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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 : April 17, 2019
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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

IBM to purchase system utility business Platform Computing to lengthen reach of Technical Computing | killexams.com Real Questions and Pass4sure dumps

ARMONK, N.Y., Oct. 11, 2011 /PRNewswire/ -- IBM (NYSE: IBM) today announced it has entered into a definitive contract to acquire Platform Computing, a privately held business headquartered in Toronto, Ontario, Canada. Platform Computing is a world leader in cluster and grid management application for distributed computing environments. The acquisition is predicted to shut within the fourth quarter of 2011, field to the pride of closing conditions. monetary terms were now not disclosed. (logo:  http://photographs.prnewswire.com/prnh/20090416/IBMLOGO)From departmental clusters to business grids, Platform Computing management software helps shoppers create, integrate and manage shared computing environments which are used in resource-intensive functions corresponding to simulations, desktop modeling and analytics. These technical and excessive efficiency computing (HPC) applications gas product development, critical business decisions and step forward science in fiscal capabilities, manufacturing, digital media, oil and fuel, lifestyles sciences, govt, and research and schooling.across agencies of all sizes, utility complexity and the volume of records continue to grow vastly, driving the want for more and more compute capability. by means of combining Platform Computing's utility with IBM high performance programs and application, IBM can stronger serve commercial enterprise valued clientele who are turning to technical computing to cut back the charge and complexity of managing and examining large quantities of statistics in a timely fashion.The mixed probability for servers, storage and programs software for technical computing is over $14 billion in 2011 and is expected to grow over eight p.c annually to $18.5 billion through 2014, in line with IDC (1).  "IBM considers the acquisition of Platform Computing to be a strategic point for the transformation of HPC into the excessive growth section of technical computing and a vital part of our smarter computing approach," stated Helene Armitage, generic manager, IBM programs software. "This acquisition will also be leveraged across IBM as we raise our IBM offerings and solutions, providing purchasers with know-how that helps draw insights to fuel critical enterprise decisions or leap forward science."Platform Computing at present serves over 2,000 customers together with 23 of the right 30 largest international organizations. example shoppers consist of CERN, Citigroup, Infineon, Pratt & Whitney, crimson Bull Racing, Sanger Institute, Statoil and tuition of Tokyo.  "Platform Computing's application has a proven track list over the past 19 years of helping consumers to speed up innovation, company analytics and IT features whereas being convenient to install and control," said Dr. Songnian Zhou, CEO, Platform Computing. "Our market-main software has enabled wide adoption of commodity clusters and enterprise grids everywhere. This acquisition will enable Platform Computing to enhanced serve our shoppers with the aid of leveraging the IBM answer portfolio and prolong the attain of our pioneering distributed computing software for broader enterprise adoption." Platform Computing's focused technical and allotted computing administration application suite enhances IBM's excessive-performance systems together with device x®, BladeCenter®, power programs™ and device Storage®, as well as the IBM utility portfolio, including Tivoli® management and WebSphere® utility infrastructure.Platform Computing's operations as of the closing can be integrated into IBM systems and know-how group. Platform Computing has about 500 employees international. The acquisition of Platform Computing will help accelerate IBM's boom in smarter computing, a key initiative in IBM's Smarter Planet strategy.  earlier this 12 months, IBM stated Smarter Planet initiatives are estimated to power $10 billion in profits for IBM by using 2015. Platform Computing's extensive network of ISVs, OEMs and others provides IBM with the opportunity to deliver disbursed computing management software to a broader set of clients whereas assisting Platform Computing's present shoppers. About IBM For more information about IBM technical computing solutions, seek advice from www.ibm.com/deepcomputing.   To study more about IBM smarter computing, visit www.ibm.com/smartercomputing.  About Platform Computing Platform Computing is a leader in cluster and grid management software ― serving greater than 2,000 of the world's most annoying corporations. For 19 years, its workload and resource management options have delivered IT responsiveness and lower expenses for enterprise and HPC functions. seek advice from www.platform.com.Footnote:  (1) source: IDC HPC person discussion board meeting, April 2011, Houston, TexasIBM, BladeCenter, energy programs, equipment Storage, equipment x, Tivoli and WebSphere are registered logos of IBM business enterprise in the united states and/or different international locations. All other enterprise/product names and repair marks may well be logos or registered trademarks of their respective groups.Media ContactTracy SullivanIBM Media relations+1 617-693-0021tsulliva@us.ibm.com

What New Language Will IBM i guide next? | killexams.com Real Questions and Pass4sure dumps

March 27, 2019 Alex Woodie

the prospect of a brand new language coming to a platform is at all times reason behind excitement. New languages carry new capabilities, or at the least faster the right way to faucet into present capabilities. The query for those living and working on the IBM i platform is what language will come next?

RPG continues to be the go-to language used with the aid of the significant majority builders on the IBM i platforms. in response to the 2019 survey with the aid of HelpSystems, eighty four percent of coders on the field use RPG. COBOL, RPG’s associate in legacy crime, is also supported within the Rational development for IBM i RDi, together with C and C++ compilers. Java has also been massive for a number of years.

within the mid-2000s, IBM made a large splash with personal home page and its partnership with Zend expertise, the backers of the open supply language, which is now managed through Rogue Wave software. extra recently, IBM has been bringing even more languages to the field, together with Python, Perl, and Node.js. Even Microsoft’s .web is now operating on IBM i via the open source Mono task, which we now have these days lined in these pages.

