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Google has launched a major revamp to its news feature at Google I/O 2018. 15 years after its launch, Google News is to offer more personalization with the help of AI. Perhaps that's surprising - surely Google has always been using AI across every feature? Well yes, to some extent. But this update brings artificial intelligence fully into the fold. It may feel strange talking about AI and news at the moment. Concern over 'echo chambers' and 'fake news' has become particularly pronounced recently. The Facebook and Cambridge Analytica scandal have thrown the spotlight on the relationship between platforms, publishers, and our data. That might explain why Google seems to be trying to counter balance the move towards greater personalization with a new feature called Full Coverage. Full Coverage has been designed by Google as a means to tackle current concerns around 'echo chambers' and polarization in discourse. Such a move highlights a greater awareness of the impact the platform can have on politics and society. It suggests by using AI in context, there's a way to get the balance right. "In order to make it easier to keep up and make sense of [today's constant flow of news and information from different sources and media][, we set out to bring our news products into one unified experience", explained Trystan Uphill in a blog post. Personalizing Google News with AI By making use of advanced machine learning and AI techniques, Google will now offer you a more personalized way to read the news. With a new 'For You' tab, Google will organize a feed of news based on everything that the search engine knows about you. This will be based on a range of things, from your browsing habits to your location. "The more you use the app, the better the app gets" Upstill explains. In a new feature called 'Newscasts' Google News will make use of natural language processing techniques to bring together wide range of sources on a single topic. It seems strange to think that Google wasn't doing this before, but in actual fact it says a lot about how the platform dictates how we understand the scope of a debate or the way a news cycle is reported and presented. With newscasts it should be easier to illustrate the sheer range of voices currently out there. Fundamentally, Google News is making its news feature smarter - where previously it relied upon keywords, there is an added dimension whereby Google's AI algorithms become much more adept at understanding how different news stories evolve, and how different things relate to one another. https://www.youtube.com/watch?v=wArETCVkS4g Tackling the impact of personalization With Full Coverage, Google News will provide a range of perspectives on a given news story. This seems to be a move to directly challenge the increased concern around online 'echo chambers.' Here's what Upstill says: "Having a productive conversation or debate requires everyone to have access to the same information. That’s why content in Full Coverage is the same for everyone—it’s an unpersonalized view of events from a range of trusted news sources." Essentially, it's about ensuring people have access to a broad overview of stories. Of course, Google is here acting a lot like a publisher or curator of news - even when giving a broad picture around a news story there still will be an element of editorializing (whether that's human or algorithmic). However, it nevertheless demonstrates that Google has some awareness of the issues around online discourse and how its artificial intelligence systems can lead to a certain degree of polarization. It's now easier to subscribe and follow your favourite news sources The evolution of digital publishing has seen the rise of subscription models for many publishers. But that hasn't always been that well-aligned for readers searching Google. However, it will now be easier to read and follow your favorite news sources on Google News. Not only will you now be able to subscribe to news sources through your Google account, you'll also be able to see paywalled content your subscribed to in your Google News feeds. That will certainly be a better reading experience. In turn, that means Google is helping to cement themselves as the go-to place for news. Of course, Google could hardly be said to be under threat. But as native applications and social media platforms have come to define the news experience for many readers in recent years, this is a way of Google staking a claim in an area in which it may be ever so slightly vulnerable.

We've teamed up with Humble Bundle once again to bring you a selection of some of our very best DevOps eBooks and videos for incredible prices. But more than that, we're also giving you the chance to support some really important charities while picking up the content you want. Go to Humble Bundle now. Get up to $1613 worth of DevOps eBooks and videos for $15! There are a diverse range of titles in the Humble Bundle. They cover all the essential components within the DevOps toolchain. This includes Automate It!, DevOps with Kubernetes, Deployment with Docker, and Ansible 2 for Beginners. With DevOps becoming an increasingly important to the way everyone builds software, this bundle of resources could prove vital for people even outside of sys admin type roles (apparently we're all sys admins now anyway). Pay at least $1 to pick up... Automate It! Effective DevOps with AWS DevOps for Web Developers [Video] Deployment with Docker Docker and Kubernetes for Java Developers Deploying and Running Docker Containers [Video] Linux Shell Scripting Solutions [Video] Three months subscription to Mapt pro for $30 ...Or pay at least $8 to get the titles above and... Ansible 2 for Beginners [Video] Practical Network Automation DevOps for Networking DevOps with Kubernetes Windows Server 2016 Automation with PowerShell Cookbook, Second Edition Learning Continuous Integration with Jenkins, Second Edition Getting Started with Kubernetes, Second Edition Mastering Puppet for Large Infrastructures [Video] ...Or pay at least $15 to get all of that and... Mastering Ansible, Second Edition Mastering DevOps [Video] Mastering Docker, Second Edition Getting Started with Docker [Video] Continuous Delivery with Docker and Jenkins Mastering Kubernetes Learning Kubernetes [Video] OpenStack Administration with Ansible 2, Second Edition Mastering Windows PowerShell 5 Administration [Video] Puppet 5 Beginner's Guide, Third Edition The Humble Bundle expires on 21 May 2018. So be quick and grab the DevOps eBooks and videos you want!

