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As we head into the new year, this week’s roundup takes one final look at the top stories, tips and tutorials of 2016.
1. Top 2016 stories: Digital business transformation leads the way for SAP – Jim O’Donnell (SearchSAP)
2016 was an eventful year for SAP, as it positioned HANA, S/4HANA and HCP as the core technologies for digital business transformation and introduced new products and partnerships.
2. What developers can learn from website outages in 2016 – Valerie Silverthorne (SearchSoftwareQuality)
Outages are going to happen, but the trick is to ensure they don’t happen to the mission-critical parts of the application. Here’s why developers need to change their mindsets.
3. The top Windows Server tutorials and tips of 2016 – Tom Walat (SearchWindowsServer)
A new operating system sparked Windows administrator’s interest in 2016, as did information about using PowerShell to manage Windows Server and ways to deflect ransomware attacks.
4. Looking back at the biggest 2016 tech trends in networking – Eamon McCarthy Earls (SearchNetworking)
In 2016 tech trends in networking, the industry witnessed the growth of analytics, Cisco’s embrace of software and services and new Ethernet standards.
5. Storage 2016 news includes Dell EMC, Nutanix IPO, Broadcom-Brocade (SearchStorage)
In 2016, EMC became part of Dell, Broadcom moved to take over Brocade, Nutanix went public, data protection expanded, and more data went on flash and cloud.
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From CIO to networking, what should the IT industry look forward to in 2017? Find out in this week’s roundup.
1. Why 2017 promises to bring more network automation systems – Antone Gonsalves (SearchNetworking)
Arista, Cisco and Juniper took steps this year toward providing better network automation systems. Next year, companies can expect to see technology from other vendors.
2. Top data center market news stories of 2016 – Tim Culverhouse (SearchDataCenter)
Outages, acquisitions and server innovations piqued the interest of enterprise IT shops in 2016. Here’s a look back at five of the biggest data center news stories from this year.
3. Seven trends in place for SD-WAN technology in 2017 – Lee Doyle (SearchSDN)
SD-WAN technology and deployment made leaps in 2016, with an increased number of use cases and suppliers. Check out seven trends on the SD-WAN horizon for 2017.
4. IT ‘cautiously optimistic’ about mobile thin clients – Ramin Edmond (SearchVirtualDesktop)
IGEL has a new mobile thin client that gives IT control over users’ Citrix, VMware or Microsoft virtual desktops and lets them make better use of existing hardware.
5. 2017 CIO priorities: If you do anything next year, do this – Niel Nickolaisen (SearchCIO)
Analytics, security, data privacy and IT specialization loom large for 2017. Here is my CIO guide for surviving another whirlwind year.
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What are some of the hottest data storage trends for 2017? Find out in this week’s roundup.
1. Hot data storage technology trends for 2017 – Dave Raffo (SearchStorage)
Learn what’s hot and what’s not-quite-so-hot on our list of data storage technology trends for the forthcoming year.
2. AWS re:Invent 2016 attendees react to host of new services – David Carty (SearchAWS)
AWS rolled out nearly two dozen new services at AWS re:Invent 2016, and conference attendees pros were quick to react to all things cloud during the keynote addresses.
3. Docker persistent storage startup beamed up to the mother ship – Beth Pariseau (SearchITOperations)
Docker persistent storage and stateful applications are the next front in the container wars, and Docker Inc. has just fired a major salvo with its acquisition of Infinit.
4. IBM’s Watson for Cybersecurity puts a new face on machine learning – Michael Heller (SearchSecurity)
The IBM Watson for Cybersecurity beta program aims to augment human intelligence, but experts question if IBM can distinguish it from other machine learning products.
5. Zero downtime goal of new industry group – Chuck Moozakis (SearchNetworking)
Networking analysts discuss if a new zero-downtime initiative will be viable and the best way to unlock the value of the hybrid cloud.
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How will the 2017 technology trends affect your organization? Find out in this week’s roundup.
1. AI, IoT, intelligent systems take center stage in 2017 technology trends – Lauren Horwitz (SearchCRM)
Experts held forth on the promise and pitfalls of technologies that are changing today’s environment at the Gilbane conference.
