Teaching an AI simple physical concepts, such as the fact that two objects can't occupy the same space, could help develop more efficient algorithms
. But these models are highly specialised and lack a general understanding of the world. As DeepMind’s researchers say in their latest paper, “something fundamental is still missing”.at DeepMind and his colleagues have created an AI called 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.
To test PLATO’s ability to understand five physical concepts such as persistence , solidity and unchangingness , the researchers used another series of simulated videos. Some showed objects obeying the laws of physics, while others depicted nonsensical actions, such as a ball rolling behind a pillar, not emerging from the other side, but then reappearing from behind another pillar further along its route.
They tasked PLATO to predict what would happen next in each video, and found that its predictions were reliably wrong for nonsensical videos, but usually correct for logical ones, suggesting the AI has an intuitive knowledge of physics.Piloto says the results show that an object-centric view of the world could give an AI a more generalised and adaptable set of abilities. “If you consider, for instance, all the different scenes that an apple might be in,” he says.
“That means they’re using an architecture that other people probably can’t use,” he says. “In science, it’s good to be reproducible so that other people can get the same results and then take them further.”at New York University says the findings could help to lower the computational requirements for training and running AI models.
“This is somewhat like teaching a kid what a car is by first teaching them what wheels and seats are,” he says. “The benefit of using object-centric representation, instead of raw visual inputs, makes AI learn intuitive physical concepts with better data efficiency.”
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