Lifeboat Foundation

Oil spill cleanup technology is a surprisingly innovative field—we learned as much in the wake of the BP Gulf disaster, when everyone from conservation biologists to barbers to Kevin Costner rushed to sell the government on their wild, sometimes literally hairy oil-sucking solutions. We had rubber goop that turned oil solid, massive bags of hair, and MIT’s previous entry into the cleanup fray, robotic oil-eating submarines.

But now the renowned science lab has a better idea: nano-magnets.

MIT researchers have developed a new technique for magnetically separating oil and water that could be used to clean up oil spills. They believe that, with their technique, the oil could be recovered for use, offsetting much of the cost of cleanup.

For the first time, scientists have used artificial intelligence to create complex, three-dimensional simulations of the Universe. It’s called the Deep Density Displacement Model, or DM, and it’s so fast and so accurate that the astrophysicists who designed it don’t even know how it does what it does.

What it does is accurately simulate the way gravity shapes the Universe over billions of years. Each simulation takes just 30 milliseconds — compared to the minutes it takes other simulations.

And, even more fascinatingly, DM learnt from the 8,000 training simulations the team fed it — vastly extrapolating from and outperforming them, able to adjust parameters in which it had not even been trained.

If you want to control a robot with your mind — and really, who doesn’t? — you currently have two options.

You can get a brain implant, in which case your control over the robot will be smooth and continuous. Or you can skip the risky, expensive surgery in favor of a device that senses your brainwaves from outside your skull — but your control over the bot will be jerky and not nearly as precise.

Continue reading “This New Mind-Controlled Robot Arm Works Without a Brain Implant” »

* Scientists Took an M.R.I. Scan of an Atom * Former NASA Flight Director Gene Kranz Restores Mission Control In Houston * Jeff Hawkins: Thousand Brains Theory of Intelligence

* Google’s robots.txt Parser is Now Open Source * Dear Agile, I’m Tired of Pretending * 4 Ways to Debug your Deep Neural Network

* How 3D printing allows scientists to grow new human hairs * NASA is testing how its new deep-space crew capsule handles a rocket emergency * Fake noise will be added to new electric cars starting today in the EU .

Some languages that have never been deciphered could be the next ones to get the machine translation treatment.

Algorithmic Intelligence Has Gotten So Good, It’s Easy To Forget It’s Artificial Artificial intelligence becomes hard to ignore when it starts taking over tasks that used to require human judgment — such as winnowing job applications or prioritizing stories in a news feed.

Machine learning, especially deep learning, is forcing a re-evaluation of how chips and systems are designed that will change the direction of the industry for decades to come.

Quantum supremacy sounds like something out of a Marvel movie. But for scientists working at the forefront of quantum computing, the hope—and hype—of this fundamentally different method of processing information is very real. Thanks to the quirky properties of quantum mechanics (here’s a nifty primer), quantum computers have the potential to massively speed up certain types of problems, particularly those that simulate nature.

Scientists are especially enthralled with the idea of marrying the quantum world with machine learning. Despite all their achievements, our silicon learning buddies remain handicapped: machine learning algorithms and traditional CPUs don’t play well, partly because the greedy algorithms tax classical computing hardware.

Add in a dose of quantum computing, however, and machine learning could potentially process complex problems beyond current abilities at a fraction of the time.

Neuromorphic computing has had little practical success in building machines that can tackle standard tests such as logistic regression or image recognition. But work by prominent researchers is combining the best of machine learning with simulated networks of spiking neurons, bringing new hope for neuromorphic breakthroughs.