Advancing Zero-Shot Spatio-Temporal Learning: A Novel Framework for AI-Generated Synthetic Intelligence Systems

Abstract

Spatio temporal learning from scratch is a revolution that allows AI generated synthetic intelligence systems to learn tasks without any previous training to the task itself. This work presents a novel combination of transformer-encoded and membrane augmented domain adaptor frame that enables the AI systems to generalize over domains at run time. Unlike existing conventional AI models that require large amounts of labeled dataset, our work addresses this problem through the use of: self-supervised meta learning, multi modal data fusion, neuro symbol representation in order to extract spatial and temporal patterns in a dataset arising from multiple data sources. Moreover, the proposed system also comprises federated learning with decentralized knowledge graphs secured by blockchain technology for ensuring private transfer of AI knowledge among different organizations. Multiple real world applications created for autonomous systems, climate prediction, smart healthcare and industrial digital twins are evaluated against the framework in which the best performance is achieved on zero shot applications. The results from the experiment demonstrate how the system can reason, interpret, and makes accurate predictions in unseen domains and still be very computationally efficient. Symbolic logic integration provides an additional aspect for explainability, while further improving the understanding of the AI decisions. By bridging the gap between zero shot learning, spatio-temporal modeling, and decentralized Artificial Intelligence, and by ensuring adaptable, efficient, and privacy considering artificial intelligence, this research paves the way for next generation of synthetic intelligence. This leads us to future directions of scaling the framework to high dimension and making the task more real time adaptable.