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While everyone is busy playing songs and giving voice commands to Amazon Alexa, the amazing voice assistant developed by Amazon is utilized by the US space agency, NASA, to organize their data-centric tasks efficiently. Chief Technology and Innovation Officer at NASA,Tom Soderstrom, said “If you have Alexa-controlled Amazon Echo smart speaker at home, tell her to enable the 'NASA Mars' app. Once done, ask Alexa anything about the Red Planet and she will come back with all the right answers. This enables serverless computing where we don't need to build for scale but for real-life work cases and get the desired results in a much cheaper way. Remember that voice as a platform is poised to give 10 times faster results. It is kind of a virtual helpdesk. Alexa doesn't need to know where the data is stored or what the passwords are to access that data. She scans and quickly provides us what we need. The only challenge now is to figure out how to communicate better with digital assistants and chatbots to make voice a more powerful medium," emphasized Soderstrom. Serverless computing gives developers the flexibility of deploying and running applications and services without thinking about scale or server management. AWS is the market leader in providing fully-managed infrastructure services, helping organizations to focus more on product development. Alexa, for example, can help JPL (federally-funded research and development centre, managed for NASA) employees scan through 400,000 sub-contracts and get the requested copy of the contract from the data-set right on the desktop in a jiffy. JPL has also integrated conference rooms with Alexa and IoT sensors which helps them solve queries quickly. One of the JPL executives also stressed on the fact that AI is not going to take away the human jobs by saying “ AI will transform industries ranging from healthcare to retail and e-commerce and auto and transportation. Sectors that won't embrace AI will be left behind, Humans are 80 percent effective and machines are also 80 percent effective. When you bring them together, they're nearly 95 percent effective” Hence, voice controlled AI- powered digital assistants are here to stay empowering Digital Transformation. How to Add an intent to your Amazon Echo skills Microsoft commits $5 billion to IoT projects Building Voice technology on IoT projects

Starting from this month Google App Engine will allow web developers to deploy Node.js web applications to its standard environment. The App Engine standard environment is nothing but container instances running on Google's infrastructure. These containers previously supported runtimes in Java 7, Java 8, Python 2.7, Go and PHP. Node.js 8 is the new addition to this long list of environments. Developers who always wanted a ready and quick platform to build web applications on Cloud scale with a very low cost to start or wanted to get rid of the burden of managing and provisioning infrastructure have found that Google App Engine is a very good choice. It has been a developer’s favorite due to its zero-config deployments, zero server management, and auto-scaling capabilities. This move from Google brings in numerous advantages such as fast deployments and automatic scaling, better developing experience, and reliable security features. Fast Deployment and Automatic scaling The app Engine standard environment is known for it’s shorter deployment time. A basic Express.js application can be deployed under a minute with the standard environment. Not only that but App Engine allows the apps to automatically scale based on the incoming traffic to that application. For example, App Engine automatically scales to zero when there is no request made for that particular application. This allows developers to implement cost-effective measures while developing or deploying their applications. Enhanced Developer Experience Google has always been striving to provide a smoother developer experience with all its products. That’s also true for this new improvement to the App Engine. The new Node.js runtime comes with no language or API restrictions. This allows developers to choose npm modules of their choice. Along with this, App Engine also provides application logs and key performance indicators in Stackdriver, which takes care of Monitoring, logging, and diagnostics for applications on the Google Cloud Platform. Reliable Security: Updating the operating system or Node.js for any major or minor versions is a tedious task. App Engine takes care of all this by automatically handling all the updates required for your application to work smoothly with all the latest features. Not only that but App Engine’s automated one-click certificate generation allows developers to serve their application under a secure HTTPS URL with their own custom domain. The relationship between Node.js and Google goes a long way beyond GCP as Node.js runs on V8, Google's open source high-performance JavaScript engine. This recent collaboration between Node.js and Google also comes with better crafted node.js libraries that allow developers to use GCP products within their node.js applications. To try out all these new features on the App Engine you can visit their official website. Building chat application with Kotlin using Node.js, the powerful Server-side JavaScript platform Node 10.0.0 released, packed with exciting new features How to deploy a Node.js application to the web using Heroku

