2024-07-23 Mark Zuckerberg.Open Source AI Is the Path Forward
Refer To：《Open Source AI Is the Path Forward》。
In the early days of high-performance computing, the major tech companies of the day each invested heavily in developing their own closed source versions of Unix. It was hard to imagine at the time that any other approach could develop such advanced software. Eventually though, open source Linux gained popularity – initially because it allowed developers to modify its code however they wanted and was more affordable, and over time because it became more advanced, more secure, and had a broader ecosystem supporting more capabilities than any closed Unix. Today, Linux is the industry standard foundation for both cloud computing and the operating systems that run most mobile devices – and we all benefit from superior products because of it.
在高性能计算的早期，当时主要的科技公司都大量投资于开发自己的封闭源版本的 Unix。那时很难想象还有其他方法能够开发出如此先进的软件。然而，开源 Linux 最终获得了普及——最初是因为它允许开发者随意修改代码且更具经济性，随着时间的推移，它变得更加先进、更安全，并且拥有比任何封闭 Unix 更广泛的生态系统，支持更多功能。如今，Linux 是云计算和运行大多数移动设备的操作系统的行业标准基础——我们都因此受益于更优质的产品。
直接的原因是反抗Windows，而不是因为开源，开源有一个更符合事实的说法：山寨。
I believe that AI will develop in a similar way. Today, several tech companies are developing leading closed models. But open source is quickly closing the gap. Last year, Llama 2 was only comparable to an older generation of models behind the frontier. This year, Llama 3 is competitive with the most advanced models and leading in some areas. Starting next year, we expect future Llama models to become the most advanced in the industry. But even before that, Llama is already leading on openness, modifiability, and cost efficiency.
我相信AI的发展会以类似的方式进行。如今，一些科技公司正在开发领先的封闭模型。但开源模型正在迅速缩小差距。去年，Llama 2仅相当于一代较老的前沿模型。今年，Llama 3已经可以与最先进的模型竞争，并在某些领域领先。从明年开始，我们预计未来的Llama模型将成为业界最先进的。但即使在此之前，Llama已经在开放性、可修改性和成本效益方面领先。
Today we’re taking the next steps towards open source AI becoming the industry standard. We’re releasing Llama 3.1 405B, the first frontier-level open source AI model, as well as new and improved Llama 3.1 70B and 8B models. In addition to having significantly better cost/performance relative to closed models, the fact that the 405B model is open will make it the best choice for fine-tuning and distilling smaller models.
今天，我们正在迈出下一步，使开源人工智能成为行业标准。我们发布了 Llama 3.1 405B，这是第一个前沿级别的开源人工智能模型，以及新改进的 Llama 3.1 70B 和 8B 模型。除了相对于封闭模型具有显著更好的性价比外，405B 模型是开源的，这将使其成为微调和提炼更小模型的最佳选择。
Beyond releasing these models, we’re working with a range of companies to grow the broader ecosystem. Amazon, Databricks, and NVIDIA are launching full suites of services to support developers fine-tuning and distilling their own models. Innovators like Groq have built low-latency, low-cost inference serving for all the new models. The models will be available on all major clouds including AWS, Azure, Google, Oracle, and more. Companies like Scale.AI, Dell, Deloitte, and others are ready to help enterprises adopt Llama and train custom models with their own data. As the community grows and more companies develop new services, we can collectively make Llama the industry standard and bring the benefits of AI to everyone.
除了发布这些模型外，我们还与一系列公司合作，以扩大整个生态系统。Amazon、Databricks和NVIDIA正在推出完整的服务套件，支持开发者微调和优化他们自己的模型。像Groq这样的创新者已经为所有新模型构建了低延迟、低成本的推理服务。这些模型将可以在包括AWS、Azure、Google、Oracle等所有主要云平台上使用。像Scale.AI、Dell、Deloitte等公司已经准备好帮助企业采用Llama，并使用他们自己的数据训练定制模型。随着社区的壮大和更多公司开发新服务，我们可以共同使Llama成为行业标准，并将AI的好处带给所有人。
最先进，行业标准，这么大的能耐为什么不出现在OpenAI之前？山寨更渴望成功，同时自我伤害的最佳策略。
Meta is committed to open source AI. I’ll outline why I believe open source is the best development stack for you, why open sourcing Llama is good for Meta, and why open source AI is good for the world and therefore a platform that will be around for the long term.
