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A beta version of V8 6.8 was announced by Google a few days ago. This release contains a lot of exciting new features such as better memory usage and improved performance. Let’s take a detailed look at what this release has in store for the web developers. V8 Engine Explained When a software is named after an 8-cylinder aircraft engine, only one thing comes to mind- Speed. V8 is an open source JavaScript Engine which was designed at the Google development center in Germany. It is used on both the browser as well as on the server side. V8 works like a usual compiler which translates the actual code into machine code for faster execution. Java developers or developers working with any modern programming language might be familiar with compilers. But what makes V8 stand out from any other compiler, is its ability to not produce any bytecode or intermediate code. The V8 team follows a six-week release cycle, where they come up with a beta of a new minor version followed by a stable release in the next few weeks. Let’s take a deep dive into the feature set of this new release. Memory The sharedFunctionInfo function or SFI, as most developers call it, holds the metadata for other functions. Usually, in function heavy-code JavaScript unnecessarily keeps these metadata functions alive, leading to a lot of memory leaks. The V8 team decided to break the dependency on SFI and reduced the actual size of the SFI itself. This approach reduces the memory consumption drastically and the V8 team is planning to further compress the size of these SFIs for optimum memory consumption. Performance Performance is imperative when it comes to engines, and the V8 team constantly strives to bring improvements to the engine. This helps developers to run high-performance application on V8. This time around they have introduced array destructuring for performance improvements. A new implementation of Object.assign improves performance, via implementation of a fast path for JavaScript. Performance for TypedArrays has been increased in instances when sorting is done using a comparison function. WebAssembly WebAssembly is nothing but a bytecode format which is executed in a web browser. This allows an application to be deployed to a device with a compliant web browser without going through any explicit installation steps. In V8 v6.8 provides trap-based bounds checking on Linux x64 platforms. This memory management optimization considerably improves WebAssembly’s execution speed. You can expect the next V8 6.9 release in a couple of months with more performance improvements. Until then you can visit their official website for a detailed breakdown of all the features in V8 6.8. WebAssembly comes to Qt. Now you can deploy your next Qt app in browser Implementing 5 Common Design Patterns in JavaScript (ES8)

Google is the new platinum member of the Linux Foundation. Google will benefit the platinum member rights and the Linux community will move towards huge financial gains. The annual membership cost for Google will be around $500,000. Linux Foundation, on the other hand, is quite thrilled to have Google as one of the platinum members.  As Google is one of the biggest contributors to and supporters of open source in the tech world. In addition to this, Google leverages one of the most important open source projects for its OS -- the Linux kernel and both Android and Chrome OS, are Linux-based. This membership also secured a seat for Sarah Novotny, Google’s Head of Open-Source strategy for the Google Cloud Platform into the Board of Directors of Linux Foundation. On this achievement, Sarah mentioned, ’Open source is an essential part of Google's culture, and we've long recognized the potential of open ecosystems to grow quickly, be more resilient and adaptable in the face of change, and create better software. The Linux Foundation is a fixture in the open source community. By working closely with the organization, we can better engage with the community-at-large and continue to build a more inclusive ecosystem where everyone can benefit.’ Google joins hands with other platinum members in the Linux Foundation, including Microsoft, Intel, Huawei, Samsung, Facebook, etc. Read more about this exciting coverage at the Linux Foundation’s official announcement. Tencent becomes a platinum member of the Linux Foundation Machine learning APIs for Google Cloud Platform Google introduces Machine Learning courses for AI beginners

