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

arXiv: https://arxiv.org/abs/1711.08378 · HTML: https://mlsanity.com/paper/1711.08378/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Nov 2017.

## Built on by (most cited first, 5 of 5)

- [SpatialBench: Benchmarking Multimodal Large Language Models for Spatial Cognition](https://mlsanity.com/paper/2511.21471/) (2025)
- [Emergent Bartering Behaviour in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2205.06760/) (2022)
- [Towards Modeling Human Attention from Eye Movements for Neural Source Code Summarization](https://mlsanity.com/paper/2305.09773/) (2023)
- [Beyond the Sum: Unlocking AI Agents Potential Through Market Forces](https://mlsanity.com/paper/2501.10388/) (2024)
- [Silico-centric Theory of Mind](https://mlsanity.com/paper/2403.09289/) (2024)

## Abstract (arXiv)

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.