Making AI meaningful again.

Artificial intelligence (AI) research enjoyed an initial period of enthusiasm in the 1970s and 80s, but this enthusiasm was tempered by a long interlude of frustration when genuinely useful AI applications failed to be forthcoming. Today, we are experiencing once again a period of enthusiasm, fired...

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Detalles Bibliográficos
Publicado en:Synthese Vol. 198; no. 3; pp. 2061 - 2082
Autores principales: Landgrebe, Jobst, Smith, Barry
Formato: Artículo
Publicado: Springer Nature Mar2021
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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        10.1007/s11229-019-02192-y
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          Landgrebe, Jobst
          Smith, Barry
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          Cognotekt GmbH, Bonner Str. 209, 50968, Cologne, Germany
          University at Buffalo, Buffalo, NY, USA
      su:
        Artificial intelligence
        Deep learning
        Machine learning
        Enthusiasm
        Frustration
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          Artificial intelligence
          Deep learning
          Machine learning
          Enthusiasm
          Frustration
      keyword:
        Basic formal ontology (BFO)
        Deep neural networks
        Logic
        Semantics
      ab: Artificial intelligence (AI) research enjoyed an initial period of enthusiasm in the 1970s and 80s, but this enthusiasm was tempered by a long interlude of frustration when genuinely useful AI applications failed to be forthcoming. Today, we are experiencing once again a period of enthusiasm, fired above all by the successes of the technology of deep neural networks or deep machine learning. In this paper we draw attention to what we take to be serious problems underlying current views of artificial intelligence encouraged by these successes, especially in the domain of language processing. We then show an alternative approach to language-centric AI, in which we identify a role for philosophy.
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    language: English
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