Summary of Creating Scalable Agi: the Open General Intelligence Framework, by Daniel A. Dollinger et al.
Creating Scalable AGI: the Open General Intelligence Framework
by Daniel A. Dollinger, Michael Singleton
First submitted to arxiv on: 24 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The recent advancements in Large Language Models (LLMs) have led to significant progress in narrow tasks such as image classification, language translation, coding, and writing. However, these models face limitations in reliability and scalability due to their siloed architectures. To address real-world challenges like medical diagnosis, quality assurance, equipment troubleshooting, and financial decision-making, a more capable Artificial General Intelligence (AGI) system is required. The paper presents the Open General Intelligence (OGI) framework, which adopts a modular approach to design intelligent systems based on human cognition principles. OGI integrates multiple specialized modules using a dynamic processing system and fabric interconnect, enabling real-time adaptability, multi-modal integration, and scalable processing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI researchers have made progress in specific tasks like image classification and language translation. But these models are limited because they can only work with one type of data at a time. To solve real-world problems, we need a better AI system that can combine many types of data together. The Open General Intelligence (OGI) framework is a new way to design AI systems that follows human thinking principles. It uses many smaller modules that work together seamlessly. |
Keywords
» Artificial intelligence » Image classification » Multi modal » Translation