(IBM i developers commonly discover themselves working with net-primarily based languages, and the IBM i world helps lots of these, together with HTML and JavaScript. youngsters, these languages run in the browser, now not the server, so they’re not truly considered IBM i-supported languages.)

So what’s subsequent for IBM i on this planet of languages that may in fact run on the power line of processors? There are rumblings that Go may well be the next in line.

Go became at the start developed by Google to streamline the development of applications running in parallel across multi-node clusters of commodity X86 containers. the primary line of Go code became laid down in 2007, and the language become launched to the general public in 2012. since then, it has turn into one of the most commonplace languages on the earth.

source: HelpSystems 2019 market survey

Go (which is now and again known as “Golang”) is a static-typed, compiled language it's syntactically comparable to C, which programmers say make it rather easy to study. The language offers elements like reminiscence defense and garbage assortment that users say support Go to create secure and performant programs. It ships with a standard library, but there are many other open source libraries purchasable too. The language features its personal built-in trying out tool, which is favored by means of some users.

Google is said to have developed Go mostly in response to Oracle taking on Java. Google had been a big user of Java before Oracle’s acquisition of sun Microsystems, and the business became reportedly involved that Java’s future would be controlled by means of Oracle. whereas it resembles C to a degree, Go is asserted to have been developed because the antithesis of C++, which its creators reportedly loathed.

In his blog “courageous New Geek,” utility engineer Tyler deal with elaborates on what makes Go tick. “Go makes it convenient to put in writing code it truly is understandable,” he writes. “There’s no magic like many enterprise Java frameworks and not one of the lovely tricks you’ll locate in most Python or Ruby codebases. The code is verbose but readable, unsophisticated but intelligible, tedious however predictable.”

Some IBMers have noted that they’re working on porting Go to IBM i. If authentic, the port would virtually actually be via PASE, which is the know-how gigantic’s favored path to bringing new utility to the platform nowadays, principally software that’s open supply.

different possible candidates for IBM i consist of Erlang, a language that traces its roots returned to 1986, when it changed into developed by Ericsson as a proprietary language for telephony functions. Erlang turned into launched as open source in 1998, and today

a further candidate may be Swift, the object-oriented, static-typed language from Apple. Swift builds noticeably upon purpose-C, which is an additional programming language from Apple that builds upon C and is the primary language for setting up iOS mobile apps.

not like objective-C, however, Swift is used extra notably on servers. truly, due to the fact that Swift become open sourced below an Apache 2 license in 2015, the language has even made it to IBM’s z systems mainframe by means of the IBM Toolkit for Swift on z/OS. might this sort of toolkit bring Swift to IBM i?

There are different languages that might eventually make their technique to IBM i. There are JVM-suitable languages like Groovy, Scala, and Kotlin that are garnering a good little bit of consideration, not to mention languages like Julia and Rust which are finding followers. The AIX team at IBM is also performing some work to support emerging compiler expertise, such as Clang and LLVM, which might portend some future PASE-level enhancements for IBM i.

Bringing new languages to IBM i seems like a good idea. after all, it puts new tools within the fingers of software builders, whereas allowing a brand new group of developers to put in writing functions that run on IBM i. however no longer everyone is overjoyed with the theory that extra syntaxes make for a better ecosystem.

as a substitute of getting current builders to gain knowledge of new languages, some say IT departments are better off bolstering their building teams with competencies like database design, challenge management, and technical writing.

For some, having a neatly-rounded construction crew that can boost, set up, and retain purposes – as antagonistic to just understanding the latest, ultimate language – is the vital component that’s missing in so many IT departments this present day.

Whichever camp you end up in – the one wanting to welcome new languages into the IBM i ecosystem or the one craving for greater neatly-rounded developers using latest applied sciences – the subject matter of application development thoughts on the IBM i platform is a vital one.

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

(Reuters) - IBM Corp referred to on Sunday it had agreed to purchase U.S. application enterprise red Hat Inc for $34 billion, together with debt, because it seeks to diversify its technology hardware and consulting business into better-margin items and services.

The transaction is by means of a long way IBM’s greatest acquisition. It underscores IBM Chief government Ginni Rometty’s efforts to expand the business’s subscription-based mostly application choices, because it faces slowing software income and waning demand for mainframe servers.

IBM, which has a market capitalization of $114 billion, can pay $a hundred ninety per share in money for pink Hat, a 63 % premium to Friday’s closing share cost.

headquartered in 1993, crimson Hat focuses on Linux operating programs, essentially the most usual category of open-source application, which changed into developed as an alternative choice to proprietary utility made by using Microsoft Corp.

Headquartered in Raleigh, North Carolina, red Hat costs prices to its corporate customers for customized features, upkeep and technical support, providing IBM a profitable supply of subscription income.

pink Hat is without doubt one of the very few groups in the cloud computing sector that has both earnings growth and free money stream, Rometty, who has been IBM’s CEO for the reason that 2012, spoke of in an interview with Reuters.

“This acquisition we're certainly doing for increase synergies. this is not about can charge synergies at all,” Rometty noted within the interview.

The acquisition illustrates how older expertise organizations are turning to dealmaking to benefit scale and fend off competitors, peculiarly in cloud computing, the place shoppers the usage of enterprise software are searching for to keep money with the aid of consolidating their supplier relationships.

