Figure 1 - Contributions of the Paper and Fair, Accessible, Interpretable and Reproducible (FAIR) AI adapted from (Halgamuge S., 2021) Artificial intelligence (AI) has seen an explosive growth over the last 20 years, largely through recent advances in machine learning (ML) - the data-centric branch of AI. A data-centric AI system consists of an AI model (a structure or architecture) and a method or learning algorithm that enables that model to derive usable information from data. Sometimes the data are exploratory, like the genomic data arriving from different parts of the world about constantly mutating viruses. To discover the presence of new variants or labels, we can feed an AI model with such uninterpreted data, so that researchers will be able to use this AI model to assign labels. Such AI models need unsupervised learning (UL) algorithms to extract information from unlabeled and uninterpreted data. We could also ask those researchers themselves to label data with appropriate variant labels, and feed both labels and genomic data to an AI model that can then use a supervised learning algorithm like deep learning (DL), so that it can serve as a predictor for known variants of the virus. If such an AI model of sufficient strength requires it to be large, deep and complex, we call it a deep neural network (DNN). Shallow neural networks, commonly referred to as Neural Networks (NNs) are data driven mathematical models consisting of about three layers of artificial neurons or nodes (several linear and nonlinear processing elements) which are interconnected through weighted connections.

F. Fallside We develop a sequential adaptation algorithm for radial basis function (RBF) neural networks of Gaussian nodes, based on the method of successive F-Projections.This method makes use of each observation efficiently in that the network mapping function so obtained is consistent with that information and is also optimal in the least L

We develop a sequential adaptation algorithm for radial basis function (RBF) neural networks of Gaussian nodes, based on the method of successive F-Projections.