Unleashing Artificial Cognition: Integrating Multiple AI Systems
This work addresses the challenge of bridging raw computation and human-like understanding in AI, though it appears incremental as it combines existing techniques like language models and vector databases in a specific demonstration environment.
The study tackled the problem of integrating multiple AI systems to achieve artificial cognition by fusing a language model with a Chess engine, resulting in a system that predicts moves and provides strategic explanations. The approach demonstrated versatility with potential applications in domains like medical diagnostics and financial forecasting.
In this study, we present an innovative fusion of language models and query analysis techniques to unlock cognition in artificial intelligence. The introduced open-source AI system seamlessly integrates a Chess engine with a language model, enabling it to predict moves and provide strategic explanations. Leveraging a vector database to achieve retrievable answer generation, our AI system elucidates its decision-making process, bridging the gap between raw computation and human-like understanding. Our choice of Chess as the demonstration environment underscores the versatility of our approach. Beyond Chess, our system holds promise for diverse applications, from medical diagnostics to financial forecasting. Our AI system is available at https://github.com/TheOpenSI/CoSMIC.git