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AI Watch Assessing Technology Readiness Levels for Artificial Intelligence

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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.

Suggested Citation

  • Fernando Martinez-Plumed & Emilia Gomez Gutierrez & Jose Hernandez-Orallo, 2020. "AI Watch Assessing Technology Readiness Levels for Artificial Intelligence," JRC Research Reports JRC122014, Joint Research Centre.
  • Handle: RePEc:ipt:iptwpa:jrc122014
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    File URL: https://publications.jrc.ec.europa.eu/repository/handle/JRC122014
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    1. Tavneet Suri, 2011. "Selection and Comparative Advantage in Technology Adoption," Econometrica, Econometric Society, vol. 79(1), pages 159-209, January.
    2. Ghoseiri, Keivan & Szidarovszky, Ferenc & Asgharpour, Mohammad Jawad, 2004. "A multi-objective train scheduling model and solution," Transportation Research Part B: Methodological, Elsevier, vol. 38(10), pages 927-952, December.
    3. Qiang Meng & Shuaian Wang & Henrik Andersson & Kristian Thun, 2014. "Containership Routing and Scheduling in Liner Shipping: Overview and Future Research Directions," Transportation Science, INFORMS, vol. 48(2), pages 265-280, May.
    4. Erik Brynjolfsson & Tom Mitchell & Daniel Rock, 2018. "What Can Machines Learn, and What Does It Mean for Occupations and the Economy?," AEA Papers and Proceedings, American Economic Association, vol. 108, pages 43-47, May.
    5. N.R. Jennings & P. Faratin & A.R. Lomuscio & S. Parsons & M.J. Wooldridge & C. Sierra, 2001. "Automated Negotiation: Prospects, Methods and Challenges," Group Decision and Negotiation, Springer, vol. 10(2), pages 199-215, March.
    6. Lex Borghans & Bas Weel, 2006. "The Division of Labour, Worker Organisation, and Technological Change," Economic Journal, Royal Economic Society, vol. 116(509), pages 45-72, February.
    7. Marwin H. S. Segler & Mike Preuss & Mark P. Waller, 2018. "Planning chemical syntheses with deep neural networks and symbolic AI," Nature, Nature, vol. 555(7698), pages 604-610, March.
    8. Nicky J. Welton & Howard H. Z. Thom, 2015. "Value of Information," Medical Decision Making, , vol. 35(5), pages 564-566, July.
    9. Thomas A. Feo & Jonathan F. Bard, 1989. "Flight Scheduling and Maintenance Base Planning," Management Science, INFORMS, vol. 35(12), pages 1415-1432, December.
    10. Erik Brynjolfsson & Daniel Rock & Chad Syverson, 2018. "Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 23-57, National Bureau of Economic Research, Inc.
    11. Sofia Samoili & Montserrat Lopez Cobo & Emilia Gomez & Giuditta De Prato & Fernando Martinez-Plumed & Blagoj Delipetrev, 2020. "AI Watch. Defining Artificial Intelligence. Towards an operational definition and taxonomy of artificial intelligence," JRC Research Reports JRC118163, Joint Research Centre.
    12. David Silver & Aja Huang & Chris J. Maddison & Arthur Guez & Laurent Sifre & George van den Driessche & Julian Schrittwieser & Ioannis Antonoglou & Veda Panneershelvam & Marc Lanctot & Sander Dieleman, 2016. "Mastering the game of Go with deep neural networks and tree search," Nature, Nature, vol. 529(7587), pages 484-489, January.
    13. David Silver & Julian Schrittwieser & Karen Simonyan & Ioannis Antonoglou & Aja Huang & Arthur Guez & Thomas Hubert & Lucas Baker & Matthew Lai & Adrian Bolton & Yutian Chen & Timothy Lillicrap & Fan , 2017. "Mastering the game of Go without human knowledge," Nature, Nature, vol. 550(7676), pages 354-359, October.
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    Keywords

    Artificial Intelligence; Technology Readiness Level; Technology;
    All these keywords.

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