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Showing 1–7 of 7 results
Advanced filters: Author: Cameron Buckner Clear advanced filters

  • For a third year in a row, we followed up with authors of several recent Comments and Perspectives in Nature Machine Intelligence about what happened after their article was published: how did the topic they wrote about develop, did they gain new insights, and what are their hopes and expectations for AI in 2022?

    • Cameron Buckner
    • Risto Miikkulainen
    • Vidushi Marda
    Special Features
    Nature Machine Intelligence
    Volume: 4, P: 5-10

  • DNN classifiers are vulnerable to small, specific perturbations in an input that seem benign to humans. To understand this phenomenon, Buckner argues that it may be necessary to treat the patterns that DNNs detect in these adversarial examples as artefacts, which may contain predictive information.

    • Cameron Buckner
    Reviews
    Nature Machine Intelligence
    Volume: 2, P: 731-736

  • Theory of Mind experiments in animals have not previously discounted the possibility that individuals follow their competitors′ behavioural cues. Here, Bugnyar et al.show that ravens consider the possibility that they are being watched when caching food, even when they cannot see a conspecific competitor.

    • Thomas Bugnyar
    • Stephan A. Reber
    • Cameron Buckner
    ResearchOpen Access
    Nature Communications
    Volume: 7, P: 1-6

  • The influence of sample composition on human neuroimaging results is unknown. Here, the authors weight a large, community-based sample to better reflect the US population and describe how applying these sample weights changes conclusions about age-related variation in brain structure.

    • Kaja Z. LeWinn
    • Margaret A. Sheridan
    • Katie A. McLaughlin
    ResearchOpen Access
    Nature Communications
    Volume: 8, P: 1-14