Eclipse Foundation announced the release of Photon release of Eclipse IDE. Also with this release, the community announced the support for Rust language. This support will give a native Eclipse IDE working experience for Rust developers. Eclipse IDE has been known for providing the IDE support and the learning demands for the Rust community. This release marks the thirteenth annual simultaneous release of Eclipse. The important features in the Photon release as follows: Full Eclipse IDE support for building, debugging, running, and packaging Rust applications and giving a good user experience for Rust development. More support for C# for editing and debugging codes, this includes syntax coloring, autocomplete suggestions, diagnostics, and navigation. The Photon release has added some more frameworks to the IDE such as RedDeer (framework for building automated test), Yasson (Java framework for providing binding with JSON documents), JGit (Git for Java), among others. It also comes with some more updates and features for dynamic language toolkit, Eclipse Modeling Framework (EMF), PHP development tools, C/C++ development tools, tools for Cloud Foundry, dark theme and improvement in background color and popup dialogs. Eclipse foundation has also introduced, what they called Language Server Protocol (LSP), with the Photon release. WIth the LSP based release, Eclipse will deliver support for popular and emerging languages in the IDE. With the normal release cycle, LSP will focus on keeping pace with the emerging tools and technologies andon the developers and their commercial needs in their future releases. For more information on the Photon project and contributing to the Eclipse community, you can check out the Eclipse Meetup event. Read more What can you expect from the upcoming Java 11 JDK? Perform Advanced Programming with Rust The top 5 reasons why Node.js could topple Java

Team at plotly excitingly announces the biggest release of plotly pythonic interface, plotly.py 3.0. This new release comes with great support to Jupyter and Jupyterlab environment, imperative manipulation techniques, animation transition, lots of performance improvements and bug fixes. Plotly is an interactive data analysis and live graphing library. The Python API allows you to access all of Plotly’s functionality from Python. The advantage of plotly is its collaborative features through which one can share, track and edit the graph real-time over web. This library is developed on the renowned JavaScript library, plotly.js equipped with numerous charts and plots such as line plots, heatmaps, histograms, bubble charts etc. What’s new in plotly.py 3.0 New widget support for Jupyter and Jupyterlab: New widget added called, FigureWidget that creates final object to be plotted with another dictionary-like object containing both data and layout objects. It is compatible with the widget frameworks. One can even hover around the plot and zoom in into regions. Manipulation Attributes:  Specific and dedicated attributes added making it easier to edit your graphs in the jupyter environment. With this set of attributes, the figures can be manipulated and graphs can be explored more in detail. Docstring support: This new support adds informative docstrings for better documentation of your python codes, classes and functions. These docstrings are directly fetched from plotly.js schema and automatically updated to python interface plotly.py. Performance Improvements Figure specs are now serialized and transferred to plotly.js over Jupyter comm protocol. Plotting speed of large data is now much faster, reducing the plotting time from 35 seconds to as low as 3 seconds for 1 million data points. Added direct support of Typed Arrays for faster access to raw data. plotly.py is considered to be a high-performance charting library through which one can plot data across different charts and graphs such as 3D graphs, statistical charts, financial charts, scientific charts and more. To know more on its different styled charts and custom control options, read the official documentation. 15 Useful Python Libraries to make your Data Science tasks Easier Visualizing 3D plots in Matplotlib 2.0 10 reasons why data scientists love Jupyter notebooks

Microsoft released version 1.0 of Open Service Broker for Azure (OSBA) along with full support for Azure SQL, Azure Database for MySQL, and Azure Database for PostgreSQL. Microsoft announced the preview of Open Service Broker for Azure (OSBA) at the KubeCon 2017. OSBA is the simplest way to connect apps running on cloud-native environment (such as Kubernetes, Cloud Foundry, and OpenShift) and rich suite of managed services available on Azure. The OSBA 1.0 ensures to connect mission-critical applications to Azure’s enterprise-grade backing services. It is also ideal to run on a containerized environment like Kubernetes. In a recent announcement of a strategic partnership between Microsoft and Red Hat to provide  OpenShift service on Azure, Microsoft demonstrated the use of OSBA using an OpenShift project template. OSBA will enable customers to deploy Azure services directly from the OpenShift console and connect them to their containerized applications running on OpenShift. It also plans to collaborate with Bitnami to bring OSBA into KubeApps, for customers to deploy solutions like WordPress built on Azure Database for MySQL and Artifactory on Azure Database for PostgreSQL. Microsoft plans 3 additional focus areas for OSBA and the Kubernetes service