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Computer Science > Machine Learning

arXiv:2311.16093 (cs)
[Submitted on 27 Nov 2023]

Title:Have we built machines that think like people?

Authors:Luca M. Schulze Buschoff, Elif Akata, Matthias Bethge, Eric
Schulz
Download a PDF of the paper titled Have we built machines that think
like people?, by Luca M. Schulze Buschoff and 3 other authors
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    Abstract:A chief goal of artificial intelligence is to build
    machines that think like people. Yet it has been argued that deep
    neural network architectures fail to accomplish this. Researchers
    have asserted these models' limitations in the domains of causal
    reasoning, intuitive physics, and intuitive psychology. Yet
    recent advancements, namely the rise of large language models,
    particularly those designed for visual processing, have rekindled
    interest in the potential to emulate human-like cognitive
    abilities. This paper evaluates the current state of vision-based
    large language models in the domains of intuitive physics, causal
    reasoning, and intuitive psychology. Through a series of
    controlled experiments, we investigate the extent to which these
    modern models grasp complex physical interactions, causal
    relationships, and intuitive understanding of others'
    preferences. Our findings reveal that, while these models
    demonstrate a notable proficiency in processing and interpreting
    visual data, they still fall short of human capabilities in these
    areas. The models exhibit a rudimentary understanding of physical
    laws and causal relationships, but their performance is hindered
    by a lack of deeper insights-a key aspect of human cognition.
    Furthermore, in tasks requiring an intuitive theory of mind, the
    models fail altogether. Our results emphasize the need for
    integrating more robust mechanisms for understanding causality,
    physical dynamics, and social cognition into modern-day,
    vision-based language models, and point out the importance of
    cognitively-inspired benchmarks.

Subjects: Machine Learning (cs.LG)
Cite as:  arXiv:2311.16093 [cs.LG]
          (or arXiv:2311.16093v1 [cs.LG] for this version)
          https://doi.org/10.48550/arXiv.2311.16093
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Luca M. Schulze Buschoff [view email]
[v1] Mon, 27 Nov 2023 18:58:34 UTC (4,181 KB)
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