑战。对于我们来说，我们的方法是，当你拥有一个像 Hugging Face 这样的开放平台时，获利的方式总是一些高级模型或某种高级模型的版本。所以我们现在有15,000家公司使用我们，并有3,000家公司支付我们使用我们的某些服务，通常他们支付附加功能，如企业功能。有些公司他们希望有安全性、用户管理，或者他们支付计算服务，例如更快的硬件运行推理和训练。我们发现了一个良好的平衡点，如果你是在社区和生态系统中做出贡献并公开你的模型的公司，那么你就能获得好处。
It's always going to be free for you and if you company more like you know taking advantage of of the platform then you contribute in different way you contribute financially right by by helping us monetize and keep working on this. So we still early on that but we've kind of like found this kind of like differentiation between the two that allows us to keep working for the community keep doing open source keep contributing in alignment with with our values and what we want to do but at the same time make it like a good business sustainable business that allows us to to scale and go our impact. You mentioned the community a few times and I think hugging face is one of the most beloved like products and communities in the A.I. World.
对于您来说，这个平台始终都是免费的，但如果您的公司更多地利用了这个平台，那么您可以通过金融上的支持来帮助我们实现货币化，让我们能够继续在这个平台上工作。虽然我们还处于早期，但我们已经发现了这两种方式之间的差异，这使我们可以为社区继续工作，保持开放源代码，并在符合我们的价值观和我们想做的事情的同时，让它成为一个良好的可持续性的商业，并允许我们扩大影响力。您多次提到了社区，我认为Hugging Face是人工智能世界中最受欢迎的产品和社区之一。
What were their specific tactics you took to build out that community or things that you felt were especially important in the early days or how did you evolve something that's so powerful from a community basis perspective. I would say just the emoji you know having having the hugging face hugging face emoji as as a logo as a name that's that's only took only took to get get the love of the community. No it's hard to say how to say we're really grateful. Some of the things that we've done that that we've been happy with is that we never hired any community manager.
你采用了哪些具体策略来建立社区，或者你觉得在早期特别重要的事情是什么，或者你是如何从社区基础角度演化出如此强大的东西的。我想说的是，只是一个表情符号，你知道，拥抱的表情符号作为标志和名称，只需这样就能获得社区的喜爱。很难说我们有多么感激。我们做过一些事情，我们已经感到满意的是，我们从来没有雇佣过任何社区经理。
And we actually it's a bit counterintuitive but it led to actually every single team members every single hugging face team members to actually share this responsibility of contributing to the community talking to the community answering to the community instead of like having a couple of like team members and you know instead of having researchers being like oh I'm not going to you know do the community work because we have this community manager.
我们实际上会有点违反直觉的想法，但这实际上导致了每个团队成员都承担了向社区贡献、与社区交流、回应社区的责任，而不是只有几个团队成员。研究人员不再像以前那样认为“我不需要做社区工作，因为我们有社区经理了”。
So for example our Twitter account the hugging face to your account everyone in the company can tweet from it. So if you're seeing the tweets from the hugging face Twitter account it's not for me it's not from like community manager it's from any of the hugging face team members.
例如，我们的 Twitter 账户是拥抱脸，所有公司的成员都可以在它上面发布推文。所以如果你在看拥抱脸 Twitter 账户的推文，那不是由我或社区经理发布的，而是由任何一个拥抱脸团队成员发布的。
Which was a kind of like a bit scary scary at the beginning especially especially as we grow we haven't had any problem yet but I apologize in advance if at some point you see like a rock, rock, rock tweets that might might be a might be a team member but yes. The smarter approach to always be able to blame someone else for exactly. Yes maybe it's going to be me actually it's going to be between the bad the bad tweets that be able to say it's an intern or something like that. Yes you must have a lot of interns just in case you're kind of smart.
一开始有点恐怖，特别是随着我们的发展，虽然还没有遇到任何问题，但事先道歉如果你看到岩石、岩石、岩石的推文可能是团队成员，但是。更聪明的做法始终是能够责备别人的方法。是的，也许会是我，实际上它会在糟糕的推文之间，能够说这是一个实习生或类似的人的错。是的，你必须聪明一点，以防万一你有很多实习生。
Yeah. Last question for me in the will open up to the audience. What do you wish more startup founders were working on or where do you think they're interesting opportunities for people to build right now.
