DeepMind's AI can apply learned knowledge to complete novel tasks

Can AI agents learn to generalize beyond its immediate experience? In a study conducted in collaboration with Stanford and the University College London, DeepMind scientists investigated whether systems could apply the knowledge they'd learned in one task to other, tangentially related tasks. They report that in environments ranging from a grid-world to an interactive 3D room generated in Unity (a game engine), their AI-driven agents correctly exploited the "compositional nature" of a language to interpret never-seen-before instructions. "[While] AI systems trained in idealized or reduced situations may fail to exhibit a compositional or systematic understanding of their experience, this competence can readily emerge when, like human learners, they have access to many examples of richly varying, multi-modal observations as they learn," wrote the contributing scientists in a preprint paper summarizing the research. "This suggests that, during training, the agent learns not only how to follow training instructions, but also general information about how word-like symbols compose and how the combination of those words affects what the agent should do in its world."