Hortonworks currently is a leader in global data management solutions partnered with Google Cloud to enhance Hortonworks Data Platform (HDP) and Hortonworks Dataflow (HDF). It has promised to deliver next-generation data analytics for hybrid and multi-cloud deployments. This partnership will enable customers to leverage new innovations from the open source community via HDP and HDF on GCP for faster business innovations. HDP’s integration with Google Cloud gives us the following features: Flexibility for ephemeral workloads: Analytical workloads which are on-demand can be managed within minutes with no add-on cost and at unlimited elastic scale. Analytics made faster: Take advantage of Apache Hive and Apache Spark for interactive query, machine learning and analytics. Automated cloud provisioning: simplifies the deployment of HDP and HDF in GCP making it easier to configure and secure workloads to make optimal use of cloud resources. In addition HDF has gone through following enhancements: Deploying Hybrid Data architecture: Smooth and secure flow of data from any source which varies from on-premise to cloud. Streaming Analytics in Real-time: Build streaming applications with ease, which will capture real-time insights without having to code a single line. With the combination of HDP, HDF and Hortonworks DataPlane Service, Hortonworks can uniquely deliver consistent metadata, security and data governance across hybrid cloud and multicloud architectures. Arun Murthy, Co-Founder & Chief Product Officer, Hortonworks said “ Partnering with Google Cloud lets our joint customers take advantage of the scalability, flexibility and agility of the cloud when running analytic and IoT workloads at scale with HDP and HDF. Together with Google Cloud, we offer enterprises an easy path to adopt cloud and, ultimately, a modern data architecture. Similarly, Google Cloud’s project management director, Sudhir Hasbe, said “ Enterprises want to be able to get smarter about both their business and their customers through advanced analytics and machine learning. Our partnership with Hortonworks will give customers the ability to quickly run data analytics, machine learning and streaming analytics workloads in GCP while enabling a bridge to hybrid or cloud-native data architectures” Refer to the Hortonworks platform blog and Google cloud blog for more information on services and enhancements. Google cloud collaborates with Unity 3D; a connected gaming experience is here How to Run Hadoop on Google Cloud – Part 1 AT&T combines with Google cloud to deliver cloud networking at scale

Google is looking for ways to improve their biometric-based authentication features for Android P, their upcoming OS. For this they are taking two major steps: First, Google has defined a better model to measure biometric security and constrain weaker authentication methods. Secondly, they are providing a common platform-provided entry point for developers to integrate biometric authentication into their apps. Google has combined secure design principles, a more attacker-aware measurement methodology, and an easy to use BiometricPrompt API for developers to integrate authentication in their devices in a simple manner. Current, biometric models quantify performance from two machine learning inspired metrics, False Accept Rate (FAR), and False Reject Rate (FRR). Both metrics do a great job of measuring the accuracy and precision of a given biometric model. However, they do not provide very useful information about its resilience against attacks. In Android 8.1, Google introduced two new metrics Spoof Accept Rate (SAR) and Imposter Accept Rate (IAR) to measure how easily an attacker can bypass a biometric authentication scheme. The SAR/IAR metrics categorize biometric authentication mechanisms as either strong or weak. While both strong and weak biometrics allowed to unlock a device, weak biometrics did not allow app developers to securely authenticate users on a device in a modality-agnostic way. This was what inspired the development of a Biometric authentication API. With Android P, mobile developers can use the BiometricPrompt API to integrate biometric authentication into their apps in a device. Developers can be assured of a consistent level of security across all devices their application runs on because BiometricPrompt only exposes strong modalities. BiometricPrompt API architecture The API is automated and easy to use. Instead of forcing app developers to implement biometric logic, the platform automatically selects an appropriate biometric to authenticate. For devices running Android O and earlier, a support library is provided for allowing applications to utilize this API across other devices. Further details on BiometricPrompt API are available on the Android developer blog. Top 5 Google I/O 2018 conference Day 1 Highlights: Android P, Android Things, ARCore, ML kit and Lighthouse Android P new features: artificial intelligence, digital wellbeing, and simplicity

The Azure team at Microsoft were highly excited to release the general availability of Azure SQL Data Sync tool for synchronization with their on-premises databases. This new tool allows database administrators to synchronize the data access between Azure SQL Database and any other SQL hosted server or local servers, both unidirectionally and bidirectionally. This new data sync tool allows you to distribute your data apps globally with a local replication available in each region, keeping data synchronization continuous across all the regions. This tool would help to significantly eradicate the connection failure and eliminate the issues related to network latency. It will also boost the response time of the applications and enhance the reliability of the application run time. Features/Capabilities of Azure SQL data Sync: Easy-to-Config - Simple and better configuration of database workflow with exciting user experience Speedy and reliable database schema refresh - Faster loading of database schemas with new Server Management Objects (SMO) library Security for Data Sync - End-to-end encryption provided for both unidirectional and bi-directional data flows with GDPR compliance. However, this particular tool would not be a true friend to DBAs as it does not support disaster recovery task. Microsoft has also made it very clear that this technology would not be supporting scaling Azure workloads, nor the Azure’s Database Migration Service. Check out the Azure SQL Data Sync Setup documentation to get started. To know more details, you can refer to the official announcement at official Microsoft web page. Get SQL Server user management right Top 10 MySQL 8 performance benchmarking aspects to know Data Exploration using Spark SQL

