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Yesterday, Airbnb open sourced MvRx (pronounced mavericks), their Android architecture. MvRx received an astounding adoption rate when it was first introduced to the engineers at Airbnb in July. Later, it became their standard way of writing screens and now it is used for nearly all of their product development. Due to its popularity among Airbnb’s app developers, they have decided to share it with all Android developers. MvRx is based on Kotlin, which helped MvRx’s developers to leverage several powerful features provided by the language to build a cleaner API. It is built on top of the following technologies and concepts: Kotlin Android Architecture Components RxJava React (conceptually) Epoxy (optional but recommended) Why you should use MvRx? Using MvRx you will be able to write simple and complex Android screens easily. MvRx also helps making certain tasks easier and faster for Android app developers such as: Saving view state and business logic in onSaveInstanceState properly Creating layouts with a toolbar, RecyclerView, and footer over and over again Executing the onSuccess and onFailure handlers for asynchronous requests Dealing with the Android lifecycle What are its core concepts? State: MvRxState is an interface that you need to extend with an immutable Kotlin data class. It contains the properties necessary to render your screen. ViewModel: All the business logic and anything other than just rendering views is handled by MvRxViewModels. It extends Google's ViewModel. The following lifecycle diagram clearly illustrates how ViewModels simplify lifecycles on Android: Source: GitHub View: MvRxView is a simple interface that users see and interact with. It is a LifecycleOwner consisting of a invalidate() function that gets called any time there is a change in the state of its ViewModels. Async: MvRx makes handling async requests like fetching from a network or a database easier using Async. It is a Kotlin sealed class with four types: Uninitialized, Loading, Success, and Fail. MvRx provides an extension to map Observable<T> to an Async<T> property on your state to make executing network requests and other actions easy with a single line of code. How can you install it? To use MvRx, you need to add the following dependency to your project in the build.gradle file: dependencies { implementation 'com.airbnb.android:mvrx:0.5.0' } To know about the other details of MvRx, you can refer to its GitHub repository and also read Airbnb’s official announcement. RxAndroid 2.1.0 is out with a newly added Async API! Dagger 2.17, a dependency injection framework for Java and Android, is now out! Did you know your idle Android device sends data to Google 10 times more often than an iOS device does to Apple?

Microsoft is now using the power of artificial intelligence in OneDrive and SharePoint. With this initiative, users can be more productive, make more informed decisions, keep more secure and better search capabilities. The increasing pressure on employees to be more productive in less time is challenging, especially taking into account the ever-increasing digital content. Microsoft aims to ease some of this pressure by providing smart solutions to store content. Smart features provided by Microsoft 365 Be Productive #1 Video and audio transcription Beginning later this year, Microsoft aims to introduce automated transcription services to be natively available for video and audio files in OneDrive and SharePoint. This will use the same AI technology available in Microsoft Stream. A full transcript will be shown directly in the viewer alongside a video or while listening to an audio file. Thus improving accessibility and search. This will further help users collaborate with others to improve productivity and quality of work. The video once made can be uploaded and published to Microsoft Stream. AI comes into the picture by providing in-video face detection and automatic captions. Source: Microsoft.com #2 Searching audio, video, and images As announced last September, Microsoft has unlocked the value of photos and images stored in OneDrive and SharePoint. Searching images will now be a cake walk as the native, secure AI will determine the location where the photos were taken, recognize objects, and extract text in photos. Video and audio files also become fully searchable owing to the transcription services mentioned earlier.   Source: Microsoft.com #3 Intelligent files recommendations The plans are to introduce a new files view to OneDrive and the Office.com home page to recommend relevant files to a user, sometime later in 2018. The intelligence of Microsoft Graph will access how a user works, who the user works with, and activity on content shared with the user across Microsoft 365. This information while collaborating on content in OneDrive and SharePoint will be used to suggest files to the user. The Tap feature in Word 2016 and Outlook 2016 intelligently recommends content stored in OneDrive and SharePoint by accessing the context of what the user is working on. Source: Microsoft.com Making informed decisions has never been easier The innovative AI used in OneDrive and SharePoint helps users make informed decisions while working with content. Smart features like File insights, Intelligent sharing, and Data insights are here to provide you with stats and facts to make life easier. Let’s suppose you have an important meeting at hand. File Insights helps viewers with an  ‘Inside look’ i.e. an important information at a glance to prep for the meeting. Source: Microsoft.com Intelligent sharing helps employees share relevant content like documents and presentations with meeting attendees. Source: Microsoft.com Finally, Data Insights will use information provided by cognitive services to set up custom workflows to organize images, trigger notifications, or invoke more extensive business processes directly in OneDrive and SharePoint with deep integration to Microsoft Flow.   