Posts Tagged ‘cognitive computing’

IBM teams with Cloudera and Hortonworks 

July 11, 2019

Dancing Dinosaur has a friend on the West coast who finally left IBM after years of complaining, swearing never to return, and has been happily working at Cloudera ever since. IBM and Cloudera this week announced a strategic partnership to develop joint go-to-market programs designed to bring advanced data and AI solutions to more organizations across the expansive Apache Hadoop ecosystem.

Graphic representing a single solution for big data analytics

Deploy a single solution for big data

The agreement builds on the long-standing relationship between IBM and Hortonworks, which merged with Cloudera this past January to create integrated solutions for data science and data management. The new agreement builds on the integrated solutions and extends them to include the Cloudera platform. “This should stop the big-data-is-dead thinking that has been cropping up,” he says, putting his best positive spin on the situation.

Unfortunately, my West coast buddy may be back at IBM sooner than he thinks. With IBM finalizing its $34 billion Red Hat acquisition yesterday, it is small additional money to just buy Horton and Cloudera and own them all as a solid big data-cloud capabilities block IBM owns.  

As IBM sees it, the companies have partnered to offer an industry-leading, enterprise-grade Hadoop distribution plus an ecosystem of integrated products and services – all designed to help organizations achieve faster analytic results at scale. As a part of this partnership, IBM promises to:

  • Resell and support of Cloudera products
  • Sell and support of Hortonworks products under a multi-year contract
  • Provide migration assistance to future Cloudera/Hortonworks unity products
  • Deliver the benefits of the combined IBM and Cloudera collaboration and investment in the open source community, along with commitment to better support analytics initiatives from the edge to AI.

IBM also will resell the Cloudera Enterprise Data Hub, Cloudera DataFlow, and Cloudera Data Science Workbench. In response, Cloudera will begin to resell IBM’s Watson Studio and BigSQL.

“By teaming more strategically with IBM we can accelerate data-driven decision making for our joint enterprise customers who want a hybrid and multi-cloud data management solution with common security and governance,” said Scott Andress, Cloudera’s Vice President of Global Channels and Alliances in the announcement. 

Cloudera enables organizations to transform complex data into clear and actionable insights. It delivers an enterprise data cloud for any data, anywhere, from the edge to AI. One obvious question: how long until IBM wants to include Cloudera as part of its own hybrid cloud? 

But IBM isn’t stopping here. It also just announced new storage solutions across AI and big data, modern data protection, hybrid multicloud, and more. These innovations will allow organizations to leverage more heterogeneous data sources and data types for deeper insights from AI and analytics, expand their ability to consolidate rapidly expanding data on IBM’s object storage, and extend modern data protection to support more workloads in hybrid cloud environments.

The key is IBM Spectrum Discover, metadata management software that provides data insight for petabyte-scale unstructured storage. The software connects to IBM Cloud Object Storage and IBM Spectrum Scale, enabling it to rapidly ingest, consolidate, and index metadata for billions of files and objects. It provides a rich metadata layer that enables storage administrators, data stewards, and data scientists to efficiently manage, classify, and gain insights from massive amounts of unstructured data. Combining that with Cloudera and Horton on the IBM’s hybrid cloud should give you a powerful data analytics solution. 

DancingDinosaur is Alan Radding, a veteran information technology analyst, writer, and ghost-writer. Follow DancingDinosaur on Twitter, @mainframeblog, and see more of his work at technologywriter.com. 

 

IBM Pushes Quantum for Business

June 20, 2019

Other major system providers pursuing quantum computing initiatives, but none are pursuing it as methodically or persistently as IBM. In a recent announcement:  IBM’s Institute for Business Value introduced a five-step roadmap to bring quantum computing to your organization.

Into IBM Q computation center: dilution refrigerators with microwave electronics (middle) that provide Q Network cloud access to 20-qubit processor. (Credit: Connie Zhou)

Start by familiarizing yourself with superposition and entanglement, which enable quantum computers to solve problems intractable for today’s conventional computers:

Superposition. A conventional computer uses binary bits that can only depict either 1 or 0. Instead, quantum computers use qubits that can depict a 1 or 0, or any combination by superposition of the qubits’ possible states. This supplies quantum computers with an exponential set of states they can explore to solve certain types of problems better than conventional computers.

Entanglement. In the quantum world, two qubits located even light-years apart can still act in ways that are strongly correlated. Quantum computing takes advantage of this entanglement to encode problems that exploit this correlation between qubits.

The quantum properties of superposition and entanglement enable quantum computers to rapidly explore an enormous set of possibilities to identify an optimal answer that could maximize business value. As future quantum computers can calculate certain answers exponentially faster than today’s conventional machines, they will enable tackling business problems that are exponentially more complex.

