An artificially intelligent controller can station a stratospheric balloon for weeks at a time without full knowledge of surrounding winds, opening up the prospect of unsupervised environmental monitoring.

The goal of an autonomous machine is to achieve an objective by making decisions while negotiating a dynamic environment. Given complete knowledge of a system’s current state, artificial intelligence and machine learning can excel at this, and even outperform humans at certain tasks — for example, when playing arcade and turn-based board games¹. But beyond the idealized world of games, real-world deployment of automated machines is hampered by environments that can be noisy and chaotic, and which are not adequately observed. The difficulty of devising long-term strategies from incomplete data can also hinder the operation of independent AI agents in real-world challenges. Writing in Nature, Bellemare et al.² describe a way forward by demonstrating that stratospheric balloons, guided by AI, can pursue a long-term strategy for positioning themselves about a location on the Equator, even when precise knowledge of buffeting winds is not known. Read More