catalog: Plans to expand the set of Azure services available in OSBA by re-enabling services such as Azure Cosmos DB and Azure Redis. These services will progress to a stable state as Microsoft will learn how customers intend to use them. They plan to continue working with the Kubernetes community to align the capabilities of the service catalog with the behavior that customers expect. With this, the cluster operator will have the ability to choose which classes/plans are available to developers. Lastly, Microsoft has a vision for the Kubernetes service catalog and the Open Service Broker API. It will enable developers to describe general requirements for a service, such as “a MySQL database of version 5.7 or higher”. Read the full coverage on Microsoft’s official blog post GitLab is moving from Azure to Google Cloud in July Announces general availability of Azure SQL Data Sync Build an IoT application with Azure IoT [Tutorial]

Microsoft recently announced Azure IoT Edge to be generally available and open source. Its preview was announced at the Microsoft Build 2017, during which the company stated how this service plans to extend cloud intelligence to edge devices. Microsoft Azure IoT Edge is a fully-managed cloud service to help enterprises generate useful insights from the data collected by the Internet of things (IoT) devices. It enables one to deploy and run Artificial Intelligence services, Azure services, and custom logic directly on the cross-platform IoT devices. This, in turn, helps deliver cloud intelligence locally as per the plan. Additional features in the Azure IoT Edge include: Support for Moby container management system: Docker which is built on Moby, an open-source platform. It allows Microsoft Azure to extend the concepts of containerization, isolation, and management from the cloud to devices at the edge. Azure IoT Device Provisioning Service: This service allows customers to securely provision huge amount of devices making edge deployments more scalable. Tooling for VSCode: VSCode allows easy module development by coding, testing, debugging, and deploying. Azure IoT Edge security manager: IoT Edge security manager acts as a tough security core for protecting the IoT Edge device and all its components by abstracting the secure silicon hardware. Automatic Device Management (ADM): ADM service allows scaled deployment of IoT Edge modules to a fleet of devices based on device metadata. When a device with the right metadata (tags) joins the fleet, ADM brings down the right modules and puts the edge device in the correct state. CI/CD pipeline with VSTS : This allows managing the complete lifecycle of the Azure IoT Edge modules from development, testing, staging, and final deployment. Broad language support for module SDKs: Azure IoT Edge supports more languages than other edge offerings in the market. It includes C#, C, Node.js, Python, and Java allowing one to program the edge modules in their choice of language. There are three components required for Azure IoT Edge deployment: Azure IoT Edge Runtime Azure IoT Hub and Edge modules. The Azure IoT Edge runtime is free and will be available as open source code. Customers would require an Azure IoT Hub instance for edge device management and deployment if they are not using one for their IoT solution already. Read full news coverage at the Microsoft Azure IoT blog post. Read Next Microsoft commits $5 billion to IoT projects Epicor partners with Microsoft Azure to adopt Cloud ERP Introduction to IOT

Till date, there has been a general perception that AI algorithms operate independently. Question marks have been raised over their ability to collaborate to perform complex tasks. Researchers at OpenAI have been working on this problem for some time now, and they seem to have found the answer. A team of AI algorithms called the OpenAI Five have managed to beat a team of human video game players in Dota 2 - the popular battle arena game. OpenAI had previously developed an algorithm which was capable of competing against human players in the single-player mode in Dota 2. This latest achievement using a team of similar algorithms modified to factor in both individual and team success has proved to be quite evolutionary. These algorithms do not communicate directly, but only through gameplay. How OpenAI Five beat the human Dota experts The OpenAI Five mastered the game of Dota 2 by initially playing against different versions of themselves. Over a period of time, they managed to learn different strategies which human players generally use - figuring out ways to attack, defend and perform a variety of other tasks. Most importantly, they learnt the art of collaboration and working as a team - something that eventually led them to beat some of the world’s top Dota 2 players. One of the founders of OpenAI, Greg Brockman thinks that this is a milestone achievement for AI - with great implications that could help humanity in a positive way. “What we’ve seen implies that coordination