Despite conventional computers’ limitations, quantum computers are not expected to replace them in the foreseeable future. Instead, hybrid quantum-conventional architectures are expected to emerge that, in effect, outsource portions of difficult problems to a quantum computer.

Already Quantum computing appears ripe to transform certain industries. For instance, current computational chemistry methods rely heavily on approximation because the exact equations cannot be solved by conventional computers. Similarly, quantum algorithms are expected to deliver accurate simulations of molecules over longer timescales, currently impossible to model precisely. This could enable life-saving drug discoveries and significantly shorten the number of years required to develop complex pharmaceuticals.

Additionally, quantum computing’s anticipated ability to solve today’s impossibly complex logistics problems could produce considerable cost savings and carbon footprint reduction. For example, consider improving the global routes of the trillion-dollar shipping industry (see Dancing Dinosaur’s recent piece on blockchain gaining traction). If quantum computing could improve container utilization and shipping volumes by even a small fraction, this could save shippers hundreds of millions of dollars. To profit from quantum computing’s advantages ahead of competitors, notes IBM, some businesses are developing expertise now to explore which use cases may benefit their own industries as soon as the technology matures.

To stimulate this type of thinking, IBM’s Institute of Business Value has come up with 5 steps to get you started:

  1. Identify your quantum champions. Assign this staff to learn more about the prospective benefits of quantum computing. Just designate some of your leading professionals as quantum champions and charge them with understanding quantum computing, its potential impact on your industry, your competitors’ response, and how your business might benefit. Have these champions report periodically to senior management to educate the organization and align progress to strategic objectives.
  2. Begin identifying quantum computing use cases and associated value propositions. Have your champions identify specific areas where quantum computing could propel your organization ahead of competitors. Have these champions monitor progress in quantum application development to track which use cases may be commercialized sooner. Finally, ensure your quantum exploration links to business results. Then select the most promising quantum computing applications, such as creating breakthrough products and services or new ways to optimize the supply chain.
  3. Experiment with real quantum systems. Demystify quantum computing by trying out a real quantum computer (IBM’s Q Experience). Have your champions get a sense for how quantum computing may solve your business problems and interface with your existing tools. A quantum solution may not be a fit for every business issue. Your champions will need to focus on solutions to address your highest priority use cases, ones that conventional computers can’t practically solve.
  4. Chart your quantum course. This entails constructing a quantum computing roadmap with viable next steps for the purpose of pursuing problems that could create formidable competitive barriers or enable sustainable business advantage. To accelerate your organization’s quantum readiness, consider joining an emerging quantum community. This can help you gain better access to technical infrastructure, evolving industry applications, and expertise that can enhance your development of specific quantum applications.
  5. Lastly, be flexible about your quantum future. Quantum computing is rapidly evolving. Seek out technologies and development toolkits that are becoming the industry standard, those around which ecosystems are coalescing. Realize that new breakthroughs may cause you to adjust your approach to your quantum development process, including changing your ecosystem partners. Similarly, your own quantum computing needs may evolve over time, particularly as you improve your understanding of which business issues can benefit most from quantum solutions.

Finally, actually have people in your organization try a quantum computer, such as through IBM’s Q program and Qiskit, a free development tool. Q provides a free 16-qubit quantum computer you don’t have to configure or keep cool and stable. That’s IBM’s headache.

DancingDinosaur is Alan Radding, a veteran information technology analyst, writer, and ghost-writer. Follow DancingDinosaur on Twitter, @mainframeblog, and see more of his work at technologywriter.com.

Syncsort Drives IBMi Security with AI

May 2, 2019

The technology security landscape looks increasingly dangerous  The problem revolves around the possible impact of AI. the impact of which is not fully clear. The hope, of course, is that AI will make security more efficient and effective.  However, the security bad actors can also jump on AI to advance their own schemes. Like a cyber version of the nuclear arms race, this has been an ongoing battle for decades. The industry has to cooperate and, specifically, share information and hope the good guys can stay a step ahead.

In the meantime, vendors like IBM and most recently Syncsort have been stepping up to  the latest challengers. Syncsort, for example, earlier this month launched its Assure Security to address the increasing sophistication of cyber attacks and expanding data privacy regulations.  In surprising ways, it turns out, data privacy and AI are closely related in the AI security battle.

Syncsort, a leader in Big Iron-to-Big Data software, announced Assure Security, which combines access control, data privacy, compliance monitoring, and risk assessment into a single product. Together, these capabilities help security officers, IBMi administrators, and Db2 administrators address critical security challenges and comply with new regulations meant to safeguard and protect the privacy of data.