IBM is hoping the deal will help it seize up with Amazon.com Inc, Alphabet Inc and Microsoft in the impulsively starting to be cloud business. IBM shares have lost pretty much a 3rd of their value during the past five years, while crimson Hat shares are up 170 percent over the equal duration.

FILE photograph: an emblem of IBM is viewed at the cellular World Congress in Barcelona, Spain February 28, 2018. REUTERS/Yves Herman/File photograph

“This deal represents the culmination of IBM’s existing partnership with red Hat, and, in our view, enables IBM to profit a extremely strategic asset to enhance its hybrid cloud initiatives,” Barclays analysts wrote in a analysis note.

They delivered that for the deal to work, it turned into crucial for IBM to uphold pink Hat’s neutrality when it got here to operating structures and retain crimson Hat’s open-supply and multi-cloud position in the market.

big BLUE

IBM became situated in 1911 and is frequent within the technology business as huge Blue, a reference to its once ubiquitous blue computer systems. It has confronted years of profits declines, because it transitions its legacy computing device maker company into new technology products and functions. Its contemporary initiatives have protected artificial intelligence and business lines around Watson, named after the supercomputer it developed.

To be certain, IBM is not any stranger to acquisitions. It got cloud infrastructure issuer Softlayer in 2013 for $2 billion, and the climate Channel’s data property for more than $2 billion in 2015. It additionally received Canadian business software maker Cognos in 2008 for $5 billion.

other large expertise organizations have additionally recently sought to reinvent themselves through acquisitions. Microsoft this year received open source software platform GitHub for $7.5 billion; chip maker Broadcom Inc agreed to purchase application maker CA Inc for very nearly $19 billion; and Adobe Inc agreed to purchase advertising utility maker Marketo for $5 billion.

one among IBM’s leading opponents, Dell applied sciences Inc, made a big bet on utility and cloud computing two years in the past, when it obtained records storage enterprise EMC for $sixty seven billion. As part of that deal, Dell inherited an eighty two % stake in virtualization application business VMware Inc.

The deal between IBM and red Hat is anticipated to shut within the second half of 2019. IBM mentioned it planned to droop its share repurchase program in 2020 and 2021 to help pay for the deal.

IBM mentioned purple Hat would proceed to be led by means of pink Hat CEO Jim Whitehurst and red Hat’s latest management group. It intends to maintain crimson Hat’s headquarters, amenities, manufacturers and practices.

a sign for pink Hat hangs on a constructing in Boston, Massachusetts, U.S., June 27, 2018. REUTERS/Brian Snyder

Lazard Ltd provided monetary counsel to IBM, alongside Goldman Sachs community Inc and JPMorgan Chase & Co, which also provided financing for the deal. Paul, Weiss, Rifkind, Wharton & Garrison LLP offered legal counsel to IBM.

Guggenheim companions LLC and Morgan Stanley were economic advisers to pink Hat, whereas Skadden, Arps, Slate, Meagher & Flom LLP offered legal tips to the business on the deal.

“figuring out first-hand how essential open, hybrid cloud applied sciences are to helping agencies unencumber price, we see the vigour of bringing these two organizations collectively, and are honored to propose IBM and commit financing for this transaction,” JPMorgan CEO Jamie Dimon stated in an announcement.

Reporting by way of Liana B. Baker and Greg Roumeliotis in manhattan; extra reporting via Stephen Nellis in San Francisco and Carl O'Donnell and Jim Finkle in new york; modifying via Rosalba O'Brien and Peter Cooney

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How To Choose The Best Master’s Degree For You | killexams.com real questions and Pass4sure dumps

More and more business schools are offering business masters degrees, from generalist programs in management to specialized master’s offerings covering topics like accounting, data analytics, finance, and marketing.

With so many options out there, choosing the best master’s degree to suit your career ambitions can be a challenge.

Before applying, candidates should know the answers to the following questions: Who are the different master’s programs for? What jobs do they lead to? What do you study on the course? What do you need to know to get accepted?

To answer those questions, we spoke with students and faculty from five of the most common business master’s degree options at some of the world’s leading business schools.

Read on to find out how to choose the best master’s degree for you.

You can find out more about the master’s degree that interests you most by clicking on one of the links below.

Masters in Accounting

Masters in Analytics

Masters in Finance

Masters in Management

Masters in Marketing

Find out more about business master’s degrees via webinar

Masters in Accounting

Find out more about Masters in Accounting via webinar


Dr Wang Jiwei, MSc in Accounting director at Singapore Management University (SMU)

Who is a Master’s in Accounting for?

Our Master of Professional Accounting (MPA) is designed to create a pathway for non-accounting graduates working or seeking to work in a field that requires specialized knowledge in accounting.

We also have a Master of Science in Accounting (MSA) which specializes in data and analytics. It’s designed for students who already have a background in accounting or business related disciplines, but wish to further their career and skills set in this data-driven era.

What skills/qualifications do you need to apply?

The overall quality of the applicant determines admission to the program. This would include prior academic performance at university, work experience, referees reports, interview scores, and GMAT/SMU Admission Test is compulsory.

Applicants must have sufficient background in math. Applicants with post-undergraduate working experience will have an added advantage. Applicants to the MSA must also have a business or accounting background.

What will you learn on a Master’s in Accounting?

Through the MPA course, students will learn about the three pillars of accounting; the business fundamentals, accounting core and the professional services which would include the following modules: management accounting, taxation, audit, corporate financial management and financial accounting.