好的。这是我在现场提问的最后一个问题了。您希望更多的初创企业家致力于什么方面，或者您认为现在有什么有趣的机会让人们去开发呢？
So I'm a bit biased on that but I wish more startup founders were actually building AI you know not just using AI systems because I think there's a big difference. So in the way I see things in the early days of software you could you know using the you could use an API you could use like weeks or square space or WordPress to build a website right and that's good that's kind of like a good way to get something up quickly and you can do beautiful things.
我有点偏袒这方面，但我希望更多的初创企业创始人实际上是在建立AI，而不仅仅是使用AI系统，因为我认为这有很大的区别。在我看来，在软件的早期阶段，您可以使用API，可以使用像Weeks、Squarespace或WordPress这样的工具来建立网站，这是不错的，这是快速上线并可以创造出美好的东西的好方法。
But I think the real power came from people actually writing code right and building building technology themselves and that's how you get kind of like the power out of these things. It's kind of like the same for AI right. You can do something quickly. And ultimately if you really want to be serious about AI you need to kind of like understand how models work, how they're trained, how you can optimize them. And that's also what's going to unlock the most potential for like truly great startups and great products and and companies that are differentiated from incumbents just like adding some like AI features.
我认为真正的力量来源于人们写出正确的代码，建造技术，这是你从这些事物中获取的力量。就像人工智能一样。你可以快速做出一些东西。最终，如果你真的想认真对待人工智能，你需要了解模型的工作原理、它们是如何训练的以及如何优化它们。这也是解锁最大潜力的关键，可以使公司真正伟大，有差别化的产品和创业公司，而不只是添加一些人工智能特性。
And these companies like you know run away email announced the release of their text video I think today or yesterday that's a good example of like really kind of like AI native startup that is really actually training models building models really kind of like doing and building AI not just using using AI. That's one thing that I usually recommend startups do or if you're just using AI just you know build your company accordingly knowing that your you know mode or you were like advantage especially 30 stage won't be so much on the technical capabilities but more on you know getting customers or getting users or any you wrote a beautiful very good like article that I would recommend. Want you to read on on modes for AI they need to then kind of like take advantage of other kind of modes than in my opinion like technical modes.
这些公司，比如你知道的那些，发布了他们的文本视频的发布公告，我想是在今天或昨天，这是一个很好的例子，说明了真正的AI本地创业公司，他们正在训练模型，构建模型，真正地利用和建立AI，而不仅仅是使用AI。这是我通常建议初创公司要做的一件事情，或者如果你只是使用AI，那就要相应地构建你的公司，知道你的竞争优势，特别是在早期开始的时候，往往不是技术能力，而是获取客户或用户。你写了一篇美丽的非常好的关于AI模式方面的文章，我建议你阅读一下，他们需要利用其他的模式，而不是单纯的技术模式。
Okay great let's open it up to the audience if there are any questions. Maybe we start in the corner right there. You mentioned something about like open sourcing thing it's good because like you give it to good actor and also like bad actor can use but like good actors probably are you have more good actors but how do you usually respond to claims like open AI that they don't say any details about their models they don't open source anything because they're afraid of AI safety.
好的，现在我们向观众开放问题环节，如果有问题可以提出来。我们可以从那个角落开始。你提到开源是件好事，因为好的参与者可以使用它，但是坏的参与者也可以使用，但是好的参与者可能会比坏的参与者多。但是，对于像开放AI这样的声明，他们不会提供任何有关其模型的详细信息，也不会开源任何东西，因为他们担心人工智能的安全性，你通常如何回应这样的声明？
I mean I respect everyone's approaches right like different organizations have different ways of like seeing seeing the future or like the current current way technology technology is building. And the fact that some technologies are built like behind closed door. You can build things like in the open you actually create much more sustainable paths in the long run for this technology to be embedded in society in general right for like regulators to be able to create the regulatory framework for for this technology is for NGOs for civil society to be able to wait.
