Google’s DeepMind AI –“Grasps Basic Laws of Physics”




Google DeepMind’s artificial intelligence team, along with researchers at the University of California, Berkeley, has trained AI machines to interact with objects in order to evaluate their properties without any prior awareness of physical laws. The team set about various trials in different virtual environments in which the AI was faced with a series of blocks and tasked with assessing their properties.

Advances in artificial intelligence have yielded intelligent machines that can achieve superhuman performance in Go, Atari, natural language processing, and complex control problems,but it is not clear that these systems can rival the scientific intuition of even a young child. The new reserach embraced the view in developmental science that human babies are endowed with a small number of separable systems of core knowledge for reasoning about objects, actions, number, space, and possibly social interactions.

When encountering novel object, humans and other animals are able to infer a wide range of physical properties such as mass, friction and deformability by interacting with themin a goal driven way. This process of active interaction is in the same spirit of a scientist performing an experiment to discover hidden facts.

The study, entitled Learning to perform physics experiments via deep reinforcement learning, explained that while recent advances in AI have achieved ‘superhuman performance’ in complex control problems and other processing tasks, the machines still lack a common sense understanding of our physical world – ‘it is not clear that these systems can rival the scientific intuition of even a young child.’

"We found," the team concluded, "that state of art deep reinforcement learning methods can learn to perform the experiments necessary to discover these hidden properties of the physical world. By systematically manipulating the problem difficulty and the cost incurred by the AI agent for performing experiments, we found that agents learn different strategies that balance the cost of gathering information against the cost of making mistakes in different situations."

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