Abstract
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.
Highlights
A great deal of public funded investment is going into Artificial Intelligence (AI) and yet the authors propose that there are still some fundamental issues with the different classifications and understanding of AI
The authors have grouped together a series of definitions that vary according to the source: AI can be defined as ―any system . . . that generates adaptive behaviour to meet goals in a range of environments can be said to be intelligent‖ [23]; AI can be seen as ―intelligent systems‘‘ that ―are expected to work, and work well in many different environments [27]
In addition to the above there are sources that argue AI is the wrong term entirely: Psychometric Artificial Intelligence (PAI) is according to [9] more suitable, since it refers to ―building informationprocessing entities capable of at least solid performance on all established, validated tests of intelligence and mental ability‘‘; An alternative view [70] argues that the lack of consistency in definitions goes beyond semantic differences, as it poses a threat to developments in the field
Summary
A great deal of public funded investment is going into AI and yet the authors propose that there are still some fundamental issues with the different classifications and understanding of AI. That generates adaptive behaviour to meet goals in a range of environments can be said to be intelligent‖ [23]; AI can be seen as ―intelligent systems‘‘ that ―are expected to work, and work well in many different environments [27] Their property of intelligence allows them to maximize the probability of success even if full knowledge of the situation is not available‘‘; AI is defined as a division of computer science, in particular, ―the study of the relation between computation and cognition‘‘ [5]; Others [61] note how AI is a ―big field‘‘ that can be defined as ―the study of agents that receive precepts from the environment and perform actions‘‘. With multiple definitions, ―progress made under one characterization of AI is not viewed as success by others who operate under a different perception of it‘‘ [70] This results in diminished confidence in the field, as well as ―promote(ing) premature conclusions of what can and cannot be accomplished and limit progress and funding along research paths‘‘.
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