我的意思是，我尊重每个人的方法，就像不同的组织有不同的方式来看待未来或目前技术的建设方式一样。有些技术是在闭门造车的情况下建立的事实。如果你能在公开的场合下建造东西，你实际上会为这种技术嵌入到社会中创造更可持续的路径，为监管机构制定监管框架，以及为非政府组织和公民社会创造机会等。
So yeah I think we were starting from a very different position philosophically speaking but you know that's not too much of a problem in my opinion for the ecosystem you know you can have different organizations with different points of use. And like the most important thing is just that your company doing our line with your company values. Any other questions right there in the middle.
嗯，我认为我们从哲学上讲开始的位置非常不同，但在我看来，这不是生态系统的太大问题，你知道可以有不同的组织有不同的用途。最重要的是，你的公司要符合你的公司价值观。还有什么其他问题吗？在中央的位置。
The paradox of how do they preserve the privacy of their data while improving the models is in housing open source models the solution or approaches like federated learning you know appreciate your thoughts.
如何在提高模型精度的同时保护数据隐私是一个悖论，放置开源模型于公共住所或使用联邦学习等方法可以成为解决方案。请您分享自己的想法。
We've been working a little bit on the conflict more like distributed or decentralized training which is still hard to do and I think nobody has really figured it out yet but that's what you asked me about my interest in the science science progress that's one of where we're really excited about to see more people working.
我们在更像分布式或去中心化培训方面进行了一些工作，这仍然很难做到，我认为没有人真正搞定它，但这就是你问我的关于科学进步方面我感兴趣的领域之一，我们对看到更多人努力工作感到非常兴奋。
But yeah the more practical answers and solutions today are on device models or there are also some more solutions that are embedded in how you train models. So for example we're leading an initiative called a big code which is releasing some I think it released a few few weeks ago the biggest open repository of code that people can train code models on it's called the stack.
但是，今天更实际的答案和解决方案在于设备型号上，或者说也有一些嵌入模型训练方式中的解决方案。例如，我们正在领导一项名为Big Code的倡议，该倡议发布了一个名为“Stack”的最大开放代码库，人们可以在其中训练代码模型。我认为它在几个星期前已经发布了。
The interesting thing about it is that it we gave the ability to opt out from these data sets before training the model. I think you've seen last week the training of the Adobe model that also have been really good at training on good data where users have actually opted in for for the training. So these are also some I think important developments in the fields where you want to be a bit more intentional about the data or more transparent about it one of the challenges is that the lot of these systems today we don't really know what they've been trained on right because there's no transparency about it.
有趣的是，在训练模型之前，我们提供了选择退出这些数据集的能力。我认为你上周看到了 Adobe 模型的训练，该模型在用户自愿参与培训的良好数据上进行了很好的训练。因此，在希望更有意识地处理数据或更透明地处理数据的领域中，这些也是一些重要的发展。其中一个挑战是，现在许多这些系统我们不知道它们是如何进行训练的，因为它们缺乏透明度。
I wish there was so that we can kind of like have a better understanding of like what you're capable of doing with which data and and then kind of like find solutions to make sure it stays like privacy preserving for for people. But there's some some good development and I think we're we're making a lot of progress there.
我希望能有某种方式，这样我们就可以更好地了解您可以使用哪些数据来做什么，并找到解决方案，确保数据对人们的隐私进行保护。但是现在有一些良好的发展，我认为我们在这方面正在取得很大的进展。
Do you think we're going to end up with just like those robots that text right now for search do you think we'll have like AI dot text or something for you know sites to be able to opt out of using any idea that's.
你觉得我们会像现在搜索引擎所用的那些机器人一样，最终采用 AI 点击文本或其他什么东西，让网站能够选择不使用任何 AI 技术吗？
Yeah, yeah, probably we'll need to have norms around that for sure around like consent for for AI. I think that's that's really important that's really important for example for artists for digital artists or non non digital artists. That's important for for attribution and distribution of value right because we want people to who are contributing to be able to be rewired for it.