Deep learning and artificial intelligence implement cognitive abilities to build specialized solutions to solve a range of problems. With growing innovations, artificial intelligence field is practically exploding. Deep learning has already shown the mettle in handling all shapes and forms of data such as text, images, video, audio, social interaction and more. There are many existing vendors such as Google, Microsoft, Amazon, and IBM constantly working towards bringing AI within an organization by providing a range of services. However,  Google no doubt is doubling down on its research for their existing deep learning techniques. The company’s latest research, AutoAugment: Learning Augmentation Policies from Data, involves a reinforcement learning algorithm to increase both the amount and the variety of data in an existing training dataset. What is AutoAugment? AutoAugment is a new latest research paper by the Google team to tackle one of the biggest hurdle faced in deep learning i.e.a huge amount of quality data available to train models. This technique finds ways to automatically augment existing data with machine learning principles. This research paper uses a procedure called data augmentation, specifically used for images that help in finding the improved data augmentation policies. The idea is creating a search space of data augmentation policies, evaluating the quality of each policy directly on the dataset. The researchers have created a search space, where each policy consist of many sub-policies. Each policy can be randomly chosen for each image in each mini-batch. A sub-policy further consists of two set of operations. Each operation is an image processing function. A search algorithm is used to find the best policy so that the neural network model provides the highest validation accuracy on large datasets. Why AutoAugment? One of the core reasons why deep learning is doing exceptionally well in computer vision is the availability of large amounts of labeled training data. A model’s performance improves as you increase the quality and the amount of training data. However, collecting quality data in order to train a model for the optimized result is a difficult task. A possible way to deal with this issue is to hardcode image symmetries into neural network architectures in order to provide optimized results. Or researchers and developers manually design data augmentation techniques such as rotation and flipping, that are extensively used to train computer vision models. However, this can be time-consuming and tedious. Now, imagine a technique which automatically augments existing data using machine learning? Google team took inspiration from the results of AutoML research which were used to build neural network architectures and optimizers to replace components of traditional systems designed by humans. They thought of doing the same to automate the procedure of data augmentation. Data augmentation has ensured improved performance by training the model about image invariances (images have many symmetries that don’t change the information present in the image) in the data domain in a way that makes a neural network unchanged to these important symmetries. The traditional deep learning models use human-designed data augmentation policies. While this technique uses reinforcement learning algorithm to find the optimal image transformation policies from the data itself. It improves the performance of computer vision models to a great extent. Advantages of using AutoAugment Using AutoAugment will automatically design custom data augmentation policies for computer vision datasets. Hence, it will select the basic image transformation operations such as flipping the image horizontally or vertically, changing the color of the image, and more. This technique automatically predicts which image transformations to combine. It also predicts the per-image probability and magnitude of the transformation used, so that the image is not always worked around in the same way. It automatically learns different transformations based on the dataset used. Using AutoAugment algorithm has ensured better augmentation policies for some of the most widely used computer vision datasets. It additionally led to better accuracy when incorporated into the training of the neural network. AutoAugment achieves a new state-of-the-art accuracy of 83.54% when augmenting ImageNet data. On CIFAR10, the error rate of 1.48% is achieved, which is 0.83% value improvement over the traditional data augmentation. Further an improved state-of-the-art error rate from 1.30% to 1.02% was achieved on the street view of house numbers (SVHN) dataset. Most importantly, you can transfer AutoAugment policies. Hence, the policy used for the ImageNet dataset can also be applied to other datasets, ultimately improving neural network performance. AutoAugment technique has shown good signs in achieving a good level of performance on popular computer vision datasets. It will continue to work across more computer vision tasks and even in other domains such as audio processing or language models. You can refer to the research paper here, to apply them to improve your model performance on relevant computer vision tasks. For complete detailed information, visit the official Google blog. Read more Google announces Cloud TPUs on the Cloud Machine Learning Engine (ML Engine) NASA’s Kepler discovers a new exoplanet using Google’s Machine Learning Google’s translation tool is now offline – and more powerful than ever thanks to AI