The MSA equips students with the essential skills to harness data for critical decision making. They learn to make use of tools such as R, Python, Tableau, and SQL for data analysis, data visualization and data management. They also can learn machine learning techniques, and understand how they can be applied to the accounting and finance work.

What jobs can you qualify for after a Master’s in Accounting?

An accounting master’s degree is versatile. Every company needs somebody who is well versed in finance and accounting. Hence, the career of an accountant is not limited to the accounting, banking and finance industry. Good accountants are in demand in every sector.

In the recent years, the role of an accountant has evolved. Many organizations are eliminating manual accounting work, because computers and robots can automatically generate accounting entries.

But at the same time, we also see a growing demand in other job functions, mainly in the areas of data processing, management analysis and financial analysis. In the current market, there is a demand for domain experts who understand finance, accounting and data technology and how the systems work.

Top tip for applying to SMU?

What will give our applicants an edge is whether they have the drive to excel, the gumption to overcome challenges and the passion to make a meaningful impact through their work.

Master’s in Accounting student Q&A


Ryan Guo Yu Sheng, SMU MSA student (part-time), manager of Global Operating Certificates at Visa in Singapore

Why pursue a Master’s in Accounting?

I have always been a tech-savvy accountancy graduate, and most of the process and data technology applied in my work was self-taught and unstructured. After completing a specialist diploma in Business Analytics in 2017, I was looking to further my knowledge and build on the basics learned in that course.

An understanding of data analysis and usage of automation and robotics have become essential to the modern finance function and this is also part of my effort in future-proofing my own career. There is an increasing focus on the application of technology to finance processes and simply relying on our IT colleagues to fill us in on technical developments is a thing of the past.

What advice do you have for prospective Master's in Accounting students?

A candidate should be technically savvy. Dealing with data requires a highly analytical and structured skillset, along with a sense of creativity for data transformation and programming. Having a conceptual understanding of accounting is important.

Masters in Analytics

Find out more about Masters in Analytics via webinar


Crystal Grant, director of admissions (pictured above), and Kalyan Talluri, MSc Business Analytics program director at Imperial College Business School (pictured below)

Who is a Master’s in Analytics for?

There's a huge range of Master's in Analytics programs available, which reflects the growing demand from candidates and employers for graduates who are able to derive business insights and competitive advantage from big data.

Some will be aimed at new or recent graduates looking to launch their career, whilst others will be suited to professionals hoping to up-skill to progress or change jobs.

What skills/qualifications do you need to apply?

Programs vary in the balance of business versus technical experience required, but if you have a passion for data analytics, quantitative skills and commercial awareness, you should be able to find a Master's in Analytics that supports your goals.

Successful candidates to our MSc Business Analytics bring a wide variety of academic and work experience. We look for strong quantitative, communication, problem-solving and team-working skills. A passion for coding is also important, as the curriculum is very practical and you should be ready to learn and use programming languages.

What will you learn on a Master’s in Analytics?

Masters in Analytics aim to help graduates and young professionals understand the content, relevance, and importance of big data problems facing businesses. Our program takes a practical approach including a variety of machine learning, statistical, and operations research techniques.

Students can expect to learn solid technical skills in programming, including R, Python and SQL, data structures, and algorithms. They’ll learn skills in optimization, econometrics and statistics, networks, and machine learning. Industry-specific application is core to the program, and students learn to apply their mastery of tools to real business problems.

What jobs can you qualify for after a Master’s in Analytics?

The most popular destination for our graduates is consulting, however others go on to work in technology, FMCG, e-commerce, finance, healthcare, and media. Graduate roles vary depending on candidates’ previous experience, but include positions like business analyst, data scientist and, consultant. Employers include Accenture, Amazon, BCG, eBay, EY, Microsoft, and Revolut.

Top tip for applying to Imperial?

Don’t just list your experience of coding and programming languages. Highlight your problem solving skills by showing how you’ve applied your technical expertise on interesting projects. Demonstrating big-picture thinking, commercial awareness, and the ability to articulate complex information in a compelling and accessible way will help your application stand out.


Master’s in Analytics student Q&A

Pinelopi Chamalelli, MSc Business Analytics student at Imperial, joining the analytics graduate scheme of a major consulting firm in London after graduation

Why pursue a Master’s in Business Analytics?

Analytics is the future; every business no matter its size collects more and more data, and desires to utilize them for a more efficient decision making. So, the opportunities we have after this program are endless not only in terms of quantity but also in terms of variety, since analytics can be applied in every single industry. imp

 What advice do you have for prospective Master's in Analytics students?

Coding skills or generally the ability to learn a new programming language or a new software package quickly is crucial. Three main programming languages we use are Python, R, and SQL, since these are widely used in the industry.

Good knowledge of linear algebra and statistics also help a lot. A good business understanding is also crucial for a successful data scientist.

Masters in Finance

Find out more about Masters in Finance via webinar

Diane Jordan, associate director of admissions, MIT Sloan

Who is a Master’s in Finance for? diane-jordan

Our Master of Finance is for individuals interested in learning the fundamentals of modern finance that emphasizes a foundation in how markets work. Students of the program come from a variety of bachelor’s degree disciplines including business, economics, finance, and significant representation from the STEM areas.

What skills/qualifications do you need to apply?

We seek smart, motivated applicants who are passionate about finance. Recent graduates, early career professionals in finance, engineers, mathematicians, physicists, computer programmers, and other high-tech professionals are encouraged to apply.

