Google today announced a handful of new consumer privacy tools for some of its most-used products, including Google Maps, YouTube and Google Assistant. The tools are meant to better allow users to control, manage and erase the data Google collects from those who use its services or prevent Google from collecting that data in the first place, in some cases. In Google Maps, there's now an incognito mode option that works similar to the one available in the Chrome browser and, more recently, YouTube . The idea is that there are times when you don't want Google to track your usage of Maps, including when you search for particular places or travel about town. There are a number of reasons why you might want to opt-out of being tracked in Maps -- maybe you don't want to taint your personalized recommendations with irrelevant data.

Google has released the TensorFlow Privacy tool, an update to its open source TensorFlow machine learning framework, that will allow developers to enhance the privacy of their AI (artificial intelligence) models. The TensorFlow framework is used the world round by AI engineers to create text, audio, and image recognition algorithms; TensorFlow Privacy will enable these projects to integrate a statistical technique known as'differential privacy'. "If we don't get something like differential privacy into TensorFlow, then we just know it won't be as easy for teams inside and outside of Google to make use of it," Carey Radebaugh told The Verge. "So, for us, it's important to get it into TensorFlow, to open source it, and to start to create this community around it." The introduction of the privacy update is in keeping with Google's principles for responsible AI development.

Pixelation has long been a familiar fig leaf to cover our visual media's most private parts. Blurred chunks of text or obscured faces and license plates show up on the news, in redacted documents, and online. The technique is nothing fancy, but it has worked well enough, because people can't see or read through the distortion. The problem, however, is that humans aren't the only image recognition masters around anymore. As computer vision becomes increasingly robust, it's starting to see things we can't.