<?xml version="1.0" encoding="utf-8"?>
<TEI xmlns="http://www.tei-c.org/ns/1.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:hal="http://hal.archives-ouvertes.fr/" xmlns:gml="http://www.opengis.net/gml/3.3/" xmlns:gmlce="http://www.opengis.net/gml/3.3/ce" version="1.1" xsi:schemaLocation="http://www.tei-c.org/ns/1.0 http://api.archives-ouvertes.fr/documents/aofr-sword.xsd">
  <teiHeader>
    <fileDesc>
      <titleStmt>
        <title>HAL TEI export of hal-04387128</title>
      </titleStmt>
      <publicationStmt>
        <distributor>CCSD</distributor>
        <availability status="restricted">
          <licence target="https://creativecommons.org/publicdomain/zero/1.0/">CC0 1.0 - Universal</licence>
        </availability>
        <date when="2026-05-17T15:32:52+02:00"/>
      </publicationStmt>
      <sourceDesc>
        <p part="N">HAL API Platform</p>
      </sourceDesc>
    </fileDesc>
  </teiHeader>
  <text>
    <body>
      <listBibl>
        <biblFull>
          <titleStmt>
            <title xml:lang="en">The AI Race: Why Current Neural Network-based Architectures are a Poor Basis for Artificial General Intelligence</title>
            <author role="aut">
              <persName>
                <forename type="first">Jérémie</forename>
                <surname>Sublime</surname>
              </persName>
              <email type="md5">e85b7af271e587a79b8300169b636063</email>
              <email type="domain">gmail.com</email>
              <idno type="idhal" notation="string">jeremie-sublime</idno>
              <idno type="idhal" notation="numeric">184388</idno>
              <idno type="halauthorid" notation="string">1118816-184388</idno>
              <idno type="ORCID">https://orcid.org/0000-0003-0508-8550</idno>
              <idno type="GOOGLE SCHOLAR">https://scholar.google.fr/citations?user=UhAxK5EAAAAJ</idno>
              <idno type="IDREF">https://www.idref.fr/197915299</idno>
              <affiliation ref="#struct-95837"/>
            </author>
            <editor role="depositor">
              <persName>
                <forename>Jérémie</forename>
                <surname>Sublime</surname>
              </persName>
              <email type="md5">e85b7af271e587a79b8300169b636063</email>
              <email type="domain">gmail.com</email>
            </editor>
          </titleStmt>
          <editionStmt>
            <edition n="v1" type="current">
              <date type="whenSubmitted">2024-01-11 10:24:41</date>
              <date type="whenModified">2024-01-17 03:48:19</date>
              <date type="whenReleased">2024-01-16 09:21:34</date>
              <date type="whenProduced">2024-01-10</date>
              <date type="whenEndEmbargoed">2024-01-11</date>
              <ref type="file" target="https://hal.science/hal-04387128v1/document">
                <date notBefore="2024-01-11"/>
              </ref>
              <ref type="file" subtype="author" n="1" target="https://hal.science/hal-04387128v1/file/15315wPg%23s.pdf" id="file-4387128-3815531">
                <date notBefore="2024-01-11"/>
              </ref>
            </edition>
            <respStmt>
              <resp>contributor</resp>
              <name key="64852">
                <persName>
                  <forename>Jérémie</forename>
                  <surname>Sublime</surname>
                </persName>
                <email type="md5">e85b7af271e587a79b8300169b636063</email>
                <email type="domain">gmail.com</email>
              </name>
            </respStmt>
          </editionStmt>
          <publicationStmt>
            <distributor>CCSD</distributor>
            <idno type="halId">hal-04387128</idno>
            <idno type="halUri">https://hal.science/hal-04387128</idno>
            <idno type="halBibtex">sublime:hal-04387128</idno>
            <idno type="halRefHtml">&lt;i&gt;Journal of Artificial Intelligence Research&lt;/i&gt;, 2024, 79, pp.41-67. &lt;a target="_blank" href="https://dx.doi.org/10.1613/jair.1.15315"&gt;&amp;#x27E8;10.1613/jair.1.15315&amp;#x27E9;&lt;/a&gt;</idno>
            <idno type="halRef">Journal of Artificial Intelligence Research, 2024, 79, pp.41-67. &amp;#x27E8;10.1613/jair.1.15315&amp;#x27E9;</idno>
            <availability status="restricted">
              <licence target="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0 - Attribution<ref corresp="#file-4387128-3815531"/></licence>
            </availability>
          </publicationStmt>
          <seriesStmt>
            <idno type="stamp" n="ISEP">Institut supérieur d'électronique de Paris</idno>
          </seriesStmt>
          <notesStmt>
            <note type="audience" n="2">International</note>
            <note type="popular" n="0">No</note>
