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C9550-512 - IBM Business Process Manager v8.5.7 Application Development using Process? - Dump Information

Vendor : IBM
Exam Code : C9550-512
Exam Name : IBM Business Process Manager v8.5.7 Application Development using Process?
Questions and Answers : 63 Q & A
Updated On : October 18, 2018
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C9550-512 Questions and Answers

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C9550-512 IBM Business Process Manager v8.5.7 Application Development using Process?

Study Guide Prepared by Killexams.com IBM Dumps Experts


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C9550-512 exam Dumps Source : IBM Business Process Manager v8.5.7 Application Development using Process?

Test Code : C9550-512
Test Name : IBM Business Process Manager v8.5.7 Application Development using Process?
Vendor Name : IBM
Q&A : 63 Real Questions

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IBM IBM Business Process Manager

IBM Launches New business process administration software and capabilities | killexams.com Real Questions and Pass4sure dumps

First name: remaining name: email handle: Password: ascertain Password: Username:

Title: C-level/President manager VP staff (associate/Analyst/and so forth.) Director

characteristic:

position in IT decision-making system: Align business & IT goals Create IT strategy examine IT needs manipulate supplier Relationships consider/Specify brands or vendors different role Authorize Purchases not concerned

Work cell: business: business size: business: road address city: Zip/postal code State/Province: country:

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IBM: We're protesting over Pentagon's $10bn winner-take-all JEDI cloud deal | killexams.com Real Questions and Pass4sure dumps

IBM has adopted Oracle in filing a bid protest towards the Pentagon's 'single-cloud' necessities for its JEDI contract, which could be price up to $10bn over 10 years.

The company filed the bid protest with the govt Accountability office simply forward of this Friday's deadline for providers to publish JEDI, or Joint enterprise defense Infrastructure, proposals.

IBM is contesting the only-seller requirement and argues that some requests choose a single vendor.

"JEDI's fundamental flaw lies in mandating a single cloud atmosphere for as much as 10 years," Sam Gordy, IBM's US Federal typical manager, talked about in a blogpost.

He also said the Pentagon is limiting competition by means of together with necessities that "mirror one supplier's inner processes" or "unnecessarily mandate definite capabilities" by means of requiring them to be in location via the submission closing date, as hostile to when the work would begin.

"Such inflexible requirements serve only one goal: to arbitrarily slender the field of bidders," stated Gordy.

"throughout the year-lengthy JEDI saga, countless issues had been raised that this solicitation is aimed toward a selected dealer. At no factor have steps been taken to alleviate these issues," he added.

SEE: Cloud v. records middle determination (ZDNet particular document) | down load the report as a PDF (TechRepublic)

The winner would assist the department of defense build a single business cloud that shops labeled counsel and enhances its weapons capabilities, while enabling the federal executive to consolidate its records facilities.

other bidders consist of Amazon net features, generally viewed because the frontrunner, and Microsoft, which introduced this week it expects to be certified for high Secret US categorised records via the primary quarter of 2019.

Oracle, Microsoft, and Google had been lobbying defense to make JEDI a multi-seller arrangement.

Oracle filed its bid protest with the GAO on August 6, based on NextGov. The company earlier this year won a ruling by the GAO over another $950m defense deal awarded to a accomplice of Amazon internet functions.

unless this week, Google was vying for a slice of the JEDI, contract. besides the fact that children, it dropped out since it lacked the certifications required to host some categorized information and argued that the deal might clash with its AI principles.

Google talked about it will have submitted a concept were it now not for the one-seller requirement.

IBM's Gordy talked about counting on a single seller would "give dangerous actors just one target to center of attention on should they wish to undermine the militia's IT spine".

Dana Deasy, the Pentagon CIO heading up the JEDI deal, informed the Washington put up that using varied suppliers would add useless complexity.

"we now have on no account built an enterprise cloud. beginning with a number of enterprises while on the same time attempting to construct out an commercial enterprise ability simply without difficulty did not make feel," Deasy observed.

old and connected coverage

Google: here's why we're pulling out of Pentagon's $10bn JEDI cloud race

Google might not bid for the branch of defense's huge cloud contract since it may conflict with its AI principles in opposition t developing weapons.

Microsoft touts its coming suitable-secret cloud certification forward of $10 billion JEDI contract time limit

Microsoft is stepping up its executive cloud campaigning simply forward of the closing of the bids for the Pentagon's $10 billion, winner-take-all JEDI cloud contract.

department of defense's updated cloud-computing contract nonetheless is winner-take-all

Microsoft and different cloud providers are keeping shut tabs on the estimated multi-billion-greenback U.S. DoD JEDI cloud contract, as it winds its means in the course of the bidding method.

Pentagon delays disputed JEDI cloud contract

The JEDI will need to wait ages longer for the award of the Pentagon's hotly-contested multi-billion "winner-take-all" cloud contract because the RFP has simply been delayed....

Google employee protest: Now Google backs off Pentagon drone AI venture

Google might not bid to renew its task Maven contract with the Pentagon after it expires in 2019.

Google worker protest: Now 'Googlers are quitting' over Pentagon drone mission

The number of personnel towards Google's venture Maven position grows, and they're now backed through a big group of academics.

Google says it won't build AI for weapons

In a collection of concepts laid out to e-book its construction of synthetic intelligence, Google additionally referred to it may not build AI for surveillance that violates "internationally authorized norms."

