As Wired notes, the algorithms now comb over large amounts of data and learn from it. In one case, the balloon temporarily floated over the Pacific Ocean to catch winds when it determined that there wouldn't be enough gusts to stay over land. There's even "reinforcement learning," which has the balloon refining its behavior even after making predictions as to what will happen. All told, the Peru vehicle made just under 20,000 tweaks to its altitude over the course of those 14 weeks, or dozens per day. The AI-based upgrade should not only keep balloons in place for longer, but help Google trim costs and expand its reach.

"Will work still be the place where we integrate individuals into societies?" asks End of Shift -- The Robots Are Taking Over, a new documentary on the future of work by filmmaker Klaus Martens that premiered last week on German and French public television (I make a brief appearance in it as well). It is a rhetorical question, and although not verbalized by the narrator before the end of the film, it is omnipresent from the first scene on and implicitly precludes all interviews and footage that Martens and his crew captured in Germany, France, Japan, and the San Francisco Bay Area. The topic is acute: An oft-cited Oxford study predicted in 2013 that software and robots will eliminate half of the human work force within the next two decades. This year's OECD report comes to a less pessimistic conclusion, emphasizing the heterogeneity of workers' tasks within occupations. It projects that "on average across the 21 OECD countries, 9 percent of jobs are automatable" (e.g. in Germany 12 percent, in France 9 percent, and in the US 9 percent), and low qualified workers will be most affected.

After the launch of Project loon, Google has been using static algorithms to make its internet balloons change altitude and stay in position for several years. Those hard coded algorithms were confined to adapt to unexpected weather patterns, which are pretty common in a place like the stratosphere. Well, those situations are history now. The Project Loon team has revealed that their engineering team is moving away from those hard coded control algorithm, instead, they are using machine learning to alter those internet balloon's behavior and make them stick to the desired flight path much longer. The company has already launched a test balloon into the stratosphere over Peru, which stayed there for 98 days, adapting to difficult wind conditions that might have sent it floating away.

This fall, customers cruising the aisles of Lowe's home improvement stores in the San Francisco Bay Area may see a new type of employee taking inventory and assisting shoppers. You won't find a nametag on this worker, but you won't confuse it with other employees, either. The new kid in town is the LoweBot, an autonomous retail service robot that scans and audits store inventory on the floor. It uses voice recognition to identify products for customers and lead them to the right shelf -- in multiple languages. The retailer is deploying LoweBots at 11 of its Bay Area stores over a seven-month period using NAVii robots made by Fellow Robots, following a successful two-year pilot program of a first-generation robot called OSHbot that was tested at one of Lowe's Orchard Supply Hardware stores.

When Google first introduced Project Loon, its internet balloons used static algorithms to change altitude and stay in position. While clever, they were limited -- Google couldn't do much to adapt to unexpected weather patterns, which are quite common tens of thousands of feet in the air. The Project Loon team has revealed that it's using artificial intelligence technology (specifically, machine learning) to alter balloons' behavior and keep them in position for much longer. One test balloon stayed in the Peruvian stratosphere for 98 days, adapting to tricky wind conditions that might have sent it drifting away. As Wired notes, the algorithms now comb over large amounts of data and learn from it.

The BayLearn Symposium aims at gathering scientists in machine learning from the San Francisco Bay Area. While it promotes community building between local researchers from academic and industrial institutions, it also welcomes visitors. This one-day event combines invited talks and posters to foster exchange of ideas. For more information, please visit www.baylearn.org

The drone turned slowly with a loud buzzing sound, red and blue lights flashing, and hit me right in the eyes with a spotlight. "Security has been notified," boomed a smooth voice from the aircraft's twin loudspeakers. Startup Aptonomy thinks this experience can keep intruders out of factories, warehouses, and other facilities more cheaply than human guards can and more effectively than cameras and alarms. I received the drone security guard treatment in a demonstration at the company's testing area on Treasure Island, an old naval base in San Francisco Bay. Cofounder Mihail Pivtoraiko says his drones will be ready to go on patrol next year.

Posted by Sara Robinson, Developer Advocate I've been playing around with Google's machine learning tools recently, especially image recognition. By far, my favorite feature of the Cloud Vision API is landmark detection: you send the API a picture of a popular (or obscure) landmark and it returns JSON with the name of the landmark, its latitude / longitude coordinates, a bounding box indicating where the landmark was found in the image, and even more landmark metadata. To see the Vision API in action and understand how it works behind the scenes, let's look at some landmarks from around the world. First, let's see what a JSON response looks like. We'll start with this image of the Eiffel Tower: If at first glance you thought this was a picture of the Eiffel Tower in Paris, you were wrong (don't worry, I was too).

Looking around at the natural world, have you ever wondered why some groups of organisms contain huge numbers of species while others are seemingly barren? Take insects as an example, animals which evolved around 480 million years ago. There are perhaps 6 million species living in all manner of environments, and occupying an incredible diversity of niches. Have you ever wondered why some groups of organisms contain huge numbers of species while others are seemingly barren? Plants have had a species production rate more than twice that of animals, while complex organisms (multicellular eukaryotes) have produced new species at a rate almost 10 times that of simpler one (protists and prokaryotes). Sex seems to have been a major catalyst for increasing the rate at which new species formed, perhaps explaining its success as an evolutionary strategy.

I was a wayward kid who grew up on the literary side of life, treating math and science as if they were pustules from the plague. So it's a little strange how I've ended up now--someone who dances daily with triple integrals, Fourier transforms, and that crown jewel of mathematics, Euler's equation. It's hard to believe I've flipped from a virtually congenital math-phobe to a professor of engineering. One day, one of my students asked me how I did it--how I changed my brain. I wanted to answer Hell--with lots of difficulty! In fact, I didn't start studying remedial math until I left the Army at age 26. If there were a textbook example of the potential for adult neural plasticity, I'd be Exhibit A. Learning math and then science as an adult gave me passage into the empowering world of engineering.