GPT 4 Level Open Source in 2024..(Llama 3 Leaks and Mistral 2.0)
TLDRThe transcript discusses the rapid advancements in open-source AI, with a focus on the potential release of an open-source model at the level of GPT-4 by Mistral AI in 2024. The company, known for its compute-efficient and ethical AI practices, aims to democratize access to advanced generative technology. Mistral's models, such as Mixr, have shown impressive performance, even surpassing some larger language models in benchmarks like the Arena ELO. Despite the challenges posed by larger companies with more resources, Mistral AI's small but accomplished team has made significant strides. The discussion also touches on the potential of Llama 3 by Meta to compete with GPT-4 while remaining freely available, which could disrupt the market and establish a new standard for open-source AI models.
Takeaways
- 🚀 Open source AI models are rapidly approaching the capabilities of GPT-4, with potential for significant advancements in 2024.
- 🔍 Sam Altman, CEO of OpenAI, has acknowledged the difficulty in catching up to GPT-4 but emphasizes the role of developers in trying.
- 🦙 Llama, a family of large language models by Meta AI, is open source and is one of the most popular models currently available.
- 🌐 Mistral AI, a European AI startup, plans to release an open-source model on par with GPT-4 in 2024, positioning itself as an alternative to larger AI companies.
- 📈 Mistral's Mixr model is reported to be six times faster than comparable models, showcasing their focus on efficiency and performance.
- 📊 Mistral AI's models support multiple languages, have natural coding abilities, and can handle long sequences, offering a competitive edge.
- 🏆 Mistral's medium model has been rated highly on benchmarks like the Arena ELO, indicating strong performance against other large language models.
- 💰 Mistral AI recently raised €385 million, suggesting significant investment in training models and infrastructure to compete with industry giants.
- 🤖 Despite being a smaller team of 22 employees, Mistral AI has made a significant impact in the AI space, rivaling larger companies with more employees.
- 🔬 The architecture of Mistral's models, including the use of a router system for task delegation, is innovative and allows for efficiency and customization.
- 🌟 There is speculation that GPT-4 uses a similar 'mixture of experts' architecture, which, if true, could pave the way for other companies to adopt this approach.
Q & A
What is the significance of open-source AI reaching the level of GPT 4 in 2024?
-The significance is that it would represent a major milestone in the democratization of advanced AI technology, allowing more developers and companies to access and customize powerful language models, which could lead to rapid innovation and a more diverse ecosystem of AI applications.
Why did Sam Altman say it's impossible to catch up to GPT 4?
-Sam Altman's statement likely refers to the immense resources, talent, and proprietary technology that OpenAI has, which makes it difficult for others to match their progress. However, he also emphasized that it's the job of developers to continually try to do so, driving the field of AI forward.
What is Mistral AI known for in the AI space?
-Mistral AI is known for its strong research orientation, providing open models with transparent access to their model weights, allowing for full customization by users. They specialize in creating compute-efficient, powerful, and useful AI models and focus on making AI models more efficient, helpful, and trustworthy.
What is the key difference between Mistral AI's Mixr model and other large language models?
-Mixr is designed with a router system that acts like a team of specialists, each handling specific types of problems. This makes the model more efficient as it selects the most relevant experts for each piece of information it processes, leading to faster inference and better performance on certain tasks.
How does Mistral AI's business model differ from traditional open-source models?
-Mistral AI provides a highly permissive license for their models while maintaining private development and funding. Their models are available for free download and use, but the datasets and weights are private, positioning the company as a European alternative to larger AI companies with a focus on ethical AI practices.
What is the arena ELO and how does it measure AI model performance?
-The arena ELO is a benchmark that measures AI model performance through user interaction. Users receive two responses to a prompt and rate which one is better. The model that receives more positive ratings increases its ELO score, providing a subjective measure of model effectiveness based on real-world use.
How does the size of Mistral AI's team compare to that of larger AI companies?
-Mistral AI has a relatively small team of 22 employees, which is significantly fewer than larger AI companies like OpenAI with 770 employees. Despite their small size, Mistral AI has made significant contributions to the AI field, highlighting their efficiency and the caliber of their team.
What is the significance of Mistral AI's funding round raising €385 million?
-The significant funding allows Mistral AI to invest heavily in training models, acquiring more GPUs, and covering server costs. It underscores the company's potential to disrupt the position of larger AI companies and their commitment to advancing AI technology.
What are the challenges that open-source AI models face in comparison to proprietary models like GPT 4?
-Open-source models face challenges such as limited access to massive proprietary datasets, potentially less talent and resources, and the need for effective product distribution and user-friendly interfaces to match the adoption rates of proprietary models.
What is the potential impact of Llama 3 being freely available and capable of competing with GPT 4?
-If Llama 3 can match or exceed the capabilities of GPT 4 while remaining freely available, it could significantly disrupt the AI market, providing an open-source alternative that empowers developers and researchers without the constraints of proprietary models.
How does the architecture of GPT 4, being a mixture of experts, influence other AI companies?
-The mixture of experts architecture allows GPT 4 to be highly efficient and effective. If this approach becomes widely known and adopted by other companies, it could lead to the development of more advanced and competitive AI models in the open-source community.