Meta 致力于开源 AI。我将概述为什么我认为开源是您最佳的开发堆栈，为什么开源 Llama 对 Meta 有利，以及为什么开源 AI 对世界有益，因此它将是一个长期存在的平台。
Why Open Source AI Is Good for Developers 开源人工智能对开发者有何好处
When I talk to developers, CEOs, and government officials across the world, I usually hear several themes:
当我与世界各地的开发者、首席执行官和政府官员交谈时，我通常会听到几个主题：
- We need to train, fine-tune, and distill our own models. Every organization has different needs that are best met with models of different sizes that are trained or fine-tuned with their specific data. On-device tasks and classification tasks require small models, while more complicated tasks require larger models. Now you’ll be able to take the most advanced Llama models, continue training them with your own data and then distill them down to a model of your optimal size – without us or anyone else seeing your data.
- 我们需要训练、微调和优化自己的模型。每个组织都有不同的需求，而这些需求最适合通过不同规模的模型来满足，这些模型需要用特定的数据进行训练或微调。设备上的任务和分类任务需要小型模型，而更复杂的任务则需要更大型的模型。现在，你可以使用最先进的Llama模型，继续用自己的数据进行训练，然后将其优化为你所需的最佳规模——而我们或其他任何人都不会看到你的数据。
- We need to control our own destiny and not get locked into a closed vendor. Many organizations don’t want to depend on models they cannot run and control themselves. They don’t want closed model providers to be able to change their model, alter their terms of use, or even stop serving them entirely. They also don’t want to get locked into a single cloud that has exclusive rights to a model. Open source enables a broad ecosystem of companies with compatible toolchains that you can move between easily.
- 我们需要掌控自己的命运，而不是被锁定在一个封闭的供应商中（我们吃的粮食不是自己种的，穿的衣服不是自己做的，生病需要开刀的话也不可能自己动手）。许多组织不想依赖他们无法自己运行和控制的模型。他们不希望封闭的模型提供商能够更改他们的模型、修改使用条款，甚至完全停止为他们提供服务。他们也不想被锁定在一个拥有模型独占权的单一云服务中。开源使得拥有兼容工具链的公司形成了广泛的生态系统，您可以轻松地在其中切换。
- We need to protect our data. Many organizations handle sensitive data that they need to secure and can’t send to closed models over cloud APIs. Other organizations simply don’t trust the closed model providers with their data. Open source addresses these issues by enabling you to run the models wherever you want. It is well-accepted that open source software tends to be more secure because it is developed more transparently.
- 我们需要保护我们的数据。许多组织处理需要保护的敏感数据，无法通过云 API 发送到封闭模型。其他组织则根本不信任封闭模型提供商处理他们的数据（可以本地运行）。开源解决了这些问题，使您可以在任何地方运行模型。人们普遍认为，开源软件往往更安全，因为它的开发过程更加透明。
- We need a model that is efficient and affordable to run. Developers can run inference on Llama 3.1 405B on their own infra at roughly 50% the cost of using closed models like GPT-4o, for both user-facing and offline inference tasks.
- 我们需要一个高效且经济实惠的模型。开发者可以在自己的基础设施上运行Llama 3.1 405B，用于用户端和离线推理任务时，成本大约是使用封闭模型（如GPT-4o）的一半（有没有问过chatGPT？）。
- We want to invest in the ecosystem that’s going to be the standard for the long term. Lots of people see that open source is advancing at a faster rate than closed models, and they want to build their systems on the architecture that will give them the greatest advantage long term.
- 我们想要投资于将成为长期标准的生态系统。很多人看到开源的发展速度快于封闭模型（谁看到了？有没有证据？），他们希望在能够为他们提供长期最大优势的架构上构建他们的系统。
Why Open Source AI Is Good for Meta 开源人工智能对 Meta 的好处
Meta’s business model is about building the best experiences and services for people. To do this, we must ensure that we always have access to the best technology, and that we’re not locking into a competitor’s closed ecosystem where they can restrict what we build.
Meta 的商业模式是为人们构建最佳体验和服务。为此，我们必须确保始终能够访问最佳技术，并且不被锁定在竞争对手的封闭生态系统中，以免限制我们的构建。
One of my formative experiences has been building our services constrained by what Apple will let us build on their platforms. Between the way they tax developers, the arbitrary rules they apply, and all the product innovations they block from shipping, it’s clear that Meta and many other companies would be freed up to build much better services for people if we could build the best versions of our products and competitors were not able to constrain what we could build. On a philosophical level, this is a major reason why I believe so strongly in building open ecosystems in AI and AR/VR for the next generation of computing.
