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000-R09 - IBM SurePOS 300 Models 330 and 33H Technical Mastery Test - Dump Information

Vendor : IBM
Exam Code : 000-R09
Exam Name : IBM SurePOS 300 Models 330 and 33H Technical Mastery Test
Questions and Answers : 54 Q & A
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000-R09 IBM SurePOS 300 Models 330 and 33H Technical Mastery Test

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000-R09 exam Dumps Source : IBM SurePOS 300 Models 330 and 33H Technical Mastery Test

Test Code : 000-R09
Test Name : IBM SurePOS 300 Models 330 and 33H Technical Mastery Test
Vendor Name : IBM
Q&A : 54 Real Questions

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IBM's Gill Zhou Is a mannequin for Working ladies in China | killexams.com Real Questions and Pass4sure dumps

throughout her eleven years at IBM, Gill Zhou has develop into a role model for chinese women in company, as evidenced by her 760,000 followers on chinese language microblogging provider Sina Weibo.

In January, when IBM introduced double-digit chinese language profits increase for 2011, the U.S. technology enormous boosted Ms. Zhou's profile again by inserting her in charge of marketing for China.

Gill Zhou Gill Zhou

Ms. Zhou's disarmingly sincere musings about work-existence balance account for her online following. She blogged in may additionally about feeling responsible for lacking her son's sixteenth-birthday dinner and the way she dealt with the situation. whereas others may cover such conflicted feelings, Ms. Zhou is aware about being an illustration and desires to exhibit her proper self.

"As a woman chief, I always need to be conscious that we play distinctive roles: professional, daughter, daughter-in-law, wife, mom, father or mother," says Ms. Zhou, forty eight, who often comprises personnel' family wants by using granting them flexibility. "or not it's under no circumstances convenient."

Ms. Zhou's elevated role is VP-advertising, communications and corporate citizenship for IBM's better China neighborhood. only the marketing a part of her title is new, changing her previous function in can charge of the company. With a personnel of 300, Ms. Zhou says she's nonetheless discovering what or not it's like to be "extra on the enterprise facet, building the alternatives."

In March, she gathered virtually 800 IBM valued clientele to talk about how expertise may bolster China's slowing increase, an experience that generated client demand for IBM in records analytics. In June, she oversaw a discuss with to China by IBM's world board of administrators.

Ms. Zhou, a veteran of Motorola, worked within the mid-2000s to ensure a clean transition when IBM sold its own computer company to China's Lenovo to focus on utility and capabilities.

IBM's China enterprise is strong today, with salary up 21.6% remaining 12 months from 2010. The nation's 2d- and third-tier cities are right away modernizing, offering enormous opportunities for IBM's crusade to think about "Smarter Cities." In Zhenjiang, as an instance, IBM is at work on a transport command middle. The enterprise regularly lends abilities to carrier projects, which fall under the "citizenship" a part of Ms. Zhou's title.

Colleagues say Ms. Zhou leads from the heart, calling her a fine listener and a talented interpreter of China for outsiders. no person meeting her for the primary time should still underestimate her electricity, colleague Patricia Yim says.

"She looks like a fragile, fascinating girl," mentioned Ms. Yim, VP-coverage and geographic growth at IBM's more suitable China group. "but she's a lady of steel."


synthetic Intelligence is Upon Us — Are We ready? | killexams.com Real Questions and Pass4sure dumps

“Philosophically, intellectually — in each manner — human society is unprepared for the upward push of synthetic intelligence.”

Henry Kissinger, How the Enlightenment Ends

artificial Intelligence (AI) is getting loads of consideration nowadays, particularly within the technology industry and in company boardrooms. AI is additionally becoming regular in consumers accepted lives. buyers don’t all the time recognize it as such, as corporate advertising experts opt to evade technical jargon and in its place use purchaser pleasant names like Siri and Alexa — however for people which are more technically inclined, the ever-present presence of AI is tough to miss.

AI is not a new concept. actually, its roots go again a couple of a long time. So why so an awful lot buzz now? is that this just yet another know-how hype that goes to fade, or does it basically have the knowledge to bring forth transformations, either first rate or bad, of epic proportions?

