|Exam Name||:||IBM Information Management Content Management OnDemand Technical Mastery Test|
|Questions and Answers||:||38 Q & A|
|Updated On||:||April 24, 2019|
|PDF Download Mirror||:||Pass4sure P2070-071 Dump|
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P2070-071 exam Dumps Source : IBM Information Management Content Management OnDemand Technical Mastery Test
Test Code : P2070-071
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Q&A : 38 Real Questions
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Now that IBM has bought its Portal, Commerce, Notes and Domino on-premises choices to HCL Industries, it looks that big Blue is pushing its chips into the center of the desk on IBM Watson and the cloud. one of the crucial tools beneath the Watson umbrella that continues to garner consideration in internet content material administration (WCM) circles is IBM Watson content Hub, so I decided to provide it a go and notice what all the fuss became about.
My first mistake changed into to google IBM Watson and register for a free trial on the ibm.com/watson page. My registration gave me entry to all types of Watson-branded tools, together with Watson Language Translator, Watson Assistant, the Watson IoT platform and the Watson Voice Agent.
but, the one product that did not have access with a free trial on IBM Watson turned into IBM Watson content Hub. After losing about 10 to 15 minutes in the IBM Cloud rabbit gap, i noticed that the IBM Watson content material Hub tool wasn't any place to be discovered after I signed up for IBM Watson.IBM is branding everything that strikes with the identify Watson. So, now what?
I headed lower back to Google and exceptionally searched the IBM Watson content material Hub term. This generated an enchanting outcomes. Google provided a couple of links, the primary of which requested me to go to IBM Watson Latvia, the 2nd requested me to go to IBM Watson Czech Republic. i used to be truly shopping from Toronto, so I discovered this reasonably perplexing. even more baffling was that the supplied URLs contained my language and nation code, en-ca, within them. regardless of all the quiz-display-winning acumen Watson possesses, IBM's flagship AI device looks to think that Latvia is a part of English-speakme Canada.
How on earth might an IBM-generated URL with embedded en-ca produce verbiage about international locations in japanese Europe? It does not make any experience.
smartly, I may still say that it doesn't make feel until you have ever attempted to internationalize a domain with IBM WebSphere Portal or IBM's Digital experience manager. these equipment don't make I18N an easy project. Seeing IBM's net content management equipment generate non-English-Canadian content for English Canadian websites tells me that no longer even IBM's own WCM specialists comprehend a way to use these IBM tools with ease.Why is IBM Watson together with Latvia and Czech Republic in a certainly English Canadian request?
anyways, I clicked on the IBM Watson Latvia hyperlink, which took me to IBM Watson Canada. From there i attempted to register for a free trial. I say tried, since it took me a few makes an attempt. You see, the registration required a password of eight characters, one uppercase letter, one lowercase letter and at least one number. I submitted the registration kind the usage of 18Indian because the password. it's two numbers, one uppercase letter and eight characters in total. however, IBM's Jeopardy-profitable AI expertise blocked my registration.
i spotted the password box changed into punitively highlighted pink, and the observe weak adjoined it. i would met all the stated password necessities, however the kind nevertheless blocked me.Watson registration password field has a secret, undocumented requirement. are you able to spot it?
I introduced a ninth letter, but the tool failed to like 18Indians both. It seemed to me like the complete registration form was damaged. As i used to be about seek AWS content Hub, I decided to throw a tenth letter into the combine, and miraculously, i used to be allowed to register.
but I need to say, all of these system faults and complications had been really beginning to bitter me, and i'd barely made it previous the registration wall. IBM has branded so many products with the Watson name that I find it overwhelming and confusing. furthermore, despite IBM's two decades in the WCM video game, it still can't seem to do I18N and search engine optimization effectively. and eventually, on whatever as simple as person registration, the business managed to provide anything so difficult and non-intuitive that it practically drove me, a professional veteran of the IT container, to a competitor.
