Artificial General Intelligence - Control of Real-time Entity (AGI CORE)
Disciplines
Artificial Intelligence and Robotics | Computer Sciences | Other Computer Sciences
Abstract (300 words maximum)
The "Artificial General Intelligence—Control of Real-time Entities" (AGI CORE) project seeks to revolutionize real-time control systems by integrating local AI systems with cloud-based large language models (LLMs) within a hybrid framework. This approach leverages a mixed modality that combines distributed local processing with centralized computation, effectively addressing critical challenges in communication, processing delays, and real-time reaction capabilities. At the core of AGI CORE is an artificial general intelligence model designed to comprehend complex contextual inputs and generate human-like, adaptive responses. By utilizing a dual-architecture—where local systems handle immediate, low-latency tasks while centralized models provide deeper analytical support—the framework achieves processing times of less than a second without sacrificing precision or accuracy. This balance between speed and depth is particularly beneficial in environments that demand rapid decision-making. The system demonstrates significant potential across a wide range of applications, including robotics, military operations, automotive control systems, and medical diagnostics. Early results indicate that the integration of distributed local systems with centralized LLMs not only mitigates common bottlenecks in communication and data processing but also enhances the system's ability to react in real time. This duality facilitates rapid recognition of visual inputs, robust image analysis, and contextual problem-solving, thereby optimizing performance in dynamic and unpredictable settings. Moreover, the AGI CORE project addresses operational disparities by establishing a versatile and adaptive platform that can seamlessly adjust to varying environmental conditions. By laying a solid foundation for an AGI model that combines adaptive reasoning, critical thinking, and rapid responsiveness, this research advances both the theoretical framework and practical applications of real-time intelligent systems. The outcomes of this study pave the way for next-generation AI control systems that are not only efficient and accurate but also resilient in the face of evolving technological challenges.
Academic department under which the project should be listed
SPCEET - Robotics and Mechatronics Engineering
Primary Investigator (PI) Name
Razvan Voicu
Artificial General Intelligence - Control of Real-time Entity (AGI CORE)
The "Artificial General Intelligence—Control of Real-time Entities" (AGI CORE) project seeks to revolutionize real-time control systems by integrating local AI systems with cloud-based large language models (LLMs) within a hybrid framework. This approach leverages a mixed modality that combines distributed local processing with centralized computation, effectively addressing critical challenges in communication, processing delays, and real-time reaction capabilities. At the core of AGI CORE is an artificial general intelligence model designed to comprehend complex contextual inputs and generate human-like, adaptive responses. By utilizing a dual-architecture—where local systems handle immediate, low-latency tasks while centralized models provide deeper analytical support—the framework achieves processing times of less than a second without sacrificing precision or accuracy. This balance between speed and depth is particularly beneficial in environments that demand rapid decision-making. The system demonstrates significant potential across a wide range of applications, including robotics, military operations, automotive control systems, and medical diagnostics. Early results indicate that the integration of distributed local systems with centralized LLMs not only mitigates common bottlenecks in communication and data processing but also enhances the system's ability to react in real time. This duality facilitates rapid recognition of visual inputs, robust image analysis, and contextual problem-solving, thereby optimizing performance in dynamic and unpredictable settings. Moreover, the AGI CORE project addresses operational disparities by establishing a versatile and adaptive platform that can seamlessly adjust to varying environmental conditions. By laying a solid foundation for an AGI model that combines adaptive reasoning, critical thinking, and rapid responsiveness, this research advances both the theoretical framework and practical applications of real-time intelligent systems. The outcomes of this study pave the way for next-generation AI control systems that are not only efficient and accurate but also resilient in the face of evolving technological challenges.