Enterprise IT Watch Blog

February 19, 2018  9:16 AM

TechTarget’s weekly roundup (2/12 – 2/19)

Michael Tidmarsh Michael Tidmarsh Profile: Michael Tidmarsh
Analytics, cybersecurity, Equifax, VMware, Zerto

Security image via FreeImages

What do you think about these new reports regarding the Equifax breach? Are you worried about your data? Check out the new details surrounding the breach in this week’s roundup.

1. Equifax breach worsens, additional consumer data exposed – Madelyn Bacon (SearchSecurity)

The Equifax breach compromised even more consumer data, including tax identification numbers, than originally reported. But the credit rating agency didn’t disclose the update.

2. Zerto Virtual Replication dives deeper into multiple clouds – Sonia Lelii (SearchDisasterRecovery)

Zerto has added to its bidirectional Virtual Replication software with multi-cloud protection for data and applications stored in AWS, Microsoft Azure and IBM Cloud.

3. Neurala claims ‘lifelong deep neural nets’ don’t forget – Nicole Laskowski (SearchCIO)

Boston startup Neurala says it has developed deep neural networks that can learn on the fly. Neurala’s COO Heather Ames explains.

4. VMware Workspace One helps Western Digital organize 3,000 apps – Alyssa Provazza (SearchEnterpriseDesktop)

The application portal in VMware Workspace One allowed IT to streamline app delivery, and the product’s cloud-based model proved the right fit for administration.

5. ExtraHop intros Reveal(x) cybersecurity analytics software – Chuck Moozakis (SearchNetworking)

ExtraHop fortifies its packet capture platform with cybersecurity analytics software, Reveal(x); Mist introduces a virtual network assistant powered by artificial intelligence.

February 12, 2018  9:49 AM

TechTarget’s weekly roundup (2/5 – 2/12)

Michael Tidmarsh Michael Tidmarsh Profile: Michael Tidmarsh
Avaya, Cisco, IBM, NetApp, salesforce

Clouds image via FreeImages

What is NetApp planning to accomplish in 2018? Find out why the company is going to narrow its priorities to fast-growth areas in this week’s roundup.

1. NetApp CEO Kurian narrows focus to flash, cloud – Garry Kranz (SearchStorage)

George Kurian became CEO of NetApp in 2015 to jump-start a push into cloud and flash storage. The vendor has bucked scant revenue growth with four straight solid quarters.

2. Cisco: Cloud Computing workloads to skyrocket by 2021 – Chuck Moozakis (SearchNetworking)

By 2021, Cisco says, cloud computing workloads will make up the lion’s share of data center traffic as enterprises expand security, IoT and other activities in the cloud.

3. Avaya adds AI voice assistant to desk phones – Jonathan Dame (SearchUnifiedCommunications)

Avaya has created an AI voice assistant application for desk phones. AI voice assistant platforms could make mundane tasks easier for office workers.

4. Salesforce buys Attic Labs, adding to Quip capabilities – Jesse Scardina (SearchSalesforce)

Quip receives back-end capabilities with open source database after Salesforce purchase of Attic Labs.

5. IBM taps Mendix for low-code platform deal – Darryl K. Taft (SearchCloudApplications)

As the market to empower citizen app developers heats up, IBM and Mendix have joined forces to deliver low-code tools for the IBM Cloud.

February 5, 2018  10:38 AM

TechTarget’s weekly roundup (1/29 – 2/5)

Michael Tidmarsh Michael Tidmarsh Profile: Michael Tidmarsh
Artificial intelligence, DevOps, Oracle, SAP, Spectre

Artificial Intelligence image via FreeImages

What do you see as the top challenges in developing an AI infrastructure for the enterprise? Find out why CIOs may have a tough time merging AI into their enterprise architectures in this week’s roundup.

1. CIOs need an AI infrastructure, but it won’t come easy – George Lawton (SearchCIO)

From picking vendors to upskilling staff, folding an AI infrastructure into enterprise architectures isn’t simple. Experts at the recent ReWork Deep Learning Summit zero in on the issues.

2. Office Depot says ‘no’ to Oracle ERP Cloud customizations – Patrick Thibodeau (SearchERP)

Office Depot believes it’s possible to run its HCM, EPM and supply chain on the Oracle ERP Cloud without customizations. The retailer says it will save money and headaches. Analysts agree.