Cross-platform app development is the rage now! And to add fuel to the fire, Xamarin has released its latest cross-platform toolkit upgrade. The latest stable release of Xamarin Forms 3 is here! Version 3 hosts new layout and styling updates to improve how developers build UI. These include updates to Visual State Manager, Flex Layout, Style Sheets, and Right-to-Left support to name a few. XAML compilation has also received specific attention with build times reduced by as much as 88% in some benchmarks. Let us look at each of the above features in detail. Visual State Manager Visual State Manager is now available in Xamarin Forms. The VSM provides a structured way to make visual changes to the user interface from code. The VSM introduces the concept of visual states. Visual states are collected in visual state groups. Developers can now define the various states for layouts and controls declaratively in XAML or C# and easily update their UI. The Xamarin.Forms Visual State Manager defines one visual state group named "CommonStates" with three visual states: Normal Disabled Focused FlexLayout FlexLayout is a new layout inspired by the web’s Flexbox. FlexLayout promotes flat, performant, and flexible UIs. It is ideal for handling distribution and spacing of content within layouts. It also provides control of the direction of layout, the justification, and alignment among other properties. FlexLayout defines six public bindable properties and five attached bindable properties that affect the size, orientation, and alignment of its child elements. StyleSheets Xamarin.Forms 3.0 introduces the ability to style an app using CSS. StyleSheets come in companionship with Flex Layouts. A style sheet consists of a list of rules, with each rule consisting of one or more selectors and a declaration block. They can be added as separate CSS files or inline with Resources. In Xamarin.Forms, CSS style sheets are parsed and evaluated at runtime, rather than compile time, and are re-parsed on use. Right-To-Left Localization Xamarin.Forms 3.0 are now equipped with FlowDirection property to make it easier to flip layouts to match language direction.  This is especially beneficial to Arabic and Hebrew scripts that flow from right-to-left. FlowDirection property apart from supporting right-to-left layouts also offers flexibility to customize layouts as seen fit by developers. Xamarin.Forms 3.0 is now available on NuGet. Read the full release notes for the list of entire bug fixes. Five reasons why Xamarin will change mobile development Hybrid Mobile apps: What you need to know Creating Hello World in Xamarin.Forms_sample

Google I/O 2018, the most anticipated conference by Google kicked off yesterday at Shoreline Amphitheatre in Mountain View, California. Seems like it was just yesterday that Google I/O 2017 was over and we were still in awe of the new AI capabilities they announced last time but here we are, with the next annual I/O event in front of us. On the 1st day, CEO Sundar Pichai delivered the keynote promising a 3-day gala event for over 7,200 attendees with a plethora of announcements and updates to Google products. I/O’18 will conduct 400+ extended events in 85 countries. Artificial intelligence was a big theme throughout. Google showcased ML Kit, a SDK for adding Google’s machine learning smarts to Android and iOS apps. New features were added to Android P, Google’s most ambitious Android update. Not to mention the release of Lighthouse 3.0, new anchor tools for multiplayer AR, updates to Google assistant, Gmail, Google Maps and more. Here are our top picks from Day 1 of Google I/O 2018. Machine Learning for Mobile Developers Google’s newly launched ML Kit SDK, allows mobile developers to make use of Google’s machine learning expertise in the development of Android and iOS apps. This kit allows integration of mobile apps with a number of pre-built Google-provided machine learning models. These models support text recognition, face detection, barcode scanning, image labeling and landmark recognition, among other things. What stands out here is the fact that the ML Kit is available both online and offline, depending on network availability and the developer’s preference. In the coming months, Google plans to add a smart reply API and a high-density face contour feature for the face detection API, in the list of currently available APIs. New Augmented Reality experiences come to Android At the Google I/O conference, Google also announced several updates to its ARCore platform focused on overcoming the limitations of existing AR-enabled smartphones. Multi-User and shared AR New cloud anchor tools will enable developers to create new types of collaborative experiences, which can be shared with multiple users across both Android and iOS devices. More surfaces to play around with Vertical Plane Detection, a new feature of ARCore, allows users to place AR objects on more surfaces, like textured walls. Another capability, Augmented Images, brings images to life just by pointing a phone at them. https://www.youtube.com/watch?v=uDs9rd7yD0I Simple AR development New ARcore updates also simplify the process of AR development for Java developers with the introduction of Sceneform. Developers can now build immersive, 3D apps, optimized for mobile without having to learn complicated APIs like OpenGL. They can use Sceneform to build AR apps from scratch as well as to add AR features to existing ones. Android P: the most ambitious Android OS yet The name for the new version is yet to be decided but judging by their trend of naming the OS after a dessert it may be Pumpkin Pie, Peppermint Patty, Or Popsicle? I’m voting for Popsicle! Apart from the name, here are the other major features of the new OS: Jetpack: Jetpack is the next generation of the Android Support Library, redefining how developers write applications for Android. Jetpack manages tedious activities like background tasks, navigation, and lifecycle management, so developers can focus on core app development. Android KTX: In the last I/O conference, Google made Kotlin language a first-class citizen for developing Android apps. Continuing on the same trend, Google announced Android KTX in I/O’18. It is a part of Jetpack that further optimizes the Kotlin developer experience across libraries, tooling, runtime, documentation, and training. Android Studio 3.2: There are 20 major features in this release of Android Studio spanning from ultra-fast Android Emulator Snapshots and Sample Data in the Layout Editor, to a brand new Energy Profiler to