And it clearly is coming at the right time.  According to Privacy Rights Clearinghouse, a non-profit corporation with a mission to advocate for data privacy there were 828 reported security incidents in 2018 resulting in the exposure of over 1.37 billion records of sensitive data. As regulations to help protect consumer and business data become stricter and more numerous, organizations must build more robust data governance and security programs to keep the data from being exploited by bad security actors for nefarious purposes.  The industry already has scrambled to comply with GDPR and the New York Department of Financial Services Cybersecurity regulations and they now must prepare for the GDPR-like California Consumer Privacy Act, which takes effect January 1, 2020.

In its own survey Syncsort found security is the number one priority among IT pros with IBMi systems. “Given the increasing sophistication of cyber attacks, it’s not surprising 41 percent of respondents reported their company experienced a security breach and 20 percent more were unsure if they even had been breached,” said David Hodgson, CPO, Syncsort. The company’s new Assure Security product leverages the wealth of IBMi security technology and the expertise to help organizations address their highest-priority challenges. This includes protecting against vulnerabilities introduced by new, open-source methods of connecting to IBMi systems, adopting new cloud services, and complying with expanded government regulations.

Of course, IBM hasn’t been sleeping through this. The company continues to push various permutations of Watson to tackle the AI security challenge. For example, IBM leverages AI to gather insights and use reasoning to identify relationships between threats, such as malicious files, suspicious IP addresses,  or even insiders. This analysis takes seconds or minutes, allowing security analysts to respond to threats up to 60 times faster.

It also relies on AI to eliminate time-consuming research tasks and provides curated analysis of risks, which reduces the amount of time security analysts require to make the critical decisions and launch an orchestrated response to counter each threat. The result, which IBM refers to as cognitive security, combines the strengths of artificial intelligence and human intelligence.

Cognitive AI in effect, learns with each interaction to proactively detect and analyze threats and provides actionable insights to security analysts making informed decisions. Such cognitive security, let’s hope, combines the strengths of artificial intelligence with human judgement.

Syncsort’s Assure Security, specifically brings together best-in-class IBMi security capabilities acquired by Syncsort into an all-in-one solution, with the flexibility for customers to license individual modules. The resulting product includes:

  • Assure  Compliance Monitoring quickly identifies security and compliance issues with real-time alerts and reports on IBMi system activity and database changes.
  • Assure Access Control provides control of access to IBMi systems and their data through a varied bundle of capabilities.
  • Assure Data Privacy protects IBMi data at-rest and in-motion from unauthorized access and theft through a combination of NIST-certified encryption, tokenization, masking, and secure file transfer capabilities.
  • Assure Security Risk Assessment examines over a dozen categories of security values, open ports, power users, and more to address vulnerabilities.

It probably won’t surprise anyone but the AI security situation is not going to be cleared up soon. Expect to see a steady stream of headlines around security hits and misses over the next few years. Just hope will get easier to separate the good guys from the bad actors and the lessons will be clear.

DancingDinosaur is Alan Radding, a veteran information technology analyst, writer, and ghost-writer. Follow DancingDinosaur on Twitter, @mainframeblog, and see more of his work at technologywriter.com.

IBM Rides Quantum Volume  to Quantum Advantage

March 19, 2019

Recently IBM announced achieving its’ highest quantum volume to date. Of course, nobody else knows what Quantum Volume is.  Quantum volume is both a measurement and a procedure developed, no surprise here, by IBM to determine how powerful a quantum computer is. Read the May 4 announcement here.

Quantum volume is not just about the number of qubits, although that is one part of it. It also includes both gate and measurement errors, device cross talk, as well as device connectivity and circuit compiler efficiency. According to IBM, the company has doubled the power of its quantum computers annually since 2017.

The upgraded processor will be available for use by developers, researchers, and programmers to explore quantum computing using a real quantum processor at no cost via the IBM Cloud. This offer has been out in various forms since May 2016 as IBM’s Q Experience.

Also announced was a new prototype of a commercial processor, which will be the core for the first IBM Q early-access commercial systems.  Dates have only been hinted at.

IBM’s recently unveiled IBM Q System One quantum computer, with a fourth-generation 20-qubit processor, which has resulted in a Quantum Volume of 16, roughly double that of the current IBM Q 20-qubit device, which have a Quantum Volume of 8.

The Q volume math goes something like this: a variety of factors determine Quantum Volume, including the number of qubits, connectivity, and coherence time, plus accounting for gate and measurement errors, device cross talk, and circuit software compiler efficiency.

In addition to producing the highest Quantum Volume to date, IBM Q System One’s performance reflects some of the lowest error rates IBM has ever measured, with an average 2-qubit gate error less than 2 percent, and its best gate achieving less than a 1 percent error rate. To build a fully-functional, large-scale, universal, fault-tolerant quantum computer, long coherence times and low error rates are required. Otherwise how could you ever be sure of the results?