IBM seems to be having a bet the farm on Watson and the cloud. but when this showcase of ineptitude is the most excellent its AI product can do, purchasers will want to supply their WCM tools from a special pasture. definitely IBM can do superior.View All movies
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NEW YORK, Dec. 28, 2018 /PRNewswire/ -- On Dec. 5 and 6, the world's largest AI event for business, The AI Summit, was held in New York. More than 350 experts, professors and business executives from the field of AI around the world attended the summit to discuss such issues as AI technology and the commercial application of AI, etc. Derek Haoyang Li, founder of Squirrel AI Learning,wasinvited to give a speech at this year's summit.
Beth Smith, IBM Watson AI general manager;Manuela Veloso, J.P. Morgan managing director and head of Global AI Research;David Carmona, Microsoft AI general manager;Steve Chien, NASA /JPL AI head;Naiomi Makovsky, Google Assistant head of global partners;James Patchett, NYCEDC president and CEO; and Derek Haoyang Li, founder of Squirrel AI Learning, were invited to the summit and gave speeches. They shared the achievements by their respective companies in AI technology, AI solution, AI strategy and other aspects, and expressed their opinions on topics that people care about, such as the changes brought by AI technology in various industries.
AI era, new opportunities for education
With the rapid expansion of big data collection and computing power, rapid progress has been made in AI technology research. The application of AI in manufacturing, services, retail, medical care and other industries is also expanding. Education, a profession that maintains the intergenerational succession of human culture, is no exception. To some extent, education must keep up with the development of AI technology, so as to ensure the historical status of mankind at the top of civilization.
On the other hand, the development of education also depends on the progress of AI technology. At this summit, Derek Haoyang Li, Squirrel AI Learning's founder, delivered a keynote speech titled "How to Give Every Child Adaptive Education". He summarized the disruptive innovation by AI in various industries, as well as the radical innovation made by Squirrel AI Learning in intelligent adaptive education, which were highly recognized by experts and scholars attending the summit.
A summary of Derek Li's keynote speech is presented below:
The upcoming AI era, like the industrial revolution, will be one of the epochal events that can change the course of human history. Specifically, the reasons why AI can have such great power are as follows:
First, it's versatile and know-it-all. IBM Watson can read more than 3,000 books, 240,000 papers, data from more than 600 experiments and 100 thousand clinical reports from patients in 17 seconds. Project Debater can debate any open conceptual topic without specific training. The versatility and know-it-all feature of AI have been widely used in every industry.
In terms of education, AI can break down and master all the nanoscale knowledge points, have a good grasp of the countless correlations between knowledge points, and provide "one-on-one tutoring" for thousands of students, which is an almost impossible task for human teachers.
Second, it can tell big stories from small things. Zuckerberg once said: "AI already can notice things that humans can't." That is gradually becoming a reality. For example, by using big data, AI algorithms and other technologies, Toutiao can judge each user's preferences through each click by users. By dividing human faces such as eyes, eyebrows and nose into millions of tiny pixels, SenseTime's face recognition is already a million times clearer than human eyes. In the same way, Netflix can detect the cognition and preference of every viewer from the data of every frame, and then find the logic of making and reshaping a film from the subtle differences. Using this trait of AI, Squirrel AI can clearly detect each student's mastery of knowledge points, knowing which knowledge points are already grasped and which are gaps left from the past, so as to help students solve their problems purposefully.
Third, it has infinite computing power. Elon Musk said: "a computer can operate at a speed of 1 million bits per second, while the speed of the human brain is only 10 bits per second." In this sense, the information processing speed of AI is much faster than that of human beings. When AI plays against a top-level human player, it can check millions of possibilities in 0.01 second and choose the optimal solution. Using the system's infinite computing power, Squirrel AI can label each question and split students' abilities, so as to clearly know the user profile of each student.
Fourth, it's self-evolving. The father of tablets Jerry Kaplan once said publicly: "in the future, AI will not only imitate human beings, but also surpass human beings." This is because AI has the ability of self-evolution. "Sunshine gathers, and light flows into my dreams." This is a line of verse created by Microsoft Xiaoice. Although it has been criticized by many experts, it's better than what 90% of human beings can do. The deep learning network of AlphaGo can help Google improve energy efficiency by 15%. It can save Google hundreds of millions of dollars in the near future. In the field of education, the evolutionary power of Squirrel AI is also amazing. It has skills that human teachers do not have. It can automatically help students make up their knowledge gaps and stimulate students' creativity and imagination.