3. IT monitoring, org discipline polish Nasdaq DevOps incident response – Beth Pariseau (SearchITOperations)

At Nasdaq, IT monitoring unified different teams’ views of infrastructure, but cultural blocking and tackling are equally important to improve incident response.

4. Microsoft rushes Spectre patch to disable Intel’s broken update – Michael Heller (SearchSecurity)

Microsoft was forced to release an out-of-band Spectre patch designed not to mitigate the vulnerability but to protect users from Intel’s broken fix.

5. SAP offers extra help on HR cloud migrations – Patrick Thibodeau (SearchHRSoftware)

SAP is trying to help on-premises HCM users make a case for moving to the cloud, as well as ease initial integration steps. But it may be hard for some European HR users to move.

February 1, 2018  12:49 PM

Instilling AI safety into robotics through reinforcement learning

Michael Tidmarsh Michael Tidmarsh Profile: Michael Tidmarsh
Artificial intelligence, Robotics

Robotics image via FreeImages

By James Kobielus (@jameskobielus)

Artificial intelligence (AI) is the perfect laughingstock. Any phenomenon that takes itself as seriously as AI is just asking to be ridiculed.

What’s even funnier is when AI comes in humanoid form, as is the case with the smart robotics that are penetrating every aspect of our lives. As Bill Vorhies discussed in his recent column, robot fails can be comedic gold.

As the brains behind autonomous devices, AI can dampen the laughter only by helping devices master their assigned tasks so well and performing them so inconspicuously that we never give them a second thought. Where robotics are concerned, this involves the trial-and-error statistics-driven approach known as reinforcement learning (RL). Under this approach, the robot explores the full range of available actions—moving, grappling, voicing, etc.–that may or may not contribute to its achieving a desired outcome.

Depending on your point of view, humor is baked into RL’s intrinsically trial-and-error process. As a robot searches for the optimal sequence of actions to achieve its intended outcome, it will of necessity take more counterproductive actions than optimal paths. If you’re the developer who’s doing the training, this might be a long, frustrating, and tedious process. You may need to revise RL procedures and the robot’s algorithmic cognition countless times till you get it to work in a way that can be generalized to future scenarios of the type for which the mechanism is being trained.

This trial-and-error RL process may be humorous to observe in a laboratory setting. But when your AI-driven robot hasn’t been trained effectively and commits these errors in production environments, it may not be funny in the least. This is amply clear from the incidents that Vorhies cites. No one will tolerate robots that routinely smash into people, endanger passengers riding in autonomous vehicles, or order products online without their owners’ authorization.

If we can draw any lesson from these incidents, it’s that robotics developers will need to incorporate the following scenarios into their RL procedures before they release their AI-powered creations to the wider world:

  • Geospatial awareness: Real-world operating environments can be very tricky for general-purpose robots to navigate successfully. The right RL could have helped the AI algorithms in this security robot learn the range of locomotion challenges in the indoor and outdoor environments where it was designed to patrol. Equipping the robot with a built-in video camera and thermal imaging wasn’t enough. No amount of trained AI could salvage it after it had rolled over into a public fountain.
  • Collision avoidance: Robots can be a hazard as much as a helpmate in many real-world environments. This is obvious with autonomous vehicles, but it’s just as disturbing for retail, office, residential, and other environments in which people might let their guard down. As demonstrated in the cited article, there’s every reason for society to build AI-driven safeguards into everyday robots so that toddlers, the disabled, and the rest of us have no need to fear that they’ll crash into us when we least expect it. Collision avoidance—a prime RL challenge—should be a standard, highly accurate algorithm in every robot. Very likely, laws and regulators will demand this in most jurisdictions before long.
  • Authenticated agency: Robots are increasingly becoming the physical manifestations of digital agents in every aspect of our lives. The smart speakers mentioned in the cited article should have been trained to refrain from placing orders for what they mistakenly interpreted as voice-activated purchase requests, but which in fact came from a small child without parental authorization. Though this could have been handled through multifactor authentication rather than through algorithmic training, it’s clear that voice-activated robots in many environmental scenarios may need to step through complex algorithms when deciding what multifactor methods to use for strong authentication and delegated permissioning. Conceivably, RL might be used to help robots more rapidly identify the most appropriate authentication, authorization, and delegation procedures to use in environments where they serve as agents for many people trying to accomplish a diverse, dynamic range of tasks.
  • Defensive maneuvering: Robots are objects that must survive both deliberate and accidental assaults that other entities—such as human beings–may inflict on them. The AI algorithms in this driverless shuttle bus should have been trained to take some sort of evasive action—such as veering a few feet in the opposite direction–to avoid the semi that inadvertently backed up into it. Defensive maneuvering will become critical for robots that are deployed into transportation, public safety, and military roles. It’s also an essential capability for robotic devices to fend off the general mischief and vandalism that is certainly going to target them wherever they’re deployed.
  • Collaborative orchestration: Robots are increasingly deployed as orchestrated ensembles rather than isolated helpmates. The AI algorithms in warehouse robots in the cited article should have been trained to work harmoniously both with each other and with the many people employed in those environments. Given the huge range of potential interaction scenarios, this is a tough challenge for RL. But it’s an essential capability that society will be demanding from swarming devices of all sorts, including the drones that patrol our skies, deliver our goods, and explore environments that are too dangerous for humans to enter.
  • Cultural sensitivity: Robots must respect people in keeping with the norms of civilized society. That includes, as noted in this article, making sure that robots’ face recognition algorithms don’t make discriminatory, demeaning, or otherwise insensitive inferences about the human beings they encounter. This will become even more critical as we deploy robots into highly social setting where they must be trained not to offend people by, for example, using an inaccurate gender-based salutation to refer to a transgender person. These kinds of distinctions can be highly tricky for actual humans to make on the fly, but that only heightens the need for RL to train AI-driven entities to avoid automated faux pas.