Nvidia recently announced that they will make Kubernetes available on its GPUs, at the Computer Vision and Pattern Recognition (CVPR) conference. Although it is not generally available, developers will be allowed to use this technology in order to test the software and provide their feedback. Source: Kubernetes on Nvidia GPUs Kubernetes on NVIDIA GPUs will allow developers and DevOps engineers to build and deploy a scalable GPU-accelerated deep learning training. It can also be used to create inference applications on multi-cloud GPU clusters. Using this novel technology, developers can handle the growing number of AI applications and services. This will be possible by automating processes such as deployment, maintenance, scheduling and operation of GPU-accelerated application containers. One can orchestrate deep learning and HPC applications on heterogeneous GPU clusters. It also includes easy-to-specify attributes such as GPU type and memory requirement. It also offers integrated metrics and monitoring capabilities for analyzing and improving GPU utilization on clusters. Interesting features of Kubernetes on Nvidia GPUs include: GPU support in Kubernetes can be used via the NVIDIA device plugin One can easily specify GPU attributes such as GPU type and memory requirements for deployment in heterogeneous GPU clusters Visualizing and monitoring GPU metrics and health with an integrated GPU monitoring stack of NVIDIA DCGM , Prometheus and Grafana Support for multiple underlying container runtimes such as Docker and CRI-O Officially supported on all NVIDIA DGX systems (DGX-1 Pascal, DGX-1 Volta and DGX Station) Read more about this exciting news on Nvidia Developer blog NVIDIA brings new deep learning updates at CVPR conference Kublr 1.9.2 for Kubernetes cluster deployment in isolated environments released! Distributed TensorFlow: Working with multiple GPUs and servers  

Google Flutter hits another release milestone on the way to version 1.0. Google Flutter, the cross-platform SDK is moving out of beta with Flutter Release Preview 1. Flutter is one of the most ambitious projects of Google in the field of cross-platform app development. Flutter apps run on the Flutter rendering engine (written in C++) and Flutter framework (written in Google's Dart language, just like Flutter apps). Google Flutter reached beta as announced at Google I/O last month. It also featured various technical sessions, on topics like UI design with Flutter and Material, mobile development with Flutter and Firebase, and architectural practices for complex Flutter apps. The shift from beta to release preview announcement was made during the keynote of the GMTC Global Front-End Conference in Beijing, China, a gathering of around a thousand front-end and mobile developers. It focuses on scenario completeness, bug fixing, and stabilization. Release preview 1 features improvements to the video player package, adding broader format support and reliability improvements. Firebase support is further extended to include Firebase Dynamic Links, an app solution for creating and handling links across multiple platforms. 32-bit iOS devices with ARMv7 chips are also added, enabling apps written with Flutter to run on older devices. Flutter release preview 1 also adds experimental instructions on adding Flutter widgets to an existing Android or iOS app. It also brings improvements to Flutter Tools. Flutter tools have a new update in the form of Flutter extension for Visual Studio Code. This extension adds a new outline view, statement completion, and the ability to launch emulators directly from Visual Studio Code. The latest Release Preview 1 SDK will be available on Flutter's site. Also, check out the Flutter app showcase. Top 5 Google I/O 2018 conference Day 1 Highlights: Android P, Android Things, ARCore, ML kit and Lighthouse 9 Most Important features in Android Studio 3.2 Google’s Android Things, developer preview 8: First look

Google Cloud announced its recent alliance with Unity at the Unite Berlin conference this week. Unity is a popular game development platform for a real-time 3D game and content creation. Google Cloud stated that they are building a suite of managed services and tools for creating connected games. This suite will be much focussed on real-time multiplayer experiences. With this Google Cloud becomes the default cloud provider helping developers build connected games using Unity. It will also assist them to easily build and scale their games. Additionally, developers will get an advantage of Google Cloud right from the Unity development environment without needing to become cloud experts. The reason Google Cloud collaborates with Unity is to create an open source for connecting players in multiplayer games. This project mainly aims at creating an open source, community-driven solutions built in collaboration with the world’s leading game companies. Unity will also be migrating all of the core infrastructure powering its services and offerings to Google Cloud. Unity will also be running its business on the same cloud that Unity game developers will develop, test and globally launch their games. John Riccitiello, Chief Executive Officer, Unity Technologies, said, “Migrating our infrastructure to Google Cloud was a decision based on the company’s impressive global reach and product quality. Now, Unity developers will be able to take advantage of the unparalleled capabilities to support their cloud needs on a global scale.” Google Cloud plans to release new products and features over the coming months. Keep yourself updated on this alliance by checking out Unity’s homepage. AI for Unity game developers: How to emulate real-world senses in your NPC agent behavior Google announces Cloud TPUs on the Cloud Machine Learning Engine (ML Engine) Unity 2D & 3D game kits simplify Unity game development for beginners

