第1集：Hugging Face首席执行官Clem Delangue谈AI领域开源与闭源的未来

这里是《无监督学习》，一档探讨快速发展的AI领域最新动态及其对全球商业影响的播客。我是Jacob Efron。今天与Jason Warner共同主持，我们非常激动地开启《无监督学习》的首期节目。首位嘉宾是Hugging Face的CEO Clem，这家公司正处于AI社区的核心位置。我们进行了一场极具启发性的对话。

This is Unsupervised Learning, a podcast about the latest in the rapidly developing AI landscape and what it means for businesses in the world. I'm Jacob Efron. I'm joined by Jason Warner, and we're excited to kick off the inaugural episode of unsupervised learning. First guest is Clem, the CEO of Hugging Face, a company really at the epicenter of the AI community. And we had a really interesting conversation.

我们探讨了Hugging Face平台的使用趋势、机器学习中闭源与开源的未来走向、企业AI团队将如何演变、AI安全的未来发展，甚至听到Clem将大型基础模型比作一级方程式赛车。请务必收听完整内容，这个类比非常有趣。尤其令人兴奋的是与曾任GitHub CTO的Jason共同主持，他与Hugging Face显然有许多共通之处，聆听他与Clem就开源优势及社区建设展开的精彩辩论真是享受。

You know, we touched on trends who's using the Hugging Face platform, the future of closed source versus open source and machine learning, how enterprise AI teams will evolve, future of AI safety, and even heard Clem compare the large foundation models to Formula One cars. So definitely stay tuned. It's a it's an interesting point. But a particularly fun one to do with Jason who was the former CTO of GitHub. Obviously, a ton in common with Hugging Face, and it was just a blast hearing him and Clem go back and forth on the benefits of open source, effectively building community.

相信听众会非常喜欢这期节目。闲话少叙，现在请收听第一期。今天我们非常荣幸地邀请到堪称完美的首位嘉宾——Hugging Face联合创始人兼CEO Clem。Hugging Face如今已成为机器学习生态系统的关键组成部分，本质上构建了机器学习界的GitHub，开发者们在此创建、发现并协作开发ML模型、数据集和应用程序。

So I think folks are really gonna enjoy this one. Without further ado, here's episode one. Today, we're excited to kick things off with a pretty much perfect first guest. Can't can't think of a better person than Clem, the cofounder and CEO of Hugging Face. Hugging Face is a is a pivotal part of the ML ecosystem today, really building the GitHub and machine learning, a place where developers create, discover, collaborate on ML models, datasets, and applications.

Hugging Face还在此基础上提供了一系列托管服务。公司已融资1.6亿美元，最近一次估值是5月份的20亿美元，投资方包括Lux、Editions、Sequoia和Co2等知名机构，目前有超过10,000家企业使用该平台。在深入探讨这些话题时，实在想不到比Clem更合适的开场嘉宾了。Clem，非常感谢你的到来。

And Hugging Face also offers a a set of hosted services on top of this. The company has raised, know, a $160,000,000, was last valued at $2,000,000,000 in May. Some great investors including Lux, Editions, Sequoia, and Co2, and over 10,000 companies using the the platform today. So as we dig into these spaces, you know, can't think of a better first guest. Clem, thanks so much for coming on.

谢谢邀请。

Thanks for having me.

那么作为开场，能否请你分享下Hugging Face的创立背景及当前业务？据我所知，这其中有个相当有趣的转型故事。

Well, you know, to kick things off, I think would just be great to get a bit of background on how Hugging Face came to be and what you do today is. As I understand it, there's a pretty interesting pivot story

没错没错，就像创业公司常有的经历对吧？

Yeah. Yeah. Yeah. As it sometimes happened for for startups. Right?

初创Hugging Face时，我们三位联合创始人分别是科学家、工程师和产品专家。当我们讨论到对AI与机器学习的共同热情时，就在思考：既能满足科学挑战性又充满乐趣的事情是什么？

When we studied Hugging Face, we were three cofounders. Right? We have a scientist, an engineer, and someone more on the product side. And when we kind of, like, talked about our shared passion for AI and machine learning, we were like, okay. What can we do that is both kind of, like, scientifically challenging but fun at the same time?

最终我们最初开发了一款类似电子宠物AI伙伴的产品，可比作更有趣版的ChatGPT。这个项目我们实际运营了三年多，首轮融资也是基于此。后来就像常发生的那样，当我们开始分享开发成果时，朋友和其他公司纷纷询问实现原理。

And we ended up starting by building this kind of, like, Tamaguchi AI AI friend, kind of like a ChatGPT, but, much more entertaining than than ChatGPT. And we actually did that for a bit more than than three years. That's that's what we raised our first rounds of of funding on. And as it sometimes happen, we kind of, like, started to share a little bit what what we were building. Some of our friends, some other companies started to ask us like, oh, how how do you do that?

这个系统表现相当出色——响应迅速且能讨论多元话题，这得益于使用了多种数据集和模型。当我们开源部分技术后，用户量和采用率立刻呈现爆发式增长。

It's it's working pretty well. It's it's really fast. It's able to talk about a lot of different topics. So using a lot of different datasets, a lot of different models. And when we started open sourcing some of that, the usage and the adoption, really blew up right from the right from the start.

对吧？就像企业开始运用这个平台，研究人员用它来分享模型、开展协作。正是这一点真正推动我们从最初的理念和产品形态，发展到如今的状态——如你所说，我们已成为企业构建机器学习、将其整合进产品、工作流或功能中最常用的平台。

Right? Like, companies started to to use this. Researchers started to use it to share their models, to collaborate. And that's really what what took us from our first kind of, like, vision and and product, to what we are now, which is, as you said, the most used platform for companies to build machine learning and integrate machine learning into their products, their workflows, or their features.

我们可不能让你跳过'抱抱脸'这个名字的故事。快说说当时的思考过程。我记得上次见面问你这个问题时，你的回答让我印象深刻。现在给广大听众讲讲这个命名的由来吧。

We we can't let you answer that question without talking about the hugging face. So so so walk us through the thought process. I will last when I remember meeting you and asking you that question, I I love the answer. So let's tell a broader audience what you're doing there. Yeah.

创业初期，我和联合创始人之间有个持续的笑话：我们要成为首家以表情符号而非三字母代码上市的公司——就像纳斯达克上市那样。我们铁了心要用表情符号，后来因为太爱'拥抱表情'这个符号，干脆就决定用它了。最初以为这个名字可能只用几周或几个月，但后来社区开始到处使用它——社交网络发帖时加上去，印在T恤或周边产品上。

The when we when we started the company, a running joke with with my cofounders was that we wanted to be the first company to go public with an emoji instead of the three letter ticker, you know, like when when you go on the Nasdaq. So we absolutely wanted an emoji. And then we were just using and loving so much the hugging face emoji that we were like, okay, let's let's go for that. At the beginning, we thought we would keep the name maybe for a few weeks, for for a few months. But then the community started to use it everywhere, to put it in social network when we were posting things, to add it to to their t shirts or to to other swag.

发展到后来我们意识到：绝对不可能改名了。必须保留这个名称，这就是为什么我们至今仍叫'Hugging Face'，并用拥抱表情作为logo。

So much so that at some point we were like, there's no way we're gonna change this. We we need to keep it, and and that's why we still have this name, hugging face and this emoji, the hugging face emoji as our logo.