Source: microsoft.com   Security Enhancements AI-powered OneDrive and SharePoint will help in securing content and ward off malicious attacks. ‘OneDrive files restore’ integrated with ‘Windows Defender Antivirus’ protects users from ransomware attacks by identifying breaches and guides them through remediation and file recovery. Users will be able to leverage the text extracted from photos and audio/video transcriptions by applying ‘Native data loss prevention (DLP)’ policies to automatically protect content thereby adhering to Intelligent compliance. Many Fortune 500 customers have already started supporting Microsoft’s bold vision to improvise content collaboration and are moving their content to OneDrive and SharePoint. Take a look at the official page for detailed information on Microsoft 365’s smart new features. Defending Democracy Program: How Microsoft is taking steps to curb increasing cybersecurity threats to democracy Microsoft claims it halted Russian spearphishing cyberattacks Microsoft’s .NET Core 2.1 now powers Bing.com    

In the annual Go user survey, the three top requests made by users for Go version 2 were better package management, error handling and the inclusion of generics. Following these requests, the Go 2 draft designs were shared yesterday to include error handling, error values, and adding generics. Note that these are not official proposals. The features, error handling and generics are in step 2 according to the Go release cycle, shown as follows. Source: Go Blog Yesterday, Google developer Russ Cox, gave a talk on design drafts for Golang 2. Go 2 draft designs were also previewed at Gophercon 2018. In his talk, he mentions that the current boilerplate contains too much code for error checks and that the error reporting is not precise enough. For example, an error while using os.Open in which the name of the file which cannot be opened, isn’t mentioned. As proper error reporting only adds to the code, most programmers don’t really bother with this despite knowing that such a practice may create confusion. The new idea, therefore, aims to add a check expression to shorten the checks while keeping them explicit. Cox also stresses on adding experience reports. These reports are difficult but necessary to implement new features. Experience reports turn abstract problems into concrete ones and are needed for changes to be implemented in Golang. They serve as a test case for evaluating a proposed solution and its effects on real-life use-cases. Regarding the inclusion of Generics, Cox mentions: “I've been examining generics recently, but I don't have in my mind a clear picture of the detailed, concrete problems that Go users need generics to solve. As a result, I can't answer a design question like whether to support generic methods, which is to say methods that are parameterized separately from the receiver. If we had a large set of real-world use cases, we could begin to answer a question like this by examining the significant ones.” Go 2 is not going to be a single release, but a sequence of releases adding features as and when they are ready. The approach is to first make features backward compatible to Go 1. Minor changes could be seen in Go 1 in a year or so. If there are no backward incompatible changes, Go 1.20 may be just declared as Go 2. The conversation for Go 2 has started, and there is a call for community help and contribution to converting the drafts into official proposals. Visit the Go page and the GitHub repository for more details. Why Golang is the fastest growing language on GitHub Golang 1.11 is here with modules and experimental WebAssembly port among other updates GoMobile: GoLang’s Foray into the Mobile World

Magic Leap, a million dollar augmented reality company, released its first mixed reality headset, earlier this month, called the “Magic Leap One - Creator Edition”. Magic Leap One is a three-part system which includes a "Lightwear" headset, a handheld controller, and a “lightpack” i.e. a wearable computer that goes in your pocket. The headset system is now available for purchase at $2295, and you can add a professional developer package for an extra $495. This Professional Developer Package provides a hub that enables you to connect the headset to a computer. The company also offers a 24-hour replacement for Magic Leap One in case of breakage. Magic Leap One features three components: Lightwear Headset Magic Leap One comes with a “lightwear” headset that includes cameras, sensors, speakers and optical relay that work in conjunction with the processor in the Lightpack. It has a spatial audio system which includes onboard speakers and 3.5 mm jack. This audio system blends and co-ordinates with the virtual objects. It also maintains the balance between the distance and intensity of the sound with great accuracy and control. One more feature that will be available soon in Magic Leap One headsets is an option for inserting a lens based on your personalized eyeglasses. Superpowered Lightpack processor What’s different in this new headset system is that the processor of this system resides in a small, wearable and portable Lightpack, which can be clipped onto your pocket. The processor consists of an integrated GPU and CPU with an 8 GB RAM and 128 GB storage capacity. It has a built-in rechargeable lithium-ion battery which can be used for upto 3 hours. It also comes with a 45-watt USB-C Power Delivery (PD) charger. Handheld Controller Magic Leap One does not require additional external sensors to deliver six degrees of freedom, also known as 6DoF. It consists of a touch-sensitive trackpad, 8-bit resolution Trigger button, Digital Bumper button, and a digital home button. For more information on Magic Leap One, check out its official website. Magic Leap’s first augmented reality headset, powered by Nvidia Tegra X2, is coming this Summer Google’s Daydream VR SDK finally adds support for two controllers Unreal Engine 4.20 released with focus on mobile and immersive (AR/VR/MR) devices  

