Predictable and Explainable AI at the Edge

Lead Research Organisation: Newcastle University
Department Name: Sch of Computing

Abstract

The project proposed focuses on the serious implications and impact of delivering AI systems in a real world setting. We aim to explore the interplay between complexity and abstraction using a promising all boolean machine learning (ML) method called the Tsetlin Machine (TM). The TM approach will be modified in an effort to improve dependability and verifiability by reducing uncertainty and unrepeatability, and by providing visualisation and analysis tools to extract extended knowledge from the background boolean representation. In addition, we will explore methods to bound the tradeoff between bits assimilated and energy usage,

With this in place, our long term direction is to promote this technology in such a way that those who are thinking more deeply about the pressing safety problems associated with the new agenda in computational autonomy, still have the opportunity to deploy AI based learning systems safely and reliably.

Publications

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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/N509528/1 30/09/2016 30/03/2022
2257500 Studentship EP/N509528/1 31/03/2019 29/06/2023 Jonathan Edwards
EP/R51309X/1 30/09/2018 29/09/2023
2257500 Studentship EP/R51309X/1 31/03/2019 29/06/2023 Jonathan Edwards