Tuesday, January 3, 2017

CES 2017 to Feature 5G

This year’s Consumer Electronics Show will feature 5G technologies. But it remains to be seen whether the technology will live up to the level of hype surrounding it.

CES 2017 kicks off this week in Las Vegas, and will attract technology companies from every spectrum. Topics will range from high-definition TVs to cutting-edge wireless technologies. More than 100,000 attendees from all over the world make CES the tech industry’s main trade show.

This year, a wide range of companies are poised to place 5G front and center. Qualcomm’s CEO Steve Mollenkopf will make 5G a centerpiece of his keynote address on Friday, while executives from Verizon, Ericsson, Sprint, 20th Century Fox Film Corp., SK Telecom and others will also discuss the move to the next-generation technology in a number of different CES panels and sessions.

Further, Intel and Ericsson have promised to show off a 5G virtual reality sports demonstration using Intel’s Voke 360 camera and Ericsson’s pre-standard 5G technology network.

Read more about CES 2017 at FierceWireless.

Saturday, December 17, 2016

Facebook is Researching AI for Apps

Facebook is already conducting research in computer vision, language understanding, machine learning, connectivity, and virtual reality, as well as on other frontiers. “Facebook’s long-term road map is focused on building foundational technologies in three areas: connectivity, artificial intelligence and virtual reality,” wrote Mike Schroepfer, CTO of Facebook, in a release. “We believe that major research and engineering breakthroughs in each of these areas will help us make more progress toward opening the world to everyone over the next decade.”

Recently, the company announced a new deep learning platform, Caffe2Go. The platform is designed to bring real-time AI to a user’s fingertips. The company is also using artificial intelligence to provide stabilization in 360-degree videos. It is also working on speech recognition to improve avatars and UI tools, and to help users interact with their VR environment with hands-free voice commands. Finally, it can use computer vision software in its Oculus Connect 3, which will help the VR headset become untethered, according to the company.

Facebook is also turning to other research platforms and infrastructures to help them advance AI. These include AutoML, an automated machine learning platform; and Lumos, a computer vision platform. The company also uses memory networks to give computers the ability to remember multiple facts, which can be used for question and answering dialogues.

Source: “How Facebook aims to advance AI” by Christina Cardoza, SD Times.


Saturday, October 15, 2016

Companies to Watch in 2017

As the technology industry continues to grow, SD Times is again presenting its annual “Companies to Watch” list. Many of the companies on this list are startups that will have to find ways to take root in an already competitive market. Others have been around for years and are looking for ways to gain new customers. Nevertheless, these are the companies expected to do well next year and think you should be watching. View the complete list here: http://sdtimes.com/companies-watch-2017/.

Tuesday, September 20, 2016

Achieving the Value of Threat Intelligence

According to Andrew Komarov, CIO of InfoArmor, Inc. Today’s cyber landscape is more sophisticated than ever before. To navigate it safely and ensure your business and customers are properly protected, you need to understand what type of threats are out there and what risks they pose to you. This is where threat intelligence comes into play. Despite the millions of attacks that happen each day, not all threats are created equal. But it is this overwhelming number of attacks that bad actors count on to cause confusion and paranoia. Putting this into perspective, for its 2016 Data Breach Investigations Report, Verizon used a dataset of 64,199 security incidents and 2,260 data breaches.

Determining which threats are relevant and aligning resources to focus on those attack vectors is where the real value in threat intelligence takes shape. To get the most out of threat intelligence, organizations must be able to customize and shape the information they collect and analyze, and compare that against their unique environment. Relying on generic data feeds will add confusion to your protection efforts and therefore obfuscate what is most important. Many organizations are challenged by sifting through the enormous amount of available data to find what is pertinent to them, which creates a huge burden for already constrained IT security resources.

Data breaches have gained widespread attention as businesses of all sizes become increasingly reliant on digital data, cloud computing, and workforce mobility. With sensitive business data stored on local machines, on enterprise databases and on cloud servers, breaching a company’s data has become as simple as gaining access to restricted networks.

