# 268 — Unsupervised Intuitive Physics from Visual Observations

This paper also introduces a new dataset called Roll4Real, a video dataset of balls rolling on both simple as well as complex terrain.
Using the Roll4Real dataset, this deep learning system builds an ‘intuition’ of real-world physics without an explicit simulator.
Separately, this type of technology could be immensely useful for ‘learning’ more complex worlds, like the patterns of quantum physics or other less obvious (?) fields. I would be particularly interested to see some sort of human-readable output that compares our human-derived models, like $g=9.8\frac{m}{s^2}$, to the output of the model’s predicted understanding of gravity, and see how they compare.