Google to unlock DeepMind's StreetLearn for teaching computer-researching agents to navigate cities TechRepublic

The StreetLearn environment depends on photos from Google road View and has been used by way of Google DeepMind to instruct a software agent to navigate a lot of western cities without reference to a map.

fb Portal gadget seeks to take video chat to the next level CNET

however will people believe the social community's new client machine after all those statistics protection complications?


Marsh Expands IBM Collaboration to deliver Blockchain Proof of coverage to greater purchasers via Salesforce Platform | killexams.com Real Questions and Pass4sure dumps

ny--(business WIRE)--

Marsh, a world leader in coverage broking and creative chance administration solutions, announced these days that it's increasing its collaboration with IBM (IBM) to deliver customers with wider access to the industry’s first commercial blockchain solution for proof of coverage.

by using blockchain know-how, Marsh and IBM are working together to transform the certificate of coverage procedure from advanced and manual, to streamlined and clear, permitting shoppers to velocity up imperative company functions reminiscent of hiring contractors.

Leveraging the IBM Blockchain Platform and IBM’s strategic relationship with Salesforce, the international leader in CRM, Marsh will work with IBM to prolong its industrial proof of coverage blockchain solution to be at once obtainable to Marsh consumers in the course of the Salesforce Platform.

the new initiative can be demonstrated nowadays at Salesforce’s Dreamforce 2018, taking location in San Francisco.

Commenting on the enlargement, Sastry Durvasula, Marsh’s Chief Digital, facts, and Analytics Officer, who will be speaking concerning the new collaboration at Dreamforce, talked about: “with the aid of making proof of insurance attainable digitally and right away for our shoppers via Salesforce, we're streamlining a key business requirement via convenient and comfortable sharing of proof of coverage.”

A distributed ledger expertise, blockchain establishes a shared, immutable listing of the entire transactions that take location within a network after which allows permissioned events entry to trusted facts in actual-time. due to the fact that proof of assurance is a key business requirement in lots of industries, this blockchain solution allows for the advent of a network of networks to supply verification on a much broader scale.

“disbursed ledger applied sciences are using effectivity throughout many industries by means of enabling legacy manual methods to operate more correctly and with stronger transparency and have confidence,” spoke of Sandip Patel, common manager, insurance business, IBM. “This innovation is an important example of how blockchain will also be used to power precise company consequences and collaboration in the assurance trade.”

Marsh introduced in April the first business proof of insurance solution, in conjunction with IBM, with enter from insurance specifications leader ACORD and customer feedback from ISN, the global chief in contractor and organisation assistance management. The blockchain is developed on the open source Hyperledger fabric technology and IBM Blockchain Platform.

Salesforce, Dreamforce and others are among the many logos of salesforce.com, inc.

About Marsh

a world chief in coverage broking and innovative risk management options, Marsh’s 30,000 colleagues propose particular person and commercial valued clientele of all sizes in over 130 countries. Marsh is a unconditionally owned subsidiary of Marsh & McLennan organizations (MMC), the leading global professional capabilities company within the areas of risk, strategy and people. With annual revenue over US$14 billion and very nearly 65,000 colleagues international, MMC helps valued clientele navigate an increasingly dynamic and complicated atmosphere via four market-main organizations. in addition to Marsh, MMC is the mother or father enterprise of guy carpenter, Mercer, and Oliver Wyman. observe Marsh on Twitter @MarshGlobal; LinkedIn; facebook; and YouTube, or subscribe to BRINK.

View source version on businesswire.com: https://www.businesswire.com/information/home/20180926005783/en/


C9550-512 IBM Business Process Manager v8.5.7 Application Development using Process?

Study Guide Prepared by Killexams.com IBM Dumps Experts


Killexams.com C9550-512 Dumps and Real Questions

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



C9550-512 exam Dumps Source : IBM Business Process Manager v8.5.7 Application Development using Process?

Test Code : C9550-512
Test Name : IBM Business Process Manager v8.5.7 Application Development using Process?
Vendor Name : IBM
Q&A : 63 Real Questions

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IBM Business Process Manager v8.5.7 Application Development using Process?

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Managing to the Next Century - The 5 Big Things For Agile Transitions | killexams.com real questions and Pass4sure dumps

Key Takeaways
  • Successful transformation needs strong leadership to support and protect agile culture
  • Help teams and stakeholders to self-organize
  • Manage your portfolio with Outcomes (not Output)
  • Systematically remove sources of waste and delay faced by Agile teams
  • Measure and improve value delivered with frequent feedback (Inspect and Adapt)
  • Moving to Agile is hard, but necessary

    As we move into the digital age, organizations increasingly want to be more agile. Maybe it is motivated by loss; loss of customers, employees, market share or brand value. It also could be driven by a need to “keep up with the Joneses” and the desire to be as cool as Amazon or Netflix. Motivation not withstanding agile transitions are common place with many organizations looking to become “more agile”.

    However, for many organizations, the journey to becoming “more agile” is littered with false starts, unhappy stakeholders and annoyed and confused leaders. Becoming agile is difficult, not just because of agility, but actually because any change is hard for organizations to embark upon. To quote Niccolo Machiavelli:

    There is nothing more difficult to take in hand, more perilous to conduct, or more uncertain in its success, than to take the lead in the introduction of a new order of things. - The Prince (1532)

    And the move to agile is a new order of things, a much more fundamental change than the adoption of a new methodology or process framework. Carlota Perez world famous economist describes the change as common-sense response to the digital age.

