A one-legged robot that can stand, hop and keep its balance on sloping or unsteady surfaces could offer a cheaper route to bipedal bots and self-balancing exoskeletons. Researchers at the Toyota Technological Institute (TTI) in Nagoya, Japan, built their robot, dubbed TTI Hopper, using simple motors and gears for less than $1000, then created an algorithm that compensates for the limited capabilities of these components. "In robotics, we sometimes use hydraulics, because they can be actuated fast," says Barkan Uğurlu, who is now at Özyeğin University in Istanbul, Turkey. "Or electric actuators that have a special spring arrangement or a strain gauge to measure forces inside. Instead, we used DC motors with gears. We only measure the joint angle, and we only used one very low-cost force sensor at the foot."

Any researcher who's focused on applying machine learning to real-world problems has likely received a response like this one: "The authors present a solution for an original and highly motivating problem, but it is an application and the significance seems limited for the machine-learning community." These words are straight from a review I received for a paper I submitted to the NeurIPS (Neural Information Processing Systems) conference, a top venue for machine-learning research. I've seen the refrain time and again in reviews of papers where my coauthors and I presented a method motivated by an application, and I've heard similar stories from countless others. This makes me wonder: If the community feels that aiming to solve high-impact real-world problems with machine learning is of limited significance, then what are we trying to achieve? The goal of artificial intelligence (pdf) is to push forward the frontier of machine intelligence. In the field of machine learning, a novel development usually means a new algorithm or procedure, or--in the case of deep learning--a new network architecture.