Failures in Perspective-taking of Multimodal AI Systems

Fuente: arXiv
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Auteurs principaux: Leonard, Bridget, Woodard, Kristin, Murray, Scott O.
Format: Preprint
Publié: 2024
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author Leonard, Bridget
Woodard, Kristin
Murray, Scott O.
author_facet Leonard, Bridget
Woodard, Kristin
Murray, Scott O.
contents This study extends previous research on spatial representations in multimodal AI systems. Although current models demonstrate a rich understanding of spatial information from images, this information is rooted in propositional representations, which differ from the analog representations employed in human and animal spatial cognition. To further explore these limitations, we apply techniques from cognitive and developmental science to assess the perspective-taking abilities of GPT-4o. Our analysis enables a comparison between the cognitive development of the human brain and that of multimodal AI, offering guidance for future research and model development.
format Preprint
id arxiv_https___arxiv_org_abs_2409_13929
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Failures in Perspective-taking of Multimodal AI Systems
Leonard, Bridget
Woodard, Kristin
Murray, Scott O.
Artificial Intelligence
This study extends previous research on spatial representations in multimodal AI systems. Although current models demonstrate a rich understanding of spatial information from images, this information is rooted in propositional representations, which differ from the analog representations employed in human and animal spatial cognition. To further explore these limitations, we apply techniques from cognitive and developmental science to assess the perspective-taking abilities of GPT-4o. Our analysis enables a comparison between the cognitive development of the human brain and that of multimodal AI, offering guidance for future research and model development.
title Failures in Perspective-taking of Multimodal AI Systems
topic Artificial Intelligence
url https://arxiv.org/abs/2409.13929