The AI Daily Brief: Artificial Intelligence News and Analysis - Zuckerberg on Why Open Source AI is the Path Forward

Episode Date: July 27, 2024

Mark Zuckerberg shares why open source AI is the future. Explore Meta’s recent release of the LLAMA 3.1 models, including the groundbreaking 405B parameter model, and Zuckerberg’s media tour promoting open source AI. Understand the significant implications for the AI space and why open source might be the key to a prosperous and secure AI-driven world. Concerned about being spied on? Tired of censored responses? AI Daily Brief listeners receive a 20% discount on Venice Pro. Visit ⁠⁠⁠https://venice.ai/nlw ⁠⁠and enter the discount code NLWDAILYBRIEF. Learn how to use AI with the world's biggest library of fun and useful tutorials: https://besuper.ai/ Use code 'podcast' for 50% off your first month. The AI Daily Brief helps you understand the most important news and discussions in AI. Subscribe to the podcast version of The AI Daily Brief wherever you listen: https://pod.link/1680633614 Subscribe to the newsletter: https://aidailybrief.beehiiv.com/ Join our Discord: https://bit.ly/aibreakdown

Today on the AI Daily Brief, why Mark Zuckerberg thinks open source AI is the path forward. The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI. To join the conversation, follow the Discord link in our show notes. Hello, friends, welcome back to the AI Daily Brief. Due to some travel this week, there will not be any episodes this weekend, and we were moving our Long Reads episode up to today. It's a perfect capstone for this week, though, I believe, where the biggest stories were, of course, meta launching its 3.1 family of models, including 405B, which became effectively the first open source model to more or less fully close the gap when it came to the state of the art

with closed source models. That is a sea change that has significant implications for the development of the AI space and was reinforced when a day later Mistral launched its Mistral Large 2 model, a similarly performant open model. Now, hold aside some of the questions around the non-commercial license of Mistral's model. It was still an enormous week for open source AI, and part of what made it so big was that Mark Zuckerberg didn't just release this model. He went on an absolute media tour selling this story. At the center of that was a blog post published on Meta's website on Wednesday alongside the release called Open Source AI is the path forward, and that's what we'll be reading now. Mark writes, 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. 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, Lama 2 was only comparable to an older generation of models behind the frontier. This year, Lama 3 is competitive with the most advanced models and leading in some areas. Starting next year, we expect future Lama models to become the most advanced in the industry. But even before that, Lama is already leading on openness, modifiability, and cost efficiency. Today, we're taking the next steps towards open source AI becoming the industry standard. We're releasing Lama 3.1 405B, the first frontier-level open source AI model, as well as new and improved Lama 3.170B 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. 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 Grock have built low latency, low-cost inference serving for all these 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 Lama and train custom models with their own data. As the community grows and more companies develop new services, we can

collectively make Lama the industry standard and bring the benefits of AI to everyone. Meta is committed to open source AI. I'll outline why I believe that open source is the best development stack for you, why open sourcing Lama 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. By the way, back to Editor's Note from NLW here. This is the point at which you can tell that this is not your normal announcement post. Yes, Zuck has gone through the quick hits of all the different pieces of information about how this thing is being launched, but then right when you would have expected him to close it, oh no, we go into the real big argument.

Section. Why Open Source AI is good for developers. When I talk to developers, CEOs, and government officials across the world, I usually hear several themes. 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 smaller models, while more complicated tasks require larger models.

Now you'll be able to take the most advanced Lama 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. The theme? 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 tool chains that you can move between easily. 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.

We need a model that is efficient and affordable to run. Developers can run inference on Lama 3.1405B on their own infra at roughly 50% the cost of using closed models like GPT40 for both user-facing and offline inference tasks. 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. Section. Why open source AI is good for META.

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. 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 taxed 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 ARVR for the next generation of computing. People often ask if I'm worried about giving up technical advantage by open sourcing Lama, but I think this misses the big picture for a few reasons. First, to ensure that we have access to the best technology and aren't locked into a closed ecosystem over time, Lama needs to develop into a full ecosystem of tools, efficiency improvements, silicon optimizations,

and other integrations. If we were the only company using Lama, this ecosystem wouldn't develop, and we'd fare no better than the closed variants of Unix. 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 Lama to become the industry standard is by being consistently competitive, efficient, and open generation after generation. 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 Lama 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. 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 standardized on our designs. We benefited from the ecosystem's innovation by open sourcing leading tools like Pytorch React and many more. This This approach has consistently worked for us when we stick with it over the long term. Today's episode is brought to you by Venice. Venice is a private, uncensored, generative AI

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discount on Venice Pro. Visit venice.a.i slash NLW and enter the discount code NLW Daily Brief. That's NLW Daily Brief, all one word. Today's episode is brought to you by Super Intelligent. As you guys know, Super Intelligent is a platform we are building to help everyone, individuals and teams, maximize their use of AI. We help you figure out how to use AI tools, as well as what to use AI for. And this is really important. The whole goal of Superintelligent is not just to give you tutorials and lessons,

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Section Why Open Source AI is good for the world. 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, the 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. 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. 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 tools may inadvertently give bad health advice. Or, in more futuristic scenarios, some worry that models may unintentionally self-replogrill or hyper-optimized goals to the detriment of humanity. Intentional harm is when a bad actor uses an AI model with the goal of causing harm. 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 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 Lama with its safety systems like Lama Guard will be safer and more secure than closed models. For this reason, most conversations around open source AI safety focus on intentional harm. 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 them 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. 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. 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. The next question is how the U.S. and Democratic nations should handle the threat of states with massive resources like China. The United States advantage is decentralization 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 U.S. 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 close 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. 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. 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. Let's build this together. With past Lama models, meta-develop them for ourselves and then release 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 Lama, and we're actively building partnerships so that more companies in the ecosystem can offer unique functionality to their customers as well. I believe the Lama 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.

All right, so back to NLW here. Couple things. One, I am fairly certain that Zuckerberg actually wrote that himself, as opposed to it being PR that he just put his name on. I'm not going to say whether he used Lama to improve the... the writing or anything like that, but it feels very much like all things that he wanted to say. Second, it feels like a foundational document for a larger argument, and Zuckerberg leaning into what he sees as his role as chief promoter of open source AI, which makes sense. This is going

to be a more contentious political conversation. There are serious headwinds against open source and the government, and so it feels to me like Zuckerberg is gearing up for a battle. For now, though, that is going to do it for today's AI Daily Brief. Until next time, peace.