Abstract: A system includes a neural network architecture. It a new type of neural network able to process statically mapped as well as temporally sequenced information with much better power utilization, data requirements and operational efficiencies. Unlike prior artificial neural network approaches, the present invention includes uniquely defined sets of relationships. The unique use of non-linear input-output mapping functions combined with a time-variant pilot function, and a deterministically bounded, fully-differentiable, nonlinear resonance field subsystem allows the present invention to be readily deployed to work with virtually any neural network architecture/implementation including photonic, opto-acoustic or other variants.

Abstract: An advanced AI system, known as Bayesian Graph-Based Retrieval-Augmented Generation with Synthetic Feedback Loop (BG-RAG-SFL), combines Bayesian evaluation, graph-based retrieval, and synthetic data feedback to create a continuously improving AI platform. The present invention integrates multiple LLMs, optimizing their performance while managing complexities across different models. Key features include a knowledge graph-based RAG system, a Bayesian evaluation network, a secondary ground-truth graph for verification, synthetic data generation for ongoing improvement, and a multi-agent verification system. The system also functions as an AI operating system capable of acting as a virtual user with screen I/O control and managing multiple computers as an intelligent process automation system.

Abstract: A system and method for enhancing or restoring audio data utilizing an artificial intelligence module, and more particularly utilizing deep neural networks and generative adversarial networks. The system and method are both able to train the artificial intelligence module to provide for different format and other characteristic-specific transforms for determining how to restore audio to source quality and even beyond. The present invention includes the steps of acquiring source data, pre-processing the source data, implementing the artificial intelligence module, indexing the data, applying transforms, and optimizing the data for a particular audio modality.