and collaboration can emerge very naturally out of the incentives”, he says. He added that substituting a human player for an algorithm to play Dota 2 in a team mode worked out very well. What is Dota 2? Dota 2 is one of the world’s most popular strategy games, played by millions across the world. In the team mode, five players collaborate to control a building or a structure by planning attacks and engaging in real-time combat. Each of the players have different strengths, weaknesses and roles within the team, and they have to optimize their capabilities to work with the team in the best possible way. Games continue to be the perfect test-bed for AI The tradition of pitting AI algorithms against expert game players has been an ongoing tradition. Last year DeepMind developed an AI algorithm AlphaGo that beat the world’s best human Go player, while another program AlphaGo Zero perfected its Go and Chess skills simply by playing against itself iteratively. Collaborative AI algorithms could be the future Beating humans in a Dota 2 team game is a rather important achievement for AI. With the commercial applications of AI on the rise, this collaborative approach used by the AI algorithms can prove to be invaluable. These algorithms, for example, can collaborate to outperform humans in a bidding war, or give faster, more accurate predictions related to certain events. One cannot rule out the possibility of them collaborating even with humans and helping them with their day to day activities in the near future. However, could there be a downside to this? Could human effort be replaced by a combination of AI algorithms working together? We will find out in due course of time, but there seems to be no evidence to suggest this...just yet. Read more Unity Machine Learning Agents: Transforming Games with Artificial Intelligence Developing Games Using AI 5 Ways Artificial Intelligence is Transforming the Gaming Industry

HashiCorp recently announced the release of a new version of its distributed service mesh, Consul 1.2.  This release supports a new feature known as Connect, which automatically changes any existing Consul cluster into a service mesh solution. It works on any platform such as physical machines, cloud, containers, schedulers, and more. HashiCorp is San Francisco based organization that helps businesses resolve development, operations, and security challenges in infrastructure, for them to focus on other business-critical tasks. Consul is one such HashiCorp’s product; it is a distributed service mesh for connecting, securing, and configuring services across any runtime platform or any public or private cloud platform. The Connect feature within the Consul 1.2, enables secure service-to-service communication with automatic TLS encryption and identity-based authorization. HashiCorp further stated the Connect feature to be free and open source. New functionalities in the Consul 1.2 Encrypted Traffic while in transit All traffic is established with Connect through a mutual TLS. It ensures traffic to be encrypted in transit and allows services to be safely deployed in low-trust environment. Connection Authorization It will allow or deny service communication by creating a service access graph with intentions. Connect uses the logical name of the service, unlike a firewall which uses IP addresses. This means rules are scale independent; it doesn’t matter if there is one web server or 100. Intentions can be configured using the UI, CLI, API, or HashiCorp Terraform. Proxy Sidecars Applications are allowed to use a lightweight proxy sidecar process to automatically establish inbound and outbound TLS connections. With this, existing applications can work with Connect without any modification. Consul ships with a built-in proxy that doesn't require external dependencies, along with third-party proxies such as Envoy. Native Integration Performance sensitive applications can natively integrate with the Consul Connect APIs to establish and accept connections without a proxy for optimal performance and security. Certificate Management Consul creates and distributes certificates using a certificate authority (CA) provider. Consul has a built-in CA system that requires no external dependencies. This CA system integrates with HashiCorp Vault, and can also be extended to support any other PKI (Public Key Infrastructure) system. Network and Cloud Independent Connect uses standard TLS over TCP/IP, which allows Connect to work on any network configuration. However, the IP advertised by the destination service should be reachable by the underlying operating system. Further, services can communicate cross-cloud without complex overlays. Know more about these functionalities in detail, by visiting HashiCorp Consul 1.2 official blog post SDLC puts process at the center of software engineering Why Agile, DevOps and Continuous Integration are here to stay: Interview with Nikhil Pathania, DevOps practitioner What is a multi layered software architecture?  