Here is a list of some of the more memorable breaches over the last 10 years:

• TK/TJ Maxx: 94 million records compromised in 2007
• Sony PlayStation Network: 77 million records compromised in 2010
• Sony Online Entertainment: 24.6 million records compromised in 2011
• Evernote: 50 million records compromised in 2013
• Living Social: 50 million records compromised in 2013
• Target: 70 million records compromised in 2013
• eBay: 145 million records compromised in 2014
• Home Depot: 56 million records compromised in 2014
• JP Morgan Chase: 76 million records compromised in 2014
• Anthem: 80 million records compromised in 2015
• OPM: 21 million records compromised in 2015
• IRS: 700,000 records compromised in 2016
• Email Service Providers: 270 million records compromised in 2016
• Banner Healthcare: 3.7 million records compromised in 2016

You can download the whitepaper here: http://resources.computerworld.com/ccd/assets/118122/detail

Tuesday, August 2, 2016

Agile Digital Transformation

The need for speed in application development is driving business today more than ever. Agile is the engine powering app development as user expectations increase. Whether creating mobile apps or high performance websites, businesses are being forced to rethink the way their entire software development process operates. The methodologies and tools that help development and delivery of quality software fast are more important than ever before. A great article by Christina Mulligan from SD Times, “Driving a Digital Transformation” discusses the forces driving agile business application development today.

Friday, July 1, 2016

Magic Quadrant for Business Intelligence and Analytics Platforms

According to a recently published Gartner report, by 2018:
  • Most business users and analysts in organizations will have access to self-service tools to prepare data for analysis as part of the shift to deploying modern BI platforms.
  • Most stand-alone self-service data preparation offerings will either have expanded into end-to-end analytical platforms or been integrated as features of existing analytics platforms.
  • Smart, governed, Hadoop-based, search-based and visual-based data discovery will converge in a single form of next-generation data discovery that will include self-service data preparation and natural-language generation.
During the past several years, the balance of power for business intelligence (BI) and analytics platform buying decisions has gradually shifted from IT to the business as the long-standing BI requirement for centrally provisioned, highly governed and scalable system-of-record reporting has been counterbalanced by the need for analytical agility and business user autonomy.

The evolution and sophistication of the self-service data preparation and data discovery capabilities available in the market has shifted the focus of buyers in the BI and analytics platform market — toward easy-to-use tools that support a full range of analytic workflow capabilities and do not require significant involvement from IT to predefine data models upfront as a prerequisite to analysis.
The shift in the BI and analytics market and the corresponding opportunity that it has created for new and innovative approaches to BI has drawn considerable attention from a diverse range of vendors. The list spans from large technology players — both those new to the space as well as longtime players trying to reinvent themselves to regain relevance — to startups backed by enormous amounts of venture capital from private equity firms. A crowded market with many new entrants, rapid evolution and constant innovation creates a difficult environment for vendors to differentiate their offerings from the competition. However, these market conditions also create an opportunity for buyers to be at the leading edge of new technology innovations in BI and analytics and to invest in new products that are better suited for Mode 2 of a bimodal delivery model than their predecessors.
Gartner's position is that organizations should initiate new BI and analytics projects using a modern platform that supports a Mode 2 delivery model, in order to take advantage of market innovation and to foster collaboration between IT and the business through an agile and iterative approach to solution development. The vendors featured in this year's Magic Quadrant present modern approaches to promoting production-ready content from Mode 2 to Mode 1, offering far greater agility than traditional top-down, IT-led initiatives — and resulting in governed analytic content that is more widely adopted by business users that are active participants in the development process. As the ability to promote user-generated content to enterprise-ready governed content improves, so it is likely that, over time, many organizations will eventually reduce the size of their enterprise system-of-record reporting platforms in favor of those that offer greater agility and deeper analytical insight.
Source: Gartner, 2016

Friday, June 17, 2016

Data Science Methodology: Best Practices for Successful Implementations

Marian University will be offering a degree program in Business Analytics starting Fall 2016. Many of our new courses draw heavily from Data Science. In the domain of Data Science, solving problems and answering questions through data analysis is standard practice. Often, data scientists construct a model to predict outcomes or discover underlying patterns, with the goal of gaining insights. Organizations can then use these insights to take actions that ideally improve future outcomes.

The flow of methodology illustrates the iterative nature of the problem-solving process. As data scientists learn more about the data and the modeling, they frequently return to a previous stage to make adjustments, iterate quickly and provide continuous value to the organization. Models are not created once, deployed and left in place as is; instead, are continually improved and adapted to evolving conditions.

You can download the White Paper here: https://form.jotformeu.com/61126366826357

Sources: Data Science Central, IBM