Exact Reasoning over Imprecise Ontologies

Bourahla, Mustapha

doi:10.1007/978-3-319-19578-0_29

Cited by 1 publication

(1 citation statement)

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“…It is stated [7] there is an issue which is commonly faced when dealing with Web-based concepts: namely, vague ontologies. Within the realm of computer science, ontologies are defined as -the definition of domain concepts (extensions) and the relations between them'' [7]. To say that an ontology is vague is when concepts are not clearly defined, i.e with vague language.…”

Section: Issues With Baseline / Ground Truth Valuementioning

confidence: 99%

A Solution to the Hyper Complex, Cross Domain Reality of Artificial Intelligence: The Hierarchy of AI

Kear¹,

Folkes²

2020

IJACSA

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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.

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Section: Issues With Baseline / Ground Truth Valuementioning

confidence: 99%