2. Multicloud computing bliss not yet a reality for all IT shops – Kristin Knapp (SearchCloudComputing)
Experts predict that multicloud computing will be a top enterprise trend in 2017, but some cloud users question whether the touted benefits are worth the jump over significant IT management hurdles.
3. SAP unveils new IoT services to help derive business value from IoT – Jim O’Donnell (SearchSAP)
SAP has released three new IoT services to help manage IoT data and get value from it: SAP Connected Goods, SAP Dynamic Edge Processing and SAP IoT Application Enablement.
4. Last ditch Senate efforts fail to stall Rule 41 changes – Peter Loshin (SearchSecurity)
After a final push to delay changes to Rule 41 failed in the Senate, the U.S. government now has much wider authority to legally search computers whose location is unknown.
5. Hedge fund returns Nexsan storage to private ownership – Garry Kranz (SearchStorage)
Nexsan storage technology is the only moneymaking asset for publicly traded Imation. The vendor is going private with help from a Louisiana hedge fund.
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By James Kobielus (@jameskobielus)
Something tells me that 2017 will be a year of intense backlash in the world at large. Judging by the results and immediate aftermath of the US presidential election, the new year does not bode well for positive, uplifting visions in high-tech or any other sector of our society.
In the IT world, we’re already deep into a backlash cycle triggered by the disturbing incidence and likely impact of online “fake news” on the just-completed political campaign. Just as disturbing is the popular backlash against data science that was triggered by the epic failure of prominent data journalists to predict Donald Trump’s defeat of Hillary Clinton. Initial indications are that the incoming president-elect will single out left-leaning Silicon Valley types for special scorn.
If we consider popular culture as a whole, what are the most likely backlashes relevant to artificial intelligent, cognitive computing, machine learning, predictive analytics, and data science that we may see in 2017? Judging by recent experience, it’s a safe bet that we’ll see the following sorts of controversies throughout the year:
- Pressure on online news sources, distributors, and aggregators to vouch for the authenticity of their content;
- Skepticism toward the efficacy of predictive analytics in general, especially when used to predict the aggregate actions, decisions, and sentiments of society at large;
- Emphasis on the actual or potential abuses of algorithmic big-data analytics in diverse application contexts, with special attention to privacy encroachment, government surveillance, digital manipulation, workplace monitoring, deception, and target-marketing overreach;
- Anxiety surrounding the lack of full algorithmic transparency and difficulties of ensuring the accountability of many real-world AI and cognitive apps;
- Worries about whether deep learning and machine learning technologies are enabling the warping of the historical record and lived experience beyond recognition;
- Apprehensions concerning the accuracy, or lack thereof, of voice, face, gesture, and other deep-learning recognition algorithms for sensitive real-world applications;
- Ridicule directed at the growing range of innovative cognitive applications and their distorting impacts on the behaviors and sentiments of culture at large;
- Handwringing over the supposed impact of AI and cognitive applications in throwing people out of work or marginalizing them into low-paying dead-end careers;
- Concern that bubbles of premature, speculative over-investment in unproven AI/cognitive technologies could precipitate future economic contractions;
- Discouragement at the supposed uselessness of cognitively boosted social-media analytics in gauging what’s truly on people’s minds; and
- Escalation of unhinged, speculative, dystopia scenarios such as autonomous AI-fueled robot “overlords” to the popular hysterias.
Clearly, none of these are new worries. It’s obviously too early to say which of these downsides will be most salient in the popular mind in 2017. Whether positive perceptions of AI and cognitive computing outweigh the negatives depends on how overall economic, political, and social trends play out in the year to come.
None will prove to be a showstopper to the spread and evolution of AI and cognitive computing in the world at large. But these themes, to varying degrees, will impact on people’s enthusiasm in embracing these innovations into the core of their lives.
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What are you expecting from AWS re:Invent 2016? Find out what to look out for in this week’s roundup.
1. AWS re:Invent conference evolves, addresses growing pains – SearchAWS staff (SearchAWS)
AWS re:Invent 2016 promises a larger venue, more sessions and a focus on technologies like microservices and Lambda. Our experts look at how the conference has changed since 2012.
2. OpenStack enterprise adoption still awaits full embrace – Robert Gates (SearchDataCenter)
OpenStack in the enterprise is more likely to see continued adoption via vendor distributions and managed services, not the raw code of big name customers such as PayPal.
