Skip to main content

A solution to the hyper complex, cross domain reality of artificial intelligence: The hierarchy of AI.

Kear, A. and Folkes, S.L., 2020. A solution to the hyper complex, cross domain reality of artificial intelligence: The hierarchy of AI. International Journal of Advanced Computer Science and Applications, 11 (3), 49 - 59.

Full text available as:

A solution to the Hyper complex, Cross domain reality of Ai.pdf - Published Version
Available under License Creative Commons Attribution.


DOI: 10.14569/IJACSA.2020.0110307


Artificial Intelligence (AI) is an umbrella term used to describe machine-based forms of learning. This can encapsulate anything from Siri, Apple's smartphone-based assistant, to Tesla's autonomous vehicles (self-driving cars). At present, there are no set criteria to classify AI. The implications of which include public uncertainty, corporate scepticism, diminished confidence, insufficient funding and limited progress. Current substantial challenges exist with AI such as the use of combinationally large search space, prediction errors against ground truth values, the use of quantum error correction strategies. These are discussed in addition to fundamental data issues across collection, sample error and quality. The concept of cross realms and domains used to inform AI, is considered. Furthermore there is the issue of the confusing range of current AI labels. This paper aims to provide a more consistent form of classification, to be used by institutions and organisations alike, as they endeavour to make AI part of their practice. In turn, this seeks to promote transparency and increase trust. This has been done through primary research, including a panel of data scientists / experts in the field, and through a literature review on existing research. The authors propose a model solution in that of the Hierarchy of AI.

Item Type:Article
Uncontrolled Keywords:Artificial intelligence; classification; ground truth value; Hierarchy of AI; Model of AI
Group:Faculty of Media & Communication
ID Code:33948
Deposited By: Symplectic RT2
Deposited On:01 May 2020 15:22
Last Modified:14 Mar 2022 14:21


Downloads per month over past year

More statistics for this item...
Repository Staff Only -