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dc.contributor.authorVoudouris, Konstantinos
dc.contributor.authorCrosby, Matthew
dc.contributor.authorBeyret, Benjamin
dc.contributor.authorHernández-Orallo, José
dc.contributor.authorShanahan, Murray
dc.contributor.authorHalina, Marta
dc.contributor.authorCheke, Lucy G
dc.date.accessioned2022-06-07T16:00:07Z
dc.date.available2022-06-07T16:00:07Z
dc.date.issued2022
dc.date.submitted2021-05-19
dc.identifier.issn1664-1078
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/337881
dc.description.abstractArtificial 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.
dc.languageen
dc.publisherFrontiers Media SA
dc.subjectPsychology
dc.subjecthuman-AI comparison
dc.subjectartificial intelligence
dc.subjectAI benchmarks
dc.subjectcomparative cognition
dc.subjectout-of-distribution testing
dc.subjectAnimal-AI Olympics
dc.subjectcognitive AI
dc.titleDirect Human-AI Comparison in the Animal-AI Environment.
dc.typeArticle
dc.date.updated2022-06-07T16:00:06Z
prism.publicationNameFront Psychol
prism.volume13
dc.identifier.doi10.17863/CAM.85287
dcterms.dateAccepted2022-03-28
rioxxterms.versionofrecord10.3389/fpsyg.2022.711821
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
dc.identifier.eissn1664-1078
cam.issuedOnline2022-05-24


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