Quantum Volume is a fundamental performance metric that measures progress in the pursuit of Quantum Advantage, the Quantum Holy Grail—the point at which quantum applications deliver a significant, practical benefit beyond what classical computers alone are capable. To achieve Quantum Advantage in the next decade, IBM believes that the industry will need to continue to double Quantum Volume every year.

Sounds like Moore’s Law all over again. IBM doesn’t deny the comparison. It writes: in 1965, Gordon Moore postulated that the number of components per integrated function would grow exponentially for classical computers. Jump to the new quantum era and IBM notes its Q system progress since 2017 presents a similar early growth pattern, supporting the premise that Quantum Volume will need to double every year and presenting a clear roadmap toward achieving Quantum Advantage.

IBM’s recently unveiled IBM Q System One quantum computer, with a fourth-generation 20-qubit processor, which has produced a Quantum Volume of 16, roughly double that of the current IBM Q 20-qubit IBM Q Network device, which has a Quantum Volume of 8.

Potential use cases, such as precisely simulating battery-cell chemistry for electric vehicles, speeding quadratic derivative models, and many others are already being investigated by IBM Q Network partners. To achieve Quantum Advantage in the 2020s, IBM believes the industry will need to continue doubling Quantum Volume every year.

In time AI should play a role expediting quantum computing.  For that, researchers will need to develop more effective AI that can identify patterns in data otherwise invisible to classical computers.

Until then how should most data centers proceed? IBM researchers suggest 3 initial steps:

  1. Develop quantum algorithms that demonstrate how quantum computers can improve AI classification accuracy.
  1. Improve feature mapping to a scale beyond the reach of the most powerful classical computers
  2. Classify data through the use of short depth circuits, allowing AI applications in the NISQ (noisy intermediate scale quantum) regime and a path forward to achieve quantum advantage for machine learning.

Sounds simple, right? Let DancingDinosaur know how you are progressing.

DancingDinosaur is Alan Radding, a veteran information technology analyst, writer, and ghost-writer. Follow DancingDinosaur on Twitter, @mainframeblog, and see more of his work at technologywriter.com.

IBM Joins with Harley-Davidson for LiveWire

March 1, 2019

I should have written this piece 40 years ago as a young man fresh out of grad school. Then I was dying for a 1200cc Harley Davidson motorcycle. My mother was dead set against it—she wouldn’t even allow me to play tackle football and has since been vindicated (You win on that, mom.). My father, too, was opposed and wouldn’t help pay for it. I had to settle for a puny little motor scooter that offered zero excitement.

In the decades since I graduated, Harley’s fortunes have plummeted as the HOG (Harley Owners Group) community aged out and few youngsters have picked up the slack. The 1200cc bike I once lusted after probably is now too heavy for me to handle. So, what is Harley to do? Redefine its classic American brand with an electric model, LiveWire.

Courtesy: Harley Davidson, IBM

With LiveWire, Harley expects to remake the motorcycle as a cloud-connected machine and promises to deliver new products for fresh motorcycle segments, broaden engagement with the brand, and strengthen the H-D dealer network. It also boldly proclaimed that Harley-Davidson will lead the electrification of motorcycling.

According to the company, Harley’s LiveWire will leverage H-D Connect, a service (available in select markets), built on thIBM AI, analytics, and IoTe IBM Cloud. This will enable it to deliver new mobility and concierge services today and leverage an expanding use of IBM AI, analytics, and IoT to enhance and evolve the rider’s experience. In order to capture this next generation of bikers, Harley is working with IBM to transform the everyday experience of riding through the latest technologies and features IBM can deliver via the cloud.

Would DancingDinosaur, an aging Harley enthusiast, plunk down the thousands it would take to buy one of these? Since I rarely use my smartphone to do anything more than check email and news, I am probably not a likely prospect for LiveWire.

Will LiveWire save Harley? Maybe; it depends on what the promised services will actually deliver. Already, I can access a wide variety of services through my car but, other than Waze, I rarely use any of those.

According to the joint IBM-Harley announcement, a fully cellular-connected electric motorcycle needed a partner that could deliver mobility solutions that would meet riders’ changing expectations, as well as enhance security. With IBM, Harley hopes to strike a balance between using data to create both intelligent and personal experiences while maintaining privacy and security, said Marc McAllister, Harley-Davidson VP Product Planning and Portfolio in the announcement.

So, based on this description, are you ready to jump to LiveWire? You probably need more details. So far, IBM and Harley have identified only three:

  1. Powering The Ride: LiveWire riders will be able to check bike vitals at any time and from any location. Information available includes features such as range, battery health, and charge level. Motorcycle status features will also support the needs of the electric bike, such as the location of charging stations. Also riders can see their motorcycle’s current map location.  Identifying charging stations could be useful.
  2. Powering Security: An alert will be sent to the owner’s phone if the motorcycle has been bumped, tampered, or moved. GPS-enabled stolen-vehicle assistance will provide peace of mind that the motorcycle’s location can be tracked. (Requires law enforcement assistance. Available in select markets).
  3. Powering Convenience: Reminders about upcoming motorcycle service requirements and other care notifications will be provided. In addition, riders will receive automated service reminders as well as safety or recall notifications.

