The on-demand model has been touted by many startups as an option that lets workers decide to work whenever they want to, and in multiple jobs, but there has also been criticism that the companies use the model to avoid paying the workers a variety of benefits that they would have paid their employees. The use of independent service providers by companies apparently in their core operations has led to worker lawsuits against many app companies including Uber, Lyft and Instacart, demanding that workers should be classified as employees with attendant benefits and not as independent contractors.
A multitude of technological developments that have been going on for years have a chance of converging to drastically affect the way we think of driving, and transportation in general. If this convergence happens the results could be nothing short of incredible, and the impact can go beyond transportation alone. Here are five technologies that may very well represent the future of driving, and in doing so, drive us towards a very interesting new world of possibilities
The value of Cisco architecture is its emphasis on embedding security spanning the extended network – including routers, switches and the data center – closing gaps across the attack continuum and significantly reducing time to detection and remediation. Specifically, Cisco is adding Cisco® Cloud Access Security (CAS), which provides visibility and data security for cloud-based applications; Identity Services Engine (ISE) enhancements, extending visibility and control for network and endpoints with new location access controls; and Threat Awareness Service, which provides organizations with threat visibility into their networks.
Universities and corporations are responding to the data analytic skills in the marketplace by launching new educational programs aimed at boosting the number of qualified data scientists. One observer noted that, over the course of the past five to six years, the number of master’s-level data science and data analytics programs at American universities has gone from the single digits to about 90. “You can’t walk onto a university campus anymore and swing a cat without somebody telling you that they have an analytics program,” says Jennifer Lewis Priestley, a professor of applied statistics and data science at Kennesaw State University, and the creator of KSU’s data science Ph.D program.
The fun is often at the edges—the differentiating features that disproportionately add value by strongly aligning to a business's or a sector's specific requirements. Some of those innovations—not all—find their way back into the core product, and that accelerates the innovation cycle. Each "player" benefits because promoting and collaborating on a common platform helps grow the market opportunity for everyone participating. Partners can focus (and differentiate) on the implementation services, support, and innovations the customers really value whilst alleviating the challenges of sustaining investment in bespoke or the alternative of proprietary solutions on their own.
According to the newly released Global Technology Adoption Index from Dell, investments in those areas are definitely leading to improvements in efficiencies and organizational growth. But cost remains a major barrier to implementation for many organizations, the study revealed. Still, there is some good news on the big data front, as more organizations report they are less unsure on what to do with it all. “While the GTAI findings remained consistent year-over-year in that 44% of organizations globally still do not know how to approach big data, we were pleased to see that gap closing in one region in particular, North America,” a Dell spokesperson told Information Management.
Computer science now applies to almost every field of work, yet universities continue to treat CS firstly as a theoretical natural science rather than an engineering discipline. Even the most cutting-edge computer science subjects such as quantum computing and deep learning are being adopted by industry today. If a computer science topic cannot be given a practical, industrial framing in the classroom, it begs the question whether that topic is worth exploring at all. One consequence of this academic focus on theory is that many computer science students leave college incapable of programming. Daniel Gelernter, CEO of Dittach writes about why he doesn’t hire computer science majors for his tech company:
Information architecture is an often misunderstood, and overlooked architectural domain that logically sits between the business and application domains. It provides a crucial link between business process and, the applications and data used by an organisation. By identifying the information assets necessary to conduct business, and how these are structured, information architecture scopes the requirements for current and future data technologies while abstracting away the complexities of application design and data management. With the current popularity of Big Data, and increasing data storage and cost, the need to understand thevalue of information derived from this data is as important as ever.
Modern enterprise apps are about everything: complex backend, rich frontend, mobile clients, traditional and NoSQL databases, Big Data, Streaming and so on. Throw those into clouds, and voila — you’ve got yourself a project worth years of development. In this article, I will discuss a bio-informatic software as a service (SaaS) product called Chorus, which was built as a public data warehousing and analytical platform for mass spectrometry data. Other features of the product include real-time visualization of raw mass-spec data. ... Frontend application should serve the images to the end-users providing the endpoint for browser and desktop apps hosted on Amazon EC2. Backend services consisting of business logic, rules, data access and security layer are also hosted on Amazon EC2.
Through the use of a common interface and APIs, Cinder abstracts the process of creating and attaching volumes to Nova compute instances. This means storage can be provided to OpenStack environments through a variety of methods. By default, Cinder volumes are created on a standard Linux server that runs Logical Volume Manager (LVM). This allows physical disks to be combined to implement redundant array of independent disks (RAID) data protection and to carve out logical volumes from a physical pool of space, called a volume group. Cinder volumes are created from a volume group called cinder-volumes, with the OpenStack administrator assigned the task of deciding exactly how this LVM group is mapped onto physical disk.
Quote for the day:
"If you really want the key to success, start by doing the opposite of what everyone else is doing." -- Brad Szollose