    No one today would propose a centralized, rigid, top-down organizational structure, where you cannot communicate across functions except through your bosses. However, that was precisely what Alfred Sloan set up at General Motors, to a great advantage at his time. With today’s communications and flexible technologies, agile creative networks make more sense and lead to much more productivity.

    Perez goes on to argue that the previous age focused on mass production. Which by its very nature is different from digital production. Mass product focuses on reducing the unit cost, and increasing quality by process optimization, conformity and standardization. Digital on the other hand, focuses on the unit size of one, where everything is unique in terms of the problem, solution and ultimate value stream. But how many organizations still manage their digital products with the same mass production paradigm with resource optimizations, standardized process and specialism of labor? The agile age requires a different approach to outcomes, work, teams and management.

    A focus on outcomes – taking ownership of the business value

    Instead of a focus on getting work done in the most cost-efficient, consistent, risk adverse manner, agile teams concentrate on delivering value in the most effective way. They focus not on the work, but instead the outcomes they are trying to deliver. Scrum, the world’s most popular agile framework, is empirical in approach. Thus, problems are broken down into small experiments with frequent observation which may result in changes. Smaller batches of work coupled with the need to concentrate on value means:

  • Ownership becomes a top priority - In most traditional organizations, decisions are made in a slow, consensus driven manner. Documents are used to store the details and are signed off to ensure that everyone knows who was involved. Change is treated as a problem with expensive management processes, review committees and escalation approaches. Agility requires decisions to be made, and work to be delivered allowing feedback and improvement. That requires ownership.
  • Value needs to be clearly understood and measured, which seems like a simple requirement - The reality is that much work does not have a clear connection to value. Measurement is also very difficult as organizations wrestle with visibility, transparency and ownership.
  • Teams must be aligned to customers – Often teams have a poor understanding of their relationship with a customer and even if they have the benefit of knowing the customer they neither are aligned to them or can get access to them. Customers are managed by the front end of the business, sales, marketing and support. Product development has to work with these organizations to get access and feedback.
  • Frequent understanding requires frequent observation – For digital products, that means delivering them to customers to be used. Delivering frequently has huge implications on every part of the development process from how requirements are managed, to how the product is tested. How the underlying software is architected also changes to support smaller more frequent changes. For non-digital or hybrid products the need to gather frequent feedback also changes the way work is done with more of a focus on frequent review, simulation and testing.  
  • Work is not owned by a manager

    In the new agile world, it is neither possible to tell people to do a particular task or plan at the same level of breadth or depth. Work is defined, managed and executed by empowered teams who are not focused on the task, but instead the outcome they are trying to achieve. Quality including technical debt is treated in the same way that value is treated allowing the team and the business to make explicit, transparent decisions on trade-offs. But moving away from traditional managed work to a more agile approach requires more than managers stepping away but requires organizations to have:

  • Clarity of accountability and ownership – In most modern organizations everyone is responsible, but no one is accountable. In an agile organization, it is clear that the team is responsible for the outcomes, the business for deciding where to focus the team in terms of value and the organization accountable for building the environment for the team to be successful. By having clear responsibilities and allowing teams to make decisions agile organizations can quickly cut away lots of the waste associated with traditional top down management approaches.
  • Management focused on building the right environment – Many managers get their position because they are good at doing the work. That leads to a culture of “parental-ism” with managers telling people what to do. For simple work this can be effective, but when work becomes complex and process variable it is very difficult requiring the manager to be always there. The management role therefore has to change to coaching, and servant leadership helping the team deal with the work and focus on the outcomes.
  • Projects replaced by products – For many organizations, work is done through projects, and at any one time there are thousands of projects being run. Each project has funding, resources and lots of documentation. People work on multiple projects and teams are created and destroyed frequently. Agility does not force an organization not to do projects, but does encourage a more focused, team centric approach to work. By reducing projects and moving to a product or customer alignment, teams can better focus on outcomes. Additional benefits are simpler planning cycles, reduced complexity of communication and reducing of the dreaded context switching problem. Projects will still exist for initiatives that cross multiple customer / product / outcome boundaries, but they will not be the norm. 
  • Teams are real

    At the very heart of the agile organization is a collection of teams, self-organized and empowered to make decisions. They have all the right skills to deliver value and are supported by an organization that fills in any gaps and helps them to get better.  At scale that means teams of teams and the adoption of practices to ensure that dependencies are effectively managed. But unlike in the world of mass production, agile organizations do not benefit from “throwing lots of bodies at a problem”, and instead encourage leaner, “smarter” organizational models. Organizations may need to change their approach to teams by:

  • Concentrating on building long lived teams – Teams are not a new concept for organizations, in fact for many they like the idea so much they put people on multiple teams. Agile organizations encourage a more focused, people centric model with teams having an identity and consistent membership. That means that work is brought to the team rather than the team forms around the work. This will require a change in thinking and process for many project management organizations.  
  • Separating of work management from people management – It is very hard to be transparent when the person who reviews you, determines your bonus and decides on promotions is working with you on a daily basis. By having a clear separation of work management and people management on agile teams encourages transparency and openness. Work is managed by the team, people development and management is either managed by some sort of community or guild, or by more traditional management.
  • Incrementally evolving the architecture towards customers – For many organizations, individuals are aligned to applications and those applications are reused in many different customer or value contexts. That leads to complexity with everything affecting everything. Modern, agile enterprise architectures focus on optimizing for agility. That may mean that some things are duplicated and that the total cost of ownership for an individual application might be higher, but ultimately the value of responding to the market outweighs that cost. Optimization moves in terms of location in the stack as it has done previous with changes to the cost of disk space or processing.
  • Diversity matters – To respond to complex problems requires experienced teams with a variety of different perspectives and skills. Diversity of opinion, idea and solution are crucial for delivering value. But for many organizations team boundaries re-enforce departmental, system and experience boundaries. For agile to work, and for organizations to deliver great customer solutions it is important to build diverse teams that challenge the status quo, look and feel different.
  • Introducing the 5 challenges of Agile Transition

    Moving to the “new order” is not easy, and many organizations have begun the journey with the adoption of Scrum, and are looking at scaling frameworks such as LeSS, SAFe and Nexus, and organizational models like the Spotify. Unfortunately, there is no single silver bullet for “transforming” your organization into an agile, digital native enterprise. Looking at organizational frameworks and scaling models are great tools and just one piece of the puzzle, as the move requires a more holistic approach to agility. It also cannot be thought of as replacing one methodology with another, but instead something more fundamental to the way the organization responds to the market and delivers value. Organizations should concentrate on 5 broad challenges.

  • Support and protect agile culture with strong leadership
  • Help teams and stakeholders to self-organize
  • Manage your portfolio with outcomes (not output)
  • Systematically remove sources of waste and delay faced by Agile teams
  • Measure and improve value delivered with frequent feedback (inspect and adapt)
  • Support and protect agile culture with strong leadership

    The culture of the organization is defined by the norms enacted by the organization in how they do everything. Leadership defines and re-enforces those norms in the actions and words. Having leaders that embody the values of agility and reinforce its principles is crucial for any transition. For example, agility requires a continuous approach to learning and understanding, but if leaders focus on mistakes and apportioning blame, then it is likely that any empirical process will not work.

    Help teams and stakeholders to self-organize

    Creating an environment where teams self-organize and are empowered to make decisions that deliver value is the foundation for agility.  Unfortunately, just telling teams to be empowered and self-organize is not an effective way to introduce those ideas. Instead change needs to be introduced incrementally and practices such as Delegation Board from management 3.0 can help slowly move teams to taking the lead.  

    Manage your portfolio with outcomes (not output)

    Agility like anything is only as good as the outcome you are focused on. By building an agile enterprise that is focused on customer outcomes rather than work you not only enable better alignment, but you also drive a clear vision and objective. Traditional organizations have always focused on the system, with only small parts of the organization being connected to the customer and the outcomes they seek. Modern, agile enterprises make outcomes in the context of customers’ needs front and center and make everyone responsible for them.

    Systematically remove sources of waste and delay faced by Agile teams

    Waste is the enemy not of efficiency but of value. Waste to agile teams is anything that stops them from delivering value and improving. Waste such as hand-offs to external departments to waste in time spent in review meetings. To become more agile everything is on the “waste removal” agenda and nothing is “set in stone”. That means that groups such as finance, audit and compliance need to be part of the change allowing them to be review existing processes with a view to change. Planning is often an area where waste is present with long term planning cycles and change management processes taking valuable time away from key stakeholders and delivery teams. In Scrum, waste is managed throughout by making it transparent in the daily Scrum and opportunities for improvement being discussed in Sprint Review and Retrospective. But outside of any particular framework agile teams look at everything with an eye to learn and improve. They also have the courage to challenge the status quo.

    Measure and improve value delivered with frequent feedback (Inspect and Adapt)

    If you were going to measure one thing to determine if an enterprise is agile it would be how frequently they get their customers, stakeholders and teams to review the delivered items. Getting real data on the use on what you have delivered, coupled with a desire to create the simplest thing to get feedback are cornerstones to agility. Famous agile organization customer centric organization Amazon famously delivers software every 11.6 seconds. Not because it wants to add more features, but because it wants to try new ideas with its customers and get feedback.

    Summary

    It seems ironic, but many organizations think of a move to agile as a hill to be climbed using traditional approaches. Build a plan, find the resources and then execute. But becoming an agile enterprise is much more than adopting a particular process framework or changing job titles. It is a fundamental change to the way in which you respond to the market. Organizational models, processes, tools and even the underlying products will change frequently in response the needs of the customer, market and stakeholders. An agile enterprise is at its heart a customer-centric learning organization. Peter Senge in his book the 5 Discipline, The Art and Practice of a learning organization describes the need for an organization to becoming focused on learning.

    “The only sustainable competitive advantage is an organization's ability to learn faster than the competition”.

    Ultimately, by concentrating on the 5 challenges of agile leadership, self-organization, becoming customer outcome centric, waste adverse and frequently delivering learning the culture of the organization, organizations will become more focused on learning and will become more agile.  

    About the Author

    Dave West is the Product Owner and CEO at Scrum.org. He is a frequent keynote at major industry conferences and is a widely published author of articles and research reports. He also is the co-author of two books, The Nexus Framework For Scaling Scrum and Head First Object-Oriented Analysis and Design. He led the development of the Rational Unified Process (RUP) for IBM/Rational. After IBM/Rational, West returned to consulting and managed Ivar Jacobson Consulting for North America. Then as VP, research director Forrester Research, he ran the software development and delivery practice. Prior to joining Scrum.org he was Chief Product Officer at Tasktop where he was responsible for product management, engineering and architecture. As a member of the company’s executive management team was also instrumental in growing Tasktop from a services business into a VC backed product business with a team of almost 100.