In recent years, a trend of "AI+ education" has emerged both at home and abroad. Data shows that in China alone, more than 50 education institutions or companies claim to have adopted the latest AI technology. But "AI+ education" doesn't happen overnight. It's about really understanding the nature of education and building your own barriers in technology and business model.
New breakthroughs in "AI+ Education"
At present, auxiliary AI tools, such as pronunciation assessment and emotion recognition, cannot get involved in children's cognitive learning process. What parents are most concerned about is how a product can help their children to learn. AI+ education will eventually return to teaching and learning. Therefore, AI adaptive education is the best application scenario of AI+ education. Squirrel AI Learning has been in the forefront in this field in China.
Since its establishment, Squirrel AI Learning has set up a core R&D team. The members respectively came from three world famous AI adaptive education enterprises RealizeIT, Knewton and ALEKS. Richard Tong was the Asia Pacific technical director for the first American AI adaptive education company Knewton. Its chief scientist Wei Cui, with a post-doctoral degree in AI from the National University of Ireland, formerly was the core scientist of RealizeIT. Its chief data scientist Dr. Dan Bindman was a co-founder of American AI adaptive learning giant ALEKS and chief architect of core product algorithms. In addition, Squirrel AI Learning and SRI have founded a joint AI lab, committed to the R&D of Squirrel AI's core algorithms. Top scientists from home and abroad, combining the theories of psychology, pedagogy, cognition and other fields with the education policy, test items and examination outlines in China, developed China's first fully proprietary Squirrel AI engine and established Squirrel AI Learning's leading position in intelligent adaptive education technology. In the past two years, Squirrel AI Learning has made new breakthroughs in the following technologies:
First, super-nano knowledge point splitting. In the intelligent adaptive learning system of Squirrel AI, a subject in junior high school has been divided into more than 30,000 knowledge points. Such super-nano splitting makes it easy for students to find the knowledge points they have or have not mastered, so as to learn with a clear objective.
Second, learning ability and learning method splitting. Squirrel AI Learning put forward the "definable, measurable and teachable ability" theory to split students' learning abilities. So far, learning abilities have been broken down to more than 500 kinds. Squirrel AI teaches children to learn by analogy or infer other things from one fact. They also pay attention to the cultivation and promotion of students' creative ability.
Third, they initiated the correlation probability of uncorrelated knowledge points. Squirrel AI builds correlations between knowledge points and uses information theory to test and teach students efficiently.
Fourth, they initiated the concept of knowledge map reconstruction based on mistakes. On the basis of finding the real reason for making mistakes, Squirrel AI adaptive learning system can develop personalized learning plans for children and finally solve their problems.
Fifth, they initiated the versus model. Using Bayes' theorem, Squirrel AI forms a versus network. Based on simulation and Squirrel AI Learning's adaptive technology and educational psychology model, machines can simulate learning and competition between students and teachers.
With these technologies, Squirrel AI can accurately locate the knowledge points of each student and continuously push the knowledge points most suitable to their intellectual development and learning ability according to each student's knowledge point mastery in the process of dynamic learning, so as to establish a personalized learning path for each student and enable them to learn the most knowledge points in the shortest time, putting an end to the "cramming model" and "excessive assignments tactic" in traditional education.
At the AI Adaptive Education Conference (AIAED) which just ended in November in Beijing, Prof. Tom Mitchell, the godfather of global machine learning, dean of CMU School of Computer Science, a member of the American Academy of Arts and Sciences and the National Academy of Engineering, an AAAS fellow and AAAI fellow, officially accepted Squirrel AI Learning's offer of the position of Chief AI Officer. As Squirrel AI Learning's first person in charge in the field of AI, Mitchell will lead a team of more than 10 AI scientists and hundreds of AI application engineers and technical teams to conduct basic AI research in the field of intelligent adaptive education, as well as the development and application of related products.