measure battery impact of the app. Material Design 2: While other Google apps like Gmail and Tasks have already gotten a recent visual update, in Android P, Google is overhauling the OS’ overall look with what people are calling Material Design 2. Google calls it Material Themes, a powerful plugin to help designers implement Material Design in their apps. This new interface is designed to be “responsive and efficient,” while feeling “cohesive” with the rest of the G Suite family of apps. Adaptive Battery: Apart from refreshing the looks, Google has been busy thinking about improving performance. Google has partnered with its AI subsidiary DeepMind on a smart battery management system for Android. Scaling IoT with Android Things 1.0 After over 100,000 SDK downloads of the Developer Preview of Android Things, Google announced the long-term release of Android Things 1.0 to developers with long-term support for production devices. App Library, allows developers to manage APKs more easily without the need to package them together in a separate zipped bundle. Visual storage layout helps in configuring the device storage allocated to apps and data for each build and helps in getting an overview of how much storage your apps require. Group sharing, where product sharing has been extended to include support for Google Groups. Updated permissions, to give developers more control over the permissions used by apps on their devices. Developers can manage their Android Things devices via a cloud-based Android Things Console. Devices themselves can manage OS and app updates, view analytics for device health and performance, and issue test builds of the software package. Lighthouse 3.0 for better web optimization A new update to Lighthouse, the web optimization tool of Google, was also announced at Google I/O. Lighthouse 3.0 offers smaller waiting periods more updates to developers to efficiently optimize their websites and audit their performance. It uses Simulated throttling, with a new Lighthouse internal auditing engine, that runs audits under normal network and CPU settings, and then estimates how long the page would take to load under mobile conditions. Lighthouse 3.0 also features a new report UI along with invocation, scoring, audit, and output changes. Other highlights Google announced the rebranding of its Google Research division to Google AI. Google made a massive “continued conversation” update to Google Assistant with Google Duplex, a new technology that enables Google's machine intelligence–powered virtual assistant, to conduct a natural conversation with a human over the phone. Google has also announced the release of the third beta of Flutter. Flutter is Google’s mobile app SDK used for creating high-quality, native user experiences on mobile. Google Photos get more AI-powered fixes such as B&W photo colorization, brightness correction and suggested rotations. Google’s first Smart Displays, the screen-enriched smart speakers, will launch in July, powered by Google Assistant and YouTube. Google Assistant is coming to Google Maps, available on iOS and Android. There are still 2 more days left for Google I/O to conclude and going by day 1 announcements, I can’t wait to see what’s next. I am especially looking forward to knowing more about Android Auto, Google’s Tour Creator,  and Google Lens. You can view the Livestream and other sessions on the Google I/O conference page. Keep visiting Packt Hub for more updates on Google I/O, Microsoft Build and other key tech conferences happening this month. Google’s Android Things, developer preview 8: First look Google open sources Seurat to bring high precision graphics to Mobile VR Microsoft Build 2018 Day 1: Azure meets Artificial Intelligence

Drive.ai has announced that it is to begin trialling a self-driving car taxi service in Frisco, Texas this Summer. The trial is to last 6 months as the organization works closely with the Frisco authorities to finalize the details of the routes and to 'educate' the public about how they can be used. But although the news has widely been presented as a step forward for the wider adoption of self-driving cars, the story in fact exposes the way in which self-driving car engineers are struggling to properly disrupt. And that's before it has even begun. Drive.ai's announcement comes shortly after a number of high profile incidents involving self-driving cars. In March, a woman was killed by an Uber self-driving car in Arizona. In May, a Waymo van was involved in a collision in Arizona too. This puts a little more pressure on Drive.ai, and means the trial will be watched particularly closely. Any further issues will only do more to make the wider public resistant to autonomous vehicles. The more issues that appear, the more the very concept of self-driving vehicles begins to look like a Silicon Valley pipe dream. It starts looking like a way for tech entrepreneurs to take advantage of underfunded public infrastructure in the name of disruption and innovation. And this is precisely the problem with the forthcoming Drive.ai trial. For the trial to work, Drive.ai are dependent on the support and collaboration of the Frisco authorities. Yes, there are some positives to this - there's an argument that the future of public life depends on a sort of hybrid of entrepreneurialism and state support. But we're not just talking about using machine learning or deep learning to better understand how to deploy resources more effectively, how to target those most in need of support. In this instance, we're talking about a slightly clunky system. It's a system everyone recognises as clunky - after all, that's why public 'education' is needed. Disruption should be frictionless. Self-driving taxis aren't. Whatever you think of Uber and Airbnb, both organisations have managed to disrupt their respective industries by building platforms that make certain transactions and interactions frictionless. However, when it comes to self-driving taxi services, things are much different. They're not frictionless at all. That's why Drive.ai are having to work with the Frisco authorities to sell the idea to the public. Disruptive tech works best when people immediately get the concept. It's the sort of thing that starts with wouldn't it be great if... No one thinks that about self-driving cars. The self-driving bit is immaterial to most users. Provided their Uber drivers are polite and get them to where they want to go, that's enough. Of course, some people might even like having a driver they can interact with (god forbid!). Sure, you