Matt，这故事太棒了。我记得在GitHub工作时，有次和人讨论各技术生态，提到机器学习领域时说'当然得用抱抱脸'，所有人都以为我在开玩笑，连连摆手说'不不不'。

Matt, I love it. I remember even when I was still at GitHub, I'm talking to some folks and they're asking about various things in various ecosystems. I'm like, oh, of course, in the ML space, got hugging face. And everyone thought I was joking. Like, no, no, no.

直到我说'Hugging Face是真实网址，你们去查'，他们后来回来才信服。虽然名字特别，但确实厉害。

Hugging face. It's an actual web address and everything. It's like, just go do it. And then they, they came back and they're like, okay, we get it now. Let's talk about that name, but awesome.

我们准备了大量深入问题，不过好久没聊了——自从ChatGPT爆红，可以说给非机器学习界扔了颗炸弹（虽然ML圈觉得'我们早玩这个了'），这段时间你们在Hugging Face的工作生活有什么变化？

And we got a whole bunch of questions that we have here that we want to go into in-depth, but it's been a little while since you and I caught up and I got to ask you, what's life been like for you in Hugging Face since ChatGPT blew up? And basically set the non ML world on fire. Feels like the ML world is basically kind of like, hey. We're we've been here. We understand this type of stuff.

突然间连我奶奶、康州老家的叔叔辈都在谈论ChatGPT这类东西。你们团队经历这种热潮后状态如何？

But all of a sudden, Chad GBT, my grandmother, you know, my uncle in Connecticut, they're all talking about it type of deal. Like, what what's life been like for you guys since that happened?

说实话非常精彩。我们对技术能力本身并不惊讶——毕竟深耕这个领域多年，亲眼见证技术发展到今天。但它的确释放了全民对AI的想象力。现在和CEO、工程副总裁或产品经理交流时，发现他们更懂AI能力了，且充满创意点子。对整个领域来说，这种全民认知突破将极大促进AI应用创新。

It's been amazing, frankly. We we were not so much surprised by the technical capabilities of it just because we've been working in the field for for some time, and and we really saw the progression to get to where we are now. But I think what it did is really unleash the creativity of everyone when it comes to thinking about AI and machine learning. So I feel like now when I talk to CEOs of companies, when I talk about VP Engineering, when I talk about product people, they understand much more the capabilities of AI, and they have tons of ideas. So, in general, for the field, I think it's going to be very positive in unleashing this creativity for everyone to see that they can use AI and machine learning for every single thing that they do.

平台数据也印证这点：ChatGPT发布后，Hugging Face的使用量呈现爆发式增长。

And we've actually seen that with usage of the platform. Since the launch of ChatGPT, actually the usage of Hugging Face has exploded.

想象一下，尤其是像卡帕西（Karpathy）发布他那些‘嘿，做个聊天GPT’之类的东西，还有所有这些指令集。感觉就像，你知道，作为比我还深入这个领域的人，虽然我也涉足已久，但这感觉像是大众市场真正爆发的临界点。2000年或2017年那波AI浪潮时，这次完全不同。现在感觉像是‘不，我们已经跨过那个阶段了’，它已成为时代精神的一部分。

Imagine it would, particularly with things like even Karpathy putting up all his, Hey, make a chat GBT, that sort of thing, and like all these instruction sets. Feels like, you know, as someone, you've been obviously in this deeper than me, but I've been in it for a long time as well, but it feels like this is the actual tipping point for mass market type of stuff. 2000 or 2017, when we had that last wave of type of AI whatnot, this is different. This feels like it's like, Nope, we're past that now. We're, it's in the zeitgeist.

如今我们已身处一个全面拥抱AI的世界。

We're an AI forward entire world at this point now.

是啊，我们从边缘走向主流了。知道吗？光是Hugging Face平台上共享的模型就超过了25万个。一月份我们的页面浏览量突破5000万——几年前谁能想到一个机器学习平台月浏览量能达到1500万呢？

Yeah. We moved from niche to mainstream. You know? I mean, when when we look at Hugging I think we we crossed 250,000 models that have been shared on the platform. I think in in January, we've had more than than 50,000,000 page views on Hugging Face, which, you know, a few years ago, you would have said that, you know, a machine learning platform would get, like, 15,000,000 page views a month.

当时人们肯定会说不可能，因为机器学习确实是个小众领域。

People would have told you, like, no way because because machine learning yes. Is a niche and Yes. And

你提到用户量激增，有没有注意到用户类型的变化？随着主流化进程，来Hugging Face的人群或你接触的对象有什么不同吗？

and you mentioned kinda like the the users, you know, obviously, you've seen a a spike in usage. You know, any any difference in kinda the type of people that are coming to Hugging Face or or that you're talking to, you know, that you that you've noticed since kind of this mainstreaming?

有趣的是应用领域和使用场景比以往更广泛了。这也与Hugging Face平台的扩展同步——几个月前还集中在NLP、文本或特定任务（比如Stable Diffusion带火的文生图），现在由于工具通用性增强，机器学习正被应用于文本、音频、时间序列等各个领域。

I think what's interesting is that it's getting broader in terms of, like, the domains and the use cases than it was before. And I mean, that that follows the expansion of of the Hugging Face platform too. You know, I I feel like a few years, a few months ago, it was mostly concentrated on, some subset of tasks, right? More like NLP, more more around text or some other specific task, for example, text to image, right, with with stable diffusion that that we've, all heard of. But now I feel like because some of these tools are quite generalists, people are applying machine learning to a whole range of domains from, you know, text to, audio, you know, time series.

我们看到越来越多公司用机器学习处理时间序列（如Uber的预计到达时间），还有生物学、化学、代码等领域。机器学习的应用范围和关注维度确实大幅拓宽了。

We're starting to see a lot of companies using machine learning for time series like your Uber ETA, for example. We're starting to see a lot of biology. We're starting to see a lot of chemistry. We're starting to see a lot of code. So it really kind of like made the scope and the domains of usage of machine learning and interests broader than it was before.

我特别好奇这点，因为你们处在独特的交叉点。就像当年在GitHub能观察整个软件业演变——见证JavaScript崛起或新语言框架涌现那样，现在你们就是机器学习界的GitHub。

I'm super curious about this too, because obviously you sit at such a unique intersection of all the things. It's like, you know, we talked about Huggy Paces, the GitHub for the ML space, but obviously like sitting inside GitHub, can watch the entire software industry evolve and understand what was happening. Was fascinating, right? You can see the rise of JavaScript at one point, or like which new language or framework or whatever was kind of emerging. You can see all that.

但换个问题：我们显然已进入后LLM时代，你认为整个生态会如何演变？开源模型在未来将扮演什么角色？

It's amazing. But now let's ask a different question here. I would say that we're living in a post LLM world at this point and all that sort of stuff. How do you see the entire ecosystem evolving? Where does open source models play in the future of this?

OpenAI对模型权重等有明确立场，但我相信代码渴望开源，模型也会如此。Hugging Face如何看待这个问题？特别是克莱姆（Clem）你的观点？

You know, OpenAI has a very specific stance on what they want to do with their models, their weights, things of that nature. But, you know, there's a as I believe code wants to be open, believe models are going want to be open too, and all that sort of stuff. How do you view this? What does what you know, how does Hugging Face think about this, but specifically Clem, too?

首先，我认为有必要提醒大家，当前所有机器学习都建立在开放科学和开源基础上。对吧？我认为这是过去几年该领域进步的首要驱动力。若非如此开放，我们可能需要十年、二十年甚至五十年才能达到今天的水平。

So, first, it's, I think, useful to remind everyone that all current machine learning is based on open science and and open source. Right? I think that's the number one driver for the progress of the field in the past few years. Right? If it wouldn't have been as open as it's been, maybe it would have taken us ten years, twenty years, fifty years to get to where we are today.

