DeepMind's AI system finds its way around simulated cities it hasn't seen before

DeepMind says it designed a system that can leverage prior knowledge to solve tasks, while at the same time exploring to gather new knowledge and plan using this new knowledge when faced with new tasks. In a paper accepted to the Conference on Computer Vision and Pattern Recognition (CVPR) 2020, researchers at the company describe an AI "planning module" that operates over episodic memories (memories of everyday events that can be explicitly stated), which they say outperforms the nearest baseline by two to three times with respect to planning and exploring. A grand challenge in AI is architecting a model that's able to enter unfamiliar environments and get to work immediately. For example, the paragon household robot would use general knowledge about homes to find cleaning supplies and acquire information it anticipates will be useful, like the location of clothes hampers in the rooms it passes. It could then leverage the newfound knowledge (i.e., hamper locations) to plan solutions for future tasks (e.g., doing the laundry) that solve the tasks more quickly.