我的一个重要经历是构建我们的服务时受限于苹果允许我们在其平台上开发的内容。苹果对开发者征收的费用、他们施加的任意规则，以及他们阻止许多产品创新发布的做法，清楚地表明，如果我们能够构建产品的最佳版本，而竞争对手不能限制我们的开发，Meta和许多其他公司将能够为人们提供更好的服务。从哲学层面上看，这也是为什么我如此坚定地相信在AI和AR/VR领域为下一代计算构建开放生态系统的一个重要原因。
Loser，孬种的思维方式，还拿着自己的AI方案跑去苹果谈合作：《Apple, Meta Have Discussed an AI Partnership》。
People often ask if I’m worried about giving up a technical advantage by open sourcing Llama, but I think this misses the big picture for a few reasons:
人们常常问我是否担心通过开源 Llama 而失去技术优势，但我认为这忽视了几个重要的方面：
First, to ensure that we have access to the best technology and aren’t locked into a closed ecosystem over the long term, Llama needs to develop into a full ecosystem of tools, efficiency improvements, silicon optimizations, and other integrations. If we were the only company using Llama, this ecosystem wouldn’t develop and we’d fare no better than the closed variants of Unix.
首先，为了确保我们能够获得最佳技术，并且在长期内不会被锁定在一个封闭的生态系统中，Llama需要发展成一个完整的生态系统，包括工具、效率提升、硅优化以及其他集成。如果只有我们一家公司使用Llama，这个生态系统将无法发展，我们的处境也不会比Unix的封闭版本好多少。
Second, I expect AI development will continue to be very competitive, which means that open sourcing any given model isn’t giving away a massive advantage over the next best models at that point in time. The path for Llama to become the industry standard is by being consistently competitive, efficient, and open generation after generation.
其次，我预计人工智能的发展将继续非常具有竞争性，这意味着开源任何给定模型并不会在当时给予相对于下一个最佳模型的巨大优势。Llama 成为行业标准的路径是代代相传地保持持续的竞争力、高效性和开放性。
Third, a key difference between Meta and closed model providers is that selling access to AI models isn’t our business model. That means openly releasing Llama doesn’t undercut our revenue, sustainability, or ability to invest in research like it does for closed providers. (This is one reason several closed providers consistently lobby governments against open source.)
第三，Meta与封闭模型提供商之间的一个关键区别在于，销售AI模型的访问权限并不是我们的商业模式。这意味着公开发布Llama不会像封闭提供商那样削弱我们的收入、可持续性或投资研究的能力。（这也是为什么一些封闭提供商一再游说政府反对开源的原因之一。）
能通过广告的现金流提供支持。
Finally, Meta has a long history of open source projects and successes. We’ve saved billions of dollars by releasing our server, network, and data center designs with Open Compute Project and having supply chains standardize on our designs. We benefited from the ecosystem’s innovations by open sourcing leading tools like PyTorch, React, and many more tools. This approach has consistently worked for us when we stick with it over the long term.
最后，Meta在开源项目和成功方面有着悠久的历史。通过与开放计算项目（Open Compute Project）分享我们的服务器、网络和数据中心设计，并使供应链标准化为我们的设计，我们节省了数十亿美元。通过开源领先的工具，如PyTorch、React以及其他许多工具，我们受益于整个生态系统的创新。长期坚持这种方法一直对我们有效。
Why Open Source AI Is Good for the World 开源人工智能为何对世界有益
I believe that open source is necessary for a positive AI future. AI has more potential than any other modern technology to increase human productivity, creativity, and quality of life – and to accelerate economic growth while unlocking progress in medical and scientific research. Open source will ensure that more people around the world have access to the benefits and opportunities of AI, that power isn’t concentrated in the hands of a small number of companies, and that the technology can be deployed more evenly and safely across society.