Unite Berlin 2018, the Unity annual developer conference, kicked off on June 19’ 2018. This three-day extravaganza will take you through a thrilling ride filled with new announcements, sessions, and workshops from the amazing creators of Unity. It’s a place to develop, network, and participate with artists, developers, filmmakers, researchers, storytellers and other creators. Day 1 was inaugurated with the promising Keynote, presented by John Riccitiello, CEO of Unity Technologies. It featured previews of upcoming unity technology, most prominently Unity’s alliance with Google Cloud to help developers build connected games. Let’s take a look at what was showcased. Connected Games with Unity and Google Cloud Unity and Google Cloud have collaborated for helping developers create real-time multiplayer games. They are building a suite of managed services and tools to help developers, test, and run connected experiences while offloading the hard work of quickly scaling game servers to Google Cloud. Games can be easily scaled to meet the needs of the players. Game developers can harness the massive power of Google cloud without having to be a cloud expert. Here’s what Google Cloud with Unity has in store: Game-Server Hosting: Streamlined resources to develop and scale hosted multiplayer games. Sample FPS: A production-quality sample project of a real-time multiplayer game. New ECS Networking Layer: Fast, flexible networking code that delivers performant multiplayer by default. Unity ML-Agents Toolkit v0.4 A new version of Unity ML-Agents Toolkit was also announced at Unite Berlin. The v0.4 toolkit hosts multiple updates as requested by the Unity community. Game developers now have the option to train environments directly from the Unity editor, rather than as built executables. Developers can simply launch the learn.py script, and then press the “play” button from within the editor to perform training. They have also launched a set of two new challenging environments, Walker and Pyramids. Walker is physics-based humanoid ragdoll and Pyramids is a complex sparse-reward environment. There are also algorithmic improvements in reinforcement learning. Agents are now trained to learn to solve tasks that were previously learned with great difficulty. Unity is also partnering with Udacity to launch Deep Reinforcement Learning Nanodegree to help students and professionals gain a deeper understanding of reinforcement learning. Augmented Reality with Project MARS Unity has also announced their Project MARS, a Mixed and Augmented Reality studio, that will be provided as a Unity extension. This studio will require almost little-to-no custom coding and will allow game developers to build AR and MR applications that intelligently interact with any real-world environment, with little-to-no custom coding. Unite Berlin - AR Keynote Reel MARS will include abstract layers for object recognition, location, and map data. It will have sample templates with simulated rooms, for testing against different environments, inside the editor.  AR-specific gizmos will be provided to easily define spatial conditions like plane size, elevation, and proximity without requiring code or precise measurements. It will also have elements such as face masks, to avatars, to entire rooms of digital art. Project MARS will be coming to Unity as an experimental package later this year. Unity has also unveiled a Facial AR Remote Component. Powered by Augmented Reality, this component can perform and capture animated characters, allowing filmmakers and CGI developers to shoot CG content with body movement, just like you would with live action. Kinematica - Machine Learning powered Animation system Unity also showcased their AI research by announcing Kinematica, an all-new ML-powered animation system. Kinematica overpowers traditional animation systems which generally require animators to explicitly define transitions. Kinematica does not have any superimposed structure, like graphs or blend trees. It generates smooth transitions and movements by applying machine learning to any data source. Game developers and animators no longer need to manually map out animation graphs. Unite Berlin 2018 - Kinematica Demo Kinematica decides in real time how to combine data clips from a single library into a sequence that matches the controller input, the environment content, and the gameplay requests. As with Project MARS, Kinematica will also be available later this year as an experimental package. New Prefab workflows The entire Prefab systems have been revamped with multiple improvements. This improved Prefab workflow is now available as a preview build. New additions include Prefab Mode, prefab variance, and nested prefabs. Prefab Mode allows faster, efficient, and safer editing of Prefabs in an isolated mode, without adding them to the actual scene. Developers can now edit the model prefabs, and the changes are propagated to all prefab variants. With Nested prefabs, teams can work on different parts of the prefab and then come together for the final asset. Predictive Personalized Placements Personalized placements bring the best of both worlds for players and the commercial business. With this new feature, game developers can create tailor-made game experiences for each player. This feature runs on an engine which is powered by predictive analytics. This prediction engine determines what to show to each player based on what will drive the highest engagement and lifetime value. This includes ad, an IAP promotion, a notification of a new feature, or a cross-promotion. And the algorithm will only get better with time. These were only a select few of the announcements presented in Unity Berlin Keynote. You can watch the full video on YouTube. Details on other sessions, seminars, and activities are available on the Unite website. GitHub for Unity 1.0 is here with Git LFS and file locking support Unity announces a new automotive division and two-day Unity AutoTech Summit Put your game face on! Unity 2018.1 is now available

