autotrain

Related articles

GraphRAG Evolves:

Understanding PathRAG and the Future of the Retrieval Augmented Generation Engine Retrieval Augmented Generative Engine (RAGE) has enhanced how we interact with large language models (LLMs). Instead of relying solely on the knowledge baked into the model during training, RAG systems can pull in relevant information from external sources, making them more accurate, up-to-date, and trustworthy. But traditional RAG, often relying on vector databases, has limitations. A new approach, leveraging knowledge graphs, is rapidly evolving, and […]

Learn More
fundamentalAGI

FundamentalAGI Blueprint

funAGI Objective: Develop a comprehensive Autonomous General Intelligence (AGI) system named FundamentalAGI (funAGI). This system integrates various advanced AI components to achieve autonomous general intelligence, leveraging multiple frameworks, real-time data processing, advanced reasoning, and a sophisticated memory system. Design will be modular for dynamic adaptation using modern object oriented programming technique primary in the Python language. Components of funAGI: the big picture Detailed Architecture and Implementation Plan 1. Cognitive Architecture 2. Multi-Modal and Multi-Model Integration […]

Learn More

aGLM

aGLM, or Autonomous General Learning Model, is designed to operate as a core model for autonomous data parsing and learning from memory in the context of artificial intelligence systems. It’s a pivotal element within a broader system called RAGE (Retrieval Augmented Generative Engine). Key aspects and functionalities of aGLM: Autonomous Learning: aGLM is built to learn autonomously from interactions and data retrievals. It continuously updates its knowledge base, refining its capabilities based on new data […]

Learn More