Open source just might have cracked the code for super-powerful AI with the new models from Mr. AI and Gemini using the concept of Mixture of Experts. This unique approach utilizes eight specialized models working together, like a Pokemon team, to handle different tasks. The gatekeeper, or team leader, decides which expert handles each task, resulting in faster run times and better results compared to a general large model. This concept is not entirely new, originating from a 1991 research paper, and has now been scaled for translation on a massive scale. If you want to try these models, you can find them on chat.lss.org and SDK of.ai, or search for them on GitHub for machine learning models. Stay tuned for a comparison between Mr. AI Large and gbt 4 in my next video! π€π₯
Key Takeaways
| Open Source AI models "Large" and "Gemini BR 1.5" challenge GPT-3.5 and D2 models |
| Both new models utilize the "Mixture of Experts" architecture for improved performance |
| Mixture of Experts uses multiple smaller models to handle specific tasks, improving efficiency and accuracy |
π€ The Rise of New AI Models and the Mixture of Experts Architecture
π The Introduction of New AI Models
gbd4, the leading AI model, faces new competition as both "Large" from Mr. AI and "Gemini BR 1.5" join the AI platform landscape. Large, while close in performance to gbd4, is unfortunately only available through the closed platforms of Microsoft’s Azora AI and Mr. AI’s LA form. Meanwhile, Gemini BR 1.5 raises eyebrows with its utilization of the groundbreaking "Mixture of Experts" architecture.
π Understanding the Mixture of Experts
In the world of AI, the traditional approach involves a single monolithic model handling all tasks. However, Mixture of Experts takes a different approach, using a team of smaller models working together to handle specific tasks. Think of them as specialized "PokΓ©mons", each excelling in unique areas such as math, coding, and language processing.
Key Takeaways
| Mixture of Experts utilizes a team of specialized models for improved efficiency |
| It allows for faster processing and reduces hardware costs |
π§ How Mixture of Experts Transforms AI Models
π Increased Efficiency Through Specialization
In contrast to a general large model, Mixture of Experts leverages eight specialized smaller models, each overseeing distinct tasks, leading to improved overall efficiency. Furthermore, it has been proven that specialization in smaller models leads to superior performance compared to general large models.
Comparison Between Mr.AI’s Mixture 87b and gbd 3.5
| Mixture 87b outperforms gbd 3.5 in Arena ELO ratings and commercial usability |
Italicized note: Mixture 87b ensures reliable performance and commercial use without constraint
π Training the "Gatekeeper"
Alongside these specialized models, the "Gatekeeper" ensures optimal task assignments during the training phase. This approach not only trains the model but also controls which expert handles specific tasks, ensuring seamless interaction between the user’s queries and the experts’ capabilities.
π The Roots and Evolution of Mixture of Experts
π Historical Origins of Mixture of Experts
Surprisingly, Mixture of Experts is not a new concept, with its origins dating back to a research paper from 1991. However, it was in 2017 when Google’s Jeff Dean and his team took this concept to a new level, scaling it for massive translation systems.
Quote: "Mixture of Experts has a rich history tracing back to a 1991 research paper, reflecting its enduring relevance and potential"
π Accessing New AI Models and Mixture of Experts
For those eager to explore these new models and delve into Mixture of Experts, platforms such as chat.lss.org for Large from Mr. AI or SDK of.ai for Mixture 87b provide a captivating starting point. Additionally, GitHub serves as a treasure trove for unearthing a multitude of AI models to experiment with.
FAQ
| Where to access AI models: chat.lss.org for Large, SDK of.ai for Mixture 87b, and GitHub for various models |
π The Future of AI and the Ongoing Battle of Models
With the emergence of new and innovative AI models utilizing Mixture of Experts, the landscape of AI technology continues to evolve. As advancements in architecture and model capabilities unfold, the possibilities for the future of AI are endless.
Conclusion
Mixture of Experts lays the foundation for a new era in AI, where specialized collaboration leads to unprecedented efficiency and performance enhancements.
In conclusion, the introduction of new AI models such as Large and Gemini BR 1.5, backed by the Mixture of Experts architecture, marks a significant step towards unlocking the full potential of AI technology. As the landscape of AI continually evolves, the use of specialized models and innovative architectures promises a future of unprecedented possibilities.
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