“The next generation of Harley-Davidson riders will demand a more engaged and personalized customer experience,” said Venkatesh Iyer, Vice President, North America IoT and Connected Solutions, Global Business Services, IBM. Introducing enhanced capabilities, he continues, via the IBM Cloud will not only enable new services immediately, but will also provide a roadmap for the journey ahead. (Huh?)

As much as DancingDinosaur aches for Harley to come roaring back with a story that will win the hearts of the HOG users who haven’t already drifted away Harley will need more than the usual buzzwords, trivial apps, and cloud hype.

DancingDinosaur is Alan Radding, a veteran information technology analyst, writer, and ghost-writer. Follow DancingDinosaur on Twitter, @mainframeblog, and see more of his work at technologywriter.com.

IBM Enhances Storage for 2019

February 14, 2019

It has been a while since DancingDinosaur last looked closely at IBM’s storage efforts. The latest 4Q18 storage briefing, actually was held on Feb. 5, 2019 but followed by more storage announcements 2/11 and 2/12 For your sake, this blog will not delve into each of these many announcements. You can, however, find them at the previous link.

Sacramento-San Joaquin River Delta–IBM RESEARCH

As IBM likes to say whenever it is trying to convey the value of data: “data is more valuable than oil.”  Maybe it is time to update this to say data is more valuable than fresh, clean water, which is quickly heading toward becoming the most precious commodity on earth.

IBM CEO Ginny Rometty, says it yet another way: “80% of the world’s data, whether it’s decades of underwriting, pricing, customer experience, risk in loans… That is all with our clients. You don’t want to share it. That is gold,” maybe more valuable even, say, the value of fresh water. But whatever metaphor you choose to use—gold, clean water, oil, something else you perceive as priceless, this represents to IBM the value of data. To preserve the value it represents this data must be economically stored, protected, made accessible, analyzed, and selectively shared. That’s where IBM’s storage comes in.

And IBM storage has been on a modest multi-year storage growth trend.  Since 2016, IBM reports shipping 700 new NVMe systems, 850 VeraStack systems, 3000 DS8880 systems, 5500 PB of capacity, attracted 6,800 new IBM Spectrum (virtualized) storage customers, and sold 3,000 Storwize All-flash system along with 12,000 all-flash arrays shipped.

The bulk of the 2/5 storage announcements fell into 4 areas:

  1. IBM storage for containers and cloud
  2. AI storage
  3. Modern data protection
  4. Cyber resiliency

Except for modern data protection, much of this may be new to Z and Power data centers. However, some of the new announcements will interest Z shops. In particular, 219-135 –Statement of direction: IBM intends to deliver Managed-from-Z, a new feature of IBM Cloud Private for Linux on IBM Z. This will enable organizations to run and manage IBM Cloud Private applications from IBM Linux on Z or LinuxONE platforms. The new capability furthers IBM’s commitment to deliver multi-cloud and multi-architecture cloud-native technologies on the platform of the customer’s choice. Watson, too, will now be available on more platforms through newly announced Watson Anywhere—a version of IBM’s cognitive platform that can run Watson on-premises, in IBM’s cloud, or any other cloud, be it private or public.

Another interesting addition to the IBM storage line, the FlashSystem 9100. IBM FlashSystem 9100, as IBM explains it, combines the performance of flash and Non-Volatile Memory Express (NVMe) end-to-end with the reliability and innovation of IBM FlashCore technology and the rich features of IBM Spectrum Virtualize, — all packed into a 2U enterprise-class storage system. Providing intensive data driven multi-cloud storage capacity, FlashSystem 9100 is deeply integrated with the software defined (virtualized) capabilities of IBM Spectrum Storage, allowing organizations to easily add multi-cloud solutions that best support their business..

Finally, 219-029 –IBM Spectrum Protect V8.1.7 and IBM Spectrum Protect Plus V10.1.3 deliver new application support and optimization for long term data retention. Think of it this way: as the value of data increases, you will want to retain and protect it in more data in more ways for longer and longer. For this you will want the kind of flexible and cost-efficient storage available through Spectrum Protect.

In addition, at Think, IBM announced Watson Anywhere, a version of Watson that runs on-premises, in IBM’s cloud, or any other cloud, be it private or public.

DancingDinosaur is Alan Radding, a veteran information technology analyst, writer, and ghost-writer. Follow DancingDinosaur on Twitter, @mainframeblog, and see more of his work at technologywriter.com.