A old standpoint

Let’s take a look at how we got here and why AI is capturing so a good deal consideration. we will revisit a bit little bit of the history of AI and the convergence of three boom vectors: Algorithmic advances, computing power, and information explosion. These vectors have their personal old landmarks as outlined beneath, until they converge round 2007, when the iPhone became first added.

the primary vector, algorithmic advances, goes returned as far as 1805 when French mathematician Adrien-Marie Legendre posted the least square components of regression which provides the groundwork for many of today’s computing device-getting to know models. In 1965 the structure for computer deep-learning using synthetic neural networks turned into first developed. Between 1986 and 1998 we saw a number of algorithmic advances: backpropagation, which permits for optimization with out human intervention; photograph consciousness; natural language processing; and Google’s noted PageRank algorithm.

The second vector, computing power, had a significant old landmark in 1965 when Intel cofounder Gordon Moore diagnosed the exponential boom in chip energy: the variety of transistors per square inch doubles every year. This grew to become known as Moore’s legislation, and has appropriately envisioned the doubling of computing vigor every 18 months to the latest day and into the foreseeable future. on the time the state-of-the-art desktop became in a position to processing within the order of 3 million FLOPS (floating-point operations per 2nd). via 1997, IBM’s Deep Blue carried out eleven giga FLOPs (11 billion FLOPS), which resulted in its victory over Gary Kasparov, the realm chess champion. In 1999 the images Processing Unit (GPU) become unveiled — a simple computing capability for deep learning. In 2002 we noticed the advent of Amazon’s internet features (AWS) making computing vigour conveniently attainable and not pricey through cloud computing. In 2004 Google launched MapReduce, which makes it possible for computing device to take care of monstrous quantities of information by using parallel processing, resulting in the introduction of Hadoop in 2006, which allowed organizations to contend with the avalanche of records produced with the aid of the web.

eventually, the third vector, data explosion, began is 1991 when the everywhere net changed into made purchasable to the general public. in the early 2000’s we noticed extensive adoption of broadband, which opened the doorways to many cyber web innovations, resulting within the debut of facebook in 2004, and Youtube in 2005. at present, the variety of cyber web clients global surpassed one thousand million.

The yr 2007 grew to become a significant landmark. it is at this point that the applied sciences start converging and intercepting new horizons as the cellular explosion came to lifestyles with Steve Job’s announcement of the iPhone in 2007. From here a few giant advances supply beginning to a renewed enthusiasm for synthetic Intelligence. In 2009, Stanford college scientists confirmed they might instruct deep-perception networks with one hundred million parameters the use of GPUs at a cost 70 times quicker than using CPUs. with the aid of 2010, 300 million smartphones had been bought, and cyber web traffic reached 20 exabytes (20 billion gigabytes) per month.

In 2011 a key milestone turned into executed. IBM Watson defeated both ideal Jeopardy champions, Brad Ruttner and Ken Jennings. Such achievement become made viable via IBM servers able to processing eighty teraFLOPS (80 trillion FLOPS). be aware that once Moore’s legislation was mentioned within the mid 60’s, the strongest computer could only method three million FLOPS.

via 2012 huge development had been made in deep getting to know for picture consciousness. Google used sixteen,000 processors to coach a deep artificial neural community to admire pictures of cats in YouTube video clips without presenting any assistance to those machines about the pictures. Convolutional Neural Networks (CNN) grew to become able to classifying pictures with a high degree of accuracy. meanwhile, the statistics explosion endured, with the variety of cellular contraptions on earth exceeding the variety of humans, generating 2.5 quintillion bytes of statistics per day by way of 2017. Computing vigour reached new heights as Google announced its Tensor Processing contraptions (TPU) in a position to one hundred eighty million teraFLOPS.

it's at this aspect within the history of artificial Intelligence that many people began to know we might not be too far from attaining, or even exceeding, what's called synthetic frequent Intelligence (AGI). To the astonishment of the world, Google’s DeepMind hit another foremost milestone when its AlphaZero algorithm realized to play through itself the games of chess, shogi and Go (Go is a really complicated video game, much more challenging than chess). now not simplest did AlphaZero study to play through itself, it defeated the top of the line computers that had been fed directions from human consultants! And it did this in just eight hours of self-play!

AlphaZero, by itself, came up with taking part in suggestions that humans had not ever idea of before — this is a big event that could explain why there's so a good deal buzz about AI at this point in time.