Over hundred conservative Facebook employees have formed an online group to protest against the company’s “intolerant” liberal culture. First reported by  the New York Times, these employees have formed an internal online group “FB’ers for Political Diversity”,  which is a space for ideological diversity within the company. Brian Amerige, a senior Facebook engineer wrote in the group, “We are a political monoculture that’s intolerant of different views. We claim to welcome all perspectives, but are quick to attack — often in mobs — anyone who presents a view that appears to be in opposition to left-leaning ideology.” Said to follow the principles of Ayn Rand, Mr. Amerige, started working at Facebook in 2012. He posted a 527-word memo about political diversity at Facebook on his personal website on 20 Aug 2018. He also proposed that Facebook employees debate their political ideas in the new group to better equip the company to host a variety of viewpoints on its platform. This activity comes as quite a surprise in Facebook’s largely liberal workplace culture - a rare sign of an organized disagreement. The last few years, Facebook witnessed many disturbing events, from the spread of misinformation by Russians, the mishandling of users’ data, and the ban of Alex Jones. Critics from the group consider these moves to be a sign that Facebook harbors an anti-conservative bias. This new group has received both praise and backlash from Facebook employees. Some say that its online posts were offensive to minorities. Per the New York Times, one engineer (name undisclosed), said “several people had lodged complaints with their managers about FB’ers for Political Diversity and were told that it had not broken any company rules.” However, some Facebook employees considered the group to be constructive and inclusive of different political viewpoints. Facebook is yet to comment on their employees’ political ideology. With Sheryl Sandberg, Facebook’s chief operating officer, scheduled to testify at a Senate hearing about social media manipulation in elections, this protest adds a one more dimension to the social complexities that Facebook often finds itself these days. For more details, read the original post on the New York Times. Facebook’s AI algorithm finds 20 Myanmar Military Officials guilty. Facebook bans another quiz app and suspends 400 more due to concerns of data misuse. Facebook is reportedly rating users on how trustworthy they are at flagging fake news.

What is Big Data as a Service (BDaaS)? Thanks to the increased adoption of cloud infrastructures, processing, storing, and analyzing huge amounts of data has never been easier. The big data revolution may have already happened, but it’s Big Data as a service, or BDaas, that’s making it a reality for many businesses and organizations. Essentially, BDaas is any service that involves managing or running big data on the cloud. The advantages of BDaas There are many advantages to using a BDaaS solution. It makes many of the aspects that managing a big data infrastructure yourself so much easier. One of the biggest advantages is that it makes managing large quantities of data possible for medium-sized businesses. Not only can it be technically and physically challenging, it can also be expensive. With BDaaS solutions that run in the cloud, companies don’t need to stump up cash up front, and operational expenses on hardware can be kept to a minimum. With cloud computing, your infrastructure requirements are fixed at a monthly or annual cost. However, it’s not just about storage and cos. BDaaS solutions sometimes offer in-built solutions for artificial intelligence and analytics, which means you can accomplish some pretty impressive results without having to have a huge team of data analysts, scientists and architects around you. The different models of BDaaS There are three different BDaaS models. These closely align with the 3 models of cloud infrastructure: IaaS, PaaS, and SaaS. Big Data Infrastructure as a Service (IaaS) – Basic data services from a cloud service provider. Big Data Platform as a Service (PaaS) – Offerings of an all-round Big Data stack like those provided by Amazon S3, EMR or RedShift. This excludes ETL and BI. Big Data Software as a Service (SaaS) – A complete Big Data stack within a single tool. How does the Big Data IaaS Model work? A good example of the IaaS model is Amazon’s AWS IaaS architecture, which combines S3 and EC2. Here, S3 acts as a data lake that can store infinite amounts of structured as well as unstructured data. EC2 acts a compute layer that can be used to implement a data service of your choice and connects to the S3 data. For the data layer you have the option of choosing from among: Hadoop – The Hadoop ecosystem can be run on an EC2 instance giving you complete control NoSQL Databases – These include MongoDB or Cassandra Relational Databases – These include PostgreSQL or MySQL For the compute layer, you can choose from among: Self-built ETL scripts that run on EC2 instances Commercial ETL tools that can run on Amazon’s infrastructure and use S3 Open source processing tools that run on AWS instances, like Kafka How does the Big Data PaaS Model work? A standard Hadoop cloud-based Big Data Infrastructure on Amazon contains the following: Data Ingestion – Logs file data from any data source Amazon