TensorFlow brings AI to the connected device

#artificialintelligence

Consumers are beginning to expect more AI-driven interactions with their devices, whether they are interacting with smart assistants or expecting more tailored content within an application. However, when considering the landscape of available AI-focused applications, the list is significantly biased toward manufacturers. So, how can a third-party app developer provide an experience that is similar in performance and interactivity to built-in AI's like Siri or Google Assistant? This is why the release of TensorFlow Lite is so significant. TensorFlow released TensorFlow Lite, this past November as an evolution of TensorFlow Mobile.


Google launches TensorFlow Lite for machine learning on mobile devices

@machinelearnbot

TensorFlow Lite for machine learning on mobile devices was first announced by Dave Burke, VP of engineering of Android at the Google I/O 2017. TensorFlow Lite is a lightweight version of Google's TensorFlow open source library that is mainly used for machine learning application by researchers and developers.


Distributed TensorFlow with GPU Support on Mesosphere DC/OS

@machinelearnbot

Today, we are excited to announce the beta release of TensorFlow in the Mesosphere DC/OS Service Catalog. Using a single command, you can now deploy distributed TensorFlow on any bare-metal, virtual, or public cloud infrastructure. As with other packages available for DC/OS, the new TensorFlow package also includes the ability to use GPUs to accelerate your machine learning and deep learning applications. In the race to leverage deep learning capabilities, data scientists specializing in deep learning are highly sought after. An efficient data science infrastructure allows you to attract the best data scientists and get the best work out of them, which gives your business a strategic advantage over competitors.


Distributed TensorFlow with GPU Support on Mesosphere DC/OS

#artificialintelligence

Today, we are excited to announce the beta release of TensorFlow in the Mesosphere DC/OS Service Catalog. Using a single command, you can now deploy distributed TensorFlow on any bare-metal, virtual, or public cloud infrastructure. As with other packages available for DC/OS, the new TensorFlow package also includes the ability to use GPUs to accelerate your machine learning and deep learning applications. In the race to leverage deep learning capabilities, data scientists specializing in deep learning are highly sought after. An efficient data science infrastructure allows you to attract the best data scientists and get the best work out of them, which gives your business a strategic advantage over competitors.


Here's how Google is preparing Android for the AI-laden future

PCWorld

The future of Android will be a lot smarter, thanks to new programming tools that Google unveiled on Wednesday. The company announced TensorFlow Lite, a version of its machine learning framework that's designed to run on smartphones and other mobile devices, during the keynote address at its Google I/O developer conference. "TensorFlow Lite will leverage a new neural network API to tap into silicon-specific accelerators, and over time we expect to see [digital signal processing chips] specifically designed for neural network inference and training," said Dave Burke, Google's vice president of engineering for Android. "We think these new capabilities will help power a next generation of on-device speech processing, visual search, augmented reality, and more." The Lite framework will be made a part of the open source TensorFlow project soon, and the neural network API will come to the next major release of Android later this year.