    Artificial Intelligence Needs To Reset | killexams.com real questions and Pass4sure dumps

    Navigating AI hypeDepositphotos

    I had the chance to interview my colleague at ArCompany, Karen Bennet, a seasoned engineering executive in platform technology, open and closed source systems and artificial intelligence technology. A former engineering lead from Yahoo!, and part of the original team who brought Red Hat to success, Karen evolved with the technological revolution, utilizing AI in expert systems in her early IBM days, and is currently laying witness to the rapid experimentation in machine learning and deep learning. Our discussions about the current state of AI have culminated into this article.

    It’s difficult to navigate AI amidst all the hype. The promises of AI, for the most part, have not come to fruition. AI is still emerging and has not become the pervasive force that has been promised. Consider the compelling stats that validate excitement in the AI hype:

  • 14X increase in the number of active AI startups since 2000
  • Investment into AI start-ups by VCs has increased 6X since 2000
  • The share of jobs requiring AI skills has grown 4.5X since 2013
  • As of 2017, Statista put out these findings:

    As of last year, only 5% of businesses worldwide have incorporated AI extensively into their processes and offerings, 32% have not yet adopted, and 22% do not have plans to.

    Statista: Adoption level of artificial intelligence (AI) in business organizations worldwide, as of 2017Statista

    Filip Pieniewski confirmed in his recent post on Venturebeat: “The AI winter is well on its way”:

    We are now in the middle of 2018 and things have changed. Not on the surface yet — the NIPS conference is still oversold, corporate PR still has AI all over its press releases, Elon Musk still keeps promising self-driving cars, and Google keeps pushing Andrew Ng’s line that AI is bigger than electricity. But this narrative is beginning to crack.

    We touted the claims of the autonomous driving car. Earlier this spring the death of a pedestrian to a self-driving vehicle raised alarms that went beyond the technology and called to question the ethics or lack thereof behind the decisions of an automated system. The trolley problem is not a simple binary choice between the life of one person to save 5 people but rather evolves into a debate of conscience, emotion and perception that now complicates the path to which a reasonable decision can be made by a machine. The conclusion from this article states:

    But the dream of a fully autonomous car may be further than we realize. There’s growing concern among AI experts that it may be years, if not decades, before self-driving systems can reliably avoid accidents.

    To use history as a predictor, both cloud and the dot net industries took about 5 years before they started impacting people in a significant way, and almost 10 years before these industries influenced major shifts in the market. We are envisioning a similar timeline for Artificial Intelligence. As Karen explains,

    To enable adoption by everyone, a product needs to be in place, one that is scalable and one that can be used by everyone–not just data scientists. This product will need to take into account the data lifecycle of capturing data, preparing it, training models and predicting. With data being stored in the cloud, data pipelines can continuously extract and prepare them to train the models which will make the predictions. The models need to continuously improve from new training data, which, in turn, will keep the models relevant and transparent. That is the objective and the promise.

    Building AI Proof of Concepts with No Significant Use Cases

    Both Karen and I have come from technology and AI start-ups. What we’ve witnessed and what we’ve realized among discussion with peers within the AI community is the widespread experimentation across a multitude of business issues, which tend to stay in the labs.

    This recent article substantiates the widespread AI pilots that are more common today:

    Vendors of AI technology are often incentivized to make their technology sound more capable than it is – but hint at more real-world traction than they actually have… Most AI applications in the enterprise are little more than ‘pilots.’ Primarily, vendor companies that sell marketing solutions, healthcare solutions and finance solutions in artificial intelligence are simply test-driving the technology. In any given industry, we find that of the hundreds of vendor companies selling AI software and technology, only about one in three will actually have the requisite skills to do artificial intelligence in the first place.

    VCs are realizing they may not see a return on their investments for some time. However, ubiquitous experimentation with very few models seeing daylight is just one of the reasons why AI is not ready for prime time.

    Can Algorithms be Accountable?

    We’ve heard of AI “black-box,” a current approach that has no visibility into how decisions are made. This practice runs in the face of banks and large institutions that have compliance standards and policies that mandate accountability. With systems operating as black boxes, there may be an inherent trust put in algorithms as long as the creation of these algorithms have been reviewed and have met some standards by critical stakeholders. This notion has been quickly disputed given the overwhelming evidence of faulty algorithms in production and the unexpected and harmful outcomes that result from them. Many of our simple systems operate as black boxes beyond the scope of any meaningful scrutiny because of intentional corporate secrecy,  the lack of adequate education and understanding how to critically examine the inputs, the outcomes and most importantly, why these results occurred. Karen concurs,

    The AI industry today is at a very early stage of being enterprise-ready. AI is very useful and ready for discovery and aiding in parsing through significant amounts of data, however it still requires human intervention as a guide to evaluate and act on the data and their outcomes.

    Karen clarifies that machine learning techniques today enable data to be labeled to identify insights. However, as part of this process, if some the data are erroneously labelled, or if there is not enough data representation, or if there are problematic data signifying bias, bad decision-making results are likely to occur. She also attests current processes continue to be refined:

    Currently, AI is all about decision support, to provide insights into a form for which business can draw conclusions. In the next phase of AI, which automates actions from the data, there are additional issues that need to be addressed like bias, explainability, privacy, diversity, ethics, and continuous model learning.

