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000-SS2 - IBM Systems Software Technical Sales Mastery V1 - Dump Information

Vendor : IBM
Exam Code : 000-SS2
Exam Name : IBM Systems Software Technical Sales Mastery V1
Questions and Answers : 32 Q & A
Updated On : December 7, 2018
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000-SS2 Questions and Answers

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000-SS2 IBM Systems Software Technical Sales Mastery V1

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000-SS2 exam Dumps Source : IBM Systems Software Technical Sales Mastery V1

Test Code : 000-SS2
Test Name : IBM Systems Software Technical Sales Mastery V1
Vendor Name : IBM
Q&A : 32 Real Questions

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IBM IBM Systems Software Technical

Is Nagios The way forward for Monitoring For IBM i? | killexams.com Real Questions and Pass4sure dumps

November 12, 2018 Alex Woodie

For many years, technology professionals grew to become to established frameworks from IBM Tivoli, CA, Hewlett Packard, and BMC (“The massive four”) to video display their hardware and application stacks. but the open supply world has caught up with these closed monitoring environments, and a venture dubbed Nagios is poised to be the go-to platform for IT monitoring, together with on IBM i.

Nagios, in case you are not widely wide-spread, is a free and open source application product that provides monitoring and alerting for servers, network apparatus, functions, and the array of capabilities that agencies increasingly count on. The software –backed by way of the obligatory recursive acronym “Nagios Ain’t Gonna Insist On Sainthood” – become in the beginning developed by using Ethan Galstad back in 2002 to run and monitor Linux methods, which turned into simply beginning to make headway in enterprise facts facilities.

over the years, the C-primarily based program has been extended to guide an array of object kinds, including host components like processor load, disk and RAM usage, and device logs for a majority of widespread working techniques, including IBM i. The energetic Nagios neighborhood has created a wide range of Nagios brokers which have prolonged the framework to an estimated 5,000 instruments.

Nagios is an open source tool that permits directors to monitor their IT gear and applications.

On the network aspect of issues, Nagios Core (as the open source product is known) screens routers, switches, and different apparatus, and supports SMTP, POP3, FTP, and SNMP protocols, among others. All faraway monitoring will also be encrypted, and it additionally presents a plug-in that lets users create their own screens in lots of languages.

records collected by using active and passive Nagios plug-ins is routed into the Nagios server, where the facts is sorted and signals are sent out. clients can consume indicators in quite a lot of approaches, including via a web browser, via SMS pagers, and through e mail. With a simple look at the Nagios dashboard on a telephone, a systems admins can see if vital servers, method, and features are alive or useless.

The Nagios Core server software also can work with a “circular-robin database” (RRD) to simplify the storage and querying of time-sequence records. With RRD, admins can dive deeper into Nagios facts and consider graphs to see efficiency traits. And with a little setup ahead of time, Nagios can also be configured to immediately respond to hobbies as they occur, or increase alerts to other individuals.

businesses that desire more than what Nagios Core has to present can locate it from Nagios enterprises, the business outfit behind the open source mission. The St. Paul, Minnesota-based mostly company, which employs Galstad, extends Nagios Core with several offerings, together with Nagio XI, which points a extra extensible front-conclusion and a MySQL-based mostly back-end. Nagios XI also brings a higher consumer journey with greater potent graphs and enhanced dashboards.

IBM i valued clientele can use Nagios to video display 15 metrics with the Nagios Plug-Ins for IBM i beta.

regrettably, Nagios companies doesn’t support IBM i. but that you could get a Nagios distribution for IBM i from a little outfit called overseas enterprise Machines. In 2017, huge Blue shipped a beta edition of an IBM i plug-in designed to feed facts from the IBM i sever into the open supply Nagios Core product or Nagios XI.

according to a developerWorks web page, the IBM i Nagios plug-in collects 15 distinct facts aspects from IBM i servers, together with issues like CPU and disk utilization, ASP usage, number of energetic jobs, and lengthy-operating SQL jobs, amongst others. Some of those services are supported by way of SNMP, while others require the open source JTOpen Java toolbox to be put in.