3. DHS hiring puts into question the cybersecurity skills shortage – Michael Heller (SearchSecurity)
A successful hiring event by the Department of Homeland Security calls into question the existence of the cybersecurity skills shortage but experts wonder if the event was an outlier.
4. Cisco patent infringement avoided, new Arista OSes OK to import – Antone Gonsalves (SearchNetworking)
Federal officials have cleared for importation to the U.S. Arista’s newer switches. U.S. trade officials had found older Arista products in violation of three Cisco patents.
5. Creative projects leave people guessing about future impact of AI – Ed Burns (SearchBusinessAnalytics)
A push is underway to write creative AI algorithms that can engage in music, film and design projects. So far, they have delivered mixed results.
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What went so wrong for election forecasters using predictive modeling? Find out in this week’s roundup.
1. How predictive modeling and forecasting failed to pick election winner – Ed Burns (SearchBusinessAnalytics)
Nearly all predictive modeling algorithms were way off in picking the winner of the presidential election. What went wrong can strike any predictive analytics project if data scientists and other analysts aren’t careful.
2. Cisco earnings show service providers are not buying – Antone Gonsalves (SearchNetworking)
The latest Cisco earnings show a drop in overall revenue, as service providers spend less. Cisco blames lower sales on macroeconomics.
3. Congress floats last-chance bill to delay Rule 41 changes – Peter Loshin (SearchSecurity)
Just two weeks before the deadline, U.S. lawmakers seek to postpone until next summer the acceptance of controversial updates to Rule 41, allowing legal access to unspecified systems.
4. Microsoft preview SQL Server on Linux, opens features across editions – Jack Vaughan (SearchSQLServer)
Microsoft looks to broaden the horizons of SQL Server, as it moves some Enterprise features to Standard Edition and introduces SQL Server on Linux.
5. OpenStack Newton storage features include data encryption – Carol Sliwa (SearchCloudStorage)
Storage updates in OpenStack’s Newton release include at-rest data encryption in Swift, a message API for async tasks in Cinder and driver-assisted migration in Manila.
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How will the Trump presidency affect the future of health IT? Find out in this week’s roundup.
1. Experts say how Trump presidency might affect the future of health IT – Kristen Lee (SearchHealthIT)
What will the future of health IT be under the new Trump administration? Health IT experts offer up predictions and reassurances for the future.
2. Public cloud and big banks finally on the same page – Trevor Jones (SearchCloudComputing)
Public cloud and big banks weren’t always a good fit, but financial juggernauts have gone beyond their four walls to test the waters of hyperscale cloud computing.
3. At DOES 2016, important lessons from the DevOps journey – Valerie Silverthorne (SearchSoftwareQuality)
DevOps is a long method of small changes in culture and process. At DOES 2016, experts who are well along their way offer their best tips. Some may surprise you.
4. Post-election Russian hacker cyberattacks evade malware detection – Michael Heller (SearchSecurity)
A rash of spear phishing attacks by Russian hacker groups were seen following the presidential election this week, but antivirus and malware detection has been failing enterprises.
5. President-elect silent on federal cybersecurity policies – Eamon McCarthy Earls (SearchNetworking)
This week, bloggers look ahead to the new administration’s cybersecurity policies, how to close gaps in app delivery management and the best way to optimize data centers.
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By James Kobielus (@jameskobielus)
Deep learning delivers extraordinary cognitive powers in the never-ending battle to distill sense from data at ever larger scales. But high performance doesn’t come cheap.
Deep learning relies on the application of multilevel neural-network algorithms to high-dimensional data objects. As such, it requires that fast-matrix manipulations in highly parallel architectures in order to identify complex, elusive patterns—such as objects, faces, voices, threats, etc.–amid big data’s “3 V” noise. As evidence for the technology’s increasingly superhuman cognitive abilities, check out research projects such as this that use it to put the Turing test to shame.
Extremely high-dimensional data is the bane of deep learning from a performance standpoint. That’s because crunching through high-dimensionality data is an exceptionally resource-intensive task, often consuming every last bit of available processors, memory, disk, and I/O thrown at it. Examples of the sorts of high-dimensional objects against which deep learning algorithms are usually applied include streaming media, photographic images, aggregated environmental feeds, rich behavioral data, and geospatial intelligence.
