Review of Algorithms for Artificial Intelligence on Low Memory Devices

The aim of the article is to conceptualise a more compact and efficient version algorithms for artificial intelligence (AI). core objective construct design self-optimising self-adapting autonomous (AutoAI) that can be applied edge analytics using real-time data. methodology based on synthesising existing knowledge AI (i.e., modelling, symbolic reasoning, modal logic), with novel concepts from neuromorphic engineering in combination deep learning reinforcement learning, neural networks, evolutionary algorithms) data science statistics, linear regression, Bayesian methods). Far-reaching implications are expected unique integration approaches analytics.