Outlines
🚀 Open Source AI's Progress Towards GPT-4 Level
The paragraph discusses the rapid progress of open source AI, with a focus on the potential for achieving GPT-4 level capabilities by 2024. It mentions recent developments and statements from CEOs that suggest this could be the pivotal year. The paragraph also highlights the role of Mistral AI, a European startup specializing in efficient AI models, which plans to release an open-source model at the GPT-4 level in 2024. Mistral AI is known for its research orientation, ethical practices, and transparent access to model weights. The company's notable achievements include Mixr, a model that outperforms others with fewer parameters, and their first large language model (LLM), Marr7B, which is available for free but not entirely open source due to private datasets and weights.
📊 Mistral's ELO Leaderboard Performance and Efficiency
This section details Mistral's impressive performance on the ELO leaderboard, a system that ranks AI models based on user ratings of their responses. Despite being a small team, Mistral's medium model ranks fourth, surpassing models from larger companies. The paragraph also emphasizes the efficiency of Mistral's models, particularly Mixr, which is significantly smaller than its competitors yet performs comparably. The discussion touches on the importance of subjective user ratings as a benchmark and the potential impact of Mistral's cost-effective models on the industry, especially considering the rate limits and costs associated with using GPT-4.
🧠 Mixr's Advanced Architecture and Mistral's Impact
The paragraph delves into the innovative architecture of Mistral's Mixr model, which functions like a team of specialized experts, each handling different types of tasks. This system, with a router that directs information to the appropriate 'expert,' allows for efficient processing and is particularly adept at tasks requiring quick thinking and large database retrieval. The paragraph also discusses the implications of GPT-4 potentially using a similar 'mixture of experts' architecture, suggesting that other companies may adopt this approach to improve their models. It highlights the importance of transparency and the potential for smaller models to compete with larger ones through longer training and fine-tuning.
🌟 Open Source Models vs. GPT-4: A Skeptical View
This section presents a skeptical perspective on whether open source models will surpass GPT-4 in the near future. It outlines five reasons why this might be unlikely, including the talent pool at Open AI, access to proprietary data, the structure of centralized teams, the productization of GPT-4, and the superior infrastructure available to companies like Google's DeepMind. The paragraph acknowledges the talent and resources of top AI labs and suggests that while independent labs may achieve commendable results, they may still fall short when compared to the likes of Open AI.
🔥 Llama's Challenge to GPT-4 and the Future of Open Source AI
The final paragraph discusses the potential of Meta's Llama models, particularly Llama 3, which is rumored to compete with GPT-4 while remaining freely available under an open-source license. This development could significantly impact the AI industry by offering a powerful, open alternative to proprietary models. The paragraph also mentions the challenges of advancing from Llama 2 to Llama 3, suggesting that it may require a shift to a 'mixture of experts' architecture. It concludes by posing a question about the future capabilities of Llama and Mistral's models and whether they can overcome the advantages of companies like Open AI and Anthropic.
Mindmap
Keywords
💡Open Source AI
💡GPT-4
💡Llama
💡Mistral
💡Mixture of Experts
💡ELO Rating
💡Apache 2.0 License
💡Ethical AI Practices
💡Cost-effectiveness
💡Benchmarks
💡Multimodality
Highlights
Open source AI is approaching the level of GPT 4, with potential availability in 2024.
Sam Altman, CEO of OpenAI, stated that catching up to GPT 4 is challenging, but developers should still strive to do so.
Mistral AI, a French startup, plans to release an open-source GPT 4 level model in 2024.
Mistral AI focuses on compute-efficient, powerful, and trustworthy AI models with a strong research orientation.
Mixr, a notable model from Mistral AI, is reported to be six times faster than comparable models with 27 billion parameters.
Mistral AI provides transparent access to their model weights, allowing full customization by users.
Mistral AI's first LLM model, Mrra 7B, is available for free download and use, though not traditionally open source.
Mistral AI positions itself as a European alternative to larger AI companies with a focus on ethical AI practices.
Mistral AI's team consists of only 22 employees, including co-founder and CEO Arthur Mench.
Mistral's medium model has been performing well on benchmarks, ranking fourth on the Arena ELO leaderboard.
Mixr 8, a version of Mistral's model with 7 billion parameters, is also outperforming several large language models.
Mistral AI has raised €385 million in funding, indicating significant investment in the company's AI models and infrastructure.
The cost-effectiveness of Mistral's models could disrupt the industry, especially if they approach the capabilities of GPT 4 at a fraction of the cost.
Llama 3, a model from Meta's AI team, is rumored to compete with GPT 4 and remain freely available under the Llama license.
Meta aims to establish Llama models as an enabling technology in the LLM market, potentially breaking OpenAI's dominance.
The architecture of GPT 4, which is speculated to be a mixture of experts model, could inspire other companies to adopt similar strategies.
Despite the advancements, challenges remain for open source models to match the talent, data, and infrastructure of top AI labs like OpenAI.
Elon Musk suggests that GPT 4 level AI could be running locally on laptops in the near future.