The Nagios server itself doesn’t run on IBM i – at the least not yet. With the Nagios on IBM i plug-in, IBM provides users with instructions on the way to deploy Nagios Core or Nagios XI on a Linux server. Technically, there should be nothing stopping from a consumer installing Nagios in a Linux LPAR on the energy methods server – and even for IBM to port the application to the IBM i OS by the use of PASE – but that has no longer took place yet.

IBM makes the case for IBM i stores to make use of Nagios in a PDF presentation on Nagios created by using IBM i company architect Tim Rowe and Wang Yun, who developed the Nagios for IBM i plug-in. whereas Navigator for i gives first rate coverage of IBM i-centric queues and metrics, that product doesn’t provide shoppers with any visibility of what’s occurring outdoor the IBM i system.

purchasers who buy Nagios XI can video display IBM i metrics graphically.

Nagios, nonetheless, provides administrators and operators with a single centralized view of all IT belongings, Rowe and Yun write. moreover the core IBM i metrics that at the moment are accessible via the Nagios plug-in, IBM i clients can get vogue and historical records, as well because the flexibility to configure metrics to their liking, not to mention the ability to create their own custom plug-ins.

IBM isn’t the only enterprise doing Nagios work on IBM i. a couple of years ago, ShapPin Cheng launched a GitHub task called check_as400 that moved records from IBM i server logs into Nagios. seeing that the extensibility and openness of Nagios, it wouldn’t be fantastic to peer impartial software carriers in the IBM i neighborhood adopt the know-how for his or her personal methods monitoring offerings.

For now, IBM presents the Nagios plug-in as a beta. Don’t be shocked to look it turn into a fully supported offering within the months to return, mainly considering the love affair that IBM has with open supply application this present day.

related reports

IBM i performance secrets and techniques revealed

What’s Cooking With IBM i On GitHub?

ASCI Job Scheduler gets Hooks for Nagios


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IBM to purchase utility business red Hat for $34 billion | killexams.com Real Questions and Pass4sure dumps

with the aid of Liana B. Baker and Greg Roumeliotis

(Reuters) - IBM Corp spoke of on Sunday it had agreed to acquire U.S. application company purple Hat Inc for $34 billion, including debt, because it seeks to diversify its expertise hardware and consulting enterprise into bigger-margin products and capabilities.

The transaction is through a ways IBM's greatest acquisition. It underscores IBM Chief govt Ginni Rometty's efforts to extend the business's subscription-based mostly application offerings, because it faces slowing utility sales and waning demand for mainframe servers.

IBM, which has a market capitalization of $114 billion, will pay $190 per share in money for crimson Hat, a 63 % premium to Friday's closing share rate.

headquartered in 1993, crimson Hat makes a speciality of Linux working systems, essentially the most typical class of open-source utility, which changed into developed as a substitute for proprietary software made by means of Microsoft Corp.

Headquartered in Raleigh, North Carolina, red Hat costs fees to its corporate purchasers for customized facets, preservation and technical help, offering IBM a profitable source of subscription profits.

purple Hat is without doubt one of the very few groups within the cloud computing sector that has each profits boom and free money circulate, Rometty, who has been IBM's CEO on account that 2012, spoke of in an interview with Reuters.

"This acquisition we are obviously doing for growth synergies. here is not about can charge synergies at all," Rometty mentioned in the interview.

The acquisition illustrates how older technology organizations are turning to dealmaking to gain scale and fend off competitors, primarily in cloud computing, the place customers using business software are searching for to save money by means of consolidating their seller relationships.

IBM is hoping the deal will help it trap up with Amazon.com Inc, Alphabet Inc and Microsoft within the all of a sudden transforming into cloud enterprise. IBM shares have misplaced basically a third of their value during the past five years, whereas crimson Hat shares are up one hundred seventy percent over the equal length.

huge BLUE

IBM turned into established in 1911 and is commonplace in the expertise business as big Blue, a reference to its once ubiquitous blue computer systems. It has confronted years of revenue declines, because it transitions its legacy desktop maker enterprise into new expertise products and functions. Its recent initiatives have covered synthetic intelligence and business lines around Watson, named after the supercomputer it developed.

