New tool searches for misconfigured Google cloud storage

GCPBucketBrute – the open source tool recently released by Rhino Security – allows pen testers to discover open buckets found on the Google Cloud Platform (GCP). The tool can also determine if privilege escalation can occur on a particular cloud instance.

“There are countless AWS S3 bucket enumerators out there online, but none (that we could find, at least) that targeted other similar storage services, such as Google Storage,” said Rhino in a blog post on February 26.

Google is staking its claim in the next big thing after cloud computing with a new line of AI-powered hardware for developers

On its website, Google Coral has product listings for a $150 motherboard, a $75 USB device to bring AI to existing systems, and a $25 camera that slots into the board. The listings were first spotted by the Verge.

"Coral offers a complete local AI toolkit that makes it easy to grow your ideas from prototype to production," writes Google in a blog post announcing Coral.

In theory, it's more than a little bit like the Raspberry Pi, the pioneering $35 minicomputer, which is mega-popular among hackers as an easy and cheap way to build experimental hardware and other oddities.

Google open-sources GPipe, a library for efficiently training large deep neural networks

If you’re in the business of training large-scale AI systems, good news: Google’s got your back. Google’s AI research division today open-sourced GPipe, a library for “efficiently” training deep neural networks (layered functions modeled after neurons) under Lingvo, a TensorFlow framework for sequence modeling. It’s applicable to any network consisting of multiple sequential layers, Google AI software engineer Yanping Huang said in a blog post, and allows researchers to “easily” scale performance.

Google AI division open sources GPipe neural network library

Google open sourced GPipe, a scalable machine learning library designed to enable users to train large-scale deep neural networks faster, more accurately, and potentially with less compute power.

The tech vendor made the library available on GitHub March 4, open sourced under the Lingo framework, a TensorFlow-based deep learning framework designed specifically for linguistic sequence models.

Google Open-Sources GPipe Library for Training Large-Scale Neural Network Models

Deep neural network (DNN) models such as BigGAN, BERT, and GPT 2.0 have demonstrated that larger DNN models produce better task performance. These huge models are however becoming increasingly difficult to train. Google this week introduced GPipe, an open-source library that dramatically improves training efficacy for large-scale neural network models.

In 2014, GoogleNet finished first in the ImageNet visual recognition challenge. The winning model consisted of four million parameters and achieved 74.8 percent accuracy. Three years later, Squeeze-and-Excitation Networks scored 82.7 percent to win the challenge with a model containing 145.8 million parameters — some 36x more than GoogleNet.

Is Google's New Lingvo Framework a Big Deal for Machine Translation?

Neural machine translation experts weigh in on Google's open sourcing of Lingvo, a sequence modeling framework built on TensorFlow.

Google Open-Sources Lingvo Framework for Sequence-To-Sequence Modeling

Natural language processing has made significant progress in the past year, but few frameworks focus directly on NLP or sequence modeling. Google Brain recently released Lingvo, a deep learning framework based on TensorFlow. Lingvo focuses on sequence-to-sequence models of language-related tasks such as machine translation, speech recognition, and speech synthesis; and significantly enhances code reuse and iteration speed. Lingvo-supported frameworks include traditional RNN sequence models, transformer models, and models that include VAE components. Lingvo is now open-sourced on GitHub.

AI Weekly: Google’s federated learning gets its day in the sun

New versions of TensorFlow, including TensorFlow 2.0 with tf.keras as a central API and TensorFlow Lite 1.0 for mobile devices, were released, as was a $150 Coral board for edge TPU applications.

Speed optimization for AI on mobile devices and a cleanup of TensorFlow’s cluttered APIs is more than cosmetic — these changes will shape how developers and businesses train AI systems. But the news that caught my eye was the release of TensorFlow for federated learning.

Google Makes Machine Learning Library Open Source

To help developers train AI agents with strong privacy guarantees, Google just released a machine learning library called TensorFlow Privacy. The library, which is open source, can be downloaded on GitHub.

Aside from training AI models with privacy, TensorFlow aims to “advance the state-of-the-art in machine learning with strong privacy guarantees.”

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TensorFlow Privacy operates on the principle of differential privacy. This is a statistical technique to maximize accuracy while balancing user information.

Differential privacy ensures that an AI model cannot encode information unique to a developer. This blocks all chances of a breach releasing a user’s identity.

Instead of gathering and storing information to learn, differential privacy enables an AI agent to acquire knowledge from patterns that show up en masse.

Google announces TensorFlow 2.0 Alpha, TensorFlow Federated, TensorFlow Privacy, and the Coral development platform

Google is fully invested in advancing the potential of artificial intelligence. The company has released a bunch of tools, documentation, tutorials, and platforms to help developers utilize machine learning for applications. TensorFlow is one of their most important projects in this field. It’s an open-source development platform, helping teams and individuals to train models via machine learning. At the 3rd annual TensorFlow Developer Summit, Google announced the first alpha release of TensorFlow 2.0. The summit also introduced a lot of other stuff, which we’ll summarize below.

Google launches TensorFlow 2.0 alpha with fewer APIs

The world’s most popular open source framework for machine learning is getting a major upgrade today with the alpha release of TensorFlow 2.0. Created by the Google Brain team, the framework is used by developers, researchers, and businesses to train and deploy machine learning models that make inferences about data.

A full release is scheduled to take place in Q2 2019.

The news was announced today at the TensorFlow Dev Summit being held at the Google Event Center in Sunnyvale, California. Since the launch of TensorFlow in November 2015, the framework has been downloaded over 41 million times and now has over 1,800 contributors from around the world, said TensorFlow engineering director Rajat Monga.

Google previews TensorFlow 2.0 alpha with focus on simplicity and ML beginners

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.

Google tool lets any AI app learn without taking all your data

A new computing tool developed by Google will let developers build AI-powered apps that respect your privacy.

Google on Wednesday released TensorFlow Federated, open-source software that incorporates federated learning, an AI training system. It works by using data that's spread out across a lot of devices, such as smartphones and tablets, to teach itself new tricks. But rather than send the data back to a central server for study, it learns on your phone or tablet itself and sends only the lesson back to the app maker.

Google is making it easier for AI developers to keep users’ data private

Google has announced a new module for its machine learning framework, TensorFlow, that lets developers improve the privacy of their AI models with just a few lines of extra code.

TensorFlow is one of the most popular tools for building machine learning applications, and it’s used by developers around the world to create programs like text, audio, and image recognition algorithms. With the introduction of TensorFlow Privacy, these developers will be able to safeguard users’ data with a statistical technique known as “differential privacy.”

The AI-Art Gold Rush Is Here

How an engineer’s accident at Google changed the art industry

Why? To make this image, the artist group Obvious used the source code and data that another artist, Robbie Barrat, had shared freely on the web.

Obvious had every right to use Barrat’s code and claim authorship of the work. Nonetheless, many criticized Christie’s for elevating the artists who played only a small part in the creation the work. This was generally read as a failure of Christie’s, particularly in the misleading way it promoted the work, rather than a need to rethink authorship of AI art.

Open-source HPC Workload Manager, Slurm Now to Have Improved Deployment Scalability on Google Cloud

Google is now sharing a new set of features for Slurm running on Google Cloud Platform (GCP) including support for preemptible VMs, custom machine types, image-based instance scaling, attachable GPUs, and customizable NFS mounts. In addition, this release features improved deployment scalability and resilience.