    Karen illustrates an example of an AI model making mistakes is seen when image captioning exposes the knowledge learned by training on images labeled with the objects they contain. This suggests that having a common sense world model of objects and people is required for an AI product to truly understand. A model only exposed to the limited number of labeled objects and limited variety in the training set will limit the efficacy of this common sense world model. Research into determining how a model treats its inputs and reaches its conclusions, in human understandable terms, is needed for enterprise. Amazon’s release of Rekognition, its facial recognition technology is an example of a technology currently in production and licensed for use while noticeable gaps exist in its effectiveness. According to a study released by the ACLU:

    ...the technology managed to confuse photos of 28 members of Congress with publicly available mug shots. Given that Amazon actively markets Rekognition to law enforcement agencies across the US, that’s simply not good enough.

    Joy Buolamwini, and MIT graduate and Founder of Algorithmic Justice League in this latest interview called for a moratorium on this technology stating it was ineffective and needed more oversight, and has appealed for more government standards into these types of systems before they are publicly released.

    AI’s Major Impediments: Mindset, Culture and Legacy

    Having to transform from legacy systems is the top barrier to implement AI into many organizations today. Mindset and culture are elements of these legacy systems that provide a systemic view into the established process, values, and business rules that have dictated, not only how organizations operate, but also why these ingrained elements will create significant hurdles for business, especially when things are currently humming nicely. Therefore, there is no real incentive to dismantle infrastructures at the moment.

    AI is a component of business transformation and while that topic has gained as much buzz as the AI hype, the investment and commitment required to make significant changes are met by hesitation. We’ve heard from companies willing to experiment on specific use cases but are unprepared for the requirements to train, re-engineer process, and revamp governance and corporate policies. For larger organizations who are compelled to make these significant investments, the question shouldn’t be one of return on investment, but rather, sustainable competitive advantage.

    The Problems with Data Integrity

    AI today needs massive amounts of data to be able to produce meaningful results but is unable to leverage experiences from another application. While Karen argued there is work in progress to overcome these limitations, the transfer of learnings is needed before models can be applied in a scalable way. There are scenarios, however, where AI can be used effectively today, such as revealing insights in images, voice, video and being able to translate languages.

    Companies are learning that focus should be on:

    1) diversity in the data, which includes proper representation across populations

    2) ensuring diverse experiences, perspectives and thinking into the creation algorithms

    3) prioritizing quality of the data over than quantity

    These are important especially as bias is introduced and trust and confidence in data degrade. For example, Turkish is a gender-neutral language, but the AI model in Google translator incorrectly predicts the gender when translating to English. As well, cancer spotting AI image recognition is only trained on fair-skinned people. From the computer vision example above, Joy Buolamwini tested these AI technologies and realized they worked more effectively on male vs. female, and on lighter vs. darker skin. The “error rates were as low as 1% on males and as high as 35% on dark females.” These issues occur because of the failure to use diverse training data. Karen concedes,

    The concept of AI is simple but the algorithms get smarter by ingesting more and more real world data, however being able to explain the decisions becomes extremely difficult. The data may be continuously changing and AI models require filters to prevent incorrect labeling such as an image of a black man being labeled as a gorilla or a panda becoming labelled as a gibbon. Enterprises relying on faulty data to make decisions will lead to ill-informed results.

    Fortunately, given AI’s nascency, very few organizations are making significant business decisions from the data today. From what we’ve witnessed most solutions produce mainly product recommendations and personalizing marketing communication. Any wrong conclusions that result from these have less societal impacts… at least for now.

    Using data to make business decisions is not new, but what has changed is the exponential increase in volume and mix of structured and unstructured data being used. AI enables us to use data from their source continuously and obtain insight much faster. This is an opportunity for businesses that have the capacity and structure to handle data volume from diverse sources. However, for other organizations, the masses of data can represent a risk because of the divergent sources and formats that make it more difficult to transform the information: emails, system logs, web pages, customer transcripts, documents, slides, informal chats, social networks and exploding rich media like images and video. Data transformation continues to be a stumbling block towards developing clean data sets, hence effective models.

    Bias is More Prevalent than We Realize

    Bias exists in many business models to minimize risk assessments, and optimize targeting opportunities and while they may produce profitable business results, they have been known to result in unintended consequences that cause individual harm and deepen economic disparities. Insurance companies may use location information or credit score data to issue higher premiums to poorer customers. Banks may approve prospects with lower credit scores, who are already debt-ridden but may be unable to afford the higher lending rates.

    There is a heightened caution surrounding bias because the introduction of AI will not only perpetuate existing biases, the result from these learning models may generalize to the point it will deepen the economic and societal divide. Bias presents itself in current algorithms to determine the likelihood of recidivism (the likelihood to re-offend) like COMPAS. The Correction Offender Management Profiling for Alternative Sanctions (COMPAS) was created by a company known as Northpointe. The goal of COMPAS was to assess the risk and prediction of criminality for defendants in pre-trial hearings. The types of questions used in the initial COMPAS research revealed enough human bias that the system perpetuated recommendations that unintentionally treated blacks, who would never go on to re-offend, more harshly by law than white defendants, who would go on to re-offend and were treated more leniently at the time of sentencing. With no public standard available, Northpointe was able to create its own definition of fairness, and develop an algorithm without third-party assessment… until recently. This article confirmed, "A Popular Algorithm Is No Better at Predicting Crimes Than Random People" …

    If this software is only as accurate as untrained people responding to an online survey, I think the courts should consider that when trying to decide how much weight to put on them in making decisions

    Karen stipulated,

    While we try to fix existing systems to minimize this bias, it is critical that models train on diverse sets of data to prevent future harms.

