Hong Kong’s subway has 10,000+ maintenance engineers, and one AI manager. If you’ve been wondering about the notable lack of updates recently, that’s because I’ve gone back to school at Stanford’s Graduate School of Business MSx program, and they’re keeping us rather busy. Ironically, while I’m working hard to further improve my management skills, this article describes humans who conceded this task altogether to an artificial intelligence algorithm, simply because it does a much better job.
JUST after midnight, the last subway car slips into its sidings in Hong Kong and an army of engineers goes to work. In a typical week, 10,000 people carry out 2600 engineering works across the system - from grinding rough rails smooth and replacing tracks to checking for damage. People might do the work, but they don’t choose what needs doing. Instead, each task is scheduled and managed by artificial intelligence. Hong Kong has one of the world’s best subway systems. It has a 99.9 per cent on time record - far better than London Underground or New York’s subway.
The main difference between normal software and Hong Kong ...
an armed gunman in a police standoff is approached by a medial robot. The machine is send to talk the gunman down, armed with a neutral voice and the individuals data footprint. Can it succeed where human officers failed? This brilliant short raises some very interesting questions about how humans see machines, what makes us trust (or distrust) another, and how our future could be profoundly changed by the interaction with automated systems.
robotic innovator Festo released this video of spherical drones flying in smart formations. The swarm acts cooperatively to maintain dynamic formations, while each individual drone is smart enough to return to a charging station when it nears the end of its battery capacity. The spheres are lighter than air balloons, equipped with electric fans for positional control. Combined with clever lighting effects, they certainly look mesmerizing.
The drones use small propellers to move, and a central computer coordinates the swarm using a system of cameras and infrared markers on each drone. The drones autonomously return to charging stations on the ground when they get low on juice.
We imagine art installations using such a system, and Festo suggests more practical applications where "indoor GPS" would be used to track products or coordinate worker robots and vehicles inside a factory.
this robotic limb is straight out of scifi, mimicking the movements of an elephant’s trunk or Doc Octopus’ bionic arms (depending on whether you prefer cute or super villain). Powered by pneumatics and made from soft materials, it is designed to safely coinhabit a human’s workspace, but there is something vaguely creepy about it.
I am in Jochen Steil’s lab, grasping a segmented, whiplashing tentacle that resists and tries to push me away. It feels strangely alive, as though I am trying to throttle a giant alien maggot. In fact, I am training a bionic elephant’s trunk to do real-world jobs like picking apples or replacing light bulbs - something non-experts haven’t been able to do until now.
Designed to bring the dexterity of an elephant’s trunk to industrial robots, the appendage I am wrestling was launched by German engineering firm Festo as a proof-of-concept in 2010. The design showed that a trunk formed of 3D-printed segments can be controlled by an array of pneumatic artificial muscles.
what if automated systems could learn from each other? Researchers have set up a classroom where one robot can enable others to learn from its experiences. The ultimate subject on the lesson plan is how do deal with those pesky humans. Enabling knowledge transfer between otherwise very dissimilar systems makes it possible for a machine to learn from human/robot interaction experiences in a wide variety of environments and situations.
The time is soon approaching when you will show up for class, and instead of a human behind a desk, your professor will be a robot. Taught by generations of robots before it, your professor will be the ultimate font of knowledge in its chosen subject.
The idea behind Taylor’s research is the creation of a true robot teacher, one whose hardware and software don’t have to be the same as its student’s for it to impart knowledge. Existing solely as virtual robots at present, Taylor’s creations teach by giving advice to their equally virtual students. By letting the robot profs give pointers and allowing the student robots time to implement their latest lesson, Taylor states that he has not only seen the transfer of knowledge, but that the student robots actually learn to surpass their teacher’s abilities.