In addition, Squirrel AI Learning not only has mastered the world's most advanced technology, but also has greatly expanded its online + offline education retail business model nationwide. Online, Squirrel AI Learning gets traffic. Students receive one-on-one tutoring online. Different from other education enterprises, 70% courses of Squirrel AI Learning are taught and lectured by AI teachers. Human teachers are responsible for the remaining 30% of teaching for monitoring and emotional help. Offline, Squirrel AI Learning opens physical learning centers in the form of franchised chain centers, cooperative schools and self-run centers in various places. So far, Squirrel AI Learning has opened more than 1,600 learning centers in over 300 cities across China.
In fact, Squirrel AI Learning's new education retail business model is also a reform of the traditional education model. On the one hand, Squirrel AI Learning not only has changed the traditional model of teaching by teachers, replacing it with teaching by AI, but also has realized intelligent management of students' learning. In the past, every student's data was opaque. Now through the Internet and AI, all students' learning process data and teaching data are collected, to provide better quality services for students. Transparent data management has changed the traditional offline education model. At present, Squirrel AI Learning has served more than one million students. The contract renewal rate is about 80% where the average rate is about 50%. It has established a good reputation among users.
Squirrel AI Learning is globally leading in both core technology and business model. But its journey of changing and revolutionize traditional education is far from over. "In terms of technology, we're testing brain wave patterns to identify changes in students' attention spans. In the future, it will be widely used." Derek Li said: "next, Squirrel AI will become a super AI teaching robot integrating personalized learning, dynamic learning objective management, human-computer dialogue, emotion and brain wave monitoring, to provide every student with high quality education and teaching services."
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If the key to survival in the knowledge era is to “learn a living” by integrating work and learning, then we must understand the relationship between education, work and technology.
Robert K. Logan, The Sixth Language: Learning a Living in the Internet Age
Corporate learning professionals have begun to achieve a better understanding of this relationship, and a new type of learning model is taking hold in the business world. The technology is new, but the learning theory has been around for some time. Organizational behavior specialists and learning theorists have been hammering out the dimensions of this type of learning model for more than 20 years. It is called experiential learning or experience-based learning.
Creating experience-based learning is now possible due to the availability of new tools and technologies. Contextual collaboration, simulation, business process modeling, workflow management and ambient intelligence (also known as smart technology) have all emerged as tools at the disposal of developers. They are used to create an increasingly seamless experience combining work and learning.
The worker’s experience is becoming highly automated through the use of virtual collaboration workplaces, real-time communication tools, business process management and business activity monitoring. The worker’s learning experience is being automated and layered over this virtual workplace through the use of expertise mining, social network analysis and smart ambient objects.
The individual’s work experience includes first-person experience, business conditions, productivity expectations, tools, time constraints, physical or virtual workplace conditions and other people. The interactions of these variables can create very complex and demanding working conditions. Legacy courseware models were never designed for this kind of flux, and a new kind of adaptive learning model has emerged. As each worker adapts to changes in the rapidly evolving business ecosystem, experience-based learning adapts to the evolving needs of the individual worker.
Tapping the Right ExperiencePractitioners who develop content and services for the individual worker have a growing number of tools they can use. Tools are selected based on the type of applications used by particular people in specific contexts. Experience-based learning tools are often native front-ends to the actual applications used by the worker.
Many of these systems have native features that can capture a set of work tasks automatically as real workers perform daily work routines. The systems then allow developers, managers and workers to “run” the routines and optimize them for desired results.
One of the most interesting aspects of experience-based learning is the individual worker’s growing content development role. Many vendors now define the expert worker in the business unit, on the line and in the field as the combined subject-matter expert, content developer and end user of their products.
Expert workers use external tools outside of productivity applications to “record” themselves traversing multiple applications. Epiance, OnDemand (part of Global Knowledge) and RWD Info Pak are examples of this kind of tool. Instructional designers may not be required to create this kind of learning, but it is based on a solid learning model.
