MIT robot uses radio waves to find and retrieve hidden objects The system combines RF signals with computer vision Robotic perception RF-Grasp is comprised of a camera on the robot’s wrist and a separate RF reader. Together, they collect tracking data and create a visual map of the environment. The system first pings the object’s RF tag to identify its location. It then determines the optimal path around the obstacles to reach the item....

The Republican presidential primary could not be in a higher level of swing, and that is bringing the candidates into our own sphere of technology. Tomorrow, current front-runner Mitt Romney is taking the time during his West Coast trip tohead to Microsoft, and speak to the firm’s political action committee. Microsoft has invited all candidates to enjoy some of its hospitality and chat at its political arm. Romney is coming to specifically discuss trade policy....

“In the western world the number of kids going into science is declining, funding is going down and the barrier to entry is proving difficult. We need to make science fun, we need to let kids know it’s not that hard.” -Christopher Kevin Howard, Libboo Co-Founder Christopher Kevin Howard is a researcher atMIT, a part-time physics instructor at Harvard and the Co-Founder of a company calledLibboo, a free community of inquisitive readers and writers around the world, created for the purpose of publishing online literature....

If you play around to make things in a workshop, you might’ve used a laser cutter to create things at one point. Usually, you would use different materials by trial and error method to reach the desired shape and function. However, researchers from MIT are making this process safer by using machine learning. The team fromMIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) has come up with a technology called SensiCut, a material sensing platform for laser cutters that warns you of potentially harmful materials....

MIT’s new ‘liquid’ neural network learns on the job — so robots can adapt to changing conditions The system draws inspiration from a tiny worm Future plans The liquid network’s small number of highly expressive neurons also makes it easier to interpret its decisions. “Just [by] changing the representation of a neuron, you can really explore some degrees of complexity you couldn’t explore otherwise,” said Hasani. In tests, the network performed promisingly in predicting future values in datasets, ranging from atmospheric chemistry to traffic patterns....