S3 Data Storage Layer Amazon EMR – A scalable set of instances that run Map/Reduce against the S3 data. Amazon RDS – A hosted MySQL database that stores the results from Map/Reduce computations. Analytics and Visualization – Using an in-house BI tool. A similar set up can be replicated using Microsoft’s Azure HDInsight. The data ingestion can be made easier with Azure Data Factory’s copy data tool. Apart from that, Azure offers several storage options like Data lake storage and Blob storage that you can use to store results from the computations. How does the Big Data SaaS model work? A fully hosted Big Data stack complete that includes everything from data storage to data visualization contains the following: Data Layer – Data needs to be pulled into a basic SQL database. An automated data warehouse does this efficiently Integration Layer – Pulls the data from the SQL database into a flexible modeling layer Processing Layer – Prepares the data based on the custom business requirements and logic provided by the user Analytics and BI Layer – Fully featured BI abilities which include visualizations, dashboards, and charts, etc. Azure Data Warehouse and AWS Redshift are the popular SaaS options that offer a complete data warehouse solution in the cloud. Their stack integrates all the four layers and is designed to be highly scalable. Google’s BigQuery is another contender that’s great for generating meaningful insights at an unmatched price-performance. Choosing the right BDaaS provider It sounds obvious, but choosing the right BDaaS provider is ultimately all about finding the solution that best suits your needs. There are a number of important factors to consider, such as workload, performance, and cost, each of which will have varying degrees of importance for you. criteria behind the classification include workload, performance and budget requirements. Here are 3 ways you might approach a BDaaS solution:Core BDaaS Core BDaaS uses a minimal platform like Hadoop with YARN and HDFS and other services like Hive. This service has gained popularity among companies which use this for any irregular workloads or as part of their larger infrastructure. They might not be as performance intensive as the other two categories. A prime example would be Elastic MapReduce or EMR provided by AWS. This integrates freely with NoSQL store, S3 Storage, DynamoDB and similar services. Given its generic nature, EMR allows a company to combine it with other services which can result in simple data pipelines to a complete infrastructure. Performance BDaaS Performance BDaaS assists businesses that are already employing a cluster-computing framework like Hadoop to further optimize their infrastructure as well as the cluster performance. Performance BDaaS is a good fit for companies that are rapidly expanding and do not wish to be burdened by having to build a data architecture and a SaaS layer. The benefit of outsourcing the infrastructure and platform is that companies can focus on specific processes that add value instead of concentrating on complicated Big Data related infrastructure. For instance, there are many third-party solutions built on top of Amazon or Azure stack that let you outsource your infrastructure and platform requirements to them. Feature BDaaS If your business is in need of additional features that may not be within the scope of Hadoop, Feature BDaaS may be the way forward. Feature BDaaS focuses on productivity as well as abstraction. It is designed to enable users to be up and using Big Data quickly and efficiently. Feature BDaaS combines both PaaS and SaaS layers. This includes web/API interfaces, and database adapters that offer a layer of abstraction from the underlying details. Businesses don’t have to spend resources and manpower setting up the cloud infrastructure. Instead, they can rely on third-party vendors like Qubole and Altiscale that are designed to set it up and running on AWS, Azure or cloud vendor of choice quickly and efficiently. Additional Tips for Choosing a Provider When evaluating a BDaaS provider for your business, cost reduction and scalability are important factors. Here are a few tips that should help you choose the right provider. Low or Zero Startup Costs – A number of BDaaS providers offer a free trial period. Therefore, theoretically, you can start seeing results before you even commit a dollar. Scalable – Growth in scale is in the very nature of a Big Data project. The solution should be easy and affordable to scale, especially in terms of storage and processing resources. Industry Footprint – It is a good idea to choose a BDaaS provider that already has an experience in your industry. This is doubly important if you are also using them for consultancy and project planning requirements. Real-Time Analysis and Feedback – The most successful Big Data projects today are those that can provide almost immediate analysis and feedback. This helps businesses to take remedial action instantly instead of working off of historical data. Managed or Self-Service – Most BDaaS providers today provide a mix of both managed as well as self-service models based on the company’s needs. It is common to find a host of technical staff working in the background to provide the client with services as needed. Conclusion The value of big data is not in the data itself, but in the insights that can be drawn after processing it and running it through robust analytics. This can help to guide and define your decision making for the future. A quick tip with regards to using Big Data: keep it small at the initial stages. This ensures the data can be checked for accuracy and the metrics derived from them are right. Once confirmed, you can go ahead with more complex and larger data projects. Gilad David Maayan is a technology writer who has worked with over 150 technology companies including SAP, Oracle, Zend, CheckPoint and Ixia. Gilad is a 3-time winner of international technical communication awards, including the STC Trans-European Merit Award and the STC Silicon Valley Award of Excellence. Over the past 7 years Gilad has headed Agile SEO, which performs strategic search marketing for leading technology brands. Together with his team, Gilad has done market research, developer relations and content strategy in 39 technology markets, lending him a broad perspective on trends, approaches and ecosystems across the tech industry. Common big data design patterns Hortonworks partner with Google Cloud to enhance their Big Data strategy Top 5 programming languages for crunching Big Data effectively Getting to know different Big data Characteristics  