Human vs. laptop Intelligence Milestones

Has Human Intelligence Already Been Defeated with the aid of Machines?

looking at these milestones, that you may’t help but ask yourself if human intelligence has already been defeated my machine intelligence. The reply is no. There are certain areas in which machines seem to have defeated the human mind, but there's much extra to human-degree intelligence than the narrow applications by which human brains were defeated. The level of complexity and skill of the human intellect is nowhere close being matched through computer systems — as a minimum not yet.

The human brain is an amazing, wondrous and mysterious introduction. Princeton researchers have shown that humans make judgements about different people inside one hundred milliseconds of meeting them. through a procedure that scientists call skinny reducing, people make judgmental selections about others corresponding to whether they are faithful, wise, excessive-repute, gay or straight, successful, adventurous, and extra. All in lower than a 2d.

Even a 3-12 months-old child is able to making visible identifications that nowadays’s most refined tremendous-powered desktop algorithms are nowhere close capable of matching.

The (small) Elephant within the Room

To prove the point, a brand new analyze discovered that artificial intelligence programs fail a vision test that a child might accomplish effortlessly. The examine presented a man-made intelligent system a living room scene which the device processed smartly, recognizing images similar to a chair, a couch, a tv, and so forth. however when the scientists added an anomalous object into the scene — the photograph of an elephant, the system grew to be very ‘confused’ and made a few mistakes, making a choice on a chair as a couch, and completely missing different objects that it had identified earlier than.

“There are all types of weird things going on that reveal how brittle present object detection programs are,” pointed out Amir Rosenfeld, a researcher at York tuition in Toronto and co-creator of the study along with his York colleague John Tsotsos and Richard Zemel of the school of Toronto.

The difficulty is because of the laborious techniques through which artificial Intelligence strategies visual impressions, one layer at a time. contrast this to how the human brain takes in a fabulous amount of counsel at once and techniques it straight away. “We open our eyes and everything happens,” pointed out Tsotsos.

Critics of the ‘over-hyping’ of artificial Intelligence are short to factor out these flaws and a lot of others. They pronounce that the AI revolution hasn’t took place yet. They remind us that the time period artificial Intelligence as it became coined within the late 1950’s to confer with the heady aspiration of realizing in utility and hardware an entity possessing human-degree intelligence, should still not be used interchangeably with computer learning. computing device studying is an algorithmic container that blends concepts from records, computing device science and other disciplines to technique facts, make predictions and assist make decisions — however it does equate to human-level intelligence.

It is obvious that we could be misusing the term artificial Intelligence as it was originally intended and that we could be making use of it too largely to discuss with a lot less competent applied sciences. it is also clear that machines are nowhere close the degree of capability of the human mind.

besides the fact that children, we will’t ignore the impressive fee of growth followed in the ultimate few years. Most scientists agree with that the synthetic Intelligence challenges faced today can be conquered prior to we suspect. really, most experts agree that we are going to achieve human-level artificial intelligence this century, many believing it is going to happen as quickly as in the next 20 to forty years.

Intelligence Explosion

in an effort to keep in mind the challenges that lay ahead, we ought to first seem at the distinctive classes of synthetic Intelligence:

1) synthetic slender Intelligence (ANI) — which you can suppose of ANI as a specialized intelligence. as an instance, the algorithms of a search engine, herbal language processing, self-riding cars, Siri, or even the self-getting to know machines that mastered the video game of Go.

2) artificial general Intelligence (AGI) — This refers to the human-stage intelligence that the usual synthetic Intelligence term become coined after. developing AGI is a much tougher assignment than growing ANI, and we are, at this element in time, not even near attaining it. This degree of intelligence requires a really widely wide-spread cognitive capability that, amongst different things, contains the ability to intent, plan, resolve problems, believe abstractly, realise complicated ideas, be trained directly, and learn from adventure.

3) artificial Superintelligence (ASI) — Nick Bostrom, author of the publication Superintelligence: Paths, dangers, options, defines superintelligence as “an mind it's plenty smarter than the most efficient human brains in nearly each field, including scientific creativity, universal knowledge and social knowledge.” An ASI agent may be trillions of instances smarter than human beings. it's where things get very horrifying. To make matters worse, once AGI is executed, ASI may additionally not be too some distance at the back of.

