Generalizing AI: Challenges and Opportunities for Plug and Play AI Solutions

Abstract

Artificial Intelligence (AI) has revolutionized today's Internet of Things (IoT) applications and services by introducing significant technological enhancements across a multitude of domains. With the deployment of the fifth generation (5G) mobile communication network, smart city visions of fast, on-demand, intelligent user-specific services are now becoming a reality. The concept of connected IoT is evolving into connected intelligent things. The advancements of both AI techniques, coupled with the sophistication of edge devices, is now leading to a new era of connected intelligence. Moving the intelligence toward end devices must account for latency demands and simplicity of selecting the type of AI technique to be used. Moreover, since most AI techniques require learning from big data sets and reasoning using a multitude of classification patterns, new simplified and collaborative solutions are now necessary more than ever. As such, the concept of introducing decentralized and distributed ‘Plug and Play’ (PnP) AI tools is now becoming more attractive given the vast numbers in edge devices, data volume and AI techniques. To this end, this article envisions a novel general AI solution that can be adapted to autonomously select the type of machine learning (ML) algorithm, the data set to be used, and provide reasoning in regards to data selection for optimal features extraction. Moreover, the solution performs the necessary training and all the necessary parameter fine-tunings to achieve the highest level of generality and simplicity for AI at the edge. We explore several aspects related to PnP-AI and its impact in the smart city ecosystem.