Controlled trial-and-error is how most robotics, edge computing, and self-driving vehicle solutions will acquire and evolve their AI smarts. To the extent that you’re capturing an AI-driven device’s RL training on video, it could prove to be the perfect “blooper reel” to show later on when your creation is a smashing success. For regulatory compliance and legal discovery purposes, this video may also help you prove that you’ve RL-trained your device in every relevant scenario, be it actual or simulated.

In the near future, a video audit log of your RL process may become required for passing muster with stakeholders who require certifications that your creations meet all reasonable “AI safety” criteria. Considering the life-or-death scenarios in which the robots of the future will serve us, this is no laughing matter.

January 29, 2018  9:41 AM

TechTarget’s weekly roundup (1/22 – 1/29)

Michael Tidmarsh Michael Tidmarsh Profile: Michael Tidmarsh
Cisco, Intel, Pure Storage, salesforce, XenApp

Virtual Desktop image via FreeImages

What effects have you seen from patching Meltdown and Spectre? Find out why the patches will hit XenApp especially hard in this week’s roundup.

1. Meltdown and Spectre patches hit XenApp performance especially hard – Alyssa Provazza (SearchVirtualDesktop)

Organizations with VDI and RDSH will likely take a performance hit after patching Meltdown and Spectre, according to test results from Lakeside Software.

2. Pure Storage FlashStack is recycler’s renewable resource – Garry Kranz (SearchStorage)

Food waste recycler Valley Proteins chose Pure Storage’s all-flash converged infrastructure to replace Dell EMC VNX-Cisco UCS for 24 TB of usable storage with Cohesity backup.

3. Salesforce Trailhead gets social boost for admins, developers – Jessica Sirkin (SearchSalesforce)

Salesforce might not own LinkedIn, but it’s creating a similar online social network of certified professionals via Trailhead, complete with vanity URLs and more detailed profiles.

4. Intel Meltdown patches pulled with little explanation – Michael Heller (SearchSecurity)

Intel claims it has determined why the Spectre and Meltdown patches caused issues on some chips. The vendor is working on a fix and suggests users don’t patch for now.

5. Cisco HyperFlex system upgrade targets hybrid cloud – Antone Gonsalves (SearchNetworking)

The Cisco HyperFlex system is now available with AppDynamics, CloudCenter and other software for managing hybrid cloud applications. The new all-in-one system scales to 64 nodes.

January 22, 2018  9:17 AM

TechTarget’s weekly roundup (1/15 – 1/22)

Michael Tidmarsh Michael Tidmarsh Profile: Michael Tidmarsh
cybersecurity, ERP, NotPetya, salesforce, SAP

Database image via FreeImages

Speculation has grown around whether or not Salesforce plans to move off of Oracle databases. Check out the details behind the possible move in this week’s roundup.

1. Salesforce databases remain Oracle, for now – Jesse Scardina (SearchSalesforce)

Oracle and Salesforce executives deny reports of Salesforce moving on from Oracle infrastructure.