To be sure, IBM is not any stranger to acquisitions. It bought cloud infrastructure company Softlayer in 2013 for $2 billion, and the climate Channel's facts property for greater than $2 billion in 2015. It additionally received Canadian business software maker Cognos in 2008 for $5 billion.

other massive expertise businesses have additionally these days sought to reinvent themselves via acquisitions. Microsoft this yr obtained open supply application platform GitHub for $7.5 billion; chip maker Broadcom Inc agreed to acquire application maker CA Inc for essentially $19 billion; and Adobe Inc agreed to acquire advertising application maker Marketo for $5 billion.

certainly one of IBM's main opponents, Dell technologies Inc, made a big wager on application and cloud computing two years ago, when it obtained records storage enterprise EMC for $67 billion. As a part of that deal, Dell inherited an eighty two percent stake in virtualization software company VMware Inc.

The deal between IBM and red Hat is anticipated to close in the 2d half of 2019. IBM observed it planned to suspend its share repurchase application in 2020 and 2021 to support pay for the deal.

IBM spoke of purple Hat would proceed to be led by way of pink Hat CEO Jim Whitehurst and red Hat's existing administration crew. It intends to retain red Hat's headquarters, facilities, brands and practices.

Lazard Ltd offered economic suggestions to IBM, alongside Goldman Sachs neighborhood Inc and JPMorgan Chase & Co, which additionally provided financing for the deal. Paul, Weiss, Rifkind, Wharton & Garrison LLP provided criminal suggestions to IBM.

Guggenheim partners LLC and Morgan Stanley had been economic advisers to red Hat, whereas Skadden, Arps, Slate, Meagher & Flom LLP offered prison suggestions to the company on the deal.

"understanding first-hand how essential open, hybrid cloud applied sciences are to helping groups unencumber cost, we see the vigour of bringing these two corporations together, and are honored to recommend IBM and commit financing for this transaction," JPMorgan CEO Jamie Dimon spoke of in a statement.

(Reporting by Liana B. Baker and Greg Roumeliotis in big apple; additional reporting by Stephen Nellis in San Francisco and Carl O'Donnell and Jim Finkle in new york; enhancing through Rosalba O'Brien and Peter Cooney)


000-SS2 IBM Systems Software Technical Sales Mastery V1

Study Guide Prepared by Killexams.com IBM Dumps Experts


Killexams.com 000-SS2 Dumps and Real Questions

100% Real Questions - Exam Pass Guarantee with High Marks - Just Memorize the Answers



000-SS2 exam Dumps Source : IBM Systems Software Technical Sales Mastery V1

Test Code : 000-SS2
Test Name : IBM Systems Software Technical Sales Mastery V1
Vendor Name : IBM
Q&A : 32 Real Questions

What a superexcellent supply trendy 000-SS2 questions that paintings in real test.
I wanted to tell you that in past in idea that i might in no way be capable of pass the 000-SS2 take a look at. However after Itake the 000-SS2 education then I came to recognise that the web offerings and material is the excellent bro! And once I gave the exams I handed it in first attempt. I knowledgeable my friends about it, moreover they beginning the 000-SS2 schooling form proper here and locating it sincerely first-rate. Its my excellent enjoy ever. Thank you



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IBM Systems Software Technical Sales Mastery V1

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The problem with analytics | killexams.com real questions and Pass4sure dumps

IBM-Bigdata-Analytics

There is a difference between knowledge and understanding. Knowledge typically comes down to knowing facts while understanding is the application of knowledge to the mastery of systems. You can know a lot while understanding very little. Just as an example, IBM’s Watson artificial intelligence system that defeated the TV Jeopardy champs a few years ago knew all there was to know about Jeopardy questions but didn’t really understand anything. Ask Watson to apply to removing your appendix its knowledge of hundreds of medical questions and you’d be disappointed and probably dead. That’s the problem with most analytics, which is why it can be a hard sell.