Meet IBM Q System One

February 1, 2019

A couple of weeks ago, IBM slipped in a new quantum machine at CES. The new machine, dubbed IBM Q System One, is designed for both scientific and commercial computing. IBM described it as the first integrated universal approximate quantum computing system.

Courtesy of IBM

Approximate refers to the short coherence time of the qubits, explains Michael Houston, manager, Analyst Relations. Or, to put it another way: how long the qubits remain stable enough to run reliable and repeatable calculations. IBM Q systems report an industry-best average of 100 microseconds. That’s not enough time for a round of golf, but probably long enough to start running some serious quantum analytics.

As described by IBM, the new machine family, the Q systems, are designed to one day tackle problems that are currently seen as too complex or too exponential in scale for classical (conventional) systems to handle. Such Q Systems may use quantum computing to find new ways to model financial data or isolate key global risk factors to make better investments or find the optimal path across global systems for ultra-efficient logistics or optimizing fleet operations for improved deliveries.

The design of IBM Q System One includes a 9x9x9 cube case constructed of half-inch thick borosilicate glass to form a sealed, airtight enclosure that opens effortlessly using roto-translation, a motor-driven rotation around two displaced axes engineered to simplify the system’s maintenance and upgrade process while minimizing downtime. Overall, the entire system was intended to enable the most stable qubits, which allows for the machine to deliver the reliable commercial use.

A series of independent aluminum and steel frames not only unify, but also decouple the system’s cryostat, control electronics, and exterior casing, helping to avoid potential vibration interference that leads to phase jitter and qubit decoherence.

The object of all of this, Houston explains, is to deliver a sophisticated, modular, and compact design optimized for stability, reliability, and continuous commercial use. For the first time ever, IBM Q System One enables universal approximate superconducting quantum computers to operate beyond the confines of the research lab.

In effect, think of the Q System One as bringing the quantum machine to the data center, starting with Q System’s design that squeezes all the quantum computing electronics, controllers, and other components into a 9x9x9 foot cube made of half-inch thick glass to create a sealed, airtight enclosure that will allow the system to cool the qubits to low Kelvin temperatures and keep them cold enough and undisturbed from any interference for long enough to perform meaningful work. All the Q System One’s components and control mechanisms are intended to keep the qubits at 10 mK  (-442F) to operate

This machine, notes IBM, should look familiar to conventional computer data center managers. Maybe, if you think a 9x9x9, half-inch thick borosilicate glass cube is a regular feature of any data center you have worked in

In effect, IBM is applying the same approach to quantum computing that it has followed for decades with its conventional computers–providing everything you need to get it operating in your data center. Just plan to bring in some trained quantum technicians, specialists, and, don’t forget, a handful of people who can program such a machine.

Other than that, the IBM Q System One consists of a number of custom components that work together–remember they said integrated: Specifically, the new machine will include:

  • Quantum hardware designed to be stable and auto-calibrated to give repeatable and predictable high-quality qubits;
  • Cryogenic engineering that delivers a continuous cold and isolated quantum environment;
  • High precision electronics in compact form factors to tightly control large numbers of qubits;
  • Quantum firmware to manage the system health and enable system upgrades without downtime for users

Are you up for it? Maybe you’d prefer to try before you buy. The IBM Q Quantum Computation Center, opening later this year in Poughkeepsie, extends the IBM Q Network to commercial quantum computing programs,

DancingDinosaur is Alan Radding, a veteran information technology analyst, writer, and ghost-writer. Follow DancingDinosaur on Twitter, @mainframeblog, and see more of his work at technologywriter.com.

Factsheets for AI

December 21, 2018

Depending on when you check in on the IBM website the primary technology trend for 2019 is quantum computing or hybrid clouds or blockchain, or artificial intelligence or any of a handful of others. Maybe IBM does have enough talented people, resources, and time to do it all well now. But somehow DancingDinosuar is dubious.

There is an old tech industry saying: you can have it right, fast, cheap—pick 2. When it comes to AI depending on your choices or patience you could win an attractive share of the projected $83 billion AI industry by 2021 or a share of the estimated $200 billion AI market by 2025, according to venturebeat.

IBM sees the technology industry at a pivotal moment in the path to mass adoption of artificial intelligence (AI). Google subsidiary DeepMind is leveraging AI to determine how to refer optometry patients. Haven Life is using AI to extend life insurance policies to people who wouldn’t traditionally be eligible, such as people with chronic illnesses and non-U.S. citizens. And Google self-driving car spinoff Waymo is tapping it to provide mobility to elderly and disabled people.

But despite the good AI is clearly capable of doing, doubts abound over its safety, transparency, and bias. IBM believes part of the problem is a lack of standard practices.

