The Rise of Open Source in AI: Meta's Llama 2 Versus OpenAI's G3PO

The Context

Meta, previously known as Facebook, is driving the open-source AI movement by releasing their Large Language Model Meta AI (LLAMA) 2.

What surprised most people is that Meta made LLAMA 2 available for commercial use, as opposed to its predecessor, which was limited to academic usage.

LLAMA 2 is expected to challenge OpenAI’s proprietary GPT models.

And this is not the first time an open-source model has challenged a closed-source model.

Remember how Stable Diffusion, an open-source text-to-image model, took the focus away from OpenAI's closed-source model - Dall-E 2.

LLAMA 2 Performance and Advantage.

Meta's LLAMA 2 comes in three different sizes (7 billion, 13 billion, and 70 billion parameters). While the smaller models are cheaper to run, the larger ones can handle more complex reasoning tasks.

LLAMA 2, despite having fewer parameters, 70 billion, than GPT 3.5's 175 billion parameters, exhibits a close performance on the Massive Multitask Language Understanding (MMLU) score, scoring 68.9, just slightly behind GPT 3.5's score of 70.0.

However, LLAMA 2 falls significantly short compared to GPT-4's MMLU rating of 86.4.

An advantage of Llama 2 is its more recent training and tuning data, cut-off in July 2023, compared to GPT 3.5's data cut-off in September 2021, providing it with more current data understanding.

In addition, LLAMA 2 uses Grouped-Query Attention (GQA), which lowers memory requirements, thus serving more requests simultaneously and reducing compute cost per word. This is critical as training and operational costs of AI models are on the rise.

Strategic Reason for Meta to Open Source LLAMA 2

1. Ecosystem Building: By open-sourcing its AI model, Meta is encouraging the creation of a global developer ecosystem around its model.
   
   This approach mirrors how tech giants like Google and Meta themselves have benefited from open-sourcing technologies like TensorFlow and React.js, respectively, which led to widespread adoption and enhancement by the global developer community.
   

2. Benchmark Setting and Industry Influence: Open-sourcing Llama 2 allows Meta to set a new industry standard. It gives Meta a chance to lead and influence the direction of AI research and its practical applications. It positions them to challenge the proprietary models from companies like OpenAI, influencing the broader conversation on how AI should be developed and used.
   

3. Competition and Market Penetration: By making Llama 2 accessible for commercial use, Meta potentially brings advanced AI capabilities to a wide range of businesses that might not have been able to access or afford such technology. This could help Meta establish a stronger foothold in the AI marketplace and potentially make their way into enterprise conversations.

Historically, high-impact open-source technologies have been integrated into various business and technological applications.

Here are a few examples of such technologies.

1. TensorFlow: Google open-sourced TensorFlow, its machine learning library, in 2015. This has led to widespread adoption and has cemented Google's reputation as a leading AI player. It has enabled various applications, from helping researchers accelerate their work to enabling businesses to build AI-driven services.

2. Linux Operating System: Linux, an open-source operating system, is a prime example of open-source success. It has been adopted across industries and has a vibrant community of developers constantly refining and expanding it. It forms the basis for Android and powers many server systems worldwide.

3. Apache Hadoop: Apache Hadoop, an open-source software framework for storing data and running applications on clusters of commodity hardware, has been widely adopted by companies like Yahoo and Facebook. It has spurred an ecosystem of big data processing tools, including Apache Spark and Hive.

What Could OpenAI Do To Counter Meta’s Threat?

As Meta continues to make waves in the AI space with LLAMA 2, OpenAI must respond strategically to ensure it remains competitive.

The game is changing, with the focus shifting from proprietary AI models to a more collaborative, open-source approach that harnesses the power of global developer communities.

Here are some detailed strategies OpenAI could adopt not just to counter Meta's threat but also to establish its position in this fast-evolving space:

1. Open-Sourcing Proprietary Models: Like Meta, OpenAI could consider releasing an open-source version of their proprietary models, such as the rumored G3PO.
   
   This would help OpenAI gain attention and maintain its standing within the AI community.
   
   The open-source model could also serve as a "teaser" to attract developers to OpenAI's more advanced and proprietary tools.

2. Differentiation Through Innovation: OpenAI could focus on out-innovating Meta by developing superior models and algorithms.
   
   They could leverage their expertise in the field to push the boundaries of AI and ML even further, offering tools that outperform LLAMA 2 in terms of capability, cost efficiency, and ease of use.

3. Expanding Collaboration: OpenAI should continue expanding its partnerships with academic institutions, tech companies, and startups to broaden its influence and usage of its models.
   
   Collaborative efforts can also drive innovative ways of utilizing and refining its models.

4. Leveraging Commercial Applications: OpenAI should identify key commercial applications and tailor their offerings to meet those needs. They should offer specialized models optimized for specific industries or applications, such as healthcare, finance, marketing, etc.

5. Training Customization Tools: OpenAI should focus on providing advanced tools and services that allow companies to customize and fine-tune the pre-trained models according to their needs. This would enable businesses to use the models more effectively, increasing their reliance on OpenAI's tools and services.

6. Pricing and Cost Effectiveness: If feasible, OpenAI could reassess its pricing strategy to provide more cost-effective solutions for businesses.

7. Focus on Developer Support and Community Building: To compete effectively with Meta, OpenAI should provide top-tier support to developers, offering robust documentation, tutorials, and interactive learning resources for their models.
   
   A vibrant, supported community could also lead to the development of innovative applications, which in turn could boost OpenAI's image and reach.

Conclusion

The battlefield is rapidly evolving as the race for AI dominance heats up.

Meta's bold move to open-source LLAMA 2, a powerful AI model, places them at the heart of the AI discussion and shifts the conversation towards more collaborative and democratized AI development.

This shift is reminiscent of industry game-changers like TensorFlow and Linux, suggesting a potentially transformative impact on AI research and applications.

However, it's not a one-horse race. OpenAI, a formidable player, has a suite of strategic options to counter Meta's threat. By leveraging open-source models, fostering innovation, and building strong partnerships, they can maintain their competitive edge.

The stakes are high, but so are the opportunities.