Review of algorithms for artificial intelligence on low memory devices

Abstract

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