WebView is used to display web content in your iOS applications.Yesterday, React Native announced a new native iOS backend to the WebView component that uses WKWebView. This WebView component will be available in the upcoming React Native 0.57 release. Apple has discouraged the use of UIWebViews for a long time. This is due to its being formally deprecated in the upcoming months. It is advised that you use the WKWebView class instead of using UIWebView for the apps that run in iOS 8 and above. You can use the useWebKit property to opt into this implementation: What problems does WKWebView solve? Earlier, to embed web content in our applications we had two options, UIWebView and WKWebView. UIWebView is the original WebView introduced in 2.0. It is known to have some pitfalls and problems. The main drawback is that it has no legitimate way to facilitate communication between the JavaScript running in the WebView and React Native. The newly introduced WKWebView aims to solve this problem. Other benefits of WKWebView over UIWebView include faster JavaScript execution and a multi-process architecture. What to consider when switching to WKWebView? You must avoid using the following properties, for the time being: Using automaticallyAdjustContentInsets and contentInsets may result in inconsistent behavior. When we add contentInsets to UIWebView, the viewport size changes (gets smaller, if the content insets are positive). In case of WKWebView, the viewport size remain unchanged. If you use the backgroundColor property there is a chance that WKWebView may render transparent backgrounds differently from UIWebView and can also flicker into view. Per React Native community, WKWebView doesn't support the scalesPageToFit property. Hence, they couldn't implement this on the WebView React Native component. To know more about the new WebViews, check out their announcement on React Native’s official website and their GitHub repository. Apple releases iOS 12 beta 2 with screen time and battery usage updates among others React Native 0.56 is now available Apple releases iOS 11.4 update with features including AirPlay 2, and HomePod among others

Gremlin, the product that's bringing chaos engineering to a huge range of organizations, announced today that it has added a new feature to its product: container discovery. Container discovery will make it easier to run chaos engineering tests alongside Docker. Chaos engineering and containers have always been closely related - arguably the loosely coupled architectural style of modern software driven by containers has, in turn, led to an increased demand for chaos engineering to improve software resiliency. Matt Fornaciari, Gremlin CTO, explains that "with today’s updates to the Gremlin platform, DevOps teams will be able to drastically improve the reliability of Docker in production." Read next: How Gremlin is making chaos engineering accessible [Interview] What does Gremlin's new container discovery feature do? Container discovery will do 2 things: it will make it easier for engineers to identify specific Docker containers, but more importantly, it will also allow them to simulate attacks or errors within those containerized environments. The real benefit of this is that it makes the testing process so much easier for engineers. Containers are, the press release notes, "often highly dynamic, ephemeral, and difficult to pinpoint at a given moment," which means identifying and isolating a particular container to run a 'chaos test' on can ordinarily be very challenging and time consuming. Gremlin has been working with the engineering team at Under Armour. Paul Osman, Senior Engineering Manager says that "we use Gremlin to test various failure scenarios and build confidence in the resiliency of our microservices." This new feature could save the engineers a lot of time, as he explains: "the ability to target containerized services with an easy-to-use UI has reduced the amount of time it takes us to do fault injection significantly." Read next: Chaos Engineering: managing complexity by breaking things Why is Docker such a big deal for Gremlin? As noted above, chaos engineering and containers are part of the same wider shift in software architectural styles. With Docker leading the way when it comes to containerization - its market share growing healthily - making it easier to perform resiliency tests on containers is incredibly important for the product. It's not a stretch to say that Gremlin have probably been working on this feature for some time, with users placing it high on their list of must-haves. Chaos engineering is still in its infancy - this year's Skill Up report found that it still remains on the periphery of many developer's awareness. However, that could quickly change, and it appears that Gremlin are working hard to make chaos engineering not only more accessible but also more appealing to companies for whom software resiliency is essential.

