Development of an Adaptive Multi-Domain Artificial Intelligence System Built using Machine Learning and Expert Systems Technologies

This paper presents a novel Adaptive Multi-Domain Artificial Intelligence Technology (AMAIT) that combines classical expert systems, gradient descent trained expert systems (GDTES), and generative AI to create a system capable of learning new problem domains and making decisions with minimal programming. The research discusses the integration of these technologies, their benefits, and the challenges they pose, particularly in terms of explainability and ethical implications. The paper delves into the development of GDTES networks, optimization techniques, and the use of large language models like LLaMA-2 for network creation. The system aims to be self-learning and adaptable, requiring human input for accuracy and compliance. The text also explores the potential and limitations of generative AI, addressing ethical concerns and the need for responsible use. Future work includes refining the system, expanding its applicability, and addressing the gap between AI and artificial general intelligence (AGI).