NVIDIA team has announced a new set of deep learning updates on their cloud computing software and hardware front during Computer Vision and Pattern Recognition Conference (CVPR 2018) held in Salt Lake City. Some of the key announcements made during the CVPR conference include Apex, an early release of a new open-source PyTorch extension, NVIDIA DALI and NVIDIA nvJPEG for efficient data optimization and image decoding, Kubernetes on NVIDIA GPUs release candidate, and runtime engine TensorRT version 4. Let’s look at some noteworthy updates made during CVPR conference: Apex Apex is an open-source PyTorch extension that includes all the required NVIDIA-maintained utilities to provide optimized and efficient mixed precision results and distributed training in PyTorch. This new extension helps machine learning engineers and data scientists to maximize deep learning training performance on NVIDIA Volta GPUs. The core promise of Apex is to provide up-to-date utilities to users as quickly as possible. Some of the notable features included are: NVIDIA PyTorch team has been inspired by the state of the art mixed precision training in tasks such as sentiment analysis, translational networks, and image classification. This has allowed them to create a set of tools to bring these methods to all levels of PyTorch users. Apex provides mixed precision utilities which are designed to improve training speed while maintaining the accuracy and stability of training in single precision. With Apex, you will now only require four or fewer line changes to the existing code to provide automatic loss scaling, automated execution of operations on FP16 or FP32, and automatic handling of master parameter conversion. In order to install/use Apex in your own development environment, you will require CUDA 9, PyTorch 0.4 or later, and Python 3. The extension is still in their early release, so we can expect the modules and utilities to undergo changes. If you want to download the code and get started with the tutorials and examples, you can visit the GitHub page. You can visit the official announcement page for more details. NVIDIA DALI and NVIDIA nvJPEG NVIDIA is using the power of GPUs with NVIDIA DALI, which utilizes the NVIDIA nvJPEG library to work on images at greater speed. This allows one to deal with performance bottleneck issues faced during image recognition and while decoding in deep learning powered computer vision applications. NVIDIA DALI is an open-source GPU-accelerated data augmentation and image loading library which can be used to optimize data pipelines (data optimization) of deep learning frameworks. You can refer to the GitHub page to learn more. NVIDIA nvJPEG is a GPU-accelerated library for JPEG decoding. You can download the release candidate for feedback and testing. This new update allows deep learning practitioners and researchers to have optimized training performance on image classification models such as ResNet-50 with MXNet, TensorFlow, and PyTorch across Amazon Web Services P3 8 GPU instances or DGX-1 systems with Volta GPUs. You can refer to the official announcement page for more details. Kubernetes on NVIDIA GPUs NVIDIA team has announced a release candidate of Kubernetes on NVIDIA GPUs which is freely available to developers for testing. This allows the enterprise to scale up training and ease up deployment to multi-cloud GPU clusters smoothly. This will ensure automated deployment, maintenance, and proper scheduling and operations of multiple GPU accelerated containers across clusters of nodes. You can arrange the growing resources on heterogeneous GPU clusters. To know more about this update, you can refer to the official announcement page. TensorRT 4 This new release of inference optimizer and runtime engine adds new layers such as recurrent neural networks, multilayer perceptrons, ONNX parser, and integration with TensorFlow to ease deep learning tasks. Moreover, it also provides the ability to execute custom neural network layers using FP16 precision and support for the Xavier SoC through NVIDIA DRIVE AI platforms. TensorRT ensures speeding up deep learning tasks such as machine translation, speech and image processing, recommender systems on GPUs. Using TensorRT across these application areas speed up the process 45x to 190x. All members of NVIDIA registered developer program can use TensorRT 4 for free. For more detailed information about the new features and updates, you can visit the developer’s official page. Read more NVIDIA open sources NVVL, library for machine learning training Nvidia’s Volta Tensor Core GPU hits performance milestones. But is it the best? Nvidia Tesla V100 GPUs publicly available in beta on Google Compute Engine and Kubernetes Engine