In data scientists’ attempts to algorithmically replicate the unfathomable intricacies of the mind, they must necessarily leverage the fastest chips, the largest clusters, and the most capacious interconnect bandwidth available to drive increasingly sophisticated deep learning algorithms. All of that assumes, of course, that these high-performance cluster-computing services are within their budgets.
What’s the optimal hardware substrate for deep learning? It would need to meet the following criteria. For high-dimensional deep learning to become more practical and pervasive, the underlying pattern-crunching hardware needs to become faster, cheaper, more scalable, and more versatile. Also, the hardware needs to become capable of processing data sets that will continue to grow in dimensionality as new sources are added, merged with other data, and analyzed by deep learning algorithms of greater sophistication. And the hardware—ranging from the chipsets and servers to the massively parallel clusters and distributed clouds—will need to keep crunching through higher-dimensionality data sets that also scale inexorably in volume, velocity, and variety.
Increasingly, many industry observers are touting graphical processing units (GPUs) are the ideal chipsets for deep learning. As discussed in this 2015 Wired article and this recent Data Science Central Post, GPUs–which were originally developed for video games and have high-performance math-processing features–may be far less hardware-intensive and less costly than general-purpose CPUs.
The Wired article mentions a Stanford researcher who used GPUs to “string together a three-computer system that could do the work of Google’s 1,000-computer cloud.” The article is also quick to point out that GPUs are pulling their deep-learning weight in production commercial and government applications, including as a complement to supercomputing resources at national laboratories. And it notes that some of the more intensive deep-learning algorithms are using GPUs to crunch through many billions of dimensions. The Data Science Central article, from a GPU hardware vendor, says that GPU technology is getting “smarter at a pace way faster than Moore’s Law,” though it offers none of the price-performance trend data needed to bolster that claim.
All of this raises the question of whether general-purpose CPUs have a future in high-performance deep learning. Some argue that general-purpose CPUs might continue to add value, either stand-alone or as a complement to GPUs, as long as they continue to improve in performance and to the extent that they’ve been optimized for high-performance, massively parallel clusters built on low-cost commodity hardware. Users such as Facebook are relying on GPU-based infrastructure to train their deep learning models, while also exploring new multi-core CPU chips that may approach the performance of GPUs in the near future.
A chipset-agnostic hybrid deep-learning hardware environment such as this may be the best approach, considering the vast range of specialized deep-learning applications and the likelihood that various hardware substrates will probably be optimized for diverse types of algorithmic analysis. In such a scenario, special-purpose “neural” chips, such as IBM SyNAPSE, may be incorporated for tasks for which neither GPUs not CPUs are optimal. FPGAs are also a credible option for deep learning.
Let’s leave quantum computing fabrics out of the discussion for now until they emerge from the laboratory suited for robust commercial deep-learning implementations. Deep learning needs serious acceleration in the here and now and shouldn’t pin its outsize performance requirements on unproven architectures that still have one foot in the lab.
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Did Microsoft downplay the Windows zero-day vulnerability? Find out why the company is coming under fire for its response in this week’s roundup.
1. Experts question Microsoft’s Windows zero-day response – Michael Heller (SearchSecurity)
A Windows zero-day disclosed by Google caught Microsoft between patch cycles, and experts questioned whether Microsoft downplayed the severity of the vulnerability.
2. Oracle IaaS has foothold with legacy shops, plays catch-up to AWS – Kristin Knapp (SearchCloudComputing)
Oracle hopes to rival public cloud giant AWS in the IaaS market, and while it could win over legacy Oracle shops, it needs to attract developers and non-Oracle users, too.
3. Cisco pledges a quicker rollout of network automation tools – Antone Gonsalves (SearchNetworking)
Cisco tells Partner Summit attendees it will move faster to deliver the network automation tools the vendor has been slow to provide.
4. Election Protection helps voters with call center technology – Jesse Scardina (SearchCRM)
Election Protection handles upward of 100,000 calls per day as Election Day nears, routing calls from high-call-volume states using Genesys.
5. Microsoft takes on Slack with new team chat app – Katherine Finnell (SearchUnifiedCommunications)
Microsoft has unveiled its new team chat app, Microsoft Teams. The app is built into Office 365 and looks to compete with Slack, Cisco Spark and Unify Circuit.