AMD has announced the support for TensorFlow v1.8 for their ROCm-enabled GPUs. This includes the Radeon Instinct MI25. ROCm stands for Radeon Open Compute and it is an open-source Hyperscale-class (HPC) platform for GPUs. The platform is programming-language independent. This is a major milestone in AMD’s efforts towards accelerating deep learning. ROCm, the Radeon Open Ecosystem is AMD’s open-source software foundation for GPU computing on Linux. Mayank Daga, Director, Deep Learning Software, AMD stated: “Our TensorFlow implementation leverages MIOpen, a library of highly optimized GPU routines for deep learning.” There is a pre-built whl package made available for a simple install similar to the installation of generic TensorFlow in Linux. They also provide a pre-built Docker image for fast installation. AMD is also working towards upstreaming all the ROCm-specific enhancements to the TensorFlow master repository in addition to supporting TensorFlow v1.8. While they work towards fully upstreaming the enhancements, AMD will be releasing and maintaining future ROCm-enabled TensorFlow versions, like v1.10. In the post, Daga stated, “We believe the future of deep learning optimization, portability, and scalability has its roots in domain-specific compilers. We are motivated by the early results of XLA, and are also working towards enabling and optimizing XLA for AMD GPUs.” Current CPUs which support PCIe Gen3 + PCIe Atomics are: AMD Ryzen CPUs AMD EPYC CPUs Intel Xeon E7 V3 or newer CPUs Intel Xeon E5 v3 or newer CPUs Intel Xeon E3 v3 or newer CPUs Intel Core i7 v4, Core i5 v4, Core i3 v4 or newer CPUs (i.e. Haswell family or newer). The installation is simple, First, you’ll need the open-source ROCm stack. Then, install the rocm library needs to be installed via APT: sudo apt update sudo apt install rocm-libs miopen-hip cxlactivitylogger And finally, you install TensorFlow itself (via AMD’s pre-built whl package): sudo apt install wget python3-pip wget http://repo.radeon.com/rocm/misc/tensorflow/tensorflow-1.8.0-cp35-cp35m-manylinux1_x86_64.whl pip3 install ./tensorflow-1.8.0-cp35-cp35m-manylinux1_x86_64.whl For more details on how to get started, visit the GitHub repository. There are also examples on image recognition, audio recognition, and multi-gpu training on ImageNet in the GPUOpen website. Nvidia unveils a new Turing architecture: “The world’s first ray tracing GPU” AMD open sources V-EZ, the Vulkan wrapper library Sugar operating system: A new OS to enhance GPU acceleration security in web apps

Yesterday, Google introduced a new Tensorflow-based framework named Dopamine, which aims to provide flexibility, stability, and reproducibility for both new and experienced RL researchers. This release also includes a set of colabs that clarify how to use the Dopamine framework. Dopamine is inspired by one of the main components in reward-motivated behavior in the brain. It also reflects a strong historical connection between neuroscience and reinforcement learning research. Its main aim is to enable a speculative research that drives radical discoveries. Dopamine framework feature highlights Ease of Use The two key considerations in Dopamine’s design are its clarity and simplicity. Its code is compact (about 15 Python files) and is well-documented. This is achieved by focusing on the Arcade Learning Environment (a mature, well-understood benchmark), and four value-based agents: DQN, C51, A carefully curated simplified variant of the Rainbow agent, and The Implicit Quantile Network agent, which was presented last month at the International Conference on Machine Learning (ICML). Reproducibility Google has provided the Dopamine code with full test coverage. These tests also serve as an additional form of documentation. Dopamine follows the recommendations given by Machado et al. (2018) on standardizing empirical evaluation with the Arcade Learning Environment. Benchmarking It is important for new researchers to be able to quickly benchmark their ideas against established methods. Following this, Google has provided the full training data of the four provided agents, across the 60 games supported by the Arcade Learning Environment. They have also provided a website where one can quickly visualize the training runs for all provided agents on all 60 games. Given below is a snapshot showcasing the training runs for the 4 agents on Seaquest, one of the Atari 2600 games supported by the Arcade Learning Environment. The x-axis represents iterations, where each iteration is 1 million game frames (4.5 hours of real-time play); the y-axis is the average score obtained per play. The shaded areas show confidence intervals from 5 independent runs. The Google community aims to empower researchers to try out new ideas, both incremental and radical with Dopamine ’s flexibility and ease-of-use. It is actively being used in Google’s research, giving them the flexibility to iterate quickly over many ideas. To know more about Dopamine in detail visit the Google AI blog. You can also check out its GitHubrepo. Build your first Reinforcement learning agent in Keras [Tutorial] Reinforcement learning model optimizes brain cancer treatment, reduces dosing cycles and improves patient quality of life OpenAI builds a reinforcement learning based system giving robots hhuman-likedexterity

Splinter, the open source tool for testing web applications using Python has now leveled up to Splinter 0.9.0. Browser actions such as visiting URLs and interacting with their items can be automated. Apart from providing a simple api, Splinter has multiple webdrivers including chrome webdriver, firefox webdriver, phantomjs webdriver, zopetest browser, and remote webdriver. It provides support to iframe, alert and executes javascript while working with both, ajax and async javascript. Two ways to install Splinter 0.9.0 Step 1: Install Python In order to install Splinter, you need to make sure that Python 2.7+  is installed. Step 2: Install Splinter There are two ways to install Splinter: Install a stable release For an official and almost bug-free version, use the terminal: $ [sudo] pip install splinter Install under-development source-code For splinter’s latest-and-greatest features and aren’t afraid of running under development code, run: $ git clone git://github.com/cobrateam/splinter.git $ cd splinter $ [sudo] python setup.py install Head over to the install guide for additional notes. Upgraded features in Splinter 0.9.0 Support for phantomjs is removed. With Chrome and Firefox headless, phantom is no longer needed. Users can now add custom options to the chrome browser. The bug related to element.find_by_text  stands resolved. When trying to do a contextual search for text, the result would include all matching text for the whole DOM instead of just those nodes that are children of the contextual node. Support was added for zope.testbrowser 5+, Flask 1+, selenium 3.14.0. Splinter can now handle webdriver StaleElementReferenceException. The lxml and cssselect has been updated  to 4.2.4  1.0.3 respectively. For a detailed explanation of features visitits  Github page. Visual Studio code July 2018 release, version 1.26 is out! OpenSSH 7.8 released! JDK 11 First Release Candidate (RC) is out with ZGC, Epsilon and more!  

