Deep neural networks (DNNs) once showed increasing alignment with primate perception as they improved on vision benchmarks, raising hopes that advances in artificial intelligence (AI) would naturally yield better models of biological vision. However, we present accumulating evidence that this alignment is now plateauing – and in some cases worsening – as DNNs scale to human or even superhuman accuracy. This divergence between artificial and biological perception may reflect the acquisition of visual strategies distinct from those of primates, and these findings challenge the view that advances in AI will naturally translate to progress in neuroscience. We argue that vision science must chart its own course, developing algorithms grounded in biological visual systems rather than optimizing for internet data.

A survey of 1,600 academics found that more than 50% have used artificial-intelligence tools while peer reviewing manuscripts.

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En la Facultad de Educación y Psicología de la Universidad de Navarra, formamos a profesionales con espíritu de servicio, pensamiento crítico, compromiso ético y ganas de aportar su conocimiento a la ...

The selective revision of beliefs in light of new evidence has been considered one of the hallmarks of human-level rationality. However, tests of this ability in other species are lacking. We examined...

Understanding how populations of cells collectively coordinate activity to produce the complex structures and behaviors that characterize multicellular organisms, and which coordinated activities, ...