Google DeepMind's artificial intelligence team, alongside 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 research project drew inspiration from child development and sought to train AI to mirror human capacity to interact with physical objects and infer properties such as mass, friction, and malleability. 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.' Lead researcher Misha Denil and his 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. In the first simulation, called Which is Heavier, the AI was given a set of four blocks which were the same size but varied in mass.