might think I'm missing the point. Self driving cars will be more efficient, right? The cost savings will be passed on to end users. Of course it might - but seen in perspective, lots of things have become more efficient or automated. It doesn't mean we're suddenly all feeling the benefits of our savings. More importantly, this isn't really disruption. You're not radically changing the way you do something based on the needs of the people that use it. Instead you're simply trying to shift their expectations to make it easier to automate jobs. In many instances we're seeing power shift from public organizations to those where technical expertise is located. And that's what's happening here. Artificial intelligence needs to be accessible to be impactful Essentially, the technology is stuck inside the Silicon Valley organizations trying to profit from it. We know for a fact the deep learning and artificial intelligence are at their most exciting and interesting when its accessible to a huge range of people. In the case of Drive.ai, the AI is just the kernel around which all these other moving parts depend - the investment, infrastructure, and acceptance of the technology. Artificial intelligence projects work best when they seem to achieve something seamlessly, not when they require a whole operation just to make it work. The initiatives being run by Drive.ai and its competitors are a tired use of AI. It's almost as if we're chasing the dream of taxi cabs that can drive themselves simply because we simply should. And while there's clearly potential for big money to be made by those organizations working hard to make it work, for many of the cities they're working with, it might not be the best option. Public transport does, after all, already exist. Drive.ai needs users to adapt to the technology Perhaps Drive.ai might just make this work. But it's going to be difficult. That's because the problems of self-driving cars are actually a little different to those many software companies face. Typically the challenge is responding to the needs of users and building the technology accordingly. In this instance, the technology is almost there. The problem facing Drive.ai and others is getting users to accept it. What we learned from CES 2018: Self-driving cars and AI chips are the rage! Apple self-driving cars are back! VoxelNet may drive the autonomous vehicles

Just when people couldn’t keep up with the excitement of the Oculus Go launched at the Facebook’s F8 conference, Google added fuel to the fire by making Lenovo Mirage Solo, the first stand-alone Daydream VR headset, available for purchase at $399.9. Lenovo Mirage Solo VR headset Let’s have a look at the features that are making this headset all the rage: Self-contained VR Headset What makes this VR headset the talk of the town is that it’s the first stand-alone Daydream VR headset. That means it doesn’t require the excess baggage of connecting the phone and then putting on the headset. All you need to do is, just put the headset on and explore the intriguing VR world sans the wires and the added complexity. The hardware inside resembles that of a mobile device. It has a Snapdragon 835 processor with 4GB RAM, and 64GB of storage. It comes with a long battery life of 2.5 hours making the entire VR experience seamless. It consists of embedded sensors along with a gyroscope, accelerometer, and magnetometer. Also, it has a microSD slot, a USB Type-C port, a power button, volume buttons, and a 3.5mm headphone jack. Position-tracking Technology Lenovo Mirage Solo comes with WorldSense, an outstanding 6 degrees of freedom motion tracking feature that helps you move around freely with headsets on, thereby, making the entire experience more immersive. WorldSense helps remove the need to set up extra sensors. It offers: Two inside-out tracking cameras Built-in proximity sensors that detect the position of nearby objects Display Lenovo Mirage Solo comes with a 5.5-inch LCD display. This is an effort to get rid of the blurring issue that happens as you move from one side to the other in the VR world. The screen has a 2,560 x 1,440-pixel resolution with a 110-degree field of view which is similar to Rift and Vive, thereby, making the VR exploration even more interactive. Design The headset body is primarily matte plastic in white color with accents of black, and gray running through it, and a solid plastic strap that wraps around the head. The Lenovo Mirage solo is a self-contained headset, which has a strong built. Yet some people find it bulky as the majority of the weight resides on the top of a wearer’s forehead. However, it is adjustable as the headset can be brought all the way around your skull. Also, the Display housing keeps the light from coming in without disturbing the image, making the headset easily movable. Sound Lenovo Mirage Solo comes with two microphones, but users need to plug in their own headphones into the 3.5-mm jack as it doesn’t come equipped with in-built speakers. Apart from the above-mentioned features, the Mirage Solo depends on the Daydream library for accessing content. The catalog has more than 350 games and apps with over 70 titles optimized for WorldSense. As you can see, Mirage Solo is not flawless. It suffers from issues such as bulky design, no built-in speakers, and limited library app content. But the pros overpower the cons in this case, and it goes without saying that Lenovo Mirage Solo is here to revolutionize the VR experience. To know more, visit the official Daydream Google Blog Oculus Go, the first stand-alone VR headset arrives! Understanding the hype behind Magic Leap’s New Augmented Reality Headsets Build a Virtual Reality Solar System in Unity for Google Cardboard