We look for: Success in academic, extracurricular, and professional endeavors; Ability to collaborate to accomplish a common goal; Ability to inspire others to achieve success; Excellent communication skills; Willingness to seek alternative solutions to existing challenges; Motivation to pursue goals.

What will you learn on a Master’s in Finance?

You’ll begin with rigorous courses in finance theory, financial mathematics, and financial accounting. You’ll be required to pass the Programming Literacy Test in either R or Python programming languages.

You’ll work with analytical tools—financial modeling, portfolio and pricing theory, statistics and data analytics, and computational methods—to help tackle multi-faceted challenges that arise in finance, from capital budgeting and cash flow analysis, to dynamic asset allocation and big-data-based investment strategies, and more.

You’ll take part in project-based courses solving real-world problems with MIT's partner corporations and culminating in presentations to corporate decision makers. You’ll choose electives from financial technology, to quantitative methods, to economics, to specialized disciplines such as healthcare finance, fixed income, mergers and acquisitions and asset management.

What jobs can you qualify for after a Master’s in Finance?

Jobs at asset managers, consulting firms, investment and corporate banks, brokerage firms, financial data providers, ratings firms, hedge funds, venture capitalists, insurance companies, public institutions, fintech, and more—from Fortune 500 companies to leading-edge boutiques.

Companies who have hired Master of Finance graduates from MIT for each of the last three years include Bain & Company, BCG, BlackRock, Deloitte, Morgan Stanley, McKinsey, and Citi.

Top tip for applying to MIT Sloan?

Get to know us and the admissions process through online chats and events to help you decide if MIT is a good fit.

Master’s in Finance student Q&A


David Smadja, student at MIT Sloan, has an offer for an associate position at a major investment bank in New York

Why pursue a Master’s in Finance?

Prior to my master’s, I graduated from a master of applied mathematics. Though I had a good understanding of the quantitative side of financial markets, I felt I was missing some important financial knowledge. To me, masters in accounting, for example, are not capable to cover everything you need to know to succeed in the field of finance like a master’s in finance does.

What advice do you have for prospective Master's in Finance students?

The role of the master’s is to teach you what’s necessary to break through the industry but I reckon that some preliminary skills are beneficial. If you’re interested in financial markets, some coding and analytics skills (statistics, optimization, AI a plus) are a minimum to have. If you’re more interested by the corporate side, a good understanding of financial statements analysis and accountability rules is crucial. 

Masters in Management

Find out more about Masters in Management via webinar


Eric Simard, associate director, MSc admissions at Ivey Business School, University of Western Ontario, Canada

Who is a Master’s in Management for?

It’s for ambitious, recent graduates who want a specialized Master’s degree, that will hone their skill-set, develop their leadership abilities and accelerate their career success. Students are admitted from a variety of disciplines, backgrounds, cultures.

This creates a unique experience in classrooms which is meant to help students understand, reflect on, and challenge their tendencies, preferences and biases.

At Ivey, students apply to a specific field of study which will allow them to focus their learning in one of three areas; Business Analytics, Digital Management, or International Business.

What skills/qualifications do you need to apply?

We evaluate applicants on a number of dimensions including; Relationship management; Leadership orientation; Openness to learning; Adaptability; Program readiness; Academic performance in their final two years of their undergraduate degree; Motivation for joining the program; Short and long-term career objectives; Pre-program experience; and International exposure/experience.

What will you learn on a Master’s in Management?

Master’s in Management programs are designed to help students bridge the gap between a solid theoretical foundation received in undergraduate studies and the practical and soft skills needed to make a difference in the early stages of their career.

Through Case-Method approach to education, Ivey MSc students are put in the position of the decision-maker and are asked to analyze data, develop alternatives and then make and defend their recommendations. We are focused on creating the next group of business leaders who are able to walk into a new role and take action.

What jobs can you qualify for after a Master’s in Management?

Graduates take positions in a variety of industries including; Technology, Management Consulting, Financial Institutions, Telecommunications, Consumer Packaged Goods, and more.

The majority of our graduates take starting positions in leadership rotational programs or in consulting firms focused in Risk, Technology or Human Capital. Many have titles such as data science associate, area manager or data analyst, and they work in a variety of organizations such as PwC, Scotiabank, Amazon, Walmart, Deloitte, Proctor & Gamble, Accenture, Sun Life Financial, BMO, Bell, EY, and Morgan Stanley.

Top tip for applying to Ivey?

Candidates should connect with our admission and recruitment team before submitting their application. They should spend time learning about what our programs have to offer by attending webinars, talking to current students, and reaching out to our MSc alumni. And they should consider and reflect on how the Ivey MSc in Management can support their short and long-term career goals.

Master’s in Management student Q&A


Deanna Hamilton, student at Ivey Business School, starting a consulting career after graduation

Why pursue a Master’s in Management?

I wanted to pursue a career in business and a Master’s in Management seemed like the perfect way for me to develop my knowledge as I did not study business during my undergrad. I also aspire to work internationally one day. Ivey’s Global Lab opportunity offered an eight-week, hands-on, international in-company experience which allowed me to immerse myself in an unfamiliar environment and work cross-culturally.

What advice do you have for prospective Master's in Management students?

Aside from the obvious skills of being a hard worker and passionate about the program, I think it is crucial for students to be open to learning and willing to adapt. It is essential for you to be willing to step out of your comfort zone to really take advantage of every opportunity on the program. Interpersonal and communication skills are also necessary for most jobs and, coming into the program, many of my classmates already had exceptional communication skills.

