Android Apps Ported to Chrome OS

When I first heard that Chrome OS will be able to run Android apps, I assumed that Chrome OS will include Android's runtime and it will be able to launch any Android app. That's not the case, Android apps are manually ported to Chrome, so that's why Google's first set of apps only includes Duolingo, Evernote, Sight Words and Vine.

Bringing more powerful apps to Chrome OS is a great idea. Making it easier to port existing mobile apps to Chrome encourages developers to go beyond web apps and write native apps that work offline, include hardware integration and work outside of the browser. While cross-platform web apps are still useful, the new Chrome apps can bring some missing features that people expect to find in native apps. "These combine the best of websites and native applications — they're available offline, are always up to date, and they can communicate with devices like USB drives & Bluetooth speakers," explains Google.



"These first apps are the result of a project called the App Runtime for Chrome (Beta), which we announced earlier this summer at Google I/O. Over the coming months, we'll be working with a select group of Android developers to add more of your favorite apps so you'll have a more seamless experience across your Android phone and Chromebook," informs Google. You can tell Google what Android apps you'd like to be ported to Chrome.



For now, the first 4 apps can only be installed in Chrome OS, but I'm sure that Google will add support for Chrome in the near future.

Google's Object Recognition Technology

Google continues to improve its image recognition technology. A Google team placed first in the classification and detection tasks of the ImageNet large-scale visual recognition challenge, the largest academic challenge in computer vision.



"Superior performance in the detection challenge requires pushing beyond annotating an image with a 'bag of labels' - a model must be able to describe a complex scene by accurately locating and identifying many objects in it," explains Google. Here's are some examples of object detection:

"This effort was accomplished by using the DistBelief infrastructure, which makes it possible to train neural networks in a distributed manner and rapidly iterate. At the core of the approach is a radically redesigned convolutional network architecture," mentions Google. The goal is to train large models for deep neural networks.



Last year, Google used the DistBelief infrastructure to improve some models used by the winning team at ImageNet and implemented the algorithms in Google+ Photos Search and later in Google Drive's search engine. Google automatically annotates images and it allows you to search for things like "car" or "laptop" and find images that include them.



Google promises to use the latest achievements to improve "Google products such as photo search, image search, YouTube, self-driving cars, and any place where it is useful to understand what is in an image as well as where things are".