Intuitive physics understanding emerges from self-supervised pretraining
  on natural videos
Authors: name: Quentin Garrido, name: Nicolas Ballas, name: Mahmoud Assran, name: Adrien Bardes, name: Laurent Najman, name: Michael Rabbat, name: Emmanuel Dupoux, name: Yann LeCun

We investigate the emergence of intuitive physics understanding in
general-purpose deep neural network models trained to predict masked regions in
natural videos. Leveraging the violation-of-expectation framework, we find that
video prediction models trained to predict outcomes in a learned representation
space demonstrate an understanding of various intuitive physics properties,
such as object permanence and shape consistency. In contrast, video prediction
in pixel space and multimodal large language models, which reason through text,
achieve performance closer to chance. Our comparisons of these architectures
reveal that jointly learning an abstract representation space while predicting
missing parts of sensory input, akin to predictive coding, is sufficient to
acquire an understanding of intuitive physics, and that even models trained on
one week of unique video achieve above chance performance. This challenges the
idea that core knowledge -- a set of innate systems to help understand the
world -- needs to be hardwired to develop an understanding of intuitive
physics.