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An algorithm created by AI firm DeepMind can distinguish between videos in which objects obey the laws of physics and ones where they don't. DeepMind has looked to developmental psychology to help AI gain a basic understanding of the physical world. Teaching artificial intelligence to understand simple physics concepts. It could lead to more capable software that takes less computational resources to train. This is somewhat like teaching a kid what a car is by first teaching them what wheels and seats are.
Real-world physics is difficult for AIs to grasp when asked to start from scratch with only training data to guide them. But DeepMind AI researcher Luis Piloto and his colleagues developed an AI, dubbed PLATO (Physics Learning through Auto-encoding and Tracking Objects) that is designed to understand that the physical world is composed of objects that follow basic physical laws.
The DeepMind AI researchers trained PLATO to identify objects and their interactions by using simulated videos of objects moving as we would expect. They then used video training data to show videos of many simple scenes to improve PLATO's performance. It uses objects at all stages of processing: representing visual inputs as a set of objects; reasoning about interactions between objects; and producing outputs.
Researcher Piloto says the results show that an object-centric view of the world could give an AI a more generalized and adaptable set of abilities. The work could lead to new avenues of AI research, and may even reveal clues about human vision and development. But implementation of PLATO is not externally viable. The findings indicate that visual animations can account for some intuitive physics learning, but not enough to account for what we see in infants.
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