因此在宏观层面，我认为重要的是记住：科学和开源越开放，我们作为领域的发展速度就越快。这确实是我们应该在生态系统中追求和培育的目标。当然，有些公司或组织基于商业目标选择开源与否也很自然——就像GitHub上并非所有公司都采用公开模式一样。

And so at the, you know, macro level, I think it's important to remember that the more open science and the more open source things are, the faster we can progress and evolve as a field. So that's really something that we should aim at and foster, I think, in the ecosystem. Now, that being said, you know, it's also fairly natural that some companies or some organizations want to open source or not open source based on also their, you know, commercial goals. Right? And that that was the same thing in on GitHub, Not not all companies were using GitHub in in the public mode.

什么...呃我

What Yeah. I

我想说的是，你提出了非常关键的一点——这一切都建立在已公开的知识和偶然实现上。模型就在那里。对于非专业人士而言可能不了解，这里的很多价值其实早已存在于学术领域，写在论文里。Hugging Face存在的意义，就是让这些知识以更易获取的形式呈现。

was gonna say, I think it's a really important point that you bring up, which is that it's all based upon public knowledge that's out there already in the incidental implementation. So the model's out there. It's one of those things where, you know, we talk about things that if you're not in the space, or maybe if you don't fully understand that a lot of the value that's derived here is academically out there already. It's written in papers. It may not be accessible in the format that we thought of it in other domains as accessible, That's what Hugging Face exists for, is to make that stuff more accessible for folks out there.

OpenAI、Anthropic等大型语言模型的卓越工作之所以能被大众获取，正是GitHub和Hugging Face对这个领域至关重要的原因。就像你想了解Linux内核可以查看代码库，想研究OpenAI的基础架构同样可以追溯其开源根基。

And the fact that OpenAI is doing this remarkable work where Anthropic or other large language models are doing this incredible work, that knowledge is available to the folks. This is exactly why GitHub and Hugging Face have been so important to the space is most things exist on GitHub. If you wanna go see how the Linux kernel works, you can go look at it. But if you wanna the basis for what OpenAI has been building upon for a while, you can go look at it and figure it out.

没错。我认为开放透明对机器学习将比传统软件更重要。因为机器学习的本质是科学驱动的——它依赖于模型架构、数据方法的科学进步。

Yeah. Yeah. And I think, the openness and transparency is going to be even more important for machine learning than it's been for traditional software. Because if you think of machine learning compared to traditional software, to me the main difference is that machine learning is science driven. It's really driven by scientific improvements of the architectures of the models, of the data approaches.

从历史上看，科学始终是开放协作的事业。科学家们希望通过发表论文推动科学进步，他们的职业发展也基于这些公开成果。

And historically, science has always been an open and collaborative endeavor. Right? I mean, scientists, they want to contribute to the advancement of science. They wanna publish research papers. They are actually evolving their careers based on their publications.

是的。因此我希望机器学习能比传统软件更开放协作。不过同时，专有或闭源方案也有存在空间——它们为机器学习创造了新的分发模式，这种现象其实一直存在。

Yep. And so I think machine learning, hopefully, is going to be even more open and more based on open and collaborative approach than than than traditional softwares. Yeah. But at the same time, I think there's there's room for proprietary or closed source approaches, especially as, you know, I think they create new and different distributions for for machine learning. And I think it's it's something that's always always happened.

就像软件领域有Squarespace、Wix这类简化技术搭建的平台，也有GitHub这样更开放的企业开发平台。Elasticsearch和Algolia等搜索公司也分别代表不同模式。这两种途径都在推动领域发展，并将长期共存。

Like, if you think about, you know, software, you have Squarespace or Wix or like platforms like that that are kind of like making it easier to to build technologies. And then you have GitHub, is kind of like more open and and more kind of like a platform for companies to to build. Or you've always had, you know, like the Elasticsearch on, on one side and, and the Algolia or like other more like close to our search, search companies on, on the other side. So I think, you know, both approaches are a part of the, of the ecosystem are contributing to moving the field forward, and will always kind of like a coexist. And the interesting thing is that on on the Hugging Face platform, we have, 250,000 models.

有趣的是，Hugging Face平台上25万个模型中，恰好半数私有化。这种情况已持续两年。最终，与其争论开源与专有孰优孰劣，不如说二者将始终并存——根据企业用例满足不同需求，提供不同工具。

Exactly half of them are private. So it's it's it's, and it's been it's been like that for for quite a while. It's it's been like that for for the past two years. And so, ultimately, you know, it's, it's good headlines to think, okay, open source is going to kill proprietary or proprietary is gonna kill open source. The truth is that you'll always have both that are kind of like feeling different needs and providing different different tools for for companies depending on their use cases.

确实。这是个非常有趣的观点。我的意思是，我记得你之前曾将OpenAI和Anthropic这类封闭模型比作这个领域的F1赛车。显然，你一直站在推动开源基础模型研发的最前沿，对吧？

Yeah. It's a it's a really interesting point. I mean, I think I've heard you before refer to these kind of closed models like OpenAI and Anthropic as, like, the Formula One cars of of the space. And, you know, I I I think, you know, you've obviously been at the forefront of leading some of the open source efforts to kind of come up with foundation model efforts. Right?

比如BigScience的Bloom项目。现在似乎出现一个趋势，像OpenAI和Anthropic这样的公司会不断在算力上投入更多资金来打造尖端模型。我想请教两个问题：第一，你认为开源社区在纯算力投入方面的极限在哪里？第二，这对F1级模型与开源替代品的适用场景意味着什么？

Like big sciences, Bloom. And, you know, one thing that seems to be happening is it seems like some of these players like OpenAI and Anthropic are gonna just throw more and more money at compute, right, for like the cutting edge model. And I wonder how you think about like one, you know, the limits of what the open source community can fund on just the pure compute side. And then two, kind of the implications of that for when are these Formula one models gonna make the most sense versus, like, the the open source alternatives?

没错。我把这些超大规模模型比作F1赛车，是因为它们本质上是优秀的营销工具。就像F1赛车本身也是绝佳的营销载体，它们确实令人印象深刻。

Yeah. The reason why I compare these, like, very large models to Formula one is because they're they're good marketing. Right? The the same way Formula one cars are are really good marketing. They are very impressive.

对吧？就像F1赛车令人叹为观止。但现实是大多数人上班并不需要F1赛车，他们需要的是价格合理、维护成本低的普通汽车。这正是我们观察到的现象。

Right? Like, the the same way Formula car is is really impressive. But the truth is, for most people out there, they don't need a formula one car to go to work. They need kind of like a good normal car that is not going to cost millions of dollars, not only to build, but also to to run and and and to drive. And that's that's what we're seeing.

某些场景下这些大型专有模型确实有用武之地。比如谷歌可能需要一个全能模型来应对各种查询，这无可厚非。

You know? Probably, kind of like these large proprietary models are going to be useful for some use cases. Let's say you're Google, you know, and you want like a very generalist model to be able to answer everything. That's that's great to have a model like that. Right?