2. SAP defends S/4HANA HCM upgrade amid questions – Patrick Thibodeau (SearchHRSoftware)

SAP, which has some 14,000 on-premises HCM customers, is extending on-premises support five years to 2030. But HCM users will have to migrate to its S/4HANA in-memory platform for support.

3. Colleges to share Oracle ERP system in effort to cut costs – Patrick Thibodeau (SearchERP)

Private, nonprofit colleges are discovering they can reduce back-office IT costs by working together. Novel approaches are emerging to reduce the financial drain of ERP systems.

4. CIA attributes NotPetya attacks to Russian spy agency – Michael Heller (SearchSecurity)

The CIA reportedly concluded that Russia’s foreign intelligence agency created and was responsible for the NotPetya attacks against Ukraine in June.

5. Cybersecurity skills shortage continues to worsen – Eamon McCarthy Earls (SearchNetworking)

This week, bloggers explore the cybersecurity skills shortage, the challenges of deploying edge computing and how best to mitigate Meltdown in a software-centric environment.

January 15, 2018  9:18 AM

TechTarget’s weekly roundup (1/8 – 1/15)

Michael Tidmarsh Michael Tidmarsh Profile: Michael Tidmarsh
AMD, Artificial intelligence, Disaster Recovery, Hybrid cloud, iot

Clouds image via FreeImages

Is 2018 the year of the hybrid cloud? Find out why cloud providers are pushing for it in this week’s roundup.

1. Providers continue to push hybrid cloud technologies in 2018 – Jim O’Reilly (SearchCloudComputing)

The hybrid cloud market changes rapidly, as major cloud providers release new services to bridge private and public platforms, but for now, management challenges remain.

2. Time-series monitoring tools give high-resolution view of IT – Beth Pariseau (SearchITOperations)

DevOps monitoring tools based on time-series databases create a steeper learning curve than traditional monitoring software, but arm IT pros with richer data for troubleshooting.

3. Frost Science Museum IT DR planning braced for worst, survived Irma – Dave Raffo (SearchDisasterRecovery)

The Frost Museum of Science on the water in Miami braced for Hurricane Irma with a solidified storage and data center infrastructure, and it suffered minimal damage from the storm.

4. AMD backtracks on Spectre vulnerabilities, plans microcode updates – Rob Wright (SearchSecurity)

AMD initially believed the Spectre vulnerabilities posed “near zero risk” to its chip, but the company this week reversed course and is planning microcode updates for its products.

5. Key considerations of AI, IoT and digital transformation – Mark Troester (IoT Agenda)

Artificial intelligence, the internet of things and digital transformation have been popular subjects over the last year. A quick scan of your favorite tech publication will likely result in multiple stories covering all three of these concepts as companies across the globe embrace them.

January 8, 2018  9:29 AM

TechTarget’s weekly roundup (1/1 – 1/8)

Michael Tidmarsh Michael Tidmarsh Profile: Michael Tidmarsh
CIO, Cloud Computing, Hadoop, Healthcare, Security

Healthcare image via FreeImages

What should the healthcare industry prepare for in 2018? Check out the four trends to watch out for in this week’s roundup.

1. Four healthcare trends to watch in 2018 – Tayla Holman (SearchHealthIT)

Healthcare organizations should be prepared to see applications for blockchain and more uses for AI in 2018. Analytics provided by EHR vendors will also emerge.

2. Huge coordinated vulnerability disclosure needed for Meltdown – Michael Heller (SearchSecurity)

Unprecedented Spectre and Meltdown CPU flaws required a vast coordinated vulnerability disclosure effort over six months and across dozens of organizations.

3. Apache Hadoop 3.0 goes GA, adds hooks for cloud and GPUs – Jack Vaughan (SearchDataManagement)

Is this the post-Hadoop era? Not in the eyes of Hadoop 3.0 backers, who see the latest update to the big data framework succeeding in machine learning applications and cloud systems.

4. Meltdown and Spectre target cloud computing environments – Ed Scannell (SearchCloudComputing)

Hackers could target cloud computing environments to exploit the Meltdown and Spectre vulnerabilities, but AWS, Microsoft and Google say their fixes are enough to bar the doors.

5. CIO goals and IT resolutions for 2018 – SearchCIO staff (SearchCIO)

Seven CIOs plus one chief digital officer give us a glimpse of their technology plans for the new year.