The answer to this problem, we’re told, is not just machine learning but Deep Machine Learning, the difference between the two being that plain old machine learning is a statistical process that could be (and used to be) replicated by hand, while the deeper variety looks several generations deep in a longitudinal analysis that quickly grows too big for mere mortals to comprehend. Deep machine learning will, theoretically, find all the interconnections and dependencies that until now we’ve had to rely on domain experts to provide, yet even then it can only happen if you happen to be gathering the right data.

With regular machine learning, when IBM talks about Watson looking inside your business and finding answers, well that can only really happen if a few PhDs are attached to the software. But with deep machine learning the idea is that the machine can do it all. No PhDs required, which would be quite a breakthrough. And so all the big companies fighting over the future of IT are building new circuits -- hardware -- for deep machine learning. Google and IBM have made recent announcements about theirs. Microsoft told me a couple weeks ago that they have similar hardware working in their lab. It’s a space race.

And it’s also at least five years from being real on any commercial scale.

Until deep machine learning is common and fast we’ll be stuck with more mundane analytics. So no, Watson isn’t going to be crunching your company numbers soon and spitting out the right answers or even the right questions to ask. We’re simply not there yet.

Which brings us to a curious thing happening at IBM. Not closing the I.M. Pei-designed lab in Somers, New York that was announced last week. That campus has been half-empty for years. Nor am I talking about the continuation of IBM’s brutal GTS layoffs, which reportedly killed another 14,000+ IBM jobs last week. What I am talking about here is the reorganization of IBM’s analytics business unit that was announced internally two weeks ago. You won’t find a news story about it… until now.

The analytics reorg labelled "One Team, One Platform" wasn’t technically a Resource Action (forced layoff). It was explained as a streamlining of business processes to improve efficiency. Citing similar moves at Google, IBM’s analytics group would no longer have employees working from home and the number of official analytics office locations worldwide would decrease from 200 to 30. People will soon be working and meeting physically together, some of them for the first time. This is good, right?

Yet the reorg is wildly unpopular within the ranks for one important reason: If for some reason affected IBMers need to keep working from home or they can’t or won’t move to their designated IBM office location, well then they are deciding to quit in the view of IBM, which means they are out of the company with no severance -- no package.

Analytics general manager Rob Thomas, who announced the reorg, has been uniformly described to me by IBMers from that division as "an idiot", which may be some internal IBM technical term, I don’t know.

The reorg, which tellingly requires some orthodox Jewish IBMers working in Israel to reverse history and relocate to either Germany or Poland, will inevitably result in a significant reduction in head count yet not have to be accounted for as one. These are stealth layoffs.

Remember that the future of IBM is supposed to be all about CAMSS (cloud, analytics, mobile, security, social) and analytics is the A in CAMSS. In one moment IBM says analytics is strategic, that they are investing and hiring in the area, yet at the same time the company is taking actions that will inevitably lead to the loss of at least some employees who are vital to the unit.

It’s the blindness of this reorg that amazes me. Heads will go no matter what, but in this case the specific heads won’t be decided by the company but by the workers, themselves. The best workers will be the most employable and therefore the quickest to go. If analytics is so strategic how can IBM allow that to happen?

I believe I know what’s happening here. Years of layoffs and unfilled positions at IBM mean that pretty much everyone remaining who isn’t in management is already very good at their jobs. There is no more fat to cut. So why not leave it up to the employees, themselves, especially if the gambit will result in very low separation expenses?

It is unlikely that any of the IBMers who are lost in this reorg will be replaced. Rather, as has become the norm at IBM in recent years, those who are left will be expected to pick up the slack.

Yes, but what about CAMSS? What about analytics being so strategic to the future of IBM?

In order to understand that we have to also understand who is IBM’s customer. Peter Drucker wrote that the purpose of the corporation is "to find a customer". Well who is IBM’s customer if the company is effectively cutting one of the divisions it is relying upon for future growth?

IBM’s customer is Wall Street.

IBM’s products are earnings-per-share, dividends, and share buy-backs, not hardware, software or computer services.

