This is a really unusual weather situation, according to the National Weather Service: Three low pressure systems in line over the entirety of North America. NASA Goddard describes them as "three atmospheric dragons." They do look like dragons! It must be a Game of Thrones' marketing ploy.
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Source: http://gizmodo.com/how-mexicos-drug-cartels-are-driving-up-the-price-of-l-1555572876
A lime shortage is threatening the U.S. food and beverage industry, with some bars and restaurants jacking up drink prices, charging extra for a slice—or refusing to serve the citrus at all. But there's another reason to rethink that margarita: The pricey limes you're buying from Mexico might be supporting drug violence.
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Source: http://www.businessinsider.com/anki-drive-and-apple-2014-3
A company called Anki made its public debut last year on one of the best stages any company could hope for — Apple's annual developer conference, WWDC.
It had been operating quietly since February 2012 to refine its product — robotic toy cars that race around a special track, all controlled by an iPhone. It's a bit like a real-life version of Mario Kart: Cars can earn powerups like faster top speeds, or weapons for disabling opponents' cars.
On the surface, it is nothing more than a toy company. But it got the attention of investment firms like Andreessen Horowitz, Two Sigma, and Index Ventures, who collectively invested $50 million. Marc Andreessen calls it "one of the best robotics startups I've ever seen." The company's chief product officer gave Business Insider an update on their progress recently.
Its $200 starter kit comes with a racetrack and two cars, and customers have so far collectively raced 42 million laps around the track. We asked Anki for sales numbers, but it declined to give out that data.
"The first problem anyone faces in robotics is positioning, or determining where your robot is in its environment," said Joe Palatucci, Anki's Chief Product Officer. "Second is reasoning. You have to give the robot a goal and it needs to determine the sequence of actions it needs to take to accomplish that goal. Last are the controls — this is the nitty-gritty, where you actually execute a task and command voltages to motors that manipulate and move the robot."
The entertainment factor can't be denied — "We eat our own dog food quite often," said Palatucci. "We have weekly tournaments at the office. It's a lot of! fun."
Here's a GIF from the WWDC presentation, in which Anki co-founder Boris Sofman demos Anki DRIVE.
The Anki DRIVE racetrack is made using a special ink that's transparent in the infrared spectrum even though our eyes register it as black. The bottoms of the cars use special cameras and lights that let them see through the ink to the bits of information encoded there, and this information is sent back wirelessly to phones 500 times per second as a car moves around the track.
Positioning: Solved.
Players are racing against autonomous cars controlled by Anki software. The cars don't have an onboard "brain" that enables them to "think" for themselves; this task is outsourced to the players' phones, which receive positioning data from the cars, then beam instructions back to the cars via Bluetooth LE, a wireless communications standard. Since the phone knows the location of all cars on the track, it can plan routes and attack other vehicles with its cars' weapons.
Implementing users' smartphones this way saves Anki money because it can offload the heavy lifting of "thinking" to a device that users already own. And Anki's software has proven to be a vicious opponent: When set to "hard" mode, the cars will beat a human player nine out of 10 times.
Reasoning: Solved.
When it comes to actually moving around a track, Anki's cars are electronically and mechanically identical. They derive their unique characteristics from Anki's software, which enables things like increased top speeds, the ability to execute 180-degree turns, or the ability to wield some weapons for blasting opponents off the track. "Because so much is driven by software," said Palatucci, "we can easily send updates to the App Store that still expand the gameplay after a single hardware purchas! e."
Controls: Solved.
Palatucci and Sofman began work on Anki DRIVE about six years ago while pursuing their PhDs in robotics at Carnegie Mellon University. After a lot of night and weekend effort, the company sought out a partnership with Apple, which it's maintained for "the better part of a year." Anki approached Apple because "mobile phones are central to what we are doing," says Palatucci. "We thought their retail stores would be a perfect way for us to distribute, and Apple got behind Bluetooth LE two years before most others."
Since its launch at WWDC, Anki DRIVE was named one of the best inventions of 2013 by TIME, and Anki even got some attention on the Ellen DeGeneres Show. In keeping with the commitment to continue adding to the cars' software, the company released several new upgrades at the beginning of the year to make for an enhanced racing experience (there's even a horn upgrade — "honk" it and opponents' cars move out of your way).