Putting Theory Into PracticeThe theoretical underpinnings of experience-based learning in the business world derive in large part from the pioneering work of David Kolb, professor of organizational behavior at the Weatherhead School of Management at Case Western Reserve University. He since has commercialized the theory and now runs a company called Experience Based Learning Systems.
Kolb based his theory on the ideas of John Dewey, an early proponent of experience as an effective learning method. Dewey’s influence also can be seen in the work of other pioneers who have influenced the development of experience-based learning, such as Grace Coyle, Eduard Lindeman and Carl Rogers.
Related theories and practices include active learning, engaged learning, situational learning, work-based learning, contextual learning, problem-based learning and service-based learning. All of these models stress the importance of the learner’s experience within a real-world context as the primary condition required for learning.
Defining the TechnologySo, what is it? Experience-based learning technologies are systems, applications and tools that merge learning with the real-time work experience of individual workers. Experience-based learning content corresponds as closely as possible to direct experience. Experience-based learning in the business world is learning that maps directly to everyday work life.
In educational environments, this is sometimes referred to as “situated learning.” Situated learning is very common in workforce retraining, rehabilitation and vocational training. It places a premium on the high-fidelity replication of not only the job’s tasks, but also the job’s environment.
Experience-based learning technology combines learning with the primary experience of workers as they perform tasks and interact with people in the real-time corporate ecosystem. The new technology is taking root in a fertile technical environment that includes real-time contextual collaboration, business process simulation, workflow management, social network analysis, expertise mining and smart wireless technology, also known as ambient intelligence.
Virtual Prairie-Dogging: Finding the Local Human Learning ObjectInstant messaging is growing rapidly in corporate markets. It is becoming one of the most common experience-based learning technologies in the workplace (real and virtual). According to data released in August 2004 from a survey conducted by the Pew Internet & American Life Project, 40 percent of all adult Internet users in the United States are using instant messaging. AOL’s annual survey on instant messaging, also conducted in 2004, found that the use of instant messaging is growing annually by 71 percent in the corporate world.
Expertise mining is the most innovative collaboration technology on the market. This technology “mines” for human experts and puts workers and experts in contact with each other via instant messaging. This is closely related to the technology known as “skill-based routing,” invented in the call-center industry.
In both cases, the most highly valued learning object is the human expert who is best qualified to help a co-worker or customer accomplish a task. This is the high-tech equivalent of what is known as “prairie-dogging,” asking the local expert in the next cubicle for help. Creating this kind of learning requires the mapping of the expertise inside a corporation. IBM, AskMe and Tacit all offer tools that map human expertise.
A growing number of companies, such as Scion, Telispark, IBM, Microsoft and Oxford are now selling wireless products that allow workers to find and work with remote experts. Oxford Technologies sells a wireless product called Remote Technical Assistance Support System (RTASS). It was developed with the U.S. Navy’s Naval Undersea Warfare Center as a “telecollaboration” solution. It not only provides local real-time performance support, but also links remote experts with workers in the field. The solution provides what the company calls an over-the-shoulder presence of a remote supervisor via collaboration software, high-performance imaging and wireless broadband connectivity.
Wireless Peripheral’s SightLink provides visual communication “between an activity point and a consultation point.” According to the company, SightLink reduces or eliminates the need for specialized personnel to be physically present when tasks that require their expertise need to be performed. With SightLink, field personnel have immediate access to a remote expert for assistance. Because the product uses a high degree of two-way visualization software, the company calls this “working at the Speed-of-Sight.”
Bottling the Blue-Collar ExperienceExperience-based learning technology is expanding rapidly into the so-called blue-collar workplaces. These workplaces include factories, shipyards, oil tankers, warehouses, auto repair shops, construction sites, mines, mills, power plants and even farms.
Forklift drivers at the Sears Warehouse in Columbus, Ohio, use touch-screen computers mounted on their forklifts. The devices provide decision support, inventory tracking and navigational advice, and even assess a worker’s performance in real time. The device (and the boss) can quickly identify productivity gaps.
