Lifeboat Foundation: Safeguarding Humanity
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Category: robotics/AI

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.

Utilizing SCKF with standard IMU/GNSS-module on each link and inter-link kinematic-constraints, we can significantly improve position/attitude estimation accuracy of the f-LASDRA necessary for stable control (less than 5cm) as compared to typical accuracy of GNSS (1-5m). Semi-distributed version of the estimation framework is also devised to address the issue of scalability. (Accepted ICRA 2019). See also https://youtu.be/oHkjB8XzxIg for operational-LASDRA).

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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.

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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).

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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.

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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 Montezuma’s Revenge due to the game’s complexity.

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This is not a story about how the polar vortex is bad—bad for the human body, bad for public transportation, bad for virtually everything in its path. This is a story about how one being among us is actually taking advantage of the historic cold snap: Cassie the bipedal robot. While humans suffer through the chill, this trunkless pair of ostrich-like legs is braving the frozen grounds of the University of Michigan, for the good of science.

“When we saw the announcement for the polar vortex, we started making plans to see how long we could operate in that kind of weather,” says roboticist Jessy Grizzle. “We were going to tie a scarf on her just so it looked cute, but we decided people would think that was keeping her warm and affecting the experiment, so we didn’t.”

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Welcome to the future.

In the first experiment of its kind, scientists have been able to translate brain signals directly into intelligible speech. It may sound like wild science fiction at first, but this feat could actually help some people with speech issues.

And yes, we could also get some futuristic computer interfaces out of this.

Key to the system is an artificial intelligence algorithm that matches what the subject is hearing with patterns of electrical activity, and then turns them into speech that actually makes sense to the listener.

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