但如果你只是想在网站上实现搜索功能，其实需要的是更小巧、更快速、经过专业数据训练后更精准且运行成本更低的模型。没人愿意为每次预测支付10美分。在Hugging Face平台上，尽管我们托管着从几百万到1800亿参数的各种模型（包括你提到的Bloom等顶级语言模型），但当前使用量最大的其实是5亿到100亿参数区间的模型。

But the truth is if you want search to work on your website, you actually want a model that is like much smaller, much faster, usually more accurate if you specialize it on your own data and just cheaper to run, right? You don't want it to cost 10¢ for every prediction that it makes. So if you look on the Hugging Face platform, what's interesting is that, even if we have models from, you know, a few million parameters, which is considered small in in today's world, up to 180,000,000,000 parameters, right? We on the platform, we host, like, the largest language models out out there, like you mentioned Bloom or or others. Actually, most of the usage today is for models from 500,000,000 to 10,000,000,000 parameters.

因为这些模型更快、更经济，经过专业数据训练后通常更精准。归根结底，这取决于具体应用场景——不同问题需要不同类型的模型来解决。

And it's just because, you know, they're faster, cheaper to run, usually more accurate if you train them on your data. So again, I mean, I think it's, it's a matter of like use cases. You'll want to use one sort of model for some use cases, others for other kind of use cases and they solve kind of like different problems, would say.

确实。我认为大多数机构并不需要全能模型，尤其是考虑到这类模型的复杂性。大型企业可能会说：'我们甚至无法预知这个模型会输出什么答案'，所以他们需要更专注、更可控的解决方案。就像编程语言的发展历程，模型领域也会出现多种模式——从F1赛车到轿车再到面包车，各种形态都会并存。

Yeah. I can't see most organizations in the world wanting the everything model, particularly with the complexities that come with those types of models at some point, particularly like these large enterprises saying, like, hey, we don't actually know the output of some of the questions that might be asked to this. And so we wanna get more specific, we wanna, like, kind of constrain some of those. And just like with, you know, just code in general, but, like, models specifically here, a bunch of different modalities are going to emerge and adapt. And, you know, they're gonna, like you said, the Formula One car to the sedan to the minivan and all that sort of stuff.

接下来我要问个略带争议的问题：Clem如何看待大语言模型？它们应该开源吗？当OpenAI等公司声称某些模型可能过于危险时，你如何从哲学层面看待这种论断？

There's gonna be a bunch of different things that exist in the world that look like Now, I'm going ask you a semi controversial question. I'm curious what your answer would be. So, what does Clem think about large language models? Should they be open? And philosophically, you know, when companies like OpenAI argue that some of the models might be too dangerous, how do you think about that whole statement?

我认为应该在安全前提下开源。从伦理角度分析，Hugging Face团队成员Irene Soleiman（她曾在OpenAI主导GPT-2的开源工作，是模型安全发布领域的权威专家）的研究表明：通过开源、透明和包容的方式发布模型，长期来看对人类更安全。简单来说，闭源会导致权力过度集中，这本身就会制造巨大风险。

Yeah. I think they should be safely open because if you should look at kind of like the underlying ethical reasoning. So part of the Hugging Face team, have, someone called Irene Soleiman, who was actually at OpenAI before and and led the release of GPT-two in open source at OpenAI before, who is one of the worldwide experts in terms of safety of releases of models. And when you look at the theory behind it, and when you try to avoid risks, you actually realize that open sourcing, open releasing, and kind of like being transparent and inclusive with these models is actually safer for humanity in the in the long term. An intuitive way to understand that is that by not releasing, not open sourcing, you actually create very big challenges in terms of concentration of power.

最终结果是，能够构建和理解模型的通常是大型科技公司或大企业。而社会其他群体则无法理解进而掌控这些技术。对吧？公共机构和政府将因缺乏理解而难以制定合理法规。被边缘化的群体也无法对其中存在的偏见——那些确实存在的偏见——表达他们的担忧。

And you end up with, organizations who are able to build, understand models, which are usually big tech or big companies. And then you have the rest of society who is not able to understand and then control this. Right? You end up with public organizations, with governments unable to understand and create regulation that makes sense. You end up with underrepresented populations who are not able to voice their concerns about some of these biases, the biases that you do find in there.

这就造成了认知鸿沟，其危害性远超没有这种鸿沟的情况。以科学为例——纵观科学发展史，它始终是开放协作的。科学家发表论文，研究成果被学界和社会吸收融合，这才是技术发展的最安全路径。反之，若将技术研发封闭起来，就会形成少数垄断机构与社会大众之间在理解力和掌控力上的巨大断层。

So you create a gap, which makes it actually much more dangerous than if you don't have this gap. And that's why, for example, science, right? If look at science in general, it's always been open and collaborative, you know? Like the scientists, they publish their research papers. It's kind of like integrated by the community and by society because that's kind of like the safest way to progress technology versus kind of like building this behind closed doors and keeping them behind closed doors where you create this, gap of understanding and ability to to control between a few organizations that are monopolistic and and kind of like keeping control and and the rest of society.

这恐怕是个值得深入探讨的长话题。我的观点与你在哲学层面高度契合——这也是我们总能在此类话题上达成共识的原因。我常试图将其概括为：唯有将事物置于阳光之下，才能真正洞悉本质。历史上我们因盲目信任X（X代表任何事物）而遭受的教训还少吗？

This is probably one of those long topics that we could get into. Though, I have a particular view, which I think is philosophically aligned with yours, which is why we probably get along well on topics such as these. But I always have tried to summarize this down. There's a saying, which is, You want put something out into the light, you'll understand what's going on, like that sort of thing. How often have we been burned in the past as a society in the world for blindly trusting X, where X is whatever it is?

这近乎是条普世真理：对X的盲目信任终将反噬我们。无论是机构、程序还是流程，概莫能外。每当与人讨论此事，我总以美国国税局为例——这个完全无法窥探内部运作的巨型黑箱系统。

And it's almost a universal truth where, if you put your blind faith trust in X, it bites us. Whether that be an institution, a program, a process, or whatever, it doesn't work. Whenever I run into somebody who's having this conversation with me, I'm like, great. Like imagine, you know, the IRS is one of these. The IRS is this one massive, black box where you have no idea what's going on inside this thing.

我们对其内部机制一无所知，这种挫败感何其常见？虽然很多人辩称AI领域不会重蹈覆辙，但我绝不相信。历史从未证明对人类X的盲目信任能带来长远福祉，这种信任终将瓦解。

You have no idea what's happening. And how often do we get frustrated by this? Now, a lot of people argue that, well, won't happen in the AI space, or the model space, because these companies don't have that same sort of structure. I just don't believe it. The history has never once proven that blind faith trust in X will work out in the long run for humanity, it always degrades.

因此我们作为代码起家的社会，应对之道就是开放系统，杜绝个人或实体垄断。这简直像是二十五年前开源软件争论的重演。所幸世界上还有你们这样的人，学术界和科学界也在推动变革。

So therefore, our reaction to that, as a society that started in code, was to open the things, so that no one person or one entity can do that. And it just feels like we're having the exact same discussion that we had about open source software twenty five years ago, and we're doing it again. But, look, thankfully, people like you exist in the world, and the academic and the scientific community is pushing it forward too.

确实。我很好奇你如何看待这场讨论的走向？显然我们现在还处于早期阶段。你认为未来两三年会如何发展？更宏观来看，这个领域过去一年已经经历了翻天覆地的变化。

Yeah. I mean, I'm curious, like, how do you see the the conversation? Obviously, we're in in early days of that conversation. You know, how do you see it evolving over the next, you know, two, three years? And and more broadly, obviously, the space has just gone through such transformational change this past year.

你觉得五年后我们会处在什么位置？

Like, where do you kinda see us in five years?

