Google's DeepMind has revealed a radical new research project designed to give AI's an imagination. The breakthrough means that systems will be able to think about their actions, and undertake'deliberate reasoning.' The radical system uses an internal'imagination encoder' that helps the AI decide what are and what aren't useful predictions about its environment. The breakthrough means that systems will be able to think about their actions, and undertake'deliberate reasoning.' The agents use an'imagination encoder'- a neural network which learns to extract any information useful for the agent's future decisions, but ignore that which is not relevant.

For some AI problems, such as playing Atari or Go, the goal is easy to define - it's winning. But how do you describe the process for performing a backflip? The difficulty of accurately describing a complex behaviour is a common problem when teaching motor skills to an artificial system. In this work we explore how sophisticated behaviors can emerge from scratch from the body interacting with the environment using only simple high-level objectives, such as moving forward without falling. Specifically, we trained agents with a variety of simulated bodies to make progress across diverse terrains, which require jumping, turning and crouching.

A hilarious new video reveals the clumsy progress of AI'parkour,' as scientists work to teach computer systems how to navigate'challenging terrains and obstacles.' DeepMind researchers have trained a number of simulated bodies, including a headless'walker,' a four-legged'ant,' and a 3D humanoid, to learn more complex behaviours as they carry out different locomotion tasks. The results, while comical, show how these systems can learn to improve their own techniques as they interact with the different environments, eventually allowing them to run, jump, crouch and turn as needed. Footage from the study offers a hilarious look into the trial-and-error process. As the team explains in the paper, the environments presented to the simulated bodies are of varying levels of difficulty.