This is the second release of Kubernetes in 2018. Kubernetes 1.11 comes with significant updates on features that revolve around maturity, scalability, and flexibility of Kubernetes.This newest version comes with storage and networking enhancements with which it is possible to plug-in any kind of infrastructure (Cloud or on-premise), into the Kubernetes system. Now let's dive into the key aspects of this release: IPVS-Based In-Cluster Service Load Balancing Promotes to General Availability IPVS consist of a simpler programming interface than iptable and delivers high-performance in-kernel load balancing. In this release it has moved to general availability where is provides better network throughput, programming latency, and scalability limits. It is not yet the default option but clusters can use it for production traffic. CoreDNS Graduates to General Availability CoreDNS has moved to general availability and is now the default option when using kubeadm. It is a flexible DNS server that directly integrates with the Kubernetes API. In comparison to the previous DNS server CoreDNS has lesser moving pasts as it is a single process that creates custom DNS entries to supports flexible uses cases. CoreDNS is also memory-safe as it is written in Go. Dynamic Kubelet Configuration Moves to Beta It has always been difficult to update Kubelet configurations in a running cluster as Kubelets are configured through command-line flags. With this feature moving to Beta, one can configure Kubelets in a live cluster through the API server. CSI enhancements Over the past few releases CSI (Container Storage Interface) has been a major focus area. This service was moved to Beta in version 1.10. In this version, the Kubernetes team continues to enhance CSI with a number of new features such as: Alpha support for raw block volumes to CSI Integrates CSI with the new kubelet plugin registration mechanism Easier to pass secrets to CSI plugins Enhanced Storage Features This release introduces online resizing of Persistent Volumes as an alpha feature. With this feature users can increase the PVs size without terminating pods or unmounting the volume. Users can update the PVC to request a new size and kubelet can resize the file system for the PVC. Dynamic maximum volume count is introduced as an alpha feature. With this new feature one can enable in-tree volume plugins to specify the number of volumes to be attached to a node, allowing the limit to vary based on the node type. In the earlier version the limits were configured through an environment variable. StorageObjectInUseProtection feature is now stable and prevents issues from deleting a Persistent Volume or a Persistent Volume Claim that is integrated to an active pod. You can know more about Kubernetes 1.11 from Kubernetes Blog and this version is available for download on GitHub. To get started with Kubernetes, check out our following books: Learning Kubernetes [Video] Kubernetes Cookbook - Second Edition Mastering Kubernetes - Second Edition Related Links VMware Kubernetes Engine (VKE) launched to offer Kubernetes-as-a-Service Rackspace now supports Kubernetes-as-a-Service Nvidia GPUs offer Kubernetes for accelerated deployments of Artificial Intelligence workloads

The Computer Literacy Project was launched by the BBC in the early eighties. Running throughout the decade, it aimed to document and explore computing and programming for a generation of British people born long before the concept even emerged. The corporation even created its own personal computer, called the BBC Micro, which people could use to learn how to program. Now, the BBC has opened up access to its entire Computer Literacy Project archive. Featuring 267 programs, 146 of which were part of the original Computer Literacy Project, it offers a valuable insight into the years when computing began to enter into the public consciousness. There are also interviews with particularly young-looking Steve Wozniak and Bill gates... Run BBC Micro software in your browser The initiative also features old BBC Micro programs that you can run in your browser. These include BASE3, which illustrates how a database works, and ENCRY3B, which shows you some simple encryption methods from the eighties. It's well worth exploring! The best Computer Literacy Project TV shows The archive features an impressive range of content. Some of it features even earlier television programs, like Tomorrow's World, a BBC technology program. Watch this clip to see what computing in the sixties looked like... https://www.youtube.com/watch?v=8bzTgbHn83Q But many of the programs were created as part of the project too. Here are some of the best, which you can watch online for free. The Silicon Factor, first broadcast in 1980 This show was a prequel to everything that we've been living