Masters in Marketing

Find out more about Masters in Marketing via webinar

Ning Li, academic program director of the MS in Marketing at Johns Hopkins, Carey School of Business

Who is a Master’s in Marketing for? ning-li

For students looking to continue in their marketing studies or students looking to pivot into marketing from another field. Those who are looking to become marketing experts will receive training in business foundations in addition to numerous high-level marketing electives.

What skills/qualifications do you need to apply?

Candidates will need undergraduate level communication and analytical skills.

What will you learn on a Master’s in Marketing?

Students will learn behavioral and quantitative methods and best marketing practices to address the most critical issues facing today’s marketer, including the impact of data and social media on marketing strategy, consumer behavior, social responsibility, competitive economics, business-to-business marketing, and more.

What jobs can you qualify for after a Master’s in Marketing?

Students can pursue careers in Marketing and Business Analytics, Marketing and Sales, General Management, Consulting, and more. Some employers who have hired our recent graduates include Amazon, China Telecom Americas, IBM, Johns Hopkins Medicine, P&G, and Tencent.

Top tip for applying to Carey?

Be Honest. No applicant is perfect and if there is a part of your application you are concerned about, be honest. Be open to why this program appeals specifically to you. This is important because honestly addressing concerns is important in marketing roles.

Are you interested in a business master’s degree?

Register now for the Master’s Week webinar series held by the Graduate Management Admission Council (GMAC).

Running from 8-to-12 April, different business schools will talk about a different master’s program each day.

Monday: Accounting; Tuesday: Business Analytics; Wednesday: Finance; Thursday: Management; Friday: Marketing

How can we design AI for the best long-term interests of humanity? | killexams.com real questions and Pass4sure dumps

Vint Cerf, who co-designed the early protocols and architecture for our modern internet, uses a parable to explain why courageous leadership is vitally important in the wake of emerging technologies like artificial intelligence.

Imagine that you are living in a tiny community at the base of a valley that’s surrounded by mountains. At the top of a distant mountain is a giant boulder. It’s been there for a long time and has never moved, so as far as your community is concerned, it just blends into the rest of the landscape. Then one day, you notice that the giant boulder looks unstable—that it’s in position to roll down the mountain, gaining speed and power as it moves, and it will destroy your community and everyone in it. In fact, you realize that perhaps you’ve been blind to its motion your entire life. That giant boulder has always been moving, little by little, but you’ve never had your eyes fully open to the subtle, minute changes happening daily: a tiny shift in the shadow it casts, the visual distance between it and next mountain over, the nearly imperceptible sound it makes as the ground crunches beneath it. You realize that as just one person, you can’t run up the mountain and stop the giant boulder on your own. You’re too small, and the boulder is too large.

But then you realize that if you can find a pebble and put it in the right spot, it will slow the boulder’s momentum and divert it just a bit. Just one pebble won’t stop the boulder from destroying the village, so you ask your entire community to join you. Pebbles in hand, every single person ascends the mountain and is prepared for the boulder—there is collaboration, and communication, and a plan to deal with the boulder as it makes its way down. People and their pebbles—not a bigger boulder—make all the difference.

Safe, beneficial technology isn’t the result of hope and happenstance. It is the product of courageous leadership and of dedicated, ongoing collaborations. But at the moment, the AI community is competitive and often working at cross-purposes.

The future of AI — and by extension, the future of humanity — is already controlled by just nine big tech titans, who are developing the frameworks, chipsets, and networks, funding the majority of research, earning the lion’s share of patents, and in the process mining our data in ways that aren’t transparent or observable to us. Six are in the US, and I call them the G-MAFIA: Google, Microsoft, Amazon, Facebook, IBM and Apple. Three are in China, and they are the BAT: Baidu, Alibaba and Tencent.

The Big Nine are under intense pressure — from Wall Street in the United States and Beijing in China — to fulfill shortsighted expectations, even at great cost to our futures. We must empower and embolden the Big Nine to shift the trajectory of artificial intelligence, because without a groundswell of support from us, they cannot and will not do it on their own.

What follows is a series of pebbles that can put humanity on a better path to the future.

The Big Nine’s leadership all promise that they are developing and promoting AI for the good of humanity. I believe that is their intent, but executing on that promise is incredibly difficult. To start, how should we define “good”? What does that word mean, exactly? This harkens back to the problems within AI’s tribes. We can’t just all agree to be “doing good” because that broad statement is far too ambiguous to guide AI’s tribes.

For example, AI’s tribes, inspired by Western moral philosopher Immanuel Kant, learn how to preprogram a system of rights and duties into certain AI systems. Killing a human is bad; keeping a human is good. The rigidity in that statement works if the AI is in a car and its only choices are to crash into a tree and injure the driver or crash into a crowd of people and kill them all. Rigid interpretations don’t solve for more complex, real-world circumstances where the choices would be more varied: crash into a tree and kill the driver; crash into a crowd and kill eight people; crash into the sidewalk and kill only a three-year-old boy. How can we possibly define what is the best version of “good” in these examples?

Again, frameworks can be useful to the Big Nine. They don’t require a mastery of philosophers. They just demand a slower, more conscientious approach. The Big Nine should take concrete steps on how it sources, trains, and uses our data, how it hires staff, and how it communicates ethical behavior within the workplace.

At every step of the process, the Big Nine should analyze its actions and determine whether or not they’re causing future harm—and then they should be able to verify that their choices are correct. This begins with clear standards on bias and transparency.

