Direct Human-AI Comparison in the Animal-AI Environment.

Autor:	Voudouris K; Leverhulme Centre for the Future of Intelligence, University of Cambridge, Cambridge, United Kingdom.; Department of Psychology, University of Cambridge, Cambridge, United Kingdom, Cambridge, United Kingdom., Crosby M; Leverhulme Centre for the Future of Intelligence, University of Cambridge, Cambridge, United Kingdom.; Department of Computing, Imperial College London, London, United Kingdom., Beyret B; Leverhulme Centre for the Future of Intelligence, University of Cambridge, Cambridge, United Kingdom.; Department of Computing, Imperial College London, London, United Kingdom., Hernández-Orallo J; Leverhulme Centre for the Future of Intelligence, University of Cambridge, Cambridge, United Kingdom.; Valencian Research Institute for Artificial Intelligence (VRAIN), Universitat Politècnica de València, València, Spain., Shanahan M; Leverhulme Centre for the Future of Intelligence, University of Cambridge, Cambridge, United Kingdom.; Department of Computing, Imperial College London, London, United Kingdom., Halina M; Leverhulme Centre for the Future of Intelligence, University of Cambridge, Cambridge, United Kingdom.; Department of Psychology, University of Cambridge, Cambridge, United Kingdom, Cambridge, United Kingdom.; Department of History and Philosophy of Science, University of Cambridge, Cambridge, United Kingdom., Cheke LG; Leverhulme Centre for the Future of Intelligence, University of Cambridge, Cambridge, United Kingdom.; Department of Psychology, University of Cambridge, Cambridge, United Kingdom, Cambridge, United Kingdom.
Jazyk:	angličtina
Zdroj:	Frontiers in psychology [Front Psychol] 2022 May 24; Vol. 13, pp. 711821. Date of Electronic Publication: 2022 May 24 (Print Publication: 2022).
DOI:	10.3389/fpsyg.2022.711821
Abstrakt:	Artificial Intelligence is making rapid and remarkable progress in the development of more sophisticated and powerful systems. However, the acknowledgement of several problems with modern machine learning approaches has prompted a shift in AI benchmarking away from task-oriented testing (such as Chess and Go) towards ability -oriented testing, in which AI systems are tested on their capacity to solve certain kinds of novel problems. The Animal-AI Environment is one such benchmark which aims to apply the ability-oriented testing used in comparative psychology to AI systems. Here, we present the first direct human-AI comparison in the Animal-AI Environment, using children aged 6-10 ( n  = 52). We found that children of all ages were significantly better than a sample of 30 AIs across most of the tests we examined, as well as performing significantly better than the two top-scoring AIs, "ironbar" and "Trrrrr," from the Animal-AI Olympics Competition 2019. While children and AIs performed similarly on basic navigational tasks, AIs performed significantly worse in more complex cognitive tests, including detour tasks, spatial elimination tasks, and object permanence tasks, indicating that AIs lack several cognitive abilities that children aged 6-10 possess. Both children and AIs performed poorly on tool-use tasks, suggesting that these tests are challenging for both biological and non-biological machines. Competing Interests: MC, BB and MS are employed by DeepMind Technologies Limited. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2022 Voudouris, Crosby, Beyret, Hernández-Orallo, Shanahan, Halina and Cheke.)
Databáze:	MEDLINE
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