Automatic object recognition in images is currently tricky. Even if a computer has the help of smart algorithms and human assistants, it may not catch everything in a given scene. Google might change that soon, though; it just detailed a new detection systemthat can easily spot lots of objects in a scene, even if they're partly obscured. The key is aneural network that can rapidly refine the criteria it's looking for without requiring a lot of extra computing power. The result is a far deeper scanning system that can both identify more objects and make better guesses -- it can spot tons of items in a living room, including (according to Google's odd example) a flying cat. The technology is still young, but the internet giant sees its recognition breakthrough helping everything fromimage searches through to self-driving cars. Don't be surprised if it gets much easier to look for things online using only vaguest of terms.
I’ve been chronicling the slow demise of B&N for years now, watching the company bleed out, drop by drop, until it has become a shell of its former value. B&N was a cultural center in places without cultural centers. It was a stopover on rainy days in New York, Chicago, and Cleveland and it was a place you could go to get your kids’ first books. That’s mostly over now. On Monday the company laid off 1,800 people. This offered a cost savings of $40 million. But that’s particularly interesting. That means each of those people made an average of $22,000 or so per year and minimum wage workers – hourly folks who are usually hit hardest during post-holiday downturns – would be making $15,000. In fact, what B&N did was fire all full time employees at 781 stores. From a former employee: On Monday morning, every single Barnes & Noble location told their full-time employees to pack up and leave. The eliminated positions were as follows: the head cashiers (those are the pe...
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