through over the last 30 years. It explores how microchips could change British industry, and what might happen if Britain fails to keep up with the rest of the world. It's well worth watching as a useful historical document of how people viewed technology at the end of the twentieth century. There are certainly some parallels with where we are today and the concerns around artificial intelligence and automation. Electronic Office, first broadcast in 1984 Electronic Office was a prophetic look at the lives we'd lead today. Okay, so it isn't all prophetic, and some it might seem strange to us today. But there's obviously much more about how we work today that would seem even stranger to anyone watching the program in the mid-eighties. With a Little Help from the Chip, first broadcast in 1985 With a Little Help from the Chip throws up plenty of interesting parallels with where we are today in terms of IoT and connected homes. It also demonstrates how technology can be used to support people who need it. It gives us an insight on an one of the earliest ways in which technology was used to provide an innovative solution to a complex social issue. The BBC reminds us that people drive innovation, not technology Yes, the archive is a fun and engaging way to look at the history of software, but it also reminds us that innovation is never set in stone. Progress and development are often uncertain (and sometimes a little bit frightening). It has taken a generation of people to get us to where we are today - and it will take generations of people to build the future.

On June 25, 2018, Wi-Fi Alliance introduced the next generation of Wi-Fi security, WPA3. It took over a decade to introduce the successor of WPA2 protocol that brings new capabilities of enhancing personal and enterprise Wi-Fi networks. Individuals along with organizations were awaiting for this update especially after last years KRACK vulnerability, which was later fixed on many devices. This update comes with a variety of added features that include more robust authentication and increased cryptographic strength for highly sensitive data markets. With this update Wi-Fi industries transit to WPA3 security, however, WPA2 devices will continue to interoperate and provide recognized security. In order to maintain flexibility of mission critical networks, WPA3 networks will: Prohibit outdated legacy protocols, Deliver the latest security methods, and Use PMF (Protected Management Frames) WPA3 security supports the market through two distinct modes of operation: WPA3-Personal and WPA3-Enterprise. WPA3-Personal If users choose passwords that fall short of typical complexity recommendation, WPA3 leverages SAE (Simultaneous Authentication of Equals) a secure key establishment protocol between devices to provide more robust protection for users against third party password guessing attempts. With this level of security enhancement your network is more resilient. WPA3-Enterprise The WPA3-Enterprise protocol proves beneficial to organizations transmitting sensitive data such as finance or government, as it provides 192-bit cryptographic strength along with additional protection to these networks. This 192-bit bundle has a consistent combination of cryptographic tools deployed across WPA3 networks. Earlier this year, Wi-Fi Alliance introduced new features and some enhancements for Wi-Fi protected access. This addition ensures that WPA2 maintains robust security protection in the evolving wireless landscape. WPA2 is still a mandatory requirement for all Wi-Fi CERTIFIED devices as it would still take some time for WPA3 market adoption to grow. Through a transitional mode of operation, WPA3 will still maintains interoperability with WPA2 devices, and Wi-Fi users can remain confident that their devices are well-protected when connected to secured Wi-Fi CERTIFIED networks. Users and Wi-Fi device vendors need not worry as WPA3 protections won’t come into action overnight; it may still take some time to evolve or maybe even many-years-long process. To get WPA3 in place you need a new router that supports it or you can hope your old one can be updated to support it. This is also true for all your gadgets. You have to buy new gadgets that support WPA3 or can hope your old devices are updated to the required standards. However, WPA3 can still connect with devices that use WPA2, so you need not worry about your device not working just because you brought in a new connectivity hardware at home. WPA3 adoption has been on a positive side as organizations such as Hewlett Packard, Qualcomm, Huawei Wireless, Intel, Cisco and many more have announced their support towards next-gen Wi-Fi security for personal and enterprise networks. Qualcomm announces a new chipset for standalone AR/VR headsets at Augmented World Expo Intel’s Spectre variant 4 patch impacts CPU performance Top 5 cybersecurity assessment tools for networking professionals