As we've exemplified above, ANI is already right here. AGI, however, continues to be only a sparkle in a scientist’s eye. As we have considered within the historic progression of artificial Intelligence, computational vigour has all the time been a pre-requisite to the next level of development. getting to AGI is no distinctive. To give you a way of the existing situation, scientists have estimated that the human brain is in a position to about 10 quadrillion computations per 2nd (cps). the area’s quickest computing device, the Tianhe-2 from China, has already overwhelmed this quantity, as it can do about 34 quadrillion cps. youngsters, the Tianhe-2 is a huge beast that expenses $390 million to build.

Ray Kurzweil, a futurist who is terribly optimistic about artificial Intelligence, estimates that AGI can become common when we are able to get 10 quadrillion cps for roughly $1,000. the use of Moore’s legislation which, as we now have considered, has accurately anticipated the increase of computing vigour to this point, it's estimated that we could be there through 2025, handiest 7 years away.

but computing vigor is just one prerequisite. as a way to achieve AGI, we nonetheless deserve to work out the way to create human-level intelligence. How are going to get there? Scientists are working on reverse engineering the human brain. The strategies have already been developed. in fact, we are in a position to emulate a 1 mm long flatworm brain with 302 neurons today. Now we simply need to determine how to scale this technique to a hundred billion neurons, the approximate dimension of the human mind. There are positive estimates that we are going to obtain this by using 2030.

There are also efforts to construct computer systems that concentrate on AI research. That ability that the computing device themselves will develop into smarter at every generation of progress, until AGI is performed. This may happen very soon. remember that computer systems can operate at exponential increase, whatever that it is complicated for us people to take into account. What appears to be progressing very slowly could unexpectedly explode into truth.

as soon as we reach AGI, given the identical iterative development on an exponential curve, it isn't complicated to foresee that ASI will arrive almost immediately after. The intelligence explosion will then attain a stage that we can’t even have an understanding of — and therein lies the hazards.

humans have dominated the earth due to our intelligence. it is easy for us to see that with intelligence comes energy. once we create an ASI agent this is vastly more intelligent than we're, what could be our destiny?

here is a extremely complex query to contemplate. Let’s look at techniques in which we can also are trying to deal with Superintelligence and affect its results.

residing with Superintelligence

If the creation of Superintelligence is inevitable, will we control it that will behave in ways that are a good option and never detrimental to humanity?

Nick Bostrom, in his book Superintelligence: Paths hazards strategies, describes a few control strategies, all of which have their own sets of competencies merits and shortcomings.

Boxing — This could be probably the most evident handle mechanisms that involves mind. you put the ASI agent in a confinement where contact with the exterior world is limited by humans. If it misbehaves you shut it down. do we expect that this Superintelligent agent received’t outsmart us in discovering a means to get away whether it is motivated to accomplish that?

Incentive strategies — We could construct an ASI in such a way that its behaviors are managed via suggestions, or incentive mechanisms. although, if the ASI obtains a ‘decisive strategic talents’, prison and financial incentives and sanctions may additionally no longer be adequate to influence its habits. The ASI may additionally internalize and increase its personal norms and ideologies and regard these human-made suggestions as meaningless.

Stunting — here's a technique wherein you impose constraints on the ASI’s cognitive capabilities. This strategy has three simple flaws: First, limiting the capabilities of an algorithm can be counter-productive; second, it is unlikely that we are capable of resisting the causes to build totally competent ASIs; Third, how will we define the brink of cognitive means and eliminate human biases? Set it incorrectly and it could actually determine by using itself how to develop into greater intelligent and skip the barriers imposed by way of people.

Tripwires — This contains setting up a set of assessments, probably without the ASI’s talents, to realize any terrible conduct or intent, and upon detection shut it down. You might, as an example, are attempting to realize if the ASI contained in a container is attempting to escape by means of organising a web connection. The difficulty right here goes lower back to the undeniable fact that you are dealing with a Superintelligent agent who could with ease subvert any tripwire devised via the human intellect.

control methods, as explained above, may have restrained effectiveness in fighting ASIs from behaving in undesirable methods. however what if we may take a unique approach to the issue and take a look at to keep away from undesirable outcomes by means of shaping what the ASIs need to do? here's referred to as Motivation option.