这是个有趣的问题。我们已学会不过度预测，因为该领域的发展速度屡屡超出预期。这种现象堪称奇迹——历史上从未有哪个领域在如此成熟主流化的同时，仍保持这般高速发展。这带来了前所未有的独特挑战。

It's an interesting question. We've learned to stop trying to predict things too much because we've been proven wrong so much with the speeds of the evolution of the fields. It's kind of like an amazing thing. I I don't think you've seen in the past a field that is becoming so mature, so mainstream, and still moving so fast at the at the same time. It creates a very, very unique challenges that we haven't seen the in the past.

我们现在逐渐认识到：机器学习是构建传统技术的新范式。正如Andres Carpathi的比喻，软件1.0是过去十五二十年的开发范式，而现在我们正进入用机器学习重构传统技术的新纪元。五年后，每间科技公司都将基于机器学习进行开发。

I think what we're starting to understand now is that machine learning is a new paradigm to build old tech. You know, like it's this analogy from Andres Carpathi, you know, like software one point zero was like the first paradigm to build software. That's what we've been doing for for the past fifteen, twenty years. Now we're entering a new paradigm, right, which is using machine learning to build old technology. And so, think in five years, every single technology company is going to build with machine learning.

事实上，到那时我们可能甚至不会称其为AI或机器学习，因为它将变得无处不在，成为构建传统技术的默认方式，以至于你都不需要专门为它命名。

As a matter of fact, maybe we won't even call it AI or machine learning then because it's gonna be so ubiquitous, and it's gonna be really kind of like the default way to build old technology that you won't even need kind of like a a name for it.

没错。看起来你们已经开始见证——显然最近几个月ChatGPT问世后，许多新兴行业或人们纷纷探索应用场景。我很好奇Hugging Face在这些新场景中扮演什么角色。比如你们聘请机器学习工程师专注医疗健康和生物科技领域，这让我觉得特别有意思。

Right. And it seems like you're you're starting to see, obviously, a lot of it seems like even the last few months post chat GBT, a lot of, you know, newer industries or folks kind of come and explore use cases. You know, I'm I'm curious what then role Hugging Face plays around some of those new use cases. Like, for example, I thought it was really interesting. You you know, you hired a machine learning engineer to kind of focus on health care and biotech.

肯定有很多不同垂直领域正在探索这些模型。你们如何考虑在帮助每个特定垂直领域时的定位？是通过社区互动，还是为这些市场提供定制化产品？你们是怎么思考这个问题的？

I'm sure there's lots of different verticals of of which are, you know, exploring these models. You know, how do you think about where you guys play in helping each of those, you know, specific verticals? You know, is it kinda just community engagement, some set of, like, tailored products for those markets? How how have you been thinking about it?

首先值得注意的是，我们看到所有领域都比以前更加同质化了。过去它们是割裂的——有NLP、计算机视觉、音频处理、生物ML。现在由于技术层面开始采用相同架构...

Well, I mean, the first interesting thing is that we're really seeing the whole fields and all the domains getting more homogeneous than they were before. Before it was very siloed. Right? You had NLP, you had computer vision, you had audio, you had, ML for biology. Now, because technologically speaking, you're starting to use the same architectures for for all.

也就是Transformer架构，这些领域间的壁垒正在被打破。我们在Hugging Face上看到越来越多人分享生物、化学、时间序列模型。作为公司，我们的目标是提供基础平台，帮助所有企业完成从传统软件到机器学习的范式转换。这意味着要凝聚分享模型的研究者社区和使用这些模型的企业。

Right? Namely namely transformers. You are starting to kind of, like, break the barriers between all these domains, and and that's what we're seeing on Hugging Face with, more and more people sharing biology, chemistry, time series models. And our our goal as a company is really to provide the right foundational platform for all companies to be able to do this paradigm switch, right, from traditional software to to machine learning. And so that means kind of, like, bringing on the community, right, of researchers sharing models and companies using these models.

这需要调整我们的工具。比如平台上很受欢迎的Spaces功能（轻松创建演示的能力），分子预测模型所需的Spaces与文本生成的就会有所不同。我们需要为新兴领域适配更多功能。而最困难的是——我认为这正是多数企业选择我们平台的主因——构建经得起未来考验的正确抽象层。

That means adapting our tools. So, for example, one feature that is very popular on the Hugging Face platform are spaces, right, the ability to create demos very, very easily. And the spaces for molecule prediction, models are going to be a little bit different than than the spaces for text generation, right? So we need to adapt and create more capabilities, for all these new domains. And then the hardest thing, and actually I think that's that's the main reason why most of the companies are are using our platform, we need to build the right abstractions for it to be future proof.

企业使用Hugging Face平台时，希望确保今天的建设六个月内不会过时。所以我们重点思考的是：如何构建能持续五年、十年、十五年的抽象工具？让企业既能享受新模型新架构的进化红利，又不必推倒重来。

Right? I think what companies want to do when when they use the Higginface platform is to make sure that what they build today is not going to be obsolete in six months. Right? So something we're thinking a lot about is, you know, how do we build the right abstractions, right tools that are going to be used not only today, not only in one year, but in five years, in ten years, in fifteen years, so that companies can take advantage of the evolution of the new models, of the new architectures, but at the same time, not get lost on them and have to start from scratch over and over again.

你们如何看待这个问题？恕我直言，这确实是当前领域的关键议题。现有最先进模型存在的情况下，很多人都在围绕GPT-3做提示工程优化。

How do you think about that? I mean, I'll shamelessly ask because I think that's that's such a big question in the space right now. Feel like there's you you have the existing state of the art models, and I feel like so many people are optimizing around, okay. Here's how GPT three works today. Let me get really good at prompt engineering.

谁知道三五年后这还是否是模型的组成部分？你们如何区分哪些是无论底层模型如何变化都重要的模块，哪些可能只是阶段性无需过度关注的事物？

Who knows if that's gonna be a part of models in three, five years? Like, how do you think about, I guess, what are like no matter what changes happen in the underlying models, these are blocks that are important versus, you know, maybe this is just kind of a moment in time thing that we don't need to focus on as much?

首先要把这个问题置于比传统范式更优先的位置——这源于我们六年前就开始的思考。比如观察我们开源库维护者处理issue和PR的方式，他们常在稳定性与追新之间权衡。其次，我们成功的秘诀在于贯通从尖端科研到商业应用的全链条认知，这对初创企业来说很不寻常。

So first, you think about it and you make it a priority much more than in the previous paradigm, I think. And that comes from our heritage of, you know, having started to think about this like six years ago now. And for example, if you look at our open source libraries and if you look at like the main maintainers of the libraries and how they reply to comments, how they prioritize the different pull requests, the different issues, they actually talk about this a lot and are doing a lot of trade offs in terms of like making it like future proof and stable versus jumping on every single trend and breaking everything, making a lot of breaking changes and things like that. Then the second thing is, and I think that's one of the things that made us successful is having the full range of understanding from very, very specialized science to commercial usage of it. It's something kind of like unusual for for startups.

我认为很少有初创企业能具备如此全面的能力，这源于我们三位创始人共同构建的完整体系。这种架构让你既能着眼于长期的科学演进，又能为企业及终端用例进行开发。这恰好弥合了从科研到生产的鸿沟——对于机器学习而言，这种转化比传统软件更具挑战性。我想这两点是最核心的差异。最后一点是，Jason你在GitHub肯定深有体会：当你成为社区的核心枢纽时，社区本身就会成为强大的助力。

I think you don't have a lot of startups that have kind of like these full range of capabilities and it comes from our history of being three founders with like the full scope. But that allows you to have kind of like the long term science evolution in mind, while at the same time kind of like building for companies and for final use cases. So that kind of bridges the gap between science to, to production, which is also something that is a bit different for machine learning than it used to be for, for traditional software. So these would be, would be, I guess, the, the two main things. And then the last one is, and Jason, you've, you've seen that with, with GitHub, I'm sure, but when you've started to be this, centerpiece for, for the community, the community itself is, is helping you a lot on that, right?