我相信开源对于积极的人工智能未来是必要的。人工智能比任何其他现代技术都有更大的潜力来提高人类的生产力、创造力和生活质量——并加速经济增长，同时推动医学和科学研究的进展。开源将确保世界上更多的人能够获得人工智能的好处和机会，权力不会集中在少数几家公司手中，并且技术可以在社会中更均匀和安全地部署。
开源约等于山寨，作为消费者，便宜的反而更贵，本人亲身体验过Linux：费劲，我看到的好处是刺激竞争对手，让微软和苹果做的更好。
There is an ongoing debate about the safety of open source AI models, and my view is that open source AI will be safer than the alternatives. I think governments will conclude it’s in their interest to support open source because it will make the world more prosperous and safer.
关于开源人工智能模型的安全性，正在进行一场持续的辩论，我认为开源人工智能将比其他选择更安全。我认为各国政府会得出结论，支持开源符合他们的利益，因为这将使世界更加繁荣和安全。
My framework for understanding safety is that we need to protect against two categories of harm: unintentional and intentional. Unintentional harm is when an AI system may cause harm even when it was not the intent of those running it to do so. For example, modern AI models may inadvertently give bad health advice. Or, in more futuristic scenarios, some worry that models may unintentionally self-replicate or hyper-optimize goals to the detriment of humanity. Intentional harm is when a bad actor uses an AI model with the goal of causing harm.
我理解安全的框架是我们需要防范两类伤害：无意的和故意的。无意的伤害是指当一个人工智能系统可能造成伤害时，即使运行它的人并没有这样的意图。例如，现代人工智能模型可能无意中给出错误的健康建议。或者，在更未来的场景中，有人担心模型可能无意中自我复制或过度优化目标，从而对人类造成损害。故意的伤害是指恶意行为者使用人工智能模型以造成伤害为目的。
缺少根据的假设，特征是既没有上文，也不会有下文。
It’s worth noting that unintentional harm covers the majority of concerns people have around AI – ranging from what influence AI systems will have on the billions of people who will use them to most of the truly catastrophic science fiction scenarios for humanity. On this front, open source should be significantly safer since the systems are more transparent and can be widely scrutinized. Historically, open source software has been more secure for this reason. Similarly, using Llama with its safety systems like Llama Guard will likely be safer and more secure than closed models. For this reason, most conversations around open source AI safety focus on intentional harm.
值得注意的是，无意伤害涵盖了人们对人工智能的主要担忧——从人工智能系统对数十亿用户的影响，到许多真正灾难性的科幻场景。在这一方面，开源应该显得更安全，因为系统更加透明，可以广泛接受审查。历史上，开源软件因此更安全。同样，使用带有安全系统的 Llama，如 Llama Guard，可能比封闭模型更安全、更可靠。因此，关于开源人工智能安全的大多数讨论集中在故意伤害上。
前后没有逻辑。
Our safety process includes rigorous testing and red-teaming to assess whether our models are capable of meaningful harm, with the goal of mitigating risks before release. Since the models are open, anyone is capable of testing for themselves as well. We must keep in mind that these models are trained by information that’s already on the internet, so the starting point when considering harm should be whether a model can facilitate more harm than information that can quickly be retrieved from Google or other search results.
我们的安全流程包括严格的测试和红队评估，以评估我们的模型是否能够造成实质性伤害，目标是在发布之前降低风险。由于这些模型是开放的，任何人都可以自己进行测试。我们必须记住，这些模型是通过互联网上已经存在的信息进行训练的，因此在考虑伤害时，起点应该是模型是否能够造成比从谷歌或其他搜索结果中快速检索到的信息更大的伤害。
这个预设等于逃避责任。
When reasoning about intentional harm, it’s helpful to distinguish between what individual or small scale actors may be able to do as opposed to what large scale actors like nation states with vast resources may be able to do.
在推理故意伤害时，区分个体或小规模行为者能够做的事情与拥有丰富资源的大规模行为者如国家能够做的事情是很有帮助的。
At some point in the future, individual bad actors may be able to use the intelligence of AI models to fabricate entirely new harms from the information available on the internet. At this point, the balance of power will be critical to AI safety. I think it will be better to live in a world where AI is widely deployed so that larger actors can check the power of smaller bad actors. This is how we’ve managed security on our social networks – our more robust AI systems identify and stop threats from less sophisticated actors who often use smaller scale AI systems. More broadly, larger institutions deploying AI at scale will promote security and stability across society. As long as everyone has access to similar generations of models – which open source promotes – then governments and institutions with more compute resources will be able to check bad actors with less compute.
