As we delve into 2024, Retrieval-Augmented Generation (RAG) and Mixture of Experts (MoE) stand out as the two key technologies in the realm of artificial intelligence (AI) to watch. These technologies are reshaping the way AI systems process information and specialise in tasks. At FLock, we believe in the immense value and innovation these technologies bring. We are keenly focused on utilising RAG and MoE to develop a more knowledgeable and intelligent AI platform, aligning perfectly with our mission to advance decentralised AI applications for the community and make them accessible to everyone.
🧠 Understanding RAG
RAG enhances large language models (LLMs) like ChatGPT by retrieving facts from external knowledge bases, grounding responses in the most accurate, current information. RAG’s ability to incorporate specific datasets — from knowledge snippets to user-specific data — enables highly relevant and personalised responses.
In FLock’s forthcoming AI Co-Creation platform, RAG plays a crucial role in harnessing community-contributed external knowledge. This capability enables the creation of specialised chatbots, each tailored to specific fields, guaranteeing deeply informed and contextually appropriate responses. Additionally, RAG allows for the development of local RAG chatbox customization, offering users the ability to build chatbots with local-specific content and perspectives, further enhancing the personalization and relevance of interactions.
RAG is poised to revolutionise both business and consumer applications, transforming foundational LLMs into dynamic, application-specific tools. In decentralised environments like Web3, RAG substantially enhances the capabilities of AI applications by tapping into extensive, decentralised information sources and improving decentralised chatbots and AI assistants.
🤖️ Exploring MoE
Mixture of Experts (MoE) is steering AI towards a modular, specialised approach, dividing tasks among multiple expert models, each proficient in specific data types or tasks, for precise, efficient problem-solving. A significant validation of MoE’s effectiveness is evident in Mistral’s implementation. Their Mixtral 8x7B model, utilising an 8 x 7B MoE configuration, has achieved performance levels comparable to ChatGPT 3.5, underscoring MoE’s capability and potential in AI. In MoE, a large AI model is segmented, activating only a few parts at a time during training and inference for efficiency, resembling a smaller model in operation. This approach is cost-effective, maintaining high quality with reduced expenses.
Looking ahead, Mistral model’s emergence signifies that using open-source models as agents is now feasible. We believe that in the coming year, open-source models with similar architectures could reach the effectiveness of GPT-4. FLock’s integration of MoE with Agent Planning involves using numerous smaller, task-specific models coordinated by a MoE-based master agent. This strategy aligns with FLock’s goal to enhance open-source models, creating a cohesive, intelligent system. Notably, the decentralisation aspect of MoE is particularly relevant to its hosting and inference stages, offering a scalable and innovative solution in AI applications.
The integration of RAG and MoE represents a significant step forward in AI, providing more efficient and tailored experiences. These technologies excel in delivering specific, up-to-date information, revolutionising how we interact with advanced AI applications. We look forward to exploring these cutting-edge developments with our community, igniting a year filled with innovation, collaboration, and progress in AI.
About FLock.io
FLock.io is a decentralized and permissionless platform for co-owned AI models and Dapps. By harnessing the synergies of Federated Learning and blockchain technologies, we address the growing demands of models and potential data breach threats, guaranteeing secure model training without revealing underlying source data, and fair rewards to data contribution and community collaboration.
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