因为社区会监督你的诚信度，当你搞砸了他们构建的内容或拖慢进度时，他们会直言不讳。所以依托社区力量、以社区为驱动，是确保当前工作价值并保持未来适应性的有效方式。

Because they're keeping you honest, they're, you know, telling you when you, when you fuck up what they, what they've been building, when you, when you slow them down. So like relying on the community and being community driven is also like a good way to make sure that what you're doing is, is useful right now and, and kind of like a future proof for most of your community members.

谈到社区，Hugging Face接下来有什么计划？你们已经实现爆发式增长，但如何平衡社区生态与未来商业化？你们会如何应对这个命题？

Speaking of community, what's next for hugging face in this way? And let's talk about, you know, your one already explosive growth, but two, how you maintain community balance and monetization at some point too. How do you, how do you approach that problem?

是的，我们为社区准备了许多激动人心的计划。正如刚才提到的，我们非常期待在生物、化学、时间序列等领域深化社区互动。同时正在开发更多科研工具，特别是将学术论文与模型中心里的模型、数据集及演示更紧密地联结起来——这方面存在巨大的创新空间。

Yes. We have a lot of exciting stuff for, for the community. As I was mentioning, we're really excited to interact more with the community in, in biology, in chemistry, in, in time series. That's, that's one. We're also working on building more tools around, science and science papers because, we feel like there's a lot of interesting things to do there, especially connecting more science papers to the models themselves that we have on the hub, the datasets and the demos.

我们还在支持初创企业生态。早期投资人Matt Hartmann（来自Betaworks）即将推出专项基金，用于扶持Hugging Face生态内的初创公司。社区层面动作很多。关于商业化与社区平衡的问题——

We also have interesting support of the startup ecosystem. So one of our earliest investors, Matt Hartmann, who was at Betaworks, is launching soon a fund that is dedicated to helping and funding startups of the Hugging Face ecosystem. So we're pretty excited about that. So a lot of stuff on the community side. Then when it comes to your question around, you know, how to do the trade off between, you know, community and monetization.

有趣的是这个问题尚未频繁出现在日常决策中，因为我们明确划分了社区贡献与商业用途的界限。例如企业开源模型属于创造社区价值的免费服务，而商业场景的大规模闭源使用则属于付费范畴。目前15000家使用我们服务的公司中，3000家已成为付费客户，从微软、彭博到Grammarly乃至小型创业团队都有涵盖。

The interesting thing is that this problem hasn't come so often yet on kind of like our day to day decisions, especially because I think we've been pretty clear on the delimitation between, you know, what are things that are really contributing to the community, right? So for example, companies sharing in open source their models, which we think is creating a lot of value for the community. And so that's something that we'll always keep in the free tier of platform compared to, you know, companies that are, for example, using without open sourcing, which are using, you know, at scale for commercial use cases, which are pretty clear kind of like paid offering. So, right now we have you said 10,000, we're actually at 15,000 companies using us. Most of these companies are using us for free, but we have 3,000 companies that are now customer of ours ranging from companies like Microsoft, like Bloomberg, all the way to small companies like Grammarly, for example, and and all the way down to kind of like smaller startups or or people starting with their with their machine learning projects.

确实。现在众多企业都在认真考虑采用机器学习方案，初创企业的理想就是成为微软、彭博这类公司的转型伙伴。我注意到有些公司鼓励用户从Hugging Face获取模型后转向他们的付费服务，而Stability等则选择与开源社区紧密合作。你们如何看待谁能最终赢得成为企业ML转型首选合作伙伴的资格？

Yeah. No. I mean, it it seems like the, you know, the dream for a lot of these companies is to be the one you mentioned, obviously, so many companies are now considering, you know, or or kind of seriously adopting their own ML use cases. And it feels like on the startup side, you know, the dream is to be able to be the partner for those companies, the Microsofts, the Bloombergs of the world, as they as they kind of go through this journey. And I'm curious, like, obviously, you know, people are using your models and they're and they're they're taking models off Hugging Face.

当企业从Hugging Face获取模型后，究竟谁有资格成为他们ML转型旅程的向导？是坚持开源路线的公司，还是那些主张私有化部署的服务商？

You have other companies that are kind of close to the open source community like Stability that are trying to, you know, work closely with companies. And then you have other folks that say, alright, take your model off Hugging Face, but then talk to us. We'll be the ones to kinda bring you on that journey. How do you think about ultimately, like, who earns the right to kinda be that partner to a Bloomberg or to, you know, some of these other companies around helping them do this ML transformation?

首先我认为会有多家非常成功的公司并存。鉴于机器学习正在成为技术建设的默认选项，就像如今存在多家千亿级科技公司那样，五年后出现多个千亿级ML公司也不足为奇——

Well, I mean, at first, I think there's gonna be multiple very successful companies. Right? Given kind of like what we're seeing with machine learning becoming the default to build all tech. The same way you have, like, a number of $100,000,000,000 plus companies, technology companies today. I wouldn't be surprised if in five years you would have like multiple $100,000,000,000 machine learning companies, right?

这完全取决于市场机遇的规模。本质上不必过度关注竞争，仔细观察会发现每家公司都有独特的市场定位和方法论。这种多样性反而会推动整个领域发展，加速机器学习的普及。我认为这完全是良性循环。

Just because of the size of opportunity. So fundamentally, don't worry too much about, you know, competition and what other people are doing. If you look very closely at what everyone is doing, they all bring something kind of like different to the table and have kind of like a different approach to the market. And I think overall it just brings the field forward and kind of like accelerates the adoption of machine learning. So I think it's all kind of positive.

实际上我们很幸运能与大家合作，对吧？从Stability这样的公司，到在Hugging Face上托管稳定扩散模型，再到OpenAI开源Whisper等模型。是的，因为最终如我所说，我们认为多家公司都有成功空间，它们都在真正推动优质机器学习的民主化。

We actually are lucky to be able to collaborate with everyone, right? From companies like Stability, hosting, stable diffusion on Hugging Face, OpenAI, open sourcing models like Whisper that are on Hugging Face. Yeah, because ultimately, as I said, we think there's room for multiple companies to be successful and all of them are really contributing to democratization of of good machine learning.

你们通过课程在教育人们AI知识方面做了大量出色工作，显然也与这些公司密切合作。我好奇的是，你认为最终企业及其AI团队会如何发展？今天我们看到一种形态，但也可以想象未来人们更专注于此，团队规模激增，或者在某些环节选择外包，当然更可能是中间路线。

You obviously do so much great work kind of teaching people about AI through courses you do. You obviously, you know, work closely with these companies. Curious, like, do you think things ultimately shake out with companies and and their own AI teams? We have one version of that world today. You could imagine a world in which, you know, people double down even more and these teams get massive or, you know, in some senses, it makes sense to outsource some of these parts or I'm sure something in between.

我既想知道你观察到的当前趋势，也想知道如果展望五到十年后，典型企业的AI团队会是什么样貌？

Curious both, like, what trends you're seeing today and then, you know, if you fast forward five, ten years, like, what does the the AI team at a typical enterprise look like?