Qlik’s new CEO Mike Capone keynoted the recently held Qlik Qonnections 2018, with some interesting feature rollouts and announcements. He also shed light on the evolution of Qlik’s two premium products - Qlikview and Qlik Sense, and shared their roadmap for the coming year. Close to 4000 developers and Business Intelligence professionals were in attendance, and were very receptive to the positive announcements made in the keynote. Let us take a quick look at some of the important announcements: Qlik continues to be the market leader Capone began the keynote by sharing some of the interesting performance metrics over the past year, which have led to Qlik being listed as a ‘Leader’ in the Gartner Magic Quadrant 2017. One of the most impressive achievements among all is the impressive customer base that Qlik boasts of, including: 9 out of the 10 major banks 8 out of the 10 major insurance companies 11 out of the 15 major global investment and securities companies With an impressive retention rate of 94%, Qlik have also managed to add close to 4000 new customers over the last year and have also doubled the developer community to over 25,000 members. These numbers mean only one thing - Qlik will continue to dominate. Migration from Qlikview to Qlik Sense There has been a lot of talk (and confusion) of late about Qlik supposedly looking to transition its focus from Qlikview to Qlik Sense. In the keynote, Capone gave us all the much needed clarity on the licensing and migration options for those looking to move from Qlikview’s guided analytics features to Qlik Sense’s self-service analytics. These are some of the important announcements in this regard: Migration from Qlikview to Qlik Sense is optional: Acknowledging some of the loyal customers who don’t want to move away from QlikView, Capone said that the migration from Qlikview to Qlik Sense is optional. For those who do want to migrate, Qlik have assured that the transition will be made as smooth as possible, and that they would be making this a priority. Single license to use both Qlikview and Qlik Sense: Qlik have made it possible for customers to get the most out of their products without having to buy multiple licenses for multiple products. With just an additional maintenance fee, they will be able to enjoy the premium features of both the tools seamlessly. Qlik venturing into cognitive analytics One of the most notable announcements of this conference was incorporating aspects of Artificial Intelligence into the Business Intelligence capabilities of the Qlik products. Qlik are aiming to improving the core associative engine that works with the available data smartly. Not just that, they have also announced the Insight Advisor feature, to auto-generate the best possible visualizations and reports. Hybrid and multi-cloud support added Qlik’s vision going forward is quite simple and straightforward - to support deployment of their applications and services in a hybrid-cloud or multi-cloud environment. Going forward, users will be able to move their Qlik Sense applications that run using a microservices-based architecture on Linux, in either public or private clouds. They will also be able to self-manage these applications with the support features provided by Qlik. New tools for Qlik developers Qonnections 2018 saw 2 important announcements made to make the lives of Qlik developers easier. Along with Qlik Branch - a platform to collaborate on projects and share innovations and new developments, Qlik also announced a new platform for developers called Qlik Core. This new platform will allow Qlik developers to leverage the offerings of IoT, edge analytics and more to design and drive innovative business models and strategies. Qlik Core is currently in the beta stage, and is expected to be generally available very soon. Interesting times ahead for Qlik In recent times, Qlik has faced stiff competition from other popular Business Intelligence tools such as Tableau, Spotfire, Microsoft’s very own Power BI - apart from the freely available tools which are easily available to customers for fast, effective business intelligence. With all the tools delivering on a similar promise and not coming out with any groundbreaking blue ocean features, it will be interesting to see how Qlik’s new offerings will fare against these sharks. The recent restructuring of the Qlik management and the downsizing happening over the past few years can make one wonder if they are struggling to keep up. However, the announcements in Qonnections 2018 indicate the company is indeed moving in a positive direction with their products, and should restore the public faith and dispel any doubts Qlik’s customers may have. How Qlik Sense is driving self-service Business Intelligence Overview of a Qlik Sense® Application’s Life Cycle QlikView Tips and Tricks

The Linux Foundation recently launched the Acumos Al Project with an aim to make AI accessible to all. Acumos AI is a platform and an open source framework, to easily build, share and deploy Artificial Intelligence, Machine Learning, and Deep learning applications. As a part of the LF Deep Learning Foundation, Acumos strives to make these AI, ML and DL technologies available to developers and data scientists everywhere. It caters to a broad range of business use cases, which include network analytics, customer care, field service and equipment repair, healthcare analytics, network security and advanced video services, and many more. Let’s have a look at what Acumos AI has in store. The Acumos AI Project, Packages tool kits such as TensorFlow and SciKit Learn and models with a common API that allows them to seamlessly connect Allows easy onboarding and training of models and tools Supports a variety of popular software languages, including Java, Python, and R Leverages modern microservices and containers in order to package and export production-ready AI applications as Docker files Includes a federated AI Model Marketplace, which is a catalog of AI models contributed by the community that can be securely shared Benefits of Acumos AI It provides a standardized platform, an easy export, and Docker-file deployment to any environment, including major public clouds, making stand-up and maintenance a breeze It has a simplified toolkit and model onboarding, which helps data scientists focus on building great AI models rather than maintaining infrastructure. The Acumos AI comprises of a Visual design editor, a drag-and-drop application design, and a chaining feature, where applications can be chained to create an array of AI services. These enable end users to deploy complicated AI apps for training and testing within minutes. Read the Acumos AI whitepaper to know more about the Acumos AI Project in detail. Kali Linux 2018.2 released How to implement In-Memory OLTP on SQL Server in Linux What to expect from upcoming Ubuntu 18.04 release

