A fascinating hypothesis is that human and animal intelligence could be explained by a few principles (rather than an encyclopedic list of heuristics). If that hypothesis was correct, we could more easily both understand our own intelligence and build intelligent machines. Just like in physics, the principles themselves would not be sufficient to predict the behavior of complex systems like brains, and substantial computation might be needed to simulate human-like intelligence. This hypothesis would suggest that studying the kind of inductive biases that humans and animals exploit could help both clarify these principles and provide inspiration for AI research and neuroscience theories. Deep learning already exploits several key inductive biases, and this work considers a larger list, focusing on those which concern mostly higher-level and sequential conscious processing. The objective of clarifying these particular principles is that they could potentially help us build AI systems benefiting from humans' abilities in terms of flexible out-of-distribution and systematic generalization, which is currently an area where a large gap exists between state-of-the-art machine learning and human intelligence.

中文翻译：

---

深度学习的高层认知归纳偏置

一个令人着迷的假设是，人类和动物的智力可以通过一些原理来解释（而不是启发式百科全书）。如果这个假设是正确的，我们可以更容易地理解我们自己的智能并建造智能机器。就像在物理学中一样，原理本身不足以预测复杂系统（如大脑）的行为，可能需要大量计算来模拟类人智力。该假设表明，研究人类和动物利用的归纳偏见既可以帮助阐明这些原理，也可以为AI研究和神经科学理论提供启发。深度学习已经利用了几个关键的归纳偏差，这项工作考虑了更大的清单，重点关注那些主要涉及高级和顺序意识处理的事物。阐明这些特定原理的目的是，它们可以潜在地帮助我们构建从人类的能力中受益的AI系统，这些系统可以灵活地进行分布式分配和系统地概括，这是当前状态之间存在较大差距的领域。最先进的机器学习和人类智能。