The second beta of iOS 12 has been released by Apple yesterday to the registered developers for testing purposes. This is two weeks after the first beta was rolled out following the much-awaited Worldwide Developers Conference. All thanks to the ongoing beta releases, beta 2 includes modifications to many of the new features which are introduced in iOS 12 such as changes to screen time, battery usage, and other smaller tweaks. Let’s have a look at the key updates that will change your iPhone or iPad for the better. Key Updates Battery Usage The usage charts that represent the activity and battery level for the past 24 hours is redesigned in iOS 12 beta 2. Also, fonts and wordings have been updated in this section. Source: macrumors Screen Time The existing toggle that helps with clearing the Screen Time data is removed. The interface which lets you add time limits to apps via the Screen Time screen has been modified. With the first beta, when you tapped an app it would go right into the limits interface. Now when you tap on an app, more information gets displayed on the app. This information includes daily average use, developer, category, and more. There's a new splash screen available for the Screen Time feature. There are also new options in screen time which lets you view your activity on either one or all devices. Notifications The new iOS 12 comes with a feature where Siri makes suggestions to the user about limiting the notifications from the sparingly used apps. Now with beta 2, the Notifications section of the Settings app has a new toggle that will allow you to get rid of the suggestions made by Siri for the individual apps. Photos Search With the iOS 12 beta 2, the Photos now support more advanced searches. So if you search for a photo taken on a specific date, say, May 15, all the photos from all years taken on May 15 will pop up. This is quite different than the iOS 12 beta 1 behavior. Also, the font of listings such as "Media Types" and "Albums" has changed. Now the listings’ font size in the Photos app is way bigger, which makes it easier for the users to read. Voice Memos A new introductory splash screen is added for Voice Memos in iOS 12 beta 2. Apart from these updates, there are also certain minor changes which are listed below: On unlocking any content using Face ID, the iPhone X now says "Scanning with Face ID." Now, on opening iPhone apps on the iPad, such as Instagram, these apps get displayed in a modern device size (iPhone 6) in both the modes namely: 1x and 2x. A new interface for auto-filling a password saved in iCloud Keychain is added. Podcasts app will now show ‘Now Playing’ indicator for the currently playing chapters. Time Travel references have been removed from the Watch app. The iOS 12 public beta will launch after iOS 12 developer beta 3 around June 26. The release date for the final version of iOS 12 is set sometime in September 2018. Also, there are some known issues regarding the latest iOS 12 beta 2 update that needs resolving. Registered developers can check out the release notes for beta 2 on the official Apple developer website. WWDC 2018 Preview: 5 Things to expect from Apple’s Developer Conference Apple releases iOS 11.4 update with features including AirPlay 2, and HomePod among others Apple introduces macOS Mojave with UX enhancements like voice memos, redesigned App Store, Apple News, & more security controls  

PyTorch Geometric is a new geometric deep learning extension library for PyTorch. With this library, you will be able to perform deep learning on graphs and other irregular graph structures using various methods and features offered by the library. Additionally, it also offers an easy-to-use mini-batch loader and helpful transforms to perform complex operations. In order to create your own simple interfaces, you can use a range of a large number of datasets offered by PyTorch Geometric library. You can use all these sets of features for performing operations on both arbitrary graphs as well as on 3D meshes or point clouds. You can find the following list of methods that are currently implemented in the library: SplineConv, Spline based CNNs which are used for irregular structured and geometric input (For eg: Graphs or meshes). You can refer to the research paper for more details. GCNConv provides a scalable approach using semi-supervised learning on graph-structured data. You can refer to the research paper for more details. ChebConv uses a generalized CNN model with fast localized spectral filtering on graphs. You can refer to the research paper for more details. NNConv uses a neural message passing algorithm for Quantum chemistry. You can refer to the research paper for more details. GATConv uses graph attention networks that operate on graph-structured data. You can refer to the research paper for more details. AGNNProp uses attention-based graph neural networks for graph-based semi-supervised learning. You can refer to the research paper for more details. SAGEConv uses representation learning on large graphs thus achieving great results in a variety of prediction tasks. You can refer to the research paper for more details. Graclus Pooling uses weighted graph cuts without Eigenvectors. You can refer to the research paper for more details. Voxel Grid Pooling In order to learn more about the installation, data handling mechanisms and a full list of implemented methods and datasets, you can refer the documentation. If you want simple hands-on examples to practice you can refer the examples/ directory. The library is currently in its first Alpha release. You can contribute to the project by raising an issue request if you notice anything unexpected. Read more Can a production ready Pytorch 1.0 give TensorFlow a tough time? Is Facebook-backed PyTorch better than Google’s TensorFlow? Python, Tensorflow, Excel and more – Data professionals reveal their top tools

