Global DDI (DNS, DHCP and IP address management) solution market has been segmented on the basis of component, application, deployment, size of organization, end use industry and geography. On the basis of component type, global DDI market has been segmented into services and solution. Additionally, global DDI market has been separated on the basis of application which includes virtualization and cloud, network security, data center transformation and network automation among others. Moreover, the evolution to IPv6 from the IPv4 Internet protocol (IP) is anticipated to contribute expressively to industrial demand. Across the globe, the cumulative focus on consciousness of impending implementations of IPv6, need to streamline IP address management and security risks among others important factors are expected to assist the growth of DDI service provider. Security plays a significant role in this development and is likely to drive the growth of DDI market during the forecast period.
There’s also the question of how to ensure that the robots’ interactions with people go smoothly. Hitch says that at first, some store employees are more reluctant than others to accept that the robots don’t pose any physical danger. Eventually, though, human workers warm up to their mechanical coworkers and often give the robots names, like Megan or Eric Jr. Robots are also learning to solve issues that are less simple, like the last-mile problem. Rui Li, the CEO and cofounder of Robby Technologies, showed off a video of his company’s new Robby 2.0, a cooler-size robot on wheels that can deliver up to “70 liters of booze” (or any other order of that size). The robots say “Excuse me” when blocked by pedestrians, and “Thank you” if the people move. Li says besides making the robots polite, the researchers also trained them to stick to one side of the sidewalk, which helped let humans know how to interact with them. “This simple change in the behavior of the robot has solved a big problem,” Li said.
An effective storage platform should combine the performance, control and management of internal data centres with the agility and scale of public cloud. This provides organisations with the ability to build and run agile environments for cloud-native and mission critical applications in their own data centres. The attraction is that it helps to solve the fundamental mismatch between infrastructure and virtual applications and aids in an organisation’s preparation to adopt DevOps practices, which cannot be fully supported by traditional infrastructure. Businesses are increasingly attracted to the DevOps model as a means to accelerate development efforts and deliver new applications and services. DevOps essentially seeks to merge two personas into one and achieve the best communication and collaboration between developers who create platforms and runtimes, and operations teams, who lead configuration management. The requirements for DevOps is to have the ability to build applications with the latest production data, distribute updates quickly with more application testing in less time, accelerate the release cycle, speed up integration testing and reduce restoration time.
With the emergence of IoT, the inheritance of technology into our lives has gone up by a notch. In the simplest terms, IoT is the concept of connecting various things, or smart devices, as they are called, to the internet. The platform of The Internet of Things brings diverse information together and provides the common language of devices and apps to communicate with each other. IoT, since its arrival, has changed the paradigm of technology entirely. Even our daily routine activities have transformed since the arrival of IoT, from how we drive, to how we make purchases, to how we make use of energy in our homes. The arrival of IoT took place at a very convenient time, when users were looking for something to make their lives more convenient and easy, which is why IoT is such a big hit! You can check out the infographic for more details about IoT.
There must be a complete management UI that encompasses the ability to seamless connect to a wide number of PLCs, CNCs and robotic systems via edge gateways, with the ability to manage devices and deploy applications and analytics at the edge. The only way to successfully implement an IoT project that scales and allows the enterprise to incorporate new types of information is to use a flexible platform that can adapt to the future. Without such platform adaptability, application development means a lot of headaches. Companies risk being stuck with a static system, which forces them to constantly invest resources—significantly, time—in developing their next generation IoT products and services. There is a strategic dimension to developing adaptive IoT products. Applying good IoT techniques and design principles will probably generate revenue value-added services that will likely generate revenue, but for enterprises to have the biggest impact they will have to leverage the network effects of a big-data ecosystem, either pre-existing or self-created. The risks of not doing so, and the rewards of achieving this, should be apparent.
