Lifeboat Foundation

About a year ago, Apple made the bold proclamation that it was zeroing in on a future where iPhones and MacBooks were created wholly of recycled materials. It was, and still is, an ambitious thought. In a technologically-charged world, many forget that nearly 100 percent of e-waste is recyclable. Apple didn’t.

Named “Daisy,” Apple’s new robot builds on its previous iteration, Liam, which Apple used to disassemble unneeded iPhones in an attempt to scrap or reuse the materials. Like her predecessor, Daisy can successfully salvage a bulk of the material needed to create brand new iPhones. All told, the robot is capable of extracting parts from nine types of iPhone, and for every 100,000 devices it manages to recover 1,900 kg (4,188 pounds) of aluminum, 770 kg of cobalt, 710 kg of copper, and 11 kg of rare earth elements — which also happen to be some of the hardest and environmentally un-friendly materials required to build the devices.

In its latest environmental progress report, Apple noted:

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Researchers in artificial intelligence can stand to make a ton of money. But this week, we actually know just how much some A.I. experts are being paid — and it’s a lot, even at a nonprofit.

OpenAI, a nonprofit research lab, paid its lead A.I. expert, Ilya Sutskever, more than $1.9 million in 2016, according to a recent public tax filing. Another researcher, Ian Goodfellow, made more than $800,000 that year, even though he was only hired in March, the New York Times reported.

As the publication points out, the figures are eye-opening and offer a bit of insight on how much A.I. researchers are being paid across the globe. Normally, this kind of data isn’t readily accessible. But since OpenAI is a nonprofit organization, it’s required by law to make these figures public.

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That raises significant issues for universities and governments. They also need A.I. expertise, both to teach the next generation of researchers and to put these technologies into practice in everything from the military to drug discovery. But they could never match the salaries being paid in the private sector.

Tax forms filed by OpenAI provide insight into the enormous salaries and bonuses paid to artificial intelligence specialists across the world.

Robots are going to make life a lot easier for amputees and people with spinal cord injuries. Take HAL from CYBERDYNE for instance: it is robotic assistive limb that improves patients’ ability to walk. HAL uses sensors to detect signals from the patient’s body to assist with desired movement.

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Humans are usually good at isolating a single voice in a crowd, but computers? Not so much — just ask anyone trying to talk to a smart speaker at a house party. Google may have a surprisingly straightforward solution, however. Its researchers have developed a deep learning system that can pick out specific voices by looking at people’s faces when they’re speaking. The team trained its neural network model to recognize individual people speaking by themselves, and then created virtual “parties” (complete with background noise) to teach the AI how to isolate multiple voices into distinct audio tracks.

The results, as you can see below, are uncanny. Even when people are clearly trying to compete with each other (such as comedians Jon Dore and Rory Scovel in the Team Coco clip above), the AI can generate a clean audio track for one person just by focusing on their face. That’s true even if the person partially obscures their face with hand gestures or a microphone.

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You might only know JPEG as the default image compression standard, but the group behind it has now branched out into something new: JPEG XS. JPEG XS is described as a new low-energy format designed to stream live video and VR, even over WiFi and 5G networks. It’s not a replacement for JPEG and the file sizes themselves won’t be smaller; it’s just that this new format is optimized specifically for lower latency and energy efficiency. In other words, JPEG is for downloading, but JPEG XS is more for streaming.

The new standard was introduced this week by the Joint Photographic Experts Group, which says that the aim of JPEG XS is to “stream the files instead of storing them in smartphones or other devices with limited memory.” So in addition to getting faster HD content on your large displays, the group also sees JPEG XS as a valuable format for faster stereoscopic VR streaming plus videos streamed by drones and self-driving cars.

“We are compressing less in order to better preserve quality, and we are making the process faster while using less energy,” says JPEG leader Touradj Ebrahimi in a statement. According to Ebrahimi, the JPEG XS video compression will be less severe than with JPEG photos — while JPEG photos are compressed by a factor of 10, JPEG XS is compressed by a factor of 6. The group promises a “visual lossless” quality to the images of JPEG XS.

Like a miniaturized Moby Dick, the pure-white fish wiggles slowly over the reef, ducking under corals and ascending, then descending again, up and down and all around. Its insides, though, are not flesh, but electronics. And its flexible tail flicking back and forth is not made of muscle and scales, but elastomer.

The Soft Robotic Fish, aka SoFi, is a hypnotic machine, the likes of which the sea has never seen before. In a paper published today in Science Robotics, MIT researchers detail the evolution of the world’s strangest fish, and describe how it could be a potentially powerful tool for scientists to study ocean life.

Scientists designed SoFi to solve several problems that bedevil oceanic robotics. Problem one: communication. Underwater vehicles are typically tethered to a boat because radio waves don’t do well in water. What SoFi’s inventors have opted for instead is sound.

And just like that, humanity draws one step closer to the singularity, the moment when the machines grow so advanced that humans become obsolete: A robot has learned to autonomously assemble an Ikea chair without throwing anything or cursing the family dog.

Researchers report today in Science Robotics that they’ve used entirely off-the-shelf parts—two industrial robot arms with force sensors and a 3D camera—to piece together one of those Stefan Ikea chairs we all had in college before it collapsed after two months of use. From planning to execution, it only took 20 minutes, compared to the human average of a lifetime of misery. It may all seem trivial, but this is in fact a big deal for robots, which struggle mightily to manipulate objects in a world built for human hands.

To start, the researchers give the pair of robot arms some basic instructions—like those cartoony illustrations, but in code. This piece goes first into this other piece, then this other, etc. Then they place the pieces in a random pattern front of the robots, which eyeball the wood with the 3D camera. So the researchers give the robots a list of tasks, then the robots take it from there.

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Sometimes sensitive data, like passwords or keys that unlock encrypted communications, are accidentally left open for anybody to see. It’s happened everywhere from the Republican National Committee to Verizon, and as long as information can be public on the internet the trend isn’t going to stop.

But researchers at software infrastructure firm Pivotal have taught AI to locate this accidentally public sensitive information in a surprising way: By looking at the code as if it were a picture. Since modern artificial intelligence is arguably better than humans at identifying minute differences in images, telling the difference between a password and normal code for a computer is just like recognizing a dog from a cat.

The best way to check whether private passwords or sensitive information has been left public today is to use hand-coded rules called “regular expressions.” These rules tell a computer to find any string of characters that meets specific criteria, like length and included characters. But passwords are all different, and this method means that the security engineer has to anticipate every kind of private data they want to guard against.

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