Palatucci kept talk of the future a bit vague, but seems most excited by the fact that simple software updates can continue to make the game a repeatably enjoyable product: "It's really exciting for potential new customers to realize Anki DRIVE is an evolving experience."
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Source: http://www.businessinsider.com/iphone-apps-crash-more-than-android-apps-2014-3
Mobile app performance management company Crittercism publish a study on the crash rate for apps on iOS and Android (PDF). Somewhat surprisingly, it says that iOS Apps crash more than Android. Chart via Statista.
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Source: http://gigaom.com/2014/03/31/why-nvidia-thinks-it-can-power-the-ai-revolution/
Smarter robots and devices are coming to a home near you, and chipmaker Nvidia wants to help make it happen. It won’t develop the algorithms that dictate their behavior or build the sensors that let them take in our world, but its graphics-processing units, or GPUs, might be a great way to handle the heavy computing necessary to make many forms of artificial intelligence a reality.
Most applications don’t use GPUs exclusively, but rather offload the most computationally intensive tasks onto them from standard microprocessors. Called GPU acceleration, the practice is very common in supercomputing workloads and it’s becoming ubiquitous in the area of computer vision and object recognition, too. In 2013, more than 80 percent of the teams participating in the ImageNet image-recognition competition utilized GPUs, said Sumit Gupta, general manager of the the Advanced Computing Group at Nvidia.
In March 2013, Google acquired DNNresearch, a deep learning startup co-created by University of Toronto professor Geoff Hinton. Part of the rationale behind that acquisition was team’s performance of Hinton’s team in the 2012 ImageNet competition, where the group’s GPU-powered deep learning models easily bested previous approaches.
Source: Nvidia
“It turns out that the deep neural network … problem is just a slam dunk for the GPU,” Gupta said. That’s because deep learning algorithms often require a lot of computing power to process their data (e.g., images or text) and extract the defining features of the things included in that data. Especially during the training phase, when the models and algorithms are being tuned for accuracy, they need to process a lot of data.
Numerous customers are using Nvidia’s Tesla GPUs for image and speech recognition, including Adobe and Chinese search giant Baidu. Nvidia is working on other aspects of machine learning as well, Gupta noted. Netflix uses them (in the Amazon Web Services cloud) to power its recommendation engine, Russian search engine Yandex uses GPUs to power its search engine, and IBM uses them to run clustering algorithms in Hadoop.
Nvidia might be so excited about machine learning because it has been pushing GPUs as a general-purpose computing platform — not just a graphics and gaming chip — for years with mixed results. The company has tried to do this by simplify programming its processors via the CUDA language it has developed, but Gupta acknowledged there’s still an overall lack of knowledge about how to use GPUs effectively. That’s why so much real innovation still remains with these large users that have the parallel-programming skills necessary to take advantage of 2,500 or more cores at a time (and even more in multi-GPU systems).
Source: Nvidia
However, Nvidia is looking beyond servers and into robotics to fuel some of its machine learning ambitions over the next decade. Last week, the company announced its Jetson TK1 development kit, which Gupta called “a supercomputing version of Raspberry Pi.” At $192, the kit is programmable using CUDA and includes all the ports one might expect to see, as well as a Tegra K1 system-on-a-chip (the latest version of Nvidia’s mobile processor) that’s comprised of a 192-core Kepler GPU, an ARM Cortex A15 CPU and 300 gigaflops of performance.
Well into the 1990s, that type of performance would have put Jetson at or near near the top of any list of the world’s fastest supercomputers.
The company is touting the kit for computer vision, security and other computations that will be critical to mainstream robotics, and Gupta raised the question of how fast the internet of things might advance if smart devices came equipped with this kind of power. While Google and Facebook might train massive artificial intelligence models across hundreds or thousands of servers (or, in Google’s case, on a quantum computer) in their data centers, one big goal is to get the resulting algorithms running on smartphones to reduce the amount of data that needs to be sent immediately to the cloud for processing. Three hundred gigaflops embedded into a Nest thermostat or a drone, for example, is nothing to sneeze at.
Nvidia expects the rise in machine learning workloads to drive “pretty good” revenue growth in the years to come, Gupta said, but beyond the obvious examples he’s not ready to predict the types of computations its GPUs will end up running. “We’ve only just figured out how to use machine learning for a few things, but in fact it’s applicable to a lot of things,” he said. With respect to the Jetson kit, he added, “We’re still trying to imagine what you can do with it.”
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