VMware recently announced its Kubernetes-as-a-Service adoption by launching VMware Kubernetes Engine (VKE) that provides a multi-cloud experience. The VKE is a fully-managed service offered through a SaaS model. It allows customers to use Kubernetes easily without having to worry about the deployment and operation of Kubernetes clusters. Kubernetes lets users manage clusters of containers while also making it easier to move applications between public hosted clouds. By adding Kubernetes on cloud, VMware offers a managed service business that will use Kubernetes containers with reduced complexities. VMware's Kubernetes engine will face a big time competition from Google Cloud and Microsoft Azure, among others. Recently, Rackspace also announced its partnership with HPE to develop a new Kubernetes-based cloud offering. VMware Kubernetes Engine (VKE) features include: VMware Smart Cluster VMware Smart Cluster is the selection of compute resources to constantly optimize resource usage, provide high availability, and reduce cost. It also enables the management of cost-effective, scalable Kubernetes clusters optimized to application requirements. Users can also have role-based access and visibility only to their predefined environment with the smart cluster. Fully Managed by VMware VMware Kubernetes Engine(VKE) is fully managed by VMware. It ensures that clusters always run in an efficient manner with multi-tenancy, seamless Kubernetes upgrades, high availability, and security. Security by default in VKE VMware Kubernetes Engine is highly secure with features like: Multi-tenancy Deep policy control Dedicated AWS accounts per organization Logical network isolation Integrated identity Access management with single sign-on Global Availability VKE has a region-agnostic user interface and is available across three AWS regions, US-East1, US-West2, and EU-West1, giving users the choice for which region to run clusters on. Read full coverage about the VMware Kubernetes Engine (VKE) on the official website. Introducing VMware Integrated OpenStack (VIO) 5.0, a new Infrastructure-as-a-Service (IaaS) cloud Nvidia GPUs offer Kubernetes for accelerated deployments of Artificial Intelligence workloads Hortonworks partner with Google Cloud to enhance their Big Data strategy  

Machine learning and Artificial Intelligence are two of most popular buzzwords today. Everyone wants to use them to their advantage, but not many know how to do it right. In a bid to promote awareness and help more developers get proficient in machine learning and AI, Google had introduced a Machine Learning Crash Course earlier this February. With the tremendous success of the program, they have now added an interactive course on image classification - the process of extracting information from images. Computer vision is a very popular use-case of machine learning and AI. Neural networks are trained with lots of image data and are then asked to classify a random image based on its characteristics. Data scientists and Machine Learning developers strive to increase the accuracy of this prediction. What is this machine learning course about? Dubbed as Machine Learning Practica, this newly added interactive course will walk the students through the basics of machine learning and its application in image classification - one of the most important use-cases of Computer Vision. They will start with understanding the basics of image classification, and go on to learn about Convolutional Neural Networks, the neural network model that can be best used for image classification. This course will also teach the readers how to build a CNN from scratch, and demonstrate the best practices in training a highly effective and accurate model for classification. Topics such as preventing over-fitting, using pre-trained models and more, are also covered. The course is primarily aimed at developers with a basic knowledge of machine learning. The examples and exercises included in this course are written in Keras - a highly popular Python library for training neural networks. While prior experience in Keras is not required, some exposure to Python programming will make it easier for you to get the best out of this course. Google’s data scientists and researchers have collaborated with the image model experts to develop this course.It contains video, interactive programming exercises as well as relevant documentation for reference. The techniques highlighted in this course are already being used to power search in Google Photos. Till date, more than 10,000 developers have benefited from this course. So, what are you waiting for? Get started with image classification in machine learning! In case you’re looking for some hands-on resources to master image classification using machine learning and deep learning, we’ve got you covered as well! Check out our books Deep Learning for Computer Vision, Tensorflow Machine Learning Cookbook, and Deep Learning with Tensorflow, Second edition to get started! Read more How machine learning as a service is transforming cloud Google announces Cloud TPUs on the Cloud Machine Learning Engine (ML Engine) Microsoft start AI School to teach Machine Learning and Artificial Intelligence