Nvidia has revealed that its Volta Tensor Core GPU has hit some significant milestones in performance. This is big news for the world of AI. It raises the bar in terms of the complexity and sophistication of the deep learning models that can be built. The Volta Tensor Core GPU has, according to the Nvidia team, has "achieved record-setting ResNet-50 performance for a single chip and single server" thanks to the updates and changes they have made. Here are the headline records and milestones the Volta Tensor Core GPU has hit, according to the team's intensive and rigorous testing: When it trains a ResNet-50, one V100 TensorCore GPU can achieve more than 1,075 images every second. That is apparently four times more than the Pascal GPU, the previous generation of Nvidia's GPU microarchitecture. Last year, one DGX-1 server supported by 8 TensorCore V100s could achieve 4,200 images a second (still a hell of a lot). Now it can achieve 7,850. One AWS P3 cloud instance supported by 8 TensorCore V100s Res-Net50 in less than 3 hours. That's three times faster than on a single TPU. But what do these advances in performance mean in practice? And has Nvidia really managed to outperform its competitors? Volta Tensor Core GPUs might not be as fast as you think Nvidia is clearly pretty excited about what it has achieved. Certainly the power of the Volta Tensor Core GPUs are impressive and not to be sniffed at. But website ExtremeTech poses a caveat. The piece argues that there are problems with using FLOPS ( floating point operations per second) as a metric for performance. This is because the mathematical formula that's used to calculate FLOPs assumes a degree of consistency in how something is processed that may be misleading. One GPU, for example, might have higher potential FLOPS but not be running at capacity. It could, of course be outperformed by an 'inferior' GPU. Other studies (this one from RiseML) have indicated that Google's TPU actually performs better than Nvidia's offering (when using a different test). Admittedly the difference wasn't huge, but enough when you consider that it's significantly cheaper than the Volta. Ultimately, the difference between the two is as much about what you want from your GPU or TPU. Google might give you a little more power but there's much less flexibility than you get with the Volta. It will be interesting to see how the competition changes over the next few years. Based on current form Nvidia and Google are going to be leading the way for some time, whoever has bragging rights about performance. Distributed TensorFlow: Working with multiple GPUs and servers Nvidia Tesla V100 GPUs publicly available in beta on Google Compute Engine and Kubernetes Engine OpenAI announces block sparse GPU kernels for accelerating neural networks

GNU Compiler Collection (GCC) is a popular Open source compiler system from the GNU Project. Recently the GNU Project announced the release of GCC 8.1 with added features for its support for languages like C, C++, Java, ADA, GO, Objective-C, among others. This release is the first stable release for GCC v8 with a lot of improvements over previous standards. This release brings an enhancement in C++ support and debugging for various languages. GNU has significantly improved the implementation of Go language to run smoothly with the latest version of Go i.e. v1.10. Apart from the compiler improvement, there has been an addition of garbage collector feature for Golang. GNU has also worked on one of the leading features of GCC compiler: Profile-driven optimization. This feature enables fail-safe compiling using several iterations of compiling and generating a profile of the runtime behavior. Based on the behavior of the program, GCC optimizes the compilation of the program. With the new improvement, GCC can detect code that is not compiled properly and can trigger the code in next instances. Some of the features that were worked on this release include the following: Improvement in compiler diagnostics enhancing the error and warning information at compile time Better Link-time optimization (LTO) using a new Dwarf format debug information. The libstdc++ library feature from C++17 and C++ 2a libraries C language extensions that support automatic vectorization for the latest AMD processors. One of the most important features to note is the support for C++ 20 (C++ 2a) which is currently under development. Also the support for the newer ARM CPUs, Intel CPUs, AMD HSA, among others is worth noting in this release. For more information about changes in GCC 8.1, you can see the changelog. Also, this release is available from GNU FTP servers. More news in application development: What’s new in Visual Studio 1.22 What is Mob Programming?

Microsoft’s kicked off Build 2018, their annual developer conference in style, with some interesting announcements related to coupling their existing products - mainly Microsoft Azure - with trending technologies such as Artificial Intelligence, IoT, Blockchain, and more. Held in Seattle, Washington over 7th and 8th May, this tech extravaganza promises to give away some exciting insights into Microsoft’s strategy for the coming year. Here is a quick recap of all the major announcements from Day 1: Azure IoT Edge open-sourced Microsoft open-sourced its Azure IoT Edge Runtime platform for their developers, in a bid to deliver cloud intelligence across all their IoT devices. Combining the power of Artificial Intelligence and the Azure services through this platform, Microsoft plans to connect over 20 billion devices globally by 2020. Microsoft also announced their partnership with Qualcomm to create smart camera-based IoT solutions, by combining the hardware from the Qualcomm Vision Intelligence Platform and the Azure services. They will also be teaming up with the China-based drone giants DJI to build a new drone SDK for Windows 10. Azure will be the official cloud solution for DJI’s commercial solutions which will utilize the AI services offered by Microsoft to analyze data for their customers. Last but not the least, Microsoft announced the first Azure Cognitive Service for the edge. Using this feature, the developers will be able to deploy AI algorithms to build powerful applications that run on these devices. Kinect returns - this time on Azure The Kinect is back! This time however, things are a bit different. Built on a technology that also runs the Microsoft Hololens, Kinect will now run on Azure rather than on the Xbox. This new and improved project will have a next-gen camera and a dedicated processor designed to handle the AI tasks to process significant amount of data before it is put up on the Azure cloud. It is worth remembering that Microsoft had also attempted to bring Kinect to the enterprise, way back in 2011, but the project failed miserably and was suspended in 2017. With a different approach this time, Microsoft are hoping to make this a resounding success. New Blockchain tools for Azure In a bid to simplify Blockchain app development on the cloud, Microsoft has announced a