PrimeTek announced a brand new theming engine PrimeReact bringing the Nova theme family and the PrimeReact Theme Designer tool to create your own themes with ease. PrimeReact theming infrastructure has been reimplemented from scratch and the Nova theme family has been introduced to give a modern and premium look to the components. Nova themes are open source under MIT license and the css ships with PrimeReact. You can experience the live demo that runs on Nova. Please note that older themes are removed as they can no longer support the new engine. The premium themes such as Ultima, Serenity, Avalon will be updated to PrimeReact 2.0.0 by mid September. Additional features in PrimeReact 2.0.0 PrimeReact designer API is a SASS based theme engine to create PrimeReact themes easily featuring over 500 variables, a demo application and a base sample theme. If you have your own style guide or need a custom theme quickly, Designer API is a perfect tool to design and bring them to existence. Designer also includes the SASS structure and whole variables of Nova theme family, so if you’d like to customize Nova, you may consider Designer as well. Team has also spent significant time on improving the overall quality of maintenance issues. “PrimeReact is very close to 2.0.0-Final release, two remaining main tasks are rewriting the Tree and TreeTable components, we plan to release 2.0.0-Final by the end of September.”, says the PrimeReact team. Is your web design responsive? Tips and tricks to optimize your responsive web design Is novelty ruining web development?  

IBM with its partners, Rocket Software and CA Technologies, have announced the launch of Zowe at the ongoing Open Source Summit in Vancouver, Canada. It is the first z/OS open source project, which is part of the Linux Foundation’s Open Mainframe Project community. Why is Zowe introduced? The rapid technology advancements and rising expectations in user experience demands  more productive and better integrated capabilities for z/OS, an operating system for IBM mainframes. Zowe enables delivery of such an environment through an extensible open source framework. It aims to create an ecosystem of Independent Software Vendors (ISVs), System Integrators, clients, and end users. By using it, development and operations teams can securely manage, control, script and develop on the mainframe like any other cloud platform. What are its components? The four main components of Zowe are: the Explorer server, API Mediation Layer, zLUX, and Zowe CLI. Source: Zowe Zowe APIs and Explorers z/OS Management Facility (z/OSMF) supports the use of REST APIs, which are public APIs that your application can use to work with system resources and can also extract system data. With the help of these REST APIs, Zowe submits jobs, works with the Job Entry Subsystem (JES) queue, and manipulates UNIX System Services (USS) or Multiple Virtual Storage (MVS) datasets. Explorers are visual representations of these APIs that are wrapped in the Zowe web UI application. They create an extensible z/OS framework that provides new z/OS REST services to transform enterprise tools and DevOps processes to incorporate new technology, languages, and modern workflows. Zowe API Mediation Layer The following are the key components of API Mediation Layer: API Gateway: It is built using Netflix Zuul and Spring Boot technology. Its purpose is to forward API requests to the appropriate corresponding service through the microservice endpoint UI. Discovery Service: It is built on Eureka and Spring Boot technology. It acts as the central point in the API Gateway that accepts announcements of REST services, and is a repository for active services. API Catalog: It is used to view the services running in API Mediation Layer. You can also view the corresponding API documentation to a service. Zowe Web UI Web UI named zLUX, modernizes and simplifies working on the mainframe.The UI works with the underlying REST APIs for data, jobs, and subsystems, and presents the information in a full-screen mode. It gives users a unifying experience where various applications can work together. Zowe Command Line Interface (CLI) Zowe CLI is used to allow user interactions from different platforms with z/OS. The platforms which can be cloud or distributed systems are able to submit jobs, issue TSO and z/OS console commands, integrate z/OS actions into scripts, and produce responses as JSON documents with the help of Zowe CLI. Currently, the Zowe is available in beta and is not intended for production use. The Zowe Leadership Committee is targeting to have a stable release by the end of the year. To know more about the launch of Zowe, refer to IBM’s announcement on their official website. IBM Files Patent for “Managing a Database Management System using a Blockchain Database” Google, IBM, RedHat and others launch Istio 1.0 service mesh for microservices IBM launches Nabla containers: A sandbox more secure than Docker containers