High profile security attacks have put cybersecurity high on the agenda. For most companies it's at best a headache and at worst a full-blown crisis. But if you're in the business of solving these problems, it only makes you more valuable. That's what has happened to Balbix. Balbix is a security solution that allows users to "predict & proactively mitigate breaches before they happen." It does this by using predictive analytics and machine learning to identify possible threats. According to TechCrunch, the company has received the series B investment from a number of different sources. This includes Singtel's Innov8 fund (based in Singapore). Balbix is bringing together machine learning and cybersecurity However, the most interesting part of the story is what Balbix is trying to do. The fact that it's seeing early signs of eager investment indicates that it's moving down the right track when it comes to cybersecurity. The company spends some time outlining how the tool works on its website. Balbix's Breach Control product uses "sensors deployed across your entire enterprise network automatically and continuously discover and monitor all devices, apps and users for hundreds of attack vectors." An 'attack vector' is really just a method of attack, like, for example, phishing or social engineering. The product then uses what the company calls the 'Balbix Brain' to analyse risks within the network. The Balbix Brain is an artificial intelligence system that is designed to do a number of things. It assesses how likely different assets and areas of the network are to be compromised, and highlights the potential impact such a compromise might have. This adds an additional level of intelligence that allows organizations that use the product to make informed decisions about how to act and what to prioritize. But Balbix BreachControl also combines chaos engineering and penetration testing by simulating small-scale attacks across a given network. "Every possible breach path is modeled across all attack vectors to calculate breach risk. " Balbix is aiming to exploit the need for improved security at an enterprise level. In an interview with TechCrunch, CEO  Gaurav Bhanga said “At enterprise scale, keeping everything up to snuff is very hard,” CEO Bangha told TechCrunch in an interview. “Most organizations have little visibility into attack surfaces, the right decisions aren’t made and projects aren’t secured.”

The .NET team has announced that have completely moved the .NET Core platform to RyuJIT, the compiler written in-house by Microsoft. The team had been long working on this shift to make the compilation faster for .NET Core applications given that web applications today take time to start up. JIT compiler is a program that converts the instructions written in .NET Core to native machine code so that it can be sent to the processor for processing action. The JIT compilers have become a standard to support the compilation for various platforms. They are an improvement over the traditional compilers which require the programs to re-compile when using on different computer systems. RyuJIT is developed by the .NET Core team as the next generation 64-bit compiler that will compile programs twice as fast. The .NET Core compiled with this JIT compiler is recorded to have 30% improved faster start-up time. Also the apps compiled with the RvyJIT produce great code that run efficiently on the servers. The most important factor that helped the performance was basing the RyuJIT to x64, shifting from x86 codebase. One of the major stability factors this will bring is that .NET programs will perform consistently across various architectures and will provide compatibility for .NET programs across the platforms like ARM, mobile, among others. This will help developers maintain a codebase that compiles on both 64-bit and 32-bit compilers and perform on both types of systems. The .NET team has promised the stability of the platform after this move and are expecting the performance to improve. The team is inviting developers to join the community and has put the documentation for the RyuJIT on the GitHub repository. Applying Single Responsibility principle from SOLID in .NET Core Microsoft Open Sources ML.NET, a cross-platform machine learning framework What is ASP.NET Core?