“The FTC takes very seriously recent press reports raising substantial concerns about the privacy practices of Facebook,” said Tom Pahl, acting director of the FTC’s bureau of consumer protection. The investigation is in response to “substantial concerns about the privacy practices of Facebook” and will look at whether Facebook engaged in “unfair acts that cause substantial injury to consumers” by sharing data with Cambridge Analytica for use in political campaigns without the knowledge of the data owners. A similar investigation has been launched in the UK by the Information Commissioner’s Office (ICO) which is charged with protecting the privacy of UK citizens. Facebook has responded to news of the FTC investigation by saying the social networking firm remains “strongly committed” to protecting people’s information and “appreciates the opportunity” to answer the FTC’s questions.
“Serverless” refers to services like AWS Lambda that offer developers a way to focus on writing application logic rather than server infrastructure. Yes, this means a developer must trust that AWS, Microsoft, or Google get that infrastructure right, but the upside to embracing these cloud back ends is huge. As such, Stackery told Governor, “Serverless is being driven by mainstream enterprises. We see them leapfrogging containers so they can take something off the shelf and move quickly.” In other words, they’d love to get into containers, but they may lack the expertise. So they’re borrowing that expertise from Amazon or another serverless vendor and skipping the container revolution. For those enterprises less willing to trust their application infrastructure to a cloud vendor, some have hoped to bring serverless “in house,” running it on-premises in a corporate datacenter, just as some hope to ape the benefits of public cloud computing in so-called private clouds in their datacenters. It’s a nice theory. Unfortunately, it doesn’t work. Not for most companies, anyway.
Despite investing in machine learning, the new survey indicates that most CIOs do not have the skilled talent, data quality and budgets to fully leverage the technology. For most CIOs, many decisions still require human input. Only 8% of respondents say their use of machine learning is substantially or highly developed, as opposed to 35% for the Internet of things or 65% for analytics. According to a McKinsey study, the three main challenges companies have related to machine learning are designing an organisational structure to support data and analytics, having an effective technology infrastructure, and ensuring senior management are involved. The study then goes on to state that organisations that can harness these capabilities effectively will be able to create significant value and differentiate themselves, while those that fail will find themselves increasingly at a disadvantage. Achieving great value from machine learning doesn’t come from just investing in new technologies, it is also necessary to make significant organisational and process changes, including approaches to talent, IT management and risk management.
Reasons for the change, Unity Technologies said, include taking advantage of new features in the C# programming language starting with version 6.0, and the ability to leverage an upgrade to the .NET 4.6 scripting runtime, which is still in the experimental stage. The company said MonoDevelop-Unity 5.9.6, the latest version of the open source IDE to ship with Unity, doesn't support many of the new C# features and can't debug C# scripts in the new experimental scripting runtime. "It [is] very important for us at Unity that we also provide a great C# IDE experience to accompany the new C# features," Unity Technologies said. Microsoft yesterday noted that Unity development was one of the first scenarios supported out-of-the-box when Visual Studio for Mac was released last year and applauded the move to make it the default IDE for Unity going forward. "This means that everyone will be able to utilize the benefits of the .NET 4.6 scripting runtime upgrade in Unity (currently an experimental feature) , including all the goodies of C# 6.0 and access to the Microsoft Azure SDK to add powerful cloud services to your games," Microsoft said.
Goodfellow is best known as the creator of generative adversarial networks (GANs), a type of artificial intelligence that makes use of two networks trained on the same data. One of the networks, called the generator, creates synthetic data, usually images, while the other network, called the discriminator, uses the same data set to determine whether the input is real. Goodfellow went through nearly a dozen examples of how different researchers have used GANs in their work, but he focused on his current main research interest, defending machine-learning systems from being fooled in the first place. He says for earlier technologies, like operating systems, defense of the technology was added afterwards, a mistake he doesn’t want made with machine learning. “I want it to be as secure as possible before we rely on it too much,” he says. GANs are very good at creating realistic adversarial examples, which end up being a very good way to train AI systems to develop a robust defense. If systems are trained on adversarial examples that they have to spot, they get better at recognizing adversarial attacks. The better those adversarial examples, the stronger the defense.
Quote for the day:
"People who think that they are being "exploited" should ask themselves whether they would be missed if they left, or whether people would say: 'Good riddance'?" -- Thomas Sowell