We live in the era of mass surveillance. Most of what we do is carefully tracked: the websites we visit, the way we spend our money and, in some places, the way we drive. Certain cities and states across the nation already collect information about driver behavior using sensors and cameras embedded in their infrastructure; that data is later shared with city planners or the Department of Transportation to help them understand what kinds of changes need to be made--a new street light here, a stop sign there, a new road over there. But who else is that data useful for? According to Nino Tarantino, CEO of the data analytics agency Octo Telematics, it could be instrumental to insurance companies as they determine rates and process accident claims--i.e., figure out how much money they'll spend on their customers.
One of the neat things about cloud, as a user, is that you don't have to worry about how many servers you need. You know -- the guy or gal who must buy the servers that make up the cloud? AWS CEO Andy Jassy told attendees at the Pacific Science Center's 14th Annual Foundations of Science Breakfast that Amazon has been using machine learning to anticipate demand for its servers. "One of the least understood aspects of AWS is that it's a giant logistics challenge, it's a really hard business to operate," he said. This is true of any cloud operation.
The analytics engine offers a turn-key, closed-loop, autonomous system that continuously monitors users, devices, applications, networks to detect anomalous or malicious behavior and offers precise actions to mitigate and prevent them, delivering the most secure workspace in the industry. Enterprises are rapidly adopting a variety of new paradigms -- mobile devices, bring your own device (BYOD), SaaS applications, and public clouds -- that boost employee productivity while offering more choice & flexibility. This, however, has adverse consequences on Security. The most notable one is that the traditional well-defined security perimeter around the data center is no longer valid and this renders traditional solutions aimed at defending that perimeter insufficient. Also, the attacks and the attack vectors are becoming highly sophisticated and the traditional threat detection techniques based on signatures and known patterns have limited effect.
Opinions expressed by Forbes Contributors are their own. The author is a Forbes contributor. The opinions expressed are those of the writer. The way that people work is evolving rapidly, disrupting industries, and reinventing organizations. Companies around the world are transforming the ways in which their workforces operate by leaning into a digital workspace model--taking a fresh approach to security while driving more meaningful workflows, simpler access, and increased productivity.
Here's an analogy using a concept that we can all relate to: a supermarket. Picture the scene: Shopping list in one hand, shopping basket in the other, you're ready to tackle your weekly shopping in your local supermarket. Your items range from fruit and vegetables to washing detergent, perhaps with some free-range eggs thrown in for good measure. Quite the eclectic mix, but you know that you'll be able to find all you need under one roof. The fact that this is possible is in itself quite remarkable.
Did You Want a Side of SLBS (Serverless BS) with Your Software or Hardware FUD? A few years ago a popular industry buzzword term theme included server less and hardware less. It turns out, serverless BS (SLBS) and hardware less are still trendy, and while some might view the cloud or software-defined data center (SDDC) virtualization, or IoT folks as the culprits, it is more widespread with plenty of bandwagon riders. To me what's ironic is that many purveyors of of SLBS also like to talk about hardware. Simple, on the one hand, there is no such thing as software that does not need hardware somewhere in the stack.
The Microsoft Data Science Virtual machine (VM) is a custom Azure VM based on Windows Server 2012 with several popular tools for data science modeling/development like: * SQL Server 2016 Developer Edition * Microsoft R server Developer Edition * Anaconda Python with Juypter notebooks * Visual Studio 2015 Community edition with language and Azure tools and * ML and Deep Learning tools like xgboost, CNTK, mxnet More information on how to use the VM can be found on the [documentation page](http://aka.ms/dsvmdoc). If are wondering about things you can do with the DSVM read this [How-To Guide to the Data Science Virtual Machine](http://aka.ms/dsvmtenthings). Here is a list of key software on the Data Science Virtual Machine and comparison between the Windows and Linux editions of the product.
Opinions expressed by Forbes Contributors are their own. The author is a Forbes contributor. The opinions expressed are those of the writer. From Jack Kerouac's "On the Road" to Bruce Springsteen's blue-collar Romeo racing in the street, cars have long been central to America's mythology and self-image. Even the type of car one drives--hybrid or convertible, minivan or sportscar--can often serve as a shorthand description of its owner.
The age of Knight Rider is upon us. As the Internet of Things (IoT) revs up the automotive industry, connected cars are becoming "devices on wheels" with in-vehicle systems connected to the Internet. At the same time, car manufacturers and software companies are redoubling their efforts to bring automated cars into widespread use. For example, Volvo announced a partnership with Microsoft to develop driverless cars for the consumer market. IoT not only will bring in new vehicle technologies, but also will completely revolutionize the car industry.
Opinions expressed by Forbes Contributors are their own. The author is a Forbes contributor. The opinions expressed are those of the writer. As I look ahead at where market and technology is headed for 2017 and beyond, I am excited about several trends. The key theme across them is one of interconnection.