Microsoft yesterday released a statement condemning the forcible separation of families at the U.S. border. The statement was made in response to public criticism of Microsoft after a blog post published earlier this year surfaced. In it, Microsoft's Azure Government team explained that it was supporting ICE - and was 'proud' to do so. In the statement, Microsoft said: Microsoft is not working with U.S. Immigration and Customs Enforcement or U.S. Customs and Border Protection on any projects related to separating children from their families at the border, and contrary to some speculation, we are not aware of Azure or Azure services being used for this purpose. As a company, Microsoft is dismayed by the forcible separation of children from their families at the border.  However, despite Microsoft's comment, it's clear that Azure Government is being used by ICE. In a post published in January, Tom Keane, a General Manager at Microsoft, wrote: ICE's decision to accelerate IT modernization using Azure Government will help them innovate faster while reducing the burden of legacy IT. The agency is currently implementing transformative technologies for homeland security and public safety, and we're proud to support this work with our mission-critical cloud. Clearly, Microsoft is distancing itself from the actions of ICE, but it may be too late. While it's unclear if Azure Government is being used by ICE as it implements the current wave of child incarceration, the link has already been formed in the minds of the public and Microsoft employees. Keane's words now have a chilling subtext. When he writes that Azure Government can help ICE employees 'make more informed decisions faster' and allow them 'to utilize deep learning capabilities to accelerate facial recognition and identification,' it's hard not to think about how the 'innovation' Microsoft is helping government agencies embrace is actually simply supporting state sanctioned violence against children. ICE has been cosying up to the tech world in 2018. Earlier this year, in April, ICE CTO spoke at a conference hosted by GitHub in Washington D.C. Although the incident was criticised in certain corners, it largely went unnoticed in the public domain. Given Microsoft's acquisition of GitHub in early June, this incident now takes on a new complexion in this strange narrative. Microsoft faces criticism from employees over relationship with ICE Gizmodo reported serious dissent from Microsoft employees. One employee told the website "this is the sort of thing that would make me question staying." Another is quoted as saying that they will "seriously consider leaving if I’m not happy with how they handle this.” The incident mirrors a number of other cases this year where employees of other major tech firms have criticized their organizations for government contracts. In May, for example, a number of Google employees quit over artificial intelligence ties to the Pentagon. However it's likely that things could get worse for Microsoft. For Google, the incident was largely internal. But given horrific reports from the U.S. border, questions around tech complicity in government actions will be propelled to the forefront of international debate.

Innovation is a word that seems to have emanated from the tech world and entered mainstream discourse. It's a term that has stuck to contemporary notions of progress and improvement. But is innovation and change really that great? Are we in danger of valorizing novelty at the expense of reliability, security and functionality? The "Boring Software" Manifesto, published on tqdev.com yesterday (18 June 2018) says yes. Written by software architect Maurits van der Schee, the "Boring Software" manifesto argues "as software developers we are tired of the false claims made by evangelists of the latest and greatest technology." Just days after we revealed data on developer attitudes to 'ninjas' and 'rockstars' the manifesto is further evidence of tension within the tech world. The tension is perhaps not so much one between 'innovators' and those concerned with ideals of security and reliability, but more about those actively selling innovation, speed, and efficiency and those with a more pragmatic approach to software engineering. Boring software vs. hyped and volatile technologies Schee's manifesto takes aim at what he calls 'hyped and volatile technologies'. He also appears to suggest that the demands of industry actually conflict with these 'hyped' technologies. Implicit in the piece is the idea is that there is a counter-industry of hype and evangelism that undermines how software can best serve industry today. 'In pursuit of "agility and craftsmanship", Schee writes, 'we have found "boring software" to be indispensable.' The most intriguing part of the manifesto features a number of examples that demonstrate the tension in the software world really clearly. For example: 3-tier applications are tried, tested and reliable; microservices, meanwhile, are hyped and volatile. Relational databases are 'simple and proven', while NoSQL is not, in Schee's view. Page reloads - also proven, whereas SPAs remain hyped. Unsurprisingly, reaction to the Boring Software manifesto is split. Many people have welcomed the intervention: https://twitter.com/overstood/status/1008956402050560000 Others, however, were more cautious. Innovation and invention only opens up new options, they argued: https://twitter.com/priyaprincess20/status/1008960699677081600 One Twitter user summed up the situation by suggesting the truth is probably somewhere between the two: https://twitter.com/ardave2002/status/1008984843403833344 This is likely to be a debate without a conclusion. However, the manifesto is a useful intervention in a discussion about how we should build software and what we should value most. What do you think about "boring software"? Is Maurits van der Schee correct? Or do we need to be open to new and emerging technologies and trends, even if they pose new challenges? Read next How Gremlin is making chaos engineering accessible [Interview] Are containers the end of virtual machines? Technical debt is damaging businesses