首先，我认为未来所有技术团队都将成为AI团队。就像我说的，因为这是构建所有技术的新范式。我们甚至可能不再称之为AI团队或ML团队，但如今开发科技产品都需要机器学习。我强烈预感企业不会仅满足于外包机器学习。

So first, I think all tech teams will be AI teams in the future. Right? Like, as I said, because it's like the new paradigm to build all tech. I'm not even sure we're going to call it AI teams or ML teams or ML engineers, but I think if you're building a technology product today, you need to do some machine learning. And one strong intuition that I have is that companies are not just going to use or like outsource machine learning.

他们会自主构建机器学习，就像第一代软件时期，人们可能认为无代码工具能解决一切。某些领域确实如此，比如Squarespace、Wix这类平台。但最终企业需要亲自编写代码来构建技术，这样才能精准满足需求，形成差异化优势。

They're going to build machine learning, you know, like same way, you know, if you look at the first generation of software, you could have said, Okay, there's gonna be like a no code tool for people to do everything. And that's happened in some segments, right? You've had, you know, the Squarespace, the Wix and these tools like that. But ultimately, companies needed and wanted to write code themselves and actually build technology because that's the way they cater to their use case. That's the way they create a differentiation compared to others.

这才是可持续的技术建设方式。机器学习同理，真正创造价值的企业会希望训练、微调、优化模型，为自身用例定制方案。现在就能看到——在我看来，最优秀的初创公司都是AI原生且全栈的，比如最近发布新品的视频编辑平台Runway ML。

That's kind of like the sustainable way of building technology. Similarly for machine learning, companies who are going to create value are going to want to train models, fine tune models, optimize models, specialize models for their own use cases. And we're already seeing it. Like, if you look, in my opinion, at the best startups out there, they're very much kind of like AI native, but also AI full stack startups. Like, I think we've got recently a new release from, Runway ML, like the video editing platform.

当你看到他们通过自主构建AI（而非仅调用API）实现的能力时，实在令人震撼。所以最终多数企业都会自主开发机器学习，技术团队自然就演变为AI团队。

When you look at what they're capable of doing by actually building AI and not just using AI through APIs or others, it's, it's mind blowing. Right? So, yeah, ultimately, I think most companies will want to build the machine learning themselves. And so most of the tech teams will end up being AI teams.

关于将自主构建基础模型（或定制模型）的能力与应用层面结合的见解非常有趣。

It's a really interesting point about marrying the power of kind of building your own, you know, foundation model with you know, or your own kind of model for your use case with the application side.

而我认为最激动人心的是——Jason你肯定有独到见解——软件在全球的采纳速度和影响力已经极快，但限制因素之一始终是软件工程师的数量。部分原因在我看来，是从咨询等其他行业转型为软件工程师门槛较高，需要时间磨练。但有趣的是，从软件工程师转向AI/机器学习工程师（无论怎么称呼）则容易得多。

And what what's yeah. What's I think is going to be very exciting is that and Jason, I'm I'm sure you have, like, an interesting opinion on that, but it feels like one of the things that's obviously, the adoption of software and the impact of software in the world has been really, really fast. But one limiting factor for me has been the number of software engineers. Part of the reason has been, in my opinion, because it's kind of like hard to go from, you know, doing like consulting or doing any other type of work to becoming a software engineers. Right?

因此第一代软件可能为机器学习铺就了更快采纳、进步和影响的道路——因为将所有软件工程师转化为机器学习工程师的速度，会远快于当年将其他从业者培养成软件工程师的速度。我很期待见证这个进程。

It takes, it takes some time to master that and to become a good software engineer. But interestingly speaking, in my opinion, it's much, much easier to switch and to move from being a software engineer to doing an AI or machine learning engineer, however you call them. So one interesting thing is that maybe the first generation of software actually paved the way for much, much faster adoption and progress and impact for for machine learning because it's going to be much, much faster to turn all software engineers into machine learning engineers than it used to be turning any other people into software engineers. So I'm excited to see, how it plays out. Right?

设想一下，或许四五年后，机器学习工程师或AI工程师的数量超过今天的软件工程师并非不可能。从这个角度看，我的联合创始人Julien长期坚持的一个观点——起初颇具争议，如今争议渐消——他认为我们今天理解的软件工程实际上是机器学习的一个子集，而非相反。机器学习并非软件工程的子领域，恰恰相反。这或许正是我们将要见证的趋势之一。

And see if like, maybe in in four to five years, it's not impossible to think that you're going to have many, many more machine learning engineers or AI engineers than you have software engineers today. And in that sense, I mean, that's an interesting thing that my my co founder has been saying, Julien, for for quite a while, which was a bit, controversial at the time, which is starting to be a bit less controversial, is, is that, it's always been saying that, software engineering as we understanding today is actually, more subset of machine learning instead of the other way around. Right? Machine learning is not so much a subset of of software engineering, but, but the other way around. And that that's one of the ways maybe we we're gonna we're going to see that happen too.

没错。记得Julian首次提出时，我深以为然，因为这揭示了某种我们尚未完全看清的编程事件视界之外的未来。我们其实已在其他领域尝试过类似突破——比如你我之间就曾讨论过这个。

Yeah. Remember Julian saying that for the first time, I really liked the the thought behind it because it really shows what's gonna be past some sort of coding event horizon that we can't really fully see yet, you know? And I think that's an important thing for us to think about. And like, you know, I think that this is we've already tried to do this in some other ways. So as an example, like, come you and I have had this conversation.

我在多个播客中提到过：GitHub宣称全球现有约1亿开发者，而Salesforce公布的活跃销售开发者数量更为庞大。观察GitHub与Salesforce在低代码/无代码平台的策略，本质上都是在构建让普通人成为各类系统开发者的机制。

I said it on a couple of different podcasts. But GitHub says that they've got a 100,000,000 developers or something like that in the world at this point. If you look at Salesforce, when they say how many active sales developers they've got, the number is dramatically higher. And then you see all these no code, low code platforms. And the thought behind what GitHub is doing versus what Salesforce is doing with these low code or no code is you're effectively trying to create mechanisms for people to to become developers of various systems.

这就像我始终将Excel高手视为程序员——他们本质上在进行数据输入输出的编程。想想他们使用的工具如此原始：我们需要键入字符组成单词，再形成可编译执行的完整指令。即便现在只接触Copilot和ChatGPT，你也能想象未来人人都能快速创造的世界——至于是否还称之为软件开发，我并不在意。

And it's that old you know, it's it's kinda the same way in which I actually view people who are excellent at manipulating Excel as doing programming because of what they're doing manipulating data in and out. And if you think about what the tools available to them are, they're so primitive. The fact that we have to type words into the systems, or characters into the systems that become words, that become fully functional sentences and all that sort of stuff, and they compile down, that's the instruction set, and I get what we're doing. Even if you have no other exposure to the system but just Copilot and ChatGPT at the moment, you should be able to imagine a world in which everybody has the ability to go create something in the future here really quickly. And whether or we call that software development in the future, I don't really care.

关键在于创造者的激增：更多人将拥有创造能力、创造好奇心，以及触手可得的创造工具。这种变革极具力量——我对此充满期待。

But the point is other people are creating. A lot more people now have the capacity to create, have the curiosity to create, and have clearly, clearly have the tools at their fingertips that are available to them to go create. And that is remarkably powerful. Like, you know, and I I'm super excited for that future and to see what happens with that.

