Google today announced an add-on for Google Sheets that applies “Simple ML” to your data that was built by the TensorFlow team to help make “machine learning accessible to all.”
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At the 2019 TensorFlow Dev Summit today, Google announced a number of updates for its open-source machine learning library aimed at research and production. The TensorFlow 2.0 alpha provides a preview of upcoming changes aimed at making ML easier for beginners.
Ahead of the 2019 TensorFlow Dev Summit, Google is announcing a new way for third-party developers to adopt differential privacy when training machine learning models. TensorFlow Privacy is designed to be easy to implement for developers already using the popular open-source ML library.
With last week’s Google Material Theme, Gmail for Android added the bolder “dangerous” warnings first introduced on the web. Powered by machine learning, Google today shared how it is leveraging TensorFlow to block 100 million additional spam messages every day.
The traditional Chinese calendar, which incorporates 12 zodiac animals, leverages moon cycles to mark the new year. In 2019, this date falls on February 5th and begins the Year of the Pig. To celebrate, Tuesday’s Google Doodle incorporates an AI Experiment that novelly uses your device’s front-facing camera to teach shadow puppetry.
Starting with the Machine Learning Crash Course in February, Google has released a number of tools and resources for developers to learn and integrate artificial intelligence. Seedbank is the latest and a home to interactive ML examples that run in the web and can be quickly edited.
During the I/O 2018 Keynote, Google announced the release of its new TPU 3.0 chip.
Many of Google’s machine learning efforts are open sourced so that developers can take advantage of the latest advancements. The latest release is for semantic image segmentation, or the technology behind the Pixel 2’s single lens portrait mode.
Back in May, Google announced AIY Projects — do-it-yourself hardware kits for experimenting with artificial intelligence. Today, Google followed up the first Voice Kit with a new Vision Kit for image recognition and TensorFlow development.
One of the many announcements from I/O 2017 was TensorFlow Lite for machine learning on mobile devices. Starting today, the Android and iOS optimized version of the ML library is now available as a developer preview.
Google has now released MobileNets, a family of computer vision models for TensorFlow. What’s special about these? They run entirely on the lower-power mobile devices that we all carry around in our pockets.
While on stage today at Google I/O 2017, CEO Sundar Pichai announced the company’s second generation TPUs (Tensor Processing Units), a cloud-computing system of software and hardware that aid in machine learning workloads.
TensorFlow (which is Google’s open source machine learning software that now powers applications like Google Translate and many Google Photos features) has now reached a major landmark: its 1.0 release. The 1.0 release was announced as part of the first annual TensorFlow Developer Summit, and Google also published details on its Developers blog.
One of the more notable features of the Snapdragon 835 processor announced at CES 2017 was TensorFlow support. In a blog post, Qualcomm notes several performance boosts as a result of optimizing the chip for Google’s machine learning framework.
Google this week has published a new version of its TensorFlow machine learning software that adds support for iOS. Google initially teased that it was working on iOS support for TensorFlow last November, but said it was unable to give a timeline. An early version of TensorFlow version 0.9 was released yesterday on GitHub, however, and it brings iOS support.
Machine learning — a branch of Artificial Intelligence that studies pattern recognition and computational learning — is at the core of many of Google‘s products. Everything from voice search to Maps‘ Street View down to Inbox‘s recently introduced Smart Replies (which are making their way into the just announced Allo) take advantage of machine learning’s incredible capabilities.
However, Google too seems to acknowledge, “great software shines brightest with great hardware underneath”. This is why, over the past several years, the company has worked on a custom ASIC (application-specific integrated circuit) named Tensor Processing Unit (TPU), and it is unveiling it today…
In a post on its Research Blog, Google today announced that it is making some significant improvements to TensorFlow. For those unfamiliar, TensorFlow is the company’s open source machine learning software that powers things like Google Translate and many Photos features. Today, Google revealed that it is adding the ability for TensorFlow to run across multiple machines at the same time with distributed computing support.
Google hasn’t been shy about sharing how it uses advanced neural networks (informally known as AI) in some of its products. The company has been teaching its machine learning tools a slew of new tricks in recent months. Google Photos uses it to easily find specific images based on your search, they equipped YouTube with the ability to better select thumbnails, reply to your emails from Gmail and made Google Translate far better at reading signs. And now, it wants to share its machine learning engine with developers, to make it even better…