January 2, 2018  8:56 AM

TechTarget’s weekly roundup (12/25 – 1/1)

Michael Tidmarsh Michael Tidmarsh Profile: Michael Tidmarsh
Cloud Computing, Cryptocurrency, Google Cloud, iot

Cloud image via FreeImages

What are your cloud provider predictions for 2018? Check out the top cloud news from 2017 in this week’s roundup.

1. Top cloud providers dominate headlines in 2017 – Kathleen Casey (SearchCloudComputing)

From the AWS and Azure machine learning partnership to Google’s grab for hybrid cloud, 2017 was an exciting year for cloud. Review some of our top stories below.

2. Most popular SearchCIO videos for IT industry leaders in 2017 – Ben Cole (SearchCIO)

Get cutting-edge technology strategy insight straight from IT industry leaders in this countdown of our top 10 most popular SearchCIO videos from 2017.

3. North Korea’s Lazarus Group sets sights on cryptocurrency – Rob Wright (SearchSecurity)

Researchers believe North Korean nation-state hackers from the Lazarus Group are targeting cryptocurrency exchanges and owners in a wave of financially motivated attacks.

4. Google Cloud Platform services engage corporate IT – Trevor Jones (SearchCloudComputing)

Google’s cloud vision to ascend in the public cloud market centers on next-gen applications, which in 2017 translated into a decidedly enterprise-centric spin.

5. Editor’s picks: Thought-provoking IoT blogs from 2017 – Sharon Shea (IoTAgenda)

Some of the most popular articles from our IoT Agenda contributor network in 2017 took a look at the use cases of IoT in various verticals, as well as the technologies vital to its success.

December 18, 2017  2:35 PM

AI development toolkits will shift toward solution orientation in 2018

Michael Tidmarsh Michael Tidmarsh Profile: Michael Tidmarsh
Artificial intelligence, Chatbots, Development

Artificial image via FreeImages

By James Kobielus (@jameskobielus)

Over the past several years, the artificial intelligence (AI) market has been converging on an open, industry-wide development framework. In the coming year, key layers of this general-purpose framework will become standard in most general-purpose AI development tools.

Going forward, AI frameworks are fundamental to developer productivity. In a world where AI is increasingly the nucleus of disruptive applications, developers will adopt tools that enable fast development of machine learning and deep learning models for specific solution domains. By year-end 2018, leading AI solution providers will offer domain-specific software development kits (SDKs) for the majority of principal commercial AI use cases.

In 2018, vendors will launch a growing range of AI SDKs that take open-source tools to the next level of solution orientation. Ultimately, the AI tool vendors who prevail will be those who recognize that each domain requires a development framework suited to its special requirements. For example, the requirements of developers of AI-infused industrial robots tend not to overlap with those who embed AI in mass-market smartphones, IoT edge devices, or embedded e-commerce chatbots. For one thing, some solution domains (such as robotics) make extensive use of reinforcement learning, as opposed to the supervised and unsupervised learning that prevail in other AI domains. For another, some domains (such as interactive chatbots) deliver AI models that drive conversational user interfaces, whereas others (such as drones) enable highly autonomous robotic endpoints. Furthermore, AI use cases vary widely in their target hardware, cloud, and application deployments.

By year-end 2018, the more diversified AI solution providers will offer domain-specific SDKs, or have partners who extend those vendors’ general-purpose SDKs, for all or most of the following AI use cases: smart mobility, virtual assistants, object recognition, computer vision, smarthome, image manipulation, mixed reality, sentiment analysis, robotics, drone, industrial IoT, gaming, and cybersecurity applications. Already, Wikibon is seeing an expanding range of AI SDKs geared to mobile, chatbot, IoT, drone, gaming, and robotics applications.

Going forward, vendors will differentiate solution-oriented development tools on their ability to speed development, training, and deployment of finished AI apps. Key differentiators will include APIs, statistical modeling interfaces, algorithm and code libraries, pretrained models, reference applications, and other functional components suited to the most common commercial, industrial, and public-sector use cases. To support the entire AI app-dev pipeline, the new generation of solution-oriented AI toolkits will also include embedded DevOps, collaboration, governance, training, and data management features suited to their various domains.

In addition, these tools will provide role- and task-oriented development interfaces tailored to the needs of the technical and domain specialists in each domain. And they will allow developers to extend and customize every interface, feature, and component to address domain-specific AI challenges.

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