            <note type="peer" n="1">Yes</note>
          </notesStmt>
          <sourceDesc>
            <biblStruct>
              <analytic>
                <title xml:lang="en">The AI Race: Why Current Neural Network-based Architectures are a Poor Basis for Artificial General Intelligence</title>
                <author role="aut">
                  <persName>
                    <forename type="first">Jérémie</forename>
                    <surname>Sublime</surname>
                  </persName>
                  <email type="md5">e85b7af271e587a79b8300169b636063</email>
                  <email type="domain">gmail.com</email>
                  <idno type="idhal" notation="string">jeremie-sublime</idno>
                  <idno type="idhal" notation="numeric">184388</idno>
                  <idno type="halauthorid" notation="string">1118816-184388</idno>
                  <idno type="ORCID">https://orcid.org/0000-0003-0508-8550</idno>
                  <idno type="GOOGLE SCHOLAR">https://scholar.google.fr/citations?user=UhAxK5EAAAAJ</idno>
                  <idno type="IDREF">https://www.idref.fr/197915299</idno>
                  <affiliation ref="#struct-95837"/>
                </author>
              </analytic>
              <monogr>
                <idno type="halJournalId" status="VALID">1353</idno>
                <idno type="issn">1076-9757</idno>
                <title level="j">Journal of Artificial Intelligence Research</title>
                <imprint>
                  <publisher>Association for the Advancement of Artificial Intelligence</publisher>
                  <biblScope unit="volume">79</biblScope>
                  <biblScope unit="pp">41-67</biblScope>
                  <date type="datePub">2024-01-10</date>
                  <date type="dateEpub">2024-01-11</date>
                </imprint>
              </monogr>
              <idno type="doi">10.1613/jair.1.15315</idno>
            </biblStruct>
          </sourceDesc>
          <profileDesc>
            <langUsage>
              <language ident="en">English</language>
            </langUsage>
            <textClass>
              <keywords scheme="author">
                <term xml:lang="en">machine learning, neural networks, change, human computer interaction, artificial intelligence</term>
              </keywords>
              <classCode scheme="halDomain" n="info.info-ai">Computer Science [cs]/Artificial Intelligence [cs.AI]</classCode>
              <classCode scheme="halTypology" n="ART">Journal articles</classCode>
              <classCode scheme="halOldTypology" n="ART">Journal articles</classCode>
              <classCode scheme="halTreeTypology" n="ART">Journal articles</classCode>
            </textClass>
            <abstract xml:lang="en">
              <p>Artificial General Intelligence is the idea that someday an hypothetical agent will arise from artificial intelligence (AI) progresses, and will surpass by far the brightest and most gifted human minds. This idea has been around since the early development of AI. Since then, scenarios on how such AI may behave towards humans have been the subject of many fictional and research works. This paper analyzes the current state of artificial intelligence progresses, and how the current AI race with the ever faster release of impressive new AI methods (that can deceive humans, outperform them at tasks we thought impossible to tackle by AI a mere decade ago, and that disrupt the job market) have raised concerns that Artificial General Intelligence (AGI) might be coming faster that we thought. In particular, we focus on 3 specific families of modern AIs to develop the idea that deep neural networks, which are the current backbone of nearly all artificial intelligence methods, are poor candidates for any AGI to arise due to their many limitations, and therefore that any threat coming from the recent AI race does not lie in AGI but in the limitations, uses, and lack of regulations of our current models and algorithms. This article appears in the AI &amp; Society track.</p>
            </abstract>
          </profileDesc>
        </biblFull>
      </listBibl>
    </body>
    <back>
      <listOrg type="structures">
        <org type="institution" xml:id="struct-95837" status="VALID">
          <idno type="ROR">https://ror.org/00yw34h52</idno>
          <orgName>Institut Supérieur d'Electronique de Paris</orgName>
          <orgName type="acronym">ISEP</orgName>
          <desc>
            <address>
              <addrLine>28 Rue Notre Dame des Champs 75006 Paris</addrLine>
              <country key="FR"/>
            </address>
            <ref type="url">http://www.isep.fr</ref>
          </desc>
        </org>
      </listOrg>
    </back>
  </text>
</TEI>