Honda technicians use Nomad Expert Technician Systems from Microvision, a wireless, wearable computer system with a head-mounted display device that beams an image directly on the retinas of technicians. The system provides access to test data, mentors and repair information while service technicians work on cars. Workers see the information they need at the point of task, without having to turn away from the job at hand. The device is wirelessly linked to the Dealership Management System (DMS) populated with electronic repair manuals, work orders, vehicle histories and part inventories. The technicians can even access messaging and collaboration technology while they work.
Prescribing a Rich Clinical ExperienceHealth-care workers now carry handheld devices that contain decision support, patient histories and encyclopedias of medical information. Nurses use bar-code devices that read patient wristbands. The readout on the device prompts nurses to provide the exact dosage of the right medicine to the right patient at the right time. To their annoyance, it also asks the nurses to provide explanations if medications are not delivered on time. Vendors in this space include MercuryMD, PatientKeeper and ePocrates.
According to ePocrates, more than 350,000 physicians, pharmacists and other health-care professionals use its handheld drug reference application, called ePocrates Rx. The application features more than 3,000 conventional drug monographs and an alternative medicine database. It includes a drug interaction guide that has a built-in function that allows the user to simultaneously check up to 30 drugs for interactions across both “ethical and alternative medications.” The product includes an infectious disease guide, clinical tables and clinical guidelines. Also included is the proprietary MedMath calculator with more than 30 of the most commonly used medical equations.
Tablet PCs have experienced wide adoption in only two markets: academic and health care. Nurses have been ambivalent about the devices, but administrators and physicians have warmed up to the new technology. Interestingly, physicians often use the devices for real-time patient education. Complex procedures can be explained quickly by showing patients simulations and diagrams of the procedure. Doctors can show patients how medical technology products work.
Not surprisingly, there is a thriving niche industry of service firms that develop medical device training and patient education content.
Sensing Smart Ambient ObjectsWorkers in many industries are now using handheld scanning devices, such as radio-frequency identification (RFID) and smart-tag readers, which scan data in bar codes and chips that are embedded in machines, devices and objects. The readouts provide static data, as well as highly contextual decision support and procedural instructions. Some of these smart tags are proactive: Machines in the plant alert workers when they need attention. In this case, content stored on the devices is generated by both human developers as well as the machines themselves when their microelectromechanical system (MEMS) sensors indicate problems.
Questra sells remote-device-monitoring software that proactively alerts service personnel when things break or when maintenance is needed. Using a form of predictive maintenance, the machines are able to “self-diagnose” themselves and send detailed alerts to remote technicians. The technicians can often fix the problem from a remote location in a sort of collaboration with the diagnostic software.
Dallas Semiconductor’s iButtons are small metal cylinders with an embedded chip that allow maintenance technicians to enter information in 32 text fields on the chip. Mining and manufacturing companies use iButtons to tag machines and equipment. The iButtons contain inventory data as well as maintenance history and short bursts of task support, helping workers quickly fix problems encountered in the past by other technicians. Each time a worker “touches” a piece of equipment with an iButton, he updates the data.
SAT provides wireless workflow solutions to the energy sector. The company’s IntelaTrac “reads an RFID tag that a worker scans on a piece of equipment and then provides device-specific data about what to check for, how often to check for it and what steps to take in the event of an abnormal reading.”
The rapid proliferation of ambient technology like smart tags, RFID and handheld devices to communicate with them has begun to create a global environment of pervasive intelligent information. Wi-Fi “hot spots,” wireless broadband Internet access and satellite connectivity have extended the virtual work experience everywhere.
Desktop computing is now being replaced by pervasive, ambient technology that distributes experience-based learning everywhere, all the time.
Sam S. Adkins is an independent learning technology researcher and chief research officer at Ambient Insight. Sam specializes in learning and productivity technology research that spans several converging technologies including simulation, mobility, business process management, collaboration, workflow management and Web services. E-mail Sam at email@example.com.
December 2004 Table of ContentsFiled under: Technology
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