更好的人工智能并不意味着更好的生物学模型
Drew Linsley1, Pinyuan Feng2, Thomas Serre1
1Brown University, Providence, RI, USA.
Trends in cognitive sciences
|December 24, 2025
概括
深度神经网络 (DNN) 最初与灵长类视觉保持一致,但随着人工智能达到超人精度,这一趋势正在逆转. 这种分歧表明,人工智能可能不会自然地促进我们对生物视觉的理解.
科学领域:
- 计算神经科学是一种神经科学.
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 深度神经网络 (DNN) 已经显示出作为生物视觉模型的前景.
- 以前的研究表明,DNN性能与灵长类动物视觉感知之间存在一致性.
- 人工智能的进步被假设为自然增强神经科学模型.
研究的目的:
- 调查DNN与灵长类动物视觉感知之间不断演变的对齐.
- 挑战人工智能进步自动有利于神经科学的假设.
- 在视觉科学中提出人工智能发展的替代方向.
主要方法:
- 对视力基准的DNN性能进行分析.
- 将DNN策略与灵长类视觉处理进行比较.
- 评估缩放DNN对感知对齐的影响.
主要成果:
- DNN与灵长类感知之间的对齐正在平稳或恶化.
- 实现人类或超人精度的DNN显示出与生物视觉的分歧.
- 这表明,与灵长类相比,DNN可能采用不同的视觉策略.
结论:
- 人工智能进步将自然导致更好的生物视觉模型的假设受到挑战.
- 视觉科学应该优先开发灵感来自生物系统的算法.
- 仅在互联网数据上优化人工智能可能不会产生对灵长类视力的洞察力.
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