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AI Watch Assessing Technology Readiness Levels for Artificial Intelligence

Fernando Martinez-Plumed (), Emilia Gomez Gutierrez () and Jose Hernandez-Orallo ()
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Fernando Martinez-Plumed: European Commission - JRC,
Emilia Gomez Gutierrez: European Commission - JRC,
Jose Hernandez-Orallo: Universitat Politècnica de Valencia

No JRC122014, JRC Working Papers from Joint Research Centre (Seville site)

Abstract: Artificial Intelligence (AI) offers the potential to transform our lives in radical ways. However, the main unanswered questions about this foreseen transformation are when and how this is going to happen. Not only do we lack the tools to determine what achievements will be attained in the near future, but we even underestimate what various technologies in AI are capable of today. Many so-called breakthroughs in AI are simply associated with highly-cited research papers or good performance on some particular benchmarks. Certainly, the translation from papers and benchmark performance to products is faster in AI than in other non-digital sectors. However, it is still the case that research breakthroughs do not directly translate to a technology that is ready to use in real-world environments. This document describes an exemplar-based methodology to categorise and assess several AI research and development technologies, by mapping them into Technology Readiness Levels (TRL) (e.g., maturity and availability levels). We first interpret the nine TRLs in the context of AI and identify different categories in AI to which they can be assigned. We then introduce new bidimensional plots, called readiness-vs-generality charts, where we see that higher TRLs are achievable for low-generality technologies focusing on narrow or specific abilities, while low TRLs are still out of reach for more general capabilities. We include numerous examples of AI technologies in a variety of fields, and show their readiness-vs-generality charts, serving as exemplars. Finally, we use the dynamics of several AI technology exemplars at different generality layers and moments of time to forecast some short-term and mid-term trends for AI.

Keywords: Artificial Intelligence; Technology Readiness Level; Technology (search for similar items in EconPapers)
Date: 2020-11
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