Visible to the public What Metrics Should We Use to Measure Commercial AI?

TitleWhat Metrics Should We Use to Measure Commercial AI?
Publication TypeJournal Article
Year of Publication2019
AuthorsHughes, Cameron, Hughes, Tracey
JournalAI Matters
Volume5
Pagination41–45
Date Publishedaug
KeywordsCollaboration, composability, Human Behavior, information assurance, Metrics, policy-based governance, pubcrawl, resilience, Resiliency, Scalability
Abstract

In AI Matters Volume 4, Issue 2, and Issue 4, we raised the notion of the possibility of an AI Cosmology in part in response to the "AI Hype Cycle" that we are currently experiencing. We posited that our current machine learning and big data era represents but one peak among several previous peaks in AI research in which each peak had accompanying "Hype Cycles". We associated each peak with an epoch in a possible AI Cosmology. We briefly explored the logic machines, cybernetics, and expert system epochs. One of the objectives of identifying these epochs was to help establish that we have been here before. In particular we've been in the territory where some application of AI research finds substantial commercial success which is then closely followed by AI fever and hype. The public's expectations are heightened only to end in disillusionment when the applications fall short. Whereas it is sometimes somewhat of a challenge even for AI researchers, educators, and practitioners to know where the reality ends and hype begins, the layperson is often in an impossible position and at the mercy of pop culture, marketing and advertising campaigns. We suggested that an AI Cosmology might help us identify a single standard model for AI that could be the foundation for a common shared understanding of what AI is and what it is not. A tool to help the layperson understand where AI has been, where it's going, and where it can't go. Something that could provide a basic road map to help the general public navigate the pitfalls of AI Hype.

URLhttps://dl.acm.org/doi/10.1145/3340470.3340479
DOI10.1145/3340470.3340479
Citation Keyhughes_what_2019