Clement，你的多元视角令人振奋。作为惯例，我们最后准备了一组快问快答——作为首位嘉宾，你将帮我们测试这个环节。Jason，我知道你最爱AGI话题，请开始吧。

Well, Clement, I mean, it's super interesting to get your your perspective on on so many of these topics. You know, going forward, we plan to kinda conclude our interviews with a standard kinda rapid fire set of of the same closing questions. And and as our first guest, you get to be our guinea pig to test some of these And so, you know, to start, I'll I'll let Jason kick it off because I know he loves the the AGI question. So, Jason.

确实痴迷AGI问题。核心就是：你真实想法如何？存在与否？若存在，时间线怎样？现阶段距离多远？若你属于相信派，认为终将实现，我们后续该预期什么？

I do I I I love the AGI question. And so and really, it just comes down to, like, what are your actual thoughts on AGI? Yes? No? It's a thing?

（继续）如果它终将到来，我们接下来应该期待或思考什么？

It's not a thing? If so, it's a thing timeline. And how close are we today if it is and all that? And, like, if you are a believer in AGI camp and think it's gonna get there, what should we expect or think about what comes next after it?

我认为AGI短期内不会到来。坦白说这个议题分散了领域注意力——机器学习本身已足够激动人心：作为构建传统技术的新范式，它能开启前所未有的可能性，根本无需AGI概念加持。

Yeah. I I don't think AGI is is coming anytime soon. I think I think it's it's frankly a distraction for for the field, I think, because, you don't need to think about AGI to make, everything that's possible with machine learning exciting. Just the perspective of, you know, being the new paradigm to build old technology and kind of like unlocking new capabilities that weren't possible before

确实。

Yeah.

这对我来说足够刺激了，而且某种程度上，我比AGI更关注这个。

Is is exciting enough for me and and some kind of, like, more focusing on on that than than AGI.

我喜欢这个观点。我非常喜欢这个观点。

I love that perspective. I love that perspective.

我想我们的第二个问题是，显然，你知道，我觉得我们在这里的Twitter上都很活跃，读同样的文章。我感觉在更广泛的AI社区里，很多时候讨论的都是同样的事情。好奇的是，你觉得现在有什么是被过度炒作，而在一般讨论中又被低估的？

I guess our our second question is, obviously, you know, I think we're all pretty active on Twitter here, you know, read the same pieces. I feel like it's the same things that that get talked about a lot of times in in the broader AI community. Curious, you know, one thing you feel like is overhyped right now and underhyped kind of in the general discourse.

所以我现在会小心对待Twitter。这是我最近注意到的一件事。

So I would be careful with with Twitter these days. It's something that I've noticed.

每天都是如此。

Every day.

是的，是的。但过去几个月里，我真的注意到，AI领域的平均观点质量和真实性，在我看来，急剧下降。所以我会非常谨慎，因为我觉得现在Twitter上有很多人在谈论AI，却知之甚少。有些内容突然走红，我简直摸不着头脑，心想这到底是怎么回事？

Yeah. Yeah. But it's it's really something that I've I've noticed in the past past few months, kind of like the the average quality and and truthfulness of, of the takes in AI, in my opinion, has been going dramatically down. So I, I would be really cautious because I feel like now on Twitter, there are a lot of people talking about AI without knowing, knowing much about it. And a little bit is going viral and I'm kind of like scratching my head and like what, what, what is, what is happening?

你知道，我想我喜欢生成式AI，对吧？如果你把它看作是一种关注新用例的方式，以及模型生成文本或图像的能力。但我们看到的是，今天大多数用例还是非生成式AI。比如，分类文本、欺诈检测、我们讨论过的时间序列预测、ETA搜索引擎，主要还是非生成式任务。社交网络的内容审核也是用非生成式模型完成的。

You know, I think I love generative AI, right? If you think about it as kind of like the way to focus on like new use cases and the ability for models to generate text or generate images. What we're seeing is that most of the use cases are today on non generative AI. So, for example, the ability to classify, you know, text, to do fraud detection, time series that we talked about, to do ETA search engine or still mostly non generative tasks. Social network moderation is is done with, like, non generative models.

所以我鼓励大家，是的，不要忘记所有那些非生成式的东西，它们现在也非常、非常令人兴奋。

So I I would encourage, yeah, people to, not not not forget about all the non generative stuff that is also super, super exciting these days.

太棒了。好吧，完全换个话题——嗯，也许也不算完全换，因为

Awesome. Alright. Flipping gears entirely from well, guess maybe not because

这就是快速问答的乐趣所在，对吧？你可以完全跳来跳去。好了。

That's the fun of the rapid fire. Right? You get to completely jump around. Alright.

那么关于AI监管和政府。在你看来，政府和监管应该在哪些方面介入AI领域？

So AI regulation and governments. Where should, in your opinion, governments and regulation get involved with AI?

我认为这应该促进更多的开放性和透明度。因为正如我之前所说，你无法控制你不理解的事物。对吧？或者说只有糟糕的控制方式。对吧？

In my opinion, this should foster more openness and transparency. Because as I said before, there's no way to control something that you don't understand. Right? Or there are only bad ways. Right?

如果你想控制和监管你不理解的东西，你只会采取错误的步骤。因此，如果要建立某种基础性监管框架，我认为应该促进更多透明度和开放性，因为这样才能让全球监管者开始理解正在发生的事情，然后制定适当的监管措施来降低风险，使其成为对人类有益的积极技术。

If you wanna control and regulate something that you don't understand, you'll only kind of, like, take the the the wrong steps. And so if there is a way to create kind of like a foundational regulation for me, it's to foster more transparency and more openness because that's what's going to allow regulators everywhere to, you know, start building understanding more what's what's happening and and then create kind of like the right regulation to to mitigate the risks and make it a positive technology for for humanity.

克莱姆，这次对话非常精彩。我觉得我们可以就任何这些方向聊上几个小时，但我觉得占用你更多时间远离Hugging Face的工作，对更广泛的机器学习社区来说是不公平的。所以我们就不多耽误你了。我相信很多听众对你和团队正在构建的东西很感兴趣。大家了解Hugging Face工作的最佳途径是什么？

Well, Clem, this has been a fascinating conversation. I feel like we could we could go hours on any of these directions, but I feel like we'd be doing a disservice to the broader ML community to take more of your time, away from Hugging Face. And so we'll we'll let you get back to the day job. I'm sure a lot of our listeners are intrigued by you and kinda what you and the team are building. You know, what's the best way for them to go learn more about what you guys are doing at Hugging Face?

是的。你可以参加Hugging Face课程。这是了解我们生态系统的很好入门方式。网址是hf.co//course。

Yeah. You can, you can take the Hugging Face course. That is like a really good introduction to our ecosystem. Right? So h f dot c o slash slash course.

你可以在Twitter和LinkedIn上关注我们。可以加入我们的Discord社区。然后探索Hugging Face Hub。它很像GitHub，你能发现很多人们没讨论但非常强大的机器学习资源，涵盖文本、音频、图像、生物、化学、时间序列等各种领域。

You can follow us on on Twitter, on on LinkedIn. You can join join our Discord to join the community. And then explore explore the Hugging Face Hub. Right? It's it's very much like GitHub, where you can find a lot of things that people are not talking about, but that are very very powerful in all sort of machine learning from like text, audio, image, biology, chemistry, time series.

所以尽情探索这个Hub吧。这也是释放你对机器学习可能性认知的好方法。

So really just explore the hub. It's a good way to also unleash your creativity about what's possible and not with machine learning.