AI farms are well suited to impoverished regions like Guizhou, where land and labor are cheap and the climate temperate enough to enable the running of large machines without expensive cooling systems. It takes only two days to train workers like Yin in basic AI tagging, or a week for the more complicated task of labeling 3D pictures.
A battle for AI supremacy is being fought one algorithm at a time.
Now, this is awesome. A stationary robot, two mobile robots, and a human cooperating to perform a task. The humanoid robot also interprets human gestures and obeys those commands.
More information: http://www.co4robots.eu/
The Co4Robots MS2 scenario consists on collaborative grasping and manipulation of an object by two agents, the TIAGo mobile manipulator and a static manipulator; and a collaborating mobile platform and stationary manipulator to facilitate loading and unloading tasks onto the mobile platform.
Another step forward in robotics self-awareness. This robot learns it’s own kinematics without human intervention and then learns to plot solution paths.
Columbia Engineering researchers have made a major advance in robotics by creating a robot that learns what it is, from scratch, with zero prior knowledge of physics, geometry, or motor dynamics. Once their robot creates a self-simulation, it can then use that self-model to adapt to different situations, to handle new tasks as well as detect and repair damage in its own body.
A saying from one of my favorite movies is, “Tie two birds together and even though they have four wings they cannot fly.” Can’t say the same about flying drones.
“We perform outdoor autonomous flying experiment of f-LASDRA, constructed with multiple ODAR-8 links connected via cable with each other. Each ODAR-8 can compensate for its own weight, rendering f-LASDRA scalable. Utilizing SCKF with IMU/GNSS-module on each link and inter-link kinematic-constraints, we attain estimation accuracy suitable for stable control (5cm: cf. 1-5m w/ GNSS).”
We perform outdoor autonomous flying experiment of f-LASDRA (flying Large-size Aerial Skeleton with Distributed Rotor Actuation), which is constructed with multiple ODAR-8 links (https://youtu.be/S3i9NspWtr0), connected via flexible cable with each other. Each ODAR-8 link can generate omni-directional force/torque and also compensate for its own weight, thereby, rendering the f-LASDRA scalable w.r.the number of links.
Since the first Roomba came out in 2002, it has seemed inevitable that one day iRobot would develop a robotic lawn mower. After all, a robot mower is basically just a Roomba that works outside, right? Of course, it’s not nearly that simple, as iRobot has spent the last decade or so discovering, but they’ve finally managed to pull it off.
More than 10 years in the making, Terra wants to do for your lawn what Roomba has done for your floors.
To make its developers’ jobs more rewarding, Facebook is now using two automated tools called Sapienz and SapFix to find and repair low-level bugs in its mobile apps. Sapienz runs the apps through many tests to figure out which actions will cause it to crash. Then, SapFix recommends a fix to developers, who review it and decide whether to accept the fix, come up with their own, or ignore the problem.
Engineers began using Sapienz to review the Facebook app in September 2017, and have gradually begun using it for the rest of the company’s apps (which include Messenger, Instagram, Facebook Lite, and Workplace). In May, the team will describe its more recent adoption of SapFix at the International Conference on Software Engineering in Montreal, Canada (and they’re hiring).
But in private settings, including meetings with the leaders of the many consulting and technology firms whose pop-up storefronts line the Davos Promenade, these executives tell a different story: They are racing to automate their own work forces to stay ahead of the competition, with little regard for the impact on workers.
DAVOS, Switzerland — They’ll never admit it in public, but many of your bosses want machines to replace you as soon as possible.
I know this because, for the past week, I’ve been mingling with corporate executives at the World Economic Forum’s annual meeting in Davos. And I’ve noticed that their answers to questions about automation depend very much on who is listening.
In public, many executives wring their hands over the negative consequences that artificial intelligence and automation could have for workers. They take part in panel discussions about building “human-centered A.I.” for the “Fourth Industrial Revolution” — Davos-speak for the corporate adoption of machine learning and other advanced technology — and talk about the need to provide a safety net for people who lose their jobs as a result of automation.
A new breed of algorithms has mastered Atari video games 10 times faster than state-of-the-art AI, with a breakthrough approach to problem solving.
Designing AI that can negotiate planning problems, especially those where rewards are not immediately obvious, is one of the most important research challenges in advancing the field.
A famous 2015 study showed Google DeepMind AI learnt to play Atari video games like Video Pinball to human level, but notoriously failed to learn a path to the first key in 1980s video game Montezuma’s Revenge due to the game’s complexity.