Epic Games CEO, Tim Sweeney has accused Google of being “irresponsible” for disclosing a major security flaw in the Fortnite Android Installer to the public eye before patch of this game was widely available. After the Fortnite installer went live, Google security engineers pointed out a security bug. This showed that installing the file (with .apk extension) shared by Epic Games, enabled the hackers to push malicious apps that could take over a user’s device. To make things even worse, the .apk file shared by Epic Games is the first step to follow while installing the Fortnite game. As mentioned in the Google thread, “Any app with the WRITE_EXTERNAL_STORAGE permission can substitute the APK immediately after the download is completed and the fingerprint is verified. This is easily done using a FileObserver. The Fortnite Installer will proceed to install the substituted (fake) APK”. Epic was quick to respond to this and took appropriate action to secure the newer Android devices from being vulnerable to the attacks. Additionally, Epic had asked Google for 90 days before making the security issue public as it would provide users with enough time to update the installers. However, last Friday, Google released a thread titled “Fortnite Installer downloads are vulnerable to hijacking” that talks about the vulnerability issues in the installer, clearly not granting Epic the requested 90 days. Google proceeded to “unrestrict the issue in line with Google’s standard disclosure practices”. Google spokesperson said that “User security is our top priority, and as part of our proactive monitoring for malware we identified a vulnerability in the Fortnite installer. We immediately notified Epic Games and they fixed the issue”. Epic games didn’t appreciate the move, and its CEO Tim Sweeney released a statement saying how “Epic genuinely appreciated Google’s effort to perform an in-depth security audit of Fortnite immediately following our release on Android, and share the results with Epic so we could speedily issue an update to fix the flaw they discovered. However, it was irresponsible of Google to publicly disclose the technical details of the flaw so quickly, while many installations had not yet been updated and were still vulnerable.” Sweeney also took to Twitter to express his disapproval regarding the situation. https://twitter.com/TimSweeneyEpic/status/1033225118405804032 https://twitter.com/TimSweeneyEpic/status/1034117758332661760 He even went ahead to say that this was Google’s attempt to “score cheap PR points” against Epic as they decided to release Fortnite via their own website instead of Google Play Store. This would have left Google out of the 30% cut it would’ve received with in-app purchases made on Fortnite Android. “Google’s security analysis efforts are appreciated and benefit the Android platform, however a company as powerful as Google should practice more responsible disclosure timing than this, and not endanger users in the course of its counter-PR efforts against Epic’s distribution of Fortnite outside of Google Play” as mentioned on the Fortnite blog. https://twitter.com/TimSweeneyEpic/status/1033226094357504000 This is not the first time that Google has been criticized, Microsoft also accused it of disclosing its vulnerabilities before patches were made widely available. Now, whether this was really a PR move by Google against Epic cannot be verified. Epic games have now come out with a 2FA or two-factor authentication to “ help protect user accounts from unauthorized access by requiring them to enter an additional code when they sign in”. Google’s incognito location tracking scandal could be the first real test of GDPR 1k+ Google employees frustrated with continued betrayal, protest against Censored Search engine project for China Google gives Artificial Intelligence full control over cooling its data centers  

The Japanese automaker giant, Toyota Motor Corp. will join hands with Uber to work collectively on autonomous autos. Toyota will make an investment of about $500 million and will value Uber at $72 billion to get self-driving cars on the road. Toyota aims to improve security and decrease transportation prices with this initiative. As for Uber, it's a chance to redeem itself in the budding autonomous transportation sector. As a part of the alliance, Toyota will manufacture Sienna vehicles, which will be equipped with Uber’s self-driving technology, and another company will operate the fleet, said a source familiar with the project. The third partner has yet to be identified. Consumers can expect "mass-production" of self-driving vehicles that would be deployed on Uber's ride sharing network. Source: BBC.com After Uber withdrew its self-driving cars owing to the autonomous Uber SUV that killed a pedestrian in a fatal crash in Tempe, Arizona, in March, the investment is a ray of hope for the company and its users. With this, Uber consumers’ growing apprehension that Uber is pulling out of the self-driving car space will be finally put to rest. As for the Uber’s investors, this collaboration will come as a relief especially after it was reported earlier this month that Uber was sinking around $1m-$2m into its autonomy work every single day thanks to the fatal crash and the expensive lawsuit that followed. This $500 billion project is expected to be piloted in 2021. The potential of self-driving cars to power car-sharing services represents a major challenge in an industry dominated by individual car ownership. For Toyota, it presents an opportunity to reinvent itself from a car maker to a mobility platform.   “This agreement and investment marks an important milestone in our transformation to a mobility company as we help provide a path for safe and secure expansion of mobility services like ride-sharing.” -Shigeki Tomoyama, executive vice president of Toyota Motor Corporation Toyota has been lagging behind in the scene of self driving cars, while Uber's troubled self-driving car efforts are in desperate need of external help. It would, therefore, be interesting to see how this joint collaboration works in favour of both, Toyota and Uber. For more details on this story, head over to Fortune’s coverage of this news. Apple self-driving cars are back! VoxelNet may drive the autonomous vehicles MIT’s Duckietown Kickstarter project aims to make learning how to program self-driving cars affordable Tesla is building its own AI hardware for self-driving cars