We agree with Lake and colleagues on their list of key ingredients for
building humanlike intelligence, including the idea that model-based reasoning
is essential. However, we favor an approach that centers on one additional
ingredient: autonomy. In particular, we aim toward agents that can both build
and exploit their own internal models, with minimal human hand-engineering. We
believe an approach centered on autonomous learning has the greatest chance of
success as we scale toward real-world complexity, tackling domains for which
ready-made formal models are not available. Here we survey several important
examples of the progress that has been made toward building autonomous agents
with humanlike abilities, and highlight some outstanding challenges.

在建立人类智能的关键要素中，我们赞同 Lake 等人的主张，其中包括了基于模型的推理是必不可少的想法，但是，我们提倡的方法则在另一个要素上更加强调，这个要素是自主性。尤其是，我们的目标是可以同时构建和利用自己内部模型的代理程序，并且是最小化人工工程的。我们相信，以自主学习为中心的方法在处理实际问题领域中扩展到现实世界复杂度时，具有最大的成功机会，这些复杂度的问题领域不存在现成的形式化模型可用。本文概述了几个重要的例子，展示了构建具有人类能力的自主代理的进展情况，并突出了一些重要的挑战。