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The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
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Video Experimental Relacionado

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One Dimensional Turing-Like Handshake Test for Motor Intelligence
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Necesitamos una prueba de Weizenbaum para la IA

Jack Stilgoe1

  • 1Department of Science and Technology Studies, University College London, London, UK. j.stilgoe@ucl.ac.uk.

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|August 10, 2023
PubMed
Resumen
Este resumen es generado por máquina.

La prueba de Turing para la inteligencia artificial (IA) está desactualizada. En lugar de preguntar si las máquinas pueden pensar, deberíamos evaluar la IA generativa en función de su utilidad para la sociedad.

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Área de la Ciencia:

  • Ciencias de la computación
  • Filosofía de la tecnología

Sus antecedentes:

  • El artículo de 1950 de Alan Turing propuso el "juego de imitación" para probar la inteligencia de las máquinas.
  • La prueba de Turing ha guiado el desarrollo de la IA, pero se enfrenta a críticas con respecto a su validez.
  • La IA ha evolucionado de un concepto académico a una realidad tecnológica.

Objetivo del estudio:

  • Reevaluar el enfoque de la investigación y el impacto social de la inteligencia artificial (IA).
  • Para cambiar la pregunta central en IA de "¿Pueden las máquinas pensar?" a "¿Son útiles estas máquinas?"
  • Incorporar la perspectiva pública en el desarrollo de la IA, inspirado en el trabajo de Joseph Weizenbaum.

Principales métodos:

  • Análisis histórico del desarrollo de la IA y la prueba de Turing.
  • Revisión de la crítica de Joseph Weizenbaum sobre el impacto social de la informática.
  • Cambio conceptual de la evaluación de la IA centrada en la inteligencia a la centrada en la utilidad.

Principales resultados:

  • La prueba de Turing, aunque históricamente significativa, puede no ser la métrica más relevante para la IA contemporánea.
  • Un punto de vista centrado en el público, centrado en el beneficio y la necesidad de la sociedad, ofrece un enfoque más pragmático de la IA.
  • La IA generativa requiere una reevaluación de su propósito e impacto más allá de las meras capacidades de "pensamiento".

Conclusiones:

  • El debate sobre el "pensamiento" de la máquina es menos relevante que evaluar la utilidad práctica y el valor social de la IA.
  • Adoptar el "es bueno" de Weizenbaum. y "¿necesitamos estas cosas?" marco es crucial para el desarrollo responsable de la IA.
  • Centrarse en la utilidad de la IA generativa guiará mejor la toma de decisiones e integración social.