Artificial intelligence (AI) is rapidly becoming one of the key technologies of this century. The majority of results in AI thus far have been achieved using deep neural networks trained with the error backpropagation learning algorithm. However, the ubiquitous adoption of this approach has highlighted some important limitations such as substantial computational cost, difficulty in quantifying uncertainty, lack of robustness, unreliability, and biological implausibility. It is possible that addressing these limitations may require schemes that are inspired and guided by neuroscience theories. One such theory, called predictive coding (PC), has shown promising performance in machine intelligence tasks, exhibiting exciting properties that make it potentially valuable for the machine learning community: PC can model information processing in different brain areas, can be used in cognitive control and robotics, and has a solid mathematical grounding in variational inference, offering a powerful inversion scheme for a specific class of continuous-state generative models. With the hope of foregrounding research in this direction, we survey the literature that has contributed to this perspective, highlighting the many ways that PC might play a role in the future of machine learning and computational intelligence at large.

翻译：人工智能正迅速成为本世纪的关键技术之一。迄今为止，人工智能的大多数成果均通过采用误差反向传播学习算法训练的深度神经网络实现。然而，该方法的广泛应用也暴露了一些重要局限，例如计算成本高昂、不确定性量化困难、鲁棒性不足、可靠性缺失以及生物学合理性缺失。解决这些局限可能需要借鉴神经科学理论启发的方案。其中一种名为预测编码的理论，在机器智能任务中展现了令人瞩目的性能，其特性对机器学习领域具有潜在价值：预测编码能够模拟不同脑区的信息处理过程，可应用于认知控制与机器人领域，并在变分推断中拥有坚实的数学基础，为特定类别的连续状态生成模型提供了强大的反演机制。为推进该方向的研究，我们综述了相关文献，重点阐述了预测编码在未来机器学习与计算智能发展中可能发挥的多重作用。