Semantic noise, the effect ensuing from the denotative and thus functional variability exhibited by different terms in different contexts, is a common concern in natural language processing (NLP). While unarguably problematic in specific applications (e.g., certain translation tasks), the main argument of this paper is that failing to observe this linguistic matter of fact as a generative effect rather than as an obstacle, leads to actual obstacles in instances where language model outputs are presented as neutral. Given that a common (...) and long-standing challenge in NLP is the interpretation of ambiguous – i.e., semantically noisy – cases, this article focuses on an exemplar ambiguity-resolution task in NLP: the problem of anaphora in Winograd schemas. The main question considered is: to what extent is the standard approach to disambiguation in NLP subject to a stagnant “image of language”? And, can a transdisciplinary, dynamic approach combining linguistics and philosophy elucidate new perspectives on these possible conceptual shortcomings? In order to answer these questions we explore the term and concept of noise, particularly in its presentation as semantic noise. Owing to its definitional plurality, and sometimes even desirable unspecificity, the term noise is thus used as proof of concept for semantic generativity being an inherent characteristic in linguistic representation, and its concept is used to interrogate assumptions admitted in the resolution of Winograd schemas. The argument is speculative and theoretical in method, and the result is an analysis which provides an account of the fundamentally dialogical and necessarily open-ended effects of semantic noise in natural language. (shrink)

La repetición y la superposición innecesaria de principios éticos similares para el desarrollo de una inteligencia artificial responsable no solo entran en conflicto, sino que esta confusión y ambigüedad pueden llegar, incluso, a resultar peligrosas si los postulados son un mero “lavado de cara” y las verdaderas intenciones se esconden detrás de intereses mezquinos. Esto aplica tanto a particulares, a empresas, como a gobiernos. El proceso de establecer leyes, normas, estándares y mejores prácticas para asegurar que la IA sea benéfica (...) para toda la sociedad es un llamado urgente para un “mejor futuro de la humanidad”. De todo lo anterior, surge este intento por traducir en solo seis principios fundamentales el cúmulo de literatura que hasta ahora se tiene y, posteriormente, defender tres máximas que podrían sintetizar no solo los principios esbozados en este libro, sino los marcos normativos vigentes con aspiraciones universalistas: responsabilidad, transparencia, seguridad, no discriminación, privacidad y sostenibilidad; y como máximas clásicas de la ética: honradez, intencionalidad y conciencia moral. (shrink)