Right now, there is no singular baseline or set of standards to evaluate bias—and there are no goals to overcome the bias that currently exists throughout AI. There is no mechanism to prioritize safety over speed and given my own experiences in China and the sheer number of safety disasters there, I’m extremely worried. Bridges and buildings routinely collapse, roads and sidewalks buckle, and there have been too many instances of food contamination to list here. (That isn’t hyperbole. There have been more than 500,000 food health scandals involving everything from baby formula and rice in just the past few years.

One of the primary causes for these problems? Chinese workplaces that incentivize cutting corners. It is absolutely chilling to imagine advanced AI systems built by teams that cut corners. Without enforceable global safety standards, the BAT have no protection from Beijing’s directives, however myopic they may be, while the G-MAFIA must answer to ill-advised market demands. There is no standard for transparency either. In the United States, the G-MAFIA, along with the American Civil Liberties Union, the New America Foundation, and the Berkman Klein Center at Harvard are part of the Partnership on AI, which is meant to promote transparency in AI research. The partnership published a terrific set of recommendations to help guide AI research in a positive direction, but those tenets are not enforceable in any way—and they’re not observed within all of the business units of the G-MAFIA. They’re not observed within the BAT, either.

The Big Nine are using flawed corpora (training data sets) that are riddled with bias. This is public knowledge. The challenge is that improving the data and learning models is a big financial liability. For example, one corpus with serious problems is ImageNet, which I’ve made reference to several times in this book. ImageNet contains 14 million labeled images, and roughly half of that labeled data comes solely from the United States.

Here in the US, a “traditional” image of a bride is a woman wearing a white dress and a veil, though in reality that image doesn’t come close to representing most people on their wedding day. There are women who get married in pantsuits, women who get married on the beach wearing colorful summery dresses, and women who get married wearing kimono and saris. In fact, my own wedding dress was a light beige color. Yet ImageNet doesn’t recognize brides in anything beyond a white dress and veil.

We also know that medical data sets are problematic. Systems being trained to recognize cancer have predominantly been ingesting photos and scans of light skin. And in the future, it could result in the misdiagnosis of people with black and brown skin. If the Big Nine knows there are problems in the corpora and aren’t doing anything about it, they’re leading AI down the wrong path.

One way forward is to turn AI on itself and evaluate all of the training data currently in use. This has been done plenty of times already—though not for the purpose of cleaning up training data. As a side project, IBM’s India Research Lab analyzed entries shortlisted for the Man Booker Prize for literature between 1969 and 2017. It revealed “the pervasiveness of gender bias and stereotype in the books on different features like occupation, introductions, and actions associated to the characters in the book.” Male characters were more likely to have higher-level jobs as directors, professors, and doctors, while female characters were more likely to be described as “teacher” or “whore.”

If it’s possible to use natural language processing, graph algorithms, and other basic machine-learning techniques to ferret out biases in literary awards, those can also be used to find biases in popular training data sets. Once problems are discovered, they should be published and then fixed. This would serve a dual purpose. Training data can suffer from entropy, which might jeopardize an entire system. With regular attention, training data can be kept healthy.

A solution would be for the Big Nine—or the G-MAFIA, at the very least—to share the costs of creating new training sets. This is a big ask since creating new corpora takes considerable time, money, and human capital. Until we’ve successfully audited our AI systems and corpora and fixed extant issues within them, the Big Nine should insist on human annotators to label content and make the entire process transparent. Then, before those corpora are used, the data should be verified. It will be an arduous and tedious process but one that would serve in the best interests of the entire field.

Yes, the Big Nine need our data. However, they should earn—rather than assume—our trust. Rather than changing the terms of service agreements using arcane, unintelligible language, or inviting us to play games, they ought to explain and disclose what they’re doing. When the Big Nine do research—either on their own or in partnership with universities and others in the AI ecosystem—they should commit to data disclosure and fully explain their motivations and expected outcomes. If they did, we might willingly participate and support their efforts. I’d be the first in line.

Understandably, data disclosure is a harder ask in China, but it’s in the best interests of citizens. The BAT should not agree to build products for the purpose of controlling and limiting the freedoms of China’s citizens and those of its partners. BAT executives must demonstrate courageous leadership. They must be willing and able to disagree with Beijing: to deny requests for surveillance, safeguard Chinese citizens’ data, and ensure that at least in the digital realm, everyone is being treated fairly and equally.

The Big Nine should pursue a sober research agenda. The goal is simple and straightforward: build technology that advances humanity without putting us at risk. One possible way to achieve this is through something called “differential technological progress,” which is often debated among AI’s tribes. It would prioritize risk-reducing AI progress over risk-increasing progress. It’s a good idea but hard to implement. For example, generative adversarial networks, which were mentioned in the scenarios, can be very risky if harnessed and used by hackers. But they’re also a path to big achievements in research. Rather than assuming that no one will repurpose AI for evil—or assuming that we can simply deal with problems as they arise—the Big Nine should develop a process to evaluate whether new basic or applied research will yield an AI whose benefits greatly outweigh any risks. To that end, any financial investment accepted or made by the Big Nine should include funding for beneficial use and risk mapping. For example, if Google pursues generative adversarial network research, it should spend a reasonable amount of time, staff resources, and money investigating, mapping, and testing the negative consequences.