new Azure service called Azure Blockchain Workbench. This service will connect the decentralized applications to the Azure cloud services such as Azure Active Directory, and will dramatically reduce the overall development time. This workbench will equip Blockchain developers with all the necessary tools to develop end-to-end Azure-based Blockchain applications without any hassle. Project Brainwave for deep learning acceleration Microsoft released a public preview of the highly anticipated Project Brainwave - a deep neural network processing solution designed to make Azure the fastest cloud to run deep learning and other AI-based solutions. Per Nadella, the CEO of Microsoft who made this announcement, Brainwave will equip the developers with the necessary processing power to deploy machine learning and deep learning models with a higher performance potential, much beyond what they can expect from a CPU or GPU. Also, this project is expected to lead to five times lower hardware latency than Google’s TPU (Tensor Processing Unit). Is Brainwave Microsoft’s answer to Google’s TPU? Check out our detailed comparison on the two chips. Other important announcements on Day 1 Apart from these significant enhancements for Azure, Microsoft also announced a whole host of features to improve their other existing offerings, including Microsoft 365, Sharepoint, Excel, and Microsoft Bot Framework, among others. Here are the major announcements in this regard: Integration of Amazon’s Alexa and Microsoft’s Cortana for Windows devices Windows Machine Learning, a new AI platform for developers to build machine learning models on the cloud was announced Updated Microsoft Bot Framework was announced, with richer voice customization Integration of Azure Search with Cognitive Services was announced, for quicker searches powered by Artificial Intelligence Azure Kubernetes services will be integrated with the Azure IoT Edge for better container orchestration Microsoft Excel will now support visualizations designed in Microsoft Power BI Microsoft 365 will make it easier to customize and publish business applications, with the help of the Microsoft Teams API In the midst of a major 3-way power struggle between Amazon, Google and Microsoft, these announcements made by Microsoft in the Build 2018 conference feel like a breath of fresh air, especially for the Microsoft developers. Their vision of transforming Microsoft’s cloud platform and other services into an intelligent cloud powered by Artificial Intelligence and other trending technologies seems to be well underway. It will be interesting to see what Google’s response will be in their Google I/O conference which will be underway tonight, and not to mention Amazon re:Invent 2018 which takes place later in the year. Here are the highlights of day 1 of Microsoft Build in under 15 minutes, if you’re interested. Serverless computing wars: AWS Lambdas vs Azure Functions Introducing Azure Sphere – A secure way of running your Internet of Things devices How to get started with Azure Stream Analytics and 7 reasons to choose it  

Offensive security released their second incremental update on Kali Linux i.e Kali Linux 2018.2.  This release comprises of all the updated packages and bug fixes from the last release in February i.e 2018.1. 2018.2 release is focused on Kernel version 4.15, which contains most-awaited patch fixes for Meltdown and Spectre malwares. Some exciting features of Kali Linux 2018.2 are: Metasploit script access made simple This release comes  for metasploit script writers. Popular metasploit scripts such as pattern_create, pattern_offset, msf- egghunter etc. can be called directly by prepending these script names with msf. These scripts were hidden under /usr/share/metasploit-framework/tools/exploit/ until this release, which made it really difficult for the writers to call these scripts. Kernel Updates Kali Linux 2018.2 is focussed on Kernel version 4.15, which includes x86 and x64 fixes focussed on Malware vulnerabilities. It also comes up with better support for AMD GPUs and support for AMD Secure Encrypted Virtualization, which allows encryption of Virtual Machine memory so that not even Hypervisor has the rights to access it. Package updates Kali Linux has a suite of in-built tools for pentesting different environments. The new release sees some significant updates to these tools. Some of them are as follows: Bloodhound is a tool which uses graph theory to reveal the path of attacks in an Active Directory environment. It has been updated to V1.5 BurpSuite has been updated to V1.7.3.1 Reaver WPS, widely used for WiFi security has been updated to V1.6.4 PixieWPF, the tool launched for pixie dust attack has been updated to V1.2.2 Hashcat, the world’s fastest and advanced password utility has been updated to V4.0.0 Ropper, the ROP gadget finder and binary information tool has been updated to V1.10.10 For the complete set of bug fixes and new features, refer to Kali Linux blog. Top-5-penetration-testing-tools-for-ethical-hackers Introduction to Penetration Testing with Kali Linux [Tutorial] Wireless Attacks in Kali Linux [ Tutorial]

Vapor, the popular web framework written in Swift has released its next major update. Vapor 3 is a complete rewrite of the existing versions and all of its related packages. The release is centered around three new features. Async: Vapor 3 is all ready to handle high levels of concurrency as it is completely non-blocking and runs on Apple’s SwiftNIO. Services: With Vapor’s new Dependency Injection framework Services, all JSON configuration files are replaced by Swift. Codable: Codable integration throughout all of Vapor brings type safety and better performance to Parsing and serializing content from HTTP messages, creating database models, and rendering views. The main focus of this release is on building a foundation for developers to work on based on the growth of Vapor and server-side Swift over the past two years. The release is updated across four major categories. Packages Vapor 3 offers a couple of new packages this release. Most notable are the MySQL and PostgreSQL packages which are now non-blocking and built on SwiftNIO. Some of these packages include: SQLite: SQLite 3 wrapper for Swift. PostgreSQL: Non-blocking, event-driven Swift client for PostgreSQL. MySQL: Pure Swift MySQL client built on non-blocking, event-driven sockets. Fluent: Swift ORM framework (queries, models, and relations) for building NoSQL and SQL database integrations. FluentSQLite: Swift ORM (queries, models, relations, etc) built