This morning I took a quick look at IBM‘s 1Q16 statement. It was interesting what was stated, how it was stated, and what was not stated. They signed $4.2 billion in Analytics and $2.6 billion in Cloud business. The rest of CAMSS was puny. If Analytics is doing better than the rest, why mess with it? How many billions have they spent to make $11 billion a year on cloud?

IBM has mostly reported revenue. Part of the company’s sales culture, it is what you sign in deals that matters. But how much profit is each division making? That is how most companies are managed. IBM‘s big money maker is services and that is losing business. IBM wants to keep Wall Street off the scent and keeping reported earnings up is critical. So they’re cutting deep with no severance. This is all about buying time until the new business gains traction. However if the new business doesn’t make as much profit, that is going to be a big problem.

This is a very scary gamble for IBM.

What this analytics reorg means is that IBM can’t see a way to reach its near-term Wall Street product objectives without cutting heads in analytics. The division was built up in anticipation of certain sales and profit targets, those targets have not been reached, so IBM will cut expenses until the numbers look right. Forget that this reorg will actually result in less -- not more -- work being done. Forget that it makes even less likely IBM achieving its goals for analytics. Forget all of that.

What it means is the A in CAMSS has already failed. That’s my understanding.


Can Anyone Stop Amazon from Winning the Industrial Internet? | killexams.com real questions and Pass4sure dumps

Executive Summary

Digital natives like Amazon have already disrupted industries such as media, publishing, travel, music, and photography. But who is likely to assume leadership in creating and capturing economic value in Type 3 products (products which have physical components, “smart” components, and connectivity)? If digital natives want to develop software-enabled solutions that siphon off significant value from industrial hardware, they must overcome three barriers: the physics of the hardware, customer intimacy, and difficulty in sharing risks. Likewise, if industrial giants want to lead in the industrial internet they will need to overcome their three barriers: software talent, digital culture, and the incumbent’s dilemma. Among the tech giants, Amazon is a likely winner in the Industrial Internet, as it has successfully fused physical with digital. Amazon understands the economic laws of analog products and is not afraid of massive up-front investments and slower growth.

Just the announcement that Jeff Bezos, Warren Buffett, and Jaime Dimon will be entering the health care space has sent shock waves for industry incumbents such as CVS, Cigna, and UnitedHealth. It also puts a fundamental question back on the agendas of CEOs in other industries: Will software eat the world, as Marc Andreessen famously quipped? Is this a warning shot that signals that other legacy industrial companies, such as Ford, Deere, and Rolls Royce are also at increased risk of being disrupted?

To start to answer that question, let’s tally up the score. There are three types of products today. Digital natives (Amazon, Google, Facebook, Microsoft, IBM) have gained competitive advantage in the first two, and the jury is still out on the third:

  • Type 1: These are “pure” information goods, where digital natives rule. An example would be Google in search, or Facebook in social networking. Their business models benefit from internet connectivity and they enjoy tremendous network effects.
  • Type 2: These are once-analog products that have now been converted into digital products, such as photography, books, and music. Here too, digital natives dominate. These products are typically sold as a service via digital distribution platforms (Audible.com for books, Spotify for music, Netflix for movies).
  • Type 3: Then there are products where input-output efficiency and reliability of the physical components are still critical but digital is becoming an integral part of the product itself (in effect, computers are being put inside products). This is the world of the Internet of Things (IOT) and the Industrial Internet.
  • Manufacturing-heavy companies such as Caterpillar, Ford, and Rolls Royce compete in this world. An aircraft engine is unlikely to become a purely digital product any time soon! Such products have three components: physical components, “smart” components (sensors, controls, microprocessors, software, and enhanced user interface), and connectivity (one machine connected to another machine; one machine connected to many machines; and many machines connected to each other in a system).

    Digital natives have already disrupted industries such as media, publishing, travel, music, and photography. But who is likely to assume leadership in creating and capturing economic value in Type 3 products: Digital natives or industry incumbents? Ford or Tesla? Rolls Royce or IBM? Caterpillar or Microsoft? Amazon, Berkshire Hathaway and JPMorgan Chase combine or UnitedHealth?

