Intelligent systems need to incorporate a significant amount of understanding of the world in terms of intuitive physics. This understanding can be gained from data in various ways. Now, group of researchers from INRIA and Facebook AI team have developed an evaluation framework which diagnoses how much a given system understands about physics. This is done by testing the system’s computation plausibility of possible physical events versus the computation of plausibility of impossible physical events. The framework is free of bias and can test a range of specific physical reasoning skills.
The team describes the first release of a benchmark dataset aimed at learning intuitive physics in an unsupervised way. The team also says that two Deep Neural Network systems trained with a future frame prediction objective and tested on the possible versus impossible discrimination task. The analysis of the results compared to human data gave new insights in the potential and limitations of next frame prediction architecture.
Physical Understanding Of A Growing Child
Intelligent systems, however advanced they may be, still lack the human understanding to recognise complex scenes. It is a difficult task since to teach, since understanding the scene involves cognizance of the spatial and temporal relationship between objects in the said scene. According to years of research, this is how a human child’s understanding of physics evolves:
- At the age of two to four months, infants are able to parse visual inputs in terms of permanent, solid and spatiotemporally continuous objects
- At the age of six months, they understand the notion of stability, support and causality
- Between eight and 10 months, they grasp the notions of gravity, inertia, and conservation of momentum in collision
- Between the age of 10 and 12 months, they learn shape constancy
It is also clearly understood by the scientific community that the intuitive understanding of physics is latent, that is, it can only be observed and measured indirectly. This is in fact a grave challenge for both evaluation and engineering purposes, making this research particularly interesting.