on SQLite 3. Auth: Authentication and Authorization layer for Fluent. JWT: JSON Web Token signing and verification. Leaf: An expressive, performant, and extensible templating language built for Swift. A complete list of packages is available in the vapor documentation. Better updated Documentation A large part of the release focuses on better documentation. Subsequently, Vapor 3 improves API docs with 100% docblock coverage including: Helpful code samples where possible. Method parameter descriptions. MARK and code re-org to help make things readable in API doc form. Also, the main docs are moving more toward a guide / tutorial feel. These guide docs cover broad use cases and practices, in contrast to the API docs which heavily focus on particular methods and protocols. Moving to Discord and introducing Books Vapor’s official team chat is now moved to Discord. The team has also announced two books (Server Side Swift with Vapor and Server-side Swift (Vapor Edition)) written specifically for Vapor 3. Benchmarks Vapor 3 introduces certain benchmarks for this release available on GitHub. The benchmarks were run on two identical Digital Ocean droplets. One for hosting the frameworks and one for running the benchmark. The benchmarker program is a small script written in Swift that runs wrk and captures the results. It is capable of doing multiple runs and averaging the results. Vapor achieved state-of-the-art results on both the plaintext benchmarks. To know further updates and other minor changes, be sure to Check out the updated website. Your First Swift Program [tutorial] Swift for TensorFlow is now open source [news] RxSwift Part 1: Where to Start? Beginning with Hot and Cold Observables [tutorial]

Google has open sourced Seurat, their VR positional-tracking tool. Google Seurat was first announced at their 2017 I/O conference to help developers bring high-precision graphics to standalone virtual reality headsets. Now, Google has open sourced Seurat to the developer community. Developers can now bring visually stunning scenes to their own VR applications while having the flexibility to customize the tool for their own workflows. Seurat can process complex 3D scenes, that couldn’t be run in real-time even on the highest performance desktop hardware, into a representation that renders efficiently on mobile hardware. How Google Seurat works Polygons are generally used to compose 3D images in computer graphics. The Polygon count refers to the number of polygons being rendered per frame. Google Seurat reduces the overall polygon count that is displayed at any given time and therefore lowers the required processing power and resources. It takes advantage of the limited viewing region, available in a mobile VR, to optimize the geometry and textures in a scene. What this means is, that Seurat takes all of the possible viewpoints that a VR user may see and removes the area of the 3D environment that they’d never be able to see. By utilizing the limited range of movement to their advantage, Seurat removes object permanence from the equation. So if users can’t see something in virtual reality, chances are it doesn’t actually exist.   On a more technical level, Google Seurat takes RGBD images (color and depth) as input and generates a textured mesh to simplify scenes. It targets a configurable number of triangles, texture size, and fill rate, to achieve this simplification process. Thus delivering immersive VR experiences on standalone headsets. This scene simplification technology was used to bring a 'Rogue One: A Star Wars Story' scene to a standalone VR experience. Developers can start working with Seurat right away with the GitHub page, containing the documentation and source code required to implement it into their projects. Alongside Seurat, Google also released Mirage Solo, the first headset on the Daydream VR platform. Top 7 modern Virtual Reality hardware systems Oculus Go, the first stand-alone VR headset arrives! Leap Motion open sources its $100 augmented reality headset, North Star  

Last month, Google announced the final preview release of Android Things, Developer Preview 8, before the upcoming stable release. Earlier this year, Google also showcased the capabilities of Android Things for creating exciting IoT products like smart displays, speakers, and 3D printers at the Consumer Electronic Show (CES) with partners like partners like Lenovo, LG, JBL, iHome, and Sony. This developer preview solidifies the chances of Android Things becoming the official IoT platform for Google. This review focuses on the improvements related to installations of the apps and managing permissions on the platform. There will be no breaking changes to the Android Things API before the stable v1.0 SDK, which is expected to be released at Google I/O this month. Let’s take a closer look at some of the notable features in this new release: Production-focused console enhancements There have been several changes made to the Android Things developer console with a focus on building and shipping production ready devices: Visual storage layout: This feature helps in configuring the device storage allocated to apps and data for each build, and helps in getting an overview of how much storage your apps require. Font/locale controls: These controls are a set of supported fonts and locales packaged into each build. Group sharing: Product sharing has been extended to include support for Google Groups. App library This is a new feature added to the Android Things ecosystem, which allows developers to manage APKs more easily without the need to package them together in a separate zipped bundle. It helps you to track individual versions of your app, review permissions, and share your app with other console users. Take a look at the official documentation for more details on App Library. Permissions This feature is quite useful to developers as it gives them more control over the permissions used by apps on their devices. When working with mobile devices, apps request permissions at runtime and the end users grant them. The early releases of Android Things used to grant these permissions automatically while booting the device. But with Developer preview 8, these permissions will be granted with the help of a new user interface in the developer console. These are just a select few of the myriad of updates available within the developer preview of Android Things. To read the entire list of changes please check out the release notes. Getting Started with Android Things Build your first Android app with Kotlin