As a result, there’s no consistent, agreed-upon way AI services should be created, tested, trained, deployed, and evaluated, observes Aleksandra Mojsilovic, head of AI foundations at IBM Research and co-director of the AI Science for Social Good program. To clear up the ambiguity surrounding AI, Mojsilovic and colleagues propose voluntary factsheets or as more formally called Supplier’s Declaration of Conformity (DoC). The goal: increasing the transparency of particular AI services and engendering trust in them.

Such factsheets alone could enable a competitive advantage to AI offers in the marketplace. Such factsheets could provide explain-ability around susceptibility to adversarial attacks—issues that must be addressed in order for AI services to be trusted along with fairness and robustness, Mojsilovic continued. Factsheets take away the black box perception of AI and render the AI system understandable by both researchers and developers.

Several core pillars form the basis for trust in AI systems: fairness, robustness, and explain-ability, the first 3 pillars.  Late in her piece, Mojsilovic introduces a fourth pillar — lineage — which concerns AI systems’ history. Factsheets would answer questions ranging from system operation and training data to underlying algorithms, test setups and results, performance benchmarks, fairness and robustness checks, intended uses, maintenance, and retraining. More granular topics might include governance strategies used to track the AI service’s data workflow, the methodologies used in testing, and bias mitigations performed on the dataset. But in Mojsilovic’s view, documents detailing the ins and outs of systems would go a long way to maintaining the public’s faith in AI.

For natural language processing algorithms specifically, the researchers propose data statements that would show how an algorithm might be generalized, how it might be deployed, and what biases it might contain.

Natural language processing systems aren’t as fraught with controversy as, say, facial recognition, but they’ve come under fire for their susceptibility to bias.  IBM, Microsoft, Accenture, Facebook, and others are actively working on automated tools that detect and minimize bias, and companies like Speechmatics and Nuance have developed solutions specifically aimed at minimizing the so-called accent gap — the tendency of voice recognition models to skew toward speakers from certain regions. But in Mojsilovic’s view, documents detailing the ins and outs of systems—factsheets–would go a long way to restoring the public’s faith in AI.

Fairness, safety, reliability, explain-ability, robustness, accountability — all agree that they are critical. Yet, to achieve trust in AI, making progress on these issues alone will not be enough; it must be accompanied with the ability to measure and communicate the performance levels of a system on each of these dimensions, she wrote. Understanding and evaluating AI systems is an issue of utmost importance for the AI community, an issue IBM believes the industry, academia, and AI practitioners should be working on together.

DancingDinosaur is Alan Radding, a veteran information technology analyst, writer, and ghost-writer. Follow DancingDinosaur on Twitter, @mainframeblog, and see more of his work at technologywriter.com.

BMC’s AMI Brings Machine Learning to Z

November 9, 2018

On Oct 18 BMC announced AMI, an automated mainframe intelligence capability that promises higher performing, self-managing mainframe environments to meet the growing demands created by digital business growth and do it through the use of AI-like capabilities.

AMI delivers a self-managing mainframe

BMC’s AMI solutions combine built-in domain expertise, machine learning, intelligent automation, and predictive analytics to help enterprises automatically manage, diagnose, heal, secure, and optimize mainframe processes. BMC doesn’t actually call it AI but they attribute all the AI buzzwords to it.

BMC cited Gartner saying: by 2020, thirty percent of data centers that fail to apply artificial intelligence and machine learning effectively in support of enterprise business will cease to be operationally and economically viable.  BMC is tapping machine learning in conjunction with its analysis of dozens of KPIs and millions of metrics a day to proactively identify, predict, and fix problems before they become an issue. In the process, BMC intends relieve the burden on enterprise teams and free up IT staff to work on high-value initiatives by removing manual processes through intelligent automation. Ultimately, the company hopes to keep its customers, as Gartner put it, operationally and economically viable.

In effect, mainframe-based organizations can benefit from BMC’s expertise in collecting deep and broad z/OS operational metrics from a variety of industry data sources, built-in world-class domain expertise, and multivariate analysis.

A lot of this already is available in the Z itself through a variety of tools, particularly zAware, described by IBM as a firmware feature consisting of an integrated set of analytic applications that monitor software running on z/OS and model normal system behavior. Its pattern recognition techniques identify unexpected messages, providing rapid diagnosis of problems caused by system changes.

But BMC is adding two new ingredients that should take this further, Autonomous Solutions and Enterprise Connectors.

Autonomous Solutions promise to enable IT operations that automatically anticipate and repair performance degradations and disruptive outages before they occur, without manual intervention. This set of intelligent, integrated solutions that compasses BMC AMI for Security Management, BMC AMI for DevOps, BMC AMI for Performance and Availability Management, and BMC AMI Cost and Capacity Management.

