Better artificial intelligence does not mean better models of biology
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909595529641984 |
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| author | Linsley, Drew Feng, Pinyuan Serre, Thomas |
| author_facet | Linsley, Drew Feng, Pinyuan Serre, Thomas |
| contents | Deep neural networks (DNNs) once showed increasing alignment with primate perception and neural responses as they improved on vision benchmarks, raising hopes that advances in AI would yield better models of biological vision. However, we show across three benchmarks that this alignment is now plateauing - and in some cases worsening - as DNNs scale to human or superhuman accuracy. This divergence may reflect the adoption of visual strategies that differ from those used by primates. These findings challenge the view that progress in artificial intelligence will naturally translate to neuroscience. We argue that vision science must chart its own course, developing algorithms grounded in biological visual systems rather than optimizing for benchmarks based on internet-scale datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_16940 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Better artificial intelligence does not mean better models of biology Linsley, Drew Feng, Pinyuan Serre, Thomas Neurons and Cognition Artificial Intelligence Computer Vision and Pattern Recognition Deep neural networks (DNNs) once showed increasing alignment with primate perception and neural responses as they improved on vision benchmarks, raising hopes that advances in AI would yield better models of biological vision. However, we show across three benchmarks that this alignment is now plateauing - and in some cases worsening - as DNNs scale to human or superhuman accuracy. This divergence may reflect the adoption of visual strategies that differ from those used by primates. These findings challenge the view that progress in artificial intelligence will naturally translate to neuroscience. We argue that vision science must chart its own course, developing algorithms grounded in biological visual systems rather than optimizing for benchmarks based on internet-scale datasets. |
| title | Better artificial intelligence does not mean better models of biology |
| topic | Neurons and Cognition Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.16940 |