The success of methods based on artificial neural networks in creating intelligent machines seems like it might pose a challenge to explanations of human cognition in terms of Bayesian inference. We argue that this is not the case and that these systems in fact offer new opportunities for Bayesian modeling. Specifically, we argue that artificial neural networks and Bayesian models of cognition lie at different levels of analysis and are complementary modeling approaches, together offering a way to understand human cognition that spans these levels. We also argue that the same perspective can be applied to intelligent machines, in which a Bayesian approach may be uniquely valuable in understanding the behavior of large, opaque artificial neural networks that are trained on proprietary data.

中文翻译：

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智能机器时代的贝叶斯


基于人工神经网络的方法在创建智能机器方面的成功似乎可能对贝叶斯推理对人类认知的解释提出挑战。我们认为事实并非如此，这些系统实际上为贝叶斯建模提供了新的机会。具体来说，我们认为人工神经网络和贝叶斯认知模型处于不同的分析层面，是互补的建模方法，共同提供了一种理解跨越这些层面的人类认知的方法。我们还认为，同样的观点也适用于智能机器，其中贝叶斯方法对于理解在专有数据上训练的大型、不透明的人工神经网络的行为可能具有独特的价值。