A requirement like this would also serve to curb expectations of fast profits. Intentionally slowing the development cycle of AI is not a popular recommendation, but it’s a vital one. It’s safer for us to think through and plan for risk in advance rather than simply reacting after something goes wrong.

In the United States, the G-MAFIA can commit to recalibrating its own hiring processes, which at present prioritize a prospective hire’s skills and whether they will ft into company culture. What this process unintentionally overlooks is someone’s personal understanding of ethics. Hilary Mason, a highly respected data scientist and the founder of Fast Forward Labs, explained a simple process for ethics screening during interviews. She recommends asking pointed questions and listening intently to a candidate’s answers. Questions like: “You’re working on a model for consumer access to a financial service. Race is a significant feature in your model, but you can’t use race. What do you do?” and “You’re asked to use network traffic data to offer loans to small businesses. It turns out that the available data doesn’t rigorously inform credit risk. What do you do?”

Depending on the answers, candidates should be hired, be hired conditionally, and required to complete unconscious bias training before they begin work, or be dis-qualified. The Big Nine can build a culture that supports ethics in AI by hiring scholars, trained ethicists, and risk analysts. Ideally, these hires would be embedded throughout the entire organization: on consumer hardware, software, and product teams; on the sales and service teams; co-leading technical programs; building networks and supply chains; in the design and strategy groups; in HR and legal; and on the marketing and communications teams.

The Big Nine should develop a process to evaluate the ethical implications of research, workflows, projects, partnerships, and products, and that process should be woven in to most of the job functions within the companies. As a gesture of trust, the Big Nine should publish that process so that we can all gain a better understanding of how decisions are made with regards to our data. Either collaboratively or individually, the Big Nine should develop a code of conduct specifically for its AI workers. It should reflect basic human rights and it should also reflect the company’s unique culture and corporate values. And if anyone violates that code, a clear and protective whistleblowing channel should be open to staff members.

Realistically, all of these measures will temporarily and negatively impact short-term revenue for the Big Nine. Investors need to allow them some breathing room.

AI is a big space, and we are only at the beginning of our ascent up the mountain. It’s time to grab our pebbles and start on the right path.

Excerpted from: The Big Nine: How the Tech Titans and Their Thinking Machines Could Warp Humanity by Amy Webb. Copyright © by Amy Webb. Published by arrangement with PublicAffairs, an imprint of Hachette Book Group.

Intel refreshes Xeon E5-2600 range for servers and workstations | killexams.com real questions and Pass4sure dumps

Intel has launched its new family of Xeon E5 processors aimed at dual socket servers and workstations, which introduces performance and efficiency improvements over the previous Xeon E5 server range.

The new 22nm Xeon E5-2600 v2 processors offer up to 45 percent greater efficiency, benchmarked using SPECpower_ssj2008, and 50 percent more performance, benchmarked using SPECVirt_sc2013, than E5-2600 v1 processors.

Diane Bryant, general manager of Intel's datacenter and connected systems group, said the new family of 18 processors is suited to handling a wide range of virtualised workloads as datacentres move towards "software-defined" compute, storage and networking.

"Workloads need to be easy to deploy, on-demand and always viable," she said at the Intel Developer Forum in San Francisco today.

The E5-2600 v2 processor range increases the maximum core count from eight cores to 12 over the previous generation and bumps the Level 3 cache from 20MB to 30MB. The maximum operating frequency is increased by 200MHz, taking it to 3.5GHz base frequency and 4GHz turbo frequency.

The lower power consumption of the v2 family has been made possible by the move from a 32nm to 22nm manufacturing process from E5-2600 v1 and the introduction of new power management features, said Bryant.

Maximum system memory is doubled to 1.5TB of DRAM and memory speed stepped up to 1,866MHz (for 1.5V memory).

Two new security features are introduced in the processor family, OS Guard, which isolates the operating system privilege from the application privilege to prevent malware from spoofing the OS, and instruction-level support for generating a random 64-bit key.

There processor family is built around three variants of Ivy Bridge-EP processor die, each targeted towards different workloads.

The first is a four to six core chip with 15MB of shared L3 cache and a 40 – 80W TDP, a six to 10 core chip with 25MB shared L3 cache and 70 to 130W TDP and a 12-core chip with 30MB L3 cache and 115 to 130W TDP. The 12-core variant also includes an additional memory controller to boost memory bandwidth.

The different processors support varying QuickPath Interconnect speeds, ranging from 6.4 to 8 GT/s, and PCI Express controllers run at 8GT/s for each SKU.

As well as the various server-targeted processors, there is the Xeon E5-2687W, which is an eight core processor running at 3.4GHz with a 150W TDP, designed for two-socket workstations.

The Intel Xeon processors E5-2600 v2 product family will power the new IBM NeXtScale System , a high-density, flexible computing platform designed for high-demand workloads such as analytics, technical computing and cloud delivery.

Intel's newest processor family will also be used in IBM's new x3650 M4 HD high-density storage server, aimed at managing big data and business-critical workloads, as well as all of IBM's two-socket systems including System x racks and towers, Flex System, iDataPlex, and BladeCenter offerings. Dell has also selected the new processors for its upcoming storage solution, the PowerEdge VRTX.

The Xeon processor E5-2600 v2 product family are shipping from today and range in price from $202 to $2,614 when bought in quantities of 1,000. Additionally three single-socket Intel Xeon processor E5-1600 parts will be offered for workstations and range in price from $294 to $1080. 

ZDNet attended IDF as a guest of Intel

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