    Given the potential risks to faulty models pervading business and society, businesses do not have governance mechanisms to police for unfair or immoral decisions that will inadvertently impact the end consumer. This is discussed under ethics.

    The Increasing Demand for Privacy

    Karen and I came from Yahoo! We worked with strong research and data teams that were able to contextualize behavior from users across our platform. We continuously studied user behavior and understood their propensities across our multitude of properties from Music, to Homepage, to Lifestyle, News etc. At that time, there was there were no strict standards or regulation for data use. Privacy was relegated to user passive agreements of the platform’s terms and conditions, similar to today.

    The recent Cambridge Analytica/Facebook scandal has brought the personal data privacy front and center. Frequent data breaches occurring at major credit institutions like Equifax and most recently, Facebook and Google + continue to compound this issue. The issue of ownership, consent and erroneous contextualization makes this a ripe topic as AI continues to iron out its kinks. The European General Data Protection Regulation (GDPR) which has come into effect May 25, 2018, will change the game for organizations, particularly those who collect, store and analyze personal user information. It will change the rules for which business operated under for many years. The unbridled use of personal information has come to a head, as the business will now come to the realization there will be significant limitations on data use and more importantly, ownership.

    We are seeing the early effects of this in location-based advertising. This $75 billion industry which is slated to grow by a 5-year 21% CAGR by 2021 continues to be impeded by the oligopoly of Facebook and Google, securing the bulk of revenues. And now, the GDPR raises the stakes to make these ad-tech companies more accountable :

    Twitter @hessiejonesTwitter @hessiejones

    The stakes are high enough that [advertisers] have to have a very high degree of confidence that what you're being told is actually in compliance. It seems like there is enough general confusion about what will ultimately constitute a violation that people are taking a broad approach to this until you can get precise about what compliance looks like.

    While regulation will eventually cripple revenues, at least for the moment, the mobile and ad platform industries are also facing increasing scrutiny from the very subjects they have been monetizing for many years: the consumer. This, coupled with the examination around established practices, will force the industry to shift their practices in the collection, aggregation, analysis, and sharing of user information.

    Operationalizing privacy will take time, significant investment (a topic that needs to be afforded more attention), and a change in mindset that will impact organizational policy, process, and culture.

    The Inevitable Coupling of AI & Ethics

    The prevailing factor of AI ensures societal benefits, including streamlining processes, increasing convenience, improving products and services, and detecting potential harms through automation. Ceding to the latter means readily measuring inputs/outputs against outcomes in renewed manufacturing processes, service, and assessment solutions, production as well as product quality.

    As discussions and news about AI persist, this term, “AI” coupled with “ethics” reveals increasingly grave concerns where AI technology can inflict societal damage that will test human conscience and values.

    CB Insights: Tech Cos Confront Ethics of AICB Insights

    Beyond individual privacy concerns, today we are seeing examples of innovation that border on the unconscionable. As stated previously, Rekognition and Face++ are being used in law enforcement and citizen surveillance while the technology is deemed to be faulty. Employees walked out in protest of Google’s decision to provide artificial intelligence to the Defense Department for the analysis of drone footage, with the goal of creating a sophisticated system to surveil cities in a project known as Project Maven. The same tech giant is also building Project Dragonfly for China, a censored search engine that also has the ability to map individual searches to identity.

    Decision-makers and regulators will need to instill new process and policies to properly assess how AI technologies are being used, for what purpose and whether there may be unintended fallout in the process. Karen pointed to new questions in determining the use of data in AI algorithms that will need to be considered:

    How do we detect sensitive data fields and anonymize them while preserving the important features of a dataset? Can we train on synthetic data as an alternative in the short term? The question we need to ask ourselves when creating the algorithm:  What fields do we require to deliver the outcomes we want? In addition, what parameters should we create to define “fairness” in the models, meaning does this treat two individuals differently? And if so, why? How do we continuously monitor for this within our systems?

    An AI Winter is a Serendipitous Opportunity to Get AI Ready

    AI has come a long way, but still needs more time to mature. In a world of increasing automation and deliberate progress towards increasing cognitive computing capabilities, the impending AI winter has afforded business the necessary time to determine how AI fits into their organization and the problems it wants to solve. The impending casualties of AI need to be addressed in policy, in governance and its impact on individuals and society.

    Its impact is far greater in this next industrial revolution as its ubiquity will become more nuanced in our lives. The leading voices of AI from Geoff Hinton, Fei Fei Lee and Andrew Ng, have called on an AI reset because Deep Learning has not yet proven to scale. The promise of AI is not waning, rather the expectations for its real arrival is pushed further out – perhaps 5-10 years. We have time to work these issues on Deep Learning, other AI methods, and the processes to effectively extract value from data. This culmination of business readiness, regulation, education, and research are necessary to bring both business and consumers up to speed and to ensure a regulatory system is in place to properly constrain technology and one that leaves humanity at the helm a little while longer.