Enterprise Connectors move business-critical data from the mainframe to the entire enterprise and simplify the enterprise-wide management of business applications. The connectors promise a complete view of enterprise data by streaming mainframe metrics and related information in real-time to a variety of data receivers, including leading Security Information and Event Management (SIEM) solutions such as Splunk, IBM QRadar, ArcSight, LogRhythm, McAfee Enterprise Security Manager, and others. Note, BMC’s AMI Data Extractor for IMS solution is available now, additional extractors will be available early in 2019.

To bolster its mainframe business further. BMC in early October announced the acquisition of the assets of CorreLog, Inc., which provides real-time security management to mainframe customers. When combined with BMC’s offerings in systems, data, and cost management, it enables end-to-end solutions to ensure the availability, performance, and security of mission critical applications and data residing on today’s modern mainframe the merged operation. CorreLog brings capabilities for security and compliance auditing professionals who need more advanced network and system security, and improved adherence to key industry standards for protecting data.

The combination of CorreLog’s security offerings with BMC’s mainframe capabilities provides organizations with enhanced security capabilities including:

Real-time visibility into security events from mainframe environments, delivered directly into SIEM/SOC systems. It also brings a wide variety of security alerts, including IBM IMS and Db2, event log correlation, which provides up-to-the second security notifications for faster remediation in the event of a breach, and a 360-degree view of mainframe threat activity. The CorreLog deal is expected to close later this quarter.

DancingDinosaur is Alan Radding, a veteran information technology analyst, writer, and ghost-writer. Follow DancingDinosaur on Twitter, @mainframeblog, and see more of his work at technologywriter.com.

IBM AI Toolset Focuses on 9 Industries

October 4, 2018

Recently, IBM introduced new AI solutions and services pre-trained for nine industries and professions including agriculture, customer service, human resources, supply chain, manufacturing, building management, automotive, marketing, and advertising. In each area the amount of data makes it more difficult for managers to keep up due to volume, velocity, and complexity of the data. The solutions generally utilize IBM’s Watson Data Platform.

For example, supply chain companies now should incorporate weather data, traffic reports, and even regulatory reports to provide a fuller picture of global supply issues. Similarly, industrial organizations are seeking to reduce product inspection resource requirements significantly through the use of visual and acoustic inspection capabilities, notes IBM.

Recent IBM research from its Institute for Business Value revealed that 82% of businesses are now considering AI deployments. Why? David Kenny, Senior Vice President, IBM Cognitive Solutions, explains: “As data flows continue to increase, people are overwhelmed by the amount of information [forcing them] to act on it every day, but luckily the information explosion coincides with another key technological advance; artificial intelligence (AI). In the 9 industries targeted by IBM, the company provides the industry-specific algorithms and system training required for making AI effective in each segment.

Let’s look at a selection of these industry segments starting with Customer Service where 77% of top performing organizations report seeing customer satisfaction as a key value driver for AI by giving customer service agents increased ability to respond quickly to questions and complex inquiries. It was first piloted at Deluxe Corporation, which saw improved response times and increased client satisfaction.

Human resources also could benefit from a ready-made AI solution. The average hiring manager flips through hundreds of applicants daily, notes IBM, spending approximately 6 seconds on each resume. This isn’t nearly enough time to make well-considered decisions. The new AI tool for HR analyzes the background of current top performing employees from diverse backgrounds and uses that data to help flag promising applicants.

In the area of industrial equipment, AI can be used to reduce product inspection resource requirements significantly by using AI-driven visual and acoustic inspection capabilities. At a time of intense global competition, manufacturers face a variety of issues that impact productivity including workforce attrition, skills-gaps, and rising raw material costs—all exacerbated by downstream defects and equipment downtime. By combining the Internet of Thing (IoT) and AI, IBM contends, manufacturers can stabilize production costs by pinpointing and predicting areas of loss; such as energy waste, equipment failures, and product quality issues.

In agriculture, farmers can use AI to gather data from multiple sources—weather, IoT-enabled tractors and irrigators, satellite imagery, and more—and see a single, overarching, predictive view of data as it relates to a farm. For the individual grower, IBM notes, this means support for making more informed decisions that help improve yield. Water, an increasingly scarce resource in large swaths of the world, including parts of the U.S., which have been experienced persistent droughts. Just remember the recent wildfires.

Subway hopes AI can increase in restaurant visits by leveraging the connection between weather and quick service (QSR) foot traffic to drive awareness of its $4.99 Foot long promotion via The Weather Channel mobile app. To build awareness and ultimately drive in-store visits to its restaurants Subway reported experiencing a 31% lift in store traffic and a 53% reduction in campaign waste due to AI.

DancingDinosaur had no opportunity to verify any results reported above. So always be skeptical of such results until they are verified to you.

DancingDinosaur is Alan Radding, a veteran information technology analyst, writer, and ghost-writer. Follow DancingDinosaur on Twitter, @mainframeblog, and see more of his work at technologywriter.com.


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