AI视觉模型越强，反而越不像人脑，提示二者发展路径正在背离。
Better artificial intelligence does not mean better models of biology
- 通过三个基准测试发现，随着AI模型性能提升，其与灵长类视觉响应的契合度已停止上升甚至下降。
- 当DNN达到人类或超人水平时，其视觉策略与灵长类生物机制出现显著差异。
- 研究呼吁神经科学应独立发展基于生物视觉的算法，而非盲目追随互联网数据集上的AI benchmark。
深度神经网络（DNNs）曾因在视觉基准上性能提升而展现出与灵长类感知和神经反应日益一致的趋势，激发了人们对人工智能进步将推动生物视觉建模的期待。然而，我们在三个基准测试中发现，这种一致性现已趋于平稳——甚至在某些情况下恶化——当DNN扩展至人类或超人级准确率时。这一分歧可能源于模型采用了与灵长类不同的视觉策略。这些发现挑战了‘人工智能进展将自然转化为神经科学洞见’的观点。我们主张，视觉科学必须走出自己的道路，开发以生物视觉系统为基础的算法，而非单纯优化依赖互联网规模数据集的基准任务。
原文摘要 · Abstract (English)
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.
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