    About Karen Bennet

    Karen Bennet, a Principle Consultant at Arcompany, is an experienced senior engineering leader with more than 25 years in the software development business in both open and closed source solutions. More recently, Karen's efforts have focused on Artificial Intelligence, enabling enterprise, particularly in the banking and automotive sectors, to experiment with AI/ML. She has extensive leadership engineering positions at Cerebri AI, IBM, Yahoo!, Trapeze and was an early leader, who helped grow Cygnus and Red Hat into sustainable businesses.


    RFPs: Time for the Dinosaurs of the Business World to Go Extinct | killexams.com real questions and Pass4sure dumps

    Modern businesses succeed when they’re fast and efficient. In the digital age, customers expect to have their needs met in “real-time,” no matter where they are on the planet. If you can’t help them, remember, someone who can, is just a click away, writes Michael Hiskey, Head of Strategy, Semarchy. 

    Modern businesses succeed when fast and efficient. In the digital age, customers expect to have their needs met in “real-time,” no matter where they are on the planet. If you can’t help them, someone who can is a click away.

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    Given this reality, it’s dismaying to see so many companies relying on one of the slowest, most obtuse practices for business projects: the RFP, or Request for Proposal. RFPs originated in the early days of the Industrial Revolution when slow communication and transportation afforded companies the time to search for and evaluate solutions.

    In the two centuries since, technology has drastically changed commercial activity, yet 95% of companies still use RFPs when they need to acquire new applications, outsource IT, and complete projects. The intentions are sensible—transform the business while reducing the costs of in-house projects—but the process no longer fits the reality.

    Resolving the RFP Paradox: Go Big by Starting Small

    Business projects are about real-world results, and when businesses need new projects, they should be looking to successful startups. By virtue of not being big, legacy players in their space, startups thrive on innovation and growth—locating and exploiting the inefficiencies of existing platforms as business needs change.

    Because startups are comprised of a relatively small group of extremely dedicated employees who are natural risk-takers, they can be lazily miscast as sandbox idealists. But there’s a reason that their total global economy value creation from 2015-2017 was $2.3 trillion, up 25.6% from the previous two-year cycle: their ability to deliver results.

    Logic would dictate that businesses, seeing growth, revenue, and value opportunities in small startups would be more comfortable with finding these innovative companies. But the RFP, that entrenched process for obtaining contractors, often disadvantages startups.

    For instance, RFPs are by nature long processes. One study found that a single RFP takes 25-50 hours of draft response, weeks or months for a finalist search, weeks or even months, travel, research, and a full project plan, at an average cost of $5,000 before a winning bid. For a company that engages in 10, 20, or more RFPs per month, costs can escalate into the hundreds of thousands of dollars before a single win.

    RFPs also have obscure age requirements, where bidders have to specify how long they’ve been in the business. The best reason for this is the abstract sense of comfort and familiarity—after all, “No one gets fired for buying from IBM.”

    But these requirements and mindsets are antithetical to the purpose of the RFP in the first place. New business projects are by definition risky. To succeed, they need quick, smart proposals that don’t break the bidder’s bank. The RFP does the opposite. Something has to give.

    The Next Generation of the RFP: Proof of Value (POV)

    Imagine the ability to quickly determine a solution’s value. That’s what a proof-of-value (POV) does. Based on Information Measurement Theory (IMT), a POV predicts a project’s risks, value, and time to completion.

    By replacing the RFP, the POV wouldn’t eliminate the process of finding capable contractors; it would reconstruct it on fairness and results. By expediting procedures and cutting to the chase, it would do away with the lethargy that stems from focusing on all the wrong pieces.

    Startups specialize in POVs because the form mimics their fast-paced, low-cost, constantly risk-evaluating innovative style. As experts in their space, small companies can easily assemble POVs, especially if they’re free from requirements of size, age, and incumbency, the arbitrary factors that are minimally correlated with the ability to deliver a successful solution.

    MVP and Growth Hacking: How Startups Quickly Create and Scale Products

    If POVs replaced RFPs, businesses would be able to quickly establish whether the bidders should advance to the next stage of the process: the Minimum Viable Product (MVP). A first iteration solution that delivers basic project needs in anticipation of a final product, an MVP immediately engages business users and develops user connectivity.

    For IT projects that are the synthesis of apps, software, and branding, the benefits of an MVP are manifold: the business learns within weeks if the solution can work. If so, startups have the platform for another one of their specialties: growth hacking.

    Growth hacking can be the final stage of a business project. Get a bunch of engineers, developers, and marketers together and let them hack their way to growth. The key is to monitor it—to give the team real-time feedback that helps it run tests to develop iteration product initiatives that combine innovation with scalability.

    Successful growth hacking isn’t likely to occur at a large company inundated with whiteboard bureaucracy. It thrives in an innovative environment—when the process for project development is quick and agile, not slow and aging.

    Conclusion

    The RFP came about during the pre-locomotive, horse-and-buggy era. The world has moved on, but the RFP has remained stubbornly in place. The mismatch has resulted in the pursuit of perfection at a high opportunity cost, bringing to mind Voltaire’s quote that “the best is the enemy of the good.” Today’s businesses have enterprise tools available to always strive for excellence. It’s time for them to rethink the path there and trust themselves to find the companies to execute.

     



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    IBM C9550-512 Exam (IBM Business Process Manager v8.5.7 Application Development using Process?) Detailed Information



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