Algorithms play role in so much of what we see online and in our day-to-day lives, helping out with everything from setting bail to finding recipes. But while the algorithms of the past were painstakingly coded by humans, the algorithms of the future will be built by robots. They'll be better, more efficient, but also nearly impossible for humans to understand.
The Robot Operating System (ROS) is a flexible framework for writing robot software. It is a collection of tools, libraries and conventions that aim to simplify the task of creating complex and robust robot behavior across a wide variety of robotic platforms. ROS is used to create application for a physical robot without depending on the actual machine, thus saving cost and time. These applications can be transferred onto the physical robot without modifications. The decision making capability of the robots can be aided with AI.
Quietly, in the background, robots are getting smarter. So much smarter that they may soon be able to accomplish a lot of our work for us. They can drive us and our stuff around, manufacture a lot of that stuff, prepare our meals and care for our elderly and infirm. This is not to say they will take over the world, but they are certainly poised to increase their population exponentially. In the forefront should be our concerns about the social upheavals that will accompany this radical change in the economics of labor.
An international team of researchers from Switzerland, Germany and the U.S. has found a way to boost the abilities of robots by using machine learning. The team used machine learning to give a robot named ANYmal--a commercially available autonomous robot the size of a large dog that can walk and navigate its surroundings--greater speed and agility. ANYbotics, the company that makes it, wanted to give the robot the ability to learn through practice rather than through programming--an alternative that could save time and money and help enhance the robot's abilities. According to the researchers' paper published in Science Robotics, robots with legs pose one of the biggest challenges in robotics: how do you teach them to walk? In particular, the dynamic and agile maneuvers of four-legged animals simply cannot be imitated or taught by existing methods devised by two-legged humans.
Tired of that smug look of satisfaction on your kid's face when they're able to find Waldo on a page faster than you can? A creative agency called RedPepper built a robot that levels the Where's Waldo playing field using a camera and machine learning AI to spot the striped traveler in as little as four-and-a-half seconds. Once the robot arm is in place, it snaps a high-res photo of a two-page Where's Waldo spread, and analyzes it for recognizable cartoon human faces using the OpenCV computer vision programming tools. To actually spot Waldo, the robot's designers trained Google's Cloud AutoML machine learning tool on a relatively small collection of 107 Waldo head and body images. Thousands of sample images are usually needed to properly train an image recognition AI, but because the robot is only dealing with simple cartoons of people, it can pick out Waldo faster than most kids--and easily dominate adults.