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  <titleInfo>
    <title>AI watch, assessing technology readiness levels for artificial intelligence</title>
  </titleInfo>
  <name type="corporate">
    <namePart>Comisión Europea</namePart>
    <namePart>Centro Común de Investigación</namePart>
    <role>
      <roleTerm authority="marcrelator" type="text">creator</roleTerm>
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  <name type="personal">
    <namePart>Hernández-Orallo, José</namePart>
  </name>
  <name type="personal">
    <namePart>Gómez, Emilia</namePart>
  </name>
  <name type="personal">
    <namePart>Martínez-Plumed, Fernando</namePart>
  </name>
  <name type="corporate">
    <namePart>Comisión Europea</namePart>
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  <originInfo>
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      <placeTerm type="code" authority="marccountry">lu</placeTerm>
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    <place>
      <placeTerm type="text">Luxembourg</placeTerm>
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    <publisher>: Publications Office of the European Union</publisher>
    <dateIssued>2020</dateIssued>
    <issuance>monographic</issuance>
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    <extent>72 p. : gráf. ; 1 documento PDF</extent>
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  <abstract>This document describes an example-based methodology to categorise and assess several AI technologies, by mapping them onto 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 a base for a broader discussion of AI technologies. Finally, we use the dynamics of several AI technology at different generality levels and moments of time to forecast some short-term and mid-term trends for AI.</abstract>
  <note type="statement of responsibility">/  Join Research Centre ;  José Hernández-Orallo, Emilia Gómez, Fernando Martínez-Plumed</note>
  <note>Bibliografía: p. 53-61</note>
  <note>Except otherwise noted, the reuse of this document is authorised under the Creative Commons Attribution 4.0 International (CC BY 4.0) licence  European Union</note>
  <subject authority="lcsh">
    <topic>Inteligencia Artificial</topic>
  </subject>
  <subject>
    <topic>AI</topic>
  </subject>
  <subject>
    <topic>IA</topic>
  </subject>
  <subject>
    <topic>ai technologies</topic>
  </subject>
  <subject>
    <topic> technology assessment</topic>
  </subject>
  <subject>
    <topic>research and development </topic>
  </subject>
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    <titleInfo>
      <title>JRC Technical Reports</title>
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  <identifier type="isbn">978-92-76-22987-2 (PDF)</identifier>
  <identifier type="issn">1831-9424  (online)</identifier>
  <identifier type="uri">https://op.europa.eu/en/publication-detail/-/publication/cb8f38a9-3385-11eb-b27b-01aa75ed71a1/language-en?WT.mc_id=Selectedpublications&amp;WT.ria_c=41957&amp;WT.ria_f=5961&amp;WT.ria_ev=search</identifier>
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  <accessCondition type="useAndReproduction">Except otherwise noted, the reuse of this document is authorised under the Creative Commons Attribution 4.0 International (CC BY 4.0) licence  European Union</accessCondition>
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