在未来的某个时刻，个别不法分子可能会利用AI模型的智能，从互联网上可用的信息中捏造出全新的危害。到那时，力量的平衡将对AI安全至关重要。我认为，生活在一个AI被广泛部署的世界中会更好，因为大规模的参与者可以制衡那些小规模的不法分子。这也是我们在社交网络上管理安全的方式——我们更强大的AI系统能够识别并阻止那些使用较小规模AI系统的不太复杂的威胁。更广泛地说，大型机构在大规模部署AI时，将促进社会的安全和稳定。只要每个人都能获得类似的模型代际（这是开源所推动的），那么拥有更多计算资源的政府和机构将能够制衡那些计算资源较少的不法分子。
The next question is how the US and democratic nations should handle the threat of states with massive resources like China. The United States’ advantage is decentralized and open innovation. Some people argue that we must close our models to prevent China from gaining access to them, but my view is that this will not work and will only disadvantage the US and its allies. Our adversaries are great at espionage, stealing models that fit on a thumb drive is relatively easy, and most tech companies are far from operating in a way that would make this more difficult. It seems most likely that a world of only closed models results in a small number of big companies plus our geopolitical adversaries having access to leading models, while startups, universities, and small businesses miss out on opportunities. Plus, constraining American innovation to closed development increases the chance that we don’t lead at all. Instead, I think our best strategy is to build a robust open ecosystem and have our leading companies work closely with our government and allies to ensure they can best take advantage of the latest advances and achieve a sustainable first-mover advantage over the long term.
下一个问题是美国和民主国家应该如何应对像某国这样拥有巨大资源的国家带来的威胁。美国的优势在于去中心化和开放的创新。有些人认为我们必须关闭模型，以防止某国获取它们，但我的看法是，这样做不会奏效，反而会使美国及其盟友处于不利地位。我们的对手擅长间谍活动，窃取可以存放在U盘上的模型相对容易，而且大多数科技公司目前的运营方式远未达到能使这变得更困难的水平。最有可能的结果是，只有封闭模型的世界会导致少数大公司和我们的地缘政治对手能够获取领先的模型，而初创公司、大学和小企业将失去机会。此外，将美国的创新限制在封闭的开发模式中，还会增加我们无法保持领先的风险。相反，我认为我们最好的策略是建立一个强大的开放生态系统，并让我们领先的公司与政府和盟友密切合作，确保他们能够充分利用最新的进展，并在长期内获得可持续的先发优势。
When you consider the opportunities ahead, remember that most of today’s leading tech companies and scientific research are built on open source software. The next generation of companies and research will use open source AI if we collectively invest in it. That includes startups just getting off the ground as well as people in universities and countries that may not have the resources to develop their own state-of-the-art AI from scratch.
当你考虑未来的机会时，请记住，今天大多数领先的科技公司和科学研究都是建立在开源软件之上的。如果我们共同投资，下一代公司和研究将会使用开源人工智能。这包括刚刚起步的初创公司，以及那些可能没有资源从头开发自己先进人工智能的大学和国家。
The bottom line is that open source AI represents the world’s best shot at harnessing this technology to create the greatest economic opportunity and security for everyone.
底线是，开源人工智能代表了全球利用这项技术为每个人创造最大经济机会和安全的最佳机会。
Let’s Build This Together 让我们一起建设这个
With past Llama models, Meta developed them for ourselves and then released them, but didn’t focus much on building a broader ecosystem. We’re taking a different approach with this release. We’re building teams internally to enable as many developers and partners as possible to use Llama, and we’re actively building partnerships so that more companies in the ecosystem can offer unique functionality to their customers as well.
在过去的 Llama 模型中，Meta 是为自己开发这些模型，然后发布，但并没有太多关注构建更广泛的生态系统。我们在这次发布中采取了不同的方法。我们正在内部组建团队，以使尽可能多的开发者和合作伙伴能够使用 Llama，并且我们正在积极建立合作关系，以便生态系统中的更多公司能够为他们的客户提供独特的功能。
I believe the Llama 3.1 release will be an inflection point in the industry where most developers begin to primarily use open source, and I expect that approach to only grow from here. I hope you’ll join us on this journey to bring the benefits of AI to everyone in the world.
我相信 Llama 3.1 的发布将成为行业的一个转折点，大多数开发者将开始主要使用开源，我预计这种趋势将从这里开始不断增长。我希望你能和我们一起踏上这段旅程，将人工智能的好处带给世界上的每一个人。
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