是的，是的，对于AI，我们可能需要确立相关规范，例如同意授权等。我认为这对于数字和非数字艺术家都非常重要。这对于归属和价值分配也非常重要，因为我们希望那些做出贡献的人能够得到应有的回报。
An interesting question that I don't think has a good solution right now but in the world where search is only kind of like a chat interface. What we want is kind of like the underlying creators of the of the content right if I if I pick up the website and before I was leaving because you know I was getting traffic on this website so I can do ads.
这是一个有趣的问题，目前我认为还没有一个很好的解决方案，但在搜索只类似于聊天接口的世界中，我们想要的是内容的底层创建者。如果我打开一个网站，并在离开前因为有流量而可以放置广告，那么我们需要知道这些内容的制作者是谁。
And if now the results of this website is actually shown on like a chat answer without mentioning me as a content creator. You know what's what's my incentive to create this content right so will people just stop building websites because they basically don't get the attribution or or or the reward for it. These are very very important questions I think we're just scratching the surface of what needs to be done for things like that to be resolved but very important questions.
如果现在这个网站的结果只是像聊天一样显示，没有提到我作为内容创建者。你知道我创造这个内容的动机是什么吗？所以人们会停止建立网站，因为基本上他们不会得到归属或回报。我认为这些是非常非常重要的问题，我们只是在涉及到解决这些问题时刚刚触及表面。
Maybe back there towards the top. So what are the modes that can be built on top of them there's there's data there's human feedback. There's maybe to some degree being good at prompting and are there any other methods that you feel startups can build modes.
可能是在顶部返回那里。那么可以在其上构建哪些模式？有数据、人类反馈，或者在一定程度上善于提示等。您觉得创业公司还可以构建哪些其他的方法？
That's a good question that depends a lot on you know your skills your background as a team what what you're excited about I think there's a lot a lot to be built around specializing for you know domain-specific domain-specific use case specific industry-specific hardware right.
那是一个很好的问题，它很大程度上取决于你所拥有的技能，以及你作为团队的背景，你对什么感到兴奋。我认为，专门针对特定领域、特定用例、特定行业相关的硬件进行专业化构建，是有很多事情可做的。
That's what I'm like most most excited about right kind of like trying to leverage some specific expertise or specific domain-specific kind of like problem that others bigger players are not going to be focusing on. As I said like for example biology chemistry time series all these domains where you don't see as much activity I think it's a good way to have in a way more more time as a startup to build your differentiation and your text stack to a point where you're not at the mercy of like 100 or start up select releasing exactly same thing as you did and get like losing losing your edge.
我最兴奋的是尝试利用某些专业特长或特定的领域问题，这些问题对于其他大型公司来说可能不是关注重点。比如生物学、化学、时间序列等领域，在这些领域中，你看不到太多的活动，我认为这是一种更好的方式，作为初创公司拥有更多的时间来建立你的差异化和技术堆栈，在这方面你不会受制于100个或更多的初创公司推出与你完全相同的产品，从而失去竞争优势。
So that that would be some of my recommendations but again I mean I've told about the story for getting face right how we started with a I time a Gucci and and the poor where we are. So you know one of the main things is just to you know start working start start building listening to what you're seeing as signals it's written on things and I'm sure you land on on something that you're excited about at some point.
这是我建议的一些内容，但是我想说的是，我已经讲过我们是如何从一个Gucci和穷困潦倒的开始走向成功的。因此，最重要的事情之一就是开始努力工作，开始建造并倾听你所看到的信号。相信你最终会找到激动人心的事情。
Okay great. I think I'm unfortunately out of time.
好的，太棒了。我觉得不幸的是，我时间已经用完了。
I think for the next 55 minutes, feel free to hang out here.
我认为在接下来的55分钟里，你可以自由地待在这里。
Thanks to stripe for being such a gracious host.
感谢Stripe公司招待周到，让我们有一个愉快的经历。
So, this is also an opportunity for you to meet other folks who are very excited and working in a...
所以，这也是一个机会让你遇到其他热情高涨并努力工作的人...
I thank you again as well. The flame is awesome.
我再次感谢你。那火焰太棒了。
Thank you so much for making that tonight.
非常感谢你今晚的付出。