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。

构建能够自主学习和思考的机器：对 Lake 等人 2017 年在《行为和脑科学》上的评论

Building Machines that Learn and Think for Themselves: Commentary on  Lake et al., Behavioral and Brain Sciences, 2017

Higher-level cognition depends on the ability to learn models of the world.
We can characterize this at the computational level as a structure-learning
problem with the goal of best identifying the prevailing causal relationships
among a set of relata. However, the computational cost of performing exact
Bayesian inference over causal models grows rapidly as the number of relata
increases. This implies that the cognitive processes underlying causal learning
must be substantially approximate. A powerful class of approximations that
focuses on the sequential absorption of successive inputs is captured by the
Neurath's ship metaphor in philosophy of science, where theory change is cast
as a stochastic and gradual process shaped as much by people's limited
willingness to abandon their current theory when considering alternatives as by
the ground truth they hope to approach. Inspired by this metaphor and by
algorithms for approximating Bayesian inference in machine learning, we propose
an algorithmic-level model of causal structure learning under which learners
represent only a single global hypothesis that they update locally as they
gather evidence. We propose a related scheme for understanding how, under these
limitations, learners choose informative interventions that manipulate the
causal system to help elucidate its workings. We find support for our approach
in the analysis of four experiments.

研究发现，认知过程会依赖世界模型的学习，而精确的贝叶斯推断成本随着关系数量的增加而急剧上升，因此表明基于前因后果关系的学习过程必须是相当近似的，而本文给出了一个基于 Neurath’s ship 比喻以及机器学习中的近似算法来探讨因果结构学习的问题