There are diverse methods within Motivation selection, as described below. once more, they have got their personal units of competencies benefits and shortcomings:

Direct Specification — This approach tries to explicitly outline a collection of suggestions or values that could be used as a compass by means of the ASI. This can also seem easy, unless you accept as true with what set of suggestions and values we would hope the ASI to be guided via. And besides the fact that we acquired that perfectly correct (which is totally not going) how would we go about expressing these suggestions and values in desktop-readable code?

Domesticity — This strategy is designed to severely restrict the scope of the ASI’s ambitions and activities. right here we would have to define what appropriate obstacles to make use of, in order that the ASI would nonetheless be useful, but most effective to the extent that it minimizes its have an effect on on the realm. once more, we run into the identical challenge raised with the stunting manage system of a way to face up to the reasons to create a more potent ASI.

indirect Normativity — here is an enchanting strategy, however as we will see later, raises many ethical questions. The theory right here is that instead of specifying the guidelines that e book the ASI’s habits at once, you define the manner by which the rules will be derived. In different phrases, in its place of telling the ASI what to do, tell it to head work out what we'd have told it to do had we primary better, and implement that. we will discuss this further once we review the thought of ‘extrapolating our volition’.

Augmentation — in this method, as a substitute of setting up a brand new motivation equipment, we delivery with an latest one and enhance its cognitive competencies to make it Superintelligent. we are talking about mind emulations and biological enhancements. do we, people, have the appropriate motivation systems to begin with? in spite of the fact that the answer is positive, what prevents it from becoming corrupt as it gets superior? And will we definitely are looking to are living in a global the place ‘brains in a field’ with full human recognition are copied, manipulated and discarded for scientific purposes?

if you haven’t realized yet the depth of the moral dilemmas which are about to hit us within the following few decades, you may also delivery to get a sense for it now. moreover confronting the perplexing complications of manipulating the mind whereas retaining human dignity, we are beginning to query complete value and motivation methods.

The (huge) Elephant in the Room

As mentioned, tips on how to manage the conduct of an ASI are restrained in effectiveness. essentially the most promising strategy seems to be the usage of Motivation alternative to design an ASI it truly is seeded with the ‘appropriate’ values to being with. but what are the ‘correct’ values? And who decides?

If we, with all of our flaws, are incapable of seeding the values with a view to drive the conduct of a more clever being, should we go away it as much as this sophisticated being to examine its personal set of values to power our behaviors?

Eliezer Yudkowsky, an American Researcher who is popularizing the thought of a friendly AI, has proposed seeding the AI with what he calls our ‘Coherent Extrapolation Volition’ (CEV). right here is how CEV is described by means of Yudkowsky:

“Our coherent extrapolation volition is our desire if we knew greater, idea faster, have been extra the individuals we would like we were, had grown up farther together; the place the extrapolation converges in preference to diverges, where our needs cohere rather than intervene; extrapolated as we wish that extrapolated, interpreted as we would like that interpreted.”

Dissecting the meaning of CEV is past the scope of this text. youngsters, what Yudkowsky is attempting to do is to get a hold of a morality model that encapsulates moral increase but keeps humanity sooner or later in cost of its personal destiny.

There are other morality fashions which are proposed, however the fundamental query is that this: What moral values do we wish to seed the AI with? If we don’t know, are we relaxed letting an all-powerful algorithm investigate the ethical values with a view to e book humanity for the rest of its existence?

Conclusion

we have some in fact large and hairy issues in front of us. We don’t know how we are able to tackle these tricky questions, however the time to birth these conversations is now. As we stated previous, AGI, and presently after, ASI, is probably going going to be a major a part of our fact in a short few a long time.

how many congressmen did you know who have a superb grasp of the considerations above? how many CEOs are thinking about how values ebook the selections of their organizations, and the way these values might influence the behavior of computer Superintelligence?

If we don’t beginning addressing these considerations now, we may additionally run out of time. The consequences are unfathomable.

counseled studying:

- How the Enlightenment Ends through Henry Kissinger

- The AI Revolution: The highway to Superintelligence by Tim city

- An govt’s e-book to AI by means of McKinsey & company

- Superintelligence: Paths, risks, techniques with the aid of Nick Bostrom

at the beginning posted at www.interceptinghorizons.com.


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IBM 000-R09 Exam (IBM SurePOS 300 Models 330 and 33H Technical Mastery Test) Detailed Information



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