    The Challenges for Digital Natives

    Value will no doubt be created in the era of smart, connected machines. We don’t expect Amazon or Microsoft or IBM to design, make, and market agricultural tractors, aircraft engines, or MR scanners. The question really is: Can digital natives develop software-enabled solutions that siphon off significant value from industrial hardware? The answer is “yes.” But it won’t be easy. It will require tremendous amounts of investments in building new capabilities for hardware companies like HP, Cisco, Dell, Samsung, and Lenovo; established software companies like Facebook, Google, Amazon, and Microsoft; and start-ups. In particular, there are three barriers they must overcome:

    1. The physics of the hardware. Companies like Rolls Royce design and manufacture jet engines. These are very complicated machines. There is hard science behind these machines. That’s much different than digital natives like Airbnb where marketing is more important than technical expertise.

    Industry incumbents have expertise in the material sciences, for instance. Further, scientific knowledge keeps improving over time. They have made heavy R&D investments—both basic and applied—to remain at the cutting-edge of the physics of the hardware. Much of this scientific knowledge is protected by patents.

    Mastery of hard science is a pre-requisite to develop software-based solutions on the hardware. These companies’ superior product/domain knowledge provides them the comparative advantage to model the asset’s performance and write high-end/high value-added software applications. A “pure” digital company can write commodity software applications. But it must acquire enough capabilities on the physics to write sophisticated apps that improve assets’ performance.

    2. Customer intimacy. Industrial giants have well-established brands, built strong customer relationships, and signed long-term service contracts. They’ve won the customer’s trust, which is why customers are willing to share data. Digital natives can work with industrial customers, but they have to first earn their trust; they must build capabilities to understand customer operations; they must match the industrials’ cumulative learning from customer interactions; they must learn to ask for the right data; and they have to hire experts in several verticals that can turn data into insights.

    3. Difficulty in sharing risks. Industrial incumbents have product knowledge, customer relationships, and field engineers on customer sites. Companies like Rolls Royce can, therefore, offer outcome deals where they guarantee customer outcomes (examples: zero downtime, higher speed, more fuel efficiency, zero operator error, greater reliability) and share risks and rewards with customers. It would be very hard for Amazon or Google to guarantee customer outcomes and take risks with businesses whose operations they know little about.

    The Challenges for Industrial Giants

    Can the industrial giants lead in the Industrial Internet? The answer is “yes.” But it won’t be easy for them, either. They too have three significant barriers to overcome:

    1. Software talent: The IT talent in industrial companies can execute projects oriented towards process efficiency and cost reduction. That talent is ill-suited to develop new, breakthrough software products that offer superior customer outcomes. For that end, they must be able to attract world-class innovators and software engineers. Is, say, Rolls Royce, in the same consideration set as Facebook and Google for young tech employees? Not, really. If so, how can the industrial giants compete to attract the best talent?

    2. Digital culture: Industrial businesses and digital businesses operate with completely different principles. The characteristics of hardware businesses include long product development cycle, Six Sigma efficiency, and long sales cycle. Software businesses have different characteristics: short product development cycle, flexibility, and short sales cycle. The industrials must build a digital culture based on concepts like lean, agile, simplicity, responsiveness, and speed. That’s a tall order for an established enterprise.

    3. The Incumbent’s Dilemma: Digital has the potential to disrupt industrial businesses. There are three ways digital strategy can cannibalize “core” industrial business. First, data and insights can help improve the productivity of machines; digital, therefore, has the potential to cannibalize future hardware sales. Second, data and insights increase the reliability of machines; digital therefore has the potential to cannibalize future service revenues. Third, software subscription and license might enable customers to do self-service. Current customers could terminate/renegotiate service contracts, and potential customers may not enter into service contracts at all. In short, it is very difficult for a company to disrupt itself.

    The future of the Industrial Internet will involve partnerships across a variety of players including tech companies and industrial companies. The key issue: Who will assume the leadership position to extract maximum economic value in such an ecosystem? Will industrial companies take the lead? Or will the digital natives take the lead? Both have a chance.

    If I were a betting man, I would place my bets on tech giants over industry incumbents. One factor that will favor digital companies in the industrial internet is technological/scientific breakthroughs that level the playing field for newcomers. For example, breakthroughs in battery technology made the electric cars possible. Electric cars are much simpler to design than cars with internal combustion engines, allowing Tesla and BYD to enter the market despite Ford’s decades of expertise. Since electrification and driverless cars go together, other tech companies such as Google, Baidu, Apple, and Lyft will also be able to enter the automotive market. Similar technological changes in jet engines and agricultural tractors can allow tech giants to gain foothold in these industries as well.

    More importantly, Amazon or Google have the resources to acquire the capabilities to master the physics and acquire customer relationships and compete with the industrial giants in the Industrial Internet. They have enough resources and some to buy them, if needed.

    Among the tech giants, Amazon is a likely winner in the Industrial Internet. It has successfully fused physical with digital. Amazon understands the economic laws of analog products and is not afraid of massive up-front investments and slower growth. Its acquisition of Whole Foods and experiments with Amazon Go grocery stores are an example. Amazon is the one company everyone’s scared of, even industrial giants.


    Machines beat us at our own game: What can we do? | killexams.com real questions and Pass4sure dumps

    In this undated publicity image released by Jeopardy Productions, Inc., host Alex Trebek, left, poses with contestants Ken Jennings, center, and Brad Rutter and a computer named Watson in Yorktown Heights, N.Y. On Monday, Feb. 14, 2011, "Jeopardy!" will begin airing two matches spread over three days between Jennings, Rutter and Watson, who was developed by IBM scientists. (AP Photo/Jeopardy Productions, Inc.) NO SALES (AP) -- Machines first out-calculated us in simple math. Then they replaced us on the assembly lines, explored places we couldn't get to, even beat our champions at chess. Now a computer called Watson has bested our best at "Jeopardy!"

    A gigantic computer created by IBM specifically to excel at answers-and-questions left two champs of the TV game show in its silicon dust after a three-day tournament, a feat that experts call a technological breakthrough.

    Watson earned $77,147, versus $24,000 for Ken Jennings and $21,600 for Brad Rutter. Jennings took it in stride writing "I for one welcome our new computer overlords" alongside his correct Final Jeopardy answer.

    The next step for the IBM machine and its programmers: taking its mastery of the arcane and applying it to help doctors plow through blizzards of medical information. Watson could also help make Internet searches far more like a conversation than the hit-or-miss things they are now.

    Watson's victory leads to the question: What can we measly humans do that amazing machines cannot do or will never do?

    The answer, like all of "Jeopardy!," comes in the form of a question: Who - not what - dreamed up Watson? While computers can calculate and construct, they cannot decide to create. So far, only humans can.

    "The way to think about this is: Can Watson decide to create Watson?" said Pradeep Khosla, dean of engineering at Carnegie Mellon University in Pittsburgh. "We are far from there. Our ability to create is what allows us to discover and create new knowledge and technology."

    Experts in the field say it is more than the spark of creation that separates man from his mechanical spawn. It is the pride creators can take, the empathy we can all have with the winners and losers, and that magical mix of adrenaline, fear and ability that kicks in when our backs are against the wall and we are in survival mode.

    What humans have that Watson, IBM's earlier chess champion Deep Blue, and all their electronic predecessors and software successors do not have and will not get is the sort of thing that makes song, romance, smiles, sadness and all that jazz. It's something the experts in computers, robotics and artificial intelligence know very well because they can't figure out how it works in people, much less duplicate it. It's that indescribable essence of humanity.

    Nevertheless, Watson, which took 25 IBM scientists four years to create, is more than just a trivia whiz, some experts say.

    Richard Doherty, a computer industry expert and research director at the Envisioneering Group in Seaford, N.Y., said he has been studying artificial intelligence for decades. He thinks IBM's advances with Watson are changing the way people think about artificial intelligence and how a computer can be programmed to give conversational answers - not merely lists of sometimes not-germane entries.

    "This is the most significant breakthrough of this century," he said. "I know the phones are ringing off the hook with interest in Watson systems. The Internet may trump Watson, but for this century, it's the most significant advance in computing."

    And yet Watson's creators say this breakthrough gives them an extra appreciation for the magnificent machines we call people.

    "I see human intelligence consuming machine intelligence, not the other way around," David Ferrucci, IBM's lead researcher on Watson, said in an interview Wednesday. "Humans are a different sort of intelligence. Our intelligence is so interconnected. The brain is so incredibly interconnected with itself, so interconnected with all the cells in our body, and has co-evolved with language and society and everything around it."

    "Humans are learning machines that live and experience the world and take in an enormous amount of information - what they see, what they taste, what they feel, and they're taking that in from the day they're born until the day they die," he said. "And they're learning from all the input all the time. We've never even created something that attempts to do that."

    The ability of a machine to learn is the essence of the field of artificial intelligence. And there have been great advances in the field, but nothing near human thinking.

    "I've been in this field for 25 years and no matter what advances we make, it's not like we feel we're getting to the finish line," said Carnegie Mellon University professor Eric Nyberg, who has worked on Watson with its IBM creators since 2007. "There's always more you can do to bring computers to human intelligence. I'm not sure we'll ever really get there."

    Bart Massey, a professor of computer science at Portland State University, quipped: "If you want to build something that thinks like a human, we have a great way to do that. It only takes like nine months and it's really fun."

    Working on computer evolution "really makes you appreciate the fact that humans are such unique things and they think such unique ways," Massey said.

    Nyberg said it is silly to think that Watson will lead to an end or a lessening of humanity. "Watson does just one task: answer questions," he said. And it gets things wrong, such as saying grasshoppers eat kosher, which Nyberg said is why humans won't turn over launch codes to it or its computer cousins.

    Take Tuesday's Final Jeopardy, which Watson flubbed and its human competitors handled with ease. The category was U.S. cities, and the clue was: "Its largest airport is named for a World War II hero; its second largest, for a World War II battle."

    The correct response was Chicago, but Watson weirdly wrote, "What is Toronto?????"

    A human would have considered Toronto and discarded it because it is a Canadian city, not a U.S. one, but that's not the type of comparative knowledge Watson has, Nyberg said.

    "A human working with Watson can get a better answer," said James Hendler, a professor of computer and cognitive science at Rensselaer Polytechnic Institute. "Using what humans are good at and what Watson is good at, together we can build systems that solve problems that neither of us can solve alone."

    That's why Paul Saffo, a longtime Silicon Valley forecaster, and others, see better search engines as the ultimate benefit from the "Jeopardy!"-playing machine.

    "We are headed toward a world where you are going to have a conversation with a machine," Saffo said. "Within five to10 years, we'll look back and roll our eyes at the idea that search queries were a string of answers and not conversations."

    The beneficiaries, IBM's Ferrucci said, could include technical support centers, hospitals, hedge funds or other businesses that need to make lots of decisions that rely on lots of data.

    For example, a medical center might use the software to better diagnose disease. Since a patient's symptoms can generate many possibilities, the advantage of a Watson-type program would be its ability to scan the medical literature faster than a human could and suggest the most likely result. A human, of course, would then have to investigate the computer's finding and make the final diagnosis.

    IBM isn't saying how much money it spent building Watson. But Doherty said the company told analysts at a recent meeting that the figure was around $30 million. Doherty believes the number is probably higher, in the "high dozens of millions."

    In a few years, Carnegie Mellon University robotic whiz Red Whittaker will be launching a robot to the moon as part of Google challenge. When it lands, the robot will make all sorts of key and crucial real-time decisions - like Neil Armstrong and Buzz Aldrin did 42 years ago - but what humans can do that machines can't will already have been done: Create the whole darn thing.

    Explore further: 'Jeopardy!' to pit humans against IBM machine

    More information: IBM's Watson: tinyurl.com/4r8w6grJeopardy: jeopardy.com

    ©2010 The Associated Press. All rights reserved. This material may not be published, broadcast, rewritten or redistributed.



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    IBM 000-SS2 Exam (IBM Systems Software Technical Sales Mastery V1) Detailed Information



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