Daily Tech Digest - January 23, 2017

The rise of the machines: lessons from history on how to adapt

Although at different stages of development and adoption, as these technologies bed in, becoming more widespread and convergent, we will see a radical shift in the way that individuals, companies and societies produce, distribute, consume and re-use goods and services. These developments are prompting widespread anxiety about what role humans will have in the new world. As the pace of change accelerates, so the alarm levels ratchet up. A University of Oxford study estimated that close to half of US jobs could be lost to automation over the next two decades. In the opposite camp, economists like James Bessen argue that, on the contrary, automation and jobs often go hand in hand. It’s impossible at this point to predict what the overall impact on employment will be. Disruption will happen; of that we can be certain. But before we swallow all of the bad news, we should take a look at history.


How to create work-life balance in tech: 7 tips from the C-suite

The overworked tech CEO is now a cultural trope for a reason: Only 65% of tech workers said they were satisfied with their work-life balance, according to a recent survey from Comparably. This is a problem, as constant work and the resulting stress can lead to health problems such as impaired sleep, depression, diabetes, and heart disease—which not only hurt the employee, but also the company, in terms of turnover and rising health insurance costs. Despite the pressure to be always on, finding the proper work/life balance is essential, experts say. "It's possible to be a tech leader and to make time for taking care of yourself," said William C. Fisher, president of Quicksilver Software, Inc. "But, it takes a willingness to make that a top priority and schedule the time."


Dublin, New York officials cite smart-tech challenges and successes

"We're on the verge of seeing smart city tech grow exponentially," he added, speaking during a visit last week to the Smart Cities Innovation Accelerator, sponsored by Harvard University in Cambridge, Mass. Cudden joined about 50 CIOs and CTOs from other cities, mostly from the U.S., to share ideas on taking advantage of the latest smart city technology. "One thing I learned from the U.S. cities is to take an entrepreneurial mindset and to use startups to address problems in cities, with something like an entrepreneurship-in-residence program," Cudden said. "A lot of cities are dealing with the same problems, and one takeaway is there's very little difference in the challenges in European cities or U.S. cities."


Introducing the Open Process Automation Forum. Finally.

Here’s the big news: 2017 marks the kick off the Open Process Automation Forum, a working group within The Open Group, a vendor- and technology-neutral industry consortium. The new Open Process Automation Forum is focused on developing a standards-based, secure and interoperable process control architecture that can be leveraged across multiple industries including oil and gas, petrochemical, mining and metals, pulp and paper, food and beverage, pharmaceutical and utilities. The concept itself is not new, as it is already playing out in The Open Group’s IT4IT Forum that is building a vendor-neutral reference architecture for managing the business of IT. Similarly, The Open Group’s Future Airborne Capability Environment (FACE) is defining approaches for using open standards with avionics systems.


New game, new rules: 3 steps to secure your bank in the digital age

It’s no secret that the sheer volume of data being collected and stored is higher now than ever before. According to EMC, by the year 2020, about 1.7 MB of new information will be created every second for every human on the planet. The data boom isn’t any different for banks—as customers access their banking information across multiple channels and touch points, they’re able to capture new and unique information about customer habits, preferences and more. That information makes banks a goldmine for hackers. The number of endpoints and systems exposed to the outside world is increasing, which means data no longer remains locked inside a data center. Instead, it proliferates outside of the four walls of business, making it vulnerable to hackers and security threats.


Dive Deep Into Deep Learning

The most remarkable thing about deep learning is that we don’t program them to perform any of the acts described above. Rather, we feed the deep learning algorithm with tons of data such as images or speeches to train it, and the algorithm figures out for itself how to recognize the desired targets. The ability of Deep Learning methods to learn complex nonlinear relations by churning high amount of data, creating features by themselves makes it stand out from the other traditional Machine Learning techniques. To know how a standard Deep Learning algorithm works, we have to follow its predecessors, neural networks. Well, some practitioners also refer Deep learning as Deep Neural Networks, which is also a choice. In short, a neural network is a family of three layers — an input layer, hidden layer, and an output layer as discussed below.


Idempotent configuration management sets things right -- no matter what

As systems architects are not perfect, many systems end up woefully underutilized -- wasting hardware, power, licences and maintenance costs -- while others lack necessary resources -- leading to performance issues. Even with the elastic resources of cloud computing, systems administrators must still ensure that the operational environment is configured correctly to optimally host the workloads. This is where idempotency comes in. Based on a mathematical concept introduced by Benjamin Peirce in 1870, the term now applies in multiple areas of computing. This does lead to confusion, as idempotency within a database is different from that in a Java program, which is different again from idempotent configuration management, which concerns modern DevOps and IT administrators.


6 EMM predictions for 2017

MDM is quickly becoming old news. Instead, Gartner coined the term EMM, pointing to a shift in mobile management to complete suites that include multiple tools for device, app and software management. And as the line is starting to blur in what defines a mobile device, Silva says MDM solutions will have "an tough battle against more full-featured EMM suites." If anything, more businesses will embrace EMM platforms and continue to use familiar MDM tools that are included in the suite. EMM also helps businesses consolidate certain practices -- like compliance regulations for example -- and deploy updates and patches across a range of complex devices all at once. "Solutions that are seamlessly integrated across all existing enterprise systems and platforms will begin to increase in popularity due to their comprehensive visibility and ease of use," says Mitch Berry, vice president of EMM at MOBI.


Raspberry Pi rival: Asus launches Tinker Board

Much as is possible with the Pi 3, the Tinker Board can be used as a PC replacement or a media center, with Asus' board supporting a custom version of the Linux-based Debian operating system and the open-source media center software Kodi. However, it won't run Raspbian, the Raspberry Pi's official, Debian-based OS. Asus is also touting the machine as a board for makers, with the Tinker Board also packing a 40-pin header with 28 general-purpose input output (GPIO) pins. These GPIO pins allow the board to control a range of hardware, and in the Pi have allowed the board to be used in modding projects ranging from robots to book scanners. Another consideration for makers is battery drain, and the Tinker Board's max power consumption is five watts.


An Introduction to Differential Privacy

Differentially private algorithms are a promising technical solution that could ease this tension, allowing analysts to perform benign aggregate analysis while guaranteeing meaningful protection of each individual’s privacy. This developing field of technology is worth considering in any system that seeks to analyze sensitive data. Although the differential privacy guarantee was conceived of only ten years ago, it has been successful in academia and industry. Researchers are rapidly inventing and improving differentially private algorithms, some of which have been adopted in both Apple’s iOS and Google’s Chrome. This article discusses the historical factors leading to differential privacy in its current form, along with a definition of differential privacy and example differentially private algorithms.



Quote for the day:


"When your values are clear to you, making decisions becomes easier." -- Roy E. Disney