Machines Who Think

McCorduck, Pamela; Cfe, Cli

doi:10.1201/9780429258985

Cited by 321 publications

(96 citation statements)

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“…Destacan con sus máquinas calculadoras John Napier (1550-1617) (1621), Wilhem Schickard (1592-1635) y Charles Babbage (1791-1871). Por su parte Leonhard Euler (1707-1783), Allen Marquand (1853-1924) y John Venn (1834-1923) trabajaron en algoritmos lógicos 32 . El desarrollo de modelos matemáticos con operativa mecánica, como el de George Boole (1815-1864), permitió pasar de la robótica clásica a la moderna tomando la computación como base 33 .…”

Section: Historia De La Robótica Antiguaunclassified

Historia de la robótica: de Arquitas de Tarento al robot Da Vinci (Parte I)

et al. 2007

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RESUMENHISTORIA DE LA ROBÓTICA: DESDE ARQUITAS DE TARENTO HASTA EL ROBOT DA VINCI. (PARTE I) La cirugía robótica es la última novedad tecnológica en urología. Para conocer como funcionan los nuevos robots es interesante conocer su historia. La fabricación de máquinas que imitan al ser humano se ha mantenido desde hace más de 4000 años. Existen referencias a King-su Tse, China clásica, que inventa un autómata en el 500 a. C. Arquitas de Tarento (hacia 400 a.C.) es considerado el padre de la ingeniería mecánica, y uno de los maestros clásicos de la robótica occidental. Figuras como Heron de Alejandría, Hsieh-Fec, Al-Jazari, Roger Bacon, Juanelo Turriano, Leonardo da Vinci, Vaucanson o von Kempelen construyeron robots en la edad media, el renacimiento y el clasicismo. En el siglo XIX existe un auge de los autómatas y se producen importantes avances en todas las ramas de la ingeniería. En 1942 Asimov publica las tres leyes de la robótica coincidiendo con el inicio de la robótica moderna, basada en los avances en mecánica, electrónica e informática. El desarrollo de robots en el terreno industrial, bélico y aeroespacial durante el siglo XX permite la aparición de robots de gran precisión, útiles en cirugía, como el robot quirúrgico da Vinci

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Section: Historia De La Robótica Antiguaunclassified

Historia de la robótica: de Arquitas de Tarento al robot Da Vinci (Parte I)

et al. 2007

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“…Frequently, some tasks are not considered AI problems any more, once they are solved without full-fledged intelligence. This phenomenon is known as the "AI effect" [124]. It would be unfair, however, to deny that some current AI systems, especially those that incorporate some learning potential, exhibit some intelligent behaviour.…”

Section: Introductionmentioning

confidence: 99%

Evaluation in artificial intelligence: from task-oriented to ability-oriented measurement

Hernández-Orallo

2016

Artif Intell Rev

108

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The evaluation of artificial intelligence systems and components is crucial for the progress of the discipline. In this paper we describe and critically assess the different ways AI systems are evaluated, and the role of components and techniques in these systems. We first focus on the traditional task-oriented evaluation approach. We identify three kinds of evaluation: human discrimination, problem benchmarks and peer confrontation. We describe some of the limitations of the many evaluation schemes and competitions in these three categories, and follow the progression of some of these tests. We then focus on a less customary (and challenging) ability-oriented evaluation approach, where a system is characterised by its (cognitive) abilities, rather than by the tasks it is designed to solve. We discuss several possibilities: the adaptation of cognitive tests used for humans and animals, the development of tests derived from algorithmic information theory or more integrated approaches under the perspective of universal psychometrics. We analyse some evaluation tests from AI that are better positioned for an ability-oriented evaluation and discuss how their problems and limitations can possibly be addressed with some of the tools and ideas that appear within the paper. Finally, we enumerate a series of lessons learnt and generic guidelines to be used when an AI evaluation scheme is under consideration.

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“…This desire to mimick human-level intelligence by creating artificially intelligent machines has occupied mankind for many centuries. For instance, mechanical men and artificial beings appear in Greek mythology and realistic human automatons had already been developed in Hellenic Egypt (McCorduck, 2004). The engineering of machines that display human-level intelligence is also referred to as strong AI (Searle, 1980) or artificial general intelligence (AGI) (Uszkoreit et al, 2007), and was the original motivation that gave rise to the field of AI (Nilsson, 2005;Newell, 1991).…”

Section: Introductionmentioning

confidence: 99%

Computational Foundations of Natural Intelligence

Gerven

2017

Preprint

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New developments in AI and neuroscience are revitalizing the quest to understanding natural intelligence, offering insight about how to equip machines with human-like capabilities. This paper reviews some of the computational principles relevant for understanding natural intelligence and, ultimately, achieving strong AI. After reviewing basic principles, a variety of computational modeling approaches is discussed. Subsequently, I concentrate on the use of artificial neural networks as a framework for modeling cognitive processes. This paper ends by outlining some of the challenges that remain to fulfill the promise of machines that show human-like intelligence.

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Machines Who Think

Cited by 321 publications

References 0 publications

Historia de la robótica: de Arquitas de Tarento al robot Da Vinci (Parte I)

Historia de la robótica: de Arquitas de Tarento al robot Da Vinci (Parte